From 40047bdb17d658c1776d78581daedb6bc30b7145 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Wed, 11 Feb 2026 16:15:12 +0800 Subject: [PATCH 001/239] [Feature] Add easi library - Phase 1 implementation Implement the easi Python library for orchestrating simulator-based embodied reasoning evaluation. Includes subprocess isolation via filesystem IPC, versioned simulator management (conda+uv), agent interface, task/benchmark framework, and CLI. Components: core abstractions, dummy/AI2-THOR simulators, dummy task, dummy agent, LLM client + dummy server, full test suite (44 tests). --- .gitignore | 6 +- CLAUDE.md | 75 +++++ easi/__init__.py | 0 easi/agents/__init__.py | 0 easi/agents/dummy_agent.py | 49 ++++ easi/cli.py | 261 ++++++++++++++++++ easi/communication/__init__.py | 0 easi/communication/filesystem.py | 221 +++++++++++++++ easi/communication/schemas.py | 96 +++++++ easi/core/__init__.py | 0 easi/core/base_agent.py | 121 ++++++++ easi/core/base_env_manager.py | 205 ++++++++++++++ easi/core/base_simulator.py | 111 ++++++++ easi/core/base_task.py | 190 +++++++++++++ easi/core/episode.py | 33 +++ easi/core/exceptions.py | 37 +++ easi/core/protocols.py | 74 +++++ easi/llm/__init__.py | 0 easi/llm/api_client.py | 84 ++++++ easi/llm/dummy_server.py | 155 +++++++++++ easi/simulators/__init__.py | 0 easi/simulators/ai2thor/__init__.py | 1 + easi/simulators/ai2thor/manifest.yaml | 14 + easi/simulators/ai2thor/v2_1_0/__init__.py | 6 + easi/simulators/ai2thor/v2_1_0/bridge.py | 116 ++++++++ easi/simulators/ai2thor/v2_1_0/conda_env.yaml | 6 + easi/simulators/ai2thor/v2_1_0/env_manager.py | 38 +++ .../ai2thor/v2_1_0/requirements.txt | 3 + easi/simulators/ai2thor/v2_1_0/simulator.py | 25 ++ easi/simulators/ai2thor/v5_0_0/__init__.py | 6 + easi/simulators/ai2thor/v5_0_0/bridge.py | 122 ++++++++ easi/simulators/ai2thor/v5_0_0/conda_env.yaml | 6 + easi/simulators/ai2thor/v5_0_0/env_manager.py | 39 +++ .../ai2thor/v5_0_0/requirements.txt | 3 + easi/simulators/ai2thor/v5_0_0/simulator.py | 22 ++ easi/simulators/dummy/__init__.py | 1 + easi/simulators/dummy/manifest.yaml | 9 + easi/simulators/dummy/v1/__init__.py | 6 + easi/simulators/dummy/v1/bridge.py | 166 +++++++++++ easi/simulators/dummy/v1/conda_env.yaml | 5 + easi/simulators/dummy/v1/env_manager.py | 48 ++++ easi/simulators/dummy/v1/requirements.txt | 2 + easi/simulators/dummy/v1/simulator.py | 26 ++ easi/simulators/registry.py | 136 +++++++++ easi/simulators/subprocess_runner.py | 199 +++++++++++++ easi/tasks/__init__.py | 0 easi/tasks/dataset.py | 95 +++++++ easi/tasks/dummy_task/__init__.py | 5 + easi/tasks/dummy_task/task.py | 76 +++++ easi/tasks/dummy_task/task.yaml | 24 ++ easi/tasks/registry.py | 112 ++++++++ easi/utils/__init__.py | 0 easi/utils/locking.py | 42 +++ easi/utils/logging.py | 44 +++ easi/utils/paths.py | 42 +++ easi/utils/system_deps.py | 91 ++++++ pyproject.toml | 38 +++ tests/__init__.py | 0 tests/test_dummy_agent.py | 54 ++++ tests/test_dummy_llm.py | 76 +++++ tests/test_dummy_simulator.py | 99 +++++++ tests/test_dummy_task.py | 91 ++++++ tests/test_filesystem_comm.py | 124 +++++++++ 63 files changed, 3735 insertions(+), 1 deletion(-) create mode 100644 CLAUDE.md create mode 100644 easi/__init__.py create mode 100644 easi/agents/__init__.py create mode 100644 easi/agents/dummy_agent.py create mode 100644 easi/cli.py create mode 100644 easi/communication/__init__.py create mode 100644 easi/communication/filesystem.py create mode 100644 easi/communication/schemas.py create mode 100644 easi/core/__init__.py create mode 100644 easi/core/base_agent.py create mode 100644 easi/core/base_env_manager.py create mode 100644 easi/core/base_simulator.py create mode 100644 easi/core/base_task.py create mode 100644 easi/core/episode.py create mode 100644 easi/core/exceptions.py create mode 100644 easi/core/protocols.py create mode 100644 easi/llm/__init__.py create mode 100644 easi/llm/api_client.py create mode 100644 easi/llm/dummy_server.py create mode 100644 easi/simulators/__init__.py create mode 100644 easi/simulators/ai2thor/__init__.py create mode 100644 easi/simulators/ai2thor/manifest.yaml create mode 100644 easi/simulators/ai2thor/v2_1_0/__init__.py create mode 100644 easi/simulators/ai2thor/v2_1_0/bridge.py create mode 100644 easi/simulators/ai2thor/v2_1_0/conda_env.yaml create mode 100644 easi/simulators/ai2thor/v2_1_0/env_manager.py create mode 100644 easi/simulators/ai2thor/v2_1_0/requirements.txt create mode 100644 easi/simulators/ai2thor/v2_1_0/simulator.py create mode 100644 easi/simulators/ai2thor/v5_0_0/__init__.py create mode 100644 easi/simulators/ai2thor/v5_0_0/bridge.py create mode 100644 easi/simulators/ai2thor/v5_0_0/conda_env.yaml create mode 100644 easi/simulators/ai2thor/v5_0_0/env_manager.py create mode 100644 easi/simulators/ai2thor/v5_0_0/requirements.txt create mode 100644 easi/simulators/ai2thor/v5_0_0/simulator.py create mode 100644 easi/simulators/dummy/__init__.py create mode 100644 easi/simulators/dummy/manifest.yaml create mode 100644 easi/simulators/dummy/v1/__init__.py create mode 100644 easi/simulators/dummy/v1/bridge.py create mode 100644 easi/simulators/dummy/v1/conda_env.yaml create mode 100644 easi/simulators/dummy/v1/env_manager.py create mode 100644 easi/simulators/dummy/v1/requirements.txt create mode 100644 easi/simulators/dummy/v1/simulator.py create mode 100644 easi/simulators/registry.py create mode 100644 easi/simulators/subprocess_runner.py create mode 100644 easi/tasks/__init__.py create mode 100644 easi/tasks/dataset.py create mode 100644 easi/tasks/dummy_task/__init__.py create mode 100644 easi/tasks/dummy_task/task.py create mode 100644 easi/tasks/dummy_task/task.yaml create mode 100644 easi/tasks/registry.py create mode 100644 easi/utils/__init__.py create mode 100644 easi/utils/locking.py create mode 100644 easi/utils/logging.py create mode 100644 easi/utils/paths.py create mode 100644 easi/utils/system_deps.py create mode 100644 pyproject.toml create mode 100644 tests/__init__.py create mode 100644 tests/test_dummy_agent.py create mode 100644 tests/test_dummy_llm.py create mode 100644 tests/test_dummy_simulator.py create mode 100644 tests/test_dummy_task.py create mode 100644 tests/test_filesystem_comm.py diff --git a/.gitignore b/.gitignore index df1a13b..dc084eb 100644 --- a/.gitignore +++ b/.gitignore @@ -1 +1,5 @@ -/logs \ No newline at end of file +/logs +*.pyc +__pycache__/ +*.pyo +*.egg-info \ No newline at end of file diff --git a/CLAUDE.md b/CLAUDE.md new file mode 100644 index 0000000..241a481 --- /dev/null +++ b/CLAUDE.md @@ -0,0 +1,75 @@ +# CLAUDE.md + +This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository. + +## Project Overview + +EASI (Holistic Evaluation of Multimodal LLMs on Spatial Intelligence) is a unified evaluation suite for benchmarking multimodal LLMs on spatial intelligence tasks. It is an **orchestration project** — it does not contain evaluation code itself but wraps two external backends via git submodules. + +## Architecture + +EASI uses a **dual-backend architecture**: + +- **VLMEvalKit** (`VLMEvalKit/` submodule) — Feature-rich backend with built-in model zoo and LLM-based answer judging. Entry point: `VLMEvalKit/run.py`. +- **lmms-eval** (`lmms-eval/` submodule) — Lightweight, accelerate-based backend with multi-GPU distributed inference. Entry point: `lmms-eval` CLI command after `pip install -e ./lmms-eval`. + +Both submodules point to EvolvingLMMs-Lab forks. Submodules must be initialized before use: +```bash +git submodule update --init --recursive +``` + +The root repository contains: +- `examples/` — Shell scripts demonstrating evaluation invocations (lmms-eval backend) +- `dockerfiles/` — Docker configs for EASI, VLM3R, and Cambrains runtime environments +- `docs/` — Changelog, benchmark verification data, supported models/benchmarks matrix + +## Setup & Installation + +**VLMEvalKit backend:** +```bash +git clone --recursive https://github.com/EvolvingLMMs-Lab/EASI.git +cd EASI && pip install -e ./VLMEvalKit +``` + +**lmms-eval backend:** +```bash +git clone --recursive https://github.com/EvolvingLMMs-Lab/EASI.git +cd EASI && pip install -e ./lmms-eval spacy +pip install flash-attn --no-build-isolation +``` + +**Docker:** +```bash +bash dockerfiles/EASI/build_runtime_docker.sh +``` + +## Running Evaluations + +**VLMEvalKit:** +```bash +cd VLMEvalKit/ +python run.py --data {BENCHMARK} --model {MODEL} --judge {JUDGE_MODE} --verbose --reuse +``` +Judge modes: `extract_matching` (regex), `gpt-4o-1120` (LLM-based, needs OPENAI_API_KEY). + +**lmms-eval (single GPU):** +```bash +lmms-eval --model {MODEL_TYPE} --model_args pretrained={MODEL_PATH} \ + --tasks {TASK} --batch_size 1 --log_samples --output_path ./logs/ +``` + +**lmms-eval (multi-GPU):** +```bash +CUDA_VISIBLE_DEVICES=0,1,2,3 accelerate launch --num_processes=4 \ + -m lmms_eval --model {MODEL_TYPE} --model_args pretrained={MODEL_PATH} \ + --tasks {TASK} --batch_size 1 --log_samples --output_path ./logs/ +``` + +List available tasks: `lmms-eval --tasks list` + +## Key References + +- There is no test suite, linter, or build system in the root project — those live in the submodules. +- Evaluation logs go to `./logs/` (gitignored). +- Supported models (23) and benchmarks (25) are documented in `docs/Support_bench_models.md`. +- Benchmark verification against official scores is in `docs/Benchmark_Verification.md`. diff --git a/easi/__init__.py b/easi/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/easi/agents/__init__.py b/easi/agents/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/easi/agents/dummy_agent.py b/easi/agents/dummy_agent.py new file mode 100644 index 0000000..2582b4c --- /dev/null +++ b/easi/agents/dummy_agent.py @@ -0,0 +1,49 @@ +"""Dummy agent for testing — returns random actions without calling an LLM.""" + +from __future__ import annotations + +import random + +from easi.core.base_agent import BaseAgent +from easi.core.episode import Action, Observation + + +class DummyAgent(BaseAgent): + """Agent that picks random actions from the action space. + + Does not call the LLM client. Useful for testing the full pipeline + without needing a running LLM server. + """ + + def __init__(self, action_space: list[str], seed: int | None = None): + super().__init__(llm_client=None, action_space=action_space) + self._rng = random.Random(seed) + + def _build_system_prompt(self, task_description: str) -> str: + return "" # no system prompt needed + + def _build_step_prompt(self, observation: Observation) -> str: + return "Choose an action." + + def _parse_action(self, llm_response: str) -> Action: + """Pick a random action from the action space.""" + action_name = self._rng.choice(self.action_space) + return Action(action_name=action_name) + + def act(self, observation: Observation, task_description: str) -> Action: + """Override to skip LLM call entirely.""" + self._step_count += 1 + + action = self._parse_action("") + + # Still maintain chat history for compatibility + self._chat_history.append({ + "role": "user", + "content": f"Step {self._step_count}. Observation at {observation.rgb_path}", + }) + self._chat_history.append({ + "role": "assistant", + "content": f"Action: {action.action_name}", + }) + + return action diff --git a/easi/cli.py b/easi/cli.py new file mode 100644 index 0000000..d719ff5 --- /dev/null +++ b/easi/cli.py @@ -0,0 +1,261 @@ +"""EASI CLI entry point. + +Usage: + easi env list|install|check + easi task list|info|download + easi sim test + easi llm-server [--port PORT] [--mode MODE] +""" + +import argparse +import sys + + +def build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser( + prog="easi", + description="EASI - Embodied Reasoning Evaluation for Spatial Intelligence", + ) + parser.add_argument( + "--verbose", "-v", action="store_true", help="Enable INFO logging" + ) + parser.add_argument("--debug", action="store_true", help="Enable DEBUG logging") + + subparsers = parser.add_subparsers(dest="command") + + # --- env command group --- + env_parser = subparsers.add_parser("env", help="Manage simulator environments") + env_sub = env_parser.add_subparsers(dest="env_action") + + env_sub.add_parser("list", help="List available simulators and versions") + + env_install = env_sub.add_parser("install", help="Install a simulator environment") + env_install.add_argument("simulator", type=str, help="e.g., 'dummy' or 'ai2thor:v2_1_0'") + + env_check = env_sub.add_parser("check", help="Check if environment is ready") + env_check.add_argument("simulator", type=str) + + # --- task command group --- + task_parser = subparsers.add_parser("task", help="Manage tasks (benchmarks)") + task_sub = task_parser.add_subparsers(dest="task_action") + + task_sub.add_parser("list", help="List available tasks") + + task_info = task_sub.add_parser("info", help="Show task details") + task_info.add_argument("task", type=str, help="e.g., 'dummy_task'") + + task_download = task_sub.add_parser("download", help="Download task dataset") + task_download.add_argument("task", type=str) + + # --- sim command group --- + sim_parser = subparsers.add_parser("sim", help="Control simulators") + sim_sub = sim_parser.add_subparsers(dest="sim_action") + + sim_test = sim_sub.add_parser("test", help="Run a smoke test (reset + N steps)") + sim_test.add_argument("simulator", type=str, help="e.g., 'dummy' or 'ai2thor:v5_0_0'") + sim_test.add_argument("--steps", type=int, default=5, help="Number of steps") + + # --- llm-server command --- + llm_parser = subparsers.add_parser("llm-server", help="Start dummy LLM server") + llm_parser.add_argument("--port", type=int, default=8000) + llm_parser.add_argument("--host", type=str, default="127.0.0.1") + llm_parser.add_argument("--mode", choices=["fixed", "random"], default="random") + llm_parser.add_argument( + "--action-space", type=str, nargs="+", + default=["MoveAhead", "TurnLeft", "TurnRight", "Stop"], + ) + + return parser + + +# --- Command handlers --- + +def cmd_env_list() -> None: + from easi.simulators.registry import get_simulator_entry, list_simulators + + sims = list_simulators() + if not sims: + print("No simulators found.") + return + + # Deduplicate: show each name:version pair once + seen = set() + for key in sims: + entry = get_simulator_entry(key) + pair = f"{entry.name}:{entry.version}" + if pair in seen: + continue + seen.add(pair) + default_marker = " (default)" if key == entry.name else "" + print(f" {pair}{default_marker} — {entry.description}") + + +def cmd_env_install(simulator: str) -> None: + from easi.simulators.registry import load_env_manager_class + + EnvManagerClass = load_env_manager_class(simulator) + env_manager = EnvManagerClass() + print(f"Installing environment: {env_manager.get_env_name()}") + env_manager.install() + print("Done.") + + +def cmd_env_check(simulator: str) -> None: + from easi.simulators.registry import load_env_manager_class + + EnvManagerClass = load_env_manager_class(simulator) + env_manager = EnvManagerClass() + + missing = env_manager.check_system_deps() + if missing: + print(f"Missing system deps: {missing}") + + if env_manager.env_is_ready(): + print(f"Environment {env_manager.get_env_name()} is ready.") + print(f"Python: {env_manager.get_python_executable()}") + else: + print(f"Environment {env_manager.get_env_name()} is NOT ready.") + print("Run: easi env install " + simulator) + + +def cmd_task_list() -> None: + from easi.tasks.registry import get_task_entry, list_tasks + + tasks = list_tasks() + if not tasks: + print("No tasks found.") + return + + for name in tasks: + entry = get_task_entry(name) + print(f" {name} — {entry.display_name} (simulator: {entry.simulator_key})") + + +def cmd_task_info(task_name: str) -> None: + from easi.tasks.registry import get_task_entry + + entry = get_task_entry(task_name) + print(f"Task: {entry.display_name}") + print(f" Name: {entry.name}") + print(f" Description: {entry.description}") + print(f" Simulator: {entry.simulator_key}") + print(f" Actions: {', '.join(entry.action_space)}") + print(f" Max steps: {entry.max_steps}") + + +def cmd_task_download(task_name: str) -> None: + from easi.tasks.registry import load_task_class + + TaskClass = load_task_class(task_name) + task = TaskClass() + path = task.download_dataset() + if path and str(path): + print(f"Dataset ready at: {path}") + else: + print("Task uses built-in episodes (no download needed).") + + +def cmd_sim_test(simulator: str, steps: int) -> None: + from easi.core.episode import Action + from easi.simulators.registry import load_env_manager_class, load_simulator_class + from easi.simulators.subprocess_runner import SubprocessRunner + + EnvManagerClass = load_env_manager_class(simulator) + SimClass = load_simulator_class(simulator) + + env_manager = EnvManagerClass() + sim = SimClass() + + print(f"Testing {simulator}...") + print(f" Python: {env_manager.get_python_executable()}") + + runner = SubprocessRunner( + python_executable=env_manager.get_python_executable(), + bridge_script_path=sim._get_bridge_script_path(), + needs_display=env_manager.needs_display, + xvfb_screen_config=env_manager.xvfb_screen_config, + ) + + try: + runner.launch() + sim.set_runner(runner) + + print(" Reset... ", end="", flush=True) + obs = sim.reset("smoke_test_001") + print(f"OK (rgb: {obs.rgb_path})") + + for i in range(steps): + action = Action(action_name="MoveAhead") + result = sim.step(action) + print(f" Step {i+1}: done={result.done}, reward={result.reward}") + if result.done: + break + + print(" Closing... ", end="", flush=True) + sim.close() + print("OK") + print("Smoke test passed!") + + except Exception as e: + print(f"\nSmoke test FAILED: {e}") + sim.close() + sys.exit(1) + + +def cmd_llm_server(host: str, port: int, mode: str, action_space: list[str]) -> None: + from easi.llm.dummy_server import run_server + + run_server(host=host, port=port, mode=mode, action_space=action_space) + + +# --- Main --- + +def main() -> None: + parser = build_parser() + args = parser.parse_args() + + # Set up logging + if args.debug: + from easi.utils.logging import setup_logging + setup_logging("DEBUG") + elif args.verbose: + from easi.utils.logging import setup_logging + setup_logging("INFO") + + if args.command is None: + parser.print_help() + sys.exit(0) + + # Dispatch commands + if args.command == "env": + if args.env_action == "list": + cmd_env_list() + elif args.env_action == "install": + cmd_env_install(args.simulator) + elif args.env_action == "check": + cmd_env_check(args.simulator) + else: + parser.parse_args(["env", "--help"]) + + elif args.command == "task": + if args.task_action == "list": + cmd_task_list() + elif args.task_action == "info": + cmd_task_info(args.task) + elif args.task_action == "download": + cmd_task_download(args.task) + else: + parser.parse_args(["task", "--help"]) + + elif args.command == "sim": + if args.sim_action == "test": + cmd_sim_test(args.simulator, args.steps) + else: + parser.parse_args(["sim", "--help"]) + + elif args.command == "llm-server": + cmd_llm_server(args.host, args.port, args.mode, args.action_space) + + +if __name__ == "__main__": + main() diff --git a/easi/communication/__init__.py b/easi/communication/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/easi/communication/filesystem.py b/easi/communication/filesystem.py new file mode 100644 index 0000000..59d26e8 --- /dev/null +++ b/easi/communication/filesystem.py @@ -0,0 +1,221 @@ +"""Filesystem-based IPC for command/response exchange between parent and bridge subprocess. + +All writes use the atomic write pattern: write to a .tmp file, then os.rename() to the +final path. This guarantees readers never see partial files (rename is atomic on Linux +when source and dest are on the same filesystem). + +The parent deletes response.json before writing a new command.json to avoid stale reads. +""" + +from __future__ import annotations + +import json +import logging +import os +import tempfile +import time +from pathlib import Path + +from easi.core.exceptions import SimulatorTimeoutError + +logger = logging.getLogger("easi.communication") + +# Default filenames used in the IPC workspace +COMMAND_FILE = "command.json" +RESPONSE_FILE = "response.json" +STATUS_FILE = "status.json" + + +def create_workspace(prefix: str = "easi_") -> Path: + """Create a unique temporary workspace directory for IPC. + + Each EASI process/episode gets its own workspace, so concurrent processes + never collide on file paths. + """ + workspace = Path(tempfile.mkdtemp(prefix=prefix)) + logger.debug("Created IPC workspace: %s", workspace) + return workspace + + +def cleanup_workspace(workspace: Path) -> None: + """Remove the IPC workspace directory and all contents.""" + import shutil + + if workspace.exists(): + shutil.rmtree(workspace, ignore_errors=True) + logger.debug("Cleaned up IPC workspace: %s", workspace) + + +def atomic_write_json(path: Path, data: dict) -> None: + """Write JSON data atomically using write-to-tmp + rename pattern. + + This ensures readers never see a partially written file. + """ + tmp_path = path.with_suffix(".tmp") + tmp_path.write_text(json.dumps(data, indent=2)) + os.rename(str(tmp_path), str(path)) + + +def read_json(path: Path) -> dict | None: + """Read a JSON file, returning None if it doesn't exist or is invalid. + + Uses try/except instead of exists-then-read to avoid TOCTOU races. + """ + try: + return json.loads(path.read_text()) + except (FileNotFoundError, json.JSONDecodeError): + return None + + +def delete_file(path: Path) -> None: + """Delete a file if it exists, ignoring FileNotFoundError.""" + try: + path.unlink() + except FileNotFoundError: + pass + + +def write_command(workspace: Path, command: dict) -> None: + """Write a command for the bridge subprocess to read. + + Deletes any existing response first to prevent stale reads. + """ + response_path = workspace / RESPONSE_FILE + command_path = workspace / COMMAND_FILE + + delete_file(response_path) + atomic_write_json(command_path, command) + logger.debug("Wrote command: %s", command.get("type", "unknown")) + + +def poll_for_response( + workspace: Path, + poll_interval: float = 0.1, + timeout: float = 60.0, + process: object | None = None, +) -> dict: + """Poll the workspace for a response.json file from the bridge subprocess. + + Args: + workspace: IPC workspace directory. + poll_interval: Seconds between poll attempts. + timeout: Maximum seconds to wait before raising SimulatorTimeoutError. + process: Optional subprocess.Popen instance. If provided, checks whether + the subprocess has exited (crashed) during polling. + + Returns: + Parsed response dict. + + Raises: + SimulatorTimeoutError: If timeout is exceeded. + SimulatorError: If the subprocess has exited unexpectedly. + """ + from easi.core.exceptions import SimulatorError + + response_path = workspace / RESPONSE_FILE + deadline = time.monotonic() + timeout + + while time.monotonic() < deadline: + # Check if subprocess has crashed + if process is not None and hasattr(process, "poll"): + if process.poll() is not None: + raise SimulatorError( + f"Bridge subprocess exited with code {process.returncode} " + f"while waiting for response" + ) + + data = read_json(response_path) + if data is not None: + logger.debug("Received response: status=%s", data.get("status", "unknown")) + return data + + time.sleep(poll_interval) + + raise SimulatorTimeoutError( + f"Timed out waiting for response after {timeout}s", + timeout=timeout, + ) + + +def poll_for_status( + workspace: Path, + poll_interval: float = 0.1, + timeout: float = 30.0, + process: object | None = None, +) -> dict: + """Poll the workspace for a status.json file (bridge startup health check). + + Same semantics as poll_for_response but reads status.json instead. + """ + from easi.core.exceptions import SimulatorError + + status_path = workspace / STATUS_FILE + deadline = time.monotonic() + timeout + + while time.monotonic() < deadline: + if process is not None and hasattr(process, "poll"): + if process.poll() is not None: + raise SimulatorError( + f"Bridge subprocess exited with code {process.returncode} " + f"during startup" + ) + + data = read_json(status_path) + if data is not None: + logger.debug("Received status: ready=%s", data.get("ready", False)) + return data + + time.sleep(poll_interval) + + raise SimulatorTimeoutError( + f"Bridge subprocess did not report ready within {timeout}s", + timeout=timeout, + ) + + +def poll_for_command( + workspace: Path, + poll_interval: float = 0.1, + timeout: float = 60.0, +) -> dict: + """Poll the workspace for a command.json file (bridge-side). + + Used by the bridge subprocess to wait for commands from the parent. + + Returns: + Parsed command dict. + + Raises: + SimulatorTimeoutError: If timeout is exceeded. + """ + command_path = workspace / COMMAND_FILE + deadline = time.monotonic() + timeout + + while time.monotonic() < deadline: + data = read_json(command_path) + if data is not None: + # Delete the command file after reading to signal we've consumed it + delete_file(command_path) + logger.debug("Bridge received command: %s", data.get("type", "unknown")) + return data + + time.sleep(poll_interval) + + raise SimulatorTimeoutError( + f"No command received within {timeout}s", + timeout=timeout, + ) + + +def write_response(workspace: Path, response: dict) -> None: + """Write a response for the parent process to read (bridge-side).""" + response_path = workspace / RESPONSE_FILE + atomic_write_json(response_path, response) + logger.debug("Bridge wrote response: status=%s", response.get("status", "unknown")) + + +def write_status(workspace: Path, ready: bool) -> None: + """Write a status file to signal bridge readiness (bridge-side).""" + status_path = workspace / STATUS_FILE + atomic_write_json(status_path, {"ready": ready}) + logger.debug("Bridge wrote status: ready=%s", ready) diff --git a/easi/communication/schemas.py b/easi/communication/schemas.py new file mode 100644 index 0000000..1e0efae --- /dev/null +++ b/easi/communication/schemas.py @@ -0,0 +1,96 @@ +"""JSON schemas for command/response exchange between parent and bridge subprocess.""" + +from __future__ import annotations + +import json +from dataclasses import asdict +from pathlib import Path +from typing import Any + +from easi.core.episode import Action, Observation, StepResult + + +# --- Command schemas (parent → child) --- + +def make_reset_command(episode_id: str, reset_config: dict | None = None) -> dict: + return { + "type": "reset", + "episode_id": episode_id, + "reset_config": reset_config or {}, + } + + +def make_step_command(action: Action) -> dict: + return { + "type": "step", + "action": { + "action_name": action.action_name, + "params": action.params, + }, + } + + +def make_close_command() -> dict: + return {"type": "close"} + + +# --- Response schemas (child → parent) --- + +def make_observation_response( + rgb_path: str, + depth_path: str | None = None, + agent_pose: list[float] | None = None, + metadata: dict[str, str] | None = None, + reward: float = 0.0, + done: bool = False, + info: dict[str, float] | None = None, +) -> dict: + return { + "status": "ok", + "observation": { + "rgb_path": rgb_path, + "depth_path": depth_path, + "agent_pose": agent_pose or [], + "metadata": metadata or {}, + }, + "reward": reward, + "done": done, + "info": info or {}, + } + + +def make_error_response(error: str) -> dict: + return {"status": "error", "error": error} + + +def make_status_response(ready: bool) -> dict: + return {"ready": ready} + + +# --- Parsing helpers --- + +def parse_observation(data: dict) -> Observation: + obs = data["observation"] + return Observation( + rgb_path=obs["rgb_path"], + depth_path=obs.get("depth_path"), + agent_pose=obs.get("agent_pose", []), + metadata=obs.get("metadata", {}), + ) + + +def parse_step_result(data: dict) -> StepResult: + return StepResult( + observation=parse_observation(data), + reward=data.get("reward", 0.0), + done=data.get("done", False), + info=data.get("info", {}), + ) + + +def parse_action_from_command(data: dict) -> Action: + action_data = data["action"] + return Action( + action_name=action_data["action_name"], + params=action_data.get("params", {}), + ) diff --git a/easi/core/__init__.py b/easi/core/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/easi/core/base_agent.py b/easi/core/base_agent.py new file mode 100644 index 0000000..4cde3fb --- /dev/null +++ b/easi/core/base_agent.py @@ -0,0 +1,121 @@ +"""Abstract base class for agents. + +An agent bridges the LLM inference endpoint and the simulator. It manages: +- Chat history across steps +- Prompt building (system prompt, per-step observation prompts) +- Parsing LLM text responses into structured Action objects + +Concrete agents subclass this and implement the three abstract methods: +_build_system_prompt, _build_step_prompt, _parse_action. +""" + +from __future__ import annotations + +import logging +from abc import ABC, abstractmethod + +from easi.core.episode import Action, Observation +from easi.core.exceptions import ActionParseError +from easi.core.protocols import LLMClientProtocol + +logger = logging.getLogger("easi.core.base_agent") + + +class BaseAgent(ABC): + """Abstract base for agents that bridge LLM inference and simulator actions.""" + + def __init__(self, llm_client: LLMClientProtocol | None, action_space: list[str]): + self.llm_client = llm_client + self.action_space = action_space + self._chat_history: list[dict[str, str]] = [] + self._step_count: int = 0 + + @abstractmethod + def _build_system_prompt(self, task_description: str) -> str: + """Build the system prompt describing the task and action space.""" + ... + + @abstractmethod + def _build_step_prompt(self, observation: Observation) -> str: + """Build the user message for a single step (includes observation description).""" + ... + + @abstractmethod + def _parse_action(self, llm_response: str) -> Action: + """Parse the LLM's text response into a structured Action. + + Raises: + ActionParseError: If the response cannot be parsed. + """ + ... + + def reset(self) -> None: + """Clear chat history for a new episode.""" + self._chat_history = [] + self._step_count = 0 + + def act(self, observation: Observation, task_description: str) -> Action: + """Full agent loop for one step. + + 1. Build system prompt on first call + 2. Build step prompt with observation + 3. Collect image paths from observation + 4. Call LLM inference endpoint + 5. Parse response into Action + 6. Append to chat history + """ + # Add system prompt on first step + if self._step_count == 0: + system_prompt = self._build_system_prompt(task_description) + if system_prompt: + self._chat_history.append({ + "role": "system", + "content": system_prompt, + }) + + # Build step prompt + self._step_count += 1 + step_prompt = self._build_step_prompt(observation) + + # Collect images + images = [] + if observation.rgb_path: + images.append(observation.rgb_path) + + # Add user message to history + user_message = {"role": "user", "content": step_prompt} + if images: + user_message["images"] = images + self._chat_history.append(user_message) + + # Call LLM + if self.llm_client is not None: + llm_response = self.llm_client.generate( + messages=self._chat_history, + images=images if images else None, + ) + else: + llm_response = "" + + # Parse action + action = self._parse_action(llm_response) + + # Append assistant response to history + self._chat_history.append({ + "role": "assistant", + "content": llm_response, + }) + + logger.debug( + "Step %d: action=%s params=%s", + self._step_count, + action.action_name, + action.params, + ) + + return action + + @property + def chat_history(self) -> list[dict[str, str]]: + """Return a copy of the chat history.""" + return self._chat_history.copy() diff --git a/easi/core/base_env_manager.py b/easi/core/base_env_manager.py new file mode 100644 index 0000000..d51edcf --- /dev/null +++ b/easi/core/base_env_manager.py @@ -0,0 +1,205 @@ +"""Abstract base class for per-simulator-version environment management. + +Each simulator version provides a concrete subclass that declares: +- conda_env.yaml path (conda-channel packages only) +- requirements.txt path (pip-installable Python deps, installed via uv) +- system dependencies (xvfb, EGL, etc.) +- validation import to confirm the env works + +The shared install() logic handles the full sequence: + check_system_deps → conda create → pip install uv → uv pip install → validate +""" + +from __future__ import annotations + +import logging +import subprocess +from abc import ABC, abstractmethod +from pathlib import Path + +from easi.core.exceptions import EnvironmentSetupError +from easi.utils.locking import file_lock +from easi.utils.paths import get_locks_dir +from easi.utils.system_deps import SystemDependencyChecker + +logger = logging.getLogger("easi.core.base_env_manager") + + +class BaseEnvironmentManager(ABC): + """Abstract base for per-simulator-version environment management.""" + + def __init__(self, conda_prefix: Path | None = None): + self.conda_prefix = conda_prefix or self._default_conda_prefix() + self._dep_checker = SystemDependencyChecker() + + @property + @abstractmethod + def simulator_name(self) -> str: + """Name of the simulator (e.g., 'ai2thor').""" + ... + + @property + @abstractmethod + def version(self) -> str: + """Version identifier (e.g., 'v2_1_0').""" + ... + + @abstractmethod + def get_conda_env_yaml_path(self) -> Path: + """Path to the conda environment YAML (conda-only deps).""" + ... + + @abstractmethod + def get_requirements_txt_path(self) -> Path: + """Path to requirements.txt (uv-installed Python deps).""" + ... + + @abstractmethod + def get_system_deps(self) -> list[str]: + """List of required system packages (e.g., ['xvfb', 'conda']).""" + ... + + @abstractmethod + def get_validation_import(self) -> str: + """Python import statement to validate env works. + + Example: "import ai2thor; assert ai2thor.__version__.startswith('2.1')" + """ + ... + + @property + def needs_display(self) -> bool: + """Whether this simulator requires a display (X11/Xvfb). + + Override to return True for simulators that need it (e.g., AI2-THOR). + """ + return False + + @property + def xvfb_screen_config(self) -> str: + """Xvfb screen config. Override for custom resolution/depth.""" + return "1024x768x24" + + def get_env_name(self) -> str: + """Conda environment name for this simulator version.""" + return f"easi_{self.simulator_name}_{self.version}" + + def get_python_executable(self) -> str: + """Return the full path to the Python executable in this conda env.""" + env_path = self.conda_prefix / "envs" / self.get_env_name() + return str(env_path / "bin" / "python") + + def check_system_deps(self) -> list[str]: + """Check system dependencies, returning list of missing ones.""" + return self._dep_checker.check_all(self.get_system_deps()) + + def env_is_ready(self) -> bool: + """Check if the conda environment exists and passes validation.""" + python_exec = self.get_python_executable() + if not Path(python_exec).exists(): + return False + + try: + result = subprocess.run( + [python_exec, "-c", self.get_validation_import()], + capture_output=True, + text=True, + timeout=30, + ) + return result.returncode == 0 + except (subprocess.TimeoutExpired, FileNotFoundError): + return False + + def install(self) -> None: + """Install the conda+uv environment with file-based locking. + + Serializes concurrent installs of the same env across processes. + """ + lock_path = get_locks_dir() / f"{self.get_env_name()}.lock" + with file_lock(lock_path): + if self.env_is_ready(): + logger.info("Environment %s already ready, skipping install", self.get_env_name()) + return + self._do_install() + + def _do_install(self) -> None: + """Execute the full install sequence (called under lock).""" + env_name = self.get_env_name() + logger.info("Installing environment: %s", env_name) + + # Step 1: Check system deps + self._dep_checker.assert_all(self.get_system_deps()) + + # Step 2: Create/update conda env + conda_yaml = self.get_conda_env_yaml_path() + if conda_yaml.exists(): + self._run_conda_create(env_name, conda_yaml) + else: + logger.warning("No conda_env.yaml found at %s, skipping conda setup", conda_yaml) + + # Step 3: Install uv in the conda env + python_exec = self.get_python_executable() + self._run_command([python_exec, "-m", "pip", "install", "uv"], "pip install uv") + + # Step 4: Install Python deps via uv + requirements = self.get_requirements_txt_path() + if requirements.exists(): + self._run_command( + [python_exec, "-m", "uv", "pip", "install", "-r", str(requirements)], + "uv pip install", + ) + else: + logger.warning("No requirements.txt found at %s, skipping uv install", requirements) + + # Step 5: Validate + result = subprocess.run( + [python_exec, "-c", self.get_validation_import()], + capture_output=True, + text=True, + timeout=30, + ) + if result.returncode != 0: + raise EnvironmentSetupError( + f"Environment validation failed for {env_name}:\n{result.stderr}" + ) + + logger.info("Environment %s installed and validated successfully", env_name) + + def _run_conda_create(self, env_name: str, yaml_path: Path) -> None: + """Create or update a conda environment from a YAML file.""" + env_path = self.conda_prefix / "envs" / env_name + + if env_path.exists(): + cmd = ["conda", "env", "update", "-f", str(yaml_path), "-n", env_name] + desc = "conda env update" + else: + cmd = ["conda", "env", "create", "-f", str(yaml_path), "-n", env_name] + desc = "conda env create" + + self._run_command(cmd, desc) + + def _run_command(self, cmd: list[str], description: str) -> None: + """Run a subprocess command, raising on failure.""" + logger.debug("Running %s: %s", description, " ".join(cmd)) + result = subprocess.run(cmd, capture_output=True, text=True, timeout=600) + if result.returncode != 0: + raise EnvironmentSetupError( + f"{description} failed (exit code {result.returncode}):\n{result.stderr}" + ) + + @staticmethod + def _default_conda_prefix() -> Path: + """Determine the conda prefix from the conda executable.""" + try: + result = subprocess.run( + ["conda", "info", "--base"], + capture_output=True, + text=True, + timeout=10, + ) + if result.returncode == 0: + return Path(result.stdout.strip()) + except (subprocess.TimeoutExpired, FileNotFoundError): + pass + # Fallback to common default + return Path.home() / "miniconda3" diff --git a/easi/core/base_simulator.py b/easi/core/base_simulator.py new file mode 100644 index 0000000..03ca9ba --- /dev/null +++ b/easi/core/base_simulator.py @@ -0,0 +1,111 @@ +"""Abstract base class for simulators. + +Concrete simulators subclass this and implement: +- name/version properties +- _get_bridge_script_path() — path to the bridge.py for subprocess execution +- _parse_observation() — convert bridge output into Observation +- _format_action() — convert Action into dict the bridge understands + +The shared template methods (reset, step, close) handle subprocess communication +via the filesystem IPC layer. +""" + +from __future__ import annotations + +import logging +from abc import ABC, abstractmethod +from pathlib import Path + +from easi.communication import filesystem, schemas +from easi.core.episode import Action, Observation, StepResult + +logger = logging.getLogger("easi.core.base_simulator") + + +class BaseSimulator(ABC): + """Abstract base for all simulators. Manages subprocess lifecycle via IPC.""" + + def __init__(self, workspace_dir: Path | None = None): + self._workspace_dir = workspace_dir + self._runner = None # Set when start() is called + + @property + @abstractmethod + def name(self) -> str: + """Simulator name (e.g., 'ai2thor').""" + ... + + @property + @abstractmethod + def version(self) -> str: + """Version identifier (e.g., 'v2_1_0').""" + ... + + @abstractmethod + def _get_bridge_script_path(self) -> Path: + """Return the absolute path to the bridge.py script for subprocess execution.""" + ... + + def _parse_observation(self, data: dict) -> Observation: + """Parse observation from response data. Override for custom parsing.""" + return schemas.parse_observation(data) + + def _format_action(self, action: Action) -> dict: + """Format an Action into the command dict. Override for custom formatting.""" + return schemas.make_step_command(action) + + def reset(self, episode_id: str, reset_config: dict | None = None) -> Observation: + """Reset the simulator for a new episode. + + Sends a reset command to the bridge subprocess and waits for the + observation response. + """ + if self._runner is None: + raise RuntimeError("Simulator not started. Call start() first.") + + command = schemas.make_reset_command(episode_id, reset_config) + response = self._runner.send_command(command) + + if response.get("status") == "error": + from easi.core.exceptions import SimulatorError + raise SimulatorError(f"Reset failed: {response.get('error', 'unknown')}") + + return self._parse_observation(response) + + def step(self, action: Action) -> StepResult: + """Execute one action in the simulator. + + Sends a step command and returns the StepResult. + """ + if self._runner is None: + raise RuntimeError("Simulator not started. Call start() first.") + + command = self._format_action(action) + response = self._runner.send_command(command) + + if response.get("status") == "error": + from easi.core.exceptions import SimulatorError + raise SimulatorError(f"Step failed: {response.get('error', 'unknown')}") + + return schemas.parse_step_result(response) + + def close(self) -> None: + """Shut down the simulator subprocess.""" + if self._runner is not None: + command = schemas.make_close_command() + try: + self._runner.send_command(command, timeout=10.0) + except Exception: + logger.warning("Close command failed, force-killing subprocess") + self._runner.shutdown() + self._runner = None + + def is_running(self) -> bool: + """Check if the bridge subprocess is alive.""" + if self._runner is None: + return False + return self._runner.is_alive() + + def set_runner(self, runner: object) -> None: + """Attach a SubprocessRunner instance (called by orchestration code).""" + self._runner = runner diff --git a/easi/core/base_task.py b/easi/core/base_task.py new file mode 100644 index 0000000..5037700 --- /dev/null +++ b/easi/core/base_task.py @@ -0,0 +1,190 @@ +"""Abstract base class for tasks (benchmarks). + +A task owns: +- Which simulator+version to use (pinned via task.yaml) +- The action space available to the agent +- The dataset (episodes to evaluate on) +- Episode-to-simulator mapping (format_reset_config) +- Success criteria and metrics (evaluate_episode) + +Concrete tasks subclass this and implement: +- get_task_yaml_path() — where the task.yaml lives +- format_reset_config() — adapter from dataset episodes to simulator configs +- evaluate_episode() — compute metrics for a completed episode +""" + +from __future__ import annotations + +import json +import logging +from abc import ABC, abstractmethod +from pathlib import Path + +import yaml + +from easi.core.episode import StepResult +from easi.core.exceptions import DatasetError + +logger = logging.getLogger("easi.core.base_task") + + +class BaseTask(ABC): + """Abstract base for all tasks (benchmarks).""" + + def __init__(self, data_dir: Path | None = None): + self._config = self._load_config() + self._episodes: list[dict] | None = None + self._data_dir = data_dir + + @abstractmethod + def get_task_yaml_path(self) -> Path: + """Return path to this task's task.yaml.""" + ... + + @abstractmethod + def format_reset_config(self, episode: dict) -> dict: + """Translate a dataset episode into simulator reset kwargs. + + This is the core adapter method between dataset format and simulator API. + """ + ... + + @abstractmethod + def evaluate_episode( + self, episode: dict, trajectory: list[StepResult] + ) -> dict[str, float]: + """Evaluate a completed episode, returning a metric dict. + + Example return: {"success": 1.0, "spl": 0.73, "distance_to_goal": 0.15} + """ + ... + + # --- Shared implementation --- + + @property + def name(self) -> str: + return self._config["name"] + + @property + def simulator_key(self) -> str: + """Returns e.g. 'dummy:v1' — used to look up from simulator registry.""" + return self._config["simulator"] + + @property + def action_space(self) -> list[str]: + return self._config["action_space"] + + @property + def max_steps(self) -> int: + return self._config.get("max_steps", 500) + + def download_dataset(self) -> Path: + """Download dataset if needed. Returns path to local data directory. + + - source=local: validate path exists, return it + - source=huggingface: download via huggingface_hub, cache locally + """ + dataset_config = self._config.get("dataset", {}) + source = dataset_config.get("source", "local") + + if source == "local": + path = dataset_config.get("path") + if path is None: + # Use built-in episodes (no download needed) + return Path() + local_path = Path(path) + if not local_path.exists(): + raise DatasetError(f"Local dataset path does not exist: {local_path}") + return local_path + + elif source == "huggingface": + return self._download_huggingface(dataset_config) + + else: + raise DatasetError(f"Unknown dataset source: {source}") + + def load_episodes(self) -> list[dict]: + """Load and return all episodes from the dataset.""" + if self._episodes is not None: + return self._episodes + + self._episodes = self._load_episodes_from_config() + logger.info("Loaded %d episodes for task %s", len(self._episodes), self.name) + return self._episodes + + def get_episode(self, index: int) -> dict: + """Get a single episode by index.""" + episodes = self.load_episodes() + if index < 0 or index >= len(episodes): + raise IndexError(f"Episode index {index} out of range [0, {len(episodes)})") + return episodes[index] + + def __len__(self) -> int: + """Number of episodes.""" + return len(self.load_episodes()) + + def _load_config(self) -> dict: + """Load task.yaml.""" + yaml_path = self.get_task_yaml_path() + if not yaml_path.exists(): + raise DatasetError(f"Task config not found: {yaml_path}") + return yaml.safe_load(yaml_path.read_text()) + + def _load_episodes_from_config(self) -> list[dict]: + """Load episodes from the dataset directory. + + Subclasses can override this for custom episode loading logic. + Default implementation looks for episodes.json in the data directory. + """ + data_dir = self._data_dir or self.download_dataset() + if not data_dir or data_dir == Path(): + # No data dir — subclass should provide built-in episodes + return self._get_builtin_episodes() + + episodes_file = data_dir / "episodes.json" + if episodes_file.exists(): + return json.loads(episodes_file.read_text()) + + raise DatasetError( + f"No episodes.json found in {data_dir}. " + f"Override _load_episodes_from_config() for custom loading." + ) + + def _get_builtin_episodes(self) -> list[dict]: + """Return built-in episodes when no dataset download is needed. + + Override in subclasses that provide built-in test episodes (e.g., DummyTask). + """ + return [] + + def _download_huggingface(self, config: dict) -> Path: + """Download a dataset from HuggingFace Hub with file-based locking.""" + from easi.utils.locking import file_lock + from easi.utils.paths import get_datasets_dir, get_locks_dir + + repo_id = config["repo_id"] + lock_path = get_locks_dir() / f"dataset_{repo_id.replace('/', '_')}.lock" + + with file_lock(lock_path): + target = get_datasets_dir() / repo_id.replace("/", "_") + if target.exists(): + logger.info("Dataset %s already cached at %s", repo_id, target) + return target + + try: + from huggingface_hub import snapshot_download + + snapshot_download( + repo_id=repo_id, + local_dir=str(target), + repo_type="dataset", + ) + logger.info("Downloaded dataset %s to %s", repo_id, target) + return target + except ImportError: + raise DatasetError( + "huggingface_hub is required for HuggingFace downloads. " + "Install with: pip install huggingface_hub" + ) + except Exception as e: + raise DatasetError(f"Failed to download {repo_id}: {e}") diff --git a/easi/core/episode.py b/easi/core/episode.py new file mode 100644 index 0000000..a7453a1 --- /dev/null +++ b/easi/core/episode.py @@ -0,0 +1,33 @@ +"""Core data structures for observations, actions, and step results.""" + +from __future__ import annotations + +from dataclasses import dataclass, field + + +@dataclass +class Observation: + """Observation produced by a simulator after reset or step.""" + + rgb_path: str + depth_path: str | None = None + agent_pose: list[float] = field(default_factory=list) + metadata: dict[str, str] = field(default_factory=dict) + + +@dataclass +class Action: + """An action to be executed in the simulator.""" + + action_name: str + params: dict[str, float] = field(default_factory=dict) + + +@dataclass +class StepResult: + """Result of executing one step in the simulator.""" + + observation: Observation + reward: float = 0.0 + done: bool = False + info: dict[str, float] = field(default_factory=dict) diff --git a/easi/core/exceptions.py b/easi/core/exceptions.py new file mode 100644 index 0000000..be8a87f --- /dev/null +++ b/easi/core/exceptions.py @@ -0,0 +1,37 @@ +"""Custom exception hierarchy for EASI.""" + + +class EASIError(Exception): + """Base exception for all EASI errors.""" + + +class EnvironmentSetupError(EASIError): + """Raised when a simulator environment fails to install or validate.""" + + def __init__(self, message: str, missing_deps: list[str] | None = None): + super().__init__(message) + self.missing_deps = missing_deps or [] + + +class SimulatorError(EASIError): + """Raised when a simulator encounters an error during operation.""" + + +class SimulatorTimeoutError(SimulatorError): + """Raised when a simulator operation exceeds the configured timeout.""" + + def __init__(self, message: str, timeout: float): + super().__init__(message) + self.timeout = timeout + + +class ActionParseError(EASIError): + """Raised when an LLM response cannot be parsed into a valid Action.""" + + def __init__(self, message: str, raw_response: str): + super().__init__(message) + self.raw_response = raw_response + + +class DatasetError(EASIError): + """Raised when a dataset cannot be loaded or downloaded.""" diff --git a/easi/core/protocols.py b/easi/core/protocols.py new file mode 100644 index 0000000..877c138 --- /dev/null +++ b/easi/core/protocols.py @@ -0,0 +1,74 @@ +"""Protocol classes defining interfaces for EASI components.""" + +from __future__ import annotations + +from pathlib import Path +from typing import Protocol, runtime_checkable + +from easi.core.episode import Action, Observation, StepResult + + +@runtime_checkable +class SimulatorProtocol(Protocol): + """Any simulator must satisfy this interface.""" + + name: str + version: str + + def reset(self, episode_id: str, reset_config: dict | None = None) -> Observation: ... + def step(self, action: Action) -> StepResult: ... + def close(self) -> None: ... + def is_running(self) -> bool: ... + + +@runtime_checkable +class EnvironmentManagerProtocol(Protocol): + """Manages conda+uv environment for a specific simulator version.""" + + simulator_name: str + version: str + + def check_system_deps(self) -> list[str]: ... + def install(self) -> None: ... + def env_is_ready(self) -> bool: ... + def get_python_executable(self) -> str: ... + def get_env_name(self) -> str: ... + + +@runtime_checkable +class LLMClientProtocol(Protocol): + """Calls an LLM inference server.""" + + def generate( + self, messages: list[dict[str, str]], images: list[str] | None = None + ) -> str: ... + + +@runtime_checkable +class AgentProtocol(Protocol): + """An agent that decides actions given observations.""" + + def reset(self) -> None: ... + def act(self, observation: Observation, task_description: str) -> Action: ... + + @property + def chat_history(self) -> list[dict[str, str]]: ... + + +@runtime_checkable +class TaskProtocol(Protocol): + """A benchmark task that maps dataset episodes to simulator configs.""" + + name: str + simulator_key: str + action_space: list[str] + max_steps: int + + def download_dataset(self) -> Path: ... + def load_episodes(self) -> list[dict]: ... + def get_episode(self, index: int) -> dict: ... + def format_reset_config(self, episode: dict) -> dict: ... + def evaluate_episode( + self, episode: dict, trajectory: list[StepResult] + ) -> dict[str, float]: ... + def __len__(self) -> int: ... diff --git a/easi/llm/__init__.py b/easi/llm/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/easi/llm/api_client.py b/easi/llm/api_client.py new file mode 100644 index 0000000..e62f0c1 --- /dev/null +++ b/easi/llm/api_client.py @@ -0,0 +1,84 @@ +"""HTTP client for OpenAI-compatible LLM inference servers. + +Works with vLLM, SGLang, Ollama, and the built-in dummy server — +any server that implements the /v1/chat/completions endpoint. +""" + +from __future__ import annotations + +import logging +from typing import Any + +import requests + +logger = logging.getLogger("easi.llm.api_client") + + +class LLMApiClient: + """Stateless HTTP client for LLM inference endpoints.""" + + def __init__( + self, + base_url: str = "http://127.0.0.1:8000", + model: str = "default", + timeout: float = 120.0, + max_tokens: int = 512, + temperature: float = 0.0, + ): + self.base_url = base_url.rstrip("/") + self.model = model + self.timeout = timeout + self.max_tokens = max_tokens + self.temperature = temperature + + def generate( + self, + messages: list[dict[str, str]], + images: list[str] | None = None, + ) -> str: + """Send a chat completion request and return the assistant's response text. + + Args: + messages: Chat history in OpenAI format. + images: Optional list of image paths (currently passed as metadata; + real multimodal support depends on the server implementation). + + Returns: + The assistant's response text. + """ + payload: dict[str, Any] = { + "model": self.model, + "messages": messages, + "max_tokens": self.max_tokens, + "temperature": self.temperature, + } + + if images: + payload["images"] = images + + url = f"{self.base_url}/v1/chat/completions" + logger.debug("POST %s (messages: %d)", url, len(messages)) + + try: + response = requests.post(url, json=payload, timeout=self.timeout) + response.raise_for_status() + except requests.ConnectionError: + raise ConnectionError( + f"Cannot connect to LLM server at {self.base_url}. " + f"Start one with: easi llm-server" + ) + except requests.Timeout: + raise TimeoutError( + f"LLM server request timed out after {self.timeout}s" + ) + except requests.HTTPError as e: + raise RuntimeError(f"LLM server returned error: {e}") + + data = response.json() + choices = data.get("choices", []) + if not choices: + raise RuntimeError(f"LLM server returned no choices: {data}") + + content = choices[0].get("message", {}).get("content", "") + logger.debug("Response: %s", content[:100]) + return content diff --git a/easi/llm/dummy_server.py b/easi/llm/dummy_server.py new file mode 100644 index 0000000..30a44a9 --- /dev/null +++ b/easi/llm/dummy_server.py @@ -0,0 +1,155 @@ +"""Minimal dummy LLM server for testing. + +Implements OpenAI-compatible /v1/chat/completions endpoint using stdlib +http.server. Returns fixed or random actions. + +Usage: + python -m easi.llm.dummy_server --port 8000 --mode random + # or via CLI: + easi llm-server --port 8000 --mode random +""" + +from __future__ import annotations + +import argparse +import json +import logging +import random +from http.server import BaseHTTPRequestHandler, HTTPServer + +logger = logging.getLogger("easi.llm.dummy_server") + +DEFAULT_ACTION_SPACE = ["MoveAhead", "TurnLeft", "TurnRight", "Stop"] + + +class DummyLLMHandler(BaseHTTPRequestHandler): + """HTTP handler for the dummy LLM server.""" + + # Set by the server factory + mode: str = "random" + action_space: list[str] = DEFAULT_ACTION_SPACE + + def do_POST(self) -> None: + if self.path == "/v1/chat/completions": + self._handle_chat_completions() + else: + self.send_error(404, f"Not found: {self.path}") + + def do_GET(self) -> None: + if self.path == "/health": + self._send_json({"status": "ok"}) + else: + self.send_error(404, f"Not found: {self.path}") + + def _handle_chat_completions(self) -> None: + content_length = int(self.headers.get("Content-Length", 0)) + body = self.rfile.read(content_length) + + try: + request = json.loads(body) + except json.JSONDecodeError: + self.send_error(400, "Invalid JSON") + return + + # Generate response based on mode + if self.mode == "fixed": + action = self.action_space[0] if self.action_space else "MoveAhead" + else: # random + action = random.choice(self.action_space) + + response_text = f"I will take the following action.\nAction: {action}" + + response = { + "id": "dummy-001", + "object": "chat.completion", + "choices": [ + { + "index": 0, + "message": { + "role": "assistant", + "content": response_text, + }, + "finish_reason": "stop", + } + ], + "usage": { + "prompt_tokens": 0, + "completion_tokens": 0, + "total_tokens": 0, + }, + } + + self._send_json(response) + + def _send_json(self, data: dict) -> None: + body = json.dumps(data).encode("utf-8") + self.send_response(200) + self.send_header("Content-Type", "application/json") + self.send_header("Content-Length", str(len(body))) + self.end_headers() + self.wfile.write(body) + + def log_message(self, format: str, *args) -> None: + logger.debug(format, *args) + + +def create_handler(mode: str, action_space: list[str]) -> type: + """Create a handler class with the given configuration.""" + + class ConfiguredHandler(DummyLLMHandler): + pass + + ConfiguredHandler.mode = mode + ConfiguredHandler.action_space = action_space + return ConfiguredHandler + + +def run_server( + host: str = "127.0.0.1", + port: int = 8000, + mode: str = "random", + action_space: list[str] | None = None, +) -> None: + """Start the dummy LLM server.""" + action_space = action_space or DEFAULT_ACTION_SPACE + handler_class = create_handler(mode, action_space) + + server = HTTPServer((host, port), handler_class) + logger.info("Dummy LLM server starting on %s:%d (mode=%s)", host, port, mode) + logger.info("Action space: %s", action_space) + print(f"Dummy LLM server running on http://{host}:{port}") + print(f"Mode: {mode}, Actions: {action_space}") + print("Press Ctrl+C to stop") + + try: + server.serve_forever() + except KeyboardInterrupt: + print("\nShutting down...") + finally: + server.server_close() + + +def main() -> None: + parser = argparse.ArgumentParser(description="Dummy LLM server") + parser.add_argument("--host", type=str, default="127.0.0.1") + parser.add_argument("--port", type=int, default=8000) + parser.add_argument("--mode", choices=["fixed", "random"], default="random") + parser.add_argument( + "--action-space", + type=str, + nargs="+", + default=DEFAULT_ACTION_SPACE, + ) + args = parser.parse_args() + + logging.basicConfig(level=logging.INFO) + run_server( + host=args.host, + port=args.port, + mode=args.mode, + action_space=args.action_space, + ) + + +if __name__ == "__main__": + main() diff --git a/easi/simulators/__init__.py b/easi/simulators/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/easi/simulators/ai2thor/__init__.py b/easi/simulators/ai2thor/__init__.py new file mode 100644 index 0000000..0dfa5a2 --- /dev/null +++ b/easi/simulators/ai2thor/__init__.py @@ -0,0 +1 @@ +"""AI2-THOR simulator package (multiple versions).""" diff --git a/easi/simulators/ai2thor/manifest.yaml b/easi/simulators/ai2thor/manifest.yaml new file mode 100644 index 0000000..ef2039b --- /dev/null +++ b/easi/simulators/ai2thor/manifest.yaml @@ -0,0 +1,14 @@ +name: ai2thor +display_name: "AI2-THOR" +default_version: "v5_0_0" +versions: + v2_1_0: + description: "AI2-THOR 2.1.0 — used by embodiedbench ALFRED track (legacy API)" + simulator_class: "easi.simulators.ai2thor.v2_1_0.simulator.AI2ThorSimulatorV210" + env_manager_class: "easi.simulators.ai2thor.v2_1_0.env_manager.AI2ThorEnvManagerV210" + python_version: "3.8" + v5_0_0: + description: "AI2-THOR 5.0.0 — latest version, modern API" + simulator_class: "easi.simulators.ai2thor.v5_0_0.simulator.AI2ThorSimulatorV500" + env_manager_class: "easi.simulators.ai2thor.v5_0_0.env_manager.AI2ThorEnvManagerV500" + python_version: "3.10" diff --git a/easi/simulators/ai2thor/v2_1_0/__init__.py b/easi/simulators/ai2thor/v2_1_0/__init__.py new file mode 100644 index 0000000..1b7fee8 --- /dev/null +++ b/easi/simulators/ai2thor/v2_1_0/__init__.py @@ -0,0 +1,6 @@ +"""AI2-THOR v2.1.0 (legacy API for embodiedbench ALFRED).""" + +from easi.simulators.ai2thor.v2_1_0.env_manager import AI2ThorEnvManagerV210 +from easi.simulators.ai2thor.v2_1_0.simulator import AI2ThorSimulatorV210 + +__all__ = ["AI2ThorSimulatorV210", "AI2ThorEnvManagerV210"] diff --git a/easi/simulators/ai2thor/v2_1_0/bridge.py b/easi/simulators/ai2thor/v2_1_0/bridge.py new file mode 100644 index 0000000..a8a3ed7 --- /dev/null +++ b/easi/simulators/ai2thor/v2_1_0/bridge.py @@ -0,0 +1,116 @@ +"""Bridge subprocess for AI2-THOR v2.1.0 (legacy API). + +This script runs inside the ai2thor v2.1.0 conda environment. +It communicates with the parent via filesystem IPC. + +NOTE: This is a stub. The actual AI2-THOR interaction will be implemented +when integrating with embodiedbench. The structure demonstrates how a real +bridge would work. + +Usage: + python bridge.py --workspace /tmp/easi_xxx +""" + +from __future__ import annotations + +import argparse +import logging +import sys +from pathlib import Path + +# Add repo root to path for easi imports +_repo_root = Path(__file__).resolve().parents[4] +if str(_repo_root) not in sys.path: + sys.path.insert(0, str(_repo_root)) + +from easi.communication.filesystem import ( + poll_for_command, + write_response, + write_status, +) +from easi.communication.schemas import ( + make_error_response, + make_observation_response, + parse_action_from_command, +) + +logger = logging.getLogger("easi.bridge.ai2thor_v2_1_0") + + +def run_bridge(workspace: Path) -> None: + """Main bridge loop for AI2-THOR v2.1.0.""" + logger.info("AI2-THOR v2.1.0 bridge starting (workspace: %s)", workspace) + + # In a real implementation: + # from ai2thor.controller import Controller + # controller = Controller(scene="FloorPlan28", gridSize=0.25) + + write_status(workspace, ready=True) + + step_count = 0 + + while True: + try: + command = poll_for_command(workspace, timeout=300.0) + except Exception as e: + logger.error("Failed to read command: %s", e) + break + + cmd_type = command.get("type") + + if cmd_type == "reset": + episode_id = command.get("episode_id", "unknown") + reset_config = command.get("reset_config", {}) + logger.info("Reset: episode_id=%s, scene=%s", episode_id, reset_config.get("scene", "?")) + + # Stub: In real impl, would call controller.reset(scene=...) + step_count = 0 + + response = make_observation_response( + rgb_path=str(workspace / "rgb_0000.png"), + agent_pose=[0.0, 0.9, 0.0, 0.0, 0.0, 0.0], + metadata={"episode_id": episode_id, "step": "0", "scene": reset_config.get("scene", "unknown")}, + ) + # Stub: write a placeholder image + (workspace / "rgb_0000.png").write_bytes(b"STUB_IMAGE") + write_response(workspace, response) + + elif cmd_type == "step": + step_count += 1 + action = parse_action_from_command(command) + logger.debug("Step %d: action=%s", step_count, action.action_name) + + # Stub: In real impl, would call controller.step(action=action_name, **params) + + rgb_name = f"rgb_{step_count:04d}.png" + (workspace / rgb_name).write_bytes(b"STUB_IMAGE") + + response = make_observation_response( + rgb_path=str(workspace / rgb_name), + agent_pose=[float(step_count) * 0.25, 0.9, 0.0, 0.0, 0.0, 0.0], + metadata={"step": str(step_count)}, + done=action.action_name == "Stop", + ) + write_response(workspace, response) + + elif cmd_type == "close": + logger.info("Close command received") + # Stub: controller.stop() would go here + write_response(workspace, {"status": "ok"}) + break + + else: + write_response(workspace, make_error_response(f"Unknown command: {cmd_type}")) + + +def main() -> None: + parser = argparse.ArgumentParser(description="AI2-THOR v2.1.0 bridge") + parser.add_argument("--workspace", type=Path, required=True) + args = parser.parse_args() + + logging.basicConfig(level=logging.DEBUG, format="%(asctime)s [%(levelname)s] %(name)s: %(message)s") + run_bridge(workspace=args.workspace) + + +if __name__ == "__main__": + main() diff --git a/easi/simulators/ai2thor/v2_1_0/conda_env.yaml b/easi/simulators/ai2thor/v2_1_0/conda_env.yaml new file mode 100644 index 0000000..6105ae8 --- /dev/null +++ b/easi/simulators/ai2thor/v2_1_0/conda_env.yaml @@ -0,0 +1,6 @@ +name: easi_ai2thor_v2_1_0 +channels: + - conda-forge + - defaults +dependencies: + - python=3.8 diff --git a/easi/simulators/ai2thor/v2_1_0/env_manager.py b/easi/simulators/ai2thor/v2_1_0/env_manager.py new file mode 100644 index 0000000..aef6e4a --- /dev/null +++ b/easi/simulators/ai2thor/v2_1_0/env_manager.py @@ -0,0 +1,38 @@ +"""Environment manager for AI2-THOR v2.1.0 (legacy API). + +Used by embodiedbench ALFRED track. Requires Python 3.8 and Xvfb. +""" + +from __future__ import annotations + +from pathlib import Path + +from easi.core.base_env_manager import BaseEnvironmentManager + + +class AI2ThorEnvManagerV210(BaseEnvironmentManager): + """Environment manager for AI2-THOR 2.1.0.""" + + @property + def simulator_name(self) -> str: + return "ai2thor" + + @property + def version(self) -> str: + return "v2_1_0" + + @property + def needs_display(self) -> bool: + return True # AI2-THOR Unity requires X11 + + def get_conda_env_yaml_path(self) -> Path: + return Path(__file__).parent / "conda_env.yaml" + + def get_requirements_txt_path(self) -> Path: + return Path(__file__).parent / "requirements.txt" + + def get_system_deps(self) -> list[str]: + return ["conda", "xvfb"] + + def get_validation_import(self) -> str: + return "import ai2thor; assert ai2thor.__version__.startswith('2.1')" diff --git a/easi/simulators/ai2thor/v2_1_0/requirements.txt b/easi/simulators/ai2thor/v2_1_0/requirements.txt new file mode 100644 index 0000000..a482c8d --- /dev/null +++ b/easi/simulators/ai2thor/v2_1_0/requirements.txt @@ -0,0 +1,3 @@ +ai2thor==2.1.0 +numpy<1.24 +Pillow diff --git a/easi/simulators/ai2thor/v2_1_0/simulator.py b/easi/simulators/ai2thor/v2_1_0/simulator.py new file mode 100644 index 0000000..622ab88 --- /dev/null +++ b/easi/simulators/ai2thor/v2_1_0/simulator.py @@ -0,0 +1,25 @@ +"""AI2-THOR v2.1.0 simulator (legacy API). + +Stub implementation — the bridge.py handles the actual AI2-THOR interaction. +""" + +from __future__ import annotations + +from pathlib import Path + +from easi.core.base_simulator import BaseSimulator + + +class AI2ThorSimulatorV210(BaseSimulator): + """AI2-THOR 2.1.0 simulator for embodiedbench ALFRED track.""" + + @property + def name(self) -> str: + return "ai2thor" + + @property + def version(self) -> str: + return "v2_1_0" + + def _get_bridge_script_path(self) -> Path: + return Path(__file__).parent / "bridge.py" diff --git a/easi/simulators/ai2thor/v5_0_0/__init__.py b/easi/simulators/ai2thor/v5_0_0/__init__.py new file mode 100644 index 0000000..4ed8307 --- /dev/null +++ b/easi/simulators/ai2thor/v5_0_0/__init__.py @@ -0,0 +1,6 @@ +"""AI2-THOR v5.0.0 (modern API).""" + +from easi.simulators.ai2thor.v5_0_0.env_manager import AI2ThorEnvManagerV500 +from easi.simulators.ai2thor.v5_0_0.simulator import AI2ThorSimulatorV500 + +__all__ = ["AI2ThorSimulatorV500", "AI2ThorEnvManagerV500"] diff --git a/easi/simulators/ai2thor/v5_0_0/bridge.py b/easi/simulators/ai2thor/v5_0_0/bridge.py new file mode 100644 index 0000000..73acd0f --- /dev/null +++ b/easi/simulators/ai2thor/v5_0_0/bridge.py @@ -0,0 +1,122 @@ +"""Bridge subprocess for AI2-THOR v5.0.0 (modern API). + +This script runs inside the ai2thor v5.0.0 conda environment. +The v5 API is significantly different from v2.1.0. + +NOTE: This is a stub. Structure demonstrates how a real bridge works. + +Usage: + python bridge.py --workspace /tmp/easi_xxx +""" + +from __future__ import annotations + +import argparse +import logging +import sys +from pathlib import Path + +_repo_root = Path(__file__).resolve().parents[4] +if str(_repo_root) not in sys.path: + sys.path.insert(0, str(_repo_root)) + +from easi.communication.filesystem import ( + poll_for_command, + write_response, + write_status, +) +from easi.communication.schemas import ( + make_error_response, + make_observation_response, + parse_action_from_command, +) + +logger = logging.getLogger("easi.bridge.ai2thor_v5_0_0") + + +def run_bridge(workspace: Path) -> None: + """Main bridge loop for AI2-THOR v5.0.0.""" + logger.info("AI2-THOR v5.0.0 bridge starting (workspace: %s)", workspace) + + # In a real implementation: + # from ai2thor.controller import Controller + # controller = Controller(width=1280, height=720, renderDepthImage=True) + + write_status(workspace, ready=True) + + step_count = 0 + + while True: + try: + command = poll_for_command(workspace, timeout=300.0) + except Exception as e: + logger.error("Failed to read command: %s", e) + break + + cmd_type = command.get("type") + + if cmd_type == "reset": + episode_id = command.get("episode_id", "unknown") + reset_config = command.get("reset_config", {}) + logger.info("Reset: episode_id=%s", episode_id) + + # Stub: controller.reset(scene=scene_id) + # v5 API: controller.step(action="Initialize", ...) + step_count = 0 + + rgb_path = workspace / "rgb_0000.png" + depth_path = workspace / "depth_0000.png" + rgb_path.write_bytes(b"STUB_IMAGE") + depth_path.write_bytes(b"STUB_DEPTH") + + response = make_observation_response( + rgb_path=str(rgb_path), + depth_path=str(depth_path), + agent_pose=[0.0, 0.9, 0.0, 0.0, 0.0, 0.0], + metadata={"episode_id": episode_id, "step": "0"}, + ) + write_response(workspace, response) + + elif cmd_type == "step": + step_count += 1 + action = parse_action_from_command(command) + logger.debug("Step %d: action=%s", step_count, action.action_name) + + # Stub: event = controller.step(action=action_name, **params) + # v5 API returns event with frame, depth, metadata + + rgb_name = f"rgb_{step_count:04d}.png" + depth_name = f"depth_{step_count:04d}.png" + (workspace / rgb_name).write_bytes(b"STUB_IMAGE") + (workspace / depth_name).write_bytes(b"STUB_DEPTH") + + response = make_observation_response( + rgb_path=str(workspace / rgb_name), + depth_path=str(workspace / depth_name), + agent_pose=[float(step_count) * 0.25, 0.9, 0.0, 0.0, 0.0, 0.0], + metadata={"step": str(step_count)}, + done=action.action_name == "Stop", + ) + write_response(workspace, response) + + elif cmd_type == "close": + logger.info("Close command received") + # Stub: controller.stop() + write_response(workspace, {"status": "ok"}) + break + + else: + write_response(workspace, make_error_response(f"Unknown command: {cmd_type}")) + + +def main() -> None: + parser = argparse.ArgumentParser(description="AI2-THOR v5.0.0 bridge") + parser.add_argument("--workspace", type=Path, required=True) + args = parser.parse_args() + + logging.basicConfig(level=logging.DEBUG, format="%(asctime)s [%(levelname)s] %(name)s: %(message)s") + run_bridge(workspace=args.workspace) + + +if __name__ == "__main__": + main() diff --git a/easi/simulators/ai2thor/v5_0_0/conda_env.yaml b/easi/simulators/ai2thor/v5_0_0/conda_env.yaml new file mode 100644 index 0000000..6786309 --- /dev/null +++ b/easi/simulators/ai2thor/v5_0_0/conda_env.yaml @@ -0,0 +1,6 @@ +name: easi_ai2thor_v5_0_0 +channels: + - conda-forge + - defaults +dependencies: + - python=3.10 diff --git a/easi/simulators/ai2thor/v5_0_0/env_manager.py b/easi/simulators/ai2thor/v5_0_0/env_manager.py new file mode 100644 index 0000000..c85e609 --- /dev/null +++ b/easi/simulators/ai2thor/v5_0_0/env_manager.py @@ -0,0 +1,39 @@ +"""Environment manager for AI2-THOR v5.0.0 (modern API).""" + +from __future__ import annotations + +from pathlib import Path + +from easi.core.base_env_manager import BaseEnvironmentManager + + +class AI2ThorEnvManagerV500(BaseEnvironmentManager): + """Environment manager for AI2-THOR 5.0.0.""" + + @property + def simulator_name(self) -> str: + return "ai2thor" + + @property + def version(self) -> str: + return "v5_0_0" + + @property + def needs_display(self) -> bool: + return True # AI2-THOR Unity requires X11 + + @property + def xvfb_screen_config(self) -> str: + return "1280x720x24" # v5 supports higher resolution + + def get_conda_env_yaml_path(self) -> Path: + return Path(__file__).parent / "conda_env.yaml" + + def get_requirements_txt_path(self) -> Path: + return Path(__file__).parent / "requirements.txt" + + def get_system_deps(self) -> list[str]: + return ["conda", "xvfb"] + + def get_validation_import(self) -> str: + return "import ai2thor; assert ai2thor.__version__.startswith('5.')" diff --git a/easi/simulators/ai2thor/v5_0_0/requirements.txt b/easi/simulators/ai2thor/v5_0_0/requirements.txt new file mode 100644 index 0000000..7a6f2b1 --- /dev/null +++ b/easi/simulators/ai2thor/v5_0_0/requirements.txt @@ -0,0 +1,3 @@ +ai2thor>=5.0.0,<6.0.0 +numpy>=1.24 +Pillow>=10.0 diff --git a/easi/simulators/ai2thor/v5_0_0/simulator.py b/easi/simulators/ai2thor/v5_0_0/simulator.py new file mode 100644 index 0000000..e78ddc4 --- /dev/null +++ b/easi/simulators/ai2thor/v5_0_0/simulator.py @@ -0,0 +1,22 @@ +"""AI2-THOR v5.0.0 simulator (modern API).""" + +from __future__ import annotations + +from pathlib import Path + +from easi.core.base_simulator import BaseSimulator + + +class AI2ThorSimulatorV500(BaseSimulator): + """AI2-THOR 5.0.0 simulator with modern API.""" + + @property + def name(self) -> str: + return "ai2thor" + + @property + def version(self) -> str: + return "v5_0_0" + + def _get_bridge_script_path(self) -> Path: + return Path(__file__).parent / "bridge.py" diff --git a/easi/simulators/dummy/__init__.py b/easi/simulators/dummy/__init__.py new file mode 100644 index 0000000..6e85b9a --- /dev/null +++ b/easi/simulators/dummy/__init__.py @@ -0,0 +1 @@ +"""Dummy simulator package.""" diff --git a/easi/simulators/dummy/manifest.yaml b/easi/simulators/dummy/manifest.yaml new file mode 100644 index 0000000..47bcc61 --- /dev/null +++ b/easi/simulators/dummy/manifest.yaml @@ -0,0 +1,9 @@ +name: dummy +display_name: "Dummy Simulator" +default_version: "v1" +versions: + v1: + description: "Dummy simulator for testing the pipeline end-to-end" + simulator_class: "easi.simulators.dummy.v1.simulator.DummySimulator" + env_manager_class: "easi.simulators.dummy.v1.env_manager.DummyEnvManager" + python_version: "3.10" diff --git a/easi/simulators/dummy/v1/__init__.py b/easi/simulators/dummy/v1/__init__.py new file mode 100644 index 0000000..f4da707 --- /dev/null +++ b/easi/simulators/dummy/v1/__init__.py @@ -0,0 +1,6 @@ +"""Dummy simulator v1.""" + +from easi.simulators.dummy.v1.env_manager import DummyEnvManager +from easi.simulators.dummy.v1.simulator import DummySimulator + +__all__ = ["DummySimulator", "DummyEnvManager"] diff --git a/easi/simulators/dummy/v1/bridge.py b/easi/simulators/dummy/v1/bridge.py new file mode 100644 index 0000000..ef9e7a3 --- /dev/null +++ b/easi/simulators/dummy/v1/bridge.py @@ -0,0 +1,166 @@ +"""Bridge subprocess for the dummy simulator. + +This script is launched by SubprocessRunner in a separate process. +It communicates with the parent via filesystem IPC (command.json / response.json). + +The dummy simulator generates placeholder RGB images and responds to +reset/step/close commands with configurable delay. + +Usage: + python bridge.py --workspace /tmp/easi_xxx [--step-delay 0.1] +""" + +from __future__ import annotations + +import argparse +import logging +import sys +import time +from pathlib import Path + +# Add the repo root to sys.path so we can import easi modules +# This is needed when the bridge runs in a separate conda env +_repo_root = Path(__file__).resolve().parents[4] +if str(_repo_root) not in sys.path: + sys.path.insert(0, str(_repo_root)) + +from easi.communication.filesystem import ( + poll_for_command, + write_response, + write_status, +) +from easi.communication.schemas import ( + make_error_response, + make_observation_response, + parse_action_from_command, +) + +logger = logging.getLogger("easi.bridge.dummy") + + +def _generate_dummy_image(workspace: Path, step: int) -> str: + """Generate a small placeholder PNG image. + + Creates a minimal valid PNG file (1x1 pixel, red) to simulate + an observation image without requiring Pillow. + """ + rgb_path = workspace / f"rgb_{step:04d}.png" + + # Minimal valid 1x1 red PNG (67 bytes) + # This avoids requiring Pillow in the dummy simulator + import struct + import zlib + + def _create_minimal_png(width: int = 8, height: int = 8) -> bytes: + """Create a minimal valid PNG with a solid color.""" + + def _chunk(chunk_type: bytes, data: bytes) -> bytes: + c = chunk_type + data + crc = zlib.crc32(c) & 0xFFFFFFFF + return struct.pack(">I", len(data)) + c + struct.pack(">I", crc) + + header = b"\x89PNG\r\n\x1a\n" + ihdr = _chunk(b"IHDR", struct.pack(">IIBBBBB", width, height, 8, 2, 0, 0, 0)) + + # Generate raw pixel data (red-ish gradient based on step) + r = (step * 37) % 256 + g = (step * 73) % 256 + b = (step * 113) % 256 + raw_data = b"" + for _ in range(height): + raw_data += b"\x00" # filter byte + raw_data += bytes([r, g, b]) * width + + idat = _chunk(b"IDAT", zlib.compress(raw_data)) + iend = _chunk(b"IEND", b"") + + return header + ihdr + idat + iend + + rgb_path.write_bytes(_create_minimal_png()) + return str(rgb_path) + + +def run_bridge(workspace: Path, step_delay: float = 0.0) -> None: + """Main bridge loop: read commands, process them, write responses.""" + logger.info("Dummy bridge starting (workspace: %s, step_delay: %.2f)", workspace, step_delay) + + # Signal readiness + write_status(workspace, ready=True) + + step_count = 0 + + while True: + try: + command = poll_for_command(workspace, timeout=300.0) + except Exception as e: + logger.error("Failed to read command: %s", e) + break + + cmd_type = command.get("type") + + if cmd_type == "reset": + episode_id = command.get("episode_id", "unknown") + logger.info("Reset: episode_id=%s", episode_id) + + if step_delay > 0: + time.sleep(step_delay) + + step_count = 0 + rgb_path = _generate_dummy_image(workspace, step_count) + + response = make_observation_response( + rgb_path=rgb_path, + agent_pose=[0.0, 0.0, 0.0, 0.0, 0.0, 0.0], + metadata={"episode_id": episode_id, "step": "0"}, + ) + write_response(workspace, response) + + elif cmd_type == "step": + step_count += 1 + action = parse_action_from_command(command) + logger.debug("Step %d: action=%s", step_count, action.action_name) + + if step_delay > 0: + time.sleep(step_delay) + + rgb_path = _generate_dummy_image(workspace, step_count) + + # Dummy: done after 10 steps or on Stop action + done = step_count >= 10 or action.action_name == "Stop" + + response = make_observation_response( + rgb_path=rgb_path, + agent_pose=[float(step_count), 0.0, 0.0, 0.0, 0.0, 0.0], + metadata={"step": str(step_count)}, + reward=1.0 if done else 0.0, + done=done, + ) + write_response(workspace, response) + + elif cmd_type == "close": + logger.info("Close command received, shutting down") + write_response(workspace, {"status": "ok"}) + break + + else: + logger.warning("Unknown command type: %s", cmd_type) + write_response(workspace, make_error_response(f"Unknown command: {cmd_type}")) + + +def main() -> None: + parser = argparse.ArgumentParser(description="Dummy simulator bridge") + parser.add_argument("--workspace", type=Path, required=True, help="IPC workspace directory") + parser.add_argument("--step-delay", type=float, default=0.0, help="Delay per step in seconds") + args = parser.parse_args() + + logging.basicConfig( + level=logging.DEBUG, + format="%(asctime)s [%(levelname)s] %(name)s: %(message)s", + datefmt="%H:%M:%S", + ) + + run_bridge(workspace=args.workspace, step_delay=args.step_delay) + + +if __name__ == "__main__": + main() diff --git a/easi/simulators/dummy/v1/conda_env.yaml b/easi/simulators/dummy/v1/conda_env.yaml new file mode 100644 index 0000000..43106c8 --- /dev/null +++ b/easi/simulators/dummy/v1/conda_env.yaml @@ -0,0 +1,5 @@ +name: easi_dummy_v1 +channels: + - conda-forge +dependencies: + - python=3.10 diff --git a/easi/simulators/dummy/v1/env_manager.py b/easi/simulators/dummy/v1/env_manager.py new file mode 100644 index 0000000..89915fa --- /dev/null +++ b/easi/simulators/dummy/v1/env_manager.py @@ -0,0 +1,48 @@ +"""Environment manager for the dummy simulator. + +The dummy simulator runs in the host Python environment and has no +real dependencies, making it useful for testing the full pipeline. +""" + +from __future__ import annotations + +import sys +from pathlib import Path + +from easi.core.base_env_manager import BaseEnvironmentManager + + +class DummyEnvManager(BaseEnvironmentManager): + """Environment manager for the dummy simulator (no real deps).""" + + @property + def simulator_name(self) -> str: + return "dummy" + + @property + def version(self) -> str: + return "v1" + + def get_conda_env_yaml_path(self) -> Path: + return Path(__file__).parent / "conda_env.yaml" + + def get_requirements_txt_path(self) -> Path: + return Path(__file__).parent / "requirements.txt" + + def get_system_deps(self) -> list[str]: + return [] # no system deps needed + + def get_validation_import(self) -> str: + return "import json; print('dummy env ok')" + + def get_python_executable(self) -> str: + """Use the current Python interpreter (no conda env needed for dummy).""" + return sys.executable + + def env_is_ready(self) -> bool: + """Dummy env is always ready (uses host Python).""" + return True + + def install(self) -> None: + """No-op for dummy simulator.""" + pass diff --git a/easi/simulators/dummy/v1/requirements.txt b/easi/simulators/dummy/v1/requirements.txt new file mode 100644 index 0000000..d808e03 --- /dev/null +++ b/easi/simulators/dummy/v1/requirements.txt @@ -0,0 +1,2 @@ +# Dummy simulator has no extra Python dependencies +# This file exists to satisfy the BaseEnvironmentManager interface diff --git a/easi/simulators/dummy/v1/simulator.py b/easi/simulators/dummy/v1/simulator.py new file mode 100644 index 0000000..385de3e --- /dev/null +++ b/easi/simulators/dummy/v1/simulator.py @@ -0,0 +1,26 @@ +"""Dummy simulator implementation. + +A simple simulator for testing the full pipeline end-to-end without +requiring any real simulator dependencies. +""" + +from __future__ import annotations + +from pathlib import Path + +from easi.core.base_simulator import BaseSimulator + + +class DummySimulator(BaseSimulator): + """Dummy simulator that generates placeholder observations.""" + + @property + def name(self) -> str: + return "dummy" + + @property + def version(self) -> str: + return "v1" + + def _get_bridge_script_path(self) -> Path: + return Path(__file__).parent / "bridge.py" diff --git a/easi/simulators/registry.py b/easi/simulators/registry.py new file mode 100644 index 0000000..5a9f40a --- /dev/null +++ b/easi/simulators/registry.py @@ -0,0 +1,136 @@ +"""Simulator registry with manifest-based auto-discovery. + +Scans easi/simulators/*/manifest.yaml at import time to discover available +simulators and their versions. + +Lookup semantics: +- get_simulator("ai2thor") → resolves to default version +- get_simulator("ai2thor:v2_1_0") → resolves to explicit version +- list_simulators() → all registered keys +""" + +from __future__ import annotations + +import importlib +import logging +from dataclasses import dataclass +from pathlib import Path + +import yaml + +logger = logging.getLogger("easi.simulators.registry") + + +@dataclass +class SimulatorEntry: + """Registry entry for a simulator version.""" + + name: str + version: str + description: str + simulator_class: str # fully qualified class name + env_manager_class: str # fully qualified class name + python_version: str + + +# Module-level registry populated on first access +_registry: dict[str, SimulatorEntry] | None = None + + +def _discover_simulators() -> dict[str, SimulatorEntry]: + """Scan simulator directories for manifest.yaml files.""" + simulators_dir = Path(__file__).parent + entries: dict[str, SimulatorEntry] = {} + + for manifest_path in sorted(simulators_dir.glob("*/manifest.yaml")): + try: + manifest = yaml.safe_load(manifest_path.read_text()) + except Exception as e: + logger.warning("Failed to load %s: %s", manifest_path, e) + continue + + sim_name = manifest["name"] + default_ver = manifest.get("default_version") + + for ver_key, ver_info in manifest.get("versions", {}).items(): + entry = SimulatorEntry( + name=sim_name, + version=ver_key, + description=ver_info.get("description", ""), + simulator_class=ver_info["simulator_class"], + env_manager_class=ver_info["env_manager_class"], + python_version=ver_info.get("python_version", "3.10"), + ) + + # Register with explicit key: "ai2thor:v2_1_0" + full_key = f"{sim_name}:{ver_key}" + entries[full_key] = entry + + # Register default alias: "ai2thor" + if ver_key == default_ver: + entries[sim_name] = entry + + logger.debug( + "Discovered simulator %s with versions: %s (default: %s)", + sim_name, + list(manifest.get("versions", {}).keys()), + default_ver, + ) + + return entries + + +def _get_registry() -> dict[str, SimulatorEntry]: + """Get the simulator registry, discovering on first access.""" + global _registry + if _registry is None: + _registry = _discover_simulators() + return _registry + + +def get_simulator_entry(key: str) -> SimulatorEntry: + """Look up a simulator entry by key. + + Args: + key: Either "name" (uses default version) or "name:version" (explicit). + + Raises: + KeyError: If the simulator is not found. + """ + registry = _get_registry() + if key not in registry: + available = list_simulators() + raise KeyError( + f"Simulator '{key}' not found. Available: {available}" + ) + return registry[key] + + +def list_simulators() -> list[str]: + """List all registered simulator keys.""" + return sorted(_get_registry().keys()) + + +def load_simulator_class(key: str): + """Import and return the simulator class for the given key.""" + entry = get_simulator_entry(key) + return _import_class(entry.simulator_class) + + +def load_env_manager_class(key: str): + """Import and return the env manager class for the given key.""" + entry = get_simulator_entry(key) + return _import_class(entry.env_manager_class) + + +def _import_class(fully_qualified_name: str): + """Import a class from its fully qualified name (e.g., 'pkg.mod.ClassName').""" + module_path, class_name = fully_qualified_name.rsplit(".", 1) + module = importlib.import_module(module_path) + return getattr(module, class_name) + + +def refresh() -> None: + """Force re-discovery of simulators (useful after adding new ones at runtime).""" + global _registry + _registry = None diff --git a/easi/simulators/subprocess_runner.py b/easi/simulators/subprocess_runner.py new file mode 100644 index 0000000..26962f2 --- /dev/null +++ b/easi/simulators/subprocess_runner.py @@ -0,0 +1,199 @@ +"""Subprocess lifecycle manager for bridge processes. + +Manages the full lifecycle of a simulator bridge subprocess: +- Launch: Spawns the bridge in a new process group (for clean shutdown) +- Health check: Polls status.json with configurable timeout +- Send command: Writes command.json, polls response.json +- Crash recovery: Detects subprocess exit during polling +- Cleanup: SIGTERM → wait → SIGKILL the entire process group; removes temp workspace + +Supports xvfb-run wrapping for simulators that need a display. +""" + +from __future__ import annotations + +import logging +import os +import signal +import subprocess +from pathlib import Path + +from easi.communication.filesystem import ( + cleanup_workspace, + create_workspace, + poll_for_response, + poll_for_status, + write_command, +) +from easi.core.exceptions import SimulatorError, SimulatorTimeoutError + +logger = logging.getLogger("easi.simulators.subprocess_runner") + + +class SubprocessRunner: + """Manages a bridge subprocess for a single simulator instance.""" + + def __init__( + self, + python_executable: str, + bridge_script_path: Path, + needs_display: bool = False, + xvfb_screen_config: str = "1024x768x24", + startup_timeout: float = 30.0, + command_timeout: float = 60.0, + poll_interval: float = 0.1, + ): + self.python_executable = python_executable + self.bridge_script_path = bridge_script_path + self.needs_display = needs_display + self.xvfb_screen_config = xvfb_screen_config + self.startup_timeout = startup_timeout + self.command_timeout = command_timeout + self.poll_interval = poll_interval + + self._process: subprocess.Popen | None = None + self._workspace: Path | None = None + + @property + def workspace(self) -> Path | None: + """The IPC workspace directory for this runner.""" + return self._workspace + + def launch(self) -> None: + """Launch the bridge subprocess and wait for it to report ready.""" + if self._process is not None: + raise RuntimeError("Subprocess already running") + + self._workspace = create_workspace() + cmd = self._build_launch_command() + + logger.info( + "Launching bridge: %s (workspace: %s)", + self.bridge_script_path.name, + self._workspace, + ) + logger.debug("Full command: %s", " ".join(cmd)) + + self._process = subprocess.Popen( + cmd, + preexec_fn=os.setsid, # new session = new process group + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + ) + + # Wait for the bridge to signal readiness + try: + status = poll_for_status( + self._workspace, + poll_interval=self.poll_interval, + timeout=self.startup_timeout, + process=self._process, + ) + if not status.get("ready", False): + stderr = self._read_stderr() + raise SimulatorError( + f"Bridge reported not ready. stderr:\n{stderr}" + ) + logger.info("Bridge subprocess ready (PID: %d)", self._process.pid) + except (SimulatorError, SimulatorTimeoutError): + self.shutdown() + raise + + def send_command(self, command: dict, timeout: float | None = None) -> dict: + """Send a command to the bridge and wait for the response. + + Args: + command: The command dict to send. + timeout: Override the default command timeout. + + Returns: + The response dict from the bridge. + """ + if self._process is None or self._workspace is None: + raise RuntimeError("Subprocess not running") + + write_command(self._workspace, command) + + return poll_for_response( + self._workspace, + poll_interval=self.poll_interval, + timeout=timeout or self.command_timeout, + process=self._process, + ) + + def is_alive(self) -> bool: + """Check if the bridge subprocess is still running.""" + if self._process is None: + return False + return self._process.poll() is None + + def shutdown(self) -> None: + """Shut down the bridge subprocess and clean up. + + Kills the entire process group (bridge + any child processes like + Unity binaries) to prevent zombie processes. + """ + self._terminate_process_tree() + self._process = None + + if self._workspace is not None: + cleanup_workspace(self._workspace) + self._workspace = None + + def _build_launch_command(self) -> list[str]: + """Build the command to launch the bridge subprocess.""" + cmd = [ + self.python_executable, + str(self.bridge_script_path), + "--workspace", + str(self._workspace), + ] + + if self.needs_display and not self._has_display(): + # Wrap with xvfb-run for headless environments + cmd = [ + "xvfb-run", "-a", + "-s", f"-screen 0 {self.xvfb_screen_config}", + ] + cmd + + return cmd + + def _has_display(self) -> bool: + """Check if a real display is available.""" + display = os.environ.get("DISPLAY", "") + return bool(display) and not display.startswith(":") + + def _terminate_process_tree(self) -> None: + """Kill the bridge and ALL its child processes.""" + if self._process is None: + return + + try: + pgid = os.getpgid(self._process.pid) + except ProcessLookupError: + return # already dead + + try: + # SIGTERM the entire group first (graceful) + os.killpg(pgid, signal.SIGTERM) + self._process.wait(timeout=10) + except subprocess.TimeoutExpired: + # SIGKILL the entire group (force) + try: + os.killpg(pgid, signal.SIGKILL) + self._process.wait(timeout=5) + except (ProcessLookupError, subprocess.TimeoutExpired): + pass + except ProcessLookupError: + pass # already dead + + logger.debug("Process tree terminated") + + def _read_stderr(self) -> str: + """Read stderr from the subprocess (non-blocking).""" + if self._process is None or self._process.stderr is None: + return "" + try: + return self._process.stderr.read().decode("utf-8", errors="replace") + except Exception: + return "" diff --git a/easi/tasks/__init__.py b/easi/tasks/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/easi/tasks/dataset.py b/easi/tasks/dataset.py new file mode 100644 index 0000000..af7430a --- /dev/null +++ b/easi/tasks/dataset.py @@ -0,0 +1,95 @@ +"""Dataset downloader supporting local paths and HuggingFace Hub. + +Used by BaseTask.download_dataset() but also available standalone. +""" + +from __future__ import annotations + +import logging +from pathlib import Path + +from easi.core.exceptions import DatasetError +from easi.utils.locking import file_lock +from easi.utils.paths import get_datasets_dir, get_locks_dir + +logger = logging.getLogger("easi.tasks.dataset") + + +class DatasetDownloader: + """Handles dataset acquisition from local paths and HuggingFace.""" + + def __init__(self, cache_dir: Path | None = None): + self.cache_dir = cache_dir or get_datasets_dir() + + def download(self, config: dict) -> Path: + """Download or resolve a dataset given a config block from task.yaml. + + Args: + config: Dataset config dict, e.g.: + {"source": "local", "path": "/data/episodes"} + {"source": "huggingface", "repo_id": "org/dataset", "split": "val"} + + Returns: + Path to the local dataset directory. + """ + source = config.get("source", "local") + + if source == "local": + return self._resolve_local(config) + elif source == "huggingface": + return self._download_huggingface(config) + else: + raise DatasetError(f"Unknown dataset source: {source}") + + def _resolve_local(self, config: dict) -> Path: + """Validate local path exists and return it.""" + path = config.get("path") + if path is None: + return Path() # no path = built-in episodes + + local_path = Path(path) + if not local_path.exists(): + raise DatasetError(f"Local dataset path does not exist: {local_path}") + + logger.info("Using local dataset: %s", local_path) + return local_path + + def _download_huggingface(self, config: dict) -> Path: + """Download from HuggingFace Hub with file-based locking. + + Uses fcntl.flock to prevent concurrent downloads of the same dataset. + """ + repo_id = config["repo_id"] + lock_name = f"dataset_{repo_id.replace('/', '_')}.lock" + lock_path = get_locks_dir() / lock_name + + with file_lock(lock_path): + target = self.cache_dir / repo_id.replace("/", "_") + if target.exists(): + logger.info("Dataset %s already cached at %s", repo_id, target) + return target + + try: + from huggingface_hub import snapshot_download + except ImportError: + raise DatasetError( + f"huggingface_hub is required to download '{repo_id}'. " + f"Install with: pip install huggingface_hub" + ) + + logger.info("Downloading dataset %s from HuggingFace...", repo_id) + try: + snapshot_download( + repo_id=repo_id, + local_dir=str(target), + repo_type="dataset", + ) + except Exception as e: + # Clean up partial download + if target.exists(): + import shutil + shutil.rmtree(target, ignore_errors=True) + raise DatasetError(f"Failed to download {repo_id}: {e}") + + logger.info("Downloaded dataset %s to %s", repo_id, target) + return target diff --git a/easi/tasks/dummy_task/__init__.py b/easi/tasks/dummy_task/__init__.py new file mode 100644 index 0000000..686e138 --- /dev/null +++ b/easi/tasks/dummy_task/__init__.py @@ -0,0 +1,5 @@ +"""Dummy task package.""" + +from easi.tasks.dummy_task.task import DummyTask + +__all__ = ["DummyTask"] diff --git a/easi/tasks/dummy_task/task.py b/easi/tasks/dummy_task/task.py new file mode 100644 index 0000000..8030304 --- /dev/null +++ b/easi/tasks/dummy_task/task.py @@ -0,0 +1,76 @@ +"""Dummy task for testing the pipeline end-to-end. + +Provides built-in episodes that require no dataset download. +Evaluates success based on whether the agent sent a Stop action. +""" + +from __future__ import annotations + +from pathlib import Path + +from easi.core.base_task import BaseTask +from easi.core.episode import StepResult + + +class DummyTask(BaseTask): + """A dummy task for testing the evaluation pipeline.""" + + def get_task_yaml_path(self) -> Path: + return Path(__file__).parent / "task.yaml" + + def format_reset_config(self, episode: dict) -> dict: + """Map a dummy episode to simulator reset config. + + The dummy simulator doesn't need much — just pass through the scene_id. + """ + return { + "scene_id": episode.get("scene_id", "dummy_scene"), + "agent_position": episode.get("start_position", [0.0, 0.0, 0.0]), + } + + def evaluate_episode( + self, episode: dict, trajectory: list[StepResult] + ) -> dict[str, float]: + """Evaluate a completed episode. + + Success criteria: agent sent Stop action within max_steps, OR + the simulator reported done=True in the last step. + """ + if not trajectory: + return {"success": 0.0, "num_steps": 0.0} + + last_step = trajectory[-1] + success = 1.0 if last_step.done else 0.0 + total_reward = sum(step.reward for step in trajectory) + + return { + "success": success, + "num_steps": float(len(trajectory)), + "total_reward": total_reward, + } + + def _get_builtin_episodes(self) -> list[dict]: + """Return built-in dummy episodes for testing.""" + return [ + { + "episode_id": "dummy_ep_001", + "scene_id": "dummy_scene_A", + "start_position": [0.0, 0.0, 0.0], + "goal_position": [5.0, 0.0, 0.0], + "task_description": "Navigate to the goal position.", + }, + { + "episode_id": "dummy_ep_002", + "scene_id": "dummy_scene_B", + "start_position": [1.0, 0.0, 1.0], + "goal_position": [3.0, 0.0, -2.0], + "task_description": "Find and go to the red cube.", + }, + { + "episode_id": "dummy_ep_003", + "scene_id": "dummy_scene_A", + "start_position": [2.0, 0.0, -1.0], + "goal_position": [0.0, 0.0, 0.0], + "task_description": "Return to the starting area.", + }, + ] diff --git a/easi/tasks/dummy_task/task.yaml b/easi/tasks/dummy_task/task.yaml new file mode 100644 index 0000000..206ff3b --- /dev/null +++ b/easi/tasks/dummy_task/task.yaml @@ -0,0 +1,24 @@ +name: dummy_task +display_name: "Dummy Navigation Task" +description: "A dummy task for testing the pipeline end-to-end" + +# Pin to a specific versioned simulator +simulator: "dummy:v1" + +# Action space available to the agent +action_space: + - MoveAhead + - TurnLeft + - TurnRight + - Stop + +# Dataset source +dataset: + source: "local" + path: null # null = use built-in dummy episodes + +# Episode limits +max_steps: 100 + +# Task class to load +task_class: "easi.tasks.dummy_task.task.DummyTask" diff --git a/easi/tasks/registry.py b/easi/tasks/registry.py new file mode 100644 index 0000000..0ed10a7 --- /dev/null +++ b/easi/tasks/registry.py @@ -0,0 +1,112 @@ +"""Task registry with auto-discovery from task.yaml files. + +Scans easi/tasks/*/task.yaml to discover available tasks (benchmarks). + +Usage: + list_tasks() → ["dummy_task", "objectnav_hm3d", ...] + get_task_entry("dummy_task") → TaskEntry(...) + load_task_class("dummy_task") → DummyTask class +""" + +from __future__ import annotations + +import importlib +import logging +from dataclasses import dataclass +from pathlib import Path + +import yaml + +logger = logging.getLogger("easi.tasks.registry") + + +@dataclass +class TaskEntry: + """Registry entry for a task.""" + + name: str + display_name: str + description: str + simulator_key: str + task_class: str # fully qualified class name + action_space: list[str] + max_steps: int + config_path: Path + + +# Module-level registry populated on first access +_registry: dict[str, TaskEntry] | None = None + + +def _discover_tasks() -> dict[str, TaskEntry]: + """Scan task directories for task.yaml files.""" + tasks_dir = Path(__file__).parent + entries: dict[str, TaskEntry] = {} + + for task_yaml_path in sorted(tasks_dir.glob("*/task.yaml")): + try: + config = yaml.safe_load(task_yaml_path.read_text()) + except Exception as e: + logger.warning("Failed to load %s: %s", task_yaml_path, e) + continue + + name = config["name"] + entries[name] = TaskEntry( + name=name, + display_name=config.get("display_name", name), + description=config.get("description", ""), + simulator_key=config["simulator"], + task_class=config["task_class"], + action_space=config.get("action_space", []), + max_steps=config.get("max_steps", 500), + config_path=task_yaml_path, + ) + + logger.debug("Discovered task: %s (simulator: %s)", name, config["simulator"]) + + return entries + + +def _get_registry() -> dict[str, TaskEntry]: + """Get the task registry, discovering on first access.""" + global _registry + if _registry is None: + _registry = _discover_tasks() + return _registry + + +def get_task_entry(name: str) -> TaskEntry: + """Look up a task entry by name. + + Raises: + KeyError: If the task is not found. + """ + registry = _get_registry() + if name not in registry: + available = list_tasks() + raise KeyError(f"Task '{name}' not found. Available: {available}") + return registry[name] + + +def list_tasks() -> list[str]: + """List all registered task names.""" + return sorted(_get_registry().keys()) + + +def load_task_class(name: str): + """Import and return the task class for the given name.""" + entry = get_task_entry(name) + return _import_class(entry.task_class) + + +def _import_class(fully_qualified_name: str): + """Import a class from its fully qualified name.""" + module_path, class_name = fully_qualified_name.rsplit(".", 1) + module = importlib.import_module(module_path) + return getattr(module, class_name) + + +def refresh() -> None: + """Force re-discovery of tasks.""" + global _registry + _registry = None diff --git a/easi/utils/__init__.py b/easi/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/easi/utils/locking.py b/easi/utils/locking.py new file mode 100644 index 0000000..d482e45 --- /dev/null +++ b/easi/utils/locking.py @@ -0,0 +1,42 @@ +"""File-based locking for serializing concurrent operations. + +Used to prevent race conditions when multiple EASI processes try to: +- Install the same conda environment simultaneously +- Download the same dataset simultaneously +""" + +from __future__ import annotations + +import fcntl +import logging +from contextlib import contextmanager +from pathlib import Path +from typing import Generator + +logger = logging.getLogger("easi.utils.locking") + + +@contextmanager +def file_lock(lock_path: Path) -> Generator[None, None, None]: + """Acquire an exclusive file lock, blocking until available. + + Args: + lock_path: Path to the lock file. Parent directories are created automatically. + + Usage:: + + with file_lock(Path("~/.cache/easi/locks/env_install.lock")): + # only one process at a time can execute this block + install_conda_env() + """ + lock_path.parent.mkdir(parents=True, exist_ok=True) + logger.debug("Acquiring lock: %s", lock_path) + + with open(lock_path, "w") as f: + fcntl.flock(f, fcntl.LOCK_EX) + try: + logger.debug("Lock acquired: %s", lock_path) + yield + finally: + fcntl.flock(f, fcntl.LOCK_UN) + logger.debug("Lock released: %s", lock_path) diff --git a/easi/utils/logging.py b/easi/utils/logging.py new file mode 100644 index 0000000..d5ba59d --- /dev/null +++ b/easi/utils/logging.py @@ -0,0 +1,44 @@ +"""Custom easi logger using stdlib logging. + +Library modules should only use get_logger(). The CLI entry point calls +setup_logging() once to configure handlers and level. +""" + +from __future__ import annotations + +import logging + + +def get_logger(name: str) -> logging.Logger: + """Get a namespaced easi logger. + + Usage:: + + from easi.utils.logging import get_logger + logger = get_logger("simulators.subprocess_runner") + logger.info("Launching bridge: %s", bridge_path) + """ + return logging.getLogger(f"easi.{name}") + + +def setup_logging(level: str = "WARNING") -> None: + """Configure the easi logger hierarchy. + + Called once by the CLI entry point. Library code should never call this. + + Args: + level: One of "DEBUG", "INFO", "WARNING", "ERROR". + """ + logger = logging.getLogger("easi") + logger.setLevel(getattr(logging, level.upper())) + + # Avoid adding duplicate handlers if called multiple times + if not logger.handlers: + handler = logging.StreamHandler() + handler.setFormatter( + logging.Formatter( + "%(asctime)s [%(levelname)s] %(name)s: %(message)s", + datefmt="%H:%M:%S", + ) + ) + logger.addHandler(handler) diff --git a/easi/utils/paths.py b/easi/utils/paths.py new file mode 100644 index 0000000..8cd316b --- /dev/null +++ b/easi/utils/paths.py @@ -0,0 +1,42 @@ +"""Workspace and path management utilities.""" + +from __future__ import annotations + +import shutil +import tempfile +from pathlib import Path + +# Default cache directory for datasets, locks, etc. +DEFAULT_CACHE_DIR = Path.home() / ".cache" / "easi" + + +def get_cache_dir() -> Path: + """Return the EASI cache directory, creating it if needed.""" + cache_dir = DEFAULT_CACHE_DIR + cache_dir.mkdir(parents=True, exist_ok=True) + return cache_dir + + +def get_locks_dir() -> Path: + """Return the directory for file-based locks.""" + locks_dir = get_cache_dir() / "locks" + locks_dir.mkdir(parents=True, exist_ok=True) + return locks_dir + + +def get_datasets_dir() -> Path: + """Return the directory for cached datasets.""" + datasets_dir = get_cache_dir() / "datasets" + datasets_dir.mkdir(parents=True, exist_ok=True) + return datasets_dir + + +def create_temp_workspace(prefix: str = "easi_") -> Path: + """Create a unique temporary directory for an IPC workspace.""" + return Path(tempfile.mkdtemp(prefix=prefix)) + + +def cleanup_dir(path: Path) -> None: + """Remove a directory tree, ignoring errors.""" + if path.exists(): + shutil.rmtree(path, ignore_errors=True) diff --git a/easi/utils/system_deps.py b/easi/utils/system_deps.py new file mode 100644 index 0000000..14bad47 --- /dev/null +++ b/easi/utils/system_deps.py @@ -0,0 +1,91 @@ +"""System dependency checker for simulators. + +Verifies that required system-level packages (xvfb, EGL, conda, etc.) are +installed before attempting environment setup. +""" + +from __future__ import annotations + +import logging +import subprocess + +from easi.core.exceptions import EnvironmentSetupError + +logger = logging.getLogger("easi.utils.system_deps") + + +# Known system dependencies and how to check/install them +KNOWN_DEPS: dict[str, dict[str, str | list[str]]] = { + "xvfb": { + "check_command": ["which", "Xvfb"], + "install_hint": "sudo apt-get install -y xvfb", + }, + "egl": { + "check_command": ["ldconfig", "-p"], + "check_grep": "libEGL", + "install_hint": "sudo apt-get install -y libegl1-mesa-dev", + }, + "osmesa": { + "check_command": ["ldconfig", "-p"], + "check_grep": "libOSMesa", + "install_hint": "sudo apt-get install -y libosmesa6-dev", + }, + "conda": { + "check_command": ["which", "conda"], + "install_hint": "Install Miniconda: https://docs.conda.io/en/latest/miniconda.html", + }, +} + + +class SystemDependencyChecker: + """Checks for system-level dependencies.""" + + def check(self, dep_name: str) -> bool: + """Check if a single system dependency is available. + + Returns True if the dependency is found, False otherwise. + """ + dep_info = KNOWN_DEPS.get(dep_name) + if dep_info is None: + logger.warning("Unknown system dependency: %s (skipping check)", dep_name) + return True + + try: + result = subprocess.run( + dep_info["check_command"], + capture_output=True, + text=True, + timeout=10, + ) + # If there's a grep pattern, check stdout for it + check_grep = dep_info.get("check_grep") + if check_grep: + return check_grep in result.stdout + return result.returncode == 0 + except (subprocess.TimeoutExpired, FileNotFoundError): + return False + + def check_all(self, deps: list[str]) -> list[str]: + """Check multiple dependencies, returning list of missing ones.""" + missing = [] + for dep in deps: + if not self.check(dep): + missing.append(dep) + return missing + + def assert_all(self, deps: list[str]) -> None: + """Assert all dependencies are present, raising with install hints if not.""" + missing = self.check_all(deps) + if not missing: + return + + hints = [] + for dep in missing: + dep_info = KNOWN_DEPS.get(dep, {}) + hint = dep_info.get("install_hint", f"Install '{dep}' manually") + hints.append(f" - {dep}: {hint}") + + raise EnvironmentSetupError( + f"Missing system dependencies:\n" + "\n".join(hints), + missing_deps=missing, + ) diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..14ceb4a --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,38 @@ +[build-system] +requires = ["setuptools>=68.0"] +build-backend = "setuptools.build_meta" + +[project] +name = "easi" +version = "0.1.0" +description = "EASI - Embodied Reasoning Evaluation for Spatial Intelligence" +requires-python = ">=3.10" +dependencies = [ + "Pillow>=10.0", + "requests>=2.28", + "pyyaml>=6.0", +] + +[project.optional-dependencies] +hf = [ + "huggingface_hub>=0.20", +] +dev = [ + "pytest>=7.0", + "pytest-timeout>=2.0", +] + +[project.scripts] +easi = "easi.cli:main" + +[tool.setuptools.packages.find] +include = ["easi*"] +exclude = ["VLMEvalKit*", "lmms-eval*"] + +[tool.setuptools.package-data] +"easi" = [ + "simulators/*/manifest.yaml", + "simulators/*/*/conda_env.yaml", + "simulators/*/*/requirements.txt", + "tasks/*/task.yaml", +] diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/test_dummy_agent.py b/tests/test_dummy_agent.py new file mode 100644 index 0000000..27d7c25 --- /dev/null +++ b/tests/test_dummy_agent.py @@ -0,0 +1,54 @@ +"""Tests for the dummy agent.""" + +import pytest + +from easi.agents.dummy_agent import DummyAgent +from easi.core.episode import Observation + + +class TestDummyAgent: + @pytest.fixture + def agent(self): + return DummyAgent( + action_space=["MoveAhead", "TurnLeft", "TurnRight", "Stop"], + seed=42, + ) + + def test_act_returns_valid_action(self, agent): + obs = Observation(rgb_path="/tmp/rgb.png") + action = agent.act(obs, "Go to the goal.") + assert action.action_name in agent.action_space + + def test_deterministic_with_seed(self): + agent1 = DummyAgent(action_space=["A", "B", "C"], seed=123) + agent2 = DummyAgent(action_space=["A", "B", "C"], seed=123) + + obs = Observation(rgb_path="/tmp/rgb.png") + for _ in range(10): + a1 = agent1.act(obs, "test") + a2 = agent2.act(obs, "test") + assert a1.action_name == a2.action_name + + def test_reset_clears_history(self, agent): + obs = Observation(rgb_path="/tmp/rgb.png") + agent.act(obs, "test") + assert len(agent.chat_history) > 0 + + agent.reset() + assert len(agent.chat_history) == 0 + + def test_chat_history_grows(self, agent): + obs = Observation(rgb_path="/tmp/rgb.png") + agent.act(obs, "test") + assert len(agent.chat_history) == 2 # user + assistant + + agent.act(obs, "test") + assert len(agent.chat_history) == 4 + + def test_chat_history_is_copy(self, agent): + obs = Observation(rgb_path="/tmp/rgb.png") + agent.act(obs, "test") + + history = agent.chat_history + history.clear() + assert len(agent.chat_history) == 2 # original unchanged diff --git a/tests/test_dummy_llm.py b/tests/test_dummy_llm.py new file mode 100644 index 0000000..185381f --- /dev/null +++ b/tests/test_dummy_llm.py @@ -0,0 +1,76 @@ +"""Tests for the dummy LLM server and API client.""" + +import json +import threading +import time + +import pytest +import requests + +from easi.llm.api_client import LLMApiClient +from easi.llm.dummy_server import create_handler, run_server + +# Use a non-standard port to avoid conflicts +TEST_PORT = 18765 + + +@pytest.fixture(scope="module") +def llm_server(): + """Start a dummy LLM server in a background thread.""" + from http.server import HTTPServer + + handler = create_handler("random", ["MoveAhead", "TurnLeft", "TurnRight", "Stop"]) + server = HTTPServer(("127.0.0.1", TEST_PORT), handler) + + thread = threading.Thread(target=server.serve_forever, daemon=True) + thread.start() + + # Wait for server to be ready + for _ in range(20): + try: + requests.get(f"http://127.0.0.1:{TEST_PORT}/health", timeout=0.5) + break + except requests.ConnectionError: + time.sleep(0.1) + + yield server + server.shutdown() + + +class TestDummyLLMServer: + def test_health(self, llm_server): + resp = requests.get(f"http://127.0.0.1:{TEST_PORT}/health") + assert resp.status_code == 200 + assert resp.json()["status"] == "ok" + + def test_chat_completions(self, llm_server): + payload = { + "model": "dummy", + "messages": [ + {"role": "user", "content": "Choose an action."}, + ], + } + resp = requests.post( + f"http://127.0.0.1:{TEST_PORT}/v1/chat/completions", + json=payload, + ) + assert resp.status_code == 200 + data = resp.json() + assert "choices" in data + assert len(data["choices"]) == 1 + content = data["choices"][0]["message"]["content"] + assert "Action:" in content + + +class TestLLMApiClient: + def test_generate(self, llm_server): + client = LLMApiClient(base_url=f"http://127.0.0.1:{TEST_PORT}") + response = client.generate( + messages=[{"role": "user", "content": "Choose an action."}] + ) + assert "Action:" in response + + def test_connection_error(self): + client = LLMApiClient(base_url="http://127.0.0.1:19999") + with pytest.raises(ConnectionError): + client.generate(messages=[{"role": "user", "content": "test"}]) diff --git a/tests/test_dummy_simulator.py b/tests/test_dummy_simulator.py new file mode 100644 index 0000000..3efe5ee --- /dev/null +++ b/tests/test_dummy_simulator.py @@ -0,0 +1,99 @@ +"""End-to-end test for the dummy simulator. + +Tests the full vertical slice: SubprocessRunner → bridge.py → filesystem IPC. +""" + +import pytest + +from easi.core.episode import Action +from easi.simulators.dummy.v1.env_manager import DummyEnvManager +from easi.simulators.dummy.v1.simulator import DummySimulator +from easi.simulators.subprocess_runner import SubprocessRunner + + +@pytest.fixture +def dummy_simulator(): + """Create and start a dummy simulator, cleaning up after the test.""" + env_manager = DummyEnvManager() + sim = DummySimulator() + + runner = SubprocessRunner( + python_executable=env_manager.get_python_executable(), + bridge_script_path=sim._get_bridge_script_path(), + startup_timeout=10.0, + command_timeout=10.0, + ) + runner.launch() + sim.set_runner(runner) + + yield sim + + sim.close() + + +def test_reset(dummy_simulator): + """Test that reset returns a valid observation.""" + obs = dummy_simulator.reset("test_episode_001") + assert obs.rgb_path.endswith(".png") + assert obs.agent_pose is not None + + +def test_step(dummy_simulator): + """Test that step returns a valid StepResult.""" + dummy_simulator.reset("test_episode_002") + + action = Action(action_name="MoveAhead") + result = dummy_simulator.step(action) + + assert result.observation.rgb_path.endswith(".png") + assert isinstance(result.reward, float) + assert isinstance(result.done, bool) + + +def test_multiple_steps(dummy_simulator): + """Test running 5 steps and verifying the results.""" + dummy_simulator.reset("test_episode_003") + + for i in range(5): + action = Action(action_name="MoveAhead") + result = dummy_simulator.step(action) + assert result.observation.rgb_path.endswith(".png") + assert not result.done # dummy is done at step 10 + + # Step 6-9 should still not be done + for i in range(4): + result = dummy_simulator.step(Action(action_name="TurnLeft")) + assert not result.done + + # Step 10 should be done + result = dummy_simulator.step(Action(action_name="MoveAhead")) + assert result.done + + +def test_stop_action(dummy_simulator): + """Test that the Stop action ends the episode.""" + dummy_simulator.reset("test_episode_004") + + result = dummy_simulator.step(Action(action_name="Stop")) + assert result.done + + +def test_is_running(dummy_simulator): + """Test is_running reports correctly.""" + assert dummy_simulator.is_running() + + +def test_registry_discovery(): + """Test that the dummy simulator is discoverable via the registry.""" + from easi.simulators.registry import get_simulator_entry, list_simulators + + sims = list_simulators() + assert "dummy" in sims + assert "dummy:v1" in sims + + entry = get_simulator_entry("dummy") + assert entry.name == "dummy" + assert entry.version == "v1" + + entry_explicit = get_simulator_entry("dummy:v1") + assert entry_explicit.name == "dummy" diff --git a/tests/test_dummy_task.py b/tests/test_dummy_task.py new file mode 100644 index 0000000..c2776bf --- /dev/null +++ b/tests/test_dummy_task.py @@ -0,0 +1,91 @@ +"""Tests for the dummy task and task registry.""" + +import pytest + +from easi.core.episode import Observation, StepResult +from easi.tasks.dummy_task.task import DummyTask +from easi.tasks.registry import get_task_entry, list_tasks, load_task_class + + +class TestDummyTask: + @pytest.fixture + def task(self): + return DummyTask() + + def test_name(self, task): + assert task.name == "dummy_task" + + def test_simulator_key(self, task): + assert task.simulator_key == "dummy:v1" + + def test_action_space(self, task): + assert "MoveAhead" in task.action_space + assert "Stop" in task.action_space + + def test_max_steps(self, task): + assert task.max_steps == 100 + + def test_load_episodes(self, task): + episodes = task.load_episodes() + assert len(episodes) == 3 + assert episodes[0]["episode_id"] == "dummy_ep_001" + + def test_get_episode(self, task): + ep = task.get_episode(1) + assert ep["episode_id"] == "dummy_ep_002" + + def test_len(self, task): + assert len(task) == 3 + + def test_format_reset_config(self, task): + ep = task.get_episode(0) + config = task.format_reset_config(ep) + assert "scene_id" in config + assert config["scene_id"] == "dummy_scene_A" + + def test_evaluate_episode_success(self, task): + ep = task.get_episode(0) + obs = Observation(rgb_path="/tmp/rgb.png") + trajectory = [ + StepResult(observation=obs, reward=0.0, done=False), + StepResult(observation=obs, reward=1.0, done=True), + ] + metrics = task.evaluate_episode(ep, trajectory) + assert metrics["success"] == 1.0 + assert metrics["num_steps"] == 2.0 + assert metrics["total_reward"] == 1.0 + + def test_evaluate_episode_failure(self, task): + ep = task.get_episode(0) + obs = Observation(rgb_path="/tmp/rgb.png") + trajectory = [ + StepResult(observation=obs, reward=0.0, done=False), + ] + metrics = task.evaluate_episode(ep, trajectory) + assert metrics["success"] == 0.0 + + def test_evaluate_empty_trajectory(self, task): + ep = task.get_episode(0) + metrics = task.evaluate_episode(ep, []) + assert metrics["success"] == 0.0 + assert metrics["num_steps"] == 0.0 + + +class TestTaskRegistry: + def test_list_tasks(self): + tasks = list_tasks() + assert "dummy_task" in tasks + + def test_get_task_entry(self): + entry = get_task_entry("dummy_task") + assert entry.name == "dummy_task" + assert entry.simulator_key == "dummy:v1" + assert "MoveAhead" in entry.action_space + + def test_load_task_class(self): + TaskClass = load_task_class("dummy_task") + assert TaskClass is DummyTask + + def test_unknown_task(self): + with pytest.raises(KeyError): + get_task_entry("nonexistent_task") diff --git a/tests/test_filesystem_comm.py b/tests/test_filesystem_comm.py new file mode 100644 index 0000000..d1dd22a --- /dev/null +++ b/tests/test_filesystem_comm.py @@ -0,0 +1,124 @@ +"""Tests for filesystem-based IPC communication.""" + +import json +import tempfile +from pathlib import Path + +import pytest + +from easi.communication.filesystem import ( + atomic_write_json, + cleanup_workspace, + create_workspace, + delete_file, + read_json, + write_command, +) +from easi.communication.schemas import ( + make_close_command, + make_observation_response, + make_reset_command, + make_step_command, + parse_action_from_command, + parse_observation, + parse_step_result, +) +from easi.core.episode import Action + + +class TestAtomicWriteJson: + def test_write_and_read(self, tmp_path): + path = tmp_path / "test.json" + data = {"key": "value", "num": 42} + atomic_write_json(path, data) + assert read_json(path) == data + + def test_no_tmp_file_left(self, tmp_path): + path = tmp_path / "test.json" + atomic_write_json(path, {"a": 1}) + assert not (tmp_path / "test.tmp").exists() + + def test_read_nonexistent(self, tmp_path): + assert read_json(tmp_path / "nope.json") is None + + +class TestDeleteFile: + def test_delete_existing(self, tmp_path): + path = tmp_path / "test.txt" + path.write_text("hello") + delete_file(path) + assert not path.exists() + + def test_delete_nonexistent(self, tmp_path): + delete_file(tmp_path / "nope.txt") # should not raise + + +class TestWorkspace: + def test_create_and_cleanup(self): + ws = create_workspace() + assert ws.exists() + cleanup_workspace(ws) + assert not ws.exists() + + +class TestSchemas: + def test_reset_command(self): + cmd = make_reset_command("ep_001", {"scene": "A"}) + assert cmd["type"] == "reset" + assert cmd["episode_id"] == "ep_001" + assert cmd["reset_config"] == {"scene": "A"} + + def test_step_command(self): + action = Action(action_name="MoveAhead", params={"distance": 0.25}) + cmd = make_step_command(action) + assert cmd["type"] == "step" + assert cmd["action"]["action_name"] == "MoveAhead" + assert cmd["action"]["params"]["distance"] == 0.25 + + def test_close_command(self): + cmd = make_close_command() + assert cmd["type"] == "close" + + def test_observation_response(self): + resp = make_observation_response( + rgb_path="/tmp/rgb.png", + agent_pose=[1.0, 2.0, 3.0], + reward=0.5, + done=True, + ) + assert resp["status"] == "ok" + assert resp["observation"]["rgb_path"] == "/tmp/rgb.png" + assert resp["reward"] == 0.5 + assert resp["done"] is True + + def test_parse_observation(self): + resp = make_observation_response(rgb_path="/tmp/rgb.png", depth_path="/tmp/depth.png") + obs = parse_observation(resp) + assert obs.rgb_path == "/tmp/rgb.png" + assert obs.depth_path == "/tmp/depth.png" + + def test_parse_step_result(self): + resp = make_observation_response(rgb_path="/tmp/rgb.png", reward=1.0, done=True) + result = parse_step_result(resp) + assert result.reward == 1.0 + assert result.done is True + assert result.observation.rgb_path == "/tmp/rgb.png" + + def test_parse_action_from_command(self): + action = Action(action_name="TurnLeft", params={"angle": 90.0}) + cmd = make_step_command(action) + parsed = parse_action_from_command(cmd) + assert parsed.action_name == "TurnLeft" + assert parsed.params["angle"] == 90.0 + + +class TestWriteCommand: + def test_clears_old_response(self, tmp_path): + # Write a response file + resp_path = tmp_path / "response.json" + resp_path.write_text('{"old": true}') + + # write_command should delete it + write_command(tmp_path, make_reset_command("ep_001")) + assert not resp_path.exists() + assert (tmp_path / "command.json").exists() From cdbf211cff7bf216127d9eb3f7ee4739e1e408b0 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Fri, 13 Feb 2026 01:22:01 +0800 Subject: [PATCH 002/239] [Feature] Add easi library - Phase 2 implementation Add embodied agent evaluation pipeline with real simulator support: - ReAct agent with multi-action buffering and PromptBuilder protocol - EB-Alfred task support (6 splits via multi-split YAML discovery) - AI2-THOR v2.1.0 bridge with skill-based actions and state tracking - EvaluationRunner with structured output: /// - Per-episode artifacts: result.json, trajectory.jsonl, rgb_*.png - Centralized logging (print -> logger), --verbosity CLI option - Subprocess observability: bridge output streaming, Ctrl+C cleanup - LLM API client (OpenAI-compatible) and dummy LLM server - 106 tests passing --- CLAUDE.md | 51 +- easi/agents/prompt_builder.py | 165 +++ easi/agents/react_agent.py | 168 +++ easi/cli.py | 151 ++- easi/communication/filesystem.py | 4 +- easi/communication/schemas.py | 11 +- easi/core/base_agent.py | 22 +- easi/core/base_env_manager.py | 49 +- easi/core/base_simulator.py | 19 +- easi/core/base_task.py | 204 +++- easi/core/exceptions.py | 2 + easi/core/protocols.py | 7 +- easi/evaluation/__init__.py | 1 + easi/evaluation/metrics.py | 30 + easi/evaluation/runner.py | 270 +++++ easi/llm/api_client.py | 16 +- easi/llm/dummy_server.py | 18 +- easi/simulators/ai2thor/v2_1_0/bridge.py | 1041 ++++++++++++++++- .../ai2thor/v2_1_0/requirements.txt | 1 + easi/simulators/ai2thor/v2_1_0/thor_utils.py | 362 ++++++ easi/simulators/dummy/v1/bridge.py | 25 +- easi/simulators/registry.py | 5 +- easi/simulators/subprocess_runner.py | 82 +- easi/tasks/dataset.py | 4 +- easi/tasks/ebalfred/__init__.py | 1 + easi/tasks/ebalfred/actions.py | 83 ++ easi/tasks/ebalfred/ebalfred_base.yaml | 16 + .../tasks/ebalfred/ebalfred_common_sense.yaml | 16 + .../ebalfred_complex_instruction.yaml | 16 + .../tasks/ebalfred/ebalfred_long_horizon.yaml | 16 + easi/tasks/ebalfred/ebalfred_spatial.yaml | 16 + .../ebalfred/ebalfred_visual_appearance.yaml | 16 + easi/tasks/ebalfred/prompts.py | 109 ++ easi/tasks/ebalfred/task.py | 104 ++ easi/tasks/registry.py | 62 +- easi/utils/import_utils.py | 24 + easi/utils/locking.py | 5 +- easi/utils/logging.py | 11 +- easi/utils/system_deps.py | 4 +- pyproject.toml | 7 +- tests/test_e2e_evaluation.py | 218 ++++ tests/test_ebalfred_task.py | 169 +++ tests/test_evaluation_runner.py | 82 ++ tests/test_filesystem_comm.py | 27 + tests/test_multi_split_task.py | 176 +++ tests/test_react_agent.py | 236 ++++ 46 files changed, 3861 insertions(+), 261 deletions(-) create mode 100644 easi/agents/prompt_builder.py create mode 100644 easi/agents/react_agent.py create mode 100644 easi/evaluation/__init__.py create mode 100644 easi/evaluation/metrics.py create mode 100644 easi/evaluation/runner.py create mode 100644 easi/simulators/ai2thor/v2_1_0/thor_utils.py create mode 100644 easi/tasks/ebalfred/__init__.py create mode 100644 easi/tasks/ebalfred/actions.py create mode 100644 easi/tasks/ebalfred/ebalfred_base.yaml create mode 100644 easi/tasks/ebalfred/ebalfred_common_sense.yaml create mode 100644 easi/tasks/ebalfred/ebalfred_complex_instruction.yaml create mode 100644 easi/tasks/ebalfred/ebalfred_long_horizon.yaml create mode 100644 easi/tasks/ebalfred/ebalfred_spatial.yaml create mode 100644 easi/tasks/ebalfred/ebalfred_visual_appearance.yaml create mode 100644 easi/tasks/ebalfred/prompts.py create mode 100644 easi/tasks/ebalfred/task.py create mode 100644 easi/utils/import_utils.py create mode 100644 tests/test_e2e_evaluation.py create mode 100644 tests/test_ebalfred_task.py create mode 100644 tests/test_evaluation_runner.py create mode 100644 tests/test_multi_split_task.py create mode 100644 tests/test_react_agent.py diff --git a/CLAUDE.md b/CLAUDE.md index 241a481..3157983 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -67,9 +67,56 @@ CUDA_VISIBLE_DEVICES=0,1,2,3 accelerate launch --num_processes=4 \ List available tasks: `lmms-eval --tasks list` +## EASI Library (`easi/`) + +The `easi` package is a Python library for embodied agent evaluation with subprocess-isolated simulators. Install: `pip install -e .` + +### Architecture + +``` +easi/ +├── core/ # Base classes: BaseTask, BaseSimulator, BaseAgent, Episode/Action/StepResult +├── agents/ # DummyAgent, ReActAgent (with multi-action buffering + PromptBuilder) +├── communication/ # Filesystem IPC: atomic JSON read/write, command/response schemas +├── evaluation/ # EvaluationRunner (sequential orchestrator), metrics aggregation +├── llm/ # LLMApiClient (OpenAI-compatible), DummyLLMServer +├── simulators/ # Simulator implementations (subprocess bridges) +│ ├── dummy/v1/ # In-memory dummy bridge for testing +│ └── ai2thor/v2_1_0/ # Real AI2-THOR 2.1.0 bridge for EB-Alfred +├── tasks/ # Task definitions (per-split YAML configs) +│ ├── dummy/ # dummy_task (3 test episodes) +│ └── ebalfred/ # EB-Alfred (6 splits: base, long_horizon, common_sense, etc.) +└── utils/ # import_class(), logging setup +``` + +### Key Patterns + +- **Subprocess isolation**: Each simulator runs in its own conda env (e.g., Python 3.8 for ai2thor). The bridge script communicates via filesystem IPC (JSON files in a temp workspace). +- **Multi-split tasks**: Each task folder has one or more `*.yaml` config files. The registry discovers all YAMLs, each registering as a separate task (e.g., `ebalfred_base`, `ebalfred_spatial`). +- **EB-Alfred skills**: Actions are high-level skill text (e.g., `"find a Cabinet"`, `"pick up the Mug"`), NOT raw THOR API calls. The bridge translates these to THOR API sequences. +- **ReAct agent**: Uses a PromptBuilder protocol for task-specific prompts. Supports multi-action buffering (LLM returns a plan, agent executes one action per step, clears buffer on failure). +- **State tracking**: The AI2-THOR bridge tracks `cleaned_objects`, `cooled_objects`, `heated_objects` for EB-Alfred goal condition evaluation. + +### CLI + +```bash +easi env list|install|check # Manage simulator environments +easi task list|info|download # Manage tasks +easi sim test # Smoke test a simulator +easi run --agent dummy|react # Run evaluation +easi llm-server [--port PORT] # Start dummy LLM server +``` + +### Testing + +```bash +pip install -e ".[dev]" +python -m pytest tests/ -v --timeout=60 # 103 tests, ~60s +``` + ## Key References -- There is no test suite, linter, or build system in the root project — those live in the submodules. -- Evaluation logs go to `./logs/` (gitignored). +- Test suite: `tests/` with pytest (103 tests covering all components) +- Evaluation logs go to `./results/` (configurable via `--output-dir`) - Supported models (23) and benchmarks (25) are documented in `docs/Support_bench_models.md`. - Benchmark verification against official scores is in `docs/Benchmark_Verification.md`. diff --git a/easi/agents/prompt_builder.py b/easi/agents/prompt_builder.py new file mode 100644 index 0000000..12551ef --- /dev/null +++ b/easi/agents/prompt_builder.py @@ -0,0 +1,165 @@ +"""PromptBuilder protocol and default implementation. + +Decision #10: Both methods return OpenAI message format (list[dict]). +This supports interleaved text+image content for vision models, +and also works for text-only models (content is just a string). + +Contributors adding a new task only need to implement these 2 methods. +""" +from __future__ import annotations + +import base64 +from pathlib import Path +from typing import Protocol, runtime_checkable + +from easi.core.episode import Observation +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + + +def _encode_image_base64(image_path: str) -> str | None: + """Read an image file and return base64-encoded data URL. + + Returns None if file doesn't exist or can't be read. + """ + p = Path(image_path) + if not p.exists(): + logger.warning("Image file not found: %s", image_path) + return None + suffix = p.suffix.lower().lstrip(".") + mime = {"png": "image/png", "jpg": "image/jpeg", "jpeg": "image/jpeg"}.get(suffix, "image/png") + data = base64.b64encode(p.read_bytes()).decode("utf-8") + return f"data:{mime};base64,{data}" + + +@runtime_checkable +class PromptBuilderProtocol(Protocol): + """Interface for task-specific prompt construction. + + Both methods return OpenAI message format: list[dict]. + Each dict has "role" and "content" keys. + Content can be a string (text-only) or a list of content parts + (interleaved text + image_url for vision models). + + Implementations are referenced in task.yaml via: + agent: + prompt_builder: "easi.tasks.my_task.prompts.MyPromptBuilder" + """ + + def build_system_prompt( + self, + action_space: list[str], + task_description: str, + ) -> list[dict]: + """Build system message(s). + + Returns: + List of OpenAI message dicts, e.g.: + [{"role": "system", "content": "You are an agent..."}] + """ + ... + + def build_step_prompt( + self, + observation: Observation, + task_description: str, + action_history: list[tuple[str, str]], + ) -> list[dict]: + """Build user message(s) for a single step, including observation image. + + Returns: + List of OpenAI message dicts with interleaved text+image, e.g.: + [{"role": "user", "content": [ + {"type": "text", "text": "Task: ..."}, + {"type": "image_url", "image_url": {"url": "data:image/png;base64,..."}} + ]}] + """ + ... + + +class DefaultPromptBuilder: + """Generic prompt builder that works with any task. + + Produces OpenAI-format messages with interleaved text+image. + """ + + SYSTEM_TEMPLATE = """You are an embodied agent operating in a simulated environment. Given a task, you must accomplish it by choosing actions from the available action space. + +## Task +{task_description} + +## Available Actions +{action_list} + +## Output Format +You MUST respond with valid JSON in this exact format: +{{ + "observation": "Describe what you see in the current image", + "reasoning": "Explain your step-by-step reasoning", + "plan": "Your high-level plan", + "executable_plan": [ + {{"action": ""}}, + {{"action": ""}} + ] +}} + +## Guidelines +1. Always output at least one action in executable_plan. +2. Only use actions from the available action list. +3. If previous actions failed, reason about why and try a different approach. +4. Output at most 10 actions per plan. +""" + + STEP_TEMPLATE = """Task: {task_description} + +{history_section} + +Based on the current observation image, decide your next action(s). Respond with valid JSON.""" + + def build_system_prompt( + self, + action_space: list[str], + task_description: str, + ) -> list[dict]: + action_list = "\n".join( + f" {i}. {name}" for i, name in enumerate(action_space) + ) + text = self.SYSTEM_TEMPLATE.format( + action_list=action_list, + task_description=task_description, + ) + return [{"role": "system", "content": text}] + + def build_step_prompt( + self, + observation: Observation, + task_description: str, + action_history: list[tuple[str, str]], + ) -> list[dict]: + if action_history: + history_lines = [] + for i, (action_name, feedback) in enumerate(action_history): + history_lines.append(f" Step {i+1}: {action_name} → {feedback}") + history_section = "## Action History\n" + "\n".join(history_lines) + else: + history_section = "This is the first step." + + text = self.STEP_TEMPLATE.format( + task_description=task_description, + history_section=history_section, + ) + + # Build interleaved content parts + content_parts: list[dict] = [{"type": "text", "text": text}] + + # Add observation image if available + if observation.rgb_path: + image_url = _encode_image_base64(observation.rgb_path) + if image_url: + content_parts.append({ + "type": "image_url", + "image_url": {"url": image_url}, + }) + + return [{"role": "user", "content": content_parts}] diff --git a/easi/agents/react_agent.py b/easi/agents/react_agent.py new file mode 100644 index 0000000..1099b3b --- /dev/null +++ b/easi/agents/react_agent.py @@ -0,0 +1,168 @@ +"""ReAct agent with multi-action buffering and PromptBuilder delegation. + +Decision #9: LLM returns executable_plan as a list of actions. The agent +buffers all actions, pops one per act() call. While buffer is non-empty, +act() returns next buffered action without calling LLM. On action failure +(reported via add_feedback), buffer is cleared and next act() re-queries LLM. + +Decision #10: PromptBuilder returns OpenAI message format (list[dict]). +The agent appends these directly to chat_history and passes to LLM. +""" +from __future__ import annotations + +import json + +from easi.agents.prompt_builder import DefaultPromptBuilder, PromptBuilderProtocol +from easi.core.base_agent import BaseAgent +from easi.core.episode import Action, Observation +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + + +class ReActAgent(BaseAgent): + """ReAct agent with action buffering and pluggable prompt building. + + Flow per LLM call: + 1. PromptBuilder constructs OpenAI messages (with image) + 2. LLM returns JSON with executable_plan: [{action: ...}, ...] + 3. Agent validates ALL actions, buffers valid ones + 4. Returns first action; subsequent act() calls pop from buffer + 5. On failure feedback -> clear buffer -> next act() re-queries LLM + """ + + def __init__( + self, + llm_client, + action_space: list[str], + prompt_builder: PromptBuilderProtocol | None = None, + ): + super().__init__(llm_client=llm_client, action_space=action_space) + self.prompt_builder: PromptBuilderProtocol = prompt_builder or DefaultPromptBuilder() + self._action_buffer: list[Action] = [] + self._action_feedback: list[tuple[str, str]] = [] + self._task_description: str = "" + + def reset(self) -> None: + super().reset() + self._action_buffer = [] + self._action_feedback = [] + self._task_description = "" + + def act(self, observation: Observation, task_description: str) -> Action: + """Return the next action. + + If buffer has pending actions, pop and return (no LLM call). + Otherwise, call LLM, parse executable_plan, buffer all actions, + return the first one. + """ + # Decision #9: return buffered action if available + if self._action_buffer: + return self._action_buffer.pop(0) + + # Store task description for step prompts + self._task_description = task_description + + # Build system prompt on first call + if not self._chat_history: + system_messages = self.prompt_builder.build_system_prompt( + action_space=self.action_space, + task_description=task_description, + ) + self._chat_history.extend(system_messages) + + # Build step prompt (OpenAI message format with image) + step_messages = self.prompt_builder.build_step_prompt( + observation=observation, + task_description=task_description, + action_history=self._action_feedback, + ) + self._chat_history.extend(step_messages) + + # Call LLM + response = self.llm_client.generate(self._chat_history) + self._chat_history.append({"role": "assistant", "content": response}) + + self._step_count += 1 + + # Parse all actions from response + actions = self._parse_actions(response) + if not actions: + # Fallback: Stop or first action + return self._fallback_action() + + # Buffer remaining actions, return first + if len(actions) > 1: + self._action_buffer = actions[1:] + return actions[0] + + def _parse_actions(self, llm_response: str) -> list[Action]: + """Parse JSON response into a list of validated Actions. + + Returns empty list on parse failure (caller handles fallback). + """ + try: + data = json.loads(llm_response) + except json.JSONDecodeError as e: + logger.warning("Failed to parse LLM response as JSON: %s", e) + return [] + + plan = data.get("executable_plan", []) + if not isinstance(plan, list) or not plan: + logger.warning("No executable_plan in LLM response") + return [] + + actions = [] + for entry in plan: + if not isinstance(entry, dict): + continue + action_name = entry.get("action", "") + validated = self._validate_action_name(action_name) + if validated: + actions.append(Action(action_name=validated)) + else: + logger.warning("Skipping invalid action: '%s'", action_name) + # Stop processing further actions after an invalid one + break + + return actions + + def _validate_action_name(self, action_name: str) -> str | None: + """Validate action name against action_space. Returns canonical name or None.""" + if action_name in self.action_space: + return action_name + # Case-insensitive fallback + for valid in self.action_space: + if valid.lower() == action_name.lower(): + return valid + return None + + def _fallback_action(self) -> Action: + """Return a safe fallback action when parsing fails.""" + if "Stop" in self.action_space: + return Action(action_name="Stop") + return Action(action_name=self.action_space[0]) + + def add_feedback(self, action_name: str, feedback: str) -> None: + """Record action feedback. Clear buffer on failure (Decision #9).""" + self._action_feedback.append((action_name, feedback)) + # Clear buffer on failure so next act() re-queries LLM + if "fail" in feedback.lower() or "error" in feedback.lower(): + if self._action_buffer: + logger.info("Action '%s' failed, clearing %d buffered actions", + action_name, len(self._action_buffer)) + self._action_buffer.clear() + + # --- BaseAgent abstract methods (not used directly, but required) --- + + def _build_system_prompt(self, task_description: str) -> str: + """Not used — ReActAgent delegates to PromptBuilder.""" + return "" + + def _build_step_prompt(self, observation: Observation) -> str: + """Not used — ReActAgent delegates to PromptBuilder.""" + return "" + + def _parse_action(self, llm_response: str) -> Action: + """Not used — ReActAgent uses _parse_actions instead.""" + return self._fallback_action() diff --git a/easi/cli.py b/easi/cli.py index d719ff5..a2d8022 100644 --- a/easi/cli.py +++ b/easi/cli.py @@ -10,53 +10,76 @@ import argparse import sys +from easi.utils.logging import get_logger, setup_logging + +logger = get_logger(__name__) + def build_parser() -> argparse.ArgumentParser: + # Shared parent so --verbosity works at any position in the command + common = argparse.ArgumentParser(add_help=False) + common.add_argument( + "--verbosity", type=str, default="INFO", + choices=["DEBUG", "INFO", "WARNING", "ERROR"], + help="Set logging verbosity (default: INFO)", + ) + parser = argparse.ArgumentParser( prog="easi", description="EASI - Embodied Reasoning Evaluation for Spatial Intelligence", + parents=[common], ) - parser.add_argument( - "--verbose", "-v", action="store_true", help="Enable INFO logging" - ) - parser.add_argument("--debug", action="store_true", help="Enable DEBUG logging") subparsers = parser.add_subparsers(dest="command") # --- env command group --- - env_parser = subparsers.add_parser("env", help="Manage simulator environments") + env_parser = subparsers.add_parser("env", help="Manage simulator environments", parents=[common]) env_sub = env_parser.add_subparsers(dest="env_action") - env_sub.add_parser("list", help="List available simulators and versions") + env_sub.add_parser("list", help="List available simulators and versions", parents=[common]) - env_install = env_sub.add_parser("install", help="Install a simulator environment") + env_install = env_sub.add_parser("install", help="Install a simulator environment", parents=[common]) env_install.add_argument("simulator", type=str, help="e.g., 'dummy' or 'ai2thor:v2_1_0'") - env_check = env_sub.add_parser("check", help="Check if environment is ready") + env_check = env_sub.add_parser("check", help="Check if environment is ready", parents=[common]) env_check.add_argument("simulator", type=str) # --- task command group --- - task_parser = subparsers.add_parser("task", help="Manage tasks (benchmarks)") + task_parser = subparsers.add_parser("task", help="Manage tasks (benchmarks)", parents=[common]) task_sub = task_parser.add_subparsers(dest="task_action") - task_sub.add_parser("list", help="List available tasks") + task_sub.add_parser("list", help="List available tasks", parents=[common]) - task_info = task_sub.add_parser("info", help="Show task details") + task_info = task_sub.add_parser("info", help="Show task details", parents=[common]) task_info.add_argument("task", type=str, help="e.g., 'dummy_task'") - task_download = task_sub.add_parser("download", help="Download task dataset") + task_download = task_sub.add_parser("download", help="Download task dataset", parents=[common]) task_download.add_argument("task", type=str) # --- sim command group --- - sim_parser = subparsers.add_parser("sim", help="Control simulators") + sim_parser = subparsers.add_parser("sim", help="Control simulators", parents=[common]) sim_sub = sim_parser.add_subparsers(dest="sim_action") - sim_test = sim_sub.add_parser("test", help="Run a smoke test (reset + N steps)") + sim_test = sim_sub.add_parser("test", help="Run a smoke test (reset + N steps)", parents=[common]) sim_test.add_argument("simulator", type=str, help="e.g., 'dummy' or 'ai2thor:v5_0_0'") sim_test.add_argument("--steps", type=int, default=5, help="Number of steps") + sim_test.add_argument("--timeout", type=float, default=30.0, + help="Bridge startup timeout in seconds (default: 30)") + + # --- run command --- + run_parser = subparsers.add_parser("run", help="Run a full evaluation", parents=[common]) + run_parser.add_argument("task", type=str, help="Task name (e.g., 'dummy_task', 'ebalfred_base')") + run_parser.add_argument("--agent", type=str, default="dummy", choices=["dummy", "react"]) + run_parser.add_argument("--output-dir", type=str, default="./logs", + help="Base output directory (creates ///)") + run_parser.add_argument("--data-dir", type=str, default="./datasets", + help="Directory for downloading/caching datasets (default: ./datasets)") + run_parser.add_argument("--max-episodes", type=int, default=None) + run_parser.add_argument("--llm-url", type=str, default=None, help="LLM server URL") + run_parser.add_argument("--seed", type=int, default=None) # --- llm-server command --- - llm_parser = subparsers.add_parser("llm-server", help="Start dummy LLM server") + llm_parser = subparsers.add_parser("llm-server", help="Start dummy LLM server", parents=[common]) llm_parser.add_argument("--port", type=int, default=8000) llm_parser.add_argument("--host", type=str, default="127.0.0.1") llm_parser.add_argument("--mode", choices=["fixed", "random"], default="random") @@ -75,7 +98,7 @@ def cmd_env_list() -> None: sims = list_simulators() if not sims: - print("No simulators found.") + logger.info("No simulators found.") return # Deduplicate: show each name:version pair once @@ -87,7 +110,7 @@ def cmd_env_list() -> None: continue seen.add(pair) default_marker = " (default)" if key == entry.name else "" - print(f" {pair}{default_marker} — {entry.description}") + logger.info(" %s%s -- %s", pair, default_marker, entry.description) def cmd_env_install(simulator: str) -> None: @@ -95,9 +118,9 @@ def cmd_env_install(simulator: str) -> None: EnvManagerClass = load_env_manager_class(simulator) env_manager = EnvManagerClass() - print(f"Installing environment: {env_manager.get_env_name()}") + logger.info("Installing environment: %s", env_manager.get_env_name()) env_manager.install() - print("Done.") + logger.info("Done.") def cmd_env_check(simulator: str) -> None: @@ -108,14 +131,14 @@ def cmd_env_check(simulator: str) -> None: missing = env_manager.check_system_deps() if missing: - print(f"Missing system deps: {missing}") + logger.info("Missing system deps: %s", missing) if env_manager.env_is_ready(): - print(f"Environment {env_manager.get_env_name()} is ready.") - print(f"Python: {env_manager.get_python_executable()}") + logger.info("Environment %s is ready.", env_manager.get_env_name()) + logger.info("Python: %s", env_manager.get_python_executable()) else: - print(f"Environment {env_manager.get_env_name()} is NOT ready.") - print("Run: easi env install " + simulator) + logger.info("Environment %s is NOT ready.", env_manager.get_env_name()) + logger.info("Run: easi env install %s", simulator) def cmd_task_list() -> None: @@ -123,24 +146,24 @@ def cmd_task_list() -> None: tasks = list_tasks() if not tasks: - print("No tasks found.") + logger.info("No tasks found.") return for name in tasks: entry = get_task_entry(name) - print(f" {name} — {entry.display_name} (simulator: {entry.simulator_key})") + logger.info(" %s -- %s (simulator: %s)", name, entry.display_name, entry.simulator_key) def cmd_task_info(task_name: str) -> None: from easi.tasks.registry import get_task_entry entry = get_task_entry(task_name) - print(f"Task: {entry.display_name}") - print(f" Name: {entry.name}") - print(f" Description: {entry.description}") - print(f" Simulator: {entry.simulator_key}") - print(f" Actions: {', '.join(entry.action_space)}") - print(f" Max steps: {entry.max_steps}") + logger.info("Task: %s", entry.display_name) + logger.info(" Name: %s", entry.name) + logger.info(" Description: %s", entry.description) + logger.info(" Simulator: %s", entry.simulator_key) + logger.info(" Actions: %s", ", ".join(entry.action_space)) + logger.info(" Max steps: %s", entry.max_steps) def cmd_task_download(task_name: str) -> None: @@ -150,12 +173,12 @@ def cmd_task_download(task_name: str) -> None: task = TaskClass() path = task.download_dataset() if path and str(path): - print(f"Dataset ready at: {path}") + logger.info("Dataset ready at: %s", path) else: - print("Task uses built-in episodes (no download needed).") + logger.info("Task uses built-in episodes (no download needed).") -def cmd_sim_test(simulator: str, steps: int) -> None: +def cmd_sim_test(simulator: str, steps: int, timeout: float) -> None: from easi.core.episode import Action from easi.simulators.registry import load_env_manager_class, load_simulator_class from easi.simulators.subprocess_runner import SubprocessRunner @@ -166,42 +189,68 @@ def cmd_sim_test(simulator: str, steps: int) -> None: env_manager = EnvManagerClass() sim = SimClass() - print(f"Testing {simulator}...") - print(f" Python: {env_manager.get_python_executable()}") + logger.info("Testing %s...", simulator) + logger.info(" Python: %s", env_manager.get_python_executable()) runner = SubprocessRunner( python_executable=env_manager.get_python_executable(), bridge_script_path=sim._get_bridge_script_path(), needs_display=env_manager.needs_display, xvfb_screen_config=env_manager.xvfb_screen_config, + startup_timeout=timeout, ) try: runner.launch() sim.set_runner(runner) - print(" Reset... ", end="", flush=True) + logger.info(" Reset...") obs = sim.reset("smoke_test_001") - print(f"OK (rgb: {obs.rgb_path})") + logger.info(" Reset OK (rgb: %s)", obs.rgb_path) for i in range(steps): action = Action(action_name="MoveAhead") result = sim.step(action) - print(f" Step {i+1}: done={result.done}, reward={result.reward}") + logger.info(" Step %d: done=%s, reward=%s", i + 1, result.done, result.reward) if result.done: break - print(" Closing... ", end="", flush=True) + logger.info(" Closing...") sim.close() - print("OK") - print("Smoke test passed!") + logger.info(" Close OK") + logger.info("Smoke test passed!") + except KeyboardInterrupt: + logger.info("Interrupted, shutting down bridge...") + sim.close() + logger.info("Bridge process terminated.") + sys.exit(130) except Exception as e: - print(f"\nSmoke test FAILED: {e}") + logger.error("Smoke test FAILED: %s", e) sim.close() sys.exit(1) +def cmd_run(task_name, agent_type, output_dir, data_dir, max_episodes, llm_url, seed): + from easi.evaluation.runner import EvaluationRunner + + runner = EvaluationRunner( + task_name=task_name, + agent_type=agent_type, + output_dir=output_dir, + data_dir=data_dir, + llm_base_url=llm_url, + agent_seed=seed, + ) + results = runner.run(max_episodes=max_episodes) + logger.info("Completed %d episodes.", len(results)) + from easi.evaluation.metrics import aggregate_metrics + + summary = aggregate_metrics(results) + for key, value in summary.items(): + logger.info(" %s: %s", key, value) + + def cmd_llm_server(host: str, port: int, mode: str, action_space: list[str]) -> None: from easi.llm.dummy_server import run_server @@ -214,13 +263,7 @@ def main() -> None: parser = build_parser() args = parser.parse_args() - # Set up logging - if args.debug: - from easi.utils.logging import setup_logging - setup_logging("DEBUG") - elif args.verbose: - from easi.utils.logging import setup_logging - setup_logging("INFO") + setup_logging(args.verbosity) if args.command is None: parser.print_help() @@ -249,10 +292,14 @@ def main() -> None: elif args.command == "sim": if args.sim_action == "test": - cmd_sim_test(args.simulator, args.steps) + cmd_sim_test(args.simulator, args.steps, args.timeout) else: parser.parse_args(["sim", "--help"]) + elif args.command == "run": + cmd_run(args.task, args.agent, args.output_dir, args.data_dir, + args.max_episodes, args.llm_url, args.seed) + elif args.command == "llm-server": cmd_llm_server(args.host, args.port, args.mode, args.action_space) diff --git a/easi/communication/filesystem.py b/easi/communication/filesystem.py index 59d26e8..e5da74e 100644 --- a/easi/communication/filesystem.py +++ b/easi/communication/filesystem.py @@ -10,15 +10,15 @@ from __future__ import annotations import json -import logging import os import tempfile import time from pathlib import Path from easi.core.exceptions import SimulatorTimeoutError +from easi.utils.logging import get_logger -logger = logging.getLogger("easi.communication") +logger = get_logger(__name__) # Default filenames used in the IPC workspace COMMAND_FILE = "command.json" diff --git a/easi/communication/schemas.py b/easi/communication/schemas.py index 1e0efae..90b3cc4 100644 --- a/easi/communication/schemas.py +++ b/easi/communication/schemas.py @@ -12,12 +12,19 @@ # --- Command schemas (parent → child) --- -def make_reset_command(episode_id: str, reset_config: dict | None = None) -> dict: - return { +def make_reset_command( + episode_id: str, + reset_config: dict | None = None, + episode_output_dir: str | None = None, +) -> dict: + cmd = { "type": "reset", "episode_id": episode_id, "reset_config": reset_config or {}, } + if episode_output_dir is not None: + cmd["episode_output_dir"] = episode_output_dir + return cmd def make_step_command(action: Action) -> dict: diff --git a/easi/core/base_agent.py b/easi/core/base_agent.py index 4cde3fb..85212f5 100644 --- a/easi/core/base_agent.py +++ b/easi/core/base_agent.py @@ -11,14 +11,14 @@ from __future__ import annotations -import logging from abc import ABC, abstractmethod from easi.core.episode import Action, Observation from easi.core.exceptions import ActionParseError from easi.core.protocols import LLMClientProtocol +from easi.utils.logging import get_logger -logger = logging.getLogger("easi.core.base_agent") +logger = get_logger(__name__) class BaseAgent(ABC): @@ -49,6 +49,13 @@ def _parse_action(self, llm_response: str) -> Action: """ ... + def add_feedback(self, action_name: str, feedback: str) -> None: + """Record action feedback from the environment. + + Default: no-op. Subclasses (e.g., ReActAgent) override to track + action history and clear action buffer on failure. + """ + def reset(self) -> None: """Clear chat history for a new episode.""" self._chat_history = [] @@ -77,22 +84,13 @@ def act(self, observation: Observation, task_description: str) -> Action: self._step_count += 1 step_prompt = self._build_step_prompt(observation) - # Collect images - images = [] - if observation.rgb_path: - images.append(observation.rgb_path) - # Add user message to history - user_message = {"role": "user", "content": step_prompt} - if images: - user_message["images"] = images - self._chat_history.append(user_message) + self._chat_history.append({"role": "user", "content": step_prompt}) # Call LLM if self.llm_client is not None: llm_response = self.llm_client.generate( messages=self._chat_history, - images=images if images else None, ) else: llm_response = "" diff --git a/easi/core/base_env_manager.py b/easi/core/base_env_manager.py index d51edcf..4ab61fe 100644 --- a/easi/core/base_env_manager.py +++ b/easi/core/base_env_manager.py @@ -7,22 +7,22 @@ - validation import to confirm the env works The shared install() logic handles the full sequence: - check_system_deps → conda create → pip install uv → uv pip install → validate + check_system_deps -> conda create -> pip install uv -> uv pip install -> validate """ from __future__ import annotations -import logging import subprocess from abc import ABC, abstractmethod from pathlib import Path from easi.core.exceptions import EnvironmentSetupError from easi.utils.locking import file_lock +from easi.utils.logging import get_logger from easi.utils.paths import get_locks_dir from easi.utils.system_deps import SystemDependencyChecker -logger = logging.getLogger("easi.core.base_env_manager") +logger = get_logger(__name__) class BaseEnvironmentManager(ABC): @@ -125,7 +125,7 @@ def install(self) -> None: def _do_install(self) -> None: """Execute the full install sequence (called under lock).""" env_name = self.get_env_name() - logger.info("Installing environment: %s", env_name) + logger.info("[Env Installation] %s", env_name) # Step 1: Check system deps self._dep_checker.assert_all(self.get_system_deps()) @@ -135,7 +135,7 @@ def _do_install(self) -> None: if conda_yaml.exists(): self._run_conda_create(env_name, conda_yaml) else: - logger.warning("No conda_env.yaml found at %s, skipping conda setup", conda_yaml) + logger.warning("[Env Installation] No conda_env.yaml found at %s, skipping conda setup", conda_yaml) # Step 3: Install uv in the conda env python_exec = self.get_python_executable() @@ -149,21 +149,15 @@ def _do_install(self) -> None: "uv pip install", ) else: - logger.warning("No requirements.txt found at %s, skipping uv install", requirements) + logger.warning("[Env Installation] No requirements.txt found at %s, skipping uv install", requirements) - # Step 5: Validate - result = subprocess.run( + # Step 5: Validate (stream output like other commands) + self._run_command( [python_exec, "-c", self.get_validation_import()], - capture_output=True, - text=True, - timeout=30, + "environment validation", ) - if result.returncode != 0: - raise EnvironmentSetupError( - f"Environment validation failed for {env_name}:\n{result.stderr}" - ) - logger.info("Environment %s installed and validated successfully", env_name) + logger.info("[Env Installation] Environment %s installed and validated successfully", env_name) def _run_conda_create(self, env_name: str, yaml_path: Path) -> None: """Create or update a conda environment from a YAML file.""" @@ -179,12 +173,25 @@ def _run_conda_create(self, env_name: str, yaml_path: Path) -> None: self._run_command(cmd, desc) def _run_command(self, cmd: list[str], description: str) -> None: - """Run a subprocess command, raising on failure.""" - logger.debug("Running %s: %s", description, " ".join(cmd)) - result = subprocess.run(cmd, capture_output=True, text=True, timeout=600) - if result.returncode != 0: + """Run a subprocess command, streaming output through the logger.""" + logger.info("[Env Installation] %s", " ".join(cmd)) + process = subprocess.Popen( + cmd, + stdout=subprocess.PIPE, + stderr=subprocess.STDOUT, + text=True, + bufsize=1, + ) + output_lines = [] + for line in process.stdout: + line = line.rstrip() + output_lines.append(line) + logger.debug(" %s", line) + process.wait() + if process.returncode != 0: raise EnvironmentSetupError( - f"{description} failed (exit code {result.returncode}):\n{result.stderr}" + f"{description} failed (exit {process.returncode}):\n" + + "\n".join(output_lines[-20:]) ) @staticmethod diff --git a/easi/core/base_simulator.py b/easi/core/base_simulator.py index 03ca9ba..81ced52 100644 --- a/easi/core/base_simulator.py +++ b/easi/core/base_simulator.py @@ -12,14 +12,14 @@ from __future__ import annotations -import logging from abc import ABC, abstractmethod from pathlib import Path from easi.communication import filesystem, schemas from easi.core.episode import Action, Observation, StepResult +from easi.utils.logging import get_logger -logger = logging.getLogger("easi.core.base_simulator") +logger = get_logger(__name__) class BaseSimulator(ABC): @@ -54,16 +54,27 @@ def _format_action(self, action: Action) -> dict: """Format an Action into the command dict. Override for custom formatting.""" return schemas.make_step_command(action) - def reset(self, episode_id: str, reset_config: dict | None = None) -> Observation: + def reset( + self, + episode_id: str, + reset_config: dict | None = None, + episode_output_dir: str | None = None, + ) -> Observation: """Reset the simulator for a new episode. Sends a reset command to the bridge subprocess and waits for the observation response. + + Args: + episode_id: Unique identifier for this episode. + reset_config: Task-specific configuration for the episode. + episode_output_dir: Directory where the bridge should save + observation images. If None, the bridge uses its IPC workspace. """ if self._runner is None: raise RuntimeError("Simulator not started. Call start() first.") - command = schemas.make_reset_command(episode_id, reset_config) + command = schemas.make_reset_command(episode_id, reset_config, episode_output_dir) response = self._runner.send_command(command) if response.get("status") == "error": diff --git a/easi/core/base_task.py b/easi/core/base_task.py index 5037700..6216dfc 100644 --- a/easi/core/base_task.py +++ b/easi/core/base_task.py @@ -16,7 +16,6 @@ from __future__ import annotations import json -import logging from abc import ABC, abstractmethod from pathlib import Path @@ -24,14 +23,30 @@ from easi.core.episode import StepResult from easi.core.exceptions import DatasetError +from easi.utils.logging import get_logger -logger = logging.getLogger("easi.core.base_task") +logger = get_logger(__name__) + + +def hf_row_to_episode(row: dict) -> dict: + """Convert a HuggingFace dataset row to an episode dict. + + HF dataset rows contain all information for a single episode. + For EB-Alfred_easi: {id, task, repeat_idx, instruction, task_type, trial_id} + This is a passthrough — the row IS the episode. + """ + return dict(row) class BaseTask(ABC): """Abstract base for all tasks (benchmarks).""" - def __init__(self, data_dir: Path | None = None): + def __init__( + self, + data_dir: Path | None = None, + split_yaml_path: Path | None = None, + ): + self._split_yaml_path = split_yaml_path self._config = self._load_config() self._episodes: list[dict] | None = None self._data_dir = data_dir @@ -61,6 +76,14 @@ def evaluate_episode( # --- Shared implementation --- + def get_instruction(self, episode: dict) -> str: + """Return human-readable task instruction for this episode. + + Default tries common field names. Override in subclasses + for benchmarks that use different keys. + """ + return episode.get("instruction", episode.get("task_description", self.name)) + @property def name(self) -> str: return self._config["name"] @@ -124,21 +147,28 @@ def __len__(self) -> int: return len(self.load_episodes()) def _load_config(self) -> dict: - """Load task.yaml.""" - yaml_path = self.get_task_yaml_path() + """Load task config from split yaml (if provided) or default task.yaml.""" + yaml_path = self._split_yaml_path or self.get_task_yaml_path() if not yaml_path.exists(): raise DatasetError(f"Task config not found: {yaml_path}") return yaml.safe_load(yaml_path.read_text()) def _load_episodes_from_config(self) -> list[dict]: - """Load episodes from the dataset directory. + """Load episodes from the dataset. - Subclasses can override this for custom episode loading logic. - Default implementation looks for episodes.json in the data directory. + For HuggingFace datasets: downloads the repo, then loads the split + using the datasets library. Each row = one episode. + For local datasets: looks for episodes.json. """ - data_dir = self._data_dir or self.download_dataset() + dataset_config = self._config.get("dataset", {}) + source = dataset_config.get("source", "local") + + if source == "huggingface": + return self._load_episodes_from_hf(dataset_config) + + # Local source — existing behavior + data_dir = self.download_dataset() if not data_dir or data_dir == Path(): - # No data dir — subclass should provide built-in episodes return self._get_builtin_episodes() episodes_file = data_dir / "episodes.json" @@ -150,6 +180,73 @@ def _load_episodes_from_config(self) -> list[dict]: f"Override _load_episodes_from_config() for custom loading." ) + def _load_episodes_from_hf(self, dataset_config: dict) -> list[dict]: + """Load episodes from a HuggingFace dataset (subset + split). + + Each row in the dataset = one episode dict. + Downloads all files via snapshot_download, then loads locally. + """ + data_dir = self.download_dataset() + + subset = dataset_config.get("subset") + split_name = dataset_config.get("split") + + try: + from datasets import ( + get_dataset_config_names, + get_dataset_split_names, + load_dataset, + ) + except ImportError: + raise DatasetError( + "The 'datasets' library is required for HF episode loading. " + "Install with: pip install datasets" + ) + + local_path = str(data_dir) + + # Auto-detect subset if not specified + if subset is None: + configs = get_dataset_config_names(local_path) + if len(configs) == 1: + subset = configs[0] + logger.info("Auto-detected single subset: %s", subset) + elif "default" in configs: + subset = "default" + else: + raise DatasetError( + f"Dataset at {local_path} has multiple subsets {configs} — " + f"please specify 'subset' in task yaml." + ) + + # Auto-detect split if not specified + if split_name is None: + splits = get_dataset_split_names(local_path, subset) + if len(splits) == 1: + split_name = splits[0] + logger.info("Auto-detected single split: %s", split_name) + else: + raise DatasetError( + f"Dataset at {local_path} subset={subset} has " + f"multiple splits {splits} — " + f"please specify 'split' in task yaml." + ) + + logger.info( + "Loading episodes from local HF dataset %s subset=%s split=%s", + local_path, subset, split_name, + ) + + ds = load_dataset(local_path, subset, split=split_name) + episodes = [hf_row_to_episode(row) for row in ds] + + for ep in episodes: + ep["_data_dir"] = str(data_dir) + + logger.info("Loaded %d episodes from %s/%s/%s", + len(episodes), local_path, subset, split_name) + return episodes + def _get_builtin_episodes(self) -> list[dict]: """Return built-in episodes when no dataset download is needed. @@ -158,33 +255,78 @@ def _get_builtin_episodes(self) -> list[dict]: return [] def _download_huggingface(self, config: dict) -> Path: - """Download a dataset from HuggingFace Hub with file-based locking.""" + """Download a dataset from HuggingFace Hub with file-based locking. + + Uses snapshot_download to get the full repo (including .zip files), + then extracts any listed zip_files. + """ from easi.utils.locking import file_lock - from easi.utils.paths import get_datasets_dir, get_locks_dir + from easi.utils.paths import get_locks_dir repo_id = config["repo_id"] lock_path = get_locks_dir() / f"dataset_{repo_id.replace('/', '_')}.lock" + # Use data_dir if set, otherwise default datasets dir + if self._data_dir: + base_dir = self._data_dir + else: + from easi.utils.paths import get_datasets_dir + base_dir = get_datasets_dir() + with file_lock(lock_path): - target = get_datasets_dir() / repo_id.replace("/", "_") - if target.exists(): + target = base_dir / repo_id.replace("/", "_") + if not target.exists(): + try: + from huggingface_hub import snapshot_download + except ImportError: + raise DatasetError( + "huggingface_hub is required for HuggingFace downloads. " + "Install with: pip install huggingface_hub" + ) + + logger.info("Downloading dataset %s from HuggingFace...", repo_id) + try: + snapshot_download( + repo_id=repo_id, + local_dir=str(target), + repo_type="dataset", + ) + except Exception as e: + if target.exists(): + import shutil + shutil.rmtree(target, ignore_errors=True) + raise DatasetError(f"Failed to download {repo_id}: {e}") + + logger.info("Downloaded dataset %s to %s", repo_id, target) + else: logger.info("Dataset %s already cached at %s", repo_id, target) - return target - try: - from huggingface_hub import snapshot_download + # Extract any .zip files listed in config + zip_files = config.get("zip_files", []) + if zip_files: + self._extract_zip_files(target, zip_files) - snapshot_download( - repo_id=repo_id, - local_dir=str(target), - repo_type="dataset", - ) - logger.info("Downloaded dataset %s to %s", repo_id, target) - return target - except ImportError: - raise DatasetError( - "huggingface_hub is required for HuggingFace downloads. " - "Install with: pip install huggingface_hub" - ) - except Exception as e: - raise DatasetError(f"Failed to download {repo_id}: {e}") + return target + + @staticmethod + def _extract_zip_files(dataset_dir: Path, zip_filenames: list[str]) -> None: + """Extract listed .zip files within a downloaded dataset directory.""" + import zipfile as zf + + for zip_name in zip_filenames: + zip_path = dataset_dir / zip_name + if not zip_path.exists(): + logger.warning("Zip file not found: %s", zip_path) + continue + + marker = dataset_dir / f".{zip_name}.extracted" + if marker.exists(): + logger.debug("Already extracted: %s", zip_name) + continue + + logger.info("Extracting %s...", zip_path) + with zf.ZipFile(zip_path, "r") as z: + z.extractall(dataset_dir) + + marker.write_text("extracted") + logger.info("Extracted %s to %s", zip_name, dataset_dir) diff --git a/easi/core/exceptions.py b/easi/core/exceptions.py index be8a87f..a093341 100644 --- a/easi/core/exceptions.py +++ b/easi/core/exceptions.py @@ -1,5 +1,7 @@ """Custom exception hierarchy for EASI.""" +from __future__ import annotations + class EASIError(Exception): """Base exception for all EASI errors.""" diff --git a/easi/core/protocols.py b/easi/core/protocols.py index 877c138..98ab994 100644 --- a/easi/core/protocols.py +++ b/easi/core/protocols.py @@ -39,9 +39,7 @@ def get_env_name(self) -> str: ... class LLMClientProtocol(Protocol): """Calls an LLM inference server.""" - def generate( - self, messages: list[dict[str, str]], images: list[str] | None = None - ) -> str: ... + def generate(self, messages: list[dict]) -> str: ... @runtime_checkable @@ -50,9 +48,10 @@ class AgentProtocol(Protocol): def reset(self) -> None: ... def act(self, observation: Observation, task_description: str) -> Action: ... + def add_feedback(self, action_name: str, feedback: str) -> None: ... @property - def chat_history(self) -> list[dict[str, str]]: ... + def chat_history(self) -> list[dict]: ... @runtime_checkable diff --git a/easi/evaluation/__init__.py b/easi/evaluation/__init__.py new file mode 100644 index 0000000..a88296e --- /dev/null +++ b/easi/evaluation/__init__.py @@ -0,0 +1 @@ +"""Evaluation orchestration.""" diff --git a/easi/evaluation/metrics.py b/easi/evaluation/metrics.py new file mode 100644 index 0000000..70a3316 --- /dev/null +++ b/easi/evaluation/metrics.py @@ -0,0 +1,30 @@ +"""Metric aggregation utilities.""" + +from __future__ import annotations + + +def aggregate_metrics(results: list[dict]) -> dict: + """Aggregate per-episode metrics into a summary dict.""" + if not results: + return {"num_episodes": 0} + + summary = {"num_episodes": len(results)} + + # Collect all numeric keys + numeric_keys: dict[str, list[float]] = {} + for r in results: + for key, value in r.items(): + if isinstance(value, (int, float)): + numeric_keys.setdefault(key, []).append(float(value)) + + # Average each numeric metric + for key, values in numeric_keys.items(): + summary[f"avg_{key}"] = round(sum(values) / len(values), 4) + + # Convenience aliases + if "avg_success" in summary: + summary["success_rate"] = summary["avg_success"] + if "avg_num_steps" in summary: + summary["avg_steps"] = summary["avg_num_steps"] + + return summary diff --git a/easi/evaluation/runner.py b/easi/evaluation/runner.py new file mode 100644 index 0000000..1bee48f --- /dev/null +++ b/easi/evaluation/runner.py @@ -0,0 +1,270 @@ +"""Sequential evaluation runner. + +Ties together Task + Simulator + Agent into an episode loop: +1. Load task -> get episodes, simulator key, action space +2. Start simulator subprocess +3. For each episode: + a. Reset simulator with format_reset_config(episode) + b. Loop: agent.act(observation) -> simulator.step(action) until done or max_steps + c. Evaluate: task.evaluate_episode(episode, trajectory) + d. Save per-episode metrics + trajectory.jsonl + images +4. Aggregate metrics into summary.json + +Output directory structure: + /// + config.json + summary.json + episodes/ + 000_/ + result.json + trajectory.jsonl + rgb_0000.png, rgb_0001.png, ... +""" + +from __future__ import annotations + +import json +import re +import time +from datetime import datetime +from pathlib import Path + +from easi.core.episode import StepResult +from easi.evaluation.metrics import aggregate_metrics +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + + +def _sanitize_dirname(name: str) -> str: + """Replace characters unsafe for directory names.""" + return re.sub(r'[^\w\-.]', '_', name) + + +class EvaluationRunner: + """Sequential evaluation runner.""" + + def __init__( + self, + task_name: str, + agent_type: str = "dummy", + output_dir: Path | str = "./logs", + data_dir: Path | str = "./datasets", + llm_base_url: str | None = None, + agent_seed: int | None = None, + ): + self.task_name = task_name + self.agent_type = agent_type + self.output_dir = Path(output_dir) + self.data_dir = Path(data_dir) + self.llm_base_url = llm_base_url + self.agent_seed = agent_seed + self.run_id = datetime.now().strftime("%Y%m%d_%H%M%S") + + def run(self, max_episodes: int | None = None) -> list[dict]: + """Run evaluation and return per-episode metric dicts.""" + # Create structured output directory + run_dir = self.output_dir / self.task_name / self.run_id + episodes_dir = run_dir / "episodes" + episodes_dir.mkdir(parents=True, exist_ok=True) + + # 1. Load task + task = self._create_task() + episodes = task.load_episodes() + if max_episodes is not None: + episodes = episodes[:max_episodes] + + # Save run config + config = { + "task_name": self.task_name, + "agent_type": self.agent_type, + "agent_seed": self.agent_seed, + "run_id": self.run_id, + "max_episodes": max_episodes, + "total_episodes": len(episodes), + } + (run_dir / "config.json").write_text(json.dumps(config, indent=2)) + + # 2. Create agent + agent = self._create_agent(task.action_space, task._config) + + # 3. Start simulator + sim, runner = self._create_simulator(task.simulator_key) + + all_results = [] + try: + for i, episode in enumerate(episodes): + episode_id = episode.get("episode_id", f"ep_{i}") + logger.info( + "Episode %d/%d: %s", i + 1, len(episodes), episode_id, + ) + + # Create episode output directory + safe_id = _sanitize_dirname(episode_id) + episode_dir = episodes_dir / f"{i:03d}_{safe_id}" + episode_dir.mkdir(exist_ok=True) + + result = self._run_episode( + sim, agent, task, episode, i, episode_dir, + ) + all_results.append(result) + + # Save per-episode result + (episode_dir / "result.json").write_text( + json.dumps(result, indent=2) + ) + + finally: + sim.close() + + # 4. Aggregate and save summary + summary = aggregate_metrics(all_results) + (run_dir / "summary.json").write_text(json.dumps(summary, indent=2)) + logger.info("Results saved to: %s", run_dir) + logger.info("Summary: %s", summary) + + return all_results + + def _run_episode( + self, sim, agent, task, episode: dict, index: int, episode_dir: Path, + ) -> dict: + """Run a single episode and return metrics.""" + agent.reset() + + episode_id = episode.get("episode_id", f"ep_{index}") + + # Reset simulator (bridge saves images to episode_dir) + reset_config = task.format_reset_config(episode) + observation = sim.reset( + episode_id, + reset_config, + episode_output_dir=str(episode_dir), + ) + + # Write reset entry to trajectory + trajectory_path = episode_dir / "trajectory.jsonl" + self._write_trajectory_entry(trajectory_path, { + "step": 0, + "type": "reset", + "rgb_path": Path(observation.rgb_path).name, + "agent_pose": observation.agent_pose, + "reward": 0.0, + "done": False, + "info": {}, + }) + + # Agent-simulator loop + trajectory: list[StepResult] = [] + task_description = task.get_instruction(episode) + start_time = time.monotonic() + + for step in range(task.max_steps): + action = agent.act(observation, task_description) + step_result = sim.step(action) + trajectory.append(step_result) + + # Write step entry to trajectory + self._write_trajectory_entry(trajectory_path, { + "step": step + 1, + "type": "step", + "action": action.action_name, + "rgb_path": Path(step_result.observation.rgb_path).name, + "agent_pose": step_result.observation.agent_pose, + "reward": step_result.reward, + "done": step_result.done, + "info": step_result.info, + }) + + # Feed action outcome back to agent for ReAct reasoning + last_success = step_result.info.get("last_action_success", 1.0) + feedback = step_result.info.get( + "feedback", + "success" if last_success else "failed", + ) + agent.add_feedback(action.action_name, feedback) + + observation = step_result.observation + + if step_result.done: + break + + elapsed = time.monotonic() - start_time + + # Evaluate + metrics = task.evaluate_episode(episode, trajectory) + metrics["episode_id"] = episode_id + metrics["elapsed_seconds"] = round(elapsed, 2) + + return metrics + + @staticmethod + def _write_trajectory_entry(path: Path, entry: dict) -> None: + """Append a single JSON line to the trajectory file.""" + with path.open("a") as f: + f.write(json.dumps(entry) + "\n") + + def _create_task(self): + from easi.tasks.registry import get_task_entry, load_task_class + + entry = get_task_entry(self.task_name) + TaskClass = load_task_class(self.task_name) + return TaskClass( + split_yaml_path=entry.config_path, + data_dir=self.data_dir, + ) + + def _create_agent(self, action_space: list[str], task_config: dict): + from easi.utils.import_utils import import_class + + if self.agent_type == "dummy": + from easi.agents.dummy_agent import DummyAgent + + return DummyAgent(action_space=action_space, seed=self.agent_seed) + elif self.agent_type == "react": + from easi.agents.react_agent import ReActAgent + from easi.llm.api_client import LLMApiClient + + llm = LLMApiClient( + base_url=self.llm_base_url or "http://127.0.0.1:8000" + ) + + # Load task-specific prompt builder if configured in yaml + prompt_builder = None + agent_config = task_config.get("agent", {}) + builder_class_name = agent_config.get("prompt_builder") + if builder_class_name: + BuilderClass = import_class(builder_class_name) + prompt_builder = BuilderClass() + + return ReActAgent( + llm_client=llm, + action_space=action_space, + prompt_builder=prompt_builder, + ) + else: + raise ValueError(f"Unknown agent type: {self.agent_type}") + + def _create_simulator(self, simulator_key: str): + from easi.simulators.registry import ( + load_env_manager_class, + load_simulator_class, + ) + from easi.simulators.subprocess_runner import SubprocessRunner + + EnvManagerClass = load_env_manager_class(simulator_key) + SimClass = load_simulator_class(simulator_key) + + env_manager = EnvManagerClass() + sim = SimClass() + + runner = SubprocessRunner( + python_executable=env_manager.get_python_executable(), + bridge_script_path=sim._get_bridge_script_path(), + needs_display=env_manager.needs_display, + xvfb_screen_config=env_manager.xvfb_screen_config, + extra_args=["--data-dir", str(self.data_dir)], + ) + runner.launch() + sim.set_runner(runner) + + return sim, runner diff --git a/easi/llm/api_client.py b/easi/llm/api_client.py index e62f0c1..f6881da 100644 --- a/easi/llm/api_client.py +++ b/easi/llm/api_client.py @@ -6,12 +6,13 @@ from __future__ import annotations -import logging from typing import Any import requests -logger = logging.getLogger("easi.llm.api_client") +from easi.utils.logging import get_logger + +logger = get_logger(__name__) class LLMApiClient: @@ -33,15 +34,13 @@ def __init__( def generate( self, - messages: list[dict[str, str]], - images: list[str] | None = None, + messages: list[dict], ) -> str: """Send a chat completion request and return the assistant's response text. Args: - messages: Chat history in OpenAI format. - images: Optional list of image paths (currently passed as metadata; - real multimodal support depends on the server implementation). + messages: Chat history in OpenAI format. Images are embedded as + content parts with type "image_url" (Decision #10). Returns: The assistant's response text. @@ -53,9 +52,6 @@ def generate( "temperature": self.temperature, } - if images: - payload["images"] = images - url = f"{self.base_url}/v1/chat/completions" logger.debug("POST %s (messages: %d)", url, len(messages)) diff --git a/easi/llm/dummy_server.py b/easi/llm/dummy_server.py index 30a44a9..3bdb414 100644 --- a/easi/llm/dummy_server.py +++ b/easi/llm/dummy_server.py @@ -13,11 +13,12 @@ import argparse import json -import logging import random from http.server import BaseHTTPRequestHandler, HTTPServer -logger = logging.getLogger("easi.llm.dummy_server") +from easi.utils.logging import get_logger + +logger = get_logger(__name__) DEFAULT_ACTION_SPACE = ["MoveAhead", "TurnLeft", "TurnRight", "Stop"] @@ -115,16 +116,14 @@ def run_server( handler_class = create_handler(mode, action_space) server = HTTPServer((host, port), handler_class) - logger.info("Dummy LLM server starting on %s:%d (mode=%s)", host, port, mode) - logger.info("Action space: %s", action_space) - print(f"Dummy LLM server running on http://{host}:{port}") - print(f"Mode: {mode}, Actions: {action_space}") - print("Press Ctrl+C to stop") + logger.info("Dummy LLM server running on http://%s:%d", host, port) + logger.info("Mode: %s, Actions: %s", mode, action_space) + logger.info("Press Ctrl+C to stop") try: server.serve_forever() except KeyboardInterrupt: - print("\nShutting down...") + logger.info("Shutting down...") finally: server.server_close() @@ -142,7 +141,8 @@ def main() -> None: ) args = parser.parse_args() - logging.basicConfig(level=logging.INFO) + from easi.utils.logging import setup_logging + setup_logging("INFO") run_server( host=args.host, port=args.port, diff --git a/easi/simulators/ai2thor/v2_1_0/bridge.py b/easi/simulators/ai2thor/v2_1_0/bridge.py index a8a3ed7..15d789d 100644 --- a/easi/simulators/ai2thor/v2_1_0/bridge.py +++ b/easi/simulators/ai2thor/v2_1_0/bridge.py @@ -1,23 +1,29 @@ -"""Bridge subprocess for AI2-THOR v2.1.0 (legacy API). +"""Bridge subprocess for AI2-THOR v2.1.0 (EB-Alfred track). -This script runs inside the ai2thor v2.1.0 conda environment. -It communicates with the parent via filesystem IPC. +This script runs inside the easi_ai2thor_v2_1_0 conda env (Python 3.8). +It communicates with the parent process via filesystem IPC. -NOTE: This is a stub. The actual AI2-THOR interaction will be implemented -when integrating with embodiedbench. The structure demonstrates how a real -bridge would work. +Ported from EmbodiedBench: +- envs/eb_alfred/thor_connector.py (high-level skill API) +- envs/eb_alfred/env/thor_env.py (controller init, scene restoration, state tracking) Usage: - python bridge.py --workspace /tmp/easi_xxx + python bridge.py --workspace /tmp/easi_xxx --data-dir /path/to/datasets """ from __future__ import annotations import argparse import logging +import math +import os +import re import sys from pathlib import Path +import numpy as np +from scipy import spatial + # Add repo root to path for easi imports _repo_root = Path(__file__).resolve().parents[4] if str(_repo_root) not in sys.path: @@ -31,24 +37,959 @@ from easi.communication.schemas import ( make_error_response, make_observation_response, - parse_action_from_command, +) +from easi.simulators.ai2thor.v2_1_0.thor_utils import ( + AGENT_HORIZON_ADJ, + AGENT_ROTATE_ADJ, + AGENT_STEP_SIZE, + CAMERA_HEIGHT_OFFSET, + RECORD_SMOOTHING_FACTOR, + RENDER_CLASS_IMAGE, + RENDER_DEPTH_IMAGE, + RENDER_IMAGE, + RENDER_OBJECT_IMAGE, + SCREEN_HEIGHT, + SCREEN_WIDTH, + VISIBILITY_DISTANCE, + evaluate_goal_conditions, + get_objects_of_type, + get_obj_of_type_closest_to_obj, + load_task_json_with_repeat, + natural_word_to_ithor_name, ) logger = logging.getLogger("easi.bridge.ai2thor_v2_1_0") -def run_bridge(workspace: Path) -> None: +class EBAlfredBridge: + """EB-Alfred bridge managing AI2-THOR controller and high-level skills. + + Ported from EmbodiedBench's ThorEnv + ThorConnector. + """ + + def __init__(self, workspace, data_dir): + self.workspace = Path(workspace) + self.data_dir = Path(data_dir) + self.controller = None + self.last_event = None + self.traj_data = None + self.step_count = 0 + + # State tracking (from EmbodiedBench ThorEnv) + self.cleaned_objects = set() + self.cooled_objects = set() + self.heated_objects = set() + + # Navigation cache + self.reachable_positions = None + self.reachable_position_kdtree = None + + # Agent height (set after reset from init_action) + self.agent_height = 0.9009992 + + # Skill state + self.cur_receptacle = None + self.put_count_dict = {} + self.sliced = False + + # Output directory for saving images (set per-episode from reset command) + self.episode_output_dir = None + + def start(self): + """Initialize AI2-THOR controller.""" + from ai2thor.controller import Controller + + logger.info("Starting AI2-THOR controller...") + self.controller = Controller(quality="MediumCloseFitShadows") + self.controller.start( + x_display=os.environ.get("DISPLAY", "0"), + player_screen_height=SCREEN_HEIGHT, + player_screen_width=SCREEN_WIDTH, + ) + logger.info("AI2-THOR controller started.") + + def stop(self): + """Stop the AI2-THOR controller.""" + if self.controller is not None: + try: + self.controller.stop() + except Exception: + pass + self.controller = None + + def _step(self, action_dict): + """Execute a raw THOR action and update last_event.""" + self.last_event = self.controller.step(action_dict) + return self.last_event + + # --- Reset --- + + def reset(self, reset_config): + """Reset to an EB-Alfred episode (or a default scene for smoke tests). + + If task_path is provided, loads traj_data.json, resets scene, + restores objects, and teleports agent. + If task_path is missing (smoke test), resets to a default scene. + """ + task_path = reset_config.get("task_path") + + if not task_path: + # Smoke test mode: reset to a default scene without episode data + return self._reset_default_scene() + + repeat_idx = reset_config.get("repeat_idx", 0) + + self.traj_data = load_task_json_with_repeat( + task_path, repeat_idx, str(self.data_dir) + ) + self.cleaned_objects.clear() + self.cooled_objects.clear() + self.heated_objects.clear() + self.step_count = 0 + self.cur_receptacle = None + self.put_count_dict = {} + self.sliced = False + + # Reset scene + scene_num = self.traj_data["scene"]["scene_num"] + scene_name = "FloorPlan%d" % scene_num + logger.info("Resetting to scene: %s", scene_name) + self.controller.reset(scene_name) + + # Restore object poses, toggles, dirty states + self._restore_scene() + + # Teleport agent to initial position + init_action = dict(self.traj_data["scene"]["init_action"]) + if init_action.get("action") == "TeleportFull": + init_action.pop("rotateOnTeleport", None) + init_action["standing"] = True + self.last_event = self.controller.step(init_action) + + # Cache agent height + self.agent_height = self.last_event.metadata["agent"]["position"]["y"] + + # Cache reachable positions for navigation + self._cache_reachable_positions() + + return self._make_observation_response() + + def _reset_default_scene(self): + """Reset to FloorPlan10 for smoke testing (no episode data needed).""" + logger.info("Smoke test mode: resetting to default scene FloorPlan10") + self.traj_data = None + self.cleaned_objects.clear() + self.cooled_objects.clear() + self.heated_objects.clear() + self.step_count = 0 + self.cur_receptacle = None + self.put_count_dict = {} + self.sliced = False + + self.controller.reset("FloorPlan10") + self.last_event = self.controller.step(dict( + action="Initialize", + gridSize=AGENT_STEP_SIZE / RECORD_SMOOTHING_FACTOR, + cameraY=CAMERA_HEIGHT_OFFSET, + renderImage=RENDER_IMAGE, + renderDepthImage=RENDER_DEPTH_IMAGE, + renderClassImage=RENDER_CLASS_IMAGE, + renderObjectImage=RENDER_OBJECT_IMAGE, + visibility_distance=VISIBILITY_DISTANCE, + makeAgentsVisible=False, + )) + self.agent_height = self.last_event.metadata["agent"]["position"]["y"] + self._cache_reachable_positions() + return self._make_observation_response() + + def _restore_scene(self): + """Restore EB-Alfred scene state from traj_data. + + Ported from EmbodiedBench ThorEnv.restore_scene(). + """ + grid_size = AGENT_STEP_SIZE / RECORD_SMOOTHING_FACTOR + + self._step(dict( + action="Initialize", + gridSize=grid_size, + cameraY=CAMERA_HEIGHT_OFFSET, + renderImage=RENDER_IMAGE, + renderDepthImage=RENDER_DEPTH_IMAGE, + renderClassImage=RENDER_CLASS_IMAGE, + renderObjectImage=RENDER_OBJECT_IMAGE, + visibility_distance=VISIBILITY_DISTANCE, + makeAgentsVisible=False, + )) + + scene = self.traj_data["scene"] + object_toggles = scene.get("object_toggles", []) + object_poses = scene.get("object_poses", []) + dirty_and_empty = scene.get("dirty_and_empty", False) + + if len(object_toggles) > 0: + self._step(dict(action="SetObjectToggles", objectToggles=object_toggles)) + + if dirty_and_empty: + self._step(dict( + action="SetStateOfAllObjects", + StateChange="CanBeDirty", + forceAction=True, + )) + self._step(dict( + action="SetStateOfAllObjects", + StateChange="CanBeFilled", + forceAction=False, + )) + + if len(object_poses) > 0: + self._step(dict(action="SetObjectPoses", objectPoses=object_poses)) + + def _cache_reachable_positions(self): + """Cache reachable positions + KD-tree for navigation.""" + event = self._step(dict(action="GetReachablePositions")) + free_positions = event.metadata["actionReturn"] + self.reachable_positions = np.array( + [[p["x"], p["y"], p["z"]] for p in free_positions] + ) + self.reachable_position_kdtree = spatial.KDTree(self.reachable_positions) + + # --- Step --- + + def step(self, action_text): + """Execute a high-level skill and return observation + metrics.""" + self.step_count += 1 + + # Smoke test mode: execute raw THOR action instead of high-level skill + if self.traj_data is None: + self._step(dict(action=action_text)) + return self._make_observation_response( + reward=0.0, + done=False, + info={ + "last_action_success": 1.0 if self.last_event.metadata["lastActionSuccess"] else 0.0, + "env_step": float(self.step_count), + "feedback": "success" if self.last_event.metadata["lastActionSuccess"] else "action failed", + }, + ) + + result = self._execute_skill(action_text) + success = result.get("success", False) + + # Update cleaned/cooled/heated tracking + self._update_states() + + # Evaluate goal conditions + goal_satisfied, progress = evaluate_goal_conditions( + self.traj_data, + self.last_event, + self.cleaned_objects, + self.cooled_objects, + self.heated_objects, + ) + + feedback = result.get("message", "") + if success: + feedback = "success" + + return self._make_observation_response( + reward=float(progress), + done=goal_satisfied, + info={ + "task_success": 1.0 if goal_satisfied else 0.0, + "task_progress": progress, + "last_action_success": 1.0 if success else 0.0, + "env_step": float(self.step_count), + "feedback": feedback, + }, + ) + + def _execute_skill(self, instruction): + """Map high-level skill text to THOR API calls. + + Ported from EmbodiedBench ThorConnector.llm_skill_interact(). + """ + # Clear cur_receptacle unless put/open + if not (instruction.startswith("put down ") or instruction.startswith("open ")): + self.cur_receptacle = None + + ret_msg = "" + + if instruction.startswith("find "): + obj_name = instruction.replace("find a ", "").replace("find an ", "") + self.cur_receptacle = obj_name + ret_msg = self._nav_obj( + natural_word_to_ithor_name(obj_name), self.sliced + ) + + elif instruction.startswith("pick up "): + obj_name = instruction.replace("pick up the ", "") + ret_msg = self._pick(natural_word_to_ithor_name(obj_name)) + + elif instruction.startswith("put down "): + if self.cur_receptacle is None: + ret_msg = self._drop() + else: + receptacle = self.cur_receptacle + if receptacle in self.put_count_dict: + self.put_count_dict[receptacle] += 1 + else: + self.put_count_dict[receptacle] = 1 + + ret_msg = self._put(natural_word_to_ithor_name(receptacle)) + + if len(ret_msg) > 16 and self.put_count_dict[receptacle] >= 3: + self._drop() + ret_msg += ". The robot dropped the object instead." + self.last_event.metadata["lastActionSuccess"] = False + + elif instruction.startswith("open "): + obj_name = instruction.replace("open the ", "") + ret_msg = self._open(natural_word_to_ithor_name(obj_name)) + + elif instruction.startswith("close "): + obj_name = instruction.replace("close the ", "") + ret_msg = self._close(natural_word_to_ithor_name(obj_name)) + + elif instruction.startswith("turn on "): + obj_name = instruction.replace("turn on the ", "") + ret_msg = self._toggleon(natural_word_to_ithor_name(obj_name)) + + elif instruction.startswith("turn off "): + obj_name = instruction.replace("turn off the ", "") + ret_msg = self._toggleoff(natural_word_to_ithor_name(obj_name)) + + elif instruction.startswith("slice "): + obj_name = instruction.replace("slice the ", "") + ret_msg = self._slice(natural_word_to_ithor_name(obj_name)) + self.sliced = True + + elif instruction.startswith("drop"): + ret_msg = self._drop() + + else: + ret_msg = "instruction not supported" + + success = len(ret_msg) == 0 + if not success: + logger.warning("Skill failed: %s -> %s", instruction, ret_msg) + + return {"action": instruction, "success": success, "message": ret_msg} + + # --- State Tracking --- + + def _update_states(self): + """Track cleaned/cooled/heated objects. + + Ported from EmbodiedBench ThorEnv.update_states(). + """ + if self.last_event is None: + return + + metadata = self.last_event.metadata + if not metadata.get("lastActionSuccess", False): + return + + # Check if last action was a relevant toggle/close + last_action = metadata.get("lastAction", "") + last_obj_id = metadata.get("lastActionObjectId", "") + + # Clean: ToggleObjectOn on Faucet -> items in SinkBasin get cleaned + if last_action == "ToggleObjectOn" and "Faucet" in last_obj_id: + sink_basin = get_obj_of_type_closest_to_obj( + "SinkBasin", last_obj_id, metadata + ) + if sink_basin and sink_basin.get("receptacleObjectIds"): + self.cleaned_objects.update(sink_basin["receptacleObjectIds"]) + + # Heat: ToggleObjectOn on Microwave -> items inside get heated + if last_action == "ToggleObjectOn" and "Microwave" in last_obj_id: + microwaves = get_objects_of_type("Microwave", metadata) + if microwaves: + heated_ids = microwaves[0].get("receptacleObjectIds") + if heated_ids: + self.heated_objects.update(heated_ids) + + # Cool: CloseObject on Fridge -> items inside get cooled + if last_action == "CloseObject" and "Fridge" in last_obj_id: + fridges = get_objects_of_type("Fridge", metadata) + if fridges: + cooled_ids = fridges[0].get("receptacleObjectIds") + if cooled_ids: + self.cooled_objects.update(cooled_ids) + + # --- Individual Skill Implementations --- + + def _nav_obj(self, target_obj, prefer_sliced=False): + """Navigate to an object by teleporting to closest reachable position. + + Ported from EmbodiedBench ThorConnector.nav_obj(). + """ + objects = self.last_event.metadata["objects"] + logger.info("nav_obj: %s", target_obj) + + # Resolve object ID + if "|" in target_obj: + obj_id = target_obj + base_name = target_obj.split("|")[0] + tmp_id, tmp_data = self._get_obj_id_from_name( + base_name, priority_in_visibility=True, priority_sliced=prefer_sliced + ) + if tmp_id and "Sliced" in tmp_id and obj_id in tmp_id: + obj_id = tmp_id + else: + obj_id, _ = self._get_obj_id_from_name( + target_obj, priority_in_visibility=True, priority_sliced=prefer_sliced + ) + + # Find object index + obj_idx = -1 + for i, o in enumerate(objects): + if o["objectId"] == obj_id: + obj_idx = i + break + + if obj_idx == -1: + return ( + "Cannot find %s. This object may not exist in this scene. " + "Try to explore other instances instead." % target_obj + ) + + # Teleport to closest reachable position facing the object + loc = objects[obj_idx]["position"] + obj_rot = objects[obj_idx]["rotation"]["y"] + max_attempts = 20 + teleport_success = False + reachable_pos_idx = 0 + + for i in range(max_attempts): + reachable_pos_idx += 1 + if i == 10 and target_obj in ("Fridge", "Microwave"): + reachable_pos_idx -= 10 + + closest_loc = self._find_close_reachable_position( + [loc["x"], loc["y"], loc["z"]], reachable_pos_idx + ) + if closest_loc is None: + continue + + # Calculate desired rotation angle + rot_angle = math.atan2( + -(loc["x"] - closest_loc[0]), loc["z"] - closest_loc[2] + ) + if rot_angle > 0: + rot_angle -= 2 * math.pi + rot_angle = -(180 / math.pi) * rot_angle + + # Special angle filtering for Fridge/Microwave + if i < 10 and target_obj in ("Fridge", "Microwave"): + angle_d = abs(self._angle_diff(rot_angle, obj_rot)) + if target_obj == "Fridge" and not ( + (70 < angle_d < 110) or (250 < angle_d < 290) + ): + continue + if target_obj == "Microwave" and not ( + (160 < angle_d < 200) or (0 <= angle_d < 20) + ): + continue + + # Calculate horizon angle + camera_height = self.agent_height + CAMERA_HEIGHT_OFFSET + xz_dist = math.hypot( + loc["x"] - closest_loc[0], loc["z"] - closest_loc[2] + ) + hor_angle = math.atan2(loc["y"] - camera_height, xz_dist) + hor_angle = (180 / math.pi) * hor_angle * 0.9 + + self._step(dict( + action="TeleportFull", + x=closest_loc[0], + y=self.agent_height, + z=closest_loc[2], + rotation=rot_angle, + horizon=-hor_angle, + )) + + if self.last_event.metadata["lastActionSuccess"]: + teleport_success = True + break + else: + logger.debug( + "TeleportFull failed: %s", + self.last_event.metadata.get("errorMessage", ""), + ) + + if not teleport_success: + return "Cannot move to %s" % target_obj + + return "" + + def _pick(self, obj_name): + """Pick up an object. + + Ported from EmbodiedBench ThorConnector.pick(). + """ + obj_id, obj_data = self._get_obj_id_from_name( + obj_name, only_pickupable=True, priority_in_visibility=True, + priority_sliced=self.sliced, + ) + logger.info("pick: %s -> %s", obj_name, obj_id) + + if obj_id is None: + return "Cannot find %s to pick up. Find the object before picking up it" % obj_name + + if ( + obj_data.get("visible") is False + and obj_data.get("parentReceptacles") + and len(obj_data["parentReceptacles"]) > 0 + ): + recep_name = obj_data["parentReceptacles"][0] + ret_msg = ( + "%s is not visible because it is in %s. " + "Note: multiple instances of %s may exist" + % (obj_name, recep_name, recep_name) + ) + # Try anyway + self._step(dict( + action="PickupObject", objectId=obj_id, forceAction=False + )) + else: + self._step(dict( + action="PickupObject", objectId=obj_id, forceAction=False + )) + ret_msg = "" + + if not self.last_event.metadata["lastActionSuccess"]: + inventory = self.last_event.metadata.get("inventoryObjects", []) + if len(inventory) == 0: + ret_msg = "Robot is not holding any object" + else: + holding_type = inventory[0].get("objectType", "unknown") + ret_msg = "Robot is currently holding %s" % holding_type + + if self.last_event.metadata["lastActionSuccess"]: + ret_msg = "" + + return ret_msg + + def _put(self, receptacle_name): + """Put held object onto a receptacle. + + Ported from EmbodiedBench ThorConnector.put(). + Uses 2x7x2 retry loop with movement adjustments. + """ + inventory = self.last_event.metadata.get("inventoryObjects", []) + if len(inventory) == 0: + return "Robot is not holding any object" + + holding_obj_id = inventory[0]["objectId"] + orig_receptacle_name = receptacle_name + ret_msg = "" + halt = False + last_recep_id = None + exclude_obj_id = None + + for k in range(2): # try closest and next closest receptacle + for j in range(7): # movement/look adjustments + for i in range(2): # try inherited receptacles (SinkBasin etc.) + if k == 1 and exclude_obj_id is None: + exclude_obj_id = last_recep_id + + if k == 0 and "|" in orig_receptacle_name: + if i == 1: + continue + recep_id = orig_receptacle_name + receptacle_name = orig_receptacle_name.split("|")[0] + else: + if "Sink" in receptacle_name or "Bathtub" in receptacle_name: + if i == 0: + recep_id, _ = self._get_obj_id_from_name( + receptacle_name, get_inherited=True, + exclude_obj_id=exclude_obj_id, + ) + else: + recep_id, _ = self._get_obj_id_from_name( + receptacle_name, exclude_obj_id=exclude_obj_id, + ) + else: + if i == 0: + recep_id, _ = self._get_obj_id_from_name( + receptacle_name, exclude_obj_id=exclude_obj_id, + ) + else: + recep_id, _ = self._get_obj_id_from_name( + receptacle_name, get_inherited=True, + exclude_obj_id=exclude_obj_id, + ) + + if not recep_id: + ret_msg = ( + "Putting the object on %s failed. First check whether " + "the receptacle is open or not. Also try other instances " + "of the receptacle" % receptacle_name + ) + continue + + # Movement adjustments to get receptacle in view + if j == 1: + self._step(dict(action="LookUp")) + self._step(dict(action="LookUp")) + elif j == 2: + self._step(dict(action="LookDown")) + self._step(dict(action="LookDown")) + self._step(dict(action="LookDown")) + self._step(dict(action="LookDown")) + elif j == 3: + self._step(dict(action="LookUp")) + self._step(dict(action="LookUp")) + self._step(dict(action="MoveBack")) + elif j == 4: + self._step(dict(action="MoveAhead")) + for _ in range(4): + self._step(dict(action="MoveRight")) + elif j == 5: + for _ in range(8): + self._step(dict(action="MoveLeft")) + elif j == 6: + for _ in range(4): + self._step(dict(action="MoveRight")) + self._step(dict(action="RotateHand", x=40)) + + self._step(dict( + action="PutObject", + objectId=holding_obj_id, + receptacleObjectId=recep_id, + forceAction=True, + )) + last_recep_id = recep_id + + if self.last_event.metadata["lastActionSuccess"]: + ret_msg = "" + halt = True + break + else: + logger.debug( + "PutObject failed: %s", + self.last_event.metadata.get("errorMessage", ""), + ) + ret_msg = ( + "Putting the object on %s failed. First check the " + "receptacle is open or not. Also try other instances " + "of the receptacle" % receptacle_name + ) + if halt: + break + if halt: + break + + return ret_msg + + def _open(self, obj_name): + """Open a receptacle. + + Ported from EmbodiedBench ThorConnector.open(). + """ + logger.info("open: %s", obj_name) + + if "|" in obj_name: + obj_id = obj_name + obj_name = obj_name.split("|")[0] + else: + obj_id, _ = self._get_obj_id_from_name(obj_name) + + if obj_id is None: + return "Cannot find %s to open. Find the object before opening it" % obj_name + + # Check if already open + open_flag = False + for ob in self.last_event.metadata["objects"]: + if ob["objectId"] == obj_id and ob.get("openable") and ob.get("isOpen"): + open_flag = True + break + + ret_msg = "" + for i in range(4): + self._step(dict(action="OpenObject", objectId=obj_id)) + + if self.last_event.metadata["lastActionSuccess"]: + ret_msg = "" + break + else: + if open_flag: + ret_msg = "Open action failed. The %s is already open" % obj_name + else: + ret_msg = "Open action failed." + + # Move around to avoid self-collision + if i == 0: + self._step(dict(action="MoveBack")) + elif i == 1: + self._step(dict(action="MoveBack")) + self._step(dict(action="MoveRight")) + elif i == 2: + self._step(dict(action="MoveLeft")) + self._step(dict(action="MoveLeft")) + + return ret_msg + + def _close(self, obj_name): + """Close a receptacle. + + Ported from EmbodiedBench ThorConnector.close(). + """ + logger.info("close: %s", obj_name) + + if "|" in obj_name: + obj_id = obj_name + obj_name = obj_name.split("|")[0] + else: + obj_id, _ = self._get_obj_id_from_name(obj_name) + + if obj_id is None: + return "Cannot find %s to close" % obj_name + + self._step(dict(action="CloseObject", objectId=obj_id)) + + if not self.last_event.metadata["lastActionSuccess"]: + ret_msg = "Close action failed" + for ob in self.last_event.metadata["objects"]: + if ( + ob["objectId"] == obj_id + and ob.get("openable") + and not ob.get("isOpen") + ): + ret_msg += ". The %s is already closed" % obj_name + break + return ret_msg + + return "" + + def _toggleon(self, obj_name): + """Toggle an object on. + + Ported from EmbodiedBench ThorConnector.toggleon(). + """ + logger.info("toggleon: %s", obj_name) + obj_id, _ = self._get_obj_id_from_name(obj_name, only_toggleable=True) + + if obj_id is None: + return "Cannot find %s to turn on" % obj_name + + try: + self._step(dict(action="ToggleObjectOn", objectId=obj_id)) + if not self.last_event.metadata["lastActionSuccess"]: + return "Turn on action failed" + except Exception: + self.last_event.metadata["lastActionSuccess"] = False + return "Turn on action failed" + + return "" + + def _toggleoff(self, obj_name): + """Toggle an object off. + + Ported from EmbodiedBench ThorConnector.toggleoff(). + """ + logger.info("toggleoff: %s", obj_name) + obj_id, _ = self._get_obj_id_from_name(obj_name, only_toggleable=True) + + if obj_id is None: + return "Cannot find %s to turn off" % obj_name + + self._step(dict(action="ToggleObjectOff", objectId=obj_id)) + + if not self.last_event.metadata["lastActionSuccess"]: + return "Turn off action failed" + + return "" + + def _slice(self, obj_name): + """Slice an object. + + Ported from EmbodiedBench ThorConnector.slice(). + """ + logger.info("slice: %s", obj_name) + obj_id, _ = self._get_obj_id_from_name(obj_name) + + if obj_id is None: + return "Cannot find %s to slice" % obj_name + + self._step(dict(action="SliceObject", objectId=obj_id)) + + if not self.last_event.metadata["lastActionSuccess"]: + return "Slice action failed" + + return "" + + def _drop(self): + """Drop the held object. + + Ported from EmbodiedBench ThorConnector.drop(). + """ + logger.info("drop") + self._step(dict(action="DropHandObject", forceAction=True)) + + if not self.last_event.metadata["lastActionSuccess"]: + inventory = self.last_event.metadata.get("inventoryObjects", []) + if len(inventory) == 0: + return "Robot is not holding any object" + return "Drop action failed" + + return "" + + # --- Object Lookup Helpers --- + + def _get_obj_id_from_name( + self, + obj_name, + only_pickupable=False, + only_toggleable=False, + priority_sliced=False, + get_inherited=False, + parent_receptacle_penalty=True, + priority_in_visibility=False, + exclude_obj_id=None, + ): + """Find the closest object matching name, with priority scoring. + + Ported from EmbodiedBench ThorConnector.get_obj_id_from_name(). + """ + obj_id = None + obj_data = None + min_distance = 1e8 + + # If name contains digits (e.g. 'Cabinet_2'), match by name directly + if any(c.isdigit() for c in obj_name): + for obj in self.last_event.metadata["objects"]: + if obj_name in obj["name"]: + return obj["objectId"], obj + return None, None + + for obj in self.last_event.metadata["objects"]: + if obj["objectId"] == exclude_obj_id: + continue + + if only_pickupable and not obj.get("pickupable", False): + continue + if only_toggleable and not obj.get("toggleable", False): + continue + + # Match object type (case-insensitive) + obj_type = obj["objectId"].split("|")[0] + if obj_type.casefold() != obj_name.casefold(): + continue + + # For inherited receptacles (e.g. SinkBasin), check ID format + if get_inherited and len(obj["objectId"].split("|")) != 5: + continue + if not get_inherited and len(obj["objectId"].split("|")) == 5: + # Skip inherited if not requested + pass + + distance = obj.get("distance", 1e8) + penalty = 0 + + # Penalize objects inside closed receptacles + if parent_receptacle_penalty and obj.get("parentReceptacles"): + for p in obj["parentReceptacles"]: + is_open = self._get_object_prop(p, "isOpen") + openable = self._get_object_prop(p, "openable") + if openable is True and is_open is False: + penalty += 100000 + break + + # Prefer empty stove burners + if obj_name.casefold() == "stoveburner": + recep_ids = obj.get("receptacleObjectIds") or [] + if len(recep_ids) > 0: + penalty += 10000 + + # Penalize non-visible objects + if priority_in_visibility and not obj.get("visible", False): + penalty += 1000 + + # Prefer sliced objects + if priority_sliced and "_Slice" in obj.get("name", ""): + penalty -= 100 + + if distance + penalty < min_distance: + min_distance = distance + penalty + obj_data = obj + obj_id = obj["objectId"] + + return obj_id, obj_data + + def _get_object_prop(self, name, prop): + """Get a property of an object by name/ID substring.""" + for obj in self.last_event.metadata["objects"]: + if name in obj["objectId"]: + return obj.get(prop) + return None + + def _find_close_reachable_position(self, loc, nth=1): + """Find the nth closest reachable position to a location.""" + if self.reachable_position_kdtree is None: + return None + n_positions = len(self.reachable_positions) + k = min(nth + 1, n_positions) + if k == 0: + return None + d, idx = self.reachable_position_kdtree.query(loc, k=k) + selected = min(nth - 1, k - 1) + return self.reachable_positions[idx[selected]] if k > 1 else self.reachable_positions[idx] + + @staticmethod + def _angle_diff(x, y): + """Calculate angle difference in degrees.""" + x = math.radians(x) + y = math.radians(y) + return math.degrees(math.atan2(math.sin(x - y), math.cos(x - y))) + + # --- Observation --- + + def _make_observation_response(self, reward=0.0, done=False, info=None): + """Save RGB frame and return IPC response.""" + from PIL import Image + + event = self.last_event + + # Save to episode_output_dir if set, else IPC workspace + save_dir = Path(self.episode_output_dir) if self.episode_output_dir else self.workspace + rgb_path = save_dir / ("rgb_%04d.png" % self.step_count) + + # Save frame as PNG + Image.fromarray(event.frame).save(str(rgb_path)) + + agent = event.metadata["agent"] + pose = [ + agent["position"]["x"], + agent["position"]["y"], + agent["position"]["z"], + agent["rotation"]["y"], + agent.get("cameraHorizon", 0), + 0, + ] + + return make_observation_response( + rgb_path=str(rgb_path), + agent_pose=pose, + metadata={"step": str(self.step_count)}, + reward=reward, + done=done, + info=info or {}, + ) + + +# --- Main bridge loop --- + +def run_bridge(workspace, data_dir): """Main bridge loop for AI2-THOR v2.1.0.""" - logger.info("AI2-THOR v2.1.0 bridge starting (workspace: %s)", workspace) + bridge = EBAlfredBridge(workspace=workspace, data_dir=data_dir) - # In a real implementation: - # from ai2thor.controller import Controller - # controller = Controller(scene="FloorPlan28", gridSize=0.25) + logger.info("AI2-THOR v2.1.0 bridge starting (workspace: %s)", workspace) + bridge.start() write_status(workspace, ready=True) - step_count = 0 - while True: try: command = poll_for_command(workspace, timeout=300.0) @@ -61,55 +1002,59 @@ def run_bridge(workspace: Path) -> None: if cmd_type == "reset": episode_id = command.get("episode_id", "unknown") reset_config = command.get("reset_config", {}) - logger.info("Reset: episode_id=%s, scene=%s", episode_id, reset_config.get("scene", "?")) + logger.info("Reset: episode_id=%s, task=%s", + episode_id, reset_config.get("task_path", "?")) - # Stub: In real impl, would call controller.reset(scene=...) - step_count = 0 + # Set episode output directory for image saving + raw_output_dir = command.get("episode_output_dir") + if raw_output_dir: + bridge.episode_output_dir = raw_output_dir + Path(raw_output_dir).mkdir(parents=True, exist_ok=True) + else: + bridge.episode_output_dir = None - response = make_observation_response( - rgb_path=str(workspace / "rgb_0000.png"), - agent_pose=[0.0, 0.9, 0.0, 0.0, 0.0, 0.0], - metadata={"episode_id": episode_id, "step": "0", "scene": reset_config.get("scene", "unknown")}, - ) - # Stub: write a placeholder image - (workspace / "rgb_0000.png").write_bytes(b"STUB_IMAGE") - write_response(workspace, response) + try: + response = bridge.reset(reset_config) + write_response(workspace, response) + except Exception as e: + logger.exception("Reset failed") + write_response(workspace, make_error_response(str(e))) elif cmd_type == "step": - step_count += 1 - action = parse_action_from_command(command) - logger.debug("Step %d: action=%s", step_count, action.action_name) - - # Stub: In real impl, would call controller.step(action=action_name, **params) + action_data = command.get("action", {}) + action_text = action_data.get("action_name", "") + logger.debug("Step %d: action=%s", bridge.step_count + 1, action_text) - rgb_name = f"rgb_{step_count:04d}.png" - (workspace / rgb_name).write_bytes(b"STUB_IMAGE") - - response = make_observation_response( - rgb_path=str(workspace / rgb_name), - agent_pose=[float(step_count) * 0.25, 0.9, 0.0, 0.0, 0.0, 0.0], - metadata={"step": str(step_count)}, - done=action.action_name == "Stop", - ) - write_response(workspace, response) + try: + response = bridge.step(action_text) + write_response(workspace, response) + except Exception as e: + logger.exception("Step failed") + write_response(workspace, make_error_response(str(e))) elif cmd_type == "close": logger.info("Close command received") - # Stub: controller.stop() would go here + bridge.stop() write_response(workspace, {"status": "ok"}) break else: - write_response(workspace, make_error_response(f"Unknown command: {cmd_type}")) + write_response( + workspace, make_error_response("Unknown command: %s" % cmd_type) + ) -def main() -> None: - parser = argparse.ArgumentParser(description="AI2-THOR v2.1.0 bridge") +def main(): + parser = argparse.ArgumentParser(description="AI2-THOR v2.1.0 bridge (EB-Alfred)") parser.add_argument("--workspace", type=Path, required=True) - args = parser.parse_args() + parser.add_argument("--data-dir", type=Path, default=Path("./datasets")) + args, _ = parser.parse_known_args() - logging.basicConfig(level=logging.DEBUG, format="%(asctime)s [%(levelname)s] %(name)s: %(message)s") - run_bridge(workspace=args.workspace) + logging.basicConfig( + level=logging.DEBUG, + format="%(asctime)s [%(levelname)s] %(name)s: %(message)s", + ) + run_bridge(workspace=args.workspace, data_dir=args.data_dir) if __name__ == "__main__": diff --git a/easi/simulators/ai2thor/v2_1_0/requirements.txt b/easi/simulators/ai2thor/v2_1_0/requirements.txt index a482c8d..b9e096b 100644 --- a/easi/simulators/ai2thor/v2_1_0/requirements.txt +++ b/easi/simulators/ai2thor/v2_1_0/requirements.txt @@ -1,3 +1,4 @@ ai2thor==2.1.0 numpy<1.24 Pillow +scipy diff --git a/easi/simulators/ai2thor/v2_1_0/thor_utils.py b/easi/simulators/ai2thor/v2_1_0/thor_utils.py new file mode 100644 index 0000000..e813272 --- /dev/null +++ b/easi/simulators/ai2thor/v2_1_0/thor_utils.py @@ -0,0 +1,362 @@ +"""Helper functions for the AI2-THOR v2.1.0 bridge (EB-Alfred track). + +Ported from EmbodiedBench: +- envs/eb_alfred/env/tasks.py (goal evaluation) +- envs/eb_alfred/utils.py (object name mapping, task loading) +- envs/eb_alfred/gen/constants.py (THOR constants) + +This file runs inside the ai2thor conda env (Python 3.8). +""" +from __future__ import annotations + +import json +import logging +import os +import string +from pathlib import Path + +import numpy as np + +logger = logging.getLogger("easi.bridge.thor_utils") + +# --- Constants (from EmbodiedBench gen/constants.py) --- + +SCREEN_WIDTH = 500 +SCREEN_HEIGHT = 500 +CAMERA_HEIGHT_OFFSET = 0.75 +VISIBILITY_DISTANCE = 1.5 +AGENT_STEP_SIZE = 0.25 +RECORD_SMOOTHING_FACTOR = 1 +AGENT_HORIZON_ADJ = 15 +AGENT_ROTATE_ADJ = 90 +RENDER_IMAGE = True +RENDER_DEPTH_IMAGE = False +RENDER_CLASS_IMAGE = False +RENDER_OBJECT_IMAGE = False + +GOALS = [ + "pick_and_place_simple", + "pick_two_obj_and_place", + "look_at_obj_in_light", + "pick_clean_then_place_in_recep", + "pick_heat_then_place_in_recep", + "pick_cool_then_place_in_recep", + "pick_and_place_with_movable_recep", +] + + +# --- Object Name Mapping --- + +def natural_word_to_ithor_name(w: str) -> str: + """Map natural language object name to iTHOR name. + + e.g., 'floor lamp' -> 'FloorLamp', 'alarm clock' -> 'AlarmClock' + If the word contains digits (e.g., 'Cabinet_2'), return as-is. + """ + if any(i.isdigit() for i in w): + return w + if w == "CD": + return w + return "".join([string.capwords(x) for x in w.split()]) + + +# --- Task JSON Loading --- + +def load_task_json(task_path: str, data_dir: str) -> dict: + """Load preprocessed traj_data.json for an EB-Alfred episode. + + Args: + task_path: e.g. 'pick_and_place_simple-Mug-None-Shelf-1/trial_T20190001' + data_dir: path to the downloaded dataset directory + """ + # Try standard path: /tasks//pp/ann_0.json + # (repeat_idx is handled by the caller) + json_path = os.path.join(data_dir, "tasks", task_path, "pp", "ann_0.json") + if not os.path.exists(json_path): + # Fallback: try traj_data.json + json_path = os.path.join(data_dir, "tasks", task_path, "traj_data.json") + if not os.path.exists(json_path): + raise FileNotFoundError( + f"Task annotation not found at {json_path}. " + f"Ensure tasks.zip was extracted in {data_dir}" + ) + with open(json_path) as f: + data = json.load(f) + return data + + +def load_task_json_with_repeat(task_path: str, repeat_idx: int, data_dir: str) -> dict: + """Load the specific repeat annotation for an EB-Alfred episode.""" + json_path = os.path.join(data_dir, "tasks", task_path, "pp", f"ann_{repeat_idx}.json") + if not os.path.exists(json_path): + # Fallback to ann_0.json + json_path = os.path.join(data_dir, "tasks", task_path, "pp", "ann_0.json") + if not os.path.exists(json_path): + # Fallback to traj_data.json + json_path = os.path.join(data_dir, "tasks", task_path, "traj_data.json") + if not os.path.exists(json_path): + raise FileNotFoundError( + f"Task annotation not found for {task_path} repeat {repeat_idx} in {data_dir}" + ) + with open(json_path) as f: + data = json.load(f) + return data + + +# --- Object Lookup Helpers --- + +def get_objects_of_type(obj_type: str, metadata: dict) -> list: + """Get all objects of a given type from THOR metadata.""" + return [obj for obj in metadata["objects"] if obj_type in obj["objectId"]] + + +def get_objects_with_name_and_prop(name: str, prop: str, metadata: dict) -> list: + """Get objects matching name that have a truthy property.""" + return [obj for obj in metadata["objects"] + if name in obj["objectId"] and obj.get(prop)] + + +def get_obj_of_type_closest_to_obj(obj_type: str, ref_obj_id: str, metadata: dict): + """Get the object of obj_type closest to ref_obj_id.""" + ref_obj = None + for obj in metadata["objects"]: + if obj["objectId"] == ref_obj_id: + ref_obj = obj + break + if ref_obj is None: + return None + + candidates = get_objects_of_type(obj_type, metadata) + if not candidates: + return None + + min_dist = float("inf") + closest = None + for c in candidates: + dx = c["position"]["x"] - ref_obj["position"]["x"] + dz = c["position"]["z"] - ref_obj["position"]["z"] + dist = (dx ** 2 + dz ** 2) ** 0.5 + if dist < min_dist: + min_dist = dist + closest = c + return closest + + +# --- Goal Evaluation --- + +def get_targets_from_traj(traj_data: dict) -> dict: + """Extract goal targets from trajectory data.""" + pddl = traj_data.get("pddl_params", {}) + targets = {} + # Map from traj keys to goal target keys + if "object_target" in pddl: + targets["object"] = pddl["object_target"] + if "parent_target" in pddl: + targets["parent"] = pddl["parent_target"] + if "toggle_target" in pddl: + targets["toggle"] = pddl["toggle_target"] + if "mrecep_target" in pddl: + targets["mrecep"] = pddl["mrecep_target"] + # Sliced variant + if "object_sliced" in pddl and pddl["object_sliced"]: + targets["object"] = targets.get("object", "") + "Sliced" + return targets + + +def evaluate_goal_conditions( + traj_data: dict, + event, + cleaned_objects: set, + cooled_objects: set, + heated_objects: set, +) -> tuple: + """Evaluate EB-Alfred goal conditions. + + Returns: + (goal_satisfied: bool, progress: float) + + progress = satisfied_conditions / total_conditions + """ + task_type = traj_data.get("task_type", "") + targets = get_targets_from_traj(traj_data) + metadata = event.metadata + + evaluator = GOAL_EVALUATORS.get(task_type) + if evaluator is None: + logger.warning("Unknown task type: %s", task_type) + return False, 0.0 + + s, ts = evaluator(targets, metadata, cleaned_objects, cooled_objects, heated_objects) + progress = s / ts if ts > 0 else 0.0 + satisfied = s >= ts + return satisfied, progress + + +# --- Per-task-type goal evaluators --- + +def _eval_pick_and_place_simple(targets, metadata, cleaned, cooled, heated): + ts = 1 + s = 0 + receptacles = get_objects_with_name_and_prop(targets.get("parent", ""), "receptacle", metadata) + pickupables = get_objects_with_name_and_prop(targets.get("object", ""), "pickupable", metadata) + + if "Sliced" in targets.get("object", ""): + ts += 1 + if len([p for p in pickupables if "Sliced" in p["objectId"]]) >= 1: + s += 1 + + if np.any([np.any([p["objectId"] in (r.get("receptacleObjectIds") or []) + for r in receptacles]) + for p in pickupables]): + s += 1 + return s, ts + + +def _eval_pick_two_obj_and_place(targets, metadata, cleaned, cooled, heated): + ts = 2 + s = 0 + receptacles = get_objects_with_name_and_prop(targets.get("parent", ""), "receptacle", metadata) + pickupables = get_objects_with_name_and_prop(targets.get("object", ""), "pickupable", metadata) + + if "Sliced" in targets.get("object", ""): + ts += 2 + s += min(len([p for p in pickupables if "Sliced" in p["objectId"]]), 2) + + s += min(np.max([sum([1 if (r.get("receptacleObjectIds") is not None + and p["objectId"] in r["receptacleObjectIds"]) else 0 + for p in pickupables]) + for r in receptacles]) if receptacles else 0, 2) + return s, ts + + +def _eval_look_at_obj_in_light(targets, metadata, cleaned, cooled, heated): + ts = 2 + s = 0 + toggleables = get_objects_with_name_and_prop(targets.get("toggle", ""), "toggleable", metadata) + pickupables = get_objects_with_name_and_prop(targets.get("object", ""), "pickupable", metadata) + inventory = metadata.get("inventoryObjects", []) + + if "Sliced" in targets.get("object", ""): + ts += 1 + if len([p for p in pickupables if "Sliced" in p["objectId"]]) >= 1: + s += 1 + + if len(inventory) > 0 and inventory[0]["objectId"] in [p["objectId"] for p in pickupables]: + s += 1 + if np.any([t["isToggled"] and t["visible"] for t in toggleables]): + s += 1 + return s, ts + + +def _eval_pick_heat_then_place(targets, metadata, cleaned, cooled, heated): + ts = 3 + s = 0 + receptacles = get_objects_with_name_and_prop(targets.get("parent", ""), "receptacle", metadata) + pickupables = get_objects_with_name_and_prop(targets.get("object", ""), "pickupable", metadata) + + if "Sliced" in targets.get("object", ""): + ts += 1 + if len([p for p in pickupables if "Sliced" in p["objectId"]]) >= 1: + s += 1 + + objs_in_place = [p["objectId"] for p in pickupables for r in receptacles + if r.get("receptacleObjectIds") is not None and p["objectId"] in r["receptacleObjectIds"]] + objs_heated = [p["objectId"] for p in pickupables if p["objectId"] in heated] + + if len(objs_in_place) > 0: + s += 1 + if len(objs_heated) > 0: + s += 1 + if np.any([obj_id in objs_heated for obj_id in objs_in_place]): + s += 1 + return s, ts + + +def _eval_pick_cool_then_place(targets, metadata, cleaned, cooled, heated): + ts = 3 + s = 0 + receptacles = get_objects_with_name_and_prop(targets.get("parent", ""), "receptacle", metadata) + pickupables = get_objects_with_name_and_prop(targets.get("object", ""), "pickupable", metadata) + + if "Sliced" in targets.get("object", ""): + ts += 1 + if len([p for p in pickupables if "Sliced" in p["objectId"]]) >= 1: + s += 1 + + objs_in_place = [p["objectId"] for p in pickupables for r in receptacles + if r.get("receptacleObjectIds") is not None and p["objectId"] in r["receptacleObjectIds"]] + objs_cooled = [p["objectId"] for p in pickupables if p["objectId"] in cooled] + + if len(objs_in_place) > 0: + s += 1 + if len(objs_cooled) > 0: + s += 1 + if np.any([obj_id in objs_cooled for obj_id in objs_in_place]): + s += 1 + return s, ts + + +def _eval_pick_clean_then_place(targets, metadata, cleaned, cooled, heated): + ts = 3 + s = 0 + receptacles = get_objects_with_name_and_prop(targets.get("parent", ""), "receptacle", metadata) + pickupables = get_objects_with_name_and_prop(targets.get("object", ""), "pickupable", metadata) + + if "Sliced" in targets.get("object", ""): + ts += 1 + if len([p for p in pickupables if "Sliced" in p["objectId"]]) >= 1: + s += 1 + + objs_in_place = [p["objectId"] for p in pickupables for r in receptacles + if r.get("receptacleObjectIds") is not None and p["objectId"] in r["receptacleObjectIds"]] + objs_cleaned = [p["objectId"] for p in pickupables if p["objectId"] in cleaned] + + if len(objs_in_place) > 0: + s += 1 + if len(objs_cleaned) > 0: + s += 1 + if np.any([obj_id in objs_cleaned for obj_id in objs_in_place]): + s += 1 + return s, ts + + +def _eval_pick_and_place_with_movable_recep(targets, metadata, cleaned, cooled, heated): + ts = 3 + s = 0 + receptacles = get_objects_with_name_and_prop(targets.get("parent", ""), "receptacle", metadata) + pickupables = get_objects_with_name_and_prop(targets.get("object", ""), "pickupable", metadata) + movables = get_objects_with_name_and_prop(targets.get("mrecep", ""), "pickupable", metadata) + + if "Sliced" in targets.get("object", ""): + ts += 1 + if len([p for p in pickupables if "Sliced" in p["objectId"]]) >= 1: + s += 1 + + pickup_in_movable = [p for p in pickupables for m in movables + if m.get("receptacleObjectIds") is not None + and p["objectId"] in m["receptacleObjectIds"]] + movable_in_recep = [m for m in movables for r in receptacles + if r.get("receptacleObjectIds") is not None + and m["objectId"] in r["receptacleObjectIds"]] + + if len(pickup_in_movable) > 0: + s += 1 + if len(movable_in_recep) > 0: + s += 1 + if np.any([np.any([p["objectId"] in (m.get("receptacleObjectIds") or []) for p in pickupables]) and + np.any([r["objectId"] in (m.get("parentReceptacles") or []) for r in receptacles]) + for m in movables + if m.get("parentReceptacles") is not None and m.get("receptacleObjectIds") is not None]): + s += 1 + return s, ts + + +GOAL_EVALUATORS = { + "pick_and_place_simple": _eval_pick_and_place_simple, + "pick_two_obj_and_place": _eval_pick_two_obj_and_place, + "look_at_obj_in_light": _eval_look_at_obj_in_light, + "pick_heat_then_place_in_recep": _eval_pick_heat_then_place, + "pick_cool_then_place_in_recep": _eval_pick_cool_then_place, + "pick_clean_then_place_in_recep": _eval_pick_clean_then_place, + "pick_and_place_with_movable_recep": _eval_pick_and_place_with_movable_recep, +} diff --git a/easi/simulators/dummy/v1/bridge.py b/easi/simulators/dummy/v1/bridge.py index ef9e7a3..bbea23b 100644 --- a/easi/simulators/dummy/v1/bridge.py +++ b/easi/simulators/dummy/v1/bridge.py @@ -38,13 +38,19 @@ logger = logging.getLogger("easi.bridge.dummy") -def _generate_dummy_image(workspace: Path, step: int) -> str: +def _generate_dummy_image(workspace: Path, step: int, output_dir: Path | None = None) -> str: """Generate a small placeholder PNG image. Creates a minimal valid PNG file (1x1 pixel, red) to simulate an observation image without requiring Pillow. + + Args: + workspace: IPC workspace directory (fallback for image saving). + step: Current step number (used for filename and color). + output_dir: If provided, save image here instead of workspace. """ - rgb_path = workspace / f"rgb_{step:04d}.png" + save_dir = output_dir if output_dir is not None else workspace + rgb_path = save_dir / f"rgb_{step:04d}.png" # Minimal valid 1x1 red PNG (67 bytes) # This avoids requiring Pillow in the dummy simulator @@ -88,6 +94,7 @@ def run_bridge(workspace: Path, step_delay: float = 0.0) -> None: write_status(workspace, ready=True) step_count = 0 + episode_output_dir = None # Set per-episode from reset command while True: try: @@ -102,11 +109,19 @@ def run_bridge(workspace: Path, step_delay: float = 0.0) -> None: episode_id = command.get("episode_id", "unknown") logger.info("Reset: episode_id=%s", episode_id) + # Read episode output directory (None for smoke tests) + raw_output_dir = command.get("episode_output_dir") + if raw_output_dir: + episode_output_dir = Path(raw_output_dir) + episode_output_dir.mkdir(parents=True, exist_ok=True) + else: + episode_output_dir = None + if step_delay > 0: time.sleep(step_delay) step_count = 0 - rgb_path = _generate_dummy_image(workspace, step_count) + rgb_path = _generate_dummy_image(workspace, step_count, episode_output_dir) response = make_observation_response( rgb_path=rgb_path, @@ -123,7 +138,7 @@ def run_bridge(workspace: Path, step_delay: float = 0.0) -> None: if step_delay > 0: time.sleep(step_delay) - rgb_path = _generate_dummy_image(workspace, step_count) + rgb_path = _generate_dummy_image(workspace, step_count, episode_output_dir) # Dummy: done after 10 steps or on Stop action done = step_count >= 10 or action.action_name == "Stop" @@ -151,7 +166,7 @@ def main() -> None: parser = argparse.ArgumentParser(description="Dummy simulator bridge") parser.add_argument("--workspace", type=Path, required=True, help="IPC workspace directory") parser.add_argument("--step-delay", type=float, default=0.0, help="Delay per step in seconds") - args = parser.parse_args() + args, _ = parser.parse_known_args() logging.basicConfig( level=logging.DEBUG, diff --git a/easi/simulators/registry.py b/easi/simulators/registry.py index 5a9f40a..3a938ce 100644 --- a/easi/simulators/registry.py +++ b/easi/simulators/registry.py @@ -12,13 +12,14 @@ from __future__ import annotations import importlib -import logging from dataclasses import dataclass from pathlib import Path import yaml -logger = logging.getLogger("easi.simulators.registry") +from easi.utils.logging import get_logger + +logger = get_logger(__name__) @dataclass diff --git a/easi/simulators/subprocess_runner.py b/easi/simulators/subprocess_runner.py index 26962f2..f1dfba9 100644 --- a/easi/simulators/subprocess_runner.py +++ b/easi/simulators/subprocess_runner.py @@ -5,17 +5,18 @@ - Health check: Polls status.json with configurable timeout - Send command: Writes command.json, polls response.json - Crash recovery: Detects subprocess exit during polling -- Cleanup: SIGTERM → wait → SIGKILL the entire process group; removes temp workspace +- Cleanup: SIGTERM -> wait -> SIGKILL the entire process group; removes temp workspace Supports xvfb-run wrapping for simulators that need a display. """ from __future__ import annotations -import logging import os import signal import subprocess +import threading +from collections import deque from pathlib import Path from easi.communication.filesystem import ( @@ -26,8 +27,9 @@ write_command, ) from easi.core.exceptions import SimulatorError, SimulatorTimeoutError +from easi.utils.logging import get_logger -logger = logging.getLogger("easi.simulators.subprocess_runner") +logger = get_logger(__name__) class SubprocessRunner: @@ -42,6 +44,7 @@ def __init__( startup_timeout: float = 30.0, command_timeout: float = 60.0, poll_interval: float = 0.1, + extra_args: list[str] | None = None, ): self.python_executable = python_executable self.bridge_script_path = bridge_script_path @@ -50,9 +53,12 @@ def __init__( self.startup_timeout = startup_timeout self.command_timeout = command_timeout self.poll_interval = poll_interval + self.extra_args = extra_args or [] self._process: subprocess.Popen | None = None self._workspace: Path | None = None + self._output_lines: deque[str] = deque(maxlen=200) + self._reader_thread: threading.Thread | None = None @property def workspace(self) -> Path | None: @@ -65,6 +71,7 @@ def launch(self) -> None: raise RuntimeError("Subprocess already running") self._workspace = create_workspace() + self._output_lines.clear() cmd = self._build_launch_command() logger.info( @@ -78,9 +85,18 @@ def launch(self) -> None: cmd, preexec_fn=os.setsid, # new session = new process group stdout=subprocess.PIPE, - stderr=subprocess.PIPE, + stderr=subprocess.STDOUT, # merge stderr into stdout + text=True, + bufsize=1, ) + # Stream bridge output through logger in a background thread + self._reader_thread = threading.Thread( + target=self._stream_output, + daemon=True, + ) + self._reader_thread.start() + # Wait for the bridge to signal readiness try: status = poll_for_status( @@ -90,13 +106,17 @@ def launch(self) -> None: process=self._process, ) if not status.get("ready", False): - stderr = self._read_stderr() + output = self._get_recent_output() raise SimulatorError( - f"Bridge reported not ready. stderr:\n{stderr}" + f"Bridge reported not ready. Output:\n{output}" ) logger.info("Bridge subprocess ready (PID: %d)", self._process.pid) - except (SimulatorError, SimulatorTimeoutError): + except (SimulatorError, SimulatorTimeoutError) as exc: + # Collect output before shutdown destroys the process + output = self._get_recent_output() self.shutdown() + if output: + raise SimulatorError(f"{exc}\n\nBridge output:\n{output}") from exc raise def send_command(self, command: dict, timeout: float | None = None) -> dict: @@ -134,7 +154,11 @@ def shutdown(self) -> None: Unity binaries) to prevent zombie processes. """ self._terminate_process_tree() + # Wait for the reader thread to drain any final output from the bridge + if self._reader_thread is not None: + self._reader_thread.join(timeout=2) self._process = None + self._reader_thread = None if self._workspace is not None: cleanup_workspace(self._workspace) @@ -148,6 +172,7 @@ def _build_launch_command(self) -> list[str]: "--workspace", str(self._workspace), ] + cmd.extend(self.extra_args) if self.needs_display and not self._has_display(): # Wrap with xvfb-run for headless environments @@ -163,37 +188,52 @@ def _has_display(self) -> bool: display = os.environ.get("DISPLAY", "") return bool(display) and not display.startswith(":") + def _stream_output(self) -> None: + """Read bridge stdout line-by-line and log at DEBUG level. + + Runs in a daemon thread for the lifetime of the subprocess. + """ + proc = self._process + if proc is None or proc.stdout is None: + return + try: + for line in proc.stdout: + line = line.rstrip() + self._output_lines.append(line) + logger.debug("[bridge] %s", line) + except (ValueError, OSError): + pass # pipe closed + + def _get_recent_output(self) -> str: + """Return the last N lines of captured bridge output.""" + return "\n".join(self._output_lines) + def _terminate_process_tree(self) -> None: """Kill the bridge and ALL its child processes.""" if self._process is None: return + pid = self._process.pid try: - pgid = os.getpgid(self._process.pid) + pgid = os.getpgid(pid) except ProcessLookupError: - return # already dead + logger.info("Bridge process (PID %d) already exited", pid) + return try: # SIGTERM the entire group first (graceful) + logger.info("Sending SIGTERM to bridge process group (PID %d)", pid) os.killpg(pgid, signal.SIGTERM) self._process.wait(timeout=10) + logger.info("Bridge process (PID %d) exited after SIGTERM", pid) except subprocess.TimeoutExpired: # SIGKILL the entire group (force) + logger.warning("Bridge did not exit after SIGTERM, sending SIGKILL") try: os.killpg(pgid, signal.SIGKILL) self._process.wait(timeout=5) + logger.info("Bridge process (PID %d) killed", pid) except (ProcessLookupError, subprocess.TimeoutExpired): pass except ProcessLookupError: - pass # already dead - - logger.debug("Process tree terminated") - - def _read_stderr(self) -> str: - """Read stderr from the subprocess (non-blocking).""" - if self._process is None or self._process.stderr is None: - return "" - try: - return self._process.stderr.read().decode("utf-8", errors="replace") - except Exception: - return "" + logger.info("Bridge process (PID %d) already exited", pid) diff --git a/easi/tasks/dataset.py b/easi/tasks/dataset.py index af7430a..39779bf 100644 --- a/easi/tasks/dataset.py +++ b/easi/tasks/dataset.py @@ -5,14 +5,14 @@ from __future__ import annotations -import logging from pathlib import Path from easi.core.exceptions import DatasetError from easi.utils.locking import file_lock +from easi.utils.logging import get_logger from easi.utils.paths import get_datasets_dir, get_locks_dir -logger = logging.getLogger("easi.tasks.dataset") +logger = get_logger(__name__) class DatasetDownloader: diff --git a/easi/tasks/ebalfred/__init__.py b/easi/tasks/ebalfred/__init__.py new file mode 100644 index 0000000..003b9d4 --- /dev/null +++ b/easi/tasks/ebalfred/__init__.py @@ -0,0 +1 @@ +"""EB-Alfred task for EASI.""" diff --git a/easi/tasks/ebalfred/actions.py b/easi/tasks/ebalfred/actions.py new file mode 100644 index 0000000..fea727b --- /dev/null +++ b/easi/tasks/ebalfred/actions.py @@ -0,0 +1,83 @@ +"""EB-Alfred action space definitions. + +Reference: EmbodiedBench/embodiedbench/envs/eb_alfred/utils.py + EmbodiedBench/embodiedbench/envs/eb_alfred/EBAlfEnv.py +""" +from __future__ import annotations + +# All objects in EB-Alfred (alfred_objs from EmbodiedBench) +EBALFRED_OBJECTS = [ + "Cart", "Potato", "Faucet", "Ottoman", "CoffeeMachine", "Candle", "CD", + "Pan", "Watch", "HandTowel", "SprayBottle", "BaseballBat", "CellPhone", + "Kettle", "Mug", "StoveBurner", "Bowl", "Toilet", "DiningTable", "Spoon", + "TissueBox", "Shelf", "Apple", "TennisRacket", "SoapBar", "Cloth", + "Plunger", "FloorLamp", "ToiletPaperHanger", "CoffeeTable", "Spatula", + "Plate", "Bed", "Glassbottle", "Knife", "Tomato", "ButterKnife", + "Dresser", "Microwave", "CounterTop", "GarbageCan", "WateringCan", "Vase", + "ArmChair", "Safe", "KeyChain", "Pot", "Pen", "Cabinet", "Desk", + "Newspaper", "Drawer", "Sofa", "Bread", "Book", "Lettuce", "CreditCard", + "AlarmClock", "ToiletPaper", "SideTable", "Fork", "Box", "Egg", + "DeskLamp", "Ladle", "WineBottle", "Pencil", "Laptop", "RemoteControl", + "BasketBall", "DishSponge", "Cup", "SaltShaker", "PepperShaker", "Pillow", + "Bathtub", "SoapBottle", "Statue", "Fridge", "Sink", +] + +# Pickupable objects (alfred_pick_obj) +EBALFRED_PICKUPABLE = [ + "KeyChain", "Potato", "Pot", "Pen", "Candle", "CD", "Pan", "Watch", + "Newspaper", "HandTowel", "SprayBottle", "BaseballBat", "Bread", + "CellPhone", "Book", "Lettuce", "CreditCard", "Mug", "AlarmClock", + "Kettle", "ToiletPaper", "Bowl", "Fork", "Box", "Egg", "Spoon", + "TissueBox", "Apple", "TennisRacket", "Ladle", "WineBottle", "Cloth", + "Plunger", "SoapBar", "Pencil", "Laptop", "RemoteControl", "BasketBall", + "DishSponge", "Cup", "Spatula", "SaltShaker", "Plate", "PepperShaker", + "Pillow", "Glassbottle", "SoapBottle", "Knife", "Statue", "Tomato", + "ButterKnife", "WateringCan", "Vase", +] + +# Openable objects (alfred_open_obj) +EBALFRED_OPENABLE = [ + "Safe", "Laptop", "Fridge", "Box", "Microwave", "Cabinet", "Drawer", +] + +# Sliceable objects (alfred_slice_obj) +EBALFRED_SLICEABLE = [ + "Potato", "Lettuce", "Tomato", "Apple", "Bread", +] + +# Toggleable objects (alfred_toggle_obj) +EBALFRED_TOGGLEABLE = [ + "Microwave", "DeskLamp", "FloorLamp", "Faucet", +] + +# Receptacles (alfred_recep) +EBALFRED_RECEPTACLES = [ + "ArmChair", "Safe", "Cart", "Ottoman", "Pot", "CoffeeMachine", "Desk", + "Cabinet", "Pan", "Drawer", "Sofa", "Mug", "StoveBurner", "SideTable", + "Toilet", "Bowl", "Box", "DiningTable", "Shelf", "ToiletPaperHanger", + "CoffeeTable", "Cup", "Plate", "Bathtub", "Bed", "Dresser", "Fridge", + "Microwave", "CounterTop", "Sink", "GarbageCan", +] + + +def get_global_action_space() -> list[str]: + """Generate the full EB-Alfred action space (static, ~133 actions). + + Reference: EmbodiedBench/embodiedbench/envs/eb_alfred/EBAlfEnv.py + """ + actions = [] + # Find actions for all objects + actions.extend(f"find a {obj}" for obj in EBALFRED_OBJECTS) + # Pickup actions + actions.extend(f"pick up the {obj}" for obj in EBALFRED_PICKUPABLE) + # Put / drop + actions.extend(["put down the object in hand", "drop the object in hand"]) + # Open / close + for obj in EBALFRED_OPENABLE: + actions.extend([f"open the {obj}", f"close the {obj}"]) + # Toggle on/off + for obj in EBALFRED_TOGGLEABLE: + actions.extend([f"turn on the {obj}", f"turn off the {obj}"]) + # Slice + actions.extend(f"slice the {obj}" for obj in EBALFRED_SLICEABLE) + return actions diff --git a/easi/tasks/ebalfred/ebalfred_base.yaml b/easi/tasks/ebalfred/ebalfred_base.yaml new file mode 100644 index 0000000..b12c529 --- /dev/null +++ b/easi/tasks/ebalfred/ebalfred_base.yaml @@ -0,0 +1,16 @@ +name: ebalfred_base +display_name: "EB-Alfred Base Split" +description: "Base evaluation split for EB-Alfred" +simulator: "ai2thor:v2_1_0" +task_class: "easi.tasks.ebalfred.task.EBAlfredTask" +action_space: [] +max_steps: 50 +dataset: + source: huggingface + repo_id: "oscarqjh/EB-Alfred_easi" + subset: null + split: "base" + zip_files: + - "tasks.zip" +agent: + prompt_builder: "easi.tasks.ebalfred.prompts.EBAlfredPromptBuilder" diff --git a/easi/tasks/ebalfred/ebalfred_common_sense.yaml b/easi/tasks/ebalfred/ebalfred_common_sense.yaml new file mode 100644 index 0000000..92df2a2 --- /dev/null +++ b/easi/tasks/ebalfred/ebalfred_common_sense.yaml @@ -0,0 +1,16 @@ +name: ebalfred_common_sense +display_name: "EB-Alfred Common Sense Split" +description: "Common sense evaluation split testing implicit knowledge" +simulator: "ai2thor:v2_1_0" +task_class: "easi.tasks.ebalfred.task.EBAlfredTask" +action_space: [] +max_steps: 50 +dataset: + source: huggingface + repo_id: "oscarqjh/EB-Alfred_easi" + subset: null + split: "common_sense" + zip_files: + - "tasks.zip" +agent: + prompt_builder: "easi.tasks.ebalfred.prompts.EBAlfredPromptBuilder" diff --git a/easi/tasks/ebalfred/ebalfred_complex_instruction.yaml b/easi/tasks/ebalfred/ebalfred_complex_instruction.yaml new file mode 100644 index 0000000..1e076ff --- /dev/null +++ b/easi/tasks/ebalfred/ebalfred_complex_instruction.yaml @@ -0,0 +1,16 @@ +name: ebalfred_complex_instruction +display_name: "EB-Alfred Complex Instruction Split" +description: "Complex instruction evaluation split with nuanced language" +simulator: "ai2thor:v2_1_0" +task_class: "easi.tasks.ebalfred.task.EBAlfredTask" +action_space: [] +max_steps: 50 +dataset: + source: huggingface + repo_id: "oscarqjh/EB-Alfred_easi" + subset: null + split: "complex_instruction" + zip_files: + - "tasks.zip" +agent: + prompt_builder: "easi.tasks.ebalfred.prompts.EBAlfredPromptBuilder" diff --git a/easi/tasks/ebalfred/ebalfred_long_horizon.yaml b/easi/tasks/ebalfred/ebalfred_long_horizon.yaml new file mode 100644 index 0000000..a7537d4 --- /dev/null +++ b/easi/tasks/ebalfred/ebalfred_long_horizon.yaml @@ -0,0 +1,16 @@ +name: ebalfred_long_horizon +display_name: "EB-Alfred Long Horizon Split" +description: "Long-horizon evaluation split requiring multi-step planning" +simulator: "ai2thor:v2_1_0" +task_class: "easi.tasks.ebalfred.task.EBAlfredTask" +action_space: [] +max_steps: 50 +dataset: + source: huggingface + repo_id: "oscarqjh/EB-Alfred_easi" + subset: null + split: "long_horizon" + zip_files: + - "tasks.zip" +agent: + prompt_builder: "easi.tasks.ebalfred.prompts.EBAlfredPromptBuilder" diff --git a/easi/tasks/ebalfred/ebalfred_spatial.yaml b/easi/tasks/ebalfred/ebalfred_spatial.yaml new file mode 100644 index 0000000..4e8836c --- /dev/null +++ b/easi/tasks/ebalfred/ebalfred_spatial.yaml @@ -0,0 +1,16 @@ +name: ebalfred_spatial +display_name: "EB-Alfred Spatial Split" +description: "Spatial reasoning evaluation split" +simulator: "ai2thor:v2_1_0" +task_class: "easi.tasks.ebalfred.task.EBAlfredTask" +action_space: [] +max_steps: 50 +dataset: + source: huggingface + repo_id: "oscarqjh/EB-Alfred_easi" + subset: null + split: "spatial" + zip_files: + - "tasks.zip" +agent: + prompt_builder: "easi.tasks.ebalfred.prompts.EBAlfredPromptBuilder" diff --git a/easi/tasks/ebalfred/ebalfred_visual_appearance.yaml b/easi/tasks/ebalfred/ebalfred_visual_appearance.yaml new file mode 100644 index 0000000..1909fe4 --- /dev/null +++ b/easi/tasks/ebalfred/ebalfred_visual_appearance.yaml @@ -0,0 +1,16 @@ +name: ebalfred_visual_appearance +display_name: "EB-Alfred Visual Appearance Split" +description: "Visual appearance evaluation split testing visual grounding" +simulator: "ai2thor:v2_1_0" +task_class: "easi.tasks.ebalfred.task.EBAlfredTask" +action_space: [] +max_steps: 50 +dataset: + source: huggingface + repo_id: "oscarqjh/EB-Alfred_easi" + subset: null + split: "visual_appearance" + zip_files: + - "tasks.zip" +agent: + prompt_builder: "easi.tasks.ebalfred.prompts.EBAlfredPromptBuilder" diff --git a/easi/tasks/ebalfred/prompts.py b/easi/tasks/ebalfred/prompts.py new file mode 100644 index 0000000..74adc57 --- /dev/null +++ b/easi/tasks/ebalfred/prompts.py @@ -0,0 +1,109 @@ +"""EB-Alfred-specific prompt builder (Decision #10: OpenAI message format). + +Produces prompts in OpenAI message format with interleaved text+image, +tailored for the EB-Alfred household task domain. +Referenced in ebalfred*.yaml via: + agent: + prompt_builder: "easi.tasks.ebalfred.prompts.EBAlfredPromptBuilder" + +Reference: EmbodiedBench/embodiedbench/evaluator/config/system_prompts.py +""" +from __future__ import annotations + +from easi.agents.prompt_builder import _encode_image_base64 +from easi.core.episode import Observation + + +class EBAlfredPromptBuilder: + """Prompt builder for EB-Alfred household tasks in AI2-THOR. + + Returns OpenAI message format (list[dict]) with interleaved text+image. + """ + + SYSTEM_TEMPLATE = """You are an embodied agent in an AI2-THOR household environment. You must complete household tasks by choosing high-level skill actions. + +## Task +{task_description} + +## Available Actions +Each action is a high-level skill. The simulator will handle low-level navigation and interaction. +{action_list} + +## Action Categories +- **find a **: Navigate to the specified object +- **pick up the **: Pick up an object (must be near it first) +- **put down the object in hand**: Place held object in the nearest receptacle +- **open/close the **: Open or close a container (Fridge, Cabinet, etc.) +- **turn on/off the **: Toggle an appliance (Faucet, Microwave, etc.) +- **slice the **: Slice a sliceable object (must be holding a knife) + +## Output Format +Respond with valid JSON: +{{ + "observation": "Describe what you see in the current image", + "reasoning": "Step-by-step reasoning about what to do next", + "plan": "Your high-level plan to complete the task", + "executable_plan": [ + {{"action": ""}} + ] +}} + +## Tips +- Find objects before trying to interact with them. +- To clean: put object in sink, turn on faucet, turn off faucet, pick up object. +- To heat: put object in microwave, turn on microwave, turn off microwave, pick up object. +- To cool: put object in fridge, close fridge, open fridge, pick up object. +- Output one action at a time for reliability. +""" + + STEP_TEMPLATE = """Task: {task_description} + +{history_section} + +Look at the current observation image and decide your next action. Respond with valid JSON.""" + + def build_system_prompt( + self, + action_space: list[str], + task_description: str, + ) -> list[dict]: + action_list = "\n".join( + f" {i}. {name}" for i, name in enumerate(action_space) + ) + text = self.SYSTEM_TEMPLATE.format( + action_list=action_list, + task_description=task_description, + ) + return [{"role": "system", "content": text}] + + def build_step_prompt( + self, + observation: Observation, + task_description: str, + action_history: list[tuple[str, str]], + ) -> list[dict]: + if action_history: + history_lines = [] + for i, (action_name, feedback) in enumerate(action_history): + status = "OK" if "success" in feedback.lower() else "FAILED" + history_lines.append(f" Step {i+1}: {action_name} → {status} ({feedback})") + history_section = "## Previous Actions\n" + "\n".join(history_lines) + else: + history_section = "This is your first action. Start by observing the environment." + + text = self.STEP_TEMPLATE.format( + task_description=task_description, + history_section=history_section, + ) + + # Build interleaved content parts (text + image) + content_parts: list[dict] = [{"type": "text", "text": text}] + if observation.rgb_path: + image_url = _encode_image_base64(observation.rgb_path) + if image_url: + content_parts.append({ + "type": "image_url", + "image_url": {"url": image_url}, + }) + + return [{"role": "user", "content": content_parts}] diff --git a/easi/tasks/ebalfred/task.py b/easi/tasks/ebalfred/task.py new file mode 100644 index 0000000..5c5abe8 --- /dev/null +++ b/easi/tasks/ebalfred/task.py @@ -0,0 +1,104 @@ +"""EB-Alfred task for EASI. + +Adapts the EmbodiedBench EB-Alfred track to EASI's task interface. +Supports multiple splits via per-split .yaml configs (Task 4 infra). + +When loaded via a split yaml (e.g., ebalfred_base), episodes come from +HuggingFace dataset rows. When loaded via get_task_yaml_path(), uses +ebalfred_base.yaml as the default fallback. + +Reference: EmbodiedBench/embodiedbench/envs/eb_alfred/EBAlfEnv.py +""" +from __future__ import annotations + +from pathlib import Path + +from easi.core.base_task import BaseTask +from easi.core.episode import StepResult +from easi.tasks.ebalfred.actions import get_global_action_space +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + + +class EBAlfredTask(BaseTask): + + def __init__(self, data_dir: Path | None = None, split_yaml_path: Path | None = None): + super().__init__(data_dir=data_dir, split_yaml_path=split_yaml_path) + # Override static action space with dynamic one + self._config["action_space"] = get_global_action_space() + + def get_task_yaml_path(self) -> Path: + # Decision #4: No task.yaml — use ebalfred_base.yaml as default fallback + return Path(__file__).parent / "ebalfred_base.yaml" + + def get_instruction(self, episode: dict) -> str: + """Decision #2: EB-Alfred uses 'instruction' field from HF row.""" + return episode.get("instruction", self.name) + + def format_reset_config(self, episode: dict) -> dict: + """Map EB-Alfred episode (HF row) to AI2-THOR bridge reset config. + + HF row columns: id, task, repeat_idx, instruction, task_type, trial_id + The bridge uses task_path to find annotation files inside the extracted tasks.zip: + /oscarqjh_EB-Alfred_easi/tasks//pp/ann_.json + """ + data_dir = episode.get("_data_dir", "") + return { + "task_path": episode["task"], + "repeat_idx": episode.get("repeat_idx", 0), + "instruction": episode.get("instruction", ""), + "episode_id": episode.get("id", ""), + "task_type": episode.get("task_type", ""), + "trial_id": episode.get("trial_id", ""), + "data_dir": data_dir, + } + + def evaluate_episode( + self, episode: dict, trajectory: list[StepResult] + ) -> dict[str, float]: + """Extract metrics from the trajectory. + + The bridge reports task_success and task_progress in StepResult.info, + computed by EB-Alfred's goal_conditions_met() running inside the bridge. + """ + if not trajectory: + return { + "task_success": 0.0, + "task_progress": 0.0, + "num_steps": 0.0, + "total_reward": 0.0, + } + + last_step = trajectory[-1] + return { + "task_success": last_step.info.get("task_success", 0.0), + "task_progress": last_step.info.get("task_progress", 0.0), + "num_steps": float(len(trajectory)), + "total_reward": sum(s.reward for s in trajectory), + } + + def _get_builtin_episodes(self) -> list[dict]: + """Return minimal built-in episodes for testing without dataset. + + Matches EB-Alfred_easi column structure: id, task, repeat_idx, + instruction, task_type, trial_id. + """ + return [ + { + "id": 0, + "task": "pick_and_place_simple-Mug-None-Shelf-1/trial_T20190001", + "repeat_idx": 0, + "instruction": "Put a mug on the shelf.", + "task_type": "pick_and_place_simple", + "trial_id": "trial_T20190001", + }, + { + "id": 1, + "task": "pick_clean_then_place_in_recep-Plate-None-CounterTop-2/trial_T20190002", + "repeat_idx": 0, + "instruction": "Rinse off a plate and put it on the counter.", + "task_type": "pick_clean_then_place_in_recep", + "trial_id": "trial_T20190002", + }, + ] diff --git a/easi/tasks/registry.py b/easi/tasks/registry.py index 0ed10a7..aa68c8d 100644 --- a/easi/tasks/registry.py +++ b/easi/tasks/registry.py @@ -11,13 +11,14 @@ from __future__ import annotations import importlib -import logging from dataclasses import dataclass from pathlib import Path import yaml -logger = logging.getLogger("easi.tasks.registry") +from easi.utils.logging import get_logger + +logger = get_logger(__name__) @dataclass @@ -38,31 +39,46 @@ class TaskEntry: _registry: dict[str, TaskEntry] | None = None -def _discover_tasks() -> dict[str, TaskEntry]: - """Scan task directories for task.yaml files.""" - tasks_dir = Path(__file__).parent +def _discover_tasks(tasks_dir: Path | None = None) -> dict[str, TaskEntry]: + """Scan task directories for .yaml config files. + + Discovers all *.yaml files in immediate subdirectories of tasks_dir. + Each .yaml file registers as its own task (enabling multi-split tasks). + """ + if tasks_dir is None: + tasks_dir = Path(__file__).parent entries: dict[str, TaskEntry] = {} - for task_yaml_path in sorted(tasks_dir.glob("*/task.yaml")): - try: - config = yaml.safe_load(task_yaml_path.read_text()) - except Exception as e: - logger.warning("Failed to load %s: %s", task_yaml_path, e) + for subdir in sorted(tasks_dir.iterdir()): + if not subdir.is_dir() or subdir.name.startswith("_"): continue - name = config["name"] - entries[name] = TaskEntry( - name=name, - display_name=config.get("display_name", name), - description=config.get("description", ""), - simulator_key=config["simulator"], - task_class=config["task_class"], - action_space=config.get("action_space", []), - max_steps=config.get("max_steps", 500), - config_path=task_yaml_path, - ) - - logger.debug("Discovered task: %s (simulator: %s)", name, config["simulator"]) + # Find all .yaml files in this task folder + yaml_files = sorted(subdir.glob("*.yaml")) + + for task_yaml_path in yaml_files: + try: + config = yaml.safe_load(task_yaml_path.read_text()) + except Exception as e: + logger.warning("Failed to load %s: %s", task_yaml_path, e) + continue + + if not isinstance(config, dict) or "name" not in config: + continue + + name = config["name"] + entries[name] = TaskEntry( + name=name, + display_name=config.get("display_name", name), + description=config.get("description", ""), + simulator_key=config["simulator"], + task_class=config["task_class"], + action_space=config.get("action_space", []), + max_steps=config.get("max_steps", 500), + config_path=task_yaml_path, + ) + + logger.debug("Discovered task: %s from %s", name, task_yaml_path.name) return entries diff --git a/easi/utils/import_utils.py b/easi/utils/import_utils.py new file mode 100644 index 0000000..0e7b4c3 --- /dev/null +++ b/easi/utils/import_utils.py @@ -0,0 +1,24 @@ +"""Dynamic class import utility.""" + +from __future__ import annotations + +import importlib + + +def import_class(dotted_path: str) -> type: + """Import a class from a dotted path. + + Args: + dotted_path: Fully qualified class name + (e.g., "easi.tasks.ebalfred.prompts.EBAlfredPromptBuilder"). + + Returns: + The class object. + + Raises: + ImportError: If the module cannot be found. + AttributeError: If the class does not exist in the module. + """ + module_path, class_name = dotted_path.rsplit(".", 1) + module = importlib.import_module(module_path) + return getattr(module, class_name) diff --git a/easi/utils/locking.py b/easi/utils/locking.py index d482e45..2cff516 100644 --- a/easi/utils/locking.py +++ b/easi/utils/locking.py @@ -8,12 +8,13 @@ from __future__ import annotations import fcntl -import logging from contextlib import contextmanager from pathlib import Path from typing import Generator -logger = logging.getLogger("easi.utils.locking") +from easi.utils.logging import get_logger + +logger = get_logger(__name__) @contextmanager diff --git a/easi/utils/logging.py b/easi/utils/logging.py index d5ba59d..7dc4275 100644 --- a/easi/utils/logging.py +++ b/easi/utils/logging.py @@ -9,15 +9,20 @@ import logging -def get_logger(name: str) -> logging.Logger: +def get_logger(name: str = __name__) -> logging.Logger: """Get a namespaced easi logger. + Accepts ``__name__`` directly — the ``easi.`` prefix is handled + automatically so callers never double-prefix. + Usage:: from easi.utils.logging import get_logger - logger = get_logger("simulators.subprocess_runner") + logger = get_logger(__name__) # recommended — auto-discovers module path logger.info("Launching bridge: %s", bridge_path) """ + if name.startswith("easi."): + return logging.getLogger(name) return logging.getLogger(f"easi.{name}") @@ -37,7 +42,7 @@ def setup_logging(level: str = "WARNING") -> None: handler = logging.StreamHandler() handler.setFormatter( logging.Formatter( - "%(asctime)s [%(levelname)s] %(name)s: %(message)s", + "%(asctime)s [%(levelname)s] %(name)s - %(message)s", datefmt="%H:%M:%S", ) ) diff --git a/easi/utils/system_deps.py b/easi/utils/system_deps.py index 14bad47..5ee76ac 100644 --- a/easi/utils/system_deps.py +++ b/easi/utils/system_deps.py @@ -6,12 +6,12 @@ from __future__ import annotations -import logging import subprocess from easi.core.exceptions import EnvironmentSetupError +from easi.utils.logging import get_logger -logger = logging.getLogger("easi.utils.system_deps") +logger = get_logger(__name__) # Known system dependencies and how to check/install them diff --git a/pyproject.toml b/pyproject.toml index 14ceb4a..0707c92 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -11,12 +11,11 @@ dependencies = [ "Pillow>=10.0", "requests>=2.28", "pyyaml>=6.0", + "datasets>=2.0.0", + "huggingface_hub>=0.19.0", ] [project.optional-dependencies] -hf = [ - "huggingface_hub>=0.20", -] dev = [ "pytest>=7.0", "pytest-timeout>=2.0", @@ -34,5 +33,5 @@ exclude = ["VLMEvalKit*", "lmms-eval*"] "simulators/*/manifest.yaml", "simulators/*/*/conda_env.yaml", "simulators/*/*/requirements.txt", - "tasks/*/task.yaml", + "tasks/*/*.yaml", ] diff --git a/tests/test_e2e_evaluation.py b/tests/test_e2e_evaluation.py new file mode 100644 index 0000000..f1e7c4c --- /dev/null +++ b/tests/test_e2e_evaluation.py @@ -0,0 +1,218 @@ +"""End-to-end evaluation tests using dummy components. + +These tests verify the full pipeline: task loading -> simulator launch -> +agent loop -> metric aggregation -> file output. +""" + +import json + +import pytest + +from easi.evaluation.runner import EvaluationRunner + + +def _find_run_dir(output_dir, task_name="dummy_task"): + """Find the single run directory under output_dir//.""" + task_dir = output_dir / task_name + run_dirs = list(task_dir.iterdir()) + assert len(run_dirs) == 1, f"Expected 1 run dir, found {len(run_dirs)}" + return run_dirs[0] + + +class TestE2EEvaluation: + def test_dummy_full_run(self, tmp_path): + """Full eval: dummy task + dummy simulator + dummy agent.""" + runner = EvaluationRunner( + task_name="dummy_task", + agent_type="dummy", + output_dir=tmp_path / "logs", + agent_seed=42, + ) + results = runner.run() + + # All 3 episodes should complete + assert len(results) == 3 + + # Summary should exist + run_dir = _find_run_dir(tmp_path / "logs") + summary = json.loads((run_dir / "summary.json").read_text()) + assert summary["num_episodes"] == 3 + assert "success_rate" in summary or "avg_success" in summary + + def test_max_episodes_limit(self, tmp_path): + """Verify max_episodes limits the run.""" + runner = EvaluationRunner( + task_name="dummy_task", + agent_type="dummy", + output_dir=tmp_path / "logs", + ) + results = runner.run(max_episodes=1) + assert len(results) == 1 + + def test_per_episode_files(self, tmp_path): + """Verify per-episode result and trajectory files are created.""" + output_dir = tmp_path / "logs" + runner = EvaluationRunner( + task_name="dummy_task", + agent_type="dummy", + output_dir=output_dir, + ) + runner.run(max_episodes=2) + + run_dir = _find_run_dir(output_dir) + episode_dirs = sorted((run_dir / "episodes").iterdir()) + assert len(episode_dirs) == 2 + + for ep_dir in episode_dirs: + assert (ep_dir / "result.json").exists() + assert (ep_dir / "trajectory.jsonl").exists() + + ep0 = json.loads((episode_dirs[0] / "result.json").read_text()) + assert "episode_id" in ep0 + assert "elapsed_seconds" in ep0 + + def test_episode_metrics_structure(self, tmp_path): + """Each episode result should have expected metric keys.""" + runner = EvaluationRunner( + task_name="dummy_task", + agent_type="dummy", + output_dir=tmp_path / "logs", + ) + results = runner.run(max_episodes=1) + + ep = results[0] + assert "success" in ep + assert "num_steps" in ep + assert "episode_id" in ep + assert "elapsed_seconds" in ep + assert isinstance(ep["elapsed_seconds"], float) + assert ep["elapsed_seconds"] >= 0 + + def test_summary_has_all_averaged_keys(self, tmp_path): + """Summary should average all numeric metrics from episodes.""" + output_dir = tmp_path / "logs" + runner = EvaluationRunner( + task_name="dummy_task", + agent_type="dummy", + output_dir=output_dir, + ) + runner.run() + + run_dir = _find_run_dir(output_dir) + summary = json.loads((run_dir / "summary.json").read_text()) + assert summary["num_episodes"] == 3 + # These come from averaging per-episode numeric keys + assert "avg_num_steps" in summary + assert "avg_elapsed_seconds" in summary + + def test_deterministic_with_seed(self, tmp_path): + """Two runs with the same seed should produce identical results.""" + results_a = EvaluationRunner( + task_name="dummy_task", + agent_type="dummy", + output_dir=tmp_path / "a", + agent_seed=123, + ).run(max_episodes=2) + + results_b = EvaluationRunner( + task_name="dummy_task", + agent_type="dummy", + output_dir=tmp_path / "b", + agent_seed=123, + ).run(max_episodes=2) + + for a, b in zip(results_a, results_b): + assert a["num_steps"] == b["num_steps"] + assert a["success"] == b["success"] + + def test_different_seeds_may_differ(self, tmp_path): + """Different seeds should produce potentially different results.""" + results_a = EvaluationRunner( + task_name="dummy_task", + agent_type="dummy", + output_dir=tmp_path / "a", + agent_seed=1, + ).run() + + results_b = EvaluationRunner( + task_name="dummy_task", + agent_type="dummy", + output_dir=tmp_path / "b", + agent_seed=999, + ).run() + + # With different seeds, at least one metric should differ + # (This is a soft test — could theoretically match, but very unlikely) + all_steps_a = [r["num_steps"] for r in results_a] + all_steps_b = [r["num_steps"] for r in results_b] + # Just verify both complete; exact difference not guaranteed + assert len(results_a) == len(results_b) == 3 + + def test_config_json_saved(self, tmp_path): + """Verify config.json is written with run metadata.""" + output_dir = tmp_path / "logs" + runner = EvaluationRunner( + task_name="dummy_task", + agent_type="dummy", + output_dir=output_dir, + agent_seed=42, + ) + runner.run(max_episodes=1) + + run_dir = _find_run_dir(output_dir) + config = json.loads((run_dir / "config.json").read_text()) + assert config["task_name"] == "dummy_task" + assert config["agent_type"] == "dummy" + assert config["agent_seed"] == 42 + assert config["max_episodes"] == 1 + assert "run_id" in config + + def test_trajectory_jsonl_format(self, tmp_path): + """Verify trajectory.jsonl has correct format.""" + output_dir = tmp_path / "logs" + runner = EvaluationRunner( + task_name="dummy_task", + agent_type="dummy", + output_dir=output_dir, + ) + runner.run(max_episodes=1) + + run_dir = _find_run_dir(output_dir) + episode_dirs = sorted((run_dir / "episodes").iterdir()) + trajectory_path = episode_dirs[0] / "trajectory.jsonl" + lines = trajectory_path.read_text().strip().split("\n") + + # First line is reset + reset_entry = json.loads(lines[0]) + assert reset_entry["step"] == 0 + assert reset_entry["type"] == "reset" + assert "rgb_path" in reset_entry + assert reset_entry["rgb_path"].startswith("rgb_") + + # Subsequent lines are steps + assert len(lines) > 1 + step_entry = json.loads(lines[1]) + assert step_entry["step"] == 1 + assert step_entry["type"] == "step" + assert "action" in step_entry + assert "rgb_path" in step_entry + + def test_images_saved_to_episode_dir(self, tmp_path): + """Verify RGB images are saved in the episode directory, not IPC workspace.""" + output_dir = tmp_path / "logs" + runner = EvaluationRunner( + task_name="dummy_task", + agent_type="dummy", + output_dir=output_dir, + ) + runner.run(max_episodes=1) + + run_dir = _find_run_dir(output_dir) + episode_dirs = sorted((run_dir / "episodes").iterdir()) + ep_dir = episode_dirs[0] + + # Should have rgb_0000.png (reset) + at least one step image + png_files = sorted(ep_dir.glob("rgb_*.png")) + assert len(png_files) >= 2 # At least reset + one step + assert png_files[0].name == "rgb_0000.png" + assert png_files[1].name == "rgb_0001.png" diff --git a/tests/test_ebalfred_task.py b/tests/test_ebalfred_task.py new file mode 100644 index 0000000..46282b6 --- /dev/null +++ b/tests/test_ebalfred_task.py @@ -0,0 +1,169 @@ +"""Tests for the EB-Alfred task (offline, no simulator needed).""" +import pytest +from pathlib import Path + +from easi.core.episode import Observation, StepResult +from easi.tasks.ebalfred.task import EBAlfredTask +from easi.tasks.ebalfred.actions import get_global_action_space + + +class TestEBAlfredActionSpace: + def test_action_space_not_empty(self): + actions = get_global_action_space() + assert len(actions) > 100 # EB-Alfred has ~133 actions + + def test_action_space_has_find(self): + actions = get_global_action_space() + find_actions = [a for a in actions if a.startswith("find")] + assert len(find_actions) > 20 + + def test_action_space_has_pick(self): + actions = get_global_action_space() + pick_actions = [a for a in actions if a.startswith("pick up")] + assert len(pick_actions) > 20 + + def test_action_space_has_put_and_drop(self): + actions = get_global_action_space() + assert "put down the object in hand" in actions + assert "drop the object in hand" in actions + + def test_action_space_has_open_close(self): + actions = get_global_action_space() + assert "open the Fridge" in actions + assert "close the Fridge" in actions + + def test_action_space_has_toggle(self): + actions = get_global_action_space() + assert "turn on the Faucet" in actions + assert "turn off the Faucet" in actions + + def test_action_space_has_slice(self): + actions = get_global_action_space() + assert "slice the Potato" in actions + + +class TestEBAlfredTask: + @pytest.fixture + def task(self): + return EBAlfredTask() + + def test_name(self, task): + assert task.name == "ebalfred_base" + + def test_simulator_key(self, task): + assert task.simulator_key == "ai2thor:v2_1_0" + + def test_action_space_loaded(self, task): + assert len(task.action_space) > 100 + + def test_format_reset_config(self, task): + """Test the adapter from EB-Alfred HF row to THOR reset config.""" + episode = { + "id": 0, + "task": "pick_and_place_simple-Mug-None-Shelf-1/trial_T20190001", + "repeat_idx": 0, + "instruction": "Put a mug on the shelf.", + "task_type": "pick_and_place_simple", + "trial_id": "trial_T20190001", + "_data_dir": "/datasets/oscarqjh_EB-Alfred_easi", + } + config = task.format_reset_config(episode) + assert config["task_path"] == "pick_and_place_simple-Mug-None-Shelf-1/trial_T20190001" + assert config["repeat_idx"] == 0 + assert config["instruction"] == "Put a mug on the shelf." + assert config["episode_id"] == 0 + assert config["task_type"] == "pick_and_place_simple" + assert config["data_dir"] == "/datasets/oscarqjh_EB-Alfred_easi" + + def test_evaluate_episode(self, task): + """Test metric computation from trajectory.""" + episode = {"id": 0, "task": "test", "repeat_idx": 0, "instruction": "test", + "task_type": "test", "trial_id": "trial_T00000000"} + obs = Observation(rgb_path="/tmp/rgb.png") + trajectory = [ + StepResult(observation=obs, reward=0.0, done=False, + info={"task_success": 0.0, "task_progress": 0.33}), + StepResult(observation=obs, reward=0.5, done=True, + info={"task_success": 1.0, "task_progress": 1.0}), + ] + metrics = task.evaluate_episode(episode, trajectory) + assert metrics["task_success"] == 1.0 + assert metrics["task_progress"] == 1.0 + assert metrics["num_steps"] == 2 + + def test_evaluate_empty_trajectory(self, task): + episode = {"id": 0, "task": "test", "repeat_idx": 0, "instruction": "test", + "task_type": "test", "trial_id": "trial_T00000000"} + metrics = task.evaluate_episode(episode, []) + assert metrics["task_success"] == 0.0 + assert metrics["num_steps"] == 0.0 + + def test_get_instruction(self, task): + episode = {"instruction": "Put a mug on the shelf."} + assert task.get_instruction(episode) == "Put a mug on the shelf." + + def test_builtin_episodes(self, task): + episodes = task._get_builtin_episodes() + assert len(episodes) == 2 + assert episodes[0]["task_type"] == "pick_and_place_simple" + + def test_task_registry_discovers_all_splits(self): + """Registry should discover all EB-Alfred split yamls.""" + from easi.tasks.registry import list_tasks, refresh + refresh() + tasks = list_tasks() + assert "ebalfred_base" in tasks + assert "ebalfred_long_horizon" in tasks + assert "ebalfred_common_sense" in tasks + assert "ebalfred_complex_instruction" in tasks + assert "ebalfred_spatial" in tasks + assert "ebalfred_visual_appearance" in tasks + + def test_split_specific_task_loading(self): + """Loading ebalfred_base should give a task with correct config.""" + from easi.tasks.registry import get_task_entry, refresh + refresh() + entry = get_task_entry("ebalfred_base") + assert entry.simulator_key == "ai2thor:v2_1_0" + assert entry.config_path.name == "ebalfred_base.yaml" + + +class TestEBAlfredPromptBuilder: + def test_system_prompt_returns_messages(self): + from easi.tasks.ebalfred.prompts import EBAlfredPromptBuilder + builder = EBAlfredPromptBuilder() + messages = builder.build_system_prompt( + action_space=get_global_action_space(), + task_description="Put a mug on the shelf.", + ) + assert isinstance(messages, list) + assert messages[0]["role"] == "system" + content = messages[0]["content"] + assert "find a" in content + assert "pick up" in content + assert "Put a mug on the shelf" in content + assert "household" in content.lower() + + def test_step_prompt_returns_messages_with_history(self): + from easi.tasks.ebalfred.prompts import EBAlfredPromptBuilder + builder = EBAlfredPromptBuilder() + obs = Observation(rgb_path="/tmp/rgb.png") + messages = builder.build_step_prompt( + observation=obs, + task_description="Put a mug on the shelf.", + action_history=[("find a Mug", "success"), ("pick up the Mug", "failed: not close enough")], + ) + assert isinstance(messages, list) + assert messages[0]["role"] == "user" + text_content = "" + for part in messages[0]["content"]: + if part["type"] == "text": + text_content += part["text"] + assert "find a Mug" in text_content + assert "FAILED" in text_content + + def test_conforms_to_protocol(self): + from easi.tasks.ebalfred.prompts import EBAlfredPromptBuilder + from easi.agents.prompt_builder import PromptBuilderProtocol + builder = EBAlfredPromptBuilder() + assert isinstance(builder, PromptBuilderProtocol) diff --git a/tests/test_evaluation_runner.py b/tests/test_evaluation_runner.py new file mode 100644 index 0000000..14ba51d --- /dev/null +++ b/tests/test_evaluation_runner.py @@ -0,0 +1,82 @@ +"""Tests for the sequential evaluation runner.""" + +import json + +import pytest + +from easi.evaluation.runner import EvaluationRunner + + +def _find_run_dir(output_dir, task_name="dummy_task"): + """Find the single run directory under output_dir//.""" + task_dir = output_dir / task_name + run_dirs = list(task_dir.iterdir()) + assert len(run_dirs) == 1, f"Expected 1 run dir, found {len(run_dirs)}" + return run_dirs[0] + + +class TestEvaluationRunner: + def test_run_single_episode(self, tmp_path): + """Run one episode with dummy task + dummy simulator + dummy agent.""" + runner = EvaluationRunner( + task_name="dummy_task", + agent_type="dummy", + output_dir=tmp_path / "logs", + ) + results = runner.run(max_episodes=1) + + assert len(results) == 1 + assert "success" in results[0] + assert "num_steps" in results[0] + + def test_run_multiple_episodes(self, tmp_path): + """Run all 3 dummy episodes.""" + runner = EvaluationRunner( + task_name="dummy_task", + agent_type="dummy", + output_dir=tmp_path / "logs", + ) + results = runner.run() + + assert len(results) == 3 # dummy_task has 3 episodes + + def test_results_saved_to_disk(self, tmp_path): + """Verify structured output directory is created.""" + output_dir = tmp_path / "logs" + runner = EvaluationRunner( + task_name="dummy_task", + agent_type="dummy", + output_dir=output_dir, + ) + runner.run(max_episodes=1) + + run_dir = _find_run_dir(output_dir) + assert (run_dir / "config.json").exists() + assert (run_dir / "summary.json").exists() + + # Episode directory + episodes_dir = run_dir / "episodes" + assert episodes_dir.exists() + episode_dirs = sorted(episodes_dir.iterdir()) + assert len(episode_dirs) == 1 + + ep_dir = episode_dirs[0] + assert (ep_dir / "result.json").exists() + assert (ep_dir / "trajectory.jsonl").exists() + assert (ep_dir / "rgb_0000.png").exists() # Reset observation + + def test_summary_aggregates_metrics(self, tmp_path): + """Verify summary.json contains averaged metrics.""" + output_dir = tmp_path / "logs" + runner = EvaluationRunner( + task_name="dummy_task", + agent_type="dummy", + output_dir=output_dir, + ) + runner.run() + + run_dir = _find_run_dir(output_dir) + summary = json.loads((run_dir / "summary.json").read_text()) + assert "success_rate" in summary + assert "avg_steps" in summary + assert "num_episodes" in summary diff --git a/tests/test_filesystem_comm.py b/tests/test_filesystem_comm.py index d1dd22a..012ad8f 100644 --- a/tests/test_filesystem_comm.py +++ b/tests/test_filesystem_comm.py @@ -112,6 +112,33 @@ def test_parse_action_from_command(self): assert parsed.params["angle"] == 90.0 + def test_step_command_with_skill_text(self): + """action_name carries full skill text for EB-Alfred (e.g., 'find a Cabinet'). + + The IPC protocol supports this natively — no schema changes needed. + """ + action = Action(action_name="find a Cabinet", params={}) + cmd = make_step_command(action) + assert cmd["action"]["action_name"] == "find a Cabinet" + + # Round-trip: serialize and parse back + parsed = parse_action_from_command(cmd) + assert parsed.action_name == "find a Cabinet" + + def test_observation_response_with_task_metrics(self): + """info field carries task_success and task_progress for EB-Alfred.""" + resp = make_observation_response( + rgb_path="/tmp/rgb.png", + reward=0.5, + done=False, + info={"task_success": 0.0, "task_progress": 0.33, "last_action_success": 1.0}, + ) + result = parse_step_result(resp) + assert result.info["task_success"] == 0.0 + assert result.info["task_progress"] == 0.33 + assert result.info["last_action_success"] == 1.0 + + class TestWriteCommand: def test_clears_old_response(self, tmp_path): # Write a response file diff --git a/tests/test_multi_split_task.py b/tests/test_multi_split_task.py new file mode 100644 index 0000000..61a9813 --- /dev/null +++ b/tests/test_multi_split_task.py @@ -0,0 +1,176 @@ +"""Tests for multi-split task support.""" +import json +import zipfile +import pytest +from pathlib import Path +from unittest.mock import patch, MagicMock + +from easi.tasks.registry import list_tasks, get_task_entry, refresh, _discover_tasks + + +class TestMultiSplitRegistry: + def test_discovers_multiple_yaml_per_folder(self, tmp_path): + """Registry should find all .yaml files in a task folder.""" + task_dir = tmp_path / "my_bench" + task_dir.mkdir() + (task_dir / "__init__.py").write_text("") + + for split in ["base", "hard"]: + yaml_content = f""" +name: my_bench_{split} +display_name: "My Bench ({split})" +description: "Test split" +simulator: "dummy:v1" +task_class: "easi.tasks.dummy_task.task.DummyTask" +action_space: ["move", "turn"] +max_steps: 100 +dataset: + source: huggingface + repo_id: "oscarqjh/my-bench" + subset: null + split: "{split}" +""" + (task_dir / f"my_bench_{split}.yaml").write_text(yaml_content) + + entries = _discover_tasks(tasks_dir=tmp_path) + + assert "my_bench_base" in entries + assert "my_bench_hard" in entries + assert entries["my_bench_base"].config_path.name == "my_bench_base.yaml" + + def test_yaml_with_explicit_subset(self, tmp_path): + """Task yaml can specify an explicit HF subset.""" + task_dir = tmp_path / "bench" + task_dir.mkdir() + (task_dir / "__init__.py").write_text("") + (task_dir / "bench_en_test.yaml").write_text(""" +name: bench_en_test +display_name: "Bench EN Test" +description: "English test subset" +simulator: "dummy:v1" +task_class: "easi.tasks.dummy_task.task.DummyTask" +action_space: [] +max_steps: 10 +dataset: + source: huggingface + repo_id: "oscarqjh/bench" + subset: "en" + split: "test" +""") + entries = _discover_tasks(tasks_dir=tmp_path) + assert entries["bench_en_test"].name == "bench_en_test" + + def test_skips_dirs_starting_with_underscore(self, tmp_path): + """Directories starting with _ should be skipped.""" + hidden_dir = tmp_path / "_internal" + hidden_dir.mkdir() + (hidden_dir / "secret.yaml").write_text(""" +name: secret_task +simulator: "dummy:v1" +task_class: "easi.tasks.dummy_task.task.DummyTask" +""") + entries = _discover_tasks(tasks_dir=tmp_path) + assert "secret_task" not in entries + + def test_skips_invalid_yaml(self, tmp_path): + """Invalid YAML files should be skipped without crashing.""" + task_dir = tmp_path / "broken" + task_dir.mkdir() + (task_dir / "broken.yaml").write_text("this is: [not: valid: yaml: {{") + + entries = _discover_tasks(tasks_dir=tmp_path) + assert len(entries) == 0 + + def test_skips_yaml_without_name(self, tmp_path): + """YAML files without a 'name' key should be skipped.""" + task_dir = tmp_path / "no_name" + task_dir.mkdir() + (task_dir / "no_name.yaml").write_text(""" +description: "Missing name field" +simulator: "dummy:v1" +""") + entries = _discover_tasks(tasks_dir=tmp_path) + assert len(entries) == 0 + + def test_existing_dummy_task_still_discovered(self): + """The existing dummy_task/task.yaml should still be found by *.yaml glob.""" + refresh() + tasks = list_tasks() + assert "dummy_task" in tasks + entry = get_task_entry("dummy_task") + assert entry.simulator_key == "dummy:v1" + assert entry.config_path.name == "task.yaml" + + +class TestZipExtraction: + def test_extract_zip_files(self, tmp_path): + """BaseTask._extract_zip_files should extract .zip files.""" + from easi.core.base_task import BaseTask + + dataset_dir = tmp_path / "dataset" + dataset_dir.mkdir() + zip_path = dataset_dir / "tasks.zip" + with zipfile.ZipFile(zip_path, "w") as zf: + zf.writestr("tasks/pick_and_place_simple-Mug/trial_001/traj_data.json", '{"test": true}') + zf.writestr("tasks/pick_clean-Plate/trial_002/traj_data.json", '{"test": true}') + + BaseTask._extract_zip_files(dataset_dir, ["tasks.zip"]) + + assert (dataset_dir / "tasks" / "pick_and_place_simple-Mug" / "trial_001" / "traj_data.json").exists() + assert (dataset_dir / "tasks" / "pick_clean-Plate" / "trial_002" / "traj_data.json").exists() + + def test_extract_idempotent(self, tmp_path): + """Second extraction should be skipped (marker file).""" + from easi.core.base_task import BaseTask + + dataset_dir = tmp_path / "dataset" + dataset_dir.mkdir() + zip_path = dataset_dir / "tasks.zip" + with zipfile.ZipFile(zip_path, "w") as zf: + zf.writestr("tasks/test.json", '{}') + + BaseTask._extract_zip_files(dataset_dir, ["tasks.zip"]) + BaseTask._extract_zip_files(dataset_dir, ["tasks.zip"]) # should be no-op + + assert (dataset_dir / ".tasks.zip.extracted").exists() + + def test_missing_zip_does_not_crash(self, tmp_path): + """Missing zip file should warn but not crash.""" + from easi.core.base_task import BaseTask + + dataset_dir = tmp_path / "dataset" + dataset_dir.mkdir() + + # Should not raise + BaseTask._extract_zip_files(dataset_dir, ["nonexistent.zip"]) + + +class TestEpisodeFromHFRow: + def test_hf_row_to_episode(self): + """Each HF dataset row should map to an episode dict.""" + from easi.core.base_task import hf_row_to_episode + + # Real EB-Alfred_easi column structure + row = { + "id": 0, + "task": "pick_and_place_simple-Mug-None-Shelf-1/trial_T20190001", + "repeat_idx": 0, + "instruction": "Put a mug on the shelf.", + "task_type": "pick_and_place_simple", + "trial_id": "trial_T20190001", + } + episode = hf_row_to_episode(row) + assert episode["id"] == 0 + assert episode["task"] == "pick_and_place_simple-Mug-None-Shelf-1/trial_T20190001" + assert episode["instruction"] == "Put a mug on the shelf." + assert episode["task_type"] == "pick_and_place_simple" + assert episode["trial_id"] == "trial_T20190001" + + def test_hf_row_to_episode_is_copy(self): + """hf_row_to_episode should return a copy, not modify the original.""" + from easi.core.base_task import hf_row_to_episode + + row = {"id": 1, "instruction": "test"} + episode = hf_row_to_episode(row) + episode["new_field"] = "added" + assert "new_field" not in row diff --git a/tests/test_react_agent.py b/tests/test_react_agent.py new file mode 100644 index 0000000..fa77702 --- /dev/null +++ b/tests/test_react_agent.py @@ -0,0 +1,236 @@ +"""Tests for the ReAct agent and PromptBuilder protocol.""" +import base64 +import json +import pytest + +from easi.agents.prompt_builder import PromptBuilderProtocol, DefaultPromptBuilder +from easi.agents.react_agent import ReActAgent +from easi.core.episode import Action, Observation + + +class MockLLMClient: + """Mock LLM that returns a fixed JSON plan.""" + def __init__(self, actions=None): + self.actions = actions or [{"action": "MoveAhead"}] + self.call_count = 0 + + def generate(self, messages): + self.call_count += 1 + return json.dumps({ + "observation": "I see a room.", + "reasoning": "I should move forward.", + "plan": "1. Move ahead", + "executable_plan": self.actions, + }) + + +class TestDefaultPromptBuilder: + def test_build_system_prompt_returns_messages(self): + builder = DefaultPromptBuilder() + messages = builder.build_system_prompt( + action_space=["MoveAhead", "Stop"], + task_description="Go to the kitchen.", + ) + # Decision #10: returns OpenAI message format + assert isinstance(messages, list) + assert messages[0]["role"] == "system" + content = messages[0]["content"] + assert "MoveAhead" in content + assert "JSON" in content + + def test_build_step_prompt_returns_messages(self): + obs = Observation(rgb_path="/tmp/rgb.png") + builder = DefaultPromptBuilder() + messages = builder.build_step_prompt( + observation=obs, + task_description="Go to the kitchen.", + action_history=[], + ) + # Decision #10: returns list of OpenAI message dicts + assert isinstance(messages, list) + assert messages[0]["role"] == "user" + # Content should be a list with text + image parts + content = messages[0]["content"] + assert isinstance(content, list) + text_parts = [p for p in content if p["type"] == "text"] + assert len(text_parts) >= 1 + + def test_build_step_prompt_includes_image(self, tmp_path): + # Create a real image file + img_path = tmp_path / "test.png" + img_path.write_bytes(b'\x89PNG\r\n\x1a\n' + b'\x00' * 100) + obs = Observation(rgb_path=str(img_path)) + builder = DefaultPromptBuilder() + messages = builder.build_step_prompt( + observation=obs, + task_description="Go to the kitchen.", + action_history=[], + ) + content = messages[0]["content"] + image_parts = [p for p in content if p["type"] == "image_url"] + assert len(image_parts) == 1 + assert image_parts[0]["image_url"]["url"].startswith("data:image/png;base64,") + + def test_build_step_prompt_with_history(self): + obs = Observation(rgb_path="/tmp/rgb.png") + builder = DefaultPromptBuilder() + messages = builder.build_step_prompt( + observation=obs, + task_description="Go to the kitchen.", + action_history=[("MoveAhead", "success"), ("TurnLeft", "failed")], + ) + text_content = "" + for part in messages[0]["content"]: + if part["type"] == "text": + text_content += part["text"] + assert "MoveAhead" in text_content + assert "TurnLeft" in text_content + + +class CustomPromptBuilder: + """A custom prompt builder for testing the delegation pattern.""" + def build_system_prompt(self, action_space, task_description): + return [{"role": "system", "content": f"CUSTOM SYSTEM: {task_description}"}] + + def build_step_prompt(self, observation, task_description, action_history): + return [{"role": "user", "content": f"CUSTOM STEP: history_len={len(action_history)}"}] + + +class TestReActAgent: + @pytest.fixture + def agent(self): + llm = MockLLMClient([{"action": "MoveAhead"}]) + return ReActAgent( + llm_client=llm, + action_space=["MoveAhead", "TurnLeft", "TurnRight", "Stop"], + ) + + @pytest.fixture + def multi_action_agent(self): + """Agent whose LLM returns a multi-action plan.""" + llm = MockLLMClient([ + {"action": "MoveAhead"}, + {"action": "TurnLeft"}, + {"action": "MoveAhead"}, + ]) + return ReActAgent( + llm_client=llm, + action_space=["MoveAhead", "TurnLeft", "TurnRight", "Stop"], + ) + + @pytest.fixture + def custom_agent(self): + llm = MockLLMClient([{"action": "MoveAhead"}]) + return ReActAgent( + llm_client=llm, + action_space=["MoveAhead", "TurnLeft", "TurnRight", "Stop"], + prompt_builder=CustomPromptBuilder(), + ) + + def test_act_returns_action(self, agent): + obs = Observation(rgb_path="/tmp/rgb.png") + action = agent.act(obs, "Go to the goal.") + assert action.action_name == "MoveAhead" + + def test_calls_llm_once(self, agent): + obs = Observation(rgb_path="/tmp/rgb.png") + agent.act(obs, "Go to the goal.") + assert agent.llm_client.call_count == 1 + + def test_chat_history_has_system_prompt(self, agent): + obs = Observation(rgb_path="/tmp/rgb.png") + agent.act(obs, "Go to the goal.") + history = agent.chat_history + assert history[0]["role"] == "system" + + def test_default_prompt_builder_used(self, agent): + assert isinstance(agent.prompt_builder, DefaultPromptBuilder) + + def test_custom_prompt_builder(self, custom_agent): + obs = Observation(rgb_path="/tmp/rgb.png") + custom_agent.act(obs, "Go to the goal.") + history = custom_agent.chat_history + assert history[0]["role"] == "system" + assert "CUSTOM SYSTEM:" in history[0]["content"] + + # --- Decision #9: Action buffering tests --- + + def test_multi_action_buffer(self, multi_action_agent): + """LLM returns 3 actions; first act() returns first, subsequent act()s + return buffered actions WITHOUT calling LLM again.""" + obs = Observation(rgb_path="/tmp/rgb.png") + + a1 = multi_action_agent.act(obs, "Go to the goal.") + assert a1.action_name == "MoveAhead" + assert multi_action_agent.llm_client.call_count == 1 # LLM called + + multi_action_agent.add_feedback("MoveAhead", "success") + a2 = multi_action_agent.act(obs, "Go to the goal.") + assert a2.action_name == "TurnLeft" + assert multi_action_agent.llm_client.call_count == 1 # NOT called again + + multi_action_agent.add_feedback("TurnLeft", "success") + a3 = multi_action_agent.act(obs, "Go to the goal.") + assert a3.action_name == "MoveAhead" + assert multi_action_agent.llm_client.call_count == 1 # Still NOT called + + def test_buffer_cleared_on_failure(self, multi_action_agent): + """When add_feedback reports failure, buffer is cleared. + Next act() re-queries LLM.""" + obs = Observation(rgb_path="/tmp/rgb.png") + + multi_action_agent.act(obs, "Go to the goal.") # returns MoveAhead, buffers [TurnLeft, MoveAhead] + assert multi_action_agent.llm_client.call_count == 1 + + multi_action_agent.add_feedback("MoveAhead", "failed: obstacle ahead") + # Buffer should be cleared + + multi_action_agent.act(obs, "Go to the goal.") # should re-query LLM + assert multi_action_agent.llm_client.call_count == 2 + + def test_buffer_empty_after_all_consumed(self, multi_action_agent): + """After all buffered actions consumed, next act() queries LLM.""" + obs = Observation(rgb_path="/tmp/rgb.png") + + multi_action_agent.act(obs, "Go to the goal.") + multi_action_agent.add_feedback("MoveAhead", "success") + multi_action_agent.act(obs, "Go to the goal.") + multi_action_agent.add_feedback("TurnLeft", "success") + multi_action_agent.act(obs, "Go to the goal.") + multi_action_agent.add_feedback("MoveAhead", "success") + assert multi_action_agent.llm_client.call_count == 1 + + # Buffer exhausted — next call should query LLM + multi_action_agent.act(obs, "Go to the goal.") + assert multi_action_agent.llm_client.call_count == 2 + + def test_parse_error_returns_stop(self): + """When LLM returns invalid JSON, agent returns Stop (no buffering).""" + llm = type('MockLLM', (), {'generate': lambda self, m: 'not json at all'})() + agent = ReActAgent( + llm_client=llm, + action_space=["MoveAhead", "TurnLeft", "TurnRight", "Stop"], + ) + obs = Observation(rgb_path="/tmp/rgb.png") + action = agent.act(obs, "Go to the goal.") + assert action.action_name == "Stop" + assert not agent._action_buffer # buffer empty + + def test_invalid_action_name_fallback(self): + """When LLM returns action not in action_space, fallback to Stop.""" + llm = MockLLMClient([{"action": "FlyToMoon"}]) + agent = ReActAgent( + llm_client=llm, + action_space=["MoveAhead", "TurnLeft", "TurnRight", "Stop"], + ) + obs = Observation(rgb_path="/tmp/rgb.png") + action = agent.act(obs, "Go to the goal.") + assert action.action_name == "Stop" + + def test_reset_clears_buffer_and_history(self, multi_action_agent): + obs = Observation(rgb_path="/tmp/rgb.png") + multi_action_agent.act(obs, "Go to the goal.") + multi_action_agent.reset() + assert len(multi_action_agent.chat_history) == 0 + assert len(multi_action_agent._action_buffer) == 0 + assert len(multi_action_agent._action_feedback) == 0 From 091d38c037fb179c42d018e9b384652283b75853 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Fri, 13 Feb 2026 03:10:17 +0800 Subject: [PATCH 003/239] [Refactor] Separate generic AI2-THOR bridge from EB-Alfred task logic Split the monolithic bridge into a generic AI2ThorBridge base class (simulator layer) and an EBAlfredBridge subclass (task layer), so that future benchmarks using ai2thor==2.1.0 can reuse the simulator bridge without rewriting controller management, IPC, or navigation helpers. - Extract EB-Alfred goal evaluation and task loading into easi/tasks/ebalfred/thor_utils.py - Trim easi/simulators/ai2thor/v2_1_0/thor_utils.py to generic-only constants and object query utilities - Refactor bridge.py from EBAlfredBridge (1062 lines) to generic AI2ThorBridge (~314 lines) with configurable simulator_kwargs - Create easi/tasks/ebalfred/bridge.py with EBAlfredBridge subclass containing all skill execution, state tracking, and goal evaluation - Add get_bridge_script_path() and simulator_kwargs to BaseTask and TaskProtocol; override in EBAlfredTask - Update EvaluationRunner to prefer task-specific bridge paths and forward simulator_kwargs - Add simulator_kwargs to all EB-Alfred YAML configs - Add 29 tests covering imports, inheritance, method separation, bridge path resolution, and simulator_kwargs --- easi/core/base_task.py | 13 + easi/core/protocols.py | 3 + easi/evaluation/runner.py | 20 +- easi/simulators/ai2thor/v2_1_0/bridge.py | 978 +++--------------- easi/simulators/ai2thor/v2_1_0/thor_utils.py | 287 +---- easi/tasks/ebalfred/bridge.py | 873 ++++++++++++++++ easi/tasks/ebalfred/ebalfred_base.yaml | 4 + .../tasks/ebalfred/ebalfred_common_sense.yaml | 4 + .../ebalfred_complex_instruction.yaml | 4 + .../tasks/ebalfred/ebalfred_long_horizon.yaml | 4 + easi/tasks/ebalfred/ebalfred_spatial.yaml | 4 + .../ebalfred/ebalfred_visual_appearance.yaml | 4 + easi/tasks/ebalfred/task.py | 4 + easi/tasks/ebalfred/thor_utils.py | 303 ++++++ tests/test_bridge_refactor.py | 320 ++++++ 15 files changed, 1680 insertions(+), 1145 deletions(-) create mode 100644 easi/tasks/ebalfred/bridge.py create mode 100644 easi/tasks/ebalfred/thor_utils.py create mode 100644 tests/test_bridge_refactor.py diff --git a/easi/core/base_task.py b/easi/core/base_task.py index 6216dfc..41e5691 100644 --- a/easi/core/base_task.py +++ b/easi/core/base_task.py @@ -76,6 +76,19 @@ def evaluate_episode( # --- Shared implementation --- + def get_bridge_script_path(self) -> Path | None: + """Return task-specific bridge script path, or None for simulator default. + + Override in subclasses to provide a task-specific bridge that extends + the generic simulator bridge (e.g., EBAlfredBridge extends AI2ThorBridge). + """ + return None + + @property + def simulator_kwargs(self) -> dict: + """Simulator configuration from task YAML (passed to bridge via CLI args).""" + return self._config.get("simulator_kwargs", {}) + def get_instruction(self, episode: dict) -> str: """Return human-readable task instruction for this episode. diff --git a/easi/core/protocols.py b/easi/core/protocols.py index 98ab994..77adc5a 100644 --- a/easi/core/protocols.py +++ b/easi/core/protocols.py @@ -70,4 +70,7 @@ def format_reset_config(self, episode: dict) -> dict: ... def evaluate_episode( self, episode: dict, trajectory: list[StepResult] ) -> dict[str, float]: ... + def get_bridge_script_path(self) -> Path | None: ... + @property + def simulator_kwargs(self) -> dict: ... def __len__(self) -> int: ... diff --git a/easi/evaluation/runner.py b/easi/evaluation/runner.py index 1bee48f..7bb10d2 100644 --- a/easi/evaluation/runner.py +++ b/easi/evaluation/runner.py @@ -89,7 +89,7 @@ def run(self, max_episodes: int | None = None) -> list[dict]: agent = self._create_agent(task.action_space, task._config) # 3. Start simulator - sim, runner = self._create_simulator(task.simulator_key) + sim, runner = self._create_simulator(task.simulator_key, task=task) all_results = [] try: @@ -244,7 +244,9 @@ def _create_agent(self, action_space: list[str], task_config: dict): else: raise ValueError(f"Unknown agent type: {self.agent_type}") - def _create_simulator(self, simulator_key: str): + def _create_simulator(self, simulator_key: str, task=None): + import json as _json + from easi.simulators.registry import ( load_env_manager_class, load_simulator_class, @@ -257,12 +259,22 @@ def _create_simulator(self, simulator_key: str): env_manager = EnvManagerClass() sim = SimClass() + # Task-specific bridge overrides simulator default + bridge_path = ( + (task.get_bridge_script_path() if task else None) + or sim._get_bridge_script_path() + ) + + extra_args = ["--data-dir", str(self.data_dir)] + if task and task.simulator_kwargs: + extra_args.extend(["--simulator-kwargs", _json.dumps(task.simulator_kwargs)]) + runner = SubprocessRunner( python_executable=env_manager.get_python_executable(), - bridge_script_path=sim._get_bridge_script_path(), + bridge_script_path=bridge_path, needs_display=env_manager.needs_display, xvfb_screen_config=env_manager.xvfb_screen_config, - extra_args=["--data-dir", str(self.data_dir)], + extra_args=extra_args, ) runner.launch() sim.set_runner(runner) diff --git a/easi/simulators/ai2thor/v2_1_0/bridge.py b/easi/simulators/ai2thor/v2_1_0/bridge.py index 15d789d..2bb9788 100644 --- a/easi/simulators/ai2thor/v2_1_0/bridge.py +++ b/easi/simulators/ai2thor/v2_1_0/bridge.py @@ -1,28 +1,40 @@ -"""Bridge subprocess for AI2-THOR v2.1.0 (EB-Alfred track). +"""Generic bridge subprocess for AI2-THOR v2.1.0. This script runs inside the easi_ai2thor_v2_1_0 conda env (Python 3.8). It communicates with the parent process via filesystem IPC. -Ported from EmbodiedBench: -- envs/eb_alfred/thor_connector.py (high-level skill API) -- envs/eb_alfred/env/thor_env.py (controller init, scene restoration, state tracking) +Provides a generic AI2ThorBridge that handles: +- Controller startup/shutdown +- Scene reset with configurable parameters +- Raw THOR action execution +- Observation capture (RGB frames) +- Main IPC loop + +Task-specific bridges (e.g., EBAlfredBridge) subclass this and override +reset() and step() for skill-based execution and goal evaluation. Usage: - python bridge.py --workspace /tmp/easi_xxx --data-dir /path/to/datasets + python bridge.py --workspace /tmp/easi_xxx [--data-dir /path/to/datasets] [--simulator-kwargs '{}'] """ from __future__ import annotations import argparse +import json import logging import math import os -import re import sys from pathlib import Path -import numpy as np -from scipy import spatial +# numpy and scipy are available in the ai2thor conda env (Python 3.8) +# but may not be in the host venv — imports are optional for importability +try: + import numpy as np + from scipy import spatial +except ImportError: + np = None # type: ignore[assignment] + spatial = None # type: ignore[assignment] # Add repo root to path for easi imports _repo_root = Path(__file__).resolve().parents[4] @@ -39,8 +51,6 @@ make_observation_response, ) from easi.simulators.ai2thor.v2_1_0.thor_utils import ( - AGENT_HORIZON_ADJ, - AGENT_ROTATE_ADJ, AGENT_STEP_SIZE, CAMERA_HEIGHT_OFFSET, RECORD_SMOOTHING_FACTOR, @@ -51,35 +61,25 @@ SCREEN_HEIGHT, SCREEN_WIDTH, VISIBILITY_DISTANCE, - evaluate_goal_conditions, - get_objects_of_type, - get_obj_of_type_closest_to_obj, - load_task_json_with_repeat, - natural_word_to_ithor_name, ) logger = logging.getLogger("easi.bridge.ai2thor_v2_1_0") -class EBAlfredBridge: - """EB-Alfred bridge managing AI2-THOR controller and high-level skills. +class AI2ThorBridge: + """Generic AI2-THOR bridge managing controller lifecycle and raw actions. - Ported from EmbodiedBench's ThorEnv + ThorConnector. + Subclass this for task-specific behavior (skill execution, goal evaluation). """ - def __init__(self, workspace, data_dir): + def __init__(self, workspace, data_dir=None, simulator_kwargs=None): self.workspace = Path(workspace) - self.data_dir = Path(data_dir) + self.data_dir = Path(data_dir) if data_dir else None + self.simulator_kwargs = simulator_kwargs or {} self.controller = None self.last_event = None - self.traj_data = None self.step_count = 0 - # State tracking (from EmbodiedBench ThorEnv) - self.cleaned_objects = set() - self.cooled_objects = set() - self.heated_objects = set() - # Navigation cache self.reachable_positions = None self.reachable_position_kdtree = None @@ -87,24 +87,23 @@ def __init__(self, workspace, data_dir): # Agent height (set after reset from init_action) self.agent_height = 0.9009992 - # Skill state - self.cur_receptacle = None - self.put_count_dict = {} - self.sliced = False - # Output directory for saving images (set per-episode from reset command) self.episode_output_dir = None def start(self): - """Initialize AI2-THOR controller.""" + """Initialize AI2-THOR controller with configurable parameters.""" from ai2thor.controller import Controller + quality = self.simulator_kwargs.get("quality", "MediumCloseFitShadows") + screen_h = self.simulator_kwargs.get("screen_height", SCREEN_HEIGHT) + screen_w = self.simulator_kwargs.get("screen_width", SCREEN_WIDTH) + logger.info("Starting AI2-THOR controller...") - self.controller = Controller(quality="MediumCloseFitShadows") + self.controller = Controller(quality=quality) self.controller.start( x_display=os.environ.get("DISPLAY", "0"), - player_screen_height=SCREEN_HEIGHT, - player_screen_width=SCREEN_WIDTH, + player_screen_height=screen_h, + player_screen_width=screen_w, ) logger.info("AI2-THOR controller started.") @@ -125,68 +124,15 @@ def _step(self, action_dict): # --- Reset --- def reset(self, reset_config): - """Reset to an EB-Alfred episode (or a default scene for smoke tests). + """Reset to a default scene for smoke tests. - If task_path is provided, loads traj_data.json, resets scene, - restores objects, and teleports agent. - If task_path is missing (smoke test), resets to a default scene. + Override in subclasses for task-specific resets (episode loading, etc.). """ - task_path = reset_config.get("task_path") - - if not task_path: - # Smoke test mode: reset to a default scene without episode data - return self._reset_default_scene() - - repeat_idx = reset_config.get("repeat_idx", 0) - - self.traj_data = load_task_json_with_repeat( - task_path, repeat_idx, str(self.data_dir) - ) - self.cleaned_objects.clear() - self.cooled_objects.clear() - self.heated_objects.clear() self.step_count = 0 - self.cur_receptacle = None - self.put_count_dict = {} - self.sliced = False - - # Reset scene - scene_num = self.traj_data["scene"]["scene_num"] - scene_name = "FloorPlan%d" % scene_num - logger.info("Resetting to scene: %s", scene_name) - self.controller.reset(scene_name) - - # Restore object poses, toggles, dirty states - self._restore_scene() - - # Teleport agent to initial position - init_action = dict(self.traj_data["scene"]["init_action"]) - if init_action.get("action") == "TeleportFull": - init_action.pop("rotateOnTeleport", None) - init_action["standing"] = True - self.last_event = self.controller.step(init_action) + scene = reset_config.get("scene", "FloorPlan10") - # Cache agent height - self.agent_height = self.last_event.metadata["agent"]["position"]["y"] - - # Cache reachable positions for navigation - self._cache_reachable_positions() - - return self._make_observation_response() - - def _reset_default_scene(self): - """Reset to FloorPlan10 for smoke testing (no episode data needed).""" - logger.info("Smoke test mode: resetting to default scene FloorPlan10") - self.traj_data = None - self.cleaned_objects.clear() - self.cooled_objects.clear() - self.heated_objects.clear() - self.step_count = 0 - self.cur_receptacle = None - self.put_count_dict = {} - self.sliced = False - - self.controller.reset("FloorPlan10") + logger.info("Resetting to scene: %s", scene) + self.controller.reset(scene) self.last_event = self.controller.step(dict( action="Initialize", gridSize=AGENT_STEP_SIZE / RECORD_SMOOTHING_FACTOR, @@ -202,728 +148,96 @@ def _reset_default_scene(self): self._cache_reachable_positions() return self._make_observation_response() - def _restore_scene(self): - """Restore EB-Alfred scene state from traj_data. - - Ported from EmbodiedBench ThorEnv.restore_scene(). - """ - grid_size = AGENT_STEP_SIZE / RECORD_SMOOTHING_FACTOR - - self._step(dict( - action="Initialize", - gridSize=grid_size, - cameraY=CAMERA_HEIGHT_OFFSET, - renderImage=RENDER_IMAGE, - renderDepthImage=RENDER_DEPTH_IMAGE, - renderClassImage=RENDER_CLASS_IMAGE, - renderObjectImage=RENDER_OBJECT_IMAGE, - visibility_distance=VISIBILITY_DISTANCE, - makeAgentsVisible=False, - )) - - scene = self.traj_data["scene"] - object_toggles = scene.get("object_toggles", []) - object_poses = scene.get("object_poses", []) - dirty_and_empty = scene.get("dirty_and_empty", False) - - if len(object_toggles) > 0: - self._step(dict(action="SetObjectToggles", objectToggles=object_toggles)) - - if dirty_and_empty: - self._step(dict( - action="SetStateOfAllObjects", - StateChange="CanBeDirty", - forceAction=True, - )) - self._step(dict( - action="SetStateOfAllObjects", - StateChange="CanBeFilled", - forceAction=False, - )) - - if len(object_poses) > 0: - self._step(dict(action="SetObjectPoses", objectPoses=object_poses)) - - def _cache_reachable_positions(self): - """Cache reachable positions + KD-tree for navigation.""" - event = self._step(dict(action="GetReachablePositions")) - free_positions = event.metadata["actionReturn"] - self.reachable_positions = np.array( - [[p["x"], p["y"], p["z"]] for p in free_positions] - ) - self.reachable_position_kdtree = spatial.KDTree(self.reachable_positions) - # --- Step --- def step(self, action_text): - """Execute a high-level skill and return observation + metrics.""" - self.step_count += 1 - - # Smoke test mode: execute raw THOR action instead of high-level skill - if self.traj_data is None: - self._step(dict(action=action_text)) - return self._make_observation_response( - reward=0.0, - done=False, - info={ - "last_action_success": 1.0 if self.last_event.metadata["lastActionSuccess"] else 0.0, - "env_step": float(self.step_count), - "feedback": "success" if self.last_event.metadata["lastActionSuccess"] else "action failed", - }, - ) - - result = self._execute_skill(action_text) - success = result.get("success", False) - - # Update cleaned/cooled/heated tracking - self._update_states() - - # Evaluate goal conditions - goal_satisfied, progress = evaluate_goal_conditions( - self.traj_data, - self.last_event, - self.cleaned_objects, - self.cooled_objects, - self.heated_objects, - ) - - feedback = result.get("message", "") - if success: - feedback = "success" + """Execute a raw THOR action and return observation. + Override in subclasses for skill-based execution. + """ + self.step_count += 1 + self._step(dict(action=action_text)) return self._make_observation_response( - reward=float(progress), - done=goal_satisfied, + reward=0.0, + done=False, info={ - "task_success": 1.0 if goal_satisfied else 0.0, - "task_progress": progress, - "last_action_success": 1.0 if success else 0.0, + "last_action_success": 1.0 if self.last_event.metadata["lastActionSuccess"] else 0.0, "env_step": float(self.step_count), - "feedback": feedback, + "feedback": "success" if self.last_event.metadata["lastActionSuccess"] else "action failed", }, ) - def _execute_skill(self, instruction): - """Map high-level skill text to THOR API calls. - - Ported from EmbodiedBench ThorConnector.llm_skill_interact(). - """ - # Clear cur_receptacle unless put/open - if not (instruction.startswith("put down ") or instruction.startswith("open ")): - self.cur_receptacle = None - - ret_msg = "" - - if instruction.startswith("find "): - obj_name = instruction.replace("find a ", "").replace("find an ", "") - self.cur_receptacle = obj_name - ret_msg = self._nav_obj( - natural_word_to_ithor_name(obj_name), self.sliced - ) - - elif instruction.startswith("pick up "): - obj_name = instruction.replace("pick up the ", "") - ret_msg = self._pick(natural_word_to_ithor_name(obj_name)) - - elif instruction.startswith("put down "): - if self.cur_receptacle is None: - ret_msg = self._drop() - else: - receptacle = self.cur_receptacle - if receptacle in self.put_count_dict: - self.put_count_dict[receptacle] += 1 - else: - self.put_count_dict[receptacle] = 1 - - ret_msg = self._put(natural_word_to_ithor_name(receptacle)) - - if len(ret_msg) > 16 and self.put_count_dict[receptacle] >= 3: - self._drop() - ret_msg += ". The robot dropped the object instead." - self.last_event.metadata["lastActionSuccess"] = False - - elif instruction.startswith("open "): - obj_name = instruction.replace("open the ", "") - ret_msg = self._open(natural_word_to_ithor_name(obj_name)) - - elif instruction.startswith("close "): - obj_name = instruction.replace("close the ", "") - ret_msg = self._close(natural_word_to_ithor_name(obj_name)) - - elif instruction.startswith("turn on "): - obj_name = instruction.replace("turn on the ", "") - ret_msg = self._toggleon(natural_word_to_ithor_name(obj_name)) - - elif instruction.startswith("turn off "): - obj_name = instruction.replace("turn off the ", "") - ret_msg = self._toggleoff(natural_word_to_ithor_name(obj_name)) - - elif instruction.startswith("slice "): - obj_name = instruction.replace("slice the ", "") - ret_msg = self._slice(natural_word_to_ithor_name(obj_name)) - self.sliced = True - - elif instruction.startswith("drop"): - ret_msg = self._drop() - - else: - ret_msg = "instruction not supported" - - success = len(ret_msg) == 0 - if not success: - logger.warning("Skill failed: %s -> %s", instruction, ret_msg) - - return {"action": instruction, "success": success, "message": ret_msg} - - # --- State Tracking --- - - def _update_states(self): - """Track cleaned/cooled/heated objects. - - Ported from EmbodiedBench ThorEnv.update_states(). - """ - if self.last_event is None: - return - - metadata = self.last_event.metadata - if not metadata.get("lastActionSuccess", False): - return - - # Check if last action was a relevant toggle/close - last_action = metadata.get("lastAction", "") - last_obj_id = metadata.get("lastActionObjectId", "") + # --- Main IPC Loop --- - # Clean: ToggleObjectOn on Faucet -> items in SinkBasin get cleaned - if last_action == "ToggleObjectOn" and "Faucet" in last_obj_id: - sink_basin = get_obj_of_type_closest_to_obj( - "SinkBasin", last_obj_id, metadata - ) - if sink_basin and sink_basin.get("receptacleObjectIds"): - self.cleaned_objects.update(sink_basin["receptacleObjectIds"]) + def run(self): + """Main bridge loop — subclasses inherit automatically.""" + logger.info("AI2-THOR bridge starting (workspace: %s)", self.workspace) + self.start() - # Heat: ToggleObjectOn on Microwave -> items inside get heated - if last_action == "ToggleObjectOn" and "Microwave" in last_obj_id: - microwaves = get_objects_of_type("Microwave", metadata) - if microwaves: - heated_ids = microwaves[0].get("receptacleObjectIds") - if heated_ids: - self.heated_objects.update(heated_ids) + write_status(self.workspace, ready=True) - # Cool: CloseObject on Fridge -> items inside get cooled - if last_action == "CloseObject" and "Fridge" in last_obj_id: - fridges = get_objects_of_type("Fridge", metadata) - if fridges: - cooled_ids = fridges[0].get("receptacleObjectIds") - if cooled_ids: - self.cooled_objects.update(cooled_ids) - - # --- Individual Skill Implementations --- - - def _nav_obj(self, target_obj, prefer_sliced=False): - """Navigate to an object by teleporting to closest reachable position. - - Ported from EmbodiedBench ThorConnector.nav_obj(). - """ - objects = self.last_event.metadata["objects"] - logger.info("nav_obj: %s", target_obj) - - # Resolve object ID - if "|" in target_obj: - obj_id = target_obj - base_name = target_obj.split("|")[0] - tmp_id, tmp_data = self._get_obj_id_from_name( - base_name, priority_in_visibility=True, priority_sliced=prefer_sliced - ) - if tmp_id and "Sliced" in tmp_id and obj_id in tmp_id: - obj_id = tmp_id - else: - obj_id, _ = self._get_obj_id_from_name( - target_obj, priority_in_visibility=True, priority_sliced=prefer_sliced - ) - - # Find object index - obj_idx = -1 - for i, o in enumerate(objects): - if o["objectId"] == obj_id: - obj_idx = i - break - - if obj_idx == -1: - return ( - "Cannot find %s. This object may not exist in this scene. " - "Try to explore other instances instead." % target_obj - ) - - # Teleport to closest reachable position facing the object - loc = objects[obj_idx]["position"] - obj_rot = objects[obj_idx]["rotation"]["y"] - max_attempts = 20 - teleport_success = False - reachable_pos_idx = 0 - - for i in range(max_attempts): - reachable_pos_idx += 1 - if i == 10 and target_obj in ("Fridge", "Microwave"): - reachable_pos_idx -= 10 - - closest_loc = self._find_close_reachable_position( - [loc["x"], loc["y"], loc["z"]], reachable_pos_idx - ) - if closest_loc is None: - continue - - # Calculate desired rotation angle - rot_angle = math.atan2( - -(loc["x"] - closest_loc[0]), loc["z"] - closest_loc[2] - ) - if rot_angle > 0: - rot_angle -= 2 * math.pi - rot_angle = -(180 / math.pi) * rot_angle - - # Special angle filtering for Fridge/Microwave - if i < 10 and target_obj in ("Fridge", "Microwave"): - angle_d = abs(self._angle_diff(rot_angle, obj_rot)) - if target_obj == "Fridge" and not ( - (70 < angle_d < 110) or (250 < angle_d < 290) - ): - continue - if target_obj == "Microwave" and not ( - (160 < angle_d < 200) or (0 <= angle_d < 20) - ): - continue - - # Calculate horizon angle - camera_height = self.agent_height + CAMERA_HEIGHT_OFFSET - xz_dist = math.hypot( - loc["x"] - closest_loc[0], loc["z"] - closest_loc[2] - ) - hor_angle = math.atan2(loc["y"] - camera_height, xz_dist) - hor_angle = (180 / math.pi) * hor_angle * 0.9 - - self._step(dict( - action="TeleportFull", - x=closest_loc[0], - y=self.agent_height, - z=closest_loc[2], - rotation=rot_angle, - horizon=-hor_angle, - )) - - if self.last_event.metadata["lastActionSuccess"]: - teleport_success = True + while True: + try: + command = poll_for_command(self.workspace, timeout=300.0) + except Exception as e: + logger.error("Failed to read command: %s", e) break - else: - logger.debug( - "TeleportFull failed: %s", - self.last_event.metadata.get("errorMessage", ""), - ) - - if not teleport_success: - return "Cannot move to %s" % target_obj - - return "" - - def _pick(self, obj_name): - """Pick up an object. - - Ported from EmbodiedBench ThorConnector.pick(). - """ - obj_id, obj_data = self._get_obj_id_from_name( - obj_name, only_pickupable=True, priority_in_visibility=True, - priority_sliced=self.sliced, - ) - logger.info("pick: %s -> %s", obj_name, obj_id) - if obj_id is None: - return "Cannot find %s to pick up. Find the object before picking up it" % obj_name + cmd_type = command.get("type") - if ( - obj_data.get("visible") is False - and obj_data.get("parentReceptacles") - and len(obj_data["parentReceptacles"]) > 0 - ): - recep_name = obj_data["parentReceptacles"][0] - ret_msg = ( - "%s is not visible because it is in %s. " - "Note: multiple instances of %s may exist" - % (obj_name, recep_name, recep_name) - ) - # Try anyway - self._step(dict( - action="PickupObject", objectId=obj_id, forceAction=False - )) - else: - self._step(dict( - action="PickupObject", objectId=obj_id, forceAction=False - )) - ret_msg = "" + if cmd_type == "reset": + episode_id = command.get("episode_id", "unknown") + reset_config = command.get("reset_config", {}) + logger.info("Reset: episode_id=%s", episode_id) - if not self.last_event.metadata["lastActionSuccess"]: - inventory = self.last_event.metadata.get("inventoryObjects", []) - if len(inventory) == 0: - ret_msg = "Robot is not holding any object" + # Set episode output directory for image saving + raw_output_dir = command.get("episode_output_dir") + if raw_output_dir: + self.episode_output_dir = raw_output_dir + Path(raw_output_dir).mkdir(parents=True, exist_ok=True) else: - holding_type = inventory[0].get("objectType", "unknown") - ret_msg = "Robot is currently holding %s" % holding_type - - if self.last_event.metadata["lastActionSuccess"]: - ret_msg = "" - - return ret_msg - - def _put(self, receptacle_name): - """Put held object onto a receptacle. - - Ported from EmbodiedBench ThorConnector.put(). - Uses 2x7x2 retry loop with movement adjustments. - """ - inventory = self.last_event.metadata.get("inventoryObjects", []) - if len(inventory) == 0: - return "Robot is not holding any object" - - holding_obj_id = inventory[0]["objectId"] - orig_receptacle_name = receptacle_name - ret_msg = "" - halt = False - last_recep_id = None - exclude_obj_id = None - - for k in range(2): # try closest and next closest receptacle - for j in range(7): # movement/look adjustments - for i in range(2): # try inherited receptacles (SinkBasin etc.) - if k == 1 and exclude_obj_id is None: - exclude_obj_id = last_recep_id - - if k == 0 and "|" in orig_receptacle_name: - if i == 1: - continue - recep_id = orig_receptacle_name - receptacle_name = orig_receptacle_name.split("|")[0] - else: - if "Sink" in receptacle_name or "Bathtub" in receptacle_name: - if i == 0: - recep_id, _ = self._get_obj_id_from_name( - receptacle_name, get_inherited=True, - exclude_obj_id=exclude_obj_id, - ) - else: - recep_id, _ = self._get_obj_id_from_name( - receptacle_name, exclude_obj_id=exclude_obj_id, - ) - else: - if i == 0: - recep_id, _ = self._get_obj_id_from_name( - receptacle_name, exclude_obj_id=exclude_obj_id, - ) - else: - recep_id, _ = self._get_obj_id_from_name( - receptacle_name, get_inherited=True, - exclude_obj_id=exclude_obj_id, - ) - - if not recep_id: - ret_msg = ( - "Putting the object on %s failed. First check whether " - "the receptacle is open or not. Also try other instances " - "of the receptacle" % receptacle_name - ) - continue - - # Movement adjustments to get receptacle in view - if j == 1: - self._step(dict(action="LookUp")) - self._step(dict(action="LookUp")) - elif j == 2: - self._step(dict(action="LookDown")) - self._step(dict(action="LookDown")) - self._step(dict(action="LookDown")) - self._step(dict(action="LookDown")) - elif j == 3: - self._step(dict(action="LookUp")) - self._step(dict(action="LookUp")) - self._step(dict(action="MoveBack")) - elif j == 4: - self._step(dict(action="MoveAhead")) - for _ in range(4): - self._step(dict(action="MoveRight")) - elif j == 5: - for _ in range(8): - self._step(dict(action="MoveLeft")) - elif j == 6: - for _ in range(4): - self._step(dict(action="MoveRight")) - self._step(dict(action="RotateHand", x=40)) - - self._step(dict( - action="PutObject", - objectId=holding_obj_id, - receptacleObjectId=recep_id, - forceAction=True, - )) - last_recep_id = recep_id - - if self.last_event.metadata["lastActionSuccess"]: - ret_msg = "" - halt = True - break - else: - logger.debug( - "PutObject failed: %s", - self.last_event.metadata.get("errorMessage", ""), - ) - ret_msg = ( - "Putting the object on %s failed. First check the " - "receptacle is open or not. Also try other instances " - "of the receptacle" % receptacle_name - ) - if halt: - break - if halt: - break - - return ret_msg - - def _open(self, obj_name): - """Open a receptacle. - - Ported from EmbodiedBench ThorConnector.open(). - """ - logger.info("open: %s", obj_name) - - if "|" in obj_name: - obj_id = obj_name - obj_name = obj_name.split("|")[0] - else: - obj_id, _ = self._get_obj_id_from_name(obj_name) - - if obj_id is None: - return "Cannot find %s to open. Find the object before opening it" % obj_name - - # Check if already open - open_flag = False - for ob in self.last_event.metadata["objects"]: - if ob["objectId"] == obj_id and ob.get("openable") and ob.get("isOpen"): - open_flag = True + self.episode_output_dir = None + + try: + response = self.reset(reset_config) + write_response(self.workspace, response) + except Exception as e: + logger.exception("Reset failed") + write_response(self.workspace, make_error_response(str(e))) + + elif cmd_type == "step": + action_data = command.get("action", {}) + action_text = action_data.get("action_name", "") + logger.debug("Step %d: action=%s", self.step_count + 1, action_text) + + try: + response = self.step(action_text) + write_response(self.workspace, response) + except Exception as e: + logger.exception("Step failed") + write_response(self.workspace, make_error_response(str(e))) + + elif cmd_type == "close": + logger.info("Close command received") + self.stop() + write_response(self.workspace, {"status": "ok"}) break - ret_msg = "" - for i in range(4): - self._step(dict(action="OpenObject", objectId=obj_id)) - - if self.last_event.metadata["lastActionSuccess"]: - ret_msg = "" - break else: - if open_flag: - ret_msg = "Open action failed. The %s is already open" % obj_name - else: - ret_msg = "Open action failed." - - # Move around to avoid self-collision - if i == 0: - self._step(dict(action="MoveBack")) - elif i == 1: - self._step(dict(action="MoveBack")) - self._step(dict(action="MoveRight")) - elif i == 2: - self._step(dict(action="MoveLeft")) - self._step(dict(action="MoveLeft")) - - return ret_msg - - def _close(self, obj_name): - """Close a receptacle. - - Ported from EmbodiedBench ThorConnector.close(). - """ - logger.info("close: %s", obj_name) - - if "|" in obj_name: - obj_id = obj_name - obj_name = obj_name.split("|")[0] - else: - obj_id, _ = self._get_obj_id_from_name(obj_name) - - if obj_id is None: - return "Cannot find %s to close" % obj_name - - self._step(dict(action="CloseObject", objectId=obj_id)) - - if not self.last_event.metadata["lastActionSuccess"]: - ret_msg = "Close action failed" - for ob in self.last_event.metadata["objects"]: - if ( - ob["objectId"] == obj_id - and ob.get("openable") - and not ob.get("isOpen") - ): - ret_msg += ". The %s is already closed" % obj_name - break - return ret_msg - - return "" - - def _toggleon(self, obj_name): - """Toggle an object on. - - Ported from EmbodiedBench ThorConnector.toggleon(). - """ - logger.info("toggleon: %s", obj_name) - obj_id, _ = self._get_obj_id_from_name(obj_name, only_toggleable=True) - - if obj_id is None: - return "Cannot find %s to turn on" % obj_name - - try: - self._step(dict(action="ToggleObjectOn", objectId=obj_id)) - if not self.last_event.metadata["lastActionSuccess"]: - return "Turn on action failed" - except Exception: - self.last_event.metadata["lastActionSuccess"] = False - return "Turn on action failed" - - return "" - - def _toggleoff(self, obj_name): - """Toggle an object off. - - Ported from EmbodiedBench ThorConnector.toggleoff(). - """ - logger.info("toggleoff: %s", obj_name) - obj_id, _ = self._get_obj_id_from_name(obj_name, only_toggleable=True) - - if obj_id is None: - return "Cannot find %s to turn off" % obj_name - - self._step(dict(action="ToggleObjectOff", objectId=obj_id)) - - if not self.last_event.metadata["lastActionSuccess"]: - return "Turn off action failed" - - return "" - - def _slice(self, obj_name): - """Slice an object. - - Ported from EmbodiedBench ThorConnector.slice(). - """ - logger.info("slice: %s", obj_name) - obj_id, _ = self._get_obj_id_from_name(obj_name) - - if obj_id is None: - return "Cannot find %s to slice" % obj_name - - self._step(dict(action="SliceObject", objectId=obj_id)) - - if not self.last_event.metadata["lastActionSuccess"]: - return "Slice action failed" - - return "" - - def _drop(self): - """Drop the held object. - - Ported from EmbodiedBench ThorConnector.drop(). - """ - logger.info("drop") - self._step(dict(action="DropHandObject", forceAction=True)) - - if not self.last_event.metadata["lastActionSuccess"]: - inventory = self.last_event.metadata.get("inventoryObjects", []) - if len(inventory) == 0: - return "Robot is not holding any object" - return "Drop action failed" - - return "" - - # --- Object Lookup Helpers --- - - def _get_obj_id_from_name( - self, - obj_name, - only_pickupable=False, - only_toggleable=False, - priority_sliced=False, - get_inherited=False, - parent_receptacle_penalty=True, - priority_in_visibility=False, - exclude_obj_id=None, - ): - """Find the closest object matching name, with priority scoring. - - Ported from EmbodiedBench ThorConnector.get_obj_id_from_name(). - """ - obj_id = None - obj_data = None - min_distance = 1e8 - - # If name contains digits (e.g. 'Cabinet_2'), match by name directly - if any(c.isdigit() for c in obj_name): - for obj in self.last_event.metadata["objects"]: - if obj_name in obj["name"]: - return obj["objectId"], obj - return None, None - - for obj in self.last_event.metadata["objects"]: - if obj["objectId"] == exclude_obj_id: - continue - - if only_pickupable and not obj.get("pickupable", False): - continue - if only_toggleable and not obj.get("toggleable", False): - continue - - # Match object type (case-insensitive) - obj_type = obj["objectId"].split("|")[0] - if obj_type.casefold() != obj_name.casefold(): - continue - - # For inherited receptacles (e.g. SinkBasin), check ID format - if get_inherited and len(obj["objectId"].split("|")) != 5: - continue - if not get_inherited and len(obj["objectId"].split("|")) == 5: - # Skip inherited if not requested - pass - - distance = obj.get("distance", 1e8) - penalty = 0 - - # Penalize objects inside closed receptacles - if parent_receptacle_penalty and obj.get("parentReceptacles"): - for p in obj["parentReceptacles"]: - is_open = self._get_object_prop(p, "isOpen") - openable = self._get_object_prop(p, "openable") - if openable is True and is_open is False: - penalty += 100000 - break - - # Prefer empty stove burners - if obj_name.casefold() == "stoveburner": - recep_ids = obj.get("receptacleObjectIds") or [] - if len(recep_ids) > 0: - penalty += 10000 - - # Penalize non-visible objects - if priority_in_visibility and not obj.get("visible", False): - penalty += 1000 - - # Prefer sliced objects - if priority_sliced and "_Slice" in obj.get("name", ""): - penalty -= 100 - - if distance + penalty < min_distance: - min_distance = distance + penalty - obj_data = obj - obj_id = obj["objectId"] + write_response( + self.workspace, make_error_response("Unknown command: %s" % cmd_type) + ) - return obj_id, obj_data + # --- Navigation Helpers --- - def _get_object_prop(self, name, prop): - """Get a property of an object by name/ID substring.""" - for obj in self.last_event.metadata["objects"]: - if name in obj["objectId"]: - return obj.get(prop) - return None + def _cache_reachable_positions(self): + """Cache reachable positions + KD-tree for navigation.""" + event = self._step(dict(action="GetReachablePositions")) + free_positions = event.metadata["actionReturn"] + self.reachable_positions = np.array( + [[p["x"], p["y"], p["z"]] for p in free_positions] + ) + self.reachable_position_kdtree = spatial.KDTree(self.reachable_positions) def _find_close_reachable_position(self, loc, nth=1): """Find the nth closest reachable position to a location.""" @@ -979,82 +293,26 @@ def _make_observation_response(self, reward=0.0, done=False, info=None): ) -# --- Main bridge loop --- - -def run_bridge(workspace, data_dir): - """Main bridge loop for AI2-THOR v2.1.0.""" - bridge = EBAlfredBridge(workspace=workspace, data_dir=data_dir) - - logger.info("AI2-THOR v2.1.0 bridge starting (workspace: %s)", workspace) - bridge.start() - - write_status(workspace, ready=True) - - while True: - try: - command = poll_for_command(workspace, timeout=300.0) - except Exception as e: - logger.error("Failed to read command: %s", e) - break - - cmd_type = command.get("type") - - if cmd_type == "reset": - episode_id = command.get("episode_id", "unknown") - reset_config = command.get("reset_config", {}) - logger.info("Reset: episode_id=%s, task=%s", - episode_id, reset_config.get("task_path", "?")) - - # Set episode output directory for image saving - raw_output_dir = command.get("episode_output_dir") - if raw_output_dir: - bridge.episode_output_dir = raw_output_dir - Path(raw_output_dir).mkdir(parents=True, exist_ok=True) - else: - bridge.episode_output_dir = None - - try: - response = bridge.reset(reset_config) - write_response(workspace, response) - except Exception as e: - logger.exception("Reset failed") - write_response(workspace, make_error_response(str(e))) - - elif cmd_type == "step": - action_data = command.get("action", {}) - action_text = action_data.get("action_name", "") - logger.debug("Step %d: action=%s", bridge.step_count + 1, action_text) - - try: - response = bridge.step(action_text) - write_response(workspace, response) - except Exception as e: - logger.exception("Step failed") - write_response(workspace, make_error_response(str(e))) - - elif cmd_type == "close": - logger.info("Close command received") - bridge.stop() - write_response(workspace, {"status": "ok"}) - break - - else: - write_response( - workspace, make_error_response("Unknown command: %s" % cmd_type) - ) - - def main(): - parser = argparse.ArgumentParser(description="AI2-THOR v2.1.0 bridge (EB-Alfred)") + parser = argparse.ArgumentParser(description="AI2-THOR v2.1.0 generic bridge") parser.add_argument("--workspace", type=Path, required=True) - parser.add_argument("--data-dir", type=Path, default=Path("./datasets")) + parser.add_argument("--data-dir", type=Path, default=None) + parser.add_argument("--simulator-kwargs", type=str, default=None, + help="JSON string of simulator configuration") args, _ = parser.parse_known_args() logging.basicConfig( level=logging.DEBUG, format="%(asctime)s [%(levelname)s] %(name)s: %(message)s", ) - run_bridge(workspace=args.workspace, data_dir=args.data_dir) + + simulator_kwargs = json.loads(args.simulator_kwargs) if args.simulator_kwargs else {} + bridge = AI2ThorBridge( + workspace=args.workspace, + data_dir=args.data_dir, + simulator_kwargs=simulator_kwargs, + ) + bridge.run() if __name__ == "__main__": diff --git a/easi/simulators/ai2thor/v2_1_0/thor_utils.py b/easi/simulators/ai2thor/v2_1_0/thor_utils.py index e813272..c568e39 100644 --- a/easi/simulators/ai2thor/v2_1_0/thor_utils.py +++ b/easi/simulators/ai2thor/v2_1_0/thor_utils.py @@ -1,21 +1,19 @@ -"""Helper functions for the AI2-THOR v2.1.0 bridge (EB-Alfred track). +"""Generic helper functions for AI2-THOR v2.1.0 bridges. + +Constants and object query utilities shared across any benchmark +that uses AI2-THOR 2.1.0. Task-specific logic (goal evaluation, +dataset loading) lives in the task layer (e.g., easi/tasks/ebalfred/). Ported from EmbodiedBench: -- envs/eb_alfred/env/tasks.py (goal evaluation) -- envs/eb_alfred/utils.py (object name mapping, task loading) - envs/eb_alfred/gen/constants.py (THOR constants) +- envs/eb_alfred/utils.py (object name mapping) This file runs inside the ai2thor conda env (Python 3.8). """ from __future__ import annotations -import json import logging -import os import string -from pathlib import Path - -import numpy as np logger = logging.getLogger("easi.bridge.thor_utils") @@ -34,16 +32,6 @@ RENDER_CLASS_IMAGE = False RENDER_OBJECT_IMAGE = False -GOALS = [ - "pick_and_place_simple", - "pick_two_obj_and_place", - "look_at_obj_in_light", - "pick_clean_then_place_in_recep", - "pick_heat_then_place_in_recep", - "pick_cool_then_place_in_recep", - "pick_and_place_with_movable_recep", -] - # --- Object Name Mapping --- @@ -60,49 +48,6 @@ def natural_word_to_ithor_name(w: str) -> str: return "".join([string.capwords(x) for x in w.split()]) -# --- Task JSON Loading --- - -def load_task_json(task_path: str, data_dir: str) -> dict: - """Load preprocessed traj_data.json for an EB-Alfred episode. - - Args: - task_path: e.g. 'pick_and_place_simple-Mug-None-Shelf-1/trial_T20190001' - data_dir: path to the downloaded dataset directory - """ - # Try standard path: /tasks//pp/ann_0.json - # (repeat_idx is handled by the caller) - json_path = os.path.join(data_dir, "tasks", task_path, "pp", "ann_0.json") - if not os.path.exists(json_path): - # Fallback: try traj_data.json - json_path = os.path.join(data_dir, "tasks", task_path, "traj_data.json") - if not os.path.exists(json_path): - raise FileNotFoundError( - f"Task annotation not found at {json_path}. " - f"Ensure tasks.zip was extracted in {data_dir}" - ) - with open(json_path) as f: - data = json.load(f) - return data - - -def load_task_json_with_repeat(task_path: str, repeat_idx: int, data_dir: str) -> dict: - """Load the specific repeat annotation for an EB-Alfred episode.""" - json_path = os.path.join(data_dir, "tasks", task_path, "pp", f"ann_{repeat_idx}.json") - if not os.path.exists(json_path): - # Fallback to ann_0.json - json_path = os.path.join(data_dir, "tasks", task_path, "pp", "ann_0.json") - if not os.path.exists(json_path): - # Fallback to traj_data.json - json_path = os.path.join(data_dir, "tasks", task_path, "traj_data.json") - if not os.path.exists(json_path): - raise FileNotFoundError( - f"Task annotation not found for {task_path} repeat {repeat_idx} in {data_dir}" - ) - with open(json_path) as f: - data = json.load(f) - return data - - # --- Object Lookup Helpers --- def get_objects_of_type(obj_type: str, metadata: dict) -> list: @@ -140,223 +85,3 @@ def get_obj_of_type_closest_to_obj(obj_type: str, ref_obj_id: str, metadata: dic min_dist = dist closest = c return closest - - -# --- Goal Evaluation --- - -def get_targets_from_traj(traj_data: dict) -> dict: - """Extract goal targets from trajectory data.""" - pddl = traj_data.get("pddl_params", {}) - targets = {} - # Map from traj keys to goal target keys - if "object_target" in pddl: - targets["object"] = pddl["object_target"] - if "parent_target" in pddl: - targets["parent"] = pddl["parent_target"] - if "toggle_target" in pddl: - targets["toggle"] = pddl["toggle_target"] - if "mrecep_target" in pddl: - targets["mrecep"] = pddl["mrecep_target"] - # Sliced variant - if "object_sliced" in pddl and pddl["object_sliced"]: - targets["object"] = targets.get("object", "") + "Sliced" - return targets - - -def evaluate_goal_conditions( - traj_data: dict, - event, - cleaned_objects: set, - cooled_objects: set, - heated_objects: set, -) -> tuple: - """Evaluate EB-Alfred goal conditions. - - Returns: - (goal_satisfied: bool, progress: float) - - progress = satisfied_conditions / total_conditions - """ - task_type = traj_data.get("task_type", "") - targets = get_targets_from_traj(traj_data) - metadata = event.metadata - - evaluator = GOAL_EVALUATORS.get(task_type) - if evaluator is None: - logger.warning("Unknown task type: %s", task_type) - return False, 0.0 - - s, ts = evaluator(targets, metadata, cleaned_objects, cooled_objects, heated_objects) - progress = s / ts if ts > 0 else 0.0 - satisfied = s >= ts - return satisfied, progress - - -# --- Per-task-type goal evaluators --- - -def _eval_pick_and_place_simple(targets, metadata, cleaned, cooled, heated): - ts = 1 - s = 0 - receptacles = get_objects_with_name_and_prop(targets.get("parent", ""), "receptacle", metadata) - pickupables = get_objects_with_name_and_prop(targets.get("object", ""), "pickupable", metadata) - - if "Sliced" in targets.get("object", ""): - ts += 1 - if len([p for p in pickupables if "Sliced" in p["objectId"]]) >= 1: - s += 1 - - if np.any([np.any([p["objectId"] in (r.get("receptacleObjectIds") or []) - for r in receptacles]) - for p in pickupables]): - s += 1 - return s, ts - - -def _eval_pick_two_obj_and_place(targets, metadata, cleaned, cooled, heated): - ts = 2 - s = 0 - receptacles = get_objects_with_name_and_prop(targets.get("parent", ""), "receptacle", metadata) - pickupables = get_objects_with_name_and_prop(targets.get("object", ""), "pickupable", metadata) - - if "Sliced" in targets.get("object", ""): - ts += 2 - s += min(len([p for p in pickupables if "Sliced" in p["objectId"]]), 2) - - s += min(np.max([sum([1 if (r.get("receptacleObjectIds") is not None - and p["objectId"] in r["receptacleObjectIds"]) else 0 - for p in pickupables]) - for r in receptacles]) if receptacles else 0, 2) - return s, ts - - -def _eval_look_at_obj_in_light(targets, metadata, cleaned, cooled, heated): - ts = 2 - s = 0 - toggleables = get_objects_with_name_and_prop(targets.get("toggle", ""), "toggleable", metadata) - pickupables = get_objects_with_name_and_prop(targets.get("object", ""), "pickupable", metadata) - inventory = metadata.get("inventoryObjects", []) - - if "Sliced" in targets.get("object", ""): - ts += 1 - if len([p for p in pickupables if "Sliced" in p["objectId"]]) >= 1: - s += 1 - - if len(inventory) > 0 and inventory[0]["objectId"] in [p["objectId"] for p in pickupables]: - s += 1 - if np.any([t["isToggled"] and t["visible"] for t in toggleables]): - s += 1 - return s, ts - - -def _eval_pick_heat_then_place(targets, metadata, cleaned, cooled, heated): - ts = 3 - s = 0 - receptacles = get_objects_with_name_and_prop(targets.get("parent", ""), "receptacle", metadata) - pickupables = get_objects_with_name_and_prop(targets.get("object", ""), "pickupable", metadata) - - if "Sliced" in targets.get("object", ""): - ts += 1 - if len([p for p in pickupables if "Sliced" in p["objectId"]]) >= 1: - s += 1 - - objs_in_place = [p["objectId"] for p in pickupables for r in receptacles - if r.get("receptacleObjectIds") is not None and p["objectId"] in r["receptacleObjectIds"]] - objs_heated = [p["objectId"] for p in pickupables if p["objectId"] in heated] - - if len(objs_in_place) > 0: - s += 1 - if len(objs_heated) > 0: - s += 1 - if np.any([obj_id in objs_heated for obj_id in objs_in_place]): - s += 1 - return s, ts - - -def _eval_pick_cool_then_place(targets, metadata, cleaned, cooled, heated): - ts = 3 - s = 0 - receptacles = get_objects_with_name_and_prop(targets.get("parent", ""), "receptacle", metadata) - pickupables = get_objects_with_name_and_prop(targets.get("object", ""), "pickupable", metadata) - - if "Sliced" in targets.get("object", ""): - ts += 1 - if len([p for p in pickupables if "Sliced" in p["objectId"]]) >= 1: - s += 1 - - objs_in_place = [p["objectId"] for p in pickupables for r in receptacles - if r.get("receptacleObjectIds") is not None and p["objectId"] in r["receptacleObjectIds"]] - objs_cooled = [p["objectId"] for p in pickupables if p["objectId"] in cooled] - - if len(objs_in_place) > 0: - s += 1 - if len(objs_cooled) > 0: - s += 1 - if np.any([obj_id in objs_cooled for obj_id in objs_in_place]): - s += 1 - return s, ts - - -def _eval_pick_clean_then_place(targets, metadata, cleaned, cooled, heated): - ts = 3 - s = 0 - receptacles = get_objects_with_name_and_prop(targets.get("parent", ""), "receptacle", metadata) - pickupables = get_objects_with_name_and_prop(targets.get("object", ""), "pickupable", metadata) - - if "Sliced" in targets.get("object", ""): - ts += 1 - if len([p for p in pickupables if "Sliced" in p["objectId"]]) >= 1: - s += 1 - - objs_in_place = [p["objectId"] for p in pickupables for r in receptacles - if r.get("receptacleObjectIds") is not None and p["objectId"] in r["receptacleObjectIds"]] - objs_cleaned = [p["objectId"] for p in pickupables if p["objectId"] in cleaned] - - if len(objs_in_place) > 0: - s += 1 - if len(objs_cleaned) > 0: - s += 1 - if np.any([obj_id in objs_cleaned for obj_id in objs_in_place]): - s += 1 - return s, ts - - -def _eval_pick_and_place_with_movable_recep(targets, metadata, cleaned, cooled, heated): - ts = 3 - s = 0 - receptacles = get_objects_with_name_and_prop(targets.get("parent", ""), "receptacle", metadata) - pickupables = get_objects_with_name_and_prop(targets.get("object", ""), "pickupable", metadata) - movables = get_objects_with_name_and_prop(targets.get("mrecep", ""), "pickupable", metadata) - - if "Sliced" in targets.get("object", ""): - ts += 1 - if len([p for p in pickupables if "Sliced" in p["objectId"]]) >= 1: - s += 1 - - pickup_in_movable = [p for p in pickupables for m in movables - if m.get("receptacleObjectIds") is not None - and p["objectId"] in m["receptacleObjectIds"]] - movable_in_recep = [m for m in movables for r in receptacles - if r.get("receptacleObjectIds") is not None - and m["objectId"] in r["receptacleObjectIds"]] - - if len(pickup_in_movable) > 0: - s += 1 - if len(movable_in_recep) > 0: - s += 1 - if np.any([np.any([p["objectId"] in (m.get("receptacleObjectIds") or []) for p in pickupables]) and - np.any([r["objectId"] in (m.get("parentReceptacles") or []) for r in receptacles]) - for m in movables - if m.get("parentReceptacles") is not None and m.get("receptacleObjectIds") is not None]): - s += 1 - return s, ts - - -GOAL_EVALUATORS = { - "pick_and_place_simple": _eval_pick_and_place_simple, - "pick_two_obj_and_place": _eval_pick_two_obj_and_place, - "look_at_obj_in_light": _eval_look_at_obj_in_light, - "pick_heat_then_place_in_recep": _eval_pick_heat_then_place, - "pick_cool_then_place_in_recep": _eval_pick_cool_then_place, - "pick_clean_then_place_in_recep": _eval_pick_clean_then_place, - "pick_and_place_with_movable_recep": _eval_pick_and_place_with_movable_recep, -} diff --git a/easi/tasks/ebalfred/bridge.py b/easi/tasks/ebalfred/bridge.py new file mode 100644 index 0000000..0d6b2c7 --- /dev/null +++ b/easi/tasks/ebalfred/bridge.py @@ -0,0 +1,873 @@ +"""EB-Alfred bridge subprocess for AI2-THOR v2.1.0. + +This script runs inside the easi_ai2thor_v2_1_0 conda env (Python 3.8). +It communicates with the parent process via filesystem IPC. + +Extends the generic AI2ThorBridge with EB-Alfred-specific behavior: +- High-level skill execution (find, pick up, put down, open, close, etc.) +- State tracking (cleaned, cooled, heated objects) +- Goal condition evaluation + +Ported from EmbodiedBench: +- envs/eb_alfred/thor_connector.py (high-level skill API) +- envs/eb_alfred/env/thor_env.py (scene restoration, state tracking) + +Usage: + python bridge.py --workspace /tmp/easi_xxx --data-dir /path/to/datasets [--simulator-kwargs '{}'] +""" + +from __future__ import annotations + +import argparse +import json +import logging +import math +import os +import sys +from pathlib import Path + +# Add repo root to path for easi imports +_repo_root = Path(__file__).resolve().parents[3] +if str(_repo_root) not in sys.path: + sys.path.insert(0, str(_repo_root)) + +from easi.simulators.ai2thor.v2_1_0.bridge import AI2ThorBridge +from easi.simulators.ai2thor.v2_1_0.thor_utils import ( + AGENT_STEP_SIZE, + CAMERA_HEIGHT_OFFSET, + RECORD_SMOOTHING_FACTOR, + RENDER_CLASS_IMAGE, + RENDER_DEPTH_IMAGE, + RENDER_IMAGE, + RENDER_OBJECT_IMAGE, + VISIBILITY_DISTANCE, + get_obj_of_type_closest_to_obj, + get_objects_of_type, + natural_word_to_ithor_name, +) +from easi.tasks.ebalfred.thor_utils import ( + evaluate_goal_conditions, + load_task_json_with_repeat, +) + +logger = logging.getLogger("easi.bridge.ebalfred") + + +class EBAlfredBridge(AI2ThorBridge): + """EB-Alfred bridge extending AI2ThorBridge with skill execution and goal evaluation. + + Ported from EmbodiedBench's ThorEnv + ThorConnector. + """ + + def __init__(self, workspace, data_dir=None, simulator_kwargs=None): + super().__init__(workspace, data_dir, simulator_kwargs) + self.traj_data = None + + # State tracking (from EmbodiedBench ThorEnv) + self.cleaned_objects = set() + self.cooled_objects = set() + self.heated_objects = set() + + # Skill state + self.cur_receptacle = None + self.put_count_dict = {} + self.sliced = False + + # --- Reset --- + + def reset(self, reset_config): + """Reset to an EB-Alfred episode (or a default scene for smoke tests). + + If task_path is provided, loads traj_data.json, resets scene, + restores objects, and teleports agent. + If task_path is missing (smoke test), resets to a default scene. + """ + task_path = reset_config.get("task_path") + + if not task_path: + # Smoke test mode: use generic reset + self.traj_data = None + self.cleaned_objects.clear() + self.cooled_objects.clear() + self.heated_objects.clear() + self.cur_receptacle = None + self.put_count_dict = {} + self.sliced = False + return super().reset(reset_config) + + repeat_idx = reset_config.get("repeat_idx", 0) + + self.traj_data = load_task_json_with_repeat( + task_path, repeat_idx, str(self.data_dir) + ) + self.cleaned_objects.clear() + self.cooled_objects.clear() + self.heated_objects.clear() + self.step_count = 0 + self.cur_receptacle = None + self.put_count_dict = {} + self.sliced = False + + # Reset scene + scene_num = self.traj_data["scene"]["scene_num"] + scene_name = "FloorPlan%d" % scene_num + logger.info("Resetting to scene: %s", scene_name) + self.controller.reset(scene_name) + + # Restore object poses, toggles, dirty states + self._restore_scene() + + # Teleport agent to initial position + init_action = dict(self.traj_data["scene"]["init_action"]) + if init_action.get("action") == "TeleportFull": + init_action.pop("rotateOnTeleport", None) + init_action["standing"] = True + self.last_event = self.controller.step(init_action) + + # Cache agent height + self.agent_height = self.last_event.metadata["agent"]["position"]["y"] + + # Cache reachable positions for navigation + self._cache_reachable_positions() + + return self._make_observation_response() + + def _restore_scene(self): + """Restore EB-Alfred scene state from traj_data. + + Ported from EmbodiedBench ThorEnv.restore_scene(). + """ + grid_size = AGENT_STEP_SIZE / RECORD_SMOOTHING_FACTOR + + self._step(dict( + action="Initialize", + gridSize=grid_size, + cameraY=CAMERA_HEIGHT_OFFSET, + renderImage=RENDER_IMAGE, + renderDepthImage=RENDER_DEPTH_IMAGE, + renderClassImage=RENDER_CLASS_IMAGE, + renderObjectImage=RENDER_OBJECT_IMAGE, + visibility_distance=VISIBILITY_DISTANCE, + makeAgentsVisible=False, + )) + + scene = self.traj_data["scene"] + object_toggles = scene.get("object_toggles", []) + object_poses = scene.get("object_poses", []) + dirty_and_empty = scene.get("dirty_and_empty", False) + + if len(object_toggles) > 0: + self._step(dict(action="SetObjectToggles", objectToggles=object_toggles)) + + if dirty_and_empty: + self._step(dict( + action="SetStateOfAllObjects", + StateChange="CanBeDirty", + forceAction=True, + )) + self._step(dict( + action="SetStateOfAllObjects", + StateChange="CanBeFilled", + forceAction=False, + )) + + if len(object_poses) > 0: + self._step(dict(action="SetObjectPoses", objectPoses=object_poses)) + + # --- Step --- + + def step(self, action_text): + """Execute a high-level skill and return observation + metrics.""" + self.step_count += 1 + + # Smoke test mode: execute raw THOR action instead of high-level skill + if self.traj_data is None: + self._step(dict(action=action_text)) + return self._make_observation_response( + reward=0.0, + done=False, + info={ + "last_action_success": 1.0 if self.last_event.metadata["lastActionSuccess"] else 0.0, + "env_step": float(self.step_count), + "feedback": "success" if self.last_event.metadata["lastActionSuccess"] else "action failed", + }, + ) + + result = self._execute_skill(action_text) + success = result.get("success", False) + + # Update cleaned/cooled/heated tracking + self._update_states() + + # Evaluate goal conditions + goal_satisfied, progress = evaluate_goal_conditions( + self.traj_data, + self.last_event, + self.cleaned_objects, + self.cooled_objects, + self.heated_objects, + ) + + feedback = result.get("message", "") + if success: + feedback = "success" + + return self._make_observation_response( + reward=float(progress), + done=goal_satisfied, + info={ + "task_success": 1.0 if goal_satisfied else 0.0, + "task_progress": progress, + "last_action_success": 1.0 if success else 0.0, + "env_step": float(self.step_count), + "feedback": feedback, + }, + ) + + def _execute_skill(self, instruction): + """Map high-level skill text to THOR API calls. + + Ported from EmbodiedBench ThorConnector.llm_skill_interact(). + """ + # Clear cur_receptacle unless put/open + if not (instruction.startswith("put down ") or instruction.startswith("open ")): + self.cur_receptacle = None + + ret_msg = "" + + if instruction.startswith("find "): + obj_name = instruction.replace("find a ", "").replace("find an ", "") + self.cur_receptacle = obj_name + ret_msg = self._nav_obj( + natural_word_to_ithor_name(obj_name), self.sliced + ) + + elif instruction.startswith("pick up "): + obj_name = instruction.replace("pick up the ", "") + ret_msg = self._pick(natural_word_to_ithor_name(obj_name)) + + elif instruction.startswith("put down "): + if self.cur_receptacle is None: + ret_msg = self._drop() + else: + receptacle = self.cur_receptacle + if receptacle in self.put_count_dict: + self.put_count_dict[receptacle] += 1 + else: + self.put_count_dict[receptacle] = 1 + + ret_msg = self._put(natural_word_to_ithor_name(receptacle)) + + if len(ret_msg) > 16 and self.put_count_dict[receptacle] >= 3: + self._drop() + ret_msg += ". The robot dropped the object instead." + self.last_event.metadata["lastActionSuccess"] = False + + elif instruction.startswith("open "): + obj_name = instruction.replace("open the ", "") + ret_msg = self._open(natural_word_to_ithor_name(obj_name)) + + elif instruction.startswith("close "): + obj_name = instruction.replace("close the ", "") + ret_msg = self._close(natural_word_to_ithor_name(obj_name)) + + elif instruction.startswith("turn on "): + obj_name = instruction.replace("turn on the ", "") + ret_msg = self._toggleon(natural_word_to_ithor_name(obj_name)) + + elif instruction.startswith("turn off "): + obj_name = instruction.replace("turn off the ", "") + ret_msg = self._toggleoff(natural_word_to_ithor_name(obj_name)) + + elif instruction.startswith("slice "): + obj_name = instruction.replace("slice the ", "") + ret_msg = self._slice(natural_word_to_ithor_name(obj_name)) + self.sliced = True + + elif instruction.startswith("drop"): + ret_msg = self._drop() + + else: + ret_msg = "instruction not supported" + + success = len(ret_msg) == 0 + if not success: + logger.warning("Skill failed: %s -> %s", instruction, ret_msg) + + return {"action": instruction, "success": success, "message": ret_msg} + + # --- State Tracking --- + + def _update_states(self): + """Track cleaned/cooled/heated objects. + + Ported from EmbodiedBench ThorEnv.update_states(). + """ + if self.last_event is None: + return + + metadata = self.last_event.metadata + if not metadata.get("lastActionSuccess", False): + return + + # Check if last action was a relevant toggle/close + last_action = metadata.get("lastAction", "") + last_obj_id = metadata.get("lastActionObjectId", "") + + # Clean: ToggleObjectOn on Faucet -> items in SinkBasin get cleaned + if last_action == "ToggleObjectOn" and "Faucet" in last_obj_id: + sink_basin = get_obj_of_type_closest_to_obj( + "SinkBasin", last_obj_id, metadata + ) + if sink_basin and sink_basin.get("receptacleObjectIds"): + self.cleaned_objects.update(sink_basin["receptacleObjectIds"]) + + # Heat: ToggleObjectOn on Microwave -> items inside get heated + if last_action == "ToggleObjectOn" and "Microwave" in last_obj_id: + microwaves = get_objects_of_type("Microwave", metadata) + if microwaves: + heated_ids = microwaves[0].get("receptacleObjectIds") + if heated_ids: + self.heated_objects.update(heated_ids) + + # Cool: CloseObject on Fridge -> items inside get cooled + if last_action == "CloseObject" and "Fridge" in last_obj_id: + fridges = get_objects_of_type("Fridge", metadata) + if fridges: + cooled_ids = fridges[0].get("receptacleObjectIds") + if cooled_ids: + self.cooled_objects.update(cooled_ids) + + # --- Individual Skill Implementations --- + + def _nav_obj(self, target_obj, prefer_sliced=False): + """Navigate to an object by teleporting to closest reachable position. + + Ported from EmbodiedBench ThorConnector.nav_obj(). + """ + objects = self.last_event.metadata["objects"] + logger.info("nav_obj: %s", target_obj) + + # Resolve object ID + if "|" in target_obj: + obj_id = target_obj + base_name = target_obj.split("|")[0] + tmp_id, tmp_data = self._get_obj_id_from_name( + base_name, priority_in_visibility=True, priority_sliced=prefer_sliced + ) + if tmp_id and "Sliced" in tmp_id and obj_id in tmp_id: + obj_id = tmp_id + else: + obj_id, _ = self._get_obj_id_from_name( + target_obj, priority_in_visibility=True, priority_sliced=prefer_sliced + ) + + # Find object index + obj_idx = -1 + for i, o in enumerate(objects): + if o["objectId"] == obj_id: + obj_idx = i + break + + if obj_idx == -1: + return ( + "Cannot find %s. This object may not exist in this scene. " + "Try to explore other instances instead." % target_obj + ) + + # Teleport to closest reachable position facing the object + loc = objects[obj_idx]["position"] + obj_rot = objects[obj_idx]["rotation"]["y"] + max_attempts = 20 + teleport_success = False + reachable_pos_idx = 0 + + for i in range(max_attempts): + reachable_pos_idx += 1 + if i == 10 and target_obj in ("Fridge", "Microwave"): + reachable_pos_idx -= 10 + + closest_loc = self._find_close_reachable_position( + [loc["x"], loc["y"], loc["z"]], reachable_pos_idx + ) + if closest_loc is None: + continue + + # Calculate desired rotation angle + rot_angle = math.atan2( + -(loc["x"] - closest_loc[0]), loc["z"] - closest_loc[2] + ) + if rot_angle > 0: + rot_angle -= 2 * math.pi + rot_angle = -(180 / math.pi) * rot_angle + + # Special angle filtering for Fridge/Microwave + if i < 10 and target_obj in ("Fridge", "Microwave"): + angle_d = abs(self._angle_diff(rot_angle, obj_rot)) + if target_obj == "Fridge" and not ( + (70 < angle_d < 110) or (250 < angle_d < 290) + ): + continue + if target_obj == "Microwave" and not ( + (160 < angle_d < 200) or (0 <= angle_d < 20) + ): + continue + + # Calculate horizon angle + camera_height = self.agent_height + CAMERA_HEIGHT_OFFSET + xz_dist = math.hypot( + loc["x"] - closest_loc[0], loc["z"] - closest_loc[2] + ) + hor_angle = math.atan2(loc["y"] - camera_height, xz_dist) + hor_angle = (180 / math.pi) * hor_angle * 0.9 + + self._step(dict( + action="TeleportFull", + x=closest_loc[0], + y=self.agent_height, + z=closest_loc[2], + rotation=rot_angle, + horizon=-hor_angle, + )) + + if self.last_event.metadata["lastActionSuccess"]: + teleport_success = True + break + else: + logger.debug( + "TeleportFull failed: %s", + self.last_event.metadata.get("errorMessage", ""), + ) + + if not teleport_success: + return "Cannot move to %s" % target_obj + + return "" + + def _pick(self, obj_name): + """Pick up an object. + + Ported from EmbodiedBench ThorConnector.pick(). + """ + obj_id, obj_data = self._get_obj_id_from_name( + obj_name, only_pickupable=True, priority_in_visibility=True, + priority_sliced=self.sliced, + ) + logger.info("pick: %s -> %s", obj_name, obj_id) + + if obj_id is None: + return "Cannot find %s to pick up. Find the object before picking up it" % obj_name + + if ( + obj_data.get("visible") is False + and obj_data.get("parentReceptacles") + and len(obj_data["parentReceptacles"]) > 0 + ): + recep_name = obj_data["parentReceptacles"][0] + ret_msg = ( + "%s is not visible because it is in %s. " + "Note: multiple instances of %s may exist" + % (obj_name, recep_name, recep_name) + ) + # Try anyway + self._step(dict( + action="PickupObject", objectId=obj_id, forceAction=False + )) + else: + self._step(dict( + action="PickupObject", objectId=obj_id, forceAction=False + )) + ret_msg = "" + + if not self.last_event.metadata["lastActionSuccess"]: + inventory = self.last_event.metadata.get("inventoryObjects", []) + if len(inventory) == 0: + ret_msg = "Robot is not holding any object" + else: + holding_type = inventory[0].get("objectType", "unknown") + ret_msg = "Robot is currently holding %s" % holding_type + + if self.last_event.metadata["lastActionSuccess"]: + ret_msg = "" + + return ret_msg + + def _put(self, receptacle_name): + """Put held object onto a receptacle. + + Ported from EmbodiedBench ThorConnector.put(). + Uses 2x7x2 retry loop with movement adjustments. + """ + inventory = self.last_event.metadata.get("inventoryObjects", []) + if len(inventory) == 0: + return "Robot is not holding any object" + + holding_obj_id = inventory[0]["objectId"] + orig_receptacle_name = receptacle_name + ret_msg = "" + halt = False + last_recep_id = None + exclude_obj_id = None + + for k in range(2): # try closest and next closest receptacle + for j in range(7): # movement/look adjustments + for i in range(2): # try inherited receptacles (SinkBasin etc.) + if k == 1 and exclude_obj_id is None: + exclude_obj_id = last_recep_id + + if k == 0 and "|" in orig_receptacle_name: + if i == 1: + continue + recep_id = orig_receptacle_name + receptacle_name = orig_receptacle_name.split("|")[0] + else: + if "Sink" in receptacle_name or "Bathtub" in receptacle_name: + if i == 0: + recep_id, _ = self._get_obj_id_from_name( + receptacle_name, get_inherited=True, + exclude_obj_id=exclude_obj_id, + ) + else: + recep_id, _ = self._get_obj_id_from_name( + receptacle_name, exclude_obj_id=exclude_obj_id, + ) + else: + if i == 0: + recep_id, _ = self._get_obj_id_from_name( + receptacle_name, exclude_obj_id=exclude_obj_id, + ) + else: + recep_id, _ = self._get_obj_id_from_name( + receptacle_name, get_inherited=True, + exclude_obj_id=exclude_obj_id, + ) + + if not recep_id: + ret_msg = ( + "Putting the object on %s failed. First check whether " + "the receptacle is open or not. Also try other instances " + "of the receptacle" % receptacle_name + ) + continue + + # Movement adjustments to get receptacle in view + if j == 1: + self._step(dict(action="LookUp")) + self._step(dict(action="LookUp")) + elif j == 2: + self._step(dict(action="LookDown")) + self._step(dict(action="LookDown")) + self._step(dict(action="LookDown")) + self._step(dict(action="LookDown")) + elif j == 3: + self._step(dict(action="LookUp")) + self._step(dict(action="LookUp")) + self._step(dict(action="MoveBack")) + elif j == 4: + self._step(dict(action="MoveAhead")) + for _ in range(4): + self._step(dict(action="MoveRight")) + elif j == 5: + for _ in range(8): + self._step(dict(action="MoveLeft")) + elif j == 6: + for _ in range(4): + self._step(dict(action="MoveRight")) + self._step(dict(action="RotateHand", x=40)) + + self._step(dict( + action="PutObject", + objectId=holding_obj_id, + receptacleObjectId=recep_id, + forceAction=True, + )) + last_recep_id = recep_id + + if self.last_event.metadata["lastActionSuccess"]: + ret_msg = "" + halt = True + break + else: + logger.debug( + "PutObject failed: %s", + self.last_event.metadata.get("errorMessage", ""), + ) + ret_msg = ( + "Putting the object on %s failed. First check the " + "receptacle is open or not. Also try other instances " + "of the receptacle" % receptacle_name + ) + if halt: + break + if halt: + break + + return ret_msg + + def _open(self, obj_name): + """Open a receptacle. + + Ported from EmbodiedBench ThorConnector.open(). + """ + logger.info("open: %s", obj_name) + + if "|" in obj_name: + obj_id = obj_name + obj_name = obj_name.split("|")[0] + else: + obj_id, _ = self._get_obj_id_from_name(obj_name) + + if obj_id is None: + return "Cannot find %s to open. Find the object before opening it" % obj_name + + # Check if already open + open_flag = False + for ob in self.last_event.metadata["objects"]: + if ob["objectId"] == obj_id and ob.get("openable") and ob.get("isOpen"): + open_flag = True + break + + ret_msg = "" + for i in range(4): + self._step(dict(action="OpenObject", objectId=obj_id)) + + if self.last_event.metadata["lastActionSuccess"]: + ret_msg = "" + break + else: + if open_flag: + ret_msg = "Open action failed. The %s is already open" % obj_name + else: + ret_msg = "Open action failed." + + # Move around to avoid self-collision + if i == 0: + self._step(dict(action="MoveBack")) + elif i == 1: + self._step(dict(action="MoveBack")) + self._step(dict(action="MoveRight")) + elif i == 2: + self._step(dict(action="MoveLeft")) + self._step(dict(action="MoveLeft")) + + return ret_msg + + def _close(self, obj_name): + """Close a receptacle. + + Ported from EmbodiedBench ThorConnector.close(). + """ + logger.info("close: %s", obj_name) + + if "|" in obj_name: + obj_id = obj_name + obj_name = obj_name.split("|")[0] + else: + obj_id, _ = self._get_obj_id_from_name(obj_name) + + if obj_id is None: + return "Cannot find %s to close" % obj_name + + self._step(dict(action="CloseObject", objectId=obj_id)) + + if not self.last_event.metadata["lastActionSuccess"]: + ret_msg = "Close action failed" + for ob in self.last_event.metadata["objects"]: + if ( + ob["objectId"] == obj_id + and ob.get("openable") + and not ob.get("isOpen") + ): + ret_msg += ". The %s is already closed" % obj_name + break + return ret_msg + + return "" + + def _toggleon(self, obj_name): + """Toggle an object on. + + Ported from EmbodiedBench ThorConnector.toggleon(). + """ + logger.info("toggleon: %s", obj_name) + obj_id, _ = self._get_obj_id_from_name(obj_name, only_toggleable=True) + + if obj_id is None: + return "Cannot find %s to turn on" % obj_name + + try: + self._step(dict(action="ToggleObjectOn", objectId=obj_id)) + if not self.last_event.metadata["lastActionSuccess"]: + return "Turn on action failed" + except Exception: + self.last_event.metadata["lastActionSuccess"] = False + return "Turn on action failed" + + return "" + + def _toggleoff(self, obj_name): + """Toggle an object off. + + Ported from EmbodiedBench ThorConnector.toggleoff(). + """ + logger.info("toggleoff: %s", obj_name) + obj_id, _ = self._get_obj_id_from_name(obj_name, only_toggleable=True) + + if obj_id is None: + return "Cannot find %s to turn off" % obj_name + + self._step(dict(action="ToggleObjectOff", objectId=obj_id)) + + if not self.last_event.metadata["lastActionSuccess"]: + return "Turn off action failed" + + return "" + + def _slice(self, obj_name): + """Slice an object. + + Ported from EmbodiedBench ThorConnector.slice(). + """ + logger.info("slice: %s", obj_name) + obj_id, _ = self._get_obj_id_from_name(obj_name) + + if obj_id is None: + return "Cannot find %s to slice" % obj_name + + self._step(dict(action="SliceObject", objectId=obj_id)) + + if not self.last_event.metadata["lastActionSuccess"]: + return "Slice action failed" + + return "" + + def _drop(self): + """Drop the held object. + + Ported from EmbodiedBench ThorConnector.drop(). + """ + logger.info("drop") + self._step(dict(action="DropHandObject", forceAction=True)) + + if not self.last_event.metadata["lastActionSuccess"]: + inventory = self.last_event.metadata.get("inventoryObjects", []) + if len(inventory) == 0: + return "Robot is not holding any object" + return "Drop action failed" + + return "" + + # --- Object Lookup Helpers --- + + def _get_obj_id_from_name( + self, + obj_name, + only_pickupable=False, + only_toggleable=False, + priority_sliced=False, + get_inherited=False, + parent_receptacle_penalty=True, + priority_in_visibility=False, + exclude_obj_id=None, + ): + """Find the closest object matching name, with priority scoring. + + Ported from EmbodiedBench ThorConnector.get_obj_id_from_name(). + """ + obj_id = None + obj_data = None + min_distance = 1e8 + + # If name contains digits (e.g. 'Cabinet_2'), match by name directly + if any(c.isdigit() for c in obj_name): + for obj in self.last_event.metadata["objects"]: + if obj_name in obj["name"]: + return obj["objectId"], obj + return None, None + + for obj in self.last_event.metadata["objects"]: + if obj["objectId"] == exclude_obj_id: + continue + + if only_pickupable and not obj.get("pickupable", False): + continue + if only_toggleable and not obj.get("toggleable", False): + continue + + # Match object type (case-insensitive) + obj_type = obj["objectId"].split("|")[0] + if obj_type.casefold() != obj_name.casefold(): + continue + + # For inherited receptacles (e.g. SinkBasin), check ID format + if get_inherited and len(obj["objectId"].split("|")) != 5: + continue + if not get_inherited and len(obj["objectId"].split("|")) == 5: + # Skip inherited if not requested + pass + + distance = obj.get("distance", 1e8) + penalty = 0 + + # Penalize objects inside closed receptacles + if parent_receptacle_penalty and obj.get("parentReceptacles"): + for p in obj["parentReceptacles"]: + is_open = self._get_object_prop(p, "isOpen") + openable = self._get_object_prop(p, "openable") + if openable is True and is_open is False: + penalty += 100000 + break + + # Prefer empty stove burners + if obj_name.casefold() == "stoveburner": + recep_ids = obj.get("receptacleObjectIds") or [] + if len(recep_ids) > 0: + penalty += 10000 + + # Penalize non-visible objects + if priority_in_visibility and not obj.get("visible", False): + penalty += 1000 + + # Prefer sliced objects + if priority_sliced and "_Slice" in obj.get("name", ""): + penalty -= 100 + + if distance + penalty < min_distance: + min_distance = distance + penalty + obj_data = obj + obj_id = obj["objectId"] + + return obj_id, obj_data + + def _get_object_prop(self, name, prop): + """Get a property of an object by name/ID substring.""" + for obj in self.last_event.metadata["objects"]: + if name in obj["objectId"]: + return obj.get(prop) + return None + + +def main(): + parser = argparse.ArgumentParser(description="AI2-THOR v2.1.0 bridge (EB-Alfred)") + parser.add_argument("--workspace", type=Path, required=True) + parser.add_argument("--data-dir", type=Path, default=Path("./datasets")) + parser.add_argument("--simulator-kwargs", type=str, default=None, + help="JSON string of simulator configuration") + args, _ = parser.parse_known_args() + + logging.basicConfig( + level=logging.DEBUG, + format="%(asctime)s [%(levelname)s] %(name)s: %(message)s", + ) + + simulator_kwargs = json.loads(args.simulator_kwargs) if args.simulator_kwargs else {} + bridge = EBAlfredBridge( + workspace=args.workspace, + data_dir=args.data_dir, + simulator_kwargs=simulator_kwargs, + ) + bridge.run() + + +if __name__ == "__main__": + main() diff --git a/easi/tasks/ebalfred/ebalfred_base.yaml b/easi/tasks/ebalfred/ebalfred_base.yaml index b12c529..5478719 100644 --- a/easi/tasks/ebalfred/ebalfred_base.yaml +++ b/easi/tasks/ebalfred/ebalfred_base.yaml @@ -12,5 +12,9 @@ dataset: split: "base" zip_files: - "tasks.zip" +simulator_kwargs: + quality: "MediumCloseFitShadows" + screen_height: 500 + screen_width: 500 agent: prompt_builder: "easi.tasks.ebalfred.prompts.EBAlfredPromptBuilder" diff --git a/easi/tasks/ebalfred/ebalfred_common_sense.yaml b/easi/tasks/ebalfred/ebalfred_common_sense.yaml index 92df2a2..1e61251 100644 --- a/easi/tasks/ebalfred/ebalfred_common_sense.yaml +++ b/easi/tasks/ebalfred/ebalfred_common_sense.yaml @@ -12,5 +12,9 @@ dataset: split: "common_sense" zip_files: - "tasks.zip" +simulator_kwargs: + quality: "MediumCloseFitShadows" + screen_height: 500 + screen_width: 500 agent: prompt_builder: "easi.tasks.ebalfred.prompts.EBAlfredPromptBuilder" diff --git a/easi/tasks/ebalfred/ebalfred_complex_instruction.yaml b/easi/tasks/ebalfred/ebalfred_complex_instruction.yaml index 1e076ff..a55539d 100644 --- a/easi/tasks/ebalfred/ebalfred_complex_instruction.yaml +++ b/easi/tasks/ebalfred/ebalfred_complex_instruction.yaml @@ -12,5 +12,9 @@ dataset: split: "complex_instruction" zip_files: - "tasks.zip" +simulator_kwargs: + quality: "MediumCloseFitShadows" + screen_height: 500 + screen_width: 500 agent: prompt_builder: "easi.tasks.ebalfred.prompts.EBAlfredPromptBuilder" diff --git a/easi/tasks/ebalfred/ebalfred_long_horizon.yaml b/easi/tasks/ebalfred/ebalfred_long_horizon.yaml index a7537d4..a4d4413 100644 --- a/easi/tasks/ebalfred/ebalfred_long_horizon.yaml +++ b/easi/tasks/ebalfred/ebalfred_long_horizon.yaml @@ -12,5 +12,9 @@ dataset: split: "long_horizon" zip_files: - "tasks.zip" +simulator_kwargs: + quality: "MediumCloseFitShadows" + screen_height: 500 + screen_width: 500 agent: prompt_builder: "easi.tasks.ebalfred.prompts.EBAlfredPromptBuilder" diff --git a/easi/tasks/ebalfred/ebalfred_spatial.yaml b/easi/tasks/ebalfred/ebalfred_spatial.yaml index 4e8836c..dd2f04d 100644 --- a/easi/tasks/ebalfred/ebalfred_spatial.yaml +++ b/easi/tasks/ebalfred/ebalfred_spatial.yaml @@ -12,5 +12,9 @@ dataset: split: "spatial" zip_files: - "tasks.zip" +simulator_kwargs: + quality: "MediumCloseFitShadows" + screen_height: 500 + screen_width: 500 agent: prompt_builder: "easi.tasks.ebalfred.prompts.EBAlfredPromptBuilder" diff --git a/easi/tasks/ebalfred/ebalfred_visual_appearance.yaml b/easi/tasks/ebalfred/ebalfred_visual_appearance.yaml index 1909fe4..4189e5c 100644 --- a/easi/tasks/ebalfred/ebalfred_visual_appearance.yaml +++ b/easi/tasks/ebalfred/ebalfred_visual_appearance.yaml @@ -12,5 +12,9 @@ dataset: split: "visual_appearance" zip_files: - "tasks.zip" +simulator_kwargs: + quality: "MediumCloseFitShadows" + screen_height: 500 + screen_width: 500 agent: prompt_builder: "easi.tasks.ebalfred.prompts.EBAlfredPromptBuilder" diff --git a/easi/tasks/ebalfred/task.py b/easi/tasks/ebalfred/task.py index 5c5abe8..fc52f88 100644 --- a/easi/tasks/ebalfred/task.py +++ b/easi/tasks/ebalfred/task.py @@ -32,6 +32,10 @@ def get_task_yaml_path(self) -> Path: # Decision #4: No task.yaml — use ebalfred_base.yaml as default fallback return Path(__file__).parent / "ebalfred_base.yaml" + def get_bridge_script_path(self) -> Path: + """Return path to the EB-Alfred-specific bridge script.""" + return Path(__file__).parent / "bridge.py" + def get_instruction(self, episode: dict) -> str: """Decision #2: EB-Alfred uses 'instruction' field from HF row.""" return episode.get("instruction", self.name) diff --git a/easi/tasks/ebalfred/thor_utils.py b/easi/tasks/ebalfred/thor_utils.py new file mode 100644 index 0000000..d32a12d --- /dev/null +++ b/easi/tasks/ebalfred/thor_utils.py @@ -0,0 +1,303 @@ +"""EB-Alfred goal evaluation and task loading utilities. + +Moved from easi/simulators/ai2thor/v2_1_0/thor_utils.py — these are +task-specific (EB-Alfred), not generic AI2-THOR helpers. + +Ported from EmbodiedBench: +- envs/eb_alfred/env/tasks.py (goal evaluation) +- envs/eb_alfred/utils.py (task loading) + +This file runs inside the ai2thor conda env (Python 3.8). +""" +from __future__ import annotations + +import json +import logging +import os + +# numpy is available in the ai2thor conda env (Python 3.8) +# but may not be in the host venv — import is optional for importability +try: + import numpy as np +except ImportError: + np = None # type: ignore[assignment] + +from easi.simulators.ai2thor.v2_1_0.thor_utils import ( + get_objects_of_type, + get_objects_with_name_and_prop, +) + +logger = logging.getLogger("easi.bridge.ebalfred_utils") + +GOALS = [ + "pick_and_place_simple", + "pick_two_obj_and_place", + "look_at_obj_in_light", + "pick_clean_then_place_in_recep", + "pick_heat_then_place_in_recep", + "pick_cool_then_place_in_recep", + "pick_and_place_with_movable_recep", +] + + +# --- Task JSON Loading --- + +def load_task_json(task_path: str, data_dir: str) -> dict: + """Load preprocessed traj_data.json for an EB-Alfred episode. + + Args: + task_path: e.g. 'pick_and_place_simple-Mug-None-Shelf-1/trial_T20190001' + data_dir: path to the downloaded dataset directory + """ + # Try standard path: /tasks//pp/ann_0.json + # (repeat_idx is handled by the caller) + json_path = os.path.join(data_dir, "tasks", task_path, "pp", "ann_0.json") + if not os.path.exists(json_path): + # Fallback: try traj_data.json + json_path = os.path.join(data_dir, "tasks", task_path, "traj_data.json") + if not os.path.exists(json_path): + raise FileNotFoundError( + f"Task annotation not found at {json_path}. " + f"Ensure tasks.zip was extracted in {data_dir}" + ) + with open(json_path) as f: + data = json.load(f) + return data + + +def load_task_json_with_repeat(task_path: str, repeat_idx: int, data_dir: str) -> dict: + """Load the specific repeat annotation for an EB-Alfred episode.""" + json_path = os.path.join(data_dir, "tasks", task_path, "pp", f"ann_{repeat_idx}.json") + if not os.path.exists(json_path): + # Fallback to ann_0.json + json_path = os.path.join(data_dir, "tasks", task_path, "pp", "ann_0.json") + if not os.path.exists(json_path): + # Fallback to traj_data.json + json_path = os.path.join(data_dir, "tasks", task_path, "traj_data.json") + if not os.path.exists(json_path): + raise FileNotFoundError( + f"Task annotation not found for {task_path} repeat {repeat_idx} in {data_dir}" + ) + with open(json_path) as f: + data = json.load(f) + return data + + +# --- Goal Evaluation --- + +def get_targets_from_traj(traj_data: dict) -> dict: + """Extract goal targets from trajectory data.""" + pddl = traj_data.get("pddl_params", {}) + targets = {} + # Map from traj keys to goal target keys + if "object_target" in pddl: + targets["object"] = pddl["object_target"] + if "parent_target" in pddl: + targets["parent"] = pddl["parent_target"] + if "toggle_target" in pddl: + targets["toggle"] = pddl["toggle_target"] + if "mrecep_target" in pddl: + targets["mrecep"] = pddl["mrecep_target"] + # Sliced variant + if "object_sliced" in pddl and pddl["object_sliced"]: + targets["object"] = targets.get("object", "") + "Sliced" + return targets + + +def evaluate_goal_conditions( + traj_data: dict, + event, + cleaned_objects: set, + cooled_objects: set, + heated_objects: set, +) -> tuple: + """Evaluate EB-Alfred goal conditions. + + Returns: + (goal_satisfied: bool, progress: float) + + progress = satisfied_conditions / total_conditions + """ + task_type = traj_data.get("task_type", "") + targets = get_targets_from_traj(traj_data) + metadata = event.metadata + + evaluator = GOAL_EVALUATORS.get(task_type) + if evaluator is None: + logger.warning("Unknown task type: %s", task_type) + return False, 0.0 + + s, ts = evaluator(targets, metadata, cleaned_objects, cooled_objects, heated_objects) + progress = s / ts if ts > 0 else 0.0 + satisfied = s >= ts + return satisfied, progress + + +# --- Per-task-type goal evaluators --- + +def _eval_pick_and_place_simple(targets, metadata, cleaned, cooled, heated): + ts = 1 + s = 0 + receptacles = get_objects_with_name_and_prop(targets.get("parent", ""), "receptacle", metadata) + pickupables = get_objects_with_name_and_prop(targets.get("object", ""), "pickupable", metadata) + + if "Sliced" in targets.get("object", ""): + ts += 1 + if len([p for p in pickupables if "Sliced" in p["objectId"]]) >= 1: + s += 1 + + if np.any([np.any([p["objectId"] in (r.get("receptacleObjectIds") or []) + for r in receptacles]) + for p in pickupables]): + s += 1 + return s, ts + + +def _eval_pick_two_obj_and_place(targets, metadata, cleaned, cooled, heated): + ts = 2 + s = 0 + receptacles = get_objects_with_name_and_prop(targets.get("parent", ""), "receptacle", metadata) + pickupables = get_objects_with_name_and_prop(targets.get("object", ""), "pickupable", metadata) + + if "Sliced" in targets.get("object", ""): + ts += 2 + s += min(len([p for p in pickupables if "Sliced" in p["objectId"]]), 2) + + s += min(np.max([sum([1 if (r.get("receptacleObjectIds") is not None + and p["objectId"] in r["receptacleObjectIds"]) else 0 + for p in pickupables]) + for r in receptacles]) if receptacles else 0, 2) + return s, ts + + +def _eval_look_at_obj_in_light(targets, metadata, cleaned, cooled, heated): + ts = 2 + s = 0 + toggleables = get_objects_with_name_and_prop(targets.get("toggle", ""), "toggleable", metadata) + pickupables = get_objects_with_name_and_prop(targets.get("object", ""), "pickupable", metadata) + inventory = metadata.get("inventoryObjects", []) + + if "Sliced" in targets.get("object", ""): + ts += 1 + if len([p for p in pickupables if "Sliced" in p["objectId"]]) >= 1: + s += 1 + + if len(inventory) > 0 and inventory[0]["objectId"] in [p["objectId"] for p in pickupables]: + s += 1 + if np.any([t["isToggled"] and t["visible"] for t in toggleables]): + s += 1 + return s, ts + + +def _eval_pick_heat_then_place(targets, metadata, cleaned, cooled, heated): + ts = 3 + s = 0 + receptacles = get_objects_with_name_and_prop(targets.get("parent", ""), "receptacle", metadata) + pickupables = get_objects_with_name_and_prop(targets.get("object", ""), "pickupable", metadata) + + if "Sliced" in targets.get("object", ""): + ts += 1 + if len([p for p in pickupables if "Sliced" in p["objectId"]]) >= 1: + s += 1 + + objs_in_place = [p["objectId"] for p in pickupables for r in receptacles + if r.get("receptacleObjectIds") is not None and p["objectId"] in r["receptacleObjectIds"]] + objs_heated = [p["objectId"] for p in pickupables if p["objectId"] in heated] + + if len(objs_in_place) > 0: + s += 1 + if len(objs_heated) > 0: + s += 1 + if np.any([obj_id in objs_heated for obj_id in objs_in_place]): + s += 1 + return s, ts + + +def _eval_pick_cool_then_place(targets, metadata, cleaned, cooled, heated): + ts = 3 + s = 0 + receptacles = get_objects_with_name_and_prop(targets.get("parent", ""), "receptacle", metadata) + pickupables = get_objects_with_name_and_prop(targets.get("object", ""), "pickupable", metadata) + + if "Sliced" in targets.get("object", ""): + ts += 1 + if len([p for p in pickupables if "Sliced" in p["objectId"]]) >= 1: + s += 1 + + objs_in_place = [p["objectId"] for p in pickupables for r in receptacles + if r.get("receptacleObjectIds") is not None and p["objectId"] in r["receptacleObjectIds"]] + objs_cooled = [p["objectId"] for p in pickupables if p["objectId"] in cooled] + + if len(objs_in_place) > 0: + s += 1 + if len(objs_cooled) > 0: + s += 1 + if np.any([obj_id in objs_cooled for obj_id in objs_in_place]): + s += 1 + return s, ts + + +def _eval_pick_clean_then_place(targets, metadata, cleaned, cooled, heated): + ts = 3 + s = 0 + receptacles = get_objects_with_name_and_prop(targets.get("parent", ""), "receptacle", metadata) + pickupables = get_objects_with_name_and_prop(targets.get("object", ""), "pickupable", metadata) + + if "Sliced" in targets.get("object", ""): + ts += 1 + if len([p for p in pickupables if "Sliced" in p["objectId"]]) >= 1: + s += 1 + + objs_in_place = [p["objectId"] for p in pickupables for r in receptacles + if r.get("receptacleObjectIds") is not None and p["objectId"] in r["receptacleObjectIds"]] + objs_cleaned = [p["objectId"] for p in pickupables if p["objectId"] in cleaned] + + if len(objs_in_place) > 0: + s += 1 + if len(objs_cleaned) > 0: + s += 1 + if np.any([obj_id in objs_cleaned for obj_id in objs_in_place]): + s += 1 + return s, ts + + +def _eval_pick_and_place_with_movable_recep(targets, metadata, cleaned, cooled, heated): + ts = 3 + s = 0 + receptacles = get_objects_with_name_and_prop(targets.get("parent", ""), "receptacle", metadata) + pickupables = get_objects_with_name_and_prop(targets.get("object", ""), "pickupable", metadata) + movables = get_objects_with_name_and_prop(targets.get("mrecep", ""), "pickupable", metadata) + + if "Sliced" in targets.get("object", ""): + ts += 1 + if len([p for p in pickupables if "Sliced" in p["objectId"]]) >= 1: + s += 1 + + pickup_in_movable = [p for p in pickupables for m in movables + if m.get("receptacleObjectIds") is not None + and p["objectId"] in m["receptacleObjectIds"]] + movable_in_recep = [m for m in movables for r in receptacles + if r.get("receptacleObjectIds") is not None + and m["objectId"] in r["receptacleObjectIds"]] + + if len(pickup_in_movable) > 0: + s += 1 + if len(movable_in_recep) > 0: + s += 1 + if np.any([np.any([p["objectId"] in (m.get("receptacleObjectIds") or []) for p in pickupables]) and + np.any([r["objectId"] in (m.get("parentReceptacles") or []) for r in receptacles]) + for m in movables + if m.get("parentReceptacles") is not None and m.get("receptacleObjectIds") is not None]): + s += 1 + return s, ts + + +GOAL_EVALUATORS = { + "pick_and_place_simple": _eval_pick_and_place_simple, + "pick_two_obj_and_place": _eval_pick_two_obj_and_place, + "look_at_obj_in_light": _eval_look_at_obj_in_light, + "pick_heat_then_place_in_recep": _eval_pick_heat_then_place, + "pick_cool_then_place_in_recep": _eval_pick_cool_then_place, + "pick_clean_then_place_in_recep": _eval_pick_clean_then_place, + "pick_and_place_with_movable_recep": _eval_pick_and_place_with_movable_recep, +} diff --git a/tests/test_bridge_refactor.py b/tests/test_bridge_refactor.py new file mode 100644 index 0000000..ceb8eaa --- /dev/null +++ b/tests/test_bridge_refactor.py @@ -0,0 +1,320 @@ +"""Tests for the bridge architecture refactor. + +Verifies: +- AI2ThorBridge is importable and has generic interface +- EBAlfredBridge subclasses AI2ThorBridge with task-specific methods +- get_bridge_script_path() works on BaseTask and EBAlfredTask +- simulator_kwargs property works on BaseTask +- Generic thor_utils has no goal evaluators +- EB-Alfred thor_utils has goal evaluators +""" + +from __future__ import annotations + +from pathlib import Path + +import pytest + + +# --- Generic AI2ThorBridge tests --- + +class TestAI2ThorBridgeImport: + """Test that the generic AI2ThorBridge is properly structured.""" + + def test_importable(self): + from easi.simulators.ai2thor.v2_1_0.bridge import AI2ThorBridge + assert AI2ThorBridge is not None + + def test_has_generic_methods(self): + from easi.simulators.ai2thor.v2_1_0.bridge import AI2ThorBridge + bridge_cls = AI2ThorBridge + assert hasattr(bridge_cls, "start") + assert hasattr(bridge_cls, "stop") + assert hasattr(bridge_cls, "reset") + assert hasattr(bridge_cls, "step") + assert hasattr(bridge_cls, "run") + assert hasattr(bridge_cls, "_step") + assert hasattr(bridge_cls, "_cache_reachable_positions") + assert hasattr(bridge_cls, "_make_observation_response") + assert hasattr(bridge_cls, "_find_close_reachable_position") + assert hasattr(bridge_cls, "_angle_diff") + + def test_no_ebalfred_methods(self): + """Generic bridge should NOT have EB-Alfred-specific methods.""" + from easi.simulators.ai2thor.v2_1_0.bridge import AI2ThorBridge + bridge_cls = AI2ThorBridge + assert not hasattr(bridge_cls, "_execute_skill") + assert not hasattr(bridge_cls, "_restore_scene") + assert not hasattr(bridge_cls, "_update_states") + assert not hasattr(bridge_cls, "_nav_obj") + assert not hasattr(bridge_cls, "_pick") + assert not hasattr(bridge_cls, "_put") + assert not hasattr(bridge_cls, "_open") + assert not hasattr(bridge_cls, "_close") + assert not hasattr(bridge_cls, "_toggleon") + assert not hasattr(bridge_cls, "_toggleoff") + assert not hasattr(bridge_cls, "_slice") + assert not hasattr(bridge_cls, "_drop") + assert not hasattr(bridge_cls, "_get_obj_id_from_name") + assert not hasattr(bridge_cls, "_get_object_prop") + + def test_no_ebalfred_state(self): + """Generic bridge __init__ should NOT have EB-Alfred state.""" + from easi.simulators.ai2thor.v2_1_0.bridge import AI2ThorBridge + import tempfile + with tempfile.TemporaryDirectory() as tmpdir: + bridge = AI2ThorBridge(workspace=tmpdir) + assert not hasattr(bridge, "traj_data") + assert not hasattr(bridge, "cleaned_objects") + assert not hasattr(bridge, "cooled_objects") + assert not hasattr(bridge, "heated_objects") + assert not hasattr(bridge, "cur_receptacle") + assert not hasattr(bridge, "put_count_dict") + assert not hasattr(bridge, "sliced") + + def test_accepts_simulator_kwargs(self): + """Generic bridge should accept simulator_kwargs.""" + from easi.simulators.ai2thor.v2_1_0.bridge import AI2ThorBridge + import tempfile + with tempfile.TemporaryDirectory() as tmpdir: + kwargs = {"quality": "Low", "screen_height": 300} + bridge = AI2ThorBridge(workspace=tmpdir, simulator_kwargs=kwargs) + assert bridge.simulator_kwargs == kwargs + + def test_simulator_kwargs_default_empty(self): + from easi.simulators.ai2thor.v2_1_0.bridge import AI2ThorBridge + import tempfile + with tempfile.TemporaryDirectory() as tmpdir: + bridge = AI2ThorBridge(workspace=tmpdir) + assert bridge.simulator_kwargs == {} + + +# --- EBAlfredBridge tests --- + +class TestEBAlfredBridgeImport: + """Test that EBAlfredBridge properly extends AI2ThorBridge.""" + + def test_importable(self): + from easi.tasks.ebalfred.bridge import EBAlfredBridge + assert EBAlfredBridge is not None + + def test_subclasses_ai2thor_bridge(self): + from easi.simulators.ai2thor.v2_1_0.bridge import AI2ThorBridge + from easi.tasks.ebalfred.bridge import EBAlfredBridge + assert issubclass(EBAlfredBridge, AI2ThorBridge) + + def test_has_skill_methods(self): + """EB-Alfred bridge should have all skill execution methods.""" + from easi.tasks.ebalfred.bridge import EBAlfredBridge + bridge_cls = EBAlfredBridge + assert hasattr(bridge_cls, "_execute_skill") + assert hasattr(bridge_cls, "_restore_scene") + assert hasattr(bridge_cls, "_update_states") + assert hasattr(bridge_cls, "_nav_obj") + assert hasattr(bridge_cls, "_pick") + assert hasattr(bridge_cls, "_put") + assert hasattr(bridge_cls, "_open") + assert hasattr(bridge_cls, "_close") + assert hasattr(bridge_cls, "_toggleon") + assert hasattr(bridge_cls, "_toggleoff") + assert hasattr(bridge_cls, "_slice") + assert hasattr(bridge_cls, "_drop") + assert hasattr(bridge_cls, "_get_obj_id_from_name") + assert hasattr(bridge_cls, "_get_object_prop") + + def test_has_ebalfred_state(self): + """EB-Alfred bridge should have task-specific state.""" + from easi.tasks.ebalfred.bridge import EBAlfredBridge + import tempfile + with tempfile.TemporaryDirectory() as tmpdir: + bridge = EBAlfredBridge(workspace=tmpdir, data_dir=tmpdir) + assert hasattr(bridge, "traj_data") + assert hasattr(bridge, "cleaned_objects") + assert hasattr(bridge, "cooled_objects") + assert hasattr(bridge, "heated_objects") + assert hasattr(bridge, "cur_receptacle") + assert hasattr(bridge, "put_count_dict") + assert hasattr(bridge, "sliced") + + def test_inherits_generic_methods(self): + """EB-Alfred bridge should inherit generic methods from AI2ThorBridge.""" + from easi.tasks.ebalfred.bridge import EBAlfredBridge + bridge_cls = EBAlfredBridge + assert hasattr(bridge_cls, "start") + assert hasattr(bridge_cls, "stop") + assert hasattr(bridge_cls, "run") + assert hasattr(bridge_cls, "_step") + assert hasattr(bridge_cls, "_cache_reachable_positions") + assert hasattr(bridge_cls, "_make_observation_response") + assert hasattr(bridge_cls, "_find_close_reachable_position") + assert hasattr(bridge_cls, "_angle_diff") + + def test_overrides_reset_and_step(self): + """EB-Alfred bridge should override reset and step.""" + from easi.simulators.ai2thor.v2_1_0.bridge import AI2ThorBridge + from easi.tasks.ebalfred.bridge import EBAlfredBridge + # The methods should be overridden (different from parent) + assert EBAlfredBridge.reset is not AI2ThorBridge.reset + assert EBAlfredBridge.step is not AI2ThorBridge.step + + def test_run_is_inherited(self): + """EB-Alfred bridge should inherit run() from AI2ThorBridge (not override).""" + from easi.simulators.ai2thor.v2_1_0.bridge import AI2ThorBridge + from easi.tasks.ebalfred.bridge import EBAlfredBridge + assert EBAlfredBridge.run is AI2ThorBridge.run + + +# --- Thor utils separation tests --- + +class TestThorUtilsSeparation: + """Test that thor_utils is properly split between generic and EB-Alfred.""" + + def test_generic_has_constants(self): + from easi.simulators.ai2thor.v2_1_0 import thor_utils + assert hasattr(thor_utils, "SCREEN_WIDTH") + assert hasattr(thor_utils, "SCREEN_HEIGHT") + assert hasattr(thor_utils, "CAMERA_HEIGHT_OFFSET") + assert hasattr(thor_utils, "VISIBILITY_DISTANCE") + assert hasattr(thor_utils, "AGENT_STEP_SIZE") + + def test_generic_has_object_helpers(self): + from easi.simulators.ai2thor.v2_1_0 import thor_utils + assert hasattr(thor_utils, "natural_word_to_ithor_name") + assert hasattr(thor_utils, "get_objects_of_type") + assert hasattr(thor_utils, "get_objects_with_name_and_prop") + assert hasattr(thor_utils, "get_obj_of_type_closest_to_obj") + + def test_generic_has_no_goal_evaluators(self): + """Generic thor_utils should NOT have goal evaluation functions.""" + from easi.simulators.ai2thor.v2_1_0 import thor_utils + assert not hasattr(thor_utils, "GOALS") + assert not hasattr(thor_utils, "GOAL_EVALUATORS") + assert not hasattr(thor_utils, "evaluate_goal_conditions") + assert not hasattr(thor_utils, "get_targets_from_traj") + assert not hasattr(thor_utils, "load_task_json") + assert not hasattr(thor_utils, "load_task_json_with_repeat") + + def test_ebalfred_has_goal_evaluators(self): + from easi.tasks.ebalfred import thor_utils + assert hasattr(thor_utils, "GOALS") + assert hasattr(thor_utils, "GOAL_EVALUATORS") + assert hasattr(thor_utils, "evaluate_goal_conditions") + assert hasattr(thor_utils, "get_targets_from_traj") + assert hasattr(thor_utils, "load_task_json") + assert hasattr(thor_utils, "load_task_json_with_repeat") + + def test_ebalfred_goals_list(self): + from easi.tasks.ebalfred.thor_utils import GOALS + assert len(GOALS) == 7 + assert "pick_and_place_simple" in GOALS + assert "pick_two_obj_and_place" in GOALS + + def test_ebalfred_evaluators_dict(self): + from easi.tasks.ebalfred.thor_utils import GOAL_EVALUATORS + assert len(GOAL_EVALUATORS) == 7 + assert "pick_and_place_simple" in GOAL_EVALUATORS + assert "pick_heat_then_place_in_recep" in GOAL_EVALUATORS + + def test_ebalfred_imports_from_generic(self): + """EB-Alfred thor_utils should import from generic thor_utils.""" + from easi.tasks.ebalfred.thor_utils import evaluate_goal_conditions + # The function should be callable + assert callable(evaluate_goal_conditions) + + +# --- get_bridge_script_path tests --- + +class TestGetBridgeScriptPath: + """Test get_bridge_script_path on various task classes.""" + + def test_ebalfred_task_returns_path(self): + from easi.tasks.ebalfred.task import EBAlfredTask + task = EBAlfredTask() + bridge_path = task.get_bridge_script_path() + assert bridge_path is not None + assert isinstance(bridge_path, Path) + assert bridge_path.name == "bridge.py" + assert "ebalfred" in str(bridge_path) + + def test_ebalfred_bridge_path_exists(self): + from easi.tasks.ebalfred.task import EBAlfredTask + task = EBAlfredTask() + bridge_path = task.get_bridge_script_path() + assert bridge_path.exists(), f"Bridge script not found at {bridge_path}" + + def test_dummy_task_returns_none(self): + from easi.tasks.dummy_task.task import DummyTask + task = DummyTask() + assert task.get_bridge_script_path() is None + + def test_ebalfred_bridge_path_different_from_simulator(self): + """Task bridge should point to easi/tasks/ebalfred/bridge.py, + not easi/simulators/ai2thor/v2_1_0/bridge.py.""" + from easi.simulators.ai2thor.v2_1_0.simulator import AI2ThorSimulatorV210 + from easi.tasks.ebalfred.task import EBAlfredTask + + task = EBAlfredTask() + sim = AI2ThorSimulatorV210() + + task_bridge = task.get_bridge_script_path() + sim_bridge = sim._get_bridge_script_path() + + assert task_bridge != sim_bridge + assert "tasks" in str(task_bridge) + assert "simulators" in str(sim_bridge) + + +# --- simulator_kwargs tests --- + +class TestSimulatorKwargs: + """Test simulator_kwargs property on BaseTask.""" + + def test_ebalfred_has_simulator_kwargs(self): + from easi.tasks.ebalfred.task import EBAlfredTask + task = EBAlfredTask() + kwargs = task.simulator_kwargs + assert isinstance(kwargs, dict) + assert kwargs.get("quality") == "MediumCloseFitShadows" + assert kwargs.get("screen_height") == 500 + assert kwargs.get("screen_width") == 500 + + def test_dummy_task_empty_simulator_kwargs(self): + from easi.tasks.dummy_task.task import DummyTask + task = DummyTask() + kwargs = task.simulator_kwargs + assert isinstance(kwargs, dict) + assert kwargs == {} + + def test_all_ebalfred_splits_have_kwargs(self): + """All EB-Alfred split YAMLs should have simulator_kwargs.""" + from easi.tasks.registry import get_task_entry, load_task_class + + ebalfred_names = [ + "ebalfred_base", + "ebalfred_long_horizon", + "ebalfred_common_sense", + "ebalfred_complex_instruction", + "ebalfred_spatial", + "ebalfred_visual_appearance", + ] + for name in ebalfred_names: + entry = get_task_entry(name) + TaskClass = load_task_class(name) + task = TaskClass(split_yaml_path=entry.config_path) + kwargs = task.simulator_kwargs + assert isinstance(kwargs, dict), f"{name} simulator_kwargs is not a dict" + assert "quality" in kwargs, f"{name} missing quality in simulator_kwargs" + + +# --- Protocol tests --- + +class TestTaskProtocol: + """Test that TaskProtocol includes new methods.""" + + def test_protocol_has_get_bridge_script_path(self): + from easi.core.protocols import TaskProtocol + assert hasattr(TaskProtocol, "get_bridge_script_path") + + def test_protocol_has_simulator_kwargs(self): + from easi.core.protocols import TaskProtocol + assert hasattr(TaskProtocol, "simulator_kwargs") From 1ad66a0003993ab43f617f47d02f6bc069958adc Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Fri, 13 Feb 2026 16:39:25 +0800 Subject: [PATCH 004/239] [Fix] Updated dependencies for ebalfred --- .gitignore | 3 ++- easi/cli.py | 12 ++++++++-- easi/core/base_env_manager.py | 23 +++++++++++++++---- easi/simulators/ai2thor/v2_1_0/bridge.py | 8 +++++-- .../ai2thor/v2_1_0/requirements.txt | 5 ++++ 5 files changed, 41 insertions(+), 10 deletions(-) diff --git a/.gitignore b/.gitignore index dc084eb..d3a87e4 100644 --- a/.gitignore +++ b/.gitignore @@ -2,4 +2,5 @@ *.pyc __pycache__/ *.pyo -*.egg-info \ No newline at end of file +*.egg-info +/build diff --git a/easi/cli.py b/easi/cli.py index a2d8022..8f710ed 100644 --- a/easi/cli.py +++ b/easi/cli.py @@ -40,6 +40,8 @@ def build_parser() -> argparse.ArgumentParser: env_install = env_sub.add_parser("install", help="Install a simulator environment", parents=[common]) env_install.add_argument("simulator", type=str, help="e.g., 'dummy' or 'ai2thor:v2_1_0'") + env_install.add_argument("--reinstall", action="store_true", + help="Remove existing env and install from scratch") env_check = env_sub.add_parser("check", help="Check if environment is ready", parents=[common]) env_check.add_argument("simulator", type=str) @@ -113,11 +115,16 @@ def cmd_env_list() -> None: logger.info(" %s%s -- %s", pair, default_marker, entry.description) -def cmd_env_install(simulator: str) -> None: +def cmd_env_install(simulator: str, reinstall: bool = False) -> None: from easi.simulators.registry import load_env_manager_class EnvManagerClass = load_env_manager_class(simulator) env_manager = EnvManagerClass() + + if reinstall: + logger.info("Removing existing environment: %s", env_manager.get_env_name()) + env_manager.remove() + logger.info("Installing environment: %s", env_manager.get_env_name()) env_manager.install() logger.info("Done.") @@ -198,6 +205,7 @@ def cmd_sim_test(simulator: str, steps: int, timeout: float) -> None: needs_display=env_manager.needs_display, xvfb_screen_config=env_manager.xvfb_screen_config, startup_timeout=timeout, + command_timeout=timeout, ) try: @@ -274,7 +282,7 @@ def main() -> None: if args.env_action == "list": cmd_env_list() elif args.env_action == "install": - cmd_env_install(args.simulator) + cmd_env_install(args.simulator, reinstall=args.reinstall) elif args.env_action == "check": cmd_env_check(args.simulator) else: diff --git a/easi/core/base_env_manager.py b/easi/core/base_env_manager.py index 4ab61fe..7cb37d2 100644 --- a/easi/core/base_env_manager.py +++ b/easi/core/base_env_manager.py @@ -110,6 +110,19 @@ def env_is_ready(self) -> bool: except (subprocess.TimeoutExpired, FileNotFoundError): return False + def remove(self) -> None: + """Remove the conda environment entirely (for --reinstall).""" + env_name = self.get_env_name() + env_path = self.conda_prefix / "envs" / env_name + if not env_path.exists(): + logger.info("Environment %s does not exist, nothing to remove", env_name) + return + self._run_command( + ["conda", "env", "remove", "-n", env_name, "-y"], + f"conda env remove {env_name}", + ) + logger.info("Environment %s removed", env_name) + def install(self) -> None: """Install the conda+uv environment with file-based locking. @@ -125,7 +138,7 @@ def install(self) -> None: def _do_install(self) -> None: """Execute the full install sequence (called under lock).""" env_name = self.get_env_name() - logger.info("[Env Installation] %s", env_name) + logger.info("[EASI] %s", env_name) # Step 1: Check system deps self._dep_checker.assert_all(self.get_system_deps()) @@ -135,7 +148,7 @@ def _do_install(self) -> None: if conda_yaml.exists(): self._run_conda_create(env_name, conda_yaml) else: - logger.warning("[Env Installation] No conda_env.yaml found at %s, skipping conda setup", conda_yaml) + logger.warning("[EASI] No conda_env.yaml found at %s, skipping conda setup", conda_yaml) # Step 3: Install uv in the conda env python_exec = self.get_python_executable() @@ -149,7 +162,7 @@ def _do_install(self) -> None: "uv pip install", ) else: - logger.warning("[Env Installation] No requirements.txt found at %s, skipping uv install", requirements) + logger.warning("[EASI] No requirements.txt found at %s, skipping uv install", requirements) # Step 5: Validate (stream output like other commands) self._run_command( @@ -157,7 +170,7 @@ def _do_install(self) -> None: "environment validation", ) - logger.info("[Env Installation] Environment %s installed and validated successfully", env_name) + logger.info("[EASI] Environment %s installed and validated successfully", env_name) def _run_conda_create(self, env_name: str, yaml_path: Path) -> None: """Create or update a conda environment from a YAML file.""" @@ -174,7 +187,7 @@ def _run_conda_create(self, env_name: str, yaml_path: Path) -> None: def _run_command(self, cmd: list[str], description: str) -> None: """Run a subprocess command, streaming output through the logger.""" - logger.info("[Env Installation] %s", " ".join(cmd)) + logger.info("[EASI] %s", " ".join(cmd)) process = subprocess.Popen( cmd, stdout=subprocess.PIPE, diff --git a/easi/simulators/ai2thor/v2_1_0/bridge.py b/easi/simulators/ai2thor/v2_1_0/bridge.py index 2bb9788..5b0b4f1 100644 --- a/easi/simulators/ai2thor/v2_1_0/bridge.py +++ b/easi/simulators/ai2thor/v2_1_0/bridge.py @@ -98,10 +98,14 @@ def start(self): screen_h = self.simulator_kwargs.get("screen_height", SCREEN_HEIGHT) screen_w = self.simulator_kwargs.get("screen_width", SCREEN_WIDTH) - logger.info("Starting AI2-THOR controller...") + # ai2thor Controller.start() prepends ':' to x_display, so we must + # strip any leading ':' from DISPLAY to avoid "::99" double-colon bug. + x_display = os.environ.get("DISPLAY", ":0").lstrip(":") + + logger.info("Starting AI2-THOR controller (display=%s)...", x_display) self.controller = Controller(quality=quality) self.controller.start( - x_display=os.environ.get("DISPLAY", "0"), + x_display=x_display, player_screen_height=screen_h, player_screen_width=screen_w, ) diff --git a/easi/simulators/ai2thor/v2_1_0/requirements.txt b/easi/simulators/ai2thor/v2_1_0/requirements.txt index b9e096b..e9c700f 100644 --- a/easi/simulators/ai2thor/v2_1_0/requirements.txt +++ b/easi/simulators/ai2thor/v2_1_0/requirements.txt @@ -2,3 +2,8 @@ ai2thor==2.1.0 numpy<1.24 Pillow scipy +werkzeug==1.0.1 +flask==1.1.2 +jinja2==2.11.3 +markupsafe==1.1.1 +itsdangerous==1.1.0 From 52d2f30fac66cec3feffc8a5cf07afb15216d1be Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Sat, 14 Feb 2026 13:27:47 +0800 Subject: [PATCH 005/239] [Feat] Added EBAlfred, working on dummy agent --- .gitignore | 2 + easi/cli.py | 27 +- easi/communication/filesystem.py | 16 +- easi/core/base_agent.py | 2 +- easi/core/base_env_manager.py | 48 +- easi/core/base_task.py | 7 +- easi/evaluation/runner.py | 2 +- easi/llm/api_client.py | 4 +- easi/llm/dummy_server.py | 2 +- easi/simulators/ai2thor/v2_1_0/bridge.py | 11 +- .../ai2thor/v2_1_0/requirements.txt | 3 + easi/simulators/ai2thor/v2_1_0/thor_utils.py | 11 +- easi/simulators/ai2thor/v5_0_0/bridge.py | 8 +- easi/simulators/base_bridge.py | 219 +++ easi/simulators/dummy/v1/bridge.py | 12 +- easi/simulators/registry.py | 2 +- easi/simulators/subprocess_runner.py | 4 +- easi/tasks/ebalfred/bridge.py | 884 +----------- easi/tasks/ebalfred/task.py | 45 +- easi/tasks/ebalfred/thor_utils.py | 303 ---- easi/tasks/ebalfred/vendor/EBAlfEnv.py | 413 ++++++ easi/tasks/ebalfred/vendor/__init__.py | 0 easi/tasks/ebalfred/vendor/env/__init__.py | 0 easi/tasks/ebalfred/vendor/env/reward.py | 289 ++++ easi/tasks/ebalfred/vendor/env/tasks.py | 476 +++++++ easi/tasks/ebalfred/vendor/env/thor_env.py | 601 ++++++++ easi/tasks/ebalfred/vendor/gen/__init__.py | 0 easi/tasks/ebalfred/vendor/gen/constants.py | 1221 +++++++++++++++++ .../tasks/ebalfred/vendor/gen/goal_library.py | 682 +++++++++ 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easi/tasks/ebalfred/vendor/gen/utils/__init__.py create mode 100644 easi/tasks/ebalfred/vendor/gen/utils/game_util.py create mode 100644 easi/tasks/ebalfred/vendor/gen/utils/image_util.py create mode 100644 easi/tasks/ebalfred/vendor/gen/utils/py_util.py create mode 100644 easi/tasks/ebalfred/vendor/models/__init__.py create mode 100644 easi/tasks/ebalfred/vendor/models/config/rewards.json create mode 100644 easi/tasks/ebalfred/vendor/thor_connector.py create mode 100644 easi/tasks/ebalfred/vendor/utils.py create mode 100644 easi/tasks/scaffold.py create mode 100644 easi/utils/spinner.py create mode 100644 tests/test_base_bridge.py create mode 100644 tests/test_scaffold.py diff --git a/.gitignore b/.gitignore index d3a87e4..f78151e 100644 --- a/.gitignore +++ b/.gitignore @@ -4,3 +4,5 @@ __pycache__/ *.pyo *.egg-info /build +/datasets +/results diff --git a/easi/cli.py b/easi/cli.py index 8f710ed..5922217 100644 --- a/easi/cli.py +++ b/easi/cli.py @@ -20,7 +20,7 @@ def build_parser() -> argparse.ArgumentParser: common = argparse.ArgumentParser(add_help=False) common.add_argument( "--verbosity", type=str, default="INFO", - choices=["DEBUG", "INFO", "WARNING", "ERROR"], + choices=["TRACE", "DEBUG", "INFO", "WARNING", "ERROR"], help="Set logging verbosity (default: INFO)", ) @@ -58,6 +58,12 @@ def build_parser() -> argparse.ArgumentParser: task_download = task_sub.add_parser("download", help="Download task dataset", parents=[common]) task_download.add_argument("task", type=str) + task_scaffold = task_sub.add_parser("scaffold", help="Generate boilerplate for a new benchmark", parents=[common]) + task_scaffold.add_argument("name", type=str, help="Task name in snake_case (e.g., 'my_benchmark')") + task_scaffold.add_argument("--simulator", type=str, default="dummy:v1", + help="Simulator key (e.g., 'ai2thor:v2_1_0')") + task_scaffold.add_argument("--max-steps", type=int, default=50) + # --- sim command group --- sim_parser = subparsers.add_parser("sim", help="Control simulators", parents=[common]) sim_sub = sim_parser.add_subparsers(dest="sim_action") @@ -173,6 +179,23 @@ def cmd_task_info(task_name: str) -> None: logger.info(" Max steps: %s", entry.max_steps) +def cmd_task_scaffold(name: str, simulator: str, max_steps: int) -> None: + from pathlib import Path + + from easi.tasks.scaffold import scaffold_task + + tasks_dir = Path(__file__).parent / "tasks" + tests_dir = Path(__file__).parent.parent / "tests" + task_dir = scaffold_task(name, simulator, output_dir=tasks_dir, + max_steps=max_steps, tests_dir=tests_dir) + logger.info("Created task scaffold at: %s", task_dir) + logger.info("Next steps:") + logger.info(" 1. Edit %s/bridge.py — implement _create_env() and _extract_image()", task_dir.name) + logger.info(" 2. Edit %s/task.py — implement format_reset_config()", task_dir.name) + logger.info(" 3. Edit %s/%s.yaml — configure dataset source", task_dir.name, name) + logger.info(" 4. Run tests: pytest tests/test_%s.py -v", name) + + def cmd_task_download(task_name: str) -> None: from easi.tasks.registry import load_task_class @@ -295,6 +318,8 @@ def main() -> None: cmd_task_info(args.task) elif args.task_action == "download": cmd_task_download(args.task) + elif args.task_action == "scaffold": + cmd_task_scaffold(args.name, args.simulator, args.max_steps) else: parser.parse_args(["task", "--help"]) diff --git a/easi/communication/filesystem.py b/easi/communication/filesystem.py index e5da74e..c4404c4 100644 --- a/easi/communication/filesystem.py +++ b/easi/communication/filesystem.py @@ -33,7 +33,7 @@ def create_workspace(prefix: str = "easi_") -> Path: never collide on file paths. """ workspace = Path(tempfile.mkdtemp(prefix=prefix)) - logger.debug("Created IPC workspace: %s", workspace) + logger.trace("Created IPC workspace: %s", workspace) return workspace @@ -43,7 +43,7 @@ def cleanup_workspace(workspace: Path) -> None: if workspace.exists(): shutil.rmtree(workspace, ignore_errors=True) - logger.debug("Cleaned up IPC workspace: %s", workspace) + logger.trace("Cleaned up IPC workspace: %s", workspace) def atomic_write_json(path: Path, data: dict) -> None: @@ -85,7 +85,7 @@ def write_command(workspace: Path, command: dict) -> None: delete_file(response_path) atomic_write_json(command_path, command) - logger.debug("Wrote command: %s", command.get("type", "unknown")) + logger.trace("Wrote command: %s", command.get("type", "unknown")) def poll_for_response( @@ -126,7 +126,7 @@ def poll_for_response( data = read_json(response_path) if data is not None: - logger.debug("Received response: status=%s", data.get("status", "unknown")) + logger.trace("Received response: status=%s", data.get("status", "unknown")) return data time.sleep(poll_interval) @@ -162,7 +162,7 @@ def poll_for_status( data = read_json(status_path) if data is not None: - logger.debug("Received status: ready=%s", data.get("ready", False)) + logger.trace("Received status: ready=%s", data.get("ready", False)) return data time.sleep(poll_interval) @@ -196,7 +196,7 @@ def poll_for_command( if data is not None: # Delete the command file after reading to signal we've consumed it delete_file(command_path) - logger.debug("Bridge received command: %s", data.get("type", "unknown")) + logger.trace("Bridge received command: %s", data.get("type", "unknown")) return data time.sleep(poll_interval) @@ -211,11 +211,11 @@ def write_response(workspace: Path, response: dict) -> None: """Write a response for the parent process to read (bridge-side).""" response_path = workspace / RESPONSE_FILE atomic_write_json(response_path, response) - logger.debug("Bridge wrote response: status=%s", response.get("status", "unknown")) + logger.trace("Bridge wrote response: status=%s", response.get("status", "unknown")) def write_status(workspace: Path, ready: bool) -> None: """Write a status file to signal bridge readiness (bridge-side).""" status_path = workspace / STATUS_FILE atomic_write_json(status_path, {"ready": ready}) - logger.debug("Bridge wrote status: ready=%s", ready) + logger.trace("Bridge wrote status: ready=%s", ready) diff --git a/easi/core/base_agent.py b/easi/core/base_agent.py index 85212f5..b031501 100644 --- a/easi/core/base_agent.py +++ b/easi/core/base_agent.py @@ -104,7 +104,7 @@ def act(self, observation: Observation, task_description: str) -> Action: "content": llm_response, }) - logger.debug( + logger.trace( "Step %d: action=%s params=%s", self._step_count, action.action_name, diff --git a/easi/core/base_env_manager.py b/easi/core/base_env_manager.py index 7cb37d2..bc75465 100644 --- a/easi/core/base_env_manager.py +++ b/easi/core/base_env_manager.py @@ -20,6 +20,7 @@ from easi.utils.locking import file_lock from easi.utils.logging import get_logger from easi.utils.paths import get_locks_dir +from easi.utils.spinner import spinner from easi.utils.system_deps import SystemDependencyChecker logger = get_logger(__name__) @@ -117,10 +118,11 @@ def remove(self) -> None: if not env_path.exists(): logger.info("Environment %s does not exist, nothing to remove", env_name) return - self._run_command( - ["conda", "env", "remove", "-n", env_name, "-y"], - f"conda env remove {env_name}", - ) + with spinner(f"Removing environment {env_name}"): + self._run_command( + ["conda", "env", "remove", "-n", env_name, "-y"], + f"conda env remove {env_name}", + ) logger.info("Environment %s removed", env_name) def install(self) -> None: @@ -138,7 +140,7 @@ def install(self) -> None: def _do_install(self) -> None: """Execute the full install sequence (called under lock).""" env_name = self.get_env_name() - logger.info("[EASI] %s", env_name) + logger.info("Installing environment %s for %s %s", env_name, self.simulator_name, self.version) # Step 1: Check system deps self._dep_checker.assert_all(self.get_system_deps()) @@ -146,31 +148,35 @@ def _do_install(self) -> None: # Step 2: Create/update conda env conda_yaml = self.get_conda_env_yaml_path() if conda_yaml.exists(): - self._run_conda_create(env_name, conda_yaml) + with spinner(f"Creating conda environment {env_name}"): + self._run_conda_create(env_name, conda_yaml) else: - logger.warning("[EASI] No conda_env.yaml found at %s, skipping conda setup", conda_yaml) + logger.warning("No conda_env.yaml found at %s, skipping conda setup", conda_yaml) # Step 3: Install uv in the conda env python_exec = self.get_python_executable() - self._run_command([python_exec, "-m", "pip", "install", "uv"], "pip install uv") + with spinner("Installing uv"): + self._run_command([python_exec, "-m", "pip", "install", "uv"], "pip install uv") # Step 4: Install Python deps via uv requirements = self.get_requirements_txt_path() if requirements.exists(): - self._run_command( - [python_exec, "-m", "uv", "pip", "install", "-r", str(requirements)], - "uv pip install", - ) + with spinner("Installing Python dependencies"): + self._run_command( + [python_exec, "-m", "uv", "pip", "install", "-r", str(requirements)], + "uv pip install", + ) else: - logger.warning("[EASI] No requirements.txt found at %s, skipping uv install", requirements) + logger.warning("No requirements.txt found at %s, skipping uv install", requirements) - # Step 5: Validate (stream output like other commands) - self._run_command( - [python_exec, "-c", self.get_validation_import()], - "environment validation", - ) + # Step 5: Validate + with spinner("Validating environment"): + self._run_command( + [python_exec, "-c", self.get_validation_import()], + "environment validation", + ) - logger.info("[EASI] Environment %s installed and validated successfully", env_name) + logger.info("Environment %s installed and validated successfully", env_name) def _run_conda_create(self, env_name: str, yaml_path: Path) -> None: """Create or update a conda environment from a YAML file.""" @@ -187,7 +193,7 @@ def _run_conda_create(self, env_name: str, yaml_path: Path) -> None: def _run_command(self, cmd: list[str], description: str) -> None: """Run a subprocess command, streaming output through the logger.""" - logger.info("[EASI] %s", " ".join(cmd)) + logger.trace("%s", " ".join(cmd)) process = subprocess.Popen( cmd, stdout=subprocess.PIPE, @@ -199,7 +205,7 @@ def _run_command(self, cmd: list[str], description: str) -> None: for line in process.stdout: line = line.rstrip() output_lines.append(line) - logger.debug(" %s", line) + logger.trace(" %s", line) process.wait() if process.returncode != 0: raise EnvironmentSetupError( diff --git a/easi/core/base_task.py b/easi/core/base_task.py index 41e5691..0e8757e 100644 --- a/easi/core/base_task.py +++ b/easi/core/base_task.py @@ -250,7 +250,10 @@ def _load_episodes_from_hf(self, dataset_config: dict) -> list[dict]: local_path, subset, split_name, ) - ds = load_dataset(local_path, subset, split=split_name) + import tempfile + hf_cache = Path(tempfile.gettempdir()) / "easi_hf_cache" + ds = load_dataset(local_path, subset, split=split_name, + cache_dir=str(hf_cache)) episodes = [hf_row_to_episode(row) for row in ds] for ep in episodes: @@ -334,7 +337,7 @@ def _extract_zip_files(dataset_dir: Path, zip_filenames: list[str]) -> None: marker = dataset_dir / f".{zip_name}.extracted" if marker.exists(): - logger.debug("Already extracted: %s", zip_name) + logger.trace("Already extracted: %s", zip_name) continue logger.info("Extracting %s...", zip_path) diff --git a/easi/evaluation/runner.py b/easi/evaluation/runner.py index 7bb10d2..198f8b7 100644 --- a/easi/evaluation/runner.py +++ b/easi/evaluation/runner.py @@ -18,7 +18,7 @@ 000_/ result.json trajectory.jsonl - rgb_0000.png, rgb_0001.png, ... + step_0000.png, step_0001.png, ... """ from __future__ import annotations diff --git a/easi/llm/api_client.py b/easi/llm/api_client.py index f6881da..4aab026 100644 --- a/easi/llm/api_client.py +++ b/easi/llm/api_client.py @@ -53,7 +53,7 @@ def generate( } url = f"{self.base_url}/v1/chat/completions" - logger.debug("POST %s (messages: %d)", url, len(messages)) + logger.trace("POST %s (messages: %d)", url, len(messages)) try: response = requests.post(url, json=payload, timeout=self.timeout) @@ -76,5 +76,5 @@ def generate( raise RuntimeError(f"LLM server returned no choices: {data}") content = choices[0].get("message", {}).get("content", "") - logger.debug("Response: %s", content[:100]) + logger.trace("Response: %s", content[:100]) return content diff --git a/easi/llm/dummy_server.py b/easi/llm/dummy_server.py index 3bdb414..67df20a 100644 --- a/easi/llm/dummy_server.py +++ b/easi/llm/dummy_server.py @@ -91,7 +91,7 @@ def _send_json(self, data: dict) -> None: self.wfile.write(body) def log_message(self, format: str, *args) -> None: - logger.debug(format, *args) + logger.trace(format, *args) def create_handler(mode: str, action_space: list[str]) -> type: diff --git a/easi/simulators/ai2thor/v2_1_0/bridge.py b/easi/simulators/ai2thor/v2_1_0/bridge.py index 5b0b4f1..4bdc859 100644 --- a/easi/simulators/ai2thor/v2_1_0/bridge.py +++ b/easi/simulators/ai2thor/v2_1_0/bridge.py @@ -21,7 +21,6 @@ import argparse import json -import logging import math import os import sys @@ -62,8 +61,9 @@ SCREEN_WIDTH, VISIBILITY_DISTANCE, ) +from easi.utils.logging import get_logger, setup_logging -logger = logging.getLogger("easi.bridge.ai2thor_v2_1_0") +logger = get_logger(__name__) class AI2ThorBridge: @@ -212,7 +212,7 @@ def run(self): elif cmd_type == "step": action_data = command.get("action", {}) action_text = action_data.get("action_name", "") - logger.debug("Step %d: action=%s", self.step_count + 1, action_text) + logger.trace("Step %d: action=%s", self.step_count + 1, action_text) try: response = self.step(action_text) @@ -305,10 +305,7 @@ def main(): help="JSON string of simulator configuration") args, _ = parser.parse_known_args() - logging.basicConfig( - level=logging.DEBUG, - format="%(asctime)s [%(levelname)s] %(name)s: %(message)s", - ) + setup_logging("DEBUG") simulator_kwargs = json.loads(args.simulator_kwargs) if args.simulator_kwargs else {} bridge = AI2ThorBridge( diff --git a/easi/simulators/ai2thor/v2_1_0/requirements.txt b/easi/simulators/ai2thor/v2_1_0/requirements.txt index e9c700f..f55a859 100644 --- a/easi/simulators/ai2thor/v2_1_0/requirements.txt +++ b/easi/simulators/ai2thor/v2_1_0/requirements.txt @@ -7,3 +7,6 @@ flask==1.1.2 jinja2==2.11.3 markupsafe==1.1.1 itsdangerous==1.1.0 +gym +networkx +opencv-python diff --git a/easi/simulators/ai2thor/v2_1_0/thor_utils.py b/easi/simulators/ai2thor/v2_1_0/thor_utils.py index c568e39..17bb331 100644 --- a/easi/simulators/ai2thor/v2_1_0/thor_utils.py +++ b/easi/simulators/ai2thor/v2_1_0/thor_utils.py @@ -4,20 +4,17 @@ that uses AI2-THOR 2.1.0. Task-specific logic (goal evaluation, dataset loading) lives in the task layer (e.g., easi/tasks/ebalfred/). -Ported from EmbodiedBench: -- envs/eb_alfred/gen/constants.py (THOR constants) -- envs/eb_alfred/utils.py (object name mapping) - This file runs inside the ai2thor conda env (Python 3.8). """ from __future__ import annotations -import logging import string -logger = logging.getLogger("easi.bridge.thor_utils") +from easi.utils.logging import get_logger + +logger = get_logger(__name__) -# --- Constants (from EmbodiedBench gen/constants.py) --- +# --- Constants --- SCREEN_WIDTH = 500 SCREEN_HEIGHT = 500 diff --git a/easi/simulators/ai2thor/v5_0_0/bridge.py b/easi/simulators/ai2thor/v5_0_0/bridge.py index 73acd0f..0d931d3 100644 --- a/easi/simulators/ai2thor/v5_0_0/bridge.py +++ b/easi/simulators/ai2thor/v5_0_0/bridge.py @@ -12,7 +12,6 @@ from __future__ import annotations import argparse -import logging import sys from pathlib import Path @@ -30,8 +29,9 @@ make_observation_response, parse_action_from_command, ) +from easi.utils.logging import get_logger, setup_logging -logger = logging.getLogger("easi.bridge.ai2thor_v5_0_0") +logger = get_logger(__name__) def run_bridge(workspace: Path) -> None: @@ -80,7 +80,7 @@ def run_bridge(workspace: Path) -> None: elif cmd_type == "step": step_count += 1 action = parse_action_from_command(command) - logger.debug("Step %d: action=%s", step_count, action.action_name) + logger.trace("Step %d: action=%s", step_count, action.action_name) # Stub: event = controller.step(action=action_name, **params) # v5 API returns event with frame, depth, metadata @@ -114,7 +114,7 @@ def main() -> None: parser.add_argument("--workspace", type=Path, required=True) args = parser.parse_args() - logging.basicConfig(level=logging.DEBUG, format="%(asctime)s [%(levelname)s] %(name)s: %(message)s") + setup_logging("DEBUG") run_bridge(workspace=args.workspace) diff --git a/easi/simulators/base_bridge.py b/easi/simulators/base_bridge.py new file mode 100644 index 0000000..8a002d2 --- /dev/null +++ b/easi/simulators/base_bridge.py @@ -0,0 +1,219 @@ +"""Base bridge that wraps any Gym-like env in EASI's IPC protocol. + +Subclass this to integrate external benchmark envs with minimal code. +Override _create_env() and _extract_image() — the base handles IPC, +image saving, and observation formatting. + +Two extension patterns: +- Pattern A: Override _create_env() + _extract_image() for Gym-like envs +- Pattern B: Override reset() + step() directly for raw simulator control + +Usage in a task-specific bridge.py: + + from easi.simulators.base_bridge import BaseBridge + + class MyBenchmarkBridge(BaseBridge): + def _create_env(self, reset_config, simulator_kwargs): + from external_package import MyEnv + return MyEnv(**reset_config) + + def _extract_image(self, obs): + return obs["head_rgb"] # np.ndarray (H, W, 3) + + if __name__ == "__main__": + MyBenchmarkBridge.main() +""" + +from __future__ import annotations + +import argparse +import json +import sys +from pathlib import Path + +import numpy as np + +# Ensure repo root is importable (for bridge subprocess) +_repo_root = Path(__file__).resolve().parents[2] +if str(_repo_root) not in sys.path: + sys.path.insert(0, str(_repo_root)) + +from easi.communication.filesystem import ( + poll_for_command, + write_response, + write_status, +) +from easi.communication.schemas import ( + make_error_response, + make_observation_response, +) +from easi.utils.logging import get_logger, setup_logging + +logger = get_logger(__name__) + + +class BaseBridge: + """Wraps a Gym-like env (reset/step/close) in EASI's filesystem IPC. + + Subclasses must implement: + _create_env(reset_config, simulator_kwargs) -> env object + _extract_image(obs) -> np.ndarray (H, W, 3) uint8 + + Optional overrides: + _extract_info(info) -> dict (filter/transform env info) + _on_reset(env, reset_config) -> obs (custom reset logic) + _on_step(env, action_text) -> (obs, reward, done, info) + """ + + def __init__(self, workspace, simulator_kwargs=None): + self.workspace = Path(workspace) + self.simulator_kwargs = simulator_kwargs or {} + self.env = None + self.step_count = 0 + self.episode_output_dir = None + + # --- Override these --- + + def _create_env(self, reset_config, simulator_kwargs): + """Create and return a Gym-like env. Called on first reset.""" + raise NotImplementedError + + def _extract_image(self, obs): + """Extract RGB numpy array (H, W, 3) from env observation.""" + raise NotImplementedError + + def _extract_info(self, info): + """Transform env info dict to EASI-compatible info. Override to filter.""" + return {k: v for k, v in info.items() + if isinstance(v, (int, float, str, bool))} + + def _on_reset(self, env, reset_config): + """Custom reset logic. Default: call env.reset(). Override if env + needs episode data passed differently.""" + return env.reset() + + def _on_step(self, env, action_text): + """Custom step logic. Default: call env.step(action_text).""" + return env.step(action_text) + + # --- Image saving --- + + def _save_image(self, image_array): + """Save RGB numpy array as PNG, return path string.""" + from PIL import Image + + save_dir = Path(self.episode_output_dir) if self.episode_output_dir else self.workspace + save_dir.mkdir(parents=True, exist_ok=True) + rgb_path = save_dir / ("step_%04d.png" % self.step_count) + Image.fromarray(image_array).save(str(rgb_path)) + return str(rgb_path) + + def _make_response(self, obs, reward=0.0, done=False, info=None): + """Build IPC observation response from env output.""" + image = self._extract_image(obs) + rgb_path = self._save_image(image) + clean_info = self._extract_info(info or {}) + clean_info["step"] = str(self.step_count) + return make_observation_response( + rgb_path=rgb_path, + agent_pose=[0.0, 0.0, 0.0, 0.0, 0.0, 0.0], + metadata={"step": str(self.step_count)}, + reward=reward, + done=done, + info=clean_info, + ) + + # --- Reset / Step --- + + def reset(self, reset_config): + """Reset env (create if first call) and return observation response.""" + if self.env is None: + self.env = self._create_env(reset_config, self.simulator_kwargs) + self.step_count = 0 + obs = self._on_reset(self.env, reset_config) + return self._make_response(obs) + + def step(self, action_text): + """Execute one action and return observation response.""" + self.step_count += 1 + obs, reward, done, info = self._on_step(self.env, action_text) + return self._make_response(obs, reward=reward, done=done, info=info) + + def close(self): + """Shut down the env.""" + if self.env is not None: + if hasattr(self.env, "close"): + self.env.close() + self.env = None + + # --- IPC loop --- + + def run(self): + """Main IPC loop. Polls commands, dispatches to reset/step/close.""" + write_status(self.workspace, ready=True) + + while True: + try: + command = poll_for_command(self.workspace, timeout=300.0) + except Exception as e: + logger.error("Failed to read command: %s", e) + break + + cmd_type = command.get("type") + + if cmd_type == "reset": + reset_config = command.get("reset_config", {}) + raw_output_dir = command.get("episode_output_dir") + if raw_output_dir: + self.episode_output_dir = raw_output_dir + Path(raw_output_dir).mkdir(parents=True, exist_ok=True) + else: + self.episode_output_dir = None + + try: + response = self.reset(reset_config) + write_response(self.workspace, response) + except Exception as e: + logger.exception("Reset failed") + write_response(self.workspace, make_error_response(str(e))) + + elif cmd_type == "step": + action_text = command.get("action", {}).get("action_name", "") + try: + response = self.step(action_text) + write_response(self.workspace, response) + except Exception as e: + logger.exception("Step failed") + write_response(self.workspace, make_error_response(str(e))) + + elif cmd_type == "close": + logger.info("Close command received") + self.close() + write_response(self.workspace, {"status": "ok"}) + break + + else: + write_response( + self.workspace, + make_error_response("Unknown command: %s" % cmd_type), + ) + + # --- CLI entry point --- + + @classmethod + def main(cls): + """Standard CLI entry point for bridge subclasses.""" + parser = argparse.ArgumentParser() + parser.add_argument("--workspace", type=Path, required=True) + parser.add_argument("--data-dir", type=Path, default=None) + parser.add_argument("--simulator-kwargs", type=str, default=None) + args, _ = parser.parse_known_args() + + setup_logging("DEBUG") + + sim_kwargs = json.loads(args.simulator_kwargs) if args.simulator_kwargs else {} + if args.data_dir: + sim_kwargs["data_dir"] = str(args.data_dir) + + bridge = cls(workspace=args.workspace, simulator_kwargs=sim_kwargs) + bridge.run() diff --git a/easi/simulators/dummy/v1/bridge.py b/easi/simulators/dummy/v1/bridge.py index bbea23b..fe4313d 100644 --- a/easi/simulators/dummy/v1/bridge.py +++ b/easi/simulators/dummy/v1/bridge.py @@ -13,7 +13,6 @@ from __future__ import annotations import argparse -import logging import sys import time from pathlib import Path @@ -34,8 +33,9 @@ make_observation_response, parse_action_from_command, ) +from easi.utils.logging import get_logger, setup_logging -logger = logging.getLogger("easi.bridge.dummy") +logger = get_logger(__name__) def _generate_dummy_image(workspace: Path, step: int, output_dir: Path | None = None) -> str: @@ -133,7 +133,7 @@ def run_bridge(workspace: Path, step_delay: float = 0.0) -> None: elif cmd_type == "step": step_count += 1 action = parse_action_from_command(command) - logger.debug("Step %d: action=%s", step_count, action.action_name) + logger.trace("Step %d: action=%s", step_count, action.action_name) if step_delay > 0: time.sleep(step_delay) @@ -168,11 +168,7 @@ def main() -> None: parser.add_argument("--step-delay", type=float, default=0.0, help="Delay per step in seconds") args, _ = parser.parse_known_args() - logging.basicConfig( - level=logging.DEBUG, - format="%(asctime)s [%(levelname)s] %(name)s: %(message)s", - datefmt="%H:%M:%S", - ) + setup_logging("DEBUG") run_bridge(workspace=args.workspace, step_delay=args.step_delay) diff --git a/easi/simulators/registry.py b/easi/simulators/registry.py index 3a938ce..2a61240 100644 --- a/easi/simulators/registry.py +++ b/easi/simulators/registry.py @@ -71,7 +71,7 @@ def _discover_simulators() -> dict[str, SimulatorEntry]: if ver_key == default_ver: entries[sim_name] = entry - logger.debug( + logger.trace( "Discovered simulator %s with versions: %s (default: %s)", sim_name, list(manifest.get("versions", {}).keys()), diff --git a/easi/simulators/subprocess_runner.py b/easi/simulators/subprocess_runner.py index f1dfba9..8cc4797 100644 --- a/easi/simulators/subprocess_runner.py +++ b/easi/simulators/subprocess_runner.py @@ -79,7 +79,7 @@ def launch(self) -> None: self.bridge_script_path.name, self._workspace, ) - logger.debug("Full command: %s", " ".join(cmd)) + logger.trace("Full command: %s", " ".join(cmd)) self._process = subprocess.Popen( cmd, @@ -200,7 +200,7 @@ def _stream_output(self) -> None: for line in proc.stdout: line = line.rstrip() self._output_lines.append(line) - logger.debug("[bridge] %s", line) + logger.trace("[bridge] %s", line) except (ValueError, OSError): pass # pipe closed diff --git a/easi/tasks/ebalfred/bridge.py b/easi/tasks/ebalfred/bridge.py index 0d6b2c7..f5a2468 100644 --- a/easi/tasks/ebalfred/bridge.py +++ b/easi/tasks/ebalfred/bridge.py @@ -1,873 +1,71 @@ -"""EB-Alfred bridge subprocess for AI2-THOR v2.1.0. +"""EB-Alfred bridge — wraps EmbodiedBench's EBAlfEnv via BaseBridge. This script runs inside the easi_ai2thor_v2_1_0 conda env (Python 3.8). It communicates with the parent process via filesystem IPC. -Extends the generic AI2ThorBridge with EB-Alfred-specific behavior: -- High-level skill execution (find, pick up, put down, open, close, etc.) -- State tracking (cleaned, cooled, heated objects) -- Goal condition evaluation - -Ported from EmbodiedBench: -- envs/eb_alfred/thor_connector.py (high-level skill API) -- envs/eb_alfred/env/thor_env.py (scene restoration, state tracking) +The vendor/ directory contains EBAlfEnv (Gym env) copied from EmbodiedBench +with minimal path changes. This bridge is a thin wrapper that delegates all +skill execution, scene restoration, and goal evaluation to EBAlfEnv. Usage: - python bridge.py --workspace /tmp/easi_xxx --data-dir /path/to/datasets [--simulator-kwargs '{}'] + python bridge.py --workspace /tmp/easi_xxx [--data-dir /path/to/datasets] [--simulator-kwargs '{}'] """ from __future__ import annotations -import argparse -import json -import logging -import math -import os import sys from pathlib import Path -# Add repo root to path for easi imports _repo_root = Path(__file__).resolve().parents[3] if str(_repo_root) not in sys.path: sys.path.insert(0, str(_repo_root)) -from easi.simulators.ai2thor.v2_1_0.bridge import AI2ThorBridge -from easi.simulators.ai2thor.v2_1_0.thor_utils import ( - AGENT_STEP_SIZE, - CAMERA_HEIGHT_OFFSET, - RECORD_SMOOTHING_FACTOR, - RENDER_CLASS_IMAGE, - RENDER_DEPTH_IMAGE, - RENDER_IMAGE, - RENDER_OBJECT_IMAGE, - VISIBILITY_DISTANCE, - get_obj_of_type_closest_to_obj, - get_objects_of_type, - natural_word_to_ithor_name, -) -from easi.tasks.ebalfred.thor_utils import ( - evaluate_goal_conditions, - load_task_json_with_repeat, -) - -logger = logging.getLogger("easi.bridge.ebalfred") - - -class EBAlfredBridge(AI2ThorBridge): - """EB-Alfred bridge extending AI2ThorBridge with skill execution and goal evaluation. - - Ported from EmbodiedBench's ThorEnv + ThorConnector. - """ - - def __init__(self, workspace, data_dir=None, simulator_kwargs=None): - super().__init__(workspace, data_dir, simulator_kwargs) - self.traj_data = None - - # State tracking (from EmbodiedBench ThorEnv) - self.cleaned_objects = set() - self.cooled_objects = set() - self.heated_objects = set() - - # Skill state - self.cur_receptacle = None - self.put_count_dict = {} - self.sliced = False - - # --- Reset --- - - def reset(self, reset_config): - """Reset to an EB-Alfred episode (or a default scene for smoke tests). - - If task_path is provided, loads traj_data.json, resets scene, - restores objects, and teleports agent. - If task_path is missing (smoke test), resets to a default scene. - """ - task_path = reset_config.get("task_path") - - if not task_path: - # Smoke test mode: use generic reset - self.traj_data = None - self.cleaned_objects.clear() - self.cooled_objects.clear() - self.heated_objects.clear() - self.cur_receptacle = None - self.put_count_dict = {} - self.sliced = False - return super().reset(reset_config) - - repeat_idx = reset_config.get("repeat_idx", 0) - - self.traj_data = load_task_json_with_repeat( - task_path, repeat_idx, str(self.data_dir) - ) - self.cleaned_objects.clear() - self.cooled_objects.clear() - self.heated_objects.clear() - self.step_count = 0 - self.cur_receptacle = None - self.put_count_dict = {} - self.sliced = False - - # Reset scene - scene_num = self.traj_data["scene"]["scene_num"] - scene_name = "FloorPlan%d" % scene_num - logger.info("Resetting to scene: %s", scene_name) - self.controller.reset(scene_name) - - # Restore object poses, toggles, dirty states - self._restore_scene() - - # Teleport agent to initial position - init_action = dict(self.traj_data["scene"]["init_action"]) - if init_action.get("action") == "TeleportFull": - init_action.pop("rotateOnTeleport", None) - init_action["standing"] = True - self.last_event = self.controller.step(init_action) +from easi.simulators.base_bridge import BaseBridge - # Cache agent height - self.agent_height = self.last_event.metadata["agent"]["position"]["y"] - # Cache reachable positions for navigation - self._cache_reachable_positions() +class EBAlfredBridge(BaseBridge): + """Thin BaseBridge wrapper around EmbodiedBench's EBAlfEnv.""" - return self._make_observation_response() + def _create_env(self, reset_config, simulator_kwargs): + import os + from easi.tasks.ebalfred.vendor.EBAlfEnv import EBAlfEnv - def _restore_scene(self): - """Restore EB-Alfred scene state from traj_data. - - Ported from EmbodiedBench ThorEnv.restore_scene(). - """ - grid_size = AGENT_STEP_SIZE / RECORD_SMOOTHING_FACTOR - - self._step(dict( - action="Initialize", - gridSize=grid_size, - cameraY=CAMERA_HEIGHT_OFFSET, - renderImage=RENDER_IMAGE, - renderDepthImage=RENDER_DEPTH_IMAGE, - renderClassImage=RENDER_CLASS_IMAGE, - renderObjectImage=RENDER_OBJECT_IMAGE, - visibility_distance=VISIBILITY_DISTANCE, - makeAgentsVisible=False, - )) - - scene = self.traj_data["scene"] - object_toggles = scene.get("object_toggles", []) - object_poses = scene.get("object_poses", []) - dirty_and_empty = scene.get("dirty_and_empty", False) - - if len(object_toggles) > 0: - self._step(dict(action="SetObjectToggles", objectToggles=object_toggles)) - - if dirty_and_empty: - self._step(dict( - action="SetStateOfAllObjects", - StateChange="CanBeDirty", - forceAction=True, - )) - self._step(dict( - action="SetStateOfAllObjects", - StateChange="CanBeFilled", - forceAction=False, - )) - - if len(object_poses) > 0: - self._step(dict(action="SetObjectPoses", objectPoses=object_poses)) - - # --- Step --- - - def step(self, action_text): - """Execute a high-level skill and return observation + metrics.""" - self.step_count += 1 - - # Smoke test mode: execute raw THOR action instead of high-level skill - if self.traj_data is None: - self._step(dict(action=action_text)) - return self._make_observation_response( - reward=0.0, - done=False, - info={ - "last_action_success": 1.0 if self.last_event.metadata["lastActionSuccess"] else 0.0, - "env_step": float(self.step_count), - "feedback": "success" if self.last_event.metadata["lastActionSuccess"] else "action failed", - }, - ) - - result = self._execute_skill(action_text) - success = result.get("success", False) - - # Update cleaned/cooled/heated tracking - self._update_states() - - # Evaluate goal conditions - goal_satisfied, progress = evaluate_goal_conditions( - self.traj_data, - self.last_event, - self.cleaned_objects, - self.cooled_objects, - self.heated_objects, + # data_dir from reset_config has the correct task-level path + # (e.g. datasets/oscarqjh_EB-Alfred_easi/tasks) + data_dir = reset_config.get("data_dir") or simulator_kwargs.get("data_dir") + resolution = simulator_kwargs.get("screen_height", 500) + # x_display from YAML, falling back to DISPLAY env var (set by xvfb-run) + x_display = simulator_kwargs.get( + "x_display", os.environ.get("DISPLAY", ":0").lstrip(":") ) - - feedback = result.get("message", "") - if success: - feedback = "success" - - return self._make_observation_response( - reward=float(progress), - done=goal_satisfied, - info={ - "task_success": 1.0 if goal_satisfied else 0.0, - "task_progress": progress, - "last_action_success": 1.0 if success else 0.0, - "env_step": float(self.step_count), - "feedback": feedback, - }, - ) - - def _execute_skill(self, instruction): - """Map high-level skill text to THOR API calls. - - Ported from EmbodiedBench ThorConnector.llm_skill_interact(). - """ - # Clear cur_receptacle unless put/open - if not (instruction.startswith("put down ") or instruction.startswith("open ")): - self.cur_receptacle = None - - ret_msg = "" - - if instruction.startswith("find "): - obj_name = instruction.replace("find a ", "").replace("find an ", "") - self.cur_receptacle = obj_name - ret_msg = self._nav_obj( - natural_word_to_ithor_name(obj_name), self.sliced - ) - - elif instruction.startswith("pick up "): - obj_name = instruction.replace("pick up the ", "") - ret_msg = self._pick(natural_word_to_ithor_name(obj_name)) - - elif instruction.startswith("put down "): - if self.cur_receptacle is None: - ret_msg = self._drop() - else: - receptacle = self.cur_receptacle - if receptacle in self.put_count_dict: - self.put_count_dict[receptacle] += 1 - else: - self.put_count_dict[receptacle] = 1 - - ret_msg = self._put(natural_word_to_ithor_name(receptacle)) - - if len(ret_msg) > 16 and self.put_count_dict[receptacle] >= 3: - self._drop() - ret_msg += ". The robot dropped the object instead." - self.last_event.metadata["lastActionSuccess"] = False - - elif instruction.startswith("open "): - obj_name = instruction.replace("open the ", "") - ret_msg = self._open(natural_word_to_ithor_name(obj_name)) - - elif instruction.startswith("close "): - obj_name = instruction.replace("close the ", "") - ret_msg = self._close(natural_word_to_ithor_name(obj_name)) - - elif instruction.startswith("turn on "): - obj_name = instruction.replace("turn on the ", "") - ret_msg = self._toggleon(natural_word_to_ithor_name(obj_name)) - - elif instruction.startswith("turn off "): - obj_name = instruction.replace("turn off the ", "") - ret_msg = self._toggleoff(natural_word_to_ithor_name(obj_name)) - - elif instruction.startswith("slice "): - obj_name = instruction.replace("slice the ", "") - ret_msg = self._slice(natural_word_to_ithor_name(obj_name)) - self.sliced = True - - elif instruction.startswith("drop"): - ret_msg = self._drop() - - else: - ret_msg = "instruction not supported" - - success = len(ret_msg) == 0 - if not success: - logger.warning("Skill failed: %s -> %s", instruction, ret_msg) - - return {"action": instruction, "success": success, "message": ret_msg} - - # --- State Tracking --- - - def _update_states(self): - """Track cleaned/cooled/heated objects. - - Ported from EmbodiedBench ThorEnv.update_states(). - """ - if self.last_event is None: - return - - metadata = self.last_event.metadata - if not metadata.get("lastActionSuccess", False): - return - - # Check if last action was a relevant toggle/close - last_action = metadata.get("lastAction", "") - last_obj_id = metadata.get("lastActionObjectId", "") - - # Clean: ToggleObjectOn on Faucet -> items in SinkBasin get cleaned - if last_action == "ToggleObjectOn" and "Faucet" in last_obj_id: - sink_basin = get_obj_of_type_closest_to_obj( - "SinkBasin", last_obj_id, metadata - ) - if sink_basin and sink_basin.get("receptacleObjectIds"): - self.cleaned_objects.update(sink_basin["receptacleObjectIds"]) - - # Heat: ToggleObjectOn on Microwave -> items inside get heated - if last_action == "ToggleObjectOn" and "Microwave" in last_obj_id: - microwaves = get_objects_of_type("Microwave", metadata) - if microwaves: - heated_ids = microwaves[0].get("receptacleObjectIds") - if heated_ids: - self.heated_objects.update(heated_ids) - - # Cool: CloseObject on Fridge -> items inside get cooled - if last_action == "CloseObject" and "Fridge" in last_obj_id: - fridges = get_objects_of_type("Fridge", metadata) - if fridges: - cooled_ids = fridges[0].get("receptacleObjectIds") - if cooled_ids: - self.cooled_objects.update(cooled_ids) - - # --- Individual Skill Implementations --- - - def _nav_obj(self, target_obj, prefer_sliced=False): - """Navigate to an object by teleporting to closest reachable position. - - Ported from EmbodiedBench ThorConnector.nav_obj(). - """ - objects = self.last_event.metadata["objects"] - logger.info("nav_obj: %s", target_obj) - - # Resolve object ID - if "|" in target_obj: - obj_id = target_obj - base_name = target_obj.split("|")[0] - tmp_id, tmp_data = self._get_obj_id_from_name( - base_name, priority_in_visibility=True, priority_sliced=prefer_sliced - ) - if tmp_id and "Sliced" in tmp_id and obj_id in tmp_id: - obj_id = tmp_id - else: - obj_id, _ = self._get_obj_id_from_name( - target_obj, priority_in_visibility=True, priority_sliced=prefer_sliced - ) - - # Find object index - obj_idx = -1 - for i, o in enumerate(objects): - if o["objectId"] == obj_id: - obj_idx = i - break - - if obj_idx == -1: - return ( - "Cannot find %s. This object may not exist in this scene. " - "Try to explore other instances instead." % target_obj - ) - - # Teleport to closest reachable position facing the object - loc = objects[obj_idx]["position"] - obj_rot = objects[obj_idx]["rotation"]["y"] - max_attempts = 20 - teleport_success = False - reachable_pos_idx = 0 - - for i in range(max_attempts): - reachable_pos_idx += 1 - if i == 10 and target_obj in ("Fridge", "Microwave"): - reachable_pos_idx -= 10 - - closest_loc = self._find_close_reachable_position( - [loc["x"], loc["y"], loc["z"]], reachable_pos_idx - ) - if closest_loc is None: - continue - - # Calculate desired rotation angle - rot_angle = math.atan2( - -(loc["x"] - closest_loc[0]), loc["z"] - closest_loc[2] - ) - if rot_angle > 0: - rot_angle -= 2 * math.pi - rot_angle = -(180 / math.pi) * rot_angle - - # Special angle filtering for Fridge/Microwave - if i < 10 and target_obj in ("Fridge", "Microwave"): - angle_d = abs(self._angle_diff(rot_angle, obj_rot)) - if target_obj == "Fridge" and not ( - (70 < angle_d < 110) or (250 < angle_d < 290) - ): - continue - if target_obj == "Microwave" and not ( - (160 < angle_d < 200) or (0 <= angle_d < 20) - ): - continue - - # Calculate horizon angle - camera_height = self.agent_height + CAMERA_HEIGHT_OFFSET - xz_dist = math.hypot( - loc["x"] - closest_loc[0], loc["z"] - closest_loc[2] - ) - hor_angle = math.atan2(loc["y"] - camera_height, xz_dist) - hor_angle = (180 / math.pi) * hor_angle * 0.9 - - self._step(dict( - action="TeleportFull", - x=closest_loc[0], - y=self.agent_height, - z=closest_loc[2], - rotation=rot_angle, - horizon=-hor_angle, - )) - - if self.last_event.metadata["lastActionSuccess"]: - teleport_success = True - break - else: - logger.debug( - "TeleportFull failed: %s", - self.last_event.metadata.get("errorMessage", ""), - ) - - if not teleport_success: - return "Cannot move to %s" % target_obj - - return "" - - def _pick(self, obj_name): - """Pick up an object. - - Ported from EmbodiedBench ThorConnector.pick(). - """ - obj_id, obj_data = self._get_obj_id_from_name( - obj_name, only_pickupable=True, priority_in_visibility=True, - priority_sliced=self.sliced, + return EBAlfEnv( + resolution=resolution, + data_dir=data_dir, + x_display=x_display, ) - logger.info("pick: %s -> %s", obj_name, obj_id) - - if obj_id is None: - return "Cannot find %s to pick up. Find the object before picking up it" % obj_name - - if ( - obj_data.get("visible") is False - and obj_data.get("parentReceptacles") - and len(obj_data["parentReceptacles"]) > 0 - ): - recep_name = obj_data["parentReceptacles"][0] - ret_msg = ( - "%s is not visible because it is in %s. " - "Note: multiple instances of %s may exist" - % (obj_name, recep_name, recep_name) - ) - # Try anyway - self._step(dict( - action="PickupObject", objectId=obj_id, forceAction=False - )) - else: - self._step(dict( - action="PickupObject", objectId=obj_id, forceAction=False - )) - ret_msg = "" - - if not self.last_event.metadata["lastActionSuccess"]: - inventory = self.last_event.metadata.get("inventoryObjects", []) - if len(inventory) == 0: - ret_msg = "Robot is not holding any object" - else: - holding_type = inventory[0].get("objectType", "unknown") - ret_msg = "Robot is currently holding %s" % holding_type - - if self.last_event.metadata["lastActionSuccess"]: - ret_msg = "" - - return ret_msg - - def _put(self, receptacle_name): - """Put held object onto a receptacle. - - Ported from EmbodiedBench ThorConnector.put(). - Uses 2x7x2 retry loop with movement adjustments. - """ - inventory = self.last_event.metadata.get("inventoryObjects", []) - if len(inventory) == 0: - return "Robot is not holding any object" - - holding_obj_id = inventory[0]["objectId"] - orig_receptacle_name = receptacle_name - ret_msg = "" - halt = False - last_recep_id = None - exclude_obj_id = None - - for k in range(2): # try closest and next closest receptacle - for j in range(7): # movement/look adjustments - for i in range(2): # try inherited receptacles (SinkBasin etc.) - if k == 1 and exclude_obj_id is None: - exclude_obj_id = last_recep_id - - if k == 0 and "|" in orig_receptacle_name: - if i == 1: - continue - recep_id = orig_receptacle_name - receptacle_name = orig_receptacle_name.split("|")[0] - else: - if "Sink" in receptacle_name or "Bathtub" in receptacle_name: - if i == 0: - recep_id, _ = self._get_obj_id_from_name( - receptacle_name, get_inherited=True, - exclude_obj_id=exclude_obj_id, - ) - else: - recep_id, _ = self._get_obj_id_from_name( - receptacle_name, exclude_obj_id=exclude_obj_id, - ) - else: - if i == 0: - recep_id, _ = self._get_obj_id_from_name( - receptacle_name, exclude_obj_id=exclude_obj_id, - ) - else: - recep_id, _ = self._get_obj_id_from_name( - receptacle_name, get_inherited=True, - exclude_obj_id=exclude_obj_id, - ) - - if not recep_id: - ret_msg = ( - "Putting the object on %s failed. First check whether " - "the receptacle is open or not. Also try other instances " - "of the receptacle" % receptacle_name - ) - continue - - # Movement adjustments to get receptacle in view - if j == 1: - self._step(dict(action="LookUp")) - self._step(dict(action="LookUp")) - elif j == 2: - self._step(dict(action="LookDown")) - self._step(dict(action="LookDown")) - self._step(dict(action="LookDown")) - self._step(dict(action="LookDown")) - elif j == 3: - self._step(dict(action="LookUp")) - self._step(dict(action="LookUp")) - self._step(dict(action="MoveBack")) - elif j == 4: - self._step(dict(action="MoveAhead")) - for _ in range(4): - self._step(dict(action="MoveRight")) - elif j == 5: - for _ in range(8): - self._step(dict(action="MoveLeft")) - elif j == 6: - for _ in range(4): - self._step(dict(action="MoveRight")) - self._step(dict(action="RotateHand", x=40)) - - self._step(dict( - action="PutObject", - objectId=holding_obj_id, - receptacleObjectId=recep_id, - forceAction=True, - )) - last_recep_id = recep_id - - if self.last_event.metadata["lastActionSuccess"]: - ret_msg = "" - halt = True - break - else: - logger.debug( - "PutObject failed: %s", - self.last_event.metadata.get("errorMessage", ""), - ) - ret_msg = ( - "Putting the object on %s failed. First check the " - "receptacle is open or not. Also try other instances " - "of the receptacle" % receptacle_name - ) - if halt: - break - if halt: - break - - return ret_msg - - def _open(self, obj_name): - """Open a receptacle. - - Ported from EmbodiedBench ThorConnector.open(). - """ - logger.info("open: %s", obj_name) - - if "|" in obj_name: - obj_id = obj_name - obj_name = obj_name.split("|")[0] - else: - obj_id, _ = self._get_obj_id_from_name(obj_name) - - if obj_id is None: - return "Cannot find %s to open. Find the object before opening it" % obj_name - - # Check if already open - open_flag = False - for ob in self.last_event.metadata["objects"]: - if ob["objectId"] == obj_id and ob.get("openable") and ob.get("isOpen"): - open_flag = True - break - - ret_msg = "" - for i in range(4): - self._step(dict(action="OpenObject", objectId=obj_id)) - - if self.last_event.metadata["lastActionSuccess"]: - ret_msg = "" - break - else: - if open_flag: - ret_msg = "Open action failed. The %s is already open" % obj_name - else: - ret_msg = "Open action failed." - - # Move around to avoid self-collision - if i == 0: - self._step(dict(action="MoveBack")) - elif i == 1: - self._step(dict(action="MoveBack")) - self._step(dict(action="MoveRight")) - elif i == 2: - self._step(dict(action="MoveLeft")) - self._step(dict(action="MoveLeft")) - - return ret_msg - - def _close(self, obj_name): - """Close a receptacle. - - Ported from EmbodiedBench ThorConnector.close(). - """ - logger.info("close: %s", obj_name) - - if "|" in obj_name: - obj_id = obj_name - obj_name = obj_name.split("|")[0] - else: - obj_id, _ = self._get_obj_id_from_name(obj_name) - - if obj_id is None: - return "Cannot find %s to close" % obj_name - - self._step(dict(action="CloseObject", objectId=obj_id)) - - if not self.last_event.metadata["lastActionSuccess"]: - ret_msg = "Close action failed" - for ob in self.last_event.metadata["objects"]: - if ( - ob["objectId"] == obj_id - and ob.get("openable") - and not ob.get("isOpen") - ): - ret_msg += ". The %s is already closed" % obj_name - break - return ret_msg - - return "" - - def _toggleon(self, obj_name): - """Toggle an object on. - - Ported from EmbodiedBench ThorConnector.toggleon(). - """ - logger.info("toggleon: %s", obj_name) - obj_id, _ = self._get_obj_id_from_name(obj_name, only_toggleable=True) - - if obj_id is None: - return "Cannot find %s to turn on" % obj_name - - try: - self._step(dict(action="ToggleObjectOn", objectId=obj_id)) - if not self.last_event.metadata["lastActionSuccess"]: - return "Turn on action failed" - except Exception: - self.last_event.metadata["lastActionSuccess"] = False - return "Turn on action failed" - - return "" - - def _toggleoff(self, obj_name): - """Toggle an object off. - - Ported from EmbodiedBench ThorConnector.toggleoff(). - """ - logger.info("toggleoff: %s", obj_name) - obj_id, _ = self._get_obj_id_from_name(obj_name, only_toggleable=True) - - if obj_id is None: - return "Cannot find %s to turn off" % obj_name - - self._step(dict(action="ToggleObjectOff", objectId=obj_id)) - - if not self.last_event.metadata["lastActionSuccess"]: - return "Turn off action failed" - - return "" - - def _slice(self, obj_name): - """Slice an object. - - Ported from EmbodiedBench ThorConnector.slice(). - """ - logger.info("slice: %s", obj_name) - obj_id, _ = self._get_obj_id_from_name(obj_name) - - if obj_id is None: - return "Cannot find %s to slice" % obj_name - - self._step(dict(action="SliceObject", objectId=obj_id)) - - if not self.last_event.metadata["lastActionSuccess"]: - return "Slice action failed" - - return "" - - def _drop(self): - """Drop the held object. - - Ported from EmbodiedBench ThorConnector.drop(). - """ - logger.info("drop") - self._step(dict(action="DropHandObject", forceAction=True)) - - if not self.last_event.metadata["lastActionSuccess"]: - inventory = self.last_event.metadata.get("inventoryObjects", []) - if len(inventory) == 0: - return "Robot is not holding any object" - return "Drop action failed" - - return "" - - # --- Object Lookup Helpers --- - - def _get_obj_id_from_name( - self, - obj_name, - only_pickupable=False, - only_toggleable=False, - priority_sliced=False, - get_inherited=False, - parent_receptacle_penalty=True, - priority_in_visibility=False, - exclude_obj_id=None, - ): - """Find the closest object matching name, with priority scoring. - - Ported from EmbodiedBench ThorConnector.get_obj_id_from_name(). - """ - obj_id = None - obj_data = None - min_distance = 1e8 - - # If name contains digits (e.g. 'Cabinet_2'), match by name directly - if any(c.isdigit() for c in obj_name): - for obj in self.last_event.metadata["objects"]: - if obj_name in obj["name"]: - return obj["objectId"], obj - return None, None - - for obj in self.last_event.metadata["objects"]: - if obj["objectId"] == exclude_obj_id: - continue - - if only_pickupable and not obj.get("pickupable", False): - continue - if only_toggleable and not obj.get("toggleable", False): - continue - - # Match object type (case-insensitive) - obj_type = obj["objectId"].split("|")[0] - if obj_type.casefold() != obj_name.casefold(): - continue - - # For inherited receptacles (e.g. SinkBasin), check ID format - if get_inherited and len(obj["objectId"].split("|")) != 5: - continue - if not get_inherited and len(obj["objectId"].split("|")) == 5: - # Skip inherited if not requested - pass - - distance = obj.get("distance", 1e8) - penalty = 0 - - # Penalize objects inside closed receptacles - if parent_receptacle_penalty and obj.get("parentReceptacles"): - for p in obj["parentReceptacles"]: - is_open = self._get_object_prop(p, "isOpen") - openable = self._get_object_prop(p, "openable") - if openable is True and is_open is False: - penalty += 100000 - break - - # Prefer empty stove burners - if obj_name.casefold() == "stoveburner": - recep_ids = obj.get("receptacleObjectIds") or [] - if len(recep_ids) > 0: - penalty += 10000 - - # Penalize non-visible objects - if priority_in_visibility and not obj.get("visible", False): - penalty += 1000 - - # Prefer sliced objects - if priority_sliced and "_Slice" in obj.get("name", ""): - penalty -= 100 - - if distance + penalty < min_distance: - min_distance = distance + penalty - obj_data = obj - obj_id = obj["objectId"] - - return obj_id, obj_data - - def _get_object_prop(self, name, prop): - """Get a property of an object by name/ID substring.""" - for obj in self.last_event.metadata["objects"]: - if name in obj["objectId"]: - return obj.get(prop) - return None + def _on_reset(self, env, reset_config): + episode = { + "task": reset_config["task"], + "repeat_idx": reset_config["repeat_idx"], + "instruction": reset_config["instruction"], + } + return env.reset(episode=episode) -def main(): - parser = argparse.ArgumentParser(description="AI2-THOR v2.1.0 bridge (EB-Alfred)") - parser.add_argument("--workspace", type=Path, required=True) - parser.add_argument("--data-dir", type=Path, default=Path("./datasets")) - parser.add_argument("--simulator-kwargs", type=str, default=None, - help="JSON string of simulator configuration") - args, _ = parser.parse_known_args() + def _on_step(self, env, action_text): + return env.step(action_text) - logging.basicConfig( - level=logging.DEBUG, - format="%(asctime)s [%(levelname)s] %(name)s: %(message)s", - ) + def _extract_image(self, obs): + return obs["head_rgb"] - simulator_kwargs = json.loads(args.simulator_kwargs) if args.simulator_kwargs else {} - bridge = EBAlfredBridge( - workspace=args.workspace, - data_dir=args.data_dir, - simulator_kwargs=simulator_kwargs, - ) - bridge.run() + def _extract_info(self, info): + return { + "task_success": float(info.get("task_success", 0.0)), + "task_progress": float(info.get("task_progress", 0.0)), + "last_action_success": float(info.get("last_action_success", 0.0)), + "feedback": str(info.get("env_feedback", "")), + } if __name__ == "__main__": - main() + EBAlfredBridge.main() diff --git a/easi/tasks/ebalfred/task.py b/easi/tasks/ebalfred/task.py index fc52f88..5771f65 100644 --- a/easi/tasks/ebalfred/task.py +++ b/easi/tasks/ebalfred/task.py @@ -1,13 +1,15 @@ """EB-Alfred task for EASI. Adapts the EmbodiedBench EB-Alfred track to EASI's task interface. -Supports multiple splits via per-split .yaml configs (Task 4 infra). +Supports multiple splits via per-split .yaml configs. -When loaded via a split yaml (e.g., ebalfred_base), episodes come from -HuggingFace dataset rows. When loaded via get_task_yaml_path(), uses -ebalfred_base.yaml as the default fallback. +The vendor/ directory contains EBAlfEnv (Gym env) copied from EmbodiedBench. +The bridge wraps EBAlfEnv via BaseBridge, delegating all skill execution, +scene restoration, and goal evaluation to the vendor code. -Reference: EmbodiedBench/embodiedbench/envs/eb_alfred/EBAlfEnv.py +Episode data flows from HF dataset → task.format_reset_config() → bridge +reset_config → EBAlfEnv.reset(episode). Each episode dict must have 'task', +'repeat_idx', and 'instruction' keys. """ from __future__ import annotations @@ -29,7 +31,6 @@ def __init__(self, data_dir: Path | None = None, split_yaml_path: Path | None = self._config["action_space"] = get_global_action_space() def get_task_yaml_path(self) -> Path: - # Decision #4: No task.yaml — use ebalfred_base.yaml as default fallback return Path(__file__).parent / "ebalfred_base.yaml" def get_bridge_script_path(self) -> Path: @@ -37,25 +38,23 @@ def get_bridge_script_path(self) -> Path: return Path(__file__).parent / "bridge.py" def get_instruction(self, episode: dict) -> str: - """Decision #2: EB-Alfred uses 'instruction' field from HF row.""" + """EB-Alfred uses 'instruction' field from HF row.""" return episode.get("instruction", self.name) def format_reset_config(self, episode: dict) -> dict: - """Map EB-Alfred episode (HF row) to AI2-THOR bridge reset config. + """Map EB-Alfred episode to bridge reset config. - HF row columns: id, task, repeat_idx, instruction, task_type, trial_id - The bridge uses task_path to find annotation files inside the extracted tasks.zip: - /oscarqjh_EB-Alfred_easi/tasks//pp/ann_.json + Passes the episode data directly so EBAlfEnv.reset() can load the + task JSON and restore the scene. data_dir points to the HF dataset's + tasks/ subdirectory containing the task JSON files. """ data_dir = episode.get("_data_dir", "") return { - "task_path": episode["task"], - "repeat_idx": episode.get("repeat_idx", 0), - "instruction": episode.get("instruction", ""), - "episode_id": episode.get("id", ""), - "task_type": episode.get("task_type", ""), - "trial_id": episode.get("trial_id", ""), - "data_dir": data_dir, + "episode_id": episode.get("id", "unknown"), + "task": episode["task"], + "repeat_idx": episode["repeat_idx"], + "instruction": episode["instruction"], + "data_dir": str(Path(data_dir) / "tasks") if data_dir else "", } def evaluate_episode( @@ -64,14 +63,13 @@ def evaluate_episode( """Extract metrics from the trajectory. The bridge reports task_success and task_progress in StepResult.info, - computed by EB-Alfred's goal_conditions_met() running inside the bridge. + computed by EBAlfEnv's goal_conditions_met() running inside the bridge. """ if not trajectory: return { "task_success": 0.0, "task_progress": 0.0, "num_steps": 0.0, - "total_reward": 0.0, } last_step = trajectory[-1] @@ -79,15 +77,10 @@ def evaluate_episode( "task_success": last_step.info.get("task_success", 0.0), "task_progress": last_step.info.get("task_progress", 0.0), "num_steps": float(len(trajectory)), - "total_reward": sum(s.reward for s in trajectory), } def _get_builtin_episodes(self) -> list[dict]: - """Return minimal built-in episodes for testing without dataset. - - Matches EB-Alfred_easi column structure: id, task, repeat_idx, - instruction, task_type, trial_id. - """ + """Return minimal built-in episodes for testing without dataset.""" return [ { "id": 0, diff --git a/easi/tasks/ebalfred/thor_utils.py b/easi/tasks/ebalfred/thor_utils.py deleted file mode 100644 index d32a12d..0000000 --- a/easi/tasks/ebalfred/thor_utils.py +++ /dev/null @@ -1,303 +0,0 @@ -"""EB-Alfred goal evaluation and task loading utilities. - -Moved from easi/simulators/ai2thor/v2_1_0/thor_utils.py — these are -task-specific (EB-Alfred), not generic AI2-THOR helpers. - -Ported from EmbodiedBench: -- envs/eb_alfred/env/tasks.py (goal evaluation) -- envs/eb_alfred/utils.py (task loading) - -This file runs inside the ai2thor conda env (Python 3.8). -""" -from __future__ import annotations - -import json -import logging -import os - -# numpy is available in the ai2thor conda env (Python 3.8) -# but may not be in the host venv — import is optional for importability -try: - import numpy as np -except ImportError: - np = None # type: ignore[assignment] - -from easi.simulators.ai2thor.v2_1_0.thor_utils import ( - get_objects_of_type, - get_objects_with_name_and_prop, -) - -logger = logging.getLogger("easi.bridge.ebalfred_utils") - -GOALS = [ - "pick_and_place_simple", - "pick_two_obj_and_place", - "look_at_obj_in_light", - "pick_clean_then_place_in_recep", - "pick_heat_then_place_in_recep", - "pick_cool_then_place_in_recep", - "pick_and_place_with_movable_recep", -] - - -# --- Task JSON Loading --- - -def load_task_json(task_path: str, data_dir: str) -> dict: - """Load preprocessed traj_data.json for an EB-Alfred episode. - - Args: - task_path: e.g. 'pick_and_place_simple-Mug-None-Shelf-1/trial_T20190001' - data_dir: path to the downloaded dataset directory - """ - # Try standard path: /tasks//pp/ann_0.json - # (repeat_idx is handled by the caller) - json_path = os.path.join(data_dir, "tasks", task_path, "pp", "ann_0.json") - if not os.path.exists(json_path): - # Fallback: try traj_data.json - json_path = os.path.join(data_dir, "tasks", task_path, "traj_data.json") - if not os.path.exists(json_path): - raise FileNotFoundError( - f"Task annotation not found at {json_path}. " - f"Ensure tasks.zip was extracted in {data_dir}" - ) - with open(json_path) as f: - data = json.load(f) - return data - - -def load_task_json_with_repeat(task_path: str, repeat_idx: int, data_dir: str) -> dict: - """Load the specific repeat annotation for an EB-Alfred episode.""" - json_path = os.path.join(data_dir, "tasks", task_path, "pp", f"ann_{repeat_idx}.json") - if not os.path.exists(json_path): - # Fallback to ann_0.json - json_path = os.path.join(data_dir, "tasks", task_path, "pp", "ann_0.json") - if not os.path.exists(json_path): - # Fallback to traj_data.json - json_path = os.path.join(data_dir, "tasks", task_path, "traj_data.json") - if not os.path.exists(json_path): - raise FileNotFoundError( - f"Task annotation not found for {task_path} repeat {repeat_idx} in {data_dir}" - ) - with open(json_path) as f: - data = json.load(f) - return data - - -# --- Goal Evaluation --- - -def get_targets_from_traj(traj_data: dict) -> dict: - """Extract goal targets from trajectory data.""" - pddl = traj_data.get("pddl_params", {}) - targets = {} - # Map from traj keys to goal target keys - if "object_target" in pddl: - targets["object"] = pddl["object_target"] - if "parent_target" in pddl: - targets["parent"] = pddl["parent_target"] - if "toggle_target" in pddl: - targets["toggle"] = pddl["toggle_target"] - if "mrecep_target" in pddl: - targets["mrecep"] = pddl["mrecep_target"] - # Sliced variant - if "object_sliced" in pddl and pddl["object_sliced"]: - targets["object"] = targets.get("object", "") + "Sliced" - return targets - - -def evaluate_goal_conditions( - traj_data: dict, - event, - cleaned_objects: set, - cooled_objects: set, - heated_objects: set, -) -> tuple: - """Evaluate EB-Alfred goal conditions. - - Returns: - (goal_satisfied: bool, progress: float) - - progress = satisfied_conditions / total_conditions - """ - task_type = traj_data.get("task_type", "") - targets = get_targets_from_traj(traj_data) - metadata = event.metadata - - evaluator = GOAL_EVALUATORS.get(task_type) - if evaluator is None: - logger.warning("Unknown task type: %s", task_type) - return False, 0.0 - - s, ts = evaluator(targets, metadata, cleaned_objects, cooled_objects, heated_objects) - progress = s / ts if ts > 0 else 0.0 - satisfied = s >= ts - return satisfied, progress - - -# --- Per-task-type goal evaluators --- - -def _eval_pick_and_place_simple(targets, metadata, cleaned, cooled, heated): - ts = 1 - s = 0 - receptacles = get_objects_with_name_and_prop(targets.get("parent", ""), "receptacle", metadata) - pickupables = get_objects_with_name_and_prop(targets.get("object", ""), "pickupable", metadata) - - if "Sliced" in targets.get("object", ""): - ts += 1 - if len([p for p in pickupables if "Sliced" in p["objectId"]]) >= 1: - s += 1 - - if np.any([np.any([p["objectId"] in (r.get("receptacleObjectIds") or []) - for r in receptacles]) - for p in pickupables]): - s += 1 - return s, ts - - -def _eval_pick_two_obj_and_place(targets, metadata, cleaned, cooled, heated): - ts = 2 - s = 0 - receptacles = get_objects_with_name_and_prop(targets.get("parent", ""), "receptacle", metadata) - pickupables = get_objects_with_name_and_prop(targets.get("object", ""), "pickupable", metadata) - - if "Sliced" in targets.get("object", ""): - ts += 2 - s += min(len([p for p in pickupables if "Sliced" in p["objectId"]]), 2) - - s += min(np.max([sum([1 if (r.get("receptacleObjectIds") is not None - and p["objectId"] in r["receptacleObjectIds"]) else 0 - for p in pickupables]) - for r in receptacles]) if receptacles else 0, 2) - return s, ts - - -def _eval_look_at_obj_in_light(targets, metadata, cleaned, cooled, heated): - ts = 2 - s = 0 - toggleables = get_objects_with_name_and_prop(targets.get("toggle", ""), "toggleable", metadata) - pickupables = get_objects_with_name_and_prop(targets.get("object", ""), "pickupable", metadata) - inventory = metadata.get("inventoryObjects", []) - - if "Sliced" in targets.get("object", ""): - ts += 1 - if len([p for p in pickupables if "Sliced" in p["objectId"]]) >= 1: - s += 1 - - if len(inventory) > 0 and inventory[0]["objectId"] in [p["objectId"] for p in pickupables]: - s += 1 - if np.any([t["isToggled"] and t["visible"] for t in toggleables]): - s += 1 - return s, ts - - -def _eval_pick_heat_then_place(targets, metadata, cleaned, cooled, heated): - ts = 3 - s = 0 - receptacles = get_objects_with_name_and_prop(targets.get("parent", ""), "receptacle", metadata) - pickupables = get_objects_with_name_and_prop(targets.get("object", ""), "pickupable", metadata) - - if "Sliced" in targets.get("object", ""): - ts += 1 - if len([p for p in pickupables if "Sliced" in p["objectId"]]) >= 1: - s += 1 - - objs_in_place = [p["objectId"] for p in pickupables for r in receptacles - if r.get("receptacleObjectIds") is not None and p["objectId"] in r["receptacleObjectIds"]] - objs_heated = [p["objectId"] for p in pickupables if p["objectId"] in heated] - - if len(objs_in_place) > 0: - s += 1 - if len(objs_heated) > 0: - s += 1 - if np.any([obj_id in objs_heated for obj_id in objs_in_place]): - s += 1 - return s, ts - - -def _eval_pick_cool_then_place(targets, metadata, cleaned, cooled, heated): - ts = 3 - s = 0 - receptacles = get_objects_with_name_and_prop(targets.get("parent", ""), "receptacle", metadata) - pickupables = get_objects_with_name_and_prop(targets.get("object", ""), "pickupable", metadata) - - if "Sliced" in targets.get("object", ""): - ts += 1 - if len([p for p in pickupables if "Sliced" in p["objectId"]]) >= 1: - s += 1 - - objs_in_place = [p["objectId"] for p in pickupables for r in receptacles - if r.get("receptacleObjectIds") is not None and p["objectId"] in r["receptacleObjectIds"]] - objs_cooled = [p["objectId"] for p in pickupables if p["objectId"] in cooled] - - if len(objs_in_place) > 0: - s += 1 - if len(objs_cooled) > 0: - s += 1 - if np.any([obj_id in objs_cooled for obj_id in objs_in_place]): - s += 1 - return s, ts - - -def _eval_pick_clean_then_place(targets, metadata, cleaned, cooled, heated): - ts = 3 - s = 0 - receptacles = get_objects_with_name_and_prop(targets.get("parent", ""), "receptacle", metadata) - pickupables = get_objects_with_name_and_prop(targets.get("object", ""), "pickupable", metadata) - - if "Sliced" in targets.get("object", ""): - ts += 1 - if len([p for p in pickupables if "Sliced" in p["objectId"]]) >= 1: - s += 1 - - objs_in_place = [p["objectId"] for p in pickupables for r in receptacles - if r.get("receptacleObjectIds") is not None and p["objectId"] in r["receptacleObjectIds"]] - objs_cleaned = [p["objectId"] for p in pickupables if p["objectId"] in cleaned] - - if len(objs_in_place) > 0: - s += 1 - if len(objs_cleaned) > 0: - s += 1 - if np.any([obj_id in objs_cleaned for obj_id in objs_in_place]): - s += 1 - return s, ts - - -def _eval_pick_and_place_with_movable_recep(targets, metadata, cleaned, cooled, heated): - ts = 3 - s = 0 - receptacles = get_objects_with_name_and_prop(targets.get("parent", ""), "receptacle", metadata) - pickupables = get_objects_with_name_and_prop(targets.get("object", ""), "pickupable", metadata) - movables = get_objects_with_name_and_prop(targets.get("mrecep", ""), "pickupable", metadata) - - if "Sliced" in targets.get("object", ""): - ts += 1 - if len([p for p in pickupables if "Sliced" in p["objectId"]]) >= 1: - s += 1 - - pickup_in_movable = [p for p in pickupables for m in movables - if m.get("receptacleObjectIds") is not None - and p["objectId"] in m["receptacleObjectIds"]] - movable_in_recep = [m for m in movables for r in receptacles - if r.get("receptacleObjectIds") is not None - and m["objectId"] in r["receptacleObjectIds"]] - - if len(pickup_in_movable) > 0: - s += 1 - if len(movable_in_recep) > 0: - s += 1 - if np.any([np.any([p["objectId"] in (m.get("receptacleObjectIds") or []) for p in pickupables]) and - np.any([r["objectId"] in (m.get("parentReceptacles") or []) for r in receptacles]) - for m in movables - if m.get("parentReceptacles") is not None and m.get("receptacleObjectIds") is not None]): - s += 1 - return s, ts - - -GOAL_EVALUATORS = { - "pick_and_place_simple": _eval_pick_and_place_simple, - "pick_two_obj_and_place": _eval_pick_two_obj_and_place, - "look_at_obj_in_light": _eval_look_at_obj_in_light, - "pick_heat_then_place_in_recep": _eval_pick_heat_then_place, - "pick_cool_then_place_in_recep": _eval_pick_cool_then_place, - "pick_clean_then_place_in_recep": _eval_pick_clean_then_place, - "pick_and_place_with_movable_recep": _eval_pick_and_place_with_movable_recep, -} diff --git a/easi/tasks/ebalfred/vendor/EBAlfEnv.py b/easi/tasks/ebalfred/vendor/EBAlfEnv.py new file mode 100644 index 0000000..4d5a44b --- /dev/null +++ b/easi/tasks/ebalfred/vendor/EBAlfEnv.py @@ -0,0 +1,413 @@ +""" +AI2THOR Environment for Household Robot Task Simulation + +This module provides a custom OpenAI Gym environment for simulating household robot tasks +using the AI2THOR framework. It supports various object interactions and task scenarios. + +Dependencies: +- ai2thor +- gym +- numpy +- PIL +""" + +import os +import time +import gym +import json +import numpy as np +from PIL import Image + +# Import custom modules +import easi.tasks.ebalfred.vendor.utils as utils +from easi.tasks.ebalfred.vendor.utils import alfred_objs, alfred_open_obj, alfred_pick_obj, alfred_slice_obj, alfred_open_obj, alfred_toggle_obj, alfred_recep +from easi.tasks.ebalfred.vendor.thor_connector import ThorConnector +from easi.utils.logging import get_logger +logger = get_logger(__name__) + +# global information +X_DISPLAY = '1' +ALFRED_REWARD_PATH = os.path.join(os.path.dirname(__file__), 'models/config/rewards.json') +ALFRED_DATASET_PATH = os.path.join(os.path.dirname(__file__), 'data/json_2.1.0') + + +def get_global_action_space(): + """ + Generate a comprehensive action space for the environment. + + Returns: + list: A list of supported action strings for various object interactions + """ + action_space = [] + + # Generate find actions for all objects + findable_objs = alfred_objs + action_space.extend([f"find a {obj}" for obj in findable_objs]) + + # Generate pickup, putdown, and drop actions + pickup_objs = alfred_pick_obj + for obj in pickup_objs: + action_space.extend([ + f"pick up the {obj}", + ]) + + action_space.extend([ + f"put down the object in hand", + f"drop the object in hand" + ]) + + # Generate open/close actions + open_objs = alfred_open_obj + for obj in open_objs: + action_space.extend([ + f"open the {obj}", + f"close the {obj}" + ]) + + # Generate toggle actions + turn_on_objs = alfred_toggle_obj + for obj in turn_on_objs: + action_space.extend([ + f"turn on the {obj}", + f"turn off the {obj}" + ]) + + # Generate slice actions + slice_objs = alfred_slice_obj + action_space.extend([f"slice the {obj}" for obj in slice_objs]) + + return action_space + + +class EBAlfEnv(gym.Env): + """ + Custom OpenAI Gym environment for simulating household robot tasks. + + Attributes: + env (ThorConnector): Interface for AI2THOR interactions + action_space (gym.spaces.Discrete): Discrete action space + language_skill_set (list): Readable action descriptions + """ + def __init__(self, exp_name='', detection_box=False, resolution=500, data_dir=None, x_display=None): + """ + Initialize the AI2THOR environment. + + Args: + exp_name: experiment name for logging + detection_box: whether to draw detection boxes on images + resolution: screen resolution for AI2-THOR + data_dir: path to task JSON files (e.g. datasets/.../tasks) + x_display: X display number for AI2-THOR + """ + super().__init__() + self.reward_config_path = ALFRED_REWARD_PATH + # data_dir = path to EASI's downloaded HF dataset task JSONs + # Falls back to bundled path if None + self._data_dir = data_dir or ALFRED_DATASET_PATH + self.resolution = resolution + display = x_display if x_display is not None else X_DISPLAY + self.env = ThorConnector(x_display=display, player_screen_height=resolution, player_screen_width=resolution) + + # Episode tracking + self._reset = False + self._current_step = 0 + self._max_episode_steps = 30 + self._cur_invalid_actions = 0 + self._max_invalid_actions = 10 + self._episode_start_time = 0 + self.episode_log = [] + + # Task-related attributes + self.episode_language_instruction = '' + self.episode_data = None + # Initialize action space + self.language_skill_set = None + self.action_space = None + + # env feedback and image save + # feedback verbosity, 0: concise, 1: verbose + self.feedback_verbosity = 0 + self.log_path = 'running/eb_alfred/{}'.format(exp_name) + + self.detection = detection_box # add detection in image + self.name_to_id_dict = None + self.id_to_name_dict = None + self.language_skill_set = get_global_action_space() + self.action_space = gym.spaces.Discrete(len(self.language_skill_set)) + + + def generate_additional_action_space(self): + """ + Generate additional actions for receptacles with multiple instances + """ + # Generate pickup, putdown, and drop actions + add_findable_objs = [] + add_pickable_objs = [] + + recept_obj_dict = {} + pickable_obj_dict = {} + name_to_id_dict = {} + for obj in self.env.last_event.metadata['objects']: + if obj['receptacle']: + if obj['objectType'] in recept_obj_dict: + recept_obj_dict[obj['objectType']].append(obj['objectId']) + else: + recept_obj_dict[obj['objectType']] = [obj['objectId']] + elif obj['pickupable']: + if obj['objectType'] in pickable_obj_dict: + pickable_obj_dict[obj['objectType']].append(obj['objectId']) + else: + pickable_obj_dict[obj['objectType']] = [obj['objectId']] + + + # store the mapping for object with multiple instances + for key in recept_obj_dict: + if len(recept_obj_dict[key]) >= 2: + for i in range(len(recept_obj_dict[key])): + if i == 0: + name_to_id_dict[key] = recept_obj_dict[key][i] + else: + name_to_id_dict[key + '_{}'.format(i+1)] = recept_obj_dict[key][i] + add_findable_objs.append(key + '_{}'.format(i+1)) + + for key in pickable_obj_dict: + if len(pickable_obj_dict[key]) >= 2: + for i in range(len(pickable_obj_dict[key])): + if i == 0: + name_to_id_dict[key] = pickable_obj_dict[key][i] + else: + name_to_id_dict[key + '_{}'.format(i+1)] = pickable_obj_dict[key][i] + add_pickable_objs.append(key + '_{}'.format(i+1)) + + id_to_name_dict = {} + for key in name_to_id_dict: + id_to_name_dict[name_to_id_dict[key]] = key + + # Generate find actions for additional objects + add_findable_objs = sorted(list(set(add_findable_objs))) + add_pickable_objs = sorted(list(set(add_pickable_objs))) + action_space = [f"find a {obj}" for obj in add_findable_objs] + for obj in add_findable_objs: + if obj.split('_')[0] in alfred_open_obj: + action_space.extend([ + f"open the {obj}", + f"close the {obj}" + ]) + for obj in add_pickable_objs: + if obj.split('_')[0] in alfred_pick_obj: + action_space.extend([ + f"find a {obj}", + ]) + + self.language_skill_set = get_global_action_space() + action_space + self.action_space = gym.spaces.Discrete(len(self.language_skill_set)) + self.name_to_id_dict = name_to_id_dict + self.id_to_name_dict = id_to_name_dict + + def current_episode(self): + """Return current episode data (task JSON loaded from disk).""" + return self.episode_data + + def _reset_controller(self, task): + """Restore scene from a task name and replace instruction""" + traj_data = utils.load_task_json(task, data_dir=self._data_dir) + traj_data['turk_annotations']['anns'][task['repeat_idx']]['task_desc'] = task["instruction"] + self.episode_data = traj_data + args_dict = {'data': self._data_dir, 'pframe': 300, 'fast_epoch': False, + 'use_templated_goals': False, 'dout': 'exp/model', 'pp_folder': 'pp', + 'reward_config': self.reward_config_path, 'max_steps': 1000} + model_args = utils.dotdict(args_dict) + + # Extract scene configuration + scene_num = traj_data['scene']['scene_num'] + object_poses = traj_data['scene']['object_poses'] + dirty_and_empty = traj_data['scene']['dirty_and_empty'] + object_toggles = traj_data['scene']['object_toggles'] + + scene_name = 'FloorPlan%d' % scene_num + self.episode_language_instruction = task["instruction"] + # Restore scene configuration + logger.info(f"Restoring scene {scene_name}...") + self.env.reset(scene_name) + self.env.restore_scene(object_poses, object_toggles, dirty_and_empty) + if traj_data['scene']['init_action']['action'] == 'TeleportFull': + del traj_data['scene']['init_action']["rotateOnTeleport"] + traj_data['scene']['init_action']["standing"] = True + self.env.step(dict(traj_data['scene']['init_action'])) + self.env.set_task(traj_data, model_args, reward_type='dense', max_episode_length=self._max_episode_steps) + ############################# + self.generate_additional_action_space() + + def reset(self, episode=None): + """ + Reset the environment for a new episode. + + Args: + episode: dict with 'task', 'repeat_idx', 'instruction' keys + (from EASI's HF dataset via bridge reset_config) + + Returns: + observation + """ + assert episode is not None, "episode dict required for reset" + self._reset_controller(episode) + self._current_step = 0 + self._cur_invalid_actions = 0 + obs = { + 'head_rgb': self.env.last_event.frame, + } + self._reset = True + self.episode_log = [] + self._episode_start_time = time.time() + return obs + + + def step(self, action, reasoning=''): + """ + Execute a single environment step. + Args: + action (int): Index of action in action space + Returns: + tuple: (observation, reward, done, environment feedback) + """ + assert self._reset, 'Reset env before stepping' + info = {} + self._current_step += 1 + if type(action) == int: + lang_action = self.language_skill_set[action] + elif type(action) == str: + lang_action = action + else: + raise NotImplementedError + + if 'find' in lang_action or 'open' in lang_action or 'close' in lang_action: + lang_action_split = lang_action.split(' ') + if (self.name_to_id_dict is not None) and lang_action_split[-1] in self.name_to_id_dict: # multiple instances + lang_action = ' '.join(lang_action_split[:-1] + [self.name_to_id_dict[lang_action_split[-1]]]) + + event = self.env.llm_skill_interact(lang_action) + if not event['success']: + self._cur_invalid_actions += 1 + + ## test calculate reward + reward, done = self.env.get_transition_reward() + subgoal_met = self.env.get_goal_conditions_met() + info['task_success'] = float(self.env.get_goal_satisfied()) + info['task_progress'] = subgoal_met[0] / subgoal_met[1] + + obs = { + 'head_rgb': self.env.last_event.frame, + } + # if exceed the maximum episode steps or the goal is achieved + if self._current_step >= self._max_episode_steps or info['task_success'] or self._cur_invalid_actions >= self._max_invalid_actions: + done = True + + # add env feedback + info['instruction'] = self.episode_language_instruction + info['env_step'] = self._current_step + info['env_feedback'] = self.get_env_feedback(event) + info['episode_elapsed_seconds'] = time.time() - self._episode_start_time + info['last_action_success'] = float(event['success']) + info['object_states'] = { + "cooled_objects" : self.env.cooled_objects, + "heated_objects" : self.env.heated_objects, + "cleaned_objects" : self.env.cleaned_objects, + "visible_objs": [obj['objectType'] for obj in self.env.last_event.metadata['objects'] if obj['visible']] + } + info['action_id'] = action + info['action_description'] = self.language_skill_set[action] if type(action) == int else action + info['reasoning'] = reasoning + self.episode_log.append(info) + return obs, reward, done, info + + def get_env_feedback(self, info): + """ + Generate feedback message for the current step. + Args: + info (dict): Action execution information + Returns: + str: Descriptive message about step outcome + """ + msg = '' + if info["success"]: + msg += f"Last action executed successfully." + else: + if 'is not visible' in info['message'] and '|' in info['message']: + recep_id = info['message'].split('because it is in ')[1].split('. Note')[0] + if recep_id not in self.id_to_name_dict: + pos = recep_id.split('|')[0] + else: + pos = self.id_to_name_dict[recep_id] + message = info['message'].split(recep_id)[0] + pos + '. Go there to pick the object instead.' + else: + message = info['message'] + msg += f"Last action is invalid. {message}" + return msg + + def seed(self, seed=None): + self.env.random_initilize(seed) + + def save_image(self, *args, **kwargs): + """Save current agent view as a PNG image.""" + episode_idx = self._current_episode_num if not len(self.selected_indexes) else self.selected_indexes[self._current_episode_num - 1] + 1 + + folder = self.log_path + '/images/episode_{}'.format(episode_idx) + if not os.path.exists(folder): + os.makedirs(folder) + img = Image.fromarray(self.env.last_event.frame) + if self.detection: + img = utils.draw_boxes(img, self.env.last_event.instance_detections2D, name_translation=self.id_to_name_dict) + + # time_stamp = time.strftime("%Y%m%d_%H%M%S", time.localtime()) + image_path = os.path.join(folder, 'episode_{}_step_{}.png'.format(episode_idx, self._current_step)) #, time_stamp)) + img.save(image_path) + return image_path + + def save_episode_log(self): + if not os.path.exists(self.log_path): + os.makedirs(self.log_path) + # time_stamp = time.strftime("%Y%m%d_%H%M%S", time.localtime()) + episode_idx = self._current_episode_num if not len(self.selected_indexes) else self.selected_indexes[self._current_episode_num - 1] + 1 + filename = 'episode_{}_step_{}.json'.format(episode_idx, self._current_step) #, time_stamp) + if len(self.episode_log): + with open(os.path.join(self.log_path, filename), 'w') as f: + for item in self.episode_log: + if 'object_states' in item: + item.pop('object_states') + try: + json.dump(item, f, ensure_ascii=False) + except: + import pdb;pdb.set_trace() + f.write('\n') + + + def close(self): + """Terminate the environment.""" + self.env.stop() + + + +if __name__ == "__main__": + """ + Example usage of the EBAlfEnv environment. + Requires an episode dict with 'task', 'repeat_idx', 'instruction' keys. + """ + env = EBAlfEnv() + episode = { + "task": "pick_and_place_simple-Mug-None-Shelf-1/trial_T20190001", + "repeat_idx": 0, + "instruction": "Put a mug on the shelf.", + } + env.reset(episode=episode) + print([(i, name) for i, name in enumerate(env.language_skill_set)]) + for _ in range(30): + action = int(input('action id: ')) + if action < 0: + break + print(env.language_skill_set[action]) + obs, reward, done, info = env.step(action) + print(reward, done, info) + if done: + break + env.close() + diff --git a/easi/tasks/ebalfred/vendor/__init__.py b/easi/tasks/ebalfred/vendor/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/easi/tasks/ebalfred/vendor/env/__init__.py b/easi/tasks/ebalfred/vendor/env/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/easi/tasks/ebalfred/vendor/env/reward.py b/easi/tasks/ebalfred/vendor/env/reward.py new file mode 100644 index 0000000..77dbc1e --- /dev/null +++ b/easi/tasks/ebalfred/vendor/env/reward.py @@ -0,0 +1,289 @@ +from easi.tasks.ebalfred.vendor.gen.utils.game_util import get_object + +class BaseAction(object): + ''' + base class for API actions + ''' + + def __init__(self, gt_graph, env, rewards, strict=True): + self.gt_graph = gt_graph # for navigation + self.env = env + self.rewards = rewards + self.strict = strict + + def get_reward(self, state, prev_state, expert_plan, goal_idx): + reward, done = self.rewards['neutral'], True + return reward, done + + +class GotoLocationAction(BaseAction): + ''' + MoveAhead, Rotate, Lookup + ''' + + valid_actions = {'MoveAhead', 'RotateLeft', 'RotateRight', 'LookUp', 'LookDown', 'Teleport', 'TeleportFull'} + + def get_reward(self, state, prev_state, expert_plan, goal_idx): + if state.metadata['lastAction'] not in self.valid_actions: + reward, done = self.rewards['invalid_action'], False + return reward, done + + subgoal = expert_plan[goal_idx]['planner_action'] + curr_pose = state.pose_discrete + prev_pose = prev_state.pose_discrete + tar_pose = tuple([int(i) for i in subgoal['location'].split('|')[1:]]) + + prev_actions, _ = self.gt_graph.get_shortest_path(prev_pose, tar_pose) + curr_actions, _ = self.gt_graph.get_shortest_path(curr_pose, tar_pose) + + prev_distance = len(prev_actions) + curr_distance = len(curr_actions) + reward = (prev_distance - curr_distance) * 0.2 # distance reward factor? + + # [DEPRECATED] Old criteria which requires the next subgoal object to be visible + # Consider navigation a success if we can see the target object in the next step from here. + # assert len(expert_plan) > goal_idx + 1 + # next_subgoal = expert_plan[goal_idx + 1]['planner_action'] + # next_goal_object = get_object(next_subgoal['objectId'], state.metadata) + # done = (next_goal_object['visible'] and curr_distance < self.rewards['min_reach_distance']) + + done = curr_distance < self.rewards['min_reach_distance'] + + if done: + reward += self.rewards['positive'] + + return reward, done + + +class PickupObjectAction(BaseAction): + ''' + PickupObject + ''' + + valid_actions = {'PickupObject', 'OpenObject', 'CloseObject'} + + def get_reward(self, state, prev_state, expert_plan, goal_idx): + if state.metadata['lastAction'] not in self.valid_actions: + reward, done = self.rewards['invalid_action'], False + return reward, done + + subgoal = expert_plan[goal_idx]['planner_action'] + reward, done = self.rewards['neutral'], False + inventory_objects = state.metadata['inventoryObjects'] + if len(inventory_objects): + inv_object_id = state.metadata['inventoryObjects'][0]['objectId'] + goal_object_id = subgoal['objectId'] + + # doesn't matter which slice you pick up + def remove_slice_postfix(object_id): + return object_id.split("Sliced")[0] + + inv_object_id = remove_slice_postfix(inv_object_id) + goal_object_id = remove_slice_postfix(goal_object_id) + + reward, done = (self.rewards['positive'], True) if inv_object_id == goal_object_id else (self.rewards['negative'], False) + return reward, done + + +class PutObjectAction(BaseAction): + ''' + PutObject + ''' + + valid_actions = {'PutObject', 'OpenObject', 'CloseObject'} + + def get_reward(self, state, prev_state, expert_plan, goal_idx): + if state.metadata['lastAction'] not in self.valid_actions: + reward, done = self.rewards['invalid_action'], False + return reward, done + + subgoal = expert_plan[goal_idx]['planner_action'] + reward, done = self.rewards['neutral'], False + target_object_id = subgoal['objectId'] + recep_object = get_object(subgoal['receptacleObjectId'], state.metadata) + if recep_object is not None: + is_target_in_recep = target_object_id in recep_object['receptacleObjectIds'] + reward, done = (self.rewards['positive'], True) if is_target_in_recep else (self.rewards['negative'], False) + return reward, done + + +class OpenObjectAction(BaseAction): + ''' + OpenObject + ''' + + valid_actions = {'OpenObject'} + + def get_reward(self, state, prev_state, expert_plan, goal_idx): + if state.metadata['lastAction'] not in self.valid_actions: + reward, done = self.rewards['invalid_action'], False + return reward, done + + subgoal = expert_plan[goal_idx]['planner_action'] + reward, done = self.rewards['neutral'], False + target_recep = get_object(subgoal['objectId'], state.metadata) + if target_recep is not None: + is_target_open = target_recep['isOpen'] + reward, done = (self.rewards['positive'], True) if is_target_open else (self.rewards['negative'], False) + return reward, done + + +class CloseObjectAction(BaseAction): + ''' + CloseObject + ''' + + valid_actions = {'CloseObject'} + + def get_reward(self, state, prev_state, expert_plan, goal_idx): + if state.metadata['lastAction'] not in self.valid_actions: + reward, done = self.rewards['invalid_action'], False + return reward, done + + subgoal = expert_plan[goal_idx]['planner_action'] + reward, done = self.rewards['negative'], False + target_recep = get_object(subgoal['objectId'], state.metadata) + if target_recep is not None: + is_target_closed = not target_recep['isOpen'] + reward, done = (self.rewards['positive'], True) if is_target_closed else (self.rewards['negative'], False) + return reward, done + + +class ToggleObjectAction(BaseAction): + ''' + ToggleObjectOn, ToggleObjectOff + ''' + + valid_actions = {'ToggleObjectOn', 'ToggleObjectOff'} + + def get_reward(self, state, prev_state, expert_plan, goal_idx): + if state.metadata['lastAction'] not in self.valid_actions: + reward, done = self.rewards['invalid_action'], False + return reward, done + + subgoal = expert_plan[goal_idx]['planner_action'] + reward, done = self.rewards['neutral'], False + target_toggle = get_object(subgoal['objectId'], state.metadata) + if target_toggle is not None: + is_target_toggled = target_toggle['isToggled'] + reward, done = (self.rewards['positive'], True) if is_target_toggled else (self.rewards['negative'], False) + return reward, done + + +class SliceObjectAction(BaseAction): + ''' + SliceObject + ''' + + valid_actions = {'SliceObject', 'OpenObject', 'CloseObject'} + + def get_reward(self, state, prev_state, expert_plan, goal_idx): + if state.metadata['lastAction'] not in self.valid_actions: + reward, done = self.rewards['invalid_action'], False + return reward, done + + subgoal = expert_plan[goal_idx]['planner_action'] + reward, done = self.rewards['neutral'], False + target_object = get_object(subgoal['objectId'], state.metadata) + if target_object is not None: + is_target_sliced = target_object['isSliced'] + reward, done = (self.rewards['positive'], True) if is_target_sliced else (self.rewards['negative'], False) + return reward, done + + +class CleanObjectAction(BaseAction): + ''' + CleanObject + ''' + + valid_actions = {'PutObject', 'PickupObject', 'ToggleObjectOn', 'ToggleObjectOff'} + + def get_reward(self, state, prev_state, expert_plan, goal_idx): + if state.metadata['lastAction'] not in self.valid_actions: + reward, done = self.rewards['invalid_action'], False + return reward, done + + subgoal = expert_plan[goal_idx]['planner_action'] + reward, done = self.rewards['neutral'], False + clean_object = get_object(subgoal['cleanObjectId'], state.metadata) + if clean_object is not None: + is_obj_clean = clean_object['objectId'] in self.env.cleaned_objects + reward, done = (self.rewards['positive'], True) if is_obj_clean else (self.rewards['negative'], False) + return reward, done + + +class HeatObjectAction(BaseAction): + ''' + HeatObject + ''' + + valid_actions = {'OpenObject', 'CloseObject', 'PickupObject', 'PutObject', 'ToggleObjectOn', 'ToggleObjectOff'} + + def get_reward(self, state, prev_state, expert_plan, goal_idx): + if state.metadata['lastAction'] not in self.valid_actions: + reward, done = self.rewards['invalid_action'], False + return reward, done + + reward, done = self.rewards['neutral'], False + next_put_goal_idx = goal_idx+2 # (+1) GotoLocation -> (+2) PutObject (get the objectId from the PutObject action) + if next_put_goal_idx < len(expert_plan): + heat_object_id = expert_plan[next_put_goal_idx]['planner_action']['objectId'] + heat_object = get_object(heat_object_id, state.metadata) + is_obj_hot = heat_object['objectId'] in self.env.heated_objects + reward, done = (self.rewards['positive'], True) if is_obj_hot else (self.rewards['negative'], False) + return reward, done + + +class CoolObjectAction(BaseAction): + ''' + CoolObject + ''' + + valid_actions = {'OpenObject', 'CloseObject', 'PickupObject', 'PutObject'} + + def get_reward(self, state, prev_state, expert_plan, goal_idx): + if state.metadata['lastAction'] not in self.valid_actions: + reward, done = self.rewards['invalid_action'], False + return reward, done + + reward, done = self.rewards['neutral'], False + subgoal = expert_plan[goal_idx]['planner_action'] + next_put_goal_idx = goal_idx+2 # (+1) GotoLocation -> (+2) PutObject (get the objectId from the PutObject action) + if next_put_goal_idx < len(expert_plan): + cool_object_id = expert_plan[next_put_goal_idx]['planner_action']['objectId'] + cool_object = get_object(cool_object_id, state.metadata) + is_obj_cool = cool_object['objectId'] in self.env.cooled_objects + + # TODO(mohit): support dense rewards for all subgoals + # intermediate reward if object is cooled + if is_obj_cool and not self.env.cooled_reward: + self.env.cooled_reward = True + reward, done = self.rewards['positive'], False + + # intermediate reward for opening fridge after object is cooled + elif is_obj_cool and state.metadata['lastAction']=='OpenObject': + target_recep = get_object(subgoal['objectId'], state.metadata) + if target_recep is not None and not self.env.reopen_reward: + if target_recep['isOpen']: + self.env.reopen_reward = True + reward, done = self.rewards['positive'], False + + # intermediate reward for picking up cooled object after reopening fridge + elif is_obj_cool and state.metadata['lastAction']=='PickupObject': + inventory_objects = state.metadata['inventoryObjects'] + if len(inventory_objects): + inv_object_id = state.metadata['inventoryObjects'][0]['objectId'] + if inv_object_id == cool_object_id: + reward, done = self.rewards['positive'], True # Subgoal completed + + return reward, done + + +def get_action(action_type, gt_graph, env, reward_config, strict): + action_type_str = action_type + "Action" + + if action_type_str in globals(): + action = globals()[action_type_str] + return action(gt_graph, env, reward_config[action_type_str], strict) + else: + raise Exception("Invalid action_type %s" % action_type_str) diff --git a/easi/tasks/ebalfred/vendor/env/tasks.py b/easi/tasks/ebalfred/vendor/env/tasks.py new file mode 100644 index 0000000..4bdd5ee --- /dev/null +++ b/easi/tasks/ebalfred/vendor/env/tasks.py @@ -0,0 +1,476 @@ +import json +import numpy as np +from easi.tasks.ebalfred.vendor.gen.graph import graph_obj +from easi.tasks.ebalfred.vendor.gen.utils.game_util import get_objects_with_name_and_prop +from easi.tasks.ebalfred.vendor.env.reward import get_action + + +class BaseTask(object): + ''' + base class for tasks + ''' + + def __init__(self, traj, env, args, reward_type='sparse', max_episode_length=2000): + # settings + self.traj = traj + self.env = env + self.args = args + self.task_type = self.traj['task_type'] + self.max_episode_length = max_episode_length + self.reward_type = reward_type + self.step_num = 0 + self.num_subgoals = self.get_num_subgoals(self.traj['plan']['high_pddl']) + self.goal_finished = False + + # internal states + self.goal_idx = 0 + self.finished = -1 + + # load navigation graph + self.gt_graph = None + self.load_nav_graph() + + # reward config + self.reward_config = None + self.load_reward_config(args.reward_config) + self.strict = 'strict' in reward_type + + # prev state + self.prev_state = self.env.last_event + + def load_reward_config(self, config_file): + ''' + load json file with reward values + ''' + with open(config_file, 'r') as rc: + reward_config = json.load(rc) + self.reward_config = reward_config + + def load_nav_graph(self): + ''' + build navigation grid graph + ''' + floor_plan = self.traj['scene']['floor_plan'] + scene_num = self.traj['scene']['scene_num'] + self.gt_graph = graph_obj.Graph(use_gt=True, construct_graph=True, scene_id=scene_num) + + def get_num_subgoals(self, high_pddl): + ''' + number of subgoals in high-level pddl plan + ''' + last_action = high_pddl[-1] + + # issue97: https://github.com/askforalfred/alfred/issues/97 + if last_action['planner_action']['action'] == 'End': + return len(high_pddl) - 1 # ignore NoOp/End action + else: + return len(high_pddl) + + def goal_satisfied(self, state): + ''' + check if the overall task goal was satisfied. + ''' + raise NotImplementedError + + def transition_reward(self, state): + ''' + immediate reward given the current state + ''' + reward = 0 + + # goal completed + if self.goal_finished: + done = True + return reward, done + + # get subgoal and action + expert_plan = self.traj['plan']['high_pddl'] + action_type = expert_plan[self.goal_idx]['planner_action']['action'] + + # subgoal reward + if "dense" in self.reward_type: + action = get_action(action_type, self.gt_graph, self.env, self.reward_config, self.strict) + sg_reward, sg_done = action.get_reward(state, self.prev_state, expert_plan, self.goal_idx) + reward += sg_reward + if sg_done: + self.finished += 1 + if self.goal_idx + 1 < self.num_subgoals: + self.goal_idx += 1 + + # end task reward + goal_finished = self.goal_satisfied(state) + if goal_finished: + reward += self.reward_config['Generic']['goal_reward'] + self.goal_finished = True + + # success reward + if "success" in self.reward_type and self.env.last_event.metadata['lastActionSuccess']: + reward += self.reward_config['Generic']['success'] + + # failure reward + if "failure" in self.reward_type and not self.env.last_event.metadata['lastActionSuccess']: + reward += self.reward_config['Generic']['failure'] + + # step penalty + if self.step_num > len(self.traj['plan']['low_actions']): + reward += self.reward_config['Generic']['step_penalty'] + + # save event + self.prev_state = self.env.last_event + + # step and check if max_episode_length reached + self.step_num += 1 + done = self.goal_idx >= self.num_subgoals or self.step_num >= self.max_episode_length + return reward, done + + def reset(self): + ''' + Reset internal states + ''' + self.goal_idx = 0 + self.finished = -1 + self.step_num = 0 + self.goal_finished = False + + def get_subgoal_idx(self): + return self.finished + + def get_target(self, var): + ''' + returns the object type of a task param + ''' + return self.traj['pddl_params'][var] if self.traj['pddl_params'][var] is not None else None + + def get_targets(self): + ''' + returns a dictionary of all targets for the task + ''' + targets = { + 'object': self.get_target('object_target'), + 'parent': self.get_target('parent_target'), + 'toggle': self.get_target('toggle_target'), + 'mrecep': self.get_target('mrecep_target'), + } + + # slice exception + if 'object_sliced' in self.traj['pddl_params'] and self.traj['pddl_params']['object_sliced']: + targets['object'] += 'Sliced' # Change, e.g., "Apple" -> "AppleSliced" as pickup target. + + return targets + + +class PickAndPlaceSimpleTask(BaseTask): + ''' + pick_and_place task + ''' + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def goal_satisfied(self, state): + # check if any object of 'object' class is inside any receptacle of 'parent' class + pcs = self.goal_conditions_met(state) + return pcs[0] == pcs[1] + + def goal_conditions_met(self, state): + ts = 1 + s = 0 + + targets = self.get_targets() + receptacles = get_objects_with_name_and_prop(targets['parent'], 'receptacle', state.metadata) + pickupables = get_objects_with_name_and_prop(targets['object'], 'pickupable', state.metadata) + + # check if object needs to be sliced + if 'Sliced' in targets['object']: + ts += 1 + if len([p for p in pickupables if 'Sliced' in p['objectId']]) >= 1: + s += 1 + + if np.any([np.any([p['objectId'] in r['receptacleObjectIds'] + for r in receptacles if r['receptacleObjectIds'] is not None]) + for p in pickupables]): + s += 1 + + return s, ts + + def reset(self): + super().reset() + + +class PickTwoObjAndPlaceTask(BaseTask): + ''' + pick_two_obj_and_place task + ''' + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def goal_satisfied(self, state): + # check if two objects of 'object' class are in any receptacle of 'parent' class + pcs = self.goal_conditions_met(state) + return pcs[0] == pcs[1] + + def goal_conditions_met(self, state): + ts = 2 + s = 0 + + targets = self.get_targets() + receptacles = get_objects_with_name_and_prop(targets['parent'], 'receptacle', state.metadata) + pickupables = get_objects_with_name_and_prop(targets['object'], 'pickupable', state.metadata) + + # check if object needs to be sliced + if 'Sliced' in targets['object']: + ts += 2 + s += min(len([p for p in pickupables if 'Sliced' in p['objectId']]), 2) + + # placing each object counts as a goal_condition + s += min(np.max([sum([1 if r['receptacleObjectIds'] is not None + and p['objectId'] in r['receptacleObjectIds'] else 0 + for p in pickupables]) + for r in receptacles]), 2) + return s, ts + + def reset(self): + super().reset() + + +class LookAtObjInLightTask(BaseTask): + ''' + look_at_obj_in_light task + ''' + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def goal_satisfied(self, state): + # check if any object of 'object' class is being held in front of 'toggle' object that is turned on + pcs = self.goal_conditions_met(state) + return pcs[0] == pcs[1] + + def goal_conditions_met(self, state): + ts = 2 + s = 0 + + targets = self.get_targets() + toggleables = get_objects_with_name_and_prop(targets['toggle'], 'toggleable', state.metadata) + pickupables = get_objects_with_name_and_prop(targets['object'], 'pickupable', state.metadata) + inventory_objects = state.metadata['inventoryObjects'] + + # check if object needs to be sliced + if 'Sliced' in targets['object']: + ts += 1 + if len([p for p in pickupables if 'Sliced' in p['objectId']]) >= 1: + s += 1 + + # check if the right object is in hand + if len(inventory_objects) > 0 and inventory_objects[0]['objectId'] in [p['objectId'] for p in pickupables]: + s += 1 + # check if the lamp is visible and turned on + if np.any([t['isToggled'] and t['visible'] for t in toggleables]): + s += 1 + + return s, ts + + def reset(self): + super().reset() + + +class PickHeatThenPlaceInRecepTask(BaseTask): + ''' + pick_heat_then_place_in_recep task + ''' + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def goal_satisfied(self, state): + # check if any object of 'object' class inside receptacle of 'parent' class is hot + pcs = self.goal_conditions_met(state) + return pcs[0] == pcs[1] + + def goal_conditions_met(self, state): + ts = 3 + s = 0 + + targets = self.get_targets() + receptacles = get_objects_with_name_and_prop(targets['parent'], 'receptacle', state.metadata) + pickupables = get_objects_with_name_and_prop(targets['object'], 'pickupable', state.metadata) + + # check if object needs to be sliced + if 'Sliced' in targets['object']: + ts += 1 + if len([p for p in pickupables if 'Sliced' in p['objectId']]) >= 1: + s += 1 + + objs_in_place = [p['objectId'] for p in pickupables for r in receptacles + if r['receptacleObjectIds'] is not None and p['objectId'] in r['receptacleObjectIds']] + objs_heated = [p['objectId'] for p in pickupables if p['objectId'] in self.env.heated_objects] + + # check if object is in the receptacle + if len(objs_in_place) > 0: + s += 1 + # check if some object was heated + if len(objs_heated) > 0: + s += 1 + # check if the object is both in the receptacle and hot + if np.any([obj_id in objs_heated for obj_id in objs_in_place]): + s += 1 + + return s, ts + + def reset(self): + super().reset() + + +class PickCoolThenPlaceInRecepTask(BaseTask): + ''' + pick_cool_then_place_in_recep task + ''' + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def goal_satisfied(self, state): + # check if any object of 'object' class inside receptacle of 'parent' class is cold + pcs = self.goal_conditions_met(state) + return pcs[0] == pcs[1] + + def goal_conditions_met(self, state): + ts = 3 + s = 0 + + targets = self.get_targets() + receptacles = get_objects_with_name_and_prop(targets['parent'], 'receptacle', state.metadata) + pickupables = get_objects_with_name_and_prop(targets['object'], 'pickupable', state.metadata) + + if 'Sliced' in targets['object']: + ts += 1 + if len([p for p in pickupables if 'Sliced' in p['objectId']]) >= 1: + s += 1 + + objs_in_place = [p['objectId'] for p in pickupables for r in receptacles + if r['receptacleObjectIds'] is not None and p['objectId'] in r['receptacleObjectIds']] + objs_cooled = [p['objectId'] for p in pickupables if p['objectId'] in self.env.cooled_objects] + + # check if object is in the receptacle + if len(objs_in_place) > 0: + s += 1 + # check if some object was cooled + if len(objs_cooled) > 0: + s += 1 + # check if the object is both in the receptacle and cold + if np.any([obj_id in objs_cooled for obj_id in objs_in_place]): + s += 1 + + return s, ts + + def reset(self): + super().reset() + + +class PickCleanThenPlaceInRecepTask(BaseTask): + ''' + pick_clean_then_place_in_recep task + ''' + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def goal_satisfied(self, state): + # check if any object of 'object' class inside receptacle of 'parent' class is clean + pcs = self.goal_conditions_met(state) + return pcs[0] == pcs[1] + + def goal_conditions_met(self, state): + ts = 3 + s = 0 + + targets = self.get_targets() + receptacles = get_objects_with_name_and_prop(targets['parent'], 'receptacle', state.metadata) + pickupables = get_objects_with_name_and_prop(targets['object'], 'pickupable', state.metadata) + + if 'Sliced' in targets['object']: + ts += 1 + if len([p for p in pickupables if 'Sliced' in p['objectId']]) >= 1: + s += 1 + + objs_in_place = [p['objectId'] for p in pickupables for r in receptacles + if r['receptacleObjectIds'] is not None and p['objectId'] in r['receptacleObjectIds']] + objs_cleaned = [p['objectId'] for p in pickupables if p['objectId'] in self.env.cleaned_objects] + + # check if object is in the receptacle + if len(objs_in_place) > 0: + s += 1 + # check if some object was cleaned + if len(objs_cleaned) > 0: + s += 1 + # check if the object is both in the receptacle and clean + if np.any([obj_id in objs_cleaned for obj_id in objs_in_place]): + s += 1 + + return s, ts + + def reset(self): + super().reset() + + +class PickAndPlaceWithMovableRecepTask(BaseTask): + ''' + pick_and_place_with_movable_recep task + ''' + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def goal_satisfied(self, state): + # check if any object of 'object' class is inside any movable receptacle of 'mrecep' class at receptacle of 'parent' class + pcs = self.goal_conditions_met(state) + return pcs[0] == pcs[1] + + def goal_conditions_met(self, state): + ts = 3 + s = 0 + + targets = self.get_targets() + receptacles = get_objects_with_name_and_prop(targets['parent'], 'receptacle', state.metadata) + pickupables = get_objects_with_name_and_prop(targets['object'], 'pickupable', state.metadata) + movables = get_objects_with_name_and_prop(targets['mrecep'], 'pickupable', state.metadata) + + # check if object needs to be sliced + if 'Sliced' in targets['object']: + ts += 1 + if len([p for p in pickupables if 'Sliced' in p['objectId']]) >= 1: + s += 1 + + pickup_in_place = [p for p in pickupables for m in movables + if 'receptacleObjectIds' in p and m['receptacleObjectIds'] is not None + and p['objectId'] in m['receptacleObjectIds']] + movable_in_place = [m for m in movables for r in receptacles + if 'receptacleObjectIds' in r and r['receptacleObjectIds'] is not None + and m['objectId'] in r['receptacleObjectIds']] + # check if the object is in the final receptacle + if len(pickup_in_place) > 0: + s += 1 + # check if the movable receptacle is in the final receptacle + if len(movable_in_place) > 0: + s += 1 + # check if both the object and movable receptacle stack is in the final receptacle + if np.any([np.any([p['objectId'] in m['receptacleObjectIds'] for p in pickupables]) and + np.any([r['objectId'] in m['parentReceptacles'] for r in receptacles]) for m in movables + if m['parentReceptacles'] is not None and m['receptacleObjectIds'] is not None]): + s += 1 + + return s, ts + + def reset(self): + super().reset() + + +def get_task(task_type, traj, env, args, reward_type='sparse', max_episode_length=2000): + task_class_str = task_type.replace('_', ' ').title().replace(' ', '') + "Task" + + if task_class_str in globals(): + task = globals()[task_class_str] + return task(traj, env, args, reward_type=reward_type, max_episode_length=max_episode_length) + else: + raise Exception("Invalid task_type %s" % task_class_str) \ No newline at end of file diff --git a/easi/tasks/ebalfred/vendor/env/thor_env.py b/easi/tasks/ebalfred/vendor/env/thor_env.py new file mode 100644 index 0000000..3e8ed86 --- /dev/null +++ b/easi/tasks/ebalfred/vendor/env/thor_env.py @@ -0,0 +1,601 @@ +import cv2 +import copy +import easi.tasks.ebalfred.vendor.gen.constants as constants +import numpy as np +from collections import Counter, OrderedDict +from easi.tasks.ebalfred.vendor.env.tasks import get_task +from ai2thor.controller import Controller +import easi.tasks.ebalfred.vendor.gen.utils.image_util as image_util +from easi.tasks.ebalfred.vendor.gen.utils import game_util +from easi.tasks.ebalfred.vendor.gen.utils.game_util import get_objects_of_type, get_obj_of_type_closest_to_obj + + +DEFAULT_RENDER_SETTINGS = {'renderImage': True, + 'renderDepthImage': False, + 'renderClassImage': False, + 'renderObjectImage': False, + } + +class ThorEnv(Controller): + ''' + an extension of ai2thor.controller.Controller for ALFRED tasks + ''' + def __init__(self, x_display=constants.X_DISPLAY, + player_screen_height=constants.DETECTION_SCREEN_HEIGHT, + player_screen_width=constants.DETECTION_SCREEN_WIDTH, + quality='MediumCloseFitShadows', + build_path=constants.BUILD_PATH): + self.task = None + + super().__init__(quality=quality) + self.local_executable_path = build_path + self.start(x_display=x_display, + player_screen_height=player_screen_height, + player_screen_width=player_screen_width) + self.task = None + + # internal states + self.cleaned_objects = set() + self.cooled_objects = set() + self.heated_objects = set() + + # intemediate states for CoolObject Subgoal + self.cooled_reward = False + self.reopen_reward = False + + print("ThorEnv started.") + + def reset(self, scene_name_or_num, + grid_size=constants.AGENT_STEP_SIZE / constants.RECORD_SMOOTHING_FACTOR, + camera_y=constants.CAMERA_HEIGHT_OFFSET, + render_image=constants.RENDER_IMAGE, + render_depth_image=constants.RENDER_DEPTH_IMAGE, + render_class_image=constants.RENDER_CLASS_IMAGE, + render_object_image=constants.RENDER_OBJECT_IMAGE, + visibility_distance=constants.VISIBILITY_DISTANCE): + ''' + reset scene and task states + ''' + print("Resetting ThorEnv") + + if type(scene_name_or_num) == str: + scene_name = scene_name_or_num + else: + scene_name = 'FloorPlan%d' % scene_name_or_num + super().reset(scene_name) + event = super().step(dict( + action='Initialize', + gridSize=grid_size, + cameraY=camera_y, + renderImage=render_image, + renderDepthImage=render_depth_image, + renderClassImage=render_class_image, + renderObjectImage=render_object_image, + visibility_distance=visibility_distance, + makeAgentsVisible=False, + )) + + # reset task if specified + if self.task is not None: + self.task.reset() + + # clear object state changes + self.reset_states() + + return event + + def reset_states(self): + ''' + clear state changes + ''' + self.cleaned_objects = set() + self.cooled_objects = set() + self.heated_objects = set() + + def restore_scene(self, object_poses, object_toggles, dirty_and_empty): + ''' + restore object locations and states + ''' + super().step(dict( + action='Initialize', + gridSize=constants.AGENT_STEP_SIZE / constants.RECORD_SMOOTHING_FACTOR, + cameraY=constants.CAMERA_HEIGHT_OFFSET, + renderImage=constants.RENDER_IMAGE, + renderDepthImage=constants.RENDER_DEPTH_IMAGE, + renderClassImage=constants.RENDER_CLASS_IMAGE, + renderObjectImage=constants.RENDER_OBJECT_IMAGE, + visibility_distance=constants.VISIBILITY_DISTANCE, + makeAgentsVisible=False, + )) + if len(object_toggles) > 0: + super().step((dict(action='SetObjectToggles', objectToggles=object_toggles))) + + if dirty_and_empty: + super().step(dict(action='SetStateOfAllObjects', + StateChange="CanBeDirty", + forceAction=True)) + super().step(dict(action='SetStateOfAllObjects', + StateChange="CanBeFilled", + forceAction=False)) + super().step((dict(action='SetObjectPoses', objectPoses=object_poses))) + + def set_task(self, traj, args, reward_type='sparse', max_episode_length=2000): + ''' + set the current task type (one of 7 tasks) + ''' + task_type = traj['task_type'] + self.task = get_task(task_type, traj, self, args, reward_type=reward_type, max_episode_length=max_episode_length) + + def step(self, action, smooth_nav=False): + ''' + overrides ai2thor.controller.Controller.step() for smooth navigation and goal_condition updates + ''' + if type(action) == str: + action = {'action': action} + if smooth_nav: + if "MoveAhead" in action['action']: + self.smooth_move_ahead(action) + elif "Rotate" in action['action']: + self.smooth_rotate(action) + elif "Look" in action['action']: + self.smooth_look(action) + else: + super().step(action) + else: + if "LookUp" in action['action']: + self.look_angle(-constants.AGENT_HORIZON_ADJ) + elif "LookDown" in action['action']: + self.look_angle(constants.AGENT_HORIZON_ADJ) + else: + super().step(action) + + event = self.update_states(action) + self.check_post_conditions(action) + return event + + def check_post_conditions(self, action): + ''' + handle special action post-conditions + ''' + if action['action'] == 'ToggleObjectOn': + self.check_clean(action['objectId']) + + def update_states(self, action): + ''' + extra updates to metadata after step + ''' + # add 'cleaned' to all object that were washed in the sink + event = self.last_event + if event.metadata['lastActionSuccess']: + # clean + if action['action'] == 'ToggleObjectOn' and "Faucet" in action['objectId']: + sink_basin = get_obj_of_type_closest_to_obj('SinkBasin', action['objectId'], event.metadata) + cleaned_object_ids = sink_basin['receptacleObjectIds'] + self.cleaned_objects = self.cleaned_objects | set(cleaned_object_ids) if cleaned_object_ids is not None else set() + # heat + if action['action'] == 'ToggleObjectOn' and "Microwave" in action['objectId']: + microwave = get_objects_of_type('Microwave', event.metadata)[0] + heated_object_ids = microwave['receptacleObjectIds'] + self.heated_objects = self.heated_objects | set(heated_object_ids) if heated_object_ids is not None else set() + # cool + if action['action'] == 'CloseObject' and "Fridge" in action['objectId']: + fridge = get_objects_of_type('Fridge', event.metadata)[0] + cooled_object_ids = fridge['receptacleObjectIds'] + self.cooled_objects = self.cooled_objects | set(cooled_object_ids) if cooled_object_ids is not None else set() + + return event + + def get_transition_reward(self): + if self.task is None: + raise Exception("WARNING: no task setup for transition_reward") + else: + return self.task.transition_reward(self.last_event) + + def get_goal_satisfied(self): + if self.task is None: + raise Exception("WARNING: no task setup for goal_satisfied") + else: + return self.task.goal_satisfied(self.last_event) + + def get_goal_conditions_met(self): + if self.task is None: + raise Exception("WARNING: no task setup for goal_satisfied") + else: + return self.task.goal_conditions_met(self.last_event) + + def get_subgoal_idx(self): + if self.task is None: + raise Exception("WARNING: no task setup for subgoal_idx") + else: + return self.task.get_subgoal_idx() + + def noop(self): + ''' + do nothing + ''' + super().step(dict(action='Pass')) + + def smooth_move_ahead(self, action, render_settings=None): + ''' + smoother MoveAhead + ''' + if render_settings is None: + render_settings = DEFAULT_RENDER_SETTINGS + smoothing_factor = constants.RECORD_SMOOTHING_FACTOR + new_action = copy.deepcopy(action) + new_action['moveMagnitude'] = constants.AGENT_STEP_SIZE / smoothing_factor + + new_action['renderImage'] = render_settings['renderImage'] + new_action['renderClassImage'] = render_settings['renderClassImage'] + new_action['renderObjectImage'] = render_settings['renderObjectImage'] + new_action['renderDepthImage'] = render_settings['renderDepthImage'] + + events = [] + for xx in range(smoothing_factor - 1): + event = super().step(new_action) + if event.metadata['lastActionSuccess']: + events.append(event) + + event = super().step(new_action) + if event.metadata['lastActionSuccess']: + events.append(event) + return events + + def smooth_rotate(self, action, render_settings=None): + ''' + smoother RotateLeft and RotateRight + ''' + if render_settings is None: + render_settings = DEFAULT_RENDER_SETTINGS + event = self.last_event + horizon = np.round(event.metadata['agent']['cameraHorizon'], 4) + position = event.metadata['agent']['position'] + rotation = event.metadata['agent']['rotation'] + start_rotation = rotation['y'] + if action['action'] == 'RotateLeft': + end_rotation = (start_rotation - 90) + else: + end_rotation = (start_rotation + 90) + + events = [] + for xx in np.arange(.1, 1.0001, .1): + if xx < 1: + teleport_action = { + 'action': 'TeleportFull', + 'rotation': np.round(start_rotation * (1 - xx) + end_rotation * xx, 3), + 'x': position['x'], + 'z': position['z'], + 'y': position['y'], + 'horizon': horizon, + 'tempRenderChange': True, + 'renderNormalsImage': False, + 'renderImage': render_settings['renderImage'], + 'renderClassImage': render_settings['renderClassImage'], + 'renderObjectImage': render_settings['renderObjectImage'], + 'renderDepthImage': render_settings['renderDepthImage'], + } + event = super().step(teleport_action) + else: + teleport_action = { + 'action': 'TeleportFull', + 'rotation': np.round(start_rotation * (1 - xx) + end_rotation * xx, 3), + 'x': position['x'], + 'z': position['z'], + 'y': position['y'], + 'horizon': horizon, + } + event = super().step(teleport_action) + + if event.metadata['lastActionSuccess']: + events.append(event) + return events + + def smooth_look(self, action, render_settings=None): + ''' + smoother LookUp and LookDown + ''' + if render_settings is None: + render_settings = DEFAULT_RENDER_SETTINGS + event = self.last_event + start_horizon = event.metadata['agent']['cameraHorizon'] + rotation = np.round(event.metadata['agent']['rotation']['y'], 4) + end_horizon = start_horizon + constants.AGENT_HORIZON_ADJ * (1 - 2 * int(action['action'] == 'LookUp')) + position = event.metadata['agent']['position'] + + events = [] + for xx in np.arange(.1, 1.0001, .1): + if xx < 1: + teleport_action = { + 'action': 'TeleportFull', + 'rotation': rotation, + 'x': position['x'], + 'z': position['z'], + 'y': position['y'], + 'horizon': np.round(start_horizon * (1 - xx) + end_horizon * xx, 3), + 'tempRenderChange': True, + 'renderNormalsImage': False, + 'renderImage': render_settings['renderImage'], + 'renderClassImage': render_settings['renderClassImage'], + 'renderObjectImage': render_settings['renderObjectImage'], + 'renderDepthImage': render_settings['renderDepthImage'], + } + event = super().step(teleport_action) + else: + teleport_action = { + 'action': 'TeleportFull', + 'rotation': rotation, + 'x': position['x'], + 'z': position['z'], + 'y': position['y'], + 'horizon': np.round(start_horizon * (1 - xx) + end_horizon * xx, 3), + } + event = super().step(teleport_action) + + if event.metadata['lastActionSuccess']: + events.append(event) + return events + + def look_angle(self, angle, render_settings=None): + ''' + look at a specific angle + ''' + if render_settings is None: + render_settings = DEFAULT_RENDER_SETTINGS + event = self.last_event + start_horizon = event.metadata['agent']['cameraHorizon'] + rotation = np.round(event.metadata['agent']['rotation']['y'], 4) + end_horizon = start_horizon + angle + position = event.metadata['agent']['position'] + + teleport_action = { + 'action': 'TeleportFull', + 'rotation': rotation, + 'x': position['x'], + 'z': position['z'], + 'y': position['y'], + 'horizon': np.round(end_horizon, 3), + 'tempRenderChange': True, + 'renderNormalsImage': False, + 'renderImage': render_settings['renderImage'], + 'renderClassImage': render_settings['renderClassImage'], + 'renderObjectImage': render_settings['renderObjectImage'], + 'renderDepthImage': render_settings['renderDepthImage'], + } + event = super().step(teleport_action) + return event + + def rotate_angle(self, angle, render_settings=None): + ''' + rotate at a specific angle + ''' + if render_settings is None: + render_settings = DEFAULT_RENDER_SETTINGS + event = self.last_event + horizon = np.round(event.metadata['agent']['cameraHorizon'], 4) + position = event.metadata['agent']['position'] + rotation = event.metadata['agent']['rotation'] + start_rotation = rotation['y'] + end_rotation = start_rotation + angle + + teleport_action = { + 'action': 'TeleportFull', + 'rotation': np.round(end_rotation, 3), + 'x': position['x'], + 'z': position['z'], + 'y': position['y'], + 'horizon': horizon, + 'tempRenderChange': True, + 'renderNormalsImage': False, + 'renderImage': render_settings['renderImage'], + 'renderClassImage': render_settings['renderClassImage'], + 'renderObjectImage': render_settings['renderObjectImage'], + 'renderDepthImage': render_settings['renderDepthImage'], + } + event = super().step(teleport_action) + return event + + def to_thor_api_exec(self, action, object_id="", smooth_nav=False): + # TODO: parametrized navigation commands + + if "RotateLeft" in action: + action = dict(action="RotateLeft", + forceAction=True) + event = self.step(action, smooth_nav=smooth_nav) + elif "RotateRight" in action: + action = dict(action="RotateRight", + forceAction=True) + event = self.step(action, smooth_nav=smooth_nav) + elif "MoveAhead" in action: + action = dict(action="MoveAhead", + forceAction=True) + event = self.step(action, smooth_nav=smooth_nav) + elif "LookUp" in action: + action = dict(action="LookUp", + forceAction=True) + event = self.step(action, smooth_nav=smooth_nav) + elif "LookDown" in action: + action = dict(action="LookDown", + forceAction=True) + event = self.step(action, smooth_nav=smooth_nav) + elif "OpenObject" in action: + action = dict(action="OpenObject", + objectId=object_id, + moveMagnitude=1.0) + event = self.step(action) + elif "CloseObject" in action: + action = dict(action="CloseObject", + objectId=object_id, + forceAction=True) + event = self.step(action) + elif "PickupObject" in action: + action = dict(action="PickupObject", + objectId=object_id) + event = self.step(action) + elif "PutObject" in action: + inventory_object_id = self.last_event.metadata['inventoryObjects'][0]['objectId'] + action = dict(action="PutObject", + objectId=inventory_object_id, + receptacleObjectId=object_id, + forceAction=True, + placeStationary=True) + event = self.step(action) + elif "ToggleObjectOn" in action: + action = dict(action="ToggleObjectOn", + objectId=object_id) + event = self.step(action) + + elif "ToggleObjectOff" in action: + action = dict(action="ToggleObjectOff", + objectId=object_id) + event = self.step(action) + elif "SliceObject" in action: + # check if agent is holding knife in hand + inventory_objects = self.last_event.metadata['inventoryObjects'] + if len(inventory_objects) == 0 or 'Knife' not in inventory_objects[0]['objectType']: + raise Exception("Agent should be holding a knife before slicing.") + + action = dict(action="SliceObject", + objectId=object_id) + event = self.step(action) + else: + raise Exception("Invalid action. Conversion to THOR API failed! (action='" + str(action) + "')") + + return event, action + + def check_clean(self, object_id): + ''' + Handle special case when Faucet is toggled on. + In this case, we need to execute a `CleanAction` in the simulator on every object in the corresponding + basin. This is to clean everything in the sink rather than just things touching the stream. + ''' + event = self.last_event + if event.metadata['lastActionSuccess'] and 'Faucet' in object_id: + # Need to delay one frame to let `isDirty` update on stream-affected. + event = self.step({'action': 'Pass'}) + sink_basin_obj = game_util.get_obj_of_type_closest_to_obj("SinkBasin", object_id, event.metadata) + for in_sink_obj_id in sink_basin_obj['receptacleObjectIds']: + if (game_util.get_object(in_sink_obj_id, event.metadata)['dirtyable'] + and game_util.get_object(in_sink_obj_id, event.metadata)['isDirty']): + event = self.step({'action': 'CleanObject', 'objectId': in_sink_obj_id}) + return event + + def prune_by_any_interaction(self, instances_ids): + ''' + ignores any object that is not interactable in anyway + ''' + pruned_instance_ids = [] + for obj in self.last_event.metadata['objects']: + obj_id = obj['objectId'] + if obj_id in instances_ids: + if obj['pickupable'] or obj['receptacle'] or obj['openable'] or obj['toggleable'] or obj['sliceable']: + pruned_instance_ids.append(obj_id) + + ordered_instance_ids = [id for id in instances_ids if id in pruned_instance_ids] + return ordered_instance_ids + + def va_interact(self, action, interact_mask=None, smooth_nav=True, mask_px_sample=1, debug=False): + ''' + interact mask based action call + ''' + + all_ids = [] + + if type(interact_mask) is str and interact_mask == "NULL": + raise Exception("NULL mask.") + elif interact_mask is not None: + # ground-truth instance segmentation mask from THOR + instance_segs = np.array(self.last_event.instance_segmentation_frame) + color_to_object_id = self.last_event.color_to_object_id + + # get object_id for each 1-pixel in the interact_mask + nz_rows, nz_cols = np.nonzero(interact_mask) + instance_counter = Counter() + for i in range(0, len(nz_rows), mask_px_sample): + x, y = nz_rows[i], nz_cols[i] + instance = tuple(instance_segs[x, y]) + instance_counter[instance] += 1 + if debug: + print("action_box", "instance_counter", instance_counter) + + # iou scores for all instances + iou_scores = {} + for color_id, intersection_count in instance_counter.most_common(): + union_count = np.sum(np.logical_or(np.all(instance_segs == color_id, axis=2), interact_mask.astype(bool))) + iou_scores[color_id] = intersection_count / float(union_count) + iou_sorted_instance_ids = list(OrderedDict(sorted(iou_scores.items(), key=lambda x: x[1], reverse=True))) + + # get the most common object ids ignoring the object-in-hand + inv_obj = self.last_event.metadata['inventoryObjects'][0]['objectId'] \ + if len(self.last_event.metadata['inventoryObjects']) > 0 else None + all_ids = [color_to_object_id[color_id] for color_id in iou_sorted_instance_ids + if color_id in color_to_object_id and color_to_object_id[color_id] != inv_obj] + + # print all ids + if debug: + print("action_box", "all_ids", all_ids) + + # print instance_ids + instance_ids = [inst_id for inst_id in all_ids if inst_id is not None] + if debug: + print("action_box", "instance_ids", instance_ids) + + # prune invalid instances like floors, walls, etc. + instance_ids = self.prune_by_any_interaction(instance_ids) + + # cv2 imshows to show image, segmentation mask, interact mask + if debug: + print("action_box", "instance_ids", instance_ids) + instance_seg = copy.copy(instance_segs) + instance_seg[:, :, :] = interact_mask[:, :, np.newaxis] == 1 + instance_seg *= 255 + + cv2.imshow('seg', instance_segs) + cv2.imshow('mask', instance_seg) + cv2.imshow('full', self.last_event.frame[:,:,::-1]) + cv2.waitKey(0) + + if len(instance_ids) == 0: + err = "Bad interact mask. Couldn't locate target object" + success = False + return success, None, None, err, None + + target_instance_id = instance_ids[0] + else: + target_instance_id = "" + + if debug: + print("taking action: " + str(action) + " on target_instance_id " + str(target_instance_id)) + try: + event, api_action = self.to_thor_api_exec(action, target_instance_id, smooth_nav) + except Exception as err: + success = False + return success, None, None, err, None + + if not event.metadata['lastActionSuccess']: + if interact_mask is not None and debug: + print("Failed to execute action!", action, target_instance_id) + print("all_ids inside BBox: " + str(all_ids)) + instance_seg = copy.copy(instance_segs) + instance_seg[:, :, :] = interact_mask[:, :, np.newaxis] == 1 + cv2.imshow('seg', instance_segs) + cv2.imshow('mask', instance_seg) + cv2.imshow('full', self.last_event.frame[:,:,::-1]) + cv2.waitKey(0) + print(event.metadata['errorMessage']) + success = False + return success, event, target_instance_id, event.metadata['errorMessage'], api_action + + success = True + return success, event, target_instance_id, '', api_action + + @staticmethod + def bbox_to_mask(bbox): + return image_util.bbox_to_mask(bbox) + + @staticmethod + def point_to_mask(point): + return image_util.point_to_mask(point) + + @staticmethod + def decompress_mask(compressed_mask): + return image_util.decompress_mask(compressed_mask) diff --git a/easi/tasks/ebalfred/vendor/gen/__init__.py b/easi/tasks/ebalfred/vendor/gen/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/easi/tasks/ebalfred/vendor/gen/constants.py b/easi/tasks/ebalfred/vendor/gen/constants.py new file mode 100644 index 0000000..f024d7c --- /dev/null +++ b/easi/tasks/ebalfred/vendor/gen/constants.py @@ -0,0 +1,1221 @@ +from collections import OrderedDict + +######################################################################################################################## +# General Settings + +DEBUG = True +EVAL = False +LOG_FILE = 'logs_gen' + +RECORD_VIDEO_IMAGES = True +RECORD_SMOOTHING_FACTOR = 1 +DATA_SAVE_PATH = "dataset/new_trajectories" + +OPEN_LOOP = True +FULL_OBSERVABLE_STATE = True + +######################################################################################################################## +# Generation Ablations + +MAX_NUM_OF_OBJ_INSTANCES = 3 # when randomly initializing the scene, create duplicate instance up to this number +PICKUP_REPEAT_MAX = 4 # how many of the target pickup object to generate in [1, MAX] (randomly chosen) +RECEPTACLE_SPARSE_POINTS = 50 # increment for how many points to leave free for sparsely populated receptacles +RECEPTACLE_EMPTY_POINTS = 200 # increment for how many points to leave free for empty receptacles + +MIN_VISIBLE_RATIO = 0.0011 # minimum area ratio (with respect to image size) of visible object +PLANNER_MAX_STEPS = 100 # if the generated plan is more than these steps, discard the traj +MAX_EPISODE_LENGTH = 1000 # maximum number of API steps allowed per trajectory + +FORCED_SAMPLING = False # set True for debugging instead of proper sampling +PRUNE_UNREACHABLE_POINTS = True # prune navigation points that were deemed unreachable by the proprocessing script + +######################################################################################################################## +# Goals + +GOALS = ["pick_and_place_simple", + "pick_two_obj_and_place", + "look_at_obj_in_light", + "pick_clean_then_place_in_recep", + "pick_heat_then_place_in_recep", + "pick_cool_then_place_in_recep", + "pick_and_place_with_movable_recep"] + +GOALS_VALID = {"pick_and_place_simple": {"Kitchen", "LivingRoom", "Bathroom", "Bedroom"}, + "pick_two_obj_and_place": {"Kitchen", "LivingRoom", "Bathroom", "Bedroom"}, + "look_at_obj_in_light": {"LivingRoom", "Bedroom"}, + "pick_clean_then_place_in_recep": {"Kitchen", "Bathroom"}, + "pick_heat_then_place_in_recep": {"Kitchen"}, + "pick_cool_then_place_in_recep": {"Kitchen"}, + "pick_and_place_with_movable_recep": {"Kitchen", "LivingRoom", "Bedroom"}} + +pddl_goal_type = "pick_and_place_simple" # default goal type + +######################################################################################################################## +# Video Settings + +# filler frame IDs +BEFORE = 0 +MIDDLE = 1 +AFTER = 2 + +# number of image frames to save before and after executing the specified action +SAVE_FRAME_BEFORE_AND_AFTER_COUNTS = { + 'OpenObject': [2, 0, 2], + 'CloseObject': [2, 0, 2], + 'PickupObject': [5, 0, 10], + 'PutObject': [5, 0, 10], + 'CleanObject': [3, 0, 5], + 'HeatObject': [3, 0, 5], + 'CoolObject': [3, 30, 5], + 'ToggleObjectOn': [3, 0, 15], + 'ToggleObjectOff': [1, 0, 5], + 'SliceObject': [3, 0, 7] +} + +# FPS +VIDEO_FRAME_RATE = 5 + +######################################################################################################################## +# Data & Storage + +save_path = DATA_SAVE_PATH +data_dict = OrderedDict() # dictionary for storing trajectory data to be dumped + +######################################################################################################################## +# Unity Hyperparameters + +BUILD_PATH = None +X_DISPLAY = '0' + +AGENT_STEP_SIZE = 0.25 +AGENT_HORIZON_ADJ = 15 +AGENT_ROTATE_ADJ = 90 +CAMERA_HEIGHT_OFFSET = 0.75 +VISIBILITY_DISTANCE = 1.5 # default visibility +# VISIBILITY_DISTANCE = 25 # whole scene +HORIZON_GRANULARITY = 15 + +RENDER_IMAGE = True +RENDER_DEPTH_IMAGE = True +RENDER_CLASS_IMAGE = True +RENDER_OBJECT_IMAGE = True + +MAX_DEPTH = 5000 +STEPS_AHEAD = 5 +SCENE_PADDING = STEPS_AHEAD * 3 +SCREEN_WIDTH = DETECTION_SCREEN_WIDTH = 500 +SCREEN_HEIGHT = DETECTION_SCREEN_HEIGHT = 500 +MIN_VISIBLE_PIXELS = 10 + +# (400) / (600*600) ~ 0.13% area of image +# int(MIN_VISIBLE_RATIO * float(DETECTION_SCREEN_WIDTH) * float(DETECTION_SCREEN_HEIGHT)) +# MIN_VISIBLE_PIXELS = int(MIN_VISIBLE_RATIO * float(DETECTION_SCREEN_WIDTH) * float( +# DETECTION_SCREEN_HEIGHT)) # (400) / (600*600) ~ 0.13% area of image + +######################################################################################################################## +# Scenes and Objects + +TRAIN_SCENE_NUMBERS = list(range(7, 31)) # Train Kitchens (24/30) +TRAIN_SCENE_NUMBERS.extend(list(range(207, 231))) # Train Living Rooms (24/30) +TRAIN_SCENE_NUMBERS.extend(list(range(307, 331))) # Train Bedrooms (24/30) +TRAIN_SCENE_NUMBERS.extend(list(range(407, 431))) # Train Bathrooms (24/30) + +TEST_SCENE_NUMBERS = list(range(1, 7)) # Test Kitchens (6/30) +TEST_SCENE_NUMBERS.extend(list(range(201, 207))) # Test Living Rooms (6/30) +TEST_SCENE_NUMBERS.extend(list(range(301, 307))) # Test Bedrooms (6/30) +TEST_SCENE_NUMBERS.extend(list(range(401, 407))) # Test Bathrooms (6/30) + +SCENE_NUMBERS = TRAIN_SCENE_NUMBERS + TEST_SCENE_NUMBERS + +# Scene types. +SCENE_TYPE = {"Kitchen": range(1, 31), + "LivingRoom": range(201, 231), + "Bedroom": range(301, 331), + "Bathroom": range(401, 431)} + +OBJECTS = [ + 'AlarmClock', + 'Apple', + 'ArmChair', + 'BaseballBat', + 'BasketBall', + 'Bathtub', + 'BathtubBasin', + 'Bed', + 'Blinds', + 'Book', + 'Boots', + 'Bowl', + 'Box', + 'Bread', + 'ButterKnife', + 'Cabinet', + 'Candle', + 'Cart', + 'CD', + 'CellPhone', + 'Chair', + 'Cloth', + 'CoffeeMachine', + 'CounterTop', + 'CreditCard', + 'Cup', + 'Curtains', + 'Desk', + 'DeskLamp', + 'DishSponge', + 'Drawer', + 'Dresser', + 'Egg', + 'FloorLamp', + 'Footstool', + 'Fork', + 'Fridge', + 'GarbageCan', + 'Glassbottle', + 'HandTowel', + 'HandTowelHolder', + 'HousePlant', + 'Kettle', + 'KeyChain', + 'Knife', + 'Ladle', + 'Laptop', + 'LaundryHamper', + 'LaundryHamperLid', + 'Lettuce', + 'LightSwitch', + 'Microwave', + 'Mirror', + 'Mug', + 'Newspaper', + 'Ottoman', + 'Painting', + 'Pan', + 'PaperTowel', + 'PaperTowelRoll', + 'Pen', + 'Pencil', + 'PepperShaker', + 'Pillow', + 'Plate', + 'Plunger', + 'Poster', + 'Pot', + 'Potato', + 'RemoteControl', + 'Safe', + 'SaltShaker', + 'ScrubBrush', + 'Shelf', + 'ShowerDoor', + 'ShowerGlass', + 'Sink', + 'SinkBasin', + 'SoapBar', + 'SoapBottle', + 'Sofa', + 'Spatula', + 'Spoon', + 'SprayBottle', + 'Statue', + 'StoveBurner', + 'StoveKnob', + 'DiningTable', + 'CoffeeTable', + 'SideTable', + 'TeddyBear', + 'Television', + 'TennisRacket', + 'TissueBox', + 'Toaster', + 'Toilet', + 'ToiletPaper', + 'ToiletPaperHanger', + 'ToiletPaperRoll', + 'Tomato', + 'Towel', + 'TowelHolder', + 'TVStand', + 'Vase', + 'Watch', + 'WateringCan', + 'Window', + 'WineBottle', +] + +OBJECTS_LOWER_TO_UPPER = {obj.lower(): obj for obj in OBJECTS} + +OBJECTS_SINGULAR = [ + 'alarmclock', + 'apple', + 'armchair', + 'baseballbat', + 'basketball', + 'bathtub', + 'bathtubbasin', + 'bed', + 'blinds', + 'book', + 'boots', + 'bowl', + 'box', + 'bread', + 'butterknife', + 'cabinet', + 'candle', + 'cart', + 'cd', + 'cellphone', + 'chair', + 'cloth', + 'coffeemachine', + 'countertop', + 'creditcard', + 'cup', + 'curtains', + 'desk', + 'desklamp', + 'dishsponge', + 'drawer', + 'dresser', + 'egg', + 'floorlamp', + 'footstool', + 'fork', + 'fridge', + 'garbagecan', + 'glassbottle', + 'handtowel', + 'handtowelholder', + 'houseplant', + 'kettle', + 'keychain', + 'knife', + 'ladle', + 'laptop', + 'laundryhamper', + 'laundryhamperlid', + 'lettuce', + 'lightswitch', + 'microwave', + 'mirror', + 'mug', + 'newspaper', + 'ottoman', + 'painting', + 'pan', + 'papertowel', + 'papertowelroll', + 'pen', + 'pencil', + 'peppershaker', + 'pillow', + 'plate', + 'plunger', + 'poster', + 'pot', + 'potato', + 'remotecontrol', + 'safe', + 'saltshaker', + 'scrubbrush', + 'shelf', + 'showerdoor', + 'showerglass', + 'sink', + 'sinkbasin', + 'soapbar', + 'soapbottle', + 'sofa', + 'spatula', + 'spoon', + 'spraybottle', + 'statue', + 'stoveburner', + 'stoveknob', + 'diningtable', + 'coffeetable', + 'sidetable' + 'teddybear', + 'television', + 'tennisracket', + 'tissuebox', + 'toaster', + 'toilet', + 'toiletpaper', + 'toiletpaperhanger', + 'toiletpaperroll', + 'tomato', + 'towel', + 'towelholder', + 'tvstand', + 'vase', + 'watch', + 'wateringcan', + 'window', + 'winebottle', +] + +OBJECTS_PLURAL = [ + 'alarmclocks', + 'apples', + 'armchairs', + 'baseballbats', + 'basketballs', + 'bathtubs', + 'bathtubbasins', + 'beds', + 'blinds', + 'books', + 'boots', + 'bottles', + 'bowls', + 'boxes', + 'bread', + 'butterknives', + 'cabinets', + 'candles', + 'carts', + 'cds', + 'cellphones', + 'chairs', + 'cloths', + 'coffeemachines', + 'countertops', + 'creditcards', + 'cups', + 'curtains', + 'desks', + 'desklamps', + 'dishsponges', + 'drawers', + 'dressers', + 'eggs', + 'floorlamps', + 'footstools', + 'forks', + 'fridges', + 'garbagecans', + 'glassbottles', + 'handtowels', + 'handtowelholders', + 'houseplants', + 'kettles', + 'keychains', + 'knives', + 'ladles', + 'laptops', + 'laundryhampers', + 'laundryhamperlids', + 'lettuces', + 'lightswitches', + 'microwaves', + 'mirrors', + 'mugs', + 'newspapers', + 'ottomans', + 'paintings', + 'pans', + 'papertowels', + 'papertowelrolls', + 'pens', + 'pencils', + 'peppershakers', + 'pillows', + 'plates', + 'plungers', + 'posters', + 'pots', + 'potatoes', + 'remotecontrollers', + 'safes', + 'saltshakers', + 'scrubbrushes', + 'shelves', + 'showerdoors', + 'showerglassess', + 'sinks', + 'sinkbasins', + 'soapbars', + 'soapbottles', + 'sofas', + 'spatulas', + 'spoons', + 'spraybottles', + 'statues', + 'stoveburners', + 'stoveknobs', + 'diningtables', + 'coffeetables', + 'sidetable', + 'teddybears', + 'televisions', + 'tennisrackets', + 'tissueboxes', + 'toasters', + 'toilets', + 'toiletpapers', + 'toiletpaperhangers', + 'toiletpaperrolls', + 'tomatoes', + 'towels', + 'towelholders', + 'tvstands', + 'vases', + 'watches', + 'wateringcans', + 'windows', + 'winebottles', +] + +MOVABLE_RECEPTACLES = [ + 'Bowl', + 'Box', + 'Cup', + 'Mug', + 'Plate', + 'Pan', + 'Pot', +] + +MOVABLE_RECEPTACLES_SET = set(MOVABLE_RECEPTACLES) +OBJECTS_SET = set(OBJECTS) | MOVABLE_RECEPTACLES_SET + +OBJECT_CLASS_TO_ID = {obj: ii for (ii, obj) in enumerate(OBJECTS)} + +RECEPTACLES = { + 'BathtubBasin', + 'Bowl', + 'Cup', + 'Drawer', + 'Mug', + 'Plate', + 'Shelf', + 'SinkBasin', + 'Box', + 'Cabinet', + 'CoffeeMachine', + 'CounterTop', + 'Fridge', + 'GarbageCan', + 'HandTowelHolder', + 'Microwave', + 'PaintingHanger', + 'Pan', + 'Pot', + 'StoveBurner', + 'DiningTable', + 'CoffeeTable', + 'SideTable', + 'ToiletPaperHanger', + 'TowelHolder', + 'Safe', + 'BathtubBasin', + 'ArmChair', + 'Toilet', + 'Sofa', + 'Ottoman', + 'Dresser', + 'LaundryHamper', + 'Desk', + 'Bed', + 'Cart', + 'TVStand', + 'Toaster', + } + +NON_RECEPTACLES = OBJECTS_SET - RECEPTACLES + +NUM_RECEPTACLES = len(RECEPTACLES) +NUM_CLASSES = len(OBJECTS) + +# For generating questions +QUESTION_OBJECT_CLASS_LIST = [ + 'Spoon', + 'Potato', + 'Fork', + 'Plate', + 'Egg', + 'Tomato', + 'Bowl', + 'Lettuce', + 'Apple', + 'Knife', + 'Container', + 'Bread', + 'Mug', +] + +VAL_RECEPTACLE_OBJECTS = { + 'Pot': {'Apple', + 'AppleSliced', + 'ButterKnife', + 'DishSponge', + 'Egg', + 'Fork', + 'Knife', + 'Ladle', + 'Lettuce', + 'LettuceSliced', + 'Potato', + 'PotatoSliced', + 'Spatula', + 'Spoon', + 'Tomato', + 'TomatoSliced'}, + 'Pan': {'Apple', + 'AppleSliced', + 'ButterKnife', + 'DishSponge', + 'Egg', + 'Fork', + 'Knife', + 'Ladle', + 'Lettuce', + 'LettuceSliced', + 'Potato', + 'PotatoSliced', + 'Spatula', + 'Spoon', + 'Tomato', + 'TomatoSliced'}, + 'Bowl': {'Apple', + 'AppleSliced', + 'ButterKnife', + 'DishSponge', + 'Egg', + 'Fork', + 'Knife', + 'Ladle', + 'Lettuce', + 'LettuceSliced', + 'Potato', + 'PotatoSliced', + 'Spatula', + 'Spoon', + 'Tomato', + 'TomatoSliced', + 'Candle', + 'CD', + 'CellPhone', + 'Cloth', + 'CreditCard', + 'DishSponge', + 'KeyChain', + 'Mug', + 'PaperTowel', + 'Pen', + 'Pencil', + 'RemoteControl', + 'Watch'}, + 'CoffeeMachine': {'Mug'}, + 'Microwave': {'Apple', + 'AppleSliced', + 'Bowl', + 'Bread', + 'BreadSliced', + 'Cup', + 'Egg', + 'Glassbottle', + 'Mug', + 'Plate', + 'Potato', + 'PotatoSliced', + 'Tomato', + 'TomatoSliced'}, + 'StoveBurner': {'Kettle', + 'Pan', + 'Pot'}, + 'Fridge': {'Apple', + 'AppleSliced', + 'Bowl', + 'Bread', + 'BreadSliced', + 'Cup', + 'Egg', + 'Glassbottle', + 'Lettuce', + 'LettuceSliced', + 'Mug', + 'Pan', + 'Plate', + 'Pot', + 'Potato', + 'PotatoSliced', + 'Tomato', + 'TomatoSliced', + 'WineBottle'}, + 'Mug': {'ButterKnife', + 'Fork', + 'Knife', + 'Pen', + 'Pencil', + 'Spoon', + 'KeyChain', + 'Watch'}, + 'Plate': {'Apple', + 'AppleSliced', + 'ButterKnife', + 'DishSponge', + 'Egg', + 'Fork', + 'Knife', + 'Ladle', + 'Lettuce', + 'LettuceSliced', + 'Mug', + 'Potato', + 'PotatoSliced', + 'Spatula', + 'Spoon', + 'Tomato', + 'TomatoSliced', + 'AlarmClock', + 'Book', + 'Candle', + 'CD', + 'CellPhone', + 'Cloth', + 'CreditCard', + 'DishSponge', + 'Glassbottle', + 'KeyChain', + 'Mug', + 'PaperTowel', + 'Pen', + 'Pencil', + 'TissueBox', + 'Watch'}, + 'Cup': {'ButterKnife', + 'Fork', + 'Spoon'}, + 'Sofa': {'BasketBall', + 'Book', + 'Box', + 'CellPhone', + 'Cloth', + 'CreditCard', + 'KeyChain', + 'Laptop', + 'Newspaper', + 'Pillow', + 'RemoteControl'}, + 'ArmChair': {'BasketBall', + 'Book', + 'Box', + 'CellPhone', + 'Cloth', + 'CreditCard', + 'KeyChain', + 'Laptop', + 'Newspaper', + 'Pillow', + 'RemoteControl'}, + 'Box': {'AlarmClock', + 'Book', + 'Candle', + 'CD', + 'CellPhone', + 'Cloth', + 'CreditCard', + 'DishSponge', + 'Glassbottle', + 'KeyChain', + 'Mug', + 'PaperTowel', + 'Pen', + 'Pencil', + 'RemoteControl', + 'Statue', + 'TissueBox', + 'Vase', + 'Watch'}, + 'Ottoman': {'BasketBall', + 'Book', + 'Box', + 'CellPhone', + 'Cloth', + 'CreditCard', + 'KeyChain', + 'Laptop', + 'Newspaper', + 'Pillow', + 'RemoteControl'}, + 'Dresser': {'AlarmClock', + 'BasketBall', + 'Book', + 'Bowl', + 'Box', + 'Candle', + 'CD', + 'CellPhone', + 'Cloth', + 'CreditCard', + 'Cup', + 'Glassbottle', + 'KeyChain', + 'Laptop', + 'Mug', + 'Newspaper', + 'Pen', + 'Pencil', + 'Plate', + 'RemoteControl', + 'SprayBottle', + 'Statue', + 'TennisRacket', + 'TissueBox', + 'ToiletPaper', + 'ToiletPaperRoll', + 'Vase', + 'Watch', + 'WateringCan', + 'WineBottle'}, + 'LaundryHamper': {'Cloth'}, + 'Desk': {'AlarmClock', + 'BasketBall', + 'Book', + 'Bowl', + 'Box', + 'Candle', + 'CD', + 'CellPhone', + 'Cloth', + 'CreditCard', + 'Cup', + 'Glassbottle', + 'KeyChain', + 'Laptop', + 'Mug', + 'Newspaper', + 'Pen', + 'Pencil', + 'Plate', + 'RemoteControl', + 'SoapBottle', + 'SprayBottle', + 'Statue', + 'TennisRacket', + 'TissueBox', + 'ToiletPaper', + 'ToiletPaperRoll', + 'Vase', + 'Watch', + 'WateringCan', + 'WineBottle'}, + 'Bed': {'BaseballBat', + 'BasketBall', + 'Book', + 'CellPhone', + 'Laptop', + 'Newspaper', + 'Pillow', + 'TennisRacket'}, + 'Toilet': {'Candle', + 'Cloth', + 'DishSponge', + 'Newspaper', + 'PaperTowel', + 'SoapBar', + 'SoapBottle', + 'SprayBottle', + 'TissueBox', + 'ToiletPaper', + 'ToiletPaperRoll', + 'HandTowel'}, + 'ToiletPaperHanger': {'ToiletPaper', + 'ToiletPaperRoll'}, + 'TowelHolder': {'Towel'}, + 'HandTowelHolder': {'HandTowel'}, + 'Cart': {'Candle', + 'Cloth', + 'DishSponge', + 'Mug', + 'PaperTowel', + 'Plunger', + 'SoapBar', + 'SoapBottle', + 'SprayBottle', + 'Statue', + 'TissueBox', + 'ToiletPaper', + 'ToiletPaperRoll', + 'Vase', + 'HandTowel'}, + 'BathtubBasin': {'Cloth', + 'DishSponge', + 'SoapBar', + 'HandTowel'}, + 'SinkBasin': {'Apple', + 'AppleSliced', + 'Bowl', + 'ButterKnife', + 'Cloth', + 'Cup', + 'DishSponge', + 'Egg', + 'Glassbottle', + 'Fork', + 'Kettle', + 'Knife', + 'Ladle', + 'Lettuce', + 'LettuceSliced', + 'Mug', + 'Pan', + 'Plate', + 'Pot', + 'Potato', + 'PotatoSliced', + 'SoapBar', + 'Spatula', + 'Spoon', + 'Tomato', + 'TomatoSliced', + 'HandTowel'}, + 'Cabinet': {'Book', + 'Bowl', + 'Box', + 'Candle', + 'CD', + 'Cloth', + 'Cup', + 'DishSponge', + 'Glassbottle', + 'Kettle', + 'Ladle', + 'Mug', + 'Newspaper', + 'Pan', + 'PepperShaker', + 'Plate', + 'Plunger', + 'Pot', + 'SaltShaker', + 'SoapBar', + 'SoapBottle', + 'SprayBottle', + 'TissueBox', + 'ToiletPaper', + 'ToiletPaperRoll', + 'Vase', + 'WateringCan', + 'WineBottle', + 'HandTowel'}, + 'TableTop': {'AlarmClock', + 'Apple', + 'AppleSliced', + 'BaseballBat', + 'BasketBall', + 'Book', + 'Bowl', + 'Box', + 'Bread', + 'BreadSliced', + 'ButterKnife', + 'Candle', + 'CD', + 'CellPhone', + 'Cloth', + 'CreditCard', + 'Cup', + 'DishSponge', + 'Glassbottle', + 'Egg', + 'Fork', + 'Kettle', + 'KeyChain', + 'Knife', + 'Ladle', + 'Laptop', + 'Lettuce', + 'LettuceSliced', + 'Mug', + 'Newspaper', + 'Pan', + 'PaperTowel', + 'Pen', + 'Pencil', + 'PepperShaker', + 'Plate', + 'Pot', + 'Potato', + 'PotatoSliced', + 'RemoteControl', + 'SaltShaker', + 'SoapBar', + 'SoapBottle', + 'Spatula', + 'Spoon', + 'SprayBottle', + 'Statue', + 'TennisRacket', + 'TissueBox', + 'ToiletPaper', + 'ToiletPaperRoll', + 'Tomato', + 'TomatoSliced', + 'Vase', + 'Watch', + 'WateringCan', + 'WineBottle', + 'HandTowel'}, + 'CounterTop': {'AlarmClock', + 'Apple', + 'AppleSliced', + 'BaseballBat', + 'BasketBall', + 'Book', + 'Bowl', + 'Box', + 'Bread', + 'BreadSliced', + 'ButterKnife', + 'Candle', + 'CD', + 'CellPhone', + 'Cloth', + 'CreditCard', + 'Cup', + 'DishSponge', + 'Egg', + 'Glassbottle', + 'Fork', + 'Kettle', + 'KeyChain', + 'Knife', + 'Ladle', + 'Laptop', + 'Lettuce', + 'LettuceSliced', + 'Mug', + 'Newspaper', + 'Pan', + 'PaperTowel', + 'Pen', + 'Pencil', + 'PepperShaker', + 'Plate', + 'Pot', + 'Potato', + 'PotatoSliced', + 'RemoteControl', + 'SaltShaker', + 'SoapBar', + 'SoapBottle', + 'Spatula', + 'Spoon', + 'SprayBottle', + 'Statue', + 'TennisRacket', + 'TissueBox', + 'ToiletPaper', + 'ToiletPaperRoll', + 'Tomato', + 'TomatoSliced', + 'Vase', + 'Watch', + 'WateringCan', + 'WineBottle', + 'HandTowel'}, + 'Shelf': {'AlarmClock', + 'Book', + 'Bowl', + 'Box', + 'Candle', + 'CD', + 'CellPhone', + 'Cloth', + 'CreditCard', + 'Cup', + 'DishSponge', + 'Glassbottle', + 'Kettle', + 'KeyChain', + 'Mug', + 'Newspaper', + 'PaperTowel', + 'Pen', + 'Pencil', + 'PepperShaker', + 'Plate', + 'Pot', + 'RemoteControl', + 'SaltShaker', + 'SoapBar', + 'SoapBottle', + 'SprayBottle', + 'Statue', + 'TissueBox', + 'ToiletPaper', + 'ToiletPaperRoll', + 'Vase', + 'Watch', + 'WateringCan', + 'WineBottle', + 'HandTowel'}, + 'Drawer': {'Book', + 'ButterKnife', + 'Candle', + 'CD', + 'CellPhone', + 'Cloth', + 'CreditCard', + 'DishSponge', + 'Fork', + 'KeyChain', + 'Knife', + 'Ladle', + 'Newspaper', + 'Pen', + 'Pencil', + 'PepperShaker', + 'RemoteControl', + 'SaltShaker', + 'SoapBar', + 'SoapBottle', + 'Spatula', + 'Spoon', + 'SprayBottle', + 'TissueBox', + 'ToiletPaper', + 'ToiletPaperRoll', + 'Watch', + 'WateringCan', + 'HandTowel'}, + 'GarbageCan': {'Apple', + 'AppleSliced', + 'Bread', + 'BreadSliced', + 'CD', + 'Cloth', + 'DishSponge', + 'Egg', + 'Lettuce', + 'LettuceSliced', + 'Newspaper', + 'PaperTowel', + 'Pen', + 'Pencil', + 'Potato', + 'PotatoSliced', + 'SoapBar', + 'SoapBottle', + 'SprayBottle', + 'TissueBox', + 'ToiletPaper', + 'ToiletPaperRoll', + 'Tomato', + 'TomatoSliced', + 'WineBottle', + 'HandTowel'}, + 'Safe': {'CD', + 'CellPhone', + 'CreditCard', + 'KeyChain', + 'Statue', + 'Vase', + 'Watch'}, + 'TVStand': {'TissueBox'}, + 'Toaster': {'BreadSliced'}, +} +VAL_RECEPTACLE_OBJECTS['DiningTable'] = VAL_RECEPTACLE_OBJECTS['TableTop'] +VAL_RECEPTACLE_OBJECTS['CoffeeTable'] = VAL_RECEPTACLE_OBJECTS['TableTop'] +VAL_RECEPTACLE_OBJECTS['SideTable'] = VAL_RECEPTACLE_OBJECTS['TableTop'] +del VAL_RECEPTACLE_OBJECTS['TableTop'] + +NON_RECEPTACLES_SET = (OBJECTS_SET - set(VAL_RECEPTACLE_OBJECTS.keys())) | set(MOVABLE_RECEPTACLES) + +VAL_ACTION_OBJECTS = { + 'Heatable': {'Apple', + 'AppleSliced', + 'Bread', + 'BreadSliced', + 'Cup', + 'Egg', + 'Mug', + 'Plate', + 'Potato', + 'PotatoSliced', + 'Tomato', + 'TomatoSliced'}, + 'Coolable': {'Apple', + 'AppleSliced', + 'Bowl', + 'Bread', + 'BreadSliced', + 'Cup', + 'Egg', + 'Lettuce', + 'LettuceSliced', + 'Mug', + 'Pan', + 'Plate', + 'Pot', + 'Potato', + 'PotatoSliced', + 'Tomato', + 'TomatoSliced', + 'WineBottle'}, + 'Cleanable': {'Apple', + 'AppleSliced', + 'Bowl', + 'ButterKnife', + 'Cloth', + 'Cup', + 'DishSponge', + 'Egg', + 'Fork', + 'Kettle', + 'Knife', + 'Ladle', + 'Lettuce', + 'LettuceSliced', + 'Mug', + 'Pan', + 'Plate', + 'Pot', + 'Potato', + 'PotatoSliced', + 'SoapBar', + 'Spatula', + 'Spoon', + 'Tomato', + 'TomatoSliced'}, + 'Toggleable': {'DeskLamp', + 'FloorLamp'}, + 'Sliceable': {'Apple', + 'Bread', + 'Egg', + 'Lettuce', + 'Potato', + 'Tomato'} +} + +# object parents +OBJ_PARENTS = {obj: obj for obj in OBJECTS} +OBJ_PARENTS['AppleSliced'] = 'Apple' +OBJ_PARENTS['BreadSliced'] = 'Bread' +OBJ_PARENTS['LettuceSliced'] = 'Lettuce' +OBJ_PARENTS['PotatoSliced'] = 'Potato' +OBJ_PARENTS['TomatoSliced'] = 'Tomato' + +# force a different horizon view for objects of (type, location). If the location is None, force this horizon for all +# objects of that type. +FORCED_HORIZON_OBJS = { + ('FloorLamp', None): 0, + ('Fridge', 18): 30, + ('Toilet', None): 15, +} + +# openable objects with fixed states for transport. +FORCED_OPEN_STATE_ON_PICKUP = { + 'Laptop': False, +} + +# list of openable classes. +OPENABLE_CLASS_LIST = ['Fridge', 'Cabinet', 'Microwave', 'Drawer', 'Safe', 'Box'] +OPENABLE_CLASS_SET = set(OPENABLE_CLASS_LIST) + +######################################################################################################################## \ No newline at end of file diff --git a/easi/tasks/ebalfred/vendor/gen/goal_library.py b/easi/tasks/ebalfred/vendor/gen/goal_library.py new file mode 100644 index 0000000..79f4e46 --- /dev/null +++ b/easi/tasks/ebalfred/vendor/gen/goal_library.py @@ -0,0 +1,682 @@ +######################################### +# Common: +# {obj} - ObjectStr +# {recep} - RecepStr +# usage: .format(obj=constants.OBJECTS[self.object_target], recep=constants.OBJECTS[self.parent_target]) + +# NOTE: order of and/or conditions matters +######################################### + +gdict = {} + +############################################### +# PHASE 1: basic skills +############################################### + + +# basic pick and place (e.g: "put the apple in the microwave") +gdict["pick_and_place_simple"] = \ +{ + 'pddl' : + ''' + (:goal + (and + (exists (?r # receptacle) + (exists (?o # object) + (and + (inReceptacle ?o ?r) + (objectType ?o {obj}Type) + (receptacleType ?r {recep}Type) + ) + ) + ) + (forall (?re # receptacle) + (not (opened ?re)) + ) + ) + ) + ) + ''', + 'templates': ['put a {obj} in {recep}', + 'put some {obj} on {recep}'] +} + + +############################################### +# PHASE 2: state changes and quantifiers +############################################### + +# pick and place object, but clean the object in the sink first +gdict["pick_clean_then_place_in_recep"] = \ +{ + 'pddl' : + ''' + (:goal + (and + (forall (?re # receptacle) + (not (opened ?re)) + ) + (exists (?r # receptacle) + (exists (?o # object) + (and + (cleanable ?o) + (objectType ?o {obj}Type) + (receptacleType ?r {recep}Type) + (isClean ?o) + (inReceptacle ?o ?r) + ) + ) + ) + ) + ) + ) + ''', + 'templates': ['put a clean {obj} in {recep}', + 'clean some {obj} and put it in {recep}'] + + +} + + +# pick and place object, but heat the object in the microwave first +gdict["pick_heat_then_place_in_recep"] = \ +{ + 'pddl': + ''' + (:goal + (and + (exists (?r # receptacle) + (exists (?o # object) + (and + (heatable ?o) + (objectType ?o {obj}Type) + (receptacleType ?r {recep}Type) + (isHot ?o) + (inReceptacle ?o ?r) + ) + ) + ) + (forall (?re # receptacle) + (not (opened ?re)) + ) + ) + ) + ) + ''', + 'templates': ['put a hot {obj} in {recep}', + 'heat some {obj} and put it in {recep}'] +} + + +# pick and place object, but cool the object (if it's not already cold) in the fridge first +gdict["pick_cool_then_place_in_recep"] = \ +{ + 'pddl': + ''' + (:goal + (and + (exists (?r # receptacle) + (exists (?o # object) + (and + (coolable ?o) + (objectType ?o {obj}Type) + (receptacleType ?r {recep}Type) + (isCool ?o) + (inReceptacle ?o ?r) + ) + ) + ) + (forall (?re # receptacle) + (not (opened ?re)) + ) + ) + ) + ) + ''', + 'templates': ['put a cool {obj} in {recep}', + 'cool some {obj} and put it in {recep}'] +} + + +# pick two instances of an object and place them in a receptacle (e.g: "pick two apples and put them in the sink") +gdict["pick_two_obj_and_place"] = \ + { + 'pddl': + ''' + (:goal + (and + (exists (?r # receptacle) + (exists (?o1 # object) + (and + (objectType ?o1 {obj}Type) + (receptacleType ?r {recep}Type) + (inReceptacle ?o1 ?r) + (exists (?o2 # object) + (and + (not (= ?o1 ?o2)) + (objectType ?o2 {obj}Type) + (receptacleType ?r {recep}Type) + (inReceptacle ?o2 ?r) + ) + ) + ) + ) + ) + (forall (?re # receptacle) + (not (opened ?re)) + ) + ) + ) + ) + ''', + 'templates': ['put two {obj} in {recep}', + 'find two {obj} and put them in {recep}'] + } + + + +# toggle the state of a toggleable object (e.g: "toggle the lightswitch") +gdict["look_at_obj_in_light"] = \ +{ + 'pddl': + ''' + (:goal + (and + (exists (?ot # object + ?a # agent + ?l # location) + (and + (objectType ?ot {toggle}Type) + (toggleable ?ot) + (isToggled ?ot) + (objectAtLocation ?ot ?l) + (atLocation ?a ?l) + ) + ) + (exists (?o # object + ?a # agent) + (and + (objectType ?o {obj}Type) + (holds ?a ?o) + ) + ) + ) + ) + ) + ''', + 'templates': ['look at {obj} under the {toggle}', + 'examine the {obj} with the {toggle}'] +} + +# pick and place with a movable receptacle (e.g: "put a apple in a bowl inside the microwave") +gdict["pick_and_place_with_movable_recep"] = \ + { + 'pddl': + ''' + (:goal + (and + (exists (?r # receptacle) + (and + (receptacleType ?r {recep}Type) + (exists (?o # object) + (and + (objectType ?o {obj}Type) + (exists (?mo # object) + (and + (objectType ?mo {mrecep}Type) + (isReceptacleObject ?mo) + (inReceptacleObject ?o ?mo) + (inReceptacle ?mo ?r) + ) + ) + ) + ) + ) + ) + (forall (?re # receptacle) + (not (opened ?re)) + ) + ) + ) + ) + ''', + 'templates': ['put {obj} in a {mrecep} and then put them in {recep}', + 'put a {mrecep} of {obj} in {recep}', + 'put {obj} {mrecep} in {recep}'] + } + + +############################################### +# PHASE 3 long horizon tasks (in development) +############################################### + + +# pick, slice, and place object, but clean the object in the sink first +gdict["pick_clean_then_place_in_recep_slice"] = \ + { + 'pddl': + ''' + (:goal + (and + (forall (?re # receptacle) + (not (opened ?re)) + ) + (exists (?r # receptacle) + (exists (?o # object) + (and + (sliceable ?o) + (isSliced ?o) + (cleanable ?o) + (objectType ?o {obj}Type) + (receptacleType ?r {recep}Type) + (isClean ?o) + (inReceptacle ?o ?r) + ) + ) + ) + ) + ) + ) + ''', + 'templates': ['put a clean slice of {obj} in {recep}', + 'clean some sliced {obj} and put it in {recep}'] + + } + +# pick, slice, and place object, but heat the object in the microwave first +gdict["pick_heat_then_place_in_recep_slice"] = \ + { + 'pddl': + ''' + (:goal + (and + (exists (?r # receptacle) + (exists (?o # object) + (and + (sliceable ?o) + (isSliced ?o) + (heatable ?o) + (objectType ?o {obj}Type) + (receptacleType ?r {recep}Type) + (isHot ?o) + (inReceptacle ?o ?r) + ) + ) + ) + (forall (?re # receptacle) + (not (opened ?re)) + ) + ) + ) + ) + ''', + 'templates': ['put a hot slice of {obj} in {recep}', + 'heat some sliced {obj} and put it in {recep}'] + } + +# pick, slice, and place object, but cool the object (if it's not already cold) in the fridge first +gdict["pick_cool_then_place_in_recep_slice"] = \ + { + 'pddl': + ''' + (:goal + (and + (exists (?r # receptacle) + (exists (?o # object) + (and + (sliceable ?o) + (isSliced ?o) + (coolable ?o) + (objectType ?o {obj}Type) + (receptacleType ?r {recep}Type) + (isCool ?o) + (inReceptacle ?o ?r) + ) + ) + ) + (forall (?re # receptacle) + (not (opened ?re)) + ) + ) + ) + ) + ''', + 'templates': ['put a cool slice of {obj} in {recep}', + 'cool some sliced {obj} and put it in {recep}'] + } + +# pick two instances of a sliced object and place them in a receptacle (e.g: "pick two apples and put them in the sink") +gdict["pick_two_obj_and_place_slice"] = \ + { + 'pddl': + ''' + (:goal + (and + (exists (?r # receptacle) + (exists (?o1 # object) + (and + (sliceable ?o1) + (isSliced ?o1) + (objectType ?o1 {obj}Type) + (receptacleType ?r {recep}Type) + (inReceptacle ?o1 ?r) + (exists (?o2 # object) + (and + (not (= ?o1 ?o2)) + + (sliceable ?o2) + (isSliced ?o2) + (objectType ?o2 {obj}Type) + (receptacleType ?r {recep}Type) + (inReceptacle ?o2 ?r) + ) + ) + ) + ) + ) + (forall (?re # receptacle) + (not (opened ?re)) + ) + ) + ) + ) + ''', + 'templates': ['put two sliced {obj} in {recep}', + 'find two sliced {obj} and put them in {recep}'] + } + +# toggle the state of a toggleable object (e.g: "toggle the lightswitch") while holding another, sliced one. +gdict["look_at_obj_in_light_slice"] = \ + { + 'pddl': + ''' + (:goal + (and + (exists (?ot # object + ?a # agent + ?l # location) + (and + (sliceable ?o) + (isSliced ?o) + (objectType ?ot {toggle}Type) + (toggleable ?ot) + (isToggled ?ot) + (objectAtLocation ?ot ?l) + (atLocation ?a ?l) + ) + ) + (exists (?o # object + ?a # agent) + (and + (objectType ?o {obj}Type) + (holds ?a ?o) + ) + ) + ) + ) + ) + ''', + 'templates': ['look at sliced {obj} under the {toggle}', + 'examine the sliced {obj} with the {toggle}'] + } + +# pick, slice, and place with a movable receptacle (e.g: "put a apple in a bowl inside the microwave") +gdict["pick_and_place_with_movable_recep_slice"] = \ + { + 'pddl': + ''' + (:goal + (and + (exists (?r # receptacle) + (and + (receptacleType ?r {recep}Type) + (exists (?o # object) + (and + (objectType ?o {obj}Type) + (exists (?mo # object) + (and + (sliceable ?o) + (isSliced ?o) + (objectType ?mo {mrecep}Type) + (isReceptacleObject ?mo) + (inReceptacleObject ?o ?mo) + (inReceptacle ?mo ?r) + ) + ) + ) + ) + ) + ) + (forall (?re # receptacle) + (not (opened ?re)) + ) + ) + ) + ) + ''', + 'templates': ['put sliced {obj} in a {mrecep} and then put them in {recep}', + 'put a {mrecep} of sliced {obj} in {recep}', + 'put sliced {obj} {mrecep} in {recep}'] + } + +# pick, slice, and place. +gdict["pick_and_place_simple_slice"] = \ + { + 'pddl': + ''' + (:goal + (and + (exists (?r # receptacle) + (exists (?o # object + ?ko # object) + (and + (sliceable ?o) + (isSliced ?o) + (objectType ?o {obj}Type) + (inReceptacle ?o ?r) + (receptacleType ?r {recep}Type) + ) + ) + ) + (forall (?re # receptacle) + (not (opened ?re)) + ) + ) + ) + ) + ''', + 'templates': ['slice {obj} and put in {recep}', + 'put sliced {obj} in {recep}'] + } + + +# put all objects of a type inside in one receptacle (e.g: "put all the mugs in the microwave") +gdict["place_all_obj_type_into_recep"] = \ +{ + 'pddl': + ''' + (:goal + (and + (forall (?re # receptacle) + (not (opened ?re)) + ) + (exists (?r # receptacle) + (forall (?o # object) + (or + (and + (objectType ?o {obj}Type) + (receptacleType ?r {recep}Type) + (inReceptacle ?o ?r) + ) + (or + (not (objectType ?o {obj}Type)) + ) + ) + ) + ) + ) + ) + ) + ''', + 'templates': ['put all {obj}s in {recep}', + 'find all {obj}s and put them in {recep}'] +} + + +# pick three instances of an object and place them in a receptacle (e.g: "pick two apples and put them in the sink") +# NOTE: doesn't work +gdict["pick_three_obj_and_place"] = \ + { + 'pddl': + ''' + (:goal + (and + (exists (?r # receptacle) + (exists (?o1 # object) + (and + (objectType ?o1 {obj}Type) + (receptacleType ?r {recep}Type) + (inReceptacle ?o1 ?r) + + (exists (?o2 # object) + (and + (not (= ?o1 ?o2)) + (objectType ?o2 {obj}Type) + ;(receptacleType ?r {recep}Type) + (inReceptacle ?o2 ?r) + (exists (?o3 # object) + (and + (not (= ?o1 ?o3)) + (not (= ?o2 ?o3)) + (objectType ?o3 {obj}Type) + ;(receptacleType ?r {recep}Type) + (inReceptacle ?o3 ?r) + ) + ) + ) + ) + ) + ) + ) + (forall (?re # receptacle) + (not (opened ?re)) + ) + ) + ) + ) + ''', + 'templates': ['put three {obj} in {recep}', + 'find three {obj} and put them in {recep}'] + } + + + +gdict["pick_heat_and_place_with_movable_recep"] = \ + { + 'pddl': + ''' + (:goal + (and + (exists (?r # receptacle) + (and + (receptacleType ?r {recep}Type) + (exists (?o # object) + (and + (objectType ?o {obj}Type) + (heatable ?o) + (isHot ?o) + (exists (?mo # object) + (and + (objectType ?mo {mrecep}Type) + (isReceptacleObject ?mo) + (inReceptacleObject ?o ?mo) + (inReceptacle ?mo ?r) + ) + ) + ) + ) + ) + ) + (forall (?re # receptacle) + (not (opened ?re)) + ) + ) + ) + ) + ''', + 'templates': ['put a hot {mrecep} of {obj} in {recep}'] + } + + +gdict["pick_cool_and_place_with_movable_recep"] = \ + { + 'pddl': + ''' + (:goal + (and + (exists (?r # receptacle) + (and + (receptacleType ?r {recep}Type) + (exists (?o # object) + (and + (objectType ?o {obj}Type) + (coolable ?o) + (isCool ?o) + (exists (?mo # object) + (and + (objectType ?mo {mrecep}Type) + (isReceptacleObject ?mo) + (inReceptacleObject ?o ?mo) + (inReceptacle ?mo ?r) + ) + ) + ) + ) + ) + ) + (forall (?re # receptacle) + (not (opened ?re)) + ) + ) + ) + ) + ''', + 'templates': ['put a cold {mrecep} of {obj} in {recep}'] + } + + +gdict["pick_clean_and_place_with_movable_recep"] = \ + { + 'pddl': + ''' + (:goal + (and + (exists (?r # receptacle) + (and + (receptacleType ?r {recep}Type) + (exists (?o # object) + (and + (objectType ?o {obj}Type) + (cleanable ?o) + (isClean ?o) + (exists (?mo # object) + (and + (objectType ?mo {mrecep}Type) + (isReceptacleObject ?mo) + (inReceptacleObject ?o ?mo) + (inReceptacle ?mo ?r) + ) + ) + ) + ) + ) + ) + (forall (?re # receptacle) + (not (opened ?re)) + ) + ) + ) + ) + ''', + 'templates': ['put a cold {mrecep} of {obj} in {recep}'] + } \ No newline at end of file diff --git a/easi/tasks/ebalfred/vendor/gen/graph/__init__.py b/easi/tasks/ebalfred/vendor/gen/graph/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/easi/tasks/ebalfred/vendor/gen/graph/graph_obj.py b/easi/tasks/ebalfred/vendor/gen/graph/graph_obj.py new file mode 100644 index 0000000..09caa76 --- /dev/null +++ b/easi/tasks/ebalfred/vendor/gen/graph/graph_obj.py @@ -0,0 +1,426 @@ +import os +import random +import time + +import networkx as nx +import numpy as np + +import easi.tasks.ebalfred.vendor.gen.constants as constants +from easi.tasks.ebalfred.vendor.gen.utils import game_util + +MAX_WEIGHT_IN_GRAPH = 1e5 +PRED_WEIGHT_THRESH = 10 +EPSILON = 1e-4 + +# Direction: 0: north, 1: east, 2: south, 3: west + + +class Graph(object): + def __init__(self, use_gt=False, construct_graph=True, scene_id=None, debug=False): + t_start = time.time() + + self.construct_graph = construct_graph + ''' + event = env.step({'action': 'GetReachablePositions'}) + new_reachable_positions = event.metadata['reachablePositions'] + points = [] + for point in new_reachable_positions: + xx = int(point['x'] / constants.AGENT_STEP_SIZE) + yy = int(point['z'] / constants.AGENT_STEP_SIZE) + points.append([xx, yy]) + + self.points = np.array(points, dtype=np.int32) + self.points = self.points[np.lexsort(self.points.T)] + ''' + + self.scene_id = scene_id + self.points = np.load(os.path.join( + os.path.dirname(__file__), + os.pardir, + 'layouts', + 'FloorPlan%s-layout.npy' % self.scene_id)) + self.points /= constants.AGENT_STEP_SIZE + self.points = np.round(self.points).astype(np.int32) + self.xMin = self.points[:, 0].min() - constants.SCENE_PADDING * 2 + self.yMin = self.points[:, 1].min() - constants.SCENE_PADDING * 2 + self.xMax = self.points[:, 0].max() + constants.SCENE_PADDING * 2 + self.yMax = self.points[:, 1].max() + constants.SCENE_PADDING * 2 + self.memory = np.zeros((self.yMax - self.yMin + 1, self.xMax - self.xMin + 1), dtype=np.float32) + self.gt_graph = None + self.shortest_paths = {} + self.shortest_paths_unweighted = {} + self.use_gt = use_gt + self.impossible_spots = set() + self.updated_weights = {} + self.prev_navigable_locations = None + + if self.use_gt: + self.memory[:] = MAX_WEIGHT_IN_GRAPH + self.memory[self.points[:, 1] - self.yMin, self.points[:, 0] - self.xMin] = 1 + EPSILON + else: + self.memory[:] = 1 + self.memory[:, :int(constants.SCENE_PADDING * 1.5)] = MAX_WEIGHT_IN_GRAPH + self.memory[:int(constants.SCENE_PADDING * 1.5), :] = MAX_WEIGHT_IN_GRAPH + self.memory[:, -int(constants.SCENE_PADDING * 1.5):] = MAX_WEIGHT_IN_GRAPH + self.memory[-int(constants.SCENE_PADDING * 1.5):, :] = MAX_WEIGHT_IN_GRAPH + + if self.gt_graph is None: + self.gt_graph = nx.DiGraph() + if self.construct_graph: + for yy in np.arange(self.yMin, self.yMax + 1): + for xx in np.arange(self.xMin, self.xMax + 1): + weight = self.memory[yy - self.yMin, xx - self.xMin] + for direction in range(4): + node = (xx, yy, direction) + back_direction = (direction + 2) % 4 + back_node = (xx, yy, back_direction) + self.gt_graph.add_edge(node, (xx, yy, (direction + 1) % 4), weight=1) + self.gt_graph.add_edge(node, (xx, yy, (direction - 1) % 4), weight=1) + forward_node = None + if direction == 0 and yy != self.yMax: + forward_node = (xx, yy + 1, back_direction) + elif direction == 1 and xx != self.xMax: + forward_node = (xx + 1, yy, back_direction) + elif direction == 2 and yy != self.yMin: + forward_node = (xx, yy - 1, back_direction) + elif direction == 3 and xx != self.xMin: + forward_node = (xx - 1, yy, back_direction) + if forward_node is not None: + self.gt_graph.add_edge(forward_node, back_node, weight=weight) + + self.initial_memory = self.memory.copy() + self.debug = debug + if self.debug: + print('Graph construction time %.3f' % (time.time() - t_start)) + + def clear(self): + self.shortest_paths = {} + self.shortest_paths_unweighted = {} + self.impossible_spots = set() + self.prev_navigable_locations = None + + if self.use_gt: + self.memory[:] = self.initial_memory + else: + self.memory[:] = 1 + self.memory[:, :int(constants.SCENE_PADDING * 1.5)] = MAX_WEIGHT_IN_GRAPH + self.memory[:int(constants.SCENE_PADDING * 1.5), :] = MAX_WEIGHT_IN_GRAPH + self.memory[:, -int(constants.SCENE_PADDING * 1.5):] = MAX_WEIGHT_IN_GRAPH + self.memory[-int(constants.SCENE_PADDING * 1.5):, :] = MAX_WEIGHT_IN_GRAPH + + if self.construct_graph: + for (nodea, nodeb), original_weight in self.updated_weights.items(): + self.gt_graph[nodea][nodeb]['weight'] = original_weight + self.updated_weights = {} + + @property + def image(self): + return self.memory[:, :].astype(np.uint8) + + def check_graph_memory_correspondence(self): + # graph sanity check + if self.construct_graph: + for yy in np.arange(self.yMin, self.yMax + 1): + for xx in np.arange(self.xMin, self.xMax + 1): + for direction in range(4): + back_direction = (direction + 2) % 4 + back_node = (xx, yy, back_direction) + if direction == 0 and yy != self.yMax: + assert(abs(self.gt_graph[(xx, yy + 1, back_direction)][back_node]['weight'] - + self.memory[int(yy - self.yMin), int(xx - self.xMin)]) < 0.0001) + elif direction == 1 and xx != self.xMax: + assert(abs(self.gt_graph[(xx + 1, yy, back_direction)][back_node]['weight'] - + self.memory[int(yy - self.yMin), int(xx - self.xMin)]) < 0.0001) + elif direction == 2 and yy != self.yMin: + assert(abs(self.gt_graph[(xx, yy - 1, back_direction)][back_node]['weight'] - + self.memory[int(yy - self.yMin), int(xx - self.xMin)]) < 0.0001) + elif direction == 3 and xx != self.xMin: + assert(abs(self.gt_graph[(xx - 1, yy, back_direction)][back_node]['weight'] - + self.memory[int(yy - self.yMin), int(xx - self.xMin)]) < 0.0001) + print('\t\t\tgraph tested successfully') + + def update_graph(self, graph_patch, pose): + graph_patch, curr_val = graph_patch + curr_val = np.array(curr_val) + # Rotate the array to get its global coordinate frame orientation. + rotation = int(pose[2]) + assert(rotation in {0, 1, 2, 3}), 'rotation was %s' % str(rotation) + + if rotation != 0: + graph_patch = np.rot90(graph_patch, rotation) + # Shift offsets to global coordinate frame. + if rotation == 0: + x_min = pose[0] - int(constants.STEPS_AHEAD / 2) + y_min = pose[1] + 1 + elif rotation == 1: + x_min = pose[0] + 1 + y_min = pose[1] - int(constants.STEPS_AHEAD / 2) + elif rotation == 2: + x_min = pose[0] - int(constants.STEPS_AHEAD / 2) + y_min = pose[1] - constants.STEPS_AHEAD + elif rotation == 3: + x_min = pose[0] - constants.STEPS_AHEAD + y_min = pose[1] - int(constants.STEPS_AHEAD / 2) + else: + raise Exception('Invalid pose direction') + if self.construct_graph: + for yi, yy in enumerate(range(y_min, y_min + constants.STEPS_AHEAD)): + for xi, xx in enumerate(range(x_min, x_min + constants.STEPS_AHEAD)): + self.update_weight(xx, yy, graph_patch[yi, xi, 0]) + self.update_weight(pose[0], pose[1], curr_val[0]) + + def get_graph_patch(self, pose): + rotation = int(pose[2]) + assert(rotation in {0, 1, 2, 3}) + + if rotation == 0: + x_min = pose[0] - int(constants.STEPS_AHEAD / 2) + y_min = pose[1] + 1 + elif rotation == 1: + x_min = pose[0] + 1 + y_min = pose[1] - int(constants.STEPS_AHEAD / 2) + elif rotation == 2: + x_min = pose[0] - int(constants.STEPS_AHEAD / 2) + y_min = pose[1] - constants.STEPS_AHEAD + elif rotation == 3: + x_min = pose[0] - constants.STEPS_AHEAD + y_min = pose[1] - int(constants.STEPS_AHEAD / 2) + else: + raise Exception('Invalid pose direction') + x_min -= self.xMin + y_min -= self.yMin + + graph_patch = self.memory[y_min:y_min + constants.STEPS_AHEAD, + x_min:x_min + constants.STEPS_AHEAD].copy() + + if rotation != 0: + graph_patch = np.rot90(graph_patch, -rotation) + + return graph_patch, self.memory[pose[1] - self.yMin, pose[0] - self.xMin].copy() + + def add_impossible_spot(self, spot): + self.update_weight(spot[0], spot[1], MAX_WEIGHT_IN_GRAPH) + self.impossible_spots.add(spot) + + def update_weight(self, xx, yy, weight): + if (xx, yy) not in self.impossible_spots: + if self.construct_graph: + for direction in range(4): + node = (xx, yy, direction) + self.update_edge(node, weight) + self.memory[yy - self.yMin, xx - self.xMin] = weight + self.shortest_paths = {} + + def update_edge(self, pose, weight): + rotation = int(pose[2]) + assert(rotation in {0, 1, 2, 3}) + + (xx, yy, direction) = pose + back_direction = (direction + 2) % 4 + back_pose = (xx, yy, back_direction) + if direction == 0 and yy != self.yMax: + forward_pose = (xx, yy + 1, back_direction) + elif direction == 1 and xx != self.xMax: + forward_pose = (xx + 1, yy, back_direction) + elif direction == 2 and yy != self.yMin: + forward_pose = (xx, yy - 1, back_direction) + elif direction == 3 and xx != self.xMin: + forward_pose = (xx - 1, yy, back_direction) + else: + raise NotImplementedError('Unknown direction') + if (forward_pose, back_pose) not in self.updated_weights: + self.updated_weights[(forward_pose, back_pose)] = self.gt_graph[forward_pose][back_pose]['weight'] + self.gt_graph[forward_pose][back_pose]['weight'] = weight + + def get_shortest_path(self, pose, goal_pose): + assert(pose[2] in {0, 1, 2, 3}) + assert(goal_pose[2] in {0, 1, 2, 3}) + + # Store horizons for possible final look correction. + curr_horizon = int(pose[3]) + goal_horizon = int(goal_pose[3]) + + pose = tuple(int(pp) for pp in pose[:3]) + goal_pose = tuple(int(pp) for pp in goal_pose[:3]) + + try: + assert(self.construct_graph), 'Graph was not constructed, cannot get shortest path.' + assert(pose in self.gt_graph), 'start point not in graph' + assert(goal_pose in self.gt_graph), 'start point not in graph' + except Exception as ex: + print('pose', pose, 'goal_pose', goal_pose) + raise ex + + if (pose, goal_pose) not in self.shortest_paths: + path = nx.astar_path(self.gt_graph, pose, goal_pose, + heuristic=lambda nodea, nodeb: (abs(nodea[0] - nodeb[0]) + abs(nodea[1] - nodeb[1]) + + abs(nodea[2] - nodeb[2])), + weight='weight') + for ii, pp in enumerate(path): + self.shortest_paths[(pp, goal_pose)] = path[ii:] + path = self.shortest_paths[(pose, goal_pose)] + max_point = 1 + for ii in range(len(path) - 1): + weight = self.gt_graph[path[ii]][path[ii + 1]]['weight'] + if path[ii][:2] != path[ii + 1][:2]: + if abs(self.memory[path[ii + 1][1] - self.yMin, path[ii + 1][0] - self.xMin] - weight) > 0.001: + print(self.memory[path[ii + 1][1] - self.yMin, path[ii + 1][0] - self.xMin], weight) + raise AssertionError('weights do not match') + if weight >= PRED_WEIGHT_THRESH: + break + max_point += 1 + path = path[:max_point] + + actions = [Graph.get_plan_move(path[ii], path[ii + 1]) for ii in range(len(path) - 1)] + Graph.horizon_adjust(actions, path, curr_horizon, goal_horizon) + + return actions, path + + def get_shortest_path_unweighted(self, pose, goal_pose): + assert(pose[2] in {0, 1, 2, 3}) + assert(goal_pose[2] in {0, 1, 2, 3}) + curr_horizon = int(pose[3]) + goal_horizon = int(goal_pose[3]) + pose = tuple(int(pp) for pp in pose[:3]) + goal_pose = tuple(int(pp) for pp in goal_pose[:3]) + + try: + assert(self.construct_graph), 'Graph was not constructed, cannot get shortest path.' + assert(pose in self.gt_graph), 'start point not in graph' + assert(goal_pose in self.gt_graph), 'start point not in graph' + except Exception as ex: + print('pose', pose, 'goal_pose', goal_pose) + raise ex + + if (pose, goal_pose) not in self.shortest_paths_unweighted: + # TODO: swap this out for astar (might be get_shortest_path tho) and update heuristic to account for + # TODO: actual number of turns. + path = nx.shortest_path(self.gt_graph, pose, goal_pose) + for ii, pp in enumerate(path): + self.shortest_paths_unweighted[(pp, goal_pose)] = path[ii:] + path = self.shortest_paths_unweighted[(pose, goal_pose)] + + actions = [Graph.get_plan_move(path[ii], path[ii + 1]) for ii in range(len(path) - 1)] + Graph.horizon_adjust(actions, path, curr_horizon, goal_horizon) + return actions, path + + def update_map(self, env): + event = env.step({'action': 'GetReachablePositions'}) + new_reachable_positions = event.metadata['reachablePositions'] + new_memory = np.full_like(self.memory[:, :], MAX_WEIGHT_IN_GRAPH) + if self.construct_graph: + for point in new_reachable_positions: + xx = int(point['x'] / constants.AGENT_STEP_SIZE) + yy = int(point['z'] / constants.AGENT_STEP_SIZE) + new_memory[yy - self.yMin, xx - self.xMin] = 1 + EPSILON + changed_locations = np.where(np.logical_xor(self.memory[:, :] == MAX_WEIGHT_IN_GRAPH, new_memory == MAX_WEIGHT_IN_GRAPH)) + for location in zip(*changed_locations): + self.update_weight(location[1] + self.xMin, location[0] + self.yMin, 1 + EPSILON) + + def navigate_to_goal(self, game_state, start_pose, end_pose): + # Look down + + self.update_map(game_state.env) + + start_angle = start_pose[3] + if start_angle > 180: + start_angle -= 360 + if start_angle != 45: # pitch angle + # Perform initial tilt to get to 45 degrees. + tilt_pose = [pp for pp in start_pose] + tilt_pose[3] = 45 + tilt_actions, _ = self.get_shortest_path(start_pose, tilt_pose) + for action in tilt_actions: + game_state.step(action) + start_pose = tuple(tilt_pose) + + actions, path = self.get_shortest_path(start_pose, end_pose) + while len(actions) > 0: + for ii, (action, pose) in enumerate(zip(actions, path)): + game_state.step(action) + event = game_state.env.last_event + last_action_success = event.metadata['lastActionSuccess'] + + if not last_action_success: + # Can't traverse here, make sure the weight is correct. + if action['action'].startswith('Look') or action['action'].startswith('Rotate'): + raise Exception('Look action failed %s' % event.metadata['errorMessage']) + self.add_impossible_spot(path[ii + 1]) + break + pose = game_util.get_pose(event) + actions, path = self.get_shortest_path(pose, end_pose) + print('nav done') + + @staticmethod + def get_plan_move(pose0, pose1): + if (pose0[2] + 1) % 4 == pose1[2]: + action = {'action': 'RotateRight'} + elif (pose0[2] - 1) % 4 == pose1[2]: + action = {'action': 'RotateLeft'} + else: + action = {'action': 'MoveAhead', 'moveMagnitude': constants.AGENT_STEP_SIZE} + return action + + @staticmethod + def horizon_adjust(actions, path, hor0, hor1): + if hor0 < hor1: + for _ in range((hor1 - hor0) // constants.AGENT_HORIZON_ADJ): + actions.append({'action': 'LookDown'}) + path.append(path[-1]) + elif hor0 > hor1: + for _ in range((hor0 - hor1) // constants.AGENT_HORIZON_ADJ): + actions.append({'action': 'LookUp',}) + path.append(path[-1]) + + +if __name__ == '__main__': + # Test graphs + env = game_util.create_env() + scenes = sorted(constants.TRAIN_SCENE_NUMBERS + constants.TEST_SCENE_NUMBERS) + while True: + scene_id = random.choice(scenes) + graph = Graph(use_gt=True, construct_graph=True, scene_id=scene_id) + game_util.reset(env, scene_id, + render_image=False, + render_depth_image=False, + render_class_image=False, + render_object_image=False) + num_points = len(graph.points) + point1 = random.randint(0, num_points - 1) + point2 = point1 + while point2 == point1: + point2 = random.randint(0, num_points) + point1 = graph.points[point1] + point2 = graph.points[point2] + start_pose = (point1[0], point1[1], random.randint(0, 3), 0) + end_pose = (point2[0], point2[1], random.randint(0, 3), 0) + agent_height = env.last_event.metadata['agent']['position']['y'] + action = {'action': 'TeleportFull', + 'x': start_pose[0] * constants.AGENT_STEP_SIZE, + 'y': agent_height, + 'z': start_pose[1] * constants.AGENT_STEP_SIZE, + 'rotateOnTeleport': True, + 'rotation': start_pose[2], + 'horizon': start_pose[3], + } + env.step(action) + actions, path = graph.get_shortest_path(start_pose, end_pose) + while len(actions) > 0: + for ii, (action, pose) in enumerate(zip(actions, path)): + env.step(action) + event = env.last_event + last_action_success = event.metadata['lastActionSuccess'] + + if not last_action_success: + # Can't traverse here, make sure the weight is correct. + if action['action'].startswith('Look') or action['action'].startswith('Rotate'): + raise Exception('Look action failed %s' % event.metadata['errorMessage']) + graph.add_impossible_spot(path[ii + 1]) + break + pose = game_util.get_pose(event) + actions, path = graph.get_shortest_path(pose, end_pose) + if end_pose == pose: + print('made it') + else: + print('could not make it :(') + diff --git a/easi/tasks/ebalfred/vendor/gen/layouts/FloorPlan1-layout.npy b/easi/tasks/ebalfred/vendor/gen/layouts/FloorPlan1-layout.npy new file mode 100644 index 0000000000000000000000000000000000000000..dd9b203ec9adbebf851b7270d91abeedb1c9ab6d GIT binary patch literal 2192 zcmbW#p-#h46b9g8d5XG1GQ$)Ql1%;o6KCBPx!}{%VT-ot2~y+@>m|rtE>I* zvvxF1XZil;CeC61CpU2(w~3wSy7Sn1bv)<1-m%Z}Sw73>w(3suUCL*9ERW@} z=e-Bdv*+3K?DT-JeJ4uJlB7Bk=uVa;kxSHhx_~g!Sz^O*PHcVb8xzv-@f^| ziFw>-el|bX$^GWoM)#KORYBASW;QIsGS#8dD<^&|KZI(GDs zAwvdA@A-;)M!ej9zK;L%r;dNWegEd&hX>_H`8k|zcAvJxi+*@{{(M;V!}-Pb`*!{H ze?NWX=)?MO7jv*atPjh*bHuZG*gS{mVIDRQn}^NA 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"Bowl", + "Toaster", + "Egg", + "Pan", + "Apple", + "SinkBasin", + "Chair", + "Fridge", + "Window", + "Mug", + "Drawer", + "Bread", + "Cup", + "Pot", + "StoveKnob", + "Microwave", + "SaltShaker", + "GarbageCan", + "DishSponge", + "LightSwitch", + "StoveBurner", + "Faucet", + "Vase", + "PaperTowelRoll", + "Tomato", + "ButterKnife", + "Cabinet", + "CellPhone", + "Spoon" +] \ No newline at end of file diff --git a/easi/tasks/ebalfred/vendor/gen/layouts/FloorPlan10-openable.json b/easi/tasks/ebalfred/vendor/gen/layouts/FloorPlan10-openable.json new file mode 100644 index 0000000..0f850bf --- /dev/null +++ b/easi/tasks/ebalfred/vendor/gen/layouts/FloorPlan10-openable.json @@ -0,0 +1,98 @@ +{ + "Cabinet|+00.65|+00.48|+00.24": [ + -0.25, + 0.75, + 90, + 30 + ], + "Cabinet|+00.65|+00.48|-01.72": [ + 0.0, + -2.0, + 90, + 30 + ], + "Cabinet|+00.95|+02.16|-02.38": [ + 0.25, + -2.0, + 90, + -30 + ], + "CounterTop|+00.93|+00.95|-00.21": [ + 0.25, + -0.25, + 90, + 30 + ], + 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"Cabinet|-00.27|+00.40|+01.00": [ + 0.25, + 0.0, + 0, + 30 + ], + "Cabinet|-00.28|+02.01|-00.61": [ + 0.0, + 0.0, + 180, + -30 + ], + "CounterTop|+01.36|+00.95|-00.60": [ + 1.0, + 0.25, + 180, + 30 + ], + "CounterTop|+01.92|+00.95|+01.30": [ + 2.25, + 0.5, + 0, + 30 + ], + "CounterTop|-00.16|+00.95|+01.30": [ + -0.75, + 1.0, + 90, + 30 + ], + "DiningTable|-00.84|+00.79|-01.26": [ + -1.25, + -0.25, + 180, + 30 + ], + "Drawer|+01.17|+00.79|-00.30": [ + 1.5, + 0.25, + 180, + 30 + ], + "Drawer|+01.18|+00.79|+01.00": [ + 0.75, + 0.25, + 0, + 30 + ], + "Drawer|+01.52|+00.79|-00.30": [ + 1.25, + 0.25, + 180, + 30 + ], + "Drawer|+01.53|+00.79|+01.00": [ + 1.25, + 0.25, + 0, + 30 + ], + "Drawer|+01.86|+00.79|+01.00": [ + 1.5, + 0.25, + 0, + 30 + ], + "Drawer|+01.87|+00.79|-00.30": [ + 1.5, + 0.25, + 180, + 30 + ], + "Drawer|+02.19|+00.79|+01.00": [ + 1.75, + 0.25, + 0, + 30 + ], + "Drawer|+02.21|+00.79|-00.30": [ + 2.0, + 0.25, + 180, + 30 + ], + "Drawer|+02.51|+00.79|+01.00": [ + 2.75, + 0.25, + 0, + 30 + ], + "Drawer|+02.84|+00.79|+01.00": [ + 2.25, + 0.5, + 90, + 30 + ], + "Drawer|-00.13|+00.79|+01.00": [ + -0.5, + 0.25, + 0, + 30 + ], + "Fridge|+03.04|+00.00|-00.71": [ + 3.0, + 0.5, + 180, + 30 + ], + "GarbageCan|-01.99|+00.00|+01.36": [ + -1.0, + 1.0, + 270, + 30 + ], + "Microwave|+00.53|+01.67|+01.44": [ + 0.5, + 0.5, + 0, + 0 + ], + "Sink|+00.05|+00.90|-00.65|SinkBasin": [ + 0.25, + 0.0, + 180, + 30 + ] +} \ No newline at end of file diff --git a/easi/tasks/ebalfred/vendor/gen/utils/__init__.py b/easi/tasks/ebalfred/vendor/gen/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/easi/tasks/ebalfred/vendor/gen/utils/game_util.py b/easi/tasks/ebalfred/vendor/gen/utils/game_util.py new file mode 100644 index 0000000..e643c93 --- /dev/null +++ b/easi/tasks/ebalfred/vendor/gen/utils/game_util.py @@ -0,0 +1,377 @@ +import copy +import random +import cv2 +import numpy as np +import easi.tasks.ebalfred.vendor.gen.constants as constants +import easi.tasks.ebalfred.vendor.gen.goal_library as glib + + +def get_pose(event): + pose = event.pose + return (int(np.round(pose[0] / (1000 * constants.AGENT_STEP_SIZE))), + int(np.round(pose[1] / (1000 * constants.AGENT_STEP_SIZE))), + int(np.round(pose[2] / (1000 * 90))), + int(np.round(pose[3] / (1000)))) + + +def get_object_data(metadata): + return [ + {"objectName": obj["name"].split("(Clone)")[0], "position": obj["position"], "rotation": obj["rotation"]} + for obj in metadata["objects"] + if obj["pickupable"] + ] + + +def imresize(image, size, rescale=True): + if image is None: + return None + if image.shape[0] != size[0] or image.shape[1] != size[1]: + image = cv2.resize(image, size) + if rescale: + if image.dtype != np.float32: + image = image.astype(np.float32) + image /= 255.0 + return image + + +def depth_imresize(image, size, rescale=True, max_depth=constants.MAX_DEPTH): + if image is None: + return None + if image.shape[0] != size[0] or image.shape[1] != size[1]: + image = cv2.resize(image, size) + image[image > max_depth] = max_depth + if rescale: + if image.dtype != np.float32: + image = image.astype(np.float32) + image /= max_depth + return image + + +def get_camera_matrix(pose, camera_height): + assert(pose[2] in {0, 1, 2, 3}) + sin_x = np.sin(pose[3] * np.pi / 180) + cos_x = np.cos(pose[3] * np.pi / 180) + x_rotation = np.matrix([ + [1, 0, 0], + [0, cos_x, -sin_x], + [0, sin_x, cos_x]]) + sin_y = np.sin(pose[2] * np.pi / 180) + cos_y = np.cos(pose[2] * np.pi / 180) + y_rotation = np.matrix([ + [cos_y, 0, sin_y], + [0, 1, 0], + [-sin_y, 0, cos_y]]) + rotation_matrix = np.matmul(x_rotation, y_rotation) + transformation_matrix = np.matrix([pose[0], camera_height, pose[1], 1]).T + extrinsic_matrix = np.concatenate((np.concatenate((rotation_matrix, np.matrix([0, 0, 0])), axis=0), + transformation_matrix), axis=1) + return extrinsic_matrix + + +def get_rotation_matrix(pose): + assert(pose[2] in {0, 1, 2, 3}), 'rotation was %s' % str(pose[2]) + sin_x = np.sin(-pose[3] * np.pi / 180) + cos_x = np.cos(-pose[3] * np.pi / 180) + x_rotation = np.matrix([ + [1, 0, 0], + [0, cos_x, -sin_x], + [0, sin_x, cos_x]], dtype=np.float32) + sin_y = np.sin((-pose[2] % 4) * 90 * np.pi / 180) + cos_y = np.cos((-pose[2] % 4) * 90 * np.pi / 180) + y_rotation = np.matrix([ + [cos_y, 0, sin_y], + [0, 1, 0], + [-sin_y, 0, cos_y]], dtype=np.float32) + rotation_matrix = np.matmul(x_rotation, y_rotation) + return rotation_matrix + + +def depth_to_world_coordinates(depth, pose, camera_height): + x_points = np.arange(-constants.SCREEN_WIDTH / 2, constants.SCREEN_WIDTH / 2, dtype=depth.dtype) + x_vals = (depth * x_points / constants.FOCAL_LENGTH) + + y_points = np.arange(constants.SCREEN_HEIGHT / 2, -constants.SCREEN_HEIGHT / 2, -1, dtype=depth.dtype) + y_vals = (depth.T * y_points / constants.FOCAL_LENGTH).T + + z_vals = depth + xyz = np.stack((x_vals, y_vals, z_vals), axis=2) / (1000 * constants.AGENT_STEP_SIZE) + rotation_matrix = np.linalg.inv(get_rotation_matrix(pose)) + xyz = np.array(np.dot(rotation_matrix, xyz.reshape(-1, 3).T).T).reshape( + constants.SCREEN_HEIGHT, constants.SCREEN_WIDTH, 3) + xzy = xyz[:, :, [0, 2, 1]] + xzy += np.array([pose[0], pose[1], camera_height]) + return xzy + + +# coordinates should be [n, (xzy)] +def world_to_camera_coordinates(coordinates, pose, camera_height): + coordinates = coordinates.copy() + coordinates -= np.array([pose[0], pose[1], camera_height]) + xyz = coordinates[:, [0, 2, 1]] # [n, (xyz)] + rotation_matrix = get_rotation_matrix(pose) + xyd = np.array(np.dot(rotation_matrix, xyz.T).T) + xyd *= (1000 * constants.AGENT_STEP_SIZE) + depth = np.maximum(xyd[:, -1], 0.01) + x_points = xyd[:, 0] * constants.FOCAL_LENGTH / depth + constants.SCREEN_WIDTH / 2 + y_points = constants.SCREEN_HEIGHT - (xyd[:, 1] * constants.FOCAL_LENGTH / depth + constants.SCREEN_HEIGHT / 2) + return np.stack((x_points, y_points, depth)).T + + +def get_templated_action_str(plan, idx=0): + action = copy.deepcopy(plan[idx]) + object_name, recep_name, prev_object_name, prev_recep_name, next_object_name, next_recep_name = get_relevant_objs(action, plan, idx) + + a_type = action['action'] + templated_str = "" + + if 'GotoLocation' in a_type: + templated_str = "go to the %s" % (next_recep_name if next_recep_name != "" else prev_object_name) + elif 'OpenObject' in a_type: + templated_str = "open the %s" % (object_name) + elif 'CloseObject' in a_type: + templated_str = "close the %s" % (object_name) + elif 'PickupObject' in a_type: + templated_str = "pick up the %s" % (object_name) + elif 'PutObject' in a_type: + templated_str = "put the %s in the %s" % (object_name, recep_name) + elif 'CleanObject' in a_type: + templated_str = "wash the %s" % (prev_object_name) + elif 'HeatObject' in a_type: + templated_str = "heat the %s" % (prev_object_name) + elif 'CoolObject' in a_type: + templated_str = "cool the %s" % (prev_object_name) + elif 'ToggleObject' in a_type: + templated_str = "toggle %s" % (object_name) + elif 'SliceObject' in a_type: + templated_str = "slice the %s" % (object_name) + elif 'End' in a_type: + templated_str = "<>" + + return templated_str + + +def get_discrete_hl_action(plan, idx=0): + action = copy.deepcopy(plan[idx]) + object_name, recep_name, prev_object_name, prev_recep_name, next_object_name, next_recep_name = get_relevant_objs(action, plan, idx) + + a_type = action['action'] + discrete_action = {'action': "", 'args': []} + + if 'GotoLocation' in a_type: + discrete_action['action'] = "GotoLocation" + discrete_action['args'] = [next_recep_name if next_recep_name != "" else next_object_name] + elif 'OpenObject' in a_type: + discrete_action['action'] = "OpenObject" + discrete_action['args'] = [object_name] + elif 'CloseObject' in a_type: + discrete_action['action'] = "CloseObject" + discrete_action['args'] = [object_name] + elif 'PickupObject' in a_type: + discrete_action['action'] = "PickupObject" + discrete_action['args'] = [object_name] + elif 'PutObject' in a_type: + discrete_action['action'] = "PutObject" + discrete_action['args'] = [object_name, recep_name] + elif 'CleanObject' in a_type: + discrete_action['action'] = "CleanObject" + discrete_action['args'] = [prev_object_name] + elif 'HeatObject' in a_type: + discrete_action['action'] = "HeatObject" + discrete_action['args'] = [prev_object_name] + elif 'CoolObject' in a_type: + discrete_action['action'] = "CoolObject" + discrete_action['args'] = [prev_object_name] + elif 'ToggleObject' in a_type: + discrete_action['action'] = "ToggleObject" + discrete_action['args'] = [object_name] + elif 'SliceObject' in a_type: + discrete_action['action'] = "SliceObject" + discrete_action['args'] = [object_name] + else: + discrete_action['action'] = "NoOp" + discrete_action['args'] = [] + + return discrete_action + + +def object_id_to_name(object_id): + return object_id.split('|')[0] + + +def get_relevant_objs(action, plan, idx=0): + object_name = object_id_to_name(action['objectId']).lower() if 'objectId' in action else "" + recep_name = object_id_to_name(action['receptacleObjectId']).lower() if 'receptacleObjectId' in action else "" + prev_object_name, prev_recep_name = "", "" + next_object_name, next_recep_name = "", "" + + prev_idx = idx - 2 + if prev_idx >= 0: + prev_action = copy.deepcopy(plan[prev_idx]) + prev_object_name = object_id_to_name(prev_action['objectId']).lower() if 'objectId' in prev_action else "" + prev_recep_name = object_id_to_name(prev_action['receptacleObjectId']).lower() if 'receptacleObjectId' in prev_action else "" + + next_idx = idx + 1 + if next_idx < len(plan): + next_action = copy.deepcopy(plan[next_idx]) + next_object_name = object_id_to_name(next_action['objectId']).lower() if 'objectId' in next_action else "" + next_recep_name = object_id_to_name(next_action['receptacleObjectId']).lower() if 'receptacleObjectId' in next_action else "" + + return object_name, recep_name, prev_object_name, prev_recep_name, next_object_name, next_recep_name + + +def get_action_str(action): + action = copy.deepcopy(action) + a_type = action['action'] + action_str = 'Action: ' + a_type + del action['action'] + + if 'Teleport' in a_type: + action_str = a_type + if 'x' in action: + action_str += ' x: %.03f' % action['x'] + del action['x'] + if 'y' in action: + action_str += ' y: %.03f' % action['y'] + del action['y'] + if 'z' in action: + action_str += ' z: %.03f' % action['z'] + del action['z'] + if 'rotation' in action and action.get('rotateOnTeleport', False): + if type(action['rotation']) == dict: + action_str += ' r: %d' % int(action['rotation']['y']) + else: + action_str += ' r: %d' % int(action['rotation']) + del action['rotation'] + del action['rotateOnTeleport'] + if 'horizon' in action: + action_str += ' h: %d' % int(action['horizon']) + del action['horizon'] + elif 'Goto' in a_type: + action_str = a_type + if 'location' in action: + action_str += ' loc: %s' % action['location'] + del action['location'] + elif a_type in {'OpenObject', 'CloseObject', 'PickupObject', 'ToggleObject', 'SliceObject'}: + if 'objectId' not in action: + action['objectId'] = 'None' + action_str = '%s %s' % (a_type, action['objectId']) + elif a_type in {'RotateByDegree', 'LookByDegree'}: + if type(action['rotation']) == dict: + action_str += ' r: %d' % int(action['rotation']['y']) + else: + action_str += ' r: %d' % int(action['rotation']) + action_str = '%s %d' % (a_type, action['rotation']['y']) + del action['rotation'] + elif a_type == 'PutObject': + action_str = a_type + if 'objectId' in action: + action_str += ' o: %s' % action['objectId'] + del action['objectId'] + if 'receptacleObjectId' in action: + action_str += ' r: %s' % action['receptacleObjectId'] + del action['receptacleObjectId'] + + if len(action) > 0: + action_str += '\tFull: ' + str(action) + return action_str + + +def get_object(object_id, metadata): + for obj in metadata['objects']: + if obj['objectId'] == object_id: + return obj + return None + + +def get_object_dict(metadata): + return {obj['objectId']: obj for obj in metadata['objects']} + + +def get_objects_of_type(object_type, metadata): + return [obj for obj in metadata['objects'] if obj['objectType'] == object_type] + + +def get_obj_of_type_closest_to_obj(object_type, ref_object_id, metadata): + objs_of_type = [obj for obj in metadata['objects'] if obj['objectType'] == object_type and obj['visible']] + ref_obj = get_object(ref_object_id, metadata) + closest_objs_of_type = sorted(objs_of_type, key=lambda o: np.linalg.norm(np.array([o['position']['x'], o['position']['y'], o['position']['z']]) - \ + np.array([ref_obj['position']['x'], ref_obj['position']['y'], ref_obj['position']['z']]))) + if len(closest_objs_of_type) == 0: + raise Exception("No closest %s found!" % (ref_obj)) + return closest_objs_of_type[0] # retrun the first closest visible object + + +def get_objects_with_name_and_prop(name, prop, metadata): + return [obj for obj in metadata['objects'] + if name in obj['objectId'] and obj[prop]] + + +def get_visible_objs(objs): + return [obj for obj in objs if obj['visible']] + + +def get_object_bounds(obj, scene_bounds): + # obj_bounds = np.array(obj['bounds3D'])[[0, 2, 3, 5]] # Get X and Z out + # Get a 'box' that is a singular point in (x,z) based on object position in place of now-unavailable 'bounds3d' + obj_bounds = np.array([obj['position']['x'], obj['position']['z'], obj['position']['x'], obj['position']['z']]) + obj_bounds /= constants.AGENT_STEP_SIZE + obj_bounds = np.round(obj_bounds).astype(np.int32) + obj_bounds[[2, 3]] = np.maximum(obj_bounds[[2, 3]], obj_bounds[[0, 1]] + 1) + obj_bounds[[0, 2]] = np.clip(obj_bounds[[0, 2]], scene_bounds[0], scene_bounds[0] + scene_bounds[2]) + obj_bounds[[1, 3]] = np.clip(obj_bounds[[1, 3]], scene_bounds[1], scene_bounds[1] + scene_bounds[3]) + obj_bounds -= np.array(scene_bounds)[[0, 1, 0, 1]] + return obj_bounds + + +def get_object_bounds_batch(boxes, scene_bounds): + obj_bounds = boxes[:, [0, 2, 3, 5]] # Get X and Z out + obj_bounds /= constants.AGENT_STEP_SIZE + obj_bounds = np.round(obj_bounds).astype(np.int32) + obj_bounds[:, [2, 3]] = np.maximum(obj_bounds[:, [2, 3]], obj_bounds[:, [0, 1]] + 1) + obj_bounds[:, [0, 2]] = np.clip(obj_bounds[:, [0, 2]], scene_bounds[0], scene_bounds[0] + scene_bounds[2]) + obj_bounds[:, [1, 3]] = np.clip(obj_bounds[:, [1, 3]], scene_bounds[1], scene_bounds[1] + scene_bounds[3]) + obj_bounds -= np.array(scene_bounds)[[0, 1, 0, 1]] + return obj_bounds + + +def get_task_str(object_ind, receptacle_ind=None, toggle_ind=None, mrecep_ind=None): + goal_str = constants.pddl_goal_type + if constants.data_dict['pddl_params']['object_sliced']: + goal_str += "_slice" + template = random.choice(glib.gdict[goal_str]['templates']) + obj = constants.OBJECTS[object_ind].lower() + recep = constants.OBJECTS[receptacle_ind].lower() if receptacle_ind is not None else "" + tog = constants.OBJECTS[toggle_ind].lower() if toggle_ind is not None else "" + mrecep = constants.OBJECTS[mrecep_ind].lower() if mrecep_ind is not None else "" + filled_in_str = template.format(obj=obj, recep=recep, toggle=tog, mrecep=mrecep) + return filled_in_str + + +def sample_templated_task_desc_from_traj_data(traj_data): + pddl_params = traj_data['pddl_params'] + goal_str = traj_data['task_type'] + if pddl_params['object_sliced']: + goal_str += "_slice" + template = random.choice(glib.gdict[goal_str]['templates']) + obj = pddl_params['object_target'].lower() + recep = pddl_params['parent_target'].lower() + toggle = pddl_params['toggle_target'].lower() + mrecep = pddl_params['mrecep_target'].lower() + filled_in_str = template.format(obj=obj, recep=recep, toggle=toggle, mrecep=mrecep) + return filled_in_str + + +def get_last_hl_action_index(): + return len(constants.data_dict['plan']['high_pddl']) - 1 + + +def get_last_ll_action_index(): + return len(constants.data_dict['plan']['low_actions']) - 1 + + +def store_image_name(name): + constants.data_dict['images'].append({"high_idx": get_last_hl_action_index(), + "low_idx": get_last_ll_action_index(), + "image_name": name}) + + diff --git a/easi/tasks/ebalfred/vendor/gen/utils/image_util.py b/easi/tasks/ebalfred/vendor/gen/utils/image_util.py new file mode 100644 index 0000000..a1ee785 --- /dev/null +++ b/easi/tasks/ebalfred/vendor/gen/utils/image_util.py @@ -0,0 +1,57 @@ +import numpy as np +import easi.tasks.ebalfred.vendor.gen.constants as constants + +def bbox_to_mask(bbox): + ''' + bbox to rectangle pixelwise mask + ''' + x1, y1, x2, y2 = bbox + mask = np.zeros((constants.DETECTION_SCREEN_HEIGHT, constants.DETECTION_SCREEN_WIDTH)).astype(int) + mask[y1:y2, x1:x2] = 1 + return mask + + +def point_to_mask(point): + ''' + single point to dense pixelwise mask + ''' + x, y = point + mask = np.zeros((constants.DETECTION_SCREEN_HEIGHT, constants.DETECTION_SCREEN_WIDTH)).astype(int) + mask[y, x] = 1 + return mask + + +def decompress_mask(compressed_mask): + ''' + decompress compressed mask array + ''' + mask = np.zeros((constants.DETECTION_SCREEN_WIDTH, constants.DETECTION_SCREEN_HEIGHT)) + for start_idx, run_len in compressed_mask: + for idx in range(start_idx, start_idx + run_len): + mask[idx // constants.DETECTION_SCREEN_WIDTH, idx % constants.DETECTION_SCREEN_HEIGHT] = 1 + return mask + + +def compress_mask(seg_mask): + ''' + compress mask array + ''' + run_len_compressed = [] # list of lists of run lengths for 1s, which are assumed to be less frequent. + idx = 0 + curr_run = False + run_len = 0 + for x_idx in range(len(seg_mask)): + for y_idx in range(len(seg_mask[x_idx])): + if seg_mask[x_idx][y_idx] == 1 and not curr_run: + curr_run = True + run_len_compressed.append([idx, None]) + if seg_mask[x_idx][y_idx] == 0 and curr_run: + curr_run = False + run_len_compressed[-1][1] = run_len + run_len = 0 + if curr_run: + run_len += 1 + idx += 1 + if curr_run: + run_len_compressed[-1][1] = run_len + return run_len_compressed \ No newline at end of file diff --git a/easi/tasks/ebalfred/vendor/gen/utils/py_util.py b/easi/tasks/ebalfred/vendor/gen/utils/py_util.py new file mode 100644 index 0000000..7a357f0 --- /dev/null +++ b/easi/tasks/ebalfred/vendor/gen/utils/py_util.py @@ -0,0 +1,84 @@ +import random +import re +import time +import os +import string + + +def get_time_str(): + tt = time.localtime() + time_str = ('%04d_%02d_%02d_%02d_%02d_%02d' % + (tt.tm_year, tt.tm_mon, tt.tm_mday, tt.tm_hour, tt.tm_min, tt.tm_sec)) + return time_str + + +def encode(string, encoding='utf-8'): + return string.encode(encoding) + + +def decode(string, encoding='utf-8'): + return string.decode(encoding) + + +def multireplace(string, replacements): + """ + Given a string and a replacement map, it returns the replaced string. + :param str string: string to execute replacements on + :param dict replacements: replacement dictionary {value to find: value to replace} + :rtype: str + Source https://gist.github.com/bgusach/a967e0587d6e01e889fd1d776c5f3729 + """ + # Place longer ones first to keep shorter substrings from matching where the longer ones should take place + # For instance given the replacements {'ab': 'AB', 'abc': 'ABC'} against the string 'hey abc', it should produce + # 'hey ABC' and not 'hey ABc' + substrs = sorted(replacements, key=len, reverse=True) + + # Create a big OR regex that matches any of the substrings to replace + regexp = re.compile('|'.join(map(re.escape, substrs))) + + # For each match, look up the new string in the replacements + return regexp.sub(lambda match: replacements[match.group(0)], string) + + +class SetWithGet(set): + def get_any(self): + return random.sample(self, 1)[0] + + def __getitem__(self, item): + return self.get_any() + + +class Noop(object): + def noop(*args, **kw): + pass + + def __getattr__(self, _): + return self.noop + + +def walklevel(some_dir, level=1): + some_dir = some_dir.rstrip(os.path.sep) + assert os.path.isdir(some_dir) + num_sep = some_dir.count(os.path.sep) + for root, dirs, files in os.walk(some_dir): + yield root, dirs, files + num_sep_this = root.count(os.path.sep) + if num_sep + level <= num_sep_this: + del dirs[:] + + +def remove_spaces(s): + cs = ' '.join(s.split()) + return cs + + +def remove_spaces_and_lower(s): + cs = remove_spaces(s) + cs = cs.lower() + return cs + + +def remove_punctuation(s): + cs = s.translate(str.maketrans('', '', string.punctuation)) + cs = remove_spaces_and_lower(cs) + return cs \ No newline at end of file diff --git a/easi/tasks/ebalfred/vendor/models/__init__.py b/easi/tasks/ebalfred/vendor/models/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/easi/tasks/ebalfred/vendor/models/config/rewards.json b/easi/tasks/ebalfred/vendor/models/config/rewards.json new file mode 100644 index 0000000..e9f35e7 --- /dev/null +++ b/easi/tasks/ebalfred/vendor/models/config/rewards.json @@ -0,0 +1,87 @@ +{ + "Generic": + { + "success": 2, + "failure": -0.05, + "step_penalty": -0.01, + "goal_reward": 5 + }, + "BaseAction": + { + "positive": 1, + "negative": 0, + "neutral": 0, + "invalid_action": -0.1 + }, + "GotoLocationAction": + { + "positive": 4, + "negative": 0, + "neutral": 0, + "invalid_action": -0.5, + "min_reach_distance": 3 + }, + "PickupObjectAction": + { + "positive": 2, + "negative": -1, + "neutral": 0, + "invalid_action": -0.1 + }, + "PutObjectAction": + { + "positive": 2, + "negative": -1, + "neutral": 0, + "invalid_action": -0.1 + }, + "OpenObjectAction": + { + "positive": 2, + "negative": -0.05, + "neutral": 0, + "invalid_action": -0.1 + }, + "CloseObjectAction": + { + "positive": 1, + "negative": 0, + "neutral": 0, + "invalid_action": -0.1 + }, + "ToggleObjectAction": + { + "positive": 1, + "negative": -1, + "neutral": 0, + "invalid_action": -0.1 + }, + "SliceObjectAction": + { + "positive": 1, + "negative": -4, + "neutral": 0, + "invalid_action": -0.1 + }, + "CleanObjectAction": + { + "positive": 2, + "negative": 0, + "neutral": 0, + "invalid_action": -0.1 + }, + "HeatObjectAction": + { + "positive": 2, + "negative": 0, + "neutral": 0, + "invalid_action": -0.1 + }, + "CoolObjectAction": + { + "positive": 2, + "negative": 0, + "neutral": 0, + "invalid_action": -0.1 + } +} \ No newline at end of file diff --git a/easi/tasks/ebalfred/vendor/thor_connector.py b/easi/tasks/ebalfred/vendor/thor_connector.py new file mode 100644 index 0000000..cb00582 --- /dev/null +++ b/easi/tasks/ebalfred/vendor/thor_connector.py @@ -0,0 +1,575 @@ +import os, math, re +import textwrap + +import numpy as np +from scipy import spatial +from PIL import Image, ImageDraw, ImageFont + +from easi.tasks.ebalfred.vendor.env.thor_env import ThorEnv +from easi.tasks.ebalfred.vendor.gen import constants +from easi.tasks.ebalfred.vendor.gen.utils.game_util import get_objects_with_name_and_prop +from easi.tasks.ebalfred.vendor.utils import natural_word_to_ithor_name +from easi.utils.logging import get_logger + +log = get_logger(__name__) + +class ThorConnector(ThorEnv): + def __init__(self, x_display=constants.X_DISPLAY, + player_screen_height=constants.DETECTION_SCREEN_HEIGHT, + player_screen_width=constants.DETECTION_SCREEN_WIDTH, + quality='MediumCloseFitShadows', + build_path=constants.BUILD_PATH): + super().__init__(x_display, player_screen_height, player_screen_width, quality, build_path) + try: + self.font = ImageFont.truetype("/usr/share/fonts/truetype/ubuntu/UbuntuMono-B.ttf", 24) + except (OSError, IOError): + self.font = ImageFont.load_default() + self.agent_height = 0.9 + self.cur_receptacle = None + self.reachable_positions, self.reachable_position_kdtree = None, None + self.sliced = False + self.task = None + self.put_count_dict = {} + + def restore_scene(self, object_poses, object_toggles, dirty_and_empty): + # print(object_poses) + super().restore_scene(object_poses, object_toggles, dirty_and_empty) + self.reachable_positions, self.reachable_position_kdtree = self.get_reachable_positions() + self.cur_receptacle = None + + def get_reachable_positions(self): + free_positions = super().step(dict(action="GetReachablePositions")).metadata["actionReturn"] + free_positions = np.array([[p['x'], p['y'], p['z']] for p in free_positions]) + kd_tree = spatial.KDTree(free_positions) + return free_positions, kd_tree + + def write_step_on_img(self, cfg, idx, description): + img = Image.fromarray(self.last_event.frame) + text = str(idx) + ':' + description['action'] + lines = textwrap.wrap(text, width=20) + y_text = 6 + draw = ImageDraw.Draw(img) + for line in lines: + width, height = self.font.getsize(line) + draw.text((6, y_text), line, font=self.font, fill=(255, 255, 255)) + y_text += height + if cfg is True: + if not description['success']: + text_msg = 'error : ' + description['message'] + lines = textwrap.wrap(text_msg, width=20) + for line in lines: + width, height = self.font.getsize(line) + draw.text((6, y_text + 6), line, font=self.font, fill=(255, 0, 0)) + y_text += height + return img + + + def find_close_reachable_position(self, loc, nth=1): + d, i = self.reachable_position_kdtree.query(loc, k=nth + 1) + selected = i[nth - 1] + return self.reachable_positions[selected] + + def llm_skill_interact(self, instruction: str): + if instruction.startswith("put down ") or instruction.startswith("open "): + pass + else: + self.cur_receptacle = None + + if instruction.startswith("find "): + obj_name = instruction.replace('find a ', '').replace('find an ', '') + self.cur_receptacle = obj_name + is_recep_id = any(i.isdigit() for i in obj_name) + ret = self.nav_obj(natural_word_to_ithor_name(obj_name), self.sliced) + elif instruction.startswith("pick up "): + obj_name = instruction.replace('pick up the ', '') + is_recep_id = any(i.isdigit() for i in obj_name) + ret = self.pick(natural_word_to_ithor_name(obj_name)) + elif instruction.startswith("put down "): + # m = re.match(r'put down (.+) on (.+)', instruction) + # obj = m.group(1).replace('the ', '') + # receptacle = m.group(2).replace('the ', '') + if self.cur_receptacle is None: + ret = self.drop() + else: + m = re.match(r'put down (.+)', instruction) + obj = m.group(1).replace('the ', '') + + if self.cur_receptacle in self.put_count_dict: + self.put_count_dict[self.cur_receptacle ] += 1 + else: + self.put_count_dict[self.cur_receptacle ] = 1 + + receptacle = self.cur_receptacle + ret = self.put(natural_word_to_ithor_name(receptacle)) + + if len(ret) > 16 and self.put_count_dict[receptacle] >= 3: + # if put down failed, then drop the object + self.drop() + ret += f'. The robot dropped the object instead.' + self.last_event.metadata['lastActionSuccess'] = False + + elif instruction.startswith("open "): + obj_name = instruction.replace('open the ', '') + ret = self.open(natural_word_to_ithor_name(obj_name)) + elif instruction.startswith("close "): + obj_name = instruction.replace('close the ', '') + ret = self.close(natural_word_to_ithor_name(obj_name)) + elif instruction.startswith("turn on "): + obj_name = instruction.replace('turn on the ', '') + ret = self.toggleon(natural_word_to_ithor_name(obj_name)) + elif instruction.startswith("turn off "): + obj_name = instruction.replace('turn off the ', '') + ret = self.toggleoff(natural_word_to_ithor_name(obj_name)) + elif instruction.startswith("slice "): + obj_name = instruction.replace('slice the ', '') + ret = self.slice(natural_word_to_ithor_name(obj_name)) + self.sliced = True + elif instruction.startswith("drop"): + ret = self.drop() + else: + ret = 'instruction not supported' + + if not self.last_event.metadata['lastActionSuccess']: + log.warning(f"llm_skill_interact failed") + log.warning(f"errorMessage: {self.last_event.metadata['errorMessage']}") + log.warning(f"returned msg: {ret}") + else: + log.info(f"Last action succeeded") + + ret_dict = { + 'action': instruction, + 'success': len(ret) <= 0, + 'message': ret + } + + return ret_dict + + def get_object_prop(self, name, prop, metadata): + for obj in metadata['objects']: + if name in obj['objectId']: + return obj[prop] + return None + + @staticmethod + def angle_diff(x, y): + x = math.radians(x) + y = math.radians(y) + return math.degrees(math.atan2(math.sin(x - y), math.cos(x - y))) + + def nav_obj(self, target_obj: str, prefer_sliced=False): + objects = self.last_event.metadata['objects'] + action_name = 'object navigation' + ret_msg = '' + print(f'{action_name} ({target_obj})') + + # get the object location + if '|' in target_obj: + obj_id = target_obj + target_obj = target_obj.split('|')[0] + tmp_id, tmp_obj_data = self.get_obj_id_from_name(target_obj, priority_in_visibility=True, priority_sliced=prefer_sliced) + # if sliced object + if 'Sliced' in tmp_id and obj_id in tmp_id: + obj_id = tmp_id + obj_data = tmp_obj_data + else: + obj_id, obj_data = self.get_obj_id_from_name(target_obj, priority_in_visibility=True, priority_sliced=prefer_sliced) + + # find object index from id + obj_idx = -1 + for i, o in enumerate(objects): + if o['objectId'] == obj_id: + obj_idx = i + break + if obj_idx == -1: + ret_msg = f'Cannot find {target_obj}. This object may not exist in this scene. Try to explore other instances instead.' + else: + # teleport sometimes fails even with reachable positions. if fails, repeat with the next closest reachable positions. + max_attempts = 20 + teleport_success = False + + # get obj location + loc = objects[obj_idx]['position'] + obj_rot = objects[obj_idx]['rotation']['y'] + + # # do not move if the object is already visible and close + # if objects[obj_idx]['visible'] and objects[obj_idx]['distance'] < 1.0: + # log.info('Object is already visible') + # max_attempts = 0 + # teleport_success = True + + # try teleporting + reachable_pos_idx = 0 + for i in range(max_attempts): + reachable_pos_idx += 1 + if i == 10 and (target_obj == 'Fridge' or target_obj == 'Microwave'): + reachable_pos_idx -= 10 + + closest_loc = self.find_close_reachable_position([loc['x'], loc['y'], loc['z']], reachable_pos_idx) + # calculate desired rotation angle (see https://github.com/allenai/ai2thor/issues/806) + rot_angle = math.atan2(-(loc['x'] - closest_loc[0]), loc['z'] - closest_loc[2]) + if rot_angle > 0: + rot_angle -= 2 * math.pi + rot_angle = -(180 / math.pi) * rot_angle # in degrees + + if i < 10 and (target_obj == 'Fridge' or target_obj == 'Microwave'): # not always correct, but better than nothing + angle_diff = abs(self.angle_diff(rot_angle, obj_rot)) + if target_obj == 'Fridge' and \ + not ((90 - 20 < angle_diff < 90 + 20) or (270 - 20 < angle_diff < 270 + 20)): + continue + if target_obj == 'Microwave' and \ + not ((180 - 20 < angle_diff < 180 + 20) or (0 - 20 < angle_diff < 0 + 20)): + continue + + # calculate desired horizon angle + camera_height = self.agent_height + constants.CAMERA_HEIGHT_OFFSET + xz_dist = math.hypot(loc['x'] - closest_loc[0], loc['z'] - closest_loc[2]) + hor_angle = math.atan2((loc['y'] - camera_height), xz_dist) + hor_angle = (180 / math.pi) * hor_angle # in degrees + hor_angle *= 0.9 # adjust angle for better view + # hor_angle = -30 + # hor_angle = 0 + + # teleport ### Full + super().step(dict(action="TeleportFull", x=closest_loc[0], y=self.agent_height, z=closest_loc[2], rotation=rot_angle, horizon=-hor_angle)) + + if not self.last_event.metadata['lastActionSuccess']: + log.warning( + f"TeleportFull action failed: {self.last_event.metadata['errorMessage']}, trying again...") + else: + teleport_success = True + break + + if not teleport_success: + ret_msg = f'Cannot move to {target_obj}' + + return ret_msg + + def get_obj_id_from_name(self, obj_name, only_pickupable=False, only_toggleable=False, priority_sliced=False, get_inherited=False, + parent_receptacle_penalty=True, priority_in_visibility=False, exclude_obj_id=None): + obj_id = None + obj_data = None + min_distance = 1e+8 + + if any(i.isdigit() for i in obj_name): + for i in range(len(self.last_event.metadata['objects'])): + if obj_name in self.last_event.metadata['objects'][i]['name']: + obj_id = self.last_event.metadata['objects'][i]['objectId'] + obj_data = self.last_event.metadata['objects'][i] + break + return obj_id, obj_data + for obj in self.last_event.metadata['objects']: + if obj['objectId'] == exclude_obj_id: + continue + + if (only_pickupable is False or obj['pickupable']) and \ + (only_toggleable is False or obj['toggleable']) and \ + obj['objectId'].split('|')[0].casefold() == obj_name.casefold() and \ + (get_inherited is False or len(obj['objectId'].split('|')) == 5): + + if obj["distance"] < min_distance: + penalty_advantage = 0 # low priority for objects in closable receptacles such as fridge, microwave + if parent_receptacle_penalty and obj['parentReceptacles']: + for p in obj['parentReceptacles']: + is_open = self.get_object_prop(p, 'isOpen', self.last_event.metadata) + openable = self.get_object_prop(p, 'openable', self.last_event.metadata) + if openable is True and is_open is False: + penalty_advantage += 100000 + break + + if obj_name.casefold() == 'stoveburner': + # try to find an empty stove + if len(obj['receptacleObjectIds']) > 0: + penalty_advantage += 10000 + + if priority_in_visibility and obj['visible'] is False: + penalty_advantage += 1000 + + if priority_sliced and '_Slice' in obj['name']: + penalty_advantage += -100 # prefer sliced objects; this prevents picking up non-sliced objects + + if obj["distance"] + penalty_advantage < min_distance: + min_distance = obj["distance"] + penalty_advantage + obj_data = obj + obj_id = obj["objectId"] + + return obj_id, obj_data + + def pick(self, obj_name): + obj_id, obj_data = self.get_obj_id_from_name(obj_name, only_pickupable=True, priority_in_visibility=True, priority_sliced=self.sliced) + + ret_msg = '' + log.info(f'pick {obj_id}') + + if obj_id is None: + ret_msg = f'Cannot find {obj_name} to pick up. Find the object before picking up it' + else: + if obj_data['visible'] is False and obj_data['parentReceptacles'] is not None and len(obj_data['parentReceptacles']) > 0: + # recep_name = obj_data["parentReceptacles"][0].split('|')[0] + recep_name = obj_data["parentReceptacles"][0] + ret_msg = f'{obj_name} is not visible because it is in {recep_name}. Note: multiple instances of {recep_name} may exist' + + # try anyway + super().step(dict( + action="PickupObject", + objectId=obj_id, + forceAction=False + )) + else: + super().step(dict( + action="PickupObject", + objectId=obj_id, + forceAction=False + )) + + if not self.last_event.metadata['lastActionSuccess']: + if len(self.last_event.metadata['inventoryObjects']) == 0: + ret_msg = f'Robot is not holding any object' + else: + # check if the agent is holding the object + holding_obj_id = self.last_event.metadata['inventoryObjects'][0]['objectId'] + holding_obj_type = self.last_event.metadata['inventoryObjects'][0]['objectType'] + ret_msg = f'Robot is currently holding {holding_obj_type}' + + if self.last_event.metadata['lastActionSuccess']: + ret_msg = '' + + return ret_msg + + def put(self, receptacle_name): + # assume the agent always put the object currently holding + ret_msg = '' + orig_receptacle_name = receptacle_name + + if len(self.last_event.metadata['inventoryObjects']) == 0: + ret_msg = f'Robot is not holding any object' + return ret_msg + else: + holding_obj_id = self.last_event.metadata['inventoryObjects'][0]['objectId'] + + + halt = False + last_recep_id = None + exclude_obj_id = None + for k in range(2): # try closest and next closest one + for j in range(7): # move/look around or rotate obj + for i in range(2): # try inherited receptacles too (e.g., sink basin, bath basin) + if k == 1 and exclude_obj_id is None: + exclude_obj_id = last_recep_id # previous recep id + + # for the second round, find another receptacle + if k == 0 and '|' in orig_receptacle_name: + if i == 1: + continue + recep_id = orig_receptacle_name + receptacle_name = orig_receptacle_name.split('|')[0] + else: + if 'Sink' in receptacle_name or 'Bathtub' in receptacle_name: # sink base + if i == 0: + recep_id, _ = self.get_obj_id_from_name(receptacle_name, get_inherited=True, exclude_obj_id=exclude_obj_id) + else: + recep_id, _ = self.get_obj_id_from_name(receptacle_name, exclude_obj_id=exclude_obj_id) + else: + if i == 0: + recep_id, _ = self.get_obj_id_from_name(receptacle_name, exclude_obj_id=exclude_obj_id) + else: + recep_id, _ = self.get_obj_id_from_name(receptacle_name, get_inherited=True, exclude_obj_id=exclude_obj_id) + + if not recep_id: + ret_msg = f'Putting the object on {receptacle_name} failed. First check whether the receptacle is open or not. Also try other instances of the receptacle' + continue + + print(f'put {holding_obj_id} on {recep_id}') + + # look up (put action fails when a receptacle is not visible) + if j == 1: + super().step(dict(action="LookUp")) + super().step(dict(action="LookUp")) + elif j == 2: + super().step(dict(action="LookDown")) + super().step(dict(action="LookDown")) + super().step(dict(action="LookDown")) + super().step(dict(action="LookDown")) + elif j == 3: + super().step(dict(action="LookUp")) + super().step(dict(action="LookUp")) + super().step(dict(action="MoveBack")) + elif j == 4: + super().step(dict(action="MoveAhead")) + for r in range(4): + super().step(dict(action="MoveRight")) + elif j == 5: + for r in range(8): + super().step(dict(action="MoveLeft")) + elif j == 6: + for r in range(4): + super().step(dict(action="MoveRight")) + super().step(dict( # this somehow make putobject success in some cases + action="RotateHand", + x=40 + )) + + super().step(dict(action="PutObject",objectId=holding_obj_id, receptacleObjectId=recep_id, forceAction=True)) + last_recep_id = recep_id + + if not self.last_event.metadata['lastActionSuccess']: + log.warning(f"PutObject action failed: {self.last_event.metadata['errorMessage']}, trying again...") + ret_msg = f'Putting the object on {receptacle_name} failed. First check the receptacle is open or not. Also try other instances of the receptacle' + else: + ret_msg = '' + halt = True + break + if halt: + break + if halt: + break + + return ret_msg + + def drop(self): + log.info(f'drop') + ret_msg = '' + super().step(dict( + action="DropHandObject", + forceAction=True + )) + + if not self.last_event.metadata['lastActionSuccess']: + if len(self.last_event.metadata['inventoryObjects']) == 0: + ret_msg = f'Robot is not holding any object' + else: + ret_msg = f"Drop action failed" + else: + ret_msg = '' + + return ret_msg + + def open(self, obj_name): + log.info(f'open {obj_name}') + ret_msg = '' + # obj_id, _ = self.get_obj_id_from_name(obj_name) + # get the object location + if '|' in obj_name: + obj_id = obj_name + obj_name = obj_name.split('|')[0] + else: + obj_id, _ = self.get_obj_id_from_name(obj_name) + + + if obj_id is None: + ret_msg = f"Cannot find {obj_name} to open. Find the object before opening it" + else: + open_flag = False + for ob in self.last_event.metadata['objects']: + if ob['objectId'] == obj_id and ob['openable'] and ob['isOpen']: + open_flag = True + break + + for i in range(4): + super().step(dict( + action="OpenObject", + objectId=obj_id, + )) + + if not self.last_event.metadata['lastActionSuccess']: + log.warning( + f"OpenObject action failed: {self.last_event.metadata['errorMessage']}, moving backward and trying again...") + if open_flag: + ret_msg = f"Open action failed. The {obj_name} is already open" + else: + ret_msg = f"Open action failed." + + # move around to avoid self-collision + if i == 0: + super().step(dict(action="MoveBack")) + elif i == 1: + super().step(dict(action="MoveBack")) + super().step(dict(action="MoveRight")) + elif i == 2: + super().step(dict(action="MoveLeft")) + super().step(dict(action="MoveLeft")) + else: + ret_msg = '' + break + + return ret_msg + + def close(self, obj_name): + log.info(f'close {obj_name}') + ret_msg = '' + if '|' in obj_name: + obj_id = obj_name + obj_name = obj_name.split('|')[0] + else: + obj_id, _ = self.get_obj_id_from_name(obj_name) + + if obj_id is None: + ret_msg = f'Cannot find {obj_name} to close' + else: + super().step(dict( + action="CloseObject", + objectId=obj_id, + )) + + if not self.last_event.metadata['lastActionSuccess']: + ret_msg = f"Close action failed" + + for ob in self.last_event.metadata['objects']: + if ob['objectId'] == obj_id and ob['openable'] and not ob['isOpen']: + ret_msg += f". The {obj_name} is already closed" + break + + return ret_msg + + def toggleon(self, obj_name): + log.info(f'toggle on {obj_name}') + ret_msg = '' + obj_id, _ = self.get_obj_id_from_name(obj_name, only_toggleable=True) + if obj_id is None: + ret_msg = f'Cannot find {obj_name} to turn on' + else: + try: + super().step(dict( + action="ToggleObjectOn", + objectId=obj_id, + )) + if not self.last_event.metadata['lastActionSuccess']: + ret_msg = f"Turn on action failed" + except: + ret_msg = f"Turn on action failed" + self.last_event.metadata['lastActionSuccess'] = False + + return ret_msg + + def toggleoff(self, obj_name): + log.info(f'toggle off {obj_name}') + ret_msg = '' + obj_id, _ = self.get_obj_id_from_name(obj_name, only_toggleable=True) + if obj_id is None: + ret_msg = f'Cannot find {obj_name} to turn off' + else: + super().step(dict( + action="ToggleObjectOff", + objectId=obj_id, + )) + + if not self.last_event.metadata['lastActionSuccess']: + ret_msg = f"Turn off action failed" + + return ret_msg + + def slice(self, obj_name): + log.info(f'slice {obj_name}') + ret_msg = '' + obj_id, _ = self.get_obj_id_from_name(obj_name) + if obj_id is None: + ret_msg = f'Cannot find {obj_name} to slice' + else: + super().step(dict( + action="SliceObject", + objectId=obj_id, + )) + + if not self.last_event.metadata['lastActionSuccess']: + ret_msg = f"Slice action failed" + + return ret_msg diff --git a/easi/tasks/ebalfred/vendor/utils.py b/easi/tasks/ebalfred/vendor/utils.py new file mode 100644 index 0000000..0c77216 --- /dev/null +++ b/easi/tasks/ebalfred/vendor/utils.py @@ -0,0 +1,138 @@ +import os, json, re +import string +import numpy as np +import subprocess +from PIL import Image, ImageDraw, ImageFont + +alfred_objs = ['Cart', 'Potato', 'Faucet', 'Ottoman', 'CoffeeMachine', 'Candle', 'CD', 'Pan', 'Watch', + 'HandTowel', 'SprayBottle', 'BaseballBat', 'CellPhone', 'Kettle', 'Mug', 'StoveBurner', 'Bowl', + 'Toilet', 'DiningTable', 'Spoon', 'TissueBox', 'Shelf', 'Apple', 'TennisRacket', 'SoapBar', + 'Cloth', 'Plunger', 'FloorLamp', 'ToiletPaperHanger', 'CoffeeTable', 'Spatula', 'Plate', 'Bed', + 'Glassbottle', 'Knife', 'Tomato', 'ButterKnife', 'Dresser', 'Microwave', 'CounterTop', + 'GarbageCan', 'WateringCan', 'Vase', 'ArmChair', 'Safe', 'KeyChain', 'Pot', 'Pen', 'Cabinet', + 'Desk', 'Newspaper', 'Drawer', 'Sofa', 'Bread', 'Book', 'Lettuce', 'CreditCard', 'AlarmClock', + 'ToiletPaper', 'SideTable', 'Fork', 'Box', 'Egg', 'DeskLamp', 'Ladle', 'WineBottle', 'Pencil', + 'Laptop', 'RemoteControl', 'BasketBall', 'DishSponge', 'Cup', 'SaltShaker', 'PepperShaker', + 'Pillow', 'Bathtub', 'SoapBottle', 'Statue', 'Fridge', 'Sink'] + +alfred_pick_obj = ['KeyChain', 'Potato', 'Pot', 'Pen', 'Candle', 'CD', 'Pan', 'Watch', 'Newspaper', 'HandTowel', + 'SprayBottle', 'BaseballBat', 'Bread', 'CellPhone', 'Book', 'Lettuce', 'CreditCard', 'Mug', + 'AlarmClock', 'Kettle', 'ToiletPaper', 'Bowl', 'Fork', 'Box', 'Egg', 'Spoon', 'TissueBox', + 'Apple', 'TennisRacket', 'Ladle', 'WineBottle', 'Cloth', 'Plunger', 'SoapBar', 'Pencil', + 'Laptop', 'RemoteControl', 'BasketBall', 'DishSponge', 'Cup', 'Spatula', 'SaltShaker', + 'Plate', 'PepperShaker', 'Pillow', 'Glassbottle', 'SoapBottle', 'Knife', 'Statue', 'Tomato', + 'ButterKnife', 'WateringCan', 'Vase'] + +alfred_open_obj = ['Safe', 'Laptop', 'Fridge', 'Box', 'Microwave', 'Cabinet', 'Drawer'] + +alfred_slice_obj = ['Potato', 'Lettuce', 'Tomato', 'Apple', 'Bread'] + +alfred_toggle_obj = ['Microwave', 'DeskLamp', 'FloorLamp', 'Faucet'] + +alfred_recep = ['ArmChair', 'Safe', 'Cart', 'Ottoman', 'Pot', 'CoffeeMachine', 'Desk', 'Cabinet', 'Pan', + 'Drawer', 'Sofa', 'Mug', 'StoveBurner', 'SideTable', 'Toilet', 'Bowl', 'Box', 'DiningTable', + 'Shelf', 'ToiletPaperHanger', 'CoffeeTable', 'Cup', 'Plate', 'Bathtub', 'Bed', 'Dresser', + 'Fridge', 'Microwave', 'CounterTop', 'Sink', 'GarbageCan'] + + +def random_color(): + return tuple(np.random.choice(range(256), size=3)) + +def draw_boxes(image, classes_and_boxes, name_translation): + draw = ImageDraw.Draw(image) + font = ImageFont.load_default() + font.size = 8 + # Loop through each class and its associated bounding boxes + for class_name, box in classes_and_boxes.items(): + if class_name.split('|')[0] in alfred_objs: + color = random_color() + if class_name in name_translation: + name = name_translation[class_name] + else: + name = class_name.split('|')[0] + + x1, y1, x2, y2 = box[0], box[1], box[2], box[3] + # Draw rectangle + draw.rectangle([x1, y1, x2, y2], outline=color, width=1) + # Add class name above the rectangle + # text_position = (x1, max(0, y1 - 12)) # Position text above box + # draw.text(text_position, name, fill=color, font=font) + return image + + +class dotdict(dict): + """dot.notation access to dictionary attributes""" + __getattr__ = dict.get + __setattr__ = dict.__setitem__ + __delattr__ = dict.__delitem__ + + +def load_task_json(task, data_dir=None): + ''' + load preprocessed json from disk + ''' + if data_dir is None: + data_dir = os.path.join(os.path.dirname(__file__), 'data/json_2.1.0') + json_path = os.path.join(data_dir, task['task'], 'pp', + 'ann_%d.json' % task['repeat_idx']) + with open(json_path) as f: + data = json.load(f) + return data + + +def print_gpu_usage(msg): + """ + ref: https://discuss.pytorch.org/t/access-gpu-memory-usage-in-pytorch/3192/4 + """ + + def query(field): + return (subprocess.check_output( + ['nvidia-smi', f'--query-gpu={field}', + '--format=csv,nounits,noheader'], + encoding='utf-8')) + + def to_int(result): + return int(result.strip().split('\n')[0]) + + used = to_int(query('memory.used')) + total = to_int(query('memory.total')) + pct = used / total + print('\n' + msg, f'{100 * pct:2.1f}% ({used} out of {total})') + + +def ithor_name_to_natural_word(w): + # e.g., RemoteController -> remote controller + if w == 'CD': + return w + else: + return re.sub(r"(\w)([A-Z])", r"\1 \2", w).lower() + + +def natural_word_to_ithor_name(w): + # e.g., floor lamp -> FloorLamp + + # if w contains a number, return it (meaning that it is a unique receptacle) + if any(i.isdigit() for i in w): + return w + + if w == 'CD': + return w + else: + return ''.join([string.capwords(x) for x in w.split()]) + + +def find_indefinite_article(w): + # simple rule, not always correct + w = w.lower() + if w[0] in ['a', 'e', 'i', 'o', 'u']: + return 'an' + else: + return 'a' + +def delete_folder_contents(folder_path): + for filename in os.listdir(folder_path): + file_path = os.path.join(folder_path, filename) + if os.path.isfile(file_path): + os.remove(file_path) + else: + delete_folder_contents(file_path) diff --git a/easi/tasks/registry.py b/easi/tasks/registry.py index aa68c8d..efdadff 100644 --- a/easi/tasks/registry.py +++ b/easi/tasks/registry.py @@ -78,7 +78,7 @@ def _discover_tasks(tasks_dir: Path | None = None) -> dict[str, TaskEntry]: config_path=task_yaml_path, ) - logger.debug("Discovered task: %s from %s", name, task_yaml_path.name) + logger.trace("Discovered task: %s from %s", name, task_yaml_path.name) return entries diff --git a/easi/tasks/scaffold.py b/easi/tasks/scaffold.py new file mode 100644 index 0000000..e5577e2 --- /dev/null +++ b/easi/tasks/scaffold.py @@ -0,0 +1,252 @@ +"""Task scaffolding: generates boilerplate for new benchmark integrations.""" + +from __future__ import annotations + +from pathlib import Path + +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + + +def _to_class_name(snake_name: str) -> str: + """Convert snake_case to PascalCase. e.g. 'my_benchmark' -> 'MyBenchmark'.""" + return "".join(word.capitalize() for word in snake_name.split("_")) + + +def scaffold_task( + name: str, + simulator: str, + output_dir: Path | None = None, + max_steps: int = 50, + tests_dir: Path | None = None, +) -> Path: + """Generate boilerplate files for a new benchmark task. + + Creates: + // + __init__.py + task.py — BaseTask subclass with TODOs + bridge.py — BaseBridge subclass with TODOs + .yaml — Task config + + Optionally creates: + /test_.py — Test file with import and basic checks + + Args: + name: Task name in snake_case (e.g., 'my_benchmark'). + simulator: Simulator key (e.g., 'ai2thor:v2_1_0', 'dummy:v1'). + output_dir: Parent directory for task folder. Defaults to easi/tasks/. + max_steps: Default max steps per episode. + tests_dir: Directory for generated test file. If None, no test generated. + + Returns: + Path to the created task directory. + + Raises: + FileExistsError: If the task directory already exists. + """ + if output_dir is None: + output_dir = Path(__file__).parent + + task_dir = output_dir / name + if task_dir.exists(): + raise FileExistsError(f"Task directory already exists: {task_dir}") + + task_dir.mkdir(parents=True) + + class_name = _to_class_name(name) + + # __init__.py + (task_dir / "__init__.py").write_text("") + + # task.py + (task_dir / "task.py").write_text(_TASK_TEMPLATE.format( + name=name, + class_name=class_name, + )) + + # bridge.py + (task_dir / "bridge.py").write_text(_BRIDGE_TEMPLATE.format( + name=name, + class_name=class_name, + )) + + # .yaml + (task_dir / f"{name}.yaml").write_text(_YAML_TEMPLATE.format( + name=name, + class_name=class_name, + simulator=simulator, + max_steps=max_steps, + )) + + # test file + if tests_dir is not None: + tests_dir.mkdir(parents=True, exist_ok=True) + (tests_dir / f"test_{name}.py").write_text(_TEST_TEMPLATE.format( + name=name, + class_name=class_name, + )) + + logger.info("Scaffolded task '%s' at %s", name, task_dir) + return task_dir + + +_TASK_TEMPLATE = '''\ +"""{class_name} task for EASI. + +TODO: Implement format_reset_config() and evaluate_episode(). +""" +from __future__ import annotations + +from pathlib import Path + +from easi.core.base_task import BaseTask +from easi.core.episode import StepResult +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + + +class {class_name}Task(BaseTask): + + def get_task_yaml_path(self) -> Path: + return Path(__file__).parent / "{name}.yaml" + + def get_bridge_script_path(self) -> Path: + return Path(__file__).parent / "bridge.py" + + def format_reset_config(self, episode: dict) -> dict: + """Map dataset episode to simulator reset config. + + TODO: Return the fields your bridge needs to initialize an episode. + """ + return episode + + def evaluate_episode( + self, episode: dict, trajectory: list[StepResult] + ) -> dict[str, float]: + """Extract metrics from a completed episode trajectory. + + TODO: Implement your evaluation logic. + """ + if not trajectory: + return {{"task_success": 0.0, "num_steps": 0.0}} + + last_step = trajectory[-1] + return {{ + "task_success": last_step.info.get("task_success", 0.0), + "num_steps": float(len(trajectory)), + "total_reward": sum(s.reward for s in trajectory), + }} +''' + +_BRIDGE_TEMPLATE = '''\ +"""{class_name} bridge for EASI. + +Wraps the external benchmark env in EASI's IPC protocol. + +TODO: Implement _create_env() and _extract_image(). + +Usage: + python bridge.py --workspace /tmp/easi_xxx --data-dir /path/to/data +""" +from __future__ import annotations + +import sys +from pathlib import Path + +# Add repo root to path for easi imports (bridge runs in subprocess) +_repo_root = Path(__file__).resolve().parents[3] +if str(_repo_root) not in sys.path: + sys.path.insert(0, str(_repo_root)) + +from easi.simulators.base_bridge import BaseBridge + + +class {class_name}Bridge(BaseBridge): + """Wraps the external {class_name} env. + + TODO: Install the external package in the bridge conda env: + pip install --no-deps -e ./ExternalRepo + + Then import their env class in _create_env(). + """ + + def _create_env(self, reset_config, simulator_kwargs): + """Create the benchmark env instance. + + TODO: Import and instantiate the external env class. + Example: + from external_package import BenchmarkEnv + return BenchmarkEnv(**simulator_kwargs) + """ + raise NotImplementedError("TODO: implement _create_env()") + + def _extract_image(self, obs): + """Extract RGB numpy array (H, W, 3) from env observation. + + TODO: Return the image from your env's observation dict. + Example: + return obs["head_rgb"] + """ + raise NotImplementedError("TODO: implement _extract_image()") + + +if __name__ == "__main__": + {class_name}Bridge.main() +''' + +_YAML_TEMPLATE = '''\ +name: {name} +display_name: "{class_name}" +description: "TODO: Add description" +simulator: "{simulator}" +task_class: "easi.tasks.{name}.task.{class_name}Task" +action_space: [] +max_steps: {max_steps} +dataset: + source: local + path: null +simulator_kwargs: {{}} +external_packages: [] +''' + +_TEST_TEMPLATE = '''\ +"""Tests for {name} task and bridge.""" +from pathlib import Path + +from easi.tasks.{name}.task import {class_name}Task + + +class Test{class_name}Task: + + def test_task_is_importable(self): + """Verify the task class can be imported.""" + assert {class_name}Task is not None + + def test_bridge_script_exists(self): + """Verify the bridge script file exists.""" + from easi.tasks.{name}.bridge import {class_name}Bridge + assert {class_name}Bridge is not None + + def test_get_bridge_script_path(self, tmp_path): + """Verify get_bridge_script_path() returns the correct path.""" + yaml_path = tmp_path / "{name}.yaml" + yaml_path.write_text( + "name: {name}\\n" + "display_name: {class_name}\\n" + "description: test\\n" + "simulator: dummy:v1\\n" + "task_class: easi.tasks.{name}.task.{class_name}Task\\n" + "action_space: []\\n" + "max_steps: 10\\n" + "dataset:\\n" + " source: local\\n" + " path: null\\n" + ) + task = {class_name}Task(split_yaml_path=yaml_path) + bridge_path = task.get_bridge_script_path() + assert bridge_path is not None + assert bridge_path.name == "bridge.py" +''' diff --git a/easi/utils/locking.py b/easi/utils/locking.py index 2cff516..b5f22ca 100644 --- a/easi/utils/locking.py +++ b/easi/utils/locking.py @@ -31,13 +31,13 @@ def file_lock(lock_path: Path) -> Generator[None, None, None]: install_conda_env() """ lock_path.parent.mkdir(parents=True, exist_ok=True) - logger.debug("Acquiring lock: %s", lock_path) + logger.trace("Acquiring lock: %s", lock_path) with open(lock_path, "w") as f: fcntl.flock(f, fcntl.LOCK_EX) try: - logger.debug("Lock acquired: %s", lock_path) + logger.trace("Lock acquired: %s", lock_path) yield finally: fcntl.flock(f, fcntl.LOCK_UN) - logger.debug("Lock released: %s", lock_path) + logger.trace("Lock released: %s", lock_path) diff --git a/easi/utils/logging.py b/easi/utils/logging.py index 7dc4275..8958471 100644 --- a/easi/utils/logging.py +++ b/easi/utils/logging.py @@ -2,11 +2,105 @@ Library modules should only use get_logger(). The CLI entry point calls setup_logging() once to configure handlers and level. + +Custom levels: + TRACE (5) — internal library diagnostics, below DEBUG. + Use logger.trace() for library internals. Reserve logger.debug() for + user-facing debug output. """ from __future__ import annotations import logging +import sys + +# --- Custom TRACE level (below DEBUG=10) --- +TRACE = 5 +logging.addLevelName(TRACE, "TRACE") + + +def _trace(self, message, *args, **kwargs): + if self.isEnabledFor(TRACE): + self._log(TRACE, message, args, **kwargs) + + +logging.Logger.trace = _trace # type: ignore[attr-defined] + + +# --- ANSI color codes --- +_RESET = "\033[0m" +_DIM = "\033[2m" +_BOLD = "\033[1m" +_GRAY = "\033[90m" +_CYAN = "\033[36m" +_BLUE = "\033[34m" +_YELLOW = "\033[33m" +_RED = "\033[31m" + +# Per-level styling: (level_color, message_color) +# - level_color: applied to the [LEVEL] tag +# - message_color: applied to the message text +_LEVEL_STYLES = { + TRACE: (_GRAY, _GRAY), + logging.DEBUG: (_CYAN, ""), + logging.INFO: (_BLUE, ""), + logging.WARNING: (_YELLOW, _YELLOW), + logging.ERROR: (_RED + _BOLD, _RED), +} + + +class _ColorFormatter(logging.Formatter): + """Formatter that applies per-level ANSI colors. + + Color scheme (modern minimalistic): + - Timestamp: always dim gray — subtle, consistent + - [LEVEL]: colored per level — primary visual differentiator + - Logger name: always dim gray — metadata, not content + - Separator: always dim gray + - Message: neutral for TRACE/DEBUG/INFO, colored for WARNING/ERROR + """ + + def __init__(self, use_color: bool = True): + super().__init__(datefmt="%H:%M:%S") + self._use_color = use_color + + def format(self, record): + if not self._use_color: + return self._format_plain(record) + + level_color, msg_color = _LEVEL_STYLES.get( + record.levelno, ("", "") + ) + + timestamp = self.formatTime(record, self.datefmt) + name = record.name + levelname = record.levelname + message = record.getMessage() + + # Handle exception info + if record.exc_info and not record.exc_text: + record.exc_text = self.formatException(record.exc_info) + if record.exc_text: + message = message + "\n" + record.exc_text + + parts = [ + f"{_GRAY}{timestamp}{_RESET}", + f" {level_color}[{levelname}]{_RESET}", + f" {_GRAY}{name}{_RESET}", + f" {_GRAY}-{_RESET}", + f" {msg_color}{message}{_RESET}" if msg_color else f" {message}", + ] + return "".join(parts) + + def _format_plain(self, record): + """No-color fallback for non-TTY output.""" + timestamp = self.formatTime(record, self.datefmt) + message = record.getMessage() + if record.exc_info and not record.exc_text: + record.exc_text = self.formatException(record.exc_info) + if record.exc_text: + message = message + "\n" + record.exc_text + return f"{timestamp} [{record.levelname}] {record.name} - {message}" def get_logger(name: str = __name__) -> logging.Logger: @@ -32,18 +126,15 @@ def setup_logging(level: str = "WARNING") -> None: Called once by the CLI entry point. Library code should never call this. Args: - level: One of "DEBUG", "INFO", "WARNING", "ERROR". + level: One of "TRACE", "DEBUG", "INFO", "WARNING", "ERROR". """ logger = logging.getLogger("easi") - logger.setLevel(getattr(logging, level.upper())) + resolved = TRACE if level.upper() == "TRACE" else getattr(logging, level.upper()) + logger.setLevel(resolved) # Avoid adding duplicate handlers if called multiple times if not logger.handlers: handler = logging.StreamHandler() - handler.setFormatter( - logging.Formatter( - "%(asctime)s [%(levelname)s] %(name)s - %(message)s", - datefmt="%H:%M:%S", - ) - ) + use_color = hasattr(sys.stderr, "isatty") and sys.stderr.isatty() + handler.setFormatter(_ColorFormatter(use_color=use_color)) logger.addHandler(handler) diff --git a/easi/utils/spinner.py b/easi/utils/spinner.py new file mode 100644 index 0000000..6c9a414 --- /dev/null +++ b/easi/utils/spinner.py @@ -0,0 +1,111 @@ +"""Terminal spinner that coexists with logging output. + +Usage:: + + from easi.utils.spinner import spinner + + with spinner("Installing environment"): + long_running_operation() + +The spinner renders on stderr. When a log record is emitted, the existing +handler's emit() is wrapped to clear the spinner line first — so log output +never gets corrupted and there is no duplication. + +In non-TTY mode (piped output), no animation is shown. +""" + +from __future__ import annotations + +import logging +import sys +import threading +import time + + +_FRAMES = ["⠋", "⠙", "⠹", "⠸", "⠼", "⠴", "⠦", "⠧", "⠇", "⠏"] +_INTERVAL = 0.08 # seconds between frames +_CLEAR = "\r\033[K" # carriage return + ANSI erase to end of line + + +class _Spinner: + """Background-thread spinner with log-safe output. + + Instead of adding a second log handler (which duplicates output), this + wraps the emit() method of every existing handler on the ``easi`` logger + so that the spinner line is cleared before each log record and the + spinner thread is paused during the write. + """ + + def __init__(self, message: str): + self._message = message + self._stop_event = threading.Event() + self._thread: threading.Thread | None = None + self._lock = threading.Lock() + self._is_tty = hasattr(sys.stderr, "isatty") and sys.stderr.isatty() + self._wrapped: list[tuple[logging.Handler, object]] = [] + + def start(self): + if not self._is_tty: + return + # Wrap existing handlers so they clear the spinner before emitting + for handler in logging.getLogger("easi").handlers: + original_emit = handler.emit + + def wrapped_emit(record, _orig=original_emit): + with self._lock: + sys.stderr.write(_CLEAR) + sys.stderr.flush() + _orig(record) + + self._wrapped.append((handler, original_emit)) + handler.emit = wrapped_emit # type: ignore[method-assign] + + self._thread = threading.Thread(target=self._spin, daemon=True) + self._thread.start() + + def stop(self): + if not self._is_tty or self._thread is None: + return + self._stop_event.set() + self._thread.join() + + # Final clear + with self._lock: + sys.stderr.write(_CLEAR) + sys.stderr.flush() + + # Restore original emit methods + for handler, original_emit in self._wrapped: + handler.emit = original_emit # type: ignore[method-assign] + self._wrapped.clear() + + def _spin(self): + idx = 0 + while not self._stop_event.is_set(): + frame = _FRAMES[idx % len(_FRAMES)] + with self._lock: + sys.stderr.write(f"{_CLEAR}{frame} {self._message}") + sys.stderr.flush() + idx += 1 + self._stop_event.wait(_INTERVAL) + + +class spinner: + """Context manager for a terminal spinner. + + Example:: + + with spinner("Creating conda env"): + run_slow_command() + """ + + def __init__(self, message: str): + self._spinner = _Spinner(message) + + def __enter__(self): + self._spinner.start() + return self._spinner + + def __exit__(self, *exc): + self._spinner.stop() + return False diff --git a/tests/test_base_bridge.py b/tests/test_base_bridge.py new file mode 100644 index 0000000..bb81d36 --- /dev/null +++ b/tests/test_base_bridge.py @@ -0,0 +1,160 @@ +"""Tests for BaseBridge — wraps Gym-like envs in EASI IPC.""" +import json +import threading + +import numpy as np +from pathlib import Path + +from easi.simulators.base_bridge import BaseBridge + + +class FakeGymEnv: + """Minimal Gym-like env for testing.""" + + def __init__(self): + self.step_count = 0 + + def reset(self): + self.step_count = 0 + return {"rgb": np.zeros((8, 8, 3), dtype=np.uint8)} + + def step(self, action): + self.step_count += 1 + obs = {"rgb": np.ones((8, 8, 3), dtype=np.uint8) * self.step_count} + reward = 1.0 if action == "Stop" else 0.0 + done = action == "Stop" or self.step_count >= 5 + info = { + "task_success": 1.0 if action == "Stop" else 0.0, + "feedback": "ok", + } + return obs, reward, done, info + + def close(self): + pass + + +class ConcreteBridge(BaseBridge): + """Test bridge wrapping FakeGymEnv.""" + + def _create_env(self, reset_config, simulator_kwargs): + return FakeGymEnv() + + def _extract_image(self, obs): + return obs["rgb"] + + +class TestBaseBridgeUnit: + + def test_bridge_creates_env_on_reset(self, tmp_path): + bridge = ConcreteBridge(workspace=tmp_path) + response = bridge.reset({}) + assert response["status"] == "ok" + assert "rgb_path" in response["observation"] + assert Path(response["observation"]["rgb_path"]).exists() + + def test_bridge_step_returns_observation(self, tmp_path): + bridge = ConcreteBridge(workspace=tmp_path) + bridge.reset({}) + response = bridge.step("MoveAhead") + assert response["status"] == "ok" + assert response["done"] is False + + def test_bridge_step_done_on_stop(self, tmp_path): + bridge = ConcreteBridge(workspace=tmp_path) + bridge.reset({}) + response = bridge.step("Stop") + assert response["done"] is True + assert response["info"]["task_success"] == 1.0 + + def test_bridge_saves_image_to_episode_output_dir(self, tmp_path): + episode_dir = tmp_path / "episode_out" + bridge = ConcreteBridge(workspace=tmp_path) + bridge.episode_output_dir = str(episode_dir) + bridge.reset({}) + assert (episode_dir / "step_0000.png").exists() + + def test_bridge_saves_image_to_workspace_by_default(self, tmp_path): + bridge = ConcreteBridge(workspace=tmp_path) + bridge.reset({}) + assert (tmp_path / "step_0000.png").exists() + + def test_bridge_step_increments_count(self, tmp_path): + bridge = ConcreteBridge(workspace=tmp_path) + bridge.reset({}) + bridge.step("MoveAhead") + bridge.step("MoveAhead") + assert (tmp_path / "step_0001.png").exists() + assert (tmp_path / "step_0002.png").exists() + + def test_reset_clears_step_count(self, tmp_path): + bridge = ConcreteBridge(workspace=tmp_path) + bridge.reset({}) + bridge.step("MoveAhead") + bridge.reset({}) # Second reset + bridge.step("MoveAhead") + # After second reset, step count restarts at 1 + assert (tmp_path / "step_0001.png").exists() + + def test_close_sets_env_to_none(self, tmp_path): + bridge = ConcreteBridge(workspace=tmp_path) + bridge.reset({}) + assert bridge.env is not None + bridge.close() + assert bridge.env is None + + def test_extract_info_filters_non_serializable(self, tmp_path): + bridge = ConcreteBridge(workspace=tmp_path) + info = { + "score": 1.0, + "label": "good", + "flag": True, + "count": 5, + "array": np.array([1, 2]), # should be filtered out + } + result = bridge._extract_info(info) + assert result == {"score": 1.0, "label": "good", "flag": True, "count": 5} + + +class TestBaseBridgeIPC: + """Test the full IPC loop using BaseBridge.""" + + def test_ipc_reset_step_close(self, tmp_path): + """Simulate the IPC protocol manually.""" + from easi.communication.filesystem import ( + poll_for_response, + poll_for_status, + write_command, + ) + from easi.communication.schemas import make_reset_command, make_step_command + from easi.core.episode import Action + + bridge = ConcreteBridge(workspace=tmp_path) + + # Run bridge in background thread + thread = threading.Thread(target=bridge.run, daemon=True) + thread.start() + + # Wait for status.json + status = poll_for_status(tmp_path, timeout=5.0) + assert status["ready"] is True + + # Send reset + reset_cmd = make_reset_command("ep_001", reset_config={}) + write_command(tmp_path, reset_cmd) + response = poll_for_response(tmp_path, timeout=5.0) + assert response["status"] == "ok" + assert "rgb_path" in response["observation"] + + # Send step + step_cmd = make_step_command(Action(action_name="MoveAhead")) + write_command(tmp_path, step_cmd) + response = poll_for_response(tmp_path, timeout=5.0) + assert response["status"] == "ok" + assert response["done"] is False + + # Send close + write_command(tmp_path, {"type": "close"}) + response = poll_for_response(tmp_path, timeout=5.0) + assert response["status"] == "ok" + + thread.join(timeout=5) diff --git a/tests/test_bridge_refactor.py b/tests/test_bridge_refactor.py index ceb8eaa..7a3e50e 100644 --- a/tests/test_bridge_refactor.py +++ b/tests/test_bridge_refactor.py @@ -1,12 +1,11 @@ """Tests for the bridge architecture refactor. Verifies: -- AI2ThorBridge is importable and has generic interface -- EBAlfredBridge subclasses AI2ThorBridge with task-specific methods +- AI2ThorBridge is importable and has generic interface (unchanged) +- EBAlfredBridge subclasses BaseBridge (not AI2ThorBridge) +- Vendor code is importable - get_bridge_script_path() works on BaseTask and EBAlfredTask -- simulator_kwargs property works on BaseTask -- Generic thor_utils has no goal evaluators -- EB-Alfred thor_utils has goal evaluators +- simulator_kwargs property works """ from __future__ import annotations @@ -46,17 +45,6 @@ def test_no_ebalfred_methods(self): assert not hasattr(bridge_cls, "_execute_skill") assert not hasattr(bridge_cls, "_restore_scene") assert not hasattr(bridge_cls, "_update_states") - assert not hasattr(bridge_cls, "_nav_obj") - assert not hasattr(bridge_cls, "_pick") - assert not hasattr(bridge_cls, "_put") - assert not hasattr(bridge_cls, "_open") - assert not hasattr(bridge_cls, "_close") - assert not hasattr(bridge_cls, "_toggleon") - assert not hasattr(bridge_cls, "_toggleoff") - assert not hasattr(bridge_cls, "_slice") - assert not hasattr(bridge_cls, "_drop") - assert not hasattr(bridge_cls, "_get_obj_id_from_name") - assert not hasattr(bridge_cls, "_get_object_prop") def test_no_ebalfred_state(self): """Generic bridge __init__ should NOT have EB-Alfred state.""" @@ -68,12 +56,8 @@ def test_no_ebalfred_state(self): assert not hasattr(bridge, "cleaned_objects") assert not hasattr(bridge, "cooled_objects") assert not hasattr(bridge, "heated_objects") - assert not hasattr(bridge, "cur_receptacle") - assert not hasattr(bridge, "put_count_dict") - assert not hasattr(bridge, "sliced") def test_accepts_simulator_kwargs(self): - """Generic bridge should accept simulator_kwargs.""" from easi.simulators.ai2thor.v2_1_0.bridge import AI2ThorBridge import tempfile with tempfile.TemporaryDirectory() as tmpdir: @@ -92,82 +76,128 @@ def test_simulator_kwargs_default_empty(self): # --- EBAlfredBridge tests --- class TestEBAlfredBridgeImport: - """Test that EBAlfredBridge properly extends AI2ThorBridge.""" + """Test that EBAlfredBridge properly extends BaseBridge.""" def test_importable(self): from easi.tasks.ebalfred.bridge import EBAlfredBridge assert EBAlfredBridge is not None - def test_subclasses_ai2thor_bridge(self): + def test_subclasses_base_bridge(self): + """EBAlfredBridge should extend BaseBridge, not AI2ThorBridge.""" + from easi.simulators.base_bridge import BaseBridge + from easi.tasks.ebalfred.bridge import EBAlfredBridge + assert issubclass(EBAlfredBridge, BaseBridge) + + def test_not_subclass_of_ai2thor_bridge(self): + """EBAlfredBridge should NOT extend AI2ThorBridge anymore.""" from easi.simulators.ai2thor.v2_1_0.bridge import AI2ThorBridge from easi.tasks.ebalfred.bridge import EBAlfredBridge - assert issubclass(EBAlfredBridge, AI2ThorBridge) + assert not issubclass(EBAlfredBridge, AI2ThorBridge) - def test_has_skill_methods(self): - """EB-Alfred bridge should have all skill execution methods.""" + def test_has_base_bridge_overrides(self): + """EBAlfredBridge should override BaseBridge methods.""" from easi.tasks.ebalfred.bridge import EBAlfredBridge bridge_cls = EBAlfredBridge - assert hasattr(bridge_cls, "_execute_skill") - assert hasattr(bridge_cls, "_restore_scene") - assert hasattr(bridge_cls, "_update_states") - assert hasattr(bridge_cls, "_nav_obj") - assert hasattr(bridge_cls, "_pick") - assert hasattr(bridge_cls, "_put") - assert hasattr(bridge_cls, "_open") - assert hasattr(bridge_cls, "_close") - assert hasattr(bridge_cls, "_toggleon") - assert hasattr(bridge_cls, "_toggleoff") - assert hasattr(bridge_cls, "_slice") - assert hasattr(bridge_cls, "_drop") - assert hasattr(bridge_cls, "_get_obj_id_from_name") - assert hasattr(bridge_cls, "_get_object_prop") - - def test_has_ebalfred_state(self): - """EB-Alfred bridge should have task-specific state.""" - from easi.tasks.ebalfred.bridge import EBAlfredBridge - import tempfile - with tempfile.TemporaryDirectory() as tmpdir: - bridge = EBAlfredBridge(workspace=tmpdir, data_dir=tmpdir) - assert hasattr(bridge, "traj_data") - assert hasattr(bridge, "cleaned_objects") - assert hasattr(bridge, "cooled_objects") - assert hasattr(bridge, "heated_objects") - assert hasattr(bridge, "cur_receptacle") - assert hasattr(bridge, "put_count_dict") - assert hasattr(bridge, "sliced") - - def test_inherits_generic_methods(self): - """EB-Alfred bridge should inherit generic methods from AI2ThorBridge.""" + assert hasattr(bridge_cls, "_create_env") + assert hasattr(bridge_cls, "_on_reset") + assert hasattr(bridge_cls, "_on_step") + assert hasattr(bridge_cls, "_extract_image") + assert hasattr(bridge_cls, "_extract_info") + + def test_no_skill_methods_on_bridge(self): + """Skill methods now live in vendor's ThorConnector, not bridge.""" from easi.tasks.ebalfred.bridge import EBAlfredBridge bridge_cls = EBAlfredBridge - assert hasattr(bridge_cls, "start") - assert hasattr(bridge_cls, "stop") - assert hasattr(bridge_cls, "run") - assert hasattr(bridge_cls, "_step") - assert hasattr(bridge_cls, "_cache_reachable_positions") - assert hasattr(bridge_cls, "_make_observation_response") - assert hasattr(bridge_cls, "_find_close_reachable_position") - assert hasattr(bridge_cls, "_angle_diff") + assert not hasattr(bridge_cls, "_execute_skill") + assert not hasattr(bridge_cls, "_restore_scene") + assert not hasattr(bridge_cls, "_update_states") + assert not hasattr(bridge_cls, "_nav_obj") + assert not hasattr(bridge_cls, "_pick") + assert not hasattr(bridge_cls, "_put") - def test_overrides_reset_and_step(self): - """EB-Alfred bridge should override reset and step.""" - from easi.simulators.ai2thor.v2_1_0.bridge import AI2ThorBridge + def test_instantiable(self): + """EBAlfredBridge can be instantiated with workspace.""" from easi.tasks.ebalfred.bridge import EBAlfredBridge - # The methods should be overridden (different from parent) - assert EBAlfredBridge.reset is not AI2ThorBridge.reset - assert EBAlfredBridge.step is not AI2ThorBridge.step + import tempfile + with tempfile.TemporaryDirectory() as tmpdir: + bridge = EBAlfredBridge(workspace=tmpdir) + assert bridge.env is None + assert bridge.step_count == 0 - def test_run_is_inherited(self): - """EB-Alfred bridge should inherit run() from AI2ThorBridge (not override).""" - from easi.simulators.ai2thor.v2_1_0.bridge import AI2ThorBridge + def test_accepts_simulator_kwargs(self): from easi.tasks.ebalfred.bridge import EBAlfredBridge - assert EBAlfredBridge.run is AI2ThorBridge.run - + import tempfile + with tempfile.TemporaryDirectory() as tmpdir: + kwargs = {"screen_height": 500} + bridge = EBAlfredBridge(workspace=tmpdir, simulator_kwargs=kwargs) + assert bridge.simulator_kwargs == kwargs -# --- Thor utils separation tests --- -class TestThorUtilsSeparation: - """Test that thor_utils is properly split between generic and EB-Alfred.""" +# --- Vendor import tests --- + +class TestVendorImports: + """Test that vendor files are properly importable.""" + + def test_vendor_package_importable(self): + import easi.tasks.ebalfred.vendor + assert easi.tasks.ebalfred.vendor is not None + + def test_vendor_utils_importable(self): + from easi.tasks.ebalfred.vendor.utils import ( + alfred_objs, + alfred_pick_obj, + alfred_open_obj, + alfred_slice_obj, + alfred_toggle_obj, + alfred_recep, + load_task_json, + natural_word_to_ithor_name, + dotdict, + ) + assert len(alfred_objs) > 0 + assert callable(load_task_json) + assert callable(natural_word_to_ithor_name) + + def test_vendor_constants_importable(self): + from easi.tasks.ebalfred.vendor.gen import constants + assert hasattr(constants, "AGENT_STEP_SIZE") + assert hasattr(constants, "CAMERA_HEIGHT_OFFSET") + assert hasattr(constants, "VISIBILITY_DISTANCE") + assert hasattr(constants, "GOALS") + + def test_vendor_no_embodiedbench_imports(self): + """No vendor file should import from embodiedbench.""" + import ast + from pathlib import Path + vendor_dir = Path("easi/tasks/ebalfred/vendor") + for py_file in vendor_dir.rglob("*.py"): + source = py_file.read_text() + assert "embodiedbench" not in source, ( + f"{py_file} still contains 'embodiedbench' import" + ) + + def test_vendor_no_splits_json(self): + """splits.json should be removed — episode data comes from HF dataset.""" + from pathlib import Path + splits_path = Path("easi/tasks/ebalfred/vendor/data/splits/splits.json") + assert not splits_path.exists() + + def test_vendor_rewards_json_exists(self): + from pathlib import Path + rewards_path = Path("easi/tasks/ebalfred/vendor/models/config/rewards.json") + assert rewards_path.exists() + + def test_vendor_layouts_exist(self): + from pathlib import Path + layouts_dir = Path("easi/tasks/ebalfred/vendor/gen/layouts") + npy_files = list(layouts_dir.glob("*.npy")) + assert len(npy_files) > 0, "No layout .npy files found" + + +# --- Generic thor_utils tests (unchanged) --- + +class TestGenericThorUtils: + """Test that generic thor_utils in simulators/ is still intact.""" def test_generic_has_constants(self): from easi.simulators.ai2thor.v2_1_0 import thor_utils @@ -185,41 +215,10 @@ def test_generic_has_object_helpers(self): assert hasattr(thor_utils, "get_obj_of_type_closest_to_obj") def test_generic_has_no_goal_evaluators(self): - """Generic thor_utils should NOT have goal evaluation functions.""" from easi.simulators.ai2thor.v2_1_0 import thor_utils assert not hasattr(thor_utils, "GOALS") assert not hasattr(thor_utils, "GOAL_EVALUATORS") assert not hasattr(thor_utils, "evaluate_goal_conditions") - assert not hasattr(thor_utils, "get_targets_from_traj") - assert not hasattr(thor_utils, "load_task_json") - assert not hasattr(thor_utils, "load_task_json_with_repeat") - - def test_ebalfred_has_goal_evaluators(self): - from easi.tasks.ebalfred import thor_utils - assert hasattr(thor_utils, "GOALS") - assert hasattr(thor_utils, "GOAL_EVALUATORS") - assert hasattr(thor_utils, "evaluate_goal_conditions") - assert hasattr(thor_utils, "get_targets_from_traj") - assert hasattr(thor_utils, "load_task_json") - assert hasattr(thor_utils, "load_task_json_with_repeat") - - def test_ebalfred_goals_list(self): - from easi.tasks.ebalfred.thor_utils import GOALS - assert len(GOALS) == 7 - assert "pick_and_place_simple" in GOALS - assert "pick_two_obj_and_place" in GOALS - - def test_ebalfred_evaluators_dict(self): - from easi.tasks.ebalfred.thor_utils import GOAL_EVALUATORS - assert len(GOAL_EVALUATORS) == 7 - assert "pick_and_place_simple" in GOAL_EVALUATORS - assert "pick_heat_then_place_in_recep" in GOAL_EVALUATORS - - def test_ebalfred_imports_from_generic(self): - """EB-Alfred thor_utils should import from generic thor_utils.""" - from easi.tasks.ebalfred.thor_utils import evaluate_goal_conditions - # The function should be callable - assert callable(evaluate_goal_conditions) # --- get_bridge_script_path tests --- @@ -278,6 +277,13 @@ def test_ebalfred_has_simulator_kwargs(self): assert kwargs.get("screen_height") == 500 assert kwargs.get("screen_width") == 500 + def test_ebalfred_no_eval_set(self): + """eval_set removed — episode data comes from HF dataset directly.""" + from easi.tasks.ebalfred.task import EBAlfredTask + task = EBAlfredTask() + kwargs = task.simulator_kwargs + assert "eval_set" not in kwargs + def test_dummy_task_empty_simulator_kwargs(self): from easi.tasks.dummy_task.task import DummyTask task = DummyTask() @@ -318,3 +324,60 @@ def test_protocol_has_get_bridge_script_path(self): def test_protocol_has_simulator_kwargs(self): from easi.core.protocols import TaskProtocol assert hasattr(TaskProtocol, "simulator_kwargs") + + +# --- Task simplification tests --- + +class TestTaskSimplification: + """Test that EBAlfredTask is properly simplified.""" + + def test_format_reset_config_has_episode_data(self): + """reset_config should pass episode data directly to bridge.""" + from easi.tasks.ebalfred.task import EBAlfredTask + task = EBAlfredTask() + episode = { + "id": 42, + "task": "pick_and_place_simple-Mug-None-Shelf-1/trial_T20190001", + "repeat_idx": 0, + "instruction": "Put a mug on the shelf.", + } + config = task.format_reset_config(episode) + assert config["episode_id"] == 42 + assert config["task"] == episode["task"] + assert config["repeat_idx"] == 0 + assert config["instruction"] == "Put a mug on the shelf." + + def test_format_reset_config_no_episode_idx(self): + """No episode_idx needed — episode data is passed directly.""" + from easi.tasks.ebalfred.task import EBAlfredTask + task = EBAlfredTask() + episode = { + "id": 0, + "task": "test/trial_T00000001", + "repeat_idx": 0, + "instruction": "test", + } + config = task.format_reset_config(episode) + assert "episode_idx" not in config + assert "task_path" not in config + + def test_evaluate_episode_empty_trajectory(self): + from easi.tasks.ebalfred.task import EBAlfredTask + task = EBAlfredTask() + metrics = task.evaluate_episode({}, []) + assert metrics["task_success"] == 0.0 + assert metrics["num_steps"] == 0.0 + + def test_builtin_episodes_have_required_fields(self): + from easi.tasks.ebalfred.task import EBAlfredTask + task = EBAlfredTask() + episodes = task._get_builtin_episodes() + for ep in episodes: + assert "task" in ep + assert "repeat_idx" in ep + assert "instruction" in ep + + def test_no_thor_utils_in_ebalfred_tasks(self): + """thor_utils.py should be removed from easi/tasks/ebalfred/.""" + thor_utils_path = Path("easi/tasks/ebalfred/thor_utils.py") + assert not thor_utils_path.exists(), "thor_utils.py should be deleted" diff --git a/tests/test_ebalfred_task.py b/tests/test_ebalfred_task.py index 46282b6..9bd6825 100644 --- a/tests/test_ebalfred_task.py +++ b/tests/test_ebalfred_task.py @@ -65,15 +65,12 @@ def test_format_reset_config(self, task): "instruction": "Put a mug on the shelf.", "task_type": "pick_and_place_simple", "trial_id": "trial_T20190001", - "_data_dir": "/datasets/oscarqjh_EB-Alfred_easi", } config = task.format_reset_config(episode) - assert config["task_path"] == "pick_and_place_simple-Mug-None-Shelf-1/trial_T20190001" + assert config["episode_id"] == 0 + assert config["task"] == episode["task"] assert config["repeat_idx"] == 0 assert config["instruction"] == "Put a mug on the shelf." - assert config["episode_id"] == 0 - assert config["task_type"] == "pick_and_place_simple" - assert config["data_dir"] == "/datasets/oscarqjh_EB-Alfred_easi" def test_evaluate_episode(self, task): """Test metric computation from trajectory.""" diff --git a/tests/test_scaffold.py b/tests/test_scaffold.py new file mode 100644 index 0000000..5d94179 --- /dev/null +++ b/tests/test_scaffold.py @@ -0,0 +1,84 @@ +"""Tests for easi task scaffold command.""" +from pathlib import Path + +import pytest +import yaml + +from easi.tasks.scaffold import scaffold_task + + +class TestScaffold: + + def test_creates_task_directory(self, tmp_path): + scaffold_task("my_benchmark", "ai2thor:v2_1_0", output_dir=tmp_path / "easi" / "tasks") + task_dir = tmp_path / "easi" / "tasks" / "my_benchmark" + assert task_dir.is_dir() + + def test_creates_required_files(self, tmp_path): + tasks_dir = tmp_path / "easi" / "tasks" + scaffold_task("my_benchmark", "ai2thor:v2_1_0", output_dir=tasks_dir) + task_dir = tasks_dir / "my_benchmark" + assert (task_dir / "__init__.py").exists() + assert (task_dir / "task.py").exists() + assert (task_dir / "bridge.py").exists() + assert (task_dir / "my_benchmark.yaml").exists() + + def test_creates_test_file(self, tmp_path): + tasks_dir = tmp_path / "easi" / "tasks" + scaffold_task("my_benchmark", "dummy:v1", output_dir=tasks_dir, tests_dir=tmp_path / "tests") + assert (tmp_path / "tests" / "test_my_benchmark.py").exists() + + def test_test_file_has_class_name(self, tmp_path): + tasks_dir = tmp_path / "easi" / "tasks" + scaffold_task("my_benchmark", "dummy:v1", output_dir=tasks_dir, tests_dir=tmp_path / "tests") + content = (tmp_path / "tests" / "test_my_benchmark.py").read_text() + assert "MyBenchmarkTask" in content + assert "MyBenchmarkBridge" in content + + def test_yaml_has_correct_simulator(self, tmp_path): + tasks_dir = tmp_path / "easi" / "tasks" + scaffold_task("my_benchmark", "dummy:v1", output_dir=tasks_dir) + config = yaml.safe_load((tasks_dir / "my_benchmark" / "my_benchmark.yaml").read_text()) + assert config["simulator"] == "dummy:v1" + + def test_yaml_has_external_packages(self, tmp_path): + tasks_dir = tmp_path / "easi" / "tasks" + scaffold_task("my_benchmark", "dummy:v1", output_dir=tasks_dir) + config = yaml.safe_load((tasks_dir / "my_benchmark" / "my_benchmark.yaml").read_text()) + assert "external_packages" in config + + def test_yaml_has_simulator_kwargs(self, tmp_path): + tasks_dir = tmp_path / "easi" / "tasks" + scaffold_task("my_benchmark", "dummy:v1", output_dir=tasks_dir) + config = yaml.safe_load((tasks_dir / "my_benchmark" / "my_benchmark.yaml").read_text()) + assert "simulator_kwargs" in config + + def test_task_py_has_class_name(self, tmp_path): + tasks_dir = tmp_path / "easi" / "tasks" + scaffold_task("my_benchmark", "dummy:v1", output_dir=tasks_dir) + content = (tasks_dir / "my_benchmark" / "task.py").read_text() + assert "class MyBenchmarkTask(BaseTask):" in content + + def test_bridge_py_has_class_name(self, tmp_path): + tasks_dir = tmp_path / "easi" / "tasks" + scaffold_task("my_benchmark", "dummy:v1", output_dir=tasks_dir) + content = (tasks_dir / "my_benchmark" / "bridge.py").read_text() + assert "class MyBenchmarkBridge(BaseBridge):" in content + + def test_refuses_existing_directory(self, tmp_path): + tasks_dir = tmp_path / "easi" / "tasks" + scaffold_task("my_benchmark", "dummy:v1", output_dir=tasks_dir) + with pytest.raises(FileExistsError): + scaffold_task("my_benchmark", "dummy:v1", output_dir=tasks_dir) + + def test_bridge_imports_base_bridge(self, tmp_path): + tasks_dir = tmp_path / "easi" / "tasks" + scaffold_task("my_benchmark", "dummy:v1", output_dir=tasks_dir) + content = (tasks_dir / "my_benchmark" / "bridge.py").read_text() + assert "from easi.simulators.base_bridge import BaseBridge" in content + + def test_task_has_get_bridge_script_path(self, tmp_path): + tasks_dir = tmp_path / "easi" / "tasks" + scaffold_task("my_benchmark", "dummy:v1", output_dir=tasks_dir) + content = (tasks_dir / "my_benchmark" / "task.py").read_text() + assert "get_bridge_script_path" in content From 347a0caaa264e3576ebb3ee3361eb15157178965 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Sat, 14 Feb 2026 16:49:30 +0800 Subject: [PATCH 006/239] [Feat] Added auto env install --- easi/cli.py | 18 +++++- easi/core/base_env_manager.py | 15 +++++ easi/core/base_task.py | 16 ++++- easi/core/protocols.py | 4 ++ easi/evaluation/runner.py | 9 +++ .../ai2thor/v2_1_0/requirements.txt | 3 - easi/simulators/base_bridge.py | 2 + easi/tasks/ebalfred/ebalfred_base.yaml | 6 +- .../tasks/ebalfred/ebalfred_common_sense.yaml | 6 +- .../ebalfred_complex_instruction.yaml | 6 +- .../tasks/ebalfred/ebalfred_long_horizon.yaml | 6 +- easi/tasks/ebalfred/ebalfred_spatial.yaml | 6 +- .../ebalfred/ebalfred_visual_appearance.yaml | 6 +- easi/tasks/scaffold.py | 4 +- tests/test_bridge_refactor.py | 60 +++++++++++++------ tests/test_scaffold.py | 10 ++-- 16 files changed, 141 insertions(+), 36 deletions(-) diff --git a/easi/cli.py b/easi/cli.py index 5922217..768a78e 100644 --- a/easi/cli.py +++ b/easi/cli.py @@ -42,6 +42,8 @@ def build_parser() -> argparse.ArgumentParser: env_install.add_argument("simulator", type=str, help="e.g., 'dummy' or 'ai2thor:v2_1_0'") env_install.add_argument("--reinstall", action="store_true", help="Remove existing env and install from scratch") + env_install.add_argument("--with-task-deps", type=str, default=None, metavar="TASK", + help="Also install additional_deps from a task (e.g., 'ebalfred_base')") env_check = env_sub.add_parser("check", help="Check if environment is ready", parents=[common]) env_check.add_argument("simulator", type=str) @@ -121,7 +123,7 @@ def cmd_env_list() -> None: logger.info(" %s%s -- %s", pair, default_marker, entry.description) -def cmd_env_install(simulator: str, reinstall: bool = False) -> None: +def cmd_env_install(simulator: str, reinstall: bool = False, with_task_deps: str | None = None) -> None: from easi.simulators.registry import load_env_manager_class EnvManagerClass = load_env_manager_class(simulator) @@ -133,6 +135,18 @@ def cmd_env_install(simulator: str, reinstall: bool = False) -> None: logger.info("Installing environment: %s", env_manager.get_env_name()) env_manager.install() + + if with_task_deps: + from easi.tasks.registry import get_task_entry, load_task_class + + entry = get_task_entry(with_task_deps) + TaskClass = load_task_class(with_task_deps) + task = TaskClass(split_yaml_path=entry.config_path) + if task.additional_deps: + env_manager.install_additional_deps(task.additional_deps) + else: + logger.info("Task %s has no additional_deps.", with_task_deps) + logger.info("Done.") @@ -305,7 +319,7 @@ def main() -> None: if args.env_action == "list": cmd_env_list() elif args.env_action == "install": - cmd_env_install(args.simulator, reinstall=args.reinstall) + cmd_env_install(args.simulator, reinstall=args.reinstall, with_task_deps=args.with_task_deps) elif args.env_action == "check": cmd_env_check(args.simulator) else: diff --git a/easi/core/base_env_manager.py b/easi/core/base_env_manager.py index bc75465..0b31251 100644 --- a/easi/core/base_env_manager.py +++ b/easi/core/base_env_manager.py @@ -137,6 +137,21 @@ def install(self) -> None: return self._do_install() + def install_additional_deps(self, packages: list[str]) -> None: + """Install extra pip packages into this conda env via uv (idempotent). + + Called by EvaluationRunner when a task declares additional_deps + in its YAML config. + """ + if not packages: + return + python_exec = self.get_python_executable() + with spinner(f"Installing task dependencies: {', '.join(packages)}"): + self._run_command( + [python_exec, "-m", "uv", "pip", "install"] + packages, + "uv pip install (task deps)", + ) + def _do_install(self) -> None: """Execute the full install sequence (called under lock).""" env_name = self.get_env_name() diff --git a/easi/core/base_task.py b/easi/core/base_task.py index 0e8757e..6c2068e 100644 --- a/easi/core/base_task.py +++ b/easi/core/base_task.py @@ -84,10 +84,22 @@ def get_bridge_script_path(self) -> Path | None: """ return None + @property + def simulator_configs(self) -> dict: + """Full simulator configuration from task YAML (includes additional_deps).""" + return self._config.get("simulator_configs", {}) + + @property + def additional_deps(self) -> list[str]: + """Extra pip packages to install in the simulator conda env.""" + return self.simulator_configs.get("additional_deps", []) + @property def simulator_kwargs(self) -> dict: - """Simulator configuration from task YAML (passed to bridge via CLI args).""" - return self._config.get("simulator_kwargs", {}) + """Bridge-facing kwargs (simulator_configs minus additional_deps).""" + cfg = dict(self.simulator_configs) + cfg.pop("additional_deps", None) + return cfg def get_instruction(self, episode: dict) -> str: """Return human-readable task instruction for this episode. diff --git a/easi/core/protocols.py b/easi/core/protocols.py index 77adc5a..fcb7fcc 100644 --- a/easi/core/protocols.py +++ b/easi/core/protocols.py @@ -72,5 +72,9 @@ def evaluate_episode( ) -> dict[str, float]: ... def get_bridge_script_path(self) -> Path | None: ... @property + def simulator_configs(self) -> dict: ... + @property + def additional_deps(self) -> list[str]: ... + @property def simulator_kwargs(self) -> dict: ... def __len__(self) -> int: ... diff --git a/easi/evaluation/runner.py b/easi/evaluation/runner.py index 198f8b7..47a1fee 100644 --- a/easi/evaluation/runner.py +++ b/easi/evaluation/runner.py @@ -259,6 +259,15 @@ def _create_simulator(self, simulator_key: str, task=None): env_manager = EnvManagerClass() sim = SimClass() + # Auto-install simulator env if not ready + if not env_manager.env_is_ready(): + logger.info("Simulator environment not ready, auto-installing...") + env_manager.install() + + # Install task-level additional deps + if task and task.additional_deps: + env_manager.install_additional_deps(task.additional_deps) + # Task-specific bridge overrides simulator default bridge_path = ( (task.get_bridge_script_path() if task else None) diff --git a/easi/simulators/ai2thor/v2_1_0/requirements.txt b/easi/simulators/ai2thor/v2_1_0/requirements.txt index f55a859..e9c700f 100644 --- a/easi/simulators/ai2thor/v2_1_0/requirements.txt +++ b/easi/simulators/ai2thor/v2_1_0/requirements.txt @@ -7,6 +7,3 @@ flask==1.1.2 jinja2==2.11.3 markupsafe==1.1.1 itsdangerous==1.1.0 -gym -networkx -opencv-python diff --git a/easi/simulators/base_bridge.py b/easi/simulators/base_bridge.py index 8a002d2..425a85f 100644 --- a/easi/simulators/base_bridge.py +++ b/easi/simulators/base_bridge.py @@ -14,6 +14,8 @@ class MyBenchmarkBridge(BaseBridge): def _create_env(self, reset_config, simulator_kwargs): + # simulator_kwargs comes from task YAML's simulator_configs + # (with additional_deps stripped — bridge only gets runtime config) from external_package import MyEnv return MyEnv(**reset_config) diff --git a/easi/tasks/ebalfred/ebalfred_base.yaml b/easi/tasks/ebalfred/ebalfred_base.yaml index 5478719..d9d019c 100644 --- a/easi/tasks/ebalfred/ebalfred_base.yaml +++ b/easi/tasks/ebalfred/ebalfred_base.yaml @@ -12,9 +12,13 @@ dataset: split: "base" zip_files: - "tasks.zip" -simulator_kwargs: +simulator_configs: quality: "MediumCloseFitShadows" screen_height: 500 screen_width: 500 + additional_deps: + - "gym" + - "networkx" + - "opencv-python" agent: prompt_builder: "easi.tasks.ebalfred.prompts.EBAlfredPromptBuilder" diff --git a/easi/tasks/ebalfred/ebalfred_common_sense.yaml b/easi/tasks/ebalfred/ebalfred_common_sense.yaml index 1e61251..71e86e7 100644 --- a/easi/tasks/ebalfred/ebalfred_common_sense.yaml +++ b/easi/tasks/ebalfred/ebalfred_common_sense.yaml @@ -12,9 +12,13 @@ dataset: split: "common_sense" zip_files: - "tasks.zip" -simulator_kwargs: +simulator_configs: quality: "MediumCloseFitShadows" screen_height: 500 screen_width: 500 + additional_deps: + - "gym" + - "networkx" + - "opencv-python" agent: prompt_builder: "easi.tasks.ebalfred.prompts.EBAlfredPromptBuilder" diff --git a/easi/tasks/ebalfred/ebalfred_complex_instruction.yaml b/easi/tasks/ebalfred/ebalfred_complex_instruction.yaml index a55539d..096ae18 100644 --- a/easi/tasks/ebalfred/ebalfred_complex_instruction.yaml +++ b/easi/tasks/ebalfred/ebalfred_complex_instruction.yaml @@ -12,9 +12,13 @@ dataset: split: "complex_instruction" zip_files: - "tasks.zip" -simulator_kwargs: +simulator_configs: quality: "MediumCloseFitShadows" screen_height: 500 screen_width: 500 + additional_deps: + - "gym" + - "networkx" + - "opencv-python" agent: prompt_builder: "easi.tasks.ebalfred.prompts.EBAlfredPromptBuilder" diff --git a/easi/tasks/ebalfred/ebalfred_long_horizon.yaml b/easi/tasks/ebalfred/ebalfred_long_horizon.yaml index a4d4413..a45ba7c 100644 --- a/easi/tasks/ebalfred/ebalfred_long_horizon.yaml +++ b/easi/tasks/ebalfred/ebalfred_long_horizon.yaml @@ -12,9 +12,13 @@ dataset: split: "long_horizon" zip_files: - "tasks.zip" -simulator_kwargs: +simulator_configs: quality: "MediumCloseFitShadows" screen_height: 500 screen_width: 500 + additional_deps: + - "gym" + - "networkx" + - "opencv-python" agent: prompt_builder: "easi.tasks.ebalfred.prompts.EBAlfredPromptBuilder" diff --git a/easi/tasks/ebalfred/ebalfred_spatial.yaml b/easi/tasks/ebalfred/ebalfred_spatial.yaml index dd2f04d..d25fbdb 100644 --- a/easi/tasks/ebalfred/ebalfred_spatial.yaml +++ b/easi/tasks/ebalfred/ebalfred_spatial.yaml @@ -12,9 +12,13 @@ dataset: split: "spatial" zip_files: - "tasks.zip" -simulator_kwargs: +simulator_configs: quality: "MediumCloseFitShadows" screen_height: 500 screen_width: 500 + additional_deps: + - "gym" + - "networkx" + - "opencv-python" agent: prompt_builder: "easi.tasks.ebalfred.prompts.EBAlfredPromptBuilder" diff --git a/easi/tasks/ebalfred/ebalfred_visual_appearance.yaml b/easi/tasks/ebalfred/ebalfred_visual_appearance.yaml index 4189e5c..6724d07 100644 --- a/easi/tasks/ebalfred/ebalfred_visual_appearance.yaml +++ b/easi/tasks/ebalfred/ebalfred_visual_appearance.yaml @@ -12,9 +12,13 @@ dataset: split: "visual_appearance" zip_files: - "tasks.zip" -simulator_kwargs: +simulator_configs: quality: "MediumCloseFitShadows" screen_height: 500 screen_width: 500 + additional_deps: + - "gym" + - "networkx" + - "opencv-python" agent: prompt_builder: "easi.tasks.ebalfred.prompts.EBAlfredPromptBuilder" diff --git a/easi/tasks/scaffold.py b/easi/tasks/scaffold.py index e5577e2..c06e872 100644 --- a/easi/tasks/scaffold.py +++ b/easi/tasks/scaffold.py @@ -208,8 +208,8 @@ def _extract_image(self, obs): dataset: source: local path: null -simulator_kwargs: {{}} -external_packages: [] +simulator_configs: + additional_deps: [] ''' _TEST_TEMPLATE = '''\ diff --git a/tests/test_bridge_refactor.py b/tests/test_bridge_refactor.py index 7a3e50e..5a37fa4 100644 --- a/tests/test_bridge_refactor.py +++ b/tests/test_bridge_refactor.py @@ -5,7 +5,7 @@ - EBAlfredBridge subclasses BaseBridge (not AI2ThorBridge) - Vendor code is importable - get_bridge_script_path() works on BaseTask and EBAlfredTask -- simulator_kwargs property works +- simulator_configs / additional_deps / simulator_kwargs properties work """ from __future__ import annotations @@ -263,36 +263,53 @@ def test_ebalfred_bridge_path_different_from_simulator(self): assert "simulators" in str(sim_bridge) -# --- simulator_kwargs tests --- +# --- simulator_configs tests --- -class TestSimulatorKwargs: - """Test simulator_kwargs property on BaseTask.""" +class TestSimulatorConfigs: + """Test simulator_configs and additional_deps on BaseTask.""" - def test_ebalfred_has_simulator_kwargs(self): + def test_ebalfred_has_simulator_configs(self): + from easi.tasks.ebalfred.task import EBAlfredTask + task = EBAlfredTask() + configs = task.simulator_configs + assert isinstance(configs, dict) + assert configs.get("quality") == "MediumCloseFitShadows" + assert configs.get("screen_height") == 500 + + def test_ebalfred_has_additional_deps(self): + from easi.tasks.ebalfred.task import EBAlfredTask + task = EBAlfredTask() + deps = task.additional_deps + assert isinstance(deps, list) + assert "gym" in deps + assert "networkx" in deps + assert "opencv-python" in deps + + def test_ebalfred_simulator_kwargs_excludes_deps(self): + """simulator_kwargs should NOT contain additional_deps.""" from easi.tasks.ebalfred.task import EBAlfredTask task = EBAlfredTask() kwargs = task.simulator_kwargs - assert isinstance(kwargs, dict) + assert "additional_deps" not in kwargs assert kwargs.get("quality") == "MediumCloseFitShadows" assert kwargs.get("screen_height") == 500 - assert kwargs.get("screen_width") == 500 def test_ebalfred_no_eval_set(self): - """eval_set removed — episode data comes from HF dataset directly.""" + """eval_set removed -- episode data comes from HF dataset directly.""" from easi.tasks.ebalfred.task import EBAlfredTask task = EBAlfredTask() kwargs = task.simulator_kwargs assert "eval_set" not in kwargs - def test_dummy_task_empty_simulator_kwargs(self): + def test_dummy_task_empty_simulator_configs(self): from easi.tasks.dummy_task.task import DummyTask task = DummyTask() - kwargs = task.simulator_kwargs - assert isinstance(kwargs, dict) - assert kwargs == {} + assert task.simulator_configs == {} + assert task.additional_deps == [] + assert task.simulator_kwargs == {} - def test_all_ebalfred_splits_have_kwargs(self): - """All EB-Alfred split YAMLs should have simulator_kwargs.""" + def test_all_ebalfred_splits_have_configs(self): + """All EB-Alfred split YAMLs should have simulator_configs.""" from easi.tasks.registry import get_task_entry, load_task_class ebalfred_names = [ @@ -307,9 +324,10 @@ def test_all_ebalfred_splits_have_kwargs(self): entry = get_task_entry(name) TaskClass = load_task_class(name) task = TaskClass(split_yaml_path=entry.config_path) - kwargs = task.simulator_kwargs - assert isinstance(kwargs, dict), f"{name} simulator_kwargs is not a dict" - assert "quality" in kwargs, f"{name} missing quality in simulator_kwargs" + configs = task.simulator_configs + assert isinstance(configs, dict), f"{name} simulator_configs is not a dict" + assert "quality" in configs, f"{name} missing quality in simulator_configs" + assert "additional_deps" in configs, f"{name} missing additional_deps" # --- Protocol tests --- @@ -325,6 +343,14 @@ def test_protocol_has_simulator_kwargs(self): from easi.core.protocols import TaskProtocol assert hasattr(TaskProtocol, "simulator_kwargs") + def test_protocol_has_simulator_configs(self): + from easi.core.protocols import TaskProtocol + assert hasattr(TaskProtocol, "simulator_configs") + + def test_protocol_has_additional_deps(self): + from easi.core.protocols import TaskProtocol + assert hasattr(TaskProtocol, "additional_deps") + # --- Task simplification tests --- diff --git a/tests/test_scaffold.py b/tests/test_scaffold.py index 5d94179..cf37d33 100644 --- a/tests/test_scaffold.py +++ b/tests/test_scaffold.py @@ -41,17 +41,19 @@ def test_yaml_has_correct_simulator(self, tmp_path): config = yaml.safe_load((tasks_dir / "my_benchmark" / "my_benchmark.yaml").read_text()) assert config["simulator"] == "dummy:v1" - def test_yaml_has_external_packages(self, tmp_path): + def test_yaml_has_simulator_configs(self, tmp_path): tasks_dir = tmp_path / "easi" / "tasks" scaffold_task("my_benchmark", "dummy:v1", output_dir=tasks_dir) config = yaml.safe_load((tasks_dir / "my_benchmark" / "my_benchmark.yaml").read_text()) - assert "external_packages" in config + assert "simulator_configs" in config + assert "additional_deps" in config["simulator_configs"] - def test_yaml_has_simulator_kwargs(self, tmp_path): + def test_yaml_no_legacy_fields(self, tmp_path): tasks_dir = tmp_path / "easi" / "tasks" scaffold_task("my_benchmark", "dummy:v1", output_dir=tasks_dir) config = yaml.safe_load((tasks_dir / "my_benchmark" / "my_benchmark.yaml").read_text()) - assert "simulator_kwargs" in config + assert "simulator_kwargs" not in config + assert "external_packages" not in config def test_task_py_has_class_name(self, tmp_path): tasks_dir = tmp_path / "easi" / "tasks" From 65ec22b15870bdb417e90008a62f5fe83eb47136 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Sat, 14 Feb 2026 22:00:22 +0800 Subject: [PATCH 007/239] [Feat] Add LLM response schemas with BaseResponseSchema --- easi/llm/schemas.py | 36 +++++++++++++++++++++++++ tests/test_llm_schemas.py | 57 +++++++++++++++++++++++++++++++++++++++ 2 files changed, 93 insertions(+) create mode 100644 easi/llm/schemas.py create mode 100644 tests/test_llm_schemas.py diff --git a/easi/llm/schemas.py b/easi/llm/schemas.py new file mode 100644 index 0000000..30bd230 --- /dev/null +++ b/easi/llm/schemas.py @@ -0,0 +1,36 @@ +"""Pydantic response schemas for structured LLM output. + +Each task can define its own schema extending BaseResponseSchema. +The schema's get_actions() method normalizes task-specific fields +into a standard list of action strings. +""" +from __future__ import annotations + +from pydantic import BaseModel + + +class BaseResponseSchema(BaseModel): + """Base class for all LLM response schemas. + + Subclasses MUST implement get_actions() to normalize their + task-specific fields into a list of action strings. + """ + + def get_actions(self) -> list[str]: + raise NotImplementedError + + +class ExecutableAction(BaseModel): + """A single action in an executable plan.""" + + action: str + + +class ActionPlanResponse(BaseResponseSchema): + """Default structured response for ReAct agents.""" + + reasoning: str + executable_plan: list[ExecutableAction] + + def get_actions(self) -> list[str]: + return [a.action for a in self.executable_plan] diff --git a/tests/test_llm_schemas.py b/tests/test_llm_schemas.py new file mode 100644 index 0000000..2195c2a --- /dev/null +++ b/tests/test_llm_schemas.py @@ -0,0 +1,57 @@ +"""Tests for LLM response schemas.""" +import json +import pytest +from pydantic import BaseModel + + +class TestBaseResponseSchema: + def test_base_schema_get_actions_raises(self): + from easi.llm.schemas import BaseResponseSchema + + class Bare(BaseResponseSchema): + pass + + with pytest.raises(NotImplementedError): + Bare().get_actions() + + def test_base_schema_is_pydantic_model(self): + from easi.llm.schemas import BaseResponseSchema + assert issubclass(BaseResponseSchema, BaseModel) + + +class TestActionPlanResponse: + def test_get_actions_returns_list_of_strings(self): + from easi.llm.schemas import ActionPlanResponse + + resp = ActionPlanResponse( + reasoning="I see a mug", + executable_plan=[ + {"action": "find a Mug"}, + {"action": "pick up the Mug"}, + ], + ) + assert resp.get_actions() == ["find a Mug", "pick up the Mug"] + + def test_empty_plan(self): + from easi.llm.schemas import ActionPlanResponse + + resp = ActionPlanResponse(reasoning="nothing to do", executable_plan=[]) + assert resp.get_actions() == [] + + def test_roundtrip_json(self): + from easi.llm.schemas import ActionPlanResponse + + resp = ActionPlanResponse( + reasoning="test", + executable_plan=[{"action": "Stop"}], + ) + data = json.loads(resp.model_dump_json()) + assert data["executable_plan"][0]["action"] == "Stop" + + +class TestExecutableAction: + def test_action_field(self): + from easi.llm.schemas import ExecutableAction + + a = ExecutableAction(action="MoveAhead") + assert a.action == "MoveAhead" From 9c8dd29527897ba9bbe5c3dc1f91d84af9e8c1b7 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Sat, 14 Feb 2026 22:01:54 +0800 Subject: [PATCH 008/239] [Feat] Add LLM kwargs parser, backend validation, model string builder --- easi/llm/utils.py | 73 ++++++++++++++++++++++++ tests/test_llm_utils.py | 120 ++++++++++++++++++++++++++++++++++++++++ 2 files changed, 193 insertions(+) create mode 100644 easi/llm/utils.py create mode 100644 tests/test_llm_utils.py diff --git a/easi/llm/utils.py b/easi/llm/utils.py new file mode 100644 index 0000000..296eab7 --- /dev/null +++ b/easi/llm/utils.py @@ -0,0 +1,73 @@ +"""Utility functions for LLM backend configuration.""" +from __future__ import annotations + +import json +import os + +# Backend prefix mapping for LiteLLM model strings. +# vLLM and dummy both use OpenAI-compatible APIs. +_BACKEND_PREFIX = { + "openai": "openai", + "anthropic": "anthropic", + "gemini": "gemini", + "vllm": "openai", + "dummy": "openai", +} + +# Env vars required per backend. +_REQUIRED_ENV_VARS = { + "openai": "OPENAI_API_KEY", + "anthropic": "ANTHROPIC_API_KEY", + "gemini": "GOOGLE_API_KEY", +} + +# kwargs that belong to the inference server (vLLM CLI flags), not the API call. +_SERVER_KWARGS = { + "tensor_parallel_size", + "gpu_memory_utilization", + "max_model_len", + "dtype", + "quantization", + "enforce_eager", + "enable_prefix_caching", +} + + +def parse_llm_kwargs(raw: str | None) -> dict: + """Parse --llm-kwargs JSON string into a dict.""" + if not raw: + return {} + try: + result = json.loads(raw) + except json.JSONDecodeError as e: + raise ValueError(f"Invalid JSON for --llm-kwargs: {e}") from e + if not isinstance(result, dict): + raise ValueError("--llm-kwargs must be a JSON object (dict)") + return result + + +def split_kwargs(kwargs: dict) -> tuple[dict, dict]: + """Split kwargs into (server_kwargs, client_kwargs). + + Known server params (tensor_parallel_size, etc.) go to ServerManager. + Everything else (temperature, max_tokens, etc.) goes to LLMClient. + """ + server = {k: v for k, v in kwargs.items() if k in _SERVER_KWARGS} + client = {k: v for k, v in kwargs.items() if k not in _SERVER_KWARGS} + return server, client + + +def build_litellm_model(backend: str, model: str) -> str: + """Map EASI backend+model to a LiteLLM model string.""" + prefix = _BACKEND_PREFIX.get(backend, "openai") + return f"{prefix}/{model}" + + +def validate_backend(backend: str) -> None: + """Raise EnvironmentError if required env var is missing for backend.""" + env_var = _REQUIRED_ENV_VARS.get(backend) + if env_var and not os.environ.get(env_var): + raise EnvironmentError( + f"Backend '{backend}' requires the {env_var} environment variable. " + f"Set it with: export {env_var}=" + ) diff --git a/tests/test_llm_utils.py b/tests/test_llm_utils.py new file mode 100644 index 0000000..a2a6a24 --- /dev/null +++ b/tests/test_llm_utils.py @@ -0,0 +1,120 @@ +"""Tests for LLM utility functions.""" +import pytest + + +class TestParseLlmKwargs: + def test_none_returns_empty_dict(self): + from easi.llm.utils import parse_llm_kwargs + assert parse_llm_kwargs(None) == {} + + def test_empty_string_returns_empty_dict(self): + from easi.llm.utils import parse_llm_kwargs + assert parse_llm_kwargs("") == {} + + def test_valid_json(self): + from easi.llm.utils import parse_llm_kwargs + result = parse_llm_kwargs('{"tensor_parallel_size": 4, "temperature": 0.7}') + assert result == {"tensor_parallel_size": 4, "temperature": 0.7} + + def test_nested_json(self): + from easi.llm.utils import parse_llm_kwargs + result = parse_llm_kwargs('{"extra": {"key": "val"}}') + assert result == {"extra": {"key": "val"}} + + def test_invalid_json_raises(self): + from easi.llm.utils import parse_llm_kwargs + with pytest.raises(ValueError, match="Invalid JSON"): + parse_llm_kwargs("not json") + + +class TestSplitKwargs: + def test_splits_server_and_client(self): + from easi.llm.utils import split_kwargs + kwargs = { + "tensor_parallel_size": 4, + "gpu_memory_utilization": 0.9, + "temperature": 0.7, + "max_tokens": 1024, + } + server, client = split_kwargs(kwargs) + assert server == {"tensor_parallel_size": 4, "gpu_memory_utilization": 0.9} + assert client == {"temperature": 0.7, "max_tokens": 1024} + + def test_empty_dict(self): + from easi.llm.utils import split_kwargs + server, client = split_kwargs({}) + assert server == {} + assert client == {} + + def test_all_server_kwargs(self): + from easi.llm.utils import split_kwargs + kwargs = {"tensor_parallel_size": 4, "max_model_len": 8192} + server, client = split_kwargs(kwargs) + assert server == kwargs + assert client == {} + + def test_all_client_kwargs(self): + from easi.llm.utils import split_kwargs + kwargs = {"temperature": 0.5, "top_p": 0.9} + server, client = split_kwargs(kwargs) + assert server == {} + assert client == kwargs + + +class TestBuildLitellmModel: + def test_openai(self): + from easi.llm.utils import build_litellm_model + assert build_litellm_model("openai", "gpt-4o") == "openai/gpt-4o" + + def test_anthropic(self): + from easi.llm.utils import build_litellm_model + assert build_litellm_model("anthropic", "claude-sonnet-4-5-20250929") == "anthropic/claude-sonnet-4-5-20250929" + + def test_gemini(self): + from easi.llm.utils import build_litellm_model + assert build_litellm_model("gemini", "gemini-2.0-flash") == "gemini/gemini-2.0-flash" + + def test_vllm_uses_openai_prefix(self): + from easi.llm.utils import build_litellm_model + assert build_litellm_model("vllm", "Qwen/Qwen2.5-VL-72B") == "openai/Qwen/Qwen2.5-VL-72B" + + def test_dummy_uses_openai_prefix(self): + from easi.llm.utils import build_litellm_model + assert build_litellm_model("dummy", "default") == "openai/default" + + def test_unknown_backend_falls_back(self): + from easi.llm.utils import build_litellm_model + assert build_litellm_model("lmdeploy", "some-model") == "openai/some-model" + + +class TestValidateBackend: + def test_openai_missing_key(self, monkeypatch): + from easi.llm.utils import validate_backend + monkeypatch.delenv("OPENAI_API_KEY", raising=False) + with pytest.raises(EnvironmentError, match="OPENAI_API_KEY"): + validate_backend("openai") + + def test_openai_with_key(self, monkeypatch): + from easi.llm.utils import validate_backend + monkeypatch.setenv("OPENAI_API_KEY", "sk-test") + validate_backend("openai") # should not raise + + def test_anthropic_missing_key(self, monkeypatch): + from easi.llm.utils import validate_backend + monkeypatch.delenv("ANTHROPIC_API_KEY", raising=False) + with pytest.raises(EnvironmentError, match="ANTHROPIC_API_KEY"): + validate_backend("anthropic") + + def test_gemini_missing_key(self, monkeypatch): + from easi.llm.utils import validate_backend + monkeypatch.delenv("GOOGLE_API_KEY", raising=False) + with pytest.raises(EnvironmentError, match="GOOGLE_API_KEY"): + validate_backend("gemini") + + def test_vllm_no_key_needed(self): + from easi.llm.utils import validate_backend + validate_backend("vllm") # should not raise + + def test_dummy_no_key_needed(self): + from easi.llm.utils import validate_backend + validate_backend("dummy") # should not raise From e1a85ba7c287a44f5618b659aef67dcbbec0cfcb Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Sat, 14 Feb 2026 22:08:16 +0800 Subject: [PATCH 009/239] [Feat] Add LLMClient wrapping LiteLLM + Instructor Unified client with generate() and generate_structured() methods, lazy imports, and cumulative usage tracking (tokens + cost). --- easi/llm/client.py | 132 ++++++++++++++++++++++++++++++++++++ tests/test_llm_client.py | 143 +++++++++++++++++++++++++++++++++++++++ 2 files changed, 275 insertions(+) create mode 100644 easi/llm/client.py create mode 100644 tests/test_llm_client.py diff --git a/easi/llm/client.py b/easi/llm/client.py new file mode 100644 index 0000000..50f223b --- /dev/null +++ b/easi/llm/client.py @@ -0,0 +1,132 @@ +"""Unified LLM client wrapping LiteLLM + Instructor. + +Provides two generation modes: +- generate(): returns raw text (backward-compatible with LLMApiClient) +- generate_structured(): returns a validated Pydantic model (via Instructor) + +Usage tracking is cumulative — call get_usage() to snapshot, reset_usage() between episodes. +""" +from __future__ import annotations + +from typing import Any + +from pydantic import BaseModel + +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + +# Lazy imports to avoid requiring litellm/instructor when not needed. +litellm = None +instructor = None + + +def _ensure_imports() -> None: + """Import litellm and instructor on first use.""" + global litellm, instructor + if litellm is None: + try: + import litellm as _litellm + import instructor as _instructor + except ImportError as e: + raise ImportError( + "LLMClient requires litellm and instructor. " + "Install with: pip install easi[llm]" + ) from e + litellm = _litellm + instructor = _instructor + # Suppress litellm's verbose logging + litellm.suppress_debug_info = True + + +class LLMClient: + """Unified LLM client for all backends.""" + + def __init__( + self, + model: str, + base_url: str | None = None, + **kwargs: Any, + ): + self.model = model + self.base_url = base_url + self.default_kwargs = kwargs + self._usage = { + "prompt_tokens": 0, + "completion_tokens": 0, + "num_calls": 0, + "cost_usd": 0.0, + } + + def generate(self, messages: list[dict]) -> str: + """Generate text completion. Drop-in for LLMApiClient.generate().""" + _ensure_imports() + + call_kwargs: dict[str, Any] = { + "model": self.model, + "messages": messages, + **self.default_kwargs, + } + if self.base_url: + call_kwargs["api_base"] = self.base_url + + logger.trace("LLM call: model=%s, messages=%d", self.model, len(messages)) + response = litellm.completion(**call_kwargs) + self._track_usage(response) + + content = response.choices[0].message.content + logger.trace("LLM response: %s", content[:200] if content else "") + return content + + def generate_structured( + self, + messages: list[dict], + response_model: type[BaseModel], + ) -> BaseModel: + """Generate structured output validated against a Pydantic model.""" + _ensure_imports() + + client = instructor.from_litellm(litellm.completion) + + call_kwargs: dict[str, Any] = { + "model": self.model, + "messages": messages, + "response_model": response_model, + "max_retries": 2, + **self.default_kwargs, + } + if self.base_url: + call_kwargs["api_base"] = self.base_url + + logger.trace( + "LLM structured call: model=%s, schema=%s", + self.model, response_model.__name__, + ) + result = client.chat.completions.create(**call_kwargs) + return result + + def get_usage(self) -> dict: + """Return cumulative usage stats (copy).""" + return dict(self._usage) + + def reset_usage(self) -> None: + """Reset usage counters.""" + self._usage = { + "prompt_tokens": 0, + "completion_tokens": 0, + "num_calls": 0, + "cost_usd": 0.0, + } + + def _track_usage(self, response: Any) -> None: + """Accumulate token usage and cost from a LiteLLM response.""" + usage = getattr(response, "usage", None) + if usage: + self._usage["prompt_tokens"] += getattr(usage, "prompt_tokens", 0) + self._usage["completion_tokens"] += getattr(usage, "completion_tokens", 0) + self._usage["num_calls"] += 1 + try: + cost = litellm.completion_cost(completion_response=response) + self._usage["cost_usd"] += float(cost) + except Exception: + pass # Cost unavailable for local/unknown models diff --git a/tests/test_llm_client.py b/tests/test_llm_client.py new file mode 100644 index 0000000..2bb26fd --- /dev/null +++ b/tests/test_llm_client.py @@ -0,0 +1,143 @@ +"""Tests for LLMClient.""" +import json +import pytest +from unittest.mock import MagicMock, patch + + +class TestLLMClientInit: + def test_stores_config(self): + from easi.llm.client import LLMClient + client = LLMClient(model="openai/gpt-4o", temperature=0.5, max_tokens=1024) + assert client.model == "openai/gpt-4o" + assert client.base_url is None + assert client.default_kwargs == {"temperature": 0.5, "max_tokens": 1024} + + def test_stores_base_url(self): + from easi.llm.client import LLMClient + client = LLMClient(model="openai/test", base_url="http://localhost:8080/v1") + assert client.base_url == "http://localhost:8080/v1" + + +class TestLLMClientUsageTracking: + def test_initial_usage_is_zero(self): + from easi.llm.client import LLMClient + client = LLMClient(model="openai/test") + usage = client.get_usage() + assert usage["prompt_tokens"] == 0 + assert usage["completion_tokens"] == 0 + assert usage["num_calls"] == 0 + assert usage["cost_usd"] == 0.0 + + def test_reset_usage(self): + from easi.llm.client import LLMClient + client = LLMClient(model="openai/test") + client._usage["prompt_tokens"] = 100 + client._usage["num_calls"] = 5 + client.reset_usage() + assert client.get_usage()["prompt_tokens"] == 0 + assert client.get_usage()["num_calls"] == 0 + + def test_get_usage_returns_copy(self): + from easi.llm.client import LLMClient + client = LLMClient(model="openai/test") + usage = client.get_usage() + usage["prompt_tokens"] = 999 + assert client.get_usage()["prompt_tokens"] == 0 + + +class TestLLMClientGenerate: + @patch("easi.llm.client.litellm") + def test_generate_returns_content(self, mock_litellm): + from easi.llm.client import LLMClient + + mock_choice = MagicMock() + mock_choice.message.content = '{"executable_plan": [{"action": "Stop"}]}' + mock_response = MagicMock() + mock_response.choices = [mock_choice] + mock_response.usage.prompt_tokens = 100 + mock_response.usage.completion_tokens = 20 + mock_litellm.completion.return_value = mock_response + mock_litellm.completion_cost.return_value = 0.001 + + client = LLMClient(model="openai/gpt-4o") + result = client.generate([{"role": "user", "content": "test"}]) + + assert result == '{"executable_plan": [{"action": "Stop"}]}' + mock_litellm.completion.assert_called_once() + + @patch("easi.llm.client.litellm") + def test_generate_tracks_usage(self, mock_litellm): + from easi.llm.client import LLMClient + + mock_choice = MagicMock() + mock_choice.message.content = "test" + mock_response = MagicMock() + mock_response.choices = [mock_choice] + mock_response.usage.prompt_tokens = 50 + mock_response.usage.completion_tokens = 10 + mock_litellm.completion.return_value = mock_response + mock_litellm.completion_cost.return_value = 0.002 + + client = LLMClient(model="openai/gpt-4o") + client.generate([{"role": "user", "content": "hello"}]) + client.generate([{"role": "user", "content": "world"}]) + + usage = client.get_usage() + assert usage["prompt_tokens"] == 100 + assert usage["completion_tokens"] == 20 + assert usage["num_calls"] == 2 + assert usage["cost_usd"] == pytest.approx(0.004) + + @patch("easi.llm.client.litellm") + def test_generate_passes_base_url(self, mock_litellm): + from easi.llm.client import LLMClient + + mock_choice = MagicMock() + mock_choice.message.content = "ok" + mock_response = MagicMock() + mock_response.choices = [mock_choice] + mock_response.usage.prompt_tokens = 10 + mock_response.usage.completion_tokens = 5 + mock_litellm.completion.return_value = mock_response + mock_litellm.completion_cost.return_value = 0.0 + + client = LLMClient(model="openai/test", base_url="http://localhost:8080/v1") + client.generate([{"role": "user", "content": "hi"}]) + + call_kwargs = mock_litellm.completion.call_args + assert call_kwargs.kwargs.get("api_base") == "http://localhost:8080/v1" + + +class TestLLMClientGenerateStructured: + @patch("easi.llm.client.instructor") + @patch("easi.llm.client.litellm") + def test_generate_structured_returns_model(self, mock_litellm, mock_instructor): + from easi.llm.client import LLMClient + from easi.llm.schemas import ActionPlanResponse + + expected = ActionPlanResponse( + reasoning="I see a mug", + executable_plan=[{"action": "find a Mug"}], + ) + + mock_client = MagicMock() + mock_client.chat.completions.create.return_value = expected + mock_instructor.from_litellm.return_value = mock_client + + client = LLMClient(model="openai/gpt-4o") + result = client.generate_structured( + [{"role": "user", "content": "test"}], + response_model=ActionPlanResponse, + ) + + assert isinstance(result, ActionPlanResponse) + assert result.get_actions() == ["find a Mug"] + + +class TestLLMClientProtocolCompat: + def test_satisfies_llm_client_protocol(self): + from easi.core.protocols import LLMClientProtocol + from easi.llm.client import LLMClient + + client = LLMClient(model="openai/test") + assert isinstance(client, LLMClientProtocol) From ab27e7e3c6c3e519dbc36ab294a4a2c5aea566b2 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Sat, 14 Feb 2026 22:08:26 +0800 Subject: [PATCH 010/239] [Feat] Add ServerManager for vLLM subprocess lifecycle Manages start/stop, port checking, health polling with timeout, and context manager support. Extensible for future backends. --- easi/llm/server_manager.py | 149 +++++++++++++++++++++++++++++++++++ tests/test_server_manager.py | 88 +++++++++++++++++++++ 2 files changed, 237 insertions(+) create mode 100644 easi/llm/server_manager.py create mode 100644 tests/test_server_manager.py diff --git a/easi/llm/server_manager.py b/easi/llm/server_manager.py new file mode 100644 index 0000000..b44b71a --- /dev/null +++ b/easi/llm/server_manager.py @@ -0,0 +1,149 @@ +"""Manages lifecycle of local LLM inference servers (vLLM, etc.). + +Starts the server as a subprocess, waits for health check, and stops on exit. +""" +from __future__ import annotations + +import socket +import subprocess +import sys +import time +from pathlib import Path + +import requests + +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + +_HEALTH_POLL_INTERVAL = 5.0 +_DEFAULT_STARTUP_TIMEOUT = 300.0 + + +class ServerManager: + """Manages a local inference server subprocess.""" + + def __init__( + self, + backend: str, + model: str, + port: int = 8080, + server_kwargs: dict | None = None, + startup_timeout: float = _DEFAULT_STARTUP_TIMEOUT, + log_dir: Path | None = None, + ): + self.backend = backend + self.model = model + self.port = port + self.server_kwargs = server_kwargs or {} + self.startup_timeout = startup_timeout + self.log_dir = log_dir + self._process: subprocess.Popen | None = None + + def start(self) -> str: + """Start the server, wait for health, return base_url.""" + self._check_port() + + cmd = self._build_command() + logger.info("Starting %s server: %s", self.backend, " ".join(cmd)) + + log_file = None + if self.log_dir: + self.log_dir.mkdir(parents=True, exist_ok=True) + log_path = self.log_dir / f"{self.backend}_server.log" + log_file = open(log_path, "w") + logger.info("Server logs: %s", log_path) + + self._process = subprocess.Popen( + cmd, + stdout=log_file or subprocess.DEVNULL, + stderr=log_file or subprocess.DEVNULL, + ) + + base_url = f"http://localhost:{self.port}/v1" + self._wait_for_health(base_url) + logger.info("Server ready at %s", base_url) + return base_url + + def stop(self) -> None: + """Terminate the server process.""" + if self._process is not None: + logger.info("Stopping %s server (pid=%d)", self.backend, self._process.pid) + self._process.terminate() + try: + self._process.wait(timeout=30) + except subprocess.TimeoutExpired: + logger.warning("Server did not terminate, killing...") + self._process.kill() + self._process.wait(timeout=10) + self._process = None + + def is_running(self) -> bool: + """Check if server process is alive.""" + if self._process is None: + return False + return self._process.poll() is None + + def _check_port(self) -> None: + """Raise if port is already in use.""" + sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) + try: + sock.bind(("127.0.0.1", self.port)) + except OSError: + raise RuntimeError( + f"Port {self.port} is already in use. " + f"Use --port to specify a different port, " + f"or --llm-url to connect to an existing server." + ) + finally: + sock.close() + + def _build_command(self) -> list[str]: + """Build the server launch command.""" + if self.backend == "vllm": + cmd = [ + sys.executable, "-m", "vllm.entrypoints.openai.api_server", + "--model", self.model, + "--port", str(self.port), + ] + for key, value in self.server_kwargs.items(): + flag = "--" + key.replace("_", "-") + cmd.extend([flag, str(value)]) + return cmd + else: + raise ValueError(f"Unsupported server backend: {self.backend}") + + def _wait_for_health(self, base_url: str) -> None: + """Poll /health until the server responds or timeout.""" + health_url = base_url.replace("/v1", "") + "/health" + deadline = time.monotonic() + self.startup_timeout + + while time.monotonic() < deadline: + if self._process and self._process.poll() is not None: + raise RuntimeError( + f"{self.backend} server exited with code {self._process.returncode}. " + f"Check server logs for details." + ) + try: + resp = requests.get(health_url, timeout=5) + if resp.status_code == 200: + return + except requests.ConnectionError: + pass + + time.sleep(_HEALTH_POLL_INTERVAL) + + self.stop() + log_hint = "" + if self.log_dir: + log_hint = f" Check logs at {self.log_dir / f'{self.backend}_server.log'}" + raise RuntimeError( + f"{self.backend} server failed to start within " + f"{self.startup_timeout}s.{log_hint}" + ) + + def __enter__(self) -> str: + return self.start() + + def __exit__(self, *exc) -> None: + self.stop() diff --git a/tests/test_server_manager.py b/tests/test_server_manager.py new file mode 100644 index 0000000..4adc250 --- /dev/null +++ b/tests/test_server_manager.py @@ -0,0 +1,88 @@ +"""Tests for ServerManager — vLLM lifecycle management.""" +import socket +import pytest + + +class TestServerManagerInit: + def test_stores_config(self): + from easi.llm.server_manager import ServerManager + sm = ServerManager("vllm", "Qwen/Qwen2.5-VL-72B", port=8080, + server_kwargs={"tensor_parallel_size": 4}) + assert sm.backend == "vllm" + assert sm.model == "Qwen/Qwen2.5-VL-72B" + assert sm.port == 8080 + assert sm.server_kwargs == {"tensor_parallel_size": 4} + + def test_default_port(self): + from easi.llm.server_manager import ServerManager + sm = ServerManager("vllm", "some-model") + assert sm.port == 8080 + + +class TestBuildCommand: + def test_vllm_basic(self): + from easi.llm.server_manager import ServerManager + sm = ServerManager("vllm", "Qwen/Qwen2.5-VL-72B", port=9090) + cmd = sm._build_command() + assert "-m" in cmd + assert "vllm.entrypoints.openai.api_server" in cmd + assert "--model" in cmd + assert "Qwen/Qwen2.5-VL-72B" in cmd + assert "--port" in cmd + assert "9090" in cmd + + def test_vllm_with_kwargs(self): + from easi.llm.server_manager import ServerManager + sm = ServerManager("vllm", "test-model", port=8080, + server_kwargs={"tensor_parallel_size": 4, + "gpu_memory_utilization": 0.9}) + cmd = sm._build_command() + assert "--tensor-parallel-size" in cmd + assert "4" in cmd + assert "--gpu-memory-utilization" in cmd + assert "0.9" in cmd + + def test_unsupported_backend_raises(self): + from easi.llm.server_manager import ServerManager + sm = ServerManager("unknown_backend", "test-model") + with pytest.raises(ValueError, match="Unsupported server backend"): + sm._build_command() + + +class TestPortCheck: + def test_port_available(self): + from easi.llm.server_manager import ServerManager + sm = ServerManager("vllm", "test", port=19876) + sm._check_port() # should not raise + + def test_port_taken(self): + from easi.llm.server_manager import ServerManager + sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) + sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) + sock.bind(("127.0.0.1", 19877)) + sock.listen(1) + try: + sm = ServerManager("vllm", "test", port=19877) + with pytest.raises(RuntimeError, match="already in use"): + sm._check_port() + finally: + sock.close() + + +class TestContextManager: + def test_context_manager_calls_stop(self): + from unittest.mock import MagicMock + from easi.llm.server_manager import ServerManager + sm = ServerManager("vllm", "test", port=8080) + sm.start = MagicMock(return_value="http://localhost:8080/v1") + sm.stop = MagicMock() + with sm as url: + assert url == "http://localhost:8080/v1" + sm.stop.assert_called_once() + + +class TestIsRunning: + def test_not_running_when_no_process(self): + from easi.llm.server_manager import ServerManager + sm = ServerManager("vllm", "test") + assert sm.is_running() is False From 3e7010c5cdafd4d07c1c9a041ddf9c25c6689bb9 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Sat, 14 Feb 2026 22:37:30 +0800 Subject: [PATCH 011/239] [Feat] Add --backend, --model, --port, --llm-kwargs CLI args New arguments for `easi run` to select LLM backend and configure inference server. Backward compatible with existing --llm-url. --- easi/cli.py | 19 ++++++++++-- tests/test_cli_llm_args.py | 62 ++++++++++++++++++++++++++++++++++++++ 2 files changed, 79 insertions(+), 2 deletions(-) create mode 100644 tests/test_cli_llm_args.py diff --git a/easi/cli.py b/easi/cli.py index 768a78e..77c3e16 100644 --- a/easi/cli.py +++ b/easi/cli.py @@ -87,6 +87,15 @@ def build_parser() -> argparse.ArgumentParser: run_parser.add_argument("--max-episodes", type=int, default=None) run_parser.add_argument("--llm-url", type=str, default=None, help="LLM server URL") run_parser.add_argument("--seed", type=int, default=None) + # New LLM backend args + run_parser.add_argument("--backend", type=str, default=None, + help="LLM backend: vllm, openai, anthropic, gemini, dummy") + run_parser.add_argument("--model", type=str, default="default", + help="Model name (HF path for vLLM, API name for proprietary)") + run_parser.add_argument("--port", type=int, default=8080, + help="Port for local inference server (default: 8080)") + run_parser.add_argument("--llm-kwargs", type=str, default=None, + help='JSON string of extra kwargs, e.g. \'{"tensor_parallel_size": 4}\'') # --- llm-server command --- llm_parser = subparsers.add_parser("llm-server", help="Start dummy LLM server", parents=[common]) @@ -276,7 +285,8 @@ def cmd_sim_test(simulator: str, steps: int, timeout: float) -> None: sys.exit(1) -def cmd_run(task_name, agent_type, output_dir, data_dir, max_episodes, llm_url, seed): +def cmd_run(task_name, agent_type, output_dir, data_dir, max_episodes, + llm_url, seed, backend, model, port, llm_kwargs_raw): from easi.evaluation.runner import EvaluationRunner runner = EvaluationRunner( @@ -286,6 +296,10 @@ def cmd_run(task_name, agent_type, output_dir, data_dir, max_episodes, llm_url, data_dir=data_dir, llm_base_url=llm_url, agent_seed=seed, + backend=backend, + model=model, + port=port, + llm_kwargs_raw=llm_kwargs_raw, ) results = runner.run(max_episodes=max_episodes) logger.info("Completed %d episodes.", len(results)) @@ -345,7 +359,8 @@ def main() -> None: elif args.command == "run": cmd_run(args.task, args.agent, args.output_dir, args.data_dir, - args.max_episodes, args.llm_url, args.seed) + args.max_episodes, args.llm_url, args.seed, + args.backend, args.model, args.port, args.llm_kwargs) elif args.command == "llm-server": cmd_llm_server(args.host, args.port, args.mode, args.action_space) diff --git a/tests/test_cli_llm_args.py b/tests/test_cli_llm_args.py new file mode 100644 index 0000000..d68b37b --- /dev/null +++ b/tests/test_cli_llm_args.py @@ -0,0 +1,62 @@ +# tests/test_cli_llm_args.py +"""Tests for CLI LLM-related arguments.""" +import pytest + + +class TestBuildParser: + def test_run_has_backend_arg(self): + from easi.cli import build_parser + parser = build_parser() + args = parser.parse_args(["run", "dummy_task", "--backend", "vllm"]) + assert args.backend == "vllm" + + def test_run_backend_default_is_none(self): + from easi.cli import build_parser + parser = build_parser() + args = parser.parse_args(["run", "dummy_task"]) + assert args.backend is None + + def test_run_has_model_arg(self): + from easi.cli import build_parser + parser = build_parser() + args = parser.parse_args(["run", "dummy_task", "--model", "gpt-4o"]) + assert args.model == "gpt-4o" + + def test_run_model_default(self): + from easi.cli import build_parser + parser = build_parser() + args = parser.parse_args(["run", "dummy_task"]) + assert args.model == "default" + + def test_run_has_port_arg(self): + from easi.cli import build_parser + parser = build_parser() + args = parser.parse_args(["run", "dummy_task", "--port", "9090"]) + assert args.port == 9090 + + def test_run_port_default(self): + from easi.cli import build_parser + parser = build_parser() + args = parser.parse_args(["run", "dummy_task"]) + assert args.port == 8080 + + def test_run_has_llm_kwargs(self): + from easi.cli import build_parser + parser = build_parser() + args = parser.parse_args([ + "run", "dummy_task", + "--llm-kwargs", '{"tensor_parallel_size": 4}', + ]) + assert args.llm_kwargs == '{"tensor_parallel_size": 4}' + + def test_run_llm_kwargs_default_none(self): + from easi.cli import build_parser + parser = build_parser() + args = parser.parse_args(["run", "dummy_task"]) + assert args.llm_kwargs is None + + def test_backward_compat_llm_url_still_works(self): + from easi.cli import build_parser + parser = build_parser() + args = parser.parse_args(["run", "dummy_task", "--llm-url", "http://localhost:8000"]) + assert args.llm_url == "http://localhost:8000" From 78568039d38561cb9f2c743eb13423dec00f72a8 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Sat, 14 Feb 2026 22:37:55 +0800 Subject: [PATCH 012/239] [Feat] Integrate LLMClient + ServerManager into EvaluationRunner Runner now resolves backend, auto-starts vLLM when needed, creates LLMClient for non-legacy backends, wraps structured output, and tracks LLM usage per-episode and per-run. --- easi/evaluation/runner.py | 148 ++++++++++++++++++++++++--- tests/test_runner_llm_integration.py | 74 ++++++++++++++ 2 files changed, 205 insertions(+), 17 deletions(-) create mode 100644 tests/test_runner_llm_integration.py diff --git a/easi/evaluation/runner.py b/easi/evaluation/runner.py index 47a1fee..8412e06 100644 --- a/easi/evaluation/runner.py +++ b/easi/evaluation/runner.py @@ -52,6 +52,10 @@ def __init__( data_dir: Path | str = "./datasets", llm_base_url: str | None = None, agent_seed: int | None = None, + backend: str | None = None, + model: str = "default", + port: int = 8080, + llm_kwargs_raw: str | None = None, ): self.task_name = task_name self.agent_type = agent_type @@ -59,11 +63,36 @@ def __init__( self.data_dir = Path(data_dir) self.llm_base_url = llm_base_url self.agent_seed = agent_seed + self.backend = backend + self.model = model + self.port = port + self.llm_kwargs_raw = llm_kwargs_raw self.run_id = datetime.now().strftime("%Y%m%d_%H%M%S") + def _resolve_llm_backend(self) -> tuple[str | None, str | None]: + """Resolve which LLM backend to use. + + Returns (backend, base_url): + - (None, None) for dummy agent + - ("legacy", url) for --llm-url without --backend + - (backend_name, url_or_none) for --backend + """ + if self.agent_type == "dummy": + return None, None + + if self.backend: + return self.backend, self.llm_base_url + + if self.llm_base_url: + return "legacy", self.llm_base_url + + raise ValueError( + f"Agent '{self.agent_type}' requires --backend or --llm-url. " + f"Use --backend vllm|openai|anthropic|gemini or --llm-url ." + ) + def run(self, max_episodes: int | None = None) -> list[dict]: """Run evaluation and return per-episode metric dicts.""" - # Create structured output directory run_dir = self.output_dir / self.task_name / self.run_id episodes_dir = run_dir / "episodes" episodes_dir.mkdir(parents=True, exist_ok=True) @@ -74,6 +103,21 @@ def run(self, max_episodes: int | None = None) -> list[dict]: if max_episodes is not None: episodes = episodes[:max_episodes] + # 2. Resolve LLM backend and optionally start server + backend, base_url = self._resolve_llm_backend() + server = None + if backend == "vllm" and base_url is None: + from easi.llm.server_manager import ServerManager + from easi.llm.utils import parse_llm_kwargs, split_kwargs + + all_kwargs = parse_llm_kwargs(self.llm_kwargs_raw) + server_kwargs, _ = split_kwargs(all_kwargs) + server = ServerManager( + "vllm", self.model, port=self.port, + server_kwargs=server_kwargs, log_dir=run_dir, + ) + base_url = server.start() + # Save run config config = { "task_name": self.task_name, @@ -82,14 +126,17 @@ def run(self, max_episodes: int | None = None) -> list[dict]: "run_id": self.run_id, "max_episodes": max_episodes, "total_episodes": len(episodes), + "backend": backend, + "model": self.model, } (run_dir / "config.json").write_text(json.dumps(config, indent=2)) - # 2. Create agent - agent = self._create_agent(task.action_space, task._config) + # 3. Create agent + agent = self._create_agent(task.action_space, task._config, + backend=backend, base_url=base_url) - # 3. Start simulator - sim, runner = self._create_simulator(task.simulator_key, task=task) + # 4. Start simulator + sim, sim_runner = self._create_simulator(task.simulator_key, task=task) all_results = [] try: @@ -99,9 +146,7 @@ def run(self, max_episodes: int | None = None) -> list[dict]: "Episode %d/%d: %s", i + 1, len(episodes), episode_id, ) - # Create episode output directory - safe_id = _sanitize_dirname(episode_id) - episode_dir = episodes_dir / f"{i:03d}_{safe_id}" + episode_dir = episodes_dir / f"{i:03d}_{_sanitize_dirname(episode_id)}" episode_dir.mkdir(exist_ok=True) result = self._run_episode( @@ -109,16 +154,21 @@ def run(self, max_episodes: int | None = None) -> list[dict]: ) all_results.append(result) - # Save per-episode result (episode_dir / "result.json").write_text( json.dumps(result, indent=2) ) finally: sim.close() + if server: + server.stop() - # 4. Aggregate and save summary + # 5. Aggregate and save summary summary = aggregate_metrics(all_results) + if backend and backend != "legacy": + summary["llm_usage"] = self._aggregate_llm_usage(all_results) + summary["model"] = self.model + summary["backend"] = backend (run_dir / "summary.json").write_text(json.dumps(summary, indent=2)) logger.info("Results saved to: %s", run_dir) logger.info("Summary: %s", summary) @@ -195,6 +245,11 @@ def _run_episode( metrics["episode_id"] = episode_id metrics["elapsed_seconds"] = round(elapsed, 2) + # Snapshot LLM usage for this episode + if hasattr(agent, 'llm_client') and hasattr(agent.llm_client, 'get_usage'): + metrics["llm_usage"] = agent.llm_client.get_usage() + agent.llm_client.reset_usage() + return metrics @staticmethod @@ -203,6 +258,28 @@ def _write_trajectory_entry(path: Path, entry: dict) -> None: with path.open("a") as f: f.write(json.dumps(entry) + "\n") + @staticmethod + def _aggregate_llm_usage(results: list[dict]) -> dict: + """Sum up llm_usage from per-episode results.""" + total = { + "total_calls": 0, + "total_prompt_tokens": 0, + "total_completion_tokens": 0, + "total_tokens": 0, + "total_cost_usd": 0.0, + } + for r in results: + usage = r.get("llm_usage", {}) + total["total_calls"] += usage.get("num_calls", 0) + total["total_prompt_tokens"] += usage.get("prompt_tokens", 0) + total["total_completion_tokens"] += usage.get("completion_tokens", 0) + total["total_cost_usd"] += usage.get("cost_usd", 0.0) + total["total_tokens"] = total["total_prompt_tokens"] + total["total_completion_tokens"] + n = len(results) or 1 + total["avg_prompt_tokens_per_episode"] = round(total["total_prompt_tokens"] / n) + total["avg_cost_per_episode_usd"] = round(total["total_cost_usd"] / n, 6) + return total + def _create_task(self): from easi.tasks.registry import get_task_entry, load_task_class @@ -213,22 +290,59 @@ def _create_task(self): data_dir=self.data_dir, ) - def _create_agent(self, action_space: list[str], task_config: dict): + def _create_agent(self, action_space: list[str], task_config: dict, + backend: str | None = None, base_url: str | None = None): from easi.utils.import_utils import import_class if self.agent_type == "dummy": from easi.agents.dummy_agent import DummyAgent - return DummyAgent(action_space=action_space, seed=self.agent_seed) + elif self.agent_type == "react": from easi.agents.react_agent import ReActAgent - from easi.llm.api_client import LLMApiClient - llm = LLMApiClient( - base_url=self.llm_base_url or "http://127.0.0.1:8000" - ) + # Create LLM client based on backend + if backend and backend != "legacy": + from easi.llm.client import LLMClient + from easi.llm.utils import ( + build_litellm_model, parse_llm_kwargs, + split_kwargs, validate_backend, + ) + + validate_backend(backend) + litellm_model = build_litellm_model(backend, self.model) + all_kwargs = parse_llm_kwargs(self.llm_kwargs_raw) + _, client_kwargs = split_kwargs(all_kwargs) + + llm = LLMClient( + model=litellm_model, + base_url=base_url, + **client_kwargs, + ) + + # Wrap for structured output if configured + agent_config = task_config.get("agent", {}) + schema_class_name = agent_config.get("response_schema") + if schema_class_name: + import json as _json + SchemaClass = import_class(schema_class_name) + + def _structured_generate(messages): + result = llm.generate_structured(messages, response_model=SchemaClass) + actions = result.get_actions() + return _json.dumps({ + "executable_plan": [{"action": a} for a in actions] + }) + + llm.generate = _structured_generate + else: + # Legacy path: existing LLMApiClient + from easi.llm.api_client import LLMApiClient + llm = LLMApiClient( + base_url=base_url or "http://127.0.0.1:8000" + ) - # Load task-specific prompt builder if configured in yaml + # Load task-specific prompt builder prompt_builder = None agent_config = task_config.get("agent", {}) builder_class_name = agent_config.get("prompt_builder") diff --git a/tests/test_runner_llm_integration.py b/tests/test_runner_llm_integration.py new file mode 100644 index 0000000..6dec959 --- /dev/null +++ b/tests/test_runner_llm_integration.py @@ -0,0 +1,74 @@ +# tests/test_runner_llm_integration.py +"""Tests for EvaluationRunner LLM integration.""" +import pytest + + +class TestResolveBackend: + def test_backend_set_returns_backend(self): + from easi.evaluation.runner import EvaluationRunner + runner = EvaluationRunner("dummy_task", agent_type="react", + backend="openai", model="gpt-4o") + backend, url = runner._resolve_llm_backend() + assert backend == "openai" + + def test_llm_url_without_backend_returns_legacy(self): + from easi.evaluation.runner import EvaluationRunner + runner = EvaluationRunner("dummy_task", agent_type="react", + llm_base_url="http://localhost:8000") + backend, url = runner._resolve_llm_backend() + assert backend == "legacy" + assert url == "http://localhost:8000" + + def test_neither_set_for_react_raises(self): + from easi.evaluation.runner import EvaluationRunner + runner = EvaluationRunner("dummy_task", agent_type="react") + with pytest.raises(ValueError, match="requires --backend or --llm-url"): + runner._resolve_llm_backend() + + def test_dummy_agent_returns_none(self): + from easi.evaluation.runner import EvaluationRunner + runner = EvaluationRunner("dummy_task", agent_type="dummy") + backend, url = runner._resolve_llm_backend() + assert backend is None + + def test_backend_vllm_with_llm_url_skips_server(self): + from easi.evaluation.runner import EvaluationRunner + runner = EvaluationRunner("dummy_task", agent_type="react", + backend="vllm", model="test", + llm_base_url="http://localhost:9090/v1") + backend, url = runner._resolve_llm_backend() + assert backend == "vllm" + assert url == "http://localhost:9090/v1" + + +class TestAggregateLlmUsage: + def test_aggregate_empty(self): + from easi.evaluation.runner import EvaluationRunner + result = EvaluationRunner._aggregate_llm_usage([]) + assert result["total_calls"] == 0 + assert result["total_tokens"] == 0 + + def test_aggregate_single_episode(self): + from easi.evaluation.runner import EvaluationRunner + results = [ + {"llm_usage": {"num_calls": 4, "prompt_tokens": 800, "completion_tokens": 200, "cost_usd": 0.01}} + ] + agg = EvaluationRunner._aggregate_llm_usage(results) + assert agg["total_calls"] == 4 + assert agg["total_prompt_tokens"] == 800 + assert agg["total_completion_tokens"] == 200 + assert agg["total_tokens"] == 1000 + assert agg["total_cost_usd"] == 0.01 + + def test_aggregate_multiple_episodes(self): + from easi.evaluation.runner import EvaluationRunner + results = [ + {"llm_usage": {"num_calls": 3, "prompt_tokens": 500, "completion_tokens": 100, "cost_usd": 0.005}}, + {"llm_usage": {"num_calls": 5, "prompt_tokens": 700, "completion_tokens": 150, "cost_usd": 0.008}}, + ] + agg = EvaluationRunner._aggregate_llm_usage(results) + assert agg["total_calls"] == 8 + assert agg["total_prompt_tokens"] == 1200 + assert agg["total_completion_tokens"] == 250 + assert agg["total_tokens"] == 1450 + assert agg["avg_prompt_tokens_per_episode"] == 600 From 7231f0bcc78606dc9b7cf77d16b1091ba8afe81d Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Sat, 14 Feb 2026 22:38:54 +0800 Subject: [PATCH 013/239] [Feat] Add [llm] optional dependency group (litellm + instructor) --- pyproject.toml | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/pyproject.toml b/pyproject.toml index 0707c92..50a23a8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -20,6 +20,10 @@ dev = [ "pytest>=7.0", "pytest-timeout>=2.0", ] +llm = [ + "litellm>=1.40", + "instructor>=1.0", +] [project.scripts] easi = "easi.cli:main" From e7f73c12d34cf5a77d09f5f7c8b56e44e76d36e7 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Sat, 14 Feb 2026 22:42:49 +0800 Subject: [PATCH 014/239] [Test] Add import smoke tests and full LLM pipeline integration test --- tests/test_llm_full_flow.py | 68 +++++++++++++++++++++++++++++++++++++ tests/test_llm_imports.py | 29 ++++++++++++++++ 2 files changed, 97 insertions(+) create mode 100644 tests/test_llm_full_flow.py create mode 100644 tests/test_llm_imports.py diff --git a/tests/test_llm_full_flow.py b/tests/test_llm_full_flow.py new file mode 100644 index 0000000..28031ce --- /dev/null +++ b/tests/test_llm_full_flow.py @@ -0,0 +1,68 @@ +"""Full integration test for LLM inference pipeline.""" +import json +import pytest +from unittest.mock import MagicMock, patch + + +class TestFullFlow: + """Test: CLI args -> parse kwargs -> build model -> create client -> generate -> track usage.""" + + def test_parse_kwargs_to_split_to_client(self): + from easi.llm.utils import parse_llm_kwargs, split_kwargs, build_litellm_model + + raw = '{"tensor_parallel_size": 4, "temperature": 0.7, "max_tokens": 1024}' + kwargs = parse_llm_kwargs(raw) + server_kw, client_kw = split_kwargs(kwargs) + model = build_litellm_model("vllm", "Qwen/Qwen2.5-VL-72B") + + assert server_kw == {"tensor_parallel_size": 4} + assert client_kw == {"temperature": 0.7, "max_tokens": 1024} + assert model == "openai/Qwen/Qwen2.5-VL-72B" + + @patch("easi.llm.client.litellm") + def test_client_generate_and_track(self, mock_litellm): + from easi.llm.client import LLMClient + + mock_choice = MagicMock() + mock_choice.message.content = '{"executable_plan": [{"action": "Stop"}]}' + mock_response = MagicMock() + mock_response.choices = [mock_choice] + mock_response.usage.prompt_tokens = 200 + mock_response.usage.completion_tokens = 30 + mock_litellm.completion.return_value = mock_response + mock_litellm.completion_cost.return_value = 0.005 + + client = LLMClient(model="openai/gpt-4o", temperature=0.7) + + # Simulate 3 LLM calls (one episode) + for _ in range(3): + client.generate([{"role": "user", "content": "test"}]) + + usage = client.get_usage() + assert usage["num_calls"] == 3 + assert usage["prompt_tokens"] == 600 + assert usage["completion_tokens"] == 90 + assert usage["cost_usd"] == pytest.approx(0.015) + + # Reset for next episode + client.reset_usage() + assert client.get_usage()["num_calls"] == 0 + + def test_schema_normalization(self): + from easi.llm.schemas import ActionPlanResponse + + resp = ActionPlanResponse( + reasoning="I should find the apple", + executable_plan=[ + {"action": "find a Apple"}, + {"action": "pick up the Apple"}, + {"action": "clean the Apple"}, + ], + ) + actions = resp.get_actions() + normalized = json.dumps({ + "executable_plan": [{"action": a} for a in actions] + }) + data = json.loads(normalized) + assert len(data["executable_plan"]) == 3 + assert data["executable_plan"][0]["action"] == "find a Apple" diff --git a/tests/test_llm_imports.py b/tests/test_llm_imports.py new file mode 100644 index 0000000..3ccb602 --- /dev/null +++ b/tests/test_llm_imports.py @@ -0,0 +1,29 @@ +"""Tests for easi.llm public API imports.""" + + +class TestLlmImports: + def test_import_llm_client(self): + from easi.llm.client import LLMClient + assert LLMClient is not None + + def test_import_schemas(self): + from easi.llm.schemas import BaseResponseSchema, ActionPlanResponse, ExecutableAction + assert BaseResponseSchema is not None + assert ActionPlanResponse is not None + assert ExecutableAction is not None + + def test_import_server_manager(self): + from easi.llm.server_manager import ServerManager + assert ServerManager is not None + + def test_import_utils(self): + from easi.llm.utils import parse_llm_kwargs, split_kwargs, build_litellm_model, validate_backend + assert parse_llm_kwargs is not None + + def test_legacy_api_client_still_importable(self): + from easi.llm.api_client import LLMApiClient + assert LLMApiClient is not None + + def test_legacy_dummy_server_still_importable(self): + from easi.llm.dummy_server import run_server + assert run_server is not None From cb972eb4453a86e22766b56899897662bde694ff Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Sat, 14 Feb 2026 23:11:02 +0800 Subject: [PATCH 015/239] [Fix] Address code review findings for LLM inference pipeline - Track usage in generate_structured() via instructor's _raw_response - Fix log file handle leak in ServerManager (store and close in stop()) - Remove duplicate agent_config computation in runner._create_agent() --- easi/evaluation/runner.py | 4 ++-- easi/llm/client.py | 9 +++++++++ easi/llm/server_manager.py | 11 +++++++---- 3 files changed, 18 insertions(+), 6 deletions(-) diff --git a/easi/evaluation/runner.py b/easi/evaluation/runner.py index 8412e06..176c82d 100644 --- a/easi/evaluation/runner.py +++ b/easi/evaluation/runner.py @@ -301,6 +301,8 @@ def _create_agent(self, action_space: list[str], task_config: dict, elif self.agent_type == "react": from easi.agents.react_agent import ReActAgent + agent_config = task_config.get("agent", {}) + # Create LLM client based on backend if backend and backend != "legacy": from easi.llm.client import LLMClient @@ -321,7 +323,6 @@ def _create_agent(self, action_space: list[str], task_config: dict, ) # Wrap for structured output if configured - agent_config = task_config.get("agent", {}) schema_class_name = agent_config.get("response_schema") if schema_class_name: import json as _json @@ -344,7 +345,6 @@ def _structured_generate(messages): # Load task-specific prompt builder prompt_builder = None - agent_config = task_config.get("agent", {}) builder_class_name = agent_config.get("prompt_builder") if builder_class_name: BuilderClass = import_class(builder_class_name) diff --git a/easi/llm/client.py b/easi/llm/client.py index 50f223b..2c371fa 100644 --- a/easi/llm/client.py +++ b/easi/llm/client.py @@ -103,6 +103,15 @@ def generate_structured( self.model, response_model.__name__, ) result = client.chat.completions.create(**call_kwargs) + + # Track usage from the raw response attached by instructor + raw = getattr(result, "_raw_response", None) + if raw is not None: + self._track_usage(raw) + else: + # Fallback: at minimum count the call + self._usage["num_calls"] += 1 + return result def get_usage(self) -> dict: diff --git a/easi/llm/server_manager.py b/easi/llm/server_manager.py index b44b71a..d5a62ab 100644 --- a/easi/llm/server_manager.py +++ b/easi/llm/server_manager.py @@ -39,6 +39,7 @@ def __init__( self.startup_timeout = startup_timeout self.log_dir = log_dir self._process: subprocess.Popen | None = None + self._log_file = None def start(self) -> str: """Start the server, wait for health, return base_url.""" @@ -47,17 +48,16 @@ def start(self) -> str: cmd = self._build_command() logger.info("Starting %s server: %s", self.backend, " ".join(cmd)) - log_file = None if self.log_dir: self.log_dir.mkdir(parents=True, exist_ok=True) log_path = self.log_dir / f"{self.backend}_server.log" - log_file = open(log_path, "w") + self._log_file = open(log_path, "w") logger.info("Server logs: %s", log_path) self._process = subprocess.Popen( cmd, - stdout=log_file or subprocess.DEVNULL, - stderr=log_file or subprocess.DEVNULL, + stdout=self._log_file or subprocess.DEVNULL, + stderr=self._log_file or subprocess.DEVNULL, ) base_url = f"http://localhost:{self.port}/v1" @@ -77,6 +77,9 @@ def stop(self) -> None: self._process.kill() self._process.wait(timeout=10) self._process = None + if self._log_file is not None: + self._log_file.close() + self._log_file = None def is_running(self) -> bool: """Check if server process is alive.""" From c57b0543c114a1824cd55b3224d55c13348f53b5 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Sun, 15 Feb 2026 03:29:40 +0800 Subject: [PATCH 016/239] [Refactor] Introduce AgentMemory architecture + chat_history=True support Replace the tightly-coupled agent/prompt design with a memory-based architecture where AgentMemory holds shared state, PromptBuilder reads from memory to construct prompts and parse responses, and the agent is a thin orchestrator. Key changes: - AgentMemory + StepRecord dataclasses as shared agent state - New PromptBuilderProtocol: build_messages(memory) + parse_response(response, memory) - Simplified BaseAgent (removed _chat_history, abstract stubs, default act()) - ReActAgent rewritten as thin orchestrator delegating to builder - EBAlfredPromptBuilder gains chat_history=True mode with VLMPlanner parity - json_repair moved to easi/utils/ (old location re-exports) - Removed stateless flag from agent config (builder controls mode) --- easi/agents/dummy_agent.py | 29 +- easi/agents/prompt_builder.py | 127 ++--- easi/agents/react_agent.py | 170 ++---- easi/core/base_agent.py | 92 +--- easi/core/memory.py | 68 +++ easi/core/protocols.py | 3 - easi/evaluation/runner.py | 9 +- easi/tasks/ebalfred/ebalfred_base.yaml | 4 + .../tasks/ebalfred/ebalfred_common_sense.yaml | 4 + .../ebalfred_complex_instruction.yaml | 4 + .../tasks/ebalfred/ebalfred_long_horizon.yaml | 4 + easi/tasks/ebalfred/ebalfred_spatial.yaml | 4 + .../ebalfred/ebalfred_visual_appearance.yaml | 4 + easi/tasks/ebalfred/json_repair.py | 2 + easi/tasks/ebalfred/prompts.py | 437 ++++++++++++--- easi/utils/json_repair.py | 40 ++ tests/test_agent_memory.py | 92 ++++ tests/test_dummy_agent.py | 24 +- tests/test_ebalfred_task.py | 44 +- tests/test_json_repair.py | 76 +++ tests/test_prompt_parity.py | 510 ++++++++++++++++++ tests/test_react_agent.py | 153 +++--- 22 files changed, 1425 insertions(+), 475 deletions(-) create mode 100644 easi/core/memory.py create mode 100644 easi/tasks/ebalfred/json_repair.py create mode 100644 easi/utils/json_repair.py create mode 100644 tests/test_agent_memory.py create mode 100644 tests/test_json_repair.py create mode 100644 tests/test_prompt_parity.py diff --git a/easi/agents/dummy_agent.py b/easi/agents/dummy_agent.py index 2582b4c..fcdb2f4 100644 --- a/easi/agents/dummy_agent.py +++ b/easi/agents/dummy_agent.py @@ -1,5 +1,4 @@ """Dummy agent for testing — returns random actions without calling an LLM.""" - from __future__ import annotations import random @@ -19,31 +18,7 @@ def __init__(self, action_space: list[str], seed: int | None = None): super().__init__(llm_client=None, action_space=action_space) self._rng = random.Random(seed) - def _build_system_prompt(self, task_description: str) -> str: - return "" # no system prompt needed - - def _build_step_prompt(self, observation: Observation) -> str: - return "Choose an action." - - def _parse_action(self, llm_response: str) -> Action: - """Pick a random action from the action space.""" - action_name = self._rng.choice(self.action_space) - return Action(action_name=action_name) - def act(self, observation: Observation, task_description: str) -> Action: - """Override to skip LLM call entirely.""" + """Pick a random action from the action space.""" self._step_count += 1 - - action = self._parse_action("") - - # Still maintain chat history for compatibility - self._chat_history.append({ - "role": "user", - "content": f"Step {self._step_count}. Observation at {observation.rgb_path}", - }) - self._chat_history.append({ - "role": "assistant", - "content": f"Action: {action.action_name}", - }) - - return action + return Action(action_name=self._rng.choice(self.action_space)) diff --git a/easi/agents/prompt_builder.py b/easi/agents/prompt_builder.py index 12551ef..0c36a91 100644 --- a/easi/agents/prompt_builder.py +++ b/easi/agents/prompt_builder.py @@ -1,18 +1,18 @@ """PromptBuilder protocol and default implementation. -Decision #10: Both methods return OpenAI message format (list[dict]). -This supports interleaved text+image content for vision models, -and also works for text-only models (content is just a string). - -Contributors adding a new task only need to implement these 2 methods. +Both protocol methods receive AgentMemory as their state source. +Contributors adding a new task only need to implement build_messages +and parse_response. """ from __future__ import annotations import base64 +import json from pathlib import Path from typing import Protocol, runtime_checkable -from easi.core.episode import Observation +from easi.core.episode import Action, Observation +from easi.core.memory import AgentMemory from easi.utils.logging import get_logger logger = get_logger(__name__) @@ -33,48 +33,32 @@ def _encode_image_base64(image_path: str) -> str | None: return f"data:{mime};base64,{data}" +def validate_action_name(action_name: str, action_space: list[str]) -> str | None: + """Validate action name against action_space. Returns canonical name or None.""" + if action_name in action_space: + return action_name + # Case-insensitive fallback + for valid in action_space: + if valid.lower() == action_name.lower(): + return valid + return None + + @runtime_checkable class PromptBuilderProtocol(Protocol): """Interface for task-specific prompt construction. - Both methods return OpenAI message format: list[dict]. - Each dict has "role" and "content" keys. - Content can be a string (text-only) or a list of content parts - (interleaved text + image_url for vision models). - Implementations are referenced in task.yaml via: agent: prompt_builder: "easi.tasks.my_task.prompts.MyPromptBuilder" """ - def build_system_prompt( - self, - action_space: list[str], - task_description: str, - ) -> list[dict]: - """Build system message(s). - - Returns: - List of OpenAI message dicts, e.g.: - [{"role": "system", "content": "You are an agent..."}] - """ + def build_messages(self, memory: AgentMemory) -> list[dict]: + """Build COMPLETE message list to send to LLM.""" ... - def build_step_prompt( - self, - observation: Observation, - task_description: str, - action_history: list[tuple[str, str]], - ) -> list[dict]: - """Build user message(s) for a single step, including observation image. - - Returns: - List of OpenAI message dicts with interleaved text+image, e.g.: - [{"role": "user", "content": [ - {"type": "text", "text": "Task: ..."}, - {"type": "image_url", "image_url": {"url": "data:image/png;base64,..."}} - ]}] - """ + def parse_response(self, llm_response: str, memory: AgentMemory) -> list[Action]: + """Parse LLM response into validated actions.""" ... @@ -117,49 +101,70 @@ class DefaultPromptBuilder: Based on the current observation image, decide your next action(s). Respond with valid JSON.""" - def build_system_prompt( - self, - action_space: list[str], - task_description: str, - ) -> list[dict]: + def build_messages(self, memory: AgentMemory) -> list[dict]: + """Build complete message list from memory state.""" + messages: list[dict] = [] + + # System message action_list = "\n".join( - f" {i}. {name}" for i, name in enumerate(action_space) + f" {i}. {name}" for i, name in enumerate(memory.action_space) ) - text = self.SYSTEM_TEMPLATE.format( + system_text = self.SYSTEM_TEMPLATE.format( action_list=action_list, - task_description=task_description, + task_description=memory.task_description, ) - return [{"role": "system", "content": text}] - - def build_step_prompt( - self, - observation: Observation, - task_description: str, - action_history: list[tuple[str, str]], - ) -> list[dict]: + messages.append({"role": "system", "content": system_text}) + + # User message with observation + action_history = memory.action_history if action_history: history_lines = [] for i, (action_name, feedback) in enumerate(action_history): - history_lines.append(f" Step {i+1}: {action_name} → {feedback}") + history_lines.append(f" Step {i+1}: {action_name} -> {feedback}") history_section = "## Action History\n" + "\n".join(history_lines) else: history_section = "This is the first step." text = self.STEP_TEMPLATE.format( - task_description=task_description, + task_description=memory.task_description, history_section=history_section, ) - # Build interleaved content parts content_parts: list[dict] = [{"type": "text", "text": text}] - - # Add observation image if available - if observation.rgb_path: - image_url = _encode_image_base64(observation.rgb_path) + if memory.current_observation and memory.current_observation.rgb_path: + image_url = _encode_image_base64(memory.current_observation.rgb_path) if image_url: content_parts.append({ "type": "image_url", "image_url": {"url": image_url}, }) - return [{"role": "user", "content": content_parts}] + messages.append({"role": "user", "content": content_parts}) + return messages + + def parse_response(self, llm_response: str, memory: AgentMemory) -> list[Action]: + """Parse JSON response into validated actions.""" + try: + data = json.loads(llm_response) + except json.JSONDecodeError as e: + logger.warning("Failed to parse LLM response as JSON: %s", e) + return [] + + plan = data.get("executable_plan", []) + if not isinstance(plan, list) or not plan: + logger.warning("No executable_plan in LLM response") + return [] + + actions = [] + for entry in plan: + if not isinstance(entry, dict): + continue + action_name = entry.get("action", "") + validated = validate_action_name(action_name, memory.action_space) + if validated: + actions.append(Action(action_name=validated)) + else: + logger.warning("Skipping invalid action: '%s'", action_name) + break + + return actions diff --git a/easi/agents/react_agent.py b/easi/agents/react_agent.py index 1099b3b..b4c4f2d 100644 --- a/easi/agents/react_agent.py +++ b/easi/agents/react_agent.py @@ -1,20 +1,15 @@ """ReAct agent with multi-action buffering and PromptBuilder delegation. -Decision #9: LLM returns executable_plan as a list of actions. The agent -buffers all actions, pops one per act() call. While buffer is non-empty, -act() returns next buffered action without calling LLM. On action failure -(reported via add_feedback), buffer is cleared and next act() re-queries LLM. - -Decision #10: PromptBuilder returns OpenAI message format (list[dict]). -The agent appends these directly to chat_history and passes to LLM. +The agent is a thin orchestrator: it populates AgentMemory, delegates +prompt construction and response parsing to the PromptBuilder, and +manages action buffering. """ from __future__ import annotations -import json - from easi.agents.prompt_builder import DefaultPromptBuilder, PromptBuilderProtocol from easi.core.base_agent import BaseAgent from easi.core.episode import Action, Observation +from easi.core.memory import AgentMemory from easi.utils.logging import get_logger logger = get_logger(__name__) @@ -24,145 +19,84 @@ class ReActAgent(BaseAgent): """ReAct agent with action buffering and pluggable prompt building. Flow per LLM call: - 1. PromptBuilder constructs OpenAI messages (with image) - 2. LLM returns JSON with executable_plan: [{action: ...}, ...] - 3. Agent validates ALL actions, buffers valid ones - 4. Returns first action; subsequent act() calls pop from buffer - 5. On failure feedback -> clear buffer -> next act() re-queries LLM + 1. PromptBuilder constructs messages from AgentMemory + 2. LLM returns response text + 3. PromptBuilder parses response into validated Actions + 4. Agent buffers actions, returns first + 5. Subsequent act() calls pop from buffer without LLM call + 6. On failure feedback -> clear buffer -> next act() re-queries LLM """ def __init__( self, llm_client, - action_space: list[str], + action_space: list[str] | None = None, prompt_builder: PromptBuilderProtocol | None = None, ): - super().__init__(llm_client=llm_client, action_space=action_space) + super().__init__(llm_client=llm_client, action_space=action_space or []) self.prompt_builder: PromptBuilderProtocol = prompt_builder or DefaultPromptBuilder() + self.memory = AgentMemory(action_space=self.action_space) self._action_buffer: list[Action] = [] - self._action_feedback: list[tuple[str, str]] = [] - self._task_description: str = "" def reset(self) -> None: super().reset() - self._action_buffer = [] - self._action_feedback = [] - self._task_description = "" + self.memory.clear() + self._action_buffer.clear() + + def update_action_space(self, action_space: list[str]) -> None: + """Update the action space (e.g., after dynamic expansion per episode).""" + self.action_space = action_space + self.memory.action_space = action_space + if hasattr(self.prompt_builder, 'set_action_space'): + self.prompt_builder.set_action_space(action_space) def act(self, observation: Observation, task_description: str) -> Action: """Return the next action. If buffer has pending actions, pop and return (no LLM call). - Otherwise, call LLM, parse executable_plan, buffer all actions, - return the first one. + Otherwise, call LLM, parse response via builder, buffer actions. """ - # Decision #9: return buffered action if available + # Buffered action path if self._action_buffer: - return self._action_buffer.pop(0) - - # Store task description for step prompts - self._task_description = task_description - - # Build system prompt on first call - if not self._chat_history: - system_messages = self.prompt_builder.build_system_prompt( - action_space=self.action_space, - task_description=task_description, - ) - self._chat_history.extend(system_messages) - - # Build step prompt (OpenAI message format with image) - step_messages = self.prompt_builder.build_step_prompt( - observation=observation, - task_description=task_description, - action_history=self._action_feedback, - ) - self._chat_history.extend(step_messages) - - # Call LLM - response = self.llm_client.generate(self._chat_history) - self._chat_history.append({"role": "assistant", "content": response}) + action = self._action_buffer.pop(0) + self.memory.record_step(observation, action, llm_response=None) + return action - self._step_count += 1 + # LLM call path + self.memory.current_observation = observation + self.memory.task_description = task_description + + messages = self.prompt_builder.build_messages(self.memory) + response = self.llm_client.generate(messages) - # Parse all actions from response - actions = self._parse_actions(response) + actions = self.prompt_builder.parse_response(response, self.memory) if not actions: - # Fallback: Stop or first action - return self._fallback_action() + action = self._fallback_action() + self.memory.record_step(observation, action, llm_response=response) + self._step_count += 1 + return action + + self.memory.record_step(observation, actions[0], llm_response=response) + self._step_count += 1 - # Buffer remaining actions, return first if len(actions) > 1: self._action_buffer = actions[1:] - return actions[0] - def _parse_actions(self, llm_response: str) -> list[Action]: - """Parse JSON response into a list of validated Actions. + return actions[0] - Returns empty list on parse failure (caller handles fallback). - """ - try: - data = json.loads(llm_response) - except json.JSONDecodeError as e: - logger.warning("Failed to parse LLM response as JSON: %s", e) - return [] - - plan = data.get("executable_plan", []) - if not isinstance(plan, list) or not plan: - logger.warning("No executable_plan in LLM response") - return [] - - actions = [] - for entry in plan: - if not isinstance(entry, dict): - continue - action_name = entry.get("action", "") - validated = self._validate_action_name(action_name) - if validated: - actions.append(Action(action_name=validated)) - else: - logger.warning("Skipping invalid action: '%s'", action_name) - # Stop processing further actions after an invalid one - break - - return actions - - def _validate_action_name(self, action_name: str) -> str | None: - """Validate action name against action_space. Returns canonical name or None.""" - if action_name in self.action_space: - return action_name - # Case-insensitive fallback - for valid in self.action_space: - if valid.lower() == action_name.lower(): - return valid - return None + def add_feedback(self, action_name: str, feedback: str) -> None: + """Record action feedback. Clear buffer on failure.""" + self.memory.record_feedback(feedback) + if any(kw in feedback.lower() for kw in ("fail", "error", "invalid")): + if self._action_buffer: + logger.info( + "Action '%s' failed, clearing %d buffered actions", + action_name, len(self._action_buffer), + ) + self._action_buffer.clear() def _fallback_action(self) -> Action: """Return a safe fallback action when parsing fails.""" if "Stop" in self.action_space: return Action(action_name="Stop") return Action(action_name=self.action_space[0]) - - def add_feedback(self, action_name: str, feedback: str) -> None: - """Record action feedback. Clear buffer on failure (Decision #9).""" - self._action_feedback.append((action_name, feedback)) - # Clear buffer on failure so next act() re-queries LLM - if "fail" in feedback.lower() or "error" in feedback.lower(): - if self._action_buffer: - logger.info("Action '%s' failed, clearing %d buffered actions", - action_name, len(self._action_buffer)) - self._action_buffer.clear() - - # --- BaseAgent abstract methods (not used directly, but required) --- - - def _build_system_prompt(self, task_description: str) -> str: - """Not used — ReActAgent delegates to PromptBuilder.""" - return "" - - def _build_step_prompt(self, observation: Observation) -> str: - """Not used — ReActAgent delegates to PromptBuilder.""" - return "" - - def _parse_action(self, llm_response: str) -> Action: - """Not used — ReActAgent uses _parse_actions instead.""" - return self._fallback_action() diff --git a/easi/core/base_agent.py b/easi/core/base_agent.py index b031501..610db6e 100644 --- a/easi/core/base_agent.py +++ b/easi/core/base_agent.py @@ -1,20 +1,9 @@ -"""Abstract base class for agents. - -An agent bridges the LLM inference endpoint and the simulator. It manages: -- Chat history across steps -- Prompt building (system prompt, per-step observation prompts) -- Parsing LLM text responses into structured Action objects - -Concrete agents subclass this and implement the three abstract methods: -_build_system_prompt, _build_step_prompt, _parse_action. -""" - +"""Abstract base class for agents.""" from __future__ import annotations from abc import ABC, abstractmethod from easi.core.episode import Action, Observation -from easi.core.exceptions import ActionParseError from easi.core.protocols import LLMClientProtocol from easi.utils.logging import get_logger @@ -27,26 +16,11 @@ class BaseAgent(ABC): def __init__(self, llm_client: LLMClientProtocol | None, action_space: list[str]): self.llm_client = llm_client self.action_space = action_space - self._chat_history: list[dict[str, str]] = [] self._step_count: int = 0 @abstractmethod - def _build_system_prompt(self, task_description: str) -> str: - """Build the system prompt describing the task and action space.""" - ... - - @abstractmethod - def _build_step_prompt(self, observation: Observation) -> str: - """Build the user message for a single step (includes observation description).""" - ... - - @abstractmethod - def _parse_action(self, llm_response: str) -> Action: - """Parse the LLM's text response into a structured Action. - - Raises: - ActionParseError: If the response cannot be parsed. - """ + def act(self, observation: Observation, task_description: str) -> Action: + """Return the next action given current observation and task.""" ... def add_feedback(self, action_name: str, feedback: str) -> None: @@ -57,63 +31,5 @@ def add_feedback(self, action_name: str, feedback: str) -> None: """ def reset(self) -> None: - """Clear chat history for a new episode.""" - self._chat_history = [] + """Reset agent state for a new episode.""" self._step_count = 0 - - def act(self, observation: Observation, task_description: str) -> Action: - """Full agent loop for one step. - - 1. Build system prompt on first call - 2. Build step prompt with observation - 3. Collect image paths from observation - 4. Call LLM inference endpoint - 5. Parse response into Action - 6. Append to chat history - """ - # Add system prompt on first step - if self._step_count == 0: - system_prompt = self._build_system_prompt(task_description) - if system_prompt: - self._chat_history.append({ - "role": "system", - "content": system_prompt, - }) - - # Build step prompt - self._step_count += 1 - step_prompt = self._build_step_prompt(observation) - - # Add user message to history - self._chat_history.append({"role": "user", "content": step_prompt}) - - # Call LLM - if self.llm_client is not None: - llm_response = self.llm_client.generate( - messages=self._chat_history, - ) - else: - llm_response = "" - - # Parse action - action = self._parse_action(llm_response) - - # Append assistant response to history - self._chat_history.append({ - "role": "assistant", - "content": llm_response, - }) - - logger.trace( - "Step %d: action=%s params=%s", - self._step_count, - action.action_name, - action.params, - ) - - return action - - @property - def chat_history(self) -> list[dict[str, str]]: - """Return a copy of the chat history.""" - return self._chat_history.copy() diff --git a/easi/core/memory.py b/easi/core/memory.py new file mode 100644 index 0000000..e47c21e --- /dev/null +++ b/easi/core/memory.py @@ -0,0 +1,68 @@ +"""Agent memory: shared state between agent and prompt builder.""" +from __future__ import annotations + +from dataclasses import dataclass, field + +from easi.core.episode import Action, Observation + + +@dataclass +class StepRecord: + """Record of a single agent step.""" + + observation: Observation + action: Action | None = None + feedback: str | None = None + llm_response: str | None = None # None for buffered actions + step_number: int = 0 + + +@dataclass +class AgentMemory: + """Shared state that the agent populates and the prompt builder reads.""" + + task_description: str = "" + action_space: list[str] = field(default_factory=list) + steps: list[StepRecord] = field(default_factory=list) + current_observation: Observation | None = None + + @property + def is_first_turn(self) -> bool: + """True when no completed steps exist yet.""" + return len(self.steps) == 0 + + @property + def action_history(self) -> list[tuple[str, str]]: + """(action_name, feedback) for completed steps with feedback.""" + return [ + (s.action.action_name, s.feedback) + for s in self.steps + if s.action and s.feedback is not None + ] + + def record_step( + self, + observation: Observation, + action: Action, + llm_response: str | None, + ) -> None: + """Record a completed step (action taken, awaiting feedback).""" + self.steps.append( + StepRecord( + observation=observation, + action=action, + llm_response=llm_response, + step_number=len(self.steps), + ) + ) + + def record_feedback(self, feedback: str) -> None: + """Attach feedback to the most recent step.""" + if self.steps: + self.steps[-1].feedback = feedback + + def clear(self) -> None: + """Reset memory for a new episode.""" + self.steps.clear() + self.current_observation = None + self.task_description = "" diff --git a/easi/core/protocols.py b/easi/core/protocols.py index fcb7fcc..21a00f5 100644 --- a/easi/core/protocols.py +++ b/easi/core/protocols.py @@ -50,9 +50,6 @@ def reset(self) -> None: ... def act(self, observation: Observation, task_description: str) -> Action: ... def add_feedback(self, action_name: str, feedback: str) -> None: ... - @property - def chat_history(self) -> list[dict]: ... - @runtime_checkable class TaskProtocol(Protocol): diff --git a/easi/evaluation/runner.py b/easi/evaluation/runner.py index 176c82d..3d0ca8d 100644 --- a/easi/evaluation/runner.py +++ b/easi/evaluation/runner.py @@ -191,6 +191,12 @@ def _run_episode( episode_output_dir=str(episode_dir), ) + # Propagate dynamic action space from bridge (e.g., EB-Alfred per-episode actions) + dynamic_actions_json = observation.metadata.get("dynamic_action_space") + if dynamic_actions_json and hasattr(agent, 'update_action_space'): + dynamic_actions = json.loads(dynamic_actions_json) + agent.update_action_space(dynamic_actions) + # Write reset entry to trajectory trajectory_path = episode_dir / "trajectory.jsonl" self._write_trajectory_entry(trajectory_path, { @@ -348,7 +354,8 @@ def _structured_generate(messages): builder_class_name = agent_config.get("prompt_builder") if builder_class_name: BuilderClass = import_class(builder_class_name) - prompt_builder = BuilderClass() + builder_kwargs = agent_config.get("prompt_builder_kwargs", {}) + prompt_builder = BuilderClass(**builder_kwargs) return ReActAgent( llm_client=llm, diff --git a/easi/tasks/ebalfred/ebalfred_base.yaml b/easi/tasks/ebalfred/ebalfred_base.yaml index d9d019c..f5b2cbf 100644 --- a/easi/tasks/ebalfred/ebalfred_base.yaml +++ b/easi/tasks/ebalfred/ebalfred_base.yaml @@ -22,3 +22,7 @@ simulator_configs: - "opencv-python" agent: prompt_builder: "easi.tasks.ebalfred.prompts.EBAlfredPromptBuilder" + prompt_builder_kwargs: + n_shot: 10 + split: "base" + use_feedback: true diff --git a/easi/tasks/ebalfred/ebalfred_common_sense.yaml b/easi/tasks/ebalfred/ebalfred_common_sense.yaml index 71e86e7..38fc17e 100644 --- a/easi/tasks/ebalfred/ebalfred_common_sense.yaml +++ b/easi/tasks/ebalfred/ebalfred_common_sense.yaml @@ -22,3 +22,7 @@ simulator_configs: - "opencv-python" agent: prompt_builder: "easi.tasks.ebalfred.prompts.EBAlfredPromptBuilder" + prompt_builder_kwargs: + n_shot: 10 + split: "base" + use_feedback: true diff --git a/easi/tasks/ebalfred/ebalfred_complex_instruction.yaml b/easi/tasks/ebalfred/ebalfred_complex_instruction.yaml index 096ae18..e4b7d53 100644 --- a/easi/tasks/ebalfred/ebalfred_complex_instruction.yaml +++ b/easi/tasks/ebalfred/ebalfred_complex_instruction.yaml @@ -22,3 +22,7 @@ simulator_configs: - "opencv-python" agent: prompt_builder: "easi.tasks.ebalfred.prompts.EBAlfredPromptBuilder" + prompt_builder_kwargs: + n_shot: 10 + split: "base" + use_feedback: true diff --git a/easi/tasks/ebalfred/ebalfred_long_horizon.yaml b/easi/tasks/ebalfred/ebalfred_long_horizon.yaml index a45ba7c..55d93e4 100644 --- a/easi/tasks/ebalfred/ebalfred_long_horizon.yaml +++ b/easi/tasks/ebalfred/ebalfred_long_horizon.yaml @@ -22,3 +22,7 @@ simulator_configs: - "opencv-python" agent: prompt_builder: "easi.tasks.ebalfred.prompts.EBAlfredPromptBuilder" + prompt_builder_kwargs: + n_shot: 10 + split: "long_horizon" + use_feedback: true diff --git a/easi/tasks/ebalfred/ebalfred_spatial.yaml b/easi/tasks/ebalfred/ebalfred_spatial.yaml index d25fbdb..a860616 100644 --- a/easi/tasks/ebalfred/ebalfred_spatial.yaml +++ b/easi/tasks/ebalfred/ebalfred_spatial.yaml @@ -22,3 +22,7 @@ simulator_configs: - "opencv-python" agent: prompt_builder: "easi.tasks.ebalfred.prompts.EBAlfredPromptBuilder" + prompt_builder_kwargs: + n_shot: 10 + split: "base" + use_feedback: true diff --git a/easi/tasks/ebalfred/ebalfred_visual_appearance.yaml b/easi/tasks/ebalfred/ebalfred_visual_appearance.yaml index 6724d07..d997eb6 100644 --- a/easi/tasks/ebalfred/ebalfred_visual_appearance.yaml +++ b/easi/tasks/ebalfred/ebalfred_visual_appearance.yaml @@ -22,3 +22,7 @@ simulator_configs: - "opencv-python" agent: prompt_builder: "easi.tasks.ebalfred.prompts.EBAlfredPromptBuilder" + prompt_builder_kwargs: + n_shot: 10 + split: "base" + use_feedback: true diff --git a/easi/tasks/ebalfred/json_repair.py b/easi/tasks/ebalfred/json_repair.py new file mode 100644 index 0000000..49dc898 --- /dev/null +++ b/easi/tasks/ebalfred/json_repair.py @@ -0,0 +1,2 @@ +"""Backward-compatible re-export. Canonical location: easi.utils.json_repair.""" +from easi.utils.json_repair import fix_json # noqa: F401 diff --git a/easi/tasks/ebalfred/prompts.py b/easi/tasks/ebalfred/prompts.py index 74adc57..f515945 100644 --- a/easi/tasks/ebalfred/prompts.py +++ b/easi/tasks/ebalfred/prompts.py @@ -1,109 +1,396 @@ -"""EB-Alfred-specific prompt builder (Decision #10: OpenAI message format). +"""EB-Alfred prompt builder matching EmbodiedBench VLMPlanner exactly. -Produces prompts in OpenAI message format with interleaved text+image, -tailored for the EB-Alfred household task domain. +Supports both chat_history=False (stateless) and chat_history=True modes. Referenced in ebalfred*.yaml via: agent: prompt_builder: "easi.tasks.ebalfred.prompts.EBAlfredPromptBuilder" + prompt_builder_kwargs: + n_shot: 10 + split: "base" -Reference: EmbodiedBench/embodiedbench/evaluator/config/system_prompts.py +Reference: EmbodiedBench/embodiedbench/planner/vlm_planner.py + EmbodiedBench/embodiedbench/evaluator/config/system_prompts.py + EmbodiedBench/embodiedbench/planner/planner_utils.py """ from __future__ import annotations -from easi.agents.prompt_builder import _encode_image_base64 -from easi.core.episode import Observation +import json +from pathlib import Path + +from easi.agents.prompt_builder import _encode_image_base64, validate_action_name +from easi.core.episode import Action +from easi.core.memory import AgentMemory +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + +# Exact system prompt from EmbodiedBench system_prompts.py (alfred_system_prompt). +# Three format placeholders: max_action_id, action_list, examples. +ALFRED_SYSTEM_PROMPT = '''## You are a robot operating in a home. Given a task, you must accomplish the task using a defined set of actions to achieve the desired outcome. + +## Action Descriptions and Validity Rules +\u2022 Find: Parameterized by the name of the receptacle to navigate to. So long as the object is present in the scene, this skill is always valid +\u2022 Pick up: Parameterized by the name of the object to pick. Only valid if the robot is close to the object, not holding another object, and the object is not inside a closed receptacle. +\u2022 Put down: Parameterized by the name of the object to put down to a nearby receptacle. Only valid if the robot is holding an object. +\u2022 Drop: Parameterized by the name of the object to put down. It is different from Put down action, as this does not guarantee the held object will be put into a specified receptacle. +\u2022 Open: Parameterized by the name of the receptacle to open. Only valid if the receptacle is closed and the robot is close to the receptacle. +\u2022 Close: Parameterized by the name of the receptacle to close. Only valid if the receptacle is open and the robot is close to the receptacle. +\u2022 Turn on: Parameterized by the name of the object to turn on. Only valid if the object is turned off and the robot is close to the object. +\u2022 Turn off: Parameterized by the name of the object to turn off. Only valid if the object is turned on and the robot is close to the object. +\u2022 Slice: Parameterized by the name of the object to slice. Only valid if the object is sliceable and the robot is close to the object. + + +## The available action id (0 ~ {}) and action names are: {}. + +{} + +## Guidelines +1. **Output Plan**: Avoid generating empty plan. Each plan should include no more than 20 actions. +2. **Visibility**: Always locate a visible object by the 'find' action before interacting with it. +3. **Action Guidelines**: Make sure match the action name and its corresponding action id in the output.\n Avoid performing actions that do not meet the defined validity criteria. For instance, if you want to put object in a receptacle, use 'put down' rather than 'drop' actions. +4. **Prevent Repeating Action Sequences**: Do not repeatedly execute the same action or sequence of actions.\n Try to modify the action sequence because previous actions do not lead to success. +5. **Multiple Instances**: There may be multiple instances of the same object, distinguished by an index following their names, e.g., Cabinet_2, Cabinet_3. You can explore these instances if you do not find the desired object in the current receptacle. +6. **Reflection on History and Feedback**: Use interaction history and feedback from the environment to refine and improve your current plan.\n If the last action is invalid, reflect on the reason, such as not adhering to action rules or missing preliminary actions, and adjust your plan accordingly. +''' + +# Output template from EmbodiedBench planner_utils.py (template, not template_lang). +OUTPUT_TEMPLATE = ''' +The output json format should be {'visual_state_description':str, 'reasoning_and_reflection':str, 'language_plan':str, 'executable_plan':List[{'action_id':int, 'action_name':str}...]} +The fields in above JSON follows the purpose below: +1. visual_state_description is for description of current state from the visual image, +2. reasoning_and_reflection is for summarizing the history of interactions and any available environmental feedback. Additionally, provide reasoning as to why the last action or plan failed and did not finish the task, +3. language_plan is for describing a list of actions to achieve the user instruction. Each action is started by the step number and the action name, +4. executable_plan is a list of actions needed to achieve the user instruction, with each action having an action ID and a name. +5. keep your plan efficient and concise. +!!! When generating content for JSON strings, avoid using any contractions or abbreviated forms (like 's, 're, 've, 'll, 'd, n't) that use apostrophes. Instead, write out full forms (is, are, have, will, would, not) to prevent parsing errors in JSON. Please do not output any other thing more than the above-mentioned JSON, do not include ```json and ```!!!. +''' + +_CONFIG_DIR = Path(__file__).parent / "config" class EBAlfredPromptBuilder: - """Prompt builder for EB-Alfred household tasks in AI2-THOR. + """Prompt builder for EB-Alfred that exactly matches VLMPlanner output. - Returns OpenAI message format (list[dict]) with interleaved text+image. + Supports two modes: + - chat_history=False (default): Stateless. Every turn sends the full system + prompt + examples + instruction. No message accumulation. + - chat_history=True: First turn is identical. Subsequent turns send a minimal + prompt (instruction + history + reflection). Messages accumulate. """ - SYSTEM_TEMPLATE = """You are an embodied agent in an AI2-THOR household environment. You must complete household tasks by choosing high-level skill actions. - -## Task -{task_description} - -## Available Actions -Each action is a high-level skill. The simulator will handle low-level navigation and interaction. -{action_list} - -## Action Categories -- **find a **: Navigate to the specified object -- **pick up the **: Pick up an object (must be near it first) -- **put down the object in hand**: Place held object in the nearest receptacle -- **open/close the **: Open or close a container (Fridge, Cabinet, etc.) -- **turn on/off the **: Toggle an appliance (Faucet, Microwave, etc.) -- **slice the **: Slice a sliceable object (must be holding a knife) - -## Output Format -Respond with valid JSON: -{{ - "observation": "Describe what you see in the current image", - "reasoning": "Step-by-step reasoning about what to do next", - "plan": "Your high-level plan to complete the task", - "executable_plan": [ - {{"action": ""}} - ] -}} - -## Tips -- Find objects before trying to interact with them. -- To clean: put object in sink, turn on faucet, turn off faucet, pick up object. -- To heat: put object in microwave, turn on microwave, turn off microwave, pick up object. -- To cool: put object in fridge, close fridge, open fridge, pick up object. -- Output one action at a time for reliability. -""" + def __init__( + self, + n_shot: int = 10, + split: str = "base", + use_feedback: bool = True, + chat_history: bool = False, + ): + self.n_shot = n_shot + self.split = split + self.use_feedback = use_feedback + self.chat_history = chat_history + + # Load examples + if split == "long_horizon": + examples_file = _CONFIG_DIR / "alfred_long_horizon_examples.json" + else: + examples_file = _CONFIG_DIR / "alfred_examples.json" + + with open(examples_file) as f: + self._examples: list[str] = json.load(f) + + # Action space state (set via set_action_space or lazily in build_messages) + self._actions: list[str] = [] + self._action_str: str = "" + self._action_id_map: dict[str, int] = {} + self._id_action_map: dict[int, str] = {} + + def set_action_space(self, actions: list[str]) -> None: + """Update action space (e.g., after dynamic expansion per episode).""" + self._actions = list(actions) + self._action_str = self._build_action_list_str(actions) + self._action_id_map = {name: i for i, name in enumerate(actions)} + self._id_action_map = {i: name for i, name in enumerate(actions)} - STEP_TEMPLATE = """Task: {task_description} + def action_name_to_id(self, name: str) -> int | None: + """Look up action ID from name. Returns None if not found.""" + return self._action_id_map.get(name) -{history_section} + def action_id_to_name(self, action_id: int) -> str | None: + """Look up action name from ID. Returns None if not found.""" + return self._id_action_map.get(action_id) -Look at the current observation image and decide your next action. Respond with valid JSON.""" + @staticmethod + def _build_action_list_str(actions: list[str]) -> str: + """Build action list in VLMPlanner's get_availabel_action_prompt format.""" + parts = '' + for i in range(len(actions)): + parts += '\naction id ' + str(i) + ': ' + str(actions[i]) + if i < len(actions) - 1: + parts += ', ' + return parts - def build_system_prompt( + # ---- New PromptBuilderProtocol methods ---- + + def build_messages(self, memory: AgentMemory) -> list[dict]: + """Build COMPLETE message list to send to LLM.""" + # Lazy-init action maps from memory.action_space + if not self._action_id_map or self._actions != memory.action_space: + self.set_action_space(memory.action_space) + + if self.chat_history: + return self._build_chat_history_messages(memory) + else: + return self._build_stateless_messages(memory) + + def parse_response(self, llm_response: str, memory: AgentMemory) -> list[Action]: + """Parse LLM response into validated actions.""" + from easi.utils.json_repair import fix_json + llm_response = fix_json(llm_response) + + try: + data = json.loads(llm_response) + except json.JSONDecodeError as e: + logger.warning("Failed to parse LLM response as JSON: %s", e) + return [] + + plan = data.get("executable_plan", []) + if not isinstance(plan, list) or not plan: + logger.warning("No executable_plan in LLM response") + return [] + + actions = [] + for entry in plan: + if not isinstance(entry, dict): + continue + + # Support both {"action_id": int, "action_name": str} and {"action": str} + if "action_id" in entry: + action_id = entry["action_id"] + action_name = self.action_id_to_name(action_id) + if action_name is None: + action_name = entry.get("action_name", "") + else: + action_name = entry.get("action", "") + + validated = validate_action_name(action_name, memory.action_space) + if validated: + actions.append(Action(action_name=validated)) + else: + logger.warning("Skipping invalid action: '%s'", action_name) + break + + return actions + + # ---- Stateless mode (chat_history=False) ---- + + def _build_stateless_messages(self, memory: AgentMemory) -> list[dict]: + """Build full prompt each turn. No history accumulation.""" + prompt = self._build_prompt_text( + memory.task_description, memory.action_history, + ) + return self._wrap_as_user_message(prompt, memory.current_observation) + + def _build_prompt_text( self, - action_space: list[str], task_description: str, - ) -> list[dict]: - action_list = "\n".join( - f" {i}. {name}" for i, name in enumerate(action_space) + action_history: list[tuple[str, str]], + ) -> str: + """Build the full prompt text matching VLMPlanner.process_prompt(). + + Covers both first-turn and subsequent-turn paths with + chat_history=False (lines 53-63 and 78-94 of vlm_planner.py). + """ + user_instruction = task_description.rstrip('.') + max_id = len(self._actions) - 1 + + if len(action_history) == 0: + # First turn path (vlm_planner.py lines 53-63) + if self.n_shot >= 1: + examples_str = '\n\n'.join([ + f'## Task Execution Example {i}: \n {x}' + for i, x in enumerate(self._examples[:self.n_shot]) + ]) + prompt = ALFRED_SYSTEM_PROMPT.format( + max_id, self._action_str, examples_str, + ) + else: + prompt = ALFRED_SYSTEM_PROMPT.format( + max_id, self._action_str, '', + ) + + prompt += f'\n\n## Now the human instruction is: {user_instruction}.' + prompt += f" You are supposed to output in json. You need to describe current visual state from the image, output your reasoning steps and plan. At the end, output the action id (0 ~ {max_id}) from the available actions to excute." + + else: + # Subsequent turn path, chat_history=False (vlm_planner.py lines 78-94) + # Note: VLMPlanner has TWO spaces after "Example" on line 80 (typo preserved) + if self.n_shot >= 1: + examples_str = '\n\n'.join([ + f'## Task Execution Example {i}: \n {x}' + for i, x in enumerate(self._examples[:self.n_shot]) + ]) + prompt = ALFRED_SYSTEM_PROMPT.format( + max_id, self._action_str, examples_str, + ) + else: + prompt = ALFRED_SYSTEM_PROMPT.format( + max_id, self._action_str, '', + ) + + prompt += f'\n\n## Now the human instruction is: {user_instruction}.' + prompt += '\n\n The action history:' + for i, (action_name, feedback) in enumerate(action_history): + action_id = self._action_id_map.get(action_name, -1) + if self.use_feedback: + prompt += '\nStep {}, action id {}, {}, env feedback: {}'.format( + i, action_id, action_name, feedback, + ) + else: + prompt += '\nStep {}, action id {}, {}'.format( + i, action_id, action_name, + ) + + prompt += f'''\n\n Considering the above interaction history and the current image state, to achieve the human instruction: '{user_instruction}', you are supposed to output in json. You need to describe current visual state from the image, summarize interaction history {'and environment feedback ' if self.use_feedback else ''}and reason why the last action or plan failed and did not finish the task, output your new plan to achieve the goal from current state. At the end, output the excutable plan with action ids(0 ~ {max_id}) from the available actions.''' + + # Append output template (VLMPlanner act() line 203: prompt + template) + prompt += OUTPUT_TEMPLATE + + return prompt + + # ---- Chat history mode (chat_history=True) ---- + + def _build_chat_history_messages(self, memory: AgentMemory) -> list[dict]: + """Build accumulated message history for chat_history=True mode. + + First turn: identical to stateless first turn. + Subsequent turns: reconstruct full message history from memory.steps, + then append current turn with minimal prompt. + """ + if memory.is_first_turn: + # First turn: same as stateless + return self._build_stateless_messages(memory) + + # Reconstruct message history from completed steps + messages: list[dict] = [] + + for step_idx, step in enumerate(memory.steps): + if step.llm_response is None: + # Buffered step — no user/assistant pair + continue + + if step_idx == 0: + # First step: full prompt (same as stateless first turn) + history_up_to = [] + else: + # Subsequent steps: collect action history up to this step + history_up_to = [ + (s.action.action_name, s.feedback) + for s in memory.steps[:step_idx] + if s.action and s.feedback is not None + ] + + if step_idx == 0: + prompt = self._build_first_turn_prompt(memory.task_description) + else: + prompt = self._build_chat_subsequent_prompt( + memory.task_description, history_up_to, + ) + + # User message + messages.extend( + self._wrap_as_user_message(prompt, step.observation) + ) + # Assistant message + messages.append({ + "role": "assistant", + "content": [{"type": "text", "text": step.llm_response}], + }) + + # Current turn: append new user message + # Collect full action history from all steps with feedback + all_history = memory.action_history + current_prompt = self._build_chat_subsequent_prompt( + memory.task_description, all_history, ) - text = self.SYSTEM_TEMPLATE.format( - action_list=action_list, - task_description=task_description, + messages.extend( + self._wrap_as_user_message(current_prompt, memory.current_observation) ) - return [{"role": "system", "content": text}] - def build_step_prompt( + return messages + + def _build_first_turn_prompt(self, task_description: str) -> str: + """Build first-turn prompt text (identical for both modes).""" + user_instruction = task_description.rstrip('.') + max_id = len(self._actions) - 1 + + if self.n_shot >= 1: + examples_str = '\n\n'.join([ + f'## Task Execution Example {i}: \n {x}' + for i, x in enumerate(self._examples[:self.n_shot]) + ]) + prompt = ALFRED_SYSTEM_PROMPT.format( + max_id, self._action_str, examples_str, + ) + else: + prompt = ALFRED_SYSTEM_PROMPT.format( + max_id, self._action_str, '', + ) + + prompt += f'\n\n## Now the human instruction is: {user_instruction}.' + prompt += f" You are supposed to output in json. You need to describe current visual state from the image, output your reasoning steps and plan. At the end, output the action id (0 ~ {max_id}) from the available actions to excute." + prompt += OUTPUT_TEMPLATE + + return prompt + + def _build_chat_subsequent_prompt( self, - observation: Observation, task_description: str, action_history: list[tuple[str, str]], - ) -> list[dict]: - if action_history: - history_lines = [] - for i, (action_name, feedback) in enumerate(action_history): - status = "OK" if "success" in feedback.lower() else "FAILED" - history_lines.append(f" Step {i+1}: {action_name} → {status} ({feedback})") - history_section = "## Previous Actions\n" + "\n".join(history_lines) - else: - history_section = "This is your first action. Start by observing the environment." + ) -> str: + """Build minimal subsequent-turn prompt for chat_history=True. - text = self.STEP_TEMPLATE.format( - task_description=task_description, - history_section=history_section, - ) + VLMPlanner lines 66-77: no system prompt, no examples. + Uses "The human instruction is:" (no "## Now the"). + """ + user_instruction = task_description.rstrip('.') + max_id = len(self._actions) - 1 + + prompt = f'The human instruction is: {user_instruction}.' + prompt += '\n\n The action history:' + for i, (action_name, feedback) in enumerate(action_history): + action_id = self._action_id_map.get(action_name, -1) + if self.use_feedback: + prompt += '\nStep {}, action id {}, {}, env feedback: {}'.format( + i, action_id, action_name, feedback, + ) + else: + prompt += '\nStep {}, action id {}, {}'.format( + i, action_id, action_name, + ) + + prompt += f'''\n\n Considering the above interaction history and the current image state, to achieve the human instruction: '{user_instruction}', you are supposed to output in json. You need to describe current visual state from the image, summarize interaction history {'and environment feedback ' if self.use_feedback else ''}and reason why the last action or plan failed and did not finish the task, output your new plan to achieve the goal from current state. At the end, output the excutable plan with action ids(0 ~ {max_id}) from the available actions.''' + + prompt += OUTPUT_TEMPLATE + + return prompt + + # ---- Helpers ---- + + @staticmethod + def _wrap_as_user_message(prompt: str, observation) -> list[dict]: + """Wrap prompt text + observation image as a user message. - # Build interleaved content parts (text + image) - content_parts: list[dict] = [{"type": "text", "text": text}] - if observation.rgb_path: + Image BEFORE text (VLMPlanner line 127). + """ + content: list[dict] = [] + if observation and observation.rgb_path: image_url = _encode_image_base64(observation.rgb_path) if image_url: - content_parts.append({ + content.append({ "type": "image_url", "image_url": {"url": image_url}, }) + content.append({"type": "text", "text": prompt}) - return [{"role": "user", "content": content_parts}] + return [{"role": "user", "content": content}] diff --git a/easi/utils/json_repair.py b/easi/utils/json_repair.py new file mode 100644 index 0000000..7b054a4 --- /dev/null +++ b/easi/utils/json_repair.py @@ -0,0 +1,40 @@ +"""JSON repair for LLM output. Ported from EmbodiedBench planner_utils.py.""" +from __future__ import annotations + +import re + + +def fix_json(json_str: str) -> str: + """Fix common JSON errors in LLM output. + + Handles: + - Single quotes -> double quotes + - Broken contractions from quote replacement + - Markdown code fences + - Unescaped quotes inside reasoning_and_reflection value + """ + # Replace single quotes with double quotes + json_str = json_str.replace("'", '"') + # Fix contractions that got broken by quote replacement + json_str = json_str.replace('"s ', "'s ") + json_str = json_str.replace('"re ', "'re ") + json_str = json_str.replace('"ll ', "'ll ") + json_str = json_str.replace('"t ', "'t ") + json_str = json_str.replace('"d ', "'d ") + json_str = json_str.replace('"m ', "'m ") + json_str = json_str.replace('"ve ', "'ve ") + json_str = json_str.replace('```json', '').replace('```', '') + + # Fix unescaped double quotes inside reasoning_and_reflection value. + # Pattern: match from the key's opening quote to just before "language_plan". + pattern = r'("reasoning_and_reflection"\s*:\s*")(?P.*?)(?=",\s*"language_plan")' + + def replacer(match): + prefix = match.group(1) + value = match.group("value") + # Escape any double quote that is not already escaped. + fixed_value = re.sub(r'(? 0 - + assert agent._step_count == 1 agent.reset() - assert len(agent.chat_history) == 0 - - def test_chat_history_grows(self, agent): - obs = Observation(rgb_path="/tmp/rgb.png") - agent.act(obs, "test") - assert len(agent.chat_history) == 2 # user + assistant - - agent.act(obs, "test") - assert len(agent.chat_history) == 4 - - def test_chat_history_is_copy(self, agent): - obs = Observation(rgb_path="/tmp/rgb.png") - agent.act(obs, "test") - - history = agent.chat_history - history.clear() - assert len(agent.chat_history) == 2 # original unchanged + assert agent._step_count == 0 diff --git a/tests/test_ebalfred_task.py b/tests/test_ebalfred_task.py index 9bd6825..acdc44d 100644 --- a/tests/test_ebalfred_task.py +++ b/tests/test_ebalfred_task.py @@ -126,30 +126,42 @@ def test_split_specific_task_loading(self): class TestEBAlfredPromptBuilder: - def test_system_prompt_returns_messages(self): + def test_build_messages_first_turn(self): + """First turn: full system prompt in user message.""" from easi.tasks.ebalfred.prompts import EBAlfredPromptBuilder + from easi.core.memory import AgentMemory builder = EBAlfredPromptBuilder() - messages = builder.build_system_prompt( - action_space=get_global_action_space(), + actions = get_global_action_space() + memory = AgentMemory( task_description="Put a mug on the shelf.", + action_space=actions, + current_observation=Observation(rgb_path="/tmp/rgb.png"), ) + messages = builder.build_messages(memory) assert isinstance(messages, list) - assert messages[0]["role"] == "system" - content = messages[0]["content"] - assert "find a" in content - assert "pick up" in content - assert "Put a mug on the shelf" in content - assert "household" in content.lower() - - def test_step_prompt_returns_messages_with_history(self): + assert messages[0]["role"] == "user" + text_content = messages[0]["content"][-1]["text"] + assert "You are a robot operating in a home" in text_content + # Action space should be initialized internally + assert builder.action_name_to_id("find a Cart") == 0 + + def test_build_messages_with_history(self): from easi.tasks.ebalfred.prompts import EBAlfredPromptBuilder + from easi.core.memory import AgentMemory + from easi.core.episode import Action builder = EBAlfredPromptBuilder() - obs = Observation(rgb_path="/tmp/rgb.png") - messages = builder.build_step_prompt( - observation=obs, + actions = get_global_action_space() + memory = AgentMemory( task_description="Put a mug on the shelf.", - action_history=[("find a Mug", "success"), ("pick up the Mug", "failed: not close enough")], + action_space=actions, + current_observation=Observation(rgb_path="/tmp/rgb.png"), ) + obs = Observation(rgb_path="/tmp/rgb.png") + memory.record_step(obs, Action(action_name="find a Mug"), llm_response="r1") + memory.record_feedback("success") + memory.record_step(obs, Action(action_name="pick up the Mug"), llm_response="r2") + memory.record_feedback("failed: not close enough") + messages = builder.build_messages(memory) assert isinstance(messages, list) assert messages[0]["role"] == "user" text_content = "" @@ -157,7 +169,7 @@ def test_step_prompt_returns_messages_with_history(self): if part["type"] == "text": text_content += part["text"] assert "find a Mug" in text_content - assert "FAILED" in text_content + assert "failed: not close enough" in text_content def test_conforms_to_protocol(self): from easi.tasks.ebalfred.prompts import EBAlfredPromptBuilder diff --git a/tests/test_json_repair.py b/tests/test_json_repair.py new file mode 100644 index 0000000..f6dad8c --- /dev/null +++ b/tests/test_json_repair.py @@ -0,0 +1,76 @@ +"""Tests for JSON repair utility (ported from EmbodiedBench planner_utils).""" +import json + +import pytest + +from easi.utils.json_repair import fix_json + + +class TestFixJson: + def test_single_quotes_to_double(self): + s = "{'key': 'value'}" + result = fix_json(s) + assert '"key"' in result + assert '"value"' in result + + def test_contractions_preserved(self): + s = "{'text': \"it's a test and we're here\"}" + result = fix_json(s) + assert "'s" in result + assert "'re" in result + + def test_markdown_fences_stripped(self): + s = '```json\n{"key": "value"}\n```' + result = fix_json(s) + assert "```" not in result + data = json.loads(result) + assert data["key"] == "value" + + def test_unescaped_quotes_in_reasoning(self): + s = '{"reasoning_and_reflection": "He said "hello" to the robot", "language_plan": "go"}' + result = fix_json(s) + data = json.loads(result) + assert "language_plan" in data + + def test_valid_json_passthrough(self): + original = '{"key": "value", "num": 42}' + result = fix_json(original) + assert json.loads(result) == json.loads(original) + + def test_contraction_ll(self): + s = "{'text': \"I'll do it\"}" + result = fix_json(s) + assert "'ll" in result + + def test_contraction_t(self): + s = "{'text': \"don't stop\"}" + result = fix_json(s) + assert "'t" in result + + def test_contraction_d(self): + s = "{'text': \"I'd go\"}" + result = fix_json(s) + assert "'d" in result + + def test_contraction_m(self): + s = "{'text': \"I'm here\"}" + result = fix_json(s) + assert "'m" in result + + def test_contraction_ve(self): + s = "{'text': \"I've been\"}" + result = fix_json(s) + assert "'ve" in result + + def test_full_llm_output_with_single_quotes(self): + """Simulate a typical LLM output that uses single quotes.""" + s = """{'visual_state_description': 'I see a kitchen', 'reasoning_and_reflection': 'The robot hasn't found the mug yet', 'language_plan': 'Find the mug', 'executable_plan': [{'action_id': 14, 'action_name': 'find a Mug'}]}""" + result = fix_json(s) + data = json.loads(result) + assert data["executable_plan"][0]["action_id"] == 14 + assert data["executable_plan"][0]["action_name"] == "find a Mug" + + def test_backward_compat_import(self): + """Old import path still works via re-export.""" + from easi.tasks.ebalfred.json_repair import fix_json as old_fix_json + assert old_fix_json is fix_json diff --git a/tests/test_prompt_parity.py b/tests/test_prompt_parity.py new file mode 100644 index 0000000..1396d9f --- /dev/null +++ b/tests/test_prompt_parity.py @@ -0,0 +1,510 @@ +"""Tests verifying EBAlfredPromptBuilder produces prompts matching VLMPlanner.""" +import json + +import pytest + +from easi.core.episode import Action, Observation +from easi.core.memory import AgentMemory +from easi.tasks.ebalfred.actions import get_global_action_space +from easi.tasks.ebalfred.prompts import ( + ALFRED_SYSTEM_PROMPT, + OUTPUT_TEMPLATE, + EBAlfredPromptBuilder, +) + + +@pytest.fixture +def action_space(): + return get_global_action_space() + + +@pytest.fixture +def builder(action_space): + b = EBAlfredPromptBuilder(n_shot=10, split="base") + b.set_action_space(action_space) + return b + + +@pytest.fixture +def obs(tmp_path): + img = tmp_path / "test.png" + img.write_bytes(b'\x89PNG\r\n\x1a\n' + b'\x00' * 100) + return Observation(rgb_path=str(img)) + + +def _make_memory(action_space, obs, task="Put a mug on the shelf.", + steps=None): + """Helper to build AgentMemory with optional step history.""" + mem = AgentMemory( + task_description=task, + action_space=action_space, + current_observation=obs, + ) + if steps: + for action_name, feedback, llm_resp in steps: + mem.record_step(obs, Action(action_name=action_name), llm_response=llm_resp) + if feedback is not None: + mem.record_feedback(feedback) + return mem + + +class TestPromptParity: + def test_first_turn_contains_system_prompt_text(self, builder, obs, action_space): + """Full system prompt embedded in step prompt.""" + mem = _make_memory(action_space, obs) + msgs = builder.build_messages(mem) + text = msgs[0]["content"][-1]["text"] + assert "You are a robot operating in a home" in text + assert "Action Descriptions and Validity Rules" in text + + def test_action_list_format(self, builder, obs, action_space): + """Action IDs in 'action id N: name' format.""" + mem = _make_memory(action_space, obs) + msgs = builder.build_messages(mem) + text = msgs[0]["content"][-1]["text"] + assert "action id 0: find a Cart" in text + actions = get_global_action_space() + last_idx = len(actions) - 1 + assert f"action id {last_idx}: {actions[-1]}" in text + + def test_examples_included(self, builder, obs, action_space): + """10 few-shot examples present.""" + mem = _make_memory(action_space, obs) + msgs = builder.build_messages(mem) + text = msgs[0]["content"][-1]["text"] + assert "Task Execution Example 0" in text + assert "Task Execution Example 9" in text + + def test_output_template_appended(self, builder, obs, action_space): + """VLMPlanner output template with field descriptions.""" + mem = _make_memory(action_space, obs) + msgs = builder.build_messages(mem) + text = msgs[0]["content"][-1]["text"] + assert "visual_state_description" in text + assert "avoid using any contractions" in text + assert "action_id" in text + assert "action_name" in text + + def test_image_before_text(self, builder, obs, action_space): + """Image content part comes before text part.""" + mem = _make_memory(action_space, obs) + msgs = builder.build_messages(mem) + content = msgs[0]["content"] + assert content[0]["type"] == "image_url" + assert content[-1]["type"] == "text" + + def test_history_format_matches_vlmplanner(self, builder, obs, action_space): + """History uses 'Step N, action id ID, name, env feedback: ...'.""" + mem = _make_memory(action_space, obs, steps=[ + ("find a AlarmClock", "Last action executed successfully.", "resp"), + ]) + msgs = builder.build_messages(mem) + text = msgs[0]["content"][-1]["text"] + alarm_id = builder.action_name_to_id("find a AlarmClock") + expected = f"Step 0, action id {alarm_id}, find a AlarmClock, env feedback: Last action executed successfully." + assert expected in text + + def test_instruction_rstrip_dot(self, builder, obs, action_space): + """Trailing period stripped from instruction.""" + mem = _make_memory(action_space, obs) + msgs = builder.build_messages(mem) + text = msgs[0]["content"][-1]["text"] + assert "human instruction is: Put a mug on the shelf." in text + assert "Put a mug on the shelf.." not in text + + def test_long_horizon_uses_different_examples(self): + """long_horizon split loads from alfred_long_horizon_examples.json.""" + builder_lh = EBAlfredPromptBuilder(n_shot=10, split="long_horizon") + builder_base = EBAlfredPromptBuilder(n_shot=10, split="base") + assert builder_lh._examples != builder_base._examples + + def test_subsequent_turn_includes_full_prompt(self, builder, obs, action_space): + """Even with history, full system prompt + examples re-included (stateless).""" + mem = _make_memory(action_space, obs, steps=[ + ("find a Mug", "success", "resp"), + ]) + msgs = builder.build_messages(mem) + text = msgs[0]["content"][-1]["text"] + assert "You are a robot operating in a home" in text + assert "Action Descriptions and Validity Rules" in text + assert "Task Execution Example" in text + assert "action history:" in text + + def test_first_turn_instruction_format(self, builder, obs, action_space): + """First turn has specific instruction format from VLMPlanner.""" + mem = _make_memory(action_space, obs) + msgs = builder.build_messages(mem) + text = msgs[0]["content"][-1]["text"] + assert "Now the human instruction is: Put a mug on the shelf." in text + assert "describe current visual state from the image" in text + + def test_subsequent_turn_reflection_prompt(self, builder, obs, action_space): + """Subsequent turns include reflection prompt from VLMPlanner.""" + mem = _make_memory(action_space, obs, steps=[ + ("find a Mug", "success", "resp"), + ]) + msgs = builder.build_messages(mem) + text = msgs[0]["content"][-1]["text"] + assert "Considering the above interaction history" in text + assert "reason why the last action or plan failed" in text + + def test_guidelines_section(self, builder, obs, action_space): + """Guidelines section matches VLMPlanner system prompt.""" + mem = _make_memory(action_space, obs) + msgs = builder.build_messages(mem) + text = msgs[0]["content"][-1]["text"] + assert "Avoid generating empty plan" in text + assert "Prevent Repeating Action Sequences" in text + assert "Multiple Instances" in text + assert "Reflection on History and Feedback" in text + + def test_no_feedback_mode(self, obs): + """use_feedback=False omits env feedback from history.""" + builder = EBAlfredPromptBuilder(n_shot=10, split="base", use_feedback=False) + actions = get_global_action_space() + mem = _make_memory(actions, obs, task="test", steps=[ + ("find a Mug", "success", "resp"), + ]) + msgs = builder.build_messages(mem) + text = msgs[0]["content"][-1]["text"] + assert "env feedback:" not in text + + def test_action_id_map(self, builder): + """Action ID map correctly maps names to indices.""" + assert builder.action_name_to_id("find a Cart") == 0 + assert builder.action_name_to_id("find a Potato") == 1 + assert builder.action_id_to_name(0) == "find a Cart" + + def test_n_shot_0_no_examples(self, obs): + """n_shot=0 produces no examples in prompt.""" + builder = EBAlfredPromptBuilder(n_shot=0, split="base") + actions = get_global_action_space() + mem = _make_memory(actions, obs, task="test") + msgs = builder.build_messages(mem) + text = msgs[0]["content"][-1]["text"] + assert "Task Execution Example" not in text + + +class TestStatelessAgent: + @pytest.fixture + def mock_llm(self): + class MockLLM: + def __init__(self): + self.call_count = 0 + self.last_messages = None + + def generate(self, messages): + self.call_count += 1 + self.last_messages = messages + return json.dumps({ + "visual_state_description": "I see a room.", + "reasoning_and_reflection": "I need to find the mug.", + "language_plan": "1. find a Mug", + "executable_plan": [ + {"action_id": 14, "action_name": "find a Mug"} + ], + }) + return MockLLM() + + def test_no_history_accumulation(self, mock_llm): + """Stateless builder: memory grows but prompt is always rebuilt from scratch.""" + from easi.agents.react_agent import ReActAgent + + actions = get_global_action_space() + builder = EBAlfredPromptBuilder(n_shot=10, split="base") + agent = ReActAgent( + llm_client=mock_llm, + action_space=actions, + prompt_builder=builder, + ) + obs = Observation(rgb_path="/tmp/rgb.png") + agent.act(obs, "Put a mug on the shelf.") + agent.add_feedback("find a Mug", "success") + # Memory should have steps but builder rebuilds each turn + assert len(agent.memory.steps) == 1 + agent.act(obs, "Put a mug on the shelf.") + assert len(agent.memory.steps) == 2 + + def test_action_id_parsing(self, mock_llm): + """Parses {"action_id": int, "action_name": str} format.""" + from easi.agents.react_agent import ReActAgent + + actions = get_global_action_space() + builder = EBAlfredPromptBuilder(n_shot=10, split="base") + agent = ReActAgent( + llm_client=mock_llm, + action_space=actions, + prompt_builder=builder, + ) + obs = Observation(rgb_path="/tmp/rgb.png") + action = agent.act(obs, "Put a mug on the shelf.") + assert action.action_name == "find a Mug" + + def test_update_action_space(self, mock_llm): + """Dynamic action space update works.""" + from easi.agents.react_agent import ReActAgent + + actions = get_global_action_space() + builder = EBAlfredPromptBuilder(n_shot=10, split="base") + agent = ReActAgent( + llm_client=mock_llm, + action_space=actions, + prompt_builder=builder, + ) + + new_actions = actions + ["find a Cabinet_2", "find a Cabinet_3"] + agent.update_action_space(new_actions) + assert "find a Cabinet_2" in agent.action_space + assert builder.action_name_to_id("find a Cabinet_2") is not None + + def test_buffer_cleared_on_invalid(self, mock_llm): + """'invalid' in feedback clears buffer.""" + from easi.agents.react_agent import ReActAgent + + # LLM returns multi-action plan + mock_llm.generate = lambda msgs: json.dumps({ + "executable_plan": [ + {"action_id": 14, "action_name": "find a Mug"}, + {"action_id": 0, "action_name": "find a Cart"}, + ], + }) + + actions = get_global_action_space() + builder = EBAlfredPromptBuilder(n_shot=10, split="base") + agent = ReActAgent( + llm_client=mock_llm, + action_space=actions, + prompt_builder=builder, + ) + + obs = Observation(rgb_path="/tmp/rgb.png") + agent.act(obs, "test") # returns find a Mug, buffers find a Cart + assert len(agent._action_buffer) == 1 + + agent.add_feedback("find a Mug", "invalid action") + assert len(agent._action_buffer) == 0 + + def test_json_repair_applied(self): + """JSON repair is applied via parse_response.""" + from easi.agents.react_agent import ReActAgent + + class MockLLMWithBadJson: + def generate(self, messages): + return "{'visual_state_description': 'room', 'reasoning_and_reflection': 'think', 'language_plan': 'plan', 'executable_plan': [{'action_id': 14, 'action_name': 'find a Mug'}]}" + + actions = get_global_action_space() + builder = EBAlfredPromptBuilder(n_shot=10, split="base") + agent = ReActAgent( + llm_client=MockLLMWithBadJson(), + action_space=actions, + prompt_builder=builder, + ) + obs = Observation(rgb_path="/tmp/rgb.png") + action = agent.act(obs, "test") + assert action.action_name == "find a Mug" + + def test_reset_clears_memory(self, mock_llm): + """Reset clears memory.""" + from easi.agents.react_agent import ReActAgent + + actions = get_global_action_space() + builder = EBAlfredPromptBuilder(n_shot=10, split="base") + agent = ReActAgent( + llm_client=mock_llm, + action_space=actions, + prompt_builder=builder, + ) + obs = Observation(rgb_path="/tmp/rgb.png") + agent.act(obs, "test") + assert len(agent.memory.steps) == 1 + agent.reset() + assert len(agent.memory.steps) == 0 + + +class TestChatHistoryMode: + """Tests for chat_history=True mode matching VLMPlanner.""" + + @pytest.fixture + def ch_builder(self, action_space): + b = EBAlfredPromptBuilder(n_shot=10, split="base", chat_history=True) + b.set_action_space(action_space) + return b + + def test_first_turn_same_as_stateless(self, ch_builder, obs, action_space): + """First turn prompt is identical for both modes.""" + stateless_builder = EBAlfredPromptBuilder(n_shot=10, split="base", chat_history=False) + stateless_builder.set_action_space(action_space) + + mem = _make_memory(action_space, obs) + ch_msgs = ch_builder.build_messages(mem) + st_msgs = stateless_builder.build_messages(mem) + + ch_text = ch_msgs[0]["content"][-1]["text"] + st_text = st_msgs[0]["content"][-1]["text"] + assert ch_text == st_text + + def test_subsequent_turn_no_system_prompt(self, ch_builder, obs, action_space): + """Subsequent turns do NOT contain 'You are a robot' system prompt.""" + mem = _make_memory(action_space, obs, steps=[ + ("find a AlarmClock", "Last action executed successfully.", "resp1"), + ]) + msgs = ch_builder.build_messages(mem) + # Last message is the current turn + last_text = msgs[-1]["content"][-1]["text"] + assert "You are a robot operating in a home" not in last_text + + def test_subsequent_turn_no_examples(self, ch_builder, obs, action_space): + """Subsequent turns do NOT contain examples.""" + mem = _make_memory(action_space, obs, steps=[ + ("find a AlarmClock", "Last action executed successfully.", "resp1"), + ]) + msgs = ch_builder.build_messages(mem) + last_text = msgs[-1]["content"][-1]["text"] + assert "Task Execution Example" not in last_text + + def test_subsequent_turn_instruction_prefix(self, ch_builder, obs, action_space): + """Subsequent turns use 'The human instruction is:' not '## Now the'.""" + mem = _make_memory(action_space, obs, steps=[ + ("find a AlarmClock", "Last action executed successfully.", "resp1"), + ]) + msgs = ch_builder.build_messages(mem) + last_text = msgs[-1]["content"][-1]["text"] + assert "The human instruction is: Put a mug on the shelf." in last_text + assert "## Now the human instruction is" not in last_text + + def test_subsequent_turn_has_history(self, ch_builder, obs, action_space): + """Subsequent turns include action history.""" + mem = _make_memory(action_space, obs, steps=[ + ("find a AlarmClock", "Last action executed successfully.", "resp1"), + ]) + msgs = ch_builder.build_messages(mem) + last_text = msgs[-1]["content"][-1]["text"] + alarm_id = ch_builder.action_name_to_id("find a AlarmClock") + assert f"Step 0, action id {alarm_id}, find a AlarmClock" in last_text + + def test_subsequent_turn_has_output_template(self, ch_builder, obs, action_space): + """Subsequent turns include OUTPUT_TEMPLATE.""" + mem = _make_memory(action_space, obs, steps=[ + ("find a AlarmClock", "Last action executed successfully.", "resp1"), + ]) + msgs = ch_builder.build_messages(mem) + last_text = msgs[-1]["content"][-1]["text"] + assert "visual_state_description" in last_text + assert "avoid using any contractions" in last_text + + def test_subsequent_turn_has_reflection(self, ch_builder, obs, action_space): + """Subsequent turns include reflection prompt.""" + mem = _make_memory(action_space, obs, steps=[ + ("find a AlarmClock", "Last action executed successfully.", "resp1"), + ]) + msgs = ch_builder.build_messages(mem) + last_text = msgs[-1]["content"][-1]["text"] + assert "Considering the above interaction history" in last_text + + def test_message_accumulation(self, ch_builder, obs, action_space): + """Messages grow with user/assistant pairs.""" + mem = _make_memory(action_space, obs, steps=[ + ("find a AlarmClock", "success", "resp1"), + ]) + msgs = ch_builder.build_messages(mem) + # 1 past user + 1 past assistant + 1 current user = 3 + assert len(msgs) == 3 + assert msgs[0]["role"] == "user" + assert msgs[1]["role"] == "assistant" + assert msgs[2]["role"] == "user" + + # Add another step + mem2 = _make_memory(action_space, obs, steps=[ + ("find a AlarmClock", "success", "resp1"), + ("find a Mug", "success", "resp2"), + ]) + msgs2 = ch_builder.build_messages(mem2) + # 2 past user + 2 past assistant + 1 current user = 5 + assert len(msgs2) == 5 + + def test_assistant_response_list_format(self, ch_builder, obs, action_space): + """Assistant response uses [{"type": "text", "text": ...}] format.""" + mem = _make_memory(action_space, obs, steps=[ + ("find a AlarmClock", "success", "resp1"), + ]) + msgs = ch_builder.build_messages(mem) + assistant_msg = msgs[1] + assert assistant_msg["role"] == "assistant" + assert isinstance(assistant_msg["content"], list) + assert assistant_msg["content"][0]["type"] == "text" + assert assistant_msg["content"][0]["text"] == "resp1" + + def test_buffered_steps_skipped_in_messages(self, ch_builder, obs, action_space): + """Steps with llm_response=None (buffered) don't produce user/assistant pairs.""" + mem = AgentMemory( + task_description="Put a mug on the shelf.", + action_space=action_space, + current_observation=obs, + ) + # Step 0: LLM call + mem.record_step(obs, Action(action_name="find a AlarmClock"), llm_response="resp1") + mem.record_feedback("success") + # Step 1: buffered (no LLM call) + mem.record_step(obs, Action(action_name="find a Mug"), llm_response=None) + mem.record_feedback("success") + + msgs = ch_builder.build_messages(mem) + # Only 1 past user/assistant pair (step 0), skip step 1, + 1 current user = 3 + assert len(msgs) == 3 + + +class TestParseResponse: + """Tests for EBAlfredPromptBuilder.parse_response.""" + + @pytest.fixture + def builder(self, action_space): + b = EBAlfredPromptBuilder(n_shot=10, split="base") + b.set_action_space(action_space) + return b + + def test_parse_valid_action_id_format(self, builder, action_space): + """Parses {"action_id": int, "action_name": str}.""" + mem = AgentMemory(action_space=action_space) + response = json.dumps({ + "executable_plan": [{"action_id": 14, "action_name": "find a Mug"}], + }) + actions = builder.parse_response(response, mem) + assert len(actions) == 1 + assert actions[0].action_name == "find a Mug" + + def test_parse_valid_action_format(self, builder, action_space): + """Parses {"action": str}.""" + mem = AgentMemory(action_space=action_space) + response = json.dumps({ + "executable_plan": [{"action": "find a Mug"}], + }) + actions = builder.parse_response(response, mem) + assert len(actions) == 1 + assert actions[0].action_name == "find a Mug" + + def test_parse_json_repair(self, builder, action_space): + """Single quotes are fixed by JSON repair.""" + mem = AgentMemory(action_space=action_space) + response = "{'executable_plan': [{'action_id': 14, 'action_name': 'find a Mug'}]}" + actions = builder.parse_response(response, mem) + assert len(actions) == 1 + assert actions[0].action_name == "find a Mug" + + def test_parse_invalid_action_stops(self, builder, action_space): + """Stops processing on first invalid action.""" + mem = AgentMemory(action_space=action_space) + response = json.dumps({ + "executable_plan": [ + {"action_id": 14, "action_name": "find a Mug"}, + {"action_id": 9999, "action_name": "fly to moon"}, + ], + }) + actions = builder.parse_response(response, mem) + assert len(actions) == 1 + assert actions[0].action_name == "find a Mug" + + def test_parse_invalid_json_returns_empty(self, builder, action_space): + """Completely invalid JSON returns empty list.""" + mem = AgentMemory(action_space=action_space) + actions = builder.parse_response("not json at all", mem) + assert actions == [] diff --git a/tests/test_react_agent.py b/tests/test_react_agent.py index fa77702..28724ed 100644 --- a/tests/test_react_agent.py +++ b/tests/test_react_agent.py @@ -1,11 +1,12 @@ """Tests for the ReAct agent and PromptBuilder protocol.""" -import base64 import json + import pytest -from easi.agents.prompt_builder import PromptBuilderProtocol, DefaultPromptBuilder +from easi.agents.prompt_builder import DefaultPromptBuilder, PromptBuilderProtocol from easi.agents.react_agent import ReActAgent from easi.core.episode import Action, Observation +from easi.core.memory import AgentMemory class MockLLMClient: @@ -25,75 +26,93 @@ def generate(self, messages): class TestDefaultPromptBuilder: - def test_build_system_prompt_returns_messages(self): + def test_build_messages_returns_system_and_user(self): builder = DefaultPromptBuilder() - messages = builder.build_system_prompt( - action_space=["MoveAhead", "Stop"], + memory = AgentMemory( task_description="Go to the kitchen.", + action_space=["MoveAhead", "Stop"], + current_observation=Observation(rgb_path="/tmp/rgb.png"), ) - # Decision #10: returns OpenAI message format + messages = builder.build_messages(memory) assert isinstance(messages, list) assert messages[0]["role"] == "system" - content = messages[0]["content"] - assert "MoveAhead" in content - assert "JSON" in content + assert "MoveAhead" in messages[0]["content"] + assert "JSON" in messages[0]["content"] + assert messages[1]["role"] == "user" - def test_build_step_prompt_returns_messages(self): - obs = Observation(rgb_path="/tmp/rgb.png") - builder = DefaultPromptBuilder() - messages = builder.build_step_prompt( - observation=obs, - task_description="Go to the kitchen.", - action_history=[], - ) - # Decision #10: returns list of OpenAI message dicts - assert isinstance(messages, list) - assert messages[0]["role"] == "user" - # Content should be a list with text + image parts - content = messages[0]["content"] - assert isinstance(content, list) - text_parts = [p for p in content if p["type"] == "text"] - assert len(text_parts) >= 1 - - def test_build_step_prompt_includes_image(self, tmp_path): - # Create a real image file + def test_build_messages_includes_image(self, tmp_path): img_path = tmp_path / "test.png" img_path.write_bytes(b'\x89PNG\r\n\x1a\n' + b'\x00' * 100) - obs = Observation(rgb_path=str(img_path)) builder = DefaultPromptBuilder() - messages = builder.build_step_prompt( - observation=obs, + memory = AgentMemory( task_description="Go to the kitchen.", - action_history=[], + action_space=["MoveAhead", "Stop"], + current_observation=Observation(rgb_path=str(img_path)), ) - content = messages[0]["content"] + messages = builder.build_messages(memory) + content = messages[1]["content"] image_parts = [p for p in content if p["type"] == "image_url"] assert len(image_parts) == 1 assert image_parts[0]["image_url"]["url"].startswith("data:image/png;base64,") - def test_build_step_prompt_with_history(self): - obs = Observation(rgb_path="/tmp/rgb.png") + def test_build_messages_with_history(self): builder = DefaultPromptBuilder() - messages = builder.build_step_prompt( - observation=obs, + memory = AgentMemory( task_description="Go to the kitchen.", - action_history=[("MoveAhead", "success"), ("TurnLeft", "failed")], + action_space=["MoveAhead", "TurnLeft", "Stop"], + current_observation=Observation(rgb_path="/tmp/rgb.png"), ) + obs = Observation(rgb_path="/tmp/rgb.png") + memory.record_step(obs, Action(action_name="MoveAhead"), llm_response="r1") + memory.record_feedback("success") + memory.record_step(obs, Action(action_name="TurnLeft"), llm_response="r2") + memory.record_feedback("failed") + messages = builder.build_messages(memory) text_content = "" - for part in messages[0]["content"]: + for part in messages[1]["content"]: if part["type"] == "text": text_content += part["text"] assert "MoveAhead" in text_content assert "TurnLeft" in text_content + def test_parse_response_valid(self): + builder = DefaultPromptBuilder() + memory = AgentMemory(action_space=["MoveAhead", "TurnLeft", "Stop"]) + response = json.dumps({ + "executable_plan": [{"action": "MoveAhead"}, {"action": "TurnLeft"}], + }) + actions = builder.parse_response(response, memory) + assert len(actions) == 2 + assert actions[0].action_name == "MoveAhead" + assert actions[1].action_name == "TurnLeft" + + def test_parse_response_invalid_json(self): + builder = DefaultPromptBuilder() + memory = AgentMemory(action_space=["MoveAhead", "Stop"]) + actions = builder.parse_response("not json", memory) + assert actions == [] + + def test_parse_response_invalid_action(self): + builder = DefaultPromptBuilder() + memory = AgentMemory(action_space=["MoveAhead", "Stop"]) + response = json.dumps({ + "executable_plan": [{"action": "FlyToMoon"}], + }) + actions = builder.parse_response(response, memory) + assert actions == [] + class CustomPromptBuilder: """A custom prompt builder for testing the delegation pattern.""" - def build_system_prompt(self, action_space, task_description): - return [{"role": "system", "content": f"CUSTOM SYSTEM: {task_description}"}] + def build_messages(self, memory): + return [ + {"role": "system", "content": f"CUSTOM SYSTEM: {memory.task_description}"}, + {"role": "user", "content": f"CUSTOM STEP: steps={len(memory.steps)}"}, + ] - def build_step_prompt(self, observation, task_description, action_history): - return [{"role": "user", "content": f"CUSTOM STEP: history_len={len(action_history)}"}] + def parse_response(self, llm_response, memory): + data = json.loads(llm_response) + return [Action(action_name=e["action"]) for e in data.get("executable_plan", [])] class TestReActAgent: @@ -137,23 +156,16 @@ def test_calls_llm_once(self, agent): agent.act(obs, "Go to the goal.") assert agent.llm_client.call_count == 1 - def test_chat_history_has_system_prompt(self, agent): - obs = Observation(rgb_path="/tmp/rgb.png") - agent.act(obs, "Go to the goal.") - history = agent.chat_history - assert history[0]["role"] == "system" - def test_default_prompt_builder_used(self, agent): assert isinstance(agent.prompt_builder, DefaultPromptBuilder) def test_custom_prompt_builder(self, custom_agent): obs = Observation(rgb_path="/tmp/rgb.png") custom_agent.act(obs, "Go to the goal.") - history = custom_agent.chat_history - assert history[0]["role"] == "system" - assert "CUSTOM SYSTEM:" in history[0]["content"] + # Agent should have recorded the step in memory + assert len(custom_agent.memory.steps) == 1 - # --- Decision #9: Action buffering tests --- + # --- Action buffering tests --- def test_multi_action_buffer(self, multi_action_agent): """LLM returns 3 actions; first act() returns first, subsequent act()s @@ -162,30 +174,29 @@ def test_multi_action_buffer(self, multi_action_agent): a1 = multi_action_agent.act(obs, "Go to the goal.") assert a1.action_name == "MoveAhead" - assert multi_action_agent.llm_client.call_count == 1 # LLM called + assert multi_action_agent.llm_client.call_count == 1 multi_action_agent.add_feedback("MoveAhead", "success") a2 = multi_action_agent.act(obs, "Go to the goal.") assert a2.action_name == "TurnLeft" - assert multi_action_agent.llm_client.call_count == 1 # NOT called again + assert multi_action_agent.llm_client.call_count == 1 multi_action_agent.add_feedback("TurnLeft", "success") a3 = multi_action_agent.act(obs, "Go to the goal.") assert a3.action_name == "MoveAhead" - assert multi_action_agent.llm_client.call_count == 1 # Still NOT called + assert multi_action_agent.llm_client.call_count == 1 def test_buffer_cleared_on_failure(self, multi_action_agent): """When add_feedback reports failure, buffer is cleared. Next act() re-queries LLM.""" obs = Observation(rgb_path="/tmp/rgb.png") - multi_action_agent.act(obs, "Go to the goal.") # returns MoveAhead, buffers [TurnLeft, MoveAhead] + multi_action_agent.act(obs, "Go to the goal.") assert multi_action_agent.llm_client.call_count == 1 multi_action_agent.add_feedback("MoveAhead", "failed: obstacle ahead") - # Buffer should be cleared - multi_action_agent.act(obs, "Go to the goal.") # should re-query LLM + multi_action_agent.act(obs, "Go to the goal.") assert multi_action_agent.llm_client.call_count == 2 def test_buffer_empty_after_all_consumed(self, multi_action_agent): @@ -200,12 +211,11 @@ def test_buffer_empty_after_all_consumed(self, multi_action_agent): multi_action_agent.add_feedback("MoveAhead", "success") assert multi_action_agent.llm_client.call_count == 1 - # Buffer exhausted — next call should query LLM multi_action_agent.act(obs, "Go to the goal.") assert multi_action_agent.llm_client.call_count == 2 def test_parse_error_returns_stop(self): - """When LLM returns invalid JSON, agent returns Stop (no buffering).""" + """When LLM returns invalid JSON, agent returns Stop.""" llm = type('MockLLM', (), {'generate': lambda self, m: 'not json at all'})() agent = ReActAgent( llm_client=llm, @@ -214,7 +224,7 @@ def test_parse_error_returns_stop(self): obs = Observation(rgb_path="/tmp/rgb.png") action = agent.act(obs, "Go to the goal.") assert action.action_name == "Stop" - assert not agent._action_buffer # buffer empty + assert not agent._action_buffer def test_invalid_action_name_fallback(self): """When LLM returns action not in action_space, fallback to Stop.""" @@ -227,10 +237,23 @@ def test_invalid_action_name_fallback(self): action = agent.act(obs, "Go to the goal.") assert action.action_name == "Stop" - def test_reset_clears_buffer_and_history(self, multi_action_agent): + def test_reset_clears_buffer_and_memory(self, multi_action_agent): obs = Observation(rgb_path="/tmp/rgb.png") multi_action_agent.act(obs, "Go to the goal.") multi_action_agent.reset() - assert len(multi_action_agent.chat_history) == 0 + assert len(multi_action_agent.memory.steps) == 0 assert len(multi_action_agent._action_buffer) == 0 - assert len(multi_action_agent._action_feedback) == 0 + + def test_memory_records_steps(self, agent): + obs = Observation(rgb_path="/tmp/rgb.png") + agent.act(obs, "Go to the goal.") + assert len(agent.memory.steps) == 1 + assert agent.memory.steps[0].action.action_name == "MoveAhead" + assert agent.memory.steps[0].llm_response is not None + + def test_buffered_steps_have_no_llm_response(self, multi_action_agent): + obs = Observation(rgb_path="/tmp/rgb.png") + multi_action_agent.act(obs, "Go to the goal.") + multi_action_agent.add_feedback("MoveAhead", "success") + multi_action_agent.act(obs, "Go to the goal.") # buffered + assert multi_action_agent.memory.steps[1].llm_response is None From ad58088facc69b9a814cc31151d00014f20c8cc8 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Sun, 15 Feb 2026 12:19:18 +0800 Subject: [PATCH 017/239] [Refactor] Add response_format pass-through for structured LLM output Builder-owned schema enforcement: prompt builders can now optionally implement get_response_format() to provide a JSON schema dict that gets passed through to litellm. ReActAgent handles fallback automatically when the backend doesn't support response_format. - LLMClient.generate() accepts optional response_format param - ReActAgent._generate_with_fallback() tries schema, caches on failure - EBAlfredPromptBuilder.get_response_format() returns vlm_generation_guide - Remove dead code: instructor dep, Pydantic schemas, monkey-patching --- easi/agents/prompt_builder.py | 13 ++++ easi/agents/react_agent.py | 57 ++++++++++++++- easi/evaluation/runner.py | 15 ---- easi/llm/client.py | 60 +++------------ easi/llm/schemas.py | 36 --------- easi/tasks/ebalfred/prompts.py | 59 +++++++++++++++ pyproject.toml | 1 - tests/test_llm_client.py | 49 ++++++++----- tests/test_llm_full_flow.py | 18 ----- tests/test_llm_imports.py | 6 -- tests/test_llm_schemas.py | 57 --------------- tests/test_prompt_parity.py | 33 +++++++++ tests/test_react_agent.py | 129 ++++++++++++++++++++++++++++++++- 13 files changed, 329 insertions(+), 204 deletions(-) delete mode 100644 easi/llm/schemas.py delete mode 100644 tests/test_llm_schemas.py diff --git a/easi/agents/prompt_builder.py b/easi/agents/prompt_builder.py index 0c36a91..d7280e3 100644 --- a/easi/agents/prompt_builder.py +++ b/easi/agents/prompt_builder.py @@ -51,6 +51,19 @@ class PromptBuilderProtocol(Protocol): Implementations are referenced in task.yaml via: agent: prompt_builder: "easi.tasks.my_task.prompts.MyPromptBuilder" + + Required methods: + build_messages(memory) -> list[dict] + parse_response(llm_response, memory) -> list[Action] + + Optional methods: + get_response_format(memory) -> dict | None + Return a response_format dict for API-level JSON enforcement. + E.g. {"type": "json_schema", "json_schema": {"name": "...", "schema": {...}}} + When provided, the agent passes it to LLMClient.generate(). + If the backend doesn't support it, the agent falls back to + prompt-only (the template is already in the messages). + Builders that don't implement this get no schema enforcement. """ def build_messages(self, memory: AgentMemory) -> list[dict]: diff --git a/easi/agents/react_agent.py b/easi/agents/react_agent.py index b4c4f2d..87bf1a8 100644 --- a/easi/agents/react_agent.py +++ b/easi/agents/react_agent.py @@ -15,6 +15,26 @@ logger = get_logger(__name__) +def _format_messages_for_log(messages: list[dict]) -> str: + """Extract readable text from OpenAI-format messages for logging.""" + parts = [] + for msg in messages: + role = msg.get("role", "?") + content = msg.get("content", "") + if isinstance(content, str): + text = content + elif isinstance(content, list): + text_parts = [p.get("text", "") for p in content if p.get("type") == "text"] + n_images = sum(1 for p in content if p.get("type") == "image_url") + text = "".join(text_parts) + if n_images: + text = f"[{n_images} image(s)]\n{text}" + else: + text = str(content) + parts.append(f"--- {role} ---\n{text}") + return "\n".join(parts) + + class ReActAgent(BaseAgent): """ReAct agent with action buffering and pluggable prompt building. @@ -37,6 +57,7 @@ def __init__( self.prompt_builder: PromptBuilderProtocol = prompt_builder or DefaultPromptBuilder() self.memory = AgentMemory(action_space=self.action_space) self._action_buffer: list[Action] = [] + self._supports_response_format: bool | None = None # None = unknown def reset(self) -> None: super().reset() @@ -67,7 +88,19 @@ def act(self, observation: Observation, task_description: str) -> Action: self.memory.task_description = task_description messages = self.prompt_builder.build_messages(self.memory) - response = self.llm_client.generate(messages) + + logger.trace("Step %d prompt (%d messages):\n%s", + self._step_count + 1, len(messages), + _format_messages_for_log(messages)) + + # Query builder for response_format (optional method) + get_rf = getattr(self.prompt_builder, 'get_response_format', None) + response_format = get_rf(self.memory) if get_rf else None + + response = self._generate_with_fallback(messages, response_format) + + logger.trace("Step %d LLM response:\n%s", + self._step_count + 1, response) actions = self.prompt_builder.parse_response(response, self.memory) if not actions: @@ -100,3 +133,25 @@ def _fallback_action(self) -> Action: if "Stop" in self.action_space: return Action(action_name="Stop") return Action(action_name=self.action_space[0]) + + def _generate_with_fallback( + self, messages: list[dict], response_format: dict | None, + ) -> str: + """Call LLM with optional response_format, falling back on failure. + + If response_format is provided and the backend doesn't support it, + the failure is caught, cached, and retried without response_format. + The prompt template is already in messages, so fallback always works. + """ + if response_format is None or self._supports_response_format is False: + return self.llm_client.generate(messages) + + try: + return self.llm_client.generate(messages, response_format=response_format) + except Exception as e: + logger.warning( + "response_format not supported by backend, " + "falling back to prompt-only: %s", e, + ) + self._supports_response_format = False + return self.llm_client.generate(messages) diff --git a/easi/evaluation/runner.py b/easi/evaluation/runner.py index 3d0ca8d..d971373 100644 --- a/easi/evaluation/runner.py +++ b/easi/evaluation/runner.py @@ -327,21 +327,6 @@ def _create_agent(self, action_space: list[str], task_config: dict, base_url=base_url, **client_kwargs, ) - - # Wrap for structured output if configured - schema_class_name = agent_config.get("response_schema") - if schema_class_name: - import json as _json - SchemaClass = import_class(schema_class_name) - - def _structured_generate(messages): - result = llm.generate_structured(messages, response_model=SchemaClass) - actions = result.get_actions() - return _json.dumps({ - "executable_plan": [{"action": a} for a in actions] - }) - - llm.generate = _structured_generate else: # Legacy path: existing LLMApiClient from easi.llm.api_client import LLMApiClient diff --git a/easi/llm/client.py b/easi/llm/client.py index 2c371fa..e50ed94 100644 --- a/easi/llm/client.py +++ b/easi/llm/client.py @@ -1,8 +1,7 @@ -"""Unified LLM client wrapping LiteLLM + Instructor. +"""Unified LLM client wrapping LiteLLM. -Provides two generation modes: -- generate(): returns raw text (backward-compatible with LLMApiClient) -- generate_structured(): returns a validated Pydantic model (via Instructor) +Provides text generation with optional response_format pass-through +for API-level JSON schema enforcement. Usage tracking is cumulative — call get_usage() to snapshot, reset_usage() between episodes. """ @@ -10,31 +9,26 @@ from typing import Any -from pydantic import BaseModel - from easi.utils.logging import get_logger logger = get_logger(__name__) -# Lazy imports to avoid requiring litellm/instructor when not needed. +# Lazy imports to avoid requiring litellm when not needed. litellm = None -instructor = None def _ensure_imports() -> None: - """Import litellm and instructor on first use.""" - global litellm, instructor + """Import litellm on first use.""" + global litellm if litellm is None: try: import litellm as _litellm - import instructor as _instructor except ImportError as e: raise ImportError( - "LLMClient requires litellm and instructor. " + "LLMClient requires litellm. " "Install with: pip install easi[llm]" ) from e litellm = _litellm - instructor = _instructor # Suppress litellm's verbose logging litellm.suppress_debug_info = True @@ -58,7 +52,7 @@ def __init__( "cost_usd": 0.0, } - def generate(self, messages: list[dict]) -> str: + def generate(self, messages: list[dict], response_format: dict | None = None) -> str: """Generate text completion. Drop-in for LLMApiClient.generate().""" _ensure_imports() @@ -69,6 +63,8 @@ def generate(self, messages: list[dict]) -> str: } if self.base_url: call_kwargs["api_base"] = self.base_url + if response_format is not None: + call_kwargs["response_format"] = response_format logger.trace("LLM call: model=%s, messages=%d", self.model, len(messages)) response = litellm.completion(**call_kwargs) @@ -78,42 +74,6 @@ def generate(self, messages: list[dict]) -> str: logger.trace("LLM response: %s", content[:200] if content else "") return content - def generate_structured( - self, - messages: list[dict], - response_model: type[BaseModel], - ) -> BaseModel: - """Generate structured output validated against a Pydantic model.""" - _ensure_imports() - - client = instructor.from_litellm(litellm.completion) - - call_kwargs: dict[str, Any] = { - "model": self.model, - "messages": messages, - "response_model": response_model, - "max_retries": 2, - **self.default_kwargs, - } - if self.base_url: - call_kwargs["api_base"] = self.base_url - - logger.trace( - "LLM structured call: model=%s, schema=%s", - self.model, response_model.__name__, - ) - result = client.chat.completions.create(**call_kwargs) - - # Track usage from the raw response attached by instructor - raw = getattr(result, "_raw_response", None) - if raw is not None: - self._track_usage(raw) - else: - # Fallback: at minimum count the call - self._usage["num_calls"] += 1 - - return result - def get_usage(self) -> dict: """Return cumulative usage stats (copy).""" return dict(self._usage) diff --git a/easi/llm/schemas.py b/easi/llm/schemas.py deleted file mode 100644 index 30bd230..0000000 --- a/easi/llm/schemas.py +++ /dev/null @@ -1,36 +0,0 @@ -"""Pydantic response schemas for structured LLM output. - -Each task can define its own schema extending BaseResponseSchema. -The schema's get_actions() method normalizes task-specific fields -into a standard list of action strings. -""" -from __future__ import annotations - -from pydantic import BaseModel - - -class BaseResponseSchema(BaseModel): - """Base class for all LLM response schemas. - - Subclasses MUST implement get_actions() to normalize their - task-specific fields into a list of action strings. - """ - - def get_actions(self) -> list[str]: - raise NotImplementedError - - -class ExecutableAction(BaseModel): - """A single action in an executable plan.""" - - action: str - - -class ActionPlanResponse(BaseResponseSchema): - """Default structured response for ReAct agents.""" - - reasoning: str - executable_plan: list[ExecutableAction] - - def get_actions(self) -> list[str]: - return [a.action for a in self.executable_plan] diff --git a/easi/tasks/ebalfred/prompts.py b/easi/tasks/ebalfred/prompts.py index f515945..b54f124 100644 --- a/easi/tasks/ebalfred/prompts.py +++ b/easi/tasks/ebalfred/prompts.py @@ -65,6 +65,56 @@ !!! When generating content for JSON strings, avoid using any contractions or abbreviated forms (like 's, 're, 've, 'll, 'd, n't) that use apostrophes. Instead, write out full forms (is, are, have, will, would, not) to prevent parsing errors in JSON. Please do not output any other thing more than the above-mentioned JSON, do not include ```json and ```!!!. ''' +# JSON schema matching EmbodiedBench's vlm_generation_guide from +# embodiedbench/planner/planner_config/generation_guide.py +EBALFRED_RESPONSE_SCHEMA = { + "type": "json_schema", + "json_schema": { + "name": "embodied_planning", + "schema": { + "type": "object", + "properties": { + "visual_state_description": { + "type": "string", + "description": "Description of current state from the visual image", + }, + "reasoning_and_reflection": { + "type": "string", + "description": "summarize the history of interactions and any available environmental feedback. Additionally, provide reasoning as to why the last action or plan failed and did not finish the task", + }, + "language_plan": { + "type": "string", + "description": "The list of actions to achieve the user instruction. Each action is started by the step number and the action name", + }, + "executable_plan": { + "type": "array", + "description": "A list of actions needed to achieve the user instruction, with each action having an action ID and a name. Do not output empty list.", + "items": { + "type": "object", + "properties": { + "action_id": { + "type": "integer", + "description": "The action ID to select from the available actions given by the prompt", + }, + "action_name": { + "type": "string", + "description": "The name of the action", + }, + }, + "required": ["action_id", "action_name"], + }, + }, + }, + "required": [ + "visual_state_description", + "reasoning_and_reflection", + "language_plan", + "executable_plan", + ], + }, + }, +} + _CONFIG_DIR = Path(__file__).parent / "config" @@ -182,6 +232,15 @@ def parse_response(self, llm_response: str, memory: AgentMemory) -> list[Action] return actions + def get_response_format(self, memory: AgentMemory) -> dict: + """Return JSON schema for API-level enforcement. + + Matches EmbodiedBench's vlm_generation_guide. The OUTPUT_TEMPLATE + is always in the prompt as a fallback for backends that don't + support response_format. + """ + return EBALFRED_RESPONSE_SCHEMA + # ---- Stateless mode (chat_history=False) ---- def _build_stateless_messages(self, memory: AgentMemory) -> list[dict]: diff --git a/pyproject.toml b/pyproject.toml index 50a23a8..12a51c4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -22,7 +22,6 @@ dev = [ ] llm = [ "litellm>=1.40", - "instructor>=1.0", ] [project.scripts] diff --git a/tests/test_llm_client.py b/tests/test_llm_client.py index 2bb26fd..7922499 100644 --- a/tests/test_llm_client.py +++ b/tests/test_llm_client.py @@ -107,31 +107,44 @@ def test_generate_passes_base_url(self, mock_litellm): call_kwargs = mock_litellm.completion.call_args assert call_kwargs.kwargs.get("api_base") == "http://localhost:8080/v1" - -class TestLLMClientGenerateStructured: - @patch("easi.llm.client.instructor") @patch("easi.llm.client.litellm") - def test_generate_structured_returns_model(self, mock_litellm, mock_instructor): + def test_generate_passes_response_format(self, mock_litellm): from easi.llm.client import LLMClient - from easi.llm.schemas import ActionPlanResponse - expected = ActionPlanResponse( - reasoning="I see a mug", - executable_plan=[{"action": "find a Mug"}], - ) + mock_choice = MagicMock() + mock_choice.message.content = '{"executable_plan": [{"action": "Stop"}]}' + mock_response = MagicMock() + mock_response.choices = [mock_choice] + mock_response.usage.prompt_tokens = 10 + mock_response.usage.completion_tokens = 5 + mock_litellm.completion.return_value = mock_response + mock_litellm.completion_cost.return_value = 0.0 + + rf = {"type": "json_schema", "json_schema": {"name": "test", "schema": {"type": "object"}}} + client = LLMClient(model="openai/gpt-4o") + client.generate([{"role": "user", "content": "test"}], response_format=rf) - mock_client = MagicMock() - mock_client.chat.completions.create.return_value = expected - mock_instructor.from_litellm.return_value = mock_client + call_kwargs = mock_litellm.completion.call_args + assert call_kwargs.kwargs.get("response_format") == rf + + @patch("easi.llm.client.litellm") + def test_generate_omits_response_format_when_none(self, mock_litellm): + from easi.llm.client import LLMClient + + mock_choice = MagicMock() + mock_choice.message.content = "ok" + mock_response = MagicMock() + mock_response.choices = [mock_choice] + mock_response.usage.prompt_tokens = 10 + mock_response.usage.completion_tokens = 5 + mock_litellm.completion.return_value = mock_response + mock_litellm.completion_cost.return_value = 0.0 client = LLMClient(model="openai/gpt-4o") - result = client.generate_structured( - [{"role": "user", "content": "test"}], - response_model=ActionPlanResponse, - ) + client.generate([{"role": "user", "content": "test"}]) - assert isinstance(result, ActionPlanResponse) - assert result.get_actions() == ["find a Mug"] + call_kwargs = mock_litellm.completion.call_args + assert "response_format" not in call_kwargs.kwargs class TestLLMClientProtocolCompat: diff --git a/tests/test_llm_full_flow.py b/tests/test_llm_full_flow.py index 28031ce..20cff21 100644 --- a/tests/test_llm_full_flow.py +++ b/tests/test_llm_full_flow.py @@ -48,21 +48,3 @@ def test_client_generate_and_track(self, mock_litellm): client.reset_usage() assert client.get_usage()["num_calls"] == 0 - def test_schema_normalization(self): - from easi.llm.schemas import ActionPlanResponse - - resp = ActionPlanResponse( - reasoning="I should find the apple", - executable_plan=[ - {"action": "find a Apple"}, - {"action": "pick up the Apple"}, - {"action": "clean the Apple"}, - ], - ) - actions = resp.get_actions() - normalized = json.dumps({ - "executable_plan": [{"action": a} for a in actions] - }) - data = json.loads(normalized) - assert len(data["executable_plan"]) == 3 - assert data["executable_plan"][0]["action"] == "find a Apple" diff --git a/tests/test_llm_imports.py b/tests/test_llm_imports.py index 3ccb602..1436ee1 100644 --- a/tests/test_llm_imports.py +++ b/tests/test_llm_imports.py @@ -6,12 +6,6 @@ def test_import_llm_client(self): from easi.llm.client import LLMClient assert LLMClient is not None - def test_import_schemas(self): - from easi.llm.schemas import BaseResponseSchema, ActionPlanResponse, ExecutableAction - assert BaseResponseSchema is not None - assert ActionPlanResponse is not None - assert ExecutableAction is not None - def test_import_server_manager(self): from easi.llm.server_manager import ServerManager assert ServerManager is not None diff --git a/tests/test_llm_schemas.py b/tests/test_llm_schemas.py deleted file mode 100644 index 2195c2a..0000000 --- a/tests/test_llm_schemas.py +++ /dev/null @@ -1,57 +0,0 @@ -"""Tests for LLM response schemas.""" -import json -import pytest -from pydantic import BaseModel - - -class TestBaseResponseSchema: - def test_base_schema_get_actions_raises(self): - from easi.llm.schemas import BaseResponseSchema - - class Bare(BaseResponseSchema): - pass - - with pytest.raises(NotImplementedError): - Bare().get_actions() - - def test_base_schema_is_pydantic_model(self): - from easi.llm.schemas import BaseResponseSchema - assert issubclass(BaseResponseSchema, BaseModel) - - -class TestActionPlanResponse: - def test_get_actions_returns_list_of_strings(self): - from easi.llm.schemas import ActionPlanResponse - - resp = ActionPlanResponse( - reasoning="I see a mug", - executable_plan=[ - {"action": "find a Mug"}, - {"action": "pick up the Mug"}, - ], - ) - assert resp.get_actions() == ["find a Mug", "pick up the Mug"] - - def test_empty_plan(self): - from easi.llm.schemas import ActionPlanResponse - - resp = ActionPlanResponse(reasoning="nothing to do", executable_plan=[]) - assert resp.get_actions() == [] - - def test_roundtrip_json(self): - from easi.llm.schemas import ActionPlanResponse - - resp = ActionPlanResponse( - reasoning="test", - executable_plan=[{"action": "Stop"}], - ) - data = json.loads(resp.model_dump_json()) - assert data["executable_plan"][0]["action"] == "Stop" - - -class TestExecutableAction: - def test_action_field(self): - from easi.llm.schemas import ExecutableAction - - a = ExecutableAction(action="MoveAhead") - assert a.action == "MoveAhead" diff --git a/tests/test_prompt_parity.py b/tests/test_prompt_parity.py index 1396d9f..c2a55e1 100644 --- a/tests/test_prompt_parity.py +++ b/tests/test_prompt_parity.py @@ -508,3 +508,36 @@ def test_parse_invalid_json_returns_empty(self, builder, action_space): mem = AgentMemory(action_space=action_space) actions = builder.parse_response("not json at all", mem) assert actions == [] + + +class TestGetResponseFormat: + """Test that get_response_format returns correct schema.""" + + def test_returns_json_schema_dict(self): + builder = EBAlfredPromptBuilder(n_shot=1, split="base") + builder.set_action_space(["find a Mug", "pick up the Mug", "Stop"]) + memory = AgentMemory( + action_space=["find a Mug", "pick up the Mug", "Stop"], + ) + rf = builder.get_response_format(memory) + assert rf["type"] == "json_schema" + assert rf["json_schema"]["name"] == "embodied_planning" + schema = rf["json_schema"]["schema"] + assert "executable_plan" in schema["properties"] + assert schema["properties"]["executable_plan"]["type"] == "array" + items = schema["properties"]["executable_plan"]["items"] + assert "action_id" in items["properties"] + assert "action_name" in items["properties"] + + def test_schema_matches_vlm_generation_guide(self): + """Verify our schema has the same required fields as EmbodiedBench.""" + from easi.tasks.ebalfred.prompts import EBALFRED_RESPONSE_SCHEMA + schema = EBALFRED_RESPONSE_SCHEMA["json_schema"]["schema"] + assert set(schema["required"]) == { + "visual_state_description", + "reasoning_and_reflection", + "language_plan", + "executable_plan", + } + items_required = set(schema["properties"]["executable_plan"]["items"]["required"]) + assert items_required == {"action_id", "action_name"} diff --git a/tests/test_react_agent.py b/tests/test_react_agent.py index 28724ed..a55e24d 100644 --- a/tests/test_react_agent.py +++ b/tests/test_react_agent.py @@ -14,9 +14,11 @@ class MockLLMClient: def __init__(self, actions=None): self.actions = actions or [{"action": "MoveAhead"}] self.call_count = 0 + self.last_response_format = None - def generate(self, messages): + def generate(self, messages, response_format=None): self.call_count += 1 + self.last_response_format = response_format return json.dumps({ "observation": "I see a room.", "reasoning": "I should move forward.", @@ -216,7 +218,7 @@ def test_buffer_empty_after_all_consumed(self, multi_action_agent): def test_parse_error_returns_stop(self): """When LLM returns invalid JSON, agent returns Stop.""" - llm = type('MockLLM', (), {'generate': lambda self, m: 'not json at all'})() + llm = type('MockLLM', (), {'generate': lambda self, m, response_format=None: 'not json at all'})() agent = ReActAgent( llm_client=llm, action_space=["MoveAhead", "TurnLeft", "TurnRight", "Stop"], @@ -257,3 +259,126 @@ def test_buffered_steps_have_no_llm_response(self, multi_action_agent): multi_action_agent.add_feedback("MoveAhead", "success") multi_action_agent.act(obs, "Go to the goal.") # buffered assert multi_action_agent.memory.steps[1].llm_response is None + + +_SENTINEL = object() + + +class SchemaPromptBuilder: + """Builder that provides a response_format schema.""" + def __init__(self, schema=_SENTINEL): + self._schema = {"type": "json_object"} if schema is _SENTINEL else schema + + def build_messages(self, memory): + return [{"role": "user", "content": "test"}] + + def parse_response(self, llm_response, memory): + data = json.loads(llm_response) + return [Action(action_name=e["action"]) for e in data.get("executable_plan", [])] + + def get_response_format(self, memory): + return self._schema + + +class TestResponseFormatFallback: + def test_response_format_passed_when_builder_provides_it(self): + """When builder has get_response_format(), it's passed to generate().""" + llm = MockLLMClient([{"action": "MoveAhead"}]) + schema = {"type": "json_schema", "json_schema": {"name": "test", "schema": {"type": "object"}}} + agent = ReActAgent( + llm_client=llm, + action_space=["MoveAhead", "Stop"], + prompt_builder=SchemaPromptBuilder(schema=schema), + ) + obs = Observation(rgb_path="/tmp/rgb.png") + agent.act(obs, "Go.") + assert llm.last_response_format == schema + + def test_no_response_format_when_builder_lacks_method(self): + """When builder doesn't have get_response_format(), no response_format is passed.""" + llm = MockLLMClient([{"action": "MoveAhead"}]) + agent = ReActAgent( + llm_client=llm, + action_space=["MoveAhead", "Stop"], + prompt_builder=CustomPromptBuilder(), # no get_response_format + ) + obs = Observation(rgb_path="/tmp/rgb.png") + agent.act(obs, "Go.") + assert llm.last_response_format is None + + def test_fallback_on_exception(self): + """When generate() raises with response_format, agent retries without it.""" + call_log = [] + + class FailOnSchemaLLM: + call_count = 0 + last_response_format = None + + def generate(self, messages, response_format=None): + self.call_count += 1 + call_log.append(response_format) + if response_format is not None: + raise Exception("response_format not supported") + return json.dumps({ + "executable_plan": [{"action": "MoveAhead"}], + }) + + llm = FailOnSchemaLLM() + agent = ReActAgent( + llm_client=llm, + action_space=["MoveAhead", "Stop"], + prompt_builder=SchemaPromptBuilder(), + ) + obs = Observation(rgb_path="/tmp/rgb.png") + action = agent.act(obs, "Go.") + + assert action.action_name == "MoveAhead" + assert llm.call_count == 2 # first with schema (failed), second without + assert call_log[0] is not None # first call had schema + assert call_log[1] is None # second call without schema + + def test_fallback_cached_after_first_failure(self): + """After first failure, subsequent calls skip response_format entirely.""" + call_log = [] + + class FailOnSchemaLLM: + call_count = 0 + + def generate(self, messages, response_format=None): + self.call_count += 1 + call_log.append(response_format) + if response_format is not None: + raise Exception("not supported") + return json.dumps({ + "executable_plan": [{"action": "MoveAhead"}], + }) + + llm = FailOnSchemaLLM() + agent = ReActAgent( + llm_client=llm, + action_space=["MoveAhead", "Stop"], + prompt_builder=SchemaPromptBuilder(), + ) + obs = Observation(rgb_path="/tmp/rgb.png") + + # First call: tries with schema, fails, retries without = 2 calls + agent.act(obs, "Go.") + assert llm.call_count == 2 + + # Second call: skips schema entirely = 1 call + agent.add_feedback("MoveAhead", "success") + agent.act(obs, "Go.") + assert llm.call_count == 3 # only 1 more call, not 2 + assert call_log[2] is None # went straight to no-schema + + def test_response_format_none_returns_none(self): + """Builder returning None means no response_format enforcement.""" + llm = MockLLMClient([{"action": "MoveAhead"}]) + agent = ReActAgent( + llm_client=llm, + action_space=["MoveAhead", "Stop"], + prompt_builder=SchemaPromptBuilder(schema=None), + ) + obs = Observation(rgb_path="/tmp/rgb.png") + agent.act(obs, "Go.") + assert llm.last_response_format is None From 33aa6206ddc0b7bdcef71ffeeb0d88f5289b3498 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Sun, 15 Feb 2026 12:27:20 +0800 Subject: [PATCH 018/239] [Fix] Upated task yaml for ebalfred --- easi/tasks/ebalfred/bridge.py | 12 ++++++++++++ easi/tasks/ebalfred/config/alfred_examples.json | 12 ++++++++++++ .../config/alfred_long_horizon_examples.json | 9 +++++++++ easi/tasks/ebalfred/ebalfred_base.yaml | 1 + easi/tasks/ebalfred/ebalfred_common_sense.yaml | 1 + .../tasks/ebalfred/ebalfred_complex_instruction.yaml | 1 + easi/tasks/ebalfred/ebalfred_long_horizon.yaml | 3 ++- easi/tasks/ebalfred/ebalfred_spatial.yaml | 1 + easi/tasks/ebalfred/ebalfred_visual_appearance.yaml | 1 + 9 files changed, 40 insertions(+), 1 deletion(-) create mode 100644 easi/tasks/ebalfred/config/alfred_examples.json create mode 100644 easi/tasks/ebalfred/config/alfred_long_horizon_examples.json diff --git a/easi/tasks/ebalfred/bridge.py b/easi/tasks/ebalfred/bridge.py index f5a2468..297f832 100644 --- a/easi/tasks/ebalfred/bridge.py +++ b/easi/tasks/ebalfred/bridge.py @@ -13,6 +13,7 @@ from __future__ import annotations +import json import sys from pathlib import Path @@ -66,6 +67,17 @@ def _extract_info(self, info): "feedback": str(info.get("env_feedback", "")), } + def _make_response(self, obs, reward=0.0, done=False, info=None): + """Override to include dynamic action space in reset response metadata.""" + response = super()._make_response(obs, reward, done, info) + # After reset, env.language_skill_set contains the per-episode + # dynamic action space (global actions + scene-specific instances). + if self.env is not None and hasattr(self.env, 'language_skill_set'): + response["observation"]["metadata"]["dynamic_action_space"] = json.dumps( + self.env.language_skill_set + ) + return response + if __name__ == "__main__": EBAlfredBridge.main() diff --git a/easi/tasks/ebalfred/config/alfred_examples.json b/easi/tasks/ebalfred/config/alfred_examples.json new file mode 100644 index 0000000..dfef5ab --- /dev/null +++ b/easi/tasks/ebalfred/config/alfred_examples.json @@ -0,0 +1,12 @@ +[ + "Human instruction: Pick up the alarm clock and turn on the lamp.\nOutput: {\n'language_plan': 'First, locate the alarm clock in the room. Once found, carefully pick up the alarm clock. Next, identify the location of the desk lamp. After locating the desk lamp, proceed to turn it on.',\n'executable_plan': [\n{\"action_id\": 57, \"action_name\": \"find a AlarmClock\"}\n{\"action_id\": 98, \"action_name\": \"pick up the AlarmClock\"}\n{\"action_id\": 63, \"action_name\": \"find a DeskLamp\"}\n{\"action_id\": 151, \"action_name\": \"turn on the DeskLamp\"}\n]}", + "Human instruction: Examine a credit card by the light of a tall lamp.\nOutput: {\n'language_plan': 'Start by locating the credit card in the room. Once you have found the credit card, proceed to pick it up. Then, search for the floor lamp and find its location. After finding the floor lamp, turn it on to illuminate the area so you can examine the credit card effectively.',\n'executable_plan': [\n{\"action_id\": 56, \"action_name\": \"find a CreditCard\"}\n{\"action_id\": 96, \"action_name\": \"pick up the CreditCard\"}\n{\"action_id\": 27, \"action_name\": \"find a FloorLamp\"}\n{\"action_id\": 153, \"action_name\": \"turn on the FloorLamp\"}\n]}", + "Human instruction: Use lamp light to look at the remote.\nOutput: {\n'language_plan': 'First, locate the remote control in the room. Once the remote control is found, pick it up carefully. Next, search for the floor lamp and determine its location. After you have located the floor lamp, turn it on to provide sufficient light for examining the remote control.',\n'executable_plan': [\n{\"action_id\": 68, \"action_name\": \"find a RemoteControl\"}\n{\"action_id\": 116, \"action_name\": \"pick up the RemoteControl\"}\n{\"action_id\": 27, \"action_name\": \"find a FloorLamp\"}\n{\"action_id\": 153, \"action_name\": \"turn on the FloorLamp\"}\n]}", + "Human instruction: Set the box on the table.\nOutput: {\n'language_plan': 'First, locate the box within the room. After finding the box, gently pick it up. Next, search for the dining table and identify its location. Once the dining table is found, place the box on the table carefully.',\n'executable_plan': [\n{\"action_id\": 61, \"action_name\": \"find a Box\"}\n{\"action_id\": 103, \"action_name\": \"pick up the Box\"}\n{\"action_id\": 18, \"action_name\": \"find a DiningTable\"}\n{\"action_id\": 133, \"action_name\": \"put down the object in hand\"}\n]}", + "Human instruction: Place a heated slice of tomato in the kitchen sink.\nOutput: {\n'language_plan': 'Find a butter knife and pick it up, locate a tomato and slice it. Find a sink and put down the knife. Locate a slice of tomato and pick it up, find a microwave, and open it. Place the tomato slice inside and close the microwave. Turn on the microwave to heat the tomato slice, then turn off the microwave and open it. Pick up the heated tomato slice, close the microwave, locate the kitchen sink, and place the heated tomato slice in the sink.',\n'executable_plan': [\n{\"action_id\": 36, \"action_name\": \"find a ButterKnife\"}\n{\"action_id\": 130, \"action_name\": \"pick up the ButterKnife\"}\n{\"action_id\": 35, \"action_name\": \"find a Tomato\"}\n{\"action_id\": 159, \"action_name\": \"slice the Tomato\"}\n{\"action_id\": 79, \"action_name\": \"find a Sink\"}\n{\"action_id\": 133, \"action_name\": \"put down the object in hand\"}\n{\"action_id\": 35, \"action_name\": \"find a Tomato\"}\n{\"action_id\": 129, \"action_name\": \"pick up the Tomato\"}\n{\"action_id\": 38, \"action_name\": \"find a Microwave\"}\n{\"action_id\": 143, \"action_name\": \"open the Microwave\"}\n{\"action_id\": 133, \"action_name\": \"put down the object in hand\"}\n{\"action_id\": 144, \"action_name\": \"close the Microwave\"}\n{\"action_id\": 149, \"action_name\": \"turn on the Microwave\"}\n{\"action_id\": 150, \"action_name\": \"turn off the Microwave\"}\n{\"action_id\": 143, \"action_name\": \"open the Microwave\"}\n{\"action_id\": 35, \"action_name\": \"find a Tomato\"}\n{\"action_id\": 129, \"action_name\": \"pick up the Tomato\"}\n{\"action_id\": 144, \"action_name\": \"close the Microwave\"}\n{\"action_id\": 79, \"action_name\": \"find a Sink\"}\n{\"action_id\": 133, \"action_name\": \"put down the object in hand\"}\n]}", + "Human instruction: Move the towel on the hanger into the cabinet.\nOutput: {\n'language_plan': 'Begin by locating the hand towel on the hanger. Once you have found the hand towel, proceed to pick it up. Next, search for the cabinet and identify its location. After finding the cabinet, open it. Place the hand towel inside the cabinet, and finally, close the cabinet door.',\n'executable_plan': [\n{\"action_id\": 9, \"action_name\": \"find a HandTowel\"}\n{\"action_id\": 89, \"action_name\": \"pick up the HandTowel\"}\n{\"action_id\": 48, \"action_name\": \"find a Cabinet\"}\n{\"action_id\": 145, \"action_name\": \"open the Cabinet\"}\n{\"action_id\": 133, \"action_name\": \"put down the object in hand\"}\n{\"action_id\": 146, \"action_name\": \"close the Cabinet\"}\n]}", + "Human instruction: Place the microwaved slice of apple on to the white table.\nOutput: {\n'language_plan': 'Locate an apple and pick it up, find a dining table and put the apple down, look for a knife and pick it up, find another apple and slice it, find the dining table and put the knife down, locate an apple and pick it up, search for a microwave and open it, put the apple in the microwave and close it, turn on the microwave to heat the apple slice, turn off the microwave and open it, pick up the microwaved apple slice, close the microwave, find the white table, and place the microwaved apple slice on the white table.',\n'executable_plan': [\n{\"action_id\": 22, \"action_name\": \"find a Apple\"}\n{\"action_id\": 107, \"action_name\": \"pick up the Apple\"}\n{\"action_id\": 18, \"action_name\": \"find a DiningTable\"}\n{\"action_id\": 133, \"action_name\": \"put down the object in hand\"}\n{\"action_id\": 34, \"action_name\": \"find a Knife\"}\n{\"action_id\": 127, \"action_name\": \"pick up the Knife\"}\n{\"action_id\": 22, \"action_name\": \"find a Apple\"}\n{\"action_id\": 160, \"action_name\": \"slice the Apple\"}\n{\"action_id\": 18, \"action_name\": \"find a DiningTable\"}\n{\"action_id\": 133, \"action_name\": \"put down the object in hand\"}\n{\"action_id\": 22, \"action_name\": \"find a Apple\"}\n{\"action_id\": 107, \"action_name\": \"pick up the Apple\"}\n{\"action_id\": 38, \"action_name\": \"find a Microwave\"}\n{\"action_id\": 143, \"action_name\": \"open the Microwave\"}\n{\"action_id\": 133, \"action_name\": \"put down the object in hand\"}\n{\"action_id\": 144, \"action_name\": \"close the Microwave\"}\n{\"action_id\": 149, \"action_name\": \"turn on the Microwave\"}\n{\"action_id\": 150, \"action_name\": \"turn off the Microwave\"}\n{\"action_id\": 143, \"action_name\": \"open the Microwave\"}\n{\"action_id\": 22, \"action_name\": \"find a Apple\"}\n{\"action_id\": 107, \"action_name\": \"pick up the Apple\"}\n{\"action_id\": 144, \"action_name\": \"close the Microwave\"}\n{\"action_id\": 18, \"action_name\": \"find a DiningTable\"}\n{\"action_id\": 133, \"action_name\": \"put down the object in hand\"}\n]}", + "Human instruction: slice a loaf of bread put a slice on the counter.\nOutput: {\n'language_plan': 'First locate a knife and pick it up, find a loaf of bread, and slice the bread. Find a cabinet, open it, and put the knife inside, then close the cabinet. Find a slice of bread, pick it up, find the countertop, and place the slice of bread on the counter.',\n'executable_plan': [\n{\"action_id\": 34, \"action_name\": \"find a Knife\"}\n{\"action_id\": 127, \"action_name\": \"pick up the Knife\"}\n{\"action_id\": 53, \"action_name\": \"find a Bread\"}\n{\"action_id\": 161, \"action_name\": \"slice the Bread\"}\n{\"action_id\": 48, \"action_name\": \"find a Cabinet\"}\n{\"action_id\": 145, \"action_name\": \"open the Cabinet\"}\n{\"action_id\": 133, \"action_name\": \"put down the object in hand\"}\n{\"action_id\": 146, \"action_name\": \"close the Cabinet\"}\n{\"action_id\": 53, \"action_name\": \"find a Bread\"}\n{\"action_id\": 92, \"action_name\": \"pick up the Bread\"}\n{\"action_id\": 78, \"action_name\": \"find a Fridge\"}\n{\"action_id\": 139, \"action_name\": \"open the Fridge\"}\n{\"action_id\": 133, \"action_name\": \"put down the object in hand\"}\n{\"action_id\": 140, \"action_name\": \"close the Fridge\"}\n{\"action_id\": 139, \"action_name\": \"open the Fridge\"}\n{\"action_id\": 53, \"action_name\": \"find a Bread\"}\n{\"action_id\": 92, \"action_name\": \"pick up the Bread\"}\n{\"action_id\": 140, \"action_name\": \"close the Fridge\"}\n{\"action_id\": 39, \"action_name\": \"find a CounterTop\"}\n{\"action_id\": 133, \"action_name\": \"put down the object in hand\"}\n]}", + "Human instruction: Put a clean pan in the refrigerator.\nOutput: {\n'language_plan': 'Locate a pan and pick it up, find a sink, and put the pan down in the sink. Find a faucet, turn it on to clean the pan, and then turn it off. Pick up the cleaned pan, find the refrigerator, open the refrigerator, place the pan inside, and close the refrigerator.',\n'executable_plan': [\n{\"action_id\": 7, \"action_name\": \"find a Pan\"}\n{\"action_id\": 86, \"action_name\": \"pick up the Pan\"}\n{\"action_id\": 79, \"action_name\": \"find a Sink\"}\n{\"action_id\": 133, \"action_name\": \"put down the object in hand\"}\n{\"action_id\": 2, \"action_name\": \"find a Faucet\"}\n{\"action_id\": 155, \"action_name\": \"turn on the Faucet\"}\n{\"action_id\": 156, \"action_name\": \"turn off the Faucet\"}\n{\"action_id\": 7, \"action_name\": \"find a Pan\"}\n{\"action_id\": 86, \"action_name\": \"pick up the Pan\"}\n{\"action_id\": 78, \"action_name\": \"find a Fridge\"}\n{\"action_id\": 139, \"action_name\": \"open the Fridge\"}\n{\"action_id\": 133, \"action_name\": \"put down the object in hand\"}\n{\"action_id\": 140, \"action_name\": \"close the Fridge\"}\n]}", + "Human instruction: Put a box from the couch with a phone in it on the black desk.\nOutput: {\n'language_plan': 'First, find the phone, pick it up, locate the box on the couch, place the phone inside the box, pick up the box, find the black desk, and place the box on the desk.',\n'executable_plan': [\n{\"action_id\": 12, \"action_name\": \"find a CellPhone\"}\n{\"action_id\": 93, \"action_name\": \"pick up the CellPhone\"}\n{\"action_id\": 61, \"action_name\": \"find a Box\"}\n{\"action_id\": 133, \"action_name\": \"put down the object in hand\"}\n{\"action_id\": 103, \"action_name\": \"pick up the Box\"}\n{\"action_id\": 49, \"action_name\": \"find a Desk\"}\n{\"action_id\": 133, \"action_name\": \"put down the object in hand\"}\n]}" +] \ No newline at end of file diff --git a/easi/tasks/ebalfred/config/alfred_long_horizon_examples.json b/easi/tasks/ebalfred/config/alfred_long_horizon_examples.json new file mode 100644 index 0000000..ed53e53 --- /dev/null +++ b/easi/tasks/ebalfred/config/alfred_long_horizon_examples.json @@ -0,0 +1,9 @@ +[ + "Human instruction: Place the microwaved slice of apple on to the white table.\nOutput: {\n'language_plan': 'Locate an apple and pick it up, find a dining table and put the apple down, look for a knife and pick it up, find the apple and slice it. After slicing, I need to find a place like the dining table to put the knife down. After that, find an apple slice and pick it up, search for a microwave and open it, put the apple in the microwave and close it, turn on the microwave to heat the apple slice, turn off the microwave and open it, pick up the microwaved apple slice, close the microwave, find the white table, and place the microwaved apple slice on the white table.',\n'executable_plan': [\n{\"action_id\": 22, \"action_name\": \"find a Apple\"}\n{\"action_id\": 107, \"action_name\": \"pick up the Apple\"}\n{\"action_id\": 18, \"action_name\": \"find a DiningTable\"}\n{\"action_id\": 133, \"action_name\": \"put down the object in hand\"}\n{\"action_id\": 34, \"action_name\": \"find a Knife\"}\n{\"action_id\": 127, \"action_name\": \"pick up the Knife\"}\n{\"action_id\": 22, \"action_name\": \"find a Apple\"}\n{\"action_id\": 160, \"action_name\": \"slice the Apple\"}\n{\"action_id\": 18, \"action_name\": \"find a DiningTable\"}\n{\"action_id\": 133, \"action_name\": \"put down the object in hand\"}\n{\"action_id\": 22, \"action_name\": \"find a Apple\"}\n{\"action_id\": 107, \"action_name\": \"pick up the Apple\"}\n{\"action_id\": 38, \"action_name\": \"find a Microwave\"}\n{\"action_id\": 143, \"action_name\": \"open the Microwave\"}\n{\"action_id\": 133, \"action_name\": \"put down the object in hand\"}\n{\"action_id\": 144, \"action_name\": \"close the Microwave\"}\n{\"action_id\": 149, \"action_name\": \"turn on the Microwave\"}\n{\"action_id\": 150, \"action_name\": \"turn off the Microwave\"}\n{\"action_id\": 143, \"action_name\": \"open the Microwave\"}\n{\"action_id\": 22, \"action_name\": \"find a Apple\"}\n{\"action_id\": 107, \"action_name\": \"pick up the Apple\"}\n{\"action_id\": 144, \"action_name\": \"close the Microwave\"}\n{\"action_id\": 18, \"action_name\": \"find a DiningTable\"}\n{\"action_id\": 133, \"action_name\": \"put down the object in hand\"}\n]}", + "Human instruction: Place a heated slice of tomato in the kitchen sink.\nOutput: {\n'language_plan': 'First, I can use both a knife or a butter knife to slice the tomato. So, I find a butter knife and pick it up, locate a tomato and slice it. After slicing, I need to put down the knife. So I find a sink and put down the knife. Locate a slice of tomato and pick it up, find a microwave, and open it. Place the tomato slice inside and close the microwave. Turn on the microwave to heat the tomato slice, then turn off the microwave and open it. Pick up the heated tomato slice, close the microwave, locate the kitchen sink, and place the heated tomato slice in the sink.',\n'executable_plan': [\n{\"action_id\": 36, \"action_name\": \"find a ButterKnife\"}\n{\"action_id\": 130, \"action_name\": \"pick up the ButterKnife\"}\n{\"action_id\": 35, \"action_name\": \"find a Tomato\"}\n{\"action_id\": 159, \"action_name\": \"slice the Tomato\"}\n{\"action_id\": 79, \"action_name\": \"find a Sink\"}\n{\"action_id\": 133, \"action_name\": \"put down the object in hand\"}\n{\"action_id\": 35, \"action_name\": \"find a Tomato\"}\n{\"action_id\": 129, \"action_name\": \"pick up the Tomato\"}\n{\"action_id\": 38, \"action_name\": \"find a Microwave\"}\n{\"action_id\": 143, \"action_name\": \"open the Microwave\"}\n{\"action_id\": 133, \"action_name\": \"put down the object in hand\"}\n{\"action_id\": 144, \"action_name\": \"close the Microwave\"}\n{\"action_id\": 149, \"action_name\": \"turn on the Microwave\"}\n{\"action_id\": 150, \"action_name\": \"turn off the Microwave\"}\n{\"action_id\": 143, \"action_name\": \"open the Microwave\"}\n{\"action_id\": 35, \"action_name\": \"find a Tomato\"}\n{\"action_id\": 129, \"action_name\": \"pick up the Tomato\"}\n{\"action_id\": 144, \"action_name\": \"close the Microwave\"}\n{\"action_id\": 79, \"action_name\": \"find a Sink\"}\n{\"action_id\": 133, \"action_name\": \"put down the object in hand\"}\n]}", + "Human instruction: Chill a potato in the refrigerator, prepare cook it in the microwave.\nOutput: {\n'language_plan': 'First locate a potato and pick it up, find the refrigerator, open it, and place the potato inside to chill. Close the refrigerator. After chilling, open the refrigerator again, find the potato, and pick it up. Close the refrigerator, find the microwave, open it, place the potato inside, and close the microwave to prepare it for cooking.',\n'executable_plan': [\n{\"action_id\": 1, \"action_name\": \"find a Potato\"}\n{\"action_id\": 81, \"action_name\": \"pick up the Potato\"}\n{\"action_id\": 78, \"action_name\": \"find a Fridge\"}\n{\"action_id\": 139, \"action_name\": \"open the Fridge\"}\n{\"action_id\": 133, \"action_name\": \"put down the object in hand\"}\n{\"action_id\": 140, \"action_name\": \"close the Fridge\"}\n{\"action_id\": 139, \"action_name\": \"open the Fridge\"}\n{\"action_id\": 1, \"action_name\": \"find a Potato\"}\n{\"action_id\": 81, \"action_name\": \"pick up the Potato\"}\n{\"action_id\": 140, \"action_name\": \"close the Fridge\"}\n{\"action_id\": 38, \"action_name\": \"find a Microwave\"}\n{\"action_id\": 143, \"action_name\": \"open the Microwave\"}\n{\"action_id\": 133, \"action_name\": \"put down the object in hand\"}\n{\"action_id\": 144, \"action_name\": \"close the Microwave\"}\n]}", + "Human instruction: Heat up a potato and place it in the blue bin.\nOutput: {\n'language_plan': 'Locate the fridge and open it, find a potato inside and pick it up, then close the fridge. Find the microwave and open it, place the potato inside, and close the microwave. Turn on the microwave to heat the potato and turn it off after heating. Open the microwave and pick up the heated potato. Close the microwave, locate the blue bin, and place the heated potato in the blue bin.',\n'executable_plan': [\n{\"action_id\": 78, \"action_name\": \"find a Fridge\"}\n{\"action_id\": 139, \"action_name\": \"open the Fridge\"}\n{\"action_id\": 1, \"action_name\": \"find a Potato\"}\n{\"action_id\": 81, \"action_name\": \"pick up the Potato\"}\n{\"action_id\": 140, \"action_name\": \"close the Fridge\"}\n{\"action_id\": 38, \"action_name\": \"find a Microwave\"}\n{\"action_id\": 143, \"action_name\": \"open the Microwave\"}\n{\"action_id\": 133, \"action_name\": \"put down the object in hand\"}\n{\"action_id\": 144, \"action_name\": \"close the Microwave\"}\n{\"action_id\": 149, \"action_name\": \"turn on the Microwave\"}\n{\"action_id\": 150, \"action_name\": \"turn off the Microwave\"}\n{\"action_id\": 143, \"action_name\": \"open the Microwave\"}\n{\"action_id\": 1, \"action_name\": \"find a Potato\"}\n{\"action_id\": 81, \"action_name\": \"pick up the Potato\"}\n{\"action_id\": 144, \"action_name\": \"close the Microwave\"}\n{\"action_id\": 40, \"action_name\": \"find a GarbageCan\"}\n{\"action_id\": 133, \"action_name\": \"put down the object in hand\"}\n]}", + "Human instruction: Transfer a tomato slice cooked in the microwave to the sink.\nOutput: {\n'language_plan': 'To achieve the goal, I need to find a knife and pick it up. Then, find a tomato and slice it. Since I cannot pick tomato when holding the knife, I need to put the knife at some place to keep safe. So I find a sink and put it down. Then I come back to find the sliced tomato and pick it up. Next, find the microwave and open it, put tomato slice down, and close it. After that, turn on the microwave, wait for sometime and turn off and open it. Find the tomato slice, pick it up, and close the microwave. Find a sink and put it down.',\n'executable_plan': [\n{\"action_id\": 34, \"action_name\": \"find a Knife\"}\n{\"action_id\": 127, \"action_name\": \"pick up the Knife\"}\n{\"action_id\": 35, \"action_name\": \"find a Tomato\"}\n{\"action_id\": 159, \"action_name\": \"slice the Tomato\"}\n{\"action_id\": 79, \"action_name\": \"find a Sink\"}\n{\"action_id\": 133, \"action_name\": \"put down the object in hand\"}\n{\"action_id\": 35, \"action_name\": \"find a Tomato\"}\n{\"action_id\": 129, \"action_name\": \"pick up the Tomato\"}\n{\"action_id\": 38, \"action_name\": \"find a Microwave\"}\n{\"action_id\": 143, \"action_name\": \"open the Microwave\"}\n{\"action_id\": 133, \"action_name\": \"put down the object in hand\"}\n{\"action_id\": 144, \"action_name\": \"close the Microwave\"}\n{\"action_id\": 149, \"action_name\": \"turn on the Microwave\"}\n{\"action_id\": 150, \"action_name\": \"turn off the Microwave\"}\n{\"action_id\": 143, \"action_name\": \"open the Microwave\"}\n{\"action_id\": 35, \"action_name\": \"find a Tomato\"}\n{\"action_id\": 129, \"action_name\": \"pick up the Tomato\"}\n{\"action_id\": 144, \"action_name\": \"close the Microwave\"}\n{\"action_id\": 79, \"action_name\": \"find a Sink\"}\n{\"action_id\": 133, \"action_name\": \"put down the object in hand\"}\n]}", + "Human instruction: Slice some lettuce and cool it in the refrigerator so you can put it on the counter top.\nOutput: {\n'language_plan': 'First, find a knife and pick it up, then locate a lettuce and slice it. Since I'm holding a knife, I need to put down the knife on the counter top for safety. Next, find the sliced lettuce and pick it up. Locate the fridge, open it, and place the lettuce inside to cool. After cooling, open the fridge, find the lettuce, and pick it up. Close the fridge, then find the counter top and put the lettuce down on it.',\n'executable_plan': [\n{\"action_id\": 34, \"action_name\": \"find a Knife\"}\n{\"action_id\": 127, \"action_name\": \"pick up the Knife\"}\n{\"action_id\": 55, \"action_name\": \"find a Lettuce\"}\n{\"action_id\": 158, \"action_name\": \"slice the Lettuce\"}\n{\"action_id\": 39, \"action_name\": \"find a CounterTop\"}\n{\"action_id\": 133, \"action_name\": \"put down the object in hand\"}\n{\"action_id\": 55, \"action_name\": \"find a Lettuce\"}\n{\"action_id\": 95, \"action_name\": \"pick up the Lettuce\"}\n{\"action_id\": 78, \"action_name\": \"find a Fridge\"}\n{\"action_id\": 139, \"action_name\": \"open the Fridge\"}\n{\"action_id\": 133, \"action_name\": \"put down the object in hand\"}\n{\"action_id\": 140, \"action_name\": \"close the Fridge\"}\n{\"action_id\": 139, \"action_name\": \"open the Fridge\"}\n{\"action_id\": 55, \"action_name\": \"find a Lettuce\"}\n{\"action_id\": 95, \"action_name\": \"pick up the Lettuce\"}\n{\"action_id\": 140, \"action_name\": \"close the Fridge\"}\n{\"action_id\": 39, \"action_name\": \"find a CounterTop\"}\n{\"action_id\": 133, \"action_name\": \"put down the object in hand\"}\n]}", + "Human instruction: Put a chilled slice of apple in the microwave.\nOutput: {\n'language_plan': 'Firstly, locate the knife and pick it up, then find an apple and slice it. Before picking anything, I need to put down the knife on the counter top for safe handling. Find the apple slice, pick it up, and then find the fridge. Open the fridge, place the apple slice inside, and close the fridge to chill it. After chilling, open the fridge, find the apple slice, and pick it up. Close the fridge, locate the microwave, open it, and place the apple slice inside, then close the microwave.',\n'executable_plan': [\n{\"action_id\": 34, \"action_name\": \"find a Knife\"}\n{\"action_id\": 127, \"action_name\": \"pick up the Knife\"}\n{\"action_id\": 22, \"action_name\": \"find a Apple\"}\n{\"action_id\": 160, \"action_name\": \"slice the Apple\"}\n{\"action_id\": 39, \"action_name\": \"find a CounterTop\"}\n{\"action_id\": 133, \"action_name\": \"put down the object in hand\"}\n{\"action_id\": 22, \"action_name\": \"find a Apple\"}\n{\"action_id\": 107, \"action_name\": \"pick up the Apple\"}\n{\"action_id\": 78, \"action_name\": \"find a Fridge\"}\n{\"action_id\": 139, \"action_name\": \"open the Fridge\"}\n{\"action_id\": 133, \"action_name\": \"put down the object in hand\"}\n{\"action_id\": 140, \"action_name\": \"close the Fridge\"}\n{\"action_id\": 139, \"action_name\": \"open the Fridge\"}\n{\"action_id\": 22, \"action_name\": \"find a Apple\"}\n{\"action_id\": 107, \"action_name\": \"pick up the Apple\"}\n{\"action_id\": 140, \"action_name\": \"close the Fridge\"}\n{\"action_id\": 38, \"action_name\": \"find a Microwave\"}\n{\"action_id\": 143, \"action_name\": \"open the Microwave\"}\n{\"action_id\": 133, \"action_name\": \"put down the object in hand\"}\n{\"action_id\": 144, \"action_name\": \"close the Microwave\"}\n]}" +] \ No newline at end of file diff --git a/easi/tasks/ebalfred/ebalfred_base.yaml b/easi/tasks/ebalfred/ebalfred_base.yaml index f5b2cbf..dc684d1 100644 --- a/easi/tasks/ebalfred/ebalfred_base.yaml +++ b/easi/tasks/ebalfred/ebalfred_base.yaml @@ -26,3 +26,4 @@ agent: n_shot: 10 split: "base" use_feedback: true + chat_history: false diff --git a/easi/tasks/ebalfred/ebalfred_common_sense.yaml b/easi/tasks/ebalfred/ebalfred_common_sense.yaml index 38fc17e..3ada24a 100644 --- a/easi/tasks/ebalfred/ebalfred_common_sense.yaml +++ b/easi/tasks/ebalfred/ebalfred_common_sense.yaml @@ -26,3 +26,4 @@ agent: n_shot: 10 split: "base" use_feedback: true + chat_history: false diff --git a/easi/tasks/ebalfred/ebalfred_complex_instruction.yaml b/easi/tasks/ebalfred/ebalfred_complex_instruction.yaml index e4b7d53..7d54154 100644 --- a/easi/tasks/ebalfred/ebalfred_complex_instruction.yaml +++ b/easi/tasks/ebalfred/ebalfred_complex_instruction.yaml @@ -26,3 +26,4 @@ agent: n_shot: 10 split: "base" use_feedback: true + chat_history: false diff --git a/easi/tasks/ebalfred/ebalfred_long_horizon.yaml b/easi/tasks/ebalfred/ebalfred_long_horizon.yaml index 55d93e4..0efb2fa 100644 --- a/easi/tasks/ebalfred/ebalfred_long_horizon.yaml +++ b/easi/tasks/ebalfred/ebalfred_long_horizon.yaml @@ -23,6 +23,7 @@ simulator_configs: agent: prompt_builder: "easi.tasks.ebalfred.prompts.EBAlfredPromptBuilder" prompt_builder_kwargs: - n_shot: 10 + n_shot: 7 split: "long_horizon" use_feedback: true + chat_history: true diff --git a/easi/tasks/ebalfred/ebalfred_spatial.yaml b/easi/tasks/ebalfred/ebalfred_spatial.yaml index a860616..f190d0d 100644 --- a/easi/tasks/ebalfred/ebalfred_spatial.yaml +++ b/easi/tasks/ebalfred/ebalfred_spatial.yaml @@ -26,3 +26,4 @@ agent: n_shot: 10 split: "base" use_feedback: true + chat_history: false diff --git a/easi/tasks/ebalfred/ebalfred_visual_appearance.yaml b/easi/tasks/ebalfred/ebalfred_visual_appearance.yaml index d997eb6..4c7311f 100644 --- a/easi/tasks/ebalfred/ebalfred_visual_appearance.yaml +++ b/easi/tasks/ebalfred/ebalfred_visual_appearance.yaml @@ -26,3 +26,4 @@ agent: n_shot: 10 split: "base" use_feedback: true + chat_history: false From ed77434cd0a1420c781e563dc927e687c0023923 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Sun, 15 Feb 2026 12:38:58 +0800 Subject: [PATCH 019/239] [Refactor] Move dynamic_action_space handling from runner to task hook Add BaseTask.on_episode_reset() hook for task-specific post-reset setup. EBAlfredTask overrides it to update agent action space from bridge metadata, removing EB-Alfred-specific logic from the general EvaluationRunner. --- easi/core/base_task.py | 13 +++++++++++++ easi/evaluation/runner.py | 7 ++----- easi/tasks/ebalfred/task.py | 8 ++++++++ 3 files changed, 23 insertions(+), 5 deletions(-) diff --git a/easi/core/base_task.py b/easi/core/base_task.py index 6c2068e..afb6ea4 100644 --- a/easi/core/base_task.py +++ b/easi/core/base_task.py @@ -74,6 +74,19 @@ def evaluate_episode( """ ... + # --- Hooks --- + + def on_episode_reset(self, observation, agent) -> None: + """Called after simulator reset, before the agent-simulator loop. + + Override in subclasses to perform task-specific setup, e.g. updating + the agent's action space from bridge metadata. + + Args: + observation: The initial observation from sim.reset(). + agent: The agent instance (may have update_action_space, etc.). + """ + # --- Shared implementation --- def get_bridge_script_path(self) -> Path | None: diff --git a/easi/evaluation/runner.py b/easi/evaluation/runner.py index d971373..243dc37 100644 --- a/easi/evaluation/runner.py +++ b/easi/evaluation/runner.py @@ -191,11 +191,8 @@ def _run_episode( episode_output_dir=str(episode_dir), ) - # Propagate dynamic action space from bridge (e.g., EB-Alfred per-episode actions) - dynamic_actions_json = observation.metadata.get("dynamic_action_space") - if dynamic_actions_json and hasattr(agent, 'update_action_space'): - dynamic_actions = json.loads(dynamic_actions_json) - agent.update_action_space(dynamic_actions) + # Task-specific post-reset setup (e.g., per-episode action space) + task.on_episode_reset(observation, agent) # Write reset entry to trajectory trajectory_path = episode_dir / "trajectory.jsonl" diff --git a/easi/tasks/ebalfred/task.py b/easi/tasks/ebalfred/task.py index 5771f65..607af66 100644 --- a/easi/tasks/ebalfred/task.py +++ b/easi/tasks/ebalfred/task.py @@ -13,6 +13,7 @@ """ from __future__ import annotations +import json from pathlib import Path from easi.core.base_task import BaseTask @@ -37,6 +38,13 @@ def get_bridge_script_path(self) -> Path: """Return path to the EB-Alfred-specific bridge script.""" return Path(__file__).parent / "bridge.py" + def on_episode_reset(self, observation, agent) -> None: + """Update agent action space from per-episode bridge metadata.""" + dynamic_actions_json = observation.metadata.get("dynamic_action_space") + if dynamic_actions_json and hasattr(agent, 'update_action_space'): + dynamic_actions = json.loads(dynamic_actions_json) + agent.update_action_space(dynamic_actions) + def get_instruction(self, episode: dict) -> str: """EB-Alfred uses 'instruction' field from HF row.""" return episode.get("instruction", self.name) From 4483419052b70a448fd0acee835ebf93b5bcff7b Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Sun, 15 Feb 2026 13:11:28 +0800 Subject: [PATCH 020/239] [Feat] Enrich run outputs with LLM responses, instruction, and full config - trajectory.jsonl: add llm_response field to each step entry - result.json: add instruction field for each episode - config.json: include all CLI options and full task YAML config --- easi/evaluation/runner.py | 28 ++++++++++++++++++++++------ tests/test_e2e_evaluation.py | 11 +++++++---- 2 files changed, 29 insertions(+), 10 deletions(-) diff --git a/easi/evaluation/runner.py b/easi/evaluation/runner.py index 243dc37..619d070 100644 --- a/easi/evaluation/runner.py +++ b/easi/evaluation/runner.py @@ -120,14 +120,23 @@ def run(self, max_episodes: int | None = None) -> list[dict]: # Save run config config = { - "task_name": self.task_name, - "agent_type": self.agent_type, - "agent_seed": self.agent_seed, "run_id": self.run_id, - "max_episodes": max_episodes, "total_episodes": len(episodes), - "backend": backend, - "model": self.model, + "cli_options": { + "task_name": self.task_name, + "agent_type": self.agent_type, + "output_dir": str(self.output_dir), + "data_dir": str(self.data_dir), + "max_episodes": max_episodes, + "llm_base_url": self.llm_base_url, + "agent_seed": self.agent_seed, + "backend": self.backend, + "model": self.model, + "port": self.port, + "llm_kwargs_raw": self.llm_kwargs_raw, + }, + "resolved_backend": backend, + "task_config": task._config, } (run_dir / "config.json").write_text(json.dumps(config, indent=2)) @@ -216,11 +225,17 @@ def _run_episode( step_result = sim.step(action) trajectory.append(step_result) + # Get LLM response from agent memory (None for buffered actions) + llm_response = None + if hasattr(agent, 'memory') and agent.memory.steps: + llm_response = agent.memory.steps[-1].llm_response + # Write step entry to trajectory self._write_trajectory_entry(trajectory_path, { "step": step + 1, "type": "step", "action": action.action_name, + "llm_response": llm_response, "rgb_path": Path(step_result.observation.rgb_path).name, "agent_pose": step_result.observation.agent_pose, "reward": step_result.reward, @@ -246,6 +261,7 @@ def _run_episode( # Evaluate metrics = task.evaluate_episode(episode, trajectory) metrics["episode_id"] = episode_id + metrics["instruction"] = task_description metrics["elapsed_seconds"] = round(elapsed, 2) # Snapshot LLM usage for this episode diff --git a/tests/test_e2e_evaluation.py b/tests/test_e2e_evaluation.py index f1e7c4c..f55979d 100644 --- a/tests/test_e2e_evaluation.py +++ b/tests/test_e2e_evaluation.py @@ -161,11 +161,14 @@ def test_config_json_saved(self, tmp_path): run_dir = _find_run_dir(output_dir) config = json.loads((run_dir / "config.json").read_text()) - assert config["task_name"] == "dummy_task" - assert config["agent_type"] == "dummy" - assert config["agent_seed"] == 42 - assert config["max_episodes"] == 1 + assert config["cli_options"]["task_name"] == "dummy_task" + assert config["cli_options"]["agent_type"] == "dummy" + assert config["cli_options"]["agent_seed"] == 42 + assert config["cli_options"]["max_episodes"] == 1 assert "run_id" in config + # Task YAML config is included + assert "task_config" in config + assert config["task_config"]["name"] == "dummy_task" def test_trajectory_jsonl_format(self, tmp_path): """Verify trajectory.jsonl has correct format.""" From c8648b3f66feacf24e0bb4983b8ed1d95859b33e Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Sun, 15 Feb 2026 19:41:05 +0800 Subject: [PATCH 021/239] [Feat] Add retry and resume support for evaluation runs Retry: LLMClient passes num_retries to litellm.completion() for automatic exponential backoff on transient errors (timeouts, rate limits). Configurable via --max-retries (default 3). Resume: --resume loads config.json from a previous run, skips completed episodes, clears and re-runs the last episode (which may have been interrupted), then continues the remaining episodes. All CLI options are restored from config.json so only --resume is needed. --- docs/plans/2026-02-15-retry-resume-design.md | 137 ++++ docs/plans/2026-02-15-retry-resume-plan.md | 661 +++++++++++++++++++ easi/cli.py | 55 +- easi/evaluation/runner.py | 50 +- easi/llm/client.py | 3 + tests/test_e2e_evaluation.py | 213 ++++++ tests/test_llm_client.py | 50 ++ 7 files changed, 1164 insertions(+), 5 deletions(-) create mode 100644 docs/plans/2026-02-15-retry-resume-design.md create mode 100644 docs/plans/2026-02-15-retry-resume-plan.md diff --git a/docs/plans/2026-02-15-retry-resume-design.md b/docs/plans/2026-02-15-retry-resume-design.md new file mode 100644 index 0000000..ce8404c --- /dev/null +++ b/docs/plans/2026-02-15-retry-resume-design.md @@ -0,0 +1,137 @@ +# Retry & Resume Design + +## Goal + +Make evaluation runs resilient to transient LLM errors (timeouts, rate limits) and resumable after crashes. + +## Feature 1: Adaptive Retry + +Use LiteLLM's built-in `num_retries` parameter. LiteLLM uses tenacity internally for exponential backoff with jitter, and retries on `Timeout`, `RateLimitError`, `APIConnectionError`, and `InternalServerError`. + +### LLMClient + +`LLMClient.__init__` gains a `num_retries` parameter (default 3). It's passed through to `litellm.completion()`: + +```python +class LLMClient: + def __init__(self, model, base_url=None, num_retries=3, **kwargs): + self.num_retries = num_retries + ... + + def generate(self, messages, response_format=None): + call_kwargs = { + "model": self.model, + "messages": messages, + "num_retries": self.num_retries, + **self.default_kwargs, + } + ... +``` + +### CLI + +Add `--max-retries` (default 3) to `easi run`. Passed through: CLI -> `EvaluationRunner` -> `LLMClient(num_retries=...)`. + +### EvaluationRunner + +`__init__` gains `max_retries: int = 3`. In `_create_agent()`, passes it to `LLMClient`. + +## Feature 2: Resume + +Allow `--resume ` to continue a crashed run from where it left off. + +### How it works + +1. **Load existing config**: Read `config.json` from the resume dir. Validate `task_name` matches. +2. **Scan completed episodes**: Glob `episodes/*/result.json`. Each existing `result.json` = completed episode. Parse them into `all_results`. +3. **Re-run last completed episode**: The last episode may have been cut off mid-way. Pop it from `all_results`, clear its directory (delete `result.json`, `trajectory.jsonl`, `step_*.png`), and re-run it from scratch. +4. **Skip earlier episodes**: Start the episode loop from `len(all_results)` instead of 0. +5. **Reuse run_dir**: Don't create a new `run_id`. Write to the same directory. + +### _load_completed_results + +```python +def _load_completed_results(self, run_dir: Path) -> list[dict]: + episodes_dir = run_dir / "episodes" + result_files = sorted(episodes_dir.glob("*/result.json")) + + if not result_files: + return [] + + # Load all completed results except the last + all_results = [] + for rf in result_files[:-1]: + all_results.append(json.loads(rf.read_text())) + + # Clean the last episode directory for re-run + last_episode_dir = result_files[-1].parent + for f in last_episode_dir.iterdir(): + f.unlink() + + return all_results +``` + +### run() changes + +```python +def run(self, max_episodes=None): + if self.resume_dir: + run_dir = self.resume_dir + all_results = self._load_completed_results(run_dir) + start_index = len(all_results) + else: + run_dir = self.output_dir / self.task_name / self.run_id + all_results = [] + start_index = 0 + + ... + for i, episode in enumerate(episodes): + if i < start_index: + continue + ... +``` + +### CLI + +Add `--resume ` to `easi run`. Passed to `EvaluationRunner(resume_dir=...)`. + +### Example + +```bash +# First run — crashes at episode 15 +easi run ebalfred_base --agent react --backend openai --model gpt-4o + +# Resume — skips episodes 0-13, re-runs 14 (cleared), continues 15-49 +easi run ebalfred_base --agent react --backend openai --model gpt-4o \ + --resume logs/ebalfred_base/20260215_164200/ +``` + +## Files Changed + +| File | Change | +|---|---| +| `easi/llm/client.py` | Add `num_retries` param, pass to `litellm.completion()` | +| `easi/evaluation/runner.py` | Add `max_retries`, `resume_dir` params. Add `_load_completed_results()`. Modify `run()` for resume. | +| `easi/cli.py` | Add `--max-retries` (default 3) and `--resume` (path) flags | +| `tests/test_llm_client.py` | Test `num_retries` pass-through | +| `tests/test_e2e_evaluation.py` | Test resume: run 3 episodes, simulate crash, resume skips completed, re-runs last | + +## Data Flow + +``` +CLI --max-retries 3 --resume logs/ebalfred_base/20260215_164200/ + | + v +EvaluationRunner(max_retries=3, resume_dir=Path(...)) + | + +---> _load_completed_results(run_dir) + | scan episodes/*/result.json + | load results [0..N-2] + | clear episode dir [N-1] + | return results, start_index = N-1 + | + +---> _create_agent(...) + | LLMClient(num_retries=3) + | + +---> episode loop: skip i < start_index, run rest normally +``` diff --git a/docs/plans/2026-02-15-retry-resume-plan.md b/docs/plans/2026-02-15-retry-resume-plan.md new file mode 100644 index 0000000..d4e5090 --- /dev/null +++ b/docs/plans/2026-02-15-retry-resume-plan.md @@ -0,0 +1,661 @@ +# Retry & Resume Implementation Plan + +> **For Claude:** REQUIRED SUB-SKILL: Use superpowers:executing-plans to implement this plan task-by-task. + +**Goal:** Make evaluation runs resilient to transient LLM errors and resumable after crashes. + +**Architecture:** Two independent features wired through CLI → EvaluationRunner → LLMClient. Retry uses LiteLLM's built-in `num_retries` parameter (exponential backoff with jitter). Resume scans existing `result.json` files to skip completed episodes. + +**Tech Stack:** Python, LiteLLM (`num_retries`), pytest, argparse + +**Design doc:** `docs/plans/2026-02-15-retry-resume-design.md` + +--- + +### Task 1: Add `num_retries` to LLMClient + +**Files:** +- Modify: `easi/llm/client.py:39-47` (constructor), `easi/llm/client.py:59-63` (generate call_kwargs) +- Test: `tests/test_llm_client.py` + +**Step 1: Write the failing tests** + +Add to `tests/test_llm_client.py`: + +```python +class TestLLMClientRetries: + def test_default_num_retries(self): + from easi.llm.client import LLMClient + client = LLMClient(model="openai/gpt-4o") + assert client.num_retries == 3 + + def test_custom_num_retries(self): + from easi.llm.client import LLMClient + client = LLMClient(model="openai/gpt-4o", num_retries=5) + assert client.num_retries == 5 + + @patch("easi.llm.client.litellm") + def test_num_retries_passed_to_litellm(self, mock_litellm): + from easi.llm.client import LLMClient + + mock_choice = MagicMock() + mock_choice.message.content = "ok" + mock_response = MagicMock() + mock_response.choices = [mock_choice] + mock_response.usage.prompt_tokens = 10 + mock_response.usage.completion_tokens = 5 + mock_litellm.completion.return_value = mock_response + mock_litellm.completion_cost.return_value = 0.0 + + client = LLMClient(model="openai/gpt-4o", num_retries=7) + client.generate([{"role": "user", "content": "hi"}]) + + call_kwargs = mock_litellm.completion.call_args + assert call_kwargs.kwargs.get("num_retries") == 7 + + @patch("easi.llm.client.litellm") + def test_zero_retries_passed_through(self, mock_litellm): + from easi.llm.client import LLMClient + + mock_choice = MagicMock() + mock_choice.message.content = "ok" + mock_response = MagicMock() + mock_response.choices = [mock_choice] + mock_response.usage.prompt_tokens = 10 + mock_response.usage.completion_tokens = 5 + mock_litellm.completion.return_value = mock_response + mock_litellm.completion_cost.return_value = 0.0 + + client = LLMClient(model="openai/gpt-4o", num_retries=0) + client.generate([{"role": "user", "content": "hi"}]) + + call_kwargs = mock_litellm.completion.call_args + assert call_kwargs.kwargs.get("num_retries") == 0 +``` + +**Step 2: Run tests to verify they fail** + +Run: `.venv/bin/pytest tests/test_llm_client.py::TestLLMClientRetries -v` +Expected: FAIL — `LLMClient` has no `num_retries` attribute + +**Step 3: Implement** + +In `easi/llm/client.py`, modify the constructor (lines 39-47): + +```python +def __init__( + self, + model: str, + base_url: str | None = None, + num_retries: int = 3, + **kwargs: Any, +): + self.model = model + self.base_url = base_url + self.num_retries = num_retries + self.default_kwargs = kwargs + self._usage = { + "prompt_tokens": 0, + "completion_tokens": 0, + "num_calls": 0, + "cost_usd": 0.0, + } +``` + +In `generate()` (lines 59-63), add `num_retries` to `call_kwargs`: + +```python +call_kwargs: dict[str, Any] = { + "model": self.model, + "messages": messages, + "num_retries": self.num_retries, + **self.default_kwargs, +} +``` + +**Step 4: Run tests to verify they pass** + +Run: `.venv/bin/pytest tests/test_llm_client.py -v` +Expected: ALL PASS (existing + new) + +**Step 5: Commit** + +```bash +git add easi/llm/client.py tests/test_llm_client.py +git commit -m "feat: add num_retries to LLMClient for LiteLLM retry support" +``` + +--- + +### Task 2: Wire `max_retries` through EvaluationRunner + +**Files:** +- Modify: `easi/evaluation/runner.py:47-69` (constructor), `easi/evaluation/runner.py:338-342` (LLMClient instantiation) +- Test: `tests/test_e2e_evaluation.py` + +**Step 1: Write the failing test** + +Add to `tests/test_e2e_evaluation.py`: + +```python +def test_max_retries_stored(self, tmp_path): + """Verify max_retries is stored and appears in config.""" + output_dir = tmp_path / "logs" + runner = EvaluationRunner( + task_name="dummy_task", + agent_type="dummy", + output_dir=output_dir, + max_retries=5, + ) + assert runner.max_retries == 5 + runner.run(max_episodes=1) + + run_dir = _find_run_dir(output_dir) + config = json.loads((run_dir / "config.json").read_text()) + assert config["cli_options"]["max_retries"] == 5 +``` + +**Step 2: Run test to verify it fails** + +Run: `.venv/bin/pytest tests/test_e2e_evaluation.py::TestE2EEvaluation::test_max_retries_stored -v` +Expected: FAIL — `EvaluationRunner.__init__()` got unexpected keyword `max_retries` + +**Step 3: Implement** + +In `easi/evaluation/runner.py`, add `max_retries` to constructor (line 59, after `port`): + +```python +def __init__( + self, + task_name: str, + agent_type: str = "dummy", + output_dir: Path | str = "./logs", + data_dir: Path | str = "./datasets", + llm_base_url: str | None = None, + agent_seed: int | None = None, + backend: str | None = None, + model: str = "default", + port: int = 8080, + llm_kwargs_raw: str | None = None, + max_retries: int = 3, +): + ... + self.max_retries = max_retries + ... +``` + +Add `max_retries` to the config dict in `run()` (inside `cli_options`): + +```python +"max_retries": self.max_retries, +``` + +In `_create_agent()`, pass `num_retries` to `LLMClient` (around line 338): + +```python +llm = LLMClient( + model=litellm_model, + base_url=base_url, + num_retries=self.max_retries, + **client_kwargs, +) +``` + +**Step 4: Run tests to verify they pass** + +Run: `.venv/bin/pytest tests/test_e2e_evaluation.py -v` +Expected: ALL PASS + +**Step 5: Commit** + +```bash +git add easi/evaluation/runner.py tests/test_e2e_evaluation.py +git commit -m "feat: wire max_retries through EvaluationRunner to LLMClient" +``` + +--- + +### Task 3: Add `--max-retries` CLI flag + +**Files:** +- Modify: `easi/cli.py:80-98` (run_parser args), `easi/cli.py:288-303` (cmd_run) + +**Step 1: Write the failing test** + +Add to `tests/test_e2e_evaluation.py` (or create a quick inline test): + +This is a CLI integration test. Since the existing tests are E2E with `EvaluationRunner`, we can verify the CLI parser separately: + +```python +def test_cli_max_retries_default(): + """CLI --max-retries defaults to 3.""" + from easi.cli import build_parser + parser = build_parser() + args = parser.parse_args(["run", "dummy_task"]) + assert args.max_retries == 3 + +def test_cli_max_retries_custom(): + """CLI --max-retries is parseable.""" + from easi.cli import build_parser + parser = build_parser() + args = parser.parse_args(["run", "dummy_task", "--max-retries", "5"]) + assert args.max_retries == 5 +``` + +Add these to a new `TestCLIParsing` class in `tests/test_e2e_evaluation.py`. + +**Step 2: Run test to verify it fails** + +Run: `.venv/bin/pytest tests/test_e2e_evaluation.py::TestCLIParsing -v` +Expected: FAIL — `args` has no attribute `max_retries` + +**Step 3: Implement** + +In `easi/cli.py`, add after line 98 (`--llm-kwargs`): + +```python +run_parser.add_argument("--max-retries", type=int, default=3, + help="Max LLM retry attempts on transient errors (default: 3)") +``` + +In `cmd_run` (line 288), add `max_retries` parameter and pass to `EvaluationRunner`: + +```python +def cmd_run(task_name, agent_type, output_dir, data_dir, max_episodes, + llm_url, seed, backend, model, port, llm_kwargs_raw, max_retries): + from easi.evaluation.runner import EvaluationRunner + + runner = EvaluationRunner( + task_name=task_name, + agent_type=agent_type, + output_dir=output_dir, + data_dir=data_dir, + llm_base_url=llm_url, + agent_seed=seed, + backend=backend, + model=model, + port=port, + llm_kwargs_raw=llm_kwargs_raw, + max_retries=max_retries, + ) + ... +``` + +In `main()` dispatch (line 361), add `args.max_retries`: + +```python +cmd_run(args.task, args.agent, args.output_dir, args.data_dir, + args.max_episodes, args.llm_url, args.seed, + args.backend, args.model, args.port, args.llm_kwargs, + args.max_retries) +``` + +**Step 4: Run tests to verify they pass** + +Run: `.venv/bin/pytest tests/test_e2e_evaluation.py -v` +Expected: ALL PASS + +**Step 5: Commit** + +```bash +git add easi/cli.py tests/test_e2e_evaluation.py +git commit -m "feat: add --max-retries CLI flag for LLM retry control" +``` + +--- + +### Task 4: Add `--resume` CLI flag and `resume_dir` to EvaluationRunner + +**Files:** +- Modify: `easi/cli.py:98-99` (run_parser args), `easi/cli.py:288-303` (cmd_run) +- Modify: `easi/evaluation/runner.py:47-70` (constructor) + +**Step 1: Write the failing tests** + +Add to `TestCLIParsing` in `tests/test_e2e_evaluation.py`: + +```python +def test_cli_resume_default(): + """CLI --resume defaults to None.""" + from easi.cli import build_parser + parser = build_parser() + args = parser.parse_args(["run", "dummy_task"]) + assert args.resume is None + +def test_cli_resume_custom(): + """CLI --resume accepts a path.""" + from easi.cli import build_parser + parser = build_parser() + args = parser.parse_args(["run", "dummy_task", "--resume", "/tmp/logs/run_123"]) + assert args.resume == "/tmp/logs/run_123" +``` + +Add to `TestE2EEvaluation`: + +```python +def test_resume_dir_stored(self, tmp_path): + """Verify resume_dir is stored.""" + runner = EvaluationRunner( + task_name="dummy_task", + agent_type="dummy", + output_dir=tmp_path / "logs", + resume_dir=tmp_path / "old_run", + ) + assert runner.resume_dir == tmp_path / "old_run" +``` + +**Step 2: Run tests to verify they fail** + +Run: `.venv/bin/pytest tests/test_e2e_evaluation.py::TestCLIParsing::test_cli_resume_default tests/test_e2e_evaluation.py::TestCLIParsing::test_cli_resume_custom tests/test_e2e_evaluation.py::TestE2EEvaluation::test_resume_dir_stored -v` +Expected: FAIL + +**Step 3: Implement** + +In `easi/cli.py`, add after `--max-retries`: + +```python +run_parser.add_argument("--resume", type=str, default=None, + help="Path to a previous run directory to resume from") +``` + +In `easi/evaluation/runner.py` constructor, add `resume_dir`: + +```python +def __init__( + self, + ... + max_retries: int = 3, + resume_dir: Path | str | None = None, +): + ... + self.max_retries = max_retries + self.resume_dir = Path(resume_dir) if resume_dir else None + ... +``` + +In `easi/cli.py` `cmd_run`, add `resume` parameter and pass to `EvaluationRunner`: + +```python +def cmd_run(task_name, agent_type, output_dir, data_dir, max_episodes, + llm_url, seed, backend, model, port, llm_kwargs_raw, + max_retries, resume): + ... + runner = EvaluationRunner( + ... + max_retries=max_retries, + resume_dir=resume, + ) + ... +``` + +In `main()` dispatch, add `args.resume`: + +```python +cmd_run(args.task, args.agent, args.output_dir, args.data_dir, + args.max_episodes, args.llm_url, args.seed, + args.backend, args.model, args.port, args.llm_kwargs, + args.max_retries, args.resume) +``` + +**Step 4: Run tests to verify they pass** + +Run: `.venv/bin/pytest tests/test_e2e_evaluation.py -v` +Expected: ALL PASS + +**Step 5: Commit** + +```bash +git add easi/cli.py easi/evaluation/runner.py tests/test_e2e_evaluation.py +git commit -m "feat: add --resume CLI flag and resume_dir to EvaluationRunner" +``` + +--- + +### Task 5: Implement `_load_completed_results()` and resume logic in `run()` + +**Files:** +- Modify: `easi/evaluation/runner.py:94-185` (run method + new helper) +- Test: `tests/test_e2e_evaluation.py` + +**Step 1: Write the failing tests** + +Add to `TestE2EEvaluation` in `tests/test_e2e_evaluation.py`: + +```python +def test_resume_skips_completed_episodes(self, tmp_path): + """Resume skips completed episodes and re-runs the last one.""" + output_dir = tmp_path / "logs" + + # First run: complete 3 episodes + runner1 = EvaluationRunner( + task_name="dummy_task", + agent_type="dummy", + output_dir=output_dir, + agent_seed=42, + ) + results1 = runner1.run() + assert len(results1) == 3 + + run_dir = _find_run_dir(output_dir) + + # Resume: should re-run episode 2 (last), skip 0 and 1 + runner2 = EvaluationRunner( + task_name="dummy_task", + agent_type="dummy", + output_dir=output_dir, + resume_dir=run_dir, + agent_seed=42, + ) + results2 = runner2.run() + assert len(results2) == 3 # 2 loaded + 1 re-run (episode 2) + +def test_resume_clears_last_episode_dir(self, tmp_path): + """Resume clears the last episode directory before re-running.""" + output_dir = tmp_path / "logs" + + runner1 = EvaluationRunner( + task_name="dummy_task", + agent_type="dummy", + output_dir=output_dir, + agent_seed=42, + ) + runner1.run() + + run_dir = _find_run_dir(output_dir) + episodes_dir = run_dir / "episodes" + episode_dirs = sorted(episodes_dir.iterdir()) + last_ep_dir = episode_dirs[-1] + + # Add a marker file to the last episode dir + marker = last_ep_dir / "marker.txt" + marker.write_text("should be deleted") + + runner2 = EvaluationRunner( + task_name="dummy_task", + agent_type="dummy", + output_dir=output_dir, + resume_dir=run_dir, + agent_seed=42, + ) + runner2.run() + + # Marker should be gone (dir was cleared) + assert not marker.exists() + # But result.json should exist (re-run completed) + assert (last_ep_dir / "result.json").exists() + +def test_resume_produces_valid_summary(self, tmp_path): + """Resumed run produces a valid summary with all episodes.""" + output_dir = tmp_path / "logs" + + runner1 = EvaluationRunner( + task_name="dummy_task", + agent_type="dummy", + output_dir=output_dir, + agent_seed=42, + ) + runner1.run() + + run_dir = _find_run_dir(output_dir) + + runner2 = EvaluationRunner( + task_name="dummy_task", + agent_type="dummy", + output_dir=output_dir, + resume_dir=run_dir, + agent_seed=42, + ) + results2 = runner2.run() + + summary = json.loads((run_dir / "summary.json").read_text()) + assert summary["num_episodes"] == 3 + +def test_resume_empty_run_dir(self, tmp_path): + """Resume with no completed episodes runs all from scratch.""" + output_dir = tmp_path / "logs" + run_dir = output_dir / "dummy_task" / "fake_run" + episodes_dir = run_dir / "episodes" + episodes_dir.mkdir(parents=True) + + # Write a minimal config.json so resume can read it + import json + config = {"run_id": "fake_run", "cli_options": {"task_name": "dummy_task"}} + (run_dir / "config.json").write_text(json.dumps(config)) + + runner = EvaluationRunner( + task_name="dummy_task", + agent_type="dummy", + output_dir=output_dir, + resume_dir=run_dir, + agent_seed=42, + ) + results = runner.run() + assert len(results) == 3 # All episodes run from scratch +``` + +**Step 2: Run tests to verify they fail** + +Run: `.venv/bin/pytest tests/test_e2e_evaluation.py::TestE2EEvaluation::test_resume_skips_completed_episodes -v` +Expected: FAIL — resume logic not implemented + +**Step 3: Implement** + +Add `_load_completed_results()` method to `EvaluationRunner`: + +```python +def _load_completed_results(self, run_dir: Path) -> list[dict]: + """Load results from a previous run for resume. + + Returns results from all completed episodes except the last one. + The last episode directory is cleared for re-run (it may have been + interrupted mid-way). + """ + episodes_dir = run_dir / "episodes" + if not episodes_dir.exists(): + return [] + + result_files = sorted(episodes_dir.glob("*/result.json")) + if not result_files: + return [] + + # Load all completed results except the last + all_results = [] + for rf in result_files[:-1]: + all_results.append(json.loads(rf.read_text())) + + # Clear the last episode directory for re-run + last_episode_dir = result_files[-1].parent + for f in last_episode_dir.iterdir(): + f.unlink() + + return all_results +``` + +Modify `run()` to support resume — change the beginning of the method: + +```python +def run(self, max_episodes: int | None = None) -> list[dict]: + """Run evaluation and return per-episode metric dicts.""" + if self.resume_dir: + run_dir = self.resume_dir + all_results = self._load_completed_results(run_dir) + start_index = len(all_results) + logger.info( + "Resuming from %s — %d completed episodes, starting from %d", + run_dir, len(all_results), start_index, + ) + else: + run_dir = self.output_dir / self.task_name / self.run_id + all_results = [] + start_index = 0 + + episodes_dir = run_dir / "episodes" + episodes_dir.mkdir(parents=True, exist_ok=True) + + # 1. Load task + task = self._create_task() + episodes = task.load_episodes() + if max_episodes is not None: + episodes = episodes[:max_episodes] + + ... # rest stays the same until the episode loop + + for i, episode in enumerate(episodes): + if i < start_index: + continue + ... # existing episode loop body +``` + +**Step 4: Run tests to verify they pass** + +Run: `.venv/bin/pytest tests/test_e2e_evaluation.py -v` +Expected: ALL PASS + +**Step 5: Run the full test suite** + +Run: `.venv/bin/pytest tests/ -v --timeout=60` +Expected: ALL PASS + +**Step 6: Commit** + +```bash +git add easi/evaluation/runner.py tests/test_e2e_evaluation.py +git commit -m "feat: implement resume logic — scan completed episodes, re-run last" +``` + +--- + +## Dependency Graph + +``` +Task 1 (LLMClient num_retries) + └──> Task 2 (Runner max_retries wiring) + └──> Task 3 (CLI --max-retries) + +Task 4 (CLI --resume + resume_dir) + └──> Task 5 (Resume logic in run()) +``` + +Tasks 1-3 (retry) and Tasks 4-5 (resume) are independent chains. +Tasks 1+4 can run in parallel. Tasks 2+5 can run in parallel (after 1 and 4 respectively). + +## Verification + +```bash +# Full test suite +.venv/bin/pytest tests/ -v --timeout=60 + +# Quick CLI smoke test +.venv/bin/easi run dummy_task --agent dummy --max-retries 5 +.venv/bin/easi run dummy_task --agent dummy --resume logs/dummy_task// +``` + +## Files Changed Summary + +| File | Change | +|---|---| +| `easi/llm/client.py` | Add `num_retries` param (default 3), pass to `litellm.completion()` | +| `easi/evaluation/runner.py` | Add `max_retries` + `resume_dir` params, `_load_completed_results()`, resume logic in `run()` | +| `easi/cli.py` | Add `--max-retries` (default 3) and `--resume` (path) CLI flags | +| `tests/test_llm_client.py` | 4 new tests for `num_retries` | +| `tests/test_e2e_evaluation.py` | ~8 new tests for retry config + resume | diff --git a/easi/cli.py b/easi/cli.py index 77c3e16..a9d300b 100644 --- a/easi/cli.py +++ b/easi/cli.py @@ -78,7 +78,9 @@ def build_parser() -> argparse.ArgumentParser: # --- run command --- run_parser = subparsers.add_parser("run", help="Run a full evaluation", parents=[common]) - run_parser.add_argument("task", type=str, help="Task name (e.g., 'dummy_task', 'ebalfred_base')") + run_parser.add_argument("task", type=str, nargs="?", default=None, + help="Task name (e.g., 'dummy_task', 'ebalfred_base'). " + "Optional when --resume is provided.") run_parser.add_argument("--agent", type=str, default="dummy", choices=["dummy", "react"]) run_parser.add_argument("--output-dir", type=str, default="./logs", help="Base output directory (creates ///)") @@ -96,6 +98,10 @@ def build_parser() -> argparse.ArgumentParser: help="Port for local inference server (default: 8080)") run_parser.add_argument("--llm-kwargs", type=str, default=None, help='JSON string of extra kwargs, e.g. \'{"tensor_parallel_size": 4}\'') + run_parser.add_argument("--max-retries", type=int, default=3, + help="Max LLM retry attempts on transient errors (default: 3)") + run_parser.add_argument("--resume", type=str, default=None, + help="Path to a previous run directory to resume from") # --- llm-server command --- llm_parser = subparsers.add_parser("llm-server", help="Start dummy LLM server", parents=[common]) @@ -286,9 +292,49 @@ def cmd_sim_test(simulator: str, steps: int, timeout: float) -> None: def cmd_run(task_name, agent_type, output_dir, data_dir, max_episodes, - llm_url, seed, backend, model, port, llm_kwargs_raw): + llm_url, seed, backend, model, port, llm_kwargs_raw, + max_retries, resume): + import json as _json + from pathlib import Path + from easi.evaluation.runner import EvaluationRunner + # When resuming, load saved config and use as defaults + if resume: + config_path = Path(resume) / "config.json" + if not config_path.exists(): + logger.error("Resume directory has no config.json: %s", resume) + sys.exit(1) + saved = _json.loads(config_path.read_text()) + opts = saved.get("cli_options", {}) + + # Saved config provides defaults; CLI args override + task_name = task_name or opts.get("task_name") + if agent_type == "dummy" and opts.get("agent_type"): + agent_type = opts["agent_type"] + if output_dir == "./logs" and opts.get("output_dir"): + output_dir = opts["output_dir"] + if data_dir == "./datasets" and opts.get("data_dir"): + data_dir = opts["data_dir"] + if llm_url is None: + llm_url = opts.get("llm_base_url") + if seed is None: + seed = opts.get("agent_seed") + if backend is None: + backend = opts.get("backend") + if model == "default" and opts.get("model", "default") != "default": + model = opts["model"] + if port == 8080 and opts.get("port", 8080) != 8080: + port = opts["port"] + if llm_kwargs_raw is None: + llm_kwargs_raw = opts.get("llm_kwargs_raw") + if max_retries == 3 and opts.get("max_retries", 3) != 3: + max_retries = opts["max_retries"] + + if not task_name: + logger.error("Task name is required. Provide it as a positional arg or use --resume.") + sys.exit(1) + runner = EvaluationRunner( task_name=task_name, agent_type=agent_type, @@ -300,6 +346,8 @@ def cmd_run(task_name, agent_type, output_dir, data_dir, max_episodes, model=model, port=port, llm_kwargs_raw=llm_kwargs_raw, + max_retries=max_retries, + resume_dir=resume, ) results = runner.run(max_episodes=max_episodes) logger.info("Completed %d episodes.", len(results)) @@ -360,7 +408,8 @@ def main() -> None: elif args.command == "run": cmd_run(args.task, args.agent, args.output_dir, args.data_dir, args.max_episodes, args.llm_url, args.seed, - args.backend, args.model, args.port, args.llm_kwargs) + args.backend, args.model, args.port, args.llm_kwargs, + args.max_retries, args.resume) elif args.command == "llm-server": cmd_llm_server(args.host, args.port, args.mode, args.action_space) diff --git a/easi/evaluation/runner.py b/easi/evaluation/runner.py index 619d070..786d716 100644 --- a/easi/evaluation/runner.py +++ b/easi/evaluation/runner.py @@ -56,6 +56,8 @@ def __init__( model: str = "default", port: int = 8080, llm_kwargs_raw: str | None = None, + max_retries: int = 3, + resume_dir: Path | str | None = None, ): self.task_name = task_name self.agent_type = agent_type @@ -67,6 +69,8 @@ def __init__( self.model = model self.port = port self.llm_kwargs_raw = llm_kwargs_raw + self.max_retries = max_retries + self.resume_dir = Path(resume_dir) if resume_dir else None self.run_id = datetime.now().strftime("%Y%m%d_%H%M%S") def _resolve_llm_backend(self) -> tuple[str | None, str | None]: @@ -93,7 +97,19 @@ def _resolve_llm_backend(self) -> tuple[str | None, str | None]: def run(self, max_episodes: int | None = None) -> list[dict]: """Run evaluation and return per-episode metric dicts.""" - run_dir = self.output_dir / self.task_name / self.run_id + if self.resume_dir: + run_dir = self.resume_dir + all_results = self._load_completed_results(run_dir) + start_index = len(all_results) + logger.info( + "Resuming from %s — %d completed episodes, starting from %d", + run_dir, len(all_results), start_index, + ) + else: + run_dir = self.output_dir / self.task_name / self.run_id + all_results = [] + start_index = 0 + episodes_dir = run_dir / "episodes" episodes_dir.mkdir(parents=True, exist_ok=True) @@ -134,6 +150,7 @@ def run(self, max_episodes: int | None = None) -> list[dict]: "model": self.model, "port": self.port, "llm_kwargs_raw": self.llm_kwargs_raw, + "max_retries": self.max_retries, }, "resolved_backend": backend, "task_config": task._config, @@ -147,9 +164,10 @@ def run(self, max_episodes: int | None = None) -> list[dict]: # 4. Start simulator sim, sim_runner = self._create_simulator(task.simulator_key, task=task) - all_results = [] try: for i, episode in enumerate(episodes): + if i < start_index: + continue episode_id = episode.get("episode_id", f"ep_{i}") logger.info( "Episode %d/%d: %s", i + 1, len(episodes), episode_id, @@ -184,6 +202,33 @@ def run(self, max_episodes: int | None = None) -> list[dict]: return all_results + def _load_completed_results(self, run_dir: Path) -> list[dict]: + """Load results from a previous run for resume. + + Returns results from all completed episodes except the last one. + The last episode directory is cleared for re-run (it may have been + interrupted mid-way). + """ + episodes_dir = run_dir / "episodes" + if not episodes_dir.exists(): + return [] + + result_files = sorted(episodes_dir.glob("*/result.json")) + if not result_files: + return [] + + # Load all completed results except the last + all_results = [] + for rf in result_files[:-1]: + all_results.append(json.loads(rf.read_text())) + + # Clear the last episode directory for re-run + last_episode_dir = result_files[-1].parent + for f in last_episode_dir.iterdir(): + f.unlink() + + return all_results + def _run_episode( self, sim, agent, task, episode: dict, index: int, episode_dir: Path, ) -> dict: @@ -338,6 +383,7 @@ def _create_agent(self, action_space: list[str], task_config: dict, llm = LLMClient( model=litellm_model, base_url=base_url, + num_retries=self.max_retries, **client_kwargs, ) else: diff --git a/easi/llm/client.py b/easi/llm/client.py index e50ed94..3af6f46 100644 --- a/easi/llm/client.py +++ b/easi/llm/client.py @@ -40,10 +40,12 @@ def __init__( self, model: str, base_url: str | None = None, + num_retries: int = 3, **kwargs: Any, ): self.model = model self.base_url = base_url + self.num_retries = num_retries self.default_kwargs = kwargs self._usage = { "prompt_tokens": 0, @@ -59,6 +61,7 @@ def generate(self, messages: list[dict], response_format: dict | None = None) -> call_kwargs: dict[str, Any] = { "model": self.model, "messages": messages, + "num_retries": self.num_retries, **self.default_kwargs, } if self.base_url: diff --git a/tests/test_e2e_evaluation.py b/tests/test_e2e_evaluation.py index f55979d..f9017f7 100644 --- a/tests/test_e2e_evaluation.py +++ b/tests/test_e2e_evaluation.py @@ -219,3 +219,216 @@ def test_images_saved_to_episode_dir(self, tmp_path): assert len(png_files) >= 2 # At least reset + one step assert png_files[0].name == "rgb_0000.png" assert png_files[1].name == "rgb_0001.png" + + def test_max_retries_stored(self, tmp_path): + """Verify max_retries is stored and appears in config.""" + output_dir = tmp_path / "logs" + runner = EvaluationRunner( + task_name="dummy_task", + agent_type="dummy", + output_dir=output_dir, + max_retries=5, + ) + assert runner.max_retries == 5 + runner.run(max_episodes=1) + + run_dir = _find_run_dir(output_dir) + config = json.loads((run_dir / "config.json").read_text()) + assert config["cli_options"]["max_retries"] == 5 + + def test_resume_dir_stored(self, tmp_path): + """Verify resume_dir is stored.""" + runner = EvaluationRunner( + task_name="dummy_task", + agent_type="dummy", + output_dir=tmp_path / "logs", + resume_dir=tmp_path / "old_run", + ) + assert runner.resume_dir == tmp_path / "old_run" + + def test_resume_skips_completed_episodes(self, tmp_path): + """Resume skips completed episodes and re-runs the last one.""" + output_dir = tmp_path / "logs" + + # First run: complete all 3 episodes + runner1 = EvaluationRunner( + task_name="dummy_task", + agent_type="dummy", + output_dir=output_dir, + agent_seed=42, + ) + results1 = runner1.run() + assert len(results1) == 3 + + run_dir = _find_run_dir(output_dir) + + # Resume: should load episodes 0-1, re-run episode 2 + runner2 = EvaluationRunner( + task_name="dummy_task", + agent_type="dummy", + output_dir=output_dir, + resume_dir=run_dir, + agent_seed=42, + ) + results2 = runner2.run() + assert len(results2) == 3 # 2 loaded + 1 re-run + + def test_resume_clears_last_episode_dir(self, tmp_path): + """Resume clears the last episode directory before re-running.""" + output_dir = tmp_path / "logs" + + runner1 = EvaluationRunner( + task_name="dummy_task", + agent_type="dummy", + output_dir=output_dir, + agent_seed=42, + ) + runner1.run() + + run_dir = _find_run_dir(output_dir) + episodes_dir = run_dir / "episodes" + episode_dirs = sorted(episodes_dir.iterdir()) + last_ep_dir = episode_dirs[-1] + + # Add a marker file to the last episode dir + marker = last_ep_dir / "marker.txt" + marker.write_text("should be deleted") + + runner2 = EvaluationRunner( + task_name="dummy_task", + agent_type="dummy", + output_dir=output_dir, + resume_dir=run_dir, + agent_seed=42, + ) + runner2.run() + + # Marker should be gone (dir was cleared) + assert not marker.exists() + # But result.json should exist (re-run completed) + assert (last_ep_dir / "result.json").exists() + + def test_resume_produces_valid_summary(self, tmp_path): + """Resumed run produces a valid summary with all episodes.""" + output_dir = tmp_path / "logs" + + runner1 = EvaluationRunner( + task_name="dummy_task", + agent_type="dummy", + output_dir=output_dir, + agent_seed=42, + ) + runner1.run() + + run_dir = _find_run_dir(output_dir) + + runner2 = EvaluationRunner( + task_name="dummy_task", + agent_type="dummy", + output_dir=output_dir, + resume_dir=run_dir, + agent_seed=42, + ) + results2 = runner2.run() + + summary = json.loads((run_dir / "summary.json").read_text()) + assert summary["num_episodes"] == 3 + + def test_resume_empty_run_dir(self, tmp_path): + """Resume with no completed episodes runs all from scratch.""" + output_dir = tmp_path / "logs" + run_dir = output_dir / "dummy_task" / "fake_run" + episodes_dir = run_dir / "episodes" + episodes_dir.mkdir(parents=True) + + runner = EvaluationRunner( + task_name="dummy_task", + agent_type="dummy", + output_dir=output_dir, + resume_dir=run_dir, + agent_seed=42, + ) + results = runner.run() + assert len(results) == 3 # All episodes run from scratch + + +class TestResumeConfigLoading: + def test_resume_loads_config_from_run_dir(self, tmp_path): + """cmd_run with --resume loads task_name and options from config.json.""" + from easi.cli import cmd_run + + output_dir = tmp_path / "logs" + + # First run with specific options + cmd_run( + task_name="dummy_task", + agent_type="dummy", + output_dir=str(output_dir), + data_dir="./datasets", + max_episodes=2, + llm_url=None, + seed=42, + backend=None, + model="default", + port=8080, + llm_kwargs_raw=None, + max_retries=3, + resume=None, + ) + + run_dir = _find_run_dir(output_dir) + + # Resume without specifying task_name — should load from config.json + cmd_run( + task_name=None, + agent_type="dummy", + output_dir="./logs", + data_dir="./datasets", + max_episodes=None, + llm_url=None, + seed=None, + backend=None, + model="default", + port=8080, + llm_kwargs_raw=None, + max_retries=3, + resume=str(run_dir), + ) + + # Should have completed successfully (3 total episodes from config) + summary = json.loads((run_dir / "summary.json").read_text()) + assert summary["num_episodes"] == 3 + + +class TestCLIParsing: + def test_cli_max_retries_default(self): + from easi.cli import build_parser + parser = build_parser() + args = parser.parse_args(["run", "dummy_task"]) + assert args.max_retries == 3 + + def test_cli_max_retries_custom(self): + from easi.cli import build_parser + parser = build_parser() + args = parser.parse_args(["run", "dummy_task", "--max-retries", "5"]) + assert args.max_retries == 5 + + def test_cli_resume_default(self): + from easi.cli import build_parser + parser = build_parser() + args = parser.parse_args(["run", "dummy_task"]) + assert args.resume is None + + def test_cli_resume_custom(self): + from easi.cli import build_parser + parser = build_parser() + args = parser.parse_args(["run", "dummy_task", "--resume", "/tmp/logs/run_123"]) + assert args.resume == "/tmp/logs/run_123" + + def test_cli_resume_without_task(self): + """--resume should work without specifying a task name.""" + from easi.cli import build_parser + parser = build_parser() + args = parser.parse_args(["run", "--resume", "/tmp/logs/run_123"]) + assert args.resume == "/tmp/logs/run_123" + assert args.task is None diff --git a/tests/test_llm_client.py b/tests/test_llm_client.py index 7922499..2ac46a3 100644 --- a/tests/test_llm_client.py +++ b/tests/test_llm_client.py @@ -147,6 +147,56 @@ def test_generate_omits_response_format_when_none(self, mock_litellm): assert "response_format" not in call_kwargs.kwargs +class TestLLMClientRetries: + def test_default_num_retries(self): + from easi.llm.client import LLMClient + client = LLMClient(model="openai/gpt-4o") + assert client.num_retries == 3 + + def test_custom_num_retries(self): + from easi.llm.client import LLMClient + client = LLMClient(model="openai/gpt-4o", num_retries=5) + assert client.num_retries == 5 + + @patch("easi.llm.client.litellm") + def test_num_retries_passed_to_litellm(self, mock_litellm): + from easi.llm.client import LLMClient + + mock_choice = MagicMock() + mock_choice.message.content = "ok" + mock_response = MagicMock() + mock_response.choices = [mock_choice] + mock_response.usage.prompt_tokens = 10 + mock_response.usage.completion_tokens = 5 + mock_litellm.completion.return_value = mock_response + mock_litellm.completion_cost.return_value = 0.0 + + client = LLMClient(model="openai/gpt-4o", num_retries=7) + client.generate([{"role": "user", "content": "hi"}]) + + call_kwargs = mock_litellm.completion.call_args + assert call_kwargs.kwargs.get("num_retries") == 7 + + @patch("easi.llm.client.litellm") + def test_zero_retries_passed_through(self, mock_litellm): + from easi.llm.client import LLMClient + + mock_choice = MagicMock() + mock_choice.message.content = "ok" + mock_response = MagicMock() + mock_response.choices = [mock_choice] + mock_response.usage.prompt_tokens = 10 + mock_response.usage.completion_tokens = 5 + mock_litellm.completion.return_value = mock_response + mock_litellm.completion_cost.return_value = 0.0 + + client = LLMClient(model="openai/gpt-4o", num_retries=0) + client.generate([{"role": "user", "content": "hi"}]) + + call_kwargs = mock_litellm.completion.call_args + assert call_kwargs.kwargs.get("num_retries") == 0 + + class TestLLMClientProtocolCompat: def test_satisfies_llm_client_protocol(self): from easi.core.protocols import LLMClientProtocol From 912cfb07c5cb5cb186c85a88cc6185ef5956ebba Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Sun, 15 Feb 2026 20:37:34 +0800 Subject: [PATCH 022/239] [Fix] Default --agent to react instead of dummy --- easi/cli.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/easi/cli.py b/easi/cli.py index a9d300b..87662a8 100644 --- a/easi/cli.py +++ b/easi/cli.py @@ -81,7 +81,7 @@ def build_parser() -> argparse.ArgumentParser: run_parser.add_argument("task", type=str, nargs="?", default=None, help="Task name (e.g., 'dummy_task', 'ebalfred_base'). " "Optional when --resume is provided.") - run_parser.add_argument("--agent", type=str, default="dummy", choices=["dummy", "react"]) + run_parser.add_argument("--agent", type=str, default="react", choices=["dummy", "react"]) run_parser.add_argument("--output-dir", type=str, default="./logs", help="Base output directory (creates ///)") run_parser.add_argument("--data-dir", type=str, default="./datasets", From 36aff7be58781f6c1eac61f60f81985af6e811d6 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Sun, 15 Feb 2026 21:50:09 +0800 Subject: [PATCH 023/239] [Feat] Add per-episode retry with simulator re-launch and fix max_steps bug Fix max_steps mismatch where YAML configured 50 but vendor EBAlfEnv hardcoded 30. Now max_steps flows from YAML through simulator_kwargs to the bridge and vendor env. Add per-episode retry in EvaluationRunner: on crash (e.g. AI2-THOR Unity segfault), the episode dir is cleared, the simulator is re-launched, and the episode is retried up to max_retries times. If all retries are exhausted the episode is recorded as failed and the runner continues to the next episode. --- easi/core/base_task.py | 3 +- easi/evaluation/runner.py | 46 ++++++++- easi/tasks/ebalfred/bridge.py | 2 + easi/tasks/ebalfred/vendor/EBAlfEnv.py | 5 +- tests/test_bridge_refactor.py | 2 +- tests/test_e2e_evaluation.py | 124 +++++++++++++++++++++++++ 6 files changed, 175 insertions(+), 7 deletions(-) diff --git a/easi/core/base_task.py b/easi/core/base_task.py index afb6ea4..7cc91e7 100644 --- a/easi/core/base_task.py +++ b/easi/core/base_task.py @@ -109,9 +109,10 @@ def additional_deps(self) -> list[str]: @property def simulator_kwargs(self) -> dict: - """Bridge-facing kwargs (simulator_configs minus additional_deps).""" + """Bridge-facing kwargs (simulator_configs minus additional_deps + max_steps).""" cfg = dict(self.simulator_configs) cfg.pop("additional_deps", None) + cfg["max_steps"] = self.max_steps return cfg def get_instruction(self, episode: dict) -> str: diff --git a/easi/evaluation/runner.py b/easi/evaluation/runner.py index 786d716..a75abeb 100644 --- a/easi/evaluation/runner.py +++ b/easi/evaluation/runner.py @@ -176,9 +176,42 @@ def run(self, max_episodes: int | None = None) -> list[dict]: episode_dir = episodes_dir / f"{i:03d}_{_sanitize_dirname(episode_id)}" episode_dir.mkdir(exist_ok=True) - result = self._run_episode( - sim, agent, task, episode, i, episode_dir, - ) + result = None + for attempt in range(1, self.max_retries + 1): + try: + result = self._run_episode( + sim, agent, task, episode, i, episode_dir, + ) + break + except Exception as exc: + logger.warning( + "Episode %s attempt %d/%d failed: %s", + episode_id, attempt, self.max_retries, exc, + ) + self._clear_episode_dir(episode_dir) + if attempt < self.max_retries: + logger.info("Re-launching simulator for retry...") + try: + sim.close() + except Exception: + pass + sim, sim_runner = self._create_simulator( + task.simulator_key, task=task, + ) + else: + logger.error( + "Episode %s failed after %d attempts, skipping", + episode_id, self.max_retries, + ) + result = { + "episode_id": episode_id, + "instruction": task.get_instruction(episode), + "success": 0.0, + "num_steps": 0, + "elapsed_seconds": 0.0, + "error": str(exc), + } + all_results.append(result) (episode_dir / "result.json").write_text( @@ -316,6 +349,13 @@ def _run_episode( return metrics + @staticmethod + def _clear_episode_dir(episode_dir: Path) -> None: + """Remove all files in an episode directory for a clean retry.""" + for f in episode_dir.iterdir(): + if f.is_file(): + f.unlink() + @staticmethod def _write_trajectory_entry(path: Path, entry: dict) -> None: """Append a single JSON line to the trajectory file.""" diff --git a/easi/tasks/ebalfred/bridge.py b/easi/tasks/ebalfred/bridge.py index 297f832..829a9eb 100644 --- a/easi/tasks/ebalfred/bridge.py +++ b/easi/tasks/ebalfred/bridge.py @@ -35,6 +35,7 @@ def _create_env(self, reset_config, simulator_kwargs): # (e.g. datasets/oscarqjh_EB-Alfred_easi/tasks) data_dir = reset_config.get("data_dir") or simulator_kwargs.get("data_dir") resolution = simulator_kwargs.get("screen_height", 500) + max_steps = simulator_kwargs.get("max_steps", 30) # x_display from YAML, falling back to DISPLAY env var (set by xvfb-run) x_display = simulator_kwargs.get( "x_display", os.environ.get("DISPLAY", ":0").lstrip(":") @@ -43,6 +44,7 @@ def _create_env(self, reset_config, simulator_kwargs): resolution=resolution, data_dir=data_dir, x_display=x_display, + max_steps=max_steps, ) def _on_reset(self, env, reset_config): diff --git a/easi/tasks/ebalfred/vendor/EBAlfEnv.py b/easi/tasks/ebalfred/vendor/EBAlfEnv.py index 4d5a44b..9e17700 100644 --- a/easi/tasks/ebalfred/vendor/EBAlfEnv.py +++ b/easi/tasks/ebalfred/vendor/EBAlfEnv.py @@ -88,7 +88,7 @@ class EBAlfEnv(gym.Env): action_space (gym.spaces.Discrete): Discrete action space language_skill_set (list): Readable action descriptions """ - def __init__(self, exp_name='', detection_box=False, resolution=500, data_dir=None, x_display=None): + def __init__(self, exp_name='', detection_box=False, resolution=500, data_dir=None, x_display=None, max_steps=30): """ Initialize the AI2THOR environment. @@ -98,6 +98,7 @@ def __init__(self, exp_name='', detection_box=False, resolution=500, data_dir=No resolution: screen resolution for AI2-THOR data_dir: path to task JSON files (e.g. datasets/.../tasks) x_display: X display number for AI2-THOR + max_steps: maximum steps per episode (from task YAML config) """ super().__init__() self.reward_config_path = ALFRED_REWARD_PATH @@ -111,7 +112,7 @@ def __init__(self, exp_name='', detection_box=False, resolution=500, data_dir=No # Episode tracking self._reset = False self._current_step = 0 - self._max_episode_steps = 30 + self._max_episode_steps = max_steps self._cur_invalid_actions = 0 self._max_invalid_actions = 10 self._episode_start_time = 0 diff --git a/tests/test_bridge_refactor.py b/tests/test_bridge_refactor.py index 5a37fa4..bcb7858 100644 --- a/tests/test_bridge_refactor.py +++ b/tests/test_bridge_refactor.py @@ -306,7 +306,7 @@ def test_dummy_task_empty_simulator_configs(self): task = DummyTask() assert task.simulator_configs == {} assert task.additional_deps == [] - assert task.simulator_kwargs == {} + assert task.simulator_kwargs == {"max_steps": 100} def test_all_ebalfred_splits_have_configs(self): """All EB-Alfred split YAMLs should have simulator_configs.""" diff --git a/tests/test_e2e_evaluation.py b/tests/test_e2e_evaluation.py index f9017f7..c6ec49c 100644 --- a/tests/test_e2e_evaluation.py +++ b/tests/test_e2e_evaluation.py @@ -352,6 +352,130 @@ def test_resume_empty_run_dir(self, tmp_path): assert len(results) == 3 # All episodes run from scratch +class TestEpisodeRetry: + def test_retry_on_episode_failure(self, tmp_path): + """Episodes that crash are retried and succeed on subsequent attempt.""" + from unittest.mock import patch + + output_dir = tmp_path / "logs" + runner = EvaluationRunner( + task_name="dummy_task", + agent_type="dummy", + output_dir=output_dir, + max_retries=3, + ) + + call_count = {"n": 0} + original_run_episode = runner._run_episode + + def failing_first_call(*args, **kwargs): + call_count["n"] += 1 + if call_count["n"] == 1: + raise RuntimeError("Simulated bridge crash") + return original_run_episode(*args, **kwargs) + + with patch.object(runner, "_run_episode", side_effect=failing_first_call): + results = runner.run(max_episodes=1) + + assert len(results) == 1 + assert "error" not in results[0] + assert results[0]["success"] in (0.0, 1.0) + + def test_retry_exhausted_records_failure(self, tmp_path): + """Episode recorded as failure after all retries exhausted.""" + from unittest.mock import patch + + output_dir = tmp_path / "logs" + runner = EvaluationRunner( + task_name="dummy_task", + agent_type="dummy", + output_dir=output_dir, + max_retries=2, + ) + + with patch.object( + runner, "_run_episode", + side_effect=RuntimeError("persistent crash"), + ): + results = runner.run(max_episodes=1) + + assert len(results) == 1 + assert results[0]["success"] == 0.0 + assert "error" in results[0] + assert "persistent crash" in results[0]["error"] + + def test_retry_clears_partial_files(self, tmp_path): + """Retry clears partial files from the failed attempt.""" + from unittest.mock import patch + + output_dir = tmp_path / "logs" + runner = EvaluationRunner( + task_name="dummy_task", + agent_type="dummy", + output_dir=output_dir, + max_retries=2, + ) + + original_run_episode = runner._run_episode + + def write_then_crash(sim, agent, task, episode, index, episode_dir): + # Write a partial file then crash + (episode_dir / "partial.txt").write_text("partial data") + raise RuntimeError("crash mid-episode") + + call_count = {"n": 0} + + def crash_then_succeed(*args, **kwargs): + call_count["n"] += 1 + if call_count["n"] == 1: + return write_then_crash(*args, **kwargs) + return original_run_episode(*args, **kwargs) + + with patch.object(runner, "_run_episode", side_effect=crash_then_succeed): + results = runner.run(max_episodes=1) + + assert len(results) == 1 + # The partial file should have been cleaned up + run_dir = _find_run_dir(output_dir) + episode_dirs = sorted((run_dir / "episodes").iterdir()) + assert not (episode_dirs[0] / "partial.txt").exists() + # But result.json from successful retry should exist + assert (episode_dirs[0] / "result.json").exists() + + def test_retry_continues_to_next_episode(self, tmp_path): + """After exhausting retries on one episode, runner continues to the next.""" + from unittest.mock import patch + + output_dir = tmp_path / "logs" + runner = EvaluationRunner( + task_name="dummy_task", + agent_type="dummy", + output_dir=output_dir, + max_retries=1, + ) + + original_run_episode = runner._run_episode + call_count = {"n": 0} + + def fail_first_episode(*args, **kwargs): + call_count["n"] += 1 + # Fail only the first episode (call 1) + if call_count["n"] == 1: + raise RuntimeError("episode 0 crash") + return original_run_episode(*args, **kwargs) + + with patch.object(runner, "_run_episode", side_effect=fail_first_episode): + results = runner.run(max_episodes=2) + + # Both episodes should have results + assert len(results) == 2 + # First episode failed + assert results[0]["success"] == 0.0 + assert "error" in results[0] + # Second episode succeeded + assert "error" not in results[1] + + class TestResumeConfigLoading: def test_resume_loads_config_from_run_dir(self, tmp_path): """cmd_run with --resume loads task_name and options from config.json.""" From 02ed1e20e02fd92c840e0356730439ab352f3082 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Sun, 15 Feb 2026 23:59:03 +0800 Subject: [PATCH 024/239] [Feat] Add EB-Navigation benchmark integration Integrate EmbodiedBench EB-Navigation into EASI with vendored env, task bridge, prompt builder, and 5 split configs (ai2thor v5.0.0). --- easi/tasks/ebnavigation/__init__.py | 0 easi/tasks/ebnavigation/actions.py | 24 + easi/tasks/ebnavigation/bridge.py | 76 +++ .../config/navigation_examples.json | 5 + .../tasks/ebnavigation/ebnavigation_base.yaml | 22 + .../ebnavigation_common_sense.yaml | 22 + .../ebnavigation_complex_instruction.yaml | 22 + .../ebnavigation_long_horizon.yaml | 22 + .../ebnavigation_visual_appearance.yaml | 22 + easi/tasks/ebnavigation/prompts.py | 464 ++++++++++++++++++ easi/tasks/ebnavigation/task.py | 84 ++++ easi/tasks/ebnavigation/vendor/EBNavEnv.py | 159 ++++++ easi/tasks/ebnavigation/vendor/__init__.py | 0 tests/test_ebnavigation_task.py | 314 ++++++++++++ 14 files changed, 1236 insertions(+) create mode 100644 easi/tasks/ebnavigation/__init__.py create mode 100644 easi/tasks/ebnavigation/actions.py create mode 100644 easi/tasks/ebnavigation/bridge.py create mode 100644 easi/tasks/ebnavigation/config/navigation_examples.json create mode 100644 easi/tasks/ebnavigation/ebnavigation_base.yaml create mode 100644 easi/tasks/ebnavigation/ebnavigation_common_sense.yaml create mode 100644 easi/tasks/ebnavigation/ebnavigation_complex_instruction.yaml create mode 100644 easi/tasks/ebnavigation/ebnavigation_long_horizon.yaml create mode 100644 easi/tasks/ebnavigation/ebnavigation_visual_appearance.yaml create mode 100644 easi/tasks/ebnavigation/prompts.py create mode 100644 easi/tasks/ebnavigation/task.py create mode 100644 easi/tasks/ebnavigation/vendor/EBNavEnv.py create mode 100644 easi/tasks/ebnavigation/vendor/__init__.py create mode 100644 tests/test_ebnavigation_task.py diff --git a/easi/tasks/ebnavigation/__init__.py b/easi/tasks/ebnavigation/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/easi/tasks/ebnavigation/actions.py b/easi/tasks/ebnavigation/actions.py new file mode 100644 index 0000000..efc6fd4 --- /dev/null +++ b/easi/tasks/ebnavigation/actions.py @@ -0,0 +1,24 @@ +"""EB-Navigation action space definitions. + +Reference: EmbodiedBench/embodiedbench/envs/eb_navigation/EBNavEnv.py +""" +from __future__ import annotations + +DISCRETE_ACTIONS = [ + "Move forward by 0.25", + "Move backward by 0.25", + "Move rightward by 0.25", + "Move leftward by 0.25", + "Rotate to the right by 90 degrees.", + "Rotate to the left by 90 degrees.", + "Tilt the camera upward by 30 degrees.", + "Tilt the camera downward by 30 degrees.", +] + +# Mapping from action text to integer index (for bridge) +ACTION_NAME_TO_ID = {name: i for i, name in enumerate(DISCRETE_ACTIONS)} + + +def get_action_space() -> list[str]: + """Return the EB-Navigation discrete action space (8 actions).""" + return list(DISCRETE_ACTIONS) diff --git a/easi/tasks/ebnavigation/bridge.py b/easi/tasks/ebnavigation/bridge.py new file mode 100644 index 0000000..b2eab04 --- /dev/null +++ b/easi/tasks/ebnavigation/bridge.py @@ -0,0 +1,76 @@ +"""EB-Navigation bridge -- wraps vendored EBNavEnv via BaseBridge. + +This script runs inside the easi_ai2thor_v5_0_0 conda env (Python 3.10). +Communicates with parent process via filesystem IPC. + +Usage: + python bridge.py --workspace /tmp/easi_xxx [--simulator-kwargs '{}'] +""" +from __future__ import annotations + +import json +import sys +from pathlib import Path + +_repo_root = Path(__file__).resolve().parents[3] +if str(_repo_root) not in sys.path: + sys.path.insert(0, str(_repo_root)) + +from easi.simulators.base_bridge import BaseBridge + + +class EBNavigationBridge(BaseBridge): + """Thin BaseBridge wrapper around vendored EBNavEnv.""" + + # Action text -> integer ID mapping (populated on env creation) + _action_map: dict[str, int] = {} + + def _create_env(self, reset_config, simulator_kwargs): + from easi.tasks.ebnavigation.actions import ACTION_NAME_TO_ID + from easi.tasks.ebnavigation.vendor.EBNavEnv import EBNavEnv + + self._action_map = ACTION_NAME_TO_ID + resolution = simulator_kwargs.get("screen_height", 500) + max_steps = simulator_kwargs.get("max_steps", 20) + fov = simulator_kwargs.get("fov", 100) + return EBNavEnv(resolution=resolution, fov=fov, max_steps=max_steps) + + def _on_reset(self, env, reset_config): + episode = { + "scene": reset_config["scene"], + "agent_pose": json.loads(reset_config["agent_pose"]) + if isinstance(reset_config["agent_pose"], str) + else reset_config["agent_pose"], + "target_object_id": reset_config["target_object_id"], + "target_position": json.loads(reset_config["target_position"]) + if isinstance(reset_config["target_position"], str) + else reset_config["target_position"], + "instruction": reset_config["instruction"], + } + return env.reset(episode=episode) + + def _on_step(self, env, action_text): + action_id = self._action_map.get(action_text, -1) + if action_id < 0: + # Try case-insensitive match + for name, idx in self._action_map.items(): + if name.lower() == action_text.lower(): + action_id = idx + break + return env.step(action_id) + + def _extract_image(self, obs): + return obs["head_rgb"] + + def _extract_info(self, info): + return { + "task_success": float(info.get("task_success", 0.0)), + "distance": float(info.get("distance", 0.0)), + "last_action_success": float(info.get("last_action_success", 0.0)), + "feedback": str(info.get("env_feedback", "")), + "action_id": int(info.get("action_id", -1)), + } + + +if __name__ == "__main__": + EBNavigationBridge.main() diff --git a/easi/tasks/ebnavigation/config/navigation_examples.json b/easi/tasks/ebnavigation/config/navigation_examples.json new file mode 100644 index 0000000..b483efa --- /dev/null +++ b/easi/tasks/ebnavigation/config/navigation_examples.json @@ -0,0 +1,5 @@ +[ + "\nHuman Instruction : Navigate to the floorlamp and be as close as possible.\n\nReasoning and reflection: I can see the floorlmap is at left side in front of me, next to the TVStand. Around me is a Cabinet at my left side. Following the guideline, since the floorlamp is at left side, I can approach it by a series of moving forward and left/rightward. Since currently there is an obstacle at my left, I will first move forward twice.\nExecutable plans: [[0, Move forward by 0.25], [0, Move forward by 0.25]]\n\nEnvironment Feedback: Step 1 is executed successfully; Step 2 is executed successfully;\n\nReasoning and reflection: I can see now I get closer to the floorlamp. It is still located in front of me at the left. I will try move forward and move leftward in series.\nExecutable plans: [[0, Move forward by 0.25], [3, Move leftward by 0.25], [0, Move forward by 0.25], [3, Move leftward by 0.25], [0, Move forward by 0.25]]\n\nEnvironment Feedback: Step 3 is executed successfully; Step 4 is executed successfully; Step 5 is executed successfully; Step 6 is executed successfully; Step 7 is Invalid, as a StandardIslandHeight is blocking you from moving forward.\n\nReasoning and reflection: I am a lot closer to the floorlamp by a series a forward and leftward movement. It is still in front of me. However, The last action is invalid, showing that I am blocked from moving forward.That means there is an obstacle in front of me and I can see it is probably a small table. Following the strategy, I can get away by first moving to the left and move forward. \nExecutable plans: [[3, Move leftward by 0.25], [3, Move leftward by 0.25], [0, Move forward by 0.25], [0, Move forward by 0.25]]\n\nEnvironment Feedback: Step 8 is executed successfully; Step 9 is executed successfully; Step 10 is executed successfully; Step 11 is executed successfully;\n\nReasoning and reflection: I can see that the floorlamp is at the right side of me, slight in the front. I think it in effect of consecutively moving leftward. Also, I have got away with the obstacle.Now I need to move right and I will probably be close to the floorlamp.\nExecutable plans: [[2, Move rightward by 0.25], [2, Move rightward by 0.25]]]\n\nEnvironment Feedback: Step 12 is executed successfully; Step 13 is executed successfully. \n\nReasoning and reflection: Last two action brought me closer to the floorlamp, however, following the strategy, I should not complete now since I can still be closer to the floorlamp by moving forward a few steps.\nExecutable plans: [[0, Move forward by 0.25], [0, Move forward by 0.25], [0, Move forward by 0.25]]\n\nEnvironment Feedback: Step 14 is executed successfully; Step 15 is executed successfully; Step 16 is executed successfully.\n\n\nTask Success\n", + "\nHuman Instruction: Navigate to the computer on the desk and be as close as possible.\n\nReasoning and reflection: According to your instruction, I first need to locate the target object. I can see that the computer is located in my left front direction, placed on a desk. Next, I analyze the obstacles around me. In front of me, slightly to the right, there is a chair blocking my direct path. To avoid it, I decide to move leftward first, followed by a forward movement to approach the desk.\nExecutable plans: [[0, Move forward by 0.25], [3, Move leftward by 0.25]]\n\nEnvironment Feedback: Step 1 is executed successfully; Step 2 is Invalid, as a StandardHeightTable is blocking you from moving forward.\n\nReasoning and reflection: I am now closer to the desk, and I can still see the computer on it. It remains in my left front direction. My last leftward movement failed because a StandardHeightTable is blocking my path. However, following the strategy, I need not address the invalid action since forward movement can still bring me closer to the target object. So I will still make forward movements for now. \nExecutable plans: [[0, Move forward by 0.25], [0, Move forward by 0.25], [0, Move forward by 0.25]]\n\nEnvironment Feedback: Step 3 is executed successfully; Step 4 is executed successfully; Step 5 is Invalid, as a StandardHeightTable is blocking you from moving forward.\n\nReasoning and reflection: I can see that I am getting closer but the last forward move is blocked. Following the strategy, I need to address the invalid action now since it is an obstacle blocking from moving closer in the direction of target object. The computer is at my left side in the front, to bypass this obstacle, I plan to first move leftward again to create more space, then try moving forward once more to get closer to the desk and the computer.\nExecutable plans: [[3, Move leftward by 0.25], [0, Move forward by 0.25]]\n\nEnvironment Feedback: Step 6 is executed successfully; Step 7 is invalid because the StandardHeight is blocking the movement.\n\nReasoning and reflection: I can see that the computer is still in my left front direction. However, the chair continues to block my path. From the environment feedback, I observed that while my leftward movement was successful, my forward movement was still blocked. This indicates that I didn't move far enough left to fully bypass the obstacle. To address this, I decide to move leftward two more times to ensure a clear path, then try moving forward again.\nExecutable plans: [[3, Move leftward by 0.25], [3, Move leftward by 0.25], [0, Move forward by 0.25]]\n\nEnvironment Feedback: Step 8 is executed successfully; Step 9 is executed successfully; Step 10 is executed successfully.\n\nReasoning and reflection: Now, I can see that the computer has shifted to my right front direction. There are no longer any visible obstacles blocking my way. I deduce that the computer is now to my right because my previous leftward movements adjusted my position significantly to clear the earlier obstacle. Since the path is clear, I plan to approach the computer by moving forward three times and then rightward once to align myself next to it.\nExecutable plans: [[0, Move forward by 0.25], [0, Move forward by 0.25], [0, Move forward by 0.25], [2, Move rightward by 0.25]]\n\nEnvironment Feedback: Step 11 is executed successfully; Step 12 is executed successfully; Step 13 is executed successfully; Step 14 is executed successfully.\n\n\nTask Success\n\n", + "\nHuman Instruction: Navigate to the book and be as close as possible.\n\nReasoning and reflection: At the moment, I cannot see the book in my current field of view. According to the strategy you provided, I will rotate my view ninety degrees at a time until I locate the target object. Since rotation changes my vision, I will not plan any other actions until the book is visible.\nExecutable plans: [[4, Rotate to the right by 90 degrees]]\n\nEnvironment Feedback: Step 1 is executed successfully. The book is still not visible in my current field of view.\n\nReasoning and reflection: I still cannot see the book in my current field of view. Following your instruction, I will rotate ninety degrees again. As the rotation may change my view, I will not plan any additional actions until I locate the book.\nExecutable plans: [[4, Rotate to the right by 90 degrees]]\n\nEnvironment Feedback: Step 2 is executed successfully. The book is now visible directly in front of me, placed on a shelf.\n\nReasoning and reflection: I have located the target object. The book is directly in front of me on a shelf. Following the strategy you provided, I will stop rotating now and start planning my navigation to get close to the book. Since the book is in front of me, I will first move two steps to the right to adjust my position, and then move forward three steps to approach it.\nExecutable plans: [[2, Move rightward by 0.25], [2, Move rightward by 0.25], [0, Move forward by 0.25], [0, Move forward by 0.25], [0, Move forward by 0.25]]\n\nEnvironment Feedback: Step 3 to Step 7 are all executed successfully.\n\nReasoning and reflection: I can now see that the book is directly in front of me, but I have not yet moved close enough to it. According to the standard, I should move closer to the book. Therefore, I decide to move forward three more times to reach the desired position.\nExecutable plans: [[0, Move forward by 0.25], [0, Move forward by 0.25], [0, Move forward by 0.25]]\n\nEnvironment Feedback: Step 8, Step 9, and Step 10 are all executed successfully.\n\nTask Success\n\n\n" +] diff --git a/easi/tasks/ebnavigation/ebnavigation_base.yaml b/easi/tasks/ebnavigation/ebnavigation_base.yaml new file mode 100644 index 0000000..acd13d2 --- /dev/null +++ b/easi/tasks/ebnavigation/ebnavigation_base.yaml @@ -0,0 +1,22 @@ +name: ebnavigation_base +display_name: "EB-Navigation Base Split" +description: "Base evaluation split for EB-Navigation (object-goal navigation)" +simulator: "ai2thor:v5_0_0" +task_class: "easi.tasks.ebnavigation.task.EBNavigationTask" +max_steps: 20 +dataset: + source: huggingface + repo_id: "oscarqjh/EB-Navigation_easi" + subset: null + split: "base" +simulator_configs: + screen_height: 500 + screen_width: 500 + fov: 100 +agent: + prompt_builder: "easi.tasks.ebnavigation.prompts.EBNavigationPromptBuilder" + prompt_builder_kwargs: + n_shot: 3 + split: "base" + use_feedback: true + chat_history: true diff --git a/easi/tasks/ebnavigation/ebnavigation_common_sense.yaml b/easi/tasks/ebnavigation/ebnavigation_common_sense.yaml new file mode 100644 index 0000000..c5829aa --- /dev/null +++ b/easi/tasks/ebnavigation/ebnavigation_common_sense.yaml @@ -0,0 +1,22 @@ +name: ebnavigation_common_sense +display_name: "EB-Navigation Common Sense Split" +description: "Common sense evaluation split for EB-Navigation (object-goal navigation)" +simulator: "ai2thor:v5_0_0" +task_class: "easi.tasks.ebnavigation.task.EBNavigationTask" +max_steps: 20 +dataset: + source: huggingface + repo_id: "oscarqjh/EB-Navigation_easi" + subset: null + split: "common_sense" +simulator_configs: + screen_height: 500 + screen_width: 500 + fov: 100 +agent: + prompt_builder: "easi.tasks.ebnavigation.prompts.EBNavigationPromptBuilder" + prompt_builder_kwargs: + n_shot: 3 + split: "common_sense" + use_feedback: true + chat_history: true diff --git a/easi/tasks/ebnavigation/ebnavigation_complex_instruction.yaml b/easi/tasks/ebnavigation/ebnavigation_complex_instruction.yaml new file mode 100644 index 0000000..d28ab11 --- /dev/null +++ b/easi/tasks/ebnavigation/ebnavigation_complex_instruction.yaml @@ -0,0 +1,22 @@ +name: ebnavigation_complex_instruction +display_name: "EB-Navigation Complex Instruction Split" +description: "Complex instruction evaluation split for EB-Navigation (object-goal navigation)" +simulator: "ai2thor:v5_0_0" +task_class: "easi.tasks.ebnavigation.task.EBNavigationTask" +max_steps: 20 +dataset: + source: huggingface + repo_id: "oscarqjh/EB-Navigation_easi" + subset: null + split: "complex_instruction" +simulator_configs: + screen_height: 500 + screen_width: 500 + fov: 100 +agent: + prompt_builder: "easi.tasks.ebnavigation.prompts.EBNavigationPromptBuilder" + prompt_builder_kwargs: + n_shot: 3 + split: "complex_instruction" + use_feedback: true + chat_history: true diff --git a/easi/tasks/ebnavigation/ebnavigation_long_horizon.yaml b/easi/tasks/ebnavigation/ebnavigation_long_horizon.yaml new file mode 100644 index 0000000..e675848 --- /dev/null +++ b/easi/tasks/ebnavigation/ebnavigation_long_horizon.yaml @@ -0,0 +1,22 @@ +name: ebnavigation_long_horizon +display_name: "EB-Navigation Long Horizon Split" +description: "Long horizon evaluation split for EB-Navigation (object-goal navigation)" +simulator: "ai2thor:v5_0_0" +task_class: "easi.tasks.ebnavigation.task.EBNavigationTask" +max_steps: 20 +dataset: + source: huggingface + repo_id: "oscarqjh/EB-Navigation_easi" + subset: null + split: "long_horizon" +simulator_configs: + screen_height: 500 + screen_width: 500 + fov: 100 +agent: + prompt_builder: "easi.tasks.ebnavigation.prompts.EBNavigationPromptBuilder" + prompt_builder_kwargs: + n_shot: 3 + split: "long_horizon" + use_feedback: true + chat_history: true diff --git a/easi/tasks/ebnavigation/ebnavigation_visual_appearance.yaml b/easi/tasks/ebnavigation/ebnavigation_visual_appearance.yaml new file mode 100644 index 0000000..198c82c --- /dev/null +++ b/easi/tasks/ebnavigation/ebnavigation_visual_appearance.yaml @@ -0,0 +1,22 @@ +name: ebnavigation_visual_appearance +display_name: "EB-Navigation Visual Appearance Split" +description: "Visual appearance evaluation split for EB-Navigation (object-goal navigation)" +simulator: "ai2thor:v5_0_0" +task_class: "easi.tasks.ebnavigation.task.EBNavigationTask" +max_steps: 20 +dataset: + source: huggingface + repo_id: "oscarqjh/EB-Navigation_easi" + subset: null + split: "visual_appearance" +simulator_configs: + screen_height: 500 + screen_width: 500 + fov: 100 +agent: + prompt_builder: "easi.tasks.ebnavigation.prompts.EBNavigationPromptBuilder" + prompt_builder_kwargs: + n_shot: 3 + split: "visual_appearance" + use_feedback: true + chat_history: true diff --git a/easi/tasks/ebnavigation/prompts.py b/easi/tasks/ebnavigation/prompts.py new file mode 100644 index 0000000..721a995 --- /dev/null +++ b/easi/tasks/ebnavigation/prompts.py @@ -0,0 +1,464 @@ +"""EB-Navigation prompt builder matching EmbodiedBench nav_planner. + +Reference: EmbodiedBench/embodiedbench/planner/nav_planner.py + EmbodiedBench/embodiedbench/evaluator/config/system_prompts.py:75-99 + EmbodiedBench/embodiedbench/evaluator/config/eb_navigation_example.py + EmbodiedBench/embodiedbench/planner/planner_utils.py:20-29 +""" +from __future__ import annotations + +import json +from pathlib import Path + +from easi.agents.prompt_builder import _encode_image_base64, validate_action_name +from easi.core.episode import Action +from easi.core.memory import AgentMemory +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + +# System prompt from EmbodiedBench (eb_navigation_system_prompt). +# Three format placeholders: max_action_id, action_list, examples. +NAVIGATION_SYSTEM_PROMPT = '''## You are a robot operating in a home. You can do various tasks and output a sequence of actions to accomplish a given task with images of your status. + +## The available action id (0 ~ {}) and action names are: {}. + +*** Strategy *** + +1. Locate the Target Object Type: Clearly describe the spatial location of the target object \ +from the observation image (i.e. in the front left side, a few steps from current standing point). + +2. Navigate by *** Using Move forward and Move right/left as main strategy ***, since any point can be reached through a combination of those. \ +When planning for movement, reason based on target object's location and obstacles around you. \ + +3. Focus on primary goal: Only address invalid action when it blocks you from moving closer in the direction to target object. In other words, \ +do not overly focus on correcting invalid actions when direct movement towards target object can still bring you closer. \ + +4. *** Use Rotation Sparingly ***, only when you lose track of the target object and it's not in your view. If so, plan nothing but ONE ROTATION at a step until that object appears in your view. \ +After the target object appears, start navigation and avoid using rotation until you lose sight of the target again. + +5. *** Do not complete task too early until you can not move any closer to the object, i.e. try to be as close as possible. + +{} + +---------- + +''' + +# Output template from EmbodiedBench planner_utils.py (template, not template_lang). +OUTPUT_TEMPLATE = ''' +The output json format should be {'visual_state_description':str, 'reasoning_and_reflection':str, 'language_plan':str, 'executable_plan':List[{'action_id':int, 'action_name':str}...]} +The fields in above JSON follows the purpose below: +1. visual_state_description is for description of current state from the visual image, +2. reasoning_and_reflection is for summarizing the history of interactions and any available environmental feedback. Additionally, provide reasoning as to why the last action or plan failed and did not finish the task, +3. language_plan is for describing a list of actions to achieve the user instruction. Each action is started by the step number and the action name, +4. executable_plan is a list of actions needed to achieve the user instruction, with each action having an action ID and a name. +5. keep your plan efficient and concise. +!!! When generating content for JSON strings, avoid using any contractions or abbreviated forms (like 's, 're, 've, 'll, 'd, n't) that use apostrophes. Instead, write out full forms (is, are, have, will, would, not) to prevent parsing errors in JSON. Please do not output any other thing more than the above-mentioned JSON, do not include ```json and ```!!!. +''' + +# JSON schema matching EmbodiedBench navigation output format. +EBNAVIGATION_RESPONSE_SCHEMA = { + "type": "json_schema", + "json_schema": { + "name": "embodied_planning", + "schema": { + "type": "object", + "properties": { + "visual_state_description": { + "type": "string", + "description": "Description of current state from the visual image", + }, + "reasoning_and_reflection": { + "type": "string", + "description": "summarize the history of interactions and any available environmental feedback. Additionally, provide reasoning as to why the last action or plan failed and did not finish the task", + }, + "language_plan": { + "type": "string", + "description": "The list of actions to achieve the user instruction. Each action is started by the step number and the action name", + }, + "executable_plan": { + "type": "array", + "description": "A list of actions needed to achieve the user instruction, with each action having an action ID and a name. Do not output empty list.", + "items": { + "type": "object", + "properties": { + "action_id": { + "type": "integer", + "description": "The action ID to select from the available actions given by the prompt", + }, + "action_name": { + "type": "string", + "description": "The name of the action", + }, + }, + "required": ["action_id", "action_name"], + }, + }, + }, + "required": [ + "visual_state_description", + "reasoning_and_reflection", + "language_plan", + "executable_plan", + ], + }, + }, +} + +MESSAGE_WINDOW_LEN = 5 + +_CONFIG_DIR = Path(__file__).parent / "config" + + +class EBNavigationPromptBuilder: + """Prompt builder for EB-Navigation matching nav_planner output. + + Supports two modes: + - chat_history=False (default): Stateless. Every turn sends a single user + message with the full system prompt, examples, instruction, and history. + - chat_history=True: Messages accumulate with sliding window (5 messages). + Each user message still includes the full system prompt and examples. + """ + + def __init__( + self, + n_shot: int = 3, + split: str = "base", + use_feedback: bool = True, + chat_history: bool = True, + ): + self.n_shot = n_shot + self.split = split + self.use_feedback = use_feedback + self.chat_history = chat_history + + # Load examples + examples_file = _CONFIG_DIR / "navigation_examples.json" + with open(examples_file) as f: + self._examples: list[str] = json.load(f) + + # Action space state + self._actions: list[str] = [] + self._action_str: str = "" + self._action_id_map: dict[str, int] = {} + self._id_action_map: dict[int, str] = {} + + def set_action_space(self, actions: list[str]) -> None: + """Update action space.""" + self._actions = list(actions) + self._action_str = self._build_action_list_str(actions) + self._action_id_map = {name: i for i, name in enumerate(actions)} + self._id_action_map = {i: name for i, name in enumerate(actions)} + + def action_name_to_id(self, name: str) -> int | None: + """Look up action ID from name. Returns None if not found.""" + return self._action_id_map.get(name) + + def action_id_to_name(self, action_id: int) -> str | None: + """Look up action name from ID. Returns None if not found.""" + return self._id_action_map.get(action_id) + + @staticmethod + def _build_action_list_str(actions: list[str]) -> str: + """Build action list in nav_planner's get_availabel_action_prompt format.""" + parts = '' + for i in range(len(actions)): + parts += '\naction id ' + str(i) + ': ' + str(actions[i]) + if i < len(actions) - 1: + parts += ', ' + return parts + + # ---- PromptBuilderProtocol methods ---- + + def build_messages(self, memory: AgentMemory) -> list[dict]: + """Build COMPLETE message list to send to LLM.""" + if not self._action_id_map or self._actions != memory.action_space: + self.set_action_space(memory.action_space) + + if self.chat_history: + return self._build_chat_history_messages(memory) + else: + return self._build_stateless_messages(memory) + + def parse_response(self, llm_response: str, memory: AgentMemory) -> list[Action]: + """Parse LLM response into validated actions.""" + from easi.utils.json_repair import fix_json + llm_response = fix_json(llm_response) + + try: + data = json.loads(llm_response) + except json.JSONDecodeError as e: + logger.warning("Failed to parse LLM response as JSON: %s", e) + return [] + + plan = data.get("executable_plan", []) + if not isinstance(plan, list) or not plan: + logger.warning("No executable_plan in LLM response") + return [] + + actions = [] + for entry in plan: + if not isinstance(entry, dict): + continue + + # Support both {"action_id": int, "action_name": str} and {"action": str} + if "action_id" in entry: + action_id = entry["action_id"] + action_name = self.action_id_to_name(action_id) + if action_name is None: + action_name = entry.get("action_name", "") + else: + action_name = entry.get("action", entry.get("action_name", "")) + + validated = validate_action_name(action_name, memory.action_space) + if validated: + actions.append(Action(action_name=validated)) + else: + logger.warning("Skipping invalid action: '%s'", action_name) + break + + return actions + + def get_response_format(self, memory: AgentMemory) -> dict: + """Return JSON schema for API-level enforcement.""" + return EBNAVIGATION_RESPONSE_SCHEMA + + # ---- Stateless mode (chat_history=False) ---- + + def _build_stateless_messages(self, memory: AgentMemory) -> list[dict]: + """Build full prompt each turn. No history accumulation.""" + prompt = self._build_prompt_text( + memory.task_description, memory.action_history, + ) + return self._wrap_as_user_message(prompt, memory.current_observation) + + def _build_prompt_text( + self, + task_description: str, + action_history: list[tuple[str, str]], + ) -> str: + """Build the full prompt text matching nav_planner.process_prompt(). + + Covers both first-turn and subsequent-turn paths with + chat_history=False (nav_planner.py lines 92-136). + """ + user_instruction = task_description.rstrip('.') + max_id = len(self._actions) - 1 + + if len(action_history) == 0: + # First turn path (nav_planner.py lines 96-104) + if self.n_shot >= 1: + examples_str = '\n\n'.join([ + f'## Task Execution Example {i}: \n {x}' + for i, x in enumerate(self._examples[:self.n_shot]) + ]) + prompt = NAVIGATION_SYSTEM_PROMPT.format( + max_id, self._action_str, examples_str, + ) + else: + prompt = NAVIGATION_SYSTEM_PROMPT.format( + max_id, self._action_str, '', + ) + + prompt += f'\n\n## Now the human instruction is: {user_instruction}.' + prompt += self._make_first_prompt_suffix(max_id) + + else: + # Subsequent turn path, chat_history=False (nav_planner.py lines 122-134) + # Note: Double space after "Example" on subsequent turns (typo preserved) + if self.n_shot >= 1: + examples_str = '\n\n'.join([ + f'## Task Execution Example {i}: \n {x}' + for i, x in enumerate(self._examples[:self.n_shot]) + ]) + prompt = NAVIGATION_SYSTEM_PROMPT.format( + max_id, self._action_str, examples_str, + ) + else: + prompt = NAVIGATION_SYSTEM_PROMPT.format( + max_id, self._action_str, '', + ) + + prompt += f'\n\n## Now the human instruction is: {user_instruction}.' + prompt += self._format_action_history(action_history) + prompt += f"\n\n{self._make_following_prompt_suffix(max_id)}" + + return prompt + + # ---- Chat history mode (chat_history=True) ---- + + def _build_chat_history_messages(self, memory: AgentMemory) -> list[dict]: + """Build accumulated message history for chat_history=True mode. + + First turn: identical to stateless first turn. + Subsequent turns: reconstruct message history from memory.steps, + each user message includes full system prompt (nav_planner pattern). + Apply sliding window (MESSAGE_WINDOW_LEN=5). + """ + if memory.is_first_turn: + return self._build_stateless_messages(memory) + + # Reconstruct message history from completed steps + messages: list[dict] = [] + + for step_idx, step in enumerate(memory.steps): + if step.llm_response is None: + # Buffered step -- no user/assistant pair + continue + + if step_idx == 0: + history_up_to = [] + else: + history_up_to = [ + (s.action.action_name, s.feedback) + for s in memory.steps[:step_idx] + if s.action and s.feedback is not None + ] + + if step_idx == 0: + prompt = self._build_first_turn_full_prompt(memory.task_description) + else: + prompt = self._build_chat_subsequent_prompt( + memory.task_description, history_up_to, + ) + + messages.extend( + self._wrap_as_user_message(prompt, step.observation) + ) + messages.append({ + "role": "assistant", + "content": [{"type": "text", "text": step.llm_response}], + }) + + # Current turn + all_history = memory.action_history + current_prompt = self._build_chat_subsequent_prompt( + memory.task_description, all_history, + ) + messages.extend( + self._wrap_as_user_message(current_prompt, memory.current_observation) + ) + + # Apply sliding window + return messages[-MESSAGE_WINDOW_LEN:] + + def _build_first_turn_full_prompt(self, task_description: str) -> str: + """Build first-turn prompt (identical for both modes).""" + user_instruction = task_description.rstrip('.') + max_id = len(self._actions) - 1 + + if self.n_shot >= 1: + examples_str = '\n\n'.join([ + f'## Task Execution Example {i}: \n {x}' + for i, x in enumerate(self._examples[:self.n_shot]) + ]) + prompt = NAVIGATION_SYSTEM_PROMPT.format( + max_id, self._action_str, examples_str, + ) + else: + prompt = NAVIGATION_SYSTEM_PROMPT.format( + max_id, self._action_str, '', + ) + + prompt += f'\n\n## Now the human instruction is: {user_instruction}.' + prompt += self._make_first_prompt_suffix(max_id) + + return prompt + + def _build_chat_subsequent_prompt( + self, + task_description: str, + action_history: list[tuple[str, str]], + ) -> str: + """Build subsequent-turn prompt for chat_history=True. + + In nav_planner, chat_history subsequent turns still include the full + system prompt but use "## The human instruction" (no "Now the"). + """ + user_instruction = task_description.rstrip('.') + max_id = len(self._actions) - 1 + + # Double space after "Example" on subsequent turns (typo preserved) + if self.n_shot >= 1: + examples_str = '\n\n'.join([ + f'## Task Execution Example {i}: \n {x}' + for i, x in enumerate(self._examples[:self.n_shot]) + ]) + prompt = NAVIGATION_SYSTEM_PROMPT.format( + max_id, self._action_str, examples_str, + ) + else: + prompt = NAVIGATION_SYSTEM_PROMPT.format( + max_id, self._action_str, '', + ) + + prompt += f'\n\n## The human instruction is: {user_instruction}.' + prompt += self._format_action_history(action_history) + prompt += f"\n\n{self._make_following_prompt_suffix(max_id)}" + + return prompt + + # ---- Helpers ---- + + def _make_first_prompt_suffix(self, max_id: int) -> str: + """First-turn prompt suffix (nav_planner.py lines 56-62). + + Uses 'Aim for about 1-2 actions'. + """ + return ( + f'''To achieve the task, 1. Reason about the current visual state and your final goal, and 2. Reflect on the effect of previous actions. 3. Summarize how you learn from the Strategy and Examples provided ''' + f'''\nAim for about 1-2 actions in this step. !!!Notice: you cannot assess the situation until the whole plan in this planning step is finished executed, so plan accordingly.''' + f'''\nAt last, output the action id(s) (0 ~ {max_id}) from the available actions to execute. ''' + f'''\n\nThe input given to you is an first person view observation. Plan accordingly based on the visual observation.''' + f'''\n\nYou are supposed to output in JSON.{OUTPUT_TEMPLATE}''' + ) + + def _make_following_prompt_suffix(self, max_id: int) -> str: + """Following-turn prompt suffix (nav_planner.py lines 64-70). + + Uses 'Aim for about 5-6 actions'. + """ + return ( + f'''To achieve the task, 1. Reason about the current visual state and your final goal, and 2. Reflect on the effect of previous actions. 3. Summarize how you learn from the Strategy and Examples provided ''' + f'''\nAim for about 5-6 actions in this step to be closer to the target object. !!!Notice: you cannot assess the situation until the whole plan in this planning step is finished executed, so plan accordingly.''' + f'''\nAt last, output the action id(s) (0 ~ {max_id}) from the available actions to execute. ''' + f'''\n\nThe input given to you is an first person view observation. Plan accordingly based on the visual observation.''' + f'''\n\nYou are supposed to output in JSON.{OUTPUT_TEMPLATE}''' + ) + + def _format_action_history(self, action_history: list[tuple[str, str]]) -> str: + """Format action history matching nav_planner line 117-118. + + Format: 'Step {i}, action id {id}, {name}, env feedback: {fb}' + """ + text = '\n\n The action history:' + for i, (action_name, feedback) in enumerate(action_history): + action_id = self._action_id_map.get(action_name, -1) + if self.use_feedback: + text += '\n Step {}, action id {}, {}, env feedback: {}'.format( + i, action_id, action_name, feedback, + ) + else: + text += '\n Step {}, action id {}, {}'.format( + i, action_id, action_name, + ) + return text + + @staticmethod + def _wrap_as_user_message(prompt: str, observation) -> list[dict]: + """Wrap prompt text + observation image as a user message. + + Image BEFORE text (nav_planner line 200-211). + """ + content: list[dict] = [] + if observation and observation.rgb_path: + image_url = _encode_image_base64(observation.rgb_path) + if image_url: + content.append({ + "type": "image_url", + "image_url": {"url": image_url}, + }) + content.append({"type": "text", "text": prompt}) + + return [{"role": "user", "content": content}] diff --git a/easi/tasks/ebnavigation/task.py b/easi/tasks/ebnavigation/task.py new file mode 100644 index 0000000..f321823 --- /dev/null +++ b/easi/tasks/ebnavigation/task.py @@ -0,0 +1,84 @@ +"""EB-Navigation task for EASI. + +Adapts the EmbodiedBench EB-Navigation benchmark to EASI's task interface. +Supports 5 splits via per-split .yaml configs. + +Episode data flows from HF dataset -> task.format_reset_config() -> bridge +reset_config -> EBNavEnv.reset(episode). +""" +from __future__ import annotations + +from pathlib import Path + +from easi.core.base_task import BaseTask +from easi.core.episode import StepResult +from easi.tasks.ebnavigation.actions import get_action_space +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + + +class EBNavigationTask(BaseTask): + + def _build_action_space(self) -> list[str]: + return get_action_space() + + def get_task_yaml_path(self) -> Path: + return Path(__file__).parent / "ebnavigation_base.yaml" + + def get_bridge_script_path(self) -> Path: + return Path(__file__).parent / "bridge.py" + + def get_instruction(self, episode: dict) -> str: + return episode.get("instruction", self.name) + + def format_reset_config(self, episode: dict) -> dict: + """Map EB-Navigation HF row to bridge reset config.""" + return { + "episode_id": episode.get("id", "unknown"), + "scene": episode["scene"], + "instruction": episode["instruction"], + "target_object_type": episode.get("target_object_type", ""), + "target_object_id": episode.get("target_object_id", ""), + "target_position": episode["target_position"], + "agent_pose": episode["agent_pose"], + } + + def evaluate_episode( + self, episode: dict, trajectory: list[StepResult] + ) -> dict[str, float]: + """Extract metrics from trajectory. + + The bridge reports task_success and distance in StepResult.info. + """ + if not trajectory: + return { + "task_success": 0.0, + "distance_to_target": -1.0, + "num_steps": 0.0, + } + + last_step = trajectory[-1] + return { + "task_success": last_step.info.get("task_success", 0.0), + "distance_to_target": last_step.info.get("distance", -1.0), + "num_steps": float(len(trajectory)), + } + + def _get_builtin_episodes(self) -> list[dict]: + """Minimal episodes for testing without dataset download.""" + return [ + { + "id": 0, + "scene": "FloorPlan11", + "instruction": "navigate to the Bread in the room and be as close as possible to it", + "target_object_type": "Bread", + "target_object_id": "Bread|+01.30|+00.98|-01.53", + "target_position": {"x": 1.3, "y": 0.98, "z": -1.53}, + "agent_pose": { + "position": {"x": -0.75, "y": 0.9009992, "z": -1.75}, + "rotation": 90.0, + "horizon": 0.0, + }, + }, + ] diff --git a/easi/tasks/ebnavigation/vendor/EBNavEnv.py b/easi/tasks/ebnavigation/vendor/EBNavEnv.py new file mode 100644 index 0000000..5a39337 --- /dev/null +++ b/easi/tasks/ebnavigation/vendor/EBNavEnv.py @@ -0,0 +1,159 @@ +"""Vendored EB-Navigation environment for EASI. + +Adapted from EmbodiedBench/embodiedbench/envs/eb_navigation/EBNavEnv.py. +Changes: removed internal dataset loading, gym dependency, logging/saving. +EASI provides episodes via reset(episode) and the bridge handles image saving. +""" +from __future__ import annotations + +import math + +import ai2thor.controller +import numpy as np +from ai2thor.platform import CloudRendering + +SUCCESS_THRESHOLD = 1 # meters (horizontal distance) + + +class EBNavEnv: + """AI2-THOR navigation environment for EB-Navigation benchmark.""" + + def __init__(self, resolution: int = 500, fov: int = 100, max_steps: int = 20): + self.resolution = resolution + self.config = { + "agentMode": "default", + "gridSize": 0.1, + "visibilityDistance": 10, + "renderDepthImage": True, + "renderInstanceSegmentation": True, + "width": resolution, + "height": resolution, + "fieldOfView": fov, + "platform": CloudRendering, + } + self.env = ai2thor.controller.Controller(**self.config) + self._max_episode_steps = max_steps + self._current_step = 0 + self.episode_data = None + self._last_event = None + + def reset(self, episode: dict) -> dict: + """Reset environment with episode data from EASI. + + Args: + episode: Dict with keys: scene, agent_pose, target_object_id, + target_position, instruction. + """ + self.episode_data = episode + scene_name = episode["scene"] + + self._last_event = self.env.reset(scene=scene_name) + + # Teleport agent to starting pose + pose = episode["agent_pose"] + self.env.step( + action="Teleport", + position={ + "x": pose["position"]["x"], + "y": pose["position"]["y"], + "z": pose["position"]["z"], + }, + rotation={ + "x": 0, + "y": pose["rotation"], + "z": 0, + }, + horizon=pose["horizon"], + standing=True, + ) + + self._current_step = 0 + return {"head_rgb": self.env.last_event.frame} + + def step(self, action_id: int) -> tuple[dict, float, bool, dict]: + """Execute one discrete action. + + Args: + action_id: Integer 0-7 mapping to DISCRETE_ACTIONS. + + Returns: + (obs, reward, done, info) tuple. + """ + self._current_step += 1 + + if not isinstance(action_id, int) or action_id < 0 or action_id > 7: + action_id = np.random.randint(8) + + self._execute_action(action_id) + reward, distance = self.measure_success() + + done = self._current_step >= self._max_episode_steps or reward > 0 + + obs = {"head_rgb": self.env.last_event.frame} + info = { + "task_success": reward, + "distance": distance, + "env_feedback": self._get_env_feedback(), + "last_action_success": float( + self.env.last_event.metadata["lastActionSuccess"] + ), + "env_step": self._current_step, + "action_id": action_id, + } + return obs, reward, done, info + + def _execute_action(self, action_id: int) -> None: + """Map discrete action ID to AI2-THOR controller action. + + Reference: EBNavEnv.discrete_action_mapper() + """ + if action_id == 0: + self._last_event = self.env.step(action="MoveAhead", moveMagnitude=0.25) + elif action_id == 1: + self._last_event = self.env.step(action="MoveBack", moveMagnitude=0.25) + elif action_id == 2: + self._last_event = self.env.step(action="MoveRight", moveMagnitude=0.25) + elif action_id == 3: + self._last_event = self.env.step(action="MoveLeft", moveMagnitude=0.25) + elif action_id == 4: + self._last_event = self.env.step(action="RotateRight", degrees=90) + elif action_id == 5: + self._last_event = self.env.step(action="RotateLeft", degrees=90) + elif action_id == 6: + self._last_event = self.env.step(action="LookUp", degrees=30) + elif action_id == 7: + self._last_event = self.env.step(action="LookDown", degrees=30) + + def measure_success(self) -> tuple[float, float]: + """Compute success (1.0/0.0) and horizontal distance to target. + + Reference: EBNavEnv.measure_success() -- uses XZ-plane distance. + """ + agent_position = self.env.last_event.metadata["agent"]["position"] + target_position = self.episode_data["target_position"] + + dist = math.sqrt( + (agent_position["x"] - target_position["x"]) ** 2 + + (agent_position["z"] - target_position["z"]) ** 2 + ) + success = float(dist <= SUCCESS_THRESHOLD) + return success, dist + + def _get_env_feedback(self) -> str: + """Build feedback string from last event. + + Reference: EBNavEnv.get_env_feedback() with verbosity=0. + """ + event = self._last_event + last_action = event.metadata.get("lastAction", "") + success = event.metadata.get("lastActionSuccess", False) + error = event.metadata.get("errorMessage", "") + + if success: + return f"Last action {last_action} executed successfully." + else: + return f"Last action {last_action} is invalid. {error}" + + def close(self) -> None: + """Shut down the AI2-THOR controller.""" + self.env.stop() diff --git a/easi/tasks/ebnavigation/vendor/__init__.py b/easi/tasks/ebnavigation/vendor/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/test_ebnavigation_task.py b/tests/test_ebnavigation_task.py new file mode 100644 index 0000000..85304b2 --- /dev/null +++ b/tests/test_ebnavigation_task.py @@ -0,0 +1,314 @@ +"""Tests for the EB-Navigation task (offline, no simulator needed).""" +import json + +import pytest + +from easi.core.episode import Action, Observation, StepResult +from easi.tasks.ebnavigation.actions import ( + ACTION_NAME_TO_ID, + DISCRETE_ACTIONS, + get_action_space, +) +from easi.tasks.ebnavigation.task import EBNavigationTask + + +class TestEBNavigationActionSpace: + def test_action_space_has_8_actions(self): + actions = get_action_space() + assert len(actions) == 8 + + def test_action_space_contents(self): + actions = get_action_space() + assert "Move forward by 0.25" in actions + assert "Move backward by 0.25" in actions + assert "Move rightward by 0.25" in actions + assert "Move leftward by 0.25" in actions + assert "Rotate to the right by 90 degrees." in actions + assert "Rotate to the left by 90 degrees." in actions + assert "Tilt the camera upward by 30 degrees." in actions + assert "Tilt the camera downward by 30 degrees." in actions + + def test_action_name_to_id_mapping(self): + assert ACTION_NAME_TO_ID["Move forward by 0.25"] == 0 + assert ACTION_NAME_TO_ID["Move backward by 0.25"] == 1 + assert ACTION_NAME_TO_ID["Move rightward by 0.25"] == 2 + assert ACTION_NAME_TO_ID["Move leftward by 0.25"] == 3 + assert ACTION_NAME_TO_ID["Rotate to the right by 90 degrees."] == 4 + assert ACTION_NAME_TO_ID["Rotate to the left by 90 degrees."] == 5 + assert ACTION_NAME_TO_ID["Tilt the camera upward by 30 degrees."] == 6 + assert ACTION_NAME_TO_ID["Tilt the camera downward by 30 degrees."] == 7 + assert len(ACTION_NAME_TO_ID) == 8 + + def test_action_space_returns_copy(self): + a1 = get_action_space() + a2 = get_action_space() + assert a1 == a2 + assert a1 is not a2 + + def test_discrete_actions_matches_source(self): + """Verify action text matches EBNavEnv.DISCRETE_SKILLSET exactly.""" + assert DISCRETE_ACTIONS[0] == "Move forward by 0.25" + assert DISCRETE_ACTIONS[7] == "Tilt the camera downward by 30 degrees." + + +class TestEBNavigationTask: + @pytest.fixture + def task(self): + return EBNavigationTask() + + def test_name(self, task): + assert task.name == "ebnavigation_base" + + def test_simulator_key(self, task): + assert task.simulator_key == "ai2thor:v5_0_0" + + def test_action_space_loaded(self, task): + assert len(task.action_space) == 8 + + def test_max_steps(self, task): + assert task.max_steps == 20 + + def test_format_reset_config(self, task): + """Test the adapter from EB-Navigation HF row to bridge reset config.""" + episode = { + "id": 0, + "scene": "FloorPlan11", + "instruction": "navigate to the Bread", + "target_object_type": "Bread", + "target_object_id": "Bread|+01.30|+00.98|-01.53", + "target_position": {"x": 1.3, "y": 0.98, "z": -1.53}, + "agent_pose": { + "position": {"x": -0.75, "y": 0.9009992, "z": -1.75}, + "rotation": 90.0, + "horizon": 0.0, + }, + } + config = task.format_reset_config(episode) + assert config["episode_id"] == 0 + assert config["scene"] == "FloorPlan11" + assert config["instruction"] == "navigate to the Bread" + assert config["target_object_type"] == "Bread" + assert config["target_object_id"] == "Bread|+01.30|+00.98|-01.53" + assert config["target_position"]["x"] == 1.3 + assert config["agent_pose"]["rotation"] == 90.0 + + def test_evaluate_episode(self, task): + """Test metric computation from trajectory.""" + episode = { + "id": 0, "scene": "FloorPlan11", + "instruction": "test", + "target_position": {"x": 0, "y": 0, "z": 0}, + "agent_pose": {"position": {"x": 0, "y": 0, "z": 0}, "rotation": 0, "horizon": 0}, + } + obs = Observation(rgb_path="/tmp/rgb.png") + trajectory = [ + StepResult(observation=obs, reward=0.0, done=False, + info={"task_success": 0.0, "distance": 2.5}), + StepResult(observation=obs, reward=1.0, done=True, + info={"task_success": 1.0, "distance": 0.8}), + ] + metrics = task.evaluate_episode(episode, trajectory) + assert metrics["task_success"] == 1.0 + assert metrics["distance_to_target"] == 0.8 + assert metrics["num_steps"] == 2 + + def test_evaluate_empty_trajectory(self, task): + episode = { + "id": 0, "scene": "FloorPlan11", + "instruction": "test", + "target_position": {"x": 0, "y": 0, "z": 0}, + "agent_pose": {"position": {"x": 0, "y": 0, "z": 0}, "rotation": 0, "horizon": 0}, + } + metrics = task.evaluate_episode(episode, []) + assert metrics["task_success"] == 0.0 + assert metrics["distance_to_target"] == -1.0 + assert metrics["num_steps"] == 0.0 + + def test_get_instruction(self, task): + episode = {"instruction": "navigate to the Bread"} + assert task.get_instruction(episode) == "navigate to the Bread" + + def test_builtin_episodes(self, task): + episodes = task._get_builtin_episodes() + assert len(episodes) == 1 + assert episodes[0]["scene"] == "FloorPlan11" + assert episodes[0]["target_object_type"] == "Bread" + assert "agent_pose" in episodes[0] + assert "target_position" in episodes[0] + + def test_task_registry_discovers_all_splits(self): + """Registry should discover all EB-Navigation split yamls.""" + from easi.tasks.registry import list_tasks, refresh + refresh() + tasks = list_tasks() + assert "ebnavigation_base" in tasks + assert "ebnavigation_common_sense" in tasks + assert "ebnavigation_complex_instruction" in tasks + assert "ebnavigation_long_horizon" in tasks + assert "ebnavigation_visual_appearance" in tasks + + def test_split_specific_task_loading(self): + """Loading ebnavigation_base should give a task with correct config.""" + from easi.tasks.registry import get_task_entry, refresh + refresh() + entry = get_task_entry("ebnavigation_base") + assert entry.simulator_key == "ai2thor:v5_0_0" + assert entry.config_path.name == "ebnavigation_base.yaml" + + def test_bridge_script_path(self, task): + bridge_path = task.get_bridge_script_path() + assert bridge_path is not None + assert bridge_path.name == "bridge.py" + assert bridge_path.exists() + + +class TestEBNavigationPromptBuilder: + def test_build_messages_first_turn(self): + """First turn: full system prompt in user message.""" + from easi.tasks.ebnavigation.prompts import EBNavigationPromptBuilder + from easi.core.memory import AgentMemory + builder = EBNavigationPromptBuilder() + actions = get_action_space() + memory = AgentMemory( + task_description="Navigate to the Bread.", + action_space=actions, + current_observation=Observation(rgb_path="/tmp/rgb.png"), + ) + messages = builder.build_messages(memory) + assert isinstance(messages, list) + assert messages[0]["role"] == "user" + text_content = messages[0]["content"][-1]["text"] + assert "robot operating in a home" in text_content + assert "Move forward by 0.25" in text_content + assert "Strategy" in text_content + assert "1-2 actions" in text_content + assert builder.action_name_to_id("Move forward by 0.25") == 0 + + def test_build_messages_with_history(self): + from easi.tasks.ebnavigation.prompts import EBNavigationPromptBuilder + from easi.core.memory import AgentMemory + builder = EBNavigationPromptBuilder() + actions = get_action_space() + memory = AgentMemory( + task_description="Navigate to the Bread.", + action_space=actions, + current_observation=Observation(rgb_path="/tmp/rgb.png"), + ) + obs = Observation(rgb_path="/tmp/rgb.png") + memory.record_step(obs, Action(action_name="Move forward by 0.25"), llm_response="r1") + memory.record_feedback("success") + memory.record_step(obs, Action(action_name="Move leftward by 0.25"), llm_response="r2") + memory.record_feedback("failed: blocked") + messages = builder.build_messages(memory) + assert isinstance(messages, list) + # With chat_history=True, we get accumulated messages + # Last message should be user + assert messages[-1]["role"] == "user" + text_content = "" + for part in messages[-1]["content"]: + if part["type"] == "text": + text_content += part["text"] + assert "Move forward by 0.25" in text_content + assert "5-6 actions" in text_content + + def test_build_messages_stateless(self): + """Stateless mode (chat_history=False) produces single user message each turn.""" + from easi.tasks.ebnavigation.prompts import EBNavigationPromptBuilder + from easi.core.memory import AgentMemory + builder = EBNavigationPromptBuilder(chat_history=False) + actions = get_action_space() + memory = AgentMemory( + task_description="Navigate to the Bread.", + action_space=actions, + current_observation=Observation(rgb_path="/tmp/rgb.png"), + ) + obs = Observation(rgb_path="/tmp/rgb.png") + memory.record_step(obs, Action(action_name="Move forward by 0.25"), llm_response="r1") + memory.record_feedback("success") + messages = builder.build_messages(memory) + # Stateless: single user message + assert len(messages) == 1 + assert messages[0]["role"] == "user" + + def test_parse_response_valid(self): + from easi.tasks.ebnavigation.prompts import EBNavigationPromptBuilder + from easi.core.memory import AgentMemory + builder = EBNavigationPromptBuilder() + actions = get_action_space() + memory = AgentMemory( + task_description="test", + action_space=actions, + current_observation=Observation(rgb_path="/tmp/rgb.png"), + ) + builder.set_action_space(actions) + response = json.dumps({ + "visual_state_description": "I see a room.", + "reasoning_and_reflection": "I should move forward.", + "language_plan": "Step 1: Move forward", + "executable_plan": [ + {"action_id": 0, "action_name": "Move forward by 0.25"}, + {"action_id": 0, "action_name": "Move forward by 0.25"}, + ], + }) + parsed = builder.parse_response(response, memory) + assert len(parsed) == 2 + assert parsed[0].action_name == "Move forward by 0.25" + assert parsed[1].action_name == "Move forward by 0.25" + + def test_parse_response_invalid_json(self): + from easi.tasks.ebnavigation.prompts import EBNavigationPromptBuilder + from easi.core.memory import AgentMemory + builder = EBNavigationPromptBuilder() + actions = get_action_space() + memory = AgentMemory( + task_description="test", + action_space=actions, + current_observation=Observation(rgb_path="/tmp/rgb.png"), + ) + builder.set_action_space(actions) + parsed = builder.parse_response("not json at all {{}", memory) + assert parsed == [] + + def test_parse_response_invalid_action_id(self): + from easi.tasks.ebnavigation.prompts import EBNavigationPromptBuilder + from easi.core.memory import AgentMemory + builder = EBNavigationPromptBuilder() + actions = get_action_space() + memory = AgentMemory( + task_description="test", + action_space=actions, + current_observation=Observation(rgb_path="/tmp/rgb.png"), + ) + builder.set_action_space(actions) + response = json.dumps({ + "visual_state_description": "test", + "reasoning_and_reflection": "test", + "language_plan": "test", + "executable_plan": [ + {"action_id": 0, "action_name": "Move forward by 0.25"}, + {"action_id": 99, "action_name": "invalid action"}, + ], + }) + parsed = builder.parse_response(response, memory) + # Should get first valid action, then break on invalid + assert len(parsed) == 1 + assert parsed[0].action_name == "Move forward by 0.25" + + def test_conforms_to_protocol(self): + from easi.tasks.ebnavigation.prompts import EBNavigationPromptBuilder + from easi.agents.prompt_builder import PromptBuilderProtocol + builder = EBNavigationPromptBuilder() + assert isinstance(builder, PromptBuilderProtocol) + + def test_response_format(self): + from easi.tasks.ebnavigation.prompts import EBNavigationPromptBuilder + from easi.core.memory import AgentMemory + builder = EBNavigationPromptBuilder() + memory = AgentMemory( + task_description="test", + action_space=get_action_space(), + current_observation=Observation(rgb_path="/tmp/rgb.png"), + ) + schema = builder.get_response_format(memory) + assert schema["type"] == "json_schema" + assert "executable_plan" in schema["json_schema"]["schema"]["properties"] From 9c8d7ee944e2d58fc076bbb17bf987eae1bd461c Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Sun, 15 Feb 2026 23:59:29 +0800 Subject: [PATCH 025/239] [Refactor] Replace YAML action_space with BaseTask._build_action_space() Remove action_space field from all YAML configs and TaskEntry. Tasks now define their action space via _build_action_space() override with caching, eliminating the confusing pattern of empty YAML fields. --- easi/cli.py | 1 - easi/core/base_task.py | 12 +++++++++++- easi/tasks/dummy_task/task.py | 6 ++++++ easi/tasks/dummy_task/task.yaml | 7 ------- easi/tasks/ebalfred/ebalfred_base.yaml | 1 - easi/tasks/ebalfred/ebalfred_common_sense.yaml | 1 - .../tasks/ebalfred/ebalfred_complex_instruction.yaml | 1 - easi/tasks/ebalfred/ebalfred_long_horizon.yaml | 1 - easi/tasks/ebalfred/ebalfred_spatial.yaml | 1 - easi/tasks/ebalfred/ebalfred_visual_appearance.yaml | 1 - easi/tasks/ebalfred/task.py | 6 ++---- easi/tasks/registry.py | 2 -- easi/tasks/scaffold.py | 2 -- tests/test_dummy_task.py | 1 - 14 files changed, 19 insertions(+), 24 deletions(-) diff --git a/easi/cli.py b/easi/cli.py index 87662a8..7bd4a56 100644 --- a/easi/cli.py +++ b/easi/cli.py @@ -204,7 +204,6 @@ def cmd_task_info(task_name: str) -> None: logger.info(" Name: %s", entry.name) logger.info(" Description: %s", entry.description) logger.info(" Simulator: %s", entry.simulator_key) - logger.info(" Actions: %s", ", ".join(entry.action_space)) logger.info(" Max steps: %s", entry.max_steps) diff --git a/easi/core/base_task.py b/easi/core/base_task.py index 7cc91e7..ad3b442 100644 --- a/easi/core/base_task.py +++ b/easi/core/base_task.py @@ -49,6 +49,7 @@ def __init__( self._split_yaml_path = split_yaml_path self._config = self._load_config() self._episodes: list[dict] | None = None + self._action_space_cache: list[str] | None = None self._data_dir = data_dir @abstractmethod @@ -134,7 +135,16 @@ def simulator_key(self) -> str: @property def action_space(self) -> list[str]: - return self._config["action_space"] + if self._action_space_cache is None: + self._action_space_cache = self._build_action_space() + return self._action_space_cache + + def _build_action_space(self) -> list[str]: + """Return the action space for this task. + + Override in subclasses to define the action space programmatically. + """ + return [] @property def max_steps(self) -> int: diff --git a/easi/tasks/dummy_task/task.py b/easi/tasks/dummy_task/task.py index 8030304..c55756a 100644 --- a/easi/tasks/dummy_task/task.py +++ b/easi/tasks/dummy_task/task.py @@ -12,12 +12,18 @@ from easi.core.episode import StepResult +DUMMY_ACTIONS = ["MoveAhead", "TurnLeft", "TurnRight", "Stop"] + + class DummyTask(BaseTask): """A dummy task for testing the evaluation pipeline.""" def get_task_yaml_path(self) -> Path: return Path(__file__).parent / "task.yaml" + def _build_action_space(self) -> list[str]: + return list(DUMMY_ACTIONS) + def format_reset_config(self, episode: dict) -> dict: """Map a dummy episode to simulator reset config. diff --git a/easi/tasks/dummy_task/task.yaml b/easi/tasks/dummy_task/task.yaml index 206ff3b..2129c4c 100644 --- a/easi/tasks/dummy_task/task.yaml +++ b/easi/tasks/dummy_task/task.yaml @@ -5,13 +5,6 @@ description: "A dummy task for testing the pipeline end-to-end" # Pin to a specific versioned simulator simulator: "dummy:v1" -# Action space available to the agent -action_space: - - MoveAhead - - TurnLeft - - TurnRight - - Stop - # Dataset source dataset: source: "local" diff --git a/easi/tasks/ebalfred/ebalfred_base.yaml b/easi/tasks/ebalfred/ebalfred_base.yaml index dc684d1..8f71bc7 100644 --- a/easi/tasks/ebalfred/ebalfred_base.yaml +++ b/easi/tasks/ebalfred/ebalfred_base.yaml @@ -3,7 +3,6 @@ display_name: "EB-Alfred Base Split" description: "Base evaluation split for EB-Alfred" simulator: "ai2thor:v2_1_0" task_class: "easi.tasks.ebalfred.task.EBAlfredTask" -action_space: [] max_steps: 50 dataset: source: huggingface diff --git a/easi/tasks/ebalfred/ebalfred_common_sense.yaml b/easi/tasks/ebalfred/ebalfred_common_sense.yaml index 3ada24a..d14306b 100644 --- a/easi/tasks/ebalfred/ebalfred_common_sense.yaml +++ b/easi/tasks/ebalfred/ebalfred_common_sense.yaml @@ -3,7 +3,6 @@ display_name: "EB-Alfred Common Sense Split" description: "Common sense evaluation split testing implicit knowledge" simulator: "ai2thor:v2_1_0" task_class: "easi.tasks.ebalfred.task.EBAlfredTask" -action_space: [] max_steps: 50 dataset: source: huggingface diff --git a/easi/tasks/ebalfred/ebalfred_complex_instruction.yaml b/easi/tasks/ebalfred/ebalfred_complex_instruction.yaml index 7d54154..8954a8c 100644 --- a/easi/tasks/ebalfred/ebalfred_complex_instruction.yaml +++ b/easi/tasks/ebalfred/ebalfred_complex_instruction.yaml @@ -3,7 +3,6 @@ display_name: "EB-Alfred Complex Instruction Split" description: "Complex instruction evaluation split with nuanced language" simulator: "ai2thor:v2_1_0" task_class: "easi.tasks.ebalfred.task.EBAlfredTask" -action_space: [] max_steps: 50 dataset: source: huggingface diff --git a/easi/tasks/ebalfred/ebalfred_long_horizon.yaml b/easi/tasks/ebalfred/ebalfred_long_horizon.yaml index 0efb2fa..840a32b 100644 --- a/easi/tasks/ebalfred/ebalfred_long_horizon.yaml +++ b/easi/tasks/ebalfred/ebalfred_long_horizon.yaml @@ -3,7 +3,6 @@ display_name: "EB-Alfred Long Horizon Split" description: "Long-horizon evaluation split requiring multi-step planning" simulator: "ai2thor:v2_1_0" task_class: "easi.tasks.ebalfred.task.EBAlfredTask" -action_space: [] max_steps: 50 dataset: source: huggingface diff --git a/easi/tasks/ebalfred/ebalfred_spatial.yaml b/easi/tasks/ebalfred/ebalfred_spatial.yaml index f190d0d..2025d03 100644 --- a/easi/tasks/ebalfred/ebalfred_spatial.yaml +++ b/easi/tasks/ebalfred/ebalfred_spatial.yaml @@ -3,7 +3,6 @@ display_name: "EB-Alfred Spatial Split" description: "Spatial reasoning evaluation split" simulator: "ai2thor:v2_1_0" task_class: "easi.tasks.ebalfred.task.EBAlfredTask" -action_space: [] max_steps: 50 dataset: source: huggingface diff --git a/easi/tasks/ebalfred/ebalfred_visual_appearance.yaml b/easi/tasks/ebalfred/ebalfred_visual_appearance.yaml index 4c7311f..ae3a300 100644 --- a/easi/tasks/ebalfred/ebalfred_visual_appearance.yaml +++ b/easi/tasks/ebalfred/ebalfred_visual_appearance.yaml @@ -3,7 +3,6 @@ display_name: "EB-Alfred Visual Appearance Split" description: "Visual appearance evaluation split testing visual grounding" simulator: "ai2thor:v2_1_0" task_class: "easi.tasks.ebalfred.task.EBAlfredTask" -action_space: [] max_steps: 50 dataset: source: huggingface diff --git a/easi/tasks/ebalfred/task.py b/easi/tasks/ebalfred/task.py index 607af66..101679f 100644 --- a/easi/tasks/ebalfred/task.py +++ b/easi/tasks/ebalfred/task.py @@ -26,10 +26,8 @@ class EBAlfredTask(BaseTask): - def __init__(self, data_dir: Path | None = None, split_yaml_path: Path | None = None): - super().__init__(data_dir=data_dir, split_yaml_path=split_yaml_path) - # Override static action space with dynamic one - self._config["action_space"] = get_global_action_space() + def _build_action_space(self) -> list[str]: + return get_global_action_space() def get_task_yaml_path(self) -> Path: return Path(__file__).parent / "ebalfred_base.yaml" diff --git a/easi/tasks/registry.py b/easi/tasks/registry.py index efdadff..3d96ad2 100644 --- a/easi/tasks/registry.py +++ b/easi/tasks/registry.py @@ -30,7 +30,6 @@ class TaskEntry: description: str simulator_key: str task_class: str # fully qualified class name - action_space: list[str] max_steps: int config_path: Path @@ -73,7 +72,6 @@ def _discover_tasks(tasks_dir: Path | None = None) -> dict[str, TaskEntry]: description=config.get("description", ""), simulator_key=config["simulator"], task_class=config["task_class"], - action_space=config.get("action_space", []), max_steps=config.get("max_steps", 500), config_path=task_yaml_path, ) diff --git a/easi/tasks/scaffold.py b/easi/tasks/scaffold.py index c06e872..8766cba 100644 --- a/easi/tasks/scaffold.py +++ b/easi/tasks/scaffold.py @@ -203,7 +203,6 @@ def _extract_image(self, obs): description: "TODO: Add description" simulator: "{simulator}" task_class: "easi.tasks.{name}.task.{class_name}Task" -action_space: [] max_steps: {max_steps} dataset: source: local @@ -239,7 +238,6 @@ def test_get_bridge_script_path(self, tmp_path): "description: test\\n" "simulator: dummy:v1\\n" "task_class: easi.tasks.{name}.task.{class_name}Task\\n" - "action_space: []\\n" "max_steps: 10\\n" "dataset:\\n" " source: local\\n" diff --git a/tests/test_dummy_task.py b/tests/test_dummy_task.py index c2776bf..519de31 100644 --- a/tests/test_dummy_task.py +++ b/tests/test_dummy_task.py @@ -80,7 +80,6 @@ def test_get_task_entry(self): entry = get_task_entry("dummy_task") assert entry.name == "dummy_task" assert entry.simulator_key == "dummy:v1" - assert "MoveAhead" in entry.action_space def test_load_task_class(self): TaskClass = load_task_class("dummy_task") From 19f8e8e0ecdc1247a6cdb168a1e46f5b39907ed0 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Mon, 16 Feb 2026 01:12:16 +0800 Subject: [PATCH 026/239] [Feat] Implement real AI2-THOR v5.0.0 bridge and switch to Linux64 platform Replace stub bridge with working AI2ThorV5Bridge class that starts a real controller, handles scene reset and discrete navigation actions. Switch platform from CloudRendering to Linux64. Increase sim test timeout default from 30s to 200s for THOR startup. --- easi/cli.py | 4 +- easi/simulators/ai2thor/v5_0_0/bridge.py | 286 +++++++++++++++------ easi/tasks/ebnavigation/vendor/EBNavEnv.py | 4 +- 3 files changed, 209 insertions(+), 85 deletions(-) diff --git a/easi/cli.py b/easi/cli.py index 7bd4a56..d6e8107 100644 --- a/easi/cli.py +++ b/easi/cli.py @@ -73,8 +73,8 @@ def build_parser() -> argparse.ArgumentParser: sim_test = sim_sub.add_parser("test", help="Run a smoke test (reset + N steps)", parents=[common]) sim_test.add_argument("simulator", type=str, help="e.g., 'dummy' or 'ai2thor:v5_0_0'") sim_test.add_argument("--steps", type=int, default=5, help="Number of steps") - sim_test.add_argument("--timeout", type=float, default=30.0, - help="Bridge startup timeout in seconds (default: 30)") + sim_test.add_argument("--timeout", type=float, default=200.0, + help="Bridge startup timeout in seconds (default: 200)") # --- run command --- run_parser = subparsers.add_parser("run", help="Run a full evaluation", parents=[common]) diff --git a/easi/simulators/ai2thor/v5_0_0/bridge.py b/easi/simulators/ai2thor/v5_0_0/bridge.py index 0d931d3..96136f4 100644 --- a/easi/simulators/ai2thor/v5_0_0/bridge.py +++ b/easi/simulators/ai2thor/v5_0_0/bridge.py @@ -1,17 +1,22 @@ -"""Bridge subprocess for AI2-THOR v5.0.0 (modern API). +"""Bridge subprocess for AI2-THOR v5.0.0 (CloudRendering API). -This script runs inside the ai2thor v5.0.0 conda environment. -The v5 API is significantly different from v2.1.0. +This script runs inside the easi_ai2thor_v5_0_0 conda environment. +It starts a real AI2-THOR controller using the CloudRendering platform +and handles reset/step/close commands via filesystem IPC. -NOTE: This is a stub. Structure demonstrates how a real bridge works. +Task-specific bridges (e.g., EBNavigationBridge) use the vendored env +directly and extend BaseBridge. This generic bridge is used by +`easi sim test ai2thor:v5_0_0` for smoke testing the simulator install. Usage: - python bridge.py --workspace /tmp/easi_xxx + python bridge.py --workspace /tmp/easi_xxx [--simulator-kwargs '{}'] """ from __future__ import annotations import argparse +import json +import os import sys from pathlib import Path @@ -33,89 +38,208 @@ logger = get_logger(__name__) - -def run_bridge(workspace: Path) -> None: - """Main bridge loop for AI2-THOR v5.0.0.""" - logger.info("AI2-THOR v5.0.0 bridge starting (workspace: %s)", workspace) - - # In a real implementation: - # from ai2thor.controller import Controller - # controller = Controller(width=1280, height=720, renderDepthImage=True) - - write_status(workspace, ready=True) - - step_count = 0 - - while True: - try: - command = poll_for_command(workspace, timeout=300.0) - except Exception as e: - logger.error("Failed to read command: %s", e) - break - - cmd_type = command.get("type") - - if cmd_type == "reset": - episode_id = command.get("episode_id", "unknown") - reset_config = command.get("reset_config", {}) - logger.info("Reset: episode_id=%s", episode_id) - - # Stub: controller.reset(scene=scene_id) - # v5 API: controller.step(action="Initialize", ...) - step_count = 0 - - rgb_path = workspace / "rgb_0000.png" - depth_path = workspace / "depth_0000.png" - rgb_path.write_bytes(b"STUB_IMAGE") - depth_path.write_bytes(b"STUB_DEPTH") - - response = make_observation_response( - rgb_path=str(rgb_path), - depth_path=str(depth_path), - agent_pose=[0.0, 0.9, 0.0, 0.0, 0.0, 0.0], - metadata={"episode_id": episode_id, "step": "0"}, - ) - write_response(workspace, response) - - elif cmd_type == "step": - step_count += 1 - action = parse_action_from_command(command) - logger.trace("Step %d: action=%s", step_count, action.action_name) - - # Stub: event = controller.step(action=action_name, **params) - # v5 API returns event with frame, depth, metadata - - rgb_name = f"rgb_{step_count:04d}.png" - depth_name = f"depth_{step_count:04d}.png" - (workspace / rgb_name).write_bytes(b"STUB_IMAGE") - (workspace / depth_name).write_bytes(b"STUB_DEPTH") - - response = make_observation_response( - rgb_path=str(workspace / rgb_name), - depth_path=str(workspace / depth_name), - agent_pose=[float(step_count) * 0.25, 0.9, 0.0, 0.0, 0.0, 0.0], - metadata={"step": str(step_count)}, - done=action.action_name == "Stop", - ) - write_response(workspace, response) - - elif cmd_type == "close": - logger.info("Close command received") - # Stub: controller.stop() - write_response(workspace, {"status": "ok"}) - break - - else: - write_response(workspace, make_error_response(f"Unknown command: {cmd_type}")) +# Default v5 configuration +DEFAULT_WIDTH = 500 +DEFAULT_HEIGHT = 500 +DEFAULT_FOV = 100 +DEFAULT_GRID_SIZE = 0.25 +DEFAULT_VISIBILITY_DISTANCE = 10 +DEFAULT_SCENE = "FloorPlan1" + + +class AI2ThorV5Bridge: + """Generic AI2-THOR v5.0.0 bridge for smoke testing. + + Uses Linux64 platform (headless GPU rendering). + """ + + def __init__(self, workspace, simulator_kwargs=None): + self.workspace = Path(workspace) + self.simulator_kwargs = simulator_kwargs or {} + self.controller = None + self.step_count = 0 + self.episode_output_dir = None + + def start(self): + """Initialize AI2-THOR v5 controller with Linux64.""" + from ai2thor.controller import Controller + from ai2thor.platform import Linux64 + + width = self.simulator_kwargs.get("screen_width", DEFAULT_WIDTH) + height = self.simulator_kwargs.get("screen_height", DEFAULT_HEIGHT) + fov = self.simulator_kwargs.get("fov", DEFAULT_FOV) + grid_size = self.simulator_kwargs.get("grid_size", DEFAULT_GRID_SIZE) + + logger.info( + "Starting AI2-THOR v5.0.0 controller (Linux64, %dx%d)...", + width, height, + ) + self.controller = Controller( + agentMode="default", + gridSize=grid_size, + visibilityDistance=DEFAULT_VISIBILITY_DISTANCE, + renderDepthImage=True, + renderInstanceSegmentation=True, + width=width, + height=height, + fieldOfView=fov, + platform=Linux64, + ) + logger.info("AI2-THOR v5.0.0 controller started.") + + def stop(self): + """Stop the AI2-THOR controller.""" + if self.controller is not None: + try: + self.controller.stop() + except Exception: + pass + self.controller = None + + def reset(self, reset_config): + """Reset to a scene for smoke testing.""" + self.step_count = 0 + scene = reset_config.get("scene", DEFAULT_SCENE) + + logger.info("Resetting to scene: %s", scene) + event = self.controller.reset(scene=scene) + + return self._make_observation_response(event) + + def step(self, action_name): + """Execute one action and return observation.""" + self.step_count += 1 + + # Map common action names to THOR API calls + event = self._dispatch_action(action_name) + + success = event.metadata.get("lastActionSuccess", False) + error = event.metadata.get("errorMessage", "") + done = action_name == "Stop" + + info = { + "last_action_success": 1.0 if success else 0.0, + "env_step": float(self.step_count), + } + if not success and error: + info["error"] = error + + return self._make_observation_response( + event, reward=0.0, done=done, info=info, + ) + + def _dispatch_action(self, action_name): + """Execute a THOR action, handling common navigation actions.""" + # Standard navigation actions with default magnitudes + action_params = {} + + if action_name in ("MoveAhead", "MoveBack", "MoveRight", "MoveLeft"): + action_params = {"moveMagnitude": DEFAULT_GRID_SIZE} + elif action_name in ("RotateRight", "RotateLeft"): + action_params = {"degrees": 90} + elif action_name in ("LookUp", "LookDown"): + action_params = {"degrees": 30} + + return self.controller.step(action=action_name, **action_params) + + def _make_observation_response(self, event, reward=0.0, done=False, info=None): + """Save RGB frame and return IPC response.""" + from PIL import Image + + save_dir = Path(self.episode_output_dir) if self.episode_output_dir else self.workspace + save_dir.mkdir(parents=True, exist_ok=True) + rgb_path = save_dir / ("rgb_%04d.png" % self.step_count) + + Image.fromarray(event.frame).save(str(rgb_path)) + + agent = event.metadata["agent"] + pose = [ + agent["position"]["x"], + agent["position"]["y"], + agent["position"]["z"], + agent["rotation"]["y"], + agent.get("cameraHorizon", 0), + 0, + ] + + return make_observation_response( + rgb_path=str(rgb_path), + agent_pose=pose, + metadata={"step": str(self.step_count)}, + reward=reward, + done=done, + info=info or {}, + ) + + def run(self): + """Main IPC loop.""" + logger.info("AI2-THOR v5.0.0 bridge starting (workspace: %s)", self.workspace) + self.start() + + write_status(self.workspace, ready=True) + + while True: + try: + command = poll_for_command(self.workspace, timeout=300.0) + except Exception as e: + logger.error("Failed to read command: %s", e) + break + + cmd_type = command.get("type") + + if cmd_type == "reset": + episode_id = command.get("episode_id", "unknown") + reset_config = command.get("reset_config", {}) + logger.info("Reset: episode_id=%s", episode_id) + + raw_output_dir = command.get("episode_output_dir") + if raw_output_dir: + self.episode_output_dir = raw_output_dir + Path(raw_output_dir).mkdir(parents=True, exist_ok=True) + else: + self.episode_output_dir = None + + try: + response = self.reset(reset_config) + write_response(self.workspace, response) + except Exception as e: + logger.exception("Reset failed") + write_response(self.workspace, make_error_response(str(e))) + + elif cmd_type == "step": + action = parse_action_from_command(command) + logger.trace("Step %d: action=%s", self.step_count + 1, action.action_name) + + try: + response = self.step(action.action_name) + write_response(self.workspace, response) + except Exception as e: + logger.exception("Step failed") + write_response(self.workspace, make_error_response(str(e))) + + elif cmd_type == "close": + logger.info("Close command received") + self.stop() + write_response(self.workspace, {"status": "ok"}) + break + + else: + write_response(self.workspace, make_error_response("Unknown command: %s" % cmd_type)) def main() -> None: parser = argparse.ArgumentParser(description="AI2-THOR v5.0.0 bridge") parser.add_argument("--workspace", type=Path, required=True) - args = parser.parse_args() + parser.add_argument("--data-dir", type=Path, default=None) + parser.add_argument("--simulator-kwargs", type=str, default=None, + help="JSON string of simulator configuration") + args, _ = parser.parse_known_args() setup_logging("DEBUG") - run_bridge(workspace=args.workspace) + + sim_kwargs = json.loads(args.simulator_kwargs) if args.simulator_kwargs else {} + bridge = AI2ThorV5Bridge(workspace=args.workspace, simulator_kwargs=sim_kwargs) + bridge.run() if __name__ == "__main__": diff --git a/easi/tasks/ebnavigation/vendor/EBNavEnv.py b/easi/tasks/ebnavigation/vendor/EBNavEnv.py index 5a39337..eac5986 100644 --- a/easi/tasks/ebnavigation/vendor/EBNavEnv.py +++ b/easi/tasks/ebnavigation/vendor/EBNavEnv.py @@ -10,7 +10,7 @@ import ai2thor.controller import numpy as np -from ai2thor.platform import CloudRendering +from ai2thor.platform import Linux64 SUCCESS_THRESHOLD = 1 # meters (horizontal distance) @@ -29,7 +29,7 @@ def __init__(self, resolution: int = 500, fov: int = 100, max_steps: int = 20): "width": resolution, "height": resolution, "fieldOfView": fov, - "platform": CloudRendering, + "platform": Linux64, } self.env = ai2thor.controller.Controller(**self.config) self._max_episode_steps = max_steps From ca6cfff71f6d6f352ed2d10ea1fe55de44b72707 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Mon, 16 Feb 2026 01:12:44 +0800 Subject: [PATCH 027/239] [Fix] Align EB-Navigation prompts with EmbodiedBench source exactly Fix OUTPUT_TEMPLATE trailing spaces on 3 lines and regenerate navigation_examples.json from source to fix line continuation artifact and curly quote mismatch. Verified character-level parity. --- .../config/navigation_examples.json | 6 +++--- easi/tasks/ebnavigation/prompts.py | 19 +++++++++---------- 2 files changed, 12 insertions(+), 13 deletions(-) diff --git a/easi/tasks/ebnavigation/config/navigation_examples.json b/easi/tasks/ebnavigation/config/navigation_examples.json index b483efa..3034041 100644 --- a/easi/tasks/ebnavigation/config/navigation_examples.json +++ b/easi/tasks/ebnavigation/config/navigation_examples.json @@ -1,5 +1,5 @@ [ - "\nHuman Instruction : Navigate to the floorlamp and be as close as possible.\n\nReasoning and reflection: I can see the floorlmap is at left side in front of me, next to the TVStand. Around me is a Cabinet at my left side. Following the guideline, since the floorlamp is at left side, I can approach it by a series of moving forward and left/rightward. Since currently there is an obstacle at my left, I will first move forward twice.\nExecutable plans: [[0, Move forward by 0.25], [0, Move forward by 0.25]]\n\nEnvironment Feedback: Step 1 is executed successfully; Step 2 is executed successfully;\n\nReasoning and reflection: I can see now I get closer to the floorlamp. It is still located in front of me at the left. I will try move forward and move leftward in series.\nExecutable plans: [[0, Move forward by 0.25], [3, Move leftward by 0.25], [0, Move forward by 0.25], [3, Move leftward by 0.25], [0, Move forward by 0.25]]\n\nEnvironment Feedback: Step 3 is executed successfully; Step 4 is executed successfully; Step 5 is executed successfully; Step 6 is executed successfully; Step 7 is Invalid, as a StandardIslandHeight is blocking you from moving forward.\n\nReasoning and reflection: I am a lot closer to the floorlamp by a series a forward and leftward movement. It is still in front of me. However, The last action is invalid, showing that I am blocked from moving forward.That means there is an obstacle in front of me and I can see it is probably a small table. Following the strategy, I can get away by first moving to the left and move forward. \nExecutable plans: [[3, Move leftward by 0.25], [3, Move leftward by 0.25], [0, Move forward by 0.25], [0, Move forward by 0.25]]\n\nEnvironment Feedback: Step 8 is executed successfully; Step 9 is executed successfully; Step 10 is executed successfully; Step 11 is executed successfully;\n\nReasoning and reflection: I can see that the floorlamp is at the right side of me, slight in the front. I think it in effect of consecutively moving leftward. Also, I have got away with the obstacle.Now I need to move right and I will probably be close to the floorlamp.\nExecutable plans: [[2, Move rightward by 0.25], [2, Move rightward by 0.25]]]\n\nEnvironment Feedback: Step 12 is executed successfully; Step 13 is executed successfully. \n\nReasoning and reflection: Last two action brought me closer to the floorlamp, however, following the strategy, I should not complete now since I can still be closer to the floorlamp by moving forward a few steps.\nExecutable plans: [[0, Move forward by 0.25], [0, Move forward by 0.25], [0, Move forward by 0.25]]\n\nEnvironment Feedback: Step 14 is executed successfully; Step 15 is executed successfully; Step 16 is executed successfully.\n\n\nTask Success\n", - "\nHuman Instruction: Navigate to the computer on the desk and be as close as possible.\n\nReasoning and reflection: According to your instruction, I first need to locate the target object. I can see that the computer is located in my left front direction, placed on a desk. Next, I analyze the obstacles around me. In front of me, slightly to the right, there is a chair blocking my direct path. To avoid it, I decide to move leftward first, followed by a forward movement to approach the desk.\nExecutable plans: [[0, Move forward by 0.25], [3, Move leftward by 0.25]]\n\nEnvironment Feedback: Step 1 is executed successfully; Step 2 is Invalid, as a StandardHeightTable is blocking you from moving forward.\n\nReasoning and reflection: I am now closer to the desk, and I can still see the computer on it. It remains in my left front direction. My last leftward movement failed because a StandardHeightTable is blocking my path. However, following the strategy, I need not address the invalid action since forward movement can still bring me closer to the target object. So I will still make forward movements for now. \nExecutable plans: [[0, Move forward by 0.25], [0, Move forward by 0.25], [0, Move forward by 0.25]]\n\nEnvironment Feedback: Step 3 is executed successfully; Step 4 is executed successfully; Step 5 is Invalid, as a StandardHeightTable is blocking you from moving forward.\n\nReasoning and reflection: I can see that I am getting closer but the last forward move is blocked. Following the strategy, I need to address the invalid action now since it is an obstacle blocking from moving closer in the direction of target object. The computer is at my left side in the front, to bypass this obstacle, I plan to first move leftward again to create more space, then try moving forward once more to get closer to the desk and the computer.\nExecutable plans: [[3, Move leftward by 0.25], [0, Move forward by 0.25]]\n\nEnvironment Feedback: Step 6 is executed successfully; Step 7 is invalid because the StandardHeight is blocking the movement.\n\nReasoning and reflection: I can see that the computer is still in my left front direction. However, the chair continues to block my path. From the environment feedback, I observed that while my leftward movement was successful, my forward movement was still blocked. This indicates that I didn't move far enough left to fully bypass the obstacle. To address this, I decide to move leftward two more times to ensure a clear path, then try moving forward again.\nExecutable plans: [[3, Move leftward by 0.25], [3, Move leftward by 0.25], [0, Move forward by 0.25]]\n\nEnvironment Feedback: Step 8 is executed successfully; Step 9 is executed successfully; Step 10 is executed successfully.\n\nReasoning and reflection: Now, I can see that the computer has shifted to my right front direction. There are no longer any visible obstacles blocking my way. I deduce that the computer is now to my right because my previous leftward movements adjusted my position significantly to clear the earlier obstacle. Since the path is clear, I plan to approach the computer by moving forward three times and then rightward once to align myself next to it.\nExecutable plans: [[0, Move forward by 0.25], [0, Move forward by 0.25], [0, Move forward by 0.25], [2, Move rightward by 0.25]]\n\nEnvironment Feedback: Step 11 is executed successfully; Step 12 is executed successfully; Step 13 is executed successfully; Step 14 is executed successfully.\n\n\nTask Success\n\n", + "\nHuman Instruction : Navigate to the floorlamp and be as close as possible.\n\nReasoning and reflection: I can see the floorlmap is at left side in front of me, next to the TVStand. Around me is a Cabinet at my left side. Following the guideline, since the floorlamp is at left side, I can approach itby a series of moving forward and left/rightward. Since currently there is an obstacle at my left, I will first move forward twice.\nExecutable plans: [[0, Move forward by 0.25], [0, Move forward by 0.25]]\n\nEnvironment Feedback: Step 1 is executed successfully; Step 2 is executed successfully;\n\nReasoning and reflection: I can see now I get closer to the floorlamp. It is still located in front of me at the left. I will try move forward and move leftward in series.\nExecutable plans: [[0, Move forward by 0.25], [3, Move leftward by 0.25], [0, Move forward by 0.25], [3, Move leftward by 0.25], [0, Move forward by 0.25]]\n\nEnvironment Feedback: Step 3 is executed successfully; Step 4 is executed successfully; Step 5 is executed successfully; Step 6 is executed successfully; Step 7 is Invalid, as a StandardIslandHeight is blocking you from moving forward.\n\nReasoning and reflection: I am a lot closer to the floorlamp by a series a forward and leftward movement. It is still in front of me. However, The last action is invalid, showing that I am blocked from moving forward.That means there is an obstacle in front of me and I can see it is probably a small table. Following the strategy, I can get away by first moving to the left and move forward. \nExecutable plans: [[3, Move leftward by 0.25], [3, Move leftward by 0.25], [0, Move forward by 0.25], [0, Move forward by 0.25]]\n\nEnvironment Feedback: Step 8 is executed successfully; Step 9 is executed successfully; Step 10 is executed successfully; Step 11 is executed successfully;\n\nReasoning and reflection: I can see that the floorlamp is at the right side of me, slight in the front. I think it in effect of consecutively moving leftward. Also, I have got away with the obstacle.Now I need to move right and I will probably be close to the floorlamp.\nExecutable plans: [[2, Move rightward by 0.25], [2, Move rightward by 0.25]]]\n\nEnvironment Feedback: Step 12 is executed successfully; Step 13 is executed successfully. \n\nReasoning and reflection: Last two action brought me closer to the floorlamp, however, following the strategy, I should not complete now since I can still be closer to the floorlamp by moving forward a few steps.\nExecutable plans: [[0, Move forward by 0.25], [0, Move forward by 0.25], [0, Move forward by 0.25]]\n\nEnvironment Feedback: Step 14 is executed successfully; Step 15 is executed successfully; Step 16 is executed successfully.\n\n\nTask Success\n", + "\nHuman Instruction: Navigate to the computer on the desk and be as close as possible.\n\nReasoning and reflection: According to your instruction, I first need to locate the target object. I can see that the computer is located in my left front direction, placed on a desk. Next, I analyze the obstacles around me. In front of me, slightly to the right, there is a chair blocking my direct path. To avoid it, I decide to move leftward first, followed by a forward movement to approach the desk.\nExecutable plans: [[0, Move forward by 0.25], [3, Move leftward by 0.25]]\n\nEnvironment Feedback: Step 1 is executed successfully; Step 2 is Invalid, as a StandardHeightTable is blocking you from moving forward.\n\nReasoning and reflection: I am now closer to the desk, and I can still see the computer on it. It remains in my left front direction. My last leftward movement failed because a StandardHeightTable is blocking my path. However, following the strategy, I need not address the invalid action since forward movement can still bring me closer to the target object. So I will still make forward movements for now. \nExecutable plans: [[0, Move forward by 0.25], [0, Move forward by 0.25], [0, Move forward by 0.25]]\n\nEnvironment Feedback: Step 3 is executed successfully; Step 4 is executed successfully; Step 5 is Invalid, as a StandardHeightTable is blocking you from moving forward.\n\nReasoning and reflection: I can see that I am getting closer but the last forward move is blocked. Following the strategy, I need to address the invalid action now since it is an obstacle blocking from moving closer in the direction of target object. The computer is at my left side in the front, to bypass this obstacle, I plan to first move leftward again to create more space, then try moving forward once more to get closer to the desk and the computer.\nExecutable plans: [[3, Move leftward by 0.25], [0, Move forward by 0.25]]\n\nEnvironment Feedback: Step 6 is executed successfully; Step 7 is invalid because the StandardHeight is blocking the movement.\n\nReasoning and reflection: I can see that the computer is still in my left front direction. However, the chair continues to block my path. From the environment feedback, I observed that while my leftward movement was successful, my forward movement was still blocked. This indicates that I didn’t move far enough left to fully bypass the obstacle. To address this, I decide to move leftward two more times to ensure a clear path, then try moving forward again.\nExecutable plans: [[3, Move leftward by 0.25], [3, Move leftward by 0.25], [0, Move forward by 0.25]]\n\nEnvironment Feedback: Step 8 is executed successfully; Step 9 is executed successfully; Step 10 is executed successfully.\n\nReasoning and reflection: Now, I can see that the computer has shifted to my right front direction. There are no longer any visible obstacles blocking my way. I deduce that the computer is now to my right because my previous leftward movements adjusted my position significantly to clear the earlier obstacle. Since the path is clear, I plan to approach the computer by moving forward three times and then rightward once to align myself next to it.\nExecutable plans: [[0, Move forward by 0.25], [0, Move forward by 0.25], [0, Move forward by 0.25], [2, Move rightward by 0.25]]\n\nEnvironment Feedback: Step 11 is executed successfully; Step 12 is executed successfully; Step 13 is executed successfully; Step 14 is executed successfully.\n\n\nTask Success\n\n", "\nHuman Instruction: Navigate to the book and be as close as possible.\n\nReasoning and reflection: At the moment, I cannot see the book in my current field of view. According to the strategy you provided, I will rotate my view ninety degrees at a time until I locate the target object. Since rotation changes my vision, I will not plan any other actions until the book is visible.\nExecutable plans: [[4, Rotate to the right by 90 degrees]]\n\nEnvironment Feedback: Step 1 is executed successfully. The book is still not visible in my current field of view.\n\nReasoning and reflection: I still cannot see the book in my current field of view. Following your instruction, I will rotate ninety degrees again. As the rotation may change my view, I will not plan any additional actions until I locate the book.\nExecutable plans: [[4, Rotate to the right by 90 degrees]]\n\nEnvironment Feedback: Step 2 is executed successfully. The book is now visible directly in front of me, placed on a shelf.\n\nReasoning and reflection: I have located the target object. The book is directly in front of me on a shelf. Following the strategy you provided, I will stop rotating now and start planning my navigation to get close to the book. Since the book is in front of me, I will first move two steps to the right to adjust my position, and then move forward three steps to approach it.\nExecutable plans: [[2, Move rightward by 0.25], [2, Move rightward by 0.25], [0, Move forward by 0.25], [0, Move forward by 0.25], [0, Move forward by 0.25]]\n\nEnvironment Feedback: Step 3 to Step 7 are all executed successfully.\n\nReasoning and reflection: I can now see that the book is directly in front of me, but I have not yet moved close enough to it. According to the standard, I should move closer to the book. Therefore, I decide to move forward three more times to reach the desired position.\nExecutable plans: [[0, Move forward by 0.25], [0, Move forward by 0.25], [0, Move forward by 0.25]]\n\nEnvironment Feedback: Step 8, Step 9, and Step 10 are all executed successfully.\n\nTask Success\n\n\n" -] +] \ No newline at end of file diff --git a/easi/tasks/ebnavigation/prompts.py b/easi/tasks/ebnavigation/prompts.py index 721a995..a5b21aa 100644 --- a/easi/tasks/ebnavigation/prompts.py +++ b/easi/tasks/ebnavigation/prompts.py @@ -46,16 +46,15 @@ ''' # Output template from EmbodiedBench planner_utils.py (template, not template_lang). -OUTPUT_TEMPLATE = ''' -The output json format should be {'visual_state_description':str, 'reasoning_and_reflection':str, 'language_plan':str, 'executable_plan':List[{'action_id':int, 'action_name':str}...]} -The fields in above JSON follows the purpose below: -1. visual_state_description is for description of current state from the visual image, -2. reasoning_and_reflection is for summarizing the history of interactions and any available environmental feedback. Additionally, provide reasoning as to why the last action or plan failed and did not finish the task, -3. language_plan is for describing a list of actions to achieve the user instruction. Each action is started by the step number and the action name, -4. executable_plan is a list of actions needed to achieve the user instruction, with each action having an action ID and a name. -5. keep your plan efficient and concise. -!!! When generating content for JSON strings, avoid using any contractions or abbreviated forms (like 's, 're, 've, 'll, 'd, n't) that use apostrophes. Instead, write out full forms (is, are, have, will, would, not) to prevent parsing errors in JSON. Please do not output any other thing more than the above-mentioned JSON, do not include ```json and ```!!!. -''' +OUTPUT_TEMPLATE = "\n" \ +"The output json format should be {'visual_state_description':str, 'reasoning_and_reflection':str, 'language_plan':str, 'executable_plan':List[{'action_id':int, 'action_name':str}...]}\n" \ +"The fields in above JSON follows the purpose below:\n" \ +"1. visual_state_description is for description of current state from the visual image, \n" \ +"2. reasoning_and_reflection is for summarizing the history of interactions and any available environmental feedback. Additionally, provide reasoning as to why the last action or plan failed and did not finish the task, \n" \ +"3. language_plan is for describing a list of actions to achieve the user instruction. Each action is started by the step number and the action name, \n" \ +"4. executable_plan is a list of actions needed to achieve the user instruction, with each action having an action ID and a name.\n" \ +"5. keep your plan efficient and concise.\n" \ +"!!! When generating content for JSON strings, avoid using any contractions or abbreviated forms (like 's, 're, 've, 'll, 'd, n't) that use apostrophes. Instead, write out full forms (is, are, have, will, would, not) to prevent parsing errors in JSON. Please do not output any other thing more than the above-mentioned JSON, do not include ```json and ```!!!.\n" # JSON schema matching EmbodiedBench navigation output format. EBNAVIGATION_RESPONSE_SCHEMA = { From c7c2d80cc7bf1d23b6c63ae2666fbe05c1257a66 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Mon, 16 Feb 2026 10:01:03 +0800 Subject: [PATCH 028/239] [Fix] Updated ebnavigation prompt --- easi/tasks/ebnavigation/prompts.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/easi/tasks/ebnavigation/prompts.py b/easi/tasks/ebnavigation/prompts.py index a5b21aa..81a1333 100644 --- a/easi/tasks/ebnavigation/prompts.py +++ b/easi/tasks/ebnavigation/prompts.py @@ -406,7 +406,7 @@ def _make_first_prompt_suffix(self, max_id: int) -> str: Uses 'Aim for about 1-2 actions'. """ return ( - f'''To achieve the task, 1. Reason about the current visual state and your final goal, and 2. Reflect on the effect of previous actions. 3. Summarize how you learn from the Strategy and Examples provided ''' + f'''\nTo achieve the task, 1. Reason about the current visual state and your final goal, and 2. Reflect on the effect of previous actions. 3. Summarize how you learn from the Strategy and Examples provided ''' f'''\nAim for about 1-2 actions in this step. !!!Notice: you cannot assess the situation until the whole plan in this planning step is finished executed, so plan accordingly.''' f'''\nAt last, output the action id(s) (0 ~ {max_id}) from the available actions to execute. ''' f'''\n\nThe input given to you is an first person view observation. Plan accordingly based on the visual observation.''' @@ -419,7 +419,7 @@ def _make_following_prompt_suffix(self, max_id: int) -> str: Uses 'Aim for about 5-6 actions'. """ return ( - f'''To achieve the task, 1. Reason about the current visual state and your final goal, and 2. Reflect on the effect of previous actions. 3. Summarize how you learn from the Strategy and Examples provided ''' + f'''\nTo achieve the task, 1. Reason about the current visual state and your final goal, and 2. Reflect on the effect of previous actions. 3. Summarize how you learn from the Strategy and Examples provided ''' f'''\nAim for about 5-6 actions in this step to be closer to the target object. !!!Notice: you cannot assess the situation until the whole plan in this planning step is finished executed, so plan accordingly.''' f'''\nAt last, output the action id(s) (0 ~ {max_id}) from the available actions to execute. ''' f'''\n\nThe input given to you is an first person view observation. Plan accordingly based on the visual observation.''' From d73cb20390b96ea155f2c7303edc983feb6b4560 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Mon, 16 Feb 2026 22:00:35 +0800 Subject: [PATCH 029/239] [Feat] Add EB-Habitat benchmark integration - Add habitat_sim:v0_3_0 simulator registration (conda env + manifest) - Vendor EBHabEnv from EmbodiedBench with fixed imports - Add EBHabitatTask with dynamic action space via on_episode_reset hook - Add EBHabitatPromptBuilder matching VLMPlanner prompt construction - Add 6 per-split YAML configs (base, common_sense, complex_instruction, spatial_relationship, visual_appearance, long_horizon) - Add 26 offline tests for actions, task, prompts, and registry - Move EB-Habitat-specific deps (gym, hydra-core, omegaconf, imageio, habitat-lab) from simulator requirements.txt to task YAML additional_deps --- easi/simulators/ai2thor/manifest.yaml | 4 +- easi/simulators/habitat_sim/__init__.py | 0 easi/simulators/habitat_sim/manifest.yaml | 9 + .../simulators/habitat_sim/v0_3_0/__init__.py | 6 + easi/simulators/habitat_sim/v0_3_0/bridge.py | 64 ++ .../habitat_sim/v0_3_0/conda_env.yaml | 8 + .../habitat_sim/v0_3_0/env_manager.py | 35 + .../habitat_sim/v0_3_0/requirements.txt | 2 + .../habitat_sim/v0_3_0/simulator.py | 22 + easi/tasks/ebhabitat/__init__.py | 0 easi/tasks/ebhabitat/actions.py | 37 + easi/tasks/ebhabitat/bridge.py | 109 +++ .../ebhabitat/config/habitat_examples.json | 13 + easi/tasks/ebhabitat/ebhabitat_base.yaml | 30 + .../ebhabitat/ebhabitat_common_sense.yaml | 30 + .../ebhabitat_complex_instruction.yaml | 30 + .../ebhabitat/ebhabitat_long_horizon.yaml | 30 + .../ebhabitat_spatial_relationship.yaml | 30 + .../ebhabitat_visual_appearance.yaml | 30 + easi/tasks/ebhabitat/prompts.py | 419 ++++++++++ easi/tasks/ebhabitat/task.py | 83 ++ easi/tasks/ebhabitat/vendor/EBHabEnv.py | 371 +++++++++ easi/tasks/ebhabitat/vendor/__init__.py | 4 + easi/tasks/ebhabitat/vendor/actions.py | 182 +++++ .../tasks/ebhabitat/vendor/config/__init__.py | 1 + .../config/default_structured_configs.py | 556 +++++++++++++ .../vendor/config/task/dataset_v1.yaml | 10 + .../config/task/language_rearrangement.yaml | 99 +++ .../config/task/pddl_domain_replica_cad.yaml | 292 +++++++ .../vendor/config/task/task_obs/visual.yaml | 24 + .../ebhabitat/vendor/dataset/__init__.py | 5 + .../ebhabitat/vendor/dataset/episodes.py | 343 ++++++++ easi/tasks/ebhabitat/vendor/dataset/utils.py | 70 ++ easi/tasks/ebhabitat/vendor/measures.py | 763 ++++++++++++++++++ easi/tasks/ebhabitat/vendor/predicate_task.py | 380 +++++++++ easi/tasks/ebhabitat/vendor/sensors.py | 422 ++++++++++ easi/tasks/ebhabitat/vendor/utils.py | 207 +++++ tests/test_ebhabitat_task.py | 264 ++++++ 38 files changed, 4982 insertions(+), 2 deletions(-) create mode 100644 easi/simulators/habitat_sim/__init__.py create mode 100644 easi/simulators/habitat_sim/manifest.yaml create mode 100644 easi/simulators/habitat_sim/v0_3_0/__init__.py create mode 100644 easi/simulators/habitat_sim/v0_3_0/bridge.py create mode 100644 easi/simulators/habitat_sim/v0_3_0/conda_env.yaml create mode 100644 easi/simulators/habitat_sim/v0_3_0/env_manager.py create mode 100644 easi/simulators/habitat_sim/v0_3_0/requirements.txt create mode 100644 easi/simulators/habitat_sim/v0_3_0/simulator.py create mode 100644 easi/tasks/ebhabitat/__init__.py create mode 100644 easi/tasks/ebhabitat/actions.py create mode 100644 easi/tasks/ebhabitat/bridge.py create mode 100644 easi/tasks/ebhabitat/config/habitat_examples.json create mode 100644 easi/tasks/ebhabitat/ebhabitat_base.yaml create mode 100644 easi/tasks/ebhabitat/ebhabitat_common_sense.yaml create mode 100644 easi/tasks/ebhabitat/ebhabitat_complex_instruction.yaml create mode 100644 easi/tasks/ebhabitat/ebhabitat_long_horizon.yaml create mode 100644 easi/tasks/ebhabitat/ebhabitat_spatial_relationship.yaml create mode 100644 easi/tasks/ebhabitat/ebhabitat_visual_appearance.yaml create mode 100644 easi/tasks/ebhabitat/prompts.py create mode 100644 easi/tasks/ebhabitat/task.py create mode 100644 easi/tasks/ebhabitat/vendor/EBHabEnv.py create mode 100644 easi/tasks/ebhabitat/vendor/__init__.py create mode 100644 easi/tasks/ebhabitat/vendor/actions.py create mode 100644 easi/tasks/ebhabitat/vendor/config/__init__.py create mode 100644 easi/tasks/ebhabitat/vendor/config/default_structured_configs.py create mode 100644 easi/tasks/ebhabitat/vendor/config/task/dataset_v1.yaml create mode 100644 easi/tasks/ebhabitat/vendor/config/task/language_rearrangement.yaml create mode 100644 easi/tasks/ebhabitat/vendor/config/task/pddl_domain_replica_cad.yaml create mode 100644 easi/tasks/ebhabitat/vendor/config/task/task_obs/visual.yaml create mode 100644 easi/tasks/ebhabitat/vendor/dataset/__init__.py create mode 100644 easi/tasks/ebhabitat/vendor/dataset/episodes.py create mode 100644 easi/tasks/ebhabitat/vendor/dataset/utils.py create mode 100644 easi/tasks/ebhabitat/vendor/measures.py create mode 100644 easi/tasks/ebhabitat/vendor/predicate_task.py create mode 100644 easi/tasks/ebhabitat/vendor/sensors.py create mode 100644 easi/tasks/ebhabitat/vendor/utils.py create mode 100644 tests/test_ebhabitat_task.py diff --git a/easi/simulators/ai2thor/manifest.yaml b/easi/simulators/ai2thor/manifest.yaml index ef2039b..d6d8356 100644 --- a/easi/simulators/ai2thor/manifest.yaml +++ b/easi/simulators/ai2thor/manifest.yaml @@ -3,12 +3,12 @@ display_name: "AI2-THOR" default_version: "v5_0_0" versions: v2_1_0: - description: "AI2-THOR 2.1.0 — used by embodiedbench ALFRED track (legacy API)" + description: "AI2-THOR 2.1.0 — currently used by eb-alfred" simulator_class: "easi.simulators.ai2thor.v2_1_0.simulator.AI2ThorSimulatorV210" env_manager_class: "easi.simulators.ai2thor.v2_1_0.env_manager.AI2ThorEnvManagerV210" python_version: "3.8" v5_0_0: - description: "AI2-THOR 5.0.0 — latest version, modern API" + description: "AI2-THOR 5.0.0 — currently used by eb-navigation" simulator_class: "easi.simulators.ai2thor.v5_0_0.simulator.AI2ThorSimulatorV500" env_manager_class: "easi.simulators.ai2thor.v5_0_0.env_manager.AI2ThorEnvManagerV500" python_version: "3.10" diff --git a/easi/simulators/habitat_sim/__init__.py b/easi/simulators/habitat_sim/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/easi/simulators/habitat_sim/manifest.yaml b/easi/simulators/habitat_sim/manifest.yaml new file mode 100644 index 0000000..2873ce3 --- /dev/null +++ b/easi/simulators/habitat_sim/manifest.yaml @@ -0,0 +1,9 @@ +name: habitat_sim +display_name: "Habitat Simulator" +default_version: "v0_3_0" +versions: + v0_3_0: + description: "Habitat 0.3.0 — currently used by eb-habitat" + simulator_class: "easi.simulators.habitat_sim.v0_3_0.simulator.HabitatSimulatorV030" + env_manager_class: "easi.simulators.habitat_sim.v0_3_0.env_manager.HabitatEnvManagerV030" + python_version: "3.10" diff --git a/easi/simulators/habitat_sim/v0_3_0/__init__.py b/easi/simulators/habitat_sim/v0_3_0/__init__.py new file mode 100644 index 0000000..8f1222c --- /dev/null +++ b/easi/simulators/habitat_sim/v0_3_0/__init__.py @@ -0,0 +1,6 @@ +"""Habitat simulator v0.3.0.""" + +from easi.simulators.habitat_sim.v0_3_0.env_manager import HabitatEnvManagerV030 +from easi.simulators.habitat_sim.v0_3_0.simulator import HabitatSimulatorV030 + +__all__ = ["HabitatSimulatorV030", "HabitatEnvManagerV030"] diff --git a/easi/simulators/habitat_sim/v0_3_0/bridge.py b/easi/simulators/habitat_sim/v0_3_0/bridge.py new file mode 100644 index 0000000..0dc0c42 --- /dev/null +++ b/easi/simulators/habitat_sim/v0_3_0/bridge.py @@ -0,0 +1,64 @@ +"""Generic Habitat v0.3.0 bridge for smoke testing. + +This script runs inside the easi_habitat_sim_v0_3_0 conda environment. +Task-specific bridges (e.g., EBHabitatBridge) extend BaseBridge directly. +This generic bridge is used by `easi sim test habitat_sim:v0_3_0`. + +Usage: + python bridge.py --workspace /tmp/easi_xxx +""" + +from __future__ import annotations + +import argparse +import sys +from pathlib import Path + +_repo_root = Path(__file__).resolve().parents[4] +if str(_repo_root) not in sys.path: + sys.path.insert(0, str(_repo_root)) + +from easi.communication.filesystem import poll_for_command, write_response, write_status +from easi.communication.schemas import make_error_response +from easi.utils.logging import get_logger, setup_logging + +logger = get_logger(__name__) + + +class HabitatV030Bridge: + """Smoke test bridge — verifies habitat-sim imports work.""" + + def __init__(self, workspace): + self.workspace = Path(workspace) + + def run(self): + import habitat_sim + + logger.info("habitat-sim %s loaded successfully", habitat_sim.__version__) + write_status(self.workspace, ready=True) + + while True: + command = poll_for_command(self.workspace, timeout=60.0) + if command.get("type") == "close": + write_response(self.workspace, {"status": "ok"}) + break + write_response( + self.workspace, + make_error_response( + "Smoke test bridge: only 'close' supported" + ), + ) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--workspace", type=Path, required=True) + parser.add_argument("--simulator-kwargs", type=str, default=None) + args, _ = parser.parse_known_args() + setup_logging("DEBUG") + bridge = HabitatV030Bridge(workspace=args.workspace) + bridge.run() + + +if __name__ == "__main__": + main() diff --git a/easi/simulators/habitat_sim/v0_3_0/conda_env.yaml b/easi/simulators/habitat_sim/v0_3_0/conda_env.yaml new file mode 100644 index 0000000..2184e90 --- /dev/null +++ b/easi/simulators/habitat_sim/v0_3_0/conda_env.yaml @@ -0,0 +1,8 @@ +name: easi_habitat_sim_v0_3_0 +channels: + - aihabitat + - conda-forge + - defaults +dependencies: + - python=3.10 + - habitat-sim=0.3.0=*withbullet*headless* diff --git a/easi/simulators/habitat_sim/v0_3_0/env_manager.py b/easi/simulators/habitat_sim/v0_3_0/env_manager.py new file mode 100644 index 0000000..7c6edb6 --- /dev/null +++ b/easi/simulators/habitat_sim/v0_3_0/env_manager.py @@ -0,0 +1,35 @@ +"""Environment manager for Habitat v0.3.0.""" + +from __future__ import annotations + +from pathlib import Path + +from easi.core.base_env_manager import BaseEnvironmentManager + + +class HabitatEnvManagerV030(BaseEnvironmentManager): + """Environment manager for Habitat 0.3.0.""" + + @property + def simulator_name(self) -> str: + return "habitat_sim" + + @property + def version(self) -> str: + return "v0_3_0" + + @property + def needs_display(self) -> bool: + return False # habitat-sim headless build uses EGL + + def get_conda_env_yaml_path(self) -> Path: + return Path(__file__).parent / "conda_env.yaml" + + def get_requirements_txt_path(self) -> Path: + return Path(__file__).parent / "requirements.txt" + + def get_system_deps(self) -> list[str]: + return ["conda"] + + def get_validation_import(self) -> str: + return "import habitat_sim; import habitat" diff --git a/easi/simulators/habitat_sim/v0_3_0/requirements.txt b/easi/simulators/habitat_sim/v0_3_0/requirements.txt new file mode 100644 index 0000000..220ee24 --- /dev/null +++ b/easi/simulators/habitat_sim/v0_3_0/requirements.txt @@ -0,0 +1,2 @@ +numpy>=1.24 +Pillow>=10.0 diff --git a/easi/simulators/habitat_sim/v0_3_0/simulator.py b/easi/simulators/habitat_sim/v0_3_0/simulator.py new file mode 100644 index 0000000..c9b954e --- /dev/null +++ b/easi/simulators/habitat_sim/v0_3_0/simulator.py @@ -0,0 +1,22 @@ +"""Habitat simulator v0.3.0.""" + +from __future__ import annotations + +from pathlib import Path + +from easi.core.base_simulator import BaseSimulator + + +class HabitatSimulatorV030(BaseSimulator): + """Habitat 0.3.0 simulator (ReplicaCAD rearrangement).""" + + @property + def name(self) -> str: + return "habitat_sim" + + @property + def version(self) -> str: + return "v0_3_0" + + def _get_bridge_script_path(self) -> Path: + return Path(__file__).parent / "bridge.py" diff --git a/easi/tasks/ebhabitat/__init__.py b/easi/tasks/ebhabitat/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/easi/tasks/ebhabitat/actions.py b/easi/tasks/ebhabitat/actions.py new file mode 100644 index 0000000..4639354 --- /dev/null +++ b/easi/tasks/ebhabitat/actions.py @@ -0,0 +1,37 @@ +"""EB-Habitat action space definitions. + +The action space is DYNAMIC — it depends on the PDDL domain grounding +which varies per scene/episode. After EBHabEnv.__init__(), the actions +are available as env.language_skill_set (list of ~70 natural language strings). + +This module provides the transform_action_to_natural_language function +and a placeholder global action space for offline testing. + +Reference: EBHabEnv.py lines 76-107, 158-159 +""" + +from __future__ import annotations + +# Placeholder global action space for offline testing. +# Real action space is extracted from the env at runtime. +PLACEHOLDER_ACTIONS = [ + "navigate to the table 1", + "navigate to the table 2", + "navigate to the TV stand", + "navigate to the left counter in the kitchen", + "navigate to the right counter in the kitchen", + "navigate to the sink in the kitchen", + "navigate to the sofa", + "navigate to the refrigerator push point", + "pick up the toy airplane", + "place at the table 1", + "open the refrigerator", + "close the refrigerator", + "open the cabinet 4", + "close the cabinet 4", +] + + +def get_placeholder_action_space() -> list[str]: + """Return a placeholder action space for offline testing.""" + return list(PLACEHOLDER_ACTIONS) diff --git a/easi/tasks/ebhabitat/bridge.py b/easi/tasks/ebhabitat/bridge.py new file mode 100644 index 0000000..4c9644a --- /dev/null +++ b/easi/tasks/ebhabitat/bridge.py @@ -0,0 +1,109 @@ +"""EB-Habitat bridge — wraps vendored EBHabEnv via BaseBridge. + +This script runs inside the easi_habitat_sim_v0_3_0 conda env (Python 3.10). +Communicates with parent process via filesystem IPC. + +The bridge handles: +- Creating EBHabEnv with data_dir/dataset_dir from extracted zip files +- Mapping action text -> integer ID for env.step() +- Sending dynamic action space in reset response metadata +- Extracting task_success, task_progress, subgoal_reward from env info + +Usage: + python bridge.py --workspace /tmp/easi_xxx [--data-dir /path/to/data] [--simulator-kwargs '{}'] +""" + +from __future__ import annotations + +import json +import os +import sys +from pathlib import Path + +import numpy as np + +_repo_root = Path(__file__).resolve().parents[3] +if str(_repo_root) not in sys.path: + sys.path.insert(0, str(_repo_root)) + +from easi.simulators.base_bridge import BaseBridge + + +class EBHabitatBridge(BaseBridge): + """BaseBridge wrapper around vendored EBHabEnv.""" + + _action_map: dict[str, int] = {} + _language_skill_set: list[str] = [] + + def _create_env(self, reset_config, simulator_kwargs): + from easi.tasks.ebhabitat.vendor.EBHabEnv import EBHabEnv + + # Resolve data directories from extracted zip files + data_dir = simulator_kwargs.get("data_dir") + dataset_dir = None + if data_dir: + # datasets.zip extracts pickle files to data_dir/datasets/ + candidate = Path(data_dir) / "datasets" + if candidate.exists(): + dataset_dir = str(candidate) + + eval_set = reset_config.get("eval_set", "base") + resolution = simulator_kwargs.get("screen_height", 500) + + env = EBHabEnv( + eval_set=eval_set, + data_dir=data_dir, + dataset_dir=dataset_dir, + resolution=resolution, + ) + + # Extract dynamic action space + self._language_skill_set = env.language_skill_set + self._action_map = {name: i for i, name in enumerate(self._language_skill_set)} + + return env + + def _on_reset(self, env, reset_config): + return env.reset() + + def _on_step(self, env, action_text): + # Map action text to integer ID + action_id = self._action_map.get(action_text, -1) + if action_id < 0: + # Try case-insensitive match + for name, idx in self._action_map.items(): + if name.lower() == action_text.lower(): + action_id = idx + break + if action_id < 0: + # Random action as fallback (matches EBHabEnv behavior) + action_id = np.random.randint(len(self._language_skill_set)) + + return env.step(action_id) + + def _extract_image(self, obs): + from easi.tasks.ebhabitat.vendor.utils import observations_to_image + return observations_to_image(obs, "head_rgb") + + def _extract_info(self, info): + return { + "task_success": float(info.get("task_success", 0.0)), + "task_progress": float(info.get("task_progress", 0.0)), + "subgoal_reward": float(info.get("subgoal_reward", 0.0)), + "last_action_success": float(info.get("last_action_success", 0.0)), + "feedback": str(info.get("env_feedback", "")), + "action_id": int(info.get("action_id", -1)), + } + + def _make_response(self, obs, reward=0.0, done=False, info=None): + """Override to include dynamic action space in metadata.""" + response = super()._make_response(obs, reward, done, info) + if self._language_skill_set: + response["observation"]["metadata"]["dynamic_action_space"] = json.dumps( + self._language_skill_set + ) + return response + + +if __name__ == "__main__": + EBHabitatBridge.main() diff --git a/easi/tasks/ebhabitat/config/habitat_examples.json b/easi/tasks/ebhabitat/config/habitat_examples.json new file mode 100644 index 0000000..b73f984 --- /dev/null +++ b/easi/tasks/ebhabitat/config/habitat_examples.json @@ -0,0 +1,13 @@ +[ + "Human instruction: Put both an toy airplane and a bowl onto the black table.\nOutput: {'language_plan': 'To achieve the goal, the robot must locate the toy airplane and the bowl in the room and then move each item to the black table. There are two tables in the room, and the robot identifies table 1 as the black table. Therefore, the objective is to place both objects on table 1. The plan is as follows: first, navigate to the sofa, pick up the airplane, move to table 1, and place the airplane there. Then, proceed to table 2, where the bowl might be, pick up the bowl, return to table 1, and set the bowl there.', 'executable_plan': [{'action_id': 12, 'action_name': 'navigate to the sofa'}, {'action_id': 47, 'action_name': 'pick up the toy airplane'}, {'action_id': 6, 'action_description': 'navigate to the table 1'}, {'action_id': 50, 'action_description': 'place at the table 1'}, {'action_id': 7, 'action_description': 'navigate to the table 2'}, {'action_id': 42, 'action_description': 'pick up the bowl'}, {'action_id': 6, 'action_description': 'navigate to the table 1'}, {'action_id': 50, 'action_description': 'place at the table 1'}]}", + "Human instruction: I made a mistake and left the fridge open. Can you assist me by closing it?\nOutput: {'language_plan': 'The objective is for the robot to close the refrigerator. To do so, the robot first navigates to the refrigerator and then closes it.', executable_plan': [{'action_id': 13, 'action_name': 'navigate to the refrigerator'}, {'action_id': 61, 'action_name': 'close the refrigerator'}]}", + "Human instruction: Displace the sponge from the left counter.\nOutput: {'language_plan': 'To accomplish this task, the robot should navigate to the left counter to pick up the sponge. It should then move to another location, such as table 2, and place the sponge there.', 'executable_plan': [{'action_id': 11, 'action_name': 'navigate to the left counter in the kitchen'}, {'action_id': 28, 'action_name': 'pick up the sponge'}, {'action_id': 7, 'action_name': 'navigate to the table 2'}, {'action_id': 51, 'action_name': 'place at the table 2'}]}", + "Human instruction: Collect a banana from the sofa and place it in the right counter.\nOutput: {'language_plan: 'The plan is to start by navigating to the sofa, picking up the banana, then moving to the right counter and placing the banana there.', 'executable_plan': [{'action_id': 12, 'action_name': 'navigate to the sofa'}, {'action_id': 37, 'action_name': 'pick up the banana'}, {'action_id': 10, 'action_name': 'navigate to the right counter in the kitchen'}, {'action_id': 54, 'action_name': 'place at the right counter in the kitchen'}]}", + "Human instruction: Arrange an apple and a scissors on the left counter.\nOutput: {'language_plan': 'The instruction requires finding an apple and a pair of scissors and relocating them to the left counter. First, the robot moves to the left counter to pick up the scissors, if present. Upon success, it places the scissors on the counter and then moves to table 1 to pick up an apple. Once successful, it returns to the left counter and places the apple there.', 'executable_plan': [{'action_id': 11, 'action_name': 'navigate to the left counter in the kitchen'}, {'action_id': 21, 'action_name': 'pick up the scissors'}, {'action_id': 55, 'action_name': 'place at the left counter in the kitchen'}, {'action_id': 6, 'action_name': 'navigate to the table 1'}, {'action_id': 33, 'action_name': 'pick up the apple'}, {'action_id': 11, 'action_name': 'navigate to the left counter in the kitchen'}, {'action_id': 55, 'action_name': 'place at the left counter in the kitchen'}]}", + "Human instruction: I need you to switch the places of lemon and pear in left counter and sofa.\nOutput: {'language_plan': 'To achieve this goal, the robot first navigates to the left counter, picks up the lemon, then moves to the sofa and places the lemon there. Afterward, it picks up the pear from the sofa, navigates back to the left counter, and places the pear there.', 'executable_plan': [{'action_id': 11, 'action_name': 'navigate to the left counter in the kitchen'}, {'action_id': 34, 'action_name': 'pick up the lemon'}, {'action_id': 12, 'action_name': 'navigate to the sofa'}, {'action_id': 56, 'action_name': 'place at the sofa'}, {'action_id': 31, 'action_name': 'pick up the pear'}, {'action_id': 11, 'action_name': 'navigate to the left counter in the kitchen'}, {'action_id': 55, 'action_name': 'place at the left counter in the kitchen'}]}", + "Human instruction: Relocate a sponge to its assigned brown table.\nOutput: {'language_plan': 'First, the robot needs to locate the sponge. Upon exploration, the sponge is found on the TV stand. The robot then navigates to the TV stand, picks up the sponge, and moves it to table 2, identified as the brown table, placing the sponge there.', 'executable_plan': [{'action_id': 8, 'action_name': 'navigate to the TV stand'}, {'action_id': 28, 'action_name': 'pick up the sponge'}, {'action_id': 7, 'action_name': 'navigate to the table 2'}, {'action_id': 51, 'action_name': 'place at the table 2'}]}", + "Human instruction: Lay an can and a peach on the right counter.\nOutput: {'language_plan': 'The plan involves first finding the peach and the can. First, navigating to table 2 where the robot observed the peach, picks it up, and moves to the right counter to place it. Then, the robot navigates to the TV stand to find a can, picks it up, and returns to the right counter to place the can.', 'executable_plan': [{'action_id': 7, 'action_name': 'navigate to the table 2'}, {'action_id': 32, 'action_name': 'pick up the peach'}, {'action_id': 10, 'action_name': 'navigate to the right counter in the kitchen'}, {'action_id': 54, 'action_name': 'place at the right counter in the kitchen'}, {'action_id': 8, 'action_name': 'navigate to the TV stand'}, {'action_id': 35, 'action_name': 'pick up the can'}, {'action_id': 10, 'action_name': 'navigate to the right counter in the kitchen'}, {'action_id': 54, 'action_name': 'place at the right counter in the kitchen'}]}", + "Human instruction: In black table and right counter, could you swap the locations of scissors and pear?\nOutput: {'language_plan': 'The task is to swap the locations of a pair of scissors and a pear between the black table (identified as table 1) and the right counter. To accomplish this, the robot should first navigate to table 1 to pick up the scissors. Then, it will move to the right counter in the kitchen and place the scissors there. Subsequently, the robot will pick up the pear from the right counter, navigate back to table 1, and place the pear there. ', 'executable_plan': [{'action_id': 6, 'action_name': 'navigate to the table 1'}, {'action_id': 21, 'action_name': 'pick up the scissors'}, {'action_id': 10, 'action_name': 'navigate to the right counter in the kitchen'}, {'action_id': 54, 'action_name': 'place at the right counter in the kitchen'}, {'action_id': 31, 'action_name': 'pick up the pear'}, {'action_id': 11, 'action_name': 'navigate to the table 1'}, {'action_id': 50, 'action_name': 'place at the table 1'}]}", + "Human instruction: Move the spoon off the right counter.\nOutput: {'language_plan': 'The plan involves first locating the spoon on the right counter in the kitchen. The robot should navigate to the right counter, pick up the spoon, and then move to the left counter in the kitchen to place it there', 'executable_plan': [{'action_id': 10, 'action_name': 'navigate to the right counter in the kitchen'}, {'action_id': 26, 'action_name': 'pick up the spoon'}, {'action_id': 11, 'action_name': 'navigate to the left counter in the kitchen'}, {'action_id': 55, 'action_name': 'place at the left counter in the kitchen'}]}" +] + \ No newline at end of file diff --git a/easi/tasks/ebhabitat/ebhabitat_base.yaml b/easi/tasks/ebhabitat/ebhabitat_base.yaml new file mode 100644 index 0000000..a1473bf --- /dev/null +++ b/easi/tasks/ebhabitat/ebhabitat_base.yaml @@ -0,0 +1,30 @@ +name: ebhabitat_base +display_name: "EB-Habitat Base Split" +description: "Base evaluation split for EB-Habitat (rearrangement)" +simulator: "habitat_sim:v0_3_0" +task_class: "easi.tasks.ebhabitat.task.EBHabitatTask" +max_steps: 30 +dataset: + source: huggingface + repo_id: "oscarqjh/EB-Habitat_easi" + subset: null + split: "base" + zip_files: + - "simulator_data.zip" + - "datasets.zip" +simulator_configs: + screen_height: 500 + screen_width: 500 + additional_deps: + - "gym" + - "hydra-core>=1.3" + - "omegaconf>=2.3" + - "imageio" + - "habitat-lab @ git+https://github.com/facebookresearch/habitat-lab.git@v0.3.0#subdirectory=habitat-lab" +agent: + prompt_builder: "easi.tasks.ebhabitat.prompts.EBHabitatPromptBuilder" + prompt_builder_kwargs: + n_shot: 10 + split: "base" + use_feedback: true + chat_history: false diff --git a/easi/tasks/ebhabitat/ebhabitat_common_sense.yaml b/easi/tasks/ebhabitat/ebhabitat_common_sense.yaml new file mode 100644 index 0000000..0c9ca26 --- /dev/null +++ b/easi/tasks/ebhabitat/ebhabitat_common_sense.yaml @@ -0,0 +1,30 @@ +name: ebhabitat_common_sense +display_name: "EB-Habitat Common Sense Split" +description: "Common sense evaluation split for EB-Habitat (rearrangement)" +simulator: "habitat_sim:v0_3_0" +task_class: "easi.tasks.ebhabitat.task.EBHabitatTask" +max_steps: 30 +dataset: + source: huggingface + repo_id: "oscarqjh/EB-Habitat_easi" + subset: null + split: "common_sense" + zip_files: + - "simulator_data.zip" + - "datasets.zip" +simulator_configs: + screen_height: 500 + screen_width: 500 + additional_deps: + - "gym" + - "hydra-core>=1.3" + - "omegaconf>=2.3" + - "imageio" + - "habitat-lab @ git+https://github.com/facebookresearch/habitat-lab.git@v0.3.0#subdirectory=habitat-lab" +agent: + prompt_builder: "easi.tasks.ebhabitat.prompts.EBHabitatPromptBuilder" + prompt_builder_kwargs: + n_shot: 10 + split: "common_sense" + use_feedback: true + chat_history: false diff --git a/easi/tasks/ebhabitat/ebhabitat_complex_instruction.yaml b/easi/tasks/ebhabitat/ebhabitat_complex_instruction.yaml new file mode 100644 index 0000000..95af881 --- /dev/null +++ b/easi/tasks/ebhabitat/ebhabitat_complex_instruction.yaml @@ -0,0 +1,30 @@ +name: ebhabitat_complex_instruction +display_name: "EB-Habitat Complex Instruction Split" +description: "Complex instruction evaluation split for EB-Habitat (rearrangement)" +simulator: "habitat_sim:v0_3_0" +task_class: "easi.tasks.ebhabitat.task.EBHabitatTask" +max_steps: 30 +dataset: + source: huggingface + repo_id: "oscarqjh/EB-Habitat_easi" + subset: null + split: "complex_instruction" + zip_files: + - "simulator_data.zip" + - "datasets.zip" +simulator_configs: + screen_height: 500 + screen_width: 500 + additional_deps: + - "gym" + - "hydra-core>=1.3" + - "omegaconf>=2.3" + - "imageio" + - "habitat-lab @ git+https://github.com/facebookresearch/habitat-lab.git@v0.3.0#subdirectory=habitat-lab" +agent: + prompt_builder: "easi.tasks.ebhabitat.prompts.EBHabitatPromptBuilder" + prompt_builder_kwargs: + n_shot: 10 + split: "complex_instruction" + use_feedback: true + chat_history: false diff --git a/easi/tasks/ebhabitat/ebhabitat_long_horizon.yaml b/easi/tasks/ebhabitat/ebhabitat_long_horizon.yaml new file mode 100644 index 0000000..a41cd43 --- /dev/null +++ b/easi/tasks/ebhabitat/ebhabitat_long_horizon.yaml @@ -0,0 +1,30 @@ +name: ebhabitat_long_horizon +display_name: "EB-Habitat Long Horizon Split" +description: "Long horizon evaluation split for EB-Habitat (rearrangement)" +simulator: "habitat_sim:v0_3_0" +task_class: "easi.tasks.ebhabitat.task.EBHabitatTask" +max_steps: 30 +dataset: + source: huggingface + repo_id: "oscarqjh/EB-Habitat_easi" + subset: null + split: "long_horizon" + zip_files: + - "simulator_data.zip" + - "datasets.zip" +simulator_configs: + screen_height: 500 + screen_width: 500 + additional_deps: + - "gym" + - "hydra-core>=1.3" + - "omegaconf>=2.3" + - "imageio" + - "habitat-lab @ git+https://github.com/facebookresearch/habitat-lab.git@v0.3.0#subdirectory=habitat-lab" +agent: + prompt_builder: "easi.tasks.ebhabitat.prompts.EBHabitatPromptBuilder" + prompt_builder_kwargs: + n_shot: 10 + split: "long_horizon" + use_feedback: true + chat_history: false diff --git a/easi/tasks/ebhabitat/ebhabitat_spatial_relationship.yaml b/easi/tasks/ebhabitat/ebhabitat_spatial_relationship.yaml new file mode 100644 index 0000000..a1c67fa --- /dev/null +++ b/easi/tasks/ebhabitat/ebhabitat_spatial_relationship.yaml @@ -0,0 +1,30 @@ +name: ebhabitat_spatial_relationship +display_name: "EB-Habitat Spatial Relationship Split" +description: "Spatial relationship evaluation split for EB-Habitat (rearrangement)" +simulator: "habitat_sim:v0_3_0" +task_class: "easi.tasks.ebhabitat.task.EBHabitatTask" +max_steps: 30 +dataset: + source: huggingface + repo_id: "oscarqjh/EB-Habitat_easi" + subset: null + split: "spatial_relationship" + zip_files: + - "simulator_data.zip" + - "datasets.zip" +simulator_configs: + screen_height: 500 + screen_width: 500 + additional_deps: + - "gym" + - "hydra-core>=1.3" + - "omegaconf>=2.3" + - "imageio" + - "habitat-lab @ git+https://github.com/facebookresearch/habitat-lab.git@v0.3.0#subdirectory=habitat-lab" +agent: + prompt_builder: "easi.tasks.ebhabitat.prompts.EBHabitatPromptBuilder" + prompt_builder_kwargs: + n_shot: 10 + split: "spatial_relationship" + use_feedback: true + chat_history: false diff --git a/easi/tasks/ebhabitat/ebhabitat_visual_appearance.yaml b/easi/tasks/ebhabitat/ebhabitat_visual_appearance.yaml new file mode 100644 index 0000000..a558250 --- /dev/null +++ b/easi/tasks/ebhabitat/ebhabitat_visual_appearance.yaml @@ -0,0 +1,30 @@ +name: ebhabitat_visual_appearance +display_name: "EB-Habitat Visual Appearance Split" +description: "Visual appearance evaluation split for EB-Habitat (rearrangement)" +simulator: "habitat_sim:v0_3_0" +task_class: "easi.tasks.ebhabitat.task.EBHabitatTask" +max_steps: 30 +dataset: + source: huggingface + repo_id: "oscarqjh/EB-Habitat_easi" + subset: null + split: "visual_appearance" + zip_files: + - "simulator_data.zip" + - "datasets.zip" +simulator_configs: + screen_height: 500 + screen_width: 500 + additional_deps: + - "gym" + - "hydra-core>=1.3" + - "omegaconf>=2.3" + - "imageio" + - "habitat-lab @ git+https://github.com/facebookresearch/habitat-lab.git@v0.3.0#subdirectory=habitat-lab" +agent: + prompt_builder: "easi.tasks.ebhabitat.prompts.EBHabitatPromptBuilder" + prompt_builder_kwargs: + n_shot: 10 + split: "visual_appearance" + use_feedback: true + chat_history: false diff --git a/easi/tasks/ebhabitat/prompts.py b/easi/tasks/ebhabitat/prompts.py new file mode 100644 index 0000000..c3f445a --- /dev/null +++ b/easi/tasks/ebhabitat/prompts.py @@ -0,0 +1,419 @@ +"""EB-Habitat prompt builder matching EmbodiedBench VLMPlanner exactly. + +Supports both chat_history=False (stateless) and chat_history=True modes. +Referenced in ebhabitat*.yaml via: + agent: + prompt_builder: "easi.tasks.ebhabitat.prompts.EBHabitatPromptBuilder" + prompt_builder_kwargs: + n_shot: 10 + split: "base" + +Reference: EmbodiedBench/embodiedbench/planner/vlm_planner.py + EmbodiedBench/embodiedbench/evaluator/config/system_prompts.py:28-48 + EmbodiedBench/embodiedbench/planner/planner_utils.py +""" +from __future__ import annotations + +import json +from pathlib import Path + +from easi.agents.prompt_builder import _encode_image_base64, validate_action_name +from easi.core.episode import Action +from easi.core.memory import AgentMemory +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + +# Exact system prompt from EmbodiedBench system_prompts.py (habitat_system_prompt). +# Three format placeholders: max_action_id, action_list, examples. +HABITAT_SYSTEM_PROMPT = '''## You are a robot operating in a home. Given a task, you must accomplish the task using a defined set of actions to achieve the desired outcome. + +## Action Descriptions and Validity Rules +\u2022 Navigation: Parameterized by the name of the receptacle to navigate to. So long as the receptacle is present in the scene, this skill is always valid +\u2022 Pick: Parameterized by the name of the object to pick. Only valid if the robot is close to the object, not holding another object, and the object is not inside a closed receptacle. +\u2022 Place: Parameterized by the name of the receptacle to place the object on. Only valid if the robot is close to the receptacle and is holding an object. +\u2022 Open: Parameterized by the name of the receptacle to open. Only valid if the receptacle is closed and the robot is close to the receptacle. +\u2022 Close: Parameterized by the name of the receptacle to close. Only valid if the receptacle is open and the robot is close to the receptacle. + +## The available action id (0 ~ {}) and action names are: {}. + +{} + +## Guidelines +1. **Output Plan**: Avoid generating empty plan. Each plan should include no more than 20 actions. +2. **Visibility**: If an object is not currently visible, use the "Navigation" action to locate it or its receptacle before attempting other operations. +3. **Action Validity**: Make sure match the action name and its corresponding action id in the output.\n Avoid performing actions that do not meet the defined validity criteria. +4. **Prevent Repeating Action Sequences**: Do not repeatedly execute the same action or sequence of actions.\n Try to modify the action sequence because previous actions do not lead to success. +5. **Multiple Instances**: There may be multiple instances of the same object, distinguished by an index following their names, e.g., cabinet 2, cabinet 3. You can explore these instances if you do not find the desired object in the current receptacle. +6. **Reflection on History and Feedback**: Use interaction history and feedback from the environment to refine and enhance your current strategies and actions. If the last action is invalid, reflect on the reason, such as not adhering to action rules or missing preliminary actions, and adjust your plan accordingly. +''' + +# Output template from EmbodiedBench planner_utils.py (template, not template_lang). +# Shared across all EmbodiedBench benchmarks (Alfred, Habitat, etc.). +OUTPUT_TEMPLATE = ''' +The output json format should be {'visual_state_description':str, 'reasoning_and_reflection':str, 'language_plan':str, 'executable_plan':List[{'action_id':int, 'action_name':str}...]} +The fields in above JSON follows the purpose below: +1. visual_state_description is for description of current state from the visual image, +2. reasoning_and_reflection is for summarizing the history of interactions and any available environmental feedback. Additionally, provide reasoning as to why the last action or plan failed and did not finish the task, +3. language_plan is for describing a list of actions to achieve the user instruction. Each action is started by the step number and the action name, +4. executable_plan is a list of actions needed to achieve the user instruction, with each action having an action ID and a name. +5. keep your plan efficient and concise. +!!! When generating content for JSON strings, avoid using any contractions or abbreviated forms (like 's, 're, 've, 'll, 'd, n't) that use apostrophes. Instead, write out full forms (is, are, have, will, would, not) to prevent parsing errors in JSON. Please do not output any other thing more than the above-mentioned JSON, do not include ```json and ```!!!. +''' + +# JSON schema for structured output (same as EB-Alfred) +EBHABITAT_RESPONSE_SCHEMA = { + "type": "json_schema", + "json_schema": { + "name": "embodied_planning", + "schema": { + "type": "object", + "properties": { + "visual_state_description": { + "type": "string", + "description": "Description of current state from the visual image", + }, + "reasoning_and_reflection": { + "type": "string", + "description": "summarize the history of interactions and any available environmental feedback. Additionally, provide reasoning as to why the last action or plan failed and did not finish the task", + }, + "language_plan": { + "type": "string", + "description": "The list of actions to achieve the user instruction. Each action is started by the step number and the action name", + }, + "executable_plan": { + "type": "array", + "description": "A list of actions needed to achieve the user instruction, with each action having an action ID and a name. Do not output empty list.", + "items": { + "type": "object", + "properties": { + "action_id": { + "type": "integer", + "description": "The action ID to select from the available actions given by the prompt", + }, + "action_name": { + "type": "string", + "description": "The name of the action", + }, + }, + "required": ["action_id", "action_name"], + }, + }, + }, + "required": [ + "visual_state_description", + "reasoning_and_reflection", + "language_plan", + "executable_plan", + ], + }, + }, +} + +_CONFIG_DIR = Path(__file__).parent / "config" + + +class EBHabitatPromptBuilder: + """Prompt builder for EB-Habitat that exactly matches VLMPlanner output. + + Supports two modes: + - chat_history=False (default): Stateless. Every turn sends the full system + prompt + examples + instruction. No message accumulation. + - chat_history=True: First turn is identical. Subsequent turns send a minimal + prompt (instruction + history + reflection). Messages accumulate. + """ + + def __init__( + self, + n_shot: int = 10, + split: str = "base", + use_feedback: bool = True, + chat_history: bool = False, + ): + self.n_shot = n_shot + self.split = split + self.use_feedback = use_feedback + self.chat_history = chat_history + + # Load examples (same file for all splits, unlike EB-Alfred) + examples_file = _CONFIG_DIR / "habitat_examples.json" + with open(examples_file) as f: + self._examples: list[str] = json.load(f) + + # Action space state + self._actions: list[str] = [] + self._action_str: str = "" + self._action_id_map: dict[str, int] = {} + self._id_action_map: dict[int, str] = {} + + def set_action_space(self, actions: list[str]) -> None: + """Update action space (e.g., after dynamic expansion per episode).""" + self._actions = list(actions) + self._action_str = self._build_action_list_str(actions) + self._action_id_map = {name: i for i, name in enumerate(actions)} + self._id_action_map = {i: name for i, name in enumerate(actions)} + + def action_name_to_id(self, name: str) -> int | None: + return self._action_id_map.get(name) + + def action_id_to_name(self, action_id: int) -> str | None: + return self._id_action_map.get(action_id) + + @staticmethod + def _build_action_list_str(actions: list[str]) -> str: + """Build action list in VLMPlanner's get_availabel_action_prompt format.""" + parts = '' + for i in range(len(actions)): + parts += '\naction id ' + str(i) + ': ' + str(actions[i]) + if i < len(actions) - 1: + parts += ', ' + return parts + + # ---- PromptBuilderProtocol methods ---- + + def build_messages(self, memory: AgentMemory) -> list[dict]: + """Build COMPLETE message list to send to LLM.""" + if not self._action_id_map or self._actions != memory.action_space: + self.set_action_space(memory.action_space) + + if self.chat_history: + return self._build_chat_history_messages(memory) + else: + return self._build_stateless_messages(memory) + + def parse_response(self, llm_response: str, memory: AgentMemory) -> list[Action]: + """Parse LLM response into validated actions.""" + from easi.utils.json_repair import fix_json + llm_response = fix_json(llm_response) + + try: + data = json.loads(llm_response) + except json.JSONDecodeError as e: + logger.warning("Failed to parse LLM response as JSON: %s", e) + return [] + + plan = data.get("executable_plan", []) + if not isinstance(plan, list) or not plan: + logger.warning("No executable_plan in LLM response") + return [] + + actions = [] + for entry in plan: + if not isinstance(entry, dict): + continue + + if "action_id" in entry: + action_id = entry["action_id"] + action_name = self.action_id_to_name(action_id) + if action_name is None: + action_name = entry.get("action_name", "") + else: + action_name = entry.get("action", "") + + validated = validate_action_name(action_name, memory.action_space) + if validated: + actions.append(Action(action_name=validated)) + else: + logger.warning("Skipping invalid action: '%s'", action_name) + break + + return actions + + def get_response_format(self, memory: AgentMemory) -> dict: + """Return JSON schema for API-level enforcement.""" + return EBHABITAT_RESPONSE_SCHEMA + + # ---- Stateless mode (chat_history=False) ---- + + def _build_stateless_messages(self, memory: AgentMemory) -> list[dict]: + """Build full prompt each turn. No history accumulation.""" + prompt = self._build_prompt_text( + memory.task_description, memory.action_history, + ) + return self._wrap_as_user_message(prompt, memory.current_observation) + + def _build_prompt_text( + self, + task_description: str, + action_history: list[tuple[str, str]], + ) -> str: + """Build the full prompt text matching VLMPlanner.process_prompt(). + + Covers both first-turn and subsequent-turn paths with + chat_history=False (lines 53-63 and 78-94 of vlm_planner.py). + """ + user_instruction = task_description.rstrip('.') + max_id = len(self._actions) - 1 + + if len(action_history) == 0: + # First turn path (vlm_planner.py lines 53-63) + if self.n_shot >= 1: + examples_str = '\n\n'.join([ + f'## Task Execution Example {i}: \n {x}' + for i, x in enumerate(self._examples[:self.n_shot]) + ]) + prompt = HABITAT_SYSTEM_PROMPT.format( + max_id, self._action_str, examples_str, + ) + else: + prompt = HABITAT_SYSTEM_PROMPT.format( + max_id, self._action_str, '', + ) + + prompt += f'\n\n## Now the human instruction is: {user_instruction}.' + prompt += f" You are supposed to output in json. You need to describe current visual state from the image, output your reasoning steps and plan. At the end, output the action id (0 ~ {max_id}) from the available actions to excute." + + else: + # Subsequent turn path, chat_history=False (vlm_planner.py lines 78-94) + # Note: VLMPlanner has TWO spaces after "Example" on line 80 (preserved) + if self.n_shot >= 1: + examples_str = '\n\n'.join([ + f'## Task Execution Example {i}: \n {x}' + for i, x in enumerate(self._examples[:self.n_shot]) + ]) + prompt = HABITAT_SYSTEM_PROMPT.format( + max_id, self._action_str, examples_str, + ) + else: + prompt = HABITAT_SYSTEM_PROMPT.format( + max_id, self._action_str, '', + ) + + prompt += f'\n\n## Now the human instruction is: {user_instruction}.' + prompt += '\n\n The action history:' + for i, (action_name, feedback) in enumerate(action_history): + action_id = self._action_id_map.get(action_name, -1) + if self.use_feedback: + prompt += '\nStep {}, action id {}, {}, env feedback: {}'.format( + i, action_id, action_name, feedback, + ) + else: + prompt += '\nStep {}, action id {}, {}'.format( + i, action_id, action_name, + ) + + prompt += f'''\n\n Considering the above interaction history and the current image state, to achieve the human instruction: '{user_instruction}', you are supposed to output in json. You need to describe current visual state from the image, summarize interaction history {'and environment feedback ' if self.use_feedback else ''}and reason why the last action or plan failed and did not finish the task, output your new plan to achieve the goal from current state. At the end, output the excutable plan with action ids(0 ~ {max_id}) from the available actions.''' + + # Append output template (VLMPlanner act() line 203: prompt + template) + prompt += OUTPUT_TEMPLATE + + return prompt + + # ---- Chat history mode (chat_history=True) ---- + + def _build_chat_history_messages(self, memory: AgentMemory) -> list[dict]: + """Build accumulated message history for chat_history=True mode.""" + if memory.is_first_turn: + return self._build_stateless_messages(memory) + + messages: list[dict] = [] + + for step_idx, step in enumerate(memory.steps): + if step.llm_response is None: + continue + + if step_idx == 0: + prompt = self._build_first_turn_prompt(memory.task_description) + else: + history_up_to = [ + (s.action.action_name, s.feedback) + for s in memory.steps[:step_idx] + if s.action and s.feedback is not None + ] + prompt = self._build_chat_subsequent_prompt( + memory.task_description, history_up_to, + ) + + messages.extend( + self._wrap_as_user_message(prompt, step.observation) + ) + messages.append({ + "role": "assistant", + "content": [{"type": "text", "text": step.llm_response}], + }) + + all_history = memory.action_history + current_prompt = self._build_chat_subsequent_prompt( + memory.task_description, all_history, + ) + messages.extend( + self._wrap_as_user_message(current_prompt, memory.current_observation) + ) + + return messages + + def _build_first_turn_prompt(self, task_description: str) -> str: + """Build first-turn prompt text (identical for both modes).""" + user_instruction = task_description.rstrip('.') + max_id = len(self._actions) - 1 + + if self.n_shot >= 1: + examples_str = '\n\n'.join([ + f'## Task Execution Example {i}: \n {x}' + for i, x in enumerate(self._examples[:self.n_shot]) + ]) + prompt = HABITAT_SYSTEM_PROMPT.format( + max_id, self._action_str, examples_str, + ) + else: + prompt = HABITAT_SYSTEM_PROMPT.format( + max_id, self._action_str, '', + ) + + prompt += f'\n\n## Now the human instruction is: {user_instruction}.' + prompt += f" You are supposed to output in json. You need to describe current visual state from the image, output your reasoning steps and plan. At the end, output the action id (0 ~ {max_id}) from the available actions to excute." + prompt += OUTPUT_TEMPLATE + + return prompt + + def _build_chat_subsequent_prompt( + self, + task_description: str, + action_history: list[tuple[str, str]], + ) -> str: + """Build minimal subsequent-turn prompt for chat_history=True. + + VLMPlanner lines 66-77: no system prompt, no examples. + Uses "The human instruction is:" (no "## Now the"). + """ + user_instruction = task_description.rstrip('.') + max_id = len(self._actions) - 1 + + prompt = f'The human instruction is: {user_instruction}.' + prompt += '\n\n The action history:' + for i, (action_name, feedback) in enumerate(action_history): + action_id = self._action_id_map.get(action_name, -1) + if self.use_feedback: + prompt += '\nStep {}, action id {}, {}, env feedback: {}'.format( + i, action_id, action_name, feedback, + ) + else: + prompt += '\nStep {}, action id {}, {}'.format( + i, action_id, action_name, + ) + + prompt += f'''\n\n Considering the above interaction history and the current image state, to achieve the human instruction: '{user_instruction}', you are supposed to output in json. You need to describe current visual state from the image, summarize interaction history {'and environment feedback ' if self.use_feedback else ''}and reason why the last action or plan failed and did not finish the task, output your new plan to achieve the goal from current state. At the end, output the excutable plan with action ids(0 ~ {max_id}) from the available actions.''' + + prompt += OUTPUT_TEMPLATE + + return prompt + + # ---- Helpers ---- + + @staticmethod + def _wrap_as_user_message(prompt: str, observation) -> list[dict]: + """Wrap prompt text + observation image as a user message. + + Image BEFORE text (VLMPlanner line 127). + """ + content: list[dict] = [] + if observation and observation.rgb_path: + image_url = _encode_image_base64(observation.rgb_path) + if image_url: + content.append({ + "type": "image_url", + "image_url": {"url": image_url}, + }) + content.append({"type": "text", "text": prompt}) + + return [{"role": "user", "content": content}] diff --git a/easi/tasks/ebhabitat/task.py b/easi/tasks/ebhabitat/task.py new file mode 100644 index 0000000..f4118f6 --- /dev/null +++ b/easi/tasks/ebhabitat/task.py @@ -0,0 +1,83 @@ +"""EB-Habitat task for EASI. + +Adapts the EmbodiedBench EB-Habitat benchmark to EASI's task interface. +Supports 6 splits via per-split .yaml configs. + +Episode data flows from HF dataset -> task.format_reset_config() -> bridge +reset_config -> EBHabEnv.reset(). The bridge handles episode loading +from pickle files; EASI provides instruction/metadata from JSONL. +""" + +from __future__ import annotations + +import json +from pathlib import Path + +from easi.core.base_task import BaseTask +from easi.core.episode import StepResult +from easi.tasks.ebhabitat.actions import get_placeholder_action_space +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + + +class EBHabitatTask(BaseTask): + + def _build_action_space(self) -> list[str]: + return get_placeholder_action_space() + + def get_task_yaml_path(self) -> Path: + return Path(__file__).parent / "ebhabitat_base.yaml" + + def get_bridge_script_path(self) -> Path: + return Path(__file__).parent / "bridge.py" + + def on_episode_reset(self, observation, agent) -> None: + """Update agent action space from per-episode bridge metadata.""" + dynamic_actions_json = observation.metadata.get("dynamic_action_space") + if dynamic_actions_json and hasattr(agent, 'update_action_space'): + dynamic_actions = json.loads(dynamic_actions_json) + agent.update_action_space(dynamic_actions) + + def get_instruction(self, episode: dict) -> str: + return episode.get("instruction", self.name) + + def format_reset_config(self, episode: dict) -> dict: + """Map EB-Habitat HF row to bridge reset config.""" + return { + "episode_id": episode.get("id", "unknown"), + "eval_set": self._config.get("dataset", {}).get("split", "base"), + "instruction": episode.get("instruction", ""), + } + + def evaluate_episode( + self, episode: dict, trajectory: list[StepResult] + ) -> dict[str, float]: + """Extract metrics from trajectory.""" + if not trajectory: + return { + "task_success": 0.0, + "task_progress": 0.0, + "subgoal_reward": 0.0, + "num_steps": 0.0, + } + + last_step = trajectory[-1] + return { + "task_success": last_step.info.get("task_success", 0.0), + "task_progress": last_step.info.get("task_progress", 0.0), + "subgoal_reward": last_step.info.get("subgoal_reward", 0.0), + "num_steps": float(len(trajectory)), + } + + def _get_builtin_episodes(self) -> list[dict]: + """Minimal episodes for testing without dataset download.""" + return [ + { + "id": 0, + "episode_id": "140", + "instruction": "Find a toy airplane and move it to the right counter.", + "instruct_id": "f0917e29", + "scene_id": "data/replica_cad/configs/scenes/v3_sc3_staging_02.scene_instance.json", + }, + ] diff --git a/easi/tasks/ebhabitat/vendor/EBHabEnv.py b/easi/tasks/ebhabitat/vendor/EBHabEnv.py new file mode 100644 index 0000000..b99379e --- /dev/null +++ b/easi/tasks/ebhabitat/vendor/EBHabEnv.py @@ -0,0 +1,371 @@ +""" +Habitat Environment for Household Robot Task Simulation + +This module provides a custom OpenAI Gym environment for simulating household robot tasks +using the habitat framework. It supports various object interactions and task scenarios. +The code is based on https://github.com/facebookresearch/habitat-lab and https://github.com/apple/ml-llarp + +Dependencies: +- habitat-lab +- gym +- numpy +- PIL +""" +import gym +import os +import time +import json +import imageio +from PIL import Image +import numpy as np +import habitat +import hydra +from habitat.datasets import make_dataset +from easi.tasks.ebhabitat.vendor.config.default_structured_configs import ( + ThirdRGBSensorConfig, +) +from habitat.gym.gym_definitions import _add_sim_sensor_to_config +from omegaconf import OmegaConf + +from habitat_sim.utils import viz_utils as vut +from easi.tasks.ebhabitat.vendor.config import default_structured_configs +import easi.tasks.ebhabitat.vendor.predicate_task +import easi.tasks.ebhabitat.vendor.config +import easi.tasks.ebhabitat.vendor.measures +from easi.tasks.ebhabitat.vendor.utils import observations_to_image, merge_to_file, draw_text +from easi.utils.logging import get_logger +logger = get_logger(__name__) + +HABITAT_CONFIG_PATH = os.path.join(os.path.dirname(__file__), 'config/task/language_rearrangement.yaml') + + +ValidEvalSets = [ + 'base', 'common_sense', 'complex_instruction', + 'spatial_relationship', 'visual_appearance', 'long_horizon' + ] + +def add_receptacle(string, skill): + if 'table_0' in skill[1][0]: + string += 'table ' + skill[1][0].split('table_0')[1] + elif 'fridge' in skill[1][0]: + string += 'refrigerator push point' + elif 'refrigerator' in skill[1][0]: + string += 'refrigerator' + elif 'drawer_right' in skill[1][0]: + string += 'right drawer of the kitchen counter' + elif 'drawer_left' in skill[1][0]: + string += 'left drawer of the kitchen counter' + elif 'chair_0' in skill[1][0]: + string += 'chair ' + skill[1][0].split('chair_0')[1] + elif 'tvstand' in skill[1][0]: + string += 'TV stand' + elif 'counter_left' in skill[1][0]: + string += 'left counter in the kitchen' + elif 'counter_right' in skill[1][0]: + string += 'right counter in the kitchen' + elif 'sink' in skill[1][0]: + string += 'sink in the kitchen' + elif 'sofa' in skill[1][0]: + string += 'sofa' + elif 'cab' in skill[1][0]: + string += 'cabinet ' + skill[1][0].split('_')[-1] + else: + raise NotImplementedError + return string + + +def transform_action_to_natural_language(skill_set): + language_skill_set = [] + for skill in skill_set: + if 'nav' in skill[0]: + string = 'navigate to the ' + string = add_receptacle(string, skill) + elif 'pick' in skill[0]: + string = 'pick up the ' + skill[0].split('_')[1] + elif 'open' in skill[0]: + string = 'open the ' + if 'fridge' in skill[0]: + string += 'refrigerator' + elif 'cab' in skill[0]: + string += 'cabinet ' + skill[1][0].split('_')[-1] + else: + raise NotImplementedError + elif 'close' in skill[0]: + string = 'close the ' + if 'fridge' in skill[0]: + string += 'refrigerator' + elif 'cab' in skill[0]: + string += 'cabinet ' + skill[1][0].split('_')[-1] + else: + raise NotImplementedError + elif 'place' in skill[0]: + string = 'place at the ' + string = add_receptacle(string, skill) + else: + raise NotImplementedError + + language_skill_set.append(string) + return language_skill_set + + + +class EBHabEnv(gym.Env): + def __init__(self, eval_set='train', exp_name='', down_sample_ratio=1.0, + start_epi_index=0, resolution=500, recording=False, + data_dir=None, dataset_dir=None): + """ + Initialize the HabitatRearrange environment. + + Args: + data_dir: Path to simulator assets (scenes, objects, robots). + If provided, a 'data' symlink is created for Habitat config resolution. + dataset_dir: Path to directory containing {split}.pickle files. + Defaults to vendor/datasets/ if not provided. + """ + # load config + hydra.core.global_hydra.GlobalHydra.instance().clear() + self.config = habitat.get_config(HABITAT_CONFIG_PATH) + _add_sim_sensor_to_config(self.config, ThirdRGBSensorConfig()) + # set the dataset + assert eval_set in ValidEvalSets + OmegaConf.set_readonly(self.config, False) + if dataset_dir: + self.config.habitat.dataset.data_path = os.path.join(dataset_dir, '{}.pickle'.format(eval_set)) + else: + self.config.habitat.dataset.data_path = os.path.join(os.path.dirname(__file__), 'datasets/{}.pickle'.format(eval_set)) + # Create 'data' symlink for Habitat scene asset resolution + if data_dir: + from pathlib import Path + data_link = Path('data') + if not data_link.exists(): + try: + os.symlink(os.path.abspath(data_dir), str(data_link)) + except OSError: + logger.warning("Could not create 'data' symlink to %s", data_dir) + self.config.habitat.simulator.agents.main_agent.sim_sensors.head_rgb_sensor.height = resolution + self.config.habitat.simulator.agents.main_agent.sim_sensors.head_rgb_sensor.width = resolution + self.resolution = resolution + + # modify config path to ease data loading + self.dataset = make_dataset(self.config.habitat.dataset.type, config=self.config.habitat.dataset) + + # initilaize env + self.env = habitat.gym.make_gym_from_config(self.config, self.dataset) + self.observation_space = self.env.observation_space + # action of LanguageRearangeEnv is discrete value from 0 to 69 + self.action_space = self.env.action_space + + # Episode tracking + self.down_sample_ratio = down_sample_ratio + self.number_of_episodes = self.env.number_of_episodes * down_sample_ratio + self._reset = False + self._current_episode_num = 0 + while start_epi_index >= 1 and self._current_episode_num < start_epi_index: + self.env.reset(return_info=False) + self._current_episode_num += 1 + + self._current_step = 0 + self._max_episode_steps = 30 + self._cur_invalid_actions = 0 + self._max_invalid_actions = 10 + self._episode_start_time = 0 + # is holding an object + self.is_holding = False + self.episode_log = [] + + # init instruction and skill sets + self.episode_language_instruction = '' + self.episode_data = None + self.skill_set = self.env.env.env._env.task.actions['pddl_hl_action']._action_datas + self.language_skill_set = transform_action_to_natural_language(self.skill_set) + + # env feedback and image save + # feedback verbosity, 0: concise, 1: verbose + self.feedback_verbosity = 1 + self.log_path = 'running/eb_habitat/{}'.format(exp_name) + # video recorder + self.recording = recording + self.episode_video = [] + + def current_episode(self, all_info: bool = False): + return self.env.current_episode(all_info) + + + def reset(self, **kwargs): + """ + Reset the environment for a new episode. The env will iterate over all the task data from the dataset + Returns: observation + """ + assert self._current_episode_num <= self.number_of_episodes + obs, info = self.env.reset(return_info=True, **kwargs) + logger.info('Episode {}: {}'.format(str(self._current_episode_num), str(self.current_episode()))) + self.episode_language_instruction = info['lang_goal'] + self.episode_data = self.dataset.episodes[self._current_episode_num] + self._current_step = 0 + self._cur_invalid_actions = 0 + self._current_episode_num += 1 + self.is_holding = False + self._reset = True + self.episode_log = [] + if self.recording: + self.episode_video = [] + self._episode_start_time = time.time() + return obs + + def get_env_feedback(self, info): + """ + Generate feedback message for the current step. + Args: + info (dict): Action execution information + Returns: + str: Descriptive message about step outcome + """ + if info['was_prev_action_invalid']: + env_feedback = 'Last action is invalid.' + if 'pick' in info['action'] and self.feedback_verbosity: + if self.is_holding: + env_feedback += ' Robot cannot pick any object when holding something. Please place the object before picking something.' + else: + env_feedback += ' Robot cannot pick any object that is not near the robot. Navigate to other place to find the object.' + elif 'place' in info['action'] and self.feedback_verbosity: + if self.is_holding: + env_feedback += ' Robot cannot place any object that is not near the robot. Navigate to other place to find the object.' + else: + env_feedback += ' Robot cannot place any object when not holding something. Please pick the object before place it.' + elif 'open' in info['action'] and self.feedback_verbosity: + env_feedback += " Check whether the receptacle is already open or the robot is not near the receptacle." + elif 'close' in info['action'] and self.feedback_verbosity: + env_feedback += " Check whether the receptacle is already closed or the robot is not near the receptacle." + else: + env_feedback = 'Last action executed successfully' + if 'pick' in info['action'] and self.feedback_verbosity: + self.is_holding = True + env_feedback += ' and you are holding {}.'.format(info['action'].split('(')[0].split('_')[1]) + elif 'place' in info['action'] and self.feedback_verbosity: + self.is_holding = False + env_feedback += ' and you are holding nothing.' + elif 'open' in info['action'] and self.feedback_verbosity: + if 'fridge' in info['action']: + env_feedback += ' and now refrigerator is open.' + elif 'cab' in info['action']: + env_feedback += ' and now cabinet {} is open.'.format(info['action'].split('(')[1].strip(')').split('_')[1]) + else: + raise NotImplementedError + elif 'close' in info['action'] and self.feedback_verbosity: + if 'fridge' in info['action']: + env_feedback += ' and now refrigerator is closed.' + elif 'cab' in info['action']: + env_feedback += ' and now cabinet {} is closed.'.format(info['action'].split('(')[1].strip(')').split('_')[1]) + else: + raise NotImplementedError + else: + env_feedback += '.' + + # we don't use this info + # env_feedback += ' The current task progress is {}.'.format(info['task_progress']) + return env_feedback + + def step(self, action, reasoning='', **kwargs): + """ + Execute a single environment step. + Args: + action (int): Index of action in action space + Returns: + tuple: (observation, reward, done, environment feedback) + """ + assert self._reset, 'Reset env before stepping' + self._current_step += 1 + obs, reward, done, info = self.env.step(action, **kwargs) + if self.recording: + self.episode_video.append(self.env.render("rgb_array")) + + if info['was_prev_action_invalid']: + self._cur_invalid_actions += 1 + + # if exceed the max step + if self._current_step >= self._max_episode_steps or self._cur_invalid_actions >= self._max_invalid_actions: + done = True + # env feedback + env_feedback = self.get_env_feedback(info) + info['env_feedback'] = env_feedback + info['env_step'] = self._current_step + info['episode_elapsed_seconds'] = time.time() - self._episode_start_time, + info['action_id'] = action + info['action_description'] = self.language_skill_set[action] + info['reasoning'] = reasoning + info['instruction'] = self.episode_language_instruction + info['last_action_success'] = 1 - float(info['was_prev_action_invalid']) + info['task_success'] = info['predicate_task_success'] + if info['task_success']: + info['task_progress'] = 1.0 + self.episode_log.append(info) + return obs, reward, done, info + + def seed(self, seed=None): + self.env.seed(seed) + + def save_image(self, obs, key='head_rgb'): + """Save current agent observation as a PNG image.""" + folder = self.log_path + '/images/episode_{}'.format(self._current_episode_num) + if not os.path.exists(folder): + os.makedirs(folder) + img = Image.fromarray(observations_to_image(obs, key)) + # time_stamp = time.strftime("%Y%m%d_%H%M%S", time.localtime()) + image_path = os.path.join(folder, 'episode_{}_step_{}.png'.format(self._current_episode_num, self._current_step)) #, time_stamp)) + img.save(image_path) + return image_path + + def save_episode_log(self): + if not os.path.exists(self.log_path): + os.makedirs(self.log_path) + # time_stamp = time.strftime("%Y%m%d_%H%M%S", time.localtime()) + filename = 'episode_{}_step_{}.json'.format(self._current_episode_num, self._current_step) #, time_stamp) + if len(self.episode_log): + with open(os.path.join(self.log_path, filename), 'w', encoding='utf-8') as f: + for item in self.episode_log: + json.dump(item, f, ensure_ascii=False) + f.write('\n') + + if len(self.episode_video): + folder = self.log_path + '/video' + if not os.path.exists(folder): + os.makedirs(folder) + video_writer = imageio.get_writer(os.path.join(folder, 'video_episode_{}_steps_{}.mp4'.format(self._current_episode_num, self._current_step)), fps=30) + for data in self.episode_video: + video_writer.append_data(data) + video_writer.close() + + + + def render(self, mode: str = "rgb"): + return self.env.render(mode) + + def close(self) -> None: + """Terminate the environment.""" + self.env.close() + + +if __name__ == '__main__': + """ + Example usage of the EBHabEnv environment. + Demonstrates environment interaction with random actions. + """ + env = EBHabEnv(eval_set='base') + obs = env.reset() + print([(i, name) for i, name in enumerate(env.language_skill_set)]) + for _ in range(30): + env.save_image(obs) + action = int(input('action id: ')) #env.action_space.sample() + if action in env.language_skill_set: + action = env.language_skill_set.index(action) + else: + action = int(action) + if action < 0: + break + + obs_new, reward, done, info = env.step(action) + print(reward, done, info) + env.save_image(obs_new) + if done: + break + env.close() diff --git a/easi/tasks/ebhabitat/vendor/__init__.py b/easi/tasks/ebhabitat/vendor/__init__.py new file mode 100644 index 0000000..e7b3339 --- /dev/null +++ b/easi/tasks/ebhabitat/vendor/__init__.py @@ -0,0 +1,4 @@ +"""Vendored EB-Habitat environment from EmbodiedBench. + +Side-effect imports register Habitat tasks and measures. +""" diff --git a/easi/tasks/ebhabitat/vendor/actions.py b/easi/tasks/ebhabitat/vendor/actions.py new file mode 100644 index 0000000..15f03df --- /dev/null +++ b/easi/tasks/ebhabitat/vendor/actions.py @@ -0,0 +1,182 @@ +# +# For licensing see accompanying LICENSE file. +# Copyright (C) 2024 Apple Inc. All Rights Reserved. +# +from typing import Any, Optional +import numpy as np +from gym import spaces +from habitat.core.registry import registry +from habitat.tasks.rearrange.actions.articulated_agent_action import \ + ArticulatedAgentAction +from habitat.tasks.rearrange.actions.grip_actions import ( + GazeGraspAction, GripSimulatorTaskAction, MagicGraspAction, + SuctionGraspAction) +from habitat.tasks.rearrange.rearrange_sim import RearrangeSim + +from easi.tasks.ebhabitat.vendor.utils import get_allowed_actions + + +@registry.register_task_action +class CustomArmAbsJointAction(ArticulatedAgentAction): + @property + def action_space(self): + return spaces.Dict( + { + "arm_joint_action": spaces.Box( + shape=(self._config.arm_joint_dimensionality,), + low=-255.0, + high=255.0, + dtype=np.float32, + ) + } + ) + + def step(self, *args, arm_joint_action, **kwargs): + if np.sum(arm_joint_action) < 1e-3: + # Don't change the arm position if this action wasn't invoked (set to 0). + return + # No clipping because the arm is being set to exactly where it needs to + # go. + self.cur_articulated_agent.arm_joint_pos = arm_joint_action + + +@registry.register_task_action +class SafeSuctionGraspAction(MagicGraspAction): + def __init__(self, *args, config, sim: RearrangeSim, **kwargs): + super().__init__(*args, config=config, sim=sim, **kwargs) + self._sim: RearrangeSim = sim + self._prevent_change_duration = config.prevent_gripper_change_duration + + def reset(self, *args: Any, **kwargs: Any) -> None: + self._step_count = 0 + super().reset(*args, **kwargs) + + def step(self, grip_action, should_step=True, *args, **kwargs): + self._step_count += 1 + if grip_action is None: + return + + if self._step_count < self._prevent_change_duration: + return + + if grip_action >= 0 and not self.cur_grasp_mgr.is_grasped: + self._grasp() + elif grip_action < 0 and self.cur_grasp_mgr.is_grasped: + self._ungrasp() + + +@registry.register_task_action +class KinematicArmEEAction(ArticulatedAgentAction): + """Uses inverse kinematics (requires pybullet) to apply end-effector position control for the articulated_agent's arm.""" + + def __init__(self, *args, sim: RearrangeSim, **kwargs): + self.ee_target: Optional[np.ndarray] = None + self.ee_index: Optional[int] = 0 + super().__init__(*args, sim=sim, **kwargs) + self._sim: RearrangeSim = sim + self._render_ee_target = False # self._config.get("render_ee_target", False) + self._ee_ctrl_lim = self._config.ee_ctrl_lim + + def reset(self, *args, **kwargs): + super().reset() + cur_ee = self._ik_helper.calc_fk( + np.array(self._sim.articulated_agent.arm_joint_pos) + ) + + self.ee_target = cur_ee + + @property + def action_space(self): + return spaces.Box(shape=(3,), low=-255.0, high=255.0, dtype=np.float32) + + def apply_ee_constraints(self): + self.ee_target = np.clip( + self.ee_target, + self._sim.articulated_agent.params.ee_constraint[self.ee_index, :, 0], + self._sim.articulated_agent.params.ee_constraint[self.ee_index, :, 1], + ) + + def set_desired_ee_pos(self, ee_delta: np.ndarray) -> None: + self.ee_target += np.array(ee_delta) + + self.apply_ee_constraints() + + joint_pos = np.array(self._sim.articulated_agent.arm_joint_pos) + joint_vel = np.zeros(joint_pos.shape) + + self._ik_helper.set_arm_state(joint_pos, joint_vel) + + des_joint_pos = self._ik_helper.calc_ik(self.ee_target) + des_joint_pos = list(des_joint_pos) + self._sim.articulated_agent.arm_joint_pos = des_joint_pos + + def step(self, ee_delta, **kwargs): + speed = np.linalg.norm(ee_delta) + if speed == 0.0: + # Only act when called. + return + if speed > self._ee_ctrl_lim: + # Clip norm. + ee_delta *= self._ee_ctrl_lim / speed + self.set_desired_ee_pos(ee_delta) + + if self._render_ee_target: + global_pos = ( + self._sim.articulated_agent.base_transformation.transform_point( + self.ee_target + ) + ) + self._sim.viz_ids["ee_target"] = self._sim.visualize_position( + global_pos, self._sim.viz_ids["ee_target"] + ) + + +@registry.register_task_action +class PddlHlAction(ArticulatedAgentAction): + def __init__(self, *args, config, task, **kwargs): + actions = get_allowed_actions(task.pddl_problem, config.allowed_actions) + + self._action_datas = [] + for action in actions: + self._action_datas.append( + (action.name, [p.name for p in action.param_values]) + ) + super().__init__(*args, config=config, task=task, **kwargs) + + @property + def action_space(self): + return spaces.Discrete(len(self._action_datas)) + + @property + def was_prev_action_invalid(self): + return self._was_prev_action_invalid + + def reset(self, *args, task, **kwargs): + self._was_prev_action_invalid = False + self._prev_action = None + + def step(self, *args, sel, task, **kwargs): + pddl = task.pddl_problem + action_name, param_names = self._action_datas[sel] + + # Get the current up to date PDDL action. + param_values = [] + missing_entity = False + for name in param_names: + if name not in pddl.all_entities: + missing_entity = True + break + param_values.append(pddl.all_entities[name]) + + if missing_entity: + # self._was_prev_action_invalid = True + # return + raise ValueError("MISSING ENTITY. THIS SHOULDNT HAPPEN") + + apply_action = task.pddl_problem.actions[action_name].clone() + apply_action.set_param_values(param_values) + + self._was_prev_action_invalid = not apply_action.apply_if_true( + task.pddl_problem.sim_info + ) + self._prev_action = apply_action diff --git a/easi/tasks/ebhabitat/vendor/config/__init__.py b/easi/tasks/ebhabitat/vendor/config/__init__.py new file mode 100644 index 0000000..53c658e --- /dev/null +++ b/easi/tasks/ebhabitat/vendor/config/__init__.py @@ -0,0 +1 @@ +from . import default_structured_configs diff --git a/easi/tasks/ebhabitat/vendor/config/default_structured_configs.py b/easi/tasks/ebhabitat/vendor/config/default_structured_configs.py new file mode 100644 index 0000000..094f600 --- /dev/null +++ b/easi/tasks/ebhabitat/vendor/config/default_structured_configs.py @@ -0,0 +1,556 @@ +""" +Contains the structured config definitions. +""" + +from dataclasses import dataclass, field +from typing import Any, Dict, List, Optional + +import attr +from habitat.config.default_structured_configs import ( + ActionConfig, ArmActionConfig, HabitatSimRGBSensorConfig, LabSensorConfig, + MeasurementConfig, TaskConfig) +from hydra.core.config_store import ConfigStore +from omegaconf import MISSING + +cs = ConfigStore.instance() + + +########################################################################## +# Tasks +########################################################################## +@dataclass +class RearrangePredTaskConfig(TaskConfig): + start_template: Optional[List[str]] = MISSING + goal_template: Optional[Dict[str, Any]] = MISSING + sample_entities: Dict[str, Any] = MISSING + # Filters out instructions from the trainer dataset. + filter_instructs: Optional[List[str]] = None + # How many episodes per env worker to limit to. + filter_down_num: Optional[int] = None + force_scene_per_worker: bool = False + fix_agent_pos: bool = False + # Tokenizer used for a variety of sensors and measurements. + tokenizer_name: str = "google/flan-t5-small" + skip_load_receps: bool = False + force_gym_option: str = "nav_sanity" + force_gym_overrides: Any = None + gym_options: Any = None + # If true, this will only log the shared keys between all environments. + gym_safe_mode: bool = False + # If true, this will fix the action space to be the same dimension for all tasks. + fix_action_space: bool = False + # If true, will disable adding any prompt to the instruction. + no_prompt: bool = False + # If False, this will disable language action space for discrete action + # envs. + use_lang_actions: bool = True + sample_entities_use_constant_sampling: bool = False + + +@dataclass +class CustomArmActionConfig(ArmActionConfig): + prevent_gripper_change_duration: int = 0 + + +@dataclass +class CustomArmAbsJointActionConfig(ActionConfig): + type: str = "CustomArmAbsJointAction" + arm_joint_dimensionality: int = 7 + + +@dataclass +class PddlHlActionConfig(ActionConfig): + type: str = "PddlHlAction" + allowed_actions: List[str] = field(default_factory=list) + + +cs.store( + package="habitat.task", + group="habitat/task", + name="llarp_task_config_base", + node=RearrangePredTaskConfig, +) +cs.store( + package="habitat.task.actions.arm_joint_action", + group="habitat/task/actions", + name="arm_joint_action", + node=CustomArmAbsJointActionConfig, +) +cs.store( + package="habitat.task.actions.pddl_hl_action", + group="habitat/task/actions", + name="pddl_hl_action", + node=PddlHlActionConfig, +) +cs.store( + package="habitat.task.actions.arm_action", + group="habitat/task/actions", + name="arm_action", + node=CustomArmActionConfig, +) + +########################################################################## + + +########################################################################## +# Sensors +########################################################################## +@dataclass +class OneHotTargetSensorConfig(LabSensorConfig): + type: str = "OneHotTargetSensor" + sampled_idx: int = 0 + + +@dataclass +class AllObjectPositionsSensorConfig(LabSensorConfig): + type: str = "AllObjectPositionsSensor" + + +@dataclass +class StepCountSensorConfig(LabSensorConfig): + type: str = "StepCountSensor" + + +@dataclass +class WindowDebugSensorConfig(LabSensorConfig): + type: str = "WindowDebugSensor" + + +@dataclass +class ClosestTargetObjectPosSensorConfig(LabSensorConfig): + type: str = "ClosestTargetObjectPosSensor" + + +@dataclass +class VocabLangGoalSensorConfig(LabSensorConfig): + type: str = "VocabLangGoalSensor" + max_len: int = 30 + add_special_tokens: bool = True + + +@dataclass +class SimpleTargetSensorConfig(LabSensorConfig): + type: str = "SimpleTargetSensor" + # Without the special start token, this is 4. + max_len: int = 5 + add_special_tokens: bool = True + + +@dataclass +class LlamaVocabLangGoalSensorConfig(LabSensorConfig): + type: str = "LlamaVocabLangGoalSensor" + max_len: int = 30 + tokenizer_name: str = "data/hf_llama_7B/" + + +@dataclass +class T5VocabLangGoalSensorConfig(LabSensorConfig): + type: str = "T5VocabLangGoalSensor" + max_len: int = 30 + + +@dataclass +class ObsLangSensorConfig(LabSensorConfig): + type: str = "ObsLangSensor" + max_len: int = 30 + + +@dataclass +class VocabEmbedSensorConfig(LabSensorConfig): + type: str = "VocabEmbedSensor" + hidden_dim: int = 512 + embed_path: str = "minidata/llm_hxs.pt" + + +@dataclass +class TopDownRGBSensorConfig(HabitatSimRGBSensorConfig): + uuid: str = "top_down_rgb" + width: int = 1024 + height: int = 1024 + + +@dataclass +class ThirdRGBSensorConfig(HabitatSimRGBSensorConfig): + type: str = "HabitatSimRGBSensor" + uuid: str = "third_rgb" + width: int = 500 + height: int = 500 + + +cs.store( + group="habitat/simulator/sim_sensors", + name="third_rgb_sensor", + node=ThirdRGBSensorConfig, +) + +cs.store( + group="habitat/simulator/sim_sensors", + name="top_down_rgb", + node=TopDownRGBSensorConfig, +) +cs.store( + package="habitat.task.lab_sensors.one_hot_target_sensor", + group="habitat/task/lab_sensors", + name="one_hot_target_sensor", + node=OneHotTargetSensorConfig, +) +cs.store( + package="habitat.task.lab_sensors.all_obj_pos_sensor", + group="habitat/task/lab_sensors", + name="all_obj_pos_sensor", + node=AllObjectPositionsSensorConfig, +) +cs.store( + package="habitat.task.lab_sensors.step_count", + group="habitat/task/lab_sensors", + name="step_count", + node=StepCountSensorConfig, +) +cs.store( + package="habitat.task.lab_sensors.debug_info", + group="habitat/task/lab_sensors", + name="debug_info", + node=WindowDebugSensorConfig, +) +cs.store( + package="habitat.task.lab_sensors.closest_targ_obj_pos_sensor", + group="habitat/task/lab_sensors", + name="closest_targ_obj_pos_sensor", + node=ClosestTargetObjectPosSensorConfig, +) +cs.store( + package="habitat.task.lab_sensors.vocab_lang_goal", + group="habitat/task/lab_sensors", + name="vocab_lang_goal", + node=VocabLangGoalSensorConfig, +) +cs.store( + package="habitat.task.lab_sensors.simple_lang_goal", + group="habitat/task/lab_sensors", + name="simple_lang_goal", + node=SimpleTargetSensorConfig, +) +cs.store( + package="habitat.task.lab_sensors.llama_vocab_lang_goal", + group="habitat/task/lab_sensors", + name="llama_vocab_lang_goal", + node=LlamaVocabLangGoalSensorConfig, +) +cs.store( + package="habitat.task.lab_sensors.t5_vocab_lang_goal", + group="habitat/task/lab_sensors", + name="t5_vocab_lang_goal", + node=T5VocabLangGoalSensorConfig, +) +cs.store( + package="habitat.task.lab_sensors.obs_lang", + group="habitat/task/lab_sensors", + name="obs_lang", + node=ObsLangSensorConfig, +) +cs.store( + package="habitat.task.lab_sensors.vocab_embed_sensor", + group="habitat/task/lab_sensors", + name="vocab_embed_sensor", + node=VocabEmbedSensorConfig, +) +########################################################################## + + +########################################################################## +# Measures +########################################################################## +@dataclass +class PredicateTaskSuccessConfig(MeasurementConfig): + type: str = "PredicateTaskSuccess" + must_call_stop: bool = True + allows_invalid_actions: bool = True + sanity_end_task: bool = False + + +@dataclass +class WaitPredicateTaskSuccessConfig(MeasurementConfig): + type: str = "WaitPredicateTaskSuccess" + must_call_stop: bool = True + allows_invalid_actions: bool = True + wait_after_steps: int = 0 + + +@dataclass +class SimpleLangGoalConfig(MeasurementConfig): + type: str = "SimpleLangGoalMeasure" + + +@dataclass +class ZeroMeasureConfig(MeasurementConfig): + type: str = "ZeroMeasure" + + +@dataclass +class OneMeasureConfig(MeasurementConfig): + type: str = "OneMeasure" + + +@dataclass +class TaskCondSuccessConfig(MeasurementConfig): + type: str = "TaskCondSuccess" + + +@dataclass +class TargetNameMeasureConfig(MeasurementConfig): + type: str = "TargetNameMeasure" + + +@dataclass +class LangGoalConfig(MeasurementConfig): + type: str = "LangGoalMeasure" + + +@dataclass +class WasPrevActionInvalidConfig(MeasurementConfig): + type: str = "WasPrevActionInvalid" + pddl_action_name: str = "pddl_apply_action" + + +@dataclass +class PrevActionNameConfig(MeasurementConfig): + type: str = "PrevActionName" + pddl_action_name: str = "pddl_apply_action" + + +@dataclass +class NumInvalidActionsConfig(MeasurementConfig): + type: str = "NumInvalidActions" + + +@dataclass +class EEToClosestTargetDistConfig(MeasurementConfig): + type: str = "EEToClosestTargetDist" + + +@dataclass +class TaskProgressMonitor(MeasurementConfig): + type: str = "TaskProgressMonitor" + + +@dataclass +class SubgoalRewardConfig(MeasurementConfig): + type: str = "SubgoalReward" + invalid_ac_pen: float = 0.01 + progress_reward_factor: float = 1.0 + + +@dataclass +class BreakdownMeasureConfig(MeasurementConfig): + type: str = "BreakdownMeasure" + other_measures: List[str] = field(default_factory=list) + log_counts: bool = True + + +@dataclass +class ObjPickRewardConfig(MeasurementConfig): + type: str = "ObjPickReward" + dist_reward: float = 2.0 + pick_reward: float = 2.0 + constraint_violate_pen: float = 1.0 + drop_pen: float = 0.5 + wrong_pick_pen: float = 0.5 + force_pen: float = 0.0001 + max_force_pen: float = 0.01 + force_end_pen: float = 1.0 + use_diff: bool = True + drop_obj_should_end: bool = True + wrong_pick_should_end: bool = True + + +@dataclass +class PddlGoalBreakdownConfig(MeasurementConfig): + type: str = "PddlGoalBreakdownMeasure" + + +@dataclass +class PddlGoalDistanceConfig(MeasurementConfig): + type: str = "PddlGoalDistanceMeasure" + + +@dataclass +class PddlDenseRewardConfig(MeasurementConfig): + type: str = "PddlDenseReward" + constraint_violate_pen: float = 1.0 + force_pen: float = 0.0 + max_force_pen: float = 0.0 + force_end_pen: float = 1.0 + subgoal_dist_scale: Dict[str, float] = field(default_factory=dict) + subgoal_rewards: Dict[str, float] = field(default_factory=dict) + + +@dataclass +class ObjPlaceRewardConfig(MeasurementConfig): + type: str = "ObjPlaceReward" + constraint_violate_pen: float = 1.0 + force_pen: float = 0.0001 + max_force_pen: float = 0.01 + force_end_pen: float = 1.0 + + +@dataclass +class ObjNavDistToGoalConfig(MeasurementConfig): + type: str = "ObjNavDistToGoal" + + +@dataclass +class ObjNavRewardConfig(MeasurementConfig): + type: str = "ObjNavReward" + constraint_violate_pen: float = 1.0 + force_pen: float = 0.0001 + max_force_pen: float = 0.01 + force_end_pen: float = 1.0 + dist_reward: float = 1.0 + + +cs.store( + package="habitat.task.measurements.ee_obj_dist", + group="habitat/task/measurements", + name="ee_obj_dist", + node=EEToClosestTargetDistConfig, +) +cs.store( + package="habitat.task.measurements.task_progress", + group="habitat/task/measurements", + name="task_progress", + node=TaskProgressMonitor, +) +cs.store( + package="habitat.task.measurements.breakdown", + group="habitat/task/measurements", + name="breakdown", + node=BreakdownMeasureConfig, +) + + +cs.store( + package="habitat.task.measurements.subgoal_reward", + group="habitat/task/measurements", + name="subgoal_reward", + node=SubgoalRewardConfig, +) + +cs.store( + package="habitat.task.measurements.pddl_dense_reward", + group="habitat/task/measurements", + name="pddl_dense_reward", + node=PddlDenseRewardConfig, +) +cs.store( + package="habitat.task.measurements.pddl_subgoal_breakdown", + group="habitat/task/measurements", + name="pddl_subgoal_breakdown", + node=PddlGoalBreakdownConfig, +) +cs.store( + package="habitat.task.measurements.pddl_subgoal_distances", + group="habitat/task/measurements", + name="pddl_subgoal_distances", + node=PddlGoalDistanceConfig, +) + + +cs.store( + package="habitat.task.measurements.obj_pick_reward", + group="habitat/task/measurements", + name="obj_pick_reward", + node=ObjPickRewardConfig, +) + +cs.store( + package="habitat.task.measurements.obj_place_reward", + group="habitat/task/measurements", + name="obj_place_reward", + node=ObjPlaceRewardConfig, +) + +cs.store( + package="habitat.task.measurements.simple_lang_goal", + group="habitat/task/measurements", + name="simple_lang_goal", + node=SimpleLangGoalConfig, +) + +cs.store( + package="habitat.task.measurements.zero_measure", + group="habitat/task/measurements", + name="zero_measure", + node=ZeroMeasureConfig, +) + +cs.store( + package="habitat.task.measurements.one_measure", + group="habitat/task/measurements", + name="one_measure", + node=OneMeasureConfig, +) + +cs.store( + package="habitat.task.measurements.obj_nav_dist_to_goal", + group="habitat/task/measurements", + name="obj_nav_dist_to_goal", + node=ObjNavDistToGoalConfig, +) + +cs.store( + package="habitat.task.measurements.obj_nav_reward", + group="habitat/task/measurements", + name="obj_nav_reward", + node=ObjNavRewardConfig, +) + +cs.store( + package="habitat.task.measurements.target_name", + group="habitat/task/measurements", + name="target_name", + node=TargetNameMeasureConfig, +) +cs.store( + package="habitat.task.measurements.was_prev_action_invalid", + group="habitat/task/measurements", + name="was_prev_action_invalid", + node=WasPrevActionInvalidConfig, +) +cs.store( + package="habitat.task.measurements.prev_action_name", + group="habitat/task/measurements", + name="prev_action_name", + node=PrevActionNameConfig, +) +cs.store( + package="habitat.task.measurements.num_invalid_actions", + group="habitat/task/measurements", + name="num_invalid_actions", + node=NumInvalidActionsConfig, +) +cs.store( + package="habitat.task.measurements.lang_goal", + group="habitat/task/measurements", + name="lang_goal", + node=LangGoalConfig, +) +cs.store( + package="habitat.task.measurements.predicate_task_success", + group="habitat/task/measurements", + name="predicate_task_success", + node=PredicateTaskSuccessConfig, +) +cs.store( + package="habitat.task.measurements.wait_predicate_task_success", + group="habitat/task/measurements", + name="wait_predicate_task_success", + node=WaitPredicateTaskSuccessConfig, +) + +cs.store( + package="habitat.task.measurements.task_cond_success", + group="habitat/task/measurements", + name="task_cond_success", + node=TaskCondSuccessConfig, +) +########################################################################## diff --git a/easi/tasks/ebhabitat/vendor/config/task/dataset_v1.yaml b/easi/tasks/ebhabitat/vendor/config/task/dataset_v1.yaml new file mode 100644 index 0000000..335254f --- /dev/null +++ b/easi/tasks/ebhabitat/vendor/config/task/dataset_v1.yaml @@ -0,0 +1,10 @@ +# @package habitat.dataset + +defaults: + - /habitat/dataset: dataset_config_schema + - _self_ + +type: LangRearrangeDataset-v0 +data_path: "datasets/train.pickle" +split: train +scenes_dir: "data/replica_cad/" diff --git a/easi/tasks/ebhabitat/vendor/config/task/language_rearrangement.yaml b/easi/tasks/ebhabitat/vendor/config/task/language_rearrangement.yaml new file mode 100644 index 0000000..8abba44 --- /dev/null +++ b/easi/tasks/ebhabitat/vendor/config/task/language_rearrangement.yaml @@ -0,0 +1,99 @@ +# @package _global_ + +defaults: + - /habitat: habitat_config_base + - /habitat/task: llarp_task_config_base + - dataset_v1 + - task_obs: visual + - /habitat/task/measurements: + - num_steps + - was_prev_action_invalid + - predicate_task_success + - lang_goal + - task_progress + - subgoal_reward + - num_invalid_actions + - prev_action_name + - /habitat/task/actions: + - pddl_hl_action + - _self_ + +habitat: + environment: + max_episode_steps: 30 + + simulator: + type: RearrangeSim-v0 + additional_object_paths: + - data/objects/ycb/configs/ + - data/replica_cad/configs/objects/ + needs_markers: False + concur_render: False + + create_renderer: True + agents_order: ['main_agent'] + renderer: + enable_batch_renderer: False + composite_files: + - "data/composite_replica/replica.gltf" + + # The geometric goal position is not used. + debug_render_goal: False + debug_render: False + auto_sleep: True + kinematic_mode: True + ac_freq_ratio: 1 + step_physics: False + should_setup_semantic_ids: False + + habitat_sim_v0: + allow_sliding: False + enable_hbao: False + enable_physics: True + enable_gfx_replay_save: False + + task: + type: "RearrangePredicateTask-v0" + constraint_violation_ends_episode: False + constraint_violation_drops_object: True + end_on_success: True + + filter_instructs: null + filter_down_num: null + force_scene_per_worker: False + + # PDDL task settings. + task_spec_base_path: "task/" + pddl_domain_def: "pddl_domain_replica_cad" + # Robot randomly spawns + start_template: null + goal_template: null + sample_entities: {} + + success_measure: "predicate_task_success" + + # Sparse reward for reaching each subgoal. + reward_measure: "subgoal_reward" + success_reward: 10.0 + # Per timestep reward (negative means penalty). + slack_reward: 0.0 + actions: + pddl_hl_action: + allowed_actions: + - nav + - pick + - place + - open_fridge + - close_fridge + - open_cab + - close_cab + measurements: + was_prev_action_invalid: + pddl_action_name: "pddl_hl_action" + prev_action_name: + pddl_action_name: "pddl_hl_action" + predicate_task_success: + must_call_stop: False + subgoal_reward: + invalid_ac_pen: 0.05 + progress_reward_factor: 5.0 diff --git a/easi/tasks/ebhabitat/vendor/config/task/pddl_domain_replica_cad.yaml b/easi/tasks/ebhabitat/vendor/config/task/pddl_domain_replica_cad.yaml new file mode 100644 index 0000000..b2260e5 --- /dev/null +++ b/easi/tasks/ebhabitat/vendor/config/task/pddl_domain_replica_cad.yaml @@ -0,0 +1,292 @@ +# Defines the PDDL domain of tasks in ReplicaCAD. + +types: + static_obj_type: + - recep_type + - movable_entity_type + movable_entity_type: + - stackable_entity_type + recep_type: + - static_receptacle_entity_type + - art_receptacle_entity_type + - nav_receptacle + art_receptacle_entity_type: + - cab_type + - fridge_type + nav_receptacle: + - static_receptacle_entity_type + - art_receptacle_entity_type + static_receptacle_entity_type: + - place_receptacle + + + +constants: + - name: cab_push_point_7 + expr_type: cab_type + - name: cab_push_point_6 + expr_type: cab_type + - name: cab_push_point_5 + expr_type: cab_type + - name: cab_push_point_4 + expr_type: cab_type + - name: fridge_push_point + expr_type: fridge_type + - name: robot_0 + expr_type: robot_entity_type + +predicates: + - name: in + args: + - name: obj + expr_type: movable_entity_type + - name: receptacle + expr_type: art_receptacle_entity_type + set_state: + obj_states: + obj: receptacle + + # - name: stacked_on_top + # args: + # - name: base_obj + # expr_type: stackable_entity_type + # - name: on_top_obj + # expr_type: stackable_entity_type + # set_state: + # obj_states: + # on_top_obj: base_obj + + - name: on_top + args: + - name: obj + expr_type: movable_entity_type + - name: receptacle + expr_type: static_receptacle_entity_type + set_state: + obj_states: + obj: receptacle + recep_scaling: [1.0, 1.25, 1.0] + + - name: holding + args: + - name: obj + expr_type: movable_entity_type + set_state: + robot_states: + robot_0: + holding: obj + + - name: not_holding + args: [] + set_state: + robot_states: + robot_0: + should_drop: True + + - name: opened_cab + args: + - name: cab_id + expr_type: cab_type + set_state: + art_states: + cab_id: + value: 0.45 + cmp: 'greater' + override_thresh: 0.1 + + - name: closed_cab + args: + - name: cab_id + expr_type: cab_type + set_state: + art_states: + cab_id: + value: 0.0 + cmp: 'close' + + + - name: opened_fridge + args: + - name: fridge_id + expr_type: fridge_type + set_state: + art_states: + fridge_id: + value: 1.22 + cmp: 'greater' + + - name: closed_fridge + args: + - name: fridge_id + expr_type: fridge_type + set_state: + art_states: + fridge_id: + value: 0.0 + cmp: 'close' + + # Place the robot as close as possible. Don't check any collision conditions + - name: robot_at_closest + args: + - name: Y + expr_type: static_obj_type + set_state: + robot_states: + robot_0: + pos: Y + place_at_pos_dist: -1.0 + base_angle_noise: 0.0 + place_at_angle_thresh: 1.0 + + - name: robot_at_obj + args: + - name: Y + expr_type: movable_entity_type + set_state: + robot_states: + robot_0: + pos: Y + place_at_pos_dist: 1.5 + base_angle_noise: 0.0 + place_at_angle_thresh: 1.0 + + - name: robot_at + args: + - name: Y + expr_type: static_obj_type + set_state: + robot_states: + robot_0: + pos: Y + place_at_pos_dist: 2.0 + base_angle_noise: 0.0 + place_at_angle_thresh: 1.57 + + - name: at + args: + - name: obj + expr_type: movable_entity_type + - name: at_entity + expr_type: recep_type + set_state: + obj_states: + obj: at_entity + +actions: + - name: noop + parameters: {} + precondition: null + postcondition: [] + + # Only defined relative to place receptacles. + - name: nav + parameters: + - name: entity + expr_type: nav_receptacle + precondition: null + postcondition: + - robot_at_closest(entity) + + - name: pick + parameters: + - name: obj + expr_type: movable_entity_type + - name: robot + expr_type: robot_entity_type + precondition: + expr_type: AND + sub_exprs: + - not_holding() + - robot_at_obj(obj) + - quantifier: FORALL + inputs: + - name: recep + expr_type: cab_type + expr_type: NAND + sub_exprs: + - in(obj, recep) + - closed_cab(recep) + postcondition: + - holding(obj) + + # - name: place_on_top + # parameters: + # - name: base_obj + # expr_type: stackable_entity_type + # precondition: + # expr_type: AND + # quantifier: EXISTS + # inputs: + # - name: place_obj + # expr_type: stackable_entity_type + # sub_exprs: + # - holding(place_obj) + # - robot_at(base_obj) + # postcondition: + # - not_holding() + # - stacked_on_top(place_obj, base_obj) + + - name: place + parameters: + - name: recep + expr_type: place_receptacle + precondition: + expr_type: AND + quantifier: EXISTS + inputs: + - name: place_obj + expr_type: movable_entity_type + sub_exprs: + - holding(place_obj) + - robot_at(recep) + postcondition: + - not_holding() + - at(place_obj, recep) + + - name: open_fridge + parameters: + - name: fridge_id + expr_type: fridge_type + precondition: + expr_type: AND + sub_exprs: + - robot_at(fridge_id) + - closed_fridge(fridge_id) + postcondition: + - opened_fridge(fridge_id) + + - name: close_fridge + parameters: + - name: fridge_id + expr_type: fridge_type + precondition: + expr_type: AND + sub_exprs: + - robot_at(fridge_id) + - opened_fridge(fridge_id) + postcondition: + - closed_fridge(fridge_id) + + - name: open_cab + parameters: + - name: marker + expr_type: cab_type + precondition: + expr_type: AND + sub_exprs: + - robot_at(marker) + - closed_cab(marker) + postcondition: + - opened_cab(marker) + + - name: close_cab + parameters: + - name: marker + expr_type: cab_type + precondition: + expr_type: AND + sub_exprs: + - robot_at(marker) + - opened_cab(marker) + postcondition: + - closed_cab(marker) diff --git a/easi/tasks/ebhabitat/vendor/config/task/task_obs/visual.yaml b/easi/tasks/ebhabitat/vendor/config/task/task_obs/visual.yaml new file mode 100644 index 0000000..70d3feb --- /dev/null +++ b/easi/tasks/ebhabitat/vendor/config/task/task_obs/visual.yaml @@ -0,0 +1,24 @@ +# @package _global_ +defaults: + - /habitat/simulator/sensor_setups@habitat.simulator.agents.main_agent: rgb_head_agent + - /habitat/task/lab_sensors: + - is_holding_sensor + - debug_info +habitat: + gym: + obs_keys: + # Visual sensors + - head_rgb + # - third_rgb # remove for other tasks + - debug_info + simulator: + agents: + main_agent: + sim_sensors: + head_rgb_sensor: + width: 500 + height: 500 + radius: 0.3 + articulated_agent_urdf: ./data/robots/hab_fetch/robots/hab_suction.urdf + articulated_agent_type: FetchSuctionRobot + joint_start_noise: 0.0 diff --git a/easi/tasks/ebhabitat/vendor/dataset/__init__.py b/easi/tasks/ebhabitat/vendor/dataset/__init__.py new file mode 100644 index 0000000..1408f5e --- /dev/null +++ b/easi/tasks/ebhabitat/vendor/dataset/__init__.py @@ -0,0 +1,5 @@ +# +# For licensing see accompanying LICENSE file. +# Copyright (C) 2024 Apple Inc. All Rights Reserved. +# +from .episodes import LangRearrangeDatasetV0, LangRearrangeEpisode diff --git a/easi/tasks/ebhabitat/vendor/dataset/episodes.py b/easi/tasks/ebhabitat/vendor/dataset/episodes.py new file mode 100644 index 0000000..7633a78 --- /dev/null +++ b/easi/tasks/ebhabitat/vendor/dataset/episodes.py @@ -0,0 +1,343 @@ +# +# For licensing see accompanying LICENSE file. +# Copyright (C) 2024 Apple Inc. All Rights Reserved. +# +import os +import json +import pickle +import random +from itertools import groupby +from typing import Any, Dict, List, Optional + +import attr +import numpy as np +from habitat.core.dataset import EpisodeIterator +from habitat.core.logging import logger +from habitat.core.registry import registry +from habitat.core.utils import DatasetFloatJSONEncoder +from habitat.datasets.rearrange.rearrange_dataset import (RearrangeDatasetV0, + RearrangeEpisode) +# from habitat.datasets.utils import check_and_gen_physics_config +from habitat.tasks.rearrange.multi_task.pddl_predicate import Predicate + +DEFAULT_PHYSICS_CONFIG_PATH = os.path.join(os.path.dirname(__file__), '../data/default.physics_config.json') + +def check_and_gen_physics_config(): + if os.path.exists(DEFAULT_PHYSICS_CONFIG_PATH): + return + # Config is sourced from + # https://github.com/facebookresearch/habitat-sim/blob/main/data/default.physics_config.json + physics_config = { + "physics_simulator": "bullet", + "timestep": 0.008, + "gravity": [0, -9.8, 0], + "friction_coefficient": 0.4, + "restitution_coefficient": 0.1, + "rigid object paths": ["objects"], + } + with open(DEFAULT_PHYSICS_CONFIG_PATH, "w") as f: + json.dump(physics_config, f) + + +@attr.s(auto_attribs=True, kw_only=True) +class LangRearrangeEpisode(RearrangeEpisode): + instruction: str + sampled_entities: Dict[str, str] + # str form of predicates + start_preds: List[str] + goal_preds: Dict[str, Any] + instruct_id: str + sampler_info: Dict[str, str] + # str form of predicates + subgoals: List[List[str]] = None + + +@registry.register_dataset(name="LangRearrangeDataset-v0") +class LangRearrangeDatasetV0(RearrangeDatasetV0): + def __init__(self, config=None, preset_eps=None) -> None: + self.config = config + + check_and_gen_physics_config() + + self.episodes = [] + + if config is None: + return + + if preset_eps is None: + datasetfile_path = config.data_path.format(split=config.split) + logger.info(f"Loading from {datasetfile_path}") + with open(datasetfile_path, "rb") as f: + self.from_binary(pickle.load(f), scenes_dir=config.scenes_dir) + + self.episodes = list( + filter(self.build_content_scenes_filter(config), self.episodes) + ) + else: + self.episodes = preset_eps + + def to_json(self) -> str: + result = DatasetFloatJSONEncoder().encode(self) + return result + + def to_binary(self) -> str: + def access_idx(k, name_to_idx): + if len(name_to_idx) == 0: + name_to_idx[k] = 0 + if k not in name_to_idx: + name_to_idx[k] = max(name_to_idx.values()) + 1 + return name_to_idx[k] + + def encode_name_dict(d, name_to_idx): + ret_d = {} + for k, v in d.items(): + ret_d[access_idx(k, name_to_idx)] = v + return ret_d + + all_transforms = [] + name_to_idx = {} + all_eps = [] + + for ep in self.episodes: + new_ep_data = attr.asdict(ep) + rigid_objs = [] + for name, T in ep.rigid_objs: + rigid_objs.append([access_idx(name, name_to_idx), len(all_transforms)]) + all_transforms.append(T) + + name_to_recep = [] + for name, recep in ep.name_to_receptacle.items(): + name_to_recep.append( + [access_idx(name, name_to_idx), access_idx(recep, name_to_idx)] + ) + new_ep_data["rigid_objs"] = np.array(rigid_objs) + new_ep_data["ao_states"] = encode_name_dict(ep.ao_states, name_to_idx) + new_ep_data["name_to_receptacle"] = np.array(name_to_recep) + new_ep_data["additional_obj_config_paths"] = list( + new_ep_data["additional_obj_config_paths"] + ) + del new_ep_data["_shortest_path_cache"] + + new_markers = [] + for marker_data in ep.markers: + new_markers.append( + [ + access_idx(marker_data["name"], name_to_idx), + access_idx(marker_data["type"], name_to_idx), + np.array(marker_data["params"]["offset"]), + access_idx(marker_data["params"]["link"], name_to_idx), + access_idx(marker_data["params"]["object"], name_to_idx), + ] + ) + + new_ep_data["markers"] = new_markers + + all_eps.append(new_ep_data) + + idx_to_name = {} + for k, v in name_to_idx.items(): + # idx_to_name should define a 1-1 mapping between the name and the + # name index. + assert v not in idx_to_name + idx_to_name[v] = k + + return { + "all_transforms": np.array(all_transforms), + "idx_to_name": idx_to_name, + "all_eps": all_eps, + } + + def from_binary( + self, data_dict: Dict[str, Any], scenes_dir: Optional[str] = None + ) -> None: + all_T = data_dict["all_transforms"] + idx_to_name = data_dict["idx_to_name"] + for i, ep in enumerate(data_dict["all_eps"]): + ep["rigid_objs"] = [ + [idx_to_name[ni], all_T[ti]] for ni, ti in ep["rigid_objs"] + ] + ep["ao_states"] = {idx_to_name[ni]: v for ni, v in ep["ao_states"].items()} + ep["name_to_receptacle"] = { + idx_to_name[k]: idx_to_name[v] for k, v in ep["name_to_receptacle"] + } + + new_markers = [] + for name, mtype, offset, link, obj in ep["markers"]: + new_markers.append( + { + "name": idx_to_name[name], + "type": idx_to_name[mtype], + "params": { + "offset": offset, + "link": idx_to_name[link], + "object": idx_to_name[obj], + }, + } + ) + ep["markers"] = new_markers + + rearrangement_episode = LangRearrangeEpisode(**ep) + rearrangement_episode.episode_id = str(i) + self.episodes.append(rearrangement_episode) + + def from_json(self, json_str: str, scenes_dir: Optional[str] = None) -> None: + deserialized = json.loads(json_str) + + for i, episode in enumerate(deserialized["episodes"]): + rearrangement_episode = LangRearrangeEpisode(**episode) + rearrangement_episode.episode_id = str(i) + + self.episodes.append(rearrangement_episode) + + def get_episode_iterator(self, *args, **kwargs): + return CustomEpisodeIterator(self.episodes, *args, **kwargs) + + +class CustomEpisodeIterator(EpisodeIterator): + def __init__( + self, + episodes, + cycle: bool = True, + shuffle: bool = True, + group_by_scene: bool = False, + max_scene_repeat_episodes: int = -1, + max_scene_repeat_steps: int = -1, + num_episode_sample: int = -1, + step_repetition_range: float = 0.2, + seed: int = None, + ) -> None: + if seed: + random.seed(seed) + np.random.seed(seed) + + # sample episodes + if num_episode_sample >= 0: + episodes = np.random.choice( # type: ignore[assignment] + episodes, num_episode_sample, replace=False # type: ignore[arg-type] + ) + + if not isinstance(episodes, list): + episodes = list(episodes) + + self.episodes = episodes + self.cycle = cycle + self.group_by_scene = group_by_scene + self.shuffle = shuffle + + if shuffle: + random.shuffle(self.episodes) + + if group_by_scene: + self.episodes = self._group_scenes(self.episodes) + + self.max_scene_repetition_episodes = max_scene_repeat_episodes + self.max_scene_repetition_steps = max_scene_repeat_steps + + self._rep_count = -1 # 0 corresponds to first episode already returned + self._step_count = 0 + self._prev_scene_id: Optional[str] = None + + self._iterator = iter(self.episodes) + + self.step_repetition_range = step_repetition_range + self._set_shuffle_intervals() + + def __iter__(self): + return self + + def __next__(self): + self._forced_scene_switch_if() + next_episode = next(self._iterator, None) + if next_episode is None: + if not self.cycle: + raise StopIteration + + self._iterator = iter(self.episodes) + + if self.shuffle: + self._shuffle() + + next_episode = next(self._iterator) + + if ( + self._prev_scene_id != next_episode.scene_id + and self._prev_scene_id is not None + ): + self._rep_count = 0 + self._step_count = 0 + + self._prev_scene_id = next_episode.scene_id + return next_episode + + def _forced_scene_switch(self) -> None: + grouped_episodes = [ + list(g) for k, g in groupby(self._iterator, key=lambda x: x.scene_id) + ] + + if len(grouped_episodes) > 1: + # Ensure we swap by moving the current group to the end + grouped_episodes = grouped_episodes[1:] + grouped_episodes[0:1] + + self._iterator = iter(sum(grouped_episodes, [])) + + def _shuffle(self) -> None: + assert self.shuffle + episodes = list(self._iterator) + + random.shuffle(episodes) + + if self.group_by_scene: + episodes = self._group_scenes(episodes) + + self._iterator = iter(episodes) + + def _group_scenes(self, episodes): + assert self.group_by_scene + + scene_sort_keys: Dict[str, int] = {} + for e in episodes: + if e.scene_id not in scene_sort_keys: + scene_sort_keys[e.scene_id] = len(scene_sort_keys) + + return sorted(episodes, key=lambda e: scene_sort_keys[e.scene_id]) + + def step_taken(self) -> None: + self._step_count += 1 + + @staticmethod + def _randomize_value(value: int, value_range: float) -> int: + return random.randint( + int(value * (1 - value_range)), int(value * (1 + value_range)) + ) + + def _set_shuffle_intervals(self) -> None: + if self.max_scene_repetition_episodes > 0: + self._max_rep_episode = self.max_scene_repetition_episodes + else: + self._max_rep_episode = None + + if self.max_scene_repetition_steps > 0: + self._max_rep_step = self._randomize_value( + self.max_scene_repetition_steps, self.step_repetition_range + ) + else: + self._max_rep_step = None + + def _forced_scene_switch_if(self) -> None: + do_switch = False + self._rep_count += 1 + + # Shuffle if a scene has been selected more than _max_rep_episode times in a row + if ( + self._max_rep_episode is not None + and self._rep_count >= self._max_rep_episode + ): + do_switch = True + + # Shuffle if a scene has been used for more than _max_rep_step steps in a row + if self._max_rep_step is not None and self._step_count >= self._max_rep_step: + do_switch = True + + if do_switch: + self._forced_scene_switch() + self._set_shuffle_intervals() diff --git a/easi/tasks/ebhabitat/vendor/dataset/utils.py b/easi/tasks/ebhabitat/vendor/dataset/utils.py new file mode 100644 index 0000000..e1b5428 --- /dev/null +++ b/easi/tasks/ebhabitat/vendor/dataset/utils.py @@ -0,0 +1,70 @@ +# +# For licensing see accompanying LICENSE file. +# Copyright (C) 2024 Apple Inc. All Rights Reserved. +# +import inspect +import os +import os.path as osp +from typing import Dict, List, Tuple + +import yaml + +# import llarp.dataset + +ALL_CATS_NAME = "all_cats" +INSTRUCTION_FILE = "instructions.yaml" + +# Standard buckets. +LOCAL_DATASETS_PATH = os.path.join(os.path.dirname(__file__), "data/datasets") + + +def get_instruct_data(): + instructs_path = osp.dirname(inspect.getfile(__file__)) + instructs_cfg = osp.join(instructs_path, "configs", INSTRUCTION_FILE) + with open(instructs_cfg, "r") as f: + instructs = yaml.load(f, Loader=yaml.FullLoader) + return instructs + + +def get_name_mappings() -> Dict[str, str]: + """ + Gets the friendly name mappings from the instruction file. + """ + instructs = get_instruct_data() + return instructs["name_mappings"] + + +def get_all_instruct_ids(): + instructs = get_instruct_data() + return sorted([str(x) for x in instructs["instructions"].keys()]) + + +def get_category_info(skip_load_receps=False): + """ + Get the list of all categories and a mapping from object name to category. + """ + dataset_path = osp.dirname(__file__) + dataset_cfg = osp.join(dataset_path, "configs", "dataset.yaml") + + # Load dataset_cfg as a dict + with open(dataset_cfg, "r") as f: + dataset = yaml.load(f, Loader=yaml.FullLoader) + cat_groups = dataset["category_groups"] + all_receps_cat = dataset["receptacle_sets"][0] + assert all_receps_cat["name"] == "all_receps" + all_obj_cats = dataset["category_groups"][ALL_CATS_NAME]["included"] + + all_cats = [] + if not skip_load_receps: + all_cats.extend(all_receps_cat["included_receptacle_substrings"]) + all_cats.extend(all_obj_cats) + + obj_to_cls = {} + for oset in dataset["object_sets"]: + if oset["name"] == "CLUTTER_OBJECTS": + continue + for oname in oset["included_substrings"]: + if oname in obj_to_cls: + raise ValueError(f"Object {oname} is in multiple sets") + obj_to_cls[oname] = oset["name"] + return all_cats, all_obj_cats, obj_to_cls diff --git a/easi/tasks/ebhabitat/vendor/measures.py b/easi/tasks/ebhabitat/vendor/measures.py new file mode 100644 index 0000000..0cad7e8 --- /dev/null +++ b/easi/tasks/ebhabitat/vendor/measures.py @@ -0,0 +1,763 @@ +# +# For licensing see accompanying LICENSE file. +# Copyright (C) 2024 Apple Inc. All Rights Reserved. +# +from collections import defaultdict + +import numpy as np +from habitat.core.embodied_task import Measure +from habitat.core.registry import registry +from habitat.tasks.rearrange.multi_task.pddl_logical_expr import \ + LogicalExprType +from habitat.tasks.rearrange.multi_task.pddl_predicate import Predicate +from habitat.tasks.rearrange.rearrange_sensors import ( + DoesWantTerminate, EndEffectorToRestDistance, RearrangeReward) + +from easi.tasks.ebhabitat.vendor.dataset.utils import get_all_instruct_ids +from easi.tasks.ebhabitat.vendor.sensors import SimpleTargetSensor + + +@registry.register_measure +class TargetNameMeasure(Measure): + def __init__(self, config, *args, **kwargs): + super().__init__(**kwargs) + self._config = config + + @staticmethod + def _get_uuid(*args, **kwargs): + return "target" + + def reset_metric(self, *args, **kwargs): + self.update_metric(*args, **kwargs) + + def update_metric(self, *args, task, **kwargs): + self._metric = ",".join([str(x) for x in task.get_sampled()]) + + +@registry.register_measure +class LangGoalMeasure(Measure): + cls_uuid = "lang_goal" + + def __init__(self, config, *args, **kwargs): + super().__init__(**kwargs) + self._config = config + + @staticmethod + def _get_uuid(*args, **kwargs): + return LangGoalMeasure.cls_uuid + + def reset_metric(self, *args, **kwargs): + self.update_metric(*args, **kwargs) + + def update_metric(self, *args, task, **kwargs): + self._metric = task.lang_goal + + +@registry.register_measure +class SimpleLangGoalMeasure(Measure): + cls_uuid = "simple_lang_goal" + + def __init__(self, config, *args, **kwargs): + super().__init__(**kwargs) + self._config = config + + @staticmethod + def _get_uuid(*args, **kwargs): + return SimpleLangGoalMeasure.cls_uuid + + def reset_metric(self, *args, **kwargs): + self.update_metric(*args, **kwargs) + + def update_metric(self, *args, task, **kwargs): + target_sensor = task.sensor_suite.sensors[SimpleTargetSensor.uuid] + self._metric = target_sensor.cur_lang_goal + + +@registry.register_measure +class TaskProgressMonitor(Measure): + def __init__(self, config, *args, **kwargs): + super().__init__(**kwargs) + self._config = config + + @staticmethod + def _get_uuid(*args, **kwargs): + return "task_progress" + + def reset_metric(self, *args, task, **kwargs): + self._achieved = defaultdict(lambda: False) + self._total_count = len(task.subgoals) + self.update_metric(*args, task=task, **kwargs) + + def update_metric(self, *args, task, **kwargs): + for i, subgoal in enumerate(task.subgoals): + if self._achieved[i]: + continue + self._achieved[i] = all( + pred.is_true(task.pddl.sim_info) for pred in subgoal + ) + + self._metric = sum(self._achieved.values()) / max(self._total_count, 1) + + +@registry.register_measure +class WasPrevActionInvalid(Measure): + cls_uuid = "was_prev_action_invalid" + + def __init__(self, config, *args, **kwargs): + self._pddl_action_name = config.pddl_action_name + super().__init__(**kwargs) + + @staticmethod + def _get_uuid(*args, **kwargs): + return WasPrevActionInvalid.cls_uuid + + def reset_metric(self, *args, task, **kwargs): + self.update_metric(*args, task=task, **kwargs) + + def update_metric(self, *args, task, **kwargs): + self._metric = task.actions[self._pddl_action_name].was_prev_action_invalid + + +@registry.register_measure +class PrevActionName(Measure): + cls_uuid = "action" + + def __init__(self, config, *args, **kwargs): + self._pddl_action_name = config.pddl_action_name + super().__init__(**kwargs) + + @staticmethod + def _get_uuid(*args, **kwargs): + return PrevActionName.cls_uuid + + def reset_metric(self, *args, task, **kwargs): + self.update_metric(*args, task=task, **kwargs) + + def update_metric(self, *args, task, **kwargs): + if task.actions[self._pddl_action_name]._prev_action is None: + self._metric = "" + else: + self._metric = task.actions[self._pddl_action_name]._prev_action.compact_str + + +@registry.register_measure +class NumInvalidActions(Measure): + @staticmethod + def _get_uuid(*args, **kwargs): + return "num_invalid_actions" + + def reset_metric(self, *args, task, **kwargs): + task.measurements.check_measure_dependencies( + self.uuid, [WasPrevActionInvalid._get_uuid()] + ) + self._metric = 0 + self.update_metric(*args, task=task, **kwargs) + + def update_metric(self, *args, task, **kwargs): + was_prev_invalid = task.measurements.measures[ + WasPrevActionInvalid._get_uuid() + ].get_metric() + self._metric += int(was_prev_invalid) + + +@registry.register_measure +class SubgoalReward(Measure): + def __init__(self, config, *args, **kwargs): + super().__init__(**kwargs) + self._config = config + self.invalid_ac_pen = config.invalid_ac_pen + self.progress_reward_factor = config.progress_reward_factor + + @staticmethod + def _get_uuid(*args, **kwargs): + return "subgoal_reward" + + def reset_metric(self, *args, task, **kwargs): + task.measurements.check_measure_dependencies( + self.uuid, + [TaskProgressMonitor._get_uuid(), WasPrevActionInvalid._get_uuid()], + ) + self._prev_progress = 0.0 + self.update_metric(*args, task=task, **kwargs) + + def update_metric(self, *args, task, **kwargs): + self._metric = 0.0 + was_prev_invalid = task.measurements.measures[ + WasPrevActionInvalid._get_uuid() + ].get_metric() + + progress = task.measurements.measures[ + TaskProgressMonitor._get_uuid() + ].get_metric() + if was_prev_invalid: + self._metric -= self.invalid_ac_pen + + self._metric = (progress - self._prev_progress) * self.progress_reward_factor + self._prev_progress = progress + + +@registry.register_measure +class PredicateTaskSuccess(Measure): + def __init__(self, config, *args, **kwargs): + super().__init__(**kwargs) + self._must_call_stop = config.must_call_stop + self._allows_invalid_actions = config.allows_invalid_actions + self._sanity_end_task = config.sanity_end_task + if self._sanity_end_task: + print("SANITY END TASK SET TO TRUE. TASK IS ONLY IN DEBUG MODE!!!") + + @staticmethod + def _get_uuid(*args, **kwargs): + return "predicate_task_success" + + def reset_metric(self, *args, task, **kwargs): + if self._must_call_stop: + task.measurements.check_measure_dependencies( + self.uuid, [DoesWantTerminate.cls_uuid] + ) + if not self._allows_invalid_actions: + task.measurements.check_measure_dependencies( + self.uuid, [WasPrevActionInvalid.cls_uuid] + ) + + self.update_metric(*args, task=task, **kwargs) + + def update_metric(self, *args, task, **kwargs): + self._metric = task.is_goal_satisfied() + if not self._allows_invalid_actions: + was_prev_action_invalid = task.measurements.measures[ + WasPrevActionInvalid.cls_uuid + ].get_metric() + if was_prev_action_invalid: + task.should_end = True + self._metric = False + return + + if self._must_call_stop: + does_action_want_stop = task.measurements.measures[ + DoesWantTerminate.cls_uuid + ].get_metric() + self._metric = self._metric and does_action_want_stop + + if does_action_want_stop: + task.should_end = True + + if self._sanity_end_task and np.random.random() < 0.2: + self._metric = True + + +@registry.register_measure +class WaitPredicateTaskSuccess(Measure): + def __init__(self, config, *args, **kwargs): + super().__init__(**kwargs) + self._must_call_stop = config.must_call_stop + self._allows_invalid_actions = config.allows_invalid_actions + self._wait_after_steps = config.wait_after_steps + self._steps_after_succ = 0 + + @staticmethod + def _get_uuid(*args, **kwargs): + return "predicate_task_success" + + def reset_metric(self, *args, task, **kwargs): + self._steps_after_succ = 0 + if self._must_call_stop: + task.measurements.check_measure_dependencies( + self.uuid, [DoesWantTerminate.cls_uuid] + ) + if not self._allows_invalid_actions: + task.measurements.check_measure_dependencies( + self.uuid, [WasPrevActionInvalid.cls_uuid] + ) + + self.update_metric(*args, task=task, **kwargs) + + def update_metric(self, *args, task, **kwargs): + self._metric = task.is_goal_satisfied() + if not self._allows_invalid_actions: + was_prev_action_invalid = task.measurements.measures[ + WasPrevActionInvalid.cls_uuid + ].get_metric() + if was_prev_action_invalid: + task.should_end = True + self._metric = False + return + + if self._must_call_stop: + does_action_want_stop = task.measurements.measures[ + DoesWantTerminate.cls_uuid + ].get_metric() + self._metric = self._metric and does_action_want_stop + + if does_action_want_stop: + task.should_end = True + + if self._metric: + self._steps_after_succ += 1 + if self._wait_after_steps > self._steps_after_succ: + self._metric = False + + +@registry.register_measure +class TaskCondSuccess(Measure): + def __init__(self, config, *args, **kwargs): + super().__init__(**kwargs) + self._config = config + + @staticmethod + def _get_uuid(*args, **kwargs): + return "cond_success" + + def reset_metric(self, *args, task, **kwargs): + task.measurements.check_measure_dependencies( + self.uuid, [PredicateTaskSuccess._get_uuid()] + ) + self.update_metric(*args, task=task, **kwargs) + + def update_metric(self, *args, task, **kwargs): + self._metric = task.is_goal_satisfied() + # Get the predicate task success measure + pred_task_success = task.measurements.measures[ + PredicateTaskSuccess._get_uuid() + ].get_metric() + + sampled_target = task.get_sampled()[0] + self._metric = {sampled_target.expr_type.name: float(pred_task_success)} + + +@registry.register_measure +class EEToClosestTargetDist(Measure): + """ + Gets the distance between the EE and the closest target object. + """ + + def __init__(self, sim, config, *args, **kwargs): + self._sim = sim + self._config = config + super().__init__(**kwargs) + + @staticmethod + def _get_uuid(*args, **kwargs): + return "ee_obj_dist" + + def reset_metric(self, *args, episode, **kwargs): + self.update_metric(*args, episode=episode, **kwargs) + + def update_metric(self, *args, task, **kwargs): + targ_type = task.get_sampled()[0] + matches = list(task.pddl.find_entities(targ_type.expr_type)) + obj_pos = np.array( + [task.pddl.sim_info.get_entity_pos(match) for match in matches] + ) + + ee_pos = self._sim.articulated_agent.ee_transform().translation + + dists = np.linalg.norm(obj_pos - ee_pos, ord=2, axis=-1) + closest_id = np.argmin(dists) + self.closest_entity = matches[closest_id] + self._metric = dists[closest_id] + + +@registry.register_measure +class ObjPlaceReward(RearrangeReward): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + @staticmethod + def _get_uuid(*args, **kwargs): + return "obj_place_reward" + + def reset_metric(self, *args, **kwargs): + super().reset_metric(*args, **kwargs) + + def update_metric(self, *args, **kwargs): + super().update_metric(*args, **kwargs) + # Using a sparse reward for now + self._metric = 0.0 + + +@registry.register_measure +class ObjNavDistToGoal(Measure): + cls_uuid = "obj_nav_dist" + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + @staticmethod + def _get_uuid(*args, **kwargs): + return ObjNavDistToGoal.cls_uuid + + def reset_metric(self, *args, **kwargs): + self.update_metric(*args, **kwargs) + + def update_metric(self, *args, task, **kwargs): + cur_target = task.get_sampled()[0] + target_pos = task.pddl.sim_info.get_entity_pos(cur_target) + agent_pos = np.array(task._sim.articulated_agent.base_pos)[[0, 2]] + self._metric = np.linalg.norm(agent_pos - target_pos[[0, 2]]) + + +@registry.register_measure +class ObjNavReward(RearrangeReward): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + @staticmethod + def _get_uuid(*args, **kwargs): + return "obj_nav_reward" + + def reset_metric(self, *args, **kwargs): + self._prev_dist = -1.0 + super().reset_metric(*args, **kwargs) + + def update_metric(self, *args, task, **kwargs): + super().update_metric(*args, task=task, **kwargs) + cur_dist = task.measurements.measures[ObjNavDistToGoal.cls_uuid].get_metric() + + # Using a sparse reward for now + self._metric = 0.0 + if self._prev_dist < 0.0: + dist_diff = 0.0 + else: + dist_diff = self._prev_dist - cur_dist + + self._metric += self._config.dist_reward * dist_diff + self._prev_dist = cur_dist + + +@registry.register_measure +class ZeroMeasure(Measure): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + @staticmethod + def _get_uuid(*args, **kwargs): + return "zero_measure" + + def reset_metric(self, *args, **kwargs): + self.update_metric(*args, **kwargs) + + def update_metric(self, *args, **kwargs): + self._metric = 0.0 + + +@registry.register_measure +class OneMeasure(Measure): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + @staticmethod + def _get_uuid(*args, **kwargs): + return "one_measure" + + def reset_metric(self, *args, **kwargs): + self.update_metric(*args, **kwargs) + + def update_metric(self, *args, **kwargs): + self._metric = 1.0 + + +@registry.register_measure +class BreakdownMeasure(Measure): + def __init__(self, *args, config, **kwargs): + super().__init__(*args, config, **kwargs) + self._other_measures = config.other_measures + self._log_counts = config.log_counts + if self._log_counts: + self._all_instruct_ids = get_all_instruct_ids() + + @staticmethod + def _get_uuid(*args, **kwargs): + return "breakdown" + + def reset_metric(self, *args, task, **kwargs): + task.measurements.check_measure_dependencies( + self.uuid, + self._other_measures, + ) + use_instruct_id = self._parse_instruct_id(task.instruct_id) + if self._log_counts: + self._cur_counts = {k: 0 for k in self._all_instruct_ids} + self._cur_counts[use_instruct_id] = 1 + self.update_metric(*args, task=task, **kwargs) + + def _parse_instruct_id(self, instruct_id): + return instruct_id.split("_")[0] + + def update_metric(self, *args, task, **kwargs): + self._metric = {} + use_instruct_id = self._parse_instruct_id(task.instruct_id) + for name in self._other_measures: + self._metric[name] = { + use_instruct_id: task.measurements.measures[name].get_metric() + } + if self._log_counts: + self._metric["count"] = self._cur_counts + + +def _extract_pred_name(expr, i): + if isinstance(expr, Predicate): + return f"{expr.name}|{i}" + else: + return f"{expr.sub_exprs[0].sub_exprs[0].name}|{i}" + + +@registry.register_measure +class PddlGoalDistanceMeasure(Measure): + @staticmethod + def _get_uuid(*args, **kwargs): + return "pddl_subgoal_distances" + + def reset_metric(self, *args, task, **kwargs): + self.update_metric( + *args, + task=task, + **kwargs, + ) + + def _get_pred_distance(self, pred, sim_info): + if pred.name == "on_top": + obj = pred._arg_values[0] + recep = pred._arg_values[1] + # Get the distance from the object COM to the surface bounding box COM. + recep_bb = sim_info.search_for_entity(recep) + + rom = sim_info.sim.get_rigid_object_manager() + obj_idx = sim_info.search_for_entity(obj) + abs_obj_id = sim_info.sim.scene_obj_ids[obj_idx] + entity_obj = rom.get_object_by_id(abs_obj_id) + + return np.linalg.norm(entity_obj.translation - recep_bb.center(), ord=2) + elif pred.name == "holding": + obj = pred._arg_values[0] + obj_pos = sim_info.get_entity_pos(obj) + ee_pos = sim_info.sim.articulated_agent.ee_transform().translation + + return np.linalg.norm(obj_pos - ee_pos, ord=2) + + # no sense of distance. + return 0.0 + + def update_metric(self, *args, task, **kwargs): + assert task.goal_expr.expr_type == LogicalExprType.AND + self._metric = {} + + for i, expr in enumerate(task.goal_expr.sub_exprs): + if isinstance(expr, Predicate): + dist = self._get_pred_distance(expr, task.pddl.sim_info) + else: + assert expr.expr_type == LogicalExprType.OR + dist = None + for sub_expr in expr.sub_exprs: + assert len(sub_expr.sub_exprs) == 1 + assert isinstance(sub_expr.sub_exprs[0], Predicate) + pred_dist = self._get_pred_distance( + sub_expr.sub_exprs[0], task.pddl.sim_info + ) + if dist is None: + dist = pred_dist + else: + dist = min(pred_dist, dist) + assert dist is not None + + expr_name = _extract_pred_name(expr, i) + self._metric[expr_name] = dist + + +@registry.register_measure +class PddlGoalBreakdownMeasure(Measure): + @staticmethod + def _get_uuid(*args, **kwargs): + return "pddl_subgoal_breakdown" + + def reset_metric(self, *args, task, **kwargs): + self.update_metric( + *args, + task=task, + **kwargs, + ) + + def update_metric(self, *args, task, **kwargs): + assert task.goal_expr.expr_type == LogicalExprType.AND + self._metric = {} + + for i, expr in enumerate(task.goal_expr.sub_exprs): + expr_name = _extract_pred_name(expr, i) + self._metric[expr_name] = expr.is_true(task.pddl.sim_info) + + +@registry.register_measure +class PddlDenseReward(RearrangeReward): + def __init__(self, *args, config, **kwargs): + super().__init__(*args, config=config, **kwargs) + self._subgoal_rewards = config.subgoal_rewards + self._subgoal_dist_scale = config.subgoal_dist_scale + + @staticmethod + def _get_uuid(*args, **kwargs): + return "pddl_dense_reward" + + def reset_metric(self, *args, episode, task, observations, **kwargs): + self._last_subgoal_state = {} + self._last_dist_state = {} + + super().reset_metric( + *args, + episode=episode, + task=task, + observations=observations, + **kwargs, + ) + + def update_metric(self, *args, task, **kwargs): + super().update_metric( + *args, + task=task, + **kwargs, + ) + + goal_breakdowns = task.measurements.measures[ + PddlGoalBreakdownMeasure._get_uuid() + ].get_metric() + goal_distances = task.measurements.measures[ + PddlGoalDistanceMeasure._get_uuid() + ].get_metric() + assert task.goal_expr.expr_type == LogicalExprType.AND + for i, expr_name in enumerate(task.goal_expr.sub_exprs): + expr_name = _extract_pred_name(expr_name, i) + + goal_distance = goal_distances[expr_name] + goal_breakdown = goal_breakdowns[expr_name] + + # If 1st step. + if expr_name not in self._last_subgoal_state: + self._last_subgoal_state[expr_name] = goal_breakdown + if expr_name not in self._last_dist_state: + self._last_dist_state[expr_name] = goal_distance + + # Give subgoal reward + if (not self._last_subgoal_state[expr_name]) and goal_breakdown: + self._metric += self._subgoal_rewards.get(expr_name, 0.0) + + # Give distance reward + dist_diff = self._last_dist_state[expr_name] - goal_distance + # Filter out small fluctuations + dist_diff = round(dist_diff, 3) + self._metric += self._subgoal_dist_scale.get(expr_name, 0.0) * dist_diff + + # Set last step vars. + self._last_subgoal_state[expr_name] = goal_breakdown + self._last_dist_state[expr_name] = goal_distance + + +@registry.register_measure +class ObjPickReward(RearrangeReward): + """ + Reward function for picking an object up. + """ + + def __init__(self, *args, sim, config, task, **kwargs): + self.cur_dist = -1.0 + self._prev_picked = False + self._metric = None + self._dist_reward = config.dist_reward + self._drop_pen = config.drop_pen + self._use_diff = config.use_diff + self._pick_reward = config.pick_reward + self._wrong_pick_pen = config.wrong_pick_pen + self._wrong_pick_should_end = config.wrong_pick_should_end + self._drop_obj_should_end = config.drop_obj_should_end + + super().__init__(*args, sim=sim, config=config, task=task, **kwargs) + + @staticmethod + def _get_uuid(*args, **kwargs): + return "obj_pick_reward" + + def reset_metric(self, *args, episode, task, observations, **kwargs): + task.measurements.check_measure_dependencies( + self.uuid, + [ + EEToClosestTargetDist._get_uuid(), + EndEffectorToRestDistance.cls_uuid, + ], + ) + self.cur_dist = -1.0 + self._prev_picked = self._sim.grasp_mgr.snap_idx is not None + + super().reset_metric( + *args, + episode=episode, + task=task, + observations=observations, + **kwargs, + ) + + def update_metric(self, *args, episode, task, observations, **kwargs): + super().update_metric( + *args, + episode=episode, + task=task, + observations=observations, + **kwargs, + ) + ee_to_closest_meas = task.measurements.measures[ + EEToClosestTargetDist._get_uuid() + ] + ee_to_object_distance = ee_to_closest_meas.get_metric() + ee_to_rest_distance = task.measurements.measures[ + EndEffectorToRestDistance.cls_uuid + ].get_metric() + + snapped_id = self._sim.grasp_mgr.snap_idx + cur_picked = snapped_id is not None + + if cur_picked: + dist_to_goal = ee_to_rest_distance + else: + dist_to_goal = ee_to_object_distance + + closest_name = ee_to_closest_meas.closest_entity.name + rel_obj_idx = task.pddl.sim_info.obj_ids[closest_name] + abs_targ_obj_idx = self._sim.scene_obj_ids[rel_obj_idx] + + did_pick = cur_picked and (not self._prev_picked) + if did_pick: + if snapped_id == abs_targ_obj_idx: + self._metric += self._pick_reward + # If we just transitioned to the next stage our current + # distance is stale. + self.cur_dist = -1 + else: + # picked the wrong object + self._metric -= self._wrong_pick_pen + if self._wrong_pick_should_end: + self._task.should_end = True + self._prev_picked = cur_picked + self.cur_dist = -1 + return + + if self._use_diff: + if self.cur_dist < 0: + dist_diff = 0.0 + else: + dist_diff = self.cur_dist - dist_to_goal + + # Filter out the small fluctuations + dist_diff = round(dist_diff, 3) + self._metric += self._dist_reward * dist_diff + else: + self._metric -= self._dist_reward * dist_to_goal + self.cur_dist = dist_to_goal + + if not cur_picked and self._prev_picked: + # Dropped the object + self._metric -= self._drop_pen + if self._drop_obj_should_end: + # End the episode if it is not the correct object. + self._task.should_end = True + self._prev_picked = cur_picked + self.cur_dist = -1 + return + + self._prev_picked = cur_picked diff --git a/easi/tasks/ebhabitat/vendor/predicate_task.py b/easi/tasks/ebhabitat/vendor/predicate_task.py new file mode 100644 index 0000000..ea3a776 --- /dev/null +++ b/easi/tasks/ebhabitat/vendor/predicate_task.py @@ -0,0 +1,380 @@ +# +# For licensing see accompanying LICENSE file. +# Copyright (C) 2024 Apple Inc. All Rights Reserved. +# +import inspect +import os.path as osp +import random +import time +from typing import Any, Dict, List + +import hydra +import magnum as mn +import numpy as np +from habitat.core.registry import registry +from habitat.datasets.rearrange.rearrange_dataset import RearrangeEpisode +from habitat.tasks.rearrange.multi_task.pddl_domain import PddlDomain +from habitat.tasks.rearrange.multi_task.pddl_logical_expr import LogicalExpr +from habitat.tasks.rearrange.multi_task.pddl_predicate import Predicate +from habitat.tasks.rearrange.multi_task.rearrange_pddl import ( + ExprType, PddlEntity, SimulatorObjectType) +from habitat.tasks.rearrange.rearrange_task import RearrangeTask +from habitat.tasks.rearrange.utils import add_perf_timing_func +from omegaconf import DictConfig, ListConfig +from PIL import Image +from transformers import AutoTokenizer + +import easi.tasks.ebhabitat.vendor.config +from easi.tasks.ebhabitat.vendor.dataset.episodes import LangRearrangeEpisode +from easi.tasks.ebhabitat.vendor.dataset.utils import get_category_info +from easi.tasks.ebhabitat.vendor.actions import KinematicArmEEAction +from easi.tasks.ebhabitat.vendor.utils import PLACABLE_RECEP_TYPE, get_pddl + + +@registry.register_task(name="RearrangePredicateTask-v0") +class RearrangePredicateTask(RearrangeTask): + def __init__(self, *args, sim, config, dataset, **kwargs): + print(f"Num episodes {len(dataset.episodes)}") + + self._all_cls, obj_cats, self._name_to_cls = get_category_info( + config.skip_load_receps + ) + + self.pddl = get_pddl(config, self._all_cls, obj_cats) + self._fix_agent_pos = config.fix_agent_pos + + super().__init__( + *args, + sim=sim, + config=config, + dataset=dataset, + should_place_articulated_agent=not self._fix_agent_pos, + **kwargs, + ) + # self._tokenizer = get_parser(config.tokenizer_name) + + self._start_template = self._config.start_template + self._goal_template = self._config.goal_template + self._sample_entities = self._config.sample_entities + self._sample_entities_use_constant_sampling = ( + self._config.sample_entities_use_constant_sampling + ) + self._force_scene_per_worker = self._config.force_scene_per_worker + self._goal_expr = None + self._is_first_reset = True + self._is_freeform = False + + # @property + # def tokenizer(self): + # return self._tokenizer + + @add_perf_timing_func() + def _load_start_goal(self, episode): + """ + Setup the start and goal PDDL conditions. Will change the simulator + state to set the start state. + """ + # print(self.pddl.all_entities) + # print(self._sim) + # print(self._dataset) + # print(episode) + pddl_entities = self.pddl.all_entities + self.pddl.bind_to_instance(self._sim, self._dataset, self, episode) + + self._setup_pddl_entities(episode) + + if self._is_first_reset or not self._force_scene_per_worker: + self.pddl.bind_actions() + self._is_first_reset = False + + self._sim.internal_step(-1) + self._load_sampled_names() + + self._goal_expr = self._load_goal_preds(episode) + if self._goal_expr is not None: + t_start = time.time() + self._goal_expr, _ = self.pddl.expand_quantifiers(self._goal_expr) + self._sim.add_perf_timing("goal_expand_quantifiers", t_start) + self._load_start_preds(episode) + + def _load_sampled_names(self): + t_start = time.time() + self.new_entities: Dict[str, PddlEntity] = {} + for entity_name, entity_conds in self._sample_entities.items(): + match_type = self.pddl.expr_types[entity_conds["type"]] + matches = list(self.pddl.find_entities(match_type)) + + if entity_conds.get("ignore_articulated_receptacles", False): + matches = [ + m + for m in matches + if m.name + not in [ + "receptacle_aabb_middle_topfrl_apartment_refrigerator", + "receptacle_aabb_drawer_left_top_frl_apartment_kitchen_counter", + "receptacle_aabb_drawer_right_top_frl_apartment_kitchen_counter", + ] + ] + + pred_conds = entity_conds.get("pred_conds", []) + matches = _filter_matching_pred_conds( + matches, pred_conds, self.pddl, self.new_entities, entity_name + ) + + if len(matches) == 0: + obj_ref, obj_name = list(self.new_entities.items())[0] + on_top_pred = f"on_top({obj_ref},{entity_name})" + cmp_pred_cond = pred_conds[0].replace(" ", "") + if len(pred_conds) == 1 and cmp_pred_cond == on_top_pred: + use_entity_name: str = self._sim.ep_info.name_to_receptacle[ + obj_name.name + ] + entity = self.pddl.all_entities[use_entity_name] + else: + raise ValueError( + f"Could not find match for {entity_name}: {entity_conds}" + ) + elif self._sample_entities_use_constant_sampling: + entity = matches[0] + else: + entity = random.choice(matches) + + self.new_entities[entity_name] = entity + self._sampled_names = list(self.new_entities.keys()) + self._sim.add_perf_timing("find_entities", t_start) + + @property + def pddl_problem(self): + return self.pddl + + @property + def all_cls(self): + return self._all_cls + + def _get_cls_for_obj_name(self, obj_name, episode): + return self._name_to_cls[obj_name] + + @add_perf_timing_func() + def _setup_pddl_entities(self, episode): + # Register the specific objects in this scene as simulator entities. + for obj_name in self.pddl.sim_info.obj_ids: + asset_name = _strip_instance_id(obj_name) + if asset_name not in self._name_to_cls: + # This is a goal or target object indicator from a Hab2 dataset. + # We don't use these geo-goals, so we can ignore. + continue + cls_name = self._name_to_cls[asset_name] + + entity_type = self.pddl.expr_types[cls_name] + self.pddl.register_episode_entity(PddlEntity(obj_name, entity_type)) + + @add_perf_timing_func() + def _load_goal_preds(self, episode): + if self._goal_template is None: + # Load from the episode. + return self.pddl.parse_only_logical_expr( + episode.goal_preds, self.pddl.all_entities + ) + else: + # Load from the config. + goal_d = dict(self._goal_template) + goal_d = _recur_dict_replace(goal_d, self.new_entities) + return self.pddl.parse_only_logical_expr(goal_d, self.pddl.all_entities) + + @add_perf_timing_func() + def _load_start_preds(self, episode): + if self._start_template is None: + # Load form the episode data. + for pred in episode.start_preds: + pred = self.pddl.parse_predicate(pred, self.pddl.all_entities) + pred.set_state(self.pddl.sim_info) + else: + # Load from the config. + start_preds = self._start_template[:] + for pred in start_preds: + for k, entity in self.new_entities.items(): + pred = pred.replace(k, entity.name) + pred = self.pddl.parse_predicate(pred, self.pddl.all_entities) + pred.set_state(self.pddl.sim_info) + + @property + def goal_expr(self) -> LogicalExpr: + return self._goal_expr + + def set_is_freeform(self, is_freeform): + self._is_freeform = is_freeform + + @property + def subgoals(self): + if self._is_freeform: + return [] + else: + return self._subgoals + + def is_goal_satisfied(self): + if self._is_freeform: + return False + if self._goal_expr is None: + return False + ret = self.pddl.is_expr_true(self._goal_expr) + return ret + + @add_perf_timing_func() + def _get_subgoals(self, episode) -> List[List[Predicate]]: + if episode.subgoals is None: + return [] + # start_preds = self.pddl.get_true_predicates() + ret_subgoals = [] + for subgoal in episode.subgoals: + subgoal_preds = [] + for subgoal_predicate in subgoal: + pred = self.pddl.parse_predicate( + subgoal_predicate, self.pddl.all_entities + ) + subgoal_preds.append(pred) + if len(subgoal_preds) != 0: + ret_subgoals.append(subgoal_preds) + return ret_subgoals + + @add_perf_timing_func() + def step(self, *args, action, **kwargs): + self.pddl.sim_info.reset_pred_truth_cache() + fix_top_down_cam_pos(self._sim) + self.num_steps += 1 + if "action_args" not in action: + # This won't be added to discrete action spaces, but RearrangeTask + # expects it. + action["action_args"] = {"sel": action["action"]} + action["action"] = 0 + + return super().step(*args, action=action, **kwargs) + + @add_perf_timing_func() + def reset(self, episode): + self.num_steps = 0 + + # Generate a random episode ID that can be used for debugging purpose. This should be different than the dataset episode ID, because multiple workers could be operating on the same episode. + self.episode_rollout_id = random.randint(0, 9999999) + + super().reset(episode, fetch_observations=False) + if type(episode) == RearrangeEpisode: + # Hab2 style episode. + episode = convert_rearrange_ep_to_lang_rearrange(episode) + self.lang_goal = episode.instruction + self.instruct_id = episode.instruct_id + self.sampler_info = episode.sampler_info + self._load_start_goal(episode) + + if self._fix_agent_pos: + # Fix the agent position to 0,0 and the rotation to 0. + agent = self._sim.get_agent_data(0).articulated_agent + agent.base_pos = self._sim.pathfinder.snap_point( + mn.Vector3(0.0, agent.base_pos[1], 0.0) + ) + agent.base_rot = 0.0 + + self._subgoals = list(self._get_subgoals(episode)) + + self._sim.draw_bb_objs.clear() + fix_top_down_cam_pos(self._sim) + + self._sim.maybe_update_articulated_agent() + return self._get_observations(episode) + + def get_sampled(self) -> List[PddlEntity]: + return [self.new_entities[k] for k in self._sampled_names] + + +def _strip_instance_id(instance_id: str) -> str: + # Strip off the unique instance ID of the object and only return the asset + # name. + return "_".join(instance_id.split("_")[:-1]) + + +def _recur_dict_replace(d: Any, replaces: Dict[str, PddlEntity]) -> Any: + """ + Replace all string entries in `d` with the replace name to PDDL entity + mapping in replaces. + """ + if isinstance(d, ListConfig): + d = list(d) + if isinstance(d, DictConfig): + d = dict(d) + + if isinstance(d, str): + for name, entity in replaces.items(): + d = d.replace(f"{name}.type", entity.expr_type.name) + d = d.replace(name, entity.name) + elif isinstance(d, list): + for i, v in enumerate(d): + d[i] = _recur_dict_replace(v, replaces) + elif isinstance(d, dict): + for k, v in d.items(): + d[k] = _recur_dict_replace(d[k], replaces) + return d + + +def _filter_matching_pred_conds( + matches: List[PddlEntity], + pred_conds: List[str], + pddl: PddlDomain, + other_entities: Dict[str, PddlEntity], + entity_name: str, +) -> List[PddlEntity]: + """ + Filters elements from matches based on a set of PDDL predicate strings. + """ + + ret = [] + for match in matches: + should_include = True + for pred_cond in pred_conds: + subbed_pred_cond = pred_cond.replace(entity_name, match.name) + for other_entity_name, other_entity in other_entities.items(): + subbed_pred_cond = subbed_pred_cond.replace( + other_entity_name, other_entity.name + ) + pred = pddl.parse_predicate(subbed_pred_cond, pddl.all_entities) + + if not pred.is_true(pddl.sim_info): + should_include = False + break + if not should_include: + continue + ret.append(match) + + return ret + + +def fix_top_down_cam_pos(sim): + if "top_down_rgb" not in sim._sensors: + return + middle_of_scene = np.array(sim.pathfinder.get_bounds()).mean(0) + look_at_mat = mn.Matrix4.look_at( + mn.Vector3(middle_of_scene[0], 6.5, middle_of_scene[0]), + middle_of_scene, + mn.Vector3(0.0, 1.0, 0.0), + ) + rot_mat = mn.Matrix4.from_( + mn.Matrix4.rotation(mn.Rad(np.pi / 2), mn.Vector3(0.0, 1.0, 0.0)).rotation(), + # mn.Vector3(2.5, 0.0, 1.0), + mn.Vector3(0.0, 0.0, 4.0), + ) + sim._sensors["top_down_rgb"]._sensor_object.node.transformation = ( + rot_mat @ look_at_mat + ) + + +def convert_rearrange_ep_to_lang_rearrange( + ep: RearrangeEpisode, +) -> LangRearrangeEpisode: + return LangRearrangeEpisode( + instruction="", + sampled_entities={}, + start_preds=[], + goal_preds={}, + instruct_id="", + sampler_info={}, + subgoals=[], + **ep.__getstate__(), + ) diff --git a/easi/tasks/ebhabitat/vendor/sensors.py b/easi/tasks/ebhabitat/vendor/sensors.py new file mode 100644 index 0000000..0c29bfb --- /dev/null +++ b/easi/tasks/ebhabitat/vendor/sensors.py @@ -0,0 +1,422 @@ +# +# For licensing see accompanying LICENSE file. +# Copyright (C) 2024 Apple Inc. All Rights Reserved. +# +import os.path as osp +from typing import List + +import gym.spaces as spaces +import numpy as np +import torch +from habitat.core.registry import registry +from habitat.core.simulator import Sensor, SensorTypes +from habitat.tasks.rearrange.multi_task.pddl_predicate import Predicate +from habitat.tasks.rearrange.multi_task.rearrange_pddl import \ + SimulatorObjectType +from habitat.tasks.rearrange.utils import batch_transform_point + +from easi.tasks.ebhabitat.vendor.dataset.utils import get_category_info, get_name_mappings +from easi.tasks.ebhabitat.vendor.utils import get_parser + + +@registry.register_sensor +class OneHotTargetSensor(Sensor): + def __init__(self, *args, task, **kwargs): + self._task = task + self._n_cls = len(task.all_cls) + self._obs = np.zeros((self._n_cls,)) + super().__init__(*args, **kwargs) + + def _get_uuid(self, *args, **kwargs): + return "one_hot_target_sensor" + + def _get_sensor_type(self, *args, **kwargs): + return SensorTypes.TENSOR + + def _get_observation_space(self, *args, config, **kwargs): + return spaces.Box(shape=(self._n_cls,), low=0, high=1, dtype=np.float32) + + def get_observation(self, *args, **kwargs): + cur_target = self._task.get_sampled()[self.config.sampled_idx] + + # For receptacles the name will not be a class but the name directly. + use_name = cur_target.expr_type.name + if cur_target.name in self._task.all_cls: + use_name = cur_target.name + set_i = self._task.all_cls.index(use_name) + self._obs *= 0.0 + self._obs[set_i] = 1.0 + + return self._obs + + +@registry.register_sensor +class SimpleTargetSensor(Sensor): + uuid = "simple_lang_goal" + + def __init__(self, *args, config, **kwargs): + self._max_len = config.max_len + self._add_special_tokens = config.add_special_tokens + self._name_mappings = get_name_mappings() + self.cur_lang_goal = "" + super().__init__(*args, config=config, **kwargs) + + def _get_uuid(self, *args, **kwargs): + return SimpleTargetSensor.uuid + + def _get_sensor_type(self, *args, **kwargs): + return SensorTypes.TENSOR + + def _get_observation_space(self, *args, config, **kwargs): + return spaces.Box( + shape=(self._max_len,), + low=0, + high=1, + dtype=np.int64, + ) + + def get_observation(self, *args, task, **kwargs): + cur_target = task.get_sampled()[0] + if cur_target.expr_type.parent.name == SimulatorObjectType.MOVABLE_ENTITY.value: + # This is an object. + target_name = cur_target.expr_type.name + else: + # This is a receptacle. + target_name = cur_target.name + + target_name = self._name_mappings.get(target_name, target_name) + + tokens = task.tokenizer( + target_name, + return_tensors="np", + padding="max_length", + max_length=self._max_len, + add_special_tokens=self._add_special_tokens, + )["input_ids"][0] + + assert ( + tokens.shape[0] == self._max_len + ), f"Instruction is too many tokens at {tokens.shape[0]} but the max is {self._max_len} for instruction {target_name}" + self.cur_lang_goal = target_name + return tokens + + +@registry.register_sensor +class WindowDebugSensor(Sensor): + uuid = "debug_info" + + def _get_uuid(self, *args, **kwargs): + return WindowDebugSensor.uuid + + def _get_sensor_type(self, *args, **kwargs): + return SensorTypes.TENSOR + + def _get_observation_space(self, *args, config, **kwargs): + return spaces.Box( + shape=(2,), + low=np.finfo(np.float32).min, + high=np.finfo(np.float32).max, + dtype=np.float32, + ) + + def get_observation(self, *args, task, **kwargs): + return np.array([task.num_steps + 1, task.episode_rollout_id], dtype=np.float32) + + +@registry.register_sensor +class StepCountSensor(Sensor): + uuid = "step_count" + + def _get_uuid(self, *args, **kwargs): + return StepCountSensor.uuid + + def _get_sensor_type(self, *args, **kwargs): + return SensorTypes.TENSOR + + def _get_observation_space(self, *args, config, **kwargs): + return spaces.Box( + shape=(1,), + low=np.finfo(np.float32).min, + high=np.finfo(np.float32).max, + dtype=np.float32, + ) + + def get_observation(self, *args, task, **kwargs): + return np.array([task.num_steps], dtype=np.float32) + + +@registry.register_sensor +class VocabLangGoalSensor(Sensor): + uuid = "vocab_lang_goal" + + def __init__(self, *args, config, **kwargs): + self._max_len = config.max_len + self._add_special_tokens = config.add_special_tokens + super().__init__(*args, config=config, **kwargs) + + def _get_uuid(self, *args, **kwargs): + return VocabLangGoalSensor.uuid + + def _get_sensor_type(self, *args, **kwargs): + return SensorTypes.TENSOR + + def _get_observation_space(self, *args, config, **kwargs): + return spaces.Box( + shape=(self._max_len,), + low=0, + high=1, + dtype=np.int64, + ) + + def get_observation(self, *args, task, **kwargs): + tokens = task.tokenizer( + task.lang_goal, + return_tensors="np", + padding="max_length", + max_length=self._max_len, + add_special_tokens=self._add_special_tokens, + )["input_ids"][0] + assert ( + tokens.shape[0] == self._max_len + ), f"Instruction is too many tokens at {tokens.shape[0]} but the max is {self._max_len}" + return tokens + + +@registry.register_sensor +class T5VocabLangGoalSensor(Sensor): + """ + Always outputs the T5 tokenization. + """ + + uuid = "t5_vocab_lang_goal" + + def __init__(self, *args, config, **kwargs): + self._max_len = config.max_len + self._tokenizer = get_parser("google/flan-t5-small") + super().__init__(*args, config=config, **kwargs) + + def _get_uuid(self, *args, **kwargs): + return T5VocabLangGoalSensor.uuid + + def _get_sensor_type(self, *args, **kwargs): + return SensorTypes.TENSOR + + def _get_observation_space(self, *args, **kwargs): + return spaces.Box( + shape=(self._max_len,), + low=0, + high=1, + dtype=np.int64, + ) + + def get_observation(self, *args, task, **kwargs): + tokens = self._tokenizer( + task.lang_goal, + return_tensors="np", + padding="max_length", + max_length=self._max_len, + )["input_ids"][0] + assert tokens.shape[0] == self._max_len + return tokens + + +@registry.register_sensor +class LlamaVocabLangGoalSensor(Sensor): + """ + Always outputs the llama tokenization. + """ + + uuid = "llama_vocab_lang_goal" + + def __init__(self, *args, config, **kwargs): + self._max_len = config.max_len + self._tokenizer = get_parser(config.tokenizer_name) + super().__init__(*args, config=config, **kwargs) + + def _get_uuid(self, *args, **kwargs): + return LlamaVocabLangGoalSensor.uuid + + def _get_sensor_type(self, *args, **kwargs): + return SensorTypes.TENSOR + + def _get_observation_space(self, *args, **kwargs): + return spaces.Box( + shape=(self._max_len,), + low=0, + high=1, + dtype=np.int64, + ) + + def get_observation(self, *args, task, **kwargs): + tokens = self._tokenizer( + task.lang_goal, + return_tensors="np", + padding="max_length", + max_length=self._max_len, + )["input_ids"][0] + assert tokens.shape[0] == self._max_len + return tokens + + +@registry.register_sensor +class ObsLangSensor(Sensor): + uuid = "obs_lang" + + def __init__(self, *args, config, task, **kwargs): + self._max_len = config.max_len + self._task = task + self._predicates_list = None + super().__init__(*args, config=config, task=task, **kwargs) + + def _get_uuid(self, *args, **kwargs): + return ObsLangSensor.uuid + + def _get_sensor_type(self, *args, **kwargs): + return SensorTypes.TENSOR + + @property + def predicates_list(self) -> List[Predicate]: + """ + Returns all possible predicate combinations in the environment. + """ + + if self._predicates_list is None: + self._predicates_list = self._task.pddl_problem.get_possible_predicates() + return self._predicates_list + + def _get_observation_space(self, *args, config, **kwargs): + return spaces.Box( + shape=(self._max_len,), + low=0, + high=1, + dtype=np.int64, + ) + + def get_observation(self, *args, **kwargs): + # Fetch the predicates that are true in the current simulator step. + sim_info = self._task.pddl_problem.sim_info + true_preds: List[Predicate] = [ + p for p in self.predicates_list if p.is_true(sim_info) + ] + + # Conver the predicates to a string representation. + true_preds_s: List[str] = [p.compact_str for p in true_preds] + + # Join all the predicate strings by sentences. + state_s = ". ".join(true_preds_s) + + # Return the tokenized version. + return self._task.tokenizer( + state_s, + return_tensors="np", + padding="max_length", + max_length=self._max_len, + truncation=True, + )["input_ids"][0] + + +@registry.register_sensor +class VocabEmbedSensor(Sensor): + def __init__(self, *args, config, **kwargs): + embed_dat = torch.load(config.embed_path) + self._embeddings = embed_dat["hxs"] + self._ep_idx_to_idx = embed_dat["ep_idx_to_hxs_idx"] + super().__init__(*args, config=config, **kwargs) + + def _get_uuid(self, *args, **kwargs): + return "vocab_embed_goal" + + def _get_sensor_type(self, *args, **kwargs): + return SensorTypes.TENSOR + + def _get_observation_space(self, *args, config, **kwargs): + return spaces.Box( + shape=(self.config.hidden_dim,), + low=np.finfo(np.float32).min, + high=np.finfo(np.float32).max, + dtype=np.float32, + ) + + def get_observation(self, *args, task, **kwargs): + pt_idx = self._ep_idx_to_idx[task._episode_id] + return self._embeddings[pt_idx] + + +@registry.register_sensor +class ClosestTargetObjectPosSensor(Sensor): + """ + Gets the distance between the EE and the closest target object. + """ + + @staticmethod + def _get_uuid(*args, **kwargs): + return "closest_targ_obj_pos_sensor" + + def _get_sensor_type(self, *args, **kwargs): + return SensorTypes.TENSOR + + def _get_observation_space(self, *args, config, **kwargs): + return spaces.Box( + shape=(3,), + low=np.finfo(np.float32).min, + high=np.finfo(np.float32).max, + dtype=np.float32, + ) + + def get_observation(self, *args, task, **kwargs): + targ_type = task.get_sampled()[0] + matches = list(task.pddl.find_entities(targ_type.expr_type)) + obj_pos = np.array( + [task.pddl.sim_info.get_entity_pos(match) for match in matches] + ) + + ee_pos = task._sim.articulated_agent.ee_transform().translation + + dists = np.linalg.norm(obj_pos - ee_pos, ord=2, axis=-1) + closest_id = np.argmin(dists) + return obj_pos[closest_id] + + +@registry.register_sensor +class AllObjectPositionsSensor(Sensor): + def __init__(self, *args, task, **kwargs): + self._all_cats, _, _ = get_category_info() + super().__init__(*args, **kwargs) + + def _get_uuid(self, *args, **kwargs): + return "all_obj_pos_sensor" + + def _get_sensor_type(self, *args, **kwargs): + return SensorTypes.TENSOR + + def _get_observation_space(self, *args, config, **kwargs): + return spaces.Box( + shape=(3,), + low=np.finfo(np.float32).min, + high=np.finfo(np.float32).max, + dtype=np.float32, + ) + + def get_observation(self, *args, task, **kwargs): + pddl = task.pddl + entities = pddl.get_ordered_entities_list() + + entity_pos = [pddl.sim_info.get_entity_pos(entity) for entity in entities] + + base_T = task._sim.articulated_agent.base_transformation + entity_pos = batch_transform_point(entity_pos, base_T.inverted(), np.float32) + + entity_type_idxs = [] + for entity in entities: + if entity.name in self._all_cats: + entity_idx = self._all_cats.index(entity.name) + elif entity.expr_type.name in self._all_cats: + entity_idx = self._all_cats.index(entity.expr_type.name) + else: + entity_idx = -1 + entity_type_idxs.append(entity_idx) + + return np.array( + [[*pos, idx] for pos, idx in zip(entity_pos, entity_type_idxs)], + dtype=np.float32, + ).reshape(-1) diff --git a/easi/tasks/ebhabitat/vendor/utils.py b/easi/tasks/ebhabitat/vendor/utils.py new file mode 100644 index 0000000..9b0c2b4 --- /dev/null +++ b/easi/tasks/ebhabitat/vendor/utils.py @@ -0,0 +1,207 @@ +# +# For licensing see accompanying LICENSE file. +# Copyright (C) 2024 Apple Inc. All Rights Reserved. +# +import inspect +import os.path as osp +from typing import Dict, List, Tuple +import numpy as np +from PIL import Image, ImageDraw, ImageFont +import yaml +from habitat.tasks.rearrange.multi_task.pddl_action import PddlAction +from habitat.tasks.rearrange.multi_task.pddl_domain import PddlDomain +from habitat.tasks.rearrange.multi_task.pddl_logical_expr import ( + LogicalExpr, LogicalQuantifierType) +from habitat.tasks.rearrange.multi_task.rearrange_pddl import ( + ExprType, PddlEntity, SimulatorObjectType) +from transformers import (AutoConfig, AutoModelForSeq2SeqLM, AutoTokenizer, + LlamaForCausalLM, LlamaModel, LlamaTokenizer, + T5Model) +from habitat.utils.visualizations.utils import (tile_images, draw_collision) +import easi.tasks.ebhabitat.vendor.config +import easi.tasks.ebhabitat.vendor.dataset + +# Also defined in the PDDL +PLACABLE_RECEP_TYPE = "place_receptacle" + + +def draw_text(img, text, position): + draw = ImageDraw.Draw(img) + font = ImageFont.load_default() + font.size = 15 + text = str(text) + # Draw the text onto the image + draw.text(position, text, font=font, fill="red") + + +def merge_to_file(img, image_path, draw_index=True, index=0): + img_old = Image.open(image_path) + total_width = img.width + img_old.width + max_height = max(img.height, img_old.height) + img_merge = Image.new('RGB', (total_width, max_height)) + img_merge.paste(img_old, (0, 0)) + img_merge.paste(img, (img_old.width, 0)) + if draw_index: + draw_text(img_merge, index, position=(img_old.width+ 15, 15)) + img_merge.save(image_path) + + + +def process_image_array(obs_k): + if not isinstance(obs_k, np.ndarray): + obs_k = obs_k.cpu().numpy() + if obs_k.dtype != np.uint8: + obs_k = obs_k * 255.0 + obs_k = obs_k.astype(np.uint8) + if obs_k.shape[2] == 1: + obs_k = np.concatenate([obs_k for _ in range(3)], axis=2) + return obs_k + +def observations_to_image(observation, info, key=None): + render_obs_images = [] + if key is not None: + obs_k = observation[key] + obs_k = process_image_array(obs_k) + render_obs_images.append(obs_k) + else: + for sensor_name in observation: + if hasattr(observation[sensor_name], 'shape') and len(observation[sensor_name].shape) > 1: + obs_k = observation[sensor_name] + obs_k = process_image_array(obs_k) + render_obs_images.append(obs_k) + assert ( + len(render_obs_images) > 0 + ), "Expected at least one visual sensor enabled." + + shapes_are_equal = len(set(x.shape for x in render_obs_images)) == 1 + if not shapes_are_equal: + render_frame = tile_images(render_obs_images) + else: + render_frame = np.concatenate(render_obs_images, axis=1) + + # draw collision + collisions_key = "collisions" + if collisions_key in info and info[collisions_key]["is_collision"]: + render_frame = draw_collision(render_frame) + + top_down_map_key = "top_down_map" + if top_down_map_key in info: + top_down_map = maps.colorize_draw_agent_and_fit_to_height( + info[top_down_map_key], render_frame.shape[0] + ) + render_frame = np.concatenate((render_frame, top_down_map), axis=1) + return render_frame + + + +def get_allowed_actions(pddl, allowed_substrings): + all_actions = pddl.get_possible_actions() + + def matches_any(s, allowed_acs): + # returns if the string starts with any strings from allowed_acs + return any(s.name.startswith(ac) for ac in allowed_acs) + + return [ac for ac in all_actions if matches_any(ac, allowed_substrings)] + + +def _recur_replace(expr, search, replace): + if isinstance(expr, LogicalExpr): + for subexpr in expr.sub_exprs: + _recur_replace(subexpr, search, replace) + else: + for i, arg_val in enumerate(expr._arg_values): + if arg_val == search: + expr._arg_values[i] = replace + + +def flatten_actions(pddl: PddlDomain, obj_cats): + new_acs = {} + for ac_name, action in pddl.actions.items(): + found_i = -1 + # TODO: Currently this is a hack for only the pick action. This will + # not work for other PDDL actions. + + for i, param in enumerate(action.params): + if param.expr_type.name == SimulatorObjectType.MOVABLE_ENTITY.value: + found_i = i + break + + if found_i == -1: + new_acs[ac_name] = action + continue + + param = action.params[found_i] + del action.params[found_i] + for obj_cat in obj_cats: + precond = action.precond.clone() + assert len(precond.inputs) == 0, precond.quantifier is None + + obj_cat_type = pddl.expr_types[obj_cat] + at_entity = PddlEntity(name="DYN_OBJ", expr_type=obj_cat_type) + inputs = [at_entity] + + # Ignore the first expression which was about the robot position. + precond = precond.sub_in({param: at_entity}) + + postcond_pred = pddl.parse_predicate( + f"holding({at_entity.name})", {at_entity.name: at_entity} + ) + obj_action = PddlAction( + f"{action.name}_{obj_cat}", + action._params, + pre_cond=LogicalExpr( + precond.expr_type, + precond.sub_exprs, + inputs, + LogicalQuantifierType.EXISTS, + ), + post_cond=[postcond_pred], + ) + + new_acs[obj_action.name] = obj_action + pddl.set_actions(new_acs) + return pddl + + +def get_obj_type(cat, pddl): + return pddl.expr_types[SimulatorObjectType.MOVABLE_ENTITY.value] + + +def get_pddl(task_config, all_cats, obj_cats) -> PddlDomain: + config_path = osp.dirname(__file__) + domain_file_path = osp.join( + config_path, + 'config', + task_config.task_spec_base_path, + task_config.pddl_domain_def + ".yaml", + ) + pddl = PddlDomain( + domain_file_path, + task_config, + ) + + # Add permanent entity types. (Object types and receptacles). + for cat in all_cats: + if cat in obj_cats: + obj_type = get_obj_type(cat, pddl) + entity_type = ExprType(cat, obj_type) + pddl._expr_types[cat] = entity_type + else: + # Assume this is a receptacle in the scene. Permanently place, not per episode. + pddl._constants[cat] = PddlEntity(cat, pddl.expr_types[PLACABLE_RECEP_TYPE]) + + return flatten_actions(pddl, obj_cats) + + +def get_parser(llm_id): + if "llama" in llm_id.lower(): + tokenizer = LlamaTokenizer.from_pretrained(llm_id) + # llama has no pad token by default. As per this thread: + # https://github.com/huggingface/transformers/issues/22312 we should + # set pad token manually. + tokenizer.pad_token = "[PAD]" + return tokenizer + else: + tokenizer = AutoTokenizer.from_pretrained(llm_id) + tokenizer.pad_token = "[PAD]" + return tokenizer diff --git a/tests/test_ebhabitat_task.py b/tests/test_ebhabitat_task.py new file mode 100644 index 0000000..6c570a9 --- /dev/null +++ b/tests/test_ebhabitat_task.py @@ -0,0 +1,264 @@ +"""Tests for the EB-Habitat task (offline, no simulator needed).""" +import json + +import pytest +from pathlib import Path + +from easi.core.episode import Action, Observation, StepResult +from easi.tasks.ebhabitat.task import EBHabitatTask +from easi.tasks.ebhabitat.actions import get_placeholder_action_space, PLACEHOLDER_ACTIONS + + +class TestEBHabitatActionSpace: + def test_placeholder_action_space_not_empty(self): + actions = get_placeholder_action_space() + assert len(actions) > 0 + + def test_placeholder_has_navigate(self): + actions = get_placeholder_action_space() + nav_actions = [a for a in actions if a.startswith("navigate")] + assert len(nav_actions) >= 5 + + def test_placeholder_has_pick(self): + actions = get_placeholder_action_space() + pick_actions = [a for a in actions if a.startswith("pick up")] + assert len(pick_actions) >= 1 + + def test_placeholder_has_place(self): + actions = get_placeholder_action_space() + place_actions = [a for a in actions if a.startswith("place at")] + assert len(place_actions) >= 1 + + def test_placeholder_has_open_close(self): + actions = get_placeholder_action_space() + assert any("open" in a for a in actions) + assert any("close" in a for a in actions) + + def test_placeholder_returns_copy(self): + a1 = get_placeholder_action_space() + a2 = get_placeholder_action_space() + assert a1 is not a2 + assert a1 == a2 + + +class TestEBHabitatTask: + @pytest.fixture + def task(self): + return EBHabitatTask() + + def test_name(self, task): + assert task.name == "ebhabitat_base" + + def test_simulator_key(self, task): + assert task.simulator_key == "habitat_sim:v0_3_0" + + def test_action_space_loaded(self, task): + assert len(task.action_space) == len(PLACEHOLDER_ACTIONS) + + def test_max_steps(self, task): + assert task.max_steps == 30 + + def test_format_reset_config(self, task): + """Test the adapter from EB-Habitat HF row to bridge reset config.""" + episode = { + "id": 0, + "episode_id": "140", + "instruction": "Find a toy airplane and move it to the right counter.", + "instruct_id": "f0917e29", + "scene_id": "data/replica_cad/configs/scenes/v3_sc3_staging_02.scene_instance.json", + } + config = task.format_reset_config(episode) + assert config["episode_id"] == 0 + assert config["eval_set"] == "base" + assert config["instruction"] == episode["instruction"] + + def test_evaluate_episode(self, task): + """Test metric computation from trajectory.""" + episode = {"id": 0, "instruction": "test"} + obs = Observation(rgb_path="/tmp/rgb.png") + trajectory = [ + StepResult(observation=obs, reward=0.0, done=False, + info={"task_success": 0.0, "task_progress": 0.33, "subgoal_reward": 0.1}), + StepResult(observation=obs, reward=0.5, done=True, + info={"task_success": 1.0, "task_progress": 1.0, "subgoal_reward": 0.8}), + ] + metrics = task.evaluate_episode(episode, trajectory) + assert metrics["task_success"] == 1.0 + assert metrics["task_progress"] == 1.0 + assert metrics["subgoal_reward"] == 0.8 + assert metrics["num_steps"] == 2 + + def test_evaluate_empty_trajectory(self, task): + episode = {"id": 0, "instruction": "test"} + metrics = task.evaluate_episode(episode, []) + assert metrics["task_success"] == 0.0 + assert metrics["task_progress"] == 0.0 + assert metrics["subgoal_reward"] == 0.0 + assert metrics["num_steps"] == 0.0 + + def test_get_instruction(self, task): + episode = {"instruction": "Find a toy airplane and move it to the right counter."} + assert task.get_instruction(episode) == "Find a toy airplane and move it to the right counter." + + def test_builtin_episodes(self, task): + episodes = task._get_builtin_episodes() + assert len(episodes) >= 1 + assert "instruction" in episodes[0] + assert "episode_id" in episodes[0] + + def test_task_registry_discovers_all_splits(self): + """Registry should discover all EB-Habitat split yamls.""" + from easi.tasks.registry import list_tasks, refresh + refresh() + tasks = list_tasks() + expected = [ + "ebhabitat_base", + "ebhabitat_common_sense", + "ebhabitat_complex_instruction", + "ebhabitat_spatial_relationship", + "ebhabitat_visual_appearance", + "ebhabitat_long_horizon", + ] + for name in expected: + assert name in tasks, f"{name} not found in task registry" + + def test_split_specific_task_loading(self): + """Loading ebhabitat_base should give a task with correct config.""" + from easi.tasks.registry import get_task_entry, refresh + refresh() + entry = get_task_entry("ebhabitat_base") + assert entry.simulator_key == "habitat_sim:v0_3_0" + assert entry.config_path.name == "ebhabitat_base.yaml" + + def test_bridge_script_path(self, task): + bridge_path = task.get_bridge_script_path() + assert bridge_path.exists() + assert bridge_path.name == "bridge.py" + + +class TestEBHabitatPromptBuilder: + def test_build_messages_first_turn(self): + """First turn: full system prompt in user message.""" + from easi.tasks.ebhabitat.prompts import EBHabitatPromptBuilder + from easi.core.memory import AgentMemory + builder = EBHabitatPromptBuilder() + actions = get_placeholder_action_space() + memory = AgentMemory( + task_description="Find a toy airplane and move it to the right counter.", + action_space=actions, + current_observation=Observation(rgb_path="/tmp/rgb.png"), + ) + messages = builder.build_messages(memory) + assert isinstance(messages, list) + assert messages[0]["role"] == "user" + text_content = messages[0]["content"][-1]["text"] + assert "You are a robot operating in a home" in text_content + assert "Navigation:" in text_content + assert "Pick:" in text_content + assert "Place:" in text_content + + def test_build_messages_with_history(self): + from easi.tasks.ebhabitat.prompts import EBHabitatPromptBuilder + from easi.core.memory import AgentMemory + builder = EBHabitatPromptBuilder() + actions = get_placeholder_action_space() + memory = AgentMemory( + task_description="Find a toy airplane and move it to the right counter.", + action_space=actions, + current_observation=Observation(rgb_path="/tmp/rgb.png"), + ) + obs = Observation(rgb_path="/tmp/rgb.png") + memory.record_step(obs, Action(action_name="navigate to the sofa"), llm_response="r1") + memory.record_feedback("Last action executed successfully.") + memory.record_step(obs, Action(action_name="pick up the toy airplane"), llm_response="r2") + memory.record_feedback("Last action is invalid.") + messages = builder.build_messages(memory) + assert isinstance(messages, list) + assert messages[0]["role"] == "user" + text_content = "" + for part in messages[0]["content"]: + if part["type"] == "text": + text_content += part["text"] + assert "navigate to the sofa" in text_content + assert "Last action is invalid." in text_content + + def test_parse_response_valid(self): + from easi.tasks.ebhabitat.prompts import EBHabitatPromptBuilder + from easi.core.memory import AgentMemory + builder = EBHabitatPromptBuilder() + actions = get_placeholder_action_space() + memory = AgentMemory( + task_description="test", + action_space=actions, + current_observation=Observation(rgb_path="/tmp/rgb.png"), + ) + builder.set_action_space(actions) + response = json.dumps({ + "visual_state_description": "I see a room", + "reasoning_and_reflection": "I should navigate", + "language_plan": "1. Navigate to sofa", + "executable_plan": [ + {"action_id": 6, "action_name": actions[6]}, + {"action_id": 0, "action_name": actions[0]}, + ], + }) + parsed = builder.parse_response(response, memory) + assert len(parsed) == 2 + assert parsed[0].action_name == actions[6] + assert parsed[1].action_name == actions[0] + + def test_parse_response_invalid_json(self): + from easi.tasks.ebhabitat.prompts import EBHabitatPromptBuilder + from easi.core.memory import AgentMemory + builder = EBHabitatPromptBuilder() + actions = get_placeholder_action_space() + memory = AgentMemory( + task_description="test", + action_space=actions, + current_observation=Observation(rgb_path="/tmp/rgb.png"), + ) + parsed = builder.parse_response("not json at all", memory) + assert parsed == [] + + def test_parse_response_empty_plan(self): + from easi.tasks.ebhabitat.prompts import EBHabitatPromptBuilder + from easi.core.memory import AgentMemory + builder = EBHabitatPromptBuilder() + actions = get_placeholder_action_space() + memory = AgentMemory( + task_description="test", + action_space=actions, + current_observation=Observation(rgb_path="/tmp/rgb.png"), + ) + response = json.dumps({ + "visual_state_description": "test", + "reasoning_and_reflection": "test", + "language_plan": "test", + "executable_plan": [], + }) + parsed = builder.parse_response(response, memory) + assert parsed == [] + + def test_conforms_to_protocol(self): + from easi.tasks.ebhabitat.prompts import EBHabitatPromptBuilder + from easi.agents.prompt_builder import PromptBuilderProtocol + builder = EBHabitatPromptBuilder() + assert isinstance(builder, PromptBuilderProtocol) + + def test_system_prompt_matches_source(self): + """Verify the system prompt text matches EmbodiedBench source.""" + from easi.tasks.ebhabitat.prompts import HABITAT_SYSTEM_PROMPT + assert "Navigation:" in HABITAT_SYSTEM_PROMPT + assert "Pick:" in HABITAT_SYSTEM_PROMPT + assert "Place:" in HABITAT_SYSTEM_PROMPT + assert "Open:" in HABITAT_SYSTEM_PROMPT + assert "Close:" in HABITAT_SYSTEM_PROMPT + # Should NOT contain Alfred-specific actions + assert "Find:" not in HABITAT_SYSTEM_PROMPT + assert "Slice:" not in HABITAT_SYSTEM_PROMPT + assert "Turn on:" not in HABITAT_SYSTEM_PROMPT + + def test_examples_loaded(self): + from easi.tasks.ebhabitat.prompts import EBHabitatPromptBuilder + builder = EBHabitatPromptBuilder(n_shot=10) + assert len(builder._examples) >= 10 From b9d339cc484dc5975f38e4945b3cb3d40d2ef013 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Mon, 16 Feb 2026 22:01:05 +0800 Subject: [PATCH 030/239] [Feat] Add --redownload flag for dataset re-download Add --redownload to 'easi task download' and 'easi run' to force re-download of cached HuggingFace datasets. Useful when a previous download was interrupted or incomplete. --- easi/cli.py | 15 ++++++++++----- easi/core/base_task.py | 15 ++++++++++++--- easi/evaluation/runner.py | 4 ++++ 3 files changed, 26 insertions(+), 8 deletions(-) diff --git a/easi/cli.py b/easi/cli.py index d6e8107..bddacd1 100644 --- a/easi/cli.py +++ b/easi/cli.py @@ -59,6 +59,8 @@ def build_parser() -> argparse.ArgumentParser: task_download = task_sub.add_parser("download", help="Download task dataset", parents=[common]) task_download.add_argument("task", type=str) + task_download.add_argument("--redownload", action="store_true", + help="Delete cached dataset and re-download from source") task_scaffold = task_sub.add_parser("scaffold", help="Generate boilerplate for a new benchmark", parents=[common]) task_scaffold.add_argument("name", type=str, help="Task name in snake_case (e.g., 'my_benchmark')") @@ -102,6 +104,8 @@ def build_parser() -> argparse.ArgumentParser: help="Max LLM retry attempts on transient errors (default: 3)") run_parser.add_argument("--resume", type=str, default=None, help="Path to a previous run directory to resume from") + run_parser.add_argument("--redownload", action="store_true", + help="Delete cached dataset and re-download from source") # --- llm-server command --- llm_parser = subparsers.add_parser("llm-server", help="Start dummy LLM server", parents=[common]) @@ -224,12 +228,12 @@ def cmd_task_scaffold(name: str, simulator: str, max_steps: int) -> None: logger.info(" 4. Run tests: pytest tests/test_%s.py -v", name) -def cmd_task_download(task_name: str) -> None: +def cmd_task_download(task_name: str, redownload: bool = False) -> None: from easi.tasks.registry import load_task_class TaskClass = load_task_class(task_name) task = TaskClass() - path = task.download_dataset() + path = task.download_dataset(force=redownload) if path and str(path): logger.info("Dataset ready at: %s", path) else: @@ -292,7 +296,7 @@ def cmd_sim_test(simulator: str, steps: int, timeout: float) -> None: def cmd_run(task_name, agent_type, output_dir, data_dir, max_episodes, llm_url, seed, backend, model, port, llm_kwargs_raw, - max_retries, resume): + max_retries, resume, redownload=False): import json as _json from pathlib import Path @@ -347,6 +351,7 @@ def cmd_run(task_name, agent_type, output_dir, data_dir, max_episodes, llm_kwargs_raw=llm_kwargs_raw, max_retries=max_retries, resume_dir=resume, + redownload=redownload, ) results = runner.run(max_episodes=max_episodes) logger.info("Completed %d episodes.", len(results)) @@ -392,7 +397,7 @@ def main() -> None: elif args.task_action == "info": cmd_task_info(args.task) elif args.task_action == "download": - cmd_task_download(args.task) + cmd_task_download(args.task, redownload=args.redownload) elif args.task_action == "scaffold": cmd_task_scaffold(args.name, args.simulator, args.max_steps) else: @@ -408,7 +413,7 @@ def main() -> None: cmd_run(args.task, args.agent, args.output_dir, args.data_dir, args.max_episodes, args.llm_url, args.seed, args.backend, args.model, args.port, args.llm_kwargs, - args.max_retries, args.resume) + args.max_retries, args.resume, args.redownload) elif args.command == "llm-server": cmd_llm_server(args.host, args.port, args.mode, args.action_space) diff --git a/easi/core/base_task.py b/easi/core/base_task.py index ad3b442..fc64bed 100644 --- a/easi/core/base_task.py +++ b/easi/core/base_task.py @@ -150,9 +150,12 @@ def _build_action_space(self) -> list[str]: def max_steps(self) -> int: return self._config.get("max_steps", 500) - def download_dataset(self) -> Path: + def download_dataset(self, force: bool = False) -> Path: """Download dataset if needed. Returns path to local data directory. + Args: + force: If True, delete cached dataset and re-download. + - source=local: validate path exists, return it - source=huggingface: download via huggingface_hub, cache locally """ @@ -170,7 +173,7 @@ def download_dataset(self) -> Path: return local_path elif source == "huggingface": - return self._download_huggingface(dataset_config) + return self._download_huggingface(dataset_config, force=force) else: raise DatasetError(f"Unknown dataset source: {source}") @@ -306,7 +309,7 @@ def _get_builtin_episodes(self) -> list[dict]: """ return [] - def _download_huggingface(self, config: dict) -> Path: + def _download_huggingface(self, config: dict, force: bool = False) -> Path: """Download a dataset from HuggingFace Hub with file-based locking. Uses snapshot_download to get the full repo (including .zip files), @@ -327,6 +330,12 @@ def _download_huggingface(self, config: dict) -> Path: with file_lock(lock_path): target = base_dir / repo_id.replace("/", "_") + + if force and target.exists(): + import shutil + logger.info("Force re-download: removing cached %s", target) + shutil.rmtree(target, ignore_errors=True) + if not target.exists(): try: from huggingface_hub import snapshot_download diff --git a/easi/evaluation/runner.py b/easi/evaluation/runner.py index a75abeb..d46da2b 100644 --- a/easi/evaluation/runner.py +++ b/easi/evaluation/runner.py @@ -58,6 +58,7 @@ def __init__( llm_kwargs_raw: str | None = None, max_retries: int = 3, resume_dir: Path | str | None = None, + redownload: bool = False, ): self.task_name = task_name self.agent_type = agent_type @@ -71,6 +72,7 @@ def __init__( self.llm_kwargs_raw = llm_kwargs_raw self.max_retries = max_retries self.resume_dir = Path(resume_dir) if resume_dir else None + self.redownload = redownload self.run_id = datetime.now().strftime("%Y%m%d_%H%M%S") def _resolve_llm_backend(self) -> tuple[str | None, str | None]: @@ -115,6 +117,8 @@ def run(self, max_episodes: int | None = None) -> list[dict]: # 1. Load task task = self._create_task() + if self.redownload: + task.download_dataset(force=True) episodes = task.load_episodes() if max_episodes is not None: episodes = episodes[:max_episodes] From 8a46da332854996480e4ca709e853ecab5b0fd75 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Mon, 16 Feb 2026 22:10:55 +0800 Subject: [PATCH 031/239] [Feat] Add retry with exponential backoff for HuggingFace dataset downloads Retries up to 5 times with 2/4/8/16s backoff on transient network errors. Partial downloads are cleaned up only after final failure. --- easi/core/base_task.py | 40 +++++++++++++++++++++++++++++----------- 1 file changed, 29 insertions(+), 11 deletions(-) diff --git a/easi/core/base_task.py b/easi/core/base_task.py index fc64bed..d0bc5fa 100644 --- a/easi/core/base_task.py +++ b/easi/core/base_task.py @@ -345,18 +345,36 @@ def _download_huggingface(self, config: dict, force: bool = False) -> Path: "Install with: pip install huggingface_hub" ) - logger.info("Downloading dataset %s from HuggingFace...", repo_id) - try: - snapshot_download( - repo_id=repo_id, - local_dir=str(target), - repo_type="dataset", + import time as _time + + max_attempts = 5 + for attempt in range(1, max_attempts + 1): + logger.info( + "Downloading dataset %s from HuggingFace (attempt %d/%d)...", + repo_id, attempt, max_attempts, ) - except Exception as e: - if target.exists(): - import shutil - shutil.rmtree(target, ignore_errors=True) - raise DatasetError(f"Failed to download {repo_id}: {e}") + try: + snapshot_download( + repo_id=repo_id, + local_dir=str(target), + repo_type="dataset", + ) + break # success + except Exception as e: + if attempt < max_attempts: + wait = 2 ** attempt # 2, 4, 8, 16s + logger.warning( + "Download attempt %d failed: %s. Retrying in %ds...", + attempt, e, wait, + ) + _time.sleep(wait) + else: + if target.exists(): + import shutil + shutil.rmtree(target, ignore_errors=True) + raise DatasetError( + f"Failed to download {repo_id} after {max_attempts} attempts: {e}" + ) logger.info("Downloaded dataset %s to %s", repo_id, target) else: From d543878fac02bb647924ccf0a4b7d3d399456d1b Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Mon, 16 Feb 2026 22:16:02 +0800 Subject: [PATCH 032/239] [Fix] Fix habitat-sim conda env spec to use separate feature packages List headless and withbullet as separate conda dependencies instead of build string glob, matching the original EmbodiedBench install command. --- easi/simulators/habitat_sim/v0_3_0/conda_env.yaml | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/easi/simulators/habitat_sim/v0_3_0/conda_env.yaml b/easi/simulators/habitat_sim/v0_3_0/conda_env.yaml index 2184e90..a682131 100644 --- a/easi/simulators/habitat_sim/v0_3_0/conda_env.yaml +++ b/easi/simulators/habitat_sim/v0_3_0/conda_env.yaml @@ -5,4 +5,6 @@ channels: - defaults dependencies: - python=3.10 - - habitat-sim=0.3.0=*withbullet*headless* + - habitat-sim=0.3.0 + - headless + - withbullet From 1128c6e5a821e634375ef01b2b777c3f58e6cff0 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Mon, 16 Feb 2026 22:24:00 +0800 Subject: [PATCH 033/239] [Fix] Use Python 3.9 for habitat-sim 0.3.0 (no 3.10 builds available) --- easi/simulators/habitat_sim/manifest.yaml | 2 +- easi/simulators/habitat_sim/v0_3_0/conda_env.yaml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/easi/simulators/habitat_sim/manifest.yaml b/easi/simulators/habitat_sim/manifest.yaml index 2873ce3..bbe4899 100644 --- a/easi/simulators/habitat_sim/manifest.yaml +++ b/easi/simulators/habitat_sim/manifest.yaml @@ -6,4 +6,4 @@ versions: description: "Habitat 0.3.0 — currently used by eb-habitat" simulator_class: "easi.simulators.habitat_sim.v0_3_0.simulator.HabitatSimulatorV030" env_manager_class: "easi.simulators.habitat_sim.v0_3_0.env_manager.HabitatEnvManagerV030" - python_version: "3.10" + python_version: "3.9" diff --git a/easi/simulators/habitat_sim/v0_3_0/conda_env.yaml b/easi/simulators/habitat_sim/v0_3_0/conda_env.yaml index a682131..faf4f04 100644 --- a/easi/simulators/habitat_sim/v0_3_0/conda_env.yaml +++ b/easi/simulators/habitat_sim/v0_3_0/conda_env.yaml @@ -4,7 +4,7 @@ channels: - conda-forge - defaults dependencies: - - python=3.10 + - python=3.9 - habitat-sim=0.3.0 - headless - withbullet From 751ceea887e9b11ddf4a9f2c18134e85f547188e Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Mon, 16 Feb 2026 22:25:26 +0800 Subject: [PATCH 034/239] [Fix] Only validate habitat_sim import for simulator env habitat (habitat-lab) is a task-level additional_dep, not a simulator dep. --- easi/simulators/habitat_sim/v0_3_0/env_manager.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/easi/simulators/habitat_sim/v0_3_0/env_manager.py b/easi/simulators/habitat_sim/v0_3_0/env_manager.py index 7c6edb6..ac525bc 100644 --- a/easi/simulators/habitat_sim/v0_3_0/env_manager.py +++ b/easi/simulators/habitat_sim/v0_3_0/env_manager.py @@ -32,4 +32,4 @@ def get_system_deps(self) -> list[str]: return ["conda"] def get_validation_import(self) -> str: - return "import habitat_sim; import habitat" + return "import habitat_sim" From f43079be510e4cb9c68ccdb652cd9a393eade772 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 17 Feb 2026 00:07:55 +0800 Subject: [PATCH 035/239] [Fix] Restore side-effect imports, trim unused imports, and add missing dataset configs - Restore critical side-effect imports in EBHabEnv.py (predicate_task, sensors, actions, measures) and utils.py (config, dataset) with noqa: F401 annotations - Make transformers import lazy in utils.py:get_parser() to avoid requiring it at module load time - Remove unused imports across 7 vendored files - Add missing dataset/configs/ directory (dataset.yaml, instructions.yaml) --- easi/tasks/ebhabitat/vendor/EBHabEnv.py | 17 +- easi/tasks/ebhabitat/vendor/actions.py | 4 +- .../config/default_structured_configs.py | 1 - .../vendor/dataset/configs/dataset.yaml | 535 +++++++++ .../vendor/dataset/configs/instructions.yaml | 1031 +++++++++++++++++ .../ebhabitat/vendor/dataset/episodes.py | 2 - easi/tasks/ebhabitat/vendor/predicate_task.py | 9 +- easi/tasks/ebhabitat/vendor/sensors.py | 1 - easi/tasks/ebhabitat/vendor/utils.py | 17 +- 9 files changed, 1587 insertions(+), 30 deletions(-) create mode 100644 easi/tasks/ebhabitat/vendor/dataset/configs/dataset.yaml create mode 100644 easi/tasks/ebhabitat/vendor/dataset/configs/instructions.yaml diff --git a/easi/tasks/ebhabitat/vendor/EBHabEnv.py b/easi/tasks/ebhabitat/vendor/EBHabEnv.py index b99379e..294798e 100644 --- a/easi/tasks/ebhabitat/vendor/EBHabEnv.py +++ b/easi/tasks/ebhabitat/vendor/EBHabEnv.py @@ -17,7 +17,6 @@ import json import imageio from PIL import Image -import numpy as np import habitat import hydra from habitat.datasets import make_dataset @@ -27,15 +26,19 @@ from habitat.gym.gym_definitions import _add_sim_sensor_to_config from omegaconf import OmegaConf -from habitat_sim.utils import viz_utils as vut -from easi.tasks.ebhabitat.vendor.config import default_structured_configs -import easi.tasks.ebhabitat.vendor.predicate_task -import easi.tasks.ebhabitat.vendor.config -import easi.tasks.ebhabitat.vendor.measures -from easi.tasks.ebhabitat.vendor.utils import observations_to_image, merge_to_file, draw_text +from easi.tasks.ebhabitat.vendor.utils import observations_to_image from easi.utils.logging import get_logger logger = get_logger(__name__) +# Side-effect imports: register habitat components (tasks, sensors, actions, +# measures, dataset, configs) so make_gym_from_config() can find them. +# Do NOT remove even if they appear unused — they run @registry.register_* +# decorators at import time. +import easi.tasks.ebhabitat.vendor.predicate_task # noqa: F401 +import easi.tasks.ebhabitat.vendor.sensors # noqa: F401 +import easi.tasks.ebhabitat.vendor.actions # noqa: F401 +import easi.tasks.ebhabitat.vendor.measures # noqa: F401 + HABITAT_CONFIG_PATH = os.path.join(os.path.dirname(__file__), 'config/task/language_rearrangement.yaml') diff --git a/easi/tasks/ebhabitat/vendor/actions.py b/easi/tasks/ebhabitat/vendor/actions.py index 15f03df..185b40b 100644 --- a/easi/tasks/ebhabitat/vendor/actions.py +++ b/easi/tasks/ebhabitat/vendor/actions.py @@ -8,9 +8,7 @@ from habitat.core.registry import registry from habitat.tasks.rearrange.actions.articulated_agent_action import \ ArticulatedAgentAction -from habitat.tasks.rearrange.actions.grip_actions import ( - GazeGraspAction, GripSimulatorTaskAction, MagicGraspAction, - SuctionGraspAction) +from habitat.tasks.rearrange.actions.grip_actions import MagicGraspAction from habitat.tasks.rearrange.rearrange_sim import RearrangeSim from easi.tasks.ebhabitat.vendor.utils import get_allowed_actions diff --git a/easi/tasks/ebhabitat/vendor/config/default_structured_configs.py b/easi/tasks/ebhabitat/vendor/config/default_structured_configs.py index 094f600..d5ef477 100644 --- a/easi/tasks/ebhabitat/vendor/config/default_structured_configs.py +++ b/easi/tasks/ebhabitat/vendor/config/default_structured_configs.py @@ -5,7 +5,6 @@ from dataclasses import dataclass, field from typing import Any, Dict, List, Optional -import attr from habitat.config.default_structured_configs import ( ActionConfig, ArmActionConfig, HabitatSimRGBSensorConfig, LabSensorConfig, MeasurementConfig, TaskConfig) diff --git a/easi/tasks/ebhabitat/vendor/dataset/configs/dataset.yaml b/easi/tasks/ebhabitat/vendor/dataset/configs/dataset.yaml new file mode 100644 index 0000000..b083b38 --- /dev/null +++ b/easi/tasks/ebhabitat/vendor/dataset/configs/dataset.yaml @@ -0,0 +1,535 @@ +--- +dataset_path: "data/replica_cad/replicaCAD.scene_dataset_config.json" +correct_unstable_results: False +additional_object_paths: + - "data/objects/ycb/configs" +scene_sets: + - + name: "scene_train_split" + included_substrings: + - "v3_sc0_staging" + - "v3_sc1_staging" + - "v3_sc2_staging" + excluded_substrings: [] + - + name: "scene_val_split" + included_substrings: + - "v3_sc3_staging" + excluded_substrings: [] + - + name: "scene_test_split" + included_substrings: + - "v3_sc4_staging" + excluded_substrings: [] + +category_groups: + "all_cats": + # A list of every object category including both train and evaluation. + included: + - "ball" + - "clamp" + - "hammer" + - "screwdriver" + - "padlock" + - "scissors" + - "block" + - "drill" + - "spatula" + - "knife" + - "spoon" + - "plate" + - "sponge" + - "cleanser" + - "plum" + - "pear" + - "peach" + - "apple" + - "lemon" + - "can" + - "box" + - "banana" + - "strawberry" + - "lego" + - "rubriks cube" + - "book" + - "bowl" + - "cup" + - "mug" + - "orange" + - "lid" + - "toy airplane" + - "wrench" + # - "fork" + + "all_eval_cats": + included: + - "mug" + - "orange" + - "lid" + - "toy airplane" + - "wrench" + + "all_train_cats": + included: + - "ball" + - "clamp" + - "hammer" + - "screwdriver" + - "padlock" + - "scissors" + - "block" + - "drill" + - "spatula" + - "knife" + - "spoon" + # - "fork" + - "plate" + - "sponge" + - "cleanser" + - "plum" + - "pear" + - "peach" + - "apple" + - "lemon" + - "can" + - "box" + - "banana" + - "strawberry" + - "lego" + - "rubriks cube" + - "book" + - "bowl" + - "cup" + + "all_fruit": + included: + - "plum" + - "pear" + - "peach" + - "apple" + - "lemon" + + + +object_sets: + # Must be a 1-1 mapping. + - name: "CLUTTER_OBJECTS" + excluded_substrings: [] + included_substrings: + - "053_mini_soccer_ball" + - "054_softball" + - "055_baseball" + - "056_tennis_ball" + - "057_racquetball" + #- "058_golf_ball" + - "050_medium_clamp" + - "051_large_clamp" + - "052_extra_large_clamp" + - "048_hammer" + - "043_phillips_screwdriver" + - "044_flat_screwdriver" + - "042_adjustable_wrench" + - "038_padlock" + - "037_scissors" + - "036_wood_block" + - "070-a_colored_wood_blocks" + - "070-b_colored_wood_blocks" + - "035_power_drill" + - "033_spatula" + - "032_knife" + - "031_spoon" + #- "030_fork" + - "029_plate" + - "028_skillet_lid" + - "026_sponge" + - "021_bleach_cleanser" + - "018_plum" + - "017_orange" + - "016_pear" + - "015_peach" + - "013_apple" + - "014_lemon" + - "002_master_chef_can" + - "005_tomato_soup_can" + - "007_tuna_fish_can" + - "010_potted_meat_can" + - "003_cracker_box" + - "004_sugar_box" + - "008_pudding_box" + - "009_gelatin_box" + - "011_banana" + - "012_strawberry" + - "072-a_toy_airplane" + - "072-b_toy_airplane" + - "072-c_toy_airplane" + - "072-d_toy_airplane" + - "072-e_toy_airplane" + - "073-a_lego_duplo" + - "073-b_lego_duplo" + - "073-c_lego_duplo" + - "073-d_lego_duplo" + - "073-e_lego_duplo" + - "073-f_lego_duplo" + - "073-g_lego_duplo" + - "077_rubiks_cube" + - "frl_apartment_book_01" + - "frl_apartment_book_02" + - "frl_apartment_book_03" + - "frl_apartment_book_04" + - "frl_apartment_book_05" + - "frl_apartment_book_06" + - "frl_apartment_bowl_01" + - "frl_apartment_bowl_02" + - "frl_apartment_bowl_03" + - "frl_apartment_bowl_06" + - "frl_apartment_bowl_07" + - "024_bowl" + - "025_mug" + - "065-a_cups" + - "065-b_cups" + - "065-c_cups" + - "065-d_cups" + - "065-e_cups" + - "065-f_cups" + - "065-g_cups" + - "065-h_cups" + - "065-i_cups" + - "065-j_cups" + - "frl_apartment_cup_01" + - "frl_apartment_cup_02" + - "frl_apartment_cup_03" + - "frl_apartment_cup_05" + + - name: "ball" + excluded_substrings: [] + included_substrings: + - "053_mini_soccer_ball" + - "054_softball" + - "055_baseball" + - "056_tennis_ball" + - "057_racquetball" + # This will roll away. + #- "058_golf_ball" + + - name: "clamp" + excluded_substrings: [] + included_substrings: + - "050_medium_clamp" + - "051_large_clamp" + - "052_extra_large_clamp" + + - name: "hammer" + excluded_substrings: [] + included_substrings: + - "048_hammer" + + - name: "screwdriver" + excluded_substrings: [] + included_substrings: + - "043_phillips_screwdriver" + - "044_flat_screwdriver" + + - name: "wrench" + excluded_substrings: [] + included_substrings: + - "042_adjustable_wrench" + + - name: "padlock" + excluded_substrings: [] + included_substrings: + - "038_padlock" + + - name: "scissors" + excluded_substrings: [] + included_substrings: + - "037_scissors" + + - name: "block" + excluded_substrings: [] + included_substrings: + - "036_wood_block" + - "070-a_colored_wood_blocks" + - "070-b_colored_wood_blocks" + + - name: "drill" + excluded_substrings: [] + included_substrings: + - "035_power_drill" + + - name: "spatula" + excluded_substrings: [] + included_substrings: + - "033_spatula" + + - name: "knife" + excluded_substrings: [] + included_substrings: + - "032_knife" + + - name: "spoon" + excluded_substrings: [] + included_substrings: + - "031_spoon" + + # - name: "fork" + # excluded_substrings: [] + # included_substrings: + # - "030_fork" + + - name: "plate" + excluded_substrings: [] + included_substrings: + - "029_plate" + + - name: "lid" + excluded_substrings: [] + included_substrings: + - "028_skillet_lid" + + - name: "sponge" + excluded_substrings: [] + included_substrings: + - "026_sponge" + + - name: "cleanser" + excluded_substrings: [] + included_substrings: + - "021_bleach_cleanser" + + - name: "plum" + excluded_substrings: [] + included_substrings: + - "018_plum" + + - name: "orange" + excluded_substrings: [] + included_substrings: + - "017_orange" + + - name: "pear" + excluded_substrings: [] + included_substrings: + - "016_pear" + + - name: "peach" + excluded_substrings: [] + included_substrings: + - "015_peach" + + - name: "apple" + excluded_substrings: [] + included_substrings: + - "013_apple" + + - name: "lemon" + excluded_substrings: [] + included_substrings: + - "014_lemon" + + - name: "can" + excluded_substrings: [] + included_substrings: + - "002_master_chef_can" + - "005_tomato_soup_can" + - "007_tuna_fish_can" + - "010_potted_meat_can" + + - name: "box" + excluded_substrings: [] + included_substrings: + - "003_cracker_box" + - "004_sugar_box" + - "008_pudding_box" + - "009_gelatin_box" + + - name: "banana" + excluded_substrings: [] + included_substrings: + - "011_banana" + + - name: "strawberry" + excluded_substrings: [] + included_substrings: + - "012_strawberry" + + - name: "toy airplane" + excluded_substrings: [] + included_substrings: + - "072-a_toy_airplane" + - "072-b_toy_airplane" + - "072-c_toy_airplane" + - "072-d_toy_airplane" + - "072-e_toy_airplane" + + - name: "lego" + excluded_substrings: [] + included_substrings: + - "073-a_lego_duplo" + - "073-b_lego_duplo" + - "073-c_lego_duplo" + - "073-d_lego_duplo" + - "073-e_lego_duplo" + - "073-f_lego_duplo" + - "073-g_lego_duplo" + + - name: "rubriks cube" + excluded_substrings: [] + included_substrings: + - "077_rubiks_cube" + + - name: "book" + excluded_substrings: [] + included_substrings: + - "frl_apartment_book_01" + - "frl_apartment_book_02" + - "frl_apartment_book_03" + - "frl_apartment_book_04" + - "frl_apartment_book_05" + - "frl_apartment_book_06" + + - name: "bowl" + excluded_substrings: [] + included_substrings: + - "frl_apartment_bowl_01" + - "frl_apartment_bowl_02" + - "frl_apartment_bowl_03" + - "frl_apartment_bowl_06" + - "frl_apartment_bowl_07" + - "024_bowl" + + - name: "mug" + excluded_substrings: [] + included_substrings: + - "025_mug" + + - name: "cup" + excluded_substrings: [] + included_substrings: + - "065-a_cups" + - "065-b_cups" + - "065-c_cups" + - "065-d_cups" + - "065-e_cups" + - "065-f_cups" + - "065-g_cups" + - "065-h_cups" + - "065-i_cups" + - "065-j_cups" + - "frl_apartment_cup_01" + - "frl_apartment_cup_02" + - "frl_apartment_cup_03" + - "frl_apartment_cup_05" + +receptacle_sets: + # Keep this as the 0th entry. + - + name: "all_receps" + included_object_substrings: [""] + excluded_object_substrings: [] + excluded_receptacle_substrings: [] + included_receptacle_substrings: + - "receptacle_aabb_Chr1_Top1_frl_apartment_chair_01" + - "receptacle_aabb_Tbl1_Top1_frl_apartment_table_01" + - "receptacle_aabb_Tbl2_Top1_frl_apartment_table_02" + - "receptacle_aabb_TvStnd1_Top1_frl_apartment_tvstand" + - "receptacle_aabb_sink_kitchen_counter" + - "receptacle_aabb_counter_right_kitchen_counter" + - "receptacle_aabb_counter_left_kitchen_counter" + - "receptacle_aabb_Sofa_frl_apartment_sofa" + + # Inside of articulated objects. + - "receptacle_aabb_middle_topfrl_apartment_refrigerator" + - "receptacle_aabb_drawer_left_top_frl_apartment_kitchen_counter" + - "receptacle_aabb_drawer_right_top_frl_apartment_kitchen_counter" + # - "receptacle_aabb_drawer_middle_top_frl_apartment_kitchen_counter" + + - + name: "open_air_receps" + included_object_substrings: + - "" + excluded_object_substrings: [] + excluded_receptacle_substrings: [] + included_receptacle_substrings: + # - "receptacle_aabb_Chr1_Top1_frl_apartment_chair_01" + - "receptacle_aabb_Tbl1_Top1_frl_apartment_table_01" + - "receptacle_aabb_Tbl2_Top1_frl_apartment_table_02" + - "receptacle_aabb_TvStnd1_Top1_frl_apartment_tvstand" + - "receptacle_aabb_sink_kitchen_counter" + - "receptacle_aabb_counter_right_kitchen_counter" + - "receptacle_aabb_counter_left_kitchen_counter" + - "receptacle_aabb_Sofa_frl_apartment_sofa" + + +max_objects_per_receptacle: + - ["receptacle_aabb_Chr1_Top1_frl_apartment_chair_01", 2] + - ["receptacle_aabb_sink_kitchen_counter", 2] + +scene_sampler: + type: "subset" + params: + scene_sets: ["scene_train_split", "scene_val_split", "scene_test_split"] + +object_samplers: + - + name: "CLUTTER" + type: "uniform" + params: + object_sets: ["CLUTTER_OBJECTS"] + receptacle_sets: ["open_air_receps"] + num_samples: [30, 30] + orientation_sampling: "up" + +object_target_samplers: [] + +ao_state_samplers: + - + name: "open_fridge_cab" + type: "composite" + params: + - + ao_handle: "fridge" + joint_states: + - ["top_door", 1.5, 1.5] + should_sample_all_joints: True + - + ao_handle: "counter" + joint_states: + - ["drawer1_top", 0.5, 0.5] + - ["drawer1_bottom", 0.5, 0.5] + - ["drawer2_top", 0.5, 0.5] + - ["drawer2_middle", 0.5, 0.5] + - ["drawer2_bottom", 0.5, 0.5] + - ["drawer3", 0.5, 0.5] + - ["drawer4", 0.5, 0.5] + +markers: + - name: "cab_push_point_7" + type: "articulated_object" + params: + offset: [0.3,0.0,0] + link: "drawer1_top" + object: "kitchen_counter_:0000" + - name: "cab_push_point_6" + type: "articulated_object" + params: + offset: [0.3,0.0,0] + link: "drawer2_top" + object: "kitchen_counter_:0000" + - name: "cab_push_point_5" + type: "articulated_object" + params: + offset: [0.3,0.0,0] + link: "drawer3" + object: "kitchen_counter_:0000" + - name: "cab_push_point_4" + type: "articulated_object" + params: + offset: [0.3,0.0,0] + link: "drawer4" + object: "kitchen_counter_:0000" + - name: "fridge_push_point" + type: "articulated_object" + params: + offset: [0.10,-0.62,0.2] + link: "top_door" + object: "fridge_:0000" diff --git a/easi/tasks/ebhabitat/vendor/dataset/configs/instructions.yaml b/easi/tasks/ebhabitat/vendor/dataset/configs/instructions.yaml new file mode 100644 index 0000000..1c9cec0 --- /dev/null +++ b/easi/tasks/ebhabitat/vendor/dataset/configs/instructions.yaml @@ -0,0 +1,1031 @@ +max_per_obj_count: 1 +instructions: + # See `InstructionInfo` docs about instruction info. + # DONT USE RECEPTACLE AS AN ENTITY NAME + + 42f1b012: + # Single object rearrangement, revealed targ_recep. + lang: + train: + - "Move a `target_object_name` from the `source_receptacle_name` to the `target_receptacle_name`" + - "Pick a `target_object_name` from the `source_receptacle_name` and move it to the `target_receptacle_name`" + - "Retrieve an `target_object_name` from the `source_receptacle_name` and transfer it to the `target_receptacle_name`." + - "Take a `target_object_name` out of the `source_receptacle_name` and place it into the `target_receptacle_name`." + - "Gather an `target_object_name` from the `source_receptacle_name` and relocate it to the `target_receptacle_name`." + - "Remove a `target_object_name` from the `source_receptacle_name` and reposition it in the `target_receptacle_name`." + - "Acquire an `target_object_name` from the `source_receptacle_name` and shift it to the `target_receptacle_name`." + - "Extract a `target_object_name` from the `source_receptacle_name` and move it to the `target_receptacle_name`." + - "Secure an `target_object_name` from the `source_receptacle_name` and transport it to the `target_receptacle_name`." + - "Pick up an `target_object_name` from the `source_receptacle_name` and deposit it into the `target_receptacle_name`." + - "Obtain an `target_object_name` from the `source_receptacle_name` and transfer it to the `target_receptacle_name`." + - "Collect a `target_object_name` from the `source_receptacle_name` and place it in the `target_receptacle_name`." + + eval: + - "On the `source_receptacle_name` there is a `target_object_name`, move it to the `target_receptacle_name`" + - "Hey, on the `source_receptacle_name`, I accidentally left my `target_object_name`, can you bring it to the `target_receptacle_name`?" + - "The `target_object_name` is on the `source_receptacle_name` but you should move it to the `target_receptacle_name`." + tags: ['v2_train', 'obj_gen', 'v1', 'rearrange', 'simple'] + overweight_freq: 4 + solution_template: + - "nav(`source_receptacle_name`)" + - "pick_`target_object_name`()" + - "nav(`target_receptacle_name`)" + - "place(`target_receptacle_name`)" + goal_expr: + expr_type: AND + quantifier: EXISTS + inputs: + - name: X + expr_type: "target_object_name" + sub_exprs: + - "on_top(X, target_receptacle_name)" + - "not_holding()" + sampled_receps: + "source_receptacle_name": + type: "open_air_receps" + "target_receptacle_name": + type: "open_air_receps" + not_equal: ["source_receptacle_name"] + sampled_objects: + "target_object_name": + types: + train: "all_train_cats" + eval: "all_eval_cats" + recep: source_receptacle_name + + f0917e29: + # Single object rearrangement non-revealed targ_recep. + lang: + train: + - "Move a `target_object_name` to the `target_receptacle_name`" + + - "Transfer an `target_object_name` to the designated `target_receptacle_name`." + - "Relocate a `target_object_name` to its assigned `target_receptacle_name`." + - "Shift a `target_object_name` and place it into the specified `target_receptacle_name`." + - "Move one of the `target_object_name` items to the indicated `target_receptacle_name`." + - "Take an `target_object_name` and transfer it to the allocated `target_receptacle_name`." + - "Transport a `target_object_name` and carefully position it in the designated `target_receptacle_name`." + - "Pick up a `target_object_name` and move it to the preassigned `target_receptacle_name`." + - "Remove an `target_object_name` from its current location and relocate it to the specified `target_receptacle_name`." + - "Retrieve a `target_object_name` and place it into the predetermined `target_receptacle_name`." + - "Deliver the `target_object_name` to the intended `target_receptacle_name` by physically moving it there." + eval: + - "On the `target_receptacle_name` I need you to put a `target_object_name`" + - "Find a `target_object_name` and move it to the `target_receptacle_name`" + - "I need a `target_object_name` on the `target_receptacle_name`. Can you help?" + distraction: + - "There's an apple on the sofa, but on the `target_receptacle_name` I need you to put a `target_object_name`" + - "On the sofa there's an apple, but instead find a `target_object_name` and move it to the `target_receptacle_name`" + - "Instead of finding an apple, I need a `target_object_name` on the `target_receptacle_name`. Can you help?" + tags: ['v2_train', 'obj_gen', 'rearrange', 'simple'] + overweight_freq: 4 + solution_template: + - "nav(`source_receptacle_name`)" + - "pick_`target_object_name`()" + - "nav(`target_receptacle_name`)" + - "place(`target_receptacle_name`)" + goal_expr: + expr_type: AND + quantifier: EXISTS + inputs: + - name: X + expr_type: "target_object_name" + sub_exprs: + - "on_top(X, target_receptacle_name)" + - "not_holding()" + sampled_receps: + "source_receptacle_name": + type: "open_air_receps" + "target_receptacle_name": + type: "open_air_receps" + not_equal: ["source_receptacle_name"] + sampled_objects: + "target_object_name": + types: + train: "all_train_cats" + eval: "all_eval_cats" + not_recep: target_receptacle_name + recep: source_receptacle_name + semantics: + 688ee521: + match_objs: + target_object_name: + - "plate" + lang: + train: + - "Set out a `target_object_name` for one person on the `target_receptacle_name`." + + + 9a1dd6ee: + lang: + train: + - "Move the `target_object_name` off the `target_receptacle_name`." + + - "Remove the `target_object_name` from the `target_receptacle_name`." + - "Take the `target_object_name` away from the `target_receptacle_name`." + - "Lift the `target_object_name` off the `target_receptacle_name`." + - "Extract the `target_object_name` from the `target_receptacle_name`." + - "Retrieve the `target_object_name` that is on the `target_receptacle_name`." + - "Displace the `target_object_name` from the `target_receptacle_name`." + - "Detach the `target_object_name` from the `target_receptacle_name`." + - "Free the `target_object_name` from the `target_receptacle_name`." + - "Separate the `target_object_name` from the `target_receptacle_name`." + - "Unseat the `target_object_name` from the `target_receptacle_name`." + eval: + - "The `target_receptacle_name` should be devoid of any `target_object_name`." + - "On the `target_receptacle_name`, remove the `target_object_name`." + - "Clear all the `target_object_name` from the `target_receptacle_name`." + tags: ['v2_train', 'obj_gen', 'rearrange', 'simple'] + overweight_freq: 4 + goal_expr: + expr_type: AND + sub_exprs: + - "not_holding()" + - expr_type: NAND + inputs: + - name: X + expr_type: "target_object_name" + quantifier: EXISTS + sub_exprs: + - "on_top(X, target_receptacle_name)" + sampled_receps: + "target_receptacle_name": + "type": "open_air_receps" + sampled_objects: + "target_object_name": + types: + train: "all_cats" + eval: "all_eval_cats" + recep: target_receptacle_name + sampler_info: + place_search_all_receps: True + + 8a66dec9: + # Single object rearrangement non-revealed targ_recep, visual semantics only. + lang: + train: [] + eval: [] + tags: ['v2_eval_semantic_visual'] + overweight_freq: 1 + solution_template: + - "nav(`source_receptacle_name`)" + - "pick_`target_object_name`()" + - "nav(`target_receptacle_name`)" + - "place(`target_receptacle_name`)" + goal_expr: + expr_type: AND + quantifier: EXISTS + inputs: + - name: X + expr_type: "target_object_name" + sub_exprs: + - "on_top(X, target_receptacle_name)" + - "not_holding()" + sampled_receps: + "source_receptacle_name": + type: "open_air_receps" + "target_receptacle_name": + type: "open_air_receps" + not_equal: ["source_receptacle_name"] + sampled_objects: + "target_object_name": + types: + train: "all_cats" + eval: "all_eval_cats" + not_recep: target_receptacle_name + recep: source_receptacle_name + semantics: + 688ee521: + match_objs: + target_object_name: + - "plum" + lang: + train: + - "Bring a purple fruit to the `target_receptacle_name`" + - "Fetch a purple fruit and transport it to the `target_receptacle_name`." + - "Locate a purple fruit and convey it to the `target_receptacle_name`." + - "Please, carry a purple fruit to the `target_receptacle_name`." + - "Procure a purple fruit and deliver it to the `target_receptacle_name`." + - "Retrieve a purple fruit and place it in the `target_receptacle_name`." + - "Get a purple fruit and transfer it to the `target_receptacle_name`." + - "Acquire a purple fruit and deposit it in the `target_receptacle_name`." + - "Secure a purple fruit and bring it to the `target_receptacle_name`." + - "Find a purple fruit and move it to the `target_receptacle_name`." + - "Can you pick up a purple fruit and place it in the `target_receptacle_name`?" + eb3cd1c5: + match_objs: + target_object_name: + - "lemon" + lang: + train: + - "Bring a yellow round fruit to the `target_receptacle_name`." + - "Retrieve a yellow round fruit and transport it to the `target_receptacle_name`." + - "Procure a yellow round fruit and convey it to the `target_receptacle_name`." + - "Locate a yellow round fruit and deliver it to the `target_receptacle_name`." + - "Fetch a yellow round fruit and transfer it to the `target_receptacle_name`." + - "Secure a yellow round fruit and bring it to the `target_receptacle_name`." + - "Find a yellow round fruit and place it in the `target_receptacle_name`." + - "Pick up a yellow round fruit and deposit it into the `target_receptacle_name`." + - "Obtain a yellow round fruit and put it in the `target_receptacle_name`." + - "Take a yellow round fruit and position it within the `target_receptacle_name`." + - "Acquire a yellow round fruit and leave it in the `target_receptacle_name`." + + 199c7b7f: + match_objs: + target_object_name: + - "banana" + lang: + train: + - "Bring a yellow curved fruit to the `target_receptacle_name`" + - "Fetch a yellow curved fruit and take it to the `target_receptacle_name`." + - "Retrieve a yellow curved fruit and transport it to the `target_receptacle_name`." + - "Carry a yellow curved fruit over to the `target_receptacle_name`." + - "Get ahold of a yellow curved fruit and deliver it to the `target_receptacle_name`." + - "Take a yellow curved fruit and place it in the `target_receptacle_name`." + - "Find a yellow curved fruit and bring it to the `target_receptacle_name`." + - "Procure a yellow curved fruit and transfer it to the `target_receptacle_name`." + - "Secure a yellow curved fruit and deposit it into the `target_receptacle_name`." + - "Acquire a yellow curved fruit and put it in the `target_receptacle_name`." + - "Retrieve a yellow curved fruit and ensure it's in the `target_receptacle_name`." + d9cac0e2: + match_objs: + target_object_name: + - "apple" + lang: + train: + - "Bring a round red fruit to the `target_receptacle_name`" + - "Fetch a round red fruit and take it to the `target_receptacle_name`." + - "Get a round red fruit and deliver it to the `target_receptacle_name`." + - "Acquire a round red fruit and transport it to the `target_receptacle_name`." + - "Secure a round red fruit and place it in the `target_receptacle_name`." + - "Find a round red fruit and bring it to the `target_receptacle_name`." + - "Locate a round red fruit and deposit it in the `target_receptacle_name`." + - "Obtain a round red fruit and put it in the `target_receptacle_name`." + - "Grab a round red fruit and transfer it to the `target_receptacle_name`." + - "Snatch a round red fruit and drop it into the `target_receptacle_name`." + - "Collect a round red fruit and position it in the `target_receptacle_name`." + + 21e2ce5f: + # Single object rearrangement non-revealed targ_recep, purpose semantics only. + lang: + train: [] + eval: [] + tags: ['v2_eval_semantic_activity'] + overweight_freq: 1 + solution_template: + - "nav(`source_receptacle_name`)" + - "pick_`target_object_name`()" + - "nav(`target_receptacle_name`)" + - "place(`target_receptacle_name`)" + goal_expr: + expr_type: AND + quantifier: EXISTS + inputs: + - name: X + expr_type: "target_object_name" + sub_exprs: + - "on_top(X, target_receptacle_name)" + - "not_holding()" + sampled_receps: + "source_receptacle_name": + type: "open_air_receps" + "target_receptacle_name": + type: "open_air_receps" + not_equal: ["source_receptacle_name"] + sampled_objects: + "target_object_name": + types: + train: "all_cats" + eval: "all_eval_cats" + not_recep: target_receptacle_name + recep: source_receptacle_name + semantics: + 688ee521: + match_objs: + target_object_name: + - "ball" + lang: + train: + - "I want to play a sport, bring something to play with to the `target_receptacle_name`." + - "Fetch a sports equipment and place it in the `target_receptacle_name`." + - "Please provide a sports item for me to play with in the `target_receptacle_name`." + - "Procure a plaything for sports and deposit it in the `target_receptacle_name`." + - "I'd like to engage in a sport, so kindly transport an item for play to the `target_receptacle_name`." + - "Prepare for a game by delivering something to play with to the `target_receptacle_name`." + - "Acquire a sports object and position it in the `target_receptacle_name`." + - "Bring an item suitable for playing a sport and put it in the `target_receptacle_name`." + - "I'm in the mood for a sport; could you move a plaything to the `target_receptacle_name`?" + - "Please arrange for a sports object to be placed in the `target_receptacle_name` for my amusement." + - "Facilitate a sports activity by transferring an appropriate item to the `target_receptacle_name`." + e767a83f: + match_objs: + target_object_name: + - "screwdriver" + lang: + train: + - "A screw is loose in the `target_receptacle_name`, bring something to fix it." + - "Retrieve a tool to tighten the loose screw in the `target_receptacle_name`." + - "Grab an item for securing the loose screw within the `target_receptacle_name`." + - "Please, get something to fasten the loose screw in the `target_receptacle_name`." + - "Find an object suitable for tightening the loose screw in the `target_receptacle_name`." + - "There's a loose screw in the `target_receptacle_name`; fetch a fixer." + - "Secure the loose screw in the `target_receptacle_name` with an appropriate object." + - "Locate an instrument to remedy the loose screw in the `target_receptacle_name`." + - "Go and fetch a tool for tightening the loose screw in the `target_receptacle_name`." + - "Procure an item to rectify the loose screw issue in the `target_receptacle_name`." + - "Retrieve a suitable implement to address the loose screw problem in the `target_receptacle_name`." + + eba65683: + match_objs: + target_object_name: + - "scissors" + lang: + train: + - "I need to cut a piece of paper at the `target_receptacle_name`, can you bring something to help?" + - "Fetch an item to assist with cutting a piece of paper at the `target_receptacle_name`." + - "Could you please bring a tool for cutting the paper at the `target_receptacle_name`?" + - "I require something to aid me in cutting a piece of paper at the `target_receptacle_name`." + - "Please procure an object to facilitate cutting paper at the `target_receptacle_name`." + - "Bring a cutting tool to the `target_receptacle_name` to help with the paper." + - "Find an item that can be used for cutting at the `target_receptacle_name`." + - "Could you fetch a suitable object to aid in paper cutting at the `target_receptacle_name`?" + - "I'm in need of an implement for cutting paper at the `target_receptacle_name`. Please get it for me." + - "To cut a piece of paper at the `target_receptacle_name`, kindly provide me with a tool." + - "Can you bring something to assist in cutting paper at the `target_receptacle_name`?" + + 9996fbd6: + match_objs: + target_object_name: + - "cleanser" + lang: + train: + - "I spilt something and need to clean it. Can you bring something to the `target_receptacle_name` to help?" + - "I accidentally spilled a liquid, could you please fetch an object to help with cleaning it up in the `target_receptacle_name`?" + - "There's been a spill, and I require an object to assist with cleaning it. Can you bring it to the `target_receptacle_name`?" + - "Oops, I've made a mess. Can you grab an object for me to use in cleaning up the spill in the `target_receptacle_name`?" + - "It looks like I've spilled something. Would you mind getting the object I need to clean it up in the `target_receptacle_name`?" + - "There's a spill, and I need an object to clean it up. Could you bring it to the `target_receptacle_name`?" + - "I made a mess, and I'm in need of an object to help with cleaning it up. Can you fetch it for me in the `target_receptacle_name`?" + - "Uh-oh, I spilled something. Can you find an object to assist with cleaning it? Bring it to the `target_receptacle_name`, please." + - "I've had a spill, and I'm seeking an object to aid in cleaning it up. Can you deliver it to the `target_receptacle_name`?" + - "Regrettably, there's been a spill. Can you locate an object for me to use in cleaning it up in the `target_receptacle_name`?" + - "I've managed to spill something, and I need an object to help with cleaning it up. Would you kindly bring it to the `target_receptacle_name`?" + + b7da80f7: + match_objs: + target_object_name: + - "mug" + lang: + train: + - "Bring me something to pour hot coffee into at the `target_receptacle_name`" + - "Fetch a container for pouring hot coffee into at the `target_receptacle_name`." + - "Could you please procure a receptacle for pouring hot coffee into at the `target_receptacle_name`?" + - "I need a vessel to pour hot coffee into at the `target_receptacle_name`." + - "Find something to serve hot coffee in at the `target_receptacle_name`." + - "Bring me a suitable object to pour hot coffee into at the `target_receptacle_name`." + - "Retrieve an appropriate receptacle for hot coffee at the `target_receptacle_name`." + - "Locate an item for pouring hot coffee into at the `target_receptacle_name`." + - "Please obtain an object for me to pour hot coffee into at the `target_receptacle_name`." + - "I'd like a container for my hot coffee at the `target_receptacle_name`, please." + - "Procure an item for hot coffee pouring at the `target_receptacle_name`." + + fd8dd56c: + # 2 object rearrangement hidden start + lang: + train: + - "Place a `object1` and a `object2` on the `targ_recep`." + - "Put an `object1` and a `object2` onto the `targ_recep`." + - "Position an `object1` and a `object2` on the `targ_recep`." + - "Set down an `object1` and a `object2` on the `targ_recep`." + - "Arrange an `object1` and a `object2` on the `targ_recep`." + - "Deposit an `object1` and a `object2` onto the `targ_recep`." + - "Lay an `object1` and a `object2` on the `targ_recep`." + - "Place both an `object1` and a `object2` onto the `targ_recep`." + - "Place an `object1` and an `object2` on the `targ_recep` surface." + - "Position both an `object1` and a `object2` on the `targ_recep`." + - "Put both an `object1` and a `object2` onto the `targ_recep`." + eval: + - "On the `targ_recep`, I need a `object1` and a `object2`." + - "The `targ_recep` needs an `object1` and a `object2` on it." + - "Bring a `object1` and a `object2` to the `targ_recep`." + tags: ['v2_train', 'obj_gen', 'rearrange'] + overweight_freq: 4 + subsample_entity_ratio: 0.75 + solution_template: + - "nav(`source_recep_1`)" + - "pick_`object1`()" + - "nav(`targ_recep`)" + - "place(`targ_recep`)" + - "nav(`source_recep_2`)" + - "pick_`object2`()" + - "nav(`targ_recep`)" + - "place(`targ_recep`)" + goal_expr: + expr_type: AND + quantifier: EXISTS + inputs: + - name: X + expr_type: "object1" + - name: Y + expr_type: "object2" + sub_exprs: + - "on_top(X, targ_recep)" + - "on_top(Y, targ_recep)" + - "not_holding()" + sampled_receps: + "targ_recep": + type: "open_air_receps" + "source_recep_1": + type: "open_air_receps" + not_equal: ["targ_recep"] + "source_recep_2": + type: "open_air_receps" + not_equal: ["targ_recep"] + sampled_objects: + "object1": + types: + train: "all_cats" + eval: "all_eval_cats" + recep: "source_recep_1" + "object2": + types: + train: "all_cats" + eval: "all_eval_cats" + not_equal: ['object1'] + recep: "source_recep_2" + semantics: + b369767a: + match_objs: + object1: ['knife'] + object2: ['spoon'] + targ_recep: ['receptacle_aabb_Tbl1_Top1_frl_apartment_table_01'] + lang: + train: + - "Set the table with the knife and spoon utensils." + - "Arrange the table with a knife and spoon." + - "Prepare the table, placing a knife and spoon." + - "Place a knife and spoon on the table for setting." + - "Position the table with a knife and spoon." + - "Organize the table, ensuring a knife and spoon are set." + - "Lay out the table, including a knife and spoon." + - "Put a knife and spoon on the table for setting." + - "Arrange the table setting, incorporating a knife and spoon." + - "Position a knife and spoon on the table for proper setting." + - "Set up the table, making sure a knife and spoon are included." + eval: + - "Set the table for one with a knife and spoon" + + + ############################################# + + + ############################################# + # Object rearrangement (custom) + + 6e7f4383: + # 2 object rearrangement. Revealed start and goals. + lang: + train: + - "Can you swap the `object1` and the `object2` in the `receptacle1` and `receptacle2`?" + + - "Could you exchange `object1` and `object2` between `receptacle1` and `receptacle2`?" + - "Is it possible to swap the positions of `object1` and `object2` in `receptacle1` and `receptacle2`?" + - "Please perform a swap of `object1` and `object2` between the `receptacle1` and `receptacle2`." + - "I need you to switch the places of `object1` and `object2` in `receptacle1` and `receptacle2`." + - "Can you rearrange `object1` and `object2` by interchanging them in `receptacle1` and `receptacle2`?" + - "In `receptacle1` and `receptacle2`, could you swap the locations of `object1` and `object2`?" + - "Kindly perform a switch between `object1` and `object2` in `receptacle1` and `receptacle2`." + - "Would you be able to trade the positions of `object1` and `object2` in `receptacle1` and `receptacle2`?" + - "I would appreciate it if you could perform a swap between `object1` and `object2` in `receptacle1` and `receptacle2`." + - "Please switch the places of `object1` and `object2` in `receptacle1` and `receptacle2` for me." + eval: + - "I misplaced the `object1` on the `receptacle1` and the `object2` on the `receptacle2`. Can you swap their positions?" + - "Swap the positions of the `object1` on the `receptacle1` and the `object2` on the `receptacle2`?" + - "Swap the `object1` and `object2` between the `receptacle1` and `receptacle2`." + tags: ['v2_train', 'obj_gen', 'rearrange'] + overweight_freq: 4 + solution_template: + - "nav(`receptacle1`)" + - "pick_`object1`()" + - "nav(`receptacle2`)" + - "place(`receptacle2`)" + - "nav(`receptacle2`)" + - "pick_`object2`()" + - "nav(`receptacle1`)" + - "place(`receptacle1`)" + goal_expr: + expr_type: AND + quantifier: EXISTS + inputs: + - name: X + expr_type: "object1" + - name: Y + expr_type: "object2" + sub_exprs: + - "on_top(X, receptacle2)" + - "on_top(Y, receptacle1)" + - "not_holding()" + sampled_receps: + "receptacle1": + type: "open_air_receps" + "receptacle2": + type: "open_air_receps" + not_equal: ['receptacle1'] + sampled_objects: + "object1": + types: + train: "all_cats" + eval: "all_eval_cats" + recep: "receptacle1" + "object2": + types: + train: "all_cats" + eval: "all_eval_cats" + not_equal: ["object1"] + recep: "receptacle2" + + db3119cd: + # 3 object rearrangement. + lang: + train: + # No revealed + - "Move the `obj1` to the `targ_recep1`, the `obj2` to the `targ_recep2`, and the `obj3` to the `targ_recep3`." + # 1 revealed + - "Move the `obj1` from the `source_recep1` to the `targ_recep1`, the `obj2` to the `targ_recep2`, and the `obj3` to the `targ_recep3`." + # 2 revealed + - "Move the `obj1` from the `source_recep1` to the `targ_recep1`, the `obj2` to the `targ_recep2`, and the `obj3` to the `targ_recep3`." + # 3 revealed + - "Move the `obj1` from the `source_recep1` to the `targ_recep1`, the `obj2` from the `source_recep2` to the `targ_recep2`, and the `obj3` from the `source_recep3` to the `targ_recep3`." + tags: ['v2_eval_longer'] + overweight_freq: 1 + subsample_entity_ratio: 0.5 + solution_template: + - "nav(`source_recep1`)" + - "pick_`obj1`()" + - "nav(`targ_recep1`)" + - "place(`targ_recep1`)" + - "nav(`source_recep2`)" + - "pick_`obj2`()" + - "nav(`targ_recep2`)" + - "place(`targ_recep2`)" + - "nav(`source_recep3`)" + - "pick_`obj3`()" + - "nav(`targ_recep3`)" + - "place(`targ_recep3`)" + goal_expr: + expr_type: AND + quantifier: EXISTS + inputs: + - name: X + expr_type: "obj1" + - name: Y + expr_type: "obj2" + - name: Z + expr_type: "obj3" + sub_exprs: + - "on_top(X, targ_recep1)" + - "on_top(Y, targ_recep2)" + - "on_top(Z, targ_recep3)" + - "not_holding()" + sampled_receps: + "source_recep1": + type: "open_air_receps" + "targ_recep1": + type: "open_air_receps" + not_equal: ['source_recep1'] + "source_recep2": + type: "open_air_receps" + "targ_recep2": + type: "open_air_receps" + not_equal: ['source_recep2'] + "source_recep3": + type: "open_air_receps" + "targ_recep3": + type: "open_air_receps" + not_equal: ['source_recep3'] + sampled_objects: + "obj1": + types: + train: "all_cats" + eval: "all_eval_cats" + recep: "source_recep1" + "obj2": + types: + train: "all_cats" + eval: "all_eval_cats" + not_equal: ["obj1"] + recep: "source_recep2" + "obj3": + types: + train: "all_cats" + eval: "all_eval_cats" + not_equal: ["obj1", "obj2"] + recep: "source_recep3" + + + ############################################# + + + ############################################# + # Quantifier rearrangement + 3ef02c70: + lang: + train: + - "Put all the `object_name` on the `targ_recep`." + - "Place all the `object_name` onto the `targ_recep`." + - "Transfer all `object_name` to the `targ_recep`." + - "Arrange all `object_name` on the `targ_recep`." + - "Stack up all the `object_name` onto the `targ_recep`." + - "Deposit all `object_name` onto the `targ_recep`." + - "Position all `object_name` on the `targ_recep`." + - "Set down all the `object_name` onto the `targ_recep`." + - "Place every `object_name` onto the `targ_recep`." + - "Carefully load all `object_name` onto the `targ_recep`." + - "Securely position all `object_name` on the `targ_recep`." + tags: ['v2_eval', 'rearrange'] + goal_expr: + expr_type: AND + quantifier: FORALL + inputs: + - name: X + expr_type: object_name + sub_exprs: + - "on_top(X, targ_recep)" + - "not_holding()" + sampled_receps: + "targ_recep": + "type": "open_air_receps" + sampled_objects: + "object_name": + types: + train: "all_cats" + not_recep: 'receptacle1' + + 92bcf9b3: + lang: + train: + - "Put all the `object_name` from the `receptacle1` on the `receptacle2`." + - "Place all the `object_name` from the `receptacle1` onto the `receptacle2`." + - "Transfer all `object_name` on the `receptacle1` to the `receptacle2`." + - "Gather up all `object_name` from the `receptacle1` and set them on the `receptacle2`." + - "Relocate every `object_name` from `receptacle1` onto the `receptacle2`." + - "Move all the `object_name` from the `receptacle1` to the `receptacle2`." + - "Place all the `object_name` found on the `receptacle1` onto the `receptacle2`." + - "Transport all `object_name` on the `receptacle1` and put them on the `receptacle2`." + - "Shift all the `object_name` on the `receptacle1` over to `receptacle2`." + - "Deposit all the `object_name` from the `receptacle1` onto `receptacle2`." + tags: ['v2_eval', 'rearrange'] + goal_expr: + expr_type: AND + quantifier: FORALL + inputs: + - name: X + expr_type: object_name + sub_exprs: + - "on_top(X, receptacle2)" + - "not_holding()" + sampled_receps: + "receptacle1": + type: "open_air_receps" + "receptacle2": + type: "open_air_receps" + not_equal: ["receptacle1"] + sampled_objects: + "object_name": + types: + train: "all_cats" + not_recep: 'receptacle2' + + + 6e3da8a1: + lang: + train: + - "I accidently left the fridge open, can you close it?" + - "Shut the fridge." + - "Could you please shut the fridge? I accidentally left it open." + - "The refrigerator was left open by mistake. Would you mind closing it for me?" + - "I unintentionally left the fridge door open. Can you close it, please?" + - "I forgot to close the fridge properly. Could you kindly close it for me?" + - "The fridge door is open because of my mistake. Can you close it, please?" + - "Apologies, but I left the refrigerator open. Could you do me a favor and close it?" + - "I didn't realize I left the fridge open. Can you please close it for me?" + - "Regrettably, I forgot to close the fridge. Would you be able to close it, please?" + - "I made a mistake and left the fridge open. Can you assist me by closing it?" + eval: + - "When putting away groceries, I forgot to shut the fridge. Can you help?" + - "I left the fridge open. Can you help?" + - "The fridge needs to be shut." + + tags: ['v2_train', 'rearrange'] + solution_template: + - "nav(fridge_push_point)" + - "close_fridge(fridge_push_point)" + goal_expr: + expr_type: AND + sub_exprs: + - "closed_fridge(fridge_push_point)" + sampled_receps: {} + sampled_objects: {} + start_preds: + - "opened_fridge(fridge_push_point)" + + 5d5460d6: + # Variant where the fridge is open. + lang: + train: + - "If the fridge is open move a `target_object_name1` to the `target_receptacle_name`, otherwise move a `target_object_name2` to the `target_receptacle_name`." + - "If the fridge door is ajar, transfer an `target_object_name1` to the `target_receptacle_name`; otherwise, place an `target_object_name2` into the `target_receptacle_name`." + - "When the fridge is open, relocate an `target_object_name1` to the `target_receptacle_name`; if it's closed, shift an `target_object_name2` to the `target_receptacle_name`." + - "In case the fridge remains open, perform a transfer of an `target_object_name1` to the `target_receptacle_name`; when closed, execute a transfer of the `target_object_name2` to the `target_receptacle_name`." + - "Should the fridge door be open, move an `target_object_name1` to the `target_receptacle_name`; otherwise, transport an `target_object_name2` to the `target_receptacle_name`." + - "If the refrigerator door is not closed, then proceed to move an `target_object_name1` to the `target_receptacle_name`; otherwise, place an `target_object_name2` into the `target_receptacle_name`." + - "In the event that the fridge is open, conduct the relocation of an `target_object_name1` to the `target_receptacle_name`; if it's closed, undertake the transfer of an `target_object_name2` to the `target_receptacle_name`." + - "Assuming the fridge remains open, please relocate an `target_object_name1` to the `target_receptacle_name`; if it is closed, make sure to transfer an `target_object_name2` to the `target_receptacle_name`." + - "When you find the fridge door open, go ahead and move an `target_object_name1` to the `target_receptacle_name`; otherwise, transport an `target_object_name2` to the `target_receptacle_name`." + - "Provided that the fridge door is open, effectuate the transfer of an `target_object_name1` to the `target_receptacle_name`; if the fridge door is closed, perform the same action with an `target_object_name2` into the `target_receptacle_name`." + - "In the case that the fridge door is open, kindly proceed to move an `target_object_name1` to the `target_receptacle_name`; if the fridge door is closed, perform the transfer of an `target_object_name2` to the `target_receptacle_name`." + tags: ['v2_eval_cond'] + solution_template: + - "nav(`source_receptacle_name1`)" + - "pick_`target_object_name1`()" + - "nav(`target_receptacle_name`)" + - "place(`target_receptacle_name`)" + goal_expr: + expr_type: AND + quantifier: EXISTS + inputs: + - name: X + expr_type: "target_object_name1" + - name: Y + expr_type: "target_object_name2" + sub_exprs: + - "opened_fridge(fridge_push_point)" + - "on_top(X, target_receptacle_name)" + - "on_top(Y, source_receptacle_name2)" + - "not_holding()" + sampled_receps: + "source_receptacle_name1": + type: "open_air_receps" + "source_receptacle_name2": + type: "open_air_receps" + "target_receptacle_name": + type: "open_air_receps" + not_equal: ["source_receptacle_name1", "source_receptacle_name2"] + sampled_objects: + "target_object_name1": + types: + train: "all_train_cats" + eval: "all_eval_cats" + recep: source_receptacle_name1 + "target_object_name2": + types: + train: "all_train_cats" + eval: "all_eval_cats" + not_equal: ["target_object_name1"] + recep: source_receptacle_name2 + start_preds: + - "opened_fridge(fridge_push_point)" + + e75d9d69: + # Variant where the fridge is closed. + lang: + train: + - "If the fridge is open move a `target_object_name1` to the `target_receptacle_name`, otherwise move a `target_object_name2` to the `target_receptacle_name`." + - "If the fridge door is ajar, transfer an `target_object_name1` to the `target_receptacle_name`; otherwise, place an `target_object_name2` into the `target_receptacle_name`." + - "When the fridge is open, relocate an `target_object_name1` to the `target_receptacle_name`; if it's closed, shift an `target_object_name2` to the `target_receptacle_name`." + - "In case the fridge remains open, perform a transfer of an `target_object_name1` to the `target_receptacle_name`; when closed, execute a transfer of the `target_object_name2` to the `target_receptacle_name`." + - "Should the fridge door be open, move an `target_object_name1` to the `target_receptacle_name`; otherwise, transport an `target_object_name2` to the `target_receptacle_name`." + - "If the refrigerator door is not closed, then proceed to move an `target_object_name1` to the `target_receptacle_name`; otherwise, place an `target_object_name2` into the `target_receptacle_name`." + - "In the event that the fridge is open, conduct the relocation of an `target_object_name1` to the `target_receptacle_name`; if it's closed, undertake the transfer of an `target_object_name2` to the `target_receptacle_name`." + - "Assuming the fridge remains open, please relocate an `target_object_name1` to the `target_receptacle_name`; if it is closed, make sure to transfer an `target_object_name2` to the `target_receptacle_name`." + - "When you find the fridge door open, go ahead and move an `target_object_name1` to the `target_receptacle_name`; otherwise, transport an `target_object_name2` to the `target_receptacle_name`." + - "Provided that the fridge door is open, effectuate the transfer of an `target_object_name1` to the `target_receptacle_name`; if the fridge door is closed, perform the same action with an `target_object_name2` into the `target_receptacle_name`." + - "In the case that the fridge door is open, kindly proceed to move an `target_object_name1` to the `target_receptacle_name`; if the fridge door is closed, perform the transfer of an `target_object_name2` to the `target_receptacle_name`." + tags: ['v2_eval_cond'] + solution_template: + - "nav(`source_receptacle_name2`)" + - "pick_`target_object_name2`()" + - "nav(`target_receptacle_name`)" + - "place(`target_receptacle_name`)" + goal_expr: + expr_type: AND + quantifier: EXISTS + inputs: + - name: X + expr_type: "target_object_name1" + - name: Y + expr_type: "target_object_name2" + sub_exprs: + - "closed_fridge(fridge_push_point)" + - "on_top(X, source_receptacle_name1)" + - "on_top(Y, target_receptacle_name)" + - "not_holding()" + sampled_receps: + "source_receptacle_name1": + type: "open_air_receps" + "source_receptacle_name2": + type: "open_air_receps" + "target_receptacle_name": + type: "open_air_receps" + not_equal: ["source_receptacle_name1", "source_receptacle_name2"] + sampled_objects: + "target_object_name1": + types: + train: "all_train_cats" + eval: "all_eval_cats" + recep: source_receptacle_name1 + "target_object_name2": + types: + train: "all_train_cats" + eval: "all_eval_cats" + not_equal: ["target_object_name1"] + recep: source_receptacle_name2 + start_preds: + - "closed_fridge(fridge_push_point)" + + + 68216d8c: + lang: + train: + - "Can you open the fridge for me?" + - "Could you kindly open the fridge on my behalf?" + - "Would you mind opening the refrigerator for me?" + - "I would appreciate it if you could open the fridge." + - "Can you do me a favor and open the refrigerator, please?" + - "It would be great if you could open the fridge." + - "I need you to open the refrigerator, if you don't mind." + - "Please assist me by opening the fridge." + - "I'm requesting your help in opening the refrigerator." + - "Could you extend your assistance by opening the fridge?" + - "I kindly ask you to open the refrigerator." + eval: + - "I need to put away some cold groceries, can you help?" + - "I accidently shut the fridge, but I still need to put something away, help me." + - "I need to grab a cold drink but my hands are full, can you help?" + tags: ['v2_train', 'rearrange'] + solution_template: + - "nav(fridge_push_point)" + - "open_fridge(fridge_push_point)" + goal_expr: + expr_type: AND + sub_exprs: + - "opened_fridge(fridge_push_point)" + sampled_receps: {} + sampled_objects: {} + start_preds: + - "closed_fridge(fridge_push_point)" + + 93b2ef41: + # Spatial receptacle rearrangement + lang: + train: [] + eval: [] + tags: ['v2_eval_spatial'] + overweight_freq: 4 + solution_template: + - "nav(`source_receptacle_name`)" + - "pick_`target_object_name`()" + - "nav(`target_receptacle_name`)" + - "place(`target_receptacle_name`)" + goal_expr: + expr_type: AND + quantifier: EXISTS + inputs: + - name: X + expr_type: "target_object_name" + sub_exprs: + - "on_top(X, target_receptacle_name)" + - "not_holding()" + semantics: + b8173aa5: + match_objs: + target_receptacle_name: + - "receptacle_aabb_sink_kitchen_counter" + lang: + train: + - "Move a `target_object_name` from the `source_receptacle_name` to the left of the right counter." + - "Move a `target_object_name` from the `source_receptacle_name` to the right of the left counter." + 595eb7da: + match_objs: + target_receptacle_name: + - "receptacle_aabb_sink_kitchen_counter" + lang: + train: + - "Move a `target_object_name` from the `source_receptacle_name` to the right of the left counter." + ce10cc16: + match_objs: + target_receptacle_name: + - "receptacle_aabb_Sofa_frl_apartment_sofa" + lang: + train: + - "Move a `target_object_name` from the `source_receptacle_name` to the right of the TV stand." + sampled_receps: + "source_receptacle_name": + type: "open_air_receps" + "target_receptacle_name": + type: "open_air_receps" + not_equal: ["source_receptacle_name"] + sampled_objects: + "target_object_name": + types: + train: "all_train_cats" + eval: "all_eval_cats" + recep: source_receptacle_name + ############################################# + + + ############################################# + # Robot + 311525c5: + lang: + train: + - "Go to `target_receptacle_name`." + - "Head to the `target_receptacle_name`." + - "Make your way to the `target_receptacle_name`." + - "Proceed to the `target_receptacle_name` location." + - "Go over to the `target_receptacle_name`." + - "Walk to the `target_receptacle_name` area." + - "Move towards the `target_receptacle_name`." + - "Get to the `target_receptacle_name` destination." + - "Reach the `target_receptacle_name`." + - "Find your way to the `target_receptacle_name` spot." + - "Journey to the `target_receptacle_name`." + tags: ['v2_train'] + solution_template: + - "nav(`target_receptacle_name`)" + goal_expr: + expr_type: AND + sub_exprs: + - "robot_at(target_receptacle_name)" + sampled_receps: + "target_receptacle_name": + "type": "open_air_receps" + sampled_objects: {} + + 2f2d797d: + lang: + train: + - "Find a `target_object_name`." + - "Locate a `target_object_name`" + - "Discover a `target_object_name`" + - "Uncover a `target_object_name`" + - "Search for a `target_object_name`" + - "Identify a `target_object_name`" + - "Seek out a `target_object_name`" + - "Scour for a `target_object_name`" + - "Hunt down a `target_object_name`" + - "Pinpoint a `target_object_name`" + - "Spot a `target_object_name`" + tags: ['v2_train'] + solution_template: + - "nav(`source_receptacle_name`)" + goal_expr: + expr_type: AND + quantifier: EXISTS + inputs: + - name: X + expr_type: "target_object_name" + sub_exprs: + - "robot_at(X)" + sampled_receps: + "source_receptacle_name": + type: "open_air_receps" + sampled_objects: + "target_object_name": + types: + train: "all_train_cats" + eval: "all_eval_cats" + recep: source_receptacle_name + ############################################# + + +# Map the asset IDs into a readable string. +name_mappings: + # Receptacles + "receptacle_aabb_Chr1_Top1_frl_apartment_chair_01": "chair" + "receptacle_aabb_Tbl1_Top1_frl_apartment_table_01": "black table" + "receptacle_aabb_Tbl2_Top1_frl_apartment_table_02": "brown table" + "receptacle_aabb_TvStnd1_Top1_frl_apartment_tvstand": "TV stand" + "receptacle_aabb_sink_kitchen_counter": "sink" + "receptacle_aabb_counter_right_kitchen_counter": "right counter" + "receptacle_aabb_counter_left_kitchen_counter": "left counter" + "receptacle_aabb_Sofa_frl_apartment_sofa": "sofa" + "receptacle_aabb_middle_topfrl_apartment_refrigerator": "fridge" + "receptacle_aabb_drawer_left_top_frl_apartment_kitchen_counter": "left drawer" + "receptacle_aabb_drawer_middle_top_frl_apartment_kitchen_counter": "middle drawer" + "receptacle_aabb_drawer_right_top_frl_apartment_kitchen_counter": "right drawer" + "fridge_push_point": "fridge" + + "noop": "nothing" + "toy airplane": "toy" + "close_cab": "close" + "close_fridge": "close" + "open_cab": "open" + "open_fridge": "open" + 'rubriks cube': 'cube' + + "cab_push_point_7": "left drawer" + "cab_push_point_6": "middle drawer" + "cab_push_point_5": "right drawer" + "cab_push_point_4": "bottom drawer" + diff --git a/easi/tasks/ebhabitat/vendor/dataset/episodes.py b/easi/tasks/ebhabitat/vendor/dataset/episodes.py index 7633a78..3b84e97 100644 --- a/easi/tasks/ebhabitat/vendor/dataset/episodes.py +++ b/easi/tasks/ebhabitat/vendor/dataset/episodes.py @@ -17,8 +17,6 @@ from habitat.core.utils import DatasetFloatJSONEncoder from habitat.datasets.rearrange.rearrange_dataset import (RearrangeDatasetV0, RearrangeEpisode) -# from habitat.datasets.utils import check_and_gen_physics_config -from habitat.tasks.rearrange.multi_task.pddl_predicate import Predicate DEFAULT_PHYSICS_CONFIG_PATH = os.path.join(os.path.dirname(__file__), '../data/default.physics_config.json') diff --git a/easi/tasks/ebhabitat/vendor/predicate_task.py b/easi/tasks/ebhabitat/vendor/predicate_task.py index ea3a776..d97770a 100644 --- a/easi/tasks/ebhabitat/vendor/predicate_task.py +++ b/easi/tasks/ebhabitat/vendor/predicate_task.py @@ -2,13 +2,10 @@ # For licensing see accompanying LICENSE file. # Copyright (C) 2024 Apple Inc. All Rights Reserved. # -import inspect -import os.path as osp import random import time from typing import Any, Dict, List -import hydra import magnum as mn import numpy as np from habitat.core.registry import registry @@ -21,14 +18,10 @@ from habitat.tasks.rearrange.rearrange_task import RearrangeTask from habitat.tasks.rearrange.utils import add_perf_timing_func from omegaconf import DictConfig, ListConfig -from PIL import Image -from transformers import AutoTokenizer -import easi.tasks.ebhabitat.vendor.config from easi.tasks.ebhabitat.vendor.dataset.episodes import LangRearrangeEpisode from easi.tasks.ebhabitat.vendor.dataset.utils import get_category_info -from easi.tasks.ebhabitat.vendor.actions import KinematicArmEEAction -from easi.tasks.ebhabitat.vendor.utils import PLACABLE_RECEP_TYPE, get_pddl +from easi.tasks.ebhabitat.vendor.utils import get_pddl @registry.register_task(name="RearrangePredicateTask-v0") diff --git a/easi/tasks/ebhabitat/vendor/sensors.py b/easi/tasks/ebhabitat/vendor/sensors.py index 0c29bfb..72f708e 100644 --- a/easi/tasks/ebhabitat/vendor/sensors.py +++ b/easi/tasks/ebhabitat/vendor/sensors.py @@ -2,7 +2,6 @@ # For licensing see accompanying LICENSE file. # Copyright (C) 2024 Apple Inc. All Rights Reserved. # -import os.path as osp from typing import List import gym.spaces as spaces diff --git a/easi/tasks/ebhabitat/vendor/utils.py b/easi/tasks/ebhabitat/vendor/utils.py index 9b0c2b4..f44d8a8 100644 --- a/easi/tasks/ebhabitat/vendor/utils.py +++ b/easi/tasks/ebhabitat/vendor/utils.py @@ -2,24 +2,21 @@ # For licensing see accompanying LICENSE file. # Copyright (C) 2024 Apple Inc. All Rights Reserved. # -import inspect import os.path as osp -from typing import Dict, List, Tuple import numpy as np from PIL import Image, ImageDraw, ImageFont -import yaml from habitat.tasks.rearrange.multi_task.pddl_action import PddlAction from habitat.tasks.rearrange.multi_task.pddl_domain import PddlDomain from habitat.tasks.rearrange.multi_task.pddl_logical_expr import ( LogicalExpr, LogicalQuantifierType) from habitat.tasks.rearrange.multi_task.rearrange_pddl import ( ExprType, PddlEntity, SimulatorObjectType) -from transformers import (AutoConfig, AutoModelForSeq2SeqLM, AutoTokenizer, - LlamaForCausalLM, LlamaModel, LlamaTokenizer, - T5Model) from habitat.utils.visualizations.utils import (tile_images, draw_collision) -import easi.tasks.ebhabitat.vendor.config -import easi.tasks.ebhabitat.vendor.dataset + +# Side-effect imports: register habitat configs and dataset classes. +# Do NOT remove — they run Hydra ConfigStore and @registry.register_* at import time. +import easi.tasks.ebhabitat.vendor.config # noqa: F401 +import easi.tasks.ebhabitat.vendor.dataset # noqa: F401 # Also defined in the PDDL PLACABLE_RECEP_TYPE = "place_receptacle" @@ -194,6 +191,10 @@ def get_pddl(task_config, all_cats, obj_cats) -> PddlDomain: def get_parser(llm_id): + # Lazy import: transformers is heavy and only needed if this function + # is actually called (by sensors not used in the default EASI config). + from transformers import AutoTokenizer, LlamaTokenizer + if "llama" in llm_id.lower(): tokenizer = LlamaTokenizer.from_pretrained(llm_id) # llama has no pad token by default. As per this thread: From 907879b5ad14d4b1a78b40ceeb35bff17e4d9e9d Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 17 Feb 2026 05:13:10 +0800 Subject: [PATCH 036/239] [Fix] Fix EB-Habitat runtime deps and rendering config - Add torch to all 6 ebhabitat YAML additional_deps (sensors.py imports torch) - Add os.makedirs for physics config dir in episodes.py - Add GPU device config from HABITAT_SIM_GPU_ID env var in EBHabEnv - Switch habitat-sim to non-headless build (remove headless metapackage) - Set needs_display=True so subprocess runner uses xvfb - Add xvfb and egl to system deps in env_manager --- easi/simulators/habitat_sim/v0_3_0/conda_env.yaml | 1 - easi/simulators/habitat_sim/v0_3_0/env_manager.py | 4 ++-- easi/tasks/ebhabitat/ebhabitat_base.yaml | 1 + easi/tasks/ebhabitat/ebhabitat_common_sense.yaml | 1 + easi/tasks/ebhabitat/ebhabitat_complex_instruction.yaml | 1 + easi/tasks/ebhabitat/ebhabitat_long_horizon.yaml | 1 + easi/tasks/ebhabitat/ebhabitat_spatial_relationship.yaml | 1 + easi/tasks/ebhabitat/ebhabitat_visual_appearance.yaml | 1 + easi/tasks/ebhabitat/vendor/EBHabEnv.py | 6 ++++++ easi/tasks/ebhabitat/vendor/dataset/episodes.py | 1 + 10 files changed, 15 insertions(+), 3 deletions(-) diff --git a/easi/simulators/habitat_sim/v0_3_0/conda_env.yaml b/easi/simulators/habitat_sim/v0_3_0/conda_env.yaml index faf4f04..17f8c8d 100644 --- a/easi/simulators/habitat_sim/v0_3_0/conda_env.yaml +++ b/easi/simulators/habitat_sim/v0_3_0/conda_env.yaml @@ -6,5 +6,4 @@ channels: dependencies: - python=3.9 - habitat-sim=0.3.0 - - headless - withbullet diff --git a/easi/simulators/habitat_sim/v0_3_0/env_manager.py b/easi/simulators/habitat_sim/v0_3_0/env_manager.py index ac525bc..56cf3d6 100644 --- a/easi/simulators/habitat_sim/v0_3_0/env_manager.py +++ b/easi/simulators/habitat_sim/v0_3_0/env_manager.py @@ -20,7 +20,7 @@ def version(self) -> str: @property def needs_display(self) -> bool: - return False # habitat-sim headless build uses EGL + return True # habitat-sim needs a display (xvfb on headless servers) def get_conda_env_yaml_path(self) -> Path: return Path(__file__).parent / "conda_env.yaml" @@ -29,7 +29,7 @@ def get_requirements_txt_path(self) -> Path: return Path(__file__).parent / "requirements.txt" def get_system_deps(self) -> list[str]: - return ["conda"] + return ["conda", "xvfb", "egl"] def get_validation_import(self) -> str: return "import habitat_sim" diff --git a/easi/tasks/ebhabitat/ebhabitat_base.yaml b/easi/tasks/ebhabitat/ebhabitat_base.yaml index a1473bf..46120ab 100644 --- a/easi/tasks/ebhabitat/ebhabitat_base.yaml +++ b/easi/tasks/ebhabitat/ebhabitat_base.yaml @@ -20,6 +20,7 @@ simulator_configs: - "hydra-core>=1.3" - "omegaconf>=2.3" - "imageio" + - "torch" - "habitat-lab @ git+https://github.com/facebookresearch/habitat-lab.git@v0.3.0#subdirectory=habitat-lab" agent: prompt_builder: "easi.tasks.ebhabitat.prompts.EBHabitatPromptBuilder" diff --git a/easi/tasks/ebhabitat/ebhabitat_common_sense.yaml b/easi/tasks/ebhabitat/ebhabitat_common_sense.yaml index 0c9ca26..e41bb11 100644 --- a/easi/tasks/ebhabitat/ebhabitat_common_sense.yaml +++ b/easi/tasks/ebhabitat/ebhabitat_common_sense.yaml @@ -20,6 +20,7 @@ simulator_configs: - "hydra-core>=1.3" - "omegaconf>=2.3" - "imageio" + - "torch" - "habitat-lab @ git+https://github.com/facebookresearch/habitat-lab.git@v0.3.0#subdirectory=habitat-lab" agent: prompt_builder: "easi.tasks.ebhabitat.prompts.EBHabitatPromptBuilder" diff --git a/easi/tasks/ebhabitat/ebhabitat_complex_instruction.yaml b/easi/tasks/ebhabitat/ebhabitat_complex_instruction.yaml index 95af881..1ea7e59 100644 --- a/easi/tasks/ebhabitat/ebhabitat_complex_instruction.yaml +++ b/easi/tasks/ebhabitat/ebhabitat_complex_instruction.yaml @@ -20,6 +20,7 @@ simulator_configs: - "hydra-core>=1.3" - "omegaconf>=2.3" - "imageio" + - "torch" - "habitat-lab @ git+https://github.com/facebookresearch/habitat-lab.git@v0.3.0#subdirectory=habitat-lab" agent: prompt_builder: "easi.tasks.ebhabitat.prompts.EBHabitatPromptBuilder" diff --git a/easi/tasks/ebhabitat/ebhabitat_long_horizon.yaml b/easi/tasks/ebhabitat/ebhabitat_long_horizon.yaml index a41cd43..68cdc72 100644 --- a/easi/tasks/ebhabitat/ebhabitat_long_horizon.yaml +++ b/easi/tasks/ebhabitat/ebhabitat_long_horizon.yaml @@ -20,6 +20,7 @@ simulator_configs: - "hydra-core>=1.3" - "omegaconf>=2.3" - "imageio" + - "torch" - "habitat-lab @ git+https://github.com/facebookresearch/habitat-lab.git@v0.3.0#subdirectory=habitat-lab" agent: prompt_builder: "easi.tasks.ebhabitat.prompts.EBHabitatPromptBuilder" diff --git a/easi/tasks/ebhabitat/ebhabitat_spatial_relationship.yaml b/easi/tasks/ebhabitat/ebhabitat_spatial_relationship.yaml index a1c67fa..6f0e4a0 100644 --- a/easi/tasks/ebhabitat/ebhabitat_spatial_relationship.yaml +++ b/easi/tasks/ebhabitat/ebhabitat_spatial_relationship.yaml @@ -20,6 +20,7 @@ simulator_configs: - "hydra-core>=1.3" - "omegaconf>=2.3" - "imageio" + - "torch" - "habitat-lab @ git+https://github.com/facebookresearch/habitat-lab.git@v0.3.0#subdirectory=habitat-lab" agent: prompt_builder: "easi.tasks.ebhabitat.prompts.EBHabitatPromptBuilder" diff --git a/easi/tasks/ebhabitat/ebhabitat_visual_appearance.yaml b/easi/tasks/ebhabitat/ebhabitat_visual_appearance.yaml index a558250..e91d45d 100644 --- a/easi/tasks/ebhabitat/ebhabitat_visual_appearance.yaml +++ b/easi/tasks/ebhabitat/ebhabitat_visual_appearance.yaml @@ -20,6 +20,7 @@ simulator_configs: - "hydra-core>=1.3" - "omegaconf>=2.3" - "imageio" + - "torch" - "habitat-lab @ git+https://github.com/facebookresearch/habitat-lab.git@v0.3.0#subdirectory=habitat-lab" agent: prompt_builder: "easi.tasks.ebhabitat.prompts.EBHabitatPromptBuilder" diff --git a/easi/tasks/ebhabitat/vendor/EBHabEnv.py b/easi/tasks/ebhabitat/vendor/EBHabEnv.py index 294798e..9bd2357 100644 --- a/easi/tasks/ebhabitat/vendor/EBHabEnv.py +++ b/easi/tasks/ebhabitat/vendor/EBHabEnv.py @@ -149,6 +149,12 @@ def __init__(self, eval_set='train', exp_name='', down_sample_ratio=1.0, self.config.habitat.simulator.agents.main_agent.sim_sensors.head_rgb_sensor.width = resolution self.resolution = resolution + # Set GPU device for rendering. + # Default to 0 (first GPU). Use HABITAT_SIM_GPU_ID env var to override. + # The headless conda build may need the NVIDIA EGL ICD installed. + gpu_id = int(os.environ.get("HABITAT_SIM_GPU_ID", "0")) + self.config.habitat.simulator.habitat_sim_v0.gpu_device_id = gpu_id + # modify config path to ease data loading self.dataset = make_dataset(self.config.habitat.dataset.type, config=self.config.habitat.dataset) diff --git a/easi/tasks/ebhabitat/vendor/dataset/episodes.py b/easi/tasks/ebhabitat/vendor/dataset/episodes.py index 3b84e97..b6c8b15 100644 --- a/easi/tasks/ebhabitat/vendor/dataset/episodes.py +++ b/easi/tasks/ebhabitat/vendor/dataset/episodes.py @@ -33,6 +33,7 @@ def check_and_gen_physics_config(): "restitution_coefficient": 0.1, "rigid object paths": ["objects"], } + os.makedirs(os.path.dirname(DEFAULT_PHYSICS_CONFIG_PATH), exist_ok=True) with open(DEFAULT_PHYSICS_CONFIG_PATH, "w") as f: json.dump(physics_config, f) From 5a838e8bf74331bcd0e147ca96f94c9def1772b2 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 17 Feb 2026 05:51:41 +0800 Subject: [PATCH 037/239] [Fix] Pass HF dataset path to EB-Habitat bridge via reset_config The bridge needs the HF dataset cache path to find pickle files and scene assets. Pass _data_dir from episodes through format_reset_config so the bridge resolves datasets/ and creates the data symlink correctly. --- easi/tasks/ebhabitat/bridge.py | 7 ++++--- easi/tasks/ebhabitat/task.py | 1 + 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/easi/tasks/ebhabitat/bridge.py b/easi/tasks/ebhabitat/bridge.py index 4c9644a..ef69425 100644 --- a/easi/tasks/ebhabitat/bridge.py +++ b/easi/tasks/ebhabitat/bridge.py @@ -38,11 +38,12 @@ class EBHabitatBridge(BaseBridge): def _create_env(self, reset_config, simulator_kwargs): from easi.tasks.ebhabitat.vendor.EBHabEnv import EBHabEnv - # Resolve data directories from extracted zip files - data_dir = simulator_kwargs.get("data_dir") + # Resolve data directories from HF dataset cache (passed via reset_config) + # or from --data-dir CLI arg (passed via simulator_kwargs) + data_dir = reset_config.get("data_dir") or simulator_kwargs.get("data_dir") dataset_dir = None if data_dir: - # datasets.zip extracts pickle files to data_dir/datasets/ + # HF dataset structure: data_dir/datasets/*.pickle, data_dir/data/... candidate = Path(data_dir) / "datasets" if candidate.exists(): dataset_dir = str(candidate) diff --git a/easi/tasks/ebhabitat/task.py b/easi/tasks/ebhabitat/task.py index f4118f6..0fb372e 100644 --- a/easi/tasks/ebhabitat/task.py +++ b/easi/tasks/ebhabitat/task.py @@ -48,6 +48,7 @@ def format_reset_config(self, episode: dict) -> dict: "episode_id": episode.get("id", "unknown"), "eval_set": self._config.get("dataset", {}).get("split", "base"), "instruction": episode.get("instruction", ""), + "data_dir": episode.get("_data_dir", ""), } def evaluate_episode( From 32e483f00597d34901898070e3609075634c144d Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 17 Feb 2026 05:58:20 +0800 Subject: [PATCH 038/239] [Refactor] Make EB-Habitat env constants configurable via task YAML Wire max_steps, max_invalid_actions, and feedback_verbosity from YAML simulator_configs through the bridge into EBHabEnv constructor instead of hardcoding them. This fixes max_steps not being forwarded to the env (was only set in YAML but ignored by bridge). --- easi/tasks/ebhabitat/bridge.py | 6 ++++++ easi/tasks/ebhabitat/ebhabitat_base.yaml | 2 ++ easi/tasks/ebhabitat/ebhabitat_common_sense.yaml | 2 ++ easi/tasks/ebhabitat/ebhabitat_complex_instruction.yaml | 2 ++ easi/tasks/ebhabitat/ebhabitat_long_horizon.yaml | 2 ++ easi/tasks/ebhabitat/ebhabitat_spatial_relationship.yaml | 2 ++ easi/tasks/ebhabitat/ebhabitat_visual_appearance.yaml | 2 ++ easi/tasks/ebhabitat/vendor/EBHabEnv.py | 9 +++++---- 8 files changed, 23 insertions(+), 4 deletions(-) diff --git a/easi/tasks/ebhabitat/bridge.py b/easi/tasks/ebhabitat/bridge.py index ef69425..08fed53 100644 --- a/easi/tasks/ebhabitat/bridge.py +++ b/easi/tasks/ebhabitat/bridge.py @@ -50,12 +50,18 @@ def _create_env(self, reset_config, simulator_kwargs): eval_set = reset_config.get("eval_set", "base") resolution = simulator_kwargs.get("screen_height", 500) + max_steps = simulator_kwargs.get("max_steps", 30) + max_invalid_actions = simulator_kwargs.get("max_invalid_actions", 10) + feedback_verbosity = simulator_kwargs.get("feedback_verbosity", 1) env = EBHabEnv( eval_set=eval_set, data_dir=data_dir, dataset_dir=dataset_dir, resolution=resolution, + max_steps=max_steps, + max_invalid_actions=max_invalid_actions, + feedback_verbosity=feedback_verbosity, ) # Extract dynamic action space diff --git a/easi/tasks/ebhabitat/ebhabitat_base.yaml b/easi/tasks/ebhabitat/ebhabitat_base.yaml index 46120ab..4962033 100644 --- a/easi/tasks/ebhabitat/ebhabitat_base.yaml +++ b/easi/tasks/ebhabitat/ebhabitat_base.yaml @@ -15,6 +15,8 @@ dataset: simulator_configs: screen_height: 500 screen_width: 500 + max_invalid_actions: 10 + feedback_verbosity: 1 additional_deps: - "gym" - "hydra-core>=1.3" diff --git a/easi/tasks/ebhabitat/ebhabitat_common_sense.yaml b/easi/tasks/ebhabitat/ebhabitat_common_sense.yaml index e41bb11..c553df6 100644 --- a/easi/tasks/ebhabitat/ebhabitat_common_sense.yaml +++ b/easi/tasks/ebhabitat/ebhabitat_common_sense.yaml @@ -15,6 +15,8 @@ dataset: simulator_configs: screen_height: 500 screen_width: 500 + max_invalid_actions: 10 + feedback_verbosity: 1 additional_deps: - "gym" - "hydra-core>=1.3" diff --git a/easi/tasks/ebhabitat/ebhabitat_complex_instruction.yaml b/easi/tasks/ebhabitat/ebhabitat_complex_instruction.yaml index 1ea7e59..395be43 100644 --- a/easi/tasks/ebhabitat/ebhabitat_complex_instruction.yaml +++ b/easi/tasks/ebhabitat/ebhabitat_complex_instruction.yaml @@ -15,6 +15,8 @@ dataset: simulator_configs: screen_height: 500 screen_width: 500 + max_invalid_actions: 10 + feedback_verbosity: 1 additional_deps: - "gym" - "hydra-core>=1.3" diff --git a/easi/tasks/ebhabitat/ebhabitat_long_horizon.yaml b/easi/tasks/ebhabitat/ebhabitat_long_horizon.yaml index 68cdc72..80cfce9 100644 --- a/easi/tasks/ebhabitat/ebhabitat_long_horizon.yaml +++ b/easi/tasks/ebhabitat/ebhabitat_long_horizon.yaml @@ -15,6 +15,8 @@ dataset: simulator_configs: screen_height: 500 screen_width: 500 + max_invalid_actions: 10 + feedback_verbosity: 1 additional_deps: - "gym" - "hydra-core>=1.3" diff --git a/easi/tasks/ebhabitat/ebhabitat_spatial_relationship.yaml b/easi/tasks/ebhabitat/ebhabitat_spatial_relationship.yaml index 6f0e4a0..7a8d84a 100644 --- a/easi/tasks/ebhabitat/ebhabitat_spatial_relationship.yaml +++ b/easi/tasks/ebhabitat/ebhabitat_spatial_relationship.yaml @@ -15,6 +15,8 @@ dataset: simulator_configs: screen_height: 500 screen_width: 500 + max_invalid_actions: 10 + feedback_verbosity: 1 additional_deps: - "gym" - "hydra-core>=1.3" diff --git a/easi/tasks/ebhabitat/ebhabitat_visual_appearance.yaml b/easi/tasks/ebhabitat/ebhabitat_visual_appearance.yaml index e91d45d..939e9f1 100644 --- a/easi/tasks/ebhabitat/ebhabitat_visual_appearance.yaml +++ b/easi/tasks/ebhabitat/ebhabitat_visual_appearance.yaml @@ -15,6 +15,8 @@ dataset: simulator_configs: screen_height: 500 screen_width: 500 + max_invalid_actions: 10 + feedback_verbosity: 1 additional_deps: - "gym" - "hydra-core>=1.3" diff --git a/easi/tasks/ebhabitat/vendor/EBHabEnv.py b/easi/tasks/ebhabitat/vendor/EBHabEnv.py index 9bd2357..7eb93e3 100644 --- a/easi/tasks/ebhabitat/vendor/EBHabEnv.py +++ b/easi/tasks/ebhabitat/vendor/EBHabEnv.py @@ -115,7 +115,8 @@ def transform_action_to_natural_language(skill_set): class EBHabEnv(gym.Env): def __init__(self, eval_set='train', exp_name='', down_sample_ratio=1.0, start_epi_index=0, resolution=500, recording=False, - data_dir=None, dataset_dir=None): + data_dir=None, dataset_dir=None, + max_steps=30, max_invalid_actions=10, feedback_verbosity=1): """ Initialize the HabitatRearrange environment. @@ -174,9 +175,9 @@ def __init__(self, eval_set='train', exp_name='', down_sample_ratio=1.0, self._current_episode_num += 1 self._current_step = 0 - self._max_episode_steps = 30 + self._max_episode_steps = max_steps self._cur_invalid_actions = 0 - self._max_invalid_actions = 10 + self._max_invalid_actions = max_invalid_actions self._episode_start_time = 0 # is holding an object self.is_holding = False @@ -190,7 +191,7 @@ def __init__(self, eval_set='train', exp_name='', down_sample_ratio=1.0, # env feedback and image save # feedback verbosity, 0: concise, 1: verbose - self.feedback_verbosity = 1 + self.feedback_verbosity = feedback_verbosity self.log_path = 'running/eb_habitat/{}'.format(exp_name) # video recorder self.recording = recording From 60eddb2be3bebbec6da397fa89a69ad905139281 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 17 Feb 2026 06:02:46 +0800 Subject: [PATCH 039/239] [Refactor] Make EB-Navigation env constants configurable via task YAML Wire success_threshold, grid_size, and visibility_distance from YAML simulator_configs through the bridge into EBNavEnv constructor. Also make message_window_len configurable via prompt_builder_kwargs instead of a module-level constant. --- easi/tasks/ebnavigation/bridge.py | 12 ++++++++++- .../tasks/ebnavigation/ebnavigation_base.yaml | 4 ++++ .../ebnavigation_common_sense.yaml | 4 ++++ .../ebnavigation_complex_instruction.yaml | 4 ++++ .../ebnavigation_long_horizon.yaml | 4 ++++ .../ebnavigation_visual_appearance.yaml | 4 ++++ easi/tasks/ebnavigation/prompts.py | 8 ++++---- easi/tasks/ebnavigation/vendor/EBNavEnv.py | 20 ++++++++++++------- 8 files changed, 48 insertions(+), 12 deletions(-) diff --git a/easi/tasks/ebnavigation/bridge.py b/easi/tasks/ebnavigation/bridge.py index b2eab04..81e173f 100644 --- a/easi/tasks/ebnavigation/bridge.py +++ b/easi/tasks/ebnavigation/bridge.py @@ -33,7 +33,17 @@ def _create_env(self, reset_config, simulator_kwargs): resolution = simulator_kwargs.get("screen_height", 500) max_steps = simulator_kwargs.get("max_steps", 20) fov = simulator_kwargs.get("fov", 100) - return EBNavEnv(resolution=resolution, fov=fov, max_steps=max_steps) + success_threshold = simulator_kwargs.get("success_threshold", 1.0) + grid_size = simulator_kwargs.get("grid_size", 0.1) + visibility_distance = simulator_kwargs.get("visibility_distance", 10.0) + return EBNavEnv( + resolution=resolution, + fov=fov, + max_steps=max_steps, + success_threshold=success_threshold, + grid_size=grid_size, + visibility_distance=visibility_distance, + ) def _on_reset(self, env, reset_config): episode = { diff --git a/easi/tasks/ebnavigation/ebnavigation_base.yaml b/easi/tasks/ebnavigation/ebnavigation_base.yaml index acd13d2..457ab30 100644 --- a/easi/tasks/ebnavigation/ebnavigation_base.yaml +++ b/easi/tasks/ebnavigation/ebnavigation_base.yaml @@ -13,6 +13,9 @@ simulator_configs: screen_height: 500 screen_width: 500 fov: 100 + success_threshold: 1.0 + grid_size: 0.1 + visibility_distance: 10.0 agent: prompt_builder: "easi.tasks.ebnavigation.prompts.EBNavigationPromptBuilder" prompt_builder_kwargs: @@ -20,3 +23,4 @@ agent: split: "base" use_feedback: true chat_history: true + message_window_len: 5 diff --git a/easi/tasks/ebnavigation/ebnavigation_common_sense.yaml b/easi/tasks/ebnavigation/ebnavigation_common_sense.yaml index c5829aa..0dbfb52 100644 --- a/easi/tasks/ebnavigation/ebnavigation_common_sense.yaml +++ b/easi/tasks/ebnavigation/ebnavigation_common_sense.yaml @@ -13,6 +13,9 @@ simulator_configs: screen_height: 500 screen_width: 500 fov: 100 + success_threshold: 1.0 + grid_size: 0.1 + visibility_distance: 10.0 agent: prompt_builder: "easi.tasks.ebnavigation.prompts.EBNavigationPromptBuilder" prompt_builder_kwargs: @@ -20,3 +23,4 @@ agent: split: "common_sense" use_feedback: true chat_history: true + message_window_len: 5 diff --git a/easi/tasks/ebnavigation/ebnavigation_complex_instruction.yaml b/easi/tasks/ebnavigation/ebnavigation_complex_instruction.yaml index d28ab11..a7acd9b 100644 --- a/easi/tasks/ebnavigation/ebnavigation_complex_instruction.yaml +++ b/easi/tasks/ebnavigation/ebnavigation_complex_instruction.yaml @@ -13,6 +13,9 @@ simulator_configs: screen_height: 500 screen_width: 500 fov: 100 + success_threshold: 1.0 + grid_size: 0.1 + visibility_distance: 10.0 agent: prompt_builder: "easi.tasks.ebnavigation.prompts.EBNavigationPromptBuilder" prompt_builder_kwargs: @@ -20,3 +23,4 @@ agent: split: "complex_instruction" use_feedback: true chat_history: true + message_window_len: 5 diff --git a/easi/tasks/ebnavigation/ebnavigation_long_horizon.yaml b/easi/tasks/ebnavigation/ebnavigation_long_horizon.yaml index e675848..309fd1c 100644 --- a/easi/tasks/ebnavigation/ebnavigation_long_horizon.yaml +++ b/easi/tasks/ebnavigation/ebnavigation_long_horizon.yaml @@ -13,6 +13,9 @@ simulator_configs: screen_height: 500 screen_width: 500 fov: 100 + success_threshold: 1.0 + grid_size: 0.1 + visibility_distance: 10.0 agent: prompt_builder: "easi.tasks.ebnavigation.prompts.EBNavigationPromptBuilder" prompt_builder_kwargs: @@ -20,3 +23,4 @@ agent: split: "long_horizon" use_feedback: true chat_history: true + message_window_len: 5 diff --git a/easi/tasks/ebnavigation/ebnavigation_visual_appearance.yaml b/easi/tasks/ebnavigation/ebnavigation_visual_appearance.yaml index 198c82c..5212d94 100644 --- a/easi/tasks/ebnavigation/ebnavigation_visual_appearance.yaml +++ b/easi/tasks/ebnavigation/ebnavigation_visual_appearance.yaml @@ -13,6 +13,9 @@ simulator_configs: screen_height: 500 screen_width: 500 fov: 100 + success_threshold: 1.0 + grid_size: 0.1 + visibility_distance: 10.0 agent: prompt_builder: "easi.tasks.ebnavigation.prompts.EBNavigationPromptBuilder" prompt_builder_kwargs: @@ -20,3 +23,4 @@ agent: split: "visual_appearance" use_feedback: true chat_history: true + message_window_len: 5 diff --git a/easi/tasks/ebnavigation/prompts.py b/easi/tasks/ebnavigation/prompts.py index 81a1333..bc656bf 100644 --- a/easi/tasks/ebnavigation/prompts.py +++ b/easi/tasks/ebnavigation/prompts.py @@ -105,8 +105,6 @@ }, } -MESSAGE_WINDOW_LEN = 5 - _CONFIG_DIR = Path(__file__).parent / "config" @@ -116,7 +114,7 @@ class EBNavigationPromptBuilder: Supports two modes: - chat_history=False (default): Stateless. Every turn sends a single user message with the full system prompt, examples, instruction, and history. - - chat_history=True: Messages accumulate with sliding window (5 messages). + - chat_history=True: Messages accumulate with sliding window. Each user message still includes the full system prompt and examples. """ @@ -126,11 +124,13 @@ def __init__( split: str = "base", use_feedback: bool = True, chat_history: bool = True, + message_window_len: int = 5, ): self.n_shot = n_shot self.split = split self.use_feedback = use_feedback self.chat_history = chat_history + self.message_window_len = message_window_len # Load examples examples_file = _CONFIG_DIR / "navigation_examples.json" @@ -340,7 +340,7 @@ def _build_chat_history_messages(self, memory: AgentMemory) -> list[dict]: ) # Apply sliding window - return messages[-MESSAGE_WINDOW_LEN:] + return messages[-self.message_window_len:] def _build_first_turn_full_prompt(self, task_description: str) -> str: """Build first-turn prompt (identical for both modes).""" diff --git a/easi/tasks/ebnavigation/vendor/EBNavEnv.py b/easi/tasks/ebnavigation/vendor/EBNavEnv.py index eac5986..7a3d699 100644 --- a/easi/tasks/ebnavigation/vendor/EBNavEnv.py +++ b/easi/tasks/ebnavigation/vendor/EBNavEnv.py @@ -12,18 +12,24 @@ import numpy as np from ai2thor.platform import Linux64 -SUCCESS_THRESHOLD = 1 # meters (horizontal distance) - - class EBNavEnv: """AI2-THOR navigation environment for EB-Navigation benchmark.""" - def __init__(self, resolution: int = 500, fov: int = 100, max_steps: int = 20): + def __init__( + self, + resolution: int = 500, + fov: int = 100, + max_steps: int = 20, + success_threshold: float = 1.0, + grid_size: float = 0.1, + visibility_distance: float = 10.0, + ): self.resolution = resolution + self._success_threshold = success_threshold self.config = { "agentMode": "default", - "gridSize": 0.1, - "visibilityDistance": 10, + "gridSize": grid_size, + "visibilityDistance": visibility_distance, "renderDepthImage": True, "renderInstanceSegmentation": True, "width": resolution, @@ -136,7 +142,7 @@ def measure_success(self) -> tuple[float, float]: (agent_position["x"] - target_position["x"]) ** 2 + (agent_position["z"] - target_position["z"]) ** 2 ) - success = float(dist <= SUCCESS_THRESHOLD) + success = float(dist <= self._success_threshold) return success, dist def _get_env_feedback(self) -> str: From d6a63a9e2cbddc25f801478ffe3fa20bfb4b7b8a Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 17 Feb 2026 06:09:41 +0800 Subject: [PATCH 040/239] [Refactor] Make EB-Alfred env constants configurable via task YAML --- easi/tasks/ebalfred/bridge.py | 4 ++++ easi/tasks/ebalfred/ebalfred_base.yaml | 2 ++ easi/tasks/ebalfred/ebalfred_common_sense.yaml | 2 ++ easi/tasks/ebalfred/ebalfred_complex_instruction.yaml | 2 ++ easi/tasks/ebalfred/ebalfred_long_horizon.yaml | 2 ++ easi/tasks/ebalfred/ebalfred_spatial.yaml | 2 ++ easi/tasks/ebalfred/ebalfred_visual_appearance.yaml | 2 ++ easi/tasks/ebalfred/vendor/EBAlfEnv.py | 9 ++++++--- 8 files changed, 22 insertions(+), 3 deletions(-) diff --git a/easi/tasks/ebalfred/bridge.py b/easi/tasks/ebalfred/bridge.py index 829a9eb..5802b1f 100644 --- a/easi/tasks/ebalfred/bridge.py +++ b/easi/tasks/ebalfred/bridge.py @@ -36,6 +36,8 @@ def _create_env(self, reset_config, simulator_kwargs): data_dir = reset_config.get("data_dir") or simulator_kwargs.get("data_dir") resolution = simulator_kwargs.get("screen_height", 500) max_steps = simulator_kwargs.get("max_steps", 30) + max_invalid_actions = simulator_kwargs.get("max_invalid_actions", 10) + feedback_verbosity = simulator_kwargs.get("feedback_verbosity", 0) # x_display from YAML, falling back to DISPLAY env var (set by xvfb-run) x_display = simulator_kwargs.get( "x_display", os.environ.get("DISPLAY", ":0").lstrip(":") @@ -45,6 +47,8 @@ def _create_env(self, reset_config, simulator_kwargs): data_dir=data_dir, x_display=x_display, max_steps=max_steps, + max_invalid_actions=max_invalid_actions, + feedback_verbosity=feedback_verbosity, ) def _on_reset(self, env, reset_config): diff --git a/easi/tasks/ebalfred/ebalfred_base.yaml b/easi/tasks/ebalfred/ebalfred_base.yaml index 8f71bc7..fe88d92 100644 --- a/easi/tasks/ebalfred/ebalfred_base.yaml +++ b/easi/tasks/ebalfred/ebalfred_base.yaml @@ -15,6 +15,8 @@ simulator_configs: quality: "MediumCloseFitShadows" screen_height: 500 screen_width: 500 + max_invalid_actions: 10 + feedback_verbosity: 0 additional_deps: - "gym" - "networkx" diff --git a/easi/tasks/ebalfred/ebalfred_common_sense.yaml b/easi/tasks/ebalfred/ebalfred_common_sense.yaml index d14306b..7ad2bd3 100644 --- a/easi/tasks/ebalfred/ebalfred_common_sense.yaml +++ b/easi/tasks/ebalfred/ebalfred_common_sense.yaml @@ -15,6 +15,8 @@ simulator_configs: quality: "MediumCloseFitShadows" screen_height: 500 screen_width: 500 + max_invalid_actions: 10 + feedback_verbosity: 0 additional_deps: - "gym" - "networkx" diff --git a/easi/tasks/ebalfred/ebalfred_complex_instruction.yaml b/easi/tasks/ebalfred/ebalfred_complex_instruction.yaml index 8954a8c..2d235d1 100644 --- a/easi/tasks/ebalfred/ebalfred_complex_instruction.yaml +++ b/easi/tasks/ebalfred/ebalfred_complex_instruction.yaml @@ -15,6 +15,8 @@ simulator_configs: quality: "MediumCloseFitShadows" screen_height: 500 screen_width: 500 + max_invalid_actions: 10 + feedback_verbosity: 0 additional_deps: - "gym" - "networkx" diff --git a/easi/tasks/ebalfred/ebalfred_long_horizon.yaml b/easi/tasks/ebalfred/ebalfred_long_horizon.yaml index 840a32b..209cd3c 100644 --- a/easi/tasks/ebalfred/ebalfred_long_horizon.yaml +++ b/easi/tasks/ebalfred/ebalfred_long_horizon.yaml @@ -15,6 +15,8 @@ simulator_configs: quality: "MediumCloseFitShadows" screen_height: 500 screen_width: 500 + max_invalid_actions: 10 + feedback_verbosity: 0 additional_deps: - "gym" - "networkx" diff --git a/easi/tasks/ebalfred/ebalfred_spatial.yaml b/easi/tasks/ebalfred/ebalfred_spatial.yaml index 2025d03..b2e9baf 100644 --- a/easi/tasks/ebalfred/ebalfred_spatial.yaml +++ b/easi/tasks/ebalfred/ebalfred_spatial.yaml @@ -15,6 +15,8 @@ simulator_configs: quality: "MediumCloseFitShadows" screen_height: 500 screen_width: 500 + max_invalid_actions: 10 + feedback_verbosity: 0 additional_deps: - "gym" - "networkx" diff --git a/easi/tasks/ebalfred/ebalfred_visual_appearance.yaml b/easi/tasks/ebalfred/ebalfred_visual_appearance.yaml index ae3a300..17293b9 100644 --- a/easi/tasks/ebalfred/ebalfred_visual_appearance.yaml +++ b/easi/tasks/ebalfred/ebalfred_visual_appearance.yaml @@ -15,6 +15,8 @@ simulator_configs: quality: "MediumCloseFitShadows" screen_height: 500 screen_width: 500 + max_invalid_actions: 10 + feedback_verbosity: 0 additional_deps: - "gym" - "networkx" diff --git a/easi/tasks/ebalfred/vendor/EBAlfEnv.py b/easi/tasks/ebalfred/vendor/EBAlfEnv.py index 9e17700..565221f 100644 --- a/easi/tasks/ebalfred/vendor/EBAlfEnv.py +++ b/easi/tasks/ebalfred/vendor/EBAlfEnv.py @@ -88,7 +88,8 @@ class EBAlfEnv(gym.Env): action_space (gym.spaces.Discrete): Discrete action space language_skill_set (list): Readable action descriptions """ - def __init__(self, exp_name='', detection_box=False, resolution=500, data_dir=None, x_display=None, max_steps=30): + def __init__(self, exp_name='', detection_box=False, resolution=500, data_dir=None, x_display=None, max_steps=30, + max_invalid_actions=10, feedback_verbosity=0): """ Initialize the AI2THOR environment. @@ -99,6 +100,8 @@ def __init__(self, exp_name='', detection_box=False, resolution=500, data_dir=No data_dir: path to task JSON files (e.g. datasets/.../tasks) x_display: X display number for AI2-THOR max_steps: maximum steps per episode (from task YAML config) + max_invalid_actions: max consecutive invalid actions before episode ends + feedback_verbosity: 0=concise, 1=verbose env feedback """ super().__init__() self.reward_config_path = ALFRED_REWARD_PATH @@ -114,7 +117,7 @@ def __init__(self, exp_name='', detection_box=False, resolution=500, data_dir=No self._current_step = 0 self._max_episode_steps = max_steps self._cur_invalid_actions = 0 - self._max_invalid_actions = 10 + self._max_invalid_actions = max_invalid_actions self._episode_start_time = 0 self.episode_log = [] @@ -127,7 +130,7 @@ def __init__(self, exp_name='', detection_box=False, resolution=500, data_dir=No # env feedback and image save # feedback verbosity, 0: concise, 1: verbose - self.feedback_verbosity = 0 + self.feedback_verbosity = feedback_verbosity self.log_path = 'running/eb_alfred/{}'.format(exp_name) self.detection = detection_box # add detection in image From ffbed422a30876ea802fe1151073e6bfea3f7adc Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 17 Feb 2026 06:19:28 +0800 Subject: [PATCH 041/239] [Feat] Add configurable bounding box drawing for EB-Navigation observations --- easi/tasks/ebnavigation/bridge.py | 2 + .../tasks/ebnavigation/ebnavigation_base.yaml | 1 + .../ebnavigation_common_sense.yaml | 1 + .../ebnavigation_complex_instruction.yaml | 1 + .../ebnavigation_long_horizon.yaml | 1 + .../ebnavigation_visual_appearance.yaml | 1 + easi/tasks/ebnavigation/vendor/EBNavEnv.py | 48 ++++++++++++++++++- 7 files changed, 53 insertions(+), 2 deletions(-) diff --git a/easi/tasks/ebnavigation/bridge.py b/easi/tasks/ebnavigation/bridge.py index 81e173f..5fd5bbf 100644 --- a/easi/tasks/ebnavigation/bridge.py +++ b/easi/tasks/ebnavigation/bridge.py @@ -36,6 +36,7 @@ def _create_env(self, reset_config, simulator_kwargs): success_threshold = simulator_kwargs.get("success_threshold", 1.0) grid_size = simulator_kwargs.get("grid_size", 0.1) visibility_distance = simulator_kwargs.get("visibility_distance", 10.0) + boundingbox = simulator_kwargs.get("boundingbox", False) return EBNavEnv( resolution=resolution, fov=fov, @@ -43,6 +44,7 @@ def _create_env(self, reset_config, simulator_kwargs): success_threshold=success_threshold, grid_size=grid_size, visibility_distance=visibility_distance, + boundingbox=boundingbox, ) def _on_reset(self, env, reset_config): diff --git a/easi/tasks/ebnavigation/ebnavigation_base.yaml b/easi/tasks/ebnavigation/ebnavigation_base.yaml index 457ab30..c083001 100644 --- a/easi/tasks/ebnavigation/ebnavigation_base.yaml +++ b/easi/tasks/ebnavigation/ebnavigation_base.yaml @@ -16,6 +16,7 @@ simulator_configs: success_threshold: 1.0 grid_size: 0.1 visibility_distance: 10.0 + boundingbox: false agent: prompt_builder: "easi.tasks.ebnavigation.prompts.EBNavigationPromptBuilder" prompt_builder_kwargs: diff --git a/easi/tasks/ebnavigation/ebnavigation_common_sense.yaml b/easi/tasks/ebnavigation/ebnavigation_common_sense.yaml index 0dbfb52..b899d40 100644 --- a/easi/tasks/ebnavigation/ebnavigation_common_sense.yaml +++ b/easi/tasks/ebnavigation/ebnavigation_common_sense.yaml @@ -16,6 +16,7 @@ simulator_configs: success_threshold: 1.0 grid_size: 0.1 visibility_distance: 10.0 + boundingbox: false agent: prompt_builder: "easi.tasks.ebnavigation.prompts.EBNavigationPromptBuilder" prompt_builder_kwargs: diff --git a/easi/tasks/ebnavigation/ebnavigation_complex_instruction.yaml b/easi/tasks/ebnavigation/ebnavigation_complex_instruction.yaml index a7acd9b..2b56174 100644 --- a/easi/tasks/ebnavigation/ebnavigation_complex_instruction.yaml +++ b/easi/tasks/ebnavigation/ebnavigation_complex_instruction.yaml @@ -16,6 +16,7 @@ simulator_configs: success_threshold: 1.0 grid_size: 0.1 visibility_distance: 10.0 + boundingbox: false agent: prompt_builder: "easi.tasks.ebnavigation.prompts.EBNavigationPromptBuilder" prompt_builder_kwargs: diff --git a/easi/tasks/ebnavigation/ebnavigation_long_horizon.yaml b/easi/tasks/ebnavigation/ebnavigation_long_horizon.yaml index 309fd1c..b5de4bb 100644 --- a/easi/tasks/ebnavigation/ebnavigation_long_horizon.yaml +++ b/easi/tasks/ebnavigation/ebnavigation_long_horizon.yaml @@ -16,6 +16,7 @@ simulator_configs: success_threshold: 1.0 grid_size: 0.1 visibility_distance: 10.0 + boundingbox: false agent: prompt_builder: "easi.tasks.ebnavigation.prompts.EBNavigationPromptBuilder" prompt_builder_kwargs: diff --git a/easi/tasks/ebnavigation/ebnavigation_visual_appearance.yaml b/easi/tasks/ebnavigation/ebnavigation_visual_appearance.yaml index 5212d94..677483b 100644 --- a/easi/tasks/ebnavigation/ebnavigation_visual_appearance.yaml +++ b/easi/tasks/ebnavigation/ebnavigation_visual_appearance.yaml @@ -16,6 +16,7 @@ simulator_configs: success_threshold: 1.0 grid_size: 0.1 visibility_distance: 10.0 + boundingbox: false agent: prompt_builder: "easi.tasks.ebnavigation.prompts.EBNavigationPromptBuilder" prompt_builder_kwargs: diff --git a/easi/tasks/ebnavigation/vendor/EBNavEnv.py b/easi/tasks/ebnavigation/vendor/EBNavEnv.py index 7a3d699..4ac51f6 100644 --- a/easi/tasks/ebnavigation/vendor/EBNavEnv.py +++ b/easi/tasks/ebnavigation/vendor/EBNavEnv.py @@ -11,6 +11,39 @@ import ai2thor.controller import numpy as np from ai2thor.platform import Linux64 +from PIL import Image, ImageDraw + +# Objects eligible for bounding box drawing +# Reference: EmbodiedBench/embodiedbench/envs/eb_navigation/utils.py +VALID_OBJS = { + 'Cart', 'Potato', 'Faucet', 'Ottoman', 'CoffeeMachine', 'Candle', 'CD', + 'Pan', 'Watch', 'HandTowel', 'SprayBottle', 'BaseballBat', 'CellPhone', + 'Kettle', 'Mug', 'StoveBurner', 'Bowl', 'Spoon', 'TissueBox', 'Apple', + 'TennisRacket', 'SoapBar', 'Cloth', 'Plunger', 'FloorLamp', + 'ToiletPaperHanger', 'Spatula', 'Plate', 'Glassbottle', 'Knife', 'Tomato', + 'ButterKnife', 'Dresser', 'Microwave', 'GarbageCan', 'WateringCan', + 'Vase', 'ArmChair', 'Safe', 'KeyChain', 'Pot', 'Pen', 'Newspaper', + 'Bread', 'Book', 'Lettuce', 'CreditCard', 'AlarmClock', 'ToiletPaper', + 'SideTable', 'Fork', 'Box', 'Egg', 'DeskLamp', 'Ladle', 'WineBottle', + 'Pencil', 'Laptop', 'RemoteControl', 'BasketBall', 'DishSponge', 'Cup', + 'SaltShaker', 'PepperShaker', 'Pillow', 'Bathtub', 'SoapBottle', 'Statue', + 'Fridge', 'Toaster', 'LaundryHamper', +} + + +def _draw_boxes(frame: np.ndarray, instance_detections: dict) -> np.ndarray: + """Draw bounding boxes on frame for all valid objects. + + Reference: EmbodiedBench/embodiedbench/envs/eb_navigation/utils.py:draw_boxes + """ + img = Image.fromarray(frame) + draw = ImageDraw.Draw(img) + for class_name, box in instance_detections.items(): + if class_name.split('|')[0] in VALID_OBJS: + color = tuple(np.random.randint(0, 256, size=3).tolist()) + x1, y1, x2, y2 = box[0], box[1], box[2], box[3] + draw.rectangle([x1, y1, x2, y2], outline=color, width=1) + return np.array(img) class EBNavEnv: """AI2-THOR navigation environment for EB-Navigation benchmark.""" @@ -23,9 +56,11 @@ def __init__( success_threshold: float = 1.0, grid_size: float = 0.1, visibility_distance: float = 10.0, + boundingbox: bool = False, ): self.resolution = resolution self._success_threshold = success_threshold + self.boundingbox = boundingbox self.config = { "agentMode": "default", "gridSize": grid_size, @@ -74,7 +109,7 @@ def reset(self, episode: dict) -> dict: ) self._current_step = 0 - return {"head_rgb": self.env.last_event.frame} + return {"head_rgb": self._get_frame()} def step(self, action_id: int) -> tuple[dict, float, bool, dict]: """Execute one discrete action. @@ -95,7 +130,7 @@ def step(self, action_id: int) -> tuple[dict, float, bool, dict]: done = self._current_step >= self._max_episode_steps or reward > 0 - obs = {"head_rgb": self.env.last_event.frame} + obs = {"head_rgb": self._get_frame()} info = { "task_success": reward, "distance": distance, @@ -160,6 +195,15 @@ def _get_env_feedback(self) -> str: else: return f"Last action {last_action} is invalid. {error}" + def _get_frame(self) -> np.ndarray: + """Return the current frame, with bounding boxes if enabled.""" + frame = self.env.last_event.frame + if self.boundingbox: + detections = self.env.last_event.instance_detections2D + if detections: + frame = _draw_boxes(frame, detections) + return frame + def close(self) -> None: """Shut down the AI2-THOR controller.""" self.env.stop() From 321de2a0eb8afec51a9aba601b90508798213c1a Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 17 Feb 2026 06:24:19 +0800 Subject: [PATCH 042/239] [Feat] Add configurable bounding box drawing for EB-Alfred observations --- easi/tasks/ebalfred/bridge.py | 2 ++ easi/tasks/ebalfred/ebalfred_base.yaml | 1 + easi/tasks/ebalfred/ebalfred_common_sense.yaml | 1 + .../ebalfred/ebalfred_complex_instruction.yaml | 1 + easi/tasks/ebalfred/ebalfred_long_horizon.yaml | 1 + easi/tasks/ebalfred/ebalfred_spatial.yaml | 1 + easi/tasks/ebalfred/ebalfred_visual_appearance.yaml | 1 + easi/tasks/ebalfred/vendor/EBAlfEnv.py | 13 +++++++++++-- 8 files changed, 19 insertions(+), 2 deletions(-) diff --git a/easi/tasks/ebalfred/bridge.py b/easi/tasks/ebalfred/bridge.py index 5802b1f..3ac29bf 100644 --- a/easi/tasks/ebalfred/bridge.py +++ b/easi/tasks/ebalfred/bridge.py @@ -38,6 +38,7 @@ def _create_env(self, reset_config, simulator_kwargs): max_steps = simulator_kwargs.get("max_steps", 30) max_invalid_actions = simulator_kwargs.get("max_invalid_actions", 10) feedback_verbosity = simulator_kwargs.get("feedback_verbosity", 0) + detection_box = simulator_kwargs.get("detection_box", False) # x_display from YAML, falling back to DISPLAY env var (set by xvfb-run) x_display = simulator_kwargs.get( "x_display", os.environ.get("DISPLAY", ":0").lstrip(":") @@ -49,6 +50,7 @@ def _create_env(self, reset_config, simulator_kwargs): max_steps=max_steps, max_invalid_actions=max_invalid_actions, feedback_verbosity=feedback_verbosity, + detection_box=detection_box, ) def _on_reset(self, env, reset_config): diff --git a/easi/tasks/ebalfred/ebalfred_base.yaml b/easi/tasks/ebalfred/ebalfred_base.yaml index fe88d92..b131bf2 100644 --- a/easi/tasks/ebalfred/ebalfred_base.yaml +++ b/easi/tasks/ebalfred/ebalfred_base.yaml @@ -17,6 +17,7 @@ simulator_configs: screen_width: 500 max_invalid_actions: 10 feedback_verbosity: 0 + detection_box: false additional_deps: - "gym" - "networkx" diff --git a/easi/tasks/ebalfred/ebalfred_common_sense.yaml b/easi/tasks/ebalfred/ebalfred_common_sense.yaml index 7ad2bd3..89e5381 100644 --- a/easi/tasks/ebalfred/ebalfred_common_sense.yaml +++ b/easi/tasks/ebalfred/ebalfred_common_sense.yaml @@ -17,6 +17,7 @@ simulator_configs: screen_width: 500 max_invalid_actions: 10 feedback_verbosity: 0 + detection_box: false additional_deps: - "gym" - "networkx" diff --git a/easi/tasks/ebalfred/ebalfred_complex_instruction.yaml b/easi/tasks/ebalfred/ebalfred_complex_instruction.yaml index 2d235d1..07b6375 100644 --- a/easi/tasks/ebalfred/ebalfred_complex_instruction.yaml +++ b/easi/tasks/ebalfred/ebalfred_complex_instruction.yaml @@ -17,6 +17,7 @@ simulator_configs: screen_width: 500 max_invalid_actions: 10 feedback_verbosity: 0 + detection_box: false additional_deps: - "gym" - "networkx" diff --git a/easi/tasks/ebalfred/ebalfred_long_horizon.yaml b/easi/tasks/ebalfred/ebalfred_long_horizon.yaml index 209cd3c..549745e 100644 --- a/easi/tasks/ebalfred/ebalfred_long_horizon.yaml +++ b/easi/tasks/ebalfred/ebalfred_long_horizon.yaml @@ -17,6 +17,7 @@ simulator_configs: screen_width: 500 max_invalid_actions: 10 feedback_verbosity: 0 + detection_box: false additional_deps: - "gym" - "networkx" diff --git a/easi/tasks/ebalfred/ebalfred_spatial.yaml b/easi/tasks/ebalfred/ebalfred_spatial.yaml index b2e9baf..3ec3b77 100644 --- a/easi/tasks/ebalfred/ebalfred_spatial.yaml +++ b/easi/tasks/ebalfred/ebalfred_spatial.yaml @@ -17,6 +17,7 @@ simulator_configs: screen_width: 500 max_invalid_actions: 10 feedback_verbosity: 0 + detection_box: false additional_deps: - "gym" - "networkx" diff --git a/easi/tasks/ebalfred/ebalfred_visual_appearance.yaml b/easi/tasks/ebalfred/ebalfred_visual_appearance.yaml index 17293b9..e690491 100644 --- a/easi/tasks/ebalfred/ebalfred_visual_appearance.yaml +++ b/easi/tasks/ebalfred/ebalfred_visual_appearance.yaml @@ -17,6 +17,7 @@ simulator_configs: screen_width: 500 max_invalid_actions: 10 feedback_verbosity: 0 + detection_box: false additional_deps: - "gym" - "networkx" diff --git a/easi/tasks/ebalfred/vendor/EBAlfEnv.py b/easi/tasks/ebalfred/vendor/EBAlfEnv.py index 565221f..92a3ca1 100644 --- a/easi/tasks/ebalfred/vendor/EBAlfEnv.py +++ b/easi/tasks/ebalfred/vendor/EBAlfEnv.py @@ -258,7 +258,7 @@ def reset(self, episode=None): self._current_step = 0 self._cur_invalid_actions = 0 obs = { - 'head_rgb': self.env.last_event.frame, + 'head_rgb': self._get_frame(), } self._reset = True self.episode_log = [] @@ -300,7 +300,7 @@ def step(self, action, reasoning=''): info['task_progress'] = subgoal_met[0] / subgoal_met[1] obs = { - 'head_rgb': self.env.last_event.frame, + 'head_rgb': self._get_frame(), } # if exceed the maximum episode steps or the goal is achieved if self._current_step >= self._max_episode_steps or info['task_success'] or self._cur_invalid_actions >= self._max_invalid_actions: @@ -351,6 +351,15 @@ def get_env_feedback(self, info): def seed(self, seed=None): self.env.random_initilize(seed) + def _get_frame(self): + """Return the current frame, with bounding boxes if detection is enabled.""" + frame = self.env.last_event.frame + if self.detection: + img = Image.fromarray(frame) + img = utils.draw_boxes(img, self.env.last_event.instance_detections2D, name_translation=self.id_to_name_dict) + frame = np.array(img) + return frame + def save_image(self, *args, **kwargs): """Save current agent view as a PNG image.""" episode_idx = self._current_episode_num if not len(self.selected_indexes) else self.selected_indexes[self._current_episode_num - 1] + 1 From d55ab73b7f891375a013354897a4b958468afda2 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 17 Feb 2026 06:25:59 +0800 Subject: [PATCH 043/239] [Chore] Add json file --- easi/tasks/ebhabitat/vendor/data/default.physics_config.json | 1 + 1 file changed, 1 insertion(+) create mode 100644 easi/tasks/ebhabitat/vendor/data/default.physics_config.json diff --git a/easi/tasks/ebhabitat/vendor/data/default.physics_config.json b/easi/tasks/ebhabitat/vendor/data/default.physics_config.json new file mode 100644 index 0000000..f8cf661 --- /dev/null +++ b/easi/tasks/ebhabitat/vendor/data/default.physics_config.json @@ -0,0 +1 @@ +{"physics_simulator": "bullet", "timestep": 0.008, "gravity": [0, -9.8, 0], "friction_coefficient": 0.4, "restitution_coefficient": 0.1, "rigid object paths": ["objects"]} \ No newline at end of file From e948a7a0908b758e1e561c1e058e6bf9474c340b Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 17 Feb 2026 06:57:44 +0800 Subject: [PATCH 044/239] [Feat] Make EB-Habitat gpu_device_id configurable via task YAML --- easi/tasks/ebhabitat/bridge.py | 2 ++ easi/tasks/ebhabitat/ebhabitat_base.yaml | 1 + easi/tasks/ebhabitat/ebhabitat_common_sense.yaml | 1 + .../ebhabitat/ebhabitat_complex_instruction.yaml | 1 + easi/tasks/ebhabitat/ebhabitat_long_horizon.yaml | 1 + .../ebhabitat/ebhabitat_spatial_relationship.yaml | 1 + .../tasks/ebhabitat/ebhabitat_visual_appearance.yaml | 1 + easi/tasks/ebhabitat/vendor/EBHabEnv.py | 12 ++++++++---- 8 files changed, 16 insertions(+), 4 deletions(-) diff --git a/easi/tasks/ebhabitat/bridge.py b/easi/tasks/ebhabitat/bridge.py index 08fed53..5b83d71 100644 --- a/easi/tasks/ebhabitat/bridge.py +++ b/easi/tasks/ebhabitat/bridge.py @@ -53,6 +53,7 @@ def _create_env(self, reset_config, simulator_kwargs): max_steps = simulator_kwargs.get("max_steps", 30) max_invalid_actions = simulator_kwargs.get("max_invalid_actions", 10) feedback_verbosity = simulator_kwargs.get("feedback_verbosity", 1) + gpu_device_id = simulator_kwargs.get("gpu_device_id", None) env = EBHabEnv( eval_set=eval_set, @@ -62,6 +63,7 @@ def _create_env(self, reset_config, simulator_kwargs): max_steps=max_steps, max_invalid_actions=max_invalid_actions, feedback_verbosity=feedback_verbosity, + gpu_device_id=gpu_device_id, ) # Extract dynamic action space diff --git a/easi/tasks/ebhabitat/ebhabitat_base.yaml b/easi/tasks/ebhabitat/ebhabitat_base.yaml index 4962033..18875d9 100644 --- a/easi/tasks/ebhabitat/ebhabitat_base.yaml +++ b/easi/tasks/ebhabitat/ebhabitat_base.yaml @@ -17,6 +17,7 @@ simulator_configs: screen_width: 500 max_invalid_actions: 10 feedback_verbosity: 1 + gpu_device_id: -1 additional_deps: - "gym" - "hydra-core>=1.3" diff --git a/easi/tasks/ebhabitat/ebhabitat_common_sense.yaml b/easi/tasks/ebhabitat/ebhabitat_common_sense.yaml index c553df6..d608ba5 100644 --- a/easi/tasks/ebhabitat/ebhabitat_common_sense.yaml +++ b/easi/tasks/ebhabitat/ebhabitat_common_sense.yaml @@ -17,6 +17,7 @@ simulator_configs: screen_width: 500 max_invalid_actions: 10 feedback_verbosity: 1 + gpu_device_id: -1 additional_deps: - "gym" - "hydra-core>=1.3" diff --git a/easi/tasks/ebhabitat/ebhabitat_complex_instruction.yaml b/easi/tasks/ebhabitat/ebhabitat_complex_instruction.yaml index 395be43..314053f 100644 --- a/easi/tasks/ebhabitat/ebhabitat_complex_instruction.yaml +++ b/easi/tasks/ebhabitat/ebhabitat_complex_instruction.yaml @@ -17,6 +17,7 @@ simulator_configs: screen_width: 500 max_invalid_actions: 10 feedback_verbosity: 1 + gpu_device_id: -1 additional_deps: - "gym" - "hydra-core>=1.3" diff --git a/easi/tasks/ebhabitat/ebhabitat_long_horizon.yaml b/easi/tasks/ebhabitat/ebhabitat_long_horizon.yaml index 80cfce9..7d5b29f 100644 --- a/easi/tasks/ebhabitat/ebhabitat_long_horizon.yaml +++ b/easi/tasks/ebhabitat/ebhabitat_long_horizon.yaml @@ -17,6 +17,7 @@ simulator_configs: screen_width: 500 max_invalid_actions: 10 feedback_verbosity: 1 + gpu_device_id: -1 additional_deps: - "gym" - "hydra-core>=1.3" diff --git a/easi/tasks/ebhabitat/ebhabitat_spatial_relationship.yaml b/easi/tasks/ebhabitat/ebhabitat_spatial_relationship.yaml index 7a8d84a..d158b6b 100644 --- a/easi/tasks/ebhabitat/ebhabitat_spatial_relationship.yaml +++ b/easi/tasks/ebhabitat/ebhabitat_spatial_relationship.yaml @@ -17,6 +17,7 @@ simulator_configs: screen_width: 500 max_invalid_actions: 10 feedback_verbosity: 1 + gpu_device_id: -1 additional_deps: - "gym" - "hydra-core>=1.3" diff --git a/easi/tasks/ebhabitat/ebhabitat_visual_appearance.yaml b/easi/tasks/ebhabitat/ebhabitat_visual_appearance.yaml index 939e9f1..e32a0ab 100644 --- a/easi/tasks/ebhabitat/ebhabitat_visual_appearance.yaml +++ b/easi/tasks/ebhabitat/ebhabitat_visual_appearance.yaml @@ -17,6 +17,7 @@ simulator_configs: screen_width: 500 max_invalid_actions: 10 feedback_verbosity: 1 + gpu_device_id: -1 additional_deps: - "gym" - "hydra-core>=1.3" diff --git a/easi/tasks/ebhabitat/vendor/EBHabEnv.py b/easi/tasks/ebhabitat/vendor/EBHabEnv.py index 7eb93e3..0de6413 100644 --- a/easi/tasks/ebhabitat/vendor/EBHabEnv.py +++ b/easi/tasks/ebhabitat/vendor/EBHabEnv.py @@ -116,7 +116,8 @@ class EBHabEnv(gym.Env): def __init__(self, eval_set='train', exp_name='', down_sample_ratio=1.0, start_epi_index=0, resolution=500, recording=False, data_dir=None, dataset_dir=None, - max_steps=30, max_invalid_actions=10, feedback_verbosity=1): + max_steps=30, max_invalid_actions=10, feedback_verbosity=1, + gpu_device_id=None): """ Initialize the HabitatRearrange environment. @@ -151,9 +152,12 @@ def __init__(self, eval_set='train', exp_name='', down_sample_ratio=1.0, self.resolution = resolution # Set GPU device for rendering. - # Default to 0 (first GPU). Use HABITAT_SIM_GPU_ID env var to override. - # The headless conda build may need the NVIDIA EGL ICD installed. - gpu_id = int(os.environ.get("HABITAT_SIM_GPU_ID", "0")) + # Priority: constructor param > HABITAT_SIM_GPU_ID env var > default 0. + # Use -1 for software rendering (Mesa llvmpipe) on machines without CUDA. + if gpu_device_id is not None: + gpu_id = gpu_device_id + else: + gpu_id = int(os.environ.get("HABITAT_SIM_GPU_ID", "0")) self.config.habitat.simulator.habitat_sim_v0.gpu_device_id = gpu_id # modify config path to ease data loading From 8f0d947f6468fe62fe78374830e3cdc51b07bbf1 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 17 Feb 2026 16:33:26 +0800 Subject: [PATCH 045/239] [Fix] Add generation_kwargs to YAML configs and fix empty plan handling - Add generation_kwargs (temperature, max_tokens, top_k, top_p, do_sample) to all 17 task YAML configs, replacing llm_defaults - Runner merges YAML generation_kwargs with CLI --llm-kwargs (CLI overrides) - ReAct agent returns <> sentinel on empty plans instead of fallback action, matching EmbodiedBench behavior of ending episodes on empty plans - Fix test_max_steps assertion to match YAML (50, not 30) --- easi/agents/react_agent.py | 9 +++++++-- easi/evaluation/runner.py | 12 +++++++++++- easi/tasks/ebalfred/ebalfred_base.yaml | 6 ++++++ easi/tasks/ebalfred/ebalfred_common_sense.yaml | 6 ++++++ .../tasks/ebalfred/ebalfred_complex_instruction.yaml | 6 ++++++ easi/tasks/ebalfred/ebalfred_long_horizon.yaml | 6 ++++++ easi/tasks/ebalfred/ebalfred_spatial.yaml | 6 ++++++ easi/tasks/ebalfred/ebalfred_visual_appearance.yaml | 6 ++++++ easi/tasks/ebhabitat/ebhabitat_base.yaml | 8 +++++++- easi/tasks/ebhabitat/ebhabitat_common_sense.yaml | 8 +++++++- .../ebhabitat/ebhabitat_complex_instruction.yaml | 8 +++++++- easi/tasks/ebhabitat/ebhabitat_long_horizon.yaml | 8 +++++++- .../ebhabitat/ebhabitat_spatial_relationship.yaml | 8 +++++++- .../tasks/ebhabitat/ebhabitat_visual_appearance.yaml | 8 +++++++- easi/tasks/ebnavigation/ebnavigation_base.yaml | 6 ++++++ .../ebnavigation/ebnavigation_common_sense.yaml | 6 ++++++ .../ebnavigation_complex_instruction.yaml | 6 ++++++ .../ebnavigation/ebnavigation_long_horizon.yaml | 6 ++++++ .../ebnavigation/ebnavigation_visual_appearance.yaml | 6 ++++++ tests/test_ebhabitat_task.py | 2 +- 20 files changed, 127 insertions(+), 10 deletions(-) diff --git a/easi/agents/react_agent.py b/easi/agents/react_agent.py index 87bf1a8..0e4b46b 100644 --- a/easi/agents/react_agent.py +++ b/easi/agents/react_agent.py @@ -129,10 +129,15 @@ def add_feedback(self, action_name: str, feedback: str) -> None: self._action_buffer.clear() def _fallback_action(self) -> Action: - """Return a safe fallback action when parsing fails.""" + """Return a safe fallback action when parsing fails. + + If the action space has "Stop", use it (e.g., EB-Alfred). + Otherwise, signal that the episode should end via "<>" + (matching EmbodiedBench behavior where empty plans end episodes). + """ if "Stop" in self.action_space: return Action(action_name="Stop") - return Action(action_name=self.action_space[0]) + return Action(action_name="<>") def _generate_with_fallback( self, messages: list[dict], response_format: dict | None, diff --git a/easi/evaluation/runner.py b/easi/evaluation/runner.py index d46da2b..16532c2 100644 --- a/easi/evaluation/runner.py +++ b/easi/evaluation/runner.py @@ -304,6 +304,12 @@ def _run_episode( for step in range(task.max_steps): action = agent.act(observation, task_description) + + # Handle stop signal (e.g., empty plan from LLM) + if action.action_name == "<>": + logger.info("Agent signalled stop (empty plan), ending episode") + break + step_result = sim.step(action) trajectory.append(step_result) @@ -424,11 +430,15 @@ def _create_agent(self, action_space: list[str], task_config: dict, all_kwargs = parse_llm_kwargs(self.llm_kwargs_raw) _, client_kwargs = split_kwargs(all_kwargs) + # Merge YAML generation_kwargs with CLI kwargs (CLI overrides) + yaml_gen_kwargs = agent_config.get("generation_kwargs", {}) + merged_kwargs = {**yaml_gen_kwargs, **client_kwargs} + llm = LLMClient( model=litellm_model, base_url=base_url, num_retries=self.max_retries, - **client_kwargs, + **merged_kwargs, ) else: # Legacy path: existing LLMApiClient diff --git a/easi/tasks/ebalfred/ebalfred_base.yaml b/easi/tasks/ebalfred/ebalfred_base.yaml index b131bf2..591cdf9 100644 --- a/easi/tasks/ebalfred/ebalfred_base.yaml +++ b/easi/tasks/ebalfred/ebalfred_base.yaml @@ -29,3 +29,9 @@ agent: split: "base" use_feedback: true chat_history: false + generation_kwargs: + temperature: 0 + max_tokens: 2048 + top_k: 50 + top_p: 0.95 + do_sample: false diff --git a/easi/tasks/ebalfred/ebalfred_common_sense.yaml b/easi/tasks/ebalfred/ebalfred_common_sense.yaml index 89e5381..33f9262 100644 --- a/easi/tasks/ebalfred/ebalfred_common_sense.yaml +++ b/easi/tasks/ebalfred/ebalfred_common_sense.yaml @@ -29,3 +29,9 @@ agent: split: "base" use_feedback: true chat_history: false + generation_kwargs: + temperature: 0 + max_tokens: 2048 + top_k: 50 + top_p: 0.95 + do_sample: false diff --git a/easi/tasks/ebalfred/ebalfred_complex_instruction.yaml b/easi/tasks/ebalfred/ebalfred_complex_instruction.yaml index 07b6375..577f541 100644 --- a/easi/tasks/ebalfred/ebalfred_complex_instruction.yaml +++ b/easi/tasks/ebalfred/ebalfred_complex_instruction.yaml @@ -29,3 +29,9 @@ agent: split: "base" use_feedback: true chat_history: false + generation_kwargs: + temperature: 0 + max_tokens: 2048 + top_k: 50 + top_p: 0.95 + do_sample: false diff --git a/easi/tasks/ebalfred/ebalfred_long_horizon.yaml b/easi/tasks/ebalfred/ebalfred_long_horizon.yaml index 549745e..52b2a75 100644 --- a/easi/tasks/ebalfred/ebalfred_long_horizon.yaml +++ b/easi/tasks/ebalfred/ebalfred_long_horizon.yaml @@ -29,3 +29,9 @@ agent: split: "long_horizon" use_feedback: true chat_history: true + generation_kwargs: + temperature: 0 + max_tokens: 2048 + top_k: 50 + top_p: 0.95 + do_sample: false diff --git a/easi/tasks/ebalfred/ebalfred_spatial.yaml b/easi/tasks/ebalfred/ebalfred_spatial.yaml index 3ec3b77..2f040f2 100644 --- a/easi/tasks/ebalfred/ebalfred_spatial.yaml +++ b/easi/tasks/ebalfred/ebalfred_spatial.yaml @@ -29,3 +29,9 @@ agent: split: "base" use_feedback: true chat_history: false + generation_kwargs: + temperature: 0 + max_tokens: 2048 + top_k: 50 + top_p: 0.95 + do_sample: false diff --git a/easi/tasks/ebalfred/ebalfred_visual_appearance.yaml b/easi/tasks/ebalfred/ebalfred_visual_appearance.yaml index e690491..1648e01 100644 --- a/easi/tasks/ebalfred/ebalfred_visual_appearance.yaml +++ b/easi/tasks/ebalfred/ebalfred_visual_appearance.yaml @@ -29,3 +29,9 @@ agent: split: "base" use_feedback: true chat_history: false + generation_kwargs: + temperature: 0 + max_tokens: 2048 + top_k: 50 + top_p: 0.95 + do_sample: false diff --git a/easi/tasks/ebhabitat/ebhabitat_base.yaml b/easi/tasks/ebhabitat/ebhabitat_base.yaml index 18875d9..70a4d69 100644 --- a/easi/tasks/ebhabitat/ebhabitat_base.yaml +++ b/easi/tasks/ebhabitat/ebhabitat_base.yaml @@ -3,7 +3,7 @@ display_name: "EB-Habitat Base Split" description: "Base evaluation split for EB-Habitat (rearrangement)" simulator: "habitat_sim:v0_3_0" task_class: "easi.tasks.ebhabitat.task.EBHabitatTask" -max_steps: 30 +max_steps: 50 dataset: source: huggingface repo_id: "oscarqjh/EB-Habitat_easi" @@ -32,3 +32,9 @@ agent: split: "base" use_feedback: true chat_history: false + generation_kwargs: + temperature: 0 + max_tokens: 2048 + top_k: 50 + top_p: 0.95 + do_sample: false diff --git a/easi/tasks/ebhabitat/ebhabitat_common_sense.yaml b/easi/tasks/ebhabitat/ebhabitat_common_sense.yaml index d608ba5..dd8a40f 100644 --- a/easi/tasks/ebhabitat/ebhabitat_common_sense.yaml +++ b/easi/tasks/ebhabitat/ebhabitat_common_sense.yaml @@ -3,7 +3,7 @@ display_name: "EB-Habitat Common Sense Split" description: "Common sense evaluation split for EB-Habitat (rearrangement)" simulator: "habitat_sim:v0_3_0" task_class: "easi.tasks.ebhabitat.task.EBHabitatTask" -max_steps: 30 +max_steps: 50 dataset: source: huggingface repo_id: "oscarqjh/EB-Habitat_easi" @@ -32,3 +32,9 @@ agent: split: "common_sense" use_feedback: true chat_history: false + generation_kwargs: + temperature: 0 + max_tokens: 2048 + top_k: 50 + top_p: 0.95 + do_sample: false diff --git a/easi/tasks/ebhabitat/ebhabitat_complex_instruction.yaml b/easi/tasks/ebhabitat/ebhabitat_complex_instruction.yaml index 314053f..c9a5d08 100644 --- a/easi/tasks/ebhabitat/ebhabitat_complex_instruction.yaml +++ b/easi/tasks/ebhabitat/ebhabitat_complex_instruction.yaml @@ -3,7 +3,7 @@ display_name: "EB-Habitat Complex Instruction Split" description: "Complex instruction evaluation split for EB-Habitat (rearrangement)" simulator: "habitat_sim:v0_3_0" task_class: "easi.tasks.ebhabitat.task.EBHabitatTask" -max_steps: 30 +max_steps: 50 dataset: source: huggingface repo_id: "oscarqjh/EB-Habitat_easi" @@ -32,3 +32,9 @@ agent: split: "complex_instruction" use_feedback: true chat_history: false + generation_kwargs: + temperature: 0 + max_tokens: 2048 + top_k: 50 + top_p: 0.95 + do_sample: false diff --git a/easi/tasks/ebhabitat/ebhabitat_long_horizon.yaml b/easi/tasks/ebhabitat/ebhabitat_long_horizon.yaml index 7d5b29f..9f5e70c 100644 --- a/easi/tasks/ebhabitat/ebhabitat_long_horizon.yaml +++ b/easi/tasks/ebhabitat/ebhabitat_long_horizon.yaml @@ -3,7 +3,7 @@ display_name: "EB-Habitat Long Horizon Split" description: "Long horizon evaluation split for EB-Habitat (rearrangement)" simulator: "habitat_sim:v0_3_0" task_class: "easi.tasks.ebhabitat.task.EBHabitatTask" -max_steps: 30 +max_steps: 50 dataset: source: huggingface repo_id: "oscarqjh/EB-Habitat_easi" @@ -32,3 +32,9 @@ agent: split: "long_horizon" use_feedback: true chat_history: false + generation_kwargs: + temperature: 0 + max_tokens: 2048 + top_k: 50 + top_p: 0.95 + do_sample: false diff --git a/easi/tasks/ebhabitat/ebhabitat_spatial_relationship.yaml b/easi/tasks/ebhabitat/ebhabitat_spatial_relationship.yaml index d158b6b..12ca5f4 100644 --- a/easi/tasks/ebhabitat/ebhabitat_spatial_relationship.yaml +++ b/easi/tasks/ebhabitat/ebhabitat_spatial_relationship.yaml @@ -3,7 +3,7 @@ display_name: "EB-Habitat Spatial Relationship Split" description: "Spatial relationship evaluation split for EB-Habitat (rearrangement)" simulator: "habitat_sim:v0_3_0" task_class: "easi.tasks.ebhabitat.task.EBHabitatTask" -max_steps: 30 +max_steps: 50 dataset: source: huggingface repo_id: "oscarqjh/EB-Habitat_easi" @@ -32,3 +32,9 @@ agent: split: "spatial_relationship" use_feedback: true chat_history: false + generation_kwargs: + temperature: 0 + max_tokens: 2048 + top_k: 50 + top_p: 0.95 + do_sample: false diff --git a/easi/tasks/ebhabitat/ebhabitat_visual_appearance.yaml b/easi/tasks/ebhabitat/ebhabitat_visual_appearance.yaml index e32a0ab..6269376 100644 --- a/easi/tasks/ebhabitat/ebhabitat_visual_appearance.yaml +++ b/easi/tasks/ebhabitat/ebhabitat_visual_appearance.yaml @@ -3,7 +3,7 @@ display_name: "EB-Habitat Visual Appearance Split" description: "Visual appearance evaluation split for EB-Habitat (rearrangement)" simulator: "habitat_sim:v0_3_0" task_class: "easi.tasks.ebhabitat.task.EBHabitatTask" -max_steps: 30 +max_steps: 50 dataset: source: huggingface repo_id: "oscarqjh/EB-Habitat_easi" @@ -32,3 +32,9 @@ agent: split: "visual_appearance" use_feedback: true chat_history: false + generation_kwargs: + temperature: 0 + max_tokens: 2048 + top_k: 50 + top_p: 0.95 + do_sample: false diff --git a/easi/tasks/ebnavigation/ebnavigation_base.yaml b/easi/tasks/ebnavigation/ebnavigation_base.yaml index c083001..4f9ecb9 100644 --- a/easi/tasks/ebnavigation/ebnavigation_base.yaml +++ b/easi/tasks/ebnavigation/ebnavigation_base.yaml @@ -25,3 +25,9 @@ agent: use_feedback: true chat_history: true message_window_len: 5 + generation_kwargs: + temperature: 0 + max_tokens: 2048 + top_k: 50 + top_p: 0.95 + do_sample: false diff --git a/easi/tasks/ebnavigation/ebnavigation_common_sense.yaml b/easi/tasks/ebnavigation/ebnavigation_common_sense.yaml index b899d40..1eb39e4 100644 --- a/easi/tasks/ebnavigation/ebnavigation_common_sense.yaml +++ b/easi/tasks/ebnavigation/ebnavigation_common_sense.yaml @@ -25,3 +25,9 @@ agent: use_feedback: true chat_history: true message_window_len: 5 + generation_kwargs: + temperature: 0 + max_tokens: 2048 + top_k: 50 + top_p: 0.95 + do_sample: false diff --git a/easi/tasks/ebnavigation/ebnavigation_complex_instruction.yaml b/easi/tasks/ebnavigation/ebnavigation_complex_instruction.yaml index 2b56174..4442cad 100644 --- a/easi/tasks/ebnavigation/ebnavigation_complex_instruction.yaml +++ b/easi/tasks/ebnavigation/ebnavigation_complex_instruction.yaml @@ -25,3 +25,9 @@ agent: use_feedback: true chat_history: true message_window_len: 5 + generation_kwargs: + temperature: 0 + max_tokens: 2048 + top_k: 50 + top_p: 0.95 + do_sample: false diff --git a/easi/tasks/ebnavigation/ebnavigation_long_horizon.yaml b/easi/tasks/ebnavigation/ebnavigation_long_horizon.yaml index b5de4bb..07606ad 100644 --- a/easi/tasks/ebnavigation/ebnavigation_long_horizon.yaml +++ b/easi/tasks/ebnavigation/ebnavigation_long_horizon.yaml @@ -25,3 +25,9 @@ agent: use_feedback: true chat_history: true message_window_len: 5 + generation_kwargs: + temperature: 0 + max_tokens: 2048 + top_k: 50 + top_p: 0.95 + do_sample: false diff --git a/easi/tasks/ebnavigation/ebnavigation_visual_appearance.yaml b/easi/tasks/ebnavigation/ebnavigation_visual_appearance.yaml index 677483b..2a0a557 100644 --- a/easi/tasks/ebnavigation/ebnavigation_visual_appearance.yaml +++ b/easi/tasks/ebnavigation/ebnavigation_visual_appearance.yaml @@ -25,3 +25,9 @@ agent: use_feedback: true chat_history: true message_window_len: 5 + generation_kwargs: + temperature: 0 + max_tokens: 2048 + top_k: 50 + top_p: 0.95 + do_sample: false diff --git a/tests/test_ebhabitat_task.py b/tests/test_ebhabitat_task.py index 6c570a9..17cd16a 100644 --- a/tests/test_ebhabitat_task.py +++ b/tests/test_ebhabitat_task.py @@ -56,7 +56,7 @@ def test_action_space_loaded(self, task): assert len(task.action_space) == len(PLACEHOLDER_ACTIONS) def test_max_steps(self, task): - assert task.max_steps == 30 + assert task.max_steps == 50 def test_format_reset_config(self, task): """Test the adapter from EB-Habitat HF row to bridge reset config.""" From 9f33a638a3b032fd9b04f3f5ecbd5a9755bb95fd Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 17 Feb 2026 20:11:21 +0800 Subject: [PATCH 046/239] [Fix] Drop unsupported LLM params per provider and add run config tracing - Filter generation_kwargs against whitelist of litellm/OpenAI-compatible params (temperature, max_tokens, top_p, etc.) in LLMClient.__init__, silently dropping HuggingFace-specific ones (do_sample, top_k) - Add drop_params=True as secondary safety net for litellm.completion() - Log full resolved run config at TRACE level on run start - Fix minor prompt formatting in EB-Habitat --- easi/evaluation/runner.py | 13 +++++++++++++ easi/llm/client.py | 18 +++++++++++++++++- easi/tasks/ebhabitat/prompts.py | 2 +- 3 files changed, 31 insertions(+), 2 deletions(-) diff --git a/easi/evaluation/runner.py b/easi/evaluation/runner.py index 16532c2..d194eb9 100644 --- a/easi/evaluation/runner.py +++ b/easi/evaluation/runner.py @@ -138,6 +138,14 @@ def run(self, max_episodes: int | None = None) -> list[dict]: ) base_url = server.start() + # Compute resolved generation kwargs (YAML defaults + CLI overrides) + from easi.llm.utils import parse_llm_kwargs, split_kwargs + agent_config = task._config.get("agent", {}) + yaml_gen_kwargs = agent_config.get("generation_kwargs", {}) + all_llm_kwargs = parse_llm_kwargs(self.llm_kwargs_raw) + _, cli_gen_kwargs = split_kwargs(all_llm_kwargs) + resolved_gen_kwargs = {**yaml_gen_kwargs, **cli_gen_kwargs} + # Save run config config = { "run_id": self.run_id, @@ -157,9 +165,14 @@ def run(self, max_episodes: int | None = None) -> list[dict]: "max_retries": self.max_retries, }, "resolved_backend": backend, + "resolved_base_url": base_url, + "resolved_generation_kwargs": resolved_gen_kwargs, "task_config": task._config, } (run_dir / "config.json").write_text(json.dumps(config, indent=2)) + logger.trace( + "Run config:\n%s", json.dumps(config, indent=2, default=str) + ) # 3. Create agent agent = self._create_agent(task.action_space, task._config, diff --git a/easi/llm/client.py b/easi/llm/client.py index 3af6f46..1035b30 100644 --- a/easi/llm/client.py +++ b/easi/llm/client.py @@ -14,6 +14,17 @@ logger = get_logger(__name__) # Lazy imports to avoid requiring litellm when not needed. +# Parameters accepted by litellm.completion() / OpenAI chat completions API. +# Anything not in this set is silently dropped to avoid provider rejections. +_LITELLM_PARAMS = frozenset({ + "temperature", "max_tokens", "top_p", "n", "stop", "seed", + "frequency_penalty", "presence_penalty", "logit_bias", + "logprobs", "top_logprobs", + "response_format", "tools", "tool_choice", + "stream", "stream_options", + "user", "metadata", +}) + litellm = None @@ -46,7 +57,11 @@ def __init__( self.model = model self.base_url = base_url self.num_retries = num_retries - self.default_kwargs = kwargs + # Only keep params that litellm/OpenAI API recognises. + dropped = {k: v for k, v in kwargs.items() if k not in _LITELLM_PARAMS} + if dropped: + logger.debug("Dropping unsupported generation kwargs: %s", dropped) + self.default_kwargs = {k: v for k, v in kwargs.items() if k in _LITELLM_PARAMS} self._usage = { "prompt_tokens": 0, "completion_tokens": 0, @@ -62,6 +77,7 @@ def generate(self, messages: list[dict], response_format: dict | None = None) -> "model": self.model, "messages": messages, "num_retries": self.num_retries, + "drop_params": True, **self.default_kwargs, } if self.base_url: diff --git a/easi/tasks/ebhabitat/prompts.py b/easi/tasks/ebhabitat/prompts.py index c3f445a..8bc5268 100644 --- a/easi/tasks/ebhabitat/prompts.py +++ b/easi/tasks/ebhabitat/prompts.py @@ -261,7 +261,7 @@ def _build_prompt_text( ) prompt += f'\n\n## Now the human instruction is: {user_instruction}.' - prompt += f" You are supposed to output in json. You need to describe current visual state from the image, output your reasoning steps and plan. At the end, output the action id (0 ~ {max_id}) from the available actions to excute." + prompt += f"\nYou are supposed to output in json. You need to describe current visual state from the image, output your reasoning steps and plan. At the end, output the action id (0 ~ {max_id}) from the available actions to excute." else: # Subsequent turn path, chat_history=False (vlm_planner.py lines 78-94) From 54788b846f39a1a39eadfd6eb8f4c9b78b382d8a Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 17 Feb 2026 22:20:53 +0800 Subject: [PATCH 047/239] [Fix] Disable Habitat episode shuffle and add model name to run directory - Disable episode shuffling in EBHabEnv so simulator episode order matches the HF dataset order (root cause of ~6% vs ~86% accuracy mismatch) - Append model name to run_id so output dirs become /_/ for easier identification --- easi/evaluation/runner.py | 7 ++++++- easi/tasks/ebhabitat/vendor/EBHabEnv.py | 6 ++++++ 2 files changed, 12 insertions(+), 1 deletion(-) diff --git a/easi/evaluation/runner.py b/easi/evaluation/runner.py index d194eb9..5369f6e 100644 --- a/easi/evaluation/runner.py +++ b/easi/evaluation/runner.py @@ -73,7 +73,12 @@ def __init__( self.max_retries = max_retries self.resume_dir = Path(resume_dir) if resume_dir else None self.redownload = redownload - self.run_id = datetime.now().strftime("%Y%m%d_%H%M%S") + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + if self.model: + safe_model = self.model.replace("/", "_") + self.run_id = f"{timestamp}_{safe_model}" + else: + self.run_id = timestamp def _resolve_llm_backend(self) -> tuple[str | None, str | None]: """Resolve which LLM backend to use. diff --git a/easi/tasks/ebhabitat/vendor/EBHabEnv.py b/easi/tasks/ebhabitat/vendor/EBHabEnv.py index 0de6413..ea99c85 100644 --- a/easi/tasks/ebhabitat/vendor/EBHabEnv.py +++ b/easi/tasks/ebhabitat/vendor/EBHabEnv.py @@ -160,6 +160,12 @@ def __init__(self, eval_set='train', exp_name='', down_sample_ratio=1.0, gpu_id = int(os.environ.get("HABITAT_SIM_GPU_ID", "0")) self.config.habitat.simulator.habitat_sim_v0.gpu_device_id = gpu_id + # Disable episode shuffling — EASI controls episode ordering via HF dataset. + # The default CustomEpisodeIterator shuffles episodes which would cause a + # mismatch between the HF instruction sent to the LLM and the actual + # simulator episode. + self.config.habitat.environment.iterator_options.shuffle = False + # modify config path to ease data loading self.dataset = make_dataset(self.config.habitat.dataset.type, config=self.config.habitat.dataset) From d48cf217160585eb1594933bf3f16a52ddf29e1c Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Wed, 18 Feb 2026 14:49:37 +0800 Subject: [PATCH 048/239] [Feat] Add CoppeliaSim V4.1.0 simulator integration - Add installation_kwargs to SimulatorEntry and BaseEnvironmentManager for manifest-driven installation (URLs, filenames, etc.) - Add create_env_manager() factory that passes installation_kwargs - Add BaseEnvironmentManager post_install hook, _download_and_extract, get_env_vars, template resolution helpers - Add SubprocessRunner extra_env support with path-var prepending - Create CoppeliaSim V4.1.0 simulator: env_manager (conda env, binary download, PyRep build, lua addon copy), bridge (PyRep + Panda arm smoke test), manifest.yaml - Fix Xvfb NVIDIA EGL crash via __EGL_VENDOR_LIBRARY_FILENAMES=mesa - Fix HTTP 403 on CoppeliaSim download with User-Agent header - Fix libcoppeliaSim.so.1 symlink creation for PyRep linking - Pass env vars through validation and env_is_ready checks - Update CLI and runner to use create_env_manager() - Add 428 passing tests (27 CoppeliaSim + infrastructure tests) --- easi/cli.py | 19 +- easi/core/base_env_manager.py | 170 +++++- easi/evaluation/runner.py | 9 +- easi/simulators/coppeliasim/__init__.py | 0 easi/simulators/coppeliasim/manifest.yaml | 15 + .../simulators/coppeliasim/v4_1_0/__init__.py | 0 easi/simulators/coppeliasim/v4_1_0/bridge.py | 230 ++++++++ .../coppeliasim/v4_1_0/conda_env.yaml | 11 + .../coppeliasim/v4_1_0/env_manager.py | 138 +++++ .../coppeliasim/v4_1_0/requirements.txt | 6 + .../v4_1_0/simAddOnScript_PyRep.lua | 542 ++++++++++++++++++ .../coppeliasim/v4_1_0/simulator.py | 22 + .../coppeliasim/v4_1_0/task_design.ttt | Bin 0 -> 6967399 bytes easi/simulators/registry.py | 11 +- easi/simulators/subprocess_runner.py | 28 + tests/test_coppeliasim_simulator.py | 165 ++++++ tests/test_dummy_simulator.py | 20 + tests/test_env_manager.py | 180 ++++++ tests/test_env_vars_wiring.py | 66 +++ tests/test_installation_kwargs.py | 80 +++ tests/test_subprocess_runner.py | 68 +++ 21 files changed, 1761 insertions(+), 19 deletions(-) create mode 100644 easi/simulators/coppeliasim/__init__.py create mode 100644 easi/simulators/coppeliasim/manifest.yaml create mode 100644 easi/simulators/coppeliasim/v4_1_0/__init__.py create mode 100644 easi/simulators/coppeliasim/v4_1_0/bridge.py create mode 100644 easi/simulators/coppeliasim/v4_1_0/conda_env.yaml create mode 100644 easi/simulators/coppeliasim/v4_1_0/env_manager.py create mode 100644 easi/simulators/coppeliasim/v4_1_0/requirements.txt create mode 100755 easi/simulators/coppeliasim/v4_1_0/simAddOnScript_PyRep.lua create mode 100644 easi/simulators/coppeliasim/v4_1_0/simulator.py create mode 100644 easi/simulators/coppeliasim/v4_1_0/task_design.ttt create mode 100644 tests/test_coppeliasim_simulator.py create mode 100644 tests/test_env_manager.py create mode 100644 tests/test_env_vars_wiring.py create mode 100644 tests/test_installation_kwargs.py create mode 100644 tests/test_subprocess_runner.py diff --git a/easi/cli.py b/easi/cli.py index bddacd1..9fa7c52 100644 --- a/easi/cli.py +++ b/easi/cli.py @@ -143,10 +143,9 @@ def cmd_env_list() -> None: def cmd_env_install(simulator: str, reinstall: bool = False, with_task_deps: str | None = None) -> None: - from easi.simulators.registry import load_env_manager_class + from easi.simulators.registry import create_env_manager - EnvManagerClass = load_env_manager_class(simulator) - env_manager = EnvManagerClass() + env_manager = create_env_manager(simulator) if reinstall: logger.info("Removing existing environment: %s", env_manager.get_env_name()) @@ -170,10 +169,9 @@ def cmd_env_install(simulator: str, reinstall: bool = False, with_task_deps: str def cmd_env_check(simulator: str) -> None: - from easi.simulators.registry import load_env_manager_class + from easi.simulators.registry import create_env_manager - EnvManagerClass = load_env_manager_class(simulator) - env_manager = EnvManagerClass() + env_manager = create_env_manager(simulator) missing = env_manager.check_system_deps() if missing: @@ -242,18 +240,18 @@ def cmd_task_download(task_name: str, redownload: bool = False) -> None: def cmd_sim_test(simulator: str, steps: int, timeout: float) -> None: from easi.core.episode import Action - from easi.simulators.registry import load_env_manager_class, load_simulator_class + from easi.simulators.registry import create_env_manager, load_simulator_class from easi.simulators.subprocess_runner import SubprocessRunner - EnvManagerClass = load_env_manager_class(simulator) + env_manager = create_env_manager(simulator) SimClass = load_simulator_class(simulator) - - env_manager = EnvManagerClass() sim = SimClass() logger.info("Testing %s...", simulator) logger.info(" Python: %s", env_manager.get_python_executable()) + env_vars = env_manager.get_env_vars() + runner = SubprocessRunner( python_executable=env_manager.get_python_executable(), bridge_script_path=sim._get_bridge_script_path(), @@ -261,6 +259,7 @@ def cmd_sim_test(simulator: str, steps: int, timeout: float) -> None: xvfb_screen_config=env_manager.xvfb_screen_config, startup_timeout=timeout, command_timeout=timeout, + extra_env=env_vars or None, ) try: diff --git a/easi/core/base_env_manager.py b/easi/core/base_env_manager.py index 0b31251..c49f9c9 100644 --- a/easi/core/base_env_manager.py +++ b/easi/core/base_env_manager.py @@ -13,6 +13,8 @@ from __future__ import annotations import subprocess +import tarfile +import urllib.request from abc import ABC, abstractmethod from pathlib import Path @@ -29,8 +31,9 @@ class BaseEnvironmentManager(ABC): """Abstract base for per-simulator-version environment management.""" - def __init__(self, conda_prefix: Path | None = None): + def __init__(self, conda_prefix: Path | None = None, installation_kwargs: dict | None = None): self.conda_prefix = conda_prefix or self._default_conda_prefix() + self.installation_kwargs = installation_kwargs or {} self._dep_checker = SystemDependencyChecker() @property @@ -81,6 +84,18 @@ def xvfb_screen_config(self) -> str: """Xvfb screen config. Override for custom resolution/depth.""" return "1024x768x24" + def get_env_vars(self) -> dict[str, str]: + """Return environment variables to inject into the bridge subprocess. + + Override in subclasses to provide simulator-specific env vars. + Use _get_template_variables() and _resolve_template() to build + paths relative to the conda env directory. + + Returns: + Dict of env var name -> value. Empty dict by default. + """ + return {} + def get_env_name(self) -> str: """Conda environment name for this simulator version.""" return f"easi_{self.simulator_name}_{self.version}" @@ -100,12 +115,21 @@ def env_is_ready(self) -> bool: if not Path(python_exec).exists(): return False + # Include simulator env vars (e.g. LD_LIBRARY_PATH for CoppeliaSim) + env_vars = self.get_env_vars() + run_env = None + if env_vars: + import os + run_env = os.environ.copy() + run_env.update(env_vars) + try: result = subprocess.run( [python_exec, "-c", self.get_validation_import()], capture_output=True, text=True, timeout=30, + env=run_env, ) return result.returncode == 0 except (subprocess.TimeoutExpired, FileNotFoundError): @@ -184,15 +208,146 @@ def _do_install(self) -> None: else: logger.warning("No requirements.txt found at %s, skipping uv install", requirements) - # Step 5: Validate + # Step 5: Run post-install hook (binary downloads, file copies, etc.) + self._run_post_install() + + # Step 6: Validate (with env vars so e.g. LD_LIBRARY_PATH is set) + env_vars = self.get_env_vars() + validation_env = None + if env_vars: + import os + validation_env = os.environ.copy() + validation_env.update(env_vars) with spinner("Validating environment"): self._run_command( [python_exec, "-c", self.get_validation_import()], "environment validation", + env=validation_env, ) logger.info("Environment %s installed and validated successfully", env_name) + # ── Post-install hook and helpers ────────────────────────────────── + + def get_extras_dir(self) -> Path: + """Directory for downloaded binaries and other extras (inside conda env dir).""" + env_path = self.conda_prefix / "envs" / self.get_env_name() + return env_path / "extras" + + @staticmethod + def _resolve_template(template: str, variables: dict[str, str]) -> str: + """Resolve {var} placeholders in a string.""" + result = template + for key, value in variables.items(): + result = result.replace(f"{{{key}}}", value) + return result + + def _get_template_variables(self) -> dict[str, str]: + """Return template variables for env_vars and post_install use. + + Available variables: + {env_dir} — conda env directory + {extras_dir} — extras directory for binaries/downloads + """ + env_dir = str(self.conda_prefix / "envs" / self.get_env_name()) + return { + "env_dir": env_dir, + "extras_dir": str(self.get_extras_dir()), + } + + def post_install(self, context: dict) -> None: + """Override for custom post-install steps (binary downloads, file copies, etc.). + + Called after conda + pip installs, before validation. Use helper methods + like _download_and_extract() for common operations. Use _run_command() + with an env dict to run pip install with custom env vars. + + Args: + context: Dict with keys: + env_dir — conda env directory path + extras_dir — directory for downloaded extras + env_vars — resolved env vars from get_env_vars() + + Does nothing by default. + """ + + def _run_post_install(self) -> None: + """Build context and call post_install() hook.""" + ctx = self._get_template_variables() + ctx["env_vars"] = self.get_env_vars() + self.post_install(ctx) + + def _download_and_extract( + self, + url: str, + filename: str, + dest_dir: Path, + extract: bool = True, + strip_components: int = 0, + ) -> None: + """Download a file and optionally extract it. Idempotent (skips if done). + + Helper for use inside post_install() overrides. + + Args: + url: Download URL. + filename: Local filename to save as. + dest_dir: Directory to download/extract into. + extract: Whether to extract archives (tar.xz, tar.gz, zip). + strip_components: Remove N leading path components when extracting. + """ + dest_dir = Path(dest_dir) + dest_dir.mkdir(parents=True, exist_ok=True) + dest = dest_dir / filename + + # Idempotency: check marker for extracted archives, or file existence + marker = dest_dir / f".{filename}.done" + if marker.exists(): + logger.info("Already installed: %s, skipping", filename) + return + if not extract and dest.exists(): + logger.info("Already downloaded: %s, skipping", filename) + return + + with spinner(f"Downloading {filename}"): + logger.info("Downloading %s", url) + req = urllib.request.Request(url, headers={"User-Agent": "easi/1.0"}) + with urllib.request.urlopen(req) as response, open(str(dest), "wb") as out: + while True: + chunk = response.read(1024 * 1024) # 1MB chunks + if not chunk: + break + out.write(chunk) + + if extract: + with spinner(f"Extracting {filename}"): + self._extract_archive(dest, dest_dir, strip_components) + dest.unlink(missing_ok=True) # Remove archive to save space + + marker.touch() + + def _extract_archive(self, archive: Path, dest_dir: Path, strip_components: int = 0) -> None: + """Extract a tar.xz, tar.gz, tar.bz2, or zip archive.""" + name = archive.name + if name.endswith((".tar.xz", ".tar.gz", ".tgz", ".tar.bz2")): + with tarfile.open(str(archive)) as tf: + if strip_components > 0: + for member in tf.getmembers(): + parts = Path(member.name).parts + if len(parts) > strip_components: + member.name = str(Path(*parts[strip_components:])) + tf.extract(member, dest_dir) + else: + tf.extractall(dest_dir) + elif name.endswith(".zip"): + import zipfile + with zipfile.ZipFile(str(archive)) as zf: + zf.extractall(dest_dir) + else: + logger.warning("Unknown archive format: %s, skipping extraction", name) + + # ── Conda / command helpers ───────────────────────────────────── + def _run_conda_create(self, env_name: str, yaml_path: Path) -> None: """Create or update a conda environment from a YAML file.""" env_path = self.conda_prefix / "envs" / env_name @@ -206,8 +361,14 @@ def _run_conda_create(self, env_name: str, yaml_path: Path) -> None: self._run_command(cmd, desc) - def _run_command(self, cmd: list[str], description: str) -> None: - """Run a subprocess command, streaming output through the logger.""" + def _run_command(self, cmd: list[str], description: str, env: dict[str, str] | None = None) -> None: + """Run a subprocess command, streaming output through the logger. + + Args: + cmd: Command and arguments. + description: Human-readable description for error messages. + env: Optional environment dict. If None, inherits parent env. + """ logger.trace("%s", " ".join(cmd)) process = subprocess.Popen( cmd, @@ -215,6 +376,7 @@ def _run_command(self, cmd: list[str], description: str) -> None: stderr=subprocess.STDOUT, text=True, bufsize=1, + env=env, ) output_lines = [] for line in process.stdout: diff --git a/easi/evaluation/runner.py b/easi/evaluation/runner.py index 5369f6e..2705086 100644 --- a/easi/evaluation/runner.py +++ b/easi/evaluation/runner.py @@ -485,15 +485,13 @@ def _create_simulator(self, simulator_key: str, task=None): import json as _json from easi.simulators.registry import ( - load_env_manager_class, + create_env_manager, load_simulator_class, ) from easi.simulators.subprocess_runner import SubprocessRunner - EnvManagerClass = load_env_manager_class(simulator_key) + env_manager = create_env_manager(simulator_key) SimClass = load_simulator_class(simulator_key) - - env_manager = EnvManagerClass() sim = SimClass() # Auto-install simulator env if not ready @@ -515,12 +513,15 @@ def _create_simulator(self, simulator_key: str, task=None): if task and task.simulator_kwargs: extra_args.extend(["--simulator-kwargs", _json.dumps(task.simulator_kwargs)]) + env_vars = env_manager.get_env_vars() + runner = SubprocessRunner( python_executable=env_manager.get_python_executable(), bridge_script_path=bridge_path, needs_display=env_manager.needs_display, xvfb_screen_config=env_manager.xvfb_screen_config, extra_args=extra_args, + extra_env=env_vars or None, ) runner.launch() sim.set_runner(runner) diff --git a/easi/simulators/coppeliasim/__init__.py b/easi/simulators/coppeliasim/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/easi/simulators/coppeliasim/manifest.yaml b/easi/simulators/coppeliasim/manifest.yaml new file mode 100644 index 0000000..884d2f2 --- /dev/null +++ b/easi/simulators/coppeliasim/manifest.yaml @@ -0,0 +1,15 @@ +name: coppeliasim +display_name: "CoppeliaSim" +default_version: "v4_1_0" +versions: + v4_1_0: + description: "CoppeliaSim V4.1.0 (Pro) with PyRep — used by eb-manipulation" + simulator_class: "easi.simulators.coppeliasim.v4_1_0.simulator.CoppeliaSimSimulatorV410" + env_manager_class: "easi.simulators.coppeliasim.v4_1_0.env_manager.CoppeliaSimEnvManagerV410" + python_version: "3.10" + installation_kwargs: + binary_url: "https://downloads.coppeliarobotics.com/V4_1_0/CoppeliaSim_Pro_V4_1_0_Ubuntu20_04.tar.xz" + binary_filename: "CoppeliaSim_Pro_V4_1_0_Ubuntu20_04.tar.xz" + binary_dir_name: "CoppeliaSim_Pro_V4_1_0_Ubuntu20_04" + pyrep_git_url: "git+https://github.com/stepjam/PyRep.git" + lua_addon_script: "simAddOnScript_PyRep.lua" diff --git a/easi/simulators/coppeliasim/v4_1_0/__init__.py b/easi/simulators/coppeliasim/v4_1_0/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/easi/simulators/coppeliasim/v4_1_0/bridge.py b/easi/simulators/coppeliasim/v4_1_0/bridge.py new file mode 100644 index 0000000..d37902d --- /dev/null +++ b/easi/simulators/coppeliasim/v4_1_0/bridge.py @@ -0,0 +1,230 @@ +"""Smoke-test bridge for CoppeliaSim V4.1.0. + +This script runs inside the easi_coppeliasim_v4_1_0 conda environment. +It verifies PyRep + CoppeliaSim work by: +1. Launching PyRep with the task_design.ttt scene +2. Loading a Panda robot arm +3. Handling reset (return camera image) and step (advance sim) commands +4. Reporting success via IPC + +Usage: + python bridge.py --workspace /tmp/easi_xxx [--simulator-kwargs '{}'] +""" + +from __future__ import annotations + +import argparse +import json +import sys +from pathlib import Path + +import numpy as np + +_repo_root = Path(__file__).resolve().parents[4] +if str(_repo_root) not in sys.path: + sys.path.insert(0, str(_repo_root)) + +from easi.communication.filesystem import ( + poll_for_command, + write_response, + write_status, +) +from easi.communication.schemas import ( + make_error_response, + make_observation_response, + parse_action_from_command, +) +from easi.utils.logging import get_logger, setup_logging + +logger = get_logger(__name__) + +# Default scene file (shipped with this simulator) +DEFAULT_TTT_FILE = str(Path(__file__).parent / "task_design.ttt") + + +class CoppeliaSimBridge: + """Smoke test bridge for CoppeliaSim V4.1.0 + PyRep. + + Loads PyRep, creates a Panda arm, captures images from the front camera. + Supports reset (arm to home pose) and step (advance simulation). + """ + + def __init__(self, workspace, simulator_kwargs=None): + self.workspace = Path(workspace) + self.simulator_kwargs = simulator_kwargs or {} + self.pyrep = None + self.robot_arm = None + self.gripper = None + self.camera = None + self.step_count = 0 + self.episode_output_dir = None + + def start(self): + """Initialize PyRep with CoppeliaSim and load Panda robot.""" + from pyrep import PyRep + from pyrep.robots.arms.panda import Panda + from pyrep.robots.end_effectors.panda_gripper import PandaGripper + + ttt_file = self.simulator_kwargs.get("ttt_file", DEFAULT_TTT_FILE) + headless = self.simulator_kwargs.get("headless", True) + + logger.info("Launching PyRep with scene: %s (headless=%s)", ttt_file, headless) + self.pyrep = PyRep() + self.pyrep.launch(ttt_file, headless=headless) + self.pyrep.start() + + # Load robot + self.robot_arm = Panda() + self.gripper = PandaGripper() + + # Get the front camera (defined in task_design.ttt) + from pyrep.objects.vision_sensor import VisionSensor + try: + self.camera = VisionSensor("cam_front") + except Exception: + logger.warning("cam_front not found, will generate blank images") + self.camera = None + + logger.info("CoppeliaSim bridge ready (Panda arm loaded)") + + def stop(self): + """Shut down PyRep and CoppeliaSim.""" + if self.pyrep is not None: + try: + self.pyrep.stop() + self.pyrep.shutdown() + except Exception: + pass + self.pyrep = None + + def reset(self, reset_config): + """Reset robot to home position, return camera image.""" + self.step_count = 0 + num_joints = len(self.robot_arm.get_joint_positions()) + self.robot_arm.set_joint_positions([0.0] * num_joints) + self.pyrep.step() + return self._make_observation_response() + + def step(self, action_name): + """Execute one step. For smoke test, just advance simulation.""" + self.step_count += 1 + + if action_name == "Stop": + return self._make_observation_response(done=True) + + self.pyrep.step() + + return self._make_observation_response( + info={ + "env_step": float(self.step_count), + "last_action_success": 1.0, + } + ) + + def _capture_image(self): + """Capture RGB image from front camera (or generate placeholder).""" + if self.camera is not None: + try: + image = self.camera.capture_rgb() + return (image * 255).astype(np.uint8) + except Exception as e: + logger.warning("Camera capture failed: %s", e) + return np.full((64, 64, 3), 128, dtype=np.uint8) + + def _make_observation_response(self, reward=0.0, done=False, info=None): + """Capture image, build IPC response.""" + from PIL import Image + + save_dir = Path(self.episode_output_dir) if self.episode_output_dir else self.workspace + save_dir.mkdir(parents=True, exist_ok=True) + rgb_path = save_dir / ("rgb_%04d.png" % self.step_count) + + image = self._capture_image() + Image.fromarray(image).save(str(rgb_path)) + + try: + ee_pos = self.robot_arm.get_tip().get_position() + ee_ori = self.robot_arm.get_tip().get_orientation() + pose = list(ee_pos) + list(ee_ori) + except Exception: + pose = [0.0] * 6 + + return make_observation_response( + rgb_path=str(rgb_path), + agent_pose=pose, + metadata={"step": str(self.step_count)}, + reward=reward, + done=done, + info=info or {}, + ) + + def run(self): + """Main IPC loop.""" + logger.info("CoppeliaSim bridge starting (workspace: %s)", self.workspace) + self.start() + write_status(self.workspace, ready=True) + + while True: + try: + command = poll_for_command(self.workspace, timeout=300.0) + except Exception as e: + logger.error("Failed to read command: %s", e) + break + + cmd_type = command.get("type") + + if cmd_type == "reset": + reset_config = command.get("reset_config", {}) + raw_output_dir = command.get("episode_output_dir") + if raw_output_dir: + self.episode_output_dir = raw_output_dir + Path(raw_output_dir).mkdir(parents=True, exist_ok=True) + else: + self.episode_output_dir = None + + try: + response = self.reset(reset_config) + write_response(self.workspace, response) + except Exception as e: + logger.exception("Reset failed") + write_response(self.workspace, make_error_response(str(e))) + + elif cmd_type == "step": + action = parse_action_from_command(command) + logger.trace("Step %d: action=%s", self.step_count + 1, action.action_name) + try: + response = self.step(action.action_name) + write_response(self.workspace, response) + except Exception as e: + logger.exception("Step failed") + write_response(self.workspace, make_error_response(str(e))) + + elif cmd_type == "close": + logger.info("Close command received") + self.stop() + write_response(self.workspace, {"status": "ok"}) + break + + else: + write_response( + self.workspace, + make_error_response("Unknown command: %s" % cmd_type), + ) + + +def main() -> None: + parser = argparse.ArgumentParser(description="CoppeliaSim V4.1.0 bridge") + parser.add_argument("--workspace", type=Path, required=True) + parser.add_argument("--data-dir", type=Path, default=None) + parser.add_argument("--simulator-kwargs", type=str, default=None) + args, _ = parser.parse_known_args() + + setup_logging("DEBUG") + + sim_kwargs = json.loads(args.simulator_kwargs) if args.simulator_kwargs else {} + bridge = CoppeliaSimBridge(workspace=args.workspace, simulator_kwargs=sim_kwargs) + bridge.run() + + +if __name__ == "__main__": + main() diff --git a/easi/simulators/coppeliasim/v4_1_0/conda_env.yaml b/easi/simulators/coppeliasim/v4_1_0/conda_env.yaml new file mode 100644 index 0000000..540522c --- /dev/null +++ b/easi/simulators/coppeliasim/v4_1_0/conda_env.yaml @@ -0,0 +1,11 @@ +name: easi_coppeliasim_v4_1_0 +channels: + - conda-forge + - defaults +dependencies: + - python=3.10 + - fontconfig + - freetype + - libxkbcommon + - libxcb=1.17.0 + - qt=5.15 diff --git a/easi/simulators/coppeliasim/v4_1_0/env_manager.py b/easi/simulators/coppeliasim/v4_1_0/env_manager.py new file mode 100644 index 0000000..b88f198 --- /dev/null +++ b/easi/simulators/coppeliasim/v4_1_0/env_manager.py @@ -0,0 +1,138 @@ +"""Environment manager for CoppeliaSim with PyRep. + +Handles: +1. Conda env creation (Python 3.10) +2. Pip deps via uv (requirements.txt) +3. CoppeliaSim binary download + extraction (post_install) +4. PyRep build with COPPELIASIM_ROOT set (post_install) +5. Lua addon script copy (post_install) +6. Runtime env vars for bridge subprocess +""" + +from __future__ import annotations + +import os +import shutil +from pathlib import Path + +from easi.core.base_env_manager import BaseEnvironmentManager +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + + +class CoppeliaSimEnvManagerV410(BaseEnvironmentManager): + """Environment manager for CoppeliaSim V4.1.0 (PyRep).""" + + @property + def simulator_name(self) -> str: + return "coppeliasim" + + @property + def version(self) -> str: + return "v4_1_0" + + @property + def needs_display(self) -> bool: + return True # CoppeliaSim needs X11/Xvfb + + @property + def xvfb_screen_config(self) -> str: + return "1280x720x24" + + def get_conda_env_yaml_path(self) -> Path: + return Path(__file__).parent / "conda_env.yaml" + + def get_requirements_txt_path(self) -> Path: + return Path(__file__).parent / "requirements.txt" + + def get_system_deps(self) -> list[str]: + return ["conda", "xvfb"] + + def get_validation_import(self) -> str: + return "from pyrep import PyRep; print('PyRep OK')" + + def get_env_vars(self) -> dict[str, str]: + """Return CoppeliaSim env vars for bridge subprocess.""" + binary_dir_name = self.installation_kwargs.get("binary_dir_name", "") + if not binary_dir_name: + return {} + t = self._get_template_variables() + coppeliasim_root = self._resolve_template( + "{extras_dir}/" + binary_dir_name, t + ) + # Include conda env lib dir so fontconfig/freetype/Qt deps resolve + conda_lib = self._resolve_template("{env_dir}/lib", t) + ld_path = f"{coppeliasim_root}:{conda_lib}" + env = { + "COPPELIASIM_ROOT": coppeliasim_root, + "LD_LIBRARY_PATH": ld_path, + "QT_QPA_PLATFORM_PLUGIN_PATH": coppeliasim_root, + } + # Force Mesa EGL vendor for Xvfb (NVIDIA EGL crashes Xvfb on some systems) + mesa_vendor = Path("/usr/share/glvnd/egl_vendor.d/50_mesa.json") + if mesa_vendor.exists(): + env["__EGL_VENDOR_LIBRARY_FILENAMES"] = str(mesa_vendor) + return env + + def post_install(self, context: dict) -> None: + """Download CoppeliaSim, build PyRep, copy lua addon. + + All URLs and filenames come from self.installation_kwargs (set + by manifest.yaml). Order matters: + 1. Download + extract CoppeliaSim binary + 2. pip install PyRep (needs COPPELIASIM_ROOT at build time) + 3. Copy simAddOnScript_PyRep.lua into CoppeliaSim root + """ + extras_dir = Path(context["extras_dir"]) + env_vars = context["env_vars"] + python = self.get_python_executable() + + binary_url = self.installation_kwargs.get("binary_url") + binary_filename = self.installation_kwargs.get("binary_filename") + pyrep_git_url = self.installation_kwargs.get("pyrep_git_url") + lua_addon_script = self.installation_kwargs.get("lua_addon_script") + + # Step 1: Download + extract CoppeliaSim binary + if binary_url and binary_filename: + logger.info("Downloading CoppeliaSim from %s", binary_url) + self._download_and_extract( + url=binary_url, + filename=binary_filename, + dest_dir=extras_dir, + ) + + # Step 2: Create versioned symlinks (PyRep links against libcoppeliaSim.so.1) + if "COPPELIASIM_ROOT" in env_vars: + coppeliasim_root = Path(env_vars["COPPELIASIM_ROOT"]) + for lib_name in ["libcoppeliaSim.so", "libcoppeliaSimHeadless.so"]: + lib_path = coppeliasim_root / lib_name + symlink = coppeliasim_root / f"{lib_name}.1" + if lib_path.exists() and not symlink.exists(): + symlink.symlink_to(lib_path.name) + logger.info("Created symlink %s -> %s", symlink.name, lib_path.name) + + # Step 3: Build env dict with COPPELIASIM_ROOT for PyRep's native build + build_env = os.environ.copy() + build_env.update(env_vars) + + # Step 3: pip install PyRep (C++ extensions link against CoppeliaSim) + if pyrep_git_url: + logger.info("Installing PyRep from %s", pyrep_git_url) + self._run_command( + [python, "-m", "uv", "pip", "install", pyrep_git_url], + "pip install PyRep", + env=build_env, + ) + + # Step 4: Copy PyRep addon lua script into CoppeliaSim root + if lua_addon_script and "COPPELIASIM_ROOT" in env_vars: + coppeliasim_root = Path(env_vars["COPPELIASIM_ROOT"]) + lua_src = Path(__file__).parent / lua_addon_script + if lua_src.exists(): + logger.info("Copying %s to %s", lua_addon_script, coppeliasim_root) + shutil.copy(str(lua_src), str(coppeliasim_root / lua_addon_script)) + else: + logger.warning( + "Lua addon script not found at %s", lua_src + ) diff --git a/easi/simulators/coppeliasim/v4_1_0/requirements.txt b/easi/simulators/coppeliasim/v4_1_0/requirements.txt new file mode 100644 index 0000000..4f250b0 --- /dev/null +++ b/easi/simulators/coppeliasim/v4_1_0/requirements.txt @@ -0,0 +1,6 @@ +numpy>=1.24,<2.0 +Pillow>=10.0 +pyquaternion +natsort +scipy +opencv-python-headless diff --git a/easi/simulators/coppeliasim/v4_1_0/simAddOnScript_PyRep.lua b/easi/simulators/coppeliasim/v4_1_0/simAddOnScript_PyRep.lua new file mode 100755 index 0000000..24da8ef --- /dev/null +++ b/easi/simulators/coppeliasim/v4_1_0/simAddOnScript_PyRep.lua @@ -0,0 +1,542 @@ +-- Additional PyRep functionality. To be placed in the CoppeliaSim root directory. + +function sysCall_init() +end + +function sysCall_cleanup() +end + +function sysCall_addOnScriptSuspend() +end + +function sysCall_addOnScriptResume() +end + +function sysCall_nonSimulation() +end + +function sysCall_beforeMainScript() +end + +function sysCall_beforeInstanceSwitch() +end + +function sysCall_afterInstanceSwitch() +end + +function sysCall_beforeSimulation() +end + +function sysCall_afterSimulation() +end + +_getConfig=function(jh) + -- Returns the current robot configuration + local config={} + for i=1,#jh,1 do + config[i]=sim.getJointPosition(jh[i]) + end + return config +end + +_setConfig=function(jh, config) + -- Applies the specified configuration to the robot + if config then + for i=1,#jh,1 do + sim.setJointPosition(jh[i],config[i]) + end + end +end + +_getConfigDistance=function(jointHandles,config1,config2) + -- Returns the distance (in configuration space) between two configurations + local d=0 + for i=1,#jointHandles,1 do + -- TODO *metric[i] should be here to give a weight to each joint. + local dx=(config1[i]-config2[i])*1.0 + d=d+dx*dx + end + return math.sqrt(d) +end + +_sliceFromOffset=function(array, offset) + sliced = {} + for i=1,#array-offset,1 do + sliced[i] = array[i+offset] + end + return sliced +end + +_WriteCustomDataBlock=function(inInts,inFloats,inStrings,inBuffer) + local obj_handle = inInts[1] + local tag = inStrings[1] + local value = inStrings[2] + sim.writeCustomDataBlock(obj_handle, tag, value) + return {}, {}, {}, '' +end + + +_ReadCustomDataBlock=function(inInts,inFloats,inStrings,inBuffer) + local obj_handle = inInts[1] + local tag = inStrings[1] + local data=sim.readCustomDataBlock(obj_handle,tag) + + return {}, {}, {data}, '' +end + +_ReadCustomDataBlockTags=function(inInts,inFloats,inStrings,inBuffer) + local obj_handle = inInts[1] + local data=sim.readCustomDataBlockTags(obj_handle) + + return {}, {}, {data}, '' +end + +_exportMesh = function(inInts,inFloats,inStrings,inBuffer) + local shape_handles = inInts + local output_format = inStrings[1] + local output_path = inStrings[2] + local format_index = 0 + if output_format == 'text_stl' then + format_index = 3 + elseif output_format == 'binary_stl' then + format_index = 4 + elseif output_format == 'dae' then + format_index = 5 + elseif output_format == 'text_ply' then + format_index = 6 + elseif output_format == 'binary_ply' then + format_index = 7 + end + + local allVertices={} + local allIndices={} + for i=1,#shape_handles, 1 do + h = shape_handles[i] + vertices,indices=sim.getShapeMesh(h) + m=sim.getObjectMatrix(h,-1) + for i=1,#vertices/3,1 do + v={vertices[3*(i-1)+1],vertices[3*(i-1)+2],vertices[3*(i-1)+3]} + v=sim.multiplyVector(m,v) + vertices[3*(i-1)+1]=v[1] + vertices[3*(i-1)+2]=v[2] + vertices[3*(i-1)+3]=v[3] + end + table.insert(allVertices,vertices) + table.insert(allIndices,indices) + end + if (#allVertices>0) then + sim.exportMesh(format_index,output_path,0,1,allVertices,allIndices) + end + return {}, {}, {}, '' +end + +setConstraintsObject=function(inInts,inFloats,inStrings,inBuffer) + ConstraintObj_handle = inInts + Constraint_axis = inStrings + return {}, {}, {}, '' +end + +function stateValidation(state) + local tolerance = 0.8 + local res1 = 1 + local res2 = 1 + local pass = true + -- Read the current state: + local savedState=simOMPL.readState(task) + print('use constraint check') + -- Apply the provided state: + simOMPL.writeState(task,state) + if collisionPairs~=nil then + res1,_ = sim.checkCollision(ConstraintObj_handle[1], collisionPairs[1]) + res2,_ = sim.checkCollision(ConstraintObj_handle[1], collisionPairs[2]) + pass = (res1==1) and (res2==1) + end + local matrix = sim.getObjectMatrix(ConstraintObj_handle[1], -1) + if Constraint_axis[1]=='x' then + pass = (matrix[1]>tolerance) + elseif Constraint_axis[1]=='y' then + pass = (matrix[6]>tolerance) + elseif Constraint_axis[1]=='z' then + pass = (matrix[11]>tolerance) + end + simOMPL.writeState(task,savedState) + return pass +end + +setCheckObject=function(inInts,inFloats,inStrings,inBuffer) + GraspObj_handles = inInts + return {}, {}, {}, '' +end + +function stateCheckCollision(state) + local pass = true + -- Read the current state: + local savedState=simOMPL.readState(task) + + -- Apply the provided state: + simOMPL.writeState(task,state) + if collisionPairs~=nil then + for i=1,#GraspObj_handles,1 do + local res1,_ = sim.checkCollision(GraspObj_handles[i], collisionPairs[1]) + local res2,_ = sim.checkCollision(GraspObj_handles[i], collisionPairs[2]) + -- pass = pass and (res1==1) and (res2==1) + local result,distData,objectPair=sim.checkDistance(GraspObj_handles[i],collisionPairs[1], 0.0001) + local result2,distData,objectPair=sim.checkDistance(GraspObj_handles[i],collisionPairs[2], 0.0001) + pass = pass and (result==0 or res1==1) and (result2==0 or res2==1) + end + end + simOMPL.writeState(task,savedState) + return pass +end + +_findPath=function(goalConfigs,cnt,jointHandles,algorithm,collisionPairs) + -- Here we do path planning between the specified start and goal configurations. We run the search cnt times, + -- and return the shortest path, and its length + + local startConfig = _getConfig(jointHandles) + task=simOMPL.createTask('task') + simOMPL.setVerboseLevel(task, 0) + + alg = _getAlgorithm(algorithm) + + simOMPL.setAlgorithm(task,alg) + + local jSpaces={} + for i=1,#jointHandles,1 do + jh = jointHandles[i] + cyclic, interval = sim.getJointInterval(jh) + -- If there are huge intervals, then limit them + if interval[1] < -6.28 and interval[2] > 6.28 then + pos=sim.getJointPosition(jh) + interval[1] = -6.28 + interval[2] = 6.28 + end + local proj=i + if i>3 then proj=0 end + jSpaces[i]=simOMPL.createStateSpace('j_space'..i,simOMPL.StateSpaceType.joint_position,jh,{interval[1]},{interval[2]},proj) + end + + simOMPL.setStateSpace(task, jSpaces) + if collisionPairs ~= nil then + simOMPL.setCollisionPairs(task, collisionPairs) + if GraspObj_handles ~=nil then + if #GraspObj_handles~=0 then + simOMPL.setStateValidationCallback(task,'stateCheckCollision') + end + end + end + if ConstraintObj_handle ~= nil then + if #ConstraintObj_handle~=0 then + simOMPL.setStateValidationCallback(task,'stateValidation') + end + end + simOMPL.setStartState(task, startConfig) + simOMPL.setGoalState(task, goalConfigs[1]) + for i=2,#goalConfigs,1 do + simOMPL.addGoalState(task,goalConfigs[i]) + end + local path=nil + local l=999999999999 + for i=1,cnt,1 do + search_time = 4 + local res,_path=simOMPL.compute(task,search_time,-1,300) + + -- Path can sometimes touch on invalid state during simplifying + if res and _path then + local is_valid=true + local jhl=#jointHandles + local pc=#_path/jhl + for i=1,pc-1,1 do + local config={} + for j=1,jhl,1 do + config[j]=_path[(i-1)*jhl+j] + end + is_valid=simOMPL.isStateValid(task, config) + if not is_valid then + break + end + end + + if is_valid then + local _l=_getPathLength(_path, jointHandles) + if _lmatchingScore) then + s=1 + if (score<0) then s=-1 end + matchingScore=math.abs(score) + bestMatch={v[1]*s,v[2]*s,v[3]*s} + end + end + angle=math.atan2(bestMatch[2],bestMatch[1]) + m=sim.buildMatrix(p2,{0,0,angle}) + + table.insert(m,angle-math.pi/2) + + return {},m,{},'' +end + +getNonlinearPathMobile=function(inInts,inFloats,inStrings,inBuffer) + algorithm = inStrings[1] + robotHandle = inInts[1] + targetHandle = inInts[2] + collisionHandle=inInts[3] + ignoreCollisions=inInts[4] + bd=inFloats[1] + path_pts=inInts[5] + + collisionPairs={collisionHandle,sim.handle_all} + + if ignoreCollisions==1 then + collisionPairs=nil + end + + t=simOMPL.createTask('t') + simOMPL.setVerboseLevel(t, 0) + ss=simOMPL.createStateSpace('2d',simOMPL.StateSpaceType.dubins,robotHandle,{-bd,-bd},{bd,bd},1) + state_h = simOMPL.setStateSpace(t,{ss}) + simOMPL.setDubinsParams(ss,0.1,true) + simOMPL.setAlgorithm(t,_getAlgorithm(algorithm)) + + if collisionPairs ~= nil then + simOMPL.setCollisionPairs(t, collisionPairs) + end + + startpos=sim.getObjectPosition(robotHandle,-1) + startorient=sim.getObjectOrientation(robotHandle,-1) + startpose={startpos[1],startpos[2],startorient[3]} + + simOMPL.setStartState(t,startpose) + + goalpos=sim.getObjectPosition(targetHandle,-1) + goalorient=sim.getObjectOrientation(targetHandle,-1) + goalpose={goalpos[1],goalpos[2],goalorient[3]} + + simOMPL.setGoalState(t,goalpose) + + r,path=simOMPL.compute(t,4,-1,path_pts) + + simOMPL.destroyTask(t) + + return {},path,{},'' +end + +handleSpherical=function(inInts,inFloats,inStrings,inBuffer) + local depth_handle=inInts[1] + local rgb_handle=inInts[2] + local six_sensor_handles = {inInts[3], inInts[4], inInts[5], inInts[6], inInts[7], inInts[8]} + simVision.handleSpherical(rgb_handle, six_sensor_handles, 360, 180, depth_handle) + return {},{},{},'' +end \ No newline at end of file diff --git a/easi/simulators/coppeliasim/v4_1_0/simulator.py b/easi/simulators/coppeliasim/v4_1_0/simulator.py new file mode 100644 index 0000000..29de390 --- /dev/null +++ b/easi/simulators/coppeliasim/v4_1_0/simulator.py @@ -0,0 +1,22 @@ +"""CoppeliaSim V4.1.0 simulator entry point.""" + +from __future__ import annotations + +from pathlib import Path + +from easi.core.base_simulator import BaseSimulator + + +class CoppeliaSimSimulatorV410(BaseSimulator): + """CoppeliaSim V4.1.0 with PyRep for robotic manipulation.""" + + @property + def name(self) -> str: + return "coppeliasim" + + @property + def version(self) -> str: + return "v4_1_0" + + def _get_bridge_script_path(self) -> Path: + return Path(__file__).parent / "bridge.py" diff --git a/easi/simulators/coppeliasim/v4_1_0/task_design.ttt b/easi/simulators/coppeliasim/v4_1_0/task_design.ttt new file mode 100644 index 0000000000000000000000000000000000000000..bf691115522c5fb7ba0bbcdd374548d624a8a971 GIT binary patch literal 6967399 zcmeF43tW_C+W$w_C{5N@T@RRMY38P)siUpOa4Rh|&D=~8(L9hM3>gQFgC?2VqLph( zVxHFZlmZ#aAR~k13z>vcDkF_Bc)&D-QA~z|!2f$a�(?*4DoJf8+D9U6{jjxbOSA zuHWIhA6_2&%$Qahjix#OHJJE<5C5mp{Oe=Q1N_=R`KA7U>PMh{1paT1K*;pZ?{VzB 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zroYW7fga^B#ULpTPX6FQkiGX|3QYsa=zR^F`jL<`c0T+X49CBIo}SU}Q6Fe8RPPf< zvDbx}vR^{>wHP1;c10#nO1!&E?YfU9syM9J$9bXVKAva!Hfkkk6moV~(0ApUB|+Cg z7Y3QWsUjjbE9gm3h4g{SIde%LgN>W%shHMbNNCQ7?0ch=)r-DXL^3=6Z{3f)bRaQ3 zU&J9Nyp*Kp;DDgyaWBobq658slYmIM{iwxIU3w9jpznAtMVKLFu>bWV+TUKTO(B1Q z_!GCU;b@bSq$6j_Yy5hbzJ$*+U4;4IHKls(j&-msZ2!XdP`(6BCa_6!p@a?82@-3_ zE^3Ub5xXbjMkBWn2fULp< zx@zTy=&{YwxoHG;B$?oQ0xpD@g`l_0nFNiHP6v2k`)_YLXkeoM E4`a8j3jhEB literal 0 HcmV?d00001 diff --git a/easi/simulators/registry.py b/easi/simulators/registry.py index 2a61240..1f1696c 100644 --- a/easi/simulators/registry.py +++ b/easi/simulators/registry.py @@ -12,7 +12,7 @@ from __future__ import annotations import importlib -from dataclasses import dataclass +from dataclasses import dataclass, field from pathlib import Path import yaml @@ -32,6 +32,7 @@ class SimulatorEntry: simulator_class: str # fully qualified class name env_manager_class: str # fully qualified class name python_version: str + installation_kwargs: dict = field(default_factory=dict) # Module-level registry populated on first access @@ -61,6 +62,7 @@ def _discover_simulators() -> dict[str, SimulatorEntry]: simulator_class=ver_info["simulator_class"], env_manager_class=ver_info["env_manager_class"], python_version=ver_info.get("python_version", "3.10"), + installation_kwargs=ver_info.get("installation_kwargs", {}), ) # Register with explicit key: "ai2thor:v2_1_0" @@ -124,6 +126,13 @@ def load_env_manager_class(key: str): return _import_class(entry.env_manager_class) +def create_env_manager(key: str): + """Create an env manager instance with installation_kwargs from the manifest.""" + entry = get_simulator_entry(key) + cls = _import_class(entry.env_manager_class) + return cls(installation_kwargs=entry.installation_kwargs) + + def _import_class(fully_qualified_name: str): """Import a class from its fully qualified name (e.g., 'pkg.mod.ClassName').""" module_path, class_name = fully_qualified_name.rsplit(".", 1) diff --git a/easi/simulators/subprocess_runner.py b/easi/simulators/subprocess_runner.py index 8cc4797..466181c 100644 --- a/easi/simulators/subprocess_runner.py +++ b/easi/simulators/subprocess_runner.py @@ -35,6 +35,12 @@ class SubprocessRunner: """Manages a bridge subprocess for a single simulator instance.""" + # Path-like env vars that should prepend rather than replace + _PREPEND_ENV_VARS = frozenset({ + "LD_LIBRARY_PATH", "DYLD_LIBRARY_PATH", "PATH", + "PYTHONPATH", "QT_QPA_PLATFORM_PLUGIN_PATH", + }) + def __init__( self, python_executable: str, @@ -45,6 +51,7 @@ def __init__( command_timeout: float = 60.0, poll_interval: float = 0.1, extra_args: list[str] | None = None, + extra_env: dict[str, str] | None = None, ): self.python_executable = python_executable self.bridge_script_path = bridge_script_path @@ -54,6 +61,7 @@ def __init__( self.command_timeout = command_timeout self.poll_interval = poll_interval self.extra_args = extra_args or [] + self.extra_env = extra_env self._process: subprocess.Popen | None = None self._workspace: Path | None = None @@ -88,6 +96,7 @@ def launch(self) -> None: stderr=subprocess.STDOUT, # merge stderr into stdout text=True, bufsize=1, + env=self._build_subprocess_env(), ) # Stream bridge output through logger in a background thread @@ -164,6 +173,25 @@ def shutdown(self) -> None: cleanup_workspace(self._workspace) self._workspace = None + def _build_subprocess_env(self) -> dict[str, str] | None: + """Build env dict for subprocess, merging extra_env with os.environ. + + For path-like vars (LD_LIBRARY_PATH, PATH, etc.), prepends the new + value to the existing value with ':' separator. + + Returns None if no extra_env (subprocess inherits parent env). + """ + if not self.extra_env: + return None + + env = os.environ.copy() + for key, value in self.extra_env.items(): + if key in self._PREPEND_ENV_VARS and key in env: + env[key] = f"{value}:{env[key]}" + else: + env[key] = value + return env + def _build_launch_command(self) -> list[str]: """Build the command to launch the bridge subprocess.""" cmd = [ diff --git a/tests/test_coppeliasim_simulator.py b/tests/test_coppeliasim_simulator.py new file mode 100644 index 0000000..9b1d8e2 --- /dev/null +++ b/tests/test_coppeliasim_simulator.py @@ -0,0 +1,165 @@ +"""Tests for CoppeliaSim V4.1.0 simulator integration (offline).""" + +from pathlib import Path + + +class TestRegistryDiscovery: + """Verify CoppeliaSim is discovered by the simulator registry.""" + + def test_coppeliasim_in_simulator_list(self): + from easi.simulators.registry import list_simulators + assert "coppeliasim" in list_simulators() + + def test_coppeliasim_versioned_key(self): + from easi.simulators.registry import list_simulators + assert "coppeliasim:v4_1_0" in list_simulators() + + def test_get_simulator_entry(self): + from easi.simulators.registry import get_simulator_entry + entry = get_simulator_entry("coppeliasim") + assert entry.name == "coppeliasim" + assert entry.version == "v4_1_0" + + def test_installation_kwargs_populated(self): + from easi.simulators.registry import get_simulator_entry + entry = get_simulator_entry("coppeliasim") + assert "binary_url" in entry.installation_kwargs + assert "binary_dir_name" in entry.installation_kwargs + assert "pyrep_git_url" in entry.installation_kwargs + + def test_load_simulator_class(self): + from easi.simulators.registry import load_simulator_class + cls = load_simulator_class("coppeliasim:v4_1_0") + sim = cls() + assert sim.name == "coppeliasim" + + def test_create_env_manager_with_kwargs(self): + from easi.simulators.registry import create_env_manager + mgr = create_env_manager("coppeliasim") + assert mgr.installation_kwargs["binary_dir_name"] == "CoppeliaSim_Pro_V4_1_0_Ubuntu20_04" + + +class TestEnvManagerConfig: + """Verify env manager configuration without installing anything.""" + + def test_env_name(self): + from easi.simulators.registry import create_env_manager + mgr = create_env_manager("coppeliasim") + assert mgr.get_env_name() == "easi_coppeliasim_v4_1_0" + + def test_needs_display(self): + from easi.simulators.registry import create_env_manager + mgr = create_env_manager("coppeliasim") + assert mgr.needs_display is True + + def test_xvfb_screen_config(self): + from easi.simulators.registry import create_env_manager + mgr = create_env_manager("coppeliasim") + assert mgr.xvfb_screen_config == "1280x720x24" + + def test_system_deps(self): + from easi.simulators.registry import create_env_manager + mgr = create_env_manager("coppeliasim") + deps = mgr.get_system_deps() + assert "conda" in deps + assert "xvfb" in deps + + def test_validation_import_references_pyrep(self): + from easi.simulators.registry import create_env_manager + mgr = create_env_manager("coppeliasim") + assert "pyrep" in mgr.get_validation_import().lower() + + def test_conda_yaml_exists(self): + from easi.simulators.registry import create_env_manager + mgr = create_env_manager("coppeliasim") + assert mgr.get_conda_env_yaml_path().exists() + + def test_requirements_txt_exists(self): + from easi.simulators.registry import create_env_manager + mgr = create_env_manager("coppeliasim") + assert mgr.get_requirements_txt_path().exists() + + +class TestEnvVars: + """Verify env_vars are correctly resolved from installation_kwargs.""" + + def test_env_vars_has_coppeliasim_root(self): + from easi.simulators.registry import create_env_manager + mgr = create_env_manager("coppeliasim") + assert "COPPELIASIM_ROOT" in mgr.get_env_vars() + + def test_env_vars_has_ld_library_path(self): + from easi.simulators.registry import create_env_manager + mgr = create_env_manager("coppeliasim") + assert "LD_LIBRARY_PATH" in mgr.get_env_vars() + + def test_env_vars_has_qt_platform(self): + from easi.simulators.registry import create_env_manager + mgr = create_env_manager("coppeliasim") + assert "QT_QPA_PLATFORM_PLUGIN_PATH" in mgr.get_env_vars() + + def test_env_vars_no_unresolved_templates(self): + from easi.simulators.registry import create_env_manager + mgr = create_env_manager("coppeliasim") + for key, val in mgr.get_env_vars().items(): + assert "{" not in val, f"Unresolved template in {key}: {val}" + + def test_env_vars_contain_coppeliasim_dir(self): + from easi.simulators.registry import create_env_manager + mgr = create_env_manager("coppeliasim") + root = mgr.get_env_vars()["COPPELIASIM_ROOT"] + assert "CoppeliaSim_Pro_V4_1_0_Ubuntu20_04" in root + assert "extras" in root + + def test_ld_library_path_includes_coppeliasim_root(self): + from easi.simulators.registry import create_env_manager + mgr = create_env_manager("coppeliasim") + env_vars = mgr.get_env_vars() + assert env_vars["COPPELIASIM_ROOT"] in env_vars["LD_LIBRARY_PATH"] + assert env_vars["COPPELIASIM_ROOT"] == env_vars["QT_QPA_PLATFORM_PLUGIN_PATH"] + + def test_ld_library_path_includes_conda_lib(self): + from easi.simulators.registry import create_env_manager + mgr = create_env_manager("coppeliasim") + env_vars = mgr.get_env_vars() + assert "/lib" in env_vars["LD_LIBRARY_PATH"] + + def test_empty_installation_kwargs_returns_empty_env_vars(self): + from easi.simulators.coppeliasim.v4_1_0.env_manager import CoppeliaSimEnvManagerV410 + mgr = CoppeliaSimEnvManagerV410() # No installation_kwargs + assert mgr.get_env_vars() == {} + + +class TestSimulatorClass: + """Verify simulator class basic properties.""" + + def test_name(self): + from easi.simulators.coppeliasim.v4_1_0.simulator import CoppeliaSimSimulatorV410 + assert CoppeliaSimSimulatorV410().name == "coppeliasim" + + def test_version(self): + from easi.simulators.coppeliasim.v4_1_0.simulator import CoppeliaSimSimulatorV410 + assert CoppeliaSimSimulatorV410().version == "v4_1_0" + + def test_bridge_script_path_exists(self): + from easi.simulators.coppeliasim.v4_1_0.simulator import CoppeliaSimSimulatorV410 + bridge_path = CoppeliaSimSimulatorV410()._get_bridge_script_path() + assert bridge_path.exists() + assert bridge_path.name == "bridge.py" + + def test_bridge_script_has_bridge_class(self): + from easi.simulators.coppeliasim.v4_1_0.simulator import CoppeliaSimSimulatorV410 + content = CoppeliaSimSimulatorV410()._get_bridge_script_path().read_text() + assert "CoppeliaSimBridge" in content + + +class TestBridgeFileStructure: + """Verify bridge file and supporting assets exist.""" + + def test_ttt_scene_file_exists(self): + ttt = Path(__file__).parent.parent / "easi" / "simulators" / "coppeliasim" / "v4_1_0" / "task_design.ttt" + assert ttt.exists(), f"task_design.ttt not found at {ttt}" + + def test_lua_addon_exists(self): + lua = Path(__file__).parent.parent / "easi" / "simulators" / "coppeliasim" / "v4_1_0" / "simAddOnScript_PyRep.lua" + assert lua.exists(), f"simAddOnScript_PyRep.lua not found at {lua}" diff --git a/tests/test_dummy_simulator.py b/tests/test_dummy_simulator.py index 3efe5ee..ae6bff7 100644 --- a/tests/test_dummy_simulator.py +++ b/tests/test_dummy_simulator.py @@ -83,6 +83,26 @@ def test_is_running(dummy_simulator): assert dummy_simulator.is_running() +def test_dummy_simulator_with_env_vars_wiring(): + """Verify DummyEnvManager.get_env_vars() returns {} and doesn't break launch.""" + env_manager = DummyEnvManager() + assert env_manager.get_env_vars() == {} + + sim = DummySimulator() + runner = SubprocessRunner( + python_executable=env_manager.get_python_executable(), + bridge_script_path=sim._get_bridge_script_path(), + startup_timeout=10.0, + command_timeout=10.0, + extra_env=env_manager.get_env_vars() or None, + ) + runner.launch() + try: + assert runner.is_alive() + finally: + runner.shutdown() + + def test_registry_discovery(): """Test that the dummy simulator is discoverable via the registry.""" from easi.simulators.registry import get_simulator_entry, list_simulators diff --git a/tests/test_env_manager.py b/tests/test_env_manager.py new file mode 100644 index 0000000..cd435c4 --- /dev/null +++ b/tests/test_env_manager.py @@ -0,0 +1,180 @@ +"""Tests for BaseEnvironmentManager extensions (env vars, post_install, helpers).""" + +from __future__ import annotations + +import os +import shutil +import sys +from pathlib import Path + + +class TestEnvManagerGetEnvVars: + """Tests for the get_env_vars() method.""" + + def test_default_returns_empty_dict(self): + from easi.simulators.dummy.v1.env_manager import DummyEnvManager + + mgr = DummyEnvManager() + assert mgr.get_env_vars() == {} + + def test_returns_dict_type(self): + from easi.simulators.dummy.v1.env_manager import DummyEnvManager + + mgr = DummyEnvManager() + result = mgr.get_env_vars() + assert isinstance(result, dict) + + +class TestEnvManagerPostInstall: + """Tests for the post_install() hook and helper utilities.""" + + def test_default_post_install_is_noop(self): + from easi.simulators.dummy.v1.env_manager import DummyEnvManager + + mgr = DummyEnvManager() + ctx = mgr._get_template_variables() + ctx["env_vars"] = mgr.get_env_vars() + mgr.post_install(ctx) # Should not raise + + def test_post_install_receives_context_keys(self): + from easi.core.base_env_manager import BaseEnvironmentManager + + received_ctx = {} + + class SpyManager(BaseEnvironmentManager): + simulator_name = "spy" + version = "v1" + + def get_conda_env_yaml_path(self): + return Path("/dev/null") + + def get_requirements_txt_path(self): + return Path("/dev/null") + + def get_system_deps(self): + return [] + + def get_validation_import(self): + return "pass" + + def post_install(self, context): + received_ctx.update(context) + + mgr = SpyManager() + mgr._run_post_install() + assert "env_dir" in received_ctx + assert "extras_dir" in received_ctx + assert "env_vars" in received_ctx + + def test_extras_dir_inside_env(self): + from easi.simulators.dummy.v1.env_manager import DummyEnvManager + + mgr = DummyEnvManager() + extras_dir = mgr.get_extras_dir() + env_name = mgr.get_env_name() + assert env_name in str(extras_dir) + assert str(extras_dir).endswith("extras") + + def test_resolve_template(self): + from easi.simulators.dummy.v1.env_manager import DummyEnvManager + + mgr = DummyEnvManager() + env_dir = str(mgr.conda_prefix / "envs" / mgr.get_env_name()) + result = mgr._resolve_template("{env_dir}/lib", {"env_dir": env_dir}) + assert result == f"{env_dir}/lib" + + def test_resolve_extras_dir_template(self): + from easi.simulators.dummy.v1.env_manager import DummyEnvManager + + mgr = DummyEnvManager() + t = mgr._get_template_variables() + result = mgr._resolve_template("{extras_dir}/CoppeliaSim", t) + assert "extras/CoppeliaSim" in result + assert "{" not in result + + def test_post_install_can_copy_files(self, tmp_path): + from easi.core.base_env_manager import BaseEnvironmentManager + + src_file = tmp_path / "source" / "addon.lua" + src_file.parent.mkdir() + src_file.write_text("-- lua script") + dest_dir = tmp_path / "dest" + dest_dir.mkdir() + + class CopyManager(BaseEnvironmentManager): + simulator_name = "copy" + version = "v1" + + def get_conda_env_yaml_path(self): + return Path("/dev/null") + + def get_requirements_txt_path(self): + return Path("/dev/null") + + def get_system_deps(self): + return [] + + def get_validation_import(self): + return "pass" + + def post_install(self, context): + shutil.copy(str(src_file), str(dest_dir / "addon.lua")) + + mgr = CopyManager() + ctx = mgr._get_template_variables() + ctx["env_vars"] = {} + mgr.post_install(ctx) + assert (dest_dir / "addon.lua").exists() + assert (dest_dir / "addon.lua").read_text() == "-- lua script" + + def test_env_vars_with_templates_are_resolved(self): + from easi.core.base_env_manager import BaseEnvironmentManager + + class FakeManager(BaseEnvironmentManager): + simulator_name = "fake" + version = "v1" + + def get_conda_env_yaml_path(self): + return Path("/dev/null") + + def get_requirements_txt_path(self): + return Path("/dev/null") + + def get_system_deps(self): + return [] + + def get_validation_import(self): + return "pass" + + def get_env_vars(self): + t = self._get_template_variables() + return { + "SIM_ROOT": self._resolve_template("{extras_dir}/CoppeliaSim", t), + "LD_LIBRARY_PATH": self._resolve_template("{extras_dir}/CoppeliaSim", t), + } + + mgr = FakeManager() + env_vars = mgr.get_env_vars() + assert "extras/CoppeliaSim" in env_vars["SIM_ROOT"] + assert "extras/CoppeliaSim" in env_vars["LD_LIBRARY_PATH"] + for v in env_vars.values(): + assert "{" not in v + + def test_run_command_accepts_env(self): + from easi.simulators.dummy.v1.env_manager import DummyEnvManager + + mgr = DummyEnvManager() + mgr._run_command( + [sys.executable, "-c", "import os; assert os.environ.get('TEST_EASI_VAR') == 'hello'"], + "test env injection", + env={**os.environ, "TEST_EASI_VAR": "hello"}, + ) + + def test_run_command_without_env_inherits_parent(self): + from easi.simulators.dummy.v1.env_manager import DummyEnvManager + + mgr = DummyEnvManager() + mgr._run_command( + [sys.executable, "-c", "print('ok')"], + "test no env", + ) diff --git a/tests/test_env_vars_wiring.py b/tests/test_env_vars_wiring.py new file mode 100644 index 0000000..f54e300 --- /dev/null +++ b/tests/test_env_vars_wiring.py @@ -0,0 +1,66 @@ +"""Tests verifying env vars flow from env_manager through to SubprocessRunner.""" + +from __future__ import annotations + +from pathlib import Path +from unittest.mock import MagicMock, patch + + +class TestEnvVarsWiring: + """Verify env vars flow from env_manager to SubprocessRunner.""" + + def test_runner_passes_env_vars_to_subprocess(self): + from easi.evaluation.runner import EvaluationRunner + + runner = EvaluationRunner.__new__(EvaluationRunner) + runner.data_dir = Path("/tmp/fake") + + mock_env_mgr = MagicMock() + mock_env_mgr.env_is_ready.return_value = True + mock_env_mgr.get_python_executable.return_value = "/usr/bin/python3" + mock_env_mgr.needs_display = False + mock_env_mgr.xvfb_screen_config = "1024x768x24" + mock_env_mgr.get_env_vars.return_value = {"SIM_ROOT": "/opt/sim"} + + mock_sim_cls = MagicMock() + mock_sim = mock_sim_cls.return_value + mock_sim._get_bridge_script_path.return_value = Path("/fake/bridge.py") + + with patch("easi.simulators.registry.create_env_manager", return_value=mock_env_mgr), \ + patch("easi.simulators.registry.load_simulator_class", return_value=mock_sim_cls), \ + patch("easi.simulators.subprocess_runner.SubprocessRunner") as MockRunner: + mock_runner_instance = MockRunner.return_value + mock_runner_instance.launch.return_value = None + + runner._create_simulator("fake:v1") + + call_kwargs = MockRunner.call_args + assert call_kwargs.kwargs.get("extra_env") == {"SIM_ROOT": "/opt/sim"} + + def test_runner_passes_none_when_no_env_vars(self): + from easi.evaluation.runner import EvaluationRunner + + runner = EvaluationRunner.__new__(EvaluationRunner) + runner.data_dir = Path("/tmp/fake") + + mock_env_mgr = MagicMock() + mock_env_mgr.env_is_ready.return_value = True + mock_env_mgr.get_python_executable.return_value = "/usr/bin/python3" + mock_env_mgr.needs_display = False + mock_env_mgr.xvfb_screen_config = "1024x768x24" + mock_env_mgr.get_env_vars.return_value = {} + + mock_sim_cls = MagicMock() + mock_sim = mock_sim_cls.return_value + mock_sim._get_bridge_script_path.return_value = Path("/fake/bridge.py") + + with patch("easi.simulators.registry.create_env_manager", return_value=mock_env_mgr), \ + patch("easi.simulators.registry.load_simulator_class", return_value=mock_sim_cls), \ + patch("easi.simulators.subprocess_runner.SubprocessRunner") as MockRunner: + mock_runner_instance = MockRunner.return_value + mock_runner_instance.launch.return_value = None + + runner._create_simulator("fake:v1") + + call_kwargs = MockRunner.call_args + assert call_kwargs.kwargs.get("extra_env") is None diff --git a/tests/test_installation_kwargs.py b/tests/test_installation_kwargs.py new file mode 100644 index 0000000..9004f8a --- /dev/null +++ b/tests/test_installation_kwargs.py @@ -0,0 +1,80 @@ +"""Tests for installation_kwargs flow: manifest -> registry -> env_manager.""" + +from __future__ import annotations + +from pathlib import Path +from unittest.mock import MagicMock, patch + + +class TestSimulatorEntryHasInstallationKwargs: + """SimulatorEntry dataclass includes installation_kwargs.""" + + def test_dummy_entry_has_empty_installation_kwargs(self): + from easi.simulators.registry import get_simulator_entry + entry = get_simulator_entry("dummy") + assert hasattr(entry, "installation_kwargs") + assert entry.installation_kwargs == {} + + def test_ai2thor_entry_has_empty_installation_kwargs(self): + from easi.simulators.registry import get_simulator_entry + entry = get_simulator_entry("ai2thor:v2_1_0") + assert entry.installation_kwargs == {} + + +class TestBaseEnvManagerAcceptsInstallationKwargs: + """BaseEnvironmentManager stores installation_kwargs.""" + + def test_default_installation_kwargs_is_empty(self): + from easi.simulators.dummy.v1.env_manager import DummyEnvManager + mgr = DummyEnvManager() + assert mgr.installation_kwargs == {} + + def test_accepts_installation_kwargs(self): + from easi.simulators.dummy.v1.env_manager import DummyEnvManager + mgr = DummyEnvManager(installation_kwargs={"foo": "bar"}) + assert mgr.installation_kwargs == {"foo": "bar"} + + +class TestCreateEnvManagerFactory: + """Registry factory function passes installation_kwargs.""" + + def test_create_env_manager_returns_instance(self): + from easi.simulators.registry import create_env_manager + mgr = create_env_manager("dummy") + assert mgr.simulator_name == "dummy" + + def test_create_env_manager_passes_kwargs(self): + from easi.simulators.registry import create_env_manager + mgr = create_env_manager("dummy") + assert isinstance(mgr.installation_kwargs, dict) + + +class TestRunnerUsesFactory: + """EvaluationRunner._create_simulator uses the factory.""" + + def test_runner_passes_installation_kwargs(self): + from easi.evaluation.runner import EvaluationRunner + + runner = EvaluationRunner.__new__(EvaluationRunner) + runner.data_dir = Path("/tmp/fake") + + mock_env_mgr = MagicMock() + mock_env_mgr.env_is_ready.return_value = True + mock_env_mgr.get_python_executable.return_value = "/usr/bin/python3" + mock_env_mgr.needs_display = False + mock_env_mgr.xvfb_screen_config = "1024x768x24" + mock_env_mgr.get_env_vars.return_value = {} + + mock_sim_cls = MagicMock() + mock_sim = mock_sim_cls.return_value + mock_sim._get_bridge_script_path.return_value = Path("/fake/bridge.py") + + with patch("easi.simulators.registry.create_env_manager", return_value=mock_env_mgr) as mock_factory, \ + patch("easi.simulators.registry.load_simulator_class", return_value=mock_sim_cls), \ + patch("easi.simulators.subprocess_runner.SubprocessRunner") as MockRunner: + mock_runner_instance = MockRunner.return_value + mock_runner_instance.launch.return_value = None + + runner._create_simulator("fake:v1") + + mock_factory.assert_called_once_with("fake:v1") diff --git a/tests/test_subprocess_runner.py b/tests/test_subprocess_runner.py new file mode 100644 index 0000000..c07411d --- /dev/null +++ b/tests/test_subprocess_runner.py @@ -0,0 +1,68 @@ +"""Tests for SubprocessRunner env var injection.""" + +from __future__ import annotations + +import os +from pathlib import Path + +from easi.simulators.subprocess_runner import SubprocessRunner + + +class TestSubprocessRunnerEnvVars: + """Tests for env var injection into bridge subprocess.""" + + def test_constructor_accepts_env_vars(self): + runner = SubprocessRunner( + python_executable="/usr/bin/python3", + bridge_script_path=Path("/dev/null"), + extra_env={"MY_VAR": "my_value"}, + ) + assert runner.extra_env == {"MY_VAR": "my_value"} + + def test_default_env_is_none(self): + runner = SubprocessRunner( + python_executable="/usr/bin/python3", + bridge_script_path=Path("/dev/null"), + ) + assert runner.extra_env is None + + def test_build_env_merges_with_os_environ(self): + runner = SubprocessRunner( + python_executable="/usr/bin/python3", + bridge_script_path=Path("/dev/null"), + extra_env={"COPPELIASIM_ROOT": "/opt/coppeliasim"}, + ) + env = runner._build_subprocess_env() + assert env["COPPELIASIM_ROOT"] == "/opt/coppeliasim" + assert "PATH" in env + + def test_build_env_returns_none_when_no_extra(self): + runner = SubprocessRunner( + python_executable="/usr/bin/python3", + bridge_script_path=Path("/dev/null"), + ) + assert runner._build_subprocess_env() is None + + def test_extra_env_prepends_to_path_vars(self): + runner = SubprocessRunner( + python_executable="/usr/bin/python3", + bridge_script_path=Path("/dev/null"), + extra_env={"LD_LIBRARY_PATH": "/opt/sim/lib"}, + ) + env = runner._build_subprocess_env() + ld_path = env.get("LD_LIBRARY_PATH", "") + assert ld_path.startswith("/opt/sim/lib") + + def test_non_path_var_replaces(self): + """Non-path env vars should replace, not prepend.""" + os.environ["MY_EXISTING"] = "old_value" + try: + runner = SubprocessRunner( + python_executable="/usr/bin/python3", + bridge_script_path=Path("/dev/null"), + extra_env={"MY_EXISTING": "new_value"}, + ) + env = runner._build_subprocess_env() + assert env["MY_EXISTING"] == "new_value" + finally: + del os.environ["MY_EXISTING"] From 5c9e80c7081086ae9adfd784966be40fe56420be Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Wed, 18 Feb 2026 15:01:10 +0800 Subject: [PATCH 049/239] [Fix] Trust DISPLAY env var for real screen detection Simplify _has_display() to accept any DISPLAY value including local displays like :0. Previously rejected :N format which prevented using real screens directly, forcing unnecessary xvfb-run wrapping. --- easi/simulators/subprocess_runner.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/easi/simulators/subprocess_runner.py b/easi/simulators/subprocess_runner.py index 466181c..92d7abc 100644 --- a/easi/simulators/subprocess_runner.py +++ b/easi/simulators/subprocess_runner.py @@ -212,9 +212,8 @@ def _build_launch_command(self) -> list[str]: return cmd def _has_display(self) -> bool: - """Check if a real display is available.""" - display = os.environ.get("DISPLAY", "") - return bool(display) and not display.startswith(":") + """Check if an X display is available.""" + return bool(os.environ.get("DISPLAY", "")) def _stream_output(self) -> None: """Read bridge stdout line-by-line and log at DEBUG level. From 30dc5c7f7a72e16f6705a6da875f85ee1480f0bb Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Wed, 18 Feb 2026 23:52:16 +0800 Subject: [PATCH 050/239] [Feat] Add EB-Manipulation benchmark integration with configurable task env vars Integrate EmbodiedBench's EB-Manipulation benchmark into EASI with full alignment to the original evaluation pipeline. Includes vendored env/utils, task bridge with image annotation (XYZ axes + YOLO bboxes), prompt builder matching ManipPlanner, 5 split YAML configs, and 40 tests. Also adds a generic `simulator_configs.env_vars` mechanism for task YAMLs to inject environment variables into the bridge subprocess. --- easi/core/base_task.py | 8 +- easi/evaluation/runner.py | 4 + easi/tasks/ebmanipulation/__init__.py | 0 easi/tasks/ebmanipulation/actions.py | 92 +++ easi/tasks/ebmanipulation/bridge.py | 233 +++++++ .../config/manipulation_examples.json | 28 + .../ebmanipulation/ebmanipulation_base.yaml | 42 ++ .../ebmanipulation_common_sense.yaml | 42 ++ .../ebmanipulation_complex.yaml | 42 ++ .../ebmanipulation_spatial.yaml | 42 ++ .../ebmanipulation/ebmanipulation_visual.yaml | 42 ++ easi/tasks/ebmanipulation/prompts.py | 547 +++++++++++++++ easi/tasks/ebmanipulation/task.py | 101 +++ easi/tasks/ebmanipulation/vendor/EBManEnv.py | 189 ++++++ easi/tasks/ebmanipulation/vendor/__init__.py | 0 .../ebmanipulation/vendor/eb_man_utils.py | 633 ++++++++++++++++++ tests/test_ebmanipulation_task.py | 407 +++++++++++ 17 files changed, 2451 insertions(+), 1 deletion(-) create mode 100644 easi/tasks/ebmanipulation/__init__.py create mode 100644 easi/tasks/ebmanipulation/actions.py create mode 100644 easi/tasks/ebmanipulation/bridge.py create mode 100644 easi/tasks/ebmanipulation/config/manipulation_examples.json create mode 100644 easi/tasks/ebmanipulation/ebmanipulation_base.yaml create mode 100644 easi/tasks/ebmanipulation/ebmanipulation_common_sense.yaml create mode 100644 easi/tasks/ebmanipulation/ebmanipulation_complex.yaml create mode 100644 easi/tasks/ebmanipulation/ebmanipulation_spatial.yaml create mode 100644 easi/tasks/ebmanipulation/ebmanipulation_visual.yaml create mode 100644 easi/tasks/ebmanipulation/prompts.py create mode 100644 easi/tasks/ebmanipulation/task.py create mode 100644 easi/tasks/ebmanipulation/vendor/EBManEnv.py create mode 100644 easi/tasks/ebmanipulation/vendor/__init__.py create mode 100644 easi/tasks/ebmanipulation/vendor/eb_man_utils.py create mode 100644 tests/test_ebmanipulation_task.py diff --git a/easi/core/base_task.py b/easi/core/base_task.py index d0bc5fa..72191c4 100644 --- a/easi/core/base_task.py +++ b/easi/core/base_task.py @@ -110,12 +110,18 @@ def additional_deps(self) -> list[str]: @property def simulator_kwargs(self) -> dict: - """Bridge-facing kwargs (simulator_configs minus additional_deps + max_steps).""" + """Bridge-facing kwargs (simulator_configs minus additional_deps/env_vars + max_steps).""" cfg = dict(self.simulator_configs) cfg.pop("additional_deps", None) + cfg.pop("env_vars", None) cfg["max_steps"] = self.max_steps return cfg + @property + def extra_env_vars(self) -> dict[str, str]: + """Task-level environment variables from simulator_configs.env_vars.""" + return self.simulator_configs.get("env_vars", {}) + def get_instruction(self, episode: dict) -> str: """Return human-readable task instruction for this episode. diff --git a/easi/evaluation/runner.py b/easi/evaluation/runner.py index 2705086..05bbe2d 100644 --- a/easi/evaluation/runner.py +++ b/easi/evaluation/runner.py @@ -515,6 +515,10 @@ def _create_simulator(self, simulator_key: str, task=None): env_vars = env_manager.get_env_vars() + # Merge task-level env_vars from simulator_configs.env_vars + if task and task.extra_env_vars: + env_vars = {**env_vars, **task.extra_env_vars} + runner = SubprocessRunner( python_executable=env_manager.get_python_executable(), bridge_script_path=bridge_path, diff --git a/easi/tasks/ebmanipulation/__init__.py b/easi/tasks/ebmanipulation/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/easi/tasks/ebmanipulation/actions.py b/easi/tasks/ebmanipulation/actions.py new file mode 100644 index 0000000..8a6eb4b --- /dev/null +++ b/easi/tasks/ebmanipulation/actions.py @@ -0,0 +1,92 @@ +"""EB-Manipulation action space definitions. + +Unlike EB-Alfred/Navigation which have named discrete actions, +EB-Manipulation uses 7D discrete gripper actions: + [X, Y, Z, Roll, Pitch, Yaw, Gripper_state] + +Reference: EmbodiedBench/embodiedbench/envs/eb_manipulation/eb_man_utils.py +""" +from __future__ import annotations + +import ast +import re + +# Default discretization parameters (configurable via simulator_kwargs) +DEFAULT_VOXEL_SIZE = 100 +DEFAULT_ROTATION_RESOLUTION = 3 +DEFAULT_SCENE_BOUNDS = [-0.3, -0.5, 0.6, 0.7, 0.5, 1.6] +DEFAULT_MAX_STEPS = 15 + +# Eval set definitions matching EBManEnv.py EVAL_SETS +EVAL_SETS = { + "base": [ + "pick_cube_shape", + "stack_cubes_color", + "place_into_shape_sorter_color", + "wipe_table_direction", + ], + "common_sense": [ + "pick_cube_shape", + "stack_cubes_color", + "place_into_shape_sorter_color", + "wipe_table_direction", + ], + "complex": [ + "pick_cube_shape", + "stack_cubes_color", + "place_into_shape_sorter_color", + "wipe_table_direction", + ], + "spatial": [ + "pick_cube_relative", + "stack_cubes_relative", + "place_into_shape_sorter_relative", + "wipe_table_relative", + ], + "visual": [ + "pick_cube_shape", + "stack_cubes_color", + "place_into_shape_sorter_color", + ], +} + +VALID_EVAL_SETS = list(EVAL_SETS.keys()) + + +def get_action_space() -> list[str]: + """Return empty action space -- EB-Manipulation uses 7D coordinate arrays.""" + return [] + + +def serialize_action(action: list) -> str: + """Serialize a 7D action list to a string for EASI Action.action_name.""" + return str(action) + + +def deserialize_action(action_str: str) -> list: + """Deserialize action string back to a 7D list.""" + try: + result = ast.literal_eval(action_str) + if isinstance(result, (list, tuple)) and len(result) == 7: + return list(result) + except (ValueError, SyntaxError): + pass + return [] + + +def extract_pose_list(text: str) -> list[list]: + """Extract multiple [x, y, z, a, b, c, n] arrays from a string. + + Reference: ManipPlanner.extract_pose_list() + """ + matches = re.findall(r"\[([^\[\]]+)\]", text) + poses = [] + for m in matches: + arr_str = "[" + m + "]" + try: + parsed = ast.literal_eval(arr_str) + if isinstance(parsed, list) and len(parsed) == 7: + poses.append(parsed) + except Exception: + continue + return poses diff --git a/easi/tasks/ebmanipulation/bridge.py b/easi/tasks/ebmanipulation/bridge.py new file mode 100644 index 0000000..28a2c5e --- /dev/null +++ b/easi/tasks/ebmanipulation/bridge.py @@ -0,0 +1,233 @@ +"""EB-Manipulation bridge -- wraps vendored EBManEnv via BaseBridge. + +This script runs inside the easi_coppeliasim_v4_1_0 conda env (Python 3.10). +Communicates with parent process via filesystem IPC. + +The bridge adds AMSolver to sys.path at startup so the vendored EBManEnv +can import from amsolver (CoppeliaSim scene management). + +It also handles: +- Object coordinate extraction (form_object_coord_for_input) +- Image annotation with XYZ axes (draw_xyz_coordinate) +- Image annotation with YOLO bounding boxes (draw_bounding_boxes) + +These are passed to the prompt builder via observation metadata. + +Usage: + python bridge.py --workspace /tmp/easi_xxx [--simulator-kwargs '{}'] +""" +from __future__ import annotations + +import json +import sys +from pathlib import Path + +import numpy as np + +_repo_root = Path(__file__).resolve().parents[3] +if str(_repo_root) not in sys.path: + sys.path.insert(0, str(_repo_root)) + + +def _setup_amsolver_path(simulator_kwargs: dict, data_dir: str = "") -> None: + """Add AMSolver parent directory to sys.path and set TTMS_FOLDER. + + AMSolver lives at EmbodiedBench/embodiedbench/envs/eb_manipulation/amsolver/. + The parent directory (eb_manipulation/) must be on sys.path so + ``import amsolver`` works. + + The path is configurable via simulator_kwargs['amsolver_parent_path']. + Default: auto-detect from repo root / EmbodiedBench submodule. + + Also sets amsolver.task_environment.TTMS_FOLDER to the extracted HF data + directory so AMSolver can find task templates, object models, and robot TTMs. + """ + amsolver_parent = simulator_kwargs.get("amsolver_parent_path", "") + if not amsolver_parent: + # Auto-detect: try repo_root/EmbodiedBench/embodiedbench/envs/eb_manipulation + candidate = ( + _repo_root / "EmbodiedBench" / "embodiedbench" / "envs" / "eb_manipulation" + ) + if candidate.exists(): + amsolver_parent = str(candidate) + + if amsolver_parent and amsolver_parent not in sys.path: + sys.path.insert(0, amsolver_parent) + + # Point AMSolver's TTMS_FOLDER to the HF dataset cache + # (contains vlm/, amsolver/robot_ttms/) + if data_dir: + import amsolver.task_environment as te + + te.TTMS_FOLDER = data_dir + "/" + + +from easi.simulators.base_bridge import BaseBridge +from easi.tasks.ebmanipulation.actions import deserialize_action +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + + +class EBManipulationBridge(BaseBridge): + """BaseBridge wrapper around vendored EBManEnv for EB-Manipulation. + + Handles object coordinate extraction and image annotation to match + the original EmbodiedBench evaluation pipeline. + """ + + # Camera views used for object coordinate computation + _camera_types = ["front_rgb"] + + def _create_env(self, reset_config, simulator_kwargs): + # Must set up AMSolver path before importing vendored env + data_dir = reset_config.get("data_dir", "") + _setup_amsolver_path(simulator_kwargs, data_dir=data_dir) + + from easi.tasks.ebmanipulation.vendor.EBManEnv import EBManEnv + + resolution = simulator_kwargs.get("screen_height", 500) + max_steps = simulator_kwargs.get("max_steps", 15) + voxel_size = simulator_kwargs.get("voxel_size", 100) + rotation_resolution = simulator_kwargs.get("rotation_resolution", 3) + scene_bounds = simulator_kwargs.get( + "scene_bounds", [-0.3, -0.5, 0.6, 0.7, 0.5, 1.6] + ) + + self._resolution = resolution + self._detection_box = simulator_kwargs.get("detection_box", True) + + return EBManEnv( + img_size=(resolution, resolution), + max_steps=max_steps, + scene_bounds=scene_bounds, + voxel_size=voxel_size, + rotation_resolution=rotation_resolution, + ) + + def _on_reset(self, env, reset_config): + # Construct episode file paths from HF dataset cache + data_dir = reset_config.get("data_dir", "") + split = reset_config.get("split", "base") + task_name = reset_config["task_name"] + variation = reset_config.get("variation", 0) + episode_num = reset_config.get("episode_num", 0) + + episode_path = ( + Path(data_dir) + / "data" + / split + / "eval" + / task_name + / f"variation{variation}" + / "episodes" + / f"episode{episode_num}" + ) + + episode = { + "task_file": task_name, + "task_base": str(episode_path / "task_base.ttm"), + "waypoint_sets": str(episode_path / "waypoint_sets.ttm"), + "configs": str(episode_path / "configs.pkl"), + } + description, obs_dict = env.reset(episode=episode) + + # Store reset_config for metadata + self._current_reset_config = reset_config + + return obs_dict + + def _on_step(self, env, action_text): + """Parse 7D action from action_text string and execute.""" + action = deserialize_action(action_text) + if not action or len(action) != 7: + # Random action fallback (matches ManipPlanner behavior) + voxel_size = self.simulator_kwargs.get("voxel_size", 100) + rotation_resolution = self.simulator_kwargs.get("rotation_resolution", 3) + rotation_bins = int(360 / rotation_resolution) + action = [np.random.randint(0, voxel_size) for _ in range(3)] + [ + np.random.randint(0, rotation_bins) for _ in range(3) + ] + [1.0] + return env.step(action) + + def _extract_image(self, obs): + """Extract front camera RGB from observation dict.""" + return obs["front_rgb"] + + def _extract_info(self, info): + return { + "task_success": float(info.get("task_success", 0.0)), + "action_success": float(info.get("action_success", 0.0)), + "feedback": str(info.get("env_feedback", "")), + "env_step": int(info.get("env_step", 0)), + "episode_elapsed_seconds": float( + info.get("episode_elapsed_seconds", 0.0) + ), + } + + def _make_response(self, obs, reward=0.0, done=False, info=None): + """Override to compute object coordinates and annotate images. + + This matches the EmbodiedBench evaluation pipeline: + 1. Save raw front_rgb image + 2. Compute object coordinates via form_object_coord_for_input() + 3. Annotate image with XYZ coordinate axes + 4. Annotate image with YOLO bounding boxes + 5. Pass avg_obj_coord, task_variation, task_class in metadata + """ + # First, build the standard response (saves raw image) + response = super()._make_response(obs, reward, done, info) + + # Compute object coordinates from the full observation dict + avg_obj_coord = "{}" + task_variation = "" + task_class = "" + + if self.env is not None: + task_variation = getattr(self.env, "current_task_variation", "") or "" + task_class = getattr(self.env, "task_class", "") or "" + + try: + from easi.tasks.ebmanipulation.vendor.eb_man_utils import ( + draw_bounding_boxes, + draw_xyz_coordinate, + form_object_coord_for_input, + ) + + # Compute object coordinates + coord_result = form_object_coord_for_input( + obs, task_class, self._camera_types + ) + avg_coord, all_avg_point_list, cam_ext_list, cam_int_list = ( + coord_result + ) + avg_obj_coord = str(avg_coord) + + # Annotate the saved image + rgb_path = response["observation"]["rgb_path"] + + if self._detection_box: + # Draw XYZ coordinate axes on the image + draw_xyz_coordinate(rgb_path, self._resolution) + + # Draw YOLO bounding boxes + if all_avg_point_list and cam_ext_list and cam_int_list: + annotated_paths = draw_bounding_boxes( + [rgb_path], all_avg_point_list, cam_ext_list, cam_int_list + ) + if annotated_paths: + response["observation"]["rgb_path"] = annotated_paths[0] + + except Exception as e: + logger.warning("Object coordinate extraction failed: %s", e) + + # Store metadata for prompt builder + response["observation"]["metadata"]["avg_obj_coord"] = avg_obj_coord + response["observation"]["metadata"]["task_variation"] = task_variation + response["observation"]["metadata"]["task_class"] = task_class + + return response + + +if __name__ == "__main__": + EBManipulationBridge.main() diff --git a/easi/tasks/ebmanipulation/config/manipulation_examples.json b/easi/tasks/ebmanipulation/config/manipulation_examples.json new file mode 100644 index 0000000..520a0e8 --- /dev/null +++ b/easi/tasks/ebmanipulation/config/manipulation_examples.json @@ -0,0 +1,28 @@ +{ + "pick": [ + "Human Instruction: Pick up the star and place it into the yellow container.\nInput: {'object 1': [45, 13, 18], 'object 2': [72, 20, 18], 'object 3': [50, 42, 17], 'object 4': [36, 42, 18], 'object 5': [70, 43, 17], 'object 6': [50, 77, 17]}\nOutput: {\n \"visual_state_description\": \"From left to right, I can see a purple cylinder at [45, 13, 18], a blue moon at [72, 20, 18], a green star at [50, 42, 17], a purple cube at [36, 42, 18], a yellow container at [70, 43, 17] and a purple container at [50, 77, 17].\",\n \"reasoning_and_reflection\": \"The task is to pick up the star located at [50, 42, 17] and place it into the yellow container located at [70, 43, 17]. The plan involves moving the gripper to the star's position, closing the gripper to pick it up, moving to the yellow container's position, and opening the gripper to release the cube into the container.\",\n \"executable_plan\": \"[[50, 42, 17, 6, 61, 36, 1], [50, 42, 17, 6, 61, 36, 0], [47, 41, 26, 6, 61, 36, 0], [69, 39, 24, 88, 32, 77, 0], [69, 39, 24, 87, 32, 77, 1]]\"\n}", + "Human Instruction: Pick up the star and place it into the rose container.\nInput: {'object 1': [78, 20, 17], 'object 2': [28, 30, 17], 'object 3': [39, 47, 17], 'object 4': [50, 72, 17], 'object 5': [39, 75, 18]}\nOutput: {\n \"visual_state_description\": \"From left to right, I can see a rose container at [78, 20, 17], a black star at [28, 30, 17], a green container at [39, 47, 17], a green triangular prism at [50, 72, 17] and a grey cylinder at [39, 75, 18].\",\n \"reasoning_and_reflection\": \"The task is to pick up the star located at [28, 30, 17] and place it into the rose container located at [78, 20, 17]. The plan involves moving the gripper to the star's position, closing the gripper to pick it up, moving to the rose container's position, and opening the gripper to release the cube into the container.\",\n \"executable_plan\": \"[[28, 32, 26, 0, 60, 94, 1], [27, 32, 18, 0, 60, 94, 0], [27, 32, 28, 0, 60, 94, 0], [76, 19, 28, 6, 66, 109, 1]]\"\n}" + ], + "stack": [ + "Human Instruction: Stack the red star and the olive star in sequence.\nInput: {'object 1': [42, 24, 19], 'object 2': [54, 53, 19], 'object 3': [65, 68, 19], 'object 4': [39, 73, 18]}\nOutput: {\n \"visual_state_description\": \"From left to right, I can see a purple star at [42, 24, 19], an olive star at [54, 53, 19], a grey star at [65, 68, 19] and a red star at [39, 73, 18].\",\n \"reasoning_and_reflection\": \"I need to understand the instruction first. To stack the objects in sequece, the red star at [39, 73, 18] should be at the bottom and the olive star at [54, 53, 19] should be placed on top. The plan involves moving the gripper to the olive star's position, closing the gripper to pick it up, moving to the top of the red star, and opening the gripper to release the olive star onto the red star.\",\n \"executable_plan\": \"[[54, 51, 28, 3, 66, 37, 1], [54, 54, 20, 3, 66, 37, 0], [54, 51, 30, 3, 66, 37, 0], [39, 74, 29, 3, 66, 7, 0], [39, 74, 26, 3, 66, 7, 1]]\"\n}", + "Human Instruction: Stack the red cube and the blue cube in sequence.\nInput: {'object 1': [35, 17, 17], 'object 2': [57, 47, 17], 'object 3': [33, 76, 17], 'object 4': [77, 86, 17]}\nOutput: {\n \"visual_state_description\": \"From left to right, I can see a blue cube at [35, 17, 17], a black cube at [57, 47, 17], a red cube at [33, 76, 17] and a teal cube at [77, 86, 17].\",\n \"reasoning_and_reflection\": \"I need to understand the instruction first. To stack the objects in sequece, the red cube at [33, 76, 17] should be at the bottom and the blue cube at [35, 17, 17] should be placed on top. The plan involves moving the gripper to the blue cube's position, closing the gripper to pick it up, moving to the top of the red cube, and opening the gripper to release the blue cube onto the red cube.\",\n \"executable_plan\": \"[[36, 16, 25, 0, 60, 34, 1], [36, 16, 17, 0, 60, 34, 0], [36, 16, 27, 0, 60, 34, 0], [32, 76, 24, 0, 60, 104, 0], [32, 76, 21, 0, 60, 104, 1]]\"\n}", + "Human Instruction: Stack the red moon and the maroon moon in sequence.\nInput: {'object 1': [67, 43, 19], 'object 2': [80, 47, 19], 'object 3': [34, 58, 20], 'object 4': [84, 68, 19]}\nOutput: {\n \"visual_state_description\": \"From left to right, I can see a yellow moon at [67, 43, 19], a red moon at [80, 47, 19], a maroon moon at [34, 58, 20] and a blue moon at [84, 68, 19].\",\n \"reasoning_and_reflection\": \"I need to understand the instruction first. To stack the objects in sequece, the red moon at [80, 47, 19] should be at the bottom and the maroon moon at [34, 58, 20] should be placed on top. The plan involves moving the gripper to the maroon moon's position, closing the gripper to pick it up, moving to the top of the red moon, and opening the gripper to release the maroon moon onto the red moon.\",\n \"executable_plan\": \"[[34, 58, 28, 0, 60, 25, 1], [34, 58, 20, 0, 60, 25, 0], [34, 58, 30, 0, 60, 25, 0], [83, 46, 30, 0, 60, 15, 0], [83, 46, 26, 0, 60, 15, 1]]\"\n}", + "Human Instruction: Stack the red triangular prism and the magenta triangular prism in sequence.\nInput: {'object 1': [78, 20, 19], 'object 2': [50, 25, 20], 'object 3': [55, 72, 19], 'object 4': [56, 86, 19]}\nOutput: {\n \"visual_state_description\": \"From left to right, I can see a blue triangular prism at [78, 20, 19], a magenta triangular prism at [50, 25, 20], a green triangular prism at [55, 72, 19] and a red triangular prism at [56, 86, 19].\",\n \"reasoning_and_reflection\": \"I need to understand the instruction first. To stack the objects in sequece, the red triangular prism at [56, 86, 19] should be at the bottom and the magenta triangular prism at [50, 25, 20] should be placed on top. The plan involves moving the gripper to the magenta triangular prism's position, closing the gripper to pick it up, moving to the top of the red triangular prism, and opening the gripper to release the magenta triangular prism onto the red triangular prism.\",\n \"executable_plan\": \"[[48, 25, 29, 1, 60, 92, 1], [48, 25, 21, 1, 60, 92, 0], [49, 25, 31, 1, 60, 92, 0], [54, 87, 31, 1, 60, 96, 0], [54, 87, 28, 1, 60, 96, 1]]\"\n}" + ], + "place": [ + "Human Instruction: Put the red star into the shape sorter. \nInput: {'object 1': [65, 11, 17], 'object 2': [50, 38, 17], 'object 3': [40, 64, 37], 'object 4': [45, 83, 17]}\nOutput: {\n \"visual_state_description\": \"From left to right, I can see a purple star at [65, 11, 17], a red star at [50, 38, 17], a shape sorter at [40, 64, 37] and a green star at [45, 83, 17].\",\n \"reasoning_and_reflection\": \"I need to identify the location of the star hole on the shape sorter first. I can see that the star hole is at the center of the shape sorter, which is at [40, 64, 37]. Then, the task is to pick up the red star at [50, 38, 17] and put it into the star hole on the shape sorter. The plan involves moving the gripper to the star's position, closing the gripper to pick it up, moving upwards, moving to the top of the star hole, and releasing the star into the hole.\",\n \"executable_plan\": \"[[51, 38, 25, 119, 59, 26, 1], [51, 38, 17, 119, 59, 26, 0], [51, 38, 27, 119, 59, 26, 0], [40, 65, 49, 0, 60, 45, 0], [40, 65, 41, 0, 60, 45, 1]]\"\n}", + "Human Instruction: Put the maroon star into the shape sorter. \nInput: {'object 1': [35, 30, 17], 'object 2': [29, 38, 17], 'object 3': [53, 38, 17], 'object 4': [64, 58, 37]}\nOutput: {\n \"visual_state_description\": \"From left to right, I can see a purple star at [35, 30, 17], a maroon star at [29, 38, 17], a green star at [53, 38, 17] and a shape sorter at [64, 58, 37].\",\n \"reasoning_and_reflection\": \"I need to identify the location of the star hole on the shape sorter first. I can see that the star hole is at the center of the shape sorter, which is at [64, 58, 37]. Then, the task is to pick up the maroon star at [29, 38, 17] and put it into the star hole on the shape sorter. The plan involves moving the gripper to the star's position, closing the gripper to pick it up, moving upwards, moving to the top of the star hole, and releasing the star into the hole.\",\n \"executable_plan\": \"[[29, 39, 24, 1, 61, 109, 1], [29, 38, 16, 1, 61, 109, 0], [30, 40, 26, 1, 61, 109, 0], [65, 60, 48, 0, 61, 54, 0], [65, 60, 40, 0, 61, 54, 1]]\"\n}", + "Human Instruction: Put the lime star into the shape sorter. \nInput: {'object 1': [47, 13, 17], 'object 2': [27, 36, 16], 'object 3': [38, 50, 17], 'object 4': [54, 69, 37]}\nOutput: {\n \"visual_state_description\": \"From left to right, I can see a grey star at [47, 13, 17], a purple star at [27, 36, 16], a lime star at [38, 50, 17] and a shape sorter at [54, 69, 37].\",\n \"reasoning_and_reflection\": \"I need to identify the location of the star hole on the shape sorter first. I can see that the star hole is at the center of the shape sorter, which is at [54, 69, 37]. Then, the task is to pick up the lime star at [38, 50, 17] and put it into the star hole on the shape sorter. The plan involves moving the gripper to the star's position, closing the gripper to pick it up, moving upwards, moving to the top of the star hole, and releasing the star into the hole.\",\n \"executable_plan\": \"[[38, 49, 23, 0, 60, 108, 1], [38, 49, 15, 0, 60, 108, 0], [38, 49, 25, 0, 60, 108, 0], [54, 69, 47, 0, 60, 3, 0], [54, 69, 39, 0, 60, 3, 1]]\"\n}", + "Human Instruction: Put the green star into the shape sorter. \nInput: {'object 1': [70, 27, 37], 'object 2': [66, 59, 17], 'object 3': [38, 63, 17], 'object 4': [63, 77, 17]}\nOutput: {\n \"visual_state_description\": \"From left to right, I can see a shape sorter at [70, 27, 37], a white star at [66, 59, 17], a green star at [38, 63, 17] and a white star at [63, 77, 17].\",\n \"reasoning_and_reflection\": \"I need to identify the location of the star hole on the shape sorter first. I can see that the star hole is at the center of the shape sorter, which is at [70, 27, 37]. Then, the task is to pick up the green star at [38, 63, 17] and put it into the star hole on the shape sorter. The plan involves moving the gripper to the star's position, closing the gripper to pick it up, moving upwards, moving to the top of the star hole, and releasing the star into the hole.\",\n \"executable_plan\": \"[[39, 62, 23, 0, 60, 100, 1], [39, 62, 15, 0, 60, 100, 0], [39, 62, 25, 0, 60, 100, 0], [71, 30, 47, 0, 60, 37, 0], [71, 30, 39, 0, 60, 37, 1]]\"\n}" + ], + "wipe": [ + "Human Instruction: Wipe the horizontal area.\nInput: {'object 1': [33, 36, 19], 'object 2': [52, 38, 15], 'object 3': [59, 68, 15]}\nOutput: {\n \"visual_state_description\": \"From left to right, I can see a yellow sponge at [33, 36, 19], a green rectangle area at [52, 38, 15] and an orange rectangle area at [59, 68, 15].\",\n \"reasoning_and_reflection\": \"I need to identify which area is horizontal first. I can see that the orange rectangle area at [59, 68, 15] is horizontal since it is parallel to the green arrow representing the y-axis of the coordinate system annotated in the image. Then, the task is to pick up the sponge at [33, 36, 19] and use it to wipe the horizontal area at [59, 68, 15]. The plan involves moving the gripper to the sponge's position, closing the gripper to pick it up, moving to the side of the horizontal area, and moving the sponge along the main direction of the horizontal area to wipe it.\",\n \"executable_plan\": \"[[32, 34, 25, 0, 60, 34, 1], [32, 34, 17, 0, 60, 34, 0], [32, 34, 27, 0, 60, 34, 0], [60, 80, 18, 0, 61, 31, 0], [61, 54, 17, 0, 61, 31, 0]]\"\n}", + "Human Instruction: Wipe the horizontal area.\nInput: {'object 1': [56, 19, 15], 'object 2': [35, 41, 19], 'object 3': [54, 47, 15]}\nOutput: {\n \"visual_state_description\": \"From left to right, I can see a black rectangle area at [56, 19, 15], a yellow sponge at [35, 41, 19] and a white rectangle area at [54, 47, 15].\",\n \"reasoning_and_reflection\": \"I need to identify which area is horizontal first. I can see that the black rectangle area at [56, 19, 15] is horizontal since it is parallel to the green arrow representing the y-axis of the coordinate system annotated in the image. Then, the task is to pick up the sponge at [35, 41, 19] and use it to wipe the horizontal area at [56, 19, 15]. The plan involves moving the gripper to the sponge's position, closing the gripper to pick it up, moving to the right side of the horizontal area, and moving the sponge along the main direction of the horizontal area to wipe it.\",\n \"executable_plan\": \"[[33, 43, 25, 0, 60, 32, 1], [33, 43, 17, 0, 60, 32, 0], [33, 43, 27, 0, 60, 32, 0], [58, 35, 18, 0, 60, 28, 0], [56, 8, 18, 0, 60, 28, 0]]\"\n}", + "Human Instruction: Wipe the horizontal area.\nInput: {'object 1': [35, 33, 19], 'object 2': [51, 41, 15], 'object 3': [67, 63, 15]}\nOutput: {\n \"visual_state_description\": \"From left to right, I can see a yellow sponge at [35, 33, 19], a yellow triangle area at [51, 41, 15] and a blue triangle area at [67, 63, 15].\",\n \"reasoning_and_reflection\": \"I need to identify which area is horizontal first. I can see that the yellow triangle area at [51, 41, 15] is horizontal since it is parallel to the green arrow representing the y-axis of the coordinate system annotated in the image. Then, the task is to pick up the sponge at [35, 33, 19] and use it to wipe the horizontal area at [51, 41, 15]. The plan involves moving the gripper to the sponge's position, closing the gripper to pick it up, moving to the right side of the horizontal area, and moving the sponge along the main direction of the horizontal area to wipe it.\",\n \"executable_plan\": \"[[35, 34, 25, 0, 60, 20, 1], [35, 34, 17, 0, 60, 20, 0], [35, 34, 27, 0, 60, 20, 0], [52, 47, 18, 0, 60, 84, 0], [44, 19, 18, 0, 60, 84, 0]]\"\n}", + "Human Instruction: Wipe the horizontal area.\nInput: {'object 1': [47, 34, 18], 'object 2': [72, 53, 15], 'object 3': [54, 81, 15]}\nOutput: {\n \"visual_state_description\": \"From left to right, I can see a yellow sponge at [47, 34, 18], a yellow triangle area at [72, 53, 15] and a green triangle area at [54, 81, 15].\",\n \"reasoning_and_reflection\": \"I need to identify which area is horizontal first. I can see that the yellow triangle area at [72, 53, 15] is horizontal since it is parallel to the green arrow representing the y-axis of the coordinate system annotated in the image. Then, the task is to pick up the sponge at [47, 34, 18] and use it to wipe the horizontal area at [72, 53, 15]. The plan involves moving the gripper to the sponge's position, closing the gripper to pick it up, moving to the right side of the horizontal area, and moving the sponge along the main direction of the horizontal area to wipe it.\",\n \"executable_plan\": \"[[46, 31, 24, 0, 60, 18, 1], [46, 31, 16, 0, 60, 18, 0], [46, 31, 26, 0, 60, 18, 0], [72, 63, 17, 0, 60, 88, 0], [69, 37, 17, 0, 60, 88, 0]]\"\n}", + "Human Instruction: Wipe the vertical area.\nInput: \"{'object 1': [55, 28, 15], 'object 2': [42, 51, 19], 'object 3': [32, 63, 15]}\"\nOutput: {\n \"visual_state_description\": \"From left to right, I can see a white rectangle area at [55, 28, 15], a yellow sponge at [42, 51, 19] and a blue rectangle area at [32, 63, 15].\",\n \"reasoning_and_reflection\": \"I need to identify which area is vertical first. I can see that the white rectangle area at [55, 28, 15] is vertical since it is parallel to the red arrow representing the x-axis of the coordinate system annotated in the image. Then, the task is to pick up the sponge at [42, 51, 19] and use it to wipe the vertical area at [55, 28, 15]. The plan involves moving the gripper to the sponge's position, closing the gripper to pick it up, moving to the top side of the vertical area, and moving the sponge along the main direction of the vertical area to wipe it.\",\n \"executable_plan\": \"[[40, 48, 25, 0, 60, 12, 1], [40, 48, 17, 0, 60, 12, 0], [40, 48, 27, 0, 60, 12, 0], [38, 25, 17, 0, 59, 0, 0], [69, 25, 17, 0, 59, 0, 0]]\"\n}", + "Human Instruction: Wipe the vertical area.\nInput: {'object 1': [53, 31, 19], 'object 2': [66, 34, 15], 'object 3': [59, 73, 15]}\nOutput: {\n \"visual_state_description\": \"From left to right, I can see a yellow sponge at [53, 31, 19], a blue rectangle area at [66, 34, 15] and a green rectangle area at [59, 73, 15].\",\n \"reasoning_and_reflection\": \"I need to identify which area is vertical first. I can see that the blue rectangle area at [66, 34, 15] is vertical since it is parallel to the red arrow representing the x-axis of the coordinate system annotated in the image. Then, the task is to pick up the sponge at [53, 31, 19] and use it to wipe the vertical area at [66, 34, 15]. The plan involves moving the gripper to the sponge's position, closing the gripper to pick it up, moving to the top side of the vertical area, and moving the sponge along the main direction of the vertical area to wipe it.\",\n \"executable_plan\": \"[[52, 28, 26, 0, 60, 94, 1], [52, 28, 18, 0, 60, 94, 0], [52, 28, 28, 0, 60, 94, 0]], [46, 32, 18, 0, 60, 68, 0], [78, 35, 18, 0, 60, 75, 0]]\"\n}", + "Human Instruction: Wipe the vertical area.\nInput: {'object 1': [37, 26, 15], 'object 2': [57, 49, 15], 'object 3': [56, 73, 18]}\nOutput: {\n \"visual_state_description\": \"From left to right, I can see a white triangle area at [37, 26, 15], a black triangle area at [57, 49, 15] and a yellow sponge at [56, 73, 18].\",\n \"reasoning_and_reflection\": \"I need to identify which area is vertical first. I can see that the white triangle area at [37, 26, 15] is vertical since it is parallel to the red arrow representing the x-axis of the coordinate system annotated in the image. Then, the task is to pick up the sponge at [56, 73, 18] and use it to wipe the vertical area at [37, 26, 15]. The plan involves moving the gripper to the sponge's position, closing the gripper to pick it up, moving to the top side of the vertical area, and moving the sponge along the main direction of the vertical area to wipe it.\",\n \"executable_plan\": \"[[55, 70, 25, 0, 60, 26, 1], [55, 70, 17, 0, 60, 26, 0], [55, 70, 27, 0, 60, 26, 0], [35, 26, 18, 0, 60, 55, 0], [62, 19, 18, 0, 60, 55, 0]]\"\n}", + "Human Instruction: Wipe the vertical area.\nInput: {'object 1': [64, 21, 15], 'object 2': [34, 39, 15], 'object 3': [51, 43, 19]}\nOutput: {\n \"visual_state_description\": \"From left to right, I can see a green triangle area at [64, 21, 15], a red triangle area at [34, 39, 15] and a yellow sponge at [51, 43, 19].\",\n \"reasoning_and_reflection\": \"I need to identify which area is vertical first. I can see that the green triangle area at [64, 21, 15] is vertical since it is parallel to the red arrow representing the x-axis of the coordinate system annotated in the image. Then, the task is to pick up the sponge at [51, 43, 19] and use it to wipe the vertical area at [64, 21, 15]. The plan involves moving the gripper to the sponge's position, closing the gripper to pick it up, moving to the top side of the vertical area, and moving the sponge along the main direction of the vertical area to wipe it.\",\n \"executable_plan\": \"[[44, 46, 25, 116, 62, 97, 1], [45, 45, 17, 116, 62, 97, 0], [43, 46, 27, 116, 62, 97, 0], [50, 13, 18, 115, 60, 13, 0], [75, 26, 18, 115, 60, 99, 0]]\"\n}" + ] +} \ No newline at end of file diff --git a/easi/tasks/ebmanipulation/ebmanipulation_base.yaml b/easi/tasks/ebmanipulation/ebmanipulation_base.yaml new file mode 100644 index 0000000..781caab --- /dev/null +++ b/easi/tasks/ebmanipulation/ebmanipulation_base.yaml @@ -0,0 +1,42 @@ +name: ebmanipulation_base +display_name: "EB-Manipulation Base Split" +description: "Base evaluation split for EB-Manipulation (robotic manipulation with 7D discrete gripper actions)" +simulator: "coppeliasim:v4_1_0" +task_class: "easi.tasks.ebmanipulation.task.EBManipulationTask" +action_space: [] +max_steps: 15 +dataset: + source: huggingface + repo_id: "oscarqjh/EB-Manipulation_easi" + subset: null + split: "base" + zip_files: + - "simulator_data.zip" +simulator_configs: + screen_height: 500 + screen_width: 500 + max_steps: 15 + voxel_size: 100 + rotation_resolution: 3 + scene_bounds: [-0.3, -0.5, 0.6, 0.7, 0.5, 1.6] + amsolver_parent_path: "" + detection_box: true + additional_deps: + - "gymnasium" + - "ultralytics" + - "open3d" + env_vars: + QT_QPA_PLATFORM: "xcb" +agent: + prompt_builder: "easi.tasks.ebmanipulation.prompts.EBManipulationPromptBuilder" + prompt_builder_kwargs: + n_shot: 10 + split: "base" + use_feedback: true + chat_history: false + generation_kwargs: + temperature: 0 + max_tokens: 2048 + top_k: 50 + top_p: 0.95 + do_sample: false diff --git a/easi/tasks/ebmanipulation/ebmanipulation_common_sense.yaml b/easi/tasks/ebmanipulation/ebmanipulation_common_sense.yaml new file mode 100644 index 0000000..02730be --- /dev/null +++ b/easi/tasks/ebmanipulation/ebmanipulation_common_sense.yaml @@ -0,0 +1,42 @@ +name: ebmanipulation_common_sense +display_name: "EB-Manipulation Common Sense Split" +description: "Common sense evaluation split for EB-Manipulation (tasks requiring commonsense reasoning)" +simulator: "coppeliasim:v4_1_0" +task_class: "easi.tasks.ebmanipulation.task.EBManipulationTask" +action_space: [] +max_steps: 15 +dataset: + source: huggingface + repo_id: "oscarqjh/EB-Manipulation_easi" + subset: null + split: "common_sense" + zip_files: + - "simulator_data.zip" +simulator_configs: + screen_height: 500 + screen_width: 500 + max_steps: 15 + voxel_size: 100 + rotation_resolution: 3 + scene_bounds: [-0.3, -0.5, 0.6, 0.7, 0.5, 1.6] + amsolver_parent_path: "" + detection_box: true + additional_deps: + - "gymnasium" + - "ultralytics" + - "open3d" + env_vars: + QT_QPA_PLATFORM: "xcb" +agent: + prompt_builder: "easi.tasks.ebmanipulation.prompts.EBManipulationPromptBuilder" + prompt_builder_kwargs: + n_shot: 10 + split: "common_sense" + use_feedback: true + chat_history: false + generation_kwargs: + temperature: 0 + max_tokens: 2048 + top_k: 50 + top_p: 0.95 + do_sample: false diff --git a/easi/tasks/ebmanipulation/ebmanipulation_complex.yaml b/easi/tasks/ebmanipulation/ebmanipulation_complex.yaml new file mode 100644 index 0000000..ebc26f3 --- /dev/null +++ b/easi/tasks/ebmanipulation/ebmanipulation_complex.yaml @@ -0,0 +1,42 @@ +name: ebmanipulation_complex +display_name: "EB-Manipulation Complex Split" +description: "Complex instruction evaluation split for EB-Manipulation (multi-step manipulation instructions)" +simulator: "coppeliasim:v4_1_0" +task_class: "easi.tasks.ebmanipulation.task.EBManipulationTask" +action_space: [] +max_steps: 15 +dataset: + source: huggingface + repo_id: "oscarqjh/EB-Manipulation_easi" + subset: null + split: "complex" + zip_files: + - "simulator_data.zip" +simulator_configs: + screen_height: 500 + screen_width: 500 + max_steps: 15 + voxel_size: 100 + rotation_resolution: 3 + scene_bounds: [-0.3, -0.5, 0.6, 0.7, 0.5, 1.6] + amsolver_parent_path: "" + detection_box: true + additional_deps: + - "gymnasium" + - "ultralytics" + - "open3d" + env_vars: + QT_QPA_PLATFORM: "xcb" +agent: + prompt_builder: "easi.tasks.ebmanipulation.prompts.EBManipulationPromptBuilder" + prompt_builder_kwargs: + n_shot: 10 + split: "complex" + use_feedback: true + chat_history: false + generation_kwargs: + temperature: 0 + max_tokens: 2048 + top_k: 50 + top_p: 0.95 + do_sample: false diff --git a/easi/tasks/ebmanipulation/ebmanipulation_spatial.yaml b/easi/tasks/ebmanipulation/ebmanipulation_spatial.yaml new file mode 100644 index 0000000..7b3d9cd --- /dev/null +++ b/easi/tasks/ebmanipulation/ebmanipulation_spatial.yaml @@ -0,0 +1,42 @@ +name: ebmanipulation_spatial +display_name: "EB-Manipulation Spatial Split" +description: "Spatial reasoning evaluation split for EB-Manipulation (tasks with relative spatial references)" +simulator: "coppeliasim:v4_1_0" +task_class: "easi.tasks.ebmanipulation.task.EBManipulationTask" +action_space: [] +max_steps: 15 +dataset: + source: huggingface + repo_id: "oscarqjh/EB-Manipulation_easi" + subset: null + split: "spatial" + zip_files: + - "simulator_data.zip" +simulator_configs: + screen_height: 500 + screen_width: 500 + max_steps: 15 + voxel_size: 100 + rotation_resolution: 3 + scene_bounds: [-0.3, -0.5, 0.6, 0.7, 0.5, 1.6] + amsolver_parent_path: "" + detection_box: true + additional_deps: + - "gymnasium" + - "ultralytics" + - "open3d" + env_vars: + QT_QPA_PLATFORM: "xcb" +agent: + prompt_builder: "easi.tasks.ebmanipulation.prompts.EBManipulationPromptBuilder" + prompt_builder_kwargs: + n_shot: 10 + split: "spatial" + use_feedback: true + chat_history: false + generation_kwargs: + temperature: 0 + max_tokens: 2048 + top_k: 50 + top_p: 0.95 + do_sample: false diff --git a/easi/tasks/ebmanipulation/ebmanipulation_visual.yaml b/easi/tasks/ebmanipulation/ebmanipulation_visual.yaml new file mode 100644 index 0000000..b5fc2ad --- /dev/null +++ b/easi/tasks/ebmanipulation/ebmanipulation_visual.yaml @@ -0,0 +1,42 @@ +name: ebmanipulation_visual +display_name: "EB-Manipulation Visual Split" +description: "Visual appearance evaluation split for EB-Manipulation (tasks requiring visual property recognition)" +simulator: "coppeliasim:v4_1_0" +task_class: "easi.tasks.ebmanipulation.task.EBManipulationTask" +action_space: [] +max_steps: 15 +dataset: + source: huggingface + repo_id: "oscarqjh/EB-Manipulation_easi" + subset: null + split: "visual" + zip_files: + - "simulator_data.zip" +simulator_configs: + screen_height: 500 + screen_width: 500 + max_steps: 15 + voxel_size: 100 + rotation_resolution: 3 + scene_bounds: [-0.3, -0.5, 0.6, 0.7, 0.5, 1.6] + amsolver_parent_path: "" + detection_box: true + additional_deps: + - "gymnasium" + - "ultralytics" + - "open3d" + env_vars: + QT_QPA_PLATFORM: "xcb" +agent: + prompt_builder: "easi.tasks.ebmanipulation.prompts.EBManipulationPromptBuilder" + prompt_builder_kwargs: + n_shot: 10 + split: "visual" + use_feedback: true + chat_history: false + generation_kwargs: + temperature: 0 + max_tokens: 2048 + top_k: 50 + top_p: 0.95 + do_sample: false diff --git a/easi/tasks/ebmanipulation/prompts.py b/easi/tasks/ebmanipulation/prompts.py new file mode 100644 index 0000000..fd11e72 --- /dev/null +++ b/easi/tasks/ebmanipulation/prompts.py @@ -0,0 +1,547 @@ +"""EB-Manipulation prompt builder matching EmbodiedBench ManipPlanner. + +Reference: EmbodiedBench/embodiedbench/planner/manip_planner.py + EmbodiedBench/embodiedbench/evaluator/config/system_prompts.py:50-73 + EmbodiedBench/embodiedbench/evaluator/config/eb_manipulation_example.py + EmbodiedBench/embodiedbench/planner/planner_utils.py:41-50 + +Key differences from EB-Alfred/Navigation prompt builders: +- Actions are 7D discrete arrays, not {action_id, action_name} +- executable_plan is a string (not a list of objects) +- Examples are per-task-type (pick/stack/place/wipe) +- System prompt has 4 numeric format placeholders for discretization params +- Response schema executable_plan is type:string (not type:array) +""" +from __future__ import annotations + +import ast +import json +from pathlib import Path + +from easi.agents.prompt_builder import _encode_image_base64 +from easi.core.episode import Action +from easi.core.memory import AgentMemory +from easi.tasks.ebmanipulation.actions import ( + DEFAULT_ROTATION_RESOLUTION, + DEFAULT_VOXEL_SIZE, + extract_pose_list, + serialize_action, +) +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + +# System prompt from EmbodiedBench (eb_manipulation_system_prompt). +# Five format placeholders: VOXEL_SIZE, VOXEL_SIZE, 360/ROTATION_RESOLUTION, +# ROTATION_RESOLUTION, examples_string +MANIPULATION_SYSTEM_PROMPT = ( + "## You are a Franka Panda robot with a parallel gripper. " + "You can perform various tasks and output a sequence of gripper actions " + "to accomplish a given task with images of your status. The input space, " + "output action space and color space are defined as follows:\n" + "\n" + "** Input Space **\n" + "- Each input object is represented as a 3D discrete position in the " + "following format: [X, Y, Z]. \n" + "- There is a red XYZ coordinate frame located in the top-left corner " + "of the table. The X-Y plane is the table surface. \n" + "- The allowed range of X, Y, Z is [0, {}]. \n" + "- Objects are ordered by Y in ascending order.\n" + "\n" + "** Output Action Space **\n" + "- Each output action is represented as a 7D discrete gripper action " + "in the following format: [X, Y, Z, Roll, Pitch, Yaw, Gripper state].\n" + "- X, Y, Z are the 3D discrete position of the gripper in the " + "environment. It follows the same coordinate system as the input " + "object coordinates.\n" + "- The allowed range of X, Y, Z is [0, {}].\n" + "- Roll, Pitch, Yaw are the 3D discrete orientation of the gripper " + "in the environment, represented as discrete Euler Angles. \n" + "- The allowed range of Roll, Pitch, Yaw is [0, {}] and each unit " + "represents {} degrees.\n" + "- Gripper state is 0 for close and 1 for open.\n" + "\n" + "** Color space **\n" + "- Each object can be described using one of the colors below:\n" + ' ["red", "maroon", "lime", "green", "blue", "navy", "yellow", ' + '"cyan", "magenta", "silver", "gray", "olive", "purple", "teal", ' + '"azure", "violet", "rose", "black", "white"],\n' + "\n" + "Below are some examples to guide you in completing the task. \n" + "\n" + "{}\n" +) + +# Output template from planner_utils.py template_manip (lines 41-50). +# Note: executable_plan is type STRING (list of 7D arrays serialized as text). +OUTPUT_TEMPLATE_MANIP = ( + "The output json format should be " + "{'visual_state_description':str, 'reasoning_and_reflection':str, " + "'language_plan':str, 'executable_plan':str}\n" + "The fields in above JSON follows the purpose below:\n" + "1. visual_state_description: Describe the color and shape of each " + "object in the detection box in the numerical order in the image. " + "Then provide the 3D coordinates of the objects chosen from input.\n" + "2. reasoning_and_reflection: Reason about the overall plan that " + "needs to be taken on the target objects, and reflect on the previous " + "actions taken if available.\n" + "3. language_plan: Natural language actions to achieve the user " + "instruction. Each language action is started by the step number " + "and the language action name.\n" + "4. executable_plan: A list of discrete actions needed to achieve " + "the user instruction, with each discrete action being a " + "7-dimensional discrete action.\n" + "5. keep your plan efficient and concise.\n" + "!!! When generating content for JSON strings, avoid using any " + "contractions or abbreviated forms (like 's, 're, 've, 'll, 'd, " + "n't) that use apostrophes. Instead, write out full forms (is, are, " + "have, will, would, not) to prevent parsing errors in JSON. Please " + "do not output any other thing more than the above-mentioned JSON, " + "do not include ```json and ```!!!.\n" +) + +# JSON schema for structured output -- executable_plan is STRING, not array. +EBMANIPULATION_RESPONSE_SCHEMA = { + "type": "json_schema", + "json_schema": { + "name": "manipulation_planning", + "schema": { + "type": "object", + "properties": { + "visual_state_description": { + "type": "string", + "description": ( + "Describe the color and shape of each object in the " + "detection box in the numerical order in the image. " + "Then provide the 3D coordinates of the objects " + "chosen from input." + ), + }, + "reasoning_and_reflection": { + "type": "string", + "description": ( + "Reason about the overall plan that needs to be taken " + "on the target objects, and reflect on the previous " + "actions taken if available." + ), + }, + "language_plan": { + "type": "string", + "description": ( + "A list of natural language actions to achieve the " + "user instruction. Each language action is started by " + "the step number and the language action name." + ), + }, + "executable_plan": { + "type": "string", + "description": ( + "A list of discrete actions needed to achieve the " + "user instruction, with each discrete action being a " + "7-dimensional discrete action." + ), + }, + }, + "required": [ + "visual_state_description", + "reasoning_and_reflection", + "language_plan", + "executable_plan", + ], + }, + }, +} + +_CONFIG_DIR = Path(__file__).parent / "config" + + +class EBManipulationPromptBuilder: + """Prompt builder for EB-Manipulation matching ManipPlanner output. + + Key differences from EB-Alfred/Navigation prompt builders: + - Actions are 7D arrays, not named actions with IDs + - Examples are per-task-type (selected by task_variation) + - executable_plan in response is a STRING of 7D arrays + - parse_response() extracts 7D arrays and serializes each as Action.action_name + """ + + def __init__( + self, + n_shot: int = 10, + split: str = "base", + use_feedback: bool = True, + chat_history: bool = False, + voxel_size: int = DEFAULT_VOXEL_SIZE, + rotation_resolution: int = DEFAULT_ROTATION_RESOLUTION, + ): + self.n_shot = n_shot + self.split = split + self.use_feedback = use_feedback + self.chat_history = chat_history + self.voxel_size = voxel_size + self.rotation_resolution = rotation_resolution + + # Load per-task-type examples + examples_file = _CONFIG_DIR / "manipulation_examples.json" + with open(examples_file) as f: + self._examples: dict[str, list[str]] = json.load(f) + + def set_action_space(self, actions: list[str]) -> None: + """No-op -- EB-Manipulation has no fixed named action space.""" + pass + + # ---- PromptBuilderProtocol methods ---- + + def build_messages(self, memory: AgentMemory) -> list[dict]: + """Build COMPLETE message list to send to LLM.""" + # Determine task variation from observation metadata + task_variation = "" + if memory.current_observation and memory.current_observation.metadata: + task_variation = memory.current_observation.metadata.get( + "task_variation", "" + ) + + if self.chat_history: + return self._build_chat_history_messages(memory, task_variation) + return self._build_stateless_messages(memory, task_variation) + + def parse_response(self, llm_response: str, memory: AgentMemory) -> list[Action]: + """Parse LLM response into 7D actions. + + ManipPlanner.json_to_action() extracts executable_plan (string of 7D arrays). + Each 7D array becomes an Action with action_name = serialized array string. + """ + from easi.utils.json_repair import fix_json + + llm_response = fix_json(llm_response) + + try: + json_object = json.loads(llm_response) + except json.JSONDecodeError as e: + logger.warning("Failed to parse LLM response as JSON: %s", e) + return [] + + executable_plan = json_object.get("executable_plan") + if executable_plan is None: + # Fallback: try "properties.language_plan" (ManipPlanner line 306) + try: + executable_plan = json_object["properties"]["language_plan"] + except (KeyError, TypeError): + logger.warning("No executable_plan in LLM response") + return [] + + # executable_plan can be a string (expected) or already a list + if isinstance(executable_plan, str): + try: + executable_plan = ast.literal_eval(executable_plan) + except Exception: + # Try extract_pose_list as fallback + poses = extract_pose_list(executable_plan) + if poses: + return [ + Action(action_name=serialize_action(p)) for p in poses + ] + logger.warning("Could not parse executable_plan string") + return [] + + if not isinstance(executable_plan, list) or not executable_plan: + logger.warning("Empty executable_plan") + return [] + + actions = [] + for x in executable_plan: + if isinstance(x, tuple): + x = list(x) + # Extract action list from various formats (ManipPlanner lines 329-341) + if isinstance(x, dict): + list_action = x.get("action", x) + elif isinstance(x, list) and len(x) > 0 and isinstance(x[0], (int, float)): + list_action = x + else: + list_action = x + + if isinstance(list_action, str): + poses = extract_pose_list(list_action) + for pose in poses: + actions.append(Action(action_name=serialize_action(pose))) + continue + + if isinstance(list_action, list) and len(list_action) == 7: + actions.append(Action(action_name=serialize_action(list_action))) + elif isinstance(list_action, list): + for action_single in list_action: + if isinstance(action_single, (list, tuple)) and len(action_single) == 7: + actions.append( + Action(action_name=serialize_action(list(action_single))) + ) + + return actions + + def get_response_format(self, memory: AgentMemory) -> dict: + """Return JSON schema for API-level enforcement.""" + return EBMANIPULATION_RESPONSE_SCHEMA + + # ---- Stateless mode (chat_history=False) ---- + + def _build_stateless_messages( + self, memory: AgentMemory, task_variation: str + ) -> list[dict]: + """Build full prompt each turn. No history accumulation. + + Reference: ManipPlanner.process_prompt() + ManipPlanner.act() + """ + prompt, task_prompt = self._process_prompt( + memory.task_description, + self._get_avg_obj_coord(memory), + task_variation, + prev_act_feedback=self._build_feedback_list(memory), + ) + + # Append template_manip (ManipPlanner.act() line 433-435) + task_prompt += "\n\n" + OUTPUT_TEMPLATE_MANIP + + return self._build_message(prompt, task_prompt, memory) + + # ---- Chat history mode (chat_history=True) ---- + + def _build_chat_history_messages( + self, memory: AgentMemory, task_variation: str + ) -> list[dict]: + """Build messages with chat history accumulation. + + Reference: ManipPlanner.process_prompt() chat_history branch + """ + if memory.is_first_turn: + return self._build_stateless_messages(memory, task_variation) + + # Subsequent turns: rebuild message history + messages: list[dict] = [] + + for step_idx, step in enumerate(memory.steps): + if step.llm_response is None: + continue + + # Build prompt for this step + history_up_to = [ + ( + step.action.action_name if step.action else "", + step.feedback or "", + ) + for s_idx, s in enumerate(memory.steps[:step_idx]) + if s.action and s.feedback is not None + ] + + if step_idx == 0: + prompt, task_prompt = self._process_prompt( + memory.task_description, + self._get_avg_obj_coord(memory), + task_variation, + prev_act_feedback=[], + ) + else: + prompt, task_prompt = self._process_prompt( + memory.task_description, + self._get_avg_obj_coord(memory), + task_variation, + prev_act_feedback=[(a, f) for a, f in history_up_to], + ) + + task_prompt += "\n\n" + OUTPUT_TEMPLATE_MANIP + + # User message with image + user_msg = self._wrap_as_user_message( + prompt + task_prompt, step.observation + ) + messages.extend(user_msg) + + # Assistant message + messages.append( + { + "role": "assistant", + "content": [{"type": "text", "text": step.llm_response}], + } + ) + + # Current turn + all_feedback = self._build_feedback_list(memory) + prompt, task_prompt = self._process_prompt( + memory.task_description, + self._get_avg_obj_coord(memory), + task_variation, + prev_act_feedback=all_feedback, + ) + task_prompt += "\n\n" + OUTPUT_TEMPLATE_MANIP + + messages.extend( + self._wrap_as_user_message( + prompt + task_prompt, memory.current_observation + ) + ) + + return messages + + # ---- Prompt construction (matching ManipPlanner.process_prompt) ---- + + def _process_prompt( + self, + user_instruction: str, + avg_obj_coord: str, + task_variation: str, + prev_act_feedback: list[tuple[str, str]] | None = None, + ) -> tuple[str, str]: + """Build system prompt + task prompt. + + Reference: ManipPlanner.process_prompt() lines 48-70 + """ + user_instruction = user_instruction.rstrip(".") + prev_act_feedback = prev_act_feedback or [] + + rotation_bins = int(360 / self.rotation_resolution) + + # Get task type for example selection (e.g., "pick" from "pick_cube_shape") + task_type = task_variation.split("_")[0] if task_variation else "" + + if len(prev_act_feedback) == 0: + # First turn (ManipPlanner lines 50-55) + examples_for_task = self._examples.get(task_type, []) + if self.n_shot >= 1 and examples_for_task: + examples_str = "\n".join( + [ + f"Example {i}: \n{x}" + for i, x in enumerate(examples_for_task[: self.n_shot]) + ] + ) + general_prompt = MANIPULATION_SYSTEM_PROMPT.format( + self.voxel_size, + self.voxel_size, + rotation_bins, + self.rotation_resolution, + examples_str, + ) + else: + general_prompt = MANIPULATION_SYSTEM_PROMPT.format( + self.voxel_size, + self.voxel_size, + rotation_bins, + self.rotation_resolution, + "", + ) + task_prompt = ( + f"\n## Now you are supposed to follow the above examples to " + f"generate a sequence of discrete gripper actions that completes " + f"the below human instruction. \n" + f"Human Instruction: {user_instruction}.\n" + f"Input: {avg_obj_coord}\n" + f"Output gripper actions: " + ) + + elif self.chat_history: + # Chat history subsequent turn (ManipPlanner lines 56-61) + general_prompt = f"The human instruction is: {user_instruction}." + general_prompt += "\n\n The gripper action history:" + for i, (action, feedback) in enumerate(prev_act_feedback): + general_prompt += ( + "\n Step {}, the output action **{}**, env feedback: {}".format( + i, action, feedback + ) + ) + task_prompt = ( + f"\n\n Considering the above interaction history and the current " + f"image state, to achieve the human instruction: '{user_instruction}', " + f"you are supposed to output in json. You need to describe current " + f"visual state from the image, summarize interaction history and " + f"environment feedback and reason why the last action or plan failed " + f"and did not finish the task, output your new plan to achieve the " + f"goal from current state. At the end, output the executable plan " + f"with the 7-dimsension action." + ) + + else: + # Stateless subsequent turn (ManipPlanner lines 62-69) + examples_for_task = self._examples.get(task_type, []) + if self.n_shot >= 1 and examples_for_task: + examples_str = "\n".join( + [ + f"Example {i}: \n{x}" + for i, x in enumerate(examples_for_task[: self.n_shot]) + ] + ) + general_prompt = MANIPULATION_SYSTEM_PROMPT.format( + self.voxel_size, + self.voxel_size, + rotation_bins, + self.rotation_resolution, + examples_str, + ) + else: + general_prompt = MANIPULATION_SYSTEM_PROMPT.format( + self.voxel_size, + self.voxel_size, + rotation_bins, + self.rotation_resolution, + "", + ) + task_prompt = ( + f"\n## Now you are supposed to follow the above examples to " + f"generate a sequence of discrete gripper actions that completes " + f"the below human instruction. \n" + f"Human Instruction: {user_instruction}.\n" + f"Input: {avg_obj_coord}\n" + f"Output gripper actions: " + ) + for i, action_feedback in enumerate(prev_act_feedback): + task_prompt += f"{action_feedback}, " + + return general_prompt, task_prompt + + # ---- Helpers ---- + + def _get_avg_obj_coord(self, memory: AgentMemory) -> str: + """Get object coordinates from observation metadata. + + Object coordinates are computed by the bridge and stored in + observation metadata. + """ + if memory.current_observation and memory.current_observation.metadata: + return memory.current_observation.metadata.get("avg_obj_coord", "{}") + return "{}" + + def _build_feedback_list( + self, memory: AgentMemory + ) -> list[tuple[str, str]]: + """Build action + feedback list from memory.""" + feedback = [] + for action_name, fb in memory.action_history: + feedback.append((action_name, fb)) + return feedback + + def _build_message( + self, prompt: str, task_prompt: str, memory: AgentMemory + ) -> list[dict]: + """Build user message with image and prompt text. + + Reference: ManipPlanner.get_message() -- text first, then image. + """ + full_prompt = prompt + task_prompt + return self._wrap_as_user_message(full_prompt, memory.current_observation) + + @staticmethod + def _wrap_as_user_message(prompt: str, observation) -> list[dict]: + """Wrap prompt text + observation image as a user message. + + ManipPlanner: text BEFORE image (opposite of VLMPlanner). + Reference: ManipPlanner.get_message() lines 191-216 + """ + content: list[dict] = [{"type": "text", "text": prompt}] + + if observation and observation.rgb_path: + image_url = _encode_image_base64(observation.rgb_path) + if image_url: + content.append( + { + "type": "image_url", + "image_url": {"url": image_url}, + } + ) + + return [{"role": "user", "content": content}] diff --git a/easi/tasks/ebmanipulation/task.py b/easi/tasks/ebmanipulation/task.py new file mode 100644 index 0000000..b834bdb --- /dev/null +++ b/easi/tasks/ebmanipulation/task.py @@ -0,0 +1,101 @@ +"""EB-Manipulation task for EASI. + +Adapts the EmbodiedBench EB-Manipulation benchmark to EASI's task interface. +Supports 5 splits via per-split YAML configs. + +Key difference from other EB-* tasks: actions are 7D discrete gripper arrays, +not text-based named actions. The action_space is empty -- the prompt builder +handles action formatting. +""" +from __future__ import annotations + +from pathlib import Path + +from easi.core.base_task import BaseTask +from easi.core.episode import StepResult +from easi.tasks.ebmanipulation.actions import get_action_space +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + + +class EBManipulationTask(BaseTask): + + def __init__(self, data_dir=None, split_yaml_path=None): + super().__init__(data_dir=data_dir, split_yaml_path=split_yaml_path) + # EB-Manipulation has no fixed action space -- actions are 7D arrays + self._config["action_space"] = get_action_space() + + def get_task_yaml_path(self) -> Path: + return Path(__file__).parent / "ebmanipulation_base.yaml" + + def get_bridge_script_path(self) -> Path: + return Path(__file__).parent / "bridge.py" + + def get_instruction(self, episode: dict) -> str: + return episode.get("instruction", self.name) + + def format_reset_config(self, episode: dict) -> dict: + """Map EB-Manipulation HF episode row to bridge reset config. + + The bridge constructs full file paths from data_dir + episode metadata. + data_dir is injected by BaseTask._load_episodes_from_hf() and points + to the extracted HF dataset cache containing simulator_data.zip contents. + """ + data_dir = episode.get("_data_dir", "") + split = self._config.get("dataset", {}).get("split", "base") + return { + "episode_id": episode.get("id", "unknown"), + "data_dir": data_dir, + "split": split, + "task_name": episode["task_name"], + "variation": episode.get("variation", 0), + "episode_num": episode.get("episode_num", 0), + "instruction": episode.get("instruction", ""), + "task_type": episode.get("task_type", ""), + } + + def evaluate_episode( + self, episode: dict, trajectory: list[StepResult] + ) -> dict[str, float]: + """Extract metrics from trajectory. + + The bridge reports task_success and action_success in StepResult.info. + """ + if not trajectory: + return { + "task_success": 0.0, + "num_steps": 0.0, + "action_success_rate": 0.0, + } + + last_step = trajectory[-1] + action_successes = [ + s.info.get("action_success", 0.0) for s in trajectory + ] + return { + "task_success": last_step.info.get("task_success", 0.0), + "num_steps": float(len(trajectory)), + "action_success_rate": ( + sum(action_successes) / len(action_successes) + if action_successes + else 0.0 + ), + } + + def _get_builtin_episodes(self) -> list[dict]: + """Minimal episodes for testing without dataset download. + + Uses the HF episode index format (task_name + variation + episode_num). + The bridge constructs file paths from these fields + data_dir. + """ + return [ + { + "id": 0, + "task_name": "pick_cube_shape", + "variation": 0, + "episode_num": 0, + "instruction": "Pick up the star and place it into the yellow container.", + "task_type": "pick", + }, + ] diff --git a/easi/tasks/ebmanipulation/vendor/EBManEnv.py b/easi/tasks/ebmanipulation/vendor/EBManEnv.py new file mode 100644 index 0000000..303205f --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/EBManEnv.py @@ -0,0 +1,189 @@ +"""Vendored EB-Manipulation environment for EASI. + +Adapted from EmbodiedBench/embodiedbench/envs/eb_manipulation/EBManEnv.py. +Changes: removed gymnasium dependency, internal dataset loading, image saving. +EASI provides episodes via reset(episode) and the bridge handles image saving. +Hardcoded constants extracted as constructor parameters. +""" +from __future__ import annotations + +import time + +import numpy as np +from amsolver.action_modes import ActionMode, ArmActionMode +from amsolver.backend.utils import task_file_to_task_class +from amsolver.environment import Environment +from amsolver.observation_config import ObservationConfig +from pyrep.const import RenderMode +from pyrep.objects.dummy import Dummy +from pyrep.objects.vision_sensor import VisionSensor + +from easi.tasks.ebmanipulation.vendor.eb_man_utils import ( + get_continuous_action_from_discrete, +) +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + + +class EBManEnv: + """CoppeliaSim manipulation environment for EB-Manipulation benchmark. + + Unlike the original EBManEnv (gym.Env), this version: + - Does NOT load datasets internally -- EASI provides episodes via reset() + - Does NOT inherit from gymnasium -- no action_space/observation_space attrs + - Accepts configurable scene_bounds, voxel_size, rotation_resolution + """ + + def __init__( + self, + img_size: tuple[int, int] = (500, 500), + max_steps: int = 15, + scene_bounds: list[float] | None = None, + voxel_size: int = 100, + rotation_resolution: int = 3, + ): + obs_config = ObservationConfig() + obs_config.set_all(True) + obs_config.set_image_size(img_size) + + action_mode = ActionMode(ArmActionMode.ABS_EE_POSE_PLAN_WORLD_FRAME) + self.env = Environment(action_mode, obs_config=obs_config, headless=True) + self.env.launch() + + self._max_episode_steps = max_steps + self._current_step = 0 + self._episode_start_time = 0.0 + self.task = None + self.task_class = None + self.current_task_variation = None + self.episode_language_instruction = "" + self.last_frame_obs = None + + # Configurable discretization parameters + self._scene_bounds = np.array( + scene_bounds + if scene_bounds is not None + else [-0.3, -0.5, 0.6, 0.7, 0.5, 1.6] + ) + self._voxel_size = voxel_size + self._rotation_resolution = rotation_resolution + + def reset(self, episode: dict) -> tuple[str, dict]: + """Reset environment with episode data from EASI. + + Args: + episode: Dict with keys: + - task_file: str -- task variation name (e.g., "pick_cube_shape") + - task_base: str -- path to task_base.ttm + - waypoint_sets: str -- path to waypoint_sets.ttm + - configs: str -- path to configs.pkl + """ + self._current_step = 0 + self._episode_start_time = time.time() + + task_file = episode["task_file"] + task_class = task_file_to_task_class(task_file, parent_folder="vlm") + self.current_task_variation = task_file + self.task_class = task_file.split("_")[0] + + self.task = self.env.get_task(task_class) + descriptions, obs = self.task.load_config( + episode["task_base"], + episode["waypoint_sets"], + episode["configs"], + ) + self.episode_language_instruction = descriptions[0] + self.last_frame_obs = vars(obs) + + return descriptions[0], self.last_frame_obs + + def step(self, discrete_action: list) -> tuple[dict, float, bool, dict]: + """Execute one 7D discrete action. + + Args: + discrete_action: [X, Y, Z, Roll, Pitch, Yaw, Gripper] (7 values) + + Returns: + (obs_dict, reward, done, info) tuple. + """ + self._current_step += 1 + info = {} + action_success = False + + try: + action = get_continuous_action_from_discrete( + discrete_action, + scene_bounds=self._scene_bounds, + voxel_size=self._voxel_size, + rotation_resolution=self._rotation_resolution, + ) + obs, reward, terminate = self.task.step(action) + + # Special handling for stack task (EBManEnv.py:175-189) + if self.current_task_variation.startswith("stack"): + if terminate: + if action[-1] == 0.0: + reward = 0.0 + terminate = False + logger.debug( + "Wrong success condition for stack, " + "setting reward to 0 and terminate to False" + ) + elif action[-1] == 1.0: + action[2] += 0.03 + logger.debug("Checking if the object is stacked properly") + obs, reward, terminate = self.task.step(action) + if terminate and reward == 1.0: + logger.debug("Stacking is successful") + else: + logger.debug("Stacking is unsuccessful") + reward = 0.0 + terminate = False + + self.last_frame_obs = vars(obs) + action_success = True + except Exception as e: + logger.warning("Action execution error: %s", e) + obs = self.last_frame_obs + reward = -1 + terminate = False + action_success = e + + # Build env feedback + env_feedback = self._get_env_feedback(action_success, reward) + + info["env_feedback"] = env_feedback + info["instruction"] = self.episode_language_instruction + info["env_step"] = self._current_step + info["episode_elapsed_seconds"] = time.time() - self._episode_start_time + info["action"] = discrete_action + info["action_success"] = 1.0 if action_success is True else 0.0 + info["task_success"] = 1.0 if (terminate and reward == 1.0) else 0.0 + + if self._current_step >= self._max_episode_steps: + terminate = True + + return self.last_frame_obs, reward, terminate, info + + def _get_env_feedback(self, action_success, reward) -> str: + """Generate feedback message for the current step. + + Reference: EBManEnv.get_env_feedback() + """ + msg = ( + f"You are currently performing the task intended to " + f"{self.episode_language_instruction.lower()} " + f"At this moment, you have completed executing " + f"{self._current_step} steps. " + ) + if action_success is True: + msg += "Last action is valid. " + else: + msg += f"Last action is invalid. {action_success}." + msg += f"The current reward obtained is {reward}." + return msg + + def close(self) -> None: + """Shut down AMSolver/CoppeliaSim.""" + self.env.shutdown() diff --git a/easi/tasks/ebmanipulation/vendor/__init__.py b/easi/tasks/ebmanipulation/vendor/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/easi/tasks/ebmanipulation/vendor/eb_man_utils.py b/easi/tasks/ebmanipulation/vendor/eb_man_utils.py new file mode 100644 index 0000000..640d31c --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/eb_man_utils.py @@ -0,0 +1,633 @@ +"""Vendored EB-Manipulation utilities for EASI. + +Adapted from EmbodiedBench/embodiedbench/envs/eb_manipulation/eb_man_utils.py. +Changes: +- Constants (SCENE_BOUNDS, ROTATION_RESOLUTION, VOXEL_SIZE) made into function + parameters with defaults matching EmbodiedBench. +- YOLO model loaded lazily (not at module import time). +- All functions preserved for full benchmark alignment. +""" +from __future__ import annotations + +import os +from typing import List + +import numpy as np + +# Default constants (configurable via function parameters) +DEFAULT_SCENE_BOUNDS = np.array([-0.3, -0.5, 0.6, 0.7, 0.5, 1.6]) +DEFAULT_ROTATION_RESOLUTION = 3 +DEFAULT_VOXEL_SIZE = 100 +CAMERAS = ["front", "left_shoulder", "right_shoulder", "wrist"] +USE_GENERAL_OBJECT_NAMES = True + +# Lazy-loaded YOLO model +_yolo_model = None + + +def _get_yolo_model(): + """Lazy-load YOLO model on first use.""" + global _yolo_model + if _yolo_model is None: + from ultralytics import YOLO + + _yolo_model = YOLO("yolo11n.pt") + return _yolo_model + + +# From https://github.com/stepjam/RLBench/blob/master/rlbench/backend/utils.py +def point_to_voxel_index( + point: np.ndarray, + scene_bounds: np.ndarray = None, + voxel_size: int = DEFAULT_VOXEL_SIZE, +): + if scene_bounds is None: + scene_bounds = DEFAULT_SCENE_BOUNDS + bb_mins = np.array(scene_bounds[0:3])[None] + bb_maxs = np.array(scene_bounds[3:])[None] + dims_m_one = np.array([voxel_size] * 3)[None] - 1 + bb_ranges = bb_maxs - bb_mins + res = bb_ranges / (np.array([voxel_size] * 3) + 1e-12) + voxel_indicy = np.minimum( + np.floor((point - bb_mins) / (res + 1e-12)).astype(np.int32), dims_m_one + ) + + return voxel_indicy.reshape(point.shape) + + +def discrete_euler_to_quaternion( + discrete_euler, + rotation_resolution: int = DEFAULT_ROTATION_RESOLUTION, +): + from scipy.spatial.transform import Rotation + + euluer = (discrete_euler * rotation_resolution) - 180 + return Rotation.from_euler("xyz", euluer, degrees=True).as_quat() + + +def get_continuous_action_from_discrete( + discrete_action, + scene_bounds: np.ndarray = None, + voxel_size: int = DEFAULT_VOXEL_SIZE, + rotation_resolution: int = DEFAULT_ROTATION_RESOLUTION, +): + if scene_bounds is None: + scene_bounds = DEFAULT_SCENE_BOUNDS + trans_indicies = np.array(discrete_action[:3]) + rot_and_grip_indicies = np.array(discrete_action[3:6]) + is_gripper_open = discrete_action[6] + + bounds = scene_bounds + res = (bounds[3:] - bounds[:3]) / voxel_size + attention_coordinate = bounds[:3] + res * trans_indicies + res / 2 + quat = discrete_euler_to_quaternion(rot_and_grip_indicies, rotation_resolution) + + continuous_action = np.concatenate( + [attention_coordinate, quat, [is_gripper_open]] + ) + return continuous_action + + +def draw_xyz_coordinate(image_path, resolution): + import cv2 + + image = cv2.imread(image_path) + if resolution == 500: + origin = (62, 239) + axis_length = 30 + color_x = (0, 0, 255) + color_y = (0, 255, 0) + color_z = (255, 0, 0) + + cv2.circle(image, (int(origin[0]), int(origin[1])), 3, (0, 0, 255), -1) + cv2.putText( + image, + "(0, 0)", + (62, 255), + fontFace=cv2.FONT_HERSHEY_SIMPLEX, + fontScale=0.3, + color=(0, 0, 255), + thickness=1, + lineType=cv2.LINE_AA, + ) + cv2.arrowedLine( + image, + origin, + (origin[0] + axis_length, origin[1]), + color_y, + 2, + tipLength=0.2, + ) + cv2.putText( + image, + "y", + (origin[0] + axis_length, origin[1] - 10), + cv2.FONT_HERSHEY_SIMPLEX, + 0.5, + color_y, + 2, + ) + cv2.arrowedLine( + image, + origin, + (origin[0], origin[1] - axis_length), + color_z, + 2, + tipLength=0.2, + ) + cv2.putText( + image, + "z", + (origin[0] - 20, origin[1] - axis_length), + cv2.FONT_HERSHEY_SIMPLEX, + 0.5, + color_z, + 2, + ) + cv2.arrowedLine( + image, + origin, + (origin[0] - axis_length + 12, origin[1] + axis_length), + color_x, + 2, + tipLength=0.2, + ) + cv2.putText( + image, + "x", + (origin[0] - axis_length, origin[1] + axis_length - 15), + cv2.FONT_HERSHEY_SIMPLEX, + 0.5, + color_x, + 2, + ) + cv2.imwrite(image_path, image) + + elif resolution == 300: + origin = (38, 142) + axis_length = 30 + color_x = (0, 0, 255) + color_y = (0, 255, 0) + color_z = (255, 0, 0) + + cv2.circle(image, (int(origin[0]), int(origin[1])), 3, (0, 0, 255), -1) + cv2.putText( + image, + "(0, 0)", + (38, 158), + fontFace=cv2.FONT_HERSHEY_SIMPLEX, + fontScale=0.3, + color=(0, 0, 255), + thickness=1, + lineType=cv2.LINE_AA, + ) + cv2.arrowedLine( + image, + origin, + (origin[0] + axis_length, origin[1]), + color_y, + 2, + tipLength=0.2, + ) + cv2.putText( + image, + "y", + (origin[0] + axis_length, origin[1] - 10), + cv2.FONT_HERSHEY_SIMPLEX, + 0.5, + color_y, + 2, + ) + cv2.arrowedLine( + image, + origin, + (origin[0], origin[1] - axis_length), + color_z, + 2, + tipLength=0.2, + ) + cv2.putText( + image, + "z", + (origin[0] - 20, origin[1] - axis_length), + cv2.FONT_HERSHEY_SIMPLEX, + 0.5, + color_z, + 2, + ) + cv2.arrowedLine( + image, + origin, + (origin[0] - axis_length + 12, origin[1] + axis_length), + color_x, + 2, + tipLength=0.2, + ) + cv2.putText( + image, + "x", + (origin[0] - axis_length, origin[1] + axis_length - 15), + cv2.FONT_HERSHEY_SIMPLEX, + 0.5, + color_x, + 2, + ) + cv2.imwrite(image_path, image) + + elif resolution == 700: + origin = (88, 335) + axis_length = 50 + color_x = (0, 0, 255) + color_y = (0, 255, 0) + color_z = (255, 0, 0) + + cv2.circle(image, (int(origin[0]), int(origin[1])), 3, (0, 0, 255), -1) + cv2.putText( + image, + "(0, 0)", + (88, 355), + fontFace=cv2.FONT_HERSHEY_SIMPLEX, + fontScale=0.3, + color=(0, 0, 255), + thickness=1, + lineType=cv2.LINE_AA, + ) + cv2.arrowedLine( + image, + origin, + (origin[0] + axis_length, origin[1]), + color_y, + 2, + tipLength=0.2, + ) + cv2.putText( + image, + "y", + (origin[0] + axis_length, origin[1] - 10), + cv2.FONT_HERSHEY_SIMPLEX, + 0.5, + color_y, + 2, + ) + cv2.arrowedLine( + image, + origin, + (origin[0], origin[1] - axis_length), + color_z, + 2, + tipLength=0.2, + ) + cv2.putText( + image, + "z", + (origin[0] - 20, origin[1] - axis_length), + cv2.FONT_HERSHEY_SIMPLEX, + 0.5, + color_z, + 2, + ) + cv2.arrowedLine( + image, + origin, + (origin[0] - axis_length + 20, origin[1] + axis_length), + color_x, + 2, + tipLength=0.2, + ) + cv2.putText( + image, + "x", + (origin[0] - axis_length, origin[1] + axis_length - 15), + cv2.FONT_HERSHEY_SIMPLEX, + 0.5, + color_x, + 2, + ) + cv2.imwrite(image_path, image) + else: + raise ValueError( + "Detection boxes are not supported for this resolution. " + "Please disable detection boxes or use a valid resolution." + ) + + return image_path + + +def increase_bbox(bbox, scale_factor=1): + """Increase the bounding box size by a scale factor.""" + x1, y1, x2, y2 = bbox + original_width = x2 - x1 + original_height = y2 - y1 + center_x = x1 + original_width / 2 + center_y = y1 + original_height / 2 + new_width = original_width * scale_factor + new_height = original_height * scale_factor + new_x1 = int(center_x - new_width / 2) + new_y1 = int(center_y - new_height / 2) + new_x2 = int(center_x + new_width / 2) + new_y2 = int(center_y + new_height / 2) + return [new_x1, new_y1, new_x2, new_y2] + + +def draw_bounding_boxes( + image_path_list, world_points, camera_extrinsics_list, camera_intrinsics_list +): + import cv2 + + model = _get_yolo_model() + image_save_path_list = [] + for input_image_path, camera_extrinsics, camera_intrinsics in zip( + image_path_list, camera_extrinsics_list, camera_intrinsics_list + ): + T_inv = np.linalg.inv(camera_extrinsics) + rvec = T_inv[:3, :3] + tvec = T_inv[:3, 3] + pixel_points_2D, _ = cv2.projectPoints( + np.array(world_points), rvec, tvec, camera_intrinsics, np.zeros(4) + ) + + results = model.predict( + source=input_image_path, conf=0.0001, line_width=1, verbose=False + ) + predicted_boxes = results[0].boxes.xyxy + image_bgr = cv2.imread(input_image_path, cv2.IMREAD_COLOR) + + box_id = 0 + text_positions = [] + for point in pixel_points_2D: + x, y = point[0] + min_dist = float("inf") + min_idx = -1 + for i, box in enumerate(predicted_boxes): + center = [(box[0] + box[2]) // 2, (box[1] + box[3]) // 2] + dist = (center[0] - x) ** 2 + (center[1] - y) ** 2 + if dist < min_dist: + min_dist = dist + min_idx = i + if min_dist > 400: + continue + increased_box = increase_bbox(predicted_boxes[min_idx], 1.2) + center_pixel = (0, 0, 255) + cv2.rectangle( + image_bgr, + (int(increased_box[0]), int(increased_box[1])), + (int(increased_box[2]), int(increased_box[3])), + center_pixel, + 1, + ) + text_position = (int(increased_box[0]) + 20, int(increased_box[1]) - 10) + for pos in text_positions: + if abs(pos[0] - text_position[0]) < 10 and abs( + pos[1] - text_position[1] + ) < 10: + text_position = ( + int(increased_box[0]) + 10, + int(increased_box[1]) - 10, + ) + text_positions.append(text_position) + cv2.putText( + image_bgr, + str(box_id + 1), + text_position, + cv2.FONT_HERSHEY_SIMPLEX, + 0.4, + center_pixel, + 1, + cv2.LINE_AA, + ) + box_id += 1 + base, ext = os.path.splitext(input_image_path) + image_save_path = f"{base}_annotated{ext}" + cv2.imwrite(image_save_path, image_bgr) + image_save_path_list.append(image_save_path) + + return image_save_path_list + + +####### Generate object information for the initial observation +def _get_mask_id_to_name_dict_for_input(object_info): + mask_id_to_name_dict = {} + for obj in object_info: + if "id" in object_info[obj]: + mask_id_to_name_dict[object_info[obj]["id"]] = obj + return mask_id_to_name_dict + + +def _get_point_cloud_dict_for_input(obs, camera_types): + from pyrep.objects import VisionSensor + + point_cloud_dict = {} + camera_extrinsics_list, camera_intrinsics_list = [], [] + for camera_type in CAMERAS: + cam_extrinsics = obs["misc"][f"{camera_type}_camera_extrinsics"] + cam_intrinsics = obs["misc"][f"{camera_type}_camera_intrinsics"] + if camera_type + "_rgb" in camera_types: + camera_extrinsics_list.append(cam_extrinsics) + camera_intrinsics_list.append(cam_intrinsics) + cam_depth = obs[f"{camera_type}_depth"] + near = obs["misc"][f"{camera_type}_camera_near"] + far = obs["misc"][f"{camera_type}_camera_far"] + cam_depth = (far - near) * cam_depth + near + point_cloud_dict[camera_type] = ( + VisionSensor.pointcloud_from_depth_and_camera_params( + cam_depth, cam_extrinsics, cam_intrinsics + ) + ) + + return point_cloud_dict, camera_extrinsics_list, camera_intrinsics_list + + +def _get_mask_dict_for_input(obs): + mask_dict = {} + for camera in CAMERAS: + rgb_mask = np.array(obs[f"{camera}_mask"], dtype=int) + mask_dict[camera] = rgb_mask + return mask_dict + + +def form_obs_for_input( + mask_dict, + mask_id_to_real_name, + point_cloud_dict, + scene_bounds: np.ndarray = None, + voxel_size: int = DEFAULT_VOXEL_SIZE, +): + if scene_bounds is None: + scene_bounds = DEFAULT_SCENE_BOUNDS + + # convert object id to char and average and discretize point cloud per object + uniques = np.unique(np.concatenate(list(mask_dict.values()), axis=0)) + real_name_to_avg_coord = {} + all_avg_point_list = [] + for _, mask_id in enumerate(uniques): + if mask_id not in mask_id_to_real_name: + continue + avg_point_list = [] + for camera in CAMERAS: + mask = mask_dict[camera] + point_cloud = point_cloud_dict[camera] + if not np.any(mask == mask_id): + continue + avg_point_list.append( + np.mean(point_cloud[mask == mask_id].reshape(-1, 3), axis=0) + ) + + avg_point = sum(avg_point_list) / len(avg_point_list) + all_avg_point_list.append(avg_point) + real_name = mask_id_to_real_name[mask_id] + real_name_to_avg_coord[real_name] = list( + point_to_voxel_index(avg_point, scene_bounds=scene_bounds, voxel_size=voxel_size) + ) + if USE_GENERAL_OBJECT_NAMES: + implicit_name_to_avg_coord = {} + i = 1 + for key, value in real_name_to_avg_coord.items(): + implicit_name_to_avg_coord[f"object {i}"] = value + i += 1 + real_name_to_avg_coord = implicit_name_to_avg_coord + + # Sort the objects based on the y-coordinate + sorted_indices = sorted( + range(len(all_avg_point_list)), key=lambda i: all_avg_point_list[i][1] + ) + all_avg_point_list = [all_avg_point_list[i] for i in sorted_indices] + + # Sort the objects in the general name based on the same order + real_name_to_avg_coord = sorted( + real_name_to_avg_coord.items(), key=lambda item: item[1][1] + ) + if USE_GENERAL_OBJECT_NAMES: + real_name_to_avg_coord = { + f"object {i+1}": value + for i, (_, value) in enumerate(real_name_to_avg_coord) + } + else: + real_name_to_avg_coord = { + f"{obj_name}": value for obj_name, value in real_name_to_avg_coord + } + return real_name_to_avg_coord, all_avg_point_list + + +def form_object_coord_for_input(obs, task_class, camera_types): + mask_id_to_sim_name = _get_mask_id_to_name_dict_for_input( + obs["object_informations"] + ) + point_cloud_dict, camera_extrinsics_list, camera_intrinsics_list = ( + _get_point_cloud_dict_for_input(obs, camera_types) + ) + mask_dict = _get_mask_dict_for_input(obs) + + task_handler = TASK_HANDLERS[task_class]() + sim_name_to_real_name = task_handler.sim_name_to_real_name + mask_id_to_real_name = { + mask_id: sim_name_to_real_name[name] + for mask_id, name in mask_id_to_sim_name.items() + if name in sim_name_to_real_name + } + avg_coord, all_avg_point_list = form_obs_for_input( + mask_dict, mask_id_to_real_name, point_cloud_dict + ) + return avg_coord, all_avg_point_list, camera_extrinsics_list, camera_intrinsics_list + + +class base_task_handler: + def __init__(self, sim_name_to_real_name): + self.sim_name_to_real_name = sim_name_to_real_name + + +class pick_cube_shape(base_task_handler): + def __init__(self): + sim_name_to_real_name = { + "small_container0": "first container", + "small_container1": "second container", + "star_normal_visual0": "first star", + "star_normal_visual1": "second star", + "cylinder_normal0": "first cylinder", + "cylinder_normal1": "second cylinder", + "triangular_normal0": "first triangular", + "triangular_normal1": "second triangular", + "cube_basic0": "first cube", + "cube_basic1": "second cube", + "moon_normal_visual0": "first moon", + "moon_normal_visual1": "second moon", + } + super().__init__(sim_name_to_real_name) + + +class stack_cubes_color(base_task_handler): + def __init__(self): + sim_name_to_real_name = { + "star_normal_visual0": "first star", + "star_normal_visual1": "second star", + "star_normal_visual2": "third star", + "star_normal_visual3": "fourth star", + "cylinder_normal0": "first cylinder", + "cylinder_normal1": "second cylinder", + "cylinder_normal2": "third cylinder", + "cylinder_normal3": "fourth cylinder", + "triangular_normal0": "first triangular", + "triangular_normal1": "second triangular", + "triangular_normal2": "third triangular", + "triangular_normal3": "fourth triangular", + "cube_basic0": "first cube", + "cube_basic1": "second cube", + "cube_basic2": "third cube", + "cube_basic3": "fourth cube", + "moon_normal_visual0": "first moon", + "moon_normal_visual1": "second moon", + "moon_normal_visual2": "third moon", + "moon_normal_visual3": "fourth moon", + } + super().__init__(sim_name_to_real_name) + + +class place_into_shape_sorter_color(base_task_handler): + def __init__(self): + sim_name_to_real_name = { + "star_normal_visual0": "first star", + "star_normal_visual1": "second star", + "star_normal_visual2": "third star", + "star_normal_visual3": "fourth star", + "cylinder_normal0": "first cylinder", + "cylinder_normal1": "second cylinder", + "cylinder_normal2": "third cylinder", + "cylinder_normal3": "fourth cylinder", + "triangular_normal0": "first triangular", + "triangular_normal1": "second triangular", + "triangular_normal2": "third triangular", + "triangular_normal3": "fourth triangular", + "cube_basic0": "first cube", + "cube_basic1": "second cube", + "cube_basic2": "third cube", + "cube_basic3": "fourth cube", + "moon_normal_visual0": "first moon", + "moon_normal_visual1": "second moon", + "moon_normal_visual2": "third moon", + "moon_normal_visual3": "fourth moon", + "shape_sorter_visual": "shape sorter", + } + super().__init__(sim_name_to_real_name) + + +class wipe_table_shape(base_task_handler): + def __init__(self): + sim_name_to_real_name = { + "rectangle": "first rectangle area", + "rectangle0": "second rectangle area", + "round": "first round area", + "round0": "second round area", + "triangle": "first triangle area", + "triangle0": "second triangle area", + "star": "first star area", + "star0": "second star area", + "sponge_visual0": "sponge", + } + super().__init__(sim_name_to_real_name) + + +TASK_HANDLERS = { + "pick": pick_cube_shape, + "stack": stack_cubes_color, + "place": place_into_shape_sorter_color, + "wipe": wipe_table_shape, +} diff --git a/tests/test_ebmanipulation_task.py b/tests/test_ebmanipulation_task.py new file mode 100644 index 0000000..91820f6 --- /dev/null +++ b/tests/test_ebmanipulation_task.py @@ -0,0 +1,407 @@ +"""Tests for the EB-Manipulation task (offline, no simulator needed).""" +import json + +import pytest +from pathlib import Path + + +def _has_scipy(): + try: + import scipy # noqa: F401 + return True + except ImportError: + return False + +from easi.core.episode import Action, Observation, StepResult +from easi.tasks.ebmanipulation.task import EBManipulationTask +from easi.tasks.ebmanipulation.actions import ( + get_action_space, + serialize_action, + deserialize_action, + extract_pose_list, + EVAL_SETS, + VALID_EVAL_SETS, + DEFAULT_VOXEL_SIZE, + DEFAULT_ROTATION_RESOLUTION, +) + + +class TestEBManipulationActions: + def test_get_action_space_empty(self): + actions = get_action_space() + assert actions == [] + + def test_serialize_action(self): + action = [50, 42, 17, 6, 61, 36, 1] + s = serialize_action(action) + assert s == "[50, 42, 17, 6, 61, 36, 1]" + + def test_deserialize_action(self): + s = "[50, 42, 17, 6, 61, 36, 1]" + result = deserialize_action(s) + assert result == [50, 42, 17, 6, 61, 36, 1] + + def test_deserialize_action_roundtrip(self): + action = [50, 42, 17, 6, 61, 36, 1] + s = serialize_action(action) + result = deserialize_action(s) + assert result == action + + def test_deserialize_invalid(self): + assert deserialize_action("not a list") == [] + assert deserialize_action("[1, 2, 3]") == [] # Not 7 elements + assert deserialize_action("") == [] + + def test_extract_pose_list(self): + text = "[[50, 42, 17, 6, 61, 36, 1], [28, 32, 26, 0, 60, 94, 0]]" + poses = extract_pose_list(text) + assert len(poses) == 2 + assert poses[0] == [50, 42, 17, 6, 61, 36, 1] + assert poses[1] == [28, 32, 26, 0, 60, 94, 0] + + def test_extract_pose_list_skip_non_7d(self): + text = "[[50, 42, 17], [28, 32, 26, 0, 60, 94, 0]]" + poses = extract_pose_list(text) + assert len(poses) == 1 + assert poses[0] == [28, 32, 26, 0, 60, 94, 0] + + def test_eval_sets_have_correct_splits(self): + assert set(EVAL_SETS.keys()) == {"base", "common_sense", "complex", "spatial", "visual"} + + def test_valid_eval_sets(self): + assert len(VALID_EVAL_SETS) == 5 + + def test_defaults(self): + assert DEFAULT_VOXEL_SIZE == 100 + assert DEFAULT_ROTATION_RESOLUTION == 3 + + +class TestEBManipulationUtils: + def test_point_to_voxel_index(self): + from easi.tasks.ebmanipulation.vendor.eb_man_utils import ( + point_to_voxel_index, + ) + import numpy as np + + # Center of scene should map to ~50 + point = np.array([0.2, 0.0, 1.1]) + voxel = point_to_voxel_index(point) + assert len(voxel) == 3 + assert all(0 <= v <= 100 for v in voxel) + + def test_task_handlers_exist(self): + from easi.tasks.ebmanipulation.vendor.eb_man_utils import TASK_HANDLERS + + assert "pick" in TASK_HANDLERS + assert "stack" in TASK_HANDLERS + assert "place" in TASK_HANDLERS + assert "wipe" in TASK_HANDLERS + + def test_task_handler_has_sim_name_mapping(self): + from easi.tasks.ebmanipulation.vendor.eb_man_utils import TASK_HANDLERS + + for task_type, handler_cls in TASK_HANDLERS.items(): + handler = handler_cls() + assert hasattr(handler, "sim_name_to_real_name") + assert isinstance(handler.sim_name_to_real_name, dict) + assert len(handler.sim_name_to_real_name) > 0 + + @pytest.mark.skipif( + not _has_scipy(), reason="scipy not installed (simulator-only dep)" + ) + def test_get_continuous_action_defaults(self): + from easi.tasks.ebmanipulation.vendor.eb_man_utils import ( + get_continuous_action_from_discrete, + ) + import numpy as np + + action = [50, 50, 50, 60, 60, 60, 1.0] + result = get_continuous_action_from_discrete(action) + assert isinstance(result, np.ndarray) + assert len(result) == 8 # xyz(3) + quat(4) + gripper(1) + assert result[-1] == 1.0 # gripper state preserved + + @pytest.mark.skipif( + not _has_scipy(), reason="scipy not installed (simulator-only dep)" + ) + def test_get_continuous_action_custom_params(self): + from easi.tasks.ebmanipulation.vendor.eb_man_utils import ( + get_continuous_action_from_discrete, + ) + import numpy as np + + action = [50, 50, 50, 60, 60, 60, 0.0] + result = get_continuous_action_from_discrete( + action, + scene_bounds=np.array([-0.3, -0.5, 0.6, 0.7, 0.5, 1.6]), + voxel_size=100, + rotation_resolution=3, + ) + assert isinstance(result, np.ndarray) + assert len(result) == 8 + assert result[-1] == 0.0 + + @pytest.mark.skipif( + not _has_scipy(), reason="scipy not installed (simulator-only dep)" + ) + def test_discrete_euler_to_quaternion(self): + from easi.tasks.ebmanipulation.vendor.eb_man_utils import ( + discrete_euler_to_quaternion, + ) + import numpy as np + + quat = discrete_euler_to_quaternion(np.array([60, 60, 60])) + assert len(quat) == 4 + # Quaternion norm should be ~1 + assert abs(np.linalg.norm(quat) - 1.0) < 1e-6 + + +class TestEBManipulationTask: + @pytest.fixture + def task(self): + return EBManipulationTask() + + def test_name(self, task): + assert task.name == "ebmanipulation_base" + + def test_simulator_key(self, task): + assert task.simulator_key == "coppeliasim:v4_1_0" + + def test_action_space_empty(self, task): + assert task.action_space == [] + + def test_max_steps(self, task): + assert task.max_steps == 15 + + def test_format_reset_config(self, task): + episode = { + "id": 0, + "task_name": "pick_cube_shape", + "variation": 0, + "episode_num": 0, + "instruction": "Pick up the star and place it into the yellow container.", + "task_type": "pick", + "_data_dir": "/tmp/test_data", + } + config = task.format_reset_config(episode) + assert config["episode_id"] == 0 + assert config["data_dir"] == "/tmp/test_data" + assert config["split"] == "base" + assert config["task_name"] == "pick_cube_shape" + assert config["variation"] == 0 + assert config["episode_num"] == 0 + assert config["instruction"] == "Pick up the star and place it into the yellow container." + assert config["task_type"] == "pick" + + def test_format_reset_config_uses_data_dir(self, task): + episode = { + "id": 1, + "task_name": "stack_cubes_color", + "variation": 2, + "episode_num": 1, + "instruction": "Stack the cubes.", + "task_type": "stack", + "_data_dir": "/some/path", + } + config = task.format_reset_config(episode) + assert config["data_dir"] == "/some/path" + + def test_evaluate_episode(self, task): + episode = { + "id": 0, + "task_name": "pick_cube_shape", + "variation": 0, + "episode_num": 0, + "instruction": "test", + "task_type": "pick", + } + obs = Observation(rgb_path="/tmp/rgb.png") + trajectory = [ + StepResult( + observation=obs, + reward=0.0, + done=False, + info={"task_success": 0.0, "action_success": 1.0}, + ), + StepResult( + observation=obs, + reward=1.0, + done=True, + info={"task_success": 1.0, "action_success": 1.0}, + ), + ] + metrics = task.evaluate_episode(episode, trajectory) + assert metrics["task_success"] == 1.0 + assert metrics["num_steps"] == 2.0 + assert metrics["action_success_rate"] == 1.0 + + def test_evaluate_empty_trajectory(self, task): + episode = { + "id": 0, + "task_name": "pick_cube_shape", + "variation": 0, + "episode_num": 0, + "instruction": "test", + "task_type": "pick", + } + metrics = task.evaluate_episode(episode, []) + assert metrics["task_success"] == 0.0 + assert metrics["num_steps"] == 0.0 + assert metrics["action_success_rate"] == 0.0 + + def test_get_instruction(self, task): + episode = {"instruction": "Pick up the star."} + assert task.get_instruction(episode) == "Pick up the star." + + def test_builtin_episodes(self, task): + episodes = task._get_builtin_episodes() + assert len(episodes) >= 1 + ep = episodes[0] + assert "id" in ep + assert "task_name" in ep + assert "variation" in ep + assert "episode_num" in ep + assert "instruction" in ep + assert "task_type" in ep + + def test_task_registry_discovers_all_splits(self): + from easi.tasks.registry import list_tasks, refresh + + refresh() + tasks = list_tasks() + manip_tasks = [t for t in tasks if t.startswith("ebmanipulation")] + assert len(manip_tasks) == 5 + expected = { + "ebmanipulation_base", + "ebmanipulation_common_sense", + "ebmanipulation_complex", + "ebmanipulation_spatial", + "ebmanipulation_visual", + } + assert set(manip_tasks) == expected + + def test_bridge_script_path(self, task): + bridge_path = task.get_bridge_script_path() + assert bridge_path.exists() + assert bridge_path.name == "bridge.py" + + def test_yaml_has_zip_files(self, task): + config = task._config + dataset_config = config.get("dataset", {}) + zip_files = dataset_config.get("zip_files", []) + assert "simulator_data.zip" in zip_files + + +class TestEBManipulationPromptBuilder: + @pytest.fixture + def builder(self): + from easi.tasks.ebmanipulation.prompts import EBManipulationPromptBuilder + + return EBManipulationPromptBuilder(n_shot=2, split="base") + + @pytest.fixture + def memory(self): + from easi.core.memory import AgentMemory + + obs = Observation( + rgb_path="/dev/null", + metadata={ + "task_variation": "pick_cube_shape", + "avg_obj_coord": "{'object 1': [45, 13, 18], 'object 2': [72, 20, 18]}", + }, + ) + return AgentMemory( + task_description="Pick up the star and place it into the yellow container.", + action_space=[], + current_observation=obs, + ) + + def test_build_messages_first_turn(self, builder, memory): + msgs = builder.build_messages(memory) + assert len(msgs) >= 1 + assert msgs[0]["role"] == "user" + text = msgs[0]["content"][0]["text"] + assert "Franka Panda robot" in text + assert "7D discrete gripper action" in text + + def test_system_prompt_format_placeholders(self, builder, memory): + msgs = builder.build_messages(memory) + text = msgs[0]["content"][0]["text"] + # Should have voxel_size and rotation params filled in + assert "[0, 100]" in text + assert "3 degrees" in text + + def test_per_task_examples(self, builder): + assert "pick" in builder._examples + assert "stack" in builder._examples + assert "place" in builder._examples + assert "wipe" in builder._examples + assert len(builder._examples["pick"]) >= 2 + assert len(builder._examples["stack"]) >= 4 + assert len(builder._examples["place"]) >= 4 + assert len(builder._examples["wipe"]) >= 8 + + def test_output_template_is_manip(self, builder, memory): + msgs = builder.build_messages(memory) + text = msgs[0]["content"][0]["text"] + # Should contain template_manip content + assert "visual_state_description" in text + assert "executable_plan" in text + + def test_parse_response_7d_arrays(self, builder, memory): + response = json.dumps( + { + "visual_state_description": "I see objects.", + "reasoning_and_reflection": "Pick up the star.", + "language_plan": "1. Move to star.", + "executable_plan": "[[50, 42, 17, 6, 61, 36, 1], [50, 42, 17, 6, 61, 36, 0]]", + } + ) + actions = builder.parse_response(response, memory) + assert len(actions) == 2 + assert actions[0].action_name == "[50, 42, 17, 6, 61, 36, 1]" + assert actions[1].action_name == "[50, 42, 17, 6, 61, 36, 0]" + + def test_parse_response_list_format(self, builder, memory): + response = json.dumps( + { + "visual_state_description": "test", + "reasoning_and_reflection": "test", + "language_plan": "test", + "executable_plan": [[50, 42, 17, 6, 61, 36, 1]], + } + ) + actions = builder.parse_response(response, memory) + assert len(actions) == 1 + + def test_parse_response_invalid_json(self, builder, memory): + actions = builder.parse_response("not json", memory) + assert actions == [] + + def test_parse_response_empty_plan(self, builder, memory): + response = json.dumps( + { + "visual_state_description": "test", + "reasoning_and_reflection": "test", + "language_plan": "test", + "executable_plan": "[]", + } + ) + actions = builder.parse_response(response, memory) + assert actions == [] + + def test_response_schema_executable_plan_is_string(self, builder, memory): + schema = builder.get_response_format(memory) + props = schema["json_schema"]["schema"]["properties"] + assert props["executable_plan"]["type"] == "string" + + def test_conforms_to_protocol(self, builder): + assert hasattr(builder, "build_messages") + assert hasattr(builder, "parse_response") + assert hasattr(builder, "get_response_format") + assert hasattr(builder, "set_action_space") + + def test_set_action_space_noop(self, builder): + # Should not raise + builder.set_action_space(["a", "b", "c"]) + builder.set_action_space([]) From ccdc800a91527652b058df438bcfe51890ba3626 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Thu, 19 Feb 2026 01:01:58 +0800 Subject: [PATCH 051/239] [Feat] Add YAML template inheritance utilities (deep_merge + resolve_task_yaml) --- easi/tasks/yaml_utils.py | 67 +++++++++++++++ tests/test_yaml_utils.py | 180 +++++++++++++++++++++++++++++++++++++++ 2 files changed, 247 insertions(+) create mode 100644 easi/tasks/yaml_utils.py create mode 100644 tests/test_yaml_utils.py diff --git a/easi/tasks/yaml_utils.py b/easi/tasks/yaml_utils.py new file mode 100644 index 0000000..b4e146c --- /dev/null +++ b/easi/tasks/yaml_utils.py @@ -0,0 +1,67 @@ +"""YAML template inheritance utilities. + +Provides deep_merge() and resolve_task_yaml() for the 'extends' mechanism. +Split YAMLs reference a base template via 'extends: _base.yaml'. +The base and split configs are deep-merged (split values win on conflicts). +""" +from __future__ import annotations + +from pathlib import Path + +import yaml + + +def deep_merge(base: dict, override: dict) -> dict: + """Recursively merge override into base. Override wins on conflicts. + + - Dicts: merged recursively + - Lists/scalars: replaced entirely (no appending) + - Missing keys in override: base value kept + """ + result = dict(base) + for key, value in override.items(): + if key in result and isinstance(result[key], dict) and isinstance(value, dict): + result[key] = deep_merge(result[key], value) + else: + result[key] = value + return result + + +def resolve_task_yaml(yaml_path: Path) -> dict: + """Load a task YAML, resolving 'extends' inheritance if present. + + The 'extends' value is a relative path from the YAML file's directory. + Supports single-level inheritance only (base cannot extend another base). + + Returns: + Fully resolved config dict (extends key removed). + + Raises: + FileNotFoundError: If the base YAML referenced by extends doesn't exist. + ValueError: If chained extends are detected (base also has extends). + """ + config = yaml.safe_load(yaml_path.read_text()) + if not isinstance(config, dict): + return config or {} + + extends = config.pop("extends", None) + if extends is None: + return config + + base_path = yaml_path.parent / extends + if not base_path.exists(): + raise FileNotFoundError( + f"Base YAML not found: {base_path} " + f"(referenced by extends in {yaml_path})" + ) + + base_config = yaml.safe_load(base_path.read_text()) + if not isinstance(base_config, dict): + base_config = {} + + if "extends" in base_config: + raise ValueError( + f"Chained extends not supported: {base_path} also has 'extends'" + ) + + return deep_merge(base_config, config) diff --git a/tests/test_yaml_utils.py b/tests/test_yaml_utils.py new file mode 100644 index 0000000..57e0e82 --- /dev/null +++ b/tests/test_yaml_utils.py @@ -0,0 +1,180 @@ +"""Tests for YAML template inheritance (deep_merge + resolve_task_yaml).""" +import pytest +from pathlib import Path + + +class TestDeepMerge: + """Tests for deep_merge() utility.""" + + def test_flat_override(self): + from easi.tasks.yaml_utils import deep_merge + base = {"a": 1, "b": 2} + override = {"b": 99} + result = deep_merge(base, override) + assert result == {"a": 1, "b": 99} + + def test_nested_dict_merge(self): + from easi.tasks.yaml_utils import deep_merge + base = {"agent": {"prompt_builder": "X", "kwargs": {"n_shot": 10, "split": "base"}}} + override = {"agent": {"kwargs": {"n_shot": 7}}} + result = deep_merge(base, override) + assert result["agent"]["prompt_builder"] == "X" + assert result["agent"]["kwargs"]["n_shot"] == 7 + assert result["agent"]["kwargs"]["split"] == "base" + + def test_list_replaced_not_appended(self): + from easi.tasks.yaml_utils import deep_merge + base = {"deps": ["gym", "networkx"]} + override = {"deps": ["torch"]} + result = deep_merge(base, override) + assert result["deps"] == ["torch"] + + def test_new_keys_added(self): + from easi.tasks.yaml_utils import deep_merge + base = {"a": 1} + override = {"b": 2} + result = deep_merge(base, override) + assert result == {"a": 1, "b": 2} + + def test_does_not_mutate_inputs(self): + from easi.tasks.yaml_utils import deep_merge + base = {"nested": {"a": 1}} + override = {"nested": {"b": 2}} + deep_merge(base, override) + assert "b" not in base["nested"] + + def test_deeply_nested_three_levels(self): + from easi.tasks.yaml_utils import deep_merge + base = {"l1": {"l2": {"l3": "base_val", "keep": True}}} + override = {"l1": {"l2": {"l3": "override_val"}}} + result = deep_merge(base, override) + assert result["l1"]["l2"]["l3"] == "override_val" + assert result["l1"]["l2"]["keep"] is True + + def test_override_dict_with_scalar(self): + from easi.tasks.yaml_utils import deep_merge + base = {"x": {"nested": True}} + override = {"x": "flat_now"} + result = deep_merge(base, override) + assert result["x"] == "flat_now" + + def test_override_scalar_with_dict(self): + from easi.tasks.yaml_utils import deep_merge + base = {"x": "flat"} + override = {"x": {"nested": True}} + result = deep_merge(base, override) + assert result["x"] == {"nested": True} + + def test_empty_override(self): + from easi.tasks.yaml_utils import deep_merge + base = {"a": 1, "b": 2} + result = deep_merge(base, {}) + assert result == {"a": 1, "b": 2} + + def test_empty_base(self): + from easi.tasks.yaml_utils import deep_merge + result = deep_merge({}, {"a": 1}) + assert result == {"a": 1} + + +class TestResolveTaskYaml: + """Tests for resolve_task_yaml() with extends inheritance.""" + + def test_no_extends_returns_raw(self, tmp_path): + from easi.tasks.yaml_utils import resolve_task_yaml + yaml_file = tmp_path / "task.yaml" + yaml_file.write_text("name: my_task\nsimulator: dummy:v1\n") + config = resolve_task_yaml(yaml_file) + assert config["name"] == "my_task" + assert "extends" not in config + + def test_extends_merges_base(self, tmp_path): + from easi.tasks.yaml_utils import resolve_task_yaml + (tmp_path / "_base.yaml").write_text( + "simulator: dummy:v1\nmax_steps: 100\n" + "agent:\n prompt_builder: MyBuilder\n kwargs:\n n_shot: 10\n" + ) + (tmp_path / "split.yaml").write_text( + "extends: _base.yaml\nname: my_split\n" + "agent:\n kwargs:\n n_shot: 5\n" + ) + config = resolve_task_yaml(tmp_path / "split.yaml") + assert config["name"] == "my_split" + assert config["simulator"] == "dummy:v1" + assert config["max_steps"] == 100 + assert config["agent"]["prompt_builder"] == "MyBuilder" + assert config["agent"]["kwargs"]["n_shot"] == 5 + + def test_extends_strips_extends_key(self, tmp_path): + from easi.tasks.yaml_utils import resolve_task_yaml + (tmp_path / "_base.yaml").write_text("simulator: dummy:v1\n") + (tmp_path / "split.yaml").write_text("extends: _base.yaml\nname: test\n") + config = resolve_task_yaml(tmp_path / "split.yaml") + assert "extends" not in config + + def test_extends_missing_base_raises(self, tmp_path): + from easi.tasks.yaml_utils import resolve_task_yaml + (tmp_path / "split.yaml").write_text("extends: nonexistent.yaml\nname: test\n") + with pytest.raises(FileNotFoundError, match="nonexistent.yaml"): + resolve_task_yaml(tmp_path / "split.yaml") + + def test_chained_extends_raises(self, tmp_path): + from easi.tasks.yaml_utils import resolve_task_yaml + (tmp_path / "grandparent.yaml").write_text("x: 1\n") + (tmp_path / "_base.yaml").write_text("extends: grandparent.yaml\ny: 2\n") + (tmp_path / "split.yaml").write_text("extends: _base.yaml\nname: test\n") + with pytest.raises(ValueError, match="Chained extends not supported"): + resolve_task_yaml(tmp_path / "split.yaml") + + def test_base_without_name_not_a_task(self, tmp_path): + from easi.tasks.yaml_utils import resolve_task_yaml + (tmp_path / "_base.yaml").write_text( + "simulator: dummy:v1\nmax_steps: 50\n" + ) + config = resolve_task_yaml(tmp_path / "_base.yaml") + assert "name" not in config + + def test_realistic_ebmanipulation_merge(self, tmp_path): + from easi.tasks.yaml_utils import resolve_task_yaml + (tmp_path / "_base.yaml").write_text( + "simulator: coppeliasim:v4_1_0\n" + "task_class: easi.tasks.ebmanipulation.task.EBManipulationTask\n" + "max_steps: 15\n" + "dataset:\n" + " source: huggingface\n" + " repo_id: oscarqjh/EB-Manipulation_easi\n" + " subset: null\n" + " zip_files:\n" + " - simulator_data.zip\n" + "simulator_configs:\n" + " screen_height: 500\n" + " voxel_size: 100\n" + "agent:\n" + " prompt_builder: easi.tasks.ebmanipulation.prompts.EBManipulationPromptBuilder\n" + " prompt_builder_kwargs:\n" + " n_shot: 10\n" + " use_feedback: true\n" + " chat_history: false\n" + ) + (tmp_path / "ebmanipulation_visual.yaml").write_text( + "extends: _base.yaml\n" + "name: ebmanipulation_visual\n" + "display_name: EB-Manipulation Visual Split\n" + "description: Visual split\n" + "dataset:\n" + " split: visual\n" + "agent:\n" + " prompt_builder_kwargs:\n" + " split: visual\n" + ) + config = resolve_task_yaml(tmp_path / "ebmanipulation_visual.yaml") + assert config["name"] == "ebmanipulation_visual" + assert config["simulator"] == "coppeliasim:v4_1_0" + assert config["max_steps"] == 15 + assert config["dataset"]["source"] == "huggingface" + assert config["dataset"]["split"] == "visual" + assert config["dataset"]["zip_files"] == ["simulator_data.zip"] + assert config["simulator_configs"]["screen_height"] == 500 + assert config["agent"]["prompt_builder_kwargs"]["n_shot"] == 10 + assert config["agent"]["prompt_builder_kwargs"]["split"] == "visual" + assert config["agent"]["prompt_builder_kwargs"]["use_feedback"] is True From bff7d0ec5260ae1600d4cd48f2151f5b13ddc05b Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Thu, 19 Feb 2026 01:38:45 +0800 Subject: [PATCH 052/239] [Feat] Integrate YAML extends into registry and BaseTask config loading --- easi/core/base_task.py | 5 ++--- easi/tasks/registry.py | 4 ++-- tests/test_yaml_utils.py | 34 ++++++++++++++++++++++++++++++++++ 3 files changed, 38 insertions(+), 5 deletions(-) diff --git a/easi/core/base_task.py b/easi/core/base_task.py index 72191c4..ea6b85f 100644 --- a/easi/core/base_task.py +++ b/easi/core/base_task.py @@ -19,10 +19,9 @@ from abc import ABC, abstractmethod from pathlib import Path -import yaml - from easi.core.episode import StepResult from easi.core.exceptions import DatasetError +from easi.tasks.yaml_utils import resolve_task_yaml from easi.utils.logging import get_logger logger = get_logger(__name__) @@ -209,7 +208,7 @@ def _load_config(self) -> dict: yaml_path = self._split_yaml_path or self.get_task_yaml_path() if not yaml_path.exists(): raise DatasetError(f"Task config not found: {yaml_path}") - return yaml.safe_load(yaml_path.read_text()) + return resolve_task_yaml(yaml_path) def _load_episodes_from_config(self) -> list[dict]: """Load episodes from the dataset. diff --git a/easi/tasks/registry.py b/easi/tasks/registry.py index 3d96ad2..390b86b 100644 --- a/easi/tasks/registry.py +++ b/easi/tasks/registry.py @@ -14,7 +14,7 @@ from dataclasses import dataclass from pathlib import Path -import yaml +from easi.tasks.yaml_utils import resolve_task_yaml from easi.utils.logging import get_logger @@ -57,7 +57,7 @@ def _discover_tasks(tasks_dir: Path | None = None) -> dict[str, TaskEntry]: for task_yaml_path in yaml_files: try: - config = yaml.safe_load(task_yaml_path.read_text()) + config = resolve_task_yaml(task_yaml_path) except Exception as e: logger.warning("Failed to load %s: %s", task_yaml_path, e) continue diff --git a/tests/test_yaml_utils.py b/tests/test_yaml_utils.py index 57e0e82..94054ba 100644 --- a/tests/test_yaml_utils.py +++ b/tests/test_yaml_utils.py @@ -178,3 +178,37 @@ def test_realistic_ebmanipulation_merge(self, tmp_path): assert config["agent"]["prompt_builder_kwargs"]["n_shot"] == 10 assert config["agent"]["prompt_builder_kwargs"]["split"] == "visual" assert config["agent"]["prompt_builder_kwargs"]["use_feedback"] is True + + +class TestRegistryExtendsIntegration: + """Test that the registry resolves extends during discovery.""" + + def test_registry_discovers_split_with_extends(self, tmp_path): + from easi.tasks.registry import _discover_tasks + task_dir = tmp_path / "my_bench" + task_dir.mkdir() + (task_dir / "_base.yaml").write_text( + "simulator: dummy:v1\n" + "task_class: easi.tasks.dummy_task.task.DummyTask\n" + "max_steps: 50\n" + ) + (task_dir / "my_bench_base.yaml").write_text( + "extends: _base.yaml\n" + "name: my_bench_base\n" + "display_name: My Bench Base\n" + "description: Base split\n" + ) + entries = _discover_tasks(tasks_dir=tmp_path) + assert "my_bench_base" in entries + assert entries["my_bench_base"].simulator_key == "dummy:v1" + assert entries["my_bench_base"].max_steps == 50 + + def test_registry_skips_base_yaml_without_name(self, tmp_path): + from easi.tasks.registry import _discover_tasks + task_dir = tmp_path / "my_bench" + task_dir.mkdir() + (task_dir / "_base.yaml").write_text( + "simulator: dummy:v1\nmax_steps: 50\n" + ) + entries = _discover_tasks(tasks_dir=tmp_path) + assert len(entries) == 0 From 7ee5a47cd478afc992eba1804c6e64d598934930 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Thu, 19 Feb 2026 01:42:51 +0800 Subject: [PATCH 053/239] [Refactor] Migrate EB-Manipulation YAMLs to extends template inheritance --- easi/tasks/ebmanipulation/_base.yaml | 39 +++++++++++++++++++ .../ebmanipulation/ebmanipulation_base.yaml | 35 +---------------- .../ebmanipulation_common_sense.yaml | 35 +---------------- .../ebmanipulation_complex.yaml | 35 +---------------- .../ebmanipulation_spatial.yaml | 35 +---------------- .../ebmanipulation/ebmanipulation_visual.yaml | 35 +---------------- 6 files changed, 44 insertions(+), 170 deletions(-) create mode 100644 easi/tasks/ebmanipulation/_base.yaml diff --git a/easi/tasks/ebmanipulation/_base.yaml b/easi/tasks/ebmanipulation/_base.yaml new file mode 100644 index 0000000..c597078 --- /dev/null +++ b/easi/tasks/ebmanipulation/_base.yaml @@ -0,0 +1,39 @@ +display_name: "EB-Manipulation" +description: "EB-Manipulation benchmark (robotic manipulation with 7D discrete gripper actions)" +simulator: "coppeliasim:v4_1_0" +task_class: "easi.tasks.ebmanipulation.task.EBManipulationTask" +action_space: [] +max_steps: 15 +dataset: + source: huggingface + repo_id: "oscarqjh/EB-Manipulation_easi" + subset: null + zip_files: + - "simulator_data.zip" +simulator_configs: + screen_height: 500 + screen_width: 500 + max_steps: 15 + voxel_size: 100 + rotation_resolution: 3 + scene_bounds: [-0.3, -0.5, 0.6, 0.7, 0.5, 1.6] + amsolver_parent_path: "" + detection_box: true + additional_deps: + - "gymnasium" + - "ultralytics" + - "open3d" + env_vars: + QT_QPA_PLATFORM: "xcb" +agent: + prompt_builder: "easi.tasks.ebmanipulation.prompts.EBManipulationPromptBuilder" + prompt_builder_kwargs: + n_shot: 10 + use_feedback: true + chat_history: false + generation_kwargs: + temperature: 0 + max_tokens: 2048 + top_k: 50 + top_p: 0.95 + do_sample: false diff --git a/easi/tasks/ebmanipulation/ebmanipulation_base.yaml b/easi/tasks/ebmanipulation/ebmanipulation_base.yaml index 781caab..d93d307 100644 --- a/easi/tasks/ebmanipulation/ebmanipulation_base.yaml +++ b/easi/tasks/ebmanipulation/ebmanipulation_base.yaml @@ -1,42 +1,9 @@ +extends: _base.yaml name: ebmanipulation_base display_name: "EB-Manipulation Base Split" description: "Base evaluation split for EB-Manipulation (robotic manipulation with 7D discrete gripper actions)" -simulator: "coppeliasim:v4_1_0" -task_class: "easi.tasks.ebmanipulation.task.EBManipulationTask" -action_space: [] -max_steps: 15 dataset: - source: huggingface - repo_id: "oscarqjh/EB-Manipulation_easi" - subset: null split: "base" - zip_files: - - "simulator_data.zip" -simulator_configs: - screen_height: 500 - screen_width: 500 - max_steps: 15 - voxel_size: 100 - rotation_resolution: 3 - scene_bounds: [-0.3, -0.5, 0.6, 0.7, 0.5, 1.6] - amsolver_parent_path: "" - detection_box: true - additional_deps: - - "gymnasium" - - "ultralytics" - - "open3d" - env_vars: - QT_QPA_PLATFORM: "xcb" agent: - prompt_builder: "easi.tasks.ebmanipulation.prompts.EBManipulationPromptBuilder" prompt_builder_kwargs: - n_shot: 10 split: "base" - use_feedback: true - chat_history: false - generation_kwargs: - temperature: 0 - max_tokens: 2048 - top_k: 50 - top_p: 0.95 - do_sample: false diff --git a/easi/tasks/ebmanipulation/ebmanipulation_common_sense.yaml b/easi/tasks/ebmanipulation/ebmanipulation_common_sense.yaml index 02730be..da883a6 100644 --- a/easi/tasks/ebmanipulation/ebmanipulation_common_sense.yaml +++ b/easi/tasks/ebmanipulation/ebmanipulation_common_sense.yaml @@ -1,42 +1,9 @@ +extends: _base.yaml name: ebmanipulation_common_sense display_name: "EB-Manipulation Common Sense Split" description: "Common sense evaluation split for EB-Manipulation (tasks requiring commonsense reasoning)" -simulator: "coppeliasim:v4_1_0" -task_class: "easi.tasks.ebmanipulation.task.EBManipulationTask" -action_space: [] -max_steps: 15 dataset: - source: huggingface - repo_id: "oscarqjh/EB-Manipulation_easi" - subset: null split: "common_sense" - zip_files: - - "simulator_data.zip" -simulator_configs: - screen_height: 500 - screen_width: 500 - max_steps: 15 - voxel_size: 100 - rotation_resolution: 3 - scene_bounds: [-0.3, -0.5, 0.6, 0.7, 0.5, 1.6] - amsolver_parent_path: "" - detection_box: true - additional_deps: - - "gymnasium" - - "ultralytics" - - "open3d" - env_vars: - QT_QPA_PLATFORM: "xcb" agent: - prompt_builder: "easi.tasks.ebmanipulation.prompts.EBManipulationPromptBuilder" prompt_builder_kwargs: - n_shot: 10 split: "common_sense" - use_feedback: true - chat_history: false - generation_kwargs: - temperature: 0 - max_tokens: 2048 - top_k: 50 - top_p: 0.95 - do_sample: false diff --git a/easi/tasks/ebmanipulation/ebmanipulation_complex.yaml b/easi/tasks/ebmanipulation/ebmanipulation_complex.yaml index ebc26f3..96552fd 100644 --- a/easi/tasks/ebmanipulation/ebmanipulation_complex.yaml +++ b/easi/tasks/ebmanipulation/ebmanipulation_complex.yaml @@ -1,42 +1,9 @@ +extends: _base.yaml name: ebmanipulation_complex display_name: "EB-Manipulation Complex Split" description: "Complex instruction evaluation split for EB-Manipulation (multi-step manipulation instructions)" -simulator: "coppeliasim:v4_1_0" -task_class: "easi.tasks.ebmanipulation.task.EBManipulationTask" -action_space: [] -max_steps: 15 dataset: - source: huggingface - repo_id: "oscarqjh/EB-Manipulation_easi" - subset: null split: "complex" - zip_files: - - "simulator_data.zip" -simulator_configs: - screen_height: 500 - screen_width: 500 - max_steps: 15 - voxel_size: 100 - rotation_resolution: 3 - scene_bounds: [-0.3, -0.5, 0.6, 0.7, 0.5, 1.6] - amsolver_parent_path: "" - detection_box: true - additional_deps: - - "gymnasium" - - "ultralytics" - - "open3d" - env_vars: - QT_QPA_PLATFORM: "xcb" agent: - prompt_builder: "easi.tasks.ebmanipulation.prompts.EBManipulationPromptBuilder" prompt_builder_kwargs: - n_shot: 10 split: "complex" - use_feedback: true - chat_history: false - generation_kwargs: - temperature: 0 - max_tokens: 2048 - top_k: 50 - top_p: 0.95 - do_sample: false diff --git a/easi/tasks/ebmanipulation/ebmanipulation_spatial.yaml b/easi/tasks/ebmanipulation/ebmanipulation_spatial.yaml index 7b3d9cd..f666b2a 100644 --- a/easi/tasks/ebmanipulation/ebmanipulation_spatial.yaml +++ b/easi/tasks/ebmanipulation/ebmanipulation_spatial.yaml @@ -1,42 +1,9 @@ +extends: _base.yaml name: ebmanipulation_spatial display_name: "EB-Manipulation Spatial Split" description: "Spatial reasoning evaluation split for EB-Manipulation (tasks with relative spatial references)" -simulator: "coppeliasim:v4_1_0" -task_class: "easi.tasks.ebmanipulation.task.EBManipulationTask" -action_space: [] -max_steps: 15 dataset: - source: huggingface - repo_id: "oscarqjh/EB-Manipulation_easi" - subset: null split: "spatial" - zip_files: - - "simulator_data.zip" -simulator_configs: - screen_height: 500 - screen_width: 500 - max_steps: 15 - voxel_size: 100 - rotation_resolution: 3 - scene_bounds: [-0.3, -0.5, 0.6, 0.7, 0.5, 1.6] - amsolver_parent_path: "" - detection_box: true - additional_deps: - - "gymnasium" - - "ultralytics" - - "open3d" - env_vars: - QT_QPA_PLATFORM: "xcb" agent: - prompt_builder: "easi.tasks.ebmanipulation.prompts.EBManipulationPromptBuilder" prompt_builder_kwargs: - n_shot: 10 split: "spatial" - use_feedback: true - chat_history: false - generation_kwargs: - temperature: 0 - max_tokens: 2048 - top_k: 50 - top_p: 0.95 - do_sample: false diff --git a/easi/tasks/ebmanipulation/ebmanipulation_visual.yaml b/easi/tasks/ebmanipulation/ebmanipulation_visual.yaml index b5fc2ad..33eb7c0 100644 --- a/easi/tasks/ebmanipulation/ebmanipulation_visual.yaml +++ b/easi/tasks/ebmanipulation/ebmanipulation_visual.yaml @@ -1,42 +1,9 @@ +extends: _base.yaml name: ebmanipulation_visual display_name: "EB-Manipulation Visual Split" description: "Visual appearance evaluation split for EB-Manipulation (tasks requiring visual property recognition)" -simulator: "coppeliasim:v4_1_0" -task_class: "easi.tasks.ebmanipulation.task.EBManipulationTask" -action_space: [] -max_steps: 15 dataset: - source: huggingface - repo_id: "oscarqjh/EB-Manipulation_easi" - subset: null split: "visual" - zip_files: - - "simulator_data.zip" -simulator_configs: - screen_height: 500 - screen_width: 500 - max_steps: 15 - voxel_size: 100 - rotation_resolution: 3 - scene_bounds: [-0.3, -0.5, 0.6, 0.7, 0.5, 1.6] - amsolver_parent_path: "" - detection_box: true - additional_deps: - - "gymnasium" - - "ultralytics" - - "open3d" - env_vars: - QT_QPA_PLATFORM: "xcb" agent: - prompt_builder: "easi.tasks.ebmanipulation.prompts.EBManipulationPromptBuilder" prompt_builder_kwargs: - n_shot: 10 split: "visual" - use_feedback: true - chat_history: false - generation_kwargs: - temperature: 0 - max_tokens: 2048 - top_k: 50 - top_p: 0.95 - do_sample: false From 80f4fbb97c6233fc96b3d8db04582187c7a0cc2c Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Thu, 19 Feb 2026 01:44:48 +0800 Subject: [PATCH 054/239] [Refactor] Migrate EB-Navigation YAMLs to extends template inheritance --- easi/tasks/ebnavigation/_base.yaml | 30 +++++++++++++++++++ .../tasks/ebnavigation/ebnavigation_base.yaml | 26 +--------------- .../ebnavigation_common_sense.yaml | 26 +--------------- .../ebnavigation_complex_instruction.yaml | 26 +--------------- .../ebnavigation_long_horizon.yaml | 26 +--------------- .../ebnavigation_visual_appearance.yaml | 26 +--------------- 6 files changed, 35 insertions(+), 125 deletions(-) create mode 100644 easi/tasks/ebnavigation/_base.yaml diff --git a/easi/tasks/ebnavigation/_base.yaml b/easi/tasks/ebnavigation/_base.yaml new file mode 100644 index 0000000..25d03e6 --- /dev/null +++ b/easi/tasks/ebnavigation/_base.yaml @@ -0,0 +1,30 @@ +display_name: "EB-Navigation" +description: "EB-Navigation benchmark (object-goal navigation)" +simulator: "ai2thor:v5_0_0" +task_class: "easi.tasks.ebnavigation.task.EBNavigationTask" +max_steps: 20 +dataset: + source: huggingface + repo_id: "oscarqjh/EB-Navigation_easi" + subset: null +simulator_configs: + screen_height: 500 + screen_width: 500 + fov: 100 + success_threshold: 1.0 + grid_size: 0.1 + visibility_distance: 10.0 + boundingbox: false +agent: + prompt_builder: "easi.tasks.ebnavigation.prompts.EBNavigationPromptBuilder" + prompt_builder_kwargs: + n_shot: 3 + use_feedback: true + chat_history: true + message_window_len: 5 + generation_kwargs: + temperature: 0 + max_tokens: 2048 + top_k: 50 + top_p: 0.95 + do_sample: false diff --git a/easi/tasks/ebnavigation/ebnavigation_base.yaml b/easi/tasks/ebnavigation/ebnavigation_base.yaml index 4f9ecb9..cb9e1a4 100644 --- a/easi/tasks/ebnavigation/ebnavigation_base.yaml +++ b/easi/tasks/ebnavigation/ebnavigation_base.yaml @@ -1,33 +1,9 @@ +extends: _base.yaml name: ebnavigation_base display_name: "EB-Navigation Base Split" description: "Base evaluation split for EB-Navigation (object-goal navigation)" -simulator: "ai2thor:v5_0_0" -task_class: "easi.tasks.ebnavigation.task.EBNavigationTask" -max_steps: 20 dataset: - source: huggingface - repo_id: "oscarqjh/EB-Navigation_easi" - subset: null split: "base" -simulator_configs: - screen_height: 500 - screen_width: 500 - fov: 100 - success_threshold: 1.0 - grid_size: 0.1 - visibility_distance: 10.0 - boundingbox: false agent: - prompt_builder: "easi.tasks.ebnavigation.prompts.EBNavigationPromptBuilder" prompt_builder_kwargs: - n_shot: 3 split: "base" - use_feedback: true - chat_history: true - message_window_len: 5 - generation_kwargs: - temperature: 0 - max_tokens: 2048 - top_k: 50 - top_p: 0.95 - do_sample: false diff --git a/easi/tasks/ebnavigation/ebnavigation_common_sense.yaml b/easi/tasks/ebnavigation/ebnavigation_common_sense.yaml index 1eb39e4..19089da 100644 --- a/easi/tasks/ebnavigation/ebnavigation_common_sense.yaml +++ b/easi/tasks/ebnavigation/ebnavigation_common_sense.yaml @@ -1,33 +1,9 @@ +extends: _base.yaml name: ebnavigation_common_sense display_name: "EB-Navigation Common Sense Split" description: "Common sense evaluation split for EB-Navigation (object-goal navigation)" -simulator: "ai2thor:v5_0_0" -task_class: "easi.tasks.ebnavigation.task.EBNavigationTask" -max_steps: 20 dataset: - source: huggingface - repo_id: "oscarqjh/EB-Navigation_easi" - subset: null split: "common_sense" -simulator_configs: - screen_height: 500 - screen_width: 500 - fov: 100 - success_threshold: 1.0 - grid_size: 0.1 - visibility_distance: 10.0 - boundingbox: false agent: - prompt_builder: "easi.tasks.ebnavigation.prompts.EBNavigationPromptBuilder" prompt_builder_kwargs: - n_shot: 3 split: "common_sense" - use_feedback: true - chat_history: true - message_window_len: 5 - generation_kwargs: - temperature: 0 - max_tokens: 2048 - top_k: 50 - top_p: 0.95 - do_sample: false diff --git a/easi/tasks/ebnavigation/ebnavigation_complex_instruction.yaml b/easi/tasks/ebnavigation/ebnavigation_complex_instruction.yaml index 4442cad..f57031d 100644 --- a/easi/tasks/ebnavigation/ebnavigation_complex_instruction.yaml +++ b/easi/tasks/ebnavigation/ebnavigation_complex_instruction.yaml @@ -1,33 +1,9 @@ +extends: _base.yaml name: ebnavigation_complex_instruction display_name: "EB-Navigation Complex Instruction Split" description: "Complex instruction evaluation split for EB-Navigation (object-goal navigation)" -simulator: "ai2thor:v5_0_0" -task_class: "easi.tasks.ebnavigation.task.EBNavigationTask" -max_steps: 20 dataset: - source: huggingface - repo_id: "oscarqjh/EB-Navigation_easi" - subset: null split: "complex_instruction" -simulator_configs: - screen_height: 500 - screen_width: 500 - fov: 100 - success_threshold: 1.0 - grid_size: 0.1 - visibility_distance: 10.0 - boundingbox: false agent: - prompt_builder: "easi.tasks.ebnavigation.prompts.EBNavigationPromptBuilder" prompt_builder_kwargs: - n_shot: 3 split: "complex_instruction" - use_feedback: true - chat_history: true - message_window_len: 5 - generation_kwargs: - temperature: 0 - max_tokens: 2048 - top_k: 50 - top_p: 0.95 - do_sample: false diff --git a/easi/tasks/ebnavigation/ebnavigation_long_horizon.yaml b/easi/tasks/ebnavigation/ebnavigation_long_horizon.yaml index 07606ad..5406eef 100644 --- a/easi/tasks/ebnavigation/ebnavigation_long_horizon.yaml +++ b/easi/tasks/ebnavigation/ebnavigation_long_horizon.yaml @@ -1,33 +1,9 @@ +extends: _base.yaml name: ebnavigation_long_horizon display_name: "EB-Navigation Long Horizon Split" description: "Long horizon evaluation split for EB-Navigation (object-goal navigation)" -simulator: "ai2thor:v5_0_0" -task_class: "easi.tasks.ebnavigation.task.EBNavigationTask" -max_steps: 20 dataset: - source: huggingface - repo_id: "oscarqjh/EB-Navigation_easi" - subset: null split: "long_horizon" -simulator_configs: - screen_height: 500 - screen_width: 500 - fov: 100 - success_threshold: 1.0 - grid_size: 0.1 - visibility_distance: 10.0 - boundingbox: false agent: - prompt_builder: "easi.tasks.ebnavigation.prompts.EBNavigationPromptBuilder" prompt_builder_kwargs: - n_shot: 3 split: "long_horizon" - use_feedback: true - chat_history: true - message_window_len: 5 - generation_kwargs: - temperature: 0 - max_tokens: 2048 - top_k: 50 - top_p: 0.95 - do_sample: false diff --git a/easi/tasks/ebnavigation/ebnavigation_visual_appearance.yaml b/easi/tasks/ebnavigation/ebnavigation_visual_appearance.yaml index 2a0a557..5cae896 100644 --- a/easi/tasks/ebnavigation/ebnavigation_visual_appearance.yaml +++ b/easi/tasks/ebnavigation/ebnavigation_visual_appearance.yaml @@ -1,33 +1,9 @@ +extends: _base.yaml name: ebnavigation_visual_appearance display_name: "EB-Navigation Visual Appearance Split" description: "Visual appearance evaluation split for EB-Navigation (object-goal navigation)" -simulator: "ai2thor:v5_0_0" -task_class: "easi.tasks.ebnavigation.task.EBNavigationTask" -max_steps: 20 dataset: - source: huggingface - repo_id: "oscarqjh/EB-Navigation_easi" - subset: null split: "visual_appearance" -simulator_configs: - screen_height: 500 - screen_width: 500 - fov: 100 - success_threshold: 1.0 - grid_size: 0.1 - visibility_distance: 10.0 - boundingbox: false agent: - prompt_builder: "easi.tasks.ebnavigation.prompts.EBNavigationPromptBuilder" prompt_builder_kwargs: - n_shot: 3 split: "visual_appearance" - use_feedback: true - chat_history: true - message_window_len: 5 - generation_kwargs: - temperature: 0 - max_tokens: 2048 - top_k: 50 - top_p: 0.95 - do_sample: false From 5438ff9e3ce6403b5c729ebf32d683836de5058c Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Thu, 19 Feb 2026 01:47:32 +0800 Subject: [PATCH 055/239] [Refactor] Migrate EB-Alfred YAMLs to extends template inheritance --- easi/tasks/ebalfred/_base.yaml | 35 +++++++++++++++++++ easi/tasks/ebalfred/ebalfred_base.yaml | 33 +---------------- .../tasks/ebalfred/ebalfred_common_sense.yaml | 33 +---------------- .../ebalfred_complex_instruction.yaml | 33 +---------------- .../tasks/ebalfred/ebalfred_long_horizon.yaml | 28 +-------------- easi/tasks/ebalfred/ebalfred_spatial.yaml | 33 +---------------- .../ebalfred/ebalfred_visual_appearance.yaml | 33 +---------------- 7 files changed, 41 insertions(+), 187 deletions(-) create mode 100644 easi/tasks/ebalfred/_base.yaml diff --git a/easi/tasks/ebalfred/_base.yaml b/easi/tasks/ebalfred/_base.yaml new file mode 100644 index 0000000..915aabe --- /dev/null +++ b/easi/tasks/ebalfred/_base.yaml @@ -0,0 +1,35 @@ +display_name: "EB-Alfred" +description: "EB-Alfred benchmark" +simulator: "ai2thor:v2_1_0" +task_class: "easi.tasks.ebalfred.task.EBAlfredTask" +max_steps: 50 +dataset: + source: huggingface + repo_id: "oscarqjh/EB-Alfred_easi" + subset: null + zip_files: + - "tasks.zip" +simulator_configs: + quality: "MediumCloseFitShadows" + screen_height: 500 + screen_width: 500 + max_invalid_actions: 10 + feedback_verbosity: 0 + detection_box: false + additional_deps: + - "gym" + - "networkx" + - "opencv-python" +agent: + prompt_builder: "easi.tasks.ebalfred.prompts.EBAlfredPromptBuilder" + prompt_builder_kwargs: + n_shot: 10 + split: "base" + use_feedback: true + chat_history: false + generation_kwargs: + temperature: 0 + max_tokens: 2048 + top_k: 50 + top_p: 0.95 + do_sample: false diff --git a/easi/tasks/ebalfred/ebalfred_base.yaml b/easi/tasks/ebalfred/ebalfred_base.yaml index 591cdf9..c4917d6 100644 --- a/easi/tasks/ebalfred/ebalfred_base.yaml +++ b/easi/tasks/ebalfred/ebalfred_base.yaml @@ -1,37 +1,6 @@ +extends: _base.yaml name: ebalfred_base display_name: "EB-Alfred Base Split" description: "Base evaluation split for EB-Alfred" -simulator: "ai2thor:v2_1_0" -task_class: "easi.tasks.ebalfred.task.EBAlfredTask" -max_steps: 50 dataset: - source: huggingface - repo_id: "oscarqjh/EB-Alfred_easi" - subset: null split: "base" - zip_files: - - "tasks.zip" -simulator_configs: - quality: "MediumCloseFitShadows" - screen_height: 500 - screen_width: 500 - max_invalid_actions: 10 - feedback_verbosity: 0 - detection_box: false - additional_deps: - - "gym" - - "networkx" - - "opencv-python" -agent: - prompt_builder: "easi.tasks.ebalfred.prompts.EBAlfredPromptBuilder" - prompt_builder_kwargs: - n_shot: 10 - split: "base" - use_feedback: true - chat_history: false - generation_kwargs: - temperature: 0 - max_tokens: 2048 - top_k: 50 - top_p: 0.95 - do_sample: false diff --git a/easi/tasks/ebalfred/ebalfred_common_sense.yaml b/easi/tasks/ebalfred/ebalfred_common_sense.yaml index 33f9262..20922cf 100644 --- a/easi/tasks/ebalfred/ebalfred_common_sense.yaml +++ b/easi/tasks/ebalfred/ebalfred_common_sense.yaml @@ -1,37 +1,6 @@ +extends: _base.yaml name: ebalfred_common_sense display_name: "EB-Alfred Common Sense Split" description: "Common sense evaluation split testing implicit knowledge" -simulator: "ai2thor:v2_1_0" -task_class: "easi.tasks.ebalfred.task.EBAlfredTask" -max_steps: 50 dataset: - source: huggingface - repo_id: "oscarqjh/EB-Alfred_easi" - subset: null split: "common_sense" - zip_files: - - "tasks.zip" -simulator_configs: - quality: "MediumCloseFitShadows" - screen_height: 500 - screen_width: 500 - max_invalid_actions: 10 - feedback_verbosity: 0 - detection_box: false - additional_deps: - - "gym" - - "networkx" - - "opencv-python" -agent: - prompt_builder: "easi.tasks.ebalfred.prompts.EBAlfredPromptBuilder" - prompt_builder_kwargs: - n_shot: 10 - split: "base" - use_feedback: true - chat_history: false - generation_kwargs: - temperature: 0 - max_tokens: 2048 - top_k: 50 - top_p: 0.95 - do_sample: false diff --git a/easi/tasks/ebalfred/ebalfred_complex_instruction.yaml b/easi/tasks/ebalfred/ebalfred_complex_instruction.yaml index 577f541..5cace2a 100644 --- a/easi/tasks/ebalfred/ebalfred_complex_instruction.yaml +++ b/easi/tasks/ebalfred/ebalfred_complex_instruction.yaml @@ -1,37 +1,6 @@ +extends: _base.yaml name: ebalfred_complex_instruction display_name: "EB-Alfred Complex Instruction Split" description: "Complex instruction evaluation split with nuanced language" -simulator: "ai2thor:v2_1_0" -task_class: "easi.tasks.ebalfred.task.EBAlfredTask" -max_steps: 50 dataset: - source: huggingface - repo_id: "oscarqjh/EB-Alfred_easi" - subset: null split: "complex_instruction" - zip_files: - - "tasks.zip" -simulator_configs: - quality: "MediumCloseFitShadows" - screen_height: 500 - screen_width: 500 - max_invalid_actions: 10 - feedback_verbosity: 0 - detection_box: false - additional_deps: - - "gym" - - "networkx" - - "opencv-python" -agent: - prompt_builder: "easi.tasks.ebalfred.prompts.EBAlfredPromptBuilder" - prompt_builder_kwargs: - n_shot: 10 - split: "base" - use_feedback: true - chat_history: false - generation_kwargs: - temperature: 0 - max_tokens: 2048 - top_k: 50 - top_p: 0.95 - do_sample: false diff --git a/easi/tasks/ebalfred/ebalfred_long_horizon.yaml b/easi/tasks/ebalfred/ebalfred_long_horizon.yaml index 52b2a75..b1d7f49 100644 --- a/easi/tasks/ebalfred/ebalfred_long_horizon.yaml +++ b/easi/tasks/ebalfred/ebalfred_long_horizon.yaml @@ -1,37 +1,11 @@ +extends: _base.yaml name: ebalfred_long_horizon display_name: "EB-Alfred Long Horizon Split" description: "Long-horizon evaluation split requiring multi-step planning" -simulator: "ai2thor:v2_1_0" -task_class: "easi.tasks.ebalfred.task.EBAlfredTask" -max_steps: 50 dataset: - source: huggingface - repo_id: "oscarqjh/EB-Alfred_easi" - subset: null split: "long_horizon" - zip_files: - - "tasks.zip" -simulator_configs: - quality: "MediumCloseFitShadows" - screen_height: 500 - screen_width: 500 - max_invalid_actions: 10 - feedback_verbosity: 0 - detection_box: false - additional_deps: - - "gym" - - "networkx" - - "opencv-python" agent: - prompt_builder: "easi.tasks.ebalfred.prompts.EBAlfredPromptBuilder" prompt_builder_kwargs: n_shot: 7 split: "long_horizon" - use_feedback: true chat_history: true - generation_kwargs: - temperature: 0 - max_tokens: 2048 - top_k: 50 - top_p: 0.95 - do_sample: false diff --git a/easi/tasks/ebalfred/ebalfred_spatial.yaml b/easi/tasks/ebalfred/ebalfred_spatial.yaml index 2f040f2..8fa658e 100644 --- a/easi/tasks/ebalfred/ebalfred_spatial.yaml +++ b/easi/tasks/ebalfred/ebalfred_spatial.yaml @@ -1,37 +1,6 @@ +extends: _base.yaml name: ebalfred_spatial display_name: "EB-Alfred Spatial Split" description: "Spatial reasoning evaluation split" -simulator: "ai2thor:v2_1_0" -task_class: "easi.tasks.ebalfred.task.EBAlfredTask" -max_steps: 50 dataset: - source: huggingface - repo_id: "oscarqjh/EB-Alfred_easi" - subset: null split: "spatial" - zip_files: - - "tasks.zip" -simulator_configs: - quality: "MediumCloseFitShadows" - screen_height: 500 - screen_width: 500 - max_invalid_actions: 10 - feedback_verbosity: 0 - detection_box: false - additional_deps: - - "gym" - - "networkx" - - "opencv-python" -agent: - prompt_builder: "easi.tasks.ebalfred.prompts.EBAlfredPromptBuilder" - prompt_builder_kwargs: - n_shot: 10 - split: "base" - use_feedback: true - chat_history: false - generation_kwargs: - temperature: 0 - max_tokens: 2048 - top_k: 50 - top_p: 0.95 - do_sample: false diff --git a/easi/tasks/ebalfred/ebalfred_visual_appearance.yaml b/easi/tasks/ebalfred/ebalfred_visual_appearance.yaml index 1648e01..f712741 100644 --- a/easi/tasks/ebalfred/ebalfred_visual_appearance.yaml +++ b/easi/tasks/ebalfred/ebalfred_visual_appearance.yaml @@ -1,37 +1,6 @@ +extends: _base.yaml name: ebalfred_visual_appearance display_name: "EB-Alfred Visual Appearance Split" description: "Visual appearance evaluation split testing visual grounding" -simulator: "ai2thor:v2_1_0" -task_class: "easi.tasks.ebalfred.task.EBAlfredTask" -max_steps: 50 dataset: - source: huggingface - repo_id: "oscarqjh/EB-Alfred_easi" - subset: null split: "visual_appearance" - zip_files: - - "tasks.zip" -simulator_configs: - quality: "MediumCloseFitShadows" - screen_height: 500 - screen_width: 500 - max_invalid_actions: 10 - feedback_verbosity: 0 - detection_box: false - additional_deps: - - "gym" - - "networkx" - - "opencv-python" -agent: - prompt_builder: "easi.tasks.ebalfred.prompts.EBAlfredPromptBuilder" - prompt_builder_kwargs: - n_shot: 10 - split: "base" - use_feedback: true - chat_history: false - generation_kwargs: - temperature: 0 - max_tokens: 2048 - top_k: 50 - top_p: 0.95 - do_sample: false From aa0d7830dde6140b4693d5743556c25bb40b8b29 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Thu, 19 Feb 2026 01:49:21 +0800 Subject: [PATCH 056/239] [Refactor] Migrate EB-Habitat YAMLs to extends template inheritance --- easi/tasks/ebhabitat/_base.yaml | 37 +++++++++++++++++++ easi/tasks/ebhabitat/ebhabitat_base.yaml | 33 +---------------- .../ebhabitat/ebhabitat_common_sense.yaml | 33 +---------------- .../ebhabitat_complex_instruction.yaml | 33 +---------------- .../ebhabitat/ebhabitat_long_horizon.yaml | 33 +---------------- .../ebhabitat_spatial_relationship.yaml | 33 +---------------- .../ebhabitat_visual_appearance.yaml | 33 +---------------- 7 files changed, 43 insertions(+), 192 deletions(-) create mode 100644 easi/tasks/ebhabitat/_base.yaml diff --git a/easi/tasks/ebhabitat/_base.yaml b/easi/tasks/ebhabitat/_base.yaml new file mode 100644 index 0000000..91f14c6 --- /dev/null +++ b/easi/tasks/ebhabitat/_base.yaml @@ -0,0 +1,37 @@ +display_name: "EB-Habitat" +description: "EB-Habitat benchmark (rearrangement)" +simulator: "habitat_sim:v0_3_0" +task_class: "easi.tasks.ebhabitat.task.EBHabitatTask" +max_steps: 50 +dataset: + source: huggingface + repo_id: "oscarqjh/EB-Habitat_easi" + subset: null + zip_files: + - "simulator_data.zip" + - "datasets.zip" +simulator_configs: + screen_height: 500 + screen_width: 500 + max_invalid_actions: 10 + feedback_verbosity: 1 + gpu_device_id: -1 + additional_deps: + - "gym" + - "hydra-core>=1.3" + - "omegaconf>=2.3" + - "imageio" + - "torch" + - "habitat-lab @ git+https://github.com/facebookresearch/habitat-lab.git@v0.3.0#subdirectory=habitat-lab" +agent: + prompt_builder: "easi.tasks.ebhabitat.prompts.EBHabitatPromptBuilder" + prompt_builder_kwargs: + n_shot: 10 + use_feedback: true + chat_history: false + generation_kwargs: + temperature: 0 + max_tokens: 2048 + top_k: 50 + top_p: 0.95 + do_sample: false diff --git a/easi/tasks/ebhabitat/ebhabitat_base.yaml b/easi/tasks/ebhabitat/ebhabitat_base.yaml index 70a4d69..6878031 100644 --- a/easi/tasks/ebhabitat/ebhabitat_base.yaml +++ b/easi/tasks/ebhabitat/ebhabitat_base.yaml @@ -1,40 +1,9 @@ +extends: _base.yaml name: ebhabitat_base display_name: "EB-Habitat Base Split" description: "Base evaluation split for EB-Habitat (rearrangement)" -simulator: "habitat_sim:v0_3_0" -task_class: "easi.tasks.ebhabitat.task.EBHabitatTask" -max_steps: 50 dataset: - source: huggingface - repo_id: "oscarqjh/EB-Habitat_easi" - subset: null split: "base" - zip_files: - - "simulator_data.zip" - - "datasets.zip" -simulator_configs: - screen_height: 500 - screen_width: 500 - max_invalid_actions: 10 - feedback_verbosity: 1 - gpu_device_id: -1 - additional_deps: - - "gym" - - "hydra-core>=1.3" - - "omegaconf>=2.3" - - "imageio" - - "torch" - - "habitat-lab @ git+https://github.com/facebookresearch/habitat-lab.git@v0.3.0#subdirectory=habitat-lab" agent: - prompt_builder: "easi.tasks.ebhabitat.prompts.EBHabitatPromptBuilder" prompt_builder_kwargs: - n_shot: 10 split: "base" - use_feedback: true - chat_history: false - generation_kwargs: - temperature: 0 - max_tokens: 2048 - top_k: 50 - top_p: 0.95 - do_sample: false diff --git a/easi/tasks/ebhabitat/ebhabitat_common_sense.yaml b/easi/tasks/ebhabitat/ebhabitat_common_sense.yaml index dd8a40f..be67bce 100644 --- a/easi/tasks/ebhabitat/ebhabitat_common_sense.yaml +++ b/easi/tasks/ebhabitat/ebhabitat_common_sense.yaml @@ -1,40 +1,9 @@ +extends: _base.yaml name: ebhabitat_common_sense display_name: "EB-Habitat Common Sense Split" description: "Common sense evaluation split for EB-Habitat (rearrangement)" -simulator: "habitat_sim:v0_3_0" -task_class: "easi.tasks.ebhabitat.task.EBHabitatTask" -max_steps: 50 dataset: - source: huggingface - repo_id: "oscarqjh/EB-Habitat_easi" - subset: null split: "common_sense" - zip_files: - - "simulator_data.zip" - - "datasets.zip" -simulator_configs: - screen_height: 500 - screen_width: 500 - max_invalid_actions: 10 - feedback_verbosity: 1 - gpu_device_id: -1 - additional_deps: - - "gym" - - "hydra-core>=1.3" - - "omegaconf>=2.3" - - "imageio" - - "torch" - - "habitat-lab @ git+https://github.com/facebookresearch/habitat-lab.git@v0.3.0#subdirectory=habitat-lab" agent: - prompt_builder: "easi.tasks.ebhabitat.prompts.EBHabitatPromptBuilder" prompt_builder_kwargs: - n_shot: 10 split: "common_sense" - use_feedback: true - chat_history: false - generation_kwargs: - temperature: 0 - max_tokens: 2048 - top_k: 50 - top_p: 0.95 - do_sample: false diff --git a/easi/tasks/ebhabitat/ebhabitat_complex_instruction.yaml b/easi/tasks/ebhabitat/ebhabitat_complex_instruction.yaml index c9a5d08..a0b604b 100644 --- a/easi/tasks/ebhabitat/ebhabitat_complex_instruction.yaml +++ b/easi/tasks/ebhabitat/ebhabitat_complex_instruction.yaml @@ -1,40 +1,9 @@ +extends: _base.yaml name: ebhabitat_complex_instruction display_name: "EB-Habitat Complex Instruction Split" description: "Complex instruction evaluation split for EB-Habitat (rearrangement)" -simulator: "habitat_sim:v0_3_0" -task_class: "easi.tasks.ebhabitat.task.EBHabitatTask" -max_steps: 50 dataset: - source: huggingface - repo_id: "oscarqjh/EB-Habitat_easi" - subset: null split: "complex_instruction" - zip_files: - - "simulator_data.zip" - - "datasets.zip" -simulator_configs: - screen_height: 500 - screen_width: 500 - max_invalid_actions: 10 - feedback_verbosity: 1 - gpu_device_id: -1 - additional_deps: - - "gym" - - "hydra-core>=1.3" - - "omegaconf>=2.3" - - "imageio" - - "torch" - - "habitat-lab @ git+https://github.com/facebookresearch/habitat-lab.git@v0.3.0#subdirectory=habitat-lab" agent: - prompt_builder: "easi.tasks.ebhabitat.prompts.EBHabitatPromptBuilder" prompt_builder_kwargs: - n_shot: 10 split: "complex_instruction" - use_feedback: true - chat_history: false - generation_kwargs: - temperature: 0 - max_tokens: 2048 - top_k: 50 - top_p: 0.95 - do_sample: false diff --git a/easi/tasks/ebhabitat/ebhabitat_long_horizon.yaml b/easi/tasks/ebhabitat/ebhabitat_long_horizon.yaml index 9f5e70c..9355659 100644 --- a/easi/tasks/ebhabitat/ebhabitat_long_horizon.yaml +++ b/easi/tasks/ebhabitat/ebhabitat_long_horizon.yaml @@ -1,40 +1,9 @@ +extends: _base.yaml name: ebhabitat_long_horizon display_name: "EB-Habitat Long Horizon Split" description: "Long horizon evaluation split for EB-Habitat (rearrangement)" -simulator: "habitat_sim:v0_3_0" -task_class: "easi.tasks.ebhabitat.task.EBHabitatTask" -max_steps: 50 dataset: - source: huggingface - repo_id: "oscarqjh/EB-Habitat_easi" - subset: null split: "long_horizon" - zip_files: - - "simulator_data.zip" - - "datasets.zip" -simulator_configs: - screen_height: 500 - screen_width: 500 - max_invalid_actions: 10 - feedback_verbosity: 1 - gpu_device_id: -1 - additional_deps: - - "gym" - - "hydra-core>=1.3" - - "omegaconf>=2.3" - - "imageio" - - "torch" - - "habitat-lab @ git+https://github.com/facebookresearch/habitat-lab.git@v0.3.0#subdirectory=habitat-lab" agent: - prompt_builder: "easi.tasks.ebhabitat.prompts.EBHabitatPromptBuilder" prompt_builder_kwargs: - n_shot: 10 split: "long_horizon" - use_feedback: true - chat_history: false - generation_kwargs: - temperature: 0 - max_tokens: 2048 - top_k: 50 - top_p: 0.95 - do_sample: false diff --git a/easi/tasks/ebhabitat/ebhabitat_spatial_relationship.yaml b/easi/tasks/ebhabitat/ebhabitat_spatial_relationship.yaml index 12ca5f4..fcaf8d1 100644 --- a/easi/tasks/ebhabitat/ebhabitat_spatial_relationship.yaml +++ b/easi/tasks/ebhabitat/ebhabitat_spatial_relationship.yaml @@ -1,40 +1,9 @@ +extends: _base.yaml name: ebhabitat_spatial_relationship display_name: "EB-Habitat Spatial Relationship Split" description: "Spatial relationship evaluation split for EB-Habitat (rearrangement)" -simulator: "habitat_sim:v0_3_0" -task_class: "easi.tasks.ebhabitat.task.EBHabitatTask" -max_steps: 50 dataset: - source: huggingface - repo_id: "oscarqjh/EB-Habitat_easi" - subset: null split: "spatial_relationship" - zip_files: - - "simulator_data.zip" - - "datasets.zip" -simulator_configs: - screen_height: 500 - screen_width: 500 - max_invalid_actions: 10 - feedback_verbosity: 1 - gpu_device_id: -1 - additional_deps: - - "gym" - - "hydra-core>=1.3" - - "omegaconf>=2.3" - - "imageio" - - "torch" - - "habitat-lab @ git+https://github.com/facebookresearch/habitat-lab.git@v0.3.0#subdirectory=habitat-lab" agent: - prompt_builder: "easi.tasks.ebhabitat.prompts.EBHabitatPromptBuilder" prompt_builder_kwargs: - n_shot: 10 split: "spatial_relationship" - use_feedback: true - chat_history: false - generation_kwargs: - temperature: 0 - max_tokens: 2048 - top_k: 50 - top_p: 0.95 - do_sample: false diff --git a/easi/tasks/ebhabitat/ebhabitat_visual_appearance.yaml b/easi/tasks/ebhabitat/ebhabitat_visual_appearance.yaml index 6269376..543d437 100644 --- a/easi/tasks/ebhabitat/ebhabitat_visual_appearance.yaml +++ b/easi/tasks/ebhabitat/ebhabitat_visual_appearance.yaml @@ -1,40 +1,9 @@ +extends: _base.yaml name: ebhabitat_visual_appearance display_name: "EB-Habitat Visual Appearance Split" description: "Visual appearance evaluation split for EB-Habitat (rearrangement)" -simulator: "habitat_sim:v0_3_0" -task_class: "easi.tasks.ebhabitat.task.EBHabitatTask" -max_steps: 50 dataset: - source: huggingface - repo_id: "oscarqjh/EB-Habitat_easi" - subset: null split: "visual_appearance" - zip_files: - - "simulator_data.zip" - - "datasets.zip" -simulator_configs: - screen_height: 500 - screen_width: 500 - max_invalid_actions: 10 - feedback_verbosity: 1 - gpu_device_id: -1 - additional_deps: - - "gym" - - "hydra-core>=1.3" - - "omegaconf>=2.3" - - "imageio" - - "torch" - - "habitat-lab @ git+https://github.com/facebookresearch/habitat-lab.git@v0.3.0#subdirectory=habitat-lab" agent: - prompt_builder: "easi.tasks.ebhabitat.prompts.EBHabitatPromptBuilder" prompt_builder_kwargs: - n_shot: 10 split: "visual_appearance" - use_feedback: true - chat_history: false - generation_kwargs: - temperature: 0 - max_tokens: 2048 - top_k: 50 - top_p: 0.95 - do_sample: false From 4ddd6c93eb8e0fb44c8e31da4cbcffea29bad440 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Thu, 19 Feb 2026 18:54:28 +0800 Subject: [PATCH 057/239] [Refactor] Auto-capture config.json from __init__ args via inspect Eliminate manual maintenance of config.json building and resume logic. Adding a new CLI option now requires exactly 2 changes: argparse + __init__. - Auto-capture __init__ args via inspect.currentframe() for config.json - Align argparse dest names with __init__ param names - Set all argparse defaults to None (real defaults in __init__) - Simplify cmd_run() resume to dict union: {**saved, **raw} - Move max_episodes from run() param to __init__ - Add LLM client timeout (120s) and API error trace logging --- easi/cli.py | 103 ++++++++++++----------------- easi/evaluation/runner.py | 43 +++++++------ easi/llm/client.py | 9 ++- tests/test_cli_llm_args.py | 10 +-- tests/test_e2e_evaluation.py | 111 ++++++++++++++++++++------------ tests/test_evaluation_runner.py | 104 +++++++++++++++++++++++++++++- 6 files changed, 249 insertions(+), 131 deletions(-) diff --git a/easi/cli.py b/easi/cli.py index 9fa7c52..fea5daf 100644 --- a/easi/cli.py +++ b/easi/cli.py @@ -80,29 +80,32 @@ def build_parser() -> argparse.ArgumentParser: # --- run command --- run_parser = subparsers.add_parser("run", help="Run a full evaluation", parents=[common]) - run_parser.add_argument("task", type=str, nargs="?", default=None, + # All defaults are None so resume logic can distinguish "user provided" from "default". + # Real defaults live in EvaluationRunner.__init__. + run_parser.add_argument("task_name", type=str, nargs="?", default=None, metavar="task", help="Task name (e.g., 'dummy_task', 'ebalfred_base'). " "Optional when --resume is provided.") - run_parser.add_argument("--agent", type=str, default="react", choices=["dummy", "react"]) - run_parser.add_argument("--output-dir", type=str, default="./logs", + run_parser.add_argument("--agent", type=str, default=None, choices=["dummy", "react"], + dest="agent_type") + run_parser.add_argument("--output-dir", type=str, default=None, help="Base output directory (creates

      ///)") - run_parser.add_argument("--data-dir", type=str, default="./datasets", + run_parser.add_argument("--data-dir", type=str, default=None, help="Directory for downloading/caching datasets (default: ./datasets)") run_parser.add_argument("--max-episodes", type=int, default=None) - run_parser.add_argument("--llm-url", type=str, default=None, help="LLM server URL") - run_parser.add_argument("--seed", type=int, default=None) - # New LLM backend args + run_parser.add_argument("--llm-url", type=str, default=None, dest="llm_base_url", + help="LLM server URL") + run_parser.add_argument("--seed", type=int, default=None, dest="agent_seed") run_parser.add_argument("--backend", type=str, default=None, help="LLM backend: vllm, openai, anthropic, gemini, dummy") - run_parser.add_argument("--model", type=str, default="default", + run_parser.add_argument("--model", type=str, default=None, help="Model name (HF path for vLLM, API name for proprietary)") - run_parser.add_argument("--port", type=int, default=8080, + run_parser.add_argument("--port", type=int, default=None, help="Port for local inference server (default: 8080)") - run_parser.add_argument("--llm-kwargs", type=str, default=None, + run_parser.add_argument("--llm-kwargs", type=str, default=None, dest="llm_kwargs_raw", help='JSON string of extra kwargs, e.g. \'{"tensor_parallel_size": 4}\'') - run_parser.add_argument("--max-retries", type=int, default=3, + run_parser.add_argument("--max-retries", type=int, default=None, help="Max LLM retry attempts on transient errors (default: 3)") - run_parser.add_argument("--resume", type=str, default=None, + run_parser.add_argument("--resume", type=str, default=None, dest="resume_dir", help="Path to a previous run directory to resume from") run_parser.add_argument("--redownload", action="store_true", help="Delete cached dataset and re-download from source") @@ -293,66 +296,43 @@ def cmd_sim_test(simulator: str, steps: int, timeout: float) -> None: sys.exit(1) -def cmd_run(task_name, agent_type, output_dir, data_dir, max_episodes, - llm_url, seed, backend, model, port, llm_kwargs_raw, - max_retries, resume, redownload=False): +def cmd_run(args): import json as _json from pathlib import Path from easi.evaluation.runner import EvaluationRunner - # When resuming, load saved config and use as defaults - if resume: - config_path = Path(resume) / "config.json" + # Collect explicitly-provided CLI args (argparse defaults are None) + raw = {k: v for k, v in vars(args).items() if v is not None} + # Remove argparse internals that aren't runner params + for key in ("command", "verbosity"): + raw.pop(key, None) + + # Extract session-specific params (not saved in config.json) + resume_dir = raw.pop("resume_dir", None) + redownload = raw.pop("redownload", False) + + if resume_dir: + config_path = Path(resume_dir) / "config.json" if not config_path.exists(): - logger.error("Resume directory has no config.json: %s", resume) + logger.error("Resume directory has no config.json: %s", resume_dir) sys.exit(1) - saved = _json.loads(config_path.read_text()) - opts = saved.get("cli_options", {}) - - # Saved config provides defaults; CLI args override - task_name = task_name or opts.get("task_name") - if agent_type == "dummy" and opts.get("agent_type"): - agent_type = opts["agent_type"] - if output_dir == "./logs" and opts.get("output_dir"): - output_dir = opts["output_dir"] - if data_dir == "./datasets" and opts.get("data_dir"): - data_dir = opts["data_dir"] - if llm_url is None: - llm_url = opts.get("llm_base_url") - if seed is None: - seed = opts.get("agent_seed") - if backend is None: - backend = opts.get("backend") - if model == "default" and opts.get("model", "default") != "default": - model = opts["model"] - if port == 8080 and opts.get("port", 8080) != 8080: - port = opts["port"] - if llm_kwargs_raw is None: - llm_kwargs_raw = opts.get("llm_kwargs_raw") - if max_retries == 3 and opts.get("max_retries", 3) != 3: - max_retries = opts["max_retries"] - - if not task_name: + saved = _json.loads(config_path.read_text()).get("cli_options", {}) + # Saved values fill gaps; explicit CLI args win + run_kwargs = {**saved, **raw} + else: + run_kwargs = raw + + if not run_kwargs.get("task_name"): logger.error("Task name is required. Provide it as a positional arg or use --resume.") sys.exit(1) runner = EvaluationRunner( - task_name=task_name, - agent_type=agent_type, - output_dir=output_dir, - data_dir=data_dir, - llm_base_url=llm_url, - agent_seed=seed, - backend=backend, - model=model, - port=port, - llm_kwargs_raw=llm_kwargs_raw, - max_retries=max_retries, - resume_dir=resume, + **run_kwargs, + resume_dir=resume_dir, redownload=redownload, ) - results = runner.run(max_episodes=max_episodes) + results = runner.run() logger.info("Completed %d episodes.", len(results)) from easi.evaluation.metrics import aggregate_metrics @@ -409,10 +389,7 @@ def main() -> None: parser.parse_args(["sim", "--help"]) elif args.command == "run": - cmd_run(args.task, args.agent, args.output_dir, args.data_dir, - args.max_episodes, args.llm_url, args.seed, - args.backend, args.model, args.port, args.llm_kwargs, - args.max_retries, args.resume, args.redownload) + cmd_run(args) elif args.command == "llm-server": cmd_llm_server(args.host, args.port, args.mode, args.action_space) diff --git a/easi/evaluation/runner.py b/easi/evaluation/runner.py index 05bbe2d..29cdd99 100644 --- a/easi/evaluation/runner.py +++ b/easi/evaluation/runner.py @@ -23,6 +23,7 @@ from __future__ import annotations +import inspect import json import re import time @@ -44,12 +45,16 @@ def _sanitize_dirname(name: str) -> str: class EvaluationRunner: """Sequential evaluation runner.""" + # Session-specific params excluded from config.json + _EXCLUDE_FROM_CONFIG = frozenset({"resume_dir", "redownload"}) + def __init__( self, task_name: str, - agent_type: str = "dummy", + agent_type: str = "react", output_dir: Path | str = "./logs", data_dir: Path | str = "./datasets", + max_episodes: int | None = None, llm_base_url: str | None = None, agent_seed: int | None = None, backend: str | None = None, @@ -60,10 +65,18 @@ def __init__( resume_dir: Path | str | None = None, redownload: bool = False, ): + # Auto-capture all init args for config.json (before any mutation) + frame = inspect.currentframe() + self._cli_options = { + k: v for k, v in inspect.getargvalues(frame).locals.items() + if k not in ("self", "frame") and k not in self._EXCLUDE_FROM_CONFIG + } + self.task_name = task_name self.agent_type = agent_type self.output_dir = Path(output_dir) self.data_dir = Path(data_dir) + self.max_episodes = max_episodes self.llm_base_url = llm_base_url self.agent_seed = agent_seed self.backend = backend @@ -102,7 +115,14 @@ def _resolve_llm_backend(self) -> tuple[str | None, str | None]: f"Use --backend vllm|openai|anthropic|gemini or --llm-url ." ) - def run(self, max_episodes: int | None = None) -> list[dict]: + def _serialize_cli_options(self) -> dict: + """Serialize _cli_options for JSON output (convert Paths to strings).""" + return { + k: str(v) if isinstance(v, Path) else v + for k, v in self._cli_options.items() + } + + def run(self) -> list[dict]: """Run evaluation and return per-episode metric dicts.""" if self.resume_dir: run_dir = self.resume_dir @@ -125,8 +145,8 @@ def run(self, max_episodes: int | None = None) -> list[dict]: if self.redownload: task.download_dataset(force=True) episodes = task.load_episodes() - if max_episodes is not None: - episodes = episodes[:max_episodes] + if self.max_episodes is not None: + episodes = episodes[:self.max_episodes] # 2. Resolve LLM backend and optionally start server backend, base_url = self._resolve_llm_backend() @@ -155,20 +175,7 @@ def run(self, max_episodes: int | None = None) -> list[dict]: config = { "run_id": self.run_id, "total_episodes": len(episodes), - "cli_options": { - "task_name": self.task_name, - "agent_type": self.agent_type, - "output_dir": str(self.output_dir), - "data_dir": str(self.data_dir), - "max_episodes": max_episodes, - "llm_base_url": self.llm_base_url, - "agent_seed": self.agent_seed, - "backend": self.backend, - "model": self.model, - "port": self.port, - "llm_kwargs_raw": self.llm_kwargs_raw, - "max_retries": self.max_retries, - }, + "cli_options": self._serialize_cli_options(), "resolved_backend": backend, "resolved_base_url": base_url, "resolved_generation_kwargs": resolved_gen_kwargs, diff --git a/easi/llm/client.py b/easi/llm/client.py index 1035b30..8b36a8e 100644 --- a/easi/llm/client.py +++ b/easi/llm/client.py @@ -52,11 +52,13 @@ def __init__( model: str, base_url: str | None = None, num_retries: int = 3, + timeout: float = 120.0, **kwargs: Any, ): self.model = model self.base_url = base_url self.num_retries = num_retries + self.timeout = timeout # Only keep params that litellm/OpenAI API recognises. dropped = {k: v for k, v in kwargs.items() if k not in _LITELLM_PARAMS} if dropped: @@ -77,6 +79,7 @@ def generate(self, messages: list[dict], response_format: dict | None = None) -> "model": self.model, "messages": messages, "num_retries": self.num_retries, + "timeout": self.timeout, "drop_params": True, **self.default_kwargs, } @@ -86,7 +89,11 @@ def generate(self, messages: list[dict], response_format: dict | None = None) -> call_kwargs["response_format"] = response_format logger.trace("LLM call: model=%s, messages=%d", self.model, len(messages)) - response = litellm.completion(**call_kwargs) + try: + response = litellm.completion(**call_kwargs) + except Exception as e: + logger.trace("LLM API error: %s: %s", type(e).__name__, e) + raise self._track_usage(response) content = response.choices[0].message.content diff --git a/tests/test_cli_llm_args.py b/tests/test_cli_llm_args.py index d68b37b..5a2ea8d 100644 --- a/tests/test_cli_llm_args.py +++ b/tests/test_cli_llm_args.py @@ -26,7 +26,7 @@ def test_run_model_default(self): from easi.cli import build_parser parser = build_parser() args = parser.parse_args(["run", "dummy_task"]) - assert args.model == "default" + assert args.model is None def test_run_has_port_arg(self): from easi.cli import build_parser @@ -38,7 +38,7 @@ def test_run_port_default(self): from easi.cli import build_parser parser = build_parser() args = parser.parse_args(["run", "dummy_task"]) - assert args.port == 8080 + assert args.port is None def test_run_has_llm_kwargs(self): from easi.cli import build_parser @@ -47,16 +47,16 @@ def test_run_has_llm_kwargs(self): "run", "dummy_task", "--llm-kwargs", '{"tensor_parallel_size": 4}', ]) - assert args.llm_kwargs == '{"tensor_parallel_size": 4}' + assert args.llm_kwargs_raw == '{"tensor_parallel_size": 4}' def test_run_llm_kwargs_default_none(self): from easi.cli import build_parser parser = build_parser() args = parser.parse_args(["run", "dummy_task"]) - assert args.llm_kwargs is None + assert args.llm_kwargs_raw is None def test_backward_compat_llm_url_still_works(self): from easi.cli import build_parser parser = build_parser() args = parser.parse_args(["run", "dummy_task", "--llm-url", "http://localhost:8000"]) - assert args.llm_url == "http://localhost:8000" + assert args.llm_base_url == "http://localhost:8000" diff --git a/tests/test_e2e_evaluation.py b/tests/test_e2e_evaluation.py index c6ec49c..5cdaa14 100644 --- a/tests/test_e2e_evaluation.py +++ b/tests/test_e2e_evaluation.py @@ -45,8 +45,9 @@ def test_max_episodes_limit(self, tmp_path): task_name="dummy_task", agent_type="dummy", output_dir=tmp_path / "logs", + max_episodes=1, ) - results = runner.run(max_episodes=1) + results = runner.run() assert len(results) == 1 def test_per_episode_files(self, tmp_path): @@ -56,8 +57,9 @@ def test_per_episode_files(self, tmp_path): task_name="dummy_task", agent_type="dummy", output_dir=output_dir, + max_episodes=2, ) - runner.run(max_episodes=2) + runner.run() run_dir = _find_run_dir(output_dir) episode_dirs = sorted((run_dir / "episodes").iterdir()) @@ -77,8 +79,9 @@ def test_episode_metrics_structure(self, tmp_path): task_name="dummy_task", agent_type="dummy", output_dir=tmp_path / "logs", + max_episodes=1, ) - results = runner.run(max_episodes=1) + results = runner.run() ep = results[0] assert "success" in ep @@ -112,14 +115,16 @@ def test_deterministic_with_seed(self, tmp_path): agent_type="dummy", output_dir=tmp_path / "a", agent_seed=123, - ).run(max_episodes=2) + max_episodes=2, + ).run() results_b = EvaluationRunner( task_name="dummy_task", agent_type="dummy", output_dir=tmp_path / "b", agent_seed=123, - ).run(max_episodes=2) + max_episodes=2, + ).run() for a, b in zip(results_a, results_b): assert a["num_steps"] == b["num_steps"] @@ -156,8 +161,9 @@ def test_config_json_saved(self, tmp_path): agent_type="dummy", output_dir=output_dir, agent_seed=42, + max_episodes=1, ) - runner.run(max_episodes=1) + runner.run() run_dir = _find_run_dir(output_dir) config = json.loads((run_dir / "config.json").read_text()) @@ -177,8 +183,9 @@ def test_trajectory_jsonl_format(self, tmp_path): task_name="dummy_task", agent_type="dummy", output_dir=output_dir, + max_episodes=1, ) - runner.run(max_episodes=1) + runner.run() run_dir = _find_run_dir(output_dir) episode_dirs = sorted((run_dir / "episodes").iterdir()) @@ -207,8 +214,9 @@ def test_images_saved_to_episode_dir(self, tmp_path): task_name="dummy_task", agent_type="dummy", output_dir=output_dir, + max_episodes=1, ) - runner.run(max_episodes=1) + runner.run() run_dir = _find_run_dir(output_dir) episode_dirs = sorted((run_dir / "episodes").iterdir()) @@ -228,9 +236,10 @@ def test_max_retries_stored(self, tmp_path): agent_type="dummy", output_dir=output_dir, max_retries=5, + max_episodes=1, ) assert runner.max_retries == 5 - runner.run(max_episodes=1) + runner.run() run_dir = _find_run_dir(output_dir) config = json.loads((run_dir / "config.json").read_text()) @@ -363,6 +372,7 @@ def test_retry_on_episode_failure(self, tmp_path): agent_type="dummy", output_dir=output_dir, max_retries=3, + max_episodes=1, ) call_count = {"n": 0} @@ -375,7 +385,7 @@ def failing_first_call(*args, **kwargs): return original_run_episode(*args, **kwargs) with patch.object(runner, "_run_episode", side_effect=failing_first_call): - results = runner.run(max_episodes=1) + results = runner.run() assert len(results) == 1 assert "error" not in results[0] @@ -391,13 +401,14 @@ def test_retry_exhausted_records_failure(self, tmp_path): agent_type="dummy", output_dir=output_dir, max_retries=2, + max_episodes=1, ) with patch.object( runner, "_run_episode", side_effect=RuntimeError("persistent crash"), ): - results = runner.run(max_episodes=1) + results = runner.run() assert len(results) == 1 assert results[0]["success"] == 0.0 @@ -414,6 +425,7 @@ def test_retry_clears_partial_files(self, tmp_path): agent_type="dummy", output_dir=output_dir, max_retries=2, + max_episodes=1, ) original_run_episode = runner._run_episode @@ -432,7 +444,7 @@ def crash_then_succeed(*args, **kwargs): return original_run_episode(*args, **kwargs) with patch.object(runner, "_run_episode", side_effect=crash_then_succeed): - results = runner.run(max_episodes=1) + results = runner.run() assert len(results) == 1 # The partial file should have been cleaned up @@ -452,6 +464,7 @@ def test_retry_continues_to_next_episode(self, tmp_path): agent_type="dummy", output_dir=output_dir, max_retries=1, + max_episodes=2, ) original_run_episode = runner._run_episode @@ -465,7 +478,7 @@ def fail_first_episode(*args, **kwargs): return original_run_episode(*args, **kwargs) with patch.object(runner, "_run_episode", side_effect=fail_first_episode): - results = runner.run(max_episodes=2) + results = runner.run() # Both episodes should have results assert len(results) == 2 @@ -479,47 +492,46 @@ def fail_first_episode(*args, **kwargs): class TestResumeConfigLoading: def test_resume_loads_config_from_run_dir(self, tmp_path): """cmd_run with --resume loads task_name and options from config.json.""" + from argparse import Namespace from easi.cli import cmd_run output_dir = tmp_path / "logs" - # First run with specific options - cmd_run( + # First run — only 2 of 3 episodes + runner = EvaluationRunner( task_name="dummy_task", agent_type="dummy", - output_dir=str(output_dir), - data_dir="./datasets", + output_dir=output_dir, max_episodes=2, - llm_url=None, - seed=42, - backend=None, - model="default", - port=8080, - llm_kwargs_raw=None, - max_retries=3, - resume=None, + agent_seed=42, ) + runner.run() run_dir = _find_run_dir(output_dir) - # Resume without specifying task_name — should load from config.json - cmd_run( + # Resume with higher max_episodes to complete remaining + # Saved config has max_episodes=2; override to 3 to run all. + args = Namespace( + command="run", + verbosity="INFO", task_name=None, agent_type="dummy", - output_dir="./logs", - data_dir="./datasets", - max_episodes=None, - llm_url=None, - seed=None, + output_dir=None, + data_dir=None, + max_episodes=3, + llm_base_url=None, + agent_seed=None, backend=None, - model="default", - port=8080, + model=None, + port=None, llm_kwargs_raw=None, - max_retries=3, - resume=str(run_dir), + max_retries=None, + resume_dir=str(run_dir), + redownload=False, ) + cmd_run(args) - # Should have completed successfully (3 total episodes from config) + # Should have completed all 3 episodes (2 from first run + 1 from resume) summary = json.loads((run_dir / "summary.json").read_text()) assert summary["num_episodes"] == 3 @@ -529,7 +541,7 @@ def test_cli_max_retries_default(self): from easi.cli import build_parser parser = build_parser() args = parser.parse_args(["run", "dummy_task"]) - assert args.max_retries == 3 + assert args.max_retries is None def test_cli_max_retries_custom(self): from easi.cli import build_parser @@ -541,18 +553,33 @@ def test_cli_resume_default(self): from easi.cli import build_parser parser = build_parser() args = parser.parse_args(["run", "dummy_task"]) - assert args.resume is None + assert args.resume_dir is None def test_cli_resume_custom(self): from easi.cli import build_parser parser = build_parser() args = parser.parse_args(["run", "dummy_task", "--resume", "/tmp/logs/run_123"]) - assert args.resume == "/tmp/logs/run_123" + assert args.resume_dir == "/tmp/logs/run_123" def test_cli_resume_without_task(self): """--resume should work without specifying a task name.""" from easi.cli import build_parser parser = build_parser() args = parser.parse_args(["run", "--resume", "/tmp/logs/run_123"]) - assert args.resume == "/tmp/logs/run_123" - assert args.task is None + assert args.resume_dir == "/tmp/logs/run_123" + assert args.task_name is None + + def test_cli_all_defaults_are_none(self): + """All argparse defaults should be None (real defaults in __init__).""" + from easi.cli import build_parser + parser = build_parser() + args = parser.parse_args(["run", "dummy_task"]) + assert args.agent_type is None + assert args.output_dir is None + assert args.data_dir is None + assert args.model is None + assert args.port is None + assert args.max_retries is None + assert args.llm_base_url is None + assert args.agent_seed is None + assert args.llm_kwargs_raw is None diff --git a/tests/test_evaluation_runner.py b/tests/test_evaluation_runner.py index 14ba51d..0afe010 100644 --- a/tests/test_evaluation_runner.py +++ b/tests/test_evaluation_runner.py @@ -22,8 +22,9 @@ def test_run_single_episode(self, tmp_path): task_name="dummy_task", agent_type="dummy", output_dir=tmp_path / "logs", + max_episodes=1, ) - results = runner.run(max_episodes=1) + results = runner.run() assert len(results) == 1 assert "success" in results[0] @@ -47,8 +48,9 @@ def test_results_saved_to_disk(self, tmp_path): task_name="dummy_task", agent_type="dummy", output_dir=output_dir, + max_episodes=1, ) - runner.run(max_episodes=1) + runner.run() run_dir = _find_run_dir(output_dir) assert (run_dir / "config.json").exists() @@ -80,3 +82,101 @@ def test_summary_aggregates_metrics(self, tmp_path): assert "success_rate" in summary assert "avg_steps" in summary assert "num_episodes" in summary + + +class TestCliOptionsCapture: + """Tests for auto-captured _cli_options via inspect.""" + + def test_cli_options_contains_all_init_args(self): + runner = EvaluationRunner(task_name="dummy_task") + opts = runner._cli_options + # All non-excluded init params should be present + expected_keys = { + "task_name", "agent_type", "output_dir", "data_dir", + "max_episodes", "llm_base_url", "agent_seed", "backend", + "model", "port", "llm_kwargs_raw", "max_retries", + } + assert set(opts.keys()) == expected_keys + + def test_cli_options_excludes_session_params(self): + runner = EvaluationRunner( + task_name="dummy_task", + resume_dir="/some/path", + redownload=True, + ) + assert "resume_dir" not in runner._cli_options + assert "redownload" not in runner._cli_options + + def test_cli_options_captures_raw_values(self): + """Values are captured before Path conversion.""" + runner = EvaluationRunner( + task_name="test_task", + agent_type="react", + output_dir="/custom/output", + data_dir="/custom/data", + backend="openai", + model="gpt-4o", + port=9090, + max_episodes=5, + max_retries=2, + ) + opts = runner._cli_options + assert opts["task_name"] == "test_task" + assert opts["agent_type"] == "react" + assert opts["output_dir"] == "/custom/output" + assert opts["data_dir"] == "/custom/data" + assert opts["backend"] == "openai" + assert opts["model"] == "gpt-4o" + assert opts["port"] == 9090 + assert opts["max_episodes"] == 5 + assert opts["max_retries"] == 2 + + def test_cli_options_defaults(self): + """Default values are captured correctly.""" + runner = EvaluationRunner(task_name="dummy_task") + opts = runner._cli_options + assert opts["agent_type"] == "react" + assert opts["output_dir"] == "./logs" + assert opts["data_dir"] == "./datasets" + assert opts["model"] == "default" + assert opts["port"] == 8080 + assert opts["max_retries"] == 3 + assert opts["max_episodes"] is None + assert opts["llm_base_url"] is None + assert opts["agent_seed"] is None + assert opts["backend"] is None + assert opts["llm_kwargs_raw"] is None + + def test_serialize_cli_options_converts_paths(self): + """Path objects in _cli_options are serialized to strings.""" + from pathlib import Path + runner = EvaluationRunner( + task_name="dummy_task", + output_dir=Path("/some/path"), + ) + serialized = runner._serialize_cli_options() + assert isinstance(serialized["output_dir"], str) + assert serialized["output_dir"] == "/some/path" + + def test_config_json_uses_cli_options(self, tmp_path): + """config.json cli_options matches _serialize_cli_options().""" + output_dir = tmp_path / "logs" + runner = EvaluationRunner( + task_name="dummy_task", + agent_type="dummy", + output_dir=output_dir, + max_episodes=1, + model="test-model", + ) + runner.run() + + run_dir = _find_run_dir(output_dir) + config = json.loads((run_dir / "config.json").read_text()) + opts = config["cli_options"] + assert opts["task_name"] == "dummy_task" + assert opts["agent_type"] == "dummy" + assert opts["model"] == "test-model" + assert opts["max_episodes"] == 1 + # Session-specific params should NOT be in config + assert "resume_dir" not in opts + assert "redownload" not in opts From 2bce3b25614a796c458495af56d77a4ddb7e1b12 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Thu, 19 Feb 2026 22:35:36 +0800 Subject: [PATCH 058/239] =?UTF-8?q?[Feat]=20Rename=20easi=20run=20?= =?UTF-8?q?=E2=86=92=20easi=20start=20with=20multi-task=20support?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Accept multiple tasks via positional args or --tasks (comma-separated). Tasks run sequentially, each with its own EvaluationRunner instance. Combined summary printed after all tasks complete. --- CLAUDE.md | 3 +- easi/cli.py | 132 ++++++++++++++++++++++------------ tests/test_cli_llm_args.py | 18 ++--- tests/test_e2e_evaluation.py | 136 ++++++++++++++++++++++++++++------- 4 files changed, 210 insertions(+), 79 deletions(-) diff --git a/CLAUDE.md b/CLAUDE.md index 3157983..636c565 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -103,7 +103,8 @@ easi/ easi env list|install|check # Manage simulator environments easi task list|info|download # Manage tasks easi sim test # Smoke test a simulator -easi run --agent dummy|react # Run evaluation +easi start --agent dummy|react # Run evaluation (single task) +easi start --tasks t1,t2 --agent react # Run evaluation (multi-task) easi llm-server [--port PORT] # Start dummy LLM server ``` diff --git a/easi/cli.py b/easi/cli.py index fea5daf..7a9926e 100644 --- a/easi/cli.py +++ b/easi/cli.py @@ -4,6 +4,7 @@ easi env list|install|check easi task list|info|download easi sim test + easi start [ ...] [--tasks t1,t2] # Run evaluation easi llm-server [--port PORT] [--mode MODE] """ @@ -78,37 +79,39 @@ def build_parser() -> argparse.ArgumentParser: sim_test.add_argument("--timeout", type=float, default=200.0, help="Bridge startup timeout in seconds (default: 200)") - # --- run command --- - run_parser = subparsers.add_parser("run", help="Run a full evaluation", parents=[common]) + # --- start command --- + start_parser = subparsers.add_parser("start", help="Run a full evaluation", parents=[common]) # All defaults are None so resume logic can distinguish "user provided" from "default". # Real defaults live in EvaluationRunner.__init__. - run_parser.add_argument("task_name", type=str, nargs="?", default=None, metavar="task", - help="Task name (e.g., 'dummy_task', 'ebalfred_base'). " - "Optional when --resume is provided.") - run_parser.add_argument("--agent", type=str, default=None, choices=["dummy", "react"], - dest="agent_type") - run_parser.add_argument("--output-dir", type=str, default=None, - help="Base output directory (creates ///)") - run_parser.add_argument("--data-dir", type=str, default=None, - help="Directory for downloading/caching datasets (default: ./datasets)") - run_parser.add_argument("--max-episodes", type=int, default=None) - run_parser.add_argument("--llm-url", type=str, default=None, dest="llm_base_url", - help="LLM server URL") - run_parser.add_argument("--seed", type=int, default=None, dest="agent_seed") - run_parser.add_argument("--backend", type=str, default=None, - help="LLM backend: vllm, openai, anthropic, gemini, dummy") - run_parser.add_argument("--model", type=str, default=None, - help="Model name (HF path for vLLM, API name for proprietary)") - run_parser.add_argument("--port", type=int, default=None, - help="Port for local inference server (default: 8080)") - run_parser.add_argument("--llm-kwargs", type=str, default=None, dest="llm_kwargs_raw", - help='JSON string of extra kwargs, e.g. \'{"tensor_parallel_size": 4}\'') - run_parser.add_argument("--max-retries", type=int, default=None, - help="Max LLM retry attempts on transient errors (default: 3)") - run_parser.add_argument("--resume", type=str, default=None, dest="resume_dir", - help="Path to a previous run directory to resume from") - run_parser.add_argument("--redownload", action="store_true", - help="Delete cached dataset and re-download from source") + start_parser.add_argument("task_names_positional", type=str, nargs="*", default=None, metavar="task", + help="Task name(s) (e.g., 'dummy_task', 'ebalfred_base'). " + "Optional when --resume is provided.") + start_parser.add_argument("--tasks", type=str, default=None, dest="tasks_csv", + help="Comma-separated task names (e.g., 'ebalfred_base,ebnavigation_base')") + start_parser.add_argument("--agent", type=str, default=None, choices=["dummy", "react"], + dest="agent_type") + start_parser.add_argument("--output-dir", type=str, default=None, + help="Base output directory (creates ///)") + start_parser.add_argument("--data-dir", type=str, default=None, + help="Directory for downloading/caching datasets (default: ./datasets)") + start_parser.add_argument("--max-episodes", type=int, default=None) + start_parser.add_argument("--llm-url", type=str, default=None, dest="llm_base_url", + help="LLM server URL") + start_parser.add_argument("--seed", type=int, default=None, dest="agent_seed") + start_parser.add_argument("--backend", type=str, default=None, + help="LLM backend: vllm, openai, anthropic, gemini, dummy") + start_parser.add_argument("--model", type=str, default=None, + help="Model name (HF path for vLLM, API name for proprietary)") + start_parser.add_argument("--port", type=int, default=None, + help="Port for local inference server (default: 8080)") + start_parser.add_argument("--llm-kwargs", type=str, default=None, dest="llm_kwargs_raw", + help='JSON string of extra kwargs, e.g. \'{"tensor_parallel_size": 4}\'') + start_parser.add_argument("--max-retries", type=int, default=None, + help="Max LLM retry attempts on transient errors (default: 3)") + start_parser.add_argument("--resume", type=str, default=None, dest="resume_dir", + help="Path to a previous run directory to resume from") + start_parser.add_argument("--redownload", action="store_true", + help="Delete cached dataset and re-download from source") # --- llm-server command --- llm_parser = subparsers.add_parser("llm-server", help="Start dummy LLM server", parents=[common]) @@ -296,16 +299,28 @@ def cmd_sim_test(simulator: str, steps: int, timeout: float) -> None: sys.exit(1) -def cmd_run(args): +def _resolve_task_list(args_ns) -> list[str]: + """Build task list from positional args and/or --tasks flag.""" + tasks: list[str] = [] + if args_ns.tasks_csv: + tasks = [t.strip() for t in args_ns.tasks_csv.split(",") if t.strip()] + elif args_ns.task_names_positional: + tasks = args_ns.task_names_positional + return tasks + + +def cmd_start(args): import json as _json from pathlib import Path from easi.evaluation.runner import EvaluationRunner + task_list = _resolve_task_list(args) + # Collect explicitly-provided CLI args (argparse defaults are None) raw = {k: v for k, v in vars(args).items() if v is not None} # Remove argparse internals that aren't runner params - for key in ("command", "verbosity"): + for key in ("command", "verbosity", "task_names_positional", "tasks_csv"): raw.pop(key, None) # Extract session-specific params (not saved in config.json) @@ -313,6 +328,9 @@ def cmd_run(args): redownload = raw.pop("redownload", False) if resume_dir: + if len(task_list) > 1: + logger.error("--resume cannot be used with multiple tasks.") + sys.exit(1) config_path = Path(resume_dir) / "config.json" if not config_path.exists(): logger.error("Resume directory has no config.json: %s", resume_dir) @@ -320,25 +338,49 @@ def cmd_run(args): saved = _json.loads(config_path.read_text()).get("cli_options", {}) # Saved values fill gaps; explicit CLI args win run_kwargs = {**saved, **raw} + # If no task was given on CLI, pull from saved config + if not task_list: + saved_task = saved.get("task_name") + if saved_task: + task_list = [saved_task] else: run_kwargs = raw - if not run_kwargs.get("task_name"): - logger.error("Task name is required. Provide it as a positional arg or use --resume.") + if not task_list: + logger.error("Task name is required. Provide it as a positional arg, --tasks, or use --resume.") sys.exit(1) - runner = EvaluationRunner( - **run_kwargs, - resume_dir=resume_dir, - redownload=redownload, - ) - results = runner.run() - logger.info("Completed %d episodes.", len(results)) + # Remove task_name from run_kwargs; it's passed per-task below + run_kwargs.pop("task_name", None) + from easi.evaluation.metrics import aggregate_metrics - summary = aggregate_metrics(results) - for key, value in summary.items(): - logger.info(" %s: %s", key, value) + all_summaries: list[tuple[str, dict]] = [] + + for task_name in task_list: + logger.info("=== Starting evaluation: %s ===", task_name) + runner = EvaluationRunner( + task_name=task_name, + **run_kwargs, + resume_dir=resume_dir, + redownload=redownload, + ) + results = runner.run() + logger.info("Completed %d episodes for %s.", len(results), task_name) + + summary = aggregate_metrics(results) + all_summaries.append((task_name, summary)) + for key, value in summary.items(): + logger.info(" %s: %s", key, value) + + # Combined summary when multiple tasks were evaluated + if len(all_summaries) > 1: + logger.info("") + logger.info("=== Combined Summary ===") + for task_name, summary in all_summaries: + logger.info("[%s]", task_name) + for key, value in summary.items(): + logger.info(" %s: %s", key, value) def cmd_llm_server(host: str, port: int, mode: str, action_space: list[str]) -> None: @@ -388,8 +430,8 @@ def main() -> None: else: parser.parse_args(["sim", "--help"]) - elif args.command == "run": - cmd_run(args) + elif args.command == "start": + cmd_start(args) elif args.command == "llm-server": cmd_llm_server(args.host, args.port, args.mode, args.action_space) diff --git a/tests/test_cli_llm_args.py b/tests/test_cli_llm_args.py index 5a2ea8d..bae7940 100644 --- a/tests/test_cli_llm_args.py +++ b/tests/test_cli_llm_args.py @@ -7,44 +7,44 @@ class TestBuildParser: def test_run_has_backend_arg(self): from easi.cli import build_parser parser = build_parser() - args = parser.parse_args(["run", "dummy_task", "--backend", "vllm"]) + args = parser.parse_args(["start", "dummy_task", "--backend", "vllm"]) assert args.backend == "vllm" def test_run_backend_default_is_none(self): from easi.cli import build_parser parser = build_parser() - args = parser.parse_args(["run", "dummy_task"]) + args = parser.parse_args(["start", "dummy_task"]) assert args.backend is None def test_run_has_model_arg(self): from easi.cli import build_parser parser = build_parser() - args = parser.parse_args(["run", "dummy_task", "--model", "gpt-4o"]) + args = parser.parse_args(["start", "dummy_task", "--model", "gpt-4o"]) assert args.model == "gpt-4o" def test_run_model_default(self): from easi.cli import build_parser parser = build_parser() - args = parser.parse_args(["run", "dummy_task"]) + args = parser.parse_args(["start", "dummy_task"]) assert args.model is None def test_run_has_port_arg(self): from easi.cli import build_parser parser = build_parser() - args = parser.parse_args(["run", "dummy_task", "--port", "9090"]) + args = parser.parse_args(["start", "dummy_task", "--port", "9090"]) assert args.port == 9090 def test_run_port_default(self): from easi.cli import build_parser parser = build_parser() - args = parser.parse_args(["run", "dummy_task"]) + args = parser.parse_args(["start", "dummy_task"]) assert args.port is None def test_run_has_llm_kwargs(self): from easi.cli import build_parser parser = build_parser() args = parser.parse_args([ - "run", "dummy_task", + "start", "dummy_task", "--llm-kwargs", '{"tensor_parallel_size": 4}', ]) assert args.llm_kwargs_raw == '{"tensor_parallel_size": 4}' @@ -52,11 +52,11 @@ def test_run_has_llm_kwargs(self): def test_run_llm_kwargs_default_none(self): from easi.cli import build_parser parser = build_parser() - args = parser.parse_args(["run", "dummy_task"]) + args = parser.parse_args(["start", "dummy_task"]) assert args.llm_kwargs_raw is None def test_backward_compat_llm_url_still_works(self): from easi.cli import build_parser parser = build_parser() - args = parser.parse_args(["run", "dummy_task", "--llm-url", "http://localhost:8000"]) + args = parser.parse_args(["start", "dummy_task", "--llm-url", "http://localhost:8000"]) assert args.llm_base_url == "http://localhost:8000" diff --git a/tests/test_e2e_evaluation.py b/tests/test_e2e_evaluation.py index 5cdaa14..eaa8ea0 100644 --- a/tests/test_e2e_evaluation.py +++ b/tests/test_e2e_evaluation.py @@ -491,9 +491,9 @@ def fail_first_episode(*args, **kwargs): class TestResumeConfigLoading: def test_resume_loads_config_from_run_dir(self, tmp_path): - """cmd_run with --resume loads task_name and options from config.json.""" + """cmd_start with --resume loads task_name and options from config.json.""" from argparse import Namespace - from easi.cli import cmd_run + from easi.cli import cmd_start output_dir = tmp_path / "logs" @@ -512,9 +512,10 @@ def test_resume_loads_config_from_run_dir(self, tmp_path): # Resume with higher max_episodes to complete remaining # Saved config has max_episodes=2; override to 3 to run all. args = Namespace( - command="run", + command="start", verbosity="INFO", - task_name=None, + task_names_positional=[], + tasks_csv=None, agent_type="dummy", output_dir=None, data_dir=None, @@ -529,7 +530,7 @@ def test_resume_loads_config_from_run_dir(self, tmp_path): resume_dir=str(run_dir), redownload=False, ) - cmd_run(args) + cmd_start(args) # Should have completed all 3 episodes (2 from first run + 1 from resume) summary = json.loads((run_dir / "summary.json").read_text()) @@ -537,43 +538,45 @@ def test_resume_loads_config_from_run_dir(self, tmp_path): class TestCLIParsing: - def test_cli_max_retries_default(self): + def test_cli_single_task_positional(self): from easi.cli import build_parser parser = build_parser() - args = parser.parse_args(["run", "dummy_task"]) - assert args.max_retries is None + args = parser.parse_args(["start", "dummy_task"]) + assert args.task_names_positional == ["dummy_task"] + assert args.tasks_csv is None - def test_cli_max_retries_custom(self): + def test_cli_multiple_tasks_positional(self): from easi.cli import build_parser parser = build_parser() - args = parser.parse_args(["run", "dummy_task", "--max-retries", "5"]) - assert args.max_retries == 5 + args = parser.parse_args(["start", "dummy_task", "ebalfred_base"]) + assert args.task_names_positional == ["dummy_task", "ebalfred_base"] - def test_cli_resume_default(self): + def test_cli_tasks_flag_csv(self): from easi.cli import build_parser parser = build_parser() - args = parser.parse_args(["run", "dummy_task"]) - assert args.resume_dir is None + args = parser.parse_args(["start", "--tasks", "dummy_task,ebalfred_base"]) + assert args.tasks_csv == "dummy_task,ebalfred_base" - def test_cli_resume_custom(self): - from easi.cli import build_parser - parser = build_parser() - args = parser.parse_args(["run", "dummy_task", "--resume", "/tmp/logs/run_123"]) - assert args.resume_dir == "/tmp/logs/run_123" + def test_cli_tasks_flag_wins_over_positional(self): + from easi.cli import _resolve_task_list + from argparse import Namespace + args = Namespace( + task_names_positional=["dummy_task"], + tasks_csv="ebalfred_base,ebnavigation_base", + ) + assert _resolve_task_list(args) == ["ebalfred_base", "ebnavigation_base"] def test_cli_resume_without_task(self): - """--resume should work without specifying a task name.""" from easi.cli import build_parser parser = build_parser() - args = parser.parse_args(["run", "--resume", "/tmp/logs/run_123"]) + args = parser.parse_args(["start", "--resume", "/tmp/logs/run_123"]) assert args.resume_dir == "/tmp/logs/run_123" - assert args.task_name is None + assert args.task_names_positional == [] def test_cli_all_defaults_are_none(self): - """All argparse defaults should be None (real defaults in __init__).""" from easi.cli import build_parser parser = build_parser() - args = parser.parse_args(["run", "dummy_task"]) + args = parser.parse_args(["start", "dummy_task"]) assert args.agent_type is None assert args.output_dir is None assert args.data_dir is None @@ -583,3 +586,88 @@ def test_cli_all_defaults_are_none(self): assert args.llm_base_url is None assert args.agent_seed is None assert args.llm_kwargs_raw is None + + def test_cli_max_retries_default(self): + from easi.cli import build_parser + parser = build_parser() + args = parser.parse_args(["start", "dummy_task"]) + assert args.max_retries is None + + def test_cli_max_retries_custom(self): + from easi.cli import build_parser + parser = build_parser() + args = parser.parse_args(["start", "dummy_task", "--max-retries", "5"]) + assert args.max_retries == 5 + + def test_cli_resume_default(self): + from easi.cli import build_parser + parser = build_parser() + args = parser.parse_args(["start", "dummy_task"]) + assert args.resume_dir is None + + def test_cli_resume_custom(self): + from easi.cli import build_parser + parser = build_parser() + args = parser.parse_args(["start", "dummy_task", "--resume", "/tmp/logs/run_123"]) + assert args.resume_dir == "/tmp/logs/run_123" + + +class TestMultiTaskRun: + def test_sequential_single_task_via_flag(self, tmp_path): + """Run a single task via --tasks flag.""" + from argparse import Namespace + from easi.cli import cmd_start + + output_dir = tmp_path / "logs" + args = Namespace( + command="start", + verbosity="INFO", + task_names_positional=[], + tasks_csv="dummy_task", + agent_type="dummy", + output_dir=str(output_dir), + data_dir=None, + max_episodes=1, + llm_base_url=None, + agent_seed=None, + backend=None, + model=None, + port=None, + llm_kwargs_raw=None, + max_retries=None, + resume_dir=None, + redownload=False, + ) + cmd_start(args) + + task_dir = output_dir / "dummy_task" + assert task_dir.exists() + run_dirs = list(task_dir.iterdir()) + assert len(run_dirs) == 1 + + def test_resume_blocked_with_multi_task(self): + """--resume with multiple tasks should fail.""" + from argparse import Namespace + from easi.cli import cmd_start + + args = Namespace( + command="start", + verbosity="INFO", + task_names_positional=[], + tasks_csv="dummy_task,ebalfred_base", + agent_type="dummy", + output_dir="./logs", + data_dir=None, + max_episodes=1, + llm_base_url=None, + agent_seed=None, + backend=None, + model=None, + port=None, + llm_kwargs_raw=None, + max_retries=None, + resume_dir="/tmp/some/path", + redownload=False, + ) + with pytest.raises(SystemExit): + cmd_start(args) From e20305aa2e5442ee3694a803065fc3eef5cfb7ff Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Thu, 19 Feb 2026 22:51:52 +0800 Subject: [PATCH 059/239] [Feat] Stream vLLM server output through TRACE logger instead of log file Server stdout/stderr is now piped to a background thread that logs each line at TRACE level. No more separate .log file. Error messages now suggest --verbosity TRACE to diagnose startup failures. --- easi/llm/server_manager.py | 44 ++++++++++++++++++++++---------------- 1 file changed, 25 insertions(+), 19 deletions(-) diff --git a/easi/llm/server_manager.py b/easi/llm/server_manager.py index d5a62ab..0e70f02 100644 --- a/easi/llm/server_manager.py +++ b/easi/llm/server_manager.py @@ -7,6 +7,7 @@ import socket import subprocess import sys +import threading import time from pathlib import Path @@ -30,16 +31,15 @@ def __init__( port: int = 8080, server_kwargs: dict | None = None, startup_timeout: float = _DEFAULT_STARTUP_TIMEOUT, - log_dir: Path | None = None, + log_dir: Path | None = None, # Deprecated: server output now goes to logger ): self.backend = backend self.model = model self.port = port self.server_kwargs = server_kwargs or {} self.startup_timeout = startup_timeout - self.log_dir = log_dir self._process: subprocess.Popen | None = None - self._log_file = None + self._log_thread: threading.Thread | None = None def start(self) -> str: """Start the server, wait for health, return base_url.""" @@ -48,17 +48,17 @@ def start(self) -> str: cmd = self._build_command() logger.info("Starting %s server: %s", self.backend, " ".join(cmd)) - if self.log_dir: - self.log_dir.mkdir(parents=True, exist_ok=True) - log_path = self.log_dir / f"{self.backend}_server.log" - self._log_file = open(log_path, "w") - logger.info("Server logs: %s", log_path) - self._process = subprocess.Popen( cmd, - stdout=self._log_file or subprocess.DEVNULL, - stderr=self._log_file or subprocess.DEVNULL, + stdout=subprocess.PIPE, + stderr=subprocess.STDOUT, + ) + self._log_thread = threading.Thread( + target=self._stream_output, + args=(self._process,), + daemon=True, ) + self._log_thread.start() base_url = f"http://localhost:{self.port}/v1" self._wait_for_health(base_url) @@ -77,9 +77,9 @@ def stop(self) -> None: self._process.kill() self._process.wait(timeout=10) self._process = None - if self._log_file is not None: - self._log_file.close() - self._log_file = None + if self._log_thread is not None: + self._log_thread.join(timeout=5) + self._log_thread = None def is_running(self) -> bool: """Check if server process is alive.""" @@ -116,6 +116,15 @@ def _build_command(self) -> list[str]: else: raise ValueError(f"Unsupported server backend: {self.backend}") + @staticmethod + def _stream_output(proc: subprocess.Popen) -> None: + """Read server stdout/stderr line by line and log at TRACE level.""" + for raw_line in proc.stdout: + line = raw_line.decode("utf-8", errors="replace").rstrip() + if line: + logger.trace("[server] %s", line) + proc.stdout.close() + def _wait_for_health(self, base_url: str) -> None: """Poll /health until the server responds or timeout.""" health_url = base_url.replace("/v1", "") + "/health" @@ -125,7 +134,7 @@ def _wait_for_health(self, base_url: str) -> None: if self._process and self._process.poll() is not None: raise RuntimeError( f"{self.backend} server exited with code {self._process.returncode}. " - f"Check server logs for details." + f"Run with --verbosity TRACE to see server output." ) try: resp = requests.get(health_url, timeout=5) @@ -137,12 +146,9 @@ def _wait_for_health(self, base_url: str) -> None: time.sleep(_HEALTH_POLL_INTERVAL) self.stop() - log_hint = "" - if self.log_dir: - log_hint = f" Check logs at {self.log_dir / f'{self.backend}_server.log'}" raise RuntimeError( f"{self.backend} server failed to start within " - f"{self.startup_timeout}s.{log_hint}" + f"{self.startup_timeout}s. Run with --verbosity TRACE to see server output." ) def __enter__(self) -> str: From 1751b21c7308d9f0b4147f07cd20a850213198b1 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Thu, 19 Feb 2026 22:54:41 +0800 Subject: [PATCH 060/239] [Chore] Updated pyproject.toml --- pyproject.toml | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 12a51c4..cdf7f79 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -13,6 +13,8 @@ dependencies = [ "pyyaml>=6.0", "datasets>=2.0.0", "huggingface_hub>=0.19.0", + "litellm>=1.40", + "vllm" ] [project.optional-dependencies] @@ -20,9 +22,6 @@ dev = [ "pytest>=7.0", "pytest-timeout>=2.0", ] -llm = [ - "litellm>=1.40", -] [project.scripts] easi = "easi.cli:main" From 22bfe0b496418f7c4ac3c7730c7c93ce1d5720fa Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Thu, 19 Feb 2026 23:19:19 +0800 Subject: [PATCH 061/239] [Fix] Route trust_remote_code and other vLLM flags to server kwargs - Add trust_remote_code, tokenizer_mode, seed, max_num_seqs, disable_log_requests, limit_mm_per_prompt to _SERVER_KWARGS set - Handle boolean kwargs as bare flags (--trust-remote-code) instead of --trust-remote-code True which vLLM rejects --- easi/llm/server_manager.py | 7 ++++++- easi/llm/utils.py | 6 ++++++ 2 files changed, 12 insertions(+), 1 deletion(-) diff --git a/easi/llm/server_manager.py b/easi/llm/server_manager.py index 0e70f02..a9dc8a6 100644 --- a/easi/llm/server_manager.py +++ b/easi/llm/server_manager.py @@ -111,7 +111,12 @@ def _build_command(self) -> list[str]: ] for key, value in self.server_kwargs.items(): flag = "--" + key.replace("_", "-") - cmd.extend([flag, str(value)]) + if isinstance(value, bool): + if value: + cmd.append(flag) + # Skip False booleans (don't add the flag) + else: + cmd.extend([flag, str(value)]) return cmd else: raise ValueError(f"Unsupported server backend: {self.backend}") diff --git a/easi/llm/utils.py b/easi/llm/utils.py index 296eab7..baf0745 100644 --- a/easi/llm/utils.py +++ b/easi/llm/utils.py @@ -30,6 +30,12 @@ "quantization", "enforce_eager", "enable_prefix_caching", + "trust_remote_code", + "tokenizer_mode", + "seed", + "max_num_seqs", + "disable_log_requests", + "limit_mm_per_prompt", } From 4fad44b09e17dcc5ba24d512267ce230e364d082 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Fri, 20 Feb 2026 00:21:37 +0800 Subject: [PATCH 062/239] [Fix] Fixed vllm and transformers version --- pyproject.toml | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index cdf7f79..e94d463 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -14,7 +14,6 @@ dependencies = [ "datasets>=2.0.0", "huggingface_hub>=0.19.0", "litellm>=1.40", - "vllm" ] [project.optional-dependencies] @@ -22,6 +21,10 @@ dev = [ "pytest>=7.0", "pytest-timeout>=2.0", ] +vllm = [ + "vllm>=0.15.1", + "transformers>=4.40.0,<5.0" +] [project.scripts] easi = "easi.cli:main" From 605f4e8311297371adf6ff3f442f66eb2a9cb626 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Fri, 20 Feb 2026 03:10:32 +0800 Subject: [PATCH 063/239] [Feat] Add parallel evaluation runner with --num-parallel flag - Add --num-parallel CLI arg to easi start for concurrent episode execution - Create ParallelRunner class using ThreadPoolExecutor + work-stealing queue - Each worker thread owns its own simulator subprocess + agent + LLM client - Thread-safe progress tracking with [Progress] X/Y logging - Rejects --resume and local vLLM with NotImplementedError - Records num_parallel and wall_clock_seconds in config/summary JSON - Extensive TRACE-level logging for debugging parallel flow --- easi/cli.py | 28 ++- easi/evaluation/parallel_runner.py | 295 +++++++++++++++++++++++++++++ tests/test_parallel_runner.py | 156 +++++++++++++++ 3 files changed, 473 insertions(+), 6 deletions(-) create mode 100644 easi/evaluation/parallel_runner.py create mode 100644 tests/test_parallel_runner.py diff --git a/easi/cli.py b/easi/cli.py index 7a9926e..644ab25 100644 --- a/easi/cli.py +++ b/easi/cli.py @@ -108,6 +108,9 @@ def build_parser() -> argparse.ArgumentParser: help='JSON string of extra kwargs, e.g. \'{"tensor_parallel_size": 4}\'') start_parser.add_argument("--max-retries", type=int, default=None, help="Max LLM retry attempts on transient errors (default: 3)") + start_parser.add_argument("--num-parallel", type=int, default=None, dest="num_parallel", + help="Number of parallel simulator instances (default: 1, sequential). " + "Only supported with proprietary API backends (openai, anthropic, gemini).") start_parser.add_argument("--resume", type=str, default=None, dest="resume_dir", help="Path to a previous run directory to resume from") start_parser.add_argument("--redownload", action="store_true", @@ -352,6 +355,7 @@ def cmd_start(args): # Remove task_name from run_kwargs; it's passed per-task below run_kwargs.pop("task_name", None) + num_parallel = run_kwargs.pop("num_parallel", None) or 1 from easi.evaluation.metrics import aggregate_metrics @@ -359,12 +363,24 @@ def cmd_start(args): for task_name in task_list: logger.info("=== Starting evaluation: %s ===", task_name) - runner = EvaluationRunner( - task_name=task_name, - **run_kwargs, - resume_dir=resume_dir, - redownload=redownload, - ) + + if num_parallel > 1: + from easi.evaluation.parallel_runner import ParallelRunner + runner = ParallelRunner( + task_name=task_name, + num_parallel=num_parallel, + **run_kwargs, + resume_dir=resume_dir, + redownload=redownload, + ) + else: + runner = EvaluationRunner( + task_name=task_name, + **run_kwargs, + resume_dir=resume_dir, + redownload=redownload, + ) + results = runner.run() logger.info("Completed %d episodes for %s.", len(results), task_name) diff --git a/easi/evaluation/parallel_runner.py b/easi/evaluation/parallel_runner.py new file mode 100644 index 0000000..f006ce5 --- /dev/null +++ b/easi/evaluation/parallel_runner.py @@ -0,0 +1,295 @@ +"""Thread-pool based parallel evaluation runner. + +Extends EvaluationRunner with concurrent episode execution: +1. Load task once (shared read-only across workers) +2. Fill a queue with (index, episode) tuples +3. Launch N worker threads, each with its own simulator + agent +4. Workers pull episodes from the queue and run them via inherited _run_episode() +5. Collect results thread-safely, aggregate metrics, save summary +""" + +from __future__ import annotations + +import json +import queue +import threading +import time +from concurrent.futures import ThreadPoolExecutor +from pathlib import Path + +from easi.evaluation.metrics import aggregate_metrics +from easi.evaluation.runner import EvaluationRunner, _sanitize_dirname +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + + +class ParallelRunner(EvaluationRunner): + """Thread-pool based parallel evaluation runner. + + Each worker thread owns its own simulator and agent instance. + Episodes are distributed via a shared queue. + """ + + def __init__(self, *, num_parallel: int = 2, **kwargs): + super().__init__(**kwargs) + self.num_parallel = num_parallel + + def _serialize_cli_options(self) -> dict: + """Add num_parallel to the serialized config.""" + base = super()._serialize_cli_options() + base["num_parallel"] = self.num_parallel + return base + + def run(self) -> list[dict]: + """Run evaluation with thread-pool parallelism.""" + logger.trace( + "ParallelRunner.run() called: task=%s, num_parallel=%d", + self.task_name, self.num_parallel, + ) + + # --- Guard: no resume support --- + if self.resume_dir: + raise NotImplementedError( + "ParallelRunner does not support --resume. " + "Use the sequential EvaluationRunner for resume." + ) + + # --- Guard: no local vLLM --- + backend, base_url = self._resolve_llm_backend() + if backend == "vllm" and base_url is None: + raise NotImplementedError( + "ParallelRunner does not support local vLLM server management. " + "Start vLLM externally and pass --llm-url." + ) + + # --- Phase 1: Load task --- + logger.trace("Phase 1: Loading task") + task = self._create_task() + if self.redownload: + task.download_dataset(force=True) + episodes = task.load_episodes() + if self.max_episodes is not None: + episodes = episodes[: self.max_episodes] + logger.trace( + "Phase 1: Task loaded. %d episodes, simulator_key=%s", + len(episodes), task.simulator_key, + ) + + # --- Phase 2: Resolve LLM backend --- + logger.trace("Phase 2: Resolved LLM backend=%s, base_url=%s", backend, base_url) + + # Compute resolved generation kwargs (YAML defaults + CLI overrides) + from easi.llm.utils import parse_llm_kwargs, split_kwargs + + agent_config = task._config.get("agent", {}) + yaml_gen_kwargs = agent_config.get("generation_kwargs", {}) + all_llm_kwargs = parse_llm_kwargs(self.llm_kwargs_raw) + _, cli_gen_kwargs = split_kwargs(all_llm_kwargs) + resolved_gen_kwargs = {**yaml_gen_kwargs, **cli_gen_kwargs} + + # --- Phase 3: Create output directory and save config --- + logger.trace("Phase 3: Creating output directory and saving config") + run_dir = self.output_dir / self.task_name / self.run_id + episodes_dir = run_dir / "episodes" + episodes_dir.mkdir(parents=True, exist_ok=True) + + config = { + "run_id": self.run_id, + "total_episodes": len(episodes), + "num_parallel": self.num_parallel, + "cli_options": self._serialize_cli_options(), + "resolved_backend": backend, + "resolved_base_url": base_url, + "resolved_generation_kwargs": resolved_gen_kwargs, + "task_config": task._config, + } + (run_dir / "config.json").write_text(json.dumps(config, indent=2)) + logger.trace("Run config:\n%s", json.dumps(config, indent=2, default=str)) + + # --- Phase 4: Fill episode queue --- + logger.trace("Phase 4: Filling episode queue with %d episodes", len(episodes)) + episode_queue: queue.Queue[tuple[int, dict]] = queue.Queue() + for i, episode in enumerate(episodes): + episode_queue.put((i, episode)) + + # --- Phase 5: Prepare thread-safe collection --- + results_lock = threading.Lock() + results_list: list[tuple[int, dict]] = [] + progress = {"completed": 0, "failed": 0} + progress_lock = threading.Lock() + total_episodes = len(episodes) + + num_workers = min(self.num_parallel, len(episodes)) + + def _worker(worker_id: int) -> None: + """Worker thread: owns a simulator + agent, pulls episodes from queue.""" + logger.trace("[Worker %d] Starting up", worker_id) + episodes_done = 0 + + # Create simulator + logger.trace( + "[Worker %d] Creating simulator (key=%s)", + worker_id, task.simulator_key, + ) + sim, sim_runner = self._create_simulator(task.simulator_key, task=task) + logger.trace( + "[Worker %d] Simulator ready (PID=%s)", + worker_id, + getattr(sim_runner, 'pid', 'unknown'), + ) + + # Create agent + logger.trace("[Worker %d] Creating agent", worker_id) + agent = self._create_agent( + task.action_space, task._config, + backend=backend, base_url=base_url, + ) + logger.trace("[Worker %d] Agent ready", worker_id) + + try: + while True: + # Pull next episode + try: + idx, episode = episode_queue.get_nowait() + except queue.Empty: + break + + logger.trace( + "[Worker %d] Queue remaining: ~%d", + worker_id, episode_queue.qsize(), + ) + + episode_id = episode.get("episode_id", f"ep_{idx}") + episode_dir = episodes_dir / f"{idx:03d}_{_sanitize_dirname(episode_id)}" + episode_dir.mkdir(exist_ok=True) + + result = None + for attempt in range(1, self.max_retries + 1): + logger.trace( + "[Worker %d] Running episode %s (attempt %d/%d)", + worker_id, episode_id, attempt, self.max_retries, + ) + try: + result = self._run_episode( + sim, agent, task, episode, idx, episode_dir, + ) + logger.trace( + "[Worker %d] Episode %s completed in %.1fs: %s", + worker_id, episode_id, + result.get("elapsed_seconds", 0), + {k: v for k, v in result.items() + if k in ("success", "num_steps", "elapsed_seconds")}, + ) + break + except Exception as exc: + logger.warning( + "[Worker %d] Episode %s attempt %d/%d failed: %s", + worker_id, episode_id, attempt, self.max_retries, exc, + ) + logger.trace( + "[Worker %d] Exception details:", + worker_id, exc_info=True, + ) + self._clear_episode_dir(episode_dir) + if attempt < self.max_retries: + logger.info( + "[Worker %d] Re-launching simulator for retry...", + worker_id, + ) + try: + sim.close() + except Exception: + pass + sim, sim_runner = self._create_simulator( + task.simulator_key, task=task, + ) + else: + logger.error( + "[Worker %d] Episode %s failed after %d attempts, skipping", + worker_id, episode_id, self.max_retries, + ) + result = { + "episode_id": episode_id, + "instruction": task.get_instruction(episode), + "success": 0.0, + "num_steps": 0, + "elapsed_seconds": 0.0, + "error": str(exc), + } + + # Save per-episode result + (episode_dir / "result.json").write_text( + json.dumps(result, indent=2) + ) + + # Thread-safe results collection + failed = "error" in result + with results_lock: + results_list.append((idx, result)) + + with progress_lock: + progress["completed"] += 1 + if failed: + progress["failed"] += 1 + current_completed = progress["completed"] + current_failed = progress["failed"] + + logger.info( + "[Progress] %d/%d episodes completed (%d failed)", + current_completed, total_episodes, current_failed, + ) + + episodes_done += 1 + + finally: + logger.trace("[Worker %d] Shutting down simulator", worker_id) + try: + sim.close() + except Exception: + pass + logger.trace( + "[Worker %d] Shutdown complete (%d episodes done)", + worker_id, episodes_done, + ) + + # --- Phase 6: Launch worker threads --- + logger.trace("Phase 6: Launching %d worker threads", num_workers) + wall_start = time.monotonic() + + with ThreadPoolExecutor(max_workers=num_workers) as executor: + futures = [] + for wid in range(num_workers): + futures.append(executor.submit(_worker, wid)) + logger.trace("All %d worker threads submitted", num_workers) + + # Wait for all workers to complete and propagate exceptions + for future in futures: + future.result() + + wall_seconds = round(time.monotonic() - wall_start, 2) + + # --- Phase 7: Sort results and aggregate --- + results_list.sort(key=lambda x: x[0]) + all_results = [r for _, r in results_list] + + num_successful = sum(1 for r in all_results if "error" not in r) + num_failed = len(all_results) - num_successful + logger.trace( + "Phase 7: Results sorted. %d successful, %d failed", + num_successful, num_failed, + ) + + # Aggregate and save summary + summary = aggregate_metrics(all_results) + summary["num_parallel"] = self.num_parallel + summary["wall_clock_seconds"] = wall_seconds + if backend and backend != "legacy": + summary["llm_usage"] = self._aggregate_llm_usage(all_results) + summary["model"] = self.model + summary["backend"] = backend + (run_dir / "summary.json").write_text(json.dumps(summary, indent=2)) + logger.info("Results saved to: %s", run_dir) + logger.info("Summary: %s", summary) + + return all_results diff --git a/tests/test_parallel_runner.py b/tests/test_parallel_runner.py new file mode 100644 index 0000000..ab75a09 --- /dev/null +++ b/tests/test_parallel_runner.py @@ -0,0 +1,156 @@ +"""Tests for ParallelRunner — parallel episode evaluation.""" +import json +import pytest + + +class TestParallelRunnerInit: + """Test ParallelRunner construction and validation.""" + + def test_inherits_evaluation_runner(self): + from easi.evaluation.parallel_runner import ParallelRunner + from easi.evaluation.runner import EvaluationRunner + assert issubclass(ParallelRunner, EvaluationRunner) + + def test_num_parallel_stored(self): + from easi.evaluation.parallel_runner import ParallelRunner + runner = ParallelRunner( + task_name="dummy_task", num_parallel=4, + agent_type="dummy", + ) + assert runner.num_parallel == 4 + + def test_defaults_to_2_workers(self): + from easi.evaluation.parallel_runner import ParallelRunner + runner = ParallelRunner( + task_name="dummy_task", + agent_type="dummy", + ) + assert runner.num_parallel == 2 + + +class TestParallelRunnerValidation: + """Test that unsupported configs are rejected.""" + + def test_resume_raises_not_implemented(self, tmp_path): + from easi.evaluation.parallel_runner import ParallelRunner + resume_dir = tmp_path / "fake_run" + resume_dir.mkdir() + (resume_dir / "config.json").write_text('{"cli_options": {}}') + + runner = ParallelRunner( + task_name="dummy_task", num_parallel=2, + agent_type="dummy", resume_dir=str(resume_dir), + ) + with pytest.raises(NotImplementedError, match="does not support --resume"): + runner.run() + + def test_local_vllm_raises_not_implemented(self, tmp_path): + from easi.evaluation.parallel_runner import ParallelRunner + runner = ParallelRunner( + task_name="dummy_task", num_parallel=2, + agent_type="react", backend="vllm", + model="some-model", output_dir=str(tmp_path), + ) + with pytest.raises(NotImplementedError, match="does not support local vLLM"): + runner.run() + + +class TestParallelRunnerWithDummy: + """Integration test: parallel run with dummy agent + dummy simulator.""" + + def test_parallel_dummy_produces_results(self, tmp_path): + """Run dummy_task with 2 workers, verify all episodes complete.""" + from easi.evaluation.parallel_runner import ParallelRunner + + runner = ParallelRunner( + task_name="dummy_task", + num_parallel=2, + agent_type="dummy", + output_dir=str(tmp_path), + max_episodes=3, + ) + results = runner.run() + + # All 3 episodes should complete + assert len(results) == 3 + + # Results should be in episode order (sorted by index) + episode_ids = [r["episode_id"] for r in results] + # dummy_task episodes have sequential ids + assert len(set(episode_ids)) == 3 # all unique + + # Summary should exist with parallel-specific fields + run_dirs = list((tmp_path / "dummy_task").iterdir()) + assert len(run_dirs) == 1 + summary_path = run_dirs[0] / "summary.json" + assert summary_path.exists() + summary = json.loads(summary_path.read_text()) + assert summary["num_parallel"] == 2 + assert "wall_clock_seconds" in summary + + def test_parallel_single_episode(self, tmp_path): + """With 1 episode and 2 workers, only 1 worker should do work.""" + from easi.evaluation.parallel_runner import ParallelRunner + + runner = ParallelRunner( + task_name="dummy_task", + num_parallel=2, + agent_type="dummy", + output_dir=str(tmp_path), + max_episodes=1, + ) + results = runner.run() + assert len(results) == 1 + + def test_config_json_records_num_parallel(self, tmp_path): + """config.json should include num_parallel.""" + from easi.evaluation.parallel_runner import ParallelRunner + + runner = ParallelRunner( + task_name="dummy_task", + num_parallel=3, + agent_type="dummy", + output_dir=str(tmp_path), + max_episodes=1, + ) + runner.run() + + run_dirs = list((tmp_path / "dummy_task").iterdir()) + config = json.loads((run_dirs[0] / "config.json").read_text()) + assert config["num_parallel"] == 3 + + def test_episode_dirs_created(self, tmp_path): + """Each episode should get its own directory with result.json.""" + from easi.evaluation.parallel_runner import ParallelRunner + + runner = ParallelRunner( + task_name="dummy_task", + num_parallel=2, + agent_type="dummy", + output_dir=str(tmp_path), + max_episodes=2, + ) + runner.run() + + run_dirs = list((tmp_path / "dummy_task").iterdir()) + episodes_dir = run_dirs[0] / "episodes" + episode_dirs = sorted(episodes_dir.iterdir()) + assert len(episode_dirs) == 2 + for ed in episode_dirs: + assert (ed / "result.json").exists() + + +class TestCLIParallelArg: + """Test --num-parallel CLI argument parsing.""" + + def test_num_parallel_parsed(self): + from easi.cli import build_parser + parser = build_parser() + args = parser.parse_args(["start", "dummy_task", "--num-parallel", "4"]) + assert args.num_parallel == 4 + + def test_num_parallel_default_is_none(self): + from easi.cli import build_parser + parser = build_parser() + args = parser.parse_args(["start", "dummy_task"]) + assert args.num_parallel is None From d522a1071e5d8a95314b0621435d38c919c0b6d3 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Fri, 20 Feb 2026 03:38:48 +0800 Subject: [PATCH 064/239] [Fix] Unify resume logic for sequential and parallel runners Refactor _load_completed_results() to scan episode dirs in ascending order, find the first incomplete episode, and clear from that point. Both sequential and parallel runners now share the same resume logic. Previously the sequential runner always dropped the last complete episode, and parallel runner didn't support resume at all. --- easi/evaluation/parallel_runner.py | 328 +++++++++++++++-------------- easi/evaluation/runner.py | 224 ++++++++++++-------- tests/test_e2e_evaluation.py | 24 +-- tests/test_parallel_runner.py | 86 ++++++-- 4 files changed, 389 insertions(+), 273 deletions(-) diff --git a/easi/evaluation/parallel_runner.py b/easi/evaluation/parallel_runner.py index f006ce5..622c15a 100644 --- a/easi/evaluation/parallel_runner.py +++ b/easi/evaluation/parallel_runner.py @@ -48,13 +48,6 @@ def run(self) -> list[dict]: self.task_name, self.num_parallel, ) - # --- Guard: no resume support --- - if self.resume_dir: - raise NotImplementedError( - "ParallelRunner does not support --resume. " - "Use the sequential EvaluationRunner for resume." - ) - # --- Guard: no local vLLM --- backend, base_url = self._resolve_llm_backend() if backend == "vllm" and base_url is None: @@ -76,7 +69,7 @@ def run(self) -> list[dict]: len(episodes), task.simulator_key, ) - # --- Phase 2: Resolve LLM backend --- + # --- Phase 2: Resolve LLM backend + handle resume --- logger.trace("Phase 2: Resolved LLM backend=%s, base_url=%s", backend, base_url) # Compute resolved generation kwargs (YAML defaults + CLI overrides) @@ -88,9 +81,23 @@ def run(self) -> list[dict]: _, cli_gen_kwargs = split_kwargs(all_llm_kwargs) resolved_gen_kwargs = {**yaml_gen_kwargs, **cli_gen_kwargs} + # Handle resume + if self.resume_dir: + run_dir = self.resume_dir + completed_results, start_index = self._load_completed_results( + run_dir, len(episodes), + ) + logger.info( + "Resuming from %s — %d completed, starting from index %d", + run_dir, len(completed_results), start_index, + ) + else: + run_dir = self.output_dir / self.task_name / self.run_id + completed_results = [] + start_index = 0 + # --- Phase 3: Create output directory and save config --- logger.trace("Phase 3: Creating output directory and saving config") - run_dir = self.output_dir / self.task_name / self.run_id episodes_dir = run_dir / "episodes" episodes_dir.mkdir(parents=True, exist_ok=True) @@ -107,167 +114,182 @@ def run(self) -> list[dict]: (run_dir / "config.json").write_text(json.dumps(config, indent=2)) logger.trace("Run config:\n%s", json.dumps(config, indent=2, default=str)) - # --- Phase 4: Fill episode queue --- - logger.trace("Phase 4: Filling episode queue with %d episodes", len(episodes)) - episode_queue: queue.Queue[tuple[int, dict]] = queue.Queue() - for i, episode in enumerate(episodes): - episode_queue.put((i, episode)) - - # --- Phase 5: Prepare thread-safe collection --- - results_lock = threading.Lock() - results_list: list[tuple[int, dict]] = [] - progress = {"completed": 0, "failed": 0} - progress_lock = threading.Lock() - total_episodes = len(episodes) - - num_workers = min(self.num_parallel, len(episodes)) - - def _worker(worker_id: int) -> None: - """Worker thread: owns a simulator + agent, pulls episodes from queue.""" - logger.trace("[Worker %d] Starting up", worker_id) - episodes_done = 0 - - # Create simulator - logger.trace( - "[Worker %d] Creating simulator (key=%s)", - worker_id, task.simulator_key, - ) - sim, sim_runner = self._create_simulator(task.simulator_key, task=task) - logger.trace( - "[Worker %d] Simulator ready (PID=%s)", - worker_id, - getattr(sim_runner, 'pid', 'unknown'), - ) - - # Create agent - logger.trace("[Worker %d] Creating agent", worker_id) - agent = self._create_agent( - task.action_space, task._config, - backend=backend, base_url=base_url, - ) - logger.trace("[Worker %d] Agent ready", worker_id) - - try: - while True: - # Pull next episode - try: - idx, episode = episode_queue.get_nowait() - except queue.Empty: - break + # Check if all episodes already complete (resume edge case) + if start_index >= len(episodes): + logger.info("All %d episodes already complete, re-aggregating summary.", len(episodes)) + all_results = completed_results + # Skip to aggregation (Phase 7) + wall_seconds = 0.0 + results_list = [(i, r) for i, r in enumerate(all_results)] + else: + # --- Phase 4: Fill episode queue (from start_index) --- + episode_queue: queue.Queue[tuple[int, dict]] = queue.Queue() + for i, episode in enumerate(episodes): + if i >= start_index: + episode_queue.put((i, episode)) + remaining = episode_queue.qsize() + logger.trace("Phase 4: Queued %d episodes (skipped %d completed)", remaining, start_index) + + # --- Phase 5: Prepare thread-safe collection --- + results_lock = threading.Lock() + new_results: list[tuple[int, dict]] = [] + progress = {"completed": 0, "failed": 0} + progress_lock = threading.Lock() + total_episodes = len(episodes) + + num_workers = min(self.num_parallel, remaining) + + def _worker(worker_id: int) -> None: + """Worker thread: owns a simulator + agent, pulls episodes from queue.""" + logger.trace("[Worker %d] Starting up", worker_id) + episodes_done = 0 + + # Create simulator + logger.trace( + "[Worker %d] Creating simulator (key=%s)", + worker_id, task.simulator_key, + ) + sim, sim_runner = self._create_simulator(task.simulator_key, task=task) + logger.trace( + "[Worker %d] Simulator ready (PID=%s)", + worker_id, + getattr(sim_runner, 'pid', 'unknown'), + ) - logger.trace( - "[Worker %d] Queue remaining: ~%d", - worker_id, episode_queue.qsize(), - ) + # Create agent + logger.trace("[Worker %d] Creating agent", worker_id) + agent = self._create_agent( + task.action_space, task._config, + backend=backend, base_url=base_url, + ) + logger.trace("[Worker %d] Agent ready", worker_id) - episode_id = episode.get("episode_id", f"ep_{idx}") - episode_dir = episodes_dir / f"{idx:03d}_{_sanitize_dirname(episode_id)}" - episode_dir.mkdir(exist_ok=True) + try: + while True: + # Pull next episode + try: + idx, episode = episode_queue.get_nowait() + except queue.Empty: + break - result = None - for attempt in range(1, self.max_retries + 1): logger.trace( - "[Worker %d] Running episode %s (attempt %d/%d)", - worker_id, episode_id, attempt, self.max_retries, + "[Worker %d] Queue remaining: ~%d", + worker_id, episode_queue.qsize(), ) - try: - result = self._run_episode( - sim, agent, task, episode, idx, episode_dir, - ) - logger.trace( - "[Worker %d] Episode %s completed in %.1fs: %s", - worker_id, episode_id, - result.get("elapsed_seconds", 0), - {k: v for k, v in result.items() - if k in ("success", "num_steps", "elapsed_seconds")}, - ) - break - except Exception as exc: - logger.warning( - "[Worker %d] Episode %s attempt %d/%d failed: %s", - worker_id, episode_id, attempt, self.max_retries, exc, - ) + + episode_id = episode.get("episode_id", f"ep_{idx}") + episode_dir = episodes_dir / f"{idx:03d}_{_sanitize_dirname(episode_id)}" + episode_dir.mkdir(exist_ok=True) + + result = None + for attempt in range(1, self.max_retries + 1): logger.trace( - "[Worker %d] Exception details:", - worker_id, exc_info=True, + "[Worker %d] Running episode %s (attempt %d/%d)", + worker_id, episode_id, attempt, self.max_retries, ) - self._clear_episode_dir(episode_dir) - if attempt < self.max_retries: - logger.info( - "[Worker %d] Re-launching simulator for retry...", - worker_id, + try: + result = self._run_episode( + sim, agent, task, episode, idx, episode_dir, ) - try: - sim.close() - except Exception: - pass - sim, sim_runner = self._create_simulator( - task.simulator_key, task=task, + logger.trace( + "[Worker %d] Episode %s completed in %.1fs: %s", + worker_id, episode_id, + result.get("elapsed_seconds", 0), + {k: v for k, v in result.items() + if k in ("success", "num_steps", "elapsed_seconds")}, ) - else: - logger.error( - "[Worker %d] Episode %s failed after %d attempts, skipping", - worker_id, episode_id, self.max_retries, + break + except Exception as exc: + logger.warning( + "[Worker %d] Episode %s attempt %d/%d failed: %s", + worker_id, episode_id, attempt, self.max_retries, exc, ) - result = { - "episode_id": episode_id, - "instruction": task.get_instruction(episode), - "success": 0.0, - "num_steps": 0, - "elapsed_seconds": 0.0, - "error": str(exc), - } - - # Save per-episode result - (episode_dir / "result.json").write_text( - json.dumps(result, indent=2) - ) + logger.trace( + "[Worker %d] Exception details:", + worker_id, exc_info=True, + ) + self._clear_episode_dir(episode_dir) + if attempt < self.max_retries: + logger.info( + "[Worker %d] Re-launching simulator for retry...", + worker_id, + ) + try: + sim.close() + except Exception: + pass + sim, sim_runner = self._create_simulator( + task.simulator_key, task=task, + ) + else: + logger.error( + "[Worker %d] Episode %s failed after %d attempts, skipping", + worker_id, episode_id, self.max_retries, + ) + result = { + "episode_id": episode_id, + "instruction": task.get_instruction(episode), + "success": 0.0, + "num_steps": 0, + "elapsed_seconds": 0.0, + "error": str(exc), + } + + # Save per-episode result + (episode_dir / "result.json").write_text( + json.dumps(result, indent=2) + ) - # Thread-safe results collection - failed = "error" in result - with results_lock: - results_list.append((idx, result)) - - with progress_lock: - progress["completed"] += 1 - if failed: - progress["failed"] += 1 - current_completed = progress["completed"] - current_failed = progress["failed"] - - logger.info( - "[Progress] %d/%d episodes completed (%d failed)", - current_completed, total_episodes, current_failed, - ) + # Thread-safe results collection + failed = "error" in result + with results_lock: + new_results.append((idx, result)) + + with progress_lock: + progress["completed"] += 1 + if failed: + progress["failed"] += 1 + current_completed = progress["completed"] + start_index + current_failed = progress["failed"] + + logger.info( + "[Progress] %d/%d episodes completed (%d failed)", + current_completed, total_episodes, current_failed, + ) - episodes_done += 1 + episodes_done += 1 - finally: - logger.trace("[Worker %d] Shutting down simulator", worker_id) - try: - sim.close() - except Exception: - pass - logger.trace( - "[Worker %d] Shutdown complete (%d episodes done)", - worker_id, episodes_done, - ) + finally: + logger.trace("[Worker %d] Shutting down simulator", worker_id) + try: + sim.close() + except Exception: + pass + logger.trace( + "[Worker %d] Shutdown complete (%d episodes done)", + worker_id, episodes_done, + ) + + # --- Phase 6: Launch worker threads --- + logger.trace("Phase 6: Launching %d worker threads", num_workers) + wall_start = time.monotonic() - # --- Phase 6: Launch worker threads --- - logger.trace("Phase 6: Launching %d worker threads", num_workers) - wall_start = time.monotonic() + with ThreadPoolExecutor(max_workers=num_workers) as executor: + futures = [] + for wid in range(num_workers): + futures.append(executor.submit(_worker, wid)) + logger.trace("All %d worker threads submitted", num_workers) - with ThreadPoolExecutor(max_workers=num_workers) as executor: - futures = [] - for wid in range(num_workers): - futures.append(executor.submit(_worker, wid)) - logger.trace("All %d worker threads submitted", num_workers) + # Wait for all workers to complete and propagate exceptions + for future in futures: + future.result() - # Wait for all workers to complete and propagate exceptions - for future in futures: - future.result() + wall_seconds = round(time.monotonic() - wall_start, 2) - wall_seconds = round(time.monotonic() - wall_start, 2) + # Merge completed results from resume with new results + new_results.sort(key=lambda x: x[0]) + results_list = [(i, r) for i, r in enumerate(completed_results)] + results_list.extend(new_results) # --- Phase 7: Sort results and aggregate --- results_list.sort(key=lambda x: x[0]) diff --git a/easi/evaluation/runner.py b/easi/evaluation/runner.py index 29cdd99..433f311 100644 --- a/easi/evaluation/runner.py +++ b/easi/evaluation/runner.py @@ -126,21 +126,13 @@ def run(self) -> list[dict]: """Run evaluation and return per-episode metric dicts.""" if self.resume_dir: run_dir = self.resume_dir - all_results = self._load_completed_results(run_dir) - start_index = len(all_results) - logger.info( - "Resuming from %s — %d completed episodes, starting from %d", - run_dir, len(all_results), start_index, - ) else: run_dir = self.output_dir / self.task_name / self.run_id - all_results = [] - start_index = 0 episodes_dir = run_dir / "episodes" episodes_dir.mkdir(parents=True, exist_ok=True) - # 1. Load task + # 1. Load task (before resume so we know total_episodes) task = self._create_task() if self.redownload: task.download_dataset(force=True) @@ -148,6 +140,17 @@ def run(self) -> list[dict]: if self.max_episodes is not None: episodes = episodes[:self.max_episodes] + # Handle resume: load completed results and find start point + if self.resume_dir: + all_results, start_index = self._load_completed_results(run_dir, len(episodes)) + logger.info( + "Resuming from %s — %d completed episodes, starting from index %d", + run_dir, len(all_results), start_index, + ) + else: + all_results = [] + start_index = 0 + # 2. Resolve LLM backend and optionally start server backend, base_url = self._resolve_llm_backend() server = None @@ -186,71 +189,77 @@ def run(self) -> list[dict]: "Run config:\n%s", json.dumps(config, indent=2, default=str) ) - # 3. Create agent - agent = self._create_agent(task.action_space, task._config, - backend=backend, base_url=base_url) - - # 4. Start simulator - sim, sim_runner = self._create_simulator(task.simulator_key, task=task) - - try: - for i, episode in enumerate(episodes): - if i < start_index: - continue - episode_id = episode.get("episode_id", f"ep_{i}") - logger.info( - "Episode %d/%d: %s", i + 1, len(episodes), episode_id, - ) - - episode_dir = episodes_dir / f"{i:03d}_{_sanitize_dirname(episode_id)}" - episode_dir.mkdir(exist_ok=True) - - result = None - for attempt in range(1, self.max_retries + 1): - try: - result = self._run_episode( - sim, agent, task, episode, i, episode_dir, - ) - break - except Exception as exc: - logger.warning( - "Episode %s attempt %d/%d failed: %s", - episode_id, attempt, self.max_retries, exc, - ) - self._clear_episode_dir(episode_dir) - if attempt < self.max_retries: - logger.info("Re-launching simulator for retry...") - try: - sim.close() - except Exception: - pass - sim, sim_runner = self._create_simulator( - task.simulator_key, task=task, - ) - else: - logger.error( - "Episode %s failed after %d attempts, skipping", - episode_id, self.max_retries, - ) - result = { - "episode_id": episode_id, - "instruction": task.get_instruction(episode), - "success": 0.0, - "num_steps": 0, - "elapsed_seconds": 0.0, - "error": str(exc), - } - - all_results.append(result) - - (episode_dir / "result.json").write_text( - json.dumps(result, indent=2) - ) - - finally: - sim.close() + # Skip simulator/agent if all episodes already complete (resume) + if start_index >= len(episodes): + logger.info("All %d episodes already complete, re-aggregating summary.", len(episodes)) if server: server.stop() + else: + # 3. Create agent + agent = self._create_agent(task.action_space, task._config, + backend=backend, base_url=base_url) + + # 4. Start simulator + sim, sim_runner = self._create_simulator(task.simulator_key, task=task) + + try: + for i, episode in enumerate(episodes): + if i < start_index: + continue + episode_id = episode.get("episode_id", f"ep_{i}") + logger.info( + "Episode %d/%d: %s", i + 1, len(episodes), episode_id, + ) + + episode_dir = episodes_dir / f"{i:03d}_{_sanitize_dirname(episode_id)}" + episode_dir.mkdir(exist_ok=True) + + result = None + for attempt in range(1, self.max_retries + 1): + try: + result = self._run_episode( + sim, agent, task, episode, i, episode_dir, + ) + break + except Exception as exc: + logger.warning( + "Episode %s attempt %d/%d failed: %s", + episode_id, attempt, self.max_retries, exc, + ) + self._clear_episode_dir(episode_dir) + if attempt < self.max_retries: + logger.info("Re-launching simulator for retry...") + try: + sim.close() + except Exception: + pass + sim, sim_runner = self._create_simulator( + task.simulator_key, task=task, + ) + else: + logger.error( + "Episode %s failed after %d attempts, skipping", + episode_id, self.max_retries, + ) + result = { + "episode_id": episode_id, + "instruction": task.get_instruction(episode), + "success": 0.0, + "num_steps": 0, + "elapsed_seconds": 0.0, + "error": str(exc), + } + + all_results.append(result) + + (episode_dir / "result.json").write_text( + json.dumps(result, indent=2) + ) + + finally: + sim.close() + if server: + server.stop() # 5. Aggregate and save summary summary = aggregate_metrics(all_results) @@ -264,32 +273,63 @@ def run(self) -> list[dict]: return all_results - def _load_completed_results(self, run_dir: Path) -> list[dict]: - """Load results from a previous run for resume. + def _load_completed_results(self, run_dir: Path, total_episodes: int) -> tuple[list[dict], int]: + """Scan episode dirs to find the first incomplete episode. + + Walks episode directories in ascending order (by index prefix). + An episode is "complete" if its directory has a valid result.json. + Returns results for all consecutive complete episodes from the start, + and clears all directories from the first incomplete episode onward. + + Args: + run_dir: The run directory containing episodes/. + total_episodes: Total number of episodes in the evaluation. - Returns results from all completed episodes except the last one. - The last episode directory is cleared for re-run (it may have been - interrupted mid-way). + Returns: + (completed_results, start_index) tuple. """ + import shutil + episodes_dir = run_dir / "episodes" if not episodes_dir.exists(): - return [] - - result_files = sorted(episodes_dir.glob("*/result.json")) - if not result_files: - return [] + return [], 0 - # Load all completed results except the last - all_results = [] - for rf in result_files[:-1]: - all_results.append(json.loads(rf.read_text())) - - # Clear the last episode directory for re-run - last_episode_dir = result_files[-1].parent - for f in last_episode_dir.iterdir(): - f.unlink() + # Collect all episode dirs, sorted by name (which starts with {i:03d}_) + episode_dirs = sorted( + [d for d in episodes_dir.iterdir() if d.is_dir()], + key=lambda d: d.name, + ) + if not episode_dirs: + return [], 0 + + # Walk in order, loading results until we hit an incomplete episode + completed_results = [] + start_index = 0 + + for ep_dir in episode_dirs: + result_file = ep_dir / "result.json" + if result_file.exists(): + try: + result = json.loads(result_file.read_text()) + completed_results.append(result) + start_index += 1 + continue + except (json.JSONDecodeError, OSError): + logger.warning("Corrupt result.json in %s, treating as incomplete", ep_dir) + # First incomplete episode found — stop here + break + + # Clear all episode dirs from start_index onward + dirs_to_clear = episode_dirs[start_index:] + if dirs_to_clear: + logger.info( + "Resume: clearing %d episode dirs from index %d onward", + len(dirs_to_clear), start_index, + ) + for d in dirs_to_clear: + shutil.rmtree(d) - return all_results + return completed_results, start_index def _run_episode( self, sim, agent, task, episode: dict, index: int, episode_dir: Path, diff --git a/tests/test_e2e_evaluation.py b/tests/test_e2e_evaluation.py index eaa8ea0..a3cf249 100644 --- a/tests/test_e2e_evaluation.py +++ b/tests/test_e2e_evaluation.py @@ -256,7 +256,7 @@ def test_resume_dir_stored(self, tmp_path): assert runner.resume_dir == tmp_path / "old_run" def test_resume_skips_completed_episodes(self, tmp_path): - """Resume skips completed episodes and re-runs the last one.""" + """Resume with all episodes complete re-aggregates but doesn't re-run.""" output_dir = tmp_path / "logs" # First run: complete all 3 episodes @@ -271,7 +271,7 @@ def test_resume_skips_completed_episodes(self, tmp_path): run_dir = _find_run_dir(output_dir) - # Resume: should load episodes 0-1, re-run episode 2 + # Resume: all episodes already complete — should load all, re-run none runner2 = EvaluationRunner( task_name="dummy_task", agent_type="dummy", @@ -280,10 +280,10 @@ def test_resume_skips_completed_episodes(self, tmp_path): agent_seed=42, ) results2 = runner2.run() - assert len(results2) == 3 # 2 loaded + 1 re-run + assert len(results2) == 3 # All loaded, none re-run - def test_resume_clears_last_episode_dir(self, tmp_path): - """Resume clears the last episode directory before re-running.""" + def test_resume_clears_from_first_incomplete(self, tmp_path): + """Resume clears directories from the first incomplete episode onward.""" output_dir = tmp_path / "logs" runner1 = EvaluationRunner( @@ -297,11 +297,9 @@ def test_resume_clears_last_episode_dir(self, tmp_path): run_dir = _find_run_dir(output_dir) episodes_dir = run_dir / "episodes" episode_dirs = sorted(episodes_dir.iterdir()) - last_ep_dir = episode_dirs[-1] - # Add a marker file to the last episode dir - marker = last_ep_dir / "marker.txt" - marker.write_text("should be deleted") + # Simulate incomplete episode 1 by removing its result.json + (episode_dirs[1] / "result.json").unlink() runner2 = EvaluationRunner( task_name="dummy_task", @@ -310,12 +308,8 @@ def test_resume_clears_last_episode_dir(self, tmp_path): resume_dir=run_dir, agent_seed=42, ) - runner2.run() - - # Marker should be gone (dir was cleared) - assert not marker.exists() - # But result.json should exist (re-run completed) - assert (last_ep_dir / "result.json").exists() + results2 = runner2.run() + assert len(results2) == 3 # 1 loaded + 2 re-run def test_resume_produces_valid_summary(self, tmp_path): """Resumed run produces a valid summary with all episodes.""" diff --git a/tests/test_parallel_runner.py b/tests/test_parallel_runner.py index ab75a09..76d0ef9 100644 --- a/tests/test_parallel_runner.py +++ b/tests/test_parallel_runner.py @@ -31,19 +31,6 @@ def test_defaults_to_2_workers(self): class TestParallelRunnerValidation: """Test that unsupported configs are rejected.""" - def test_resume_raises_not_implemented(self, tmp_path): - from easi.evaluation.parallel_runner import ParallelRunner - resume_dir = tmp_path / "fake_run" - resume_dir.mkdir() - (resume_dir / "config.json").write_text('{"cli_options": {}}') - - runner = ParallelRunner( - task_name="dummy_task", num_parallel=2, - agent_type="dummy", resume_dir=str(resume_dir), - ) - with pytest.raises(NotImplementedError, match="does not support --resume"): - runner.run() - def test_local_vllm_raises_not_implemented(self, tmp_path): from easi.evaluation.parallel_runner import ParallelRunner runner = ParallelRunner( @@ -154,3 +141,76 @@ def test_num_parallel_default_is_none(self): parser = build_parser() args = parser.parse_args(["start", "dummy_task"]) assert args.num_parallel is None + + +class TestParallelResume: + """Test resume support for parallel runner.""" + + def test_parallel_resume_basic(self, tmp_path): + """Parallel run can be resumed after partial completion.""" + from easi.evaluation.parallel_runner import ParallelRunner + + # First run: complete only 1 of 3 episodes + runner1 = ParallelRunner( + task_name="dummy_task", num_parallel=2, + agent_type="dummy", output_dir=str(tmp_path), + max_episodes=1, + ) + results1 = runner1.run() + assert len(results1) == 1 + + run_dir = list((tmp_path / "dummy_task").iterdir())[0] + + # Resume: should complete remaining episodes + runner2 = ParallelRunner( + task_name="dummy_task", num_parallel=2, + agent_type="dummy", output_dir=str(tmp_path), + resume_dir=str(run_dir), + ) + results2 = runner2.run() + assert len(results2) == 3 # 1 loaded + 2 re-run + + def test_parallel_resume_all_complete(self, tmp_path): + """Resuming a fully complete parallel run re-aggregates.""" + from easi.evaluation.parallel_runner import ParallelRunner + + runner1 = ParallelRunner( + task_name="dummy_task", num_parallel=2, + agent_type="dummy", output_dir=str(tmp_path), + ) + runner1.run() + + run_dir = list((tmp_path / "dummy_task").iterdir())[0] + + runner2 = ParallelRunner( + task_name="dummy_task", num_parallel=2, + agent_type="dummy", output_dir=str(tmp_path), + resume_dir=str(run_dir), + ) + results2 = runner2.run() + assert len(results2) == 3 + + def test_parallel_resume_with_gap(self, tmp_path): + """Resume clears from first incomplete even if later episodes exist.""" + from easi.evaluation.parallel_runner import ParallelRunner + + runner1 = ParallelRunner( + task_name="dummy_task", num_parallel=2, + agent_type="dummy", output_dir=str(tmp_path), + ) + runner1.run() + + run_dir = list((tmp_path / "dummy_task").iterdir())[0] + episodes_dir = run_dir / "episodes" + episode_dirs = sorted(episodes_dir.iterdir()) + + # Simulate gap: remove result.json from episode 1 + (episode_dirs[1] / "result.json").unlink() + + runner2 = ParallelRunner( + task_name="dummy_task", num_parallel=2, + agent_type="dummy", output_dir=str(tmp_path), + resume_dir=str(run_dir), + ) + results2 = runner2.run() + assert len(results2) == 3 # 1 loaded + 2 re-run From f4edcf693cdf6aa2f38a39650af921e1267bc258 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Fri, 20 Feb 2026 16:59:09 +0800 Subject: [PATCH 065/239] [Feat] Add TDW v1.11.23 simulator integration for HAZARD benchmark Register ThreeDWorld (TDW) as a new simulator in EASI with manifest, env manager (Xvfb + conda), simulator class, and a generic bridge that handles Controller lifecycle, scene loading, and image capture via TDW OutputData. Includes 16 offline tests. --- easi/simulators/tdw/__init__.py | 1 + easi/simulators/tdw/manifest.yaml | 9 + easi/simulators/tdw/v1_11_23/__init__.py | 1 + easi/simulators/tdw/v1_11_23/bridge.py | 290 ++++++++++++++++++ easi/simulators/tdw/v1_11_23/conda_env.yaml | 6 + easi/simulators/tdw/v1_11_23/env_manager.py | 37 +++ easi/simulators/tdw/v1_11_23/requirements.txt | 4 + easi/simulators/tdw/v1_11_23/simulator.py | 24 ++ tests/test_tdw_simulator.py | 117 +++++++ 9 files changed, 489 insertions(+) create mode 100644 easi/simulators/tdw/__init__.py create mode 100644 easi/simulators/tdw/manifest.yaml create mode 100644 easi/simulators/tdw/v1_11_23/__init__.py create mode 100644 easi/simulators/tdw/v1_11_23/bridge.py create mode 100644 easi/simulators/tdw/v1_11_23/conda_env.yaml create mode 100644 easi/simulators/tdw/v1_11_23/env_manager.py create mode 100644 easi/simulators/tdw/v1_11_23/requirements.txt create mode 100644 easi/simulators/tdw/v1_11_23/simulator.py create mode 100644 tests/test_tdw_simulator.py diff --git a/easi/simulators/tdw/__init__.py b/easi/simulators/tdw/__init__.py new file mode 100644 index 0000000..bb2ee48 --- /dev/null +++ b/easi/simulators/tdw/__init__.py @@ -0,0 +1 @@ +"""ThreeDWorld (TDW) simulator package.""" diff --git a/easi/simulators/tdw/manifest.yaml b/easi/simulators/tdw/manifest.yaml new file mode 100644 index 0000000..2e2fa12 --- /dev/null +++ b/easi/simulators/tdw/manifest.yaml @@ -0,0 +1,9 @@ +name: tdw +display_name: "ThreeDWorld (TDW)" +default_version: "v1_11_23" +versions: + v1_11_23: + description: "TDW 1.11.23 — used by HAZARD benchmark" + simulator_class: "easi.simulators.tdw.v1_11_23.simulator.TDWSimulator" + env_manager_class: "easi.simulators.tdw.v1_11_23.env_manager.TDWEnvManager" + python_version: "3.10" diff --git a/easi/simulators/tdw/v1_11_23/__init__.py b/easi/simulators/tdw/v1_11_23/__init__.py new file mode 100644 index 0000000..d556c9a --- /dev/null +++ b/easi/simulators/tdw/v1_11_23/__init__.py @@ -0,0 +1 @@ +"""TDW v1.11.23 simulator.""" diff --git a/easi/simulators/tdw/v1_11_23/bridge.py b/easi/simulators/tdw/v1_11_23/bridge.py new file mode 100644 index 0000000..fb19a7c --- /dev/null +++ b/easi/simulators/tdw/v1_11_23/bridge.py @@ -0,0 +1,290 @@ +"""Bridge subprocess for TDW v1.11.23. + +This script runs inside the easi_tdw_v1_11_23 conda env (Python 3.10). +It communicates with the parent process via filesystem IPC. + +Provides a TDWBridge that handles: +- Controller startup/shutdown (auto-launches Unity build) +- Scene loading and image capture +- Main IPC loop (reset/step/close) + +Task-specific bridges (e.g., HAZARD) will subclass this for +scenario-specific scene setup (LogPlayback, hazard controllers). + +Usage: + python bridge.py --workspace /tmp/easi_xxx [--simulator-kwargs '{}'] +""" +from __future__ import annotations + +import argparse +import io +import json +import sys +from pathlib import Path + +import numpy as np +from PIL import Image + +# Add repo root to path for easi imports +_repo_root = Path(__file__).resolve().parents[4] +if str(_repo_root) not in sys.path: + sys.path.insert(0, str(_repo_root)) + +from easi.communication.filesystem import ( + poll_for_command, + write_response, + write_status, +) +from easi.communication.schemas import ( + make_error_response, + make_observation_response, +) +from easi.utils.logging import get_logger, setup_logging + +logger = get_logger(__name__) + +# Default TDW settings +DEFAULT_PORT = 1071 +DEFAULT_SCREEN_SIZE = 512 +DEFAULT_SCENE = "tdw_room" + + +class TDWBridge: + """Generic TDW bridge managing Controller lifecycle and basic actions. + + Subclass this for task-specific behavior (HAZARD scenario controllers). + """ + + def __init__(self, workspace, simulator_kwargs=None): + self.workspace = Path(workspace) + self.simulator_kwargs = simulator_kwargs or {} + self.controller = None + self.step_count = 0 + self.episode_output_dir = None + self._screen_size = self.simulator_kwargs.get( + "screen_size", DEFAULT_SCREEN_SIZE + ) + + def start(self): + """Initialize TDW Controller (auto-launches Unity build).""" + from tdw.controller import Controller + + port = self.simulator_kwargs.get("port", DEFAULT_PORT) + launch_build = self.simulator_kwargs.get("launch_build", True) + + logger.info( + "Starting TDW Controller (port=%d, screen_size=%d, launch_build=%s)...", + port, self._screen_size, launch_build, + ) + self.controller = Controller( + port=port, + check_version=True, + launch_build=launch_build, + ) + logger.info("TDW Controller started.") + + def stop(self): + """Shutdown the TDW Controller and Unity process.""" + if self.controller is not None: + try: + self.controller.communicate({"$type": "terminate"}) + except Exception: + pass + self.controller = None + + def reset(self, reset_config): + """Reset to a scene for smoke tests. + + Override in subclasses for task-specific resets (LogPlayback, etc.). + """ + self.step_count = 0 + scene = reset_config.get("scene", DEFAULT_SCENE) + + logger.info("Resetting to scene: %s", scene) + + # Load scene and configure rendering + commands = [ + self.controller.get_add_scene(scene), + {"$type": "set_screen_size", + "width": self._screen_size, "height": self._screen_size}, + {"$type": "set_render_quality", "render_quality": 5}, + ] + + # Create a third-person camera + commands.extend([ + {"$type": "create_avatar", "type": "A_Img_Caps_Kinematic", + "id": "a"}, + {"$type": "teleport_avatar_to", + "position": {"x": 0, "y": 1.5, "z": -2}, "avatar_id": "a"}, + {"$type": "look_at_position", + "position": {"x": 0, "y": 0.5, "z": 0}, "avatar_id": "a"}, + {"$type": "set_pass_masks", + "pass_masks": ["_img"], "avatar_id": "a"}, + {"$type": "send_images", "frequency": "always"}, + ]) + + resp = self.controller.communicate(commands) + return self._make_observation_response(resp) + + def step(self, action_text): + """Execute a basic action and return observation. + + For the smoke test, steps just advance physics. + Override in subclasses for task-specific actions. + """ + self.step_count += 1 + + # Default: step physics forward + resp = self.controller.communicate([ + {"$type": "step_physics", "frames": 10}, + ]) + return self._make_observation_response( + resp, + reward=0.0, + done=False, + info={"env_step": float(self.step_count)}, + ) + + # --- Main IPC Loop --- + + def run(self): + """Main bridge loop — reads IPC commands, dispatches to handlers.""" + logger.info("TDW bridge starting (workspace: %s)", self.workspace) + self.start() + + write_status(self.workspace, ready=True) + + while True: + try: + command = poll_for_command(self.workspace, timeout=300.0) + except Exception as e: + logger.error("Failed to read command: %s", e) + break + + cmd_type = command.get("type") + + if cmd_type == "reset": + episode_id = command.get("episode_id", "unknown") + reset_config = command.get("reset_config", {}) + logger.info("Reset: episode_id=%s", episode_id) + + raw_output_dir = command.get("episode_output_dir") + if raw_output_dir: + self.episode_output_dir = raw_output_dir + Path(raw_output_dir).mkdir(parents=True, exist_ok=True) + else: + self.episode_output_dir = None + + try: + response = self.reset(reset_config) + write_response(self.workspace, response) + except Exception as e: + logger.exception("Reset failed") + write_response(self.workspace, make_error_response(str(e))) + + elif cmd_type == "step": + action_data = command.get("action", {}) + action_text = action_data.get("action_name", "") + logger.trace( + "Step %d: action=%s", self.step_count + 1, action_text + ) + + try: + response = self.step(action_text) + write_response(self.workspace, response) + except Exception as e: + logger.exception("Step failed") + write_response(self.workspace, make_error_response(str(e))) + + elif cmd_type == "close": + logger.info("Close command received") + self.stop() + write_response(self.workspace, {"status": "ok"}) + break + + else: + write_response( + self.workspace, + make_error_response("Unknown command: %s" % cmd_type), + ) + + # --- Image extraction --- + + def _extract_rgb_from_response(self, resp): + """Parse TDW response bytes to extract RGB image as numpy array. + + TDW returns OutputData in resp. We look for Images data + (type id "imag") and extract the RGB pass. + + Returns: + numpy array (H, W, 3) uint8, or None if no image found. + """ + from tdw.output_data import OutputData, Images + + for i in range(len(resp) - 1): + r_id = OutputData.get_data_type_id(resp[i]) + if r_id == "imag": + images = Images(resp[i]) + # Get the first image pass (should be _img / RGB) + for j in range(images.get_num_passes()): + if images.get_pass_mask(j) == "_img": + # TDW Images are PNG-encoded bytes + img_bytes = images.get_image(j) + pil_img = Image.open(io.BytesIO(img_bytes)) + return np.array(pil_img)[:, :, :3] # Drop alpha + return None + + def _make_observation_response(self, resp, reward=0.0, done=False, info=None): + """Extract image from TDW response and build IPC response.""" + rgb_array = self._extract_rgb_from_response(resp) + + save_dir = ( + Path(self.episode_output_dir) + if self.episode_output_dir + else self.workspace + ) + rgb_path = save_dir / ("rgb_%04d.png" % self.step_count) + + if rgb_array is not None: + Image.fromarray(rgb_array).save(str(rgb_path)) + else: + # Fallback: generate a minimal placeholder image + logger.warning("No image in TDW response, generating placeholder") + placeholder = np.zeros( + (self._screen_size, self._screen_size, 3), dtype=np.uint8 + ) + Image.fromarray(placeholder).save(str(rgb_path)) + + return make_observation_response( + rgb_path=str(rgb_path), + agent_pose=[0.0, 1.5, -2.0, 0.0, 0.0, 0.0], + metadata={"step": str(self.step_count)}, + reward=reward, + done=done, + info=info or {}, + ) + + +def main(): + parser = argparse.ArgumentParser(description="TDW v1.11.23 bridge") + parser.add_argument("--workspace", type=Path, required=True) + parser.add_argument( + "--simulator-kwargs", type=str, default=None, + help="JSON string of simulator configuration", + ) + args, _ = parser.parse_known_args() + + setup_logging("DEBUG") + + simulator_kwargs = ( + json.loads(args.simulator_kwargs) if args.simulator_kwargs else {} + ) + bridge = TDWBridge( + workspace=args.workspace, + simulator_kwargs=simulator_kwargs, + ) + bridge.run() + + +if __name__ == "__main__": + main() diff --git a/easi/simulators/tdw/v1_11_23/conda_env.yaml b/easi/simulators/tdw/v1_11_23/conda_env.yaml new file mode 100644 index 0000000..0b30b07 --- /dev/null +++ b/easi/simulators/tdw/v1_11_23/conda_env.yaml @@ -0,0 +1,6 @@ +name: easi_tdw_v1_11_23 +channels: + - conda-forge + - defaults +dependencies: + - python=3.10 diff --git a/easi/simulators/tdw/v1_11_23/env_manager.py b/easi/simulators/tdw/v1_11_23/env_manager.py new file mode 100644 index 0000000..9558d1e --- /dev/null +++ b/easi/simulators/tdw/v1_11_23/env_manager.py @@ -0,0 +1,37 @@ +"""Environment manager for TDW v1.11.23. + +Used by HAZARD benchmark. Requires Python 3.10 and Xvfb (Unity build needs X11). +""" +from __future__ import annotations + +from pathlib import Path + +from easi.core.base_env_manager import BaseEnvironmentManager + + +class TDWEnvManager(BaseEnvironmentManager): + """Environment manager for TDW 1.11.23.""" + + @property + def simulator_name(self) -> str: + return "tdw" + + @property + def version(self) -> str: + return "v1_11_23" + + @property + def needs_display(self) -> bool: + return True # TDW Unity build requires X11 + + def get_conda_env_yaml_path(self) -> Path: + return Path(__file__).parent / "conda_env.yaml" + + def get_requirements_txt_path(self) -> Path: + return Path(__file__).parent / "requirements.txt" + + def get_system_deps(self) -> list[str]: + return ["conda", "xvfb"] + + def get_validation_import(self) -> str: + return "from tdw.controller import Controller; print('tdw ok')" diff --git a/easi/simulators/tdw/v1_11_23/requirements.txt b/easi/simulators/tdw/v1_11_23/requirements.txt new file mode 100644 index 0000000..dc314cb --- /dev/null +++ b/easi/simulators/tdw/v1_11_23/requirements.txt @@ -0,0 +1,4 @@ +tdw==1.11.23.0 +setuptools<71 +numpy>=1.24 +Pillow>=10.0 diff --git a/easi/simulators/tdw/v1_11_23/simulator.py b/easi/simulators/tdw/v1_11_23/simulator.py new file mode 100644 index 0000000..7cab842 --- /dev/null +++ b/easi/simulators/tdw/v1_11_23/simulator.py @@ -0,0 +1,24 @@ +"""TDW v1.11.23 simulator. + +Stub implementation — the bridge.py handles the actual TDW interaction. +""" +from __future__ import annotations + +from pathlib import Path + +from easi.core.base_simulator import BaseSimulator + + +class TDWSimulator(BaseSimulator): + """TDW 1.11.23 simulator for HAZARD benchmark.""" + + @property + def name(self) -> str: + return "tdw" + + @property + def version(self) -> str: + return "v1_11_23" + + def _get_bridge_script_path(self) -> Path: + return Path(__file__).parent / "bridge.py" diff --git a/tests/test_tdw_simulator.py b/tests/test_tdw_simulator.py new file mode 100644 index 0000000..30369fb --- /dev/null +++ b/tests/test_tdw_simulator.py @@ -0,0 +1,117 @@ +"""Tests for TDW simulator integration (offline, no TDW needed).""" +import pytest +from pathlib import Path + + +class TestTDWManifest: + """Test that manifest.yaml is correctly structured.""" + + def test_manifest_exists(self): + manifest = Path("easi/simulators/tdw/manifest.yaml") + assert manifest.exists() + + def test_manifest_contents(self): + import yaml + with open("easi/simulators/tdw/manifest.yaml") as f: + data = yaml.safe_load(f) + assert data["name"] == "tdw" + assert "v1_11_23" in data["versions"] + assert data["default_version"] == "v1_11_23" + + def test_manifest_classes_importable(self): + import yaml + with open("easi/simulators/tdw/manifest.yaml") as f: + data = yaml.safe_load(f) + v = data["versions"]["v1_11_23"] + assert "simulator.TDWSimulator" in v["simulator_class"] + assert "env_manager.TDWEnvManager" in v["env_manager_class"] + + +class TestTDWSimulator: + """Test TDWSimulator class.""" + + def test_import(self): + from easi.simulators.tdw.v1_11_23.simulator import TDWSimulator + sim = TDWSimulator() + assert sim.name == "tdw" + assert sim.version == "v1_11_23" + + def test_bridge_script_path(self): + from easi.simulators.tdw.v1_11_23.simulator import TDWSimulator + sim = TDWSimulator() + bridge_path = sim._get_bridge_script_path() + assert bridge_path.exists() + assert bridge_path.name == "bridge.py" + + +class TestTDWEnvManager: + """Test TDWEnvManager class.""" + + def test_import(self): + from easi.simulators.tdw.v1_11_23.env_manager import TDWEnvManager + mgr = TDWEnvManager() + assert mgr.simulator_name == "tdw" + assert mgr.version == "v1_11_23" + + def test_env_name(self): + from easi.simulators.tdw.v1_11_23.env_manager import TDWEnvManager + mgr = TDWEnvManager() + assert mgr.get_env_name() == "easi_tdw_v1_11_23" + + def test_needs_display(self): + from easi.simulators.tdw.v1_11_23.env_manager import TDWEnvManager + mgr = TDWEnvManager() + assert mgr.needs_display is True + + def test_system_deps(self): + from easi.simulators.tdw.v1_11_23.env_manager import TDWEnvManager + mgr = TDWEnvManager() + deps = mgr.get_system_deps() + assert "conda" in deps + assert "xvfb" in deps + + def test_conda_env_yaml_exists(self): + from easi.simulators.tdw.v1_11_23.env_manager import TDWEnvManager + mgr = TDWEnvManager() + assert mgr.get_conda_env_yaml_path().exists() + + def test_requirements_txt_exists(self): + from easi.simulators.tdw.v1_11_23.env_manager import TDWEnvManager + mgr = TDWEnvManager() + assert mgr.get_requirements_txt_path().exists() + + def test_validation_import(self): + from easi.simulators.tdw.v1_11_23.env_manager import TDWEnvManager + mgr = TDWEnvManager() + val = mgr.get_validation_import() + assert "tdw" in val + + +class TestTDWBridgeSyntax: + """Test that bridge.py is valid Python (without importing TDW).""" + + def test_bridge_syntax(self): + import ast + bridge_path = Path("easi/simulators/tdw/v1_11_23/bridge.py") + ast.parse(bridge_path.read_text()) + + +class TestTDWRegistry: + """Test that registry discovers TDW simulator.""" + + def test_env_list_includes_tdw(self): + from easi.simulators.registry import list_simulators + sims = list_simulators() + assert "tdw:v1_11_23" in sims + + def test_load_simulator_class(self): + from easi.simulators.registry import load_simulator_class + SimClass = load_simulator_class("tdw:v1_11_23") + sim = SimClass() + assert sim.name == "tdw" + + def test_create_env_manager(self): + from easi.simulators.registry import create_env_manager + mgr = create_env_manager("tdw:v1_11_23") + assert mgr.simulator_name == "tdw" + assert mgr.needs_display is True From b36d99c3b93fad1e88a6d0e6fcbeedb771a335a6 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Fri, 20 Feb 2026 18:40:28 +0800 Subject: [PATCH 066/239] feat: add EpisodeRecord and pluggable aggregate_results to BaseTask --- easi/core/base_task.py | 29 ++++++- easi/core/episode.py | 13 ++++ easi/evaluation/metrics.py | 47 ++++++++--- tests/test_metrics.py | 155 +++++++++++++++++++++++++++++++++++++ 4 files changed, 234 insertions(+), 10 deletions(-) create mode 100644 tests/test_metrics.py diff --git a/easi/core/base_task.py b/easi/core/base_task.py index ea6b85f..3ce3846 100644 --- a/easi/core/base_task.py +++ b/easi/core/base_task.py @@ -19,7 +19,7 @@ from abc import ABC, abstractmethod from pathlib import Path -from easi.core.episode import StepResult +from easi.core.episode import EpisodeRecord, StepResult from easi.core.exceptions import DatasetError from easi.tasks.yaml_utils import resolve_task_yaml from easi.utils.logging import get_logger @@ -74,6 +74,33 @@ def evaluate_episode( """ ... + def aggregate_results( + self, records: list["EpisodeRecord"] + ) -> dict[str, float]: + """Aggregate metrics across all completed episodes. + + Default implementation: averages all numeric keys from each + record's episode_results. Override in subclasses for custom + aggregation logic (e.g., weighted scores, category breakdowns, + trajectory-level analysis). + + Args: + records: List of EpisodeRecord objects. Each contains: + - episode: raw dataset row dict + - trajectory: list of StepResult objects + - episode_results: dict from evaluate_episode() + + Returns: + Summary metrics dict (placed under "metrics" in summary.json). + """ + from easi.evaluation.metrics import default_aggregate + logger.info( + "Using default_aggregate for task '%s'. " + "Override aggregate_results() in your task class for custom aggregation.", + self.name, + ) + return default_aggregate(records) + # --- Hooks --- def on_episode_reset(self, observation, agent) -> None: diff --git a/easi/core/episode.py b/easi/core/episode.py index a7453a1..a334ab2 100644 --- a/easi/core/episode.py +++ b/easi/core/episode.py @@ -31,3 +31,16 @@ class StepResult: reward: float = 0.0 done: bool = False info: dict[str, float] = field(default_factory=dict) + + +@dataclass +class EpisodeRecord: + """Bundles all data for one completed episode. + + Used by aggregate_results() to give the aggregation function + access to both the raw episode data and the full trajectory. + """ + + episode: dict + trajectory: list[StepResult] + episode_results: dict[str, float] diff --git a/easi/evaluation/metrics.py b/easi/evaluation/metrics.py index 70a3316..910ea88 100644 --- a/easi/evaluation/metrics.py +++ b/easi/evaluation/metrics.py @@ -1,19 +1,28 @@ """Metric aggregation utilities.""" - from __future__ import annotations +from easi.core.episode import EpisodeRecord -def aggregate_metrics(results: list[dict]) -> dict: - """Aggregate per-episode metrics into a summary dict.""" - if not results: - return {"num_episodes": 0} - summary = {"num_episodes": len(results)} +def default_aggregate(records: list[EpisodeRecord]) -> dict: + """Default aggregation: average all numeric keys from episode_results. - # Collect all numeric keys + Args: + records: List of EpisodeRecord objects (one per episode). + + Returns: + Summary dict with avg_ for each numeric key, + plus convenience aliases (success_rate, avg_steps). + """ + if not records: + return {} + + summary: dict = {} + + # Collect all numeric keys from episode_results numeric_keys: dict[str, list[float]] = {} - for r in results: - for key, value in r.items(): + for r in records: + for key, value in r.episode_results.items(): if isinstance(value, (int, float)): numeric_keys.setdefault(key, []).append(float(value)) @@ -24,7 +33,27 @@ def aggregate_metrics(results: list[dict]) -> dict: # Convenience aliases if "avg_success" in summary: summary["success_rate"] = summary["avg_success"] + if "avg_task_success" in summary: + summary["success_rate"] = summary["avg_task_success"] if "avg_num_steps" in summary: summary["avg_steps"] = summary["avg_num_steps"] return summary + + +def aggregate_metrics(results: list[dict]) -> dict: + """Legacy aggregate function for backward compatibility. + + Wraps default_aggregate() by converting plain dicts to EpisodeRecords. + New code should use task.aggregate_results() directly. + """ + if not results: + return {"num_episodes": 0} + + records = [ + EpisodeRecord(episode={}, trajectory=[], episode_results=r) + for r in results + ] + summary = {"num_episodes": len(results)} + summary.update(default_aggregate(records)) + return summary diff --git a/tests/test_metrics.py b/tests/test_metrics.py new file mode 100644 index 0000000..d78d1f2 --- /dev/null +++ b/tests/test_metrics.py @@ -0,0 +1,155 @@ +"""Tests for the pluggable metric system.""" +import pytest +from pathlib import Path + +from easi.core.episode import EpisodeRecord, Observation, StepResult + + +def _make_step(info=None, reward=0.0, done=False): + """Helper to create a StepResult with given info.""" + obs = Observation(rgb_path="/tmp/fake.png") + return StepResult(observation=obs, reward=reward, done=done, info=info or {}) + + +class TestEpisodeRecord: + """Test the EpisodeRecord dataclass.""" + + def test_create(self): + record = EpisodeRecord( + episode={"episode_id": "ep1"}, + trajectory=[_make_step()], + episode_results={"success": 1.0}, + ) + assert record.episode["episode_id"] == "ep1" + assert len(record.trajectory) == 1 + assert record.episode_results["success"] == 1.0 + + def test_defaults(self): + record = EpisodeRecord( + episode={}, + trajectory=[], + episode_results={}, + ) + assert record.episode == {} + assert record.trajectory == [] + assert record.episode_results == {} + + +class TestDefaultAggregate: + """Test default_aggregate (extracted from aggregate_metrics).""" + + def test_empty_results(self): + from easi.evaluation.metrics import default_aggregate + assert default_aggregate([]) == {} + + def test_averages_numeric_keys(self): + from easi.evaluation.metrics import default_aggregate + records = [ + EpisodeRecord({}, [], {"task_success": 1.0, "num_steps": 10.0}), + EpisodeRecord({}, [], {"task_success": 0.0, "num_steps": 20.0}), + ] + agg = default_aggregate(records) + assert agg["avg_task_success"] == 0.5 + assert agg["avg_num_steps"] == 15.0 + + def test_ignores_non_numeric_keys_in_episode_results(self): + from easi.evaluation.metrics import default_aggregate + records = [ + EpisodeRecord({}, [], {"task_success": 1.0, "note": "good"}), + ] + agg = default_aggregate(records) + assert "avg_task_success" in agg + assert "avg_note" not in agg + + def test_convenience_aliases(self): + from easi.evaluation.metrics import default_aggregate + records = [ + EpisodeRecord({}, [], {"success": 1.0, "num_steps": 5.0}), + EpisodeRecord({}, [], {"success": 0.0, "num_steps": 15.0}), + ] + agg = default_aggregate(records) + assert agg.get("success_rate") == 0.5 + assert agg.get("avg_steps") == 10.0 + + +class TestBaseTaskAggregateResults: + """Test BaseTask.aggregate_results() default implementation.""" + + def _make_task(self): + """Create a minimal concrete BaseTask.""" + from easi.core.base_task import BaseTask + + class MinimalTask(BaseTask): + def get_task_yaml_path(self): + return Path("/dev/null") + def format_reset_config(self, episode): + return {} + def evaluate_episode(self, episode, trajectory): + return {"success": 1.0} + def _load_config(self): + return {"name": "test", "simulator": "dummy:v1"} + return MinimalTask() + + def test_default_aggregate_averages(self): + task = self._make_task() + records = [ + EpisodeRecord({}, [], {"success": 1.0, "num_steps": 10.0}), + EpisodeRecord({}, [], {"success": 0.0, "num_steps": 20.0}), + ] + agg = task.aggregate_results(records) + assert agg["avg_success"] == 0.5 + assert agg["avg_num_steps"] == 15.0 + + def test_default_aggregate_empty(self): + task = self._make_task() + assert task.aggregate_results([]) == {} + + def test_custom_override(self): + """Subclass can override aggregate_results for custom logic.""" + from easi.core.base_task import BaseTask + + class CustomTask(BaseTask): + def get_task_yaml_path(self): + return Path("/dev/null") + def format_reset_config(self, episode): + return {} + def evaluate_episode(self, episode, trajectory): + return {} + def _load_config(self): + return {"name": "test", "simulator": "dummy:v1"} + def aggregate_results(self, records): + n_success = sum( + 1 for r in records + if r.episode_results.get("success", 0) > 0 + ) + total_steps = sum( + len(r.trajectory) for r in records + ) + return { + "success_rate": n_success / len(records) if records else 0, + "total_steps": float(total_steps), + } + + task = CustomTask() + records = [ + EpisodeRecord({}, [_make_step(), _make_step()], {"success": 1.0}), + EpisodeRecord({}, [_make_step()], {"success": 0.0}), + ] + agg = task.aggregate_results(records) + assert agg["success_rate"] == 0.5 + assert agg["total_steps"] == 3.0 + + +class TestAggregateMetricsBackwardCompat: + """Verify old aggregate_metrics() still works for any existing callers.""" + + def test_aggregate_metrics_unchanged(self): + from easi.evaluation.metrics import aggregate_metrics + results = [ + {"success": 1.0, "num_steps": 10.0}, + {"success": 0.0, "num_steps": 20.0}, + ] + summary = aggregate_metrics(results) + assert summary["num_episodes"] == 2 + assert summary["avg_success"] == 0.5 + assert summary["success_rate"] == 0.5 From 789d0225702088a59be9dd5c285105b898938c3f Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Fri, 20 Feb 2026 19:37:54 +0800 Subject: [PATCH 067/239] feat: integrate aggregate_results into runners, nest metrics in summary.json --- easi/evaluation/parallel_runner.py | 24 ++++++++++++++++++---- easi/evaluation/runner.py | 30 +++++++++++++++++++++++----- tests/test_e2e_evaluation.py | 10 ++++++---- tests/test_evaluation_runner.py | 7 ++++--- tests/test_metrics.py | 32 ++++++++++++++++++++++++++++++ 5 files changed, 87 insertions(+), 16 deletions(-) diff --git a/easi/evaluation/parallel_runner.py b/easi/evaluation/parallel_runner.py index 622c15a..0dbd765 100644 --- a/easi/evaluation/parallel_runner.py +++ b/easi/evaluation/parallel_runner.py @@ -17,7 +17,7 @@ from concurrent.futures import ThreadPoolExecutor from pathlib import Path -from easi.evaluation.metrics import aggregate_metrics +from easi.core.episode import EpisodeRecord from easi.evaluation.runner import EvaluationRunner, _sanitize_dirname from easi.utils.logging import get_logger @@ -235,9 +235,13 @@ def _worker(worker_id: int) -> None: "error": str(exc), } - # Save per-episode result + # Save per-episode result (strip internal keys) + result_to_save = { + k: v for k, v in result.items() + if not k.startswith("_") + } (episode_dir / "result.json").write_text( - json.dumps(result, indent=2) + json.dumps(result_to_save, indent=2) ) # Thread-safe results collection @@ -302,8 +306,20 @@ def _worker(worker_id: int) -> None: num_successful, num_failed, ) + # Build EpisodeRecords for aggregate_results + records = [] + for r in all_results: + trajectory = r.pop("_trajectory", []) + episode = r.pop("_episode", {}) + records.append(EpisodeRecord( + episode=episode, + trajectory=trajectory, + episode_results=r, + )) + # Aggregate and save summary - summary = aggregate_metrics(all_results) + metric_results = task.aggregate_results(records) + summary = {"num_episodes": len(all_results), "metrics": metric_results} summary["num_parallel"] = self.num_parallel summary["wall_clock_seconds"] = wall_seconds if backend and backend != "legacy": diff --git a/easi/evaluation/runner.py b/easi/evaluation/runner.py index 433f311..e2f3d07 100644 --- a/easi/evaluation/runner.py +++ b/easi/evaluation/runner.py @@ -30,8 +30,7 @@ from datetime import datetime from pathlib import Path -from easi.core.episode import StepResult -from easi.evaluation.metrics import aggregate_metrics +from easi.core.episode import EpisodeRecord, StepResult from easi.utils.logging import get_logger logger = get_logger(__name__) @@ -252,8 +251,13 @@ def run(self) -> list[dict]: all_results.append(result) + # Save per-episode result (strip internal keys) + result_to_save = { + k: v for k, v in result.items() + if not k.startswith("_") + } (episode_dir / "result.json").write_text( - json.dumps(result, indent=2) + json.dumps(result_to_save, indent=2) ) finally: @@ -261,8 +265,20 @@ def run(self) -> list[dict]: if server: server.stop() - # 5. Aggregate and save summary - summary = aggregate_metrics(all_results) + # 5. Build EpisodeRecords for aggregate_results + records = [] + for r in all_results: + trajectory = r.pop("_trajectory", []) + episode = r.pop("_episode", {}) + records.append(EpisodeRecord( + episode=episode, + trajectory=trajectory, + episode_results=r, + )) + + # 6. Aggregate and save summary + metric_results = task.aggregate_results(records) + summary = {"num_episodes": len(all_results), "metrics": metric_results} if backend and backend != "legacy": summary["llm_usage"] = self._aggregate_llm_usage(all_results) summary["model"] = self.model @@ -417,6 +433,10 @@ def _run_episode( metrics["instruction"] = task_description metrics["elapsed_seconds"] = round(elapsed, 2) + # Attach trajectory and episode for aggregate_results() + metrics["_trajectory"] = trajectory + metrics["_episode"] = episode + # Snapshot LLM usage for this episode if hasattr(agent, 'llm_client') and hasattr(agent.llm_client, 'get_usage'): metrics["llm_usage"] = agent.llm_client.get_usage() diff --git a/tests/test_e2e_evaluation.py b/tests/test_e2e_evaluation.py index a3cf249..bf72477 100644 --- a/tests/test_e2e_evaluation.py +++ b/tests/test_e2e_evaluation.py @@ -37,7 +37,8 @@ def test_dummy_full_run(self, tmp_path): run_dir = _find_run_dir(tmp_path / "logs") summary = json.loads((run_dir / "summary.json").read_text()) assert summary["num_episodes"] == 3 - assert "success_rate" in summary or "avg_success" in summary + assert "metrics" in summary + assert "success_rate" in summary["metrics"] or "avg_success" in summary["metrics"] def test_max_episodes_limit(self, tmp_path): """Verify max_episodes limits the run.""" @@ -104,9 +105,10 @@ def test_summary_has_all_averaged_keys(self, tmp_path): run_dir = _find_run_dir(output_dir) summary = json.loads((run_dir / "summary.json").read_text()) assert summary["num_episodes"] == 3 - # These come from averaging per-episode numeric keys - assert "avg_num_steps" in summary - assert "avg_elapsed_seconds" in summary + # These come from averaging per-episode numeric keys (nested under "metrics") + assert "metrics" in summary + assert "avg_num_steps" in summary["metrics"] + assert "avg_elapsed_seconds" in summary["metrics"] def test_deterministic_with_seed(self, tmp_path): """Two runs with the same seed should produce identical results.""" diff --git a/tests/test_evaluation_runner.py b/tests/test_evaluation_runner.py index 0afe010..8d2be50 100644 --- a/tests/test_evaluation_runner.py +++ b/tests/test_evaluation_runner.py @@ -68,7 +68,7 @@ def test_results_saved_to_disk(self, tmp_path): assert (ep_dir / "rgb_0000.png").exists() # Reset observation def test_summary_aggregates_metrics(self, tmp_path): - """Verify summary.json contains averaged metrics.""" + """Verify summary.json contains averaged metrics nested under 'metrics'.""" output_dir = tmp_path / "logs" runner = EvaluationRunner( task_name="dummy_task", @@ -79,9 +79,10 @@ def test_summary_aggregates_metrics(self, tmp_path): run_dir = _find_run_dir(output_dir) summary = json.loads((run_dir / "summary.json").read_text()) - assert "success_rate" in summary - assert "avg_steps" in summary assert "num_episodes" in summary + assert "metrics" in summary + assert "success_rate" in summary["metrics"] + assert "avg_steps" in summary["metrics"] class TestCliOptionsCapture: diff --git a/tests/test_metrics.py b/tests/test_metrics.py index d78d1f2..096b5a2 100644 --- a/tests/test_metrics.py +++ b/tests/test_metrics.py @@ -153,3 +153,35 @@ def test_aggregate_metrics_unchanged(self): assert summary["num_episodes"] == 2 assert summary["avg_success"] == 0.5 assert summary["success_rate"] == 0.5 + + +class TestSummaryJsonStructure: + """Test that summary.json nests metrics under 'metrics' key.""" + + def test_metrics_nested_in_summary(self, tmp_path): + """Run dummy_task and verify metrics are under summary['metrics'].""" + from easi.evaluation.runner import EvaluationRunner + + runner = EvaluationRunner( + task_name="dummy_task", + agent_type="dummy", + output_dir=str(tmp_path), + max_episodes=2, + ) + runner.run() + + import json + summary_path = list(tmp_path.rglob("summary.json"))[0] + summary = json.loads(summary_path.read_text()) + + # Metrics should be nested + assert "metrics" in summary + assert isinstance(summary["metrics"], dict) + assert len(summary["metrics"]) > 0 + + # Metadata should be at top level + assert summary["num_episodes"] == 2 + + # Metric keys should NOT be at the top level + assert "avg_success" not in summary + assert "success_rate" not in summary From 5413684a42c021320e474ce7bdbbb0258bdc8e92 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Fri, 20 Feb 2026 20:04:26 +0800 Subject: [PATCH 068/239] [Chore] Remove redundant comments in prompt_builder and react_agent --- easi/agents/prompt_builder.py | 2 -- easi/agents/react_agent.py | 3 +-- 2 files changed, 1 insertion(+), 4 deletions(-) diff --git a/easi/agents/prompt_builder.py b/easi/agents/prompt_builder.py index d7280e3..975130d 100644 --- a/easi/agents/prompt_builder.py +++ b/easi/agents/prompt_builder.py @@ -61,8 +61,6 @@ class PromptBuilderProtocol(Protocol): Return a response_format dict for API-level JSON enforcement. E.g. {"type": "json_schema", "json_schema": {"name": "...", "schema": {...}}} When provided, the agent passes it to LLMClient.generate(). - If the backend doesn't support it, the agent falls back to - prompt-only (the template is already in the messages). Builders that don't implement this get no schema enforcement. """ diff --git a/easi/agents/react_agent.py b/easi/agents/react_agent.py index 0e4b46b..78e8f63 100644 --- a/easi/agents/react_agent.py +++ b/easi/agents/react_agent.py @@ -131,9 +131,8 @@ def add_feedback(self, action_name: str, feedback: str) -> None: def _fallback_action(self) -> Action: """Return a safe fallback action when parsing fails. - If the action space has "Stop", use it (e.g., EB-Alfred). + If the action space has "Stop", use it. Otherwise, signal that the episode should end via "<>" - (matching EmbodiedBench behavior where empty plans end episodes). """ if "Stop" in self.action_space: return Action(action_name="Stop") From 5835faed16a0c02620e941023afb7ff91c3272a7 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Fri, 20 Feb 2026 20:45:05 +0800 Subject: [PATCH 069/239] [Docs] Update CLAUDE.md and add AGENT.md for AI-assisted development --- AGENT.md | 629 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ CLAUDE.md | 232 ++++++++++++-------- 2 files changed, 775 insertions(+), 86 deletions(-) create mode 100644 AGENT.md diff --git a/AGENT.md b/AGENT.md new file mode 100644 index 0000000..5f568e2 --- /dev/null +++ b/AGENT.md @@ -0,0 +1,629 @@ +# AGENT.md — AI-Assisted Development Guide + +This document helps AI coding agents (and human developers) understand the EASI library and contribute new benchmarks effectively. + +## How the Library Works + +EASI evaluates embodied AI agents in interactive simulators. The core loop: + +``` +for each episode in dataset: + sim.reset(task.format_reset_config(episode)) + while not done and step < max_steps: + action = agent.act(observation, instruction) + observation = sim.step(action) + result = task.evaluate_episode(episode, trajectory) +summary = task.aggregate_results(all_records) +``` + +Four components plug together: + +| Component | Role | Lives in | +|---|---|---| +| **Task** | Defines episodes, action space, success metrics | `easi/tasks//` | +| **Simulator** | Runs the 3D environment in a subprocess | `easi/simulators//` | +| **Bridge** | Wraps the simulator's Python API for IPC | `easi/tasks//bridge.py` or `easi/simulators///bridge.py` | +| **Agent** | Decides actions (DummyAgent or ReActAgent+LLM) | `easi/agents/` | + +--- + +## Adding a New Benchmark (Step-by-Step) + +This is the most common contribution. Follow the existing task structure exactly. + +### Prerequisites + +- The simulator is already integrated (check `easi env list`). If not, see "Adding a New Simulator" below. +- The benchmark's dataset is on HuggingFace (or you have local episodes). +- You have the benchmark's source code to reference for environment setup and evaluation logic. + +### Step 1: Create the Task Folder + +``` +easi/tasks// +├── __init__.py # Empty +├── task.py # Task class (required) +├── bridge.py # Bridge script (if task needs custom env wrapping) +├── actions.py # Action space definitions (if static) +├── prompts.py # PromptBuilder for LLM interaction (optional) +├── _base.yaml # Base config (or split configs) +├── _base.yaml # Shared config for multi-split tasks +├── config/ # Few-shot examples, etc. (optional) +└── vendor/ # Vendored benchmark env code (optional) + └── __init__.py +``` + +Use `easi task scaffold ` to generate boilerplate, then customize. + +### Step 2: Define the Task YAML + +Every task needs at least one `.yaml` config file. The registry auto-discovers all `easi/tasks/*/*.yaml` files (excluding files without a `name` key). + +**Minimal config (single-split task):** + +```yaml +name: my_benchmark +display_name: "My Benchmark" +description: "Description of the benchmark" +simulator: "ai2thor:v5_0_0" +task_class: "easi.tasks.my_benchmark.task.MyBenchmarkTask" +max_steps: 50 +dataset: + source: huggingface + repo_id: "username/my-benchmark-dataset" + subset: null + split: "test" +``` + +**Multi-split task (recommended for benchmarks with difficulty splits):** + +```yaml +# _base.yaml — shared config, NOT registered as a task (no `name` key) +display_name: "My Benchmark" +simulator: "ai2thor:v5_0_0" +task_class: "easi.tasks.my_benchmark.task.MyBenchmarkTask" +max_steps: 50 +dataset: + source: huggingface + repo_id: "username/my-benchmark-dataset" +simulator_configs: + screen_height: 500 + screen_width: 500 + additional_deps: + - "gym" +agent: + prompt_builder: "easi.tasks.my_benchmark.prompts.MyPromptBuilder" + prompt_builder_kwargs: + n_shot: 3 + generation_kwargs: + temperature: 0 + max_tokens: 2048 +``` + +```yaml +# my_benchmark_base.yaml — registered as task "my_benchmark_base" +extends: _base.yaml +name: my_benchmark_base +display_name: "My Benchmark Base Split" +dataset: + split: "base" +``` + +```yaml +# my_benchmark_hard.yaml — registered as task "my_benchmark_hard" +extends: _base.yaml +name: my_benchmark_hard +display_name: "My Benchmark Hard Split" +dataset: + split: "hard" +``` + +**YAML fields reference:** + +| Field | Required | Description | +|---|---|---| +| `name` | Yes | Task name used in CLI (`easi start `) | +| `display_name` | No | Human-readable name | +| `description` | No | Task description | +| `simulator` | Yes | Simulator key, e.g. `"ai2thor:v5_0_0"` or `"dummy:v1"` | +| `task_class` | Yes | Dotted import path to your Task class | +| `max_steps` | No | Max steps per episode (default: 500) | +| `dataset.source` | Yes | `"huggingface"` or `"local"` | +| `dataset.repo_id` | HF only | HuggingFace repo ID | +| `dataset.split` | HF only | Dataset split name | +| `dataset.subset` | No | Dataset subset (auto-detected if single) | +| `dataset.zip_files` | No | List of zip files to extract after download | +| `simulator_configs` | No | Dict passed to bridge as `simulator_kwargs` | +| `simulator_configs.additional_deps` | No | Extra pip packages for the simulator env | +| `simulator_configs.env_vars` | No | Environment variables for bridge subprocess | +| `agent.prompt_builder` | No | Dotted path to PromptBuilder class | +| `agent.prompt_builder_kwargs` | No | Kwargs passed to PromptBuilder constructor | +| `agent.generation_kwargs` | No | LLM generation defaults (temperature, max_tokens, etc.) | +| `extends` | No | Relative path to base YAML for template inheritance | + +### Step 3: Implement the Task Class + +Subclass `BaseTask` and implement 3 abstract methods: + +```python +"""My benchmark task for EASI.""" +from __future__ import annotations + +from pathlib import Path + +from easi.core.base_task import BaseTask +from easi.core.episode import StepResult + +class MyBenchmarkTask(BaseTask): + + def get_task_yaml_path(self) -> Path: + """Return path to the default YAML config.""" + return Path(__file__).parent / "my_benchmark_base.yaml" + + def format_reset_config(self, episode: dict) -> dict: + """Map a dataset row to simulator reset kwargs. + + The returned dict is passed to bridge.reset(reset_config). + Include everything the bridge needs to initialize the episode. + """ + return { + "episode_id": episode.get("id", "unknown"), + "scene": episode["scene"], + "instruction": episode["instruction"], + # Add all fields your bridge needs + } + + def evaluate_episode( + self, episode: dict, trajectory: list[StepResult] + ) -> dict[str, float]: + """Score a completed episode. + + Args: + episode: The raw dataset row dict. + trajectory: List of StepResult from the agent-simulator loop. + Each StepResult has: observation, reward, done, info. + + Returns: + Dict of metric_name -> float. These are saved to result.json + and passed to aggregate_results(). + """ + if not trajectory: + return {"task_success": 0.0, "num_steps": 0.0} + + last_step = trajectory[-1] + return { + "task_success": last_step.info.get("task_success", 0.0), + "num_steps": float(len(trajectory)), + } +``` + +**Optional overrides:** + +```python + # Static action space (if not dynamic per-episode) + def _build_action_space(self) -> list[str]: + return ["MoveForward", "TurnLeft", "TurnRight", "Stop"] + + # Custom bridge script (if task needs special env wrapping) + def get_bridge_script_path(self) -> Path: + return Path(__file__).parent / "bridge.py" + + # Extract instruction from episode (if field name differs) + def get_instruction(self, episode: dict) -> str: + return episode.get("task_description", self.name) + + # Dynamic action space per episode (e.g., EB-Alfred) + def on_episode_reset(self, observation, agent) -> None: + new_actions = observation.metadata.get("action_space", "").split(",") + if new_actions and hasattr(agent, "update_action_space"): + agent.update_action_space(new_actions) + + # Custom cross-episode aggregation + def aggregate_results(self, records): + """Custom aggregation with access to trajectories and episode data. + + Args: + records: list[EpisodeRecord], each with: + - record.episode: raw dataset row dict + - record.trajectory: list[StepResult] + - record.episode_results: dict from evaluate_episode() + """ + n = len(records) + successes = sum(r.episode_results.get("task_success", 0) for r in records) + return { + "success_rate": round(successes / n, 4) if n else 0.0, + "avg_steps": round( + sum(r.episode_results.get("num_steps", 0) for r in records) / n, 2 + ) if n else 0.0, + } + + # Built-in episodes for testing without dataset download + def _get_builtin_episodes(self) -> list[dict]: + return [{"id": 0, "scene": "TestScene", "instruction": "test"}] +``` + +### Step 4: Implement the Bridge (if needed) + +If your benchmark uses a vendored environment that differs from the simulator's default bridge, create a task-specific bridge. The bridge runs as a **subprocess** in the simulator's conda env. + +```python +"""My benchmark bridge — wraps vendored env via BaseBridge. + +This script runs inside the simulator's conda env (e.g., Python 3.10). +""" +from __future__ import annotations + +import sys +from pathlib import Path + +# Ensure repo root is importable +_repo_root = Path(__file__).resolve().parents[3] +if str(_repo_root) not in sys.path: + sys.path.insert(0, str(_repo_root)) + +from easi.simulators.base_bridge import BaseBridge + + +class MyBenchmarkBridge(BaseBridge): + """Wraps vendored MyEnv via BaseBridge.""" + + def _create_env(self, reset_config, simulator_kwargs): + """Create the environment. Called once on first reset.""" + from easi.tasks.my_benchmark.vendor.my_env import MyEnv + resolution = simulator_kwargs.get("screen_height", 500) + return MyEnv(resolution=resolution) + + def _on_reset(self, env, reset_config): + """Reset with episode data. Return observation.""" + return env.reset(scene=reset_config["scene"]) + + def _on_step(self, env, action_text): + """Execute action. Return (obs, reward, done, info) tuple.""" + return env.step(action_text) + + def _extract_image(self, obs): + """Extract RGB numpy array (H, W, 3) from observation.""" + return obs["rgb"] # np.ndarray + + def _extract_info(self, info): + """Filter info dict to JSON-serializable values.""" + return { + "task_success": float(info.get("success", 0.0)), + "feedback": str(info.get("feedback", "")), + } + + +if __name__ == "__main__": + MyBenchmarkBridge.main() +``` + +**BaseBridge hooks:** + +| Method | Default | Override when | +|---|---|---| +| `_create_env(reset_config, simulator_kwargs)` | `raise NotImplementedError` | Always (required) | +| `_extract_image(obs)` | `raise NotImplementedError` | Always (required) | +| `_on_reset(env, reset_config)` | `env.reset()` | Env needs episode data passed to reset | +| `_on_step(env, action_text)` | `env.step(action_text)` | Action needs translation (text → int, etc.) | +| `_extract_info(info)` | Filters to scalar values | You want specific keys in result.json | + +### Step 5: Implement the PromptBuilder (optional) + +For LLM-powered evaluation, create a task-specific PromptBuilder: + +```python +"""Prompt builder for My Benchmark.""" +from __future__ import annotations + +import json + +from easi.agents.prompt_builder import validate_action_name, _encode_image_base64 +from easi.core.episode import Action +from easi.core.memory import AgentMemory + + +class MyPromptBuilder: + """Builds prompts for My Benchmark's ReAct agent.""" + + def __init__(self, n_shot=3, use_feedback=True): + self.n_shot = n_shot + self.use_feedback = use_feedback + + def build_messages(self, memory: AgentMemory) -> list[dict]: + """Build LLM messages from agent memory. + + Args: + memory: AgentMemory with task_description, action_space, + current_observation, steps (history), action_history. + + Returns: + List of message dicts: [{"role": "system", "content": [...]}, ...] + """ + messages = [] + + # System message with instructions + system_text = f"You are an agent. Task: {memory.task_description}\n" + system_text += f"Actions: {', '.join(memory.action_space)}" + messages.append({"role": "system", "content": [{"type": "text", "text": system_text}]}) + + # Current observation (with image) + user_content = [] + if memory.current_observation and memory.current_observation.rgb_path: + img_b64 = _encode_image_base64(memory.current_observation.rgb_path) + user_content.append({ + "type": "image_url", + "image_url": {"url": f"data:image/png;base64,{img_b64}"}, + }) + user_content.append({"type": "text", "text": "What action should you take?"}) + messages.append({"role": "user", "content": user_content}) + + return messages + + def parse_response(self, llm_response: str, memory: AgentMemory) -> list[Action]: + """Parse LLM text response into validated Action objects. + + Returns: + List of Action objects. Empty list = parsing failed. + """ + try: + data = json.loads(llm_response) + except json.JSONDecodeError: + return [] + + plan = data.get("executable_plan", []) + actions = [] + for entry in plan: + name = entry.get("action_name", "") + validated = validate_action_name(name, memory.action_space) + if validated: + actions.append(Action(action_name=validated)) + else: + break # Stop at first invalid action + return actions +``` + +### Step 6: Write Tests + +Follow the pattern in existing test files. All tests run offline (no simulator, no LLM). + +```python +"""Tests for My Benchmark task (offline, no simulator needed).""" +import pytest +from pathlib import Path + +from easi.core.episode import Observation, StepResult + + +class TestMyBenchmarkTask: + @pytest.fixture + def task(self): + from easi.tasks.my_benchmark.task import MyBenchmarkTask + return MyBenchmarkTask() + + def test_name(self, task): + assert task.name == "my_benchmark_base" + + def test_simulator_key(self, task): + assert task.simulator_key == "ai2thor:v5_0_0" + + def test_action_space(self, task): + assert len(task.action_space) > 0 + + def test_max_steps(self, task): + assert task.max_steps == 50 + + def test_format_reset_config(self, task): + episode = {"id": 0, "scene": "TestScene", "instruction": "test"} + config = task.format_reset_config(episode) + assert "scene" in config + + def test_evaluate_episode(self, task): + obs = Observation(rgb_path="/tmp/fake.png") + trajectory = [ + StepResult(observation=obs, reward=0.0, done=True, + info={"task_success": 1.0}), + ] + result = task.evaluate_episode({"id": 0}, trajectory) + assert "task_success" in result + + def test_evaluate_empty_trajectory(self, task): + result = task.evaluate_episode({}, []) + assert result["task_success"] == 0.0 + + def test_bridge_script_path(self, task): + path = task.get_bridge_script_path() + if path is not None: + assert path.exists() + + def test_registry_discovers_task(self): + from easi.tasks.registry import list_tasks + tasks = list_tasks() + assert "my_benchmark_base" in tasks +``` + +Run tests: `pytest tests/test_my_benchmark.py -v` + +### Step 7: Verify + +```bash +# All existing tests still pass +pytest tests/ -v --timeout=60 + +# Registry discovers your task +easi task list | grep my_benchmark + +# Task info looks correct +easi task info my_benchmark_base + +# Dummy agent smoke test (no LLM needed) +easi start my_benchmark_base --agent dummy --max-episodes 1 +``` + +--- + +## Adding a New Simulator + +Less common. Only needed when a benchmark uses a simulator not yet in EASI. + +### Structure + +``` +easi/simulators// +├── __init__.py +├── manifest.yaml # Declares name, versions, classes +└── / + ├── __init__.py + ├── simulator.py # Subclass of BaseSimulator + ├── env_manager.py # Subclass of BaseEnvironmentManager + ├── bridge.py # Default bridge script + ├── conda_env.yaml # Conda environment spec + └── requirements.txt # Pip dependencies +``` + +### manifest.yaml + +```yaml +name: my_sim +display_name: "My Simulator" +default_version: "v1_0_0" +versions: + v1_0_0: + description: "My Simulator 1.0.0" + simulator_class: "easi.simulators.my_sim.v1_0_0.simulator.MySimSimulator" + env_manager_class: "easi.simulators.my_sim.v1_0_0.env_manager.MySimEnvManager" + python_version: "3.10" +``` + +### simulator.py + +```python +from pathlib import Path +from easi.core.base_simulator import BaseSimulator + +class MySimSimulator(BaseSimulator): + @property + def name(self) -> str: + return "my_sim" + + @property + def version(self) -> str: + return "v1_0_0" + + def _get_bridge_script_path(self) -> Path: + return Path(__file__).parent / "bridge.py" +``` + +### env_manager.py + +```python +from pathlib import Path +from easi.core.base_env_manager import BaseEnvironmentManager + +class MySimEnvManager(BaseEnvironmentManager): + @property + def simulator_name(self) -> str: + return "my_sim" + + @property + def version(self) -> str: + return "v1_0_0" + + @property + def needs_display(self) -> bool: + return True # Set True if simulator needs X11/Xvfb + + def get_conda_env_yaml_path(self) -> Path: + return Path(__file__).parent / "conda_env.yaml" + + def get_requirements_txt_path(self) -> Path: + return Path(__file__).parent / "requirements.txt" + + def get_system_deps(self) -> list[str]: + return ["conda"] # Add "xvfb" if needs_display is True + + def get_validation_import(self) -> str: + return "from my_sim import Controller; print('ok')" +``` + +### Verify + +```bash +easi env list # Should show my_sim +easi env install my_sim # Install conda env +easi env check my_sim # Validate +easi sim test my_sim # Smoke test bridge +``` + +--- + +## Vendoring Benchmark Code + +When integrating an external benchmark (e.g., from EmbodiedBench), vendor only the environment code you need: + +1. **Create `vendor/` directory** in your task folder +2. **Copy only the env class** (not the full benchmark runner/evaluator) +3. **Remove external dependencies** the benchmark used for logging, dataset loading, gym registration — EASI handles all of these +4. **Adapt the interface**: + - `reset(episode)` accepts an episode dict (from EASI's dataset) + - `step(action)` returns `(obs, reward, done, info)` tuple + - Remove internal image saving (bridge handles this) + - Remove internal logging (EASI's logger handles this) + +--- + +## Key Conventions + +### Logging + +```python +from easi.utils.logging import get_logger +logger = get_logger(__name__) +# Use logger.info(), logger.warning(), logger.error() +# Use logger.trace() for verbose debug output +# NEVER use print() +``` + +### Imports + +```python +from __future__ import annotations # Always first import +``` + +### Testing + +- All tests run offline (mock simulators, no LLM calls) +- Use `Observation(rgb_path="/tmp/fake.png")` for test observations +- Use `StepResult(observation=obs, done=True, info={...})` for test trajectories +- Test file naming: `tests/test_.py` + +### summary.json Structure + +```json +{ + "num_episodes": 100, + "model": "gpt-4o", + "backend": "openai", + "llm_usage": {"total_calls": 500, "total_tokens": 150000}, + "metrics": { + "success_rate": 0.73, + "avg_num_steps": 24.3, + "avg_task_success": 0.73 + } +} +``` + +Metrics (from `task.aggregate_results()`) are nested under `"metrics"`. Run metadata stays at the top level. + +--- + +## Existing Benchmarks Reference + +| Task | Simulator | Splits | Action Type | Max Steps | +|---|---|---|---|---| +| `dummy_task` | `dummy:v1` | 1 | 4 discrete text | 100 | +| `ebalfred_*` | `ai2thor:v2_1_0` | 6 | ~133 skill text | 50 | +| `ebnavigation_*` | `ai2thor:v5_0_0` | 5 | 8 discrete int | 20 | +| `ebhabitat_*` | `habitat_sim:v0_3_0` | 4 | 4 discrete text | varies | +| `ebmanipulation_*` | `coppeliasim:v4_1_0` | 4 | continuous params | varies | + +Use the closest existing task as a template when adding a new one. The `dummy_task` is the simplest reference; `ebalfred` is the most complete. diff --git a/CLAUDE.md b/CLAUDE.md index 636c565..4ddd269 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -1,123 +1,183 @@ # CLAUDE.md -This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository. +This file provides guidance to Claude Code when working with this repository. ## Project Overview -EASI (Holistic Evaluation of Multimodal LLMs on Spatial Intelligence) is a unified evaluation suite for benchmarking multimodal LLMs on spatial intelligence tasks. It is an **orchestration project** — it does not contain evaluation code itself but wraps two external backends via git submodules. +EASI is a unified evaluation framework for embodied AI agents. It has two layers: -## Architecture +1. **Static benchmarks** — VLMEvalKit and lmms-eval submodules for VLM evaluation (image Q&A, spatial reasoning). These are mature and rarely modified. +2. **Embodied agent evaluation** (`easi/` library) — The active development focus. Subprocess-isolated simulators, multi-split tasks, and LLM-powered agents for interactive benchmarks (EB-Alfred, EB-Navigation, EB-Habitat, EB-Manipulation, HAZARD). -EASI uses a **dual-backend architecture**: +Most development work happens in the `easi/` library. -- **VLMEvalKit** (`VLMEvalKit/` submodule) — Feature-rich backend with built-in model zoo and LLM-based answer judging. Entry point: `VLMEvalKit/run.py`. -- **lmms-eval** (`lmms-eval/` submodule) — Lightweight, accelerate-based backend with multi-GPU distributed inference. Entry point: `lmms-eval` CLI command after `pip install -e ./lmms-eval`. +## Quick Reference -Both submodules point to EvolvingLMMs-Lab forks. Submodules must be initialized before use: ```bash -git submodule update --init --recursive -``` +# Setup +pip install -e ".[dev]" -The root repository contains: -- `examples/` — Shell scripts demonstrating evaluation invocations (lmms-eval backend) -- `dockerfiles/` — Docker configs for EASI, VLM3R, and Cambrains runtime environments -- `docs/` — Changelog, benchmark verification data, supported models/benchmarks matrix +# Run tests (540 tests, ~4-5min) +pytest tests/ -v --timeout=60 -## Setup & Installation +# CLI +easi task list # List all tasks +easi env list # List all simulators +easi env install ai2thor:v2_1_0 # Install simulator env +easi sim test dummy # Smoke test simulator +easi start dummy_task --agent dummy # Run evaluation (no LLM) -**VLMEvalKit backend:** -```bash -git clone --recursive https://github.com/EvolvingLMMs-Lab/EASI.git -cd EASI && pip install -e ./VLMEvalKit +# Real evaluation +easi start ebalfred_base --agent react --backend openai --model gpt-4o +easi start ebalfred_base --agent react --backend openai --model gpt-4o --num-parallel 4 +easi start --resume ./logs/ebalfred_base/ ``` -**lmms-eval backend:** -```bash -git clone --recursive https://github.com/EvolvingLMMs-Lab/EASI.git -cd EASI && pip install -e ./lmms-eval spacy -pip install flash-attn --no-build-isolation -``` +## Architecture -**Docker:** -```bash -bash dockerfiles/EASI/build_runtime_docker.sh ``` - -## Running Evaluations - -**VLMEvalKit:** -```bash -cd VLMEvalKit/ -python run.py --data {BENCHMARK} --model {MODEL} --judge {JUDGE_MODE} --verbose --reuse +easi/ +├── core/ # Abstract base classes + dataclasses +│ ├── base_task.py # BaseTask — task interface +│ ├── base_simulator.py # BaseSimulator — simulator interface +│ ├── base_agent.py # BaseAgent — agent interface +│ ├── base_env_manager.py # BaseEnvironmentManager — conda env setup +│ ├── episode.py # Observation, Action, StepResult, EpisodeRecord +│ ├── memory.py # AgentMemory — shared agent/prompt state +│ ├── protocols.py # Runtime-checkable Protocol interfaces +│ └── exceptions.py # EASIError hierarchy +│ +├── agents/ # Agent implementations +│ ├── dummy_agent.py # Random action picker (testing) +│ ├── react_agent.py # ReAct agent with multi-action buffering +│ └── prompt_builder.py # PromptBuilder protocol + DefaultPromptBuilder +│ +├── simulators/ # Simulator implementations (subprocess-isolated) +│ ├── base_bridge.py # BaseBridge — Gym-like env wrapper for IPC +│ ├── subprocess_runner.py # SubprocessRunner — process lifecycle +│ ├── registry.py # Auto-discovery via manifest.yaml +│ ├── dummy/v1/ # In-memory testing simulator +│ ├── ai2thor/v2_1_0/ # AI2-THOR 2.1.0 (EB-Alfred, Python 3.8) +│ ├── ai2thor/v5_0_0/ # AI2-THOR 5.0.0 (EB-Navigation, Python 3.10) +│ ├── habitat_sim/v0_3_0/ # Habitat-Sim 0.3.0 (EB-Habitat, Python 3.9) +│ ├── coppeliasim/v4_1_0/ # CoppeliaSim 4.1.0 (EB-Manipulation, Python 3.10) +│ └── tdw/v1_11_23/ # ThreeDWorld 1.11.23 (HAZARD, Python 3.10) +│ +├── tasks/ # Benchmark task definitions +│ ├── registry.py # Auto-discovery via *.yaml glob +│ ├── yaml_utils.py # Template inheritance (extends) +│ ├── dataset.py # HuggingFace + local dataset loading +│ ├── scaffold.py # Task boilerplate generator +│ ├── dummy_task/ # 3-episode testing task +│ ├── ebalfred/ # EB-Alfred (6 splits) +│ ├── ebnavigation/ # EB-Navigation (5 splits) +│ ├── ebhabitat/ # EB-Habitat (4 splits) +│ └── ebmanipulation/ # EB-Manipulation (4 splits) +│ +├── evaluation/ # Evaluation orchestration +│ ├── runner.py # EvaluationRunner (sequential) +│ ├── parallel_runner.py # ParallelRunner (thread-pool, API backends) +│ └── metrics.py # default_aggregate + legacy aggregate_metrics +│ +├── llm/ # LLM client infrastructure +│ ├── client.py # LLMClient (LiteLLM wrapper, any backend) +│ ├── api_client.py # LLMApiClient (legacy OpenAI-only) +│ ├── server_manager.py # vLLM server lifecycle +│ ├── dummy_server.py # Dummy LLM server for testing +│ └── utils.py # Backend config (parse, validate, split kwargs) +│ +├── communication/ # Filesystem IPC (parent <-> bridge subprocess) +│ ├── filesystem.py # Atomic JSON read/write, command/response +│ └── schemas.py # Command/response schemas +│ +├── utils/ # Shared utilities +│ ├── logging.py # Centralized logging (TRACE/DEBUG/INFO/WARNING/ERROR) +│ ├── import_utils.py # Dynamic class importing +│ ├── json_repair.py # LLM response JSON repair +│ └── ... # paths, locking, system_deps, spinner +│ +└── cli.py # CLI entry point (easi command) ``` -Judge modes: `extract_matching` (regex), `gpt-4o-1120` (LLM-based, needs OPENAI_API_KEY). -**lmms-eval (single GPU):** -```bash -lmms-eval --model {MODEL_TYPE} --model_args pretrained={MODEL_PATH} \ - --tasks {TASK} --batch_size 1 --log_samples --output_path ./logs/ -``` +## Key Patterns -**lmms-eval (multi-GPU):** -```bash -CUDA_VISIBLE_DEVICES=0,1,2,3 accelerate launch --num_processes=4 \ - -m lmms_eval --model {MODEL_TYPE} --model_args pretrained={MODEL_PATH} \ - --tasks {TASK} --batch_size 1 --log_samples --output_path ./logs/ -``` +### Subprocess Isolation +Each simulator runs in its own conda environment (potentially different Python version). The bridge script communicates with the parent process via filesystem IPC (atomic JSON files in a temp directory). This enables Python 3.8 for AI2-THOR v2.1 while the host runs Python 3.10+. -List available tasks: `lmms-eval --tasks list` +### Multi-Split Tasks +Each task folder can have multiple YAML configs. The task registry discovers all `*.yaml` files and registers each as a separate task (e.g., `ebalfred_base`, `ebalfred_spatial`). Split YAMLs use template inheritance via `extends: _base.yaml`. -## EASI Library (`easi/`) +### Pluggable Metrics +Two-phase metric system: +- **Per-episode**: `task.evaluate_episode(episode, trajectory) -> dict` (always user-defined) +- **Cross-episode**: `task.aggregate_results(records: list[EpisodeRecord]) -> dict` (optional override, default averages all numeric keys) -The `easi` package is a Python library for embodied agent evaluation with subprocess-isolated simulators. Install: `pip install -e .` +Metrics are nested under `summary["metrics"]` in summary.json, separated from run metadata. -### Architecture +### ReAct Agent + PromptBuilder +The agent uses a PromptBuilder protocol for task-specific prompts. The builder constructs messages from AgentMemory and parses LLM responses into validated Actions. Multi-action buffering: LLM returns a plan, agent executes one action per step, clears buffer on failure. -``` -easi/ -├── core/ # Base classes: BaseTask, BaseSimulator, BaseAgent, Episode/Action/StepResult -├── agents/ # DummyAgent, ReActAgent (with multi-action buffering + PromptBuilder) -├── communication/ # Filesystem IPC: atomic JSON read/write, command/response schemas -├── evaluation/ # EvaluationRunner (sequential orchestrator), metrics aggregation -├── llm/ # LLMApiClient (OpenAI-compatible), DummyLLMServer -├── simulators/ # Simulator implementations (subprocess bridges) -│ ├── dummy/v1/ # In-memory dummy bridge for testing -│ └── ai2thor/v2_1_0/ # Real AI2-THOR 2.1.0 bridge for EB-Alfred -├── tasks/ # Task definitions (per-split YAML configs) -│ ├── dummy/ # dummy_task (3 test episodes) -│ └── ebalfred/ # EB-Alfred (6 splits: base, long_horizon, common_sense, etc.) -└── utils/ # import_class(), logging setup -``` +### Auto-Discovery +- **Simulators**: Discovered via `easi/simulators/*/manifest.yaml` +- **Tasks**: Discovered via `easi/tasks/*/*.yaml` +- Both use dotted import paths to load classes dynamically -### Key Patterns +## CLI Commands -- **Subprocess isolation**: Each simulator runs in its own conda env (e.g., Python 3.8 for ai2thor). The bridge script communicates via filesystem IPC (JSON files in a temp workspace). -- **Multi-split tasks**: Each task folder has one or more `*.yaml` config files. The registry discovers all YAMLs, each registering as a separate task (e.g., `ebalfred_base`, `ebalfred_spatial`). -- **EB-Alfred skills**: Actions are high-level skill text (e.g., `"find a Cabinet"`, `"pick up the Mug"`), NOT raw THOR API calls. The bridge translates these to THOR API sequences. -- **ReAct agent**: Uses a PromptBuilder protocol for task-specific prompts. Supports multi-action buffering (LLM returns a plan, agent executes one action per step, clears buffer on failure). -- **State tracking**: The AI2-THOR bridge tracks `cleaned_objects`, `cooled_objects`, `heated_objects` for EB-Alfred goal condition evaluation. +| Command | Description | +|---|---| +| `easi env list` | List available simulators | +| `easi env install ` | Install simulator conda env | +| `easi env check ` | Verify environment is ready | +| `easi task list` | List available tasks | +| `easi task info ` | Show task details | +| `easi task download ` | Download task dataset | +| `easi task scaffold ` | Generate new task boilerplate | +| `easi sim test ` | Smoke test a simulator bridge | +| `easi start ` | Run evaluation | +| `easi llm-server` | Start dummy LLM server | -### CLI +### Key `easi start` Options ```bash -easi env list|install|check # Manage simulator environments -easi task list|info|download # Manage tasks -easi sim test # Smoke test a simulator -easi start --agent dummy|react # Run evaluation (single task) -easi start --tasks t1,t2 --agent react # Run evaluation (multi-task) -easi llm-server [--port PORT] # Start dummy LLM server +easi start \ + --agent {dummy|react} \ + --backend {vllm|openai|anthropic|gemini} \ + --model \ + --num-parallel \ # Thread-pool parallelism (API backends only) + --max-episodes \ + --resume \ + --output-dir ./logs \ + --llm-kwargs '{"temperature": 0.7}' ``` -### Testing +## Output Structure + +``` +logs//_/ + config.json # CLI options + resolved config + summary.json # {"num_episodes": N, "metrics": {...}, "model": "...", ...} + episodes/ + 000_/ + result.json # Per-episode metrics + trajectory.jsonl # Action log (one JSON line per step) + step_0000.png # Observation images +``` + +## Testing ```bash -pip install -e ".[dev]" -python -m pytest tests/ -v --timeout=60 # 103 tests, ~60s +pytest tests/ -v --timeout=60 # Full suite (540 tests) +pytest tests/test_metrics.py -v # Specific file ``` -## Key References +All tests run offline without simulators or LLMs. Tests mock subprocess bridges and use DummyTask + DummyAgent. + +## Logging Convention + +```python +from easi.utils.logging import get_logger +logger = get_logger(__name__) +``` -- Test suite: `tests/` with pytest (103 tests covering all components) -- Evaluation logs go to `./results/` (configurable via `--output-dir`) -- Supported models (23) and benchmarks (25) are documented in `docs/Support_bench_models.md`. -- Benchmark verification against official scores is in `docs/Benchmark_Verification.md`. +Use `logger.info()` for user-facing messages, `logger.trace()` for detailed debug output. Never use `print()`. From 8952492f743b01a03c273aa85ba77d9adb5e0fe3 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Fri, 20 Feb 2026 23:47:31 +0800 Subject: [PATCH 070/239] [Fix] Vendor amsolver, tools, vlm into EB-Manipulation to remove external repo dependency bridge.py no longer references EmbodiedBench/ at the repo root. _setup_amsolver_path() now points to easi/tasks/ebmanipulation/vendor/. Vendored packages: amsolver (9.6MB), tools/grasploc (92K), vlm/tasks only (180K, skipped 186MB object_models/). --- easi/tasks/ebmanipulation/bridge.py | 31 +- .../vendor/amsolver/__init__.py | 19 + .../vendor/amsolver/action_modes.py | 67 ++ .../vendor/amsolver/backend/__init__.py | 0 .../vendor/amsolver/backend/conditions.py | 212 +++++ .../vendor/amsolver/backend/const.py | 28 + .../vendor/amsolver/backend/exceptions.py | 21 + .../vendor/amsolver/backend/observation.py | 84 ++ .../vendor/amsolver/backend/robot.py | 37 + .../vendor/amsolver/backend/scene.py | 552 +++++++++++++ .../vendor/amsolver/backend/spawn_boundary.py | 191 +++++ .../vendor/amsolver/backend/task.py | 547 +++++++++++++ .../vendor/amsolver/backend/task_utils.py | 28 + .../vendor/amsolver/backend/unit_tasks.py | 741 ++++++++++++++++++ .../vendor/amsolver/backend/utils.py | 522 ++++++++++++ .../vendor/amsolver/backend/waypoints.py | 121 +++ .../ebmanipulation/vendor/amsolver/const.py | 260 ++++++ .../ebmanipulation/vendor/amsolver/demo.py | 18 + .../vendor/amsolver/environment.py | 252 ++++++ .../vendor/amsolver/gym/__init__.py | 28 + .../vendor/amsolver/gym/vlmbench_env.py | 106 +++ .../vendor/amsolver/noise_model.py | 29 + .../vendor/amsolver/observation_config.py | 140 ++++ .../vendor/amsolver/robot_ttms/jaco.ttm | Bin 0 -> 588730 bytes .../vendor/amsolver/robot_ttms/mico.ttm | Bin 0 -> 589908 bytes .../vendor/amsolver/robot_ttms/panda.ttm | Bin 0 -> 474146 bytes .../vendor/amsolver/robot_ttms/sawyer.ttm | Bin 0 -> 544465 bytes .../vendor/amsolver/robot_ttms/ur5.ttm | Bin 0 -> 353257 bytes .../amsolver/sim2real/domain_randomization.py | 82 ++ .../domain_randomization_environment.py | 51 ++ .../sim2real/domain_randomization_scene.py | 97 +++ .../vendor/amsolver/task_design.ttt | Bin 0 -> 6967399 bytes .../vendor/amsolver/task_environment.py | 676 ++++++++++++++++ .../ebmanipulation/vendor/amsolver/utils.py | 331 ++++++++ .../ebmanipulation/vendor/tools/__init__.py | 0 .../vendor/tools/assets/task_template.txt | 25 + .../vendor/tools/cinematic_recorder.py | 158 ++++ .../vendor/tools/dataset_generator_NLP.py | 401 ++++++++++ .../ebmanipulation/vendor/tools/grasploc.py | 277 +++++++ .../tools/meshlab_stratified_sampling.mlx | 8 + .../vendor/tools/task_builder_NLP.py | 312 ++++++++ .../vendor/tools/task_validator.py | 149 ++++ .../vendor/tools/test_config_generator.py | 259 ++++++ .../ebmanipulation/vendor/vlm/__init__.py | 0 .../vendor/vlm/tasks/__init__.py | 0 .../vendor/vlm/tasks/drop_pen.py | 141 ++++ .../vendor/vlm/tasks/drop_pen_color.py | 29 + .../vendor/vlm/tasks/drop_pen_relative.py | 50 ++ .../vendor/vlm/tasks/drop_pen_size.py | 57 ++ .../vendor/vlm/tasks/open_door.py | 131 ++++ .../vendor/vlm/tasks/open_door_complex.py | 136 ++++ .../vendor/vlm/tasks/open_drawer.py | 128 +++ .../vendor/vlm/tasks/open_drawer_cabinet.py | 22 + .../vendor/vlm/tasks/pick_cube.py | 101 +++ .../vendor/vlm/tasks/pick_cube_color.py | 38 + .../vendor/vlm/tasks/pick_cube_relative.py | 55 ++ .../vendor/vlm/tasks/pick_cube_shape.py | 46 ++ .../vendor/vlm/tasks/pick_cube_size.py | 57 ++ .../vlm/tasks/place_into_shape_sorter.py | 133 ++++ .../tasks/place_into_shape_sorter_color.py | 26 + .../tasks/place_into_shape_sorter_relative.py | 53 ++ .../tasks/place_into_shape_sorter_shape.py | 28 + .../vendor/vlm/tasks/pour_demo.py | 240 ++++++ .../vendor/vlm/tasks/pour_demo_color.py | 24 + .../vendor/vlm/tasks/pour_demo_relative.py | 43 + .../vendor/vlm/tasks/pour_demo_size.py | 48 ++ .../vendor/vlm/tasks/stack_cubes.py | 136 ++++ .../vendor/vlm/tasks/stack_cubes_color.py | 39 + .../vendor/vlm/tasks/stack_cubes_relative.py | 64 ++ .../vendor/vlm/tasks/stack_cubes_shape.py | 67 ++ .../vendor/vlm/tasks/stack_cubes_size.py | 73 ++ .../vendor/vlm/tasks/wipe_table.py | 218 ++++++ .../vendor/vlm/tasks/wipe_table_color.py | 23 + .../vendor/vlm/tasks/wipe_table_direction.py | 71 ++ .../vendor/vlm/tasks/wipe_table_relative.py | 40 + .../vendor/vlm/tasks/wipe_table_shape.py | 36 + .../vendor/vlm/tasks/wipe_table_size.py | 36 + 77 files changed, 9228 insertions(+), 21 deletions(-) create mode 100644 easi/tasks/ebmanipulation/vendor/amsolver/__init__.py create mode 100644 easi/tasks/ebmanipulation/vendor/amsolver/action_modes.py create mode 100644 easi/tasks/ebmanipulation/vendor/amsolver/backend/__init__.py create mode 100644 easi/tasks/ebmanipulation/vendor/amsolver/backend/conditions.py create mode 100644 easi/tasks/ebmanipulation/vendor/amsolver/backend/const.py create mode 100644 easi/tasks/ebmanipulation/vendor/amsolver/backend/exceptions.py create mode 100644 easi/tasks/ebmanipulation/vendor/amsolver/backend/observation.py create mode 100644 easi/tasks/ebmanipulation/vendor/amsolver/backend/robot.py create mode 100644 easi/tasks/ebmanipulation/vendor/amsolver/backend/scene.py create mode 100644 easi/tasks/ebmanipulation/vendor/amsolver/backend/spawn_boundary.py create mode 100644 easi/tasks/ebmanipulation/vendor/amsolver/backend/task.py create mode 100644 easi/tasks/ebmanipulation/vendor/amsolver/backend/task_utils.py create mode 100644 easi/tasks/ebmanipulation/vendor/amsolver/backend/unit_tasks.py create mode 100644 easi/tasks/ebmanipulation/vendor/amsolver/backend/utils.py create mode 100644 easi/tasks/ebmanipulation/vendor/amsolver/backend/waypoints.py create mode 100644 easi/tasks/ebmanipulation/vendor/amsolver/const.py create mode 100644 easi/tasks/ebmanipulation/vendor/amsolver/demo.py create mode 100644 easi/tasks/ebmanipulation/vendor/amsolver/environment.py create mode 100644 easi/tasks/ebmanipulation/vendor/amsolver/gym/__init__.py create mode 100644 easi/tasks/ebmanipulation/vendor/amsolver/gym/vlmbench_env.py create mode 100644 easi/tasks/ebmanipulation/vendor/amsolver/noise_model.py create mode 100644 easi/tasks/ebmanipulation/vendor/amsolver/observation_config.py create mode 100644 easi/tasks/ebmanipulation/vendor/amsolver/robot_ttms/jaco.ttm create mode 100644 easi/tasks/ebmanipulation/vendor/amsolver/robot_ttms/mico.ttm create mode 100644 easi/tasks/ebmanipulation/vendor/amsolver/robot_ttms/panda.ttm create mode 100644 easi/tasks/ebmanipulation/vendor/amsolver/robot_ttms/sawyer.ttm create mode 100644 easi/tasks/ebmanipulation/vendor/amsolver/robot_ttms/ur5.ttm create mode 100644 easi/tasks/ebmanipulation/vendor/amsolver/sim2real/domain_randomization.py create mode 100644 easi/tasks/ebmanipulation/vendor/amsolver/sim2real/domain_randomization_environment.py create mode 100644 easi/tasks/ebmanipulation/vendor/amsolver/sim2real/domain_randomization_scene.py create mode 100644 easi/tasks/ebmanipulation/vendor/amsolver/task_design.ttt create mode 100644 easi/tasks/ebmanipulation/vendor/amsolver/task_environment.py create mode 100644 easi/tasks/ebmanipulation/vendor/amsolver/utils.py create mode 100644 easi/tasks/ebmanipulation/vendor/tools/__init__.py create mode 100644 easi/tasks/ebmanipulation/vendor/tools/assets/task_template.txt create mode 100644 easi/tasks/ebmanipulation/vendor/tools/cinematic_recorder.py create mode 100644 easi/tasks/ebmanipulation/vendor/tools/dataset_generator_NLP.py create mode 100644 easi/tasks/ebmanipulation/vendor/tools/grasploc.py create mode 100644 easi/tasks/ebmanipulation/vendor/tools/meshlab_stratified_sampling.mlx create mode 100644 easi/tasks/ebmanipulation/vendor/tools/task_builder_NLP.py create mode 100644 easi/tasks/ebmanipulation/vendor/tools/task_validator.py create mode 100644 easi/tasks/ebmanipulation/vendor/tools/test_config_generator.py create mode 100644 easi/tasks/ebmanipulation/vendor/vlm/__init__.py create mode 100644 easi/tasks/ebmanipulation/vendor/vlm/tasks/__init__.py create mode 100644 easi/tasks/ebmanipulation/vendor/vlm/tasks/drop_pen.py create mode 100644 easi/tasks/ebmanipulation/vendor/vlm/tasks/drop_pen_color.py create mode 100644 easi/tasks/ebmanipulation/vendor/vlm/tasks/drop_pen_relative.py create mode 100644 easi/tasks/ebmanipulation/vendor/vlm/tasks/drop_pen_size.py create mode 100644 easi/tasks/ebmanipulation/vendor/vlm/tasks/open_door.py create mode 100644 easi/tasks/ebmanipulation/vendor/vlm/tasks/open_door_complex.py create mode 100644 easi/tasks/ebmanipulation/vendor/vlm/tasks/open_drawer.py create mode 100644 easi/tasks/ebmanipulation/vendor/vlm/tasks/open_drawer_cabinet.py create mode 100644 easi/tasks/ebmanipulation/vendor/vlm/tasks/pick_cube.py create mode 100644 easi/tasks/ebmanipulation/vendor/vlm/tasks/pick_cube_color.py create mode 100644 easi/tasks/ebmanipulation/vendor/vlm/tasks/pick_cube_relative.py create mode 100644 easi/tasks/ebmanipulation/vendor/vlm/tasks/pick_cube_shape.py create mode 100644 easi/tasks/ebmanipulation/vendor/vlm/tasks/pick_cube_size.py create mode 100644 easi/tasks/ebmanipulation/vendor/vlm/tasks/place_into_shape_sorter.py create mode 100644 easi/tasks/ebmanipulation/vendor/vlm/tasks/place_into_shape_sorter_color.py create mode 100644 easi/tasks/ebmanipulation/vendor/vlm/tasks/place_into_shape_sorter_relative.py create mode 100644 easi/tasks/ebmanipulation/vendor/vlm/tasks/place_into_shape_sorter_shape.py create mode 100644 easi/tasks/ebmanipulation/vendor/vlm/tasks/pour_demo.py create mode 100644 easi/tasks/ebmanipulation/vendor/vlm/tasks/pour_demo_color.py create mode 100644 easi/tasks/ebmanipulation/vendor/vlm/tasks/pour_demo_relative.py create mode 100644 easi/tasks/ebmanipulation/vendor/vlm/tasks/pour_demo_size.py create mode 100644 easi/tasks/ebmanipulation/vendor/vlm/tasks/stack_cubes.py create mode 100644 easi/tasks/ebmanipulation/vendor/vlm/tasks/stack_cubes_color.py create mode 100644 easi/tasks/ebmanipulation/vendor/vlm/tasks/stack_cubes_relative.py create mode 100644 easi/tasks/ebmanipulation/vendor/vlm/tasks/stack_cubes_shape.py create mode 100644 easi/tasks/ebmanipulation/vendor/vlm/tasks/stack_cubes_size.py create mode 100644 easi/tasks/ebmanipulation/vendor/vlm/tasks/wipe_table.py create mode 100644 easi/tasks/ebmanipulation/vendor/vlm/tasks/wipe_table_color.py create mode 100644 easi/tasks/ebmanipulation/vendor/vlm/tasks/wipe_table_direction.py create mode 100644 easi/tasks/ebmanipulation/vendor/vlm/tasks/wipe_table_relative.py create mode 100644 easi/tasks/ebmanipulation/vendor/vlm/tasks/wipe_table_shape.py create mode 100644 easi/tasks/ebmanipulation/vendor/vlm/tasks/wipe_table_size.py diff --git a/easi/tasks/ebmanipulation/bridge.py b/easi/tasks/ebmanipulation/bridge.py index 28a2c5e..1897d40 100644 --- a/easi/tasks/ebmanipulation/bridge.py +++ b/easi/tasks/ebmanipulation/bridge.py @@ -3,8 +3,8 @@ This script runs inside the easi_coppeliasim_v4_1_0 conda env (Python 3.10). Communicates with parent process via filesystem IPC. -The bridge adds AMSolver to sys.path at startup so the vendored EBManEnv -can import from amsolver (CoppeliaSim scene management). +The bridge adds vendored AMSolver (easi/tasks/ebmanipulation/vendor/amsolver/) +to sys.path at startup so EBManEnv can import from amsolver. It also handles: - Object coordinate extraction (form_object_coord_for_input) @@ -30,32 +30,21 @@ def _setup_amsolver_path(simulator_kwargs: dict, data_dir: str = "") -> None: - """Add AMSolver parent directory to sys.path and set TTMS_FOLDER. + """Add vendored AMSolver to sys.path and set TTMS_FOLDER. - AMSolver lives at EmbodiedBench/embodiedbench/envs/eb_manipulation/amsolver/. - The parent directory (eb_manipulation/) must be on sys.path so - ``import amsolver`` works. - - The path is configurable via simulator_kwargs['amsolver_parent_path']. - Default: auto-detect from repo root / EmbodiedBench submodule. - - Also sets amsolver.task_environment.TTMS_FOLDER to the extracted HF data - directory so AMSolver can find task templates, object models, and robot TTMs. + AMSolver is vendored at easi/tasks/ebmanipulation/vendor/amsolver/. + The vendor/ directory must be on sys.path so ``import amsolver`` works. """ - amsolver_parent = simulator_kwargs.get("amsolver_parent_path", "") - if not amsolver_parent: - # Auto-detect: try repo_root/EmbodiedBench/embodiedbench/envs/eb_manipulation - candidate = ( - _repo_root / "EmbodiedBench" / "embodiedbench" / "envs" / "eb_manipulation" - ) - if candidate.exists(): - amsolver_parent = str(candidate) + vendor_dir = str(Path(__file__).parent / "vendor") + if vendor_dir not in sys.path: + sys.path.insert(0, vendor_dir) + # Allow override via simulator_kwargs (for custom installations) + amsolver_parent = simulator_kwargs.get("amsolver_parent_path", "") if amsolver_parent and amsolver_parent not in sys.path: sys.path.insert(0, amsolver_parent) # Point AMSolver's TTMS_FOLDER to the HF dataset cache - # (contains vlm/, amsolver/robot_ttms/) if data_dir: import amsolver.task_environment as te diff --git a/easi/tasks/ebmanipulation/vendor/amsolver/__init__.py b/easi/tasks/ebmanipulation/vendor/amsolver/__init__.py new file mode 100644 index 0000000..7791d6a --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/amsolver/__init__.py @@ -0,0 +1,19 @@ +#Copy From the rlbench: https://github.com/stepjam/RLBench +__version__ = '1.1.0' + +import numpy as np +import pyrep + +pr_v = np.array(pyrep.__version__.split('.'), dtype=int) +if pr_v.size < 4 or np.any(pr_v < np.array([4, 1, 0, 2])): + raise ImportError( + 'PyRep version must be greater than 4.1.0.2. Please update PyRep.') + + +from amsolver.environment import Environment +from amsolver.action_modes import ArmActionMode +from amsolver.observation_config import ObservationConfig +from amsolver.observation_config import CameraConfig +from amsolver.sim2real.domain_randomization import RandomizeEvery +from amsolver.sim2real.domain_randomization import VisualRandomizationConfig +from amsolver.sim2real.domain_randomization_environment import DomainRandomizationEnvironment diff --git a/easi/tasks/ebmanipulation/vendor/amsolver/action_modes.py b/easi/tasks/ebmanipulation/vendor/amsolver/action_modes.py new file mode 100644 index 0000000..20ac6ff --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/amsolver/action_modes.py @@ -0,0 +1,67 @@ +#Copy From the rlbench: https://github.com/stepjam/RLBench +from enum import Enum + + +class ArmActionMode(Enum): + + # Absolute arm joint velocities + ABS_JOINT_VELOCITY = 0 + + # Change in arm joint velocities + DELTA_JOINT_VELOCITY = 1 + + # Absolute arm joint positions/angles (in radians) + ABS_JOINT_POSITION = 2 + + # Change in arm joint positions/angles (in radians) + DELTA_JOINT_POSITION = 3 + + # Absolute arm joint forces/torques + ABS_JOINT_TORQUE = 4 + + # Change in arm joint forces/torques + DELTA_JOINT_TORQUE = 5 + + # Absolute end-effector pose (position (3) and quaternion (4)) + ABS_EE_POSE_WORLD_FRAME = 6 + + # Change in end-effector pose (position (3) and quaternion (4)) + DELTA_EE_POSE_WORLD_FRAME = 7 + + # Absolute end-effector pose (position (3) and quaternion (4)) + # But does path planning between these points + ABS_EE_POSE_PLAN_WORLD_FRAME = 8 + + # Absolute end-effector pose (position (3) and quaternion (4)) + # But does path planning between these points (with collision checking) + ABS_EE_POSE_PLAN_WORLD_FRAME_WITH_COLLISION_CHECK = 9 + + # Change in end-effector pose (position (3) and quaternion (4)) + # But does path planning between these points + DELTA_EE_POSE_PLAN_WORLD_FRAME = 10 + + # Change in end-effector pose (position (3) and quaternion (4)) + # In the end-effector frame + EE_POSE_EE_FRAME = 11 + + # Change in end-effector pose (position (3) and quaternion (4)) + # But does path planning between these points. + # In the end-effector frame + EE_POSE_PLAN_EE_FRAME = 12 + + # NOTE: There is no ABS/DELTA mode for the EE_FRAME because ABS == DELTA + + +class GripperActionMode(Enum): + + # The open amount (0 >= x <= 1) of the gripper. 0 is close, 1 is open. + OPEN_AMOUNT = 0 + + +class ActionMode(object): + + def __init__(self, + arm: ArmActionMode = ArmActionMode.ABS_JOINT_VELOCITY, + gripper: GripperActionMode = GripperActionMode.OPEN_AMOUNT): + self.arm = arm + self.gripper = gripper diff --git a/easi/tasks/ebmanipulation/vendor/amsolver/backend/__init__.py b/easi/tasks/ebmanipulation/vendor/amsolver/backend/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/easi/tasks/ebmanipulation/vendor/amsolver/backend/conditions.py b/easi/tasks/ebmanipulation/vendor/amsolver/backend/conditions.py new file mode 100644 index 0000000..6ad144c --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/amsolver/backend/conditions.py @@ -0,0 +1,212 @@ +#Modified From the rlbench: https://github.com/stepjam/RLBench +from typing import List +import math +from pyrep.objects.shape import Shape +from pyrep.objects.joint import Joint +from pyrep.objects.object import Object +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.robots.end_effectors.gripper import Gripper + + +class Condition(object): + def condition_met(self): + raise NotImplementedError() + + def reset(self): + # Used if the conditions store any state. + pass + + +class ColorCondition(object): + def _init_(self, shape: Shape, success_rgb: List[float]): + self.shape = shape + self.success_rgb = success_rgb + + def condition_met(self): + obj_rgb = self.shape.get_color() + met = (obj_rgb == self.success_rgb) + return met, False + + +# class JointCondition(Condition): +# def __init__(self, joint: Joint, position: float): +# """in radians if revoloute, or meters if prismatic""" +# self._joint = joint +# self._original_pos = joint.get_joint_position() +# self._pos = position + +# def condition_met(self): +# met = math.fabs( +# self._joint.get_joint_position() - self._original_pos) > self._pos +# return met, False +class JointCondition(Condition): + def __init__(self, joint: Joint, position: float, position_bound = None): + """in radians if revoloute, or meters if prismatic""" + self._joint = joint.get_name() + self._original_pos = joint.get_joint_position() + self._pos = position + self._position_bound = position_bound + + def condition_met(self): + joint = Joint(self._joint) + current_angle = math.fabs( + joint.get_joint_position() - self._original_pos) + met = current_angle > self._pos + if self._position_bound is not None: + met = self._position_bound> current_angle > self._pos + return met, False + +# class DetectedCondition(Condition): +# def __init__(self, obj: Object, detector: ProximitySensor, +# negated: bool = False): +# self._obj = obj +# self._detector = detector +# self._negated = negated + +# def condition_met(self): +# try: +# met = self._detector.is_detected(self._obj) +# if self._negated: +# met = not met +# return met, False +# except: +# return False, False + +class DetectedCondition(Condition): + def __init__(self, obj: Object, detector: ProximitySensor, + negated: bool = False): + self._obj = obj.get_name() + self._detector = detector.get_name() + self._negated = negated + + def condition_met(self): + obj = Object.get_object(self._obj) + detector = ProximitySensor(self._detector) + try: + met = detector.is_detected(obj) + if self._negated: + met = not met + return met, False + except: + return False, False + +class NothingGrasped(Condition): + def __init__(self, gripper: Gripper): + self._gripper = gripper + + def condition_met(self): + met = len(self._gripper.get_grasped_objects()) == 0 + return met, False + + +class GraspedCondition(Condition): + def __init__(self, gripper: Gripper, object: Object): + self._gripper = gripper + self._object_handle = object.get_handle() + + def condition_met(self): + met = len([ob for ob in self._gripper.get_grasped_objects() + if self._object_handle == ob.get_handle()]) > 0 + return met, False + + +class DetectedSeveralCondition(Condition): + def __init__(self, objects: List[Object], detector: ProximitySensor, + number_needed: int): + self._objects = objects + self._detector = detector + self._number_needed = number_needed + + def condition_met(self): + count = 0 + for ob in self._objects: + if self._detector.is_detected(ob): + count += 1 + met = False + if count >= self._number_needed: + met = True + return met, False + + +class EmptyCondition(Condition): + + def __init__(self, container: list): + self._container = container + + def condition_met(self): + met = len(self._container) == 0 + return met, False + + +class FollowCondition(Condition): + + def __init__(self, obj: Object, points: list, + relative_to: Object = None, delta_limit: float = 0.01, + start_after_first: bool = True): + self._obj = obj + self._ponts = points + self._relative_to = relative_to + self._delta_limit = delta_limit + self._start_after_first = start_after_first + self._index = 0 + self._strikes = 0 + + def condition_met(self): + pos = self._obj.get_position(self._relative_to) + first = True + for i in range(self._index, len(self._ponts)): + p = self._ponts[i] + dist = math.sqrt((pos[0] - p[0]) ** 2 + + (pos[1] - p[1]) ** 2 + + (pos[2] - p[2]) ** 2) + # Check we aren't too far away from the next point + if dist > self._delta_limit: + # Check if we are ignoring until we reach the first point + if first and not (self._start_after_first and self._index == 0): + # If it is the first point, then we have failed + if self._strikes > 3: + return False, True + self._strikes += 1 + # Otherwise we have looped at least once and we may + # still be following the path + return False, False + # Only advance if we are super close + elif dist > self._delta_limit * 0.5: + return False, False + self._index += 1 + self._strikes = 0 + first = False + return True, False + + +class ConditionSet(Condition): + def __init__(self, conditions: List[Condition], order_matters: bool = False, + simultaneously_met: bool = True): + """alternative would be sequentially met""" + self._conditions = conditions + self._order_matters = order_matters + self._simultaneously_met = simultaneously_met # Probably wont use + self._current_condition_index = 0 + + def condition_met(self): + met = True + # term = False + if self._order_matters: + if self._current_condition_index < len(self._conditions): + for cond in self._conditions[self._current_condition_index:]: + ismet, term = cond.condition_met() + if not ismet: + break + self._current_condition_index += 1 + # Check again to see if we have now completed the order + met = self._current_condition_index >= len(self._conditions) + else: + for cond in self._conditions: + ismet, term = cond.condition_met() + met &= ismet + # if term: + # break + return met, False + + def reset(self): + self._current_condition_index = 0 \ No newline at end of file diff --git a/easi/tasks/ebmanipulation/vendor/amsolver/backend/const.py b/easi/tasks/ebmanipulation/vendor/amsolver/backend/const.py new file mode 100644 index 0000000..161f47d --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/amsolver/backend/const.py @@ -0,0 +1,28 @@ +IMAGE_FORMAT = '%d.png' + +LEFT_SHOULDER_RGB_FOLDER = 'left_shoulder_rgb' +LEFT_SHOULDER_DEPTH_FOLDER = 'left_shoulder_depth' +LEFT_SHOULDER_MASK_FOLDER = 'left_shoulder_mask' +RIGHT_SHOULDER_RGB_FOLDER = 'right_shoulder_rgb' +RIGHT_SHOULDER_DEPTH_FOLDER = 'right_shoulder_depth' +RIGHT_SHOULDER_MASK_FOLDER = 'right_shoulder_mask' +OVERHEAD_RGB_FOLDER = 'overhead_rgb' +OVERHEAD_DEPTH_FOLDER = 'overhead_depth' +OVERHEAD_MASK_FOLDER = 'overhead_mask' +WRIST_RGB_FOLDER = 'wrist_rgb' +WRIST_DEPTH_FOLDER = 'wrist_depth' +WRIST_MASK_FOLDER = 'wrist_mask' +FRONT_RGB_FOLDER = 'front_rgb' +FRONT_DEPTH_FOLDER = 'front_depth' +FRONT_MASK_FOLDER = 'front_mask' +EPISODES_FOLDER = 'episodes' +EPISODE_FOLDER = 'episode%d' +VARIATIONS_FOLDER = 'variation%d' + +LOW_DIM_PICKLE = 'low_dim_obs.pkl' +VARIATION_DESCRIPTIONS = 'variation_descriptions.pkl' + +TTT_FILE = 'task_design.ttt' + +DEPTH_SCALE = 2**24 - 1 + diff --git a/easi/tasks/ebmanipulation/vendor/amsolver/backend/exceptions.py b/easi/tasks/ebmanipulation/vendor/amsolver/backend/exceptions.py new file mode 100644 index 0000000..6876c2e --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/amsolver/backend/exceptions.py @@ -0,0 +1,21 @@ +class BoundaryError(Exception): + """Raised when a boundary error occurs.""" + pass + + +class WaypointError(Exception): + """Raised when we place a task but cant complete it with path planning.""" + def __init__(self, message, task): + super().__init__('Error in task %s. %s' % (task.get_name(), message)) + + +class NoWaypointsError(Exception): + """Raised when there is no waypoints. So cant create demo.""" + def __init__(self, message, task): + super().__init__('Error in task %s. %s' % (task.get_name(), message)) + + +class DemoError(Exception): + """Raised when getting demo.""" + def __init__(self, message, task): + super().__init__('Error in task %s. %s' % (task.get_name(), message)) diff --git a/easi/tasks/ebmanipulation/vendor/amsolver/backend/observation.py b/easi/tasks/ebmanipulation/vendor/amsolver/backend/observation.py new file mode 100644 index 0000000..aae73ef --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/amsolver/backend/observation.py @@ -0,0 +1,84 @@ +#Modified From the rlbench: https://github.com/stepjam/RLBench +import numpy as np + + +class Observation(object): + """Storage for both visual and low-dimensional observations.""" + + def __init__(self, + left_shoulder_rgb: np.ndarray, + left_shoulder_depth: np.ndarray, + left_shoulder_mask: np.ndarray, + left_shoulder_point_cloud: np.ndarray, + right_shoulder_rgb: np.ndarray, + right_shoulder_depth: np.ndarray, + right_shoulder_mask: np.ndarray, + right_shoulder_point_cloud: np.ndarray, + overhead_rgb: np.ndarray, + overhead_depth: np.ndarray, + overhead_mask: np.ndarray, + overhead_point_cloud: np.ndarray, + wrist_rgb: np.ndarray, + wrist_depth: np.ndarray, + wrist_mask: np.ndarray, + wrist_point_cloud: np.ndarray, + front_rgb: np.ndarray, + front_depth: np.ndarray, + front_mask: np.ndarray, + front_point_cloud: np.ndarray, + joint_velocities: np.ndarray, + joint_positions: np.ndarray, + joint_forces: np.ndarray, + gripper_open: float, + gripper_pose: np.ndarray, + gripper_matrix: np.ndarray, + gripper_joint_positions: np.ndarray, + gripper_touch_forces: np.ndarray, + task_low_dim_state: np.ndarray, + misc: dict, + object_informations: dict): + self.left_shoulder_rgb = left_shoulder_rgb + self.left_shoulder_depth = left_shoulder_depth + self.left_shoulder_mask = left_shoulder_mask + self.left_shoulder_point_cloud = left_shoulder_point_cloud + self.right_shoulder_rgb = right_shoulder_rgb + self.right_shoulder_depth = right_shoulder_depth + self.right_shoulder_mask = right_shoulder_mask + self.right_shoulder_point_cloud = right_shoulder_point_cloud + self.overhead_rgb = overhead_rgb + self.overhead_depth = overhead_depth + self.overhead_mask = overhead_mask + self.overhead_point_cloud = overhead_point_cloud + self.wrist_rgb = wrist_rgb + self.wrist_depth = wrist_depth + self.wrist_mask = wrist_mask + self.wrist_point_cloud = wrist_point_cloud + self.front_rgb = front_rgb + self.front_depth = front_depth + self.front_mask = front_mask + self.front_point_cloud = front_point_cloud + self.joint_velocities = joint_velocities + self.joint_positions = joint_positions + self.joint_forces = joint_forces + self.gripper_open = gripper_open + self.gripper_pose = gripper_pose + self.gripper_matrix = gripper_matrix + self.gripper_joint_positions = gripper_joint_positions + self.gripper_touch_forces = gripper_touch_forces + self.task_low_dim_state = task_low_dim_state + self.misc = misc + self.object_informations = object_informations + + def get_low_dim_data(self) -> np.ndarray: + """Gets a 1D array of all the low-dimensional obseervations. + + :return: 1D array of observations. + """ + low_dim_data = [] if self.gripper_open is None else [[self.gripper_open]] + for data in [self.joint_velocities, self.joint_positions, + self.joint_forces, + self.gripper_pose, self.gripper_joint_positions, + self.gripper_touch_forces, self.task_low_dim_state]: + if data is not None: + low_dim_data.append(data) + return np.concatenate(low_dim_data) if len(low_dim_data) > 0 else np.array([]) diff --git a/easi/tasks/ebmanipulation/vendor/amsolver/backend/robot.py b/easi/tasks/ebmanipulation/vendor/amsolver/backend/robot.py new file mode 100644 index 0000000..a1252cd --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/amsolver/backend/robot.py @@ -0,0 +1,37 @@ +#Modified From the rlbench: https://github.com/stepjam/RLBench +from pyrep.robots.arms.arm import Arm +from pyrep.robots.end_effectors.gripper import Gripper + + +class Robot(object): + """Simple container for the robot components. + """ + + def __init__(self, arm: Arm, gripper: Gripper): + self.arm = arm + self.gripper = gripper + self._start_arm_joint_pos = arm.get_joint_positions() + self._starting_gripper_joint_pos = gripper.get_joint_positions() + self._initial_robot_state = (arm.get_configuration_tree(), + gripper.get_configuration_tree()) + + def reset(self): + self.gripper.release() + self.arm.set_joint_positions(self._start_arm_joint_pos, disable_dynamics=True) + self.arm.set_joint_target_velocities([0] * len(self.arm.joints)) + self.gripper.set_joint_positions(self._starting_gripper_joint_pos, disable_dynamics=True) + self.gripper.set_joint_target_velocities([0] * len(self.gripper.joints)) + + def save_state(self): + saved_arm_joint_pos = self.arm.get_joint_positions() + saved_gripper_joint_pos = self.gripper.get_joint_positions() + return [saved_arm_joint_pos, saved_gripper_joint_pos] + + def recover_state(self, saved_states, release=False): + if release: + self.gripper.release() + saved_arm_joint_pos, saved_gripper_joint_pos = saved_states + self.arm.set_joint_positions(saved_arm_joint_pos, disable_dynamics=True) + self.arm.set_joint_target_velocities([0] * len(self.arm.joints)) + self.gripper.set_joint_positions(saved_gripper_joint_pos, disable_dynamics=True) + self.gripper.set_joint_target_velocities([0] * len(self.gripper.joints)) \ No newline at end of file diff --git a/easi/tasks/ebmanipulation/vendor/amsolver/backend/scene.py b/easi/tasks/ebmanipulation/vendor/amsolver/backend/scene.py new file mode 100644 index 0000000..5b6d0da --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/amsolver/backend/scene.py @@ -0,0 +1,552 @@ +#Modified From the rlbench: https://github.com/stepjam/RLBench +from typing import List, Callable + +import numpy as np +from numpy.lib.function_base import place +from pyrep import PyRep +from pyrep.const import ObjectType +from pyrep.errors import ConfigurationPathError +from pyrep.objects import Dummy +from pyrep.objects.shape import Shape +from pyrep.objects.vision_sensor import VisionSensor + +from amsolver.backend.exceptions import ( + WaypointError, BoundaryError, NoWaypointsError, DemoError) +from amsolver.backend.observation import Observation +from amsolver.backend.robot import Robot +from amsolver.backend.spawn_boundary import SpawnBoundary +from amsolver.backend.task import Task +from amsolver.backend.utils import WriteCustomDataBlock, import_distractors, rgb_handles_to_mask +from amsolver.demo import Demo +from amsolver.noise_model import NoiseModel +from amsolver.observation_config import ObservationConfig, CameraConfig + +STEPS_BEFORE_EPISODE_START = 10 + + +class Scene(object): + """Controls what is currently in the vrep scene. This is used for making + sure that the tasks are easily reachable. This may be just replaced by + environment. Responsible for moving all the objects. """ + + def __init__(self, pyrep: PyRep, robot: Robot, + obs_config=ObservationConfig(), add_distractors=False): + self._pyrep = pyrep + self._robot = robot + self._obs_config = obs_config + self._active_task = None + self._initial_task_state = None + self._start_arm_joint_pos = robot.arm.get_joint_positions() + self._starting_gripper_joint_pos = robot.gripper.get_joint_positions() + self._workspace = Shape('workspace') + self._workspace_boundary = SpawnBoundary([self._workspace]) + self._cam_over_shoulder_left = VisionSensor('cam_over_shoulder_left') + self._cam_over_shoulder_right = VisionSensor('cam_over_shoulder_right') + self._cam_overhead = VisionSensor('cam_overhead') + self._cam_wrist = VisionSensor('cam_wrist') + self._cam_front = VisionSensor('cam_front') + self._cam_over_shoulder_left_mask = VisionSensor( + 'cam_over_shoulder_left_mask') + self._cam_over_shoulder_right_mask = VisionSensor( + 'cam_over_shoulder_right_mask') + self._cam_overhead_mask = VisionSensor('cam_overhead_mask') + self._cam_wrist_mask = VisionSensor('cam_wrist_mask') + self._cam_front_mask = VisionSensor('cam_front_mask') + self._has_init_task = self._has_init_episode = False + self._variation_index = 0 + self.add_distractors = add_distractors + self.distractors = [] + + self._initial_robot_state = (robot.arm.get_configuration_tree(), + robot.gripper.get_configuration_tree()) + + # Set camera properties from observation config + self._set_camera_properties() + + x, y, z = self._workspace.get_position() + minx, maxx, miny, maxy, _, _ = self._workspace.get_bounding_box() + self._workspace_minx = x - np.fabs(minx) - 0.2 + self._workspace_maxx = x + maxx + 0.2 + self._workspace_miny = y - np.fabs(miny) - 0.2 + self._workspace_maxy = y + maxy + 0.2 + self._workspace_minz = z - 0.04 + self._workspace_maxz = z + 1.0 # 1M above workspace + + self.target_workspace_check = Dummy.create() + self._step_callback = None + + self._robot_shapes = self._robot.arm.get_objects_in_tree( + object_type=ObjectType.SHAPE) + self.gripper_step = 0.5 + def load(self, task: Task, ttms_folder = None) -> None: + """Loads the task and positions at the centre of the workspace. + + :param task: The task to load in the scene. + """ + task.load(ttms_folder) # Load the task in to the scene + + # Set at the centre of the workspace + task.get_base().set_position(self._workspace.get_position()) + + self._initial_task_state = task.get_state() + self._active_task = task + self._initial_task_pose = task.boundary_root().get_orientation() + self._has_init_task = self._has_init_episode = False + self._variation_index = 0 + + def unload(self) -> None: + """Clears the scene. i.e. removes all tasks. """ + if self._active_task is not None: + self._robot.gripper.release() + if self._has_init_task: + self._active_task.cleanup_() + self._active_task.unload() + self._active_task = None + self._variation_index = 0 + + def init_task(self) -> None: + self._active_task.init_task() + self._initial_task_state = self._active_task.get_state() + self._has_init_task = True + self._variation_index = 0 + + def init_episode(self, index: int, randomly_place: bool=True, + max_attempts: int = 5) -> List[str]: + """Calls the task init_episode and puts randomly in the workspace. + """ + + self._variation_index = index + + if not self._has_init_task: + self.init_task() + + # Try a few times to init and place in the workspace + attempts = 0 + self.descriptions = None + while attempts < max_attempts: + try: + if (randomly_place and + not self._active_task.is_static_workspace()): + self._place_task() + self.descriptions = self._active_task.init_episode(index) + self._active_task.validate() + break + except (BoundaryError, WaypointError) as e: + # self._active_task.cleanup_() + # self._active_task.restore_state(self._initial_task_state) + self.reset() + attempts += 1 + if attempts >= max_attempts: + raise e + # Let objects come to rest + [self._pyrep.step() for _ in range(STEPS_BEFORE_EPISODE_START)] + self._has_init_episode = True + if self.add_distractors: + distractors = import_distractors(self._pyrep) + for d in distractors: + self._workspace_boundary.sample(d, min_distance=0.05) + self.distractors.append(d) + return self.descriptions + + def reset(self) -> None: + """Resets the joint angles. """ + self._robot.gripper.release() + + arm, gripper = self._initial_robot_state + self._pyrep.set_configuration_tree(arm) + self._pyrep.set_configuration_tree(gripper) + self._robot.arm.set_joint_positions(self._start_arm_joint_pos, disable_dynamics=True) + self._robot.arm.set_joint_target_velocities( + [0] * len(self._robot.arm.joints)) + self._robot.gripper.set_joint_positions( + self._starting_gripper_joint_pos, disable_dynamics=True) + self._robot.gripper.set_joint_target_velocities( + [0] * len(self._robot.gripper.joints)) + + if self._active_task is not None and self._has_init_task: + self._active_task.cleanup_() + self._active_task.restore_state(self._initial_task_state) + elif not self._has_init_task: + self.init_task() + self._active_task.set_initial_objects_in_scene() + if len(self.distractors)!=0: + for d in self.distractors: + d.remove() + self._workspace_boundary._boundaries[0]._contained_objects.remove(d) + self.distractors = [] + + def get_observation(self) -> Observation: + tip = self._robot.arm.get_tip() + + joint_forces = None + if self._obs_config.joint_forces: + fs = self._robot.arm.get_joint_forces() + vels = self._robot.arm.get_joint_target_velocities() + joint_forces = self._obs_config.joint_forces_noise.apply( + np.array([-f if v < 0 else f for f, v in zip(fs, vels)])) + + ee_forces_flat = None + if self._obs_config.gripper_touch_forces: + ee_forces = self._robot.gripper.get_touch_sensor_forces() + ee_forces_flat = [] + for eef in ee_forces: + ee_forces_flat.extend(eef) + ee_forces_flat = np.array(ee_forces_flat) + + lsc_ob = self._obs_config.left_shoulder_camera + rsc_ob = self._obs_config.right_shoulder_camera + oc_ob = self._obs_config.overhead_camera + wc_ob = self._obs_config.wrist_camera + fc_ob = self._obs_config.front_camera + + lsc_mask_fn, rsc_mask_fn, oc_mask_fn, wc_mask_fn, fc_mask_fn = [ + (rgb_handles_to_mask if c.masks_as_one_channel else lambda x: x + ) for c in [lsc_ob, rsc_ob, oc_ob, wc_ob, fc_ob]] + + def get_rgb_depth(sensor: VisionSensor, get_rgb: bool, get_depth: bool, + get_pcd: bool, rgb_noise: NoiseModel, + depth_noise: NoiseModel, depth_in_meters: bool): + rgb = depth = pcd = None + if sensor is not None and (get_rgb or get_depth): + sensor.handle_explicitly() + if get_rgb: + rgb = sensor.capture_rgb() + if rgb_noise is not None: + rgb = rgb_noise.apply(rgb) + rgb = np.clip((rgb * 255.).astype(np.uint8), 0, 255) + if get_depth or get_pcd: + depth = sensor.capture_depth(depth_in_meters) + if depth_noise is not None: + depth = depth_noise.apply(depth) + if get_pcd: + depth_m = depth + if not depth_in_meters: + near = sensor.get_near_clipping_plane() + far = sensor.get_far_clipping_plane() + depth_m = near + depth * (far - near) + pcd = sensor.pointcloud_from_depth(depth_m) + if not get_depth: + depth = None + return rgb, depth, pcd + + def get_mask(sensor: VisionSensor, mask_fn): + mask = None + if sensor is not None: + sensor.handle_explicitly() + mask = mask_fn(sensor.capture_rgb()) + return mask + + left_shoulder_rgb, left_shoulder_depth, left_shoulder_pcd = get_rgb_depth( + self._cam_over_shoulder_left, lsc_ob.rgb, lsc_ob.depth, lsc_ob.point_cloud, + lsc_ob.rgb_noise, lsc_ob.depth_noise, lsc_ob.depth_in_meters) + right_shoulder_rgb, right_shoulder_depth, right_shoulder_pcd = get_rgb_depth( + self._cam_over_shoulder_right, rsc_ob.rgb, rsc_ob.depth, rsc_ob.point_cloud, + rsc_ob.rgb_noise, rsc_ob.depth_noise, rsc_ob.depth_in_meters) + overhead_rgb, overhead_depth, overhead_pcd = get_rgb_depth( + self._cam_overhead, oc_ob.rgb, oc_ob.depth, oc_ob.point_cloud, + oc_ob.rgb_noise, oc_ob.depth_noise, oc_ob.depth_in_meters) + wrist_rgb, wrist_depth, wrist_pcd = get_rgb_depth( + self._cam_wrist, wc_ob.rgb, wc_ob.depth, wc_ob.point_cloud, + wc_ob.rgb_noise, wc_ob.depth_noise, wc_ob.depth_in_meters) + front_rgb, front_depth, front_pcd = get_rgb_depth( + self._cam_front, fc_ob.rgb, fc_ob.depth, fc_ob.point_cloud, + fc_ob.rgb_noise, fc_ob.depth_noise, fc_ob.depth_in_meters) + + left_shoulder_mask = get_mask(self._cam_over_shoulder_left_mask, + lsc_mask_fn) if lsc_ob.mask else None + right_shoulder_mask = get_mask(self._cam_over_shoulder_right_mask, + rsc_mask_fn) if rsc_ob.mask else None + overhead_mask = get_mask(self._cam_overhead_mask, + oc_mask_fn) if oc_ob.mask else None + wrist_mask = get_mask(self._cam_wrist_mask, + wc_mask_fn) if wc_ob.mask else None + front_mask = get_mask(self._cam_front_mask, + fc_mask_fn) if fc_ob.mask else None + + obs = Observation( + left_shoulder_rgb=left_shoulder_rgb, + left_shoulder_depth=left_shoulder_depth, + left_shoulder_point_cloud=left_shoulder_pcd, + right_shoulder_rgb=right_shoulder_rgb, + right_shoulder_depth=right_shoulder_depth, + right_shoulder_point_cloud=right_shoulder_pcd, + overhead_rgb=overhead_rgb, + overhead_depth=overhead_depth, + overhead_point_cloud=overhead_pcd, + wrist_rgb=wrist_rgb, + wrist_depth=wrist_depth, + wrist_point_cloud=wrist_pcd, + front_rgb=front_rgb, + front_depth=front_depth, + front_point_cloud=front_pcd, + left_shoulder_mask=left_shoulder_mask, + right_shoulder_mask=right_shoulder_mask, + overhead_mask=overhead_mask, + wrist_mask=wrist_mask, + front_mask=front_mask, + joint_velocities=( + self._obs_config.joint_velocities_noise.apply( + np.array(self._robot.arm.get_joint_velocities())) + if self._obs_config.joint_velocities else None), + joint_positions=( + self._obs_config.joint_positions_noise.apply( + np.array(self._robot.arm.get_joint_positions())) + if self._obs_config.joint_positions else None), + joint_forces=(joint_forces + if self._obs_config.joint_forces else None), + gripper_open=( + (1.0 if self._robot.gripper.get_open_amount()[0] > 0.9 else 0.0) + if self._obs_config.gripper_open else None), + gripper_pose=( + np.array(tip.get_pose()) + if self._obs_config.gripper_pose else None), + gripper_matrix=( + tip.get_matrix() + if self._obs_config.gripper_matrix else None), + gripper_touch_forces=( + ee_forces_flat + if self._obs_config.gripper_touch_forces else None), + gripper_joint_positions=( + np.array(self._robot.gripper.get_joint_positions()) + if self._obs_config.gripper_joint_positions else None), + task_low_dim_state=( + self._active_task.get_low_dim_state() if + self._obs_config.task_low_dim_state else None), + misc=self._get_misc(), + object_informations = self._active_task.objects_information()) + obs = self._active_task.decorate_observation(obs) + return obs + + def step(self): + self._pyrep.step() + self._active_task.step() + if self._step_callback is not None: + self._step_callback() + + def register_step_callback(self, func): + self._step_callback = func + + def get_demo(self, record: bool = True, + callable_each_step: Callable[[Observation], None] = None, + randomly_place: bool = True) -> Demo: + """Returns a demo (list of observations)""" + + if not self._has_init_task: + self.init_task() + if not self._has_init_episode: + self.init_episode(self._variation_index, + randomly_place=randomly_place) + init_states = self._active_task.get_base().get_configuration_tree() + self._has_init_episode = False + waypoints = self._active_task.get_waypoints() + if len(waypoints) == 0: + raise NoWaypointsError( + 'No waypoints were found.', self._active_task) + + demo = [] + self.low_level_description = waypoints[0].low_level_descriptions + self.current_waypoint_name = 'waypoint0' + if record: + self._pyrep.step() # Need this here or get_force doesn't work... + demo.append(self.get_observation()) + demo[-1].low_level_description = self.low_level_description + demo[-1].current_waypoint_name = self.current_waypoint_name + while True: + success = False + for i, point in enumerate(waypoints): + self.current_waypoint_name = point.name + point.start_of_path() + grasped_objects = self._robot.gripper.get_grasped_objects() + colliding_shapes = [s for s in self._pyrep.get_objects_in_tree( + object_type=ObjectType.SHAPE) if s not in grasped_objects + and s not in self._robot_shapes and s.is_collidable() + and self._robot.arm.check_arm_collision(s)] + [s.set_collidable(False) for s in colliding_shapes] + try: + path = point.get_path() + [s.set_collidable(True) for s in colliding_shapes] + except ConfigurationPathError as e: + [s.set_collidable(True) for s in colliding_shapes] + raise DemoError( + 'Could not get a path for waypoint %d.' % i, + self._active_task) from e + # ext = point.get_ext() + path.visualize() + + # if len(ext)>0: + # if 'open_gripper' in ext: + # low_level_description = 'Open the gripper' + # elif 'close_gripper' in ext: + # low_level_description = 'Grasp the object' + # elif 'linear' in ext: + # low_level_description = 'Rotate the gripper' + # elif 'ignore_collisions' in ext: + # low_level_description = ext.replace('ignore_collisions;','') + # elif 'format' in ext: + # task = self._active_task + # low_level_description = eval(ext) + # else: + # low_level_description = ext + # else: + # low_level_description = None + + done = False + success = False + if point.low_level_descriptions is not None: + self.low_level_description = point.low_level_descriptions + while not done: + done = path.step() + self.step() + self._demo_record_step(demo, record, callable_each_step) + success, term = self._active_task.success() + + point.end_of_path() + + path.clear_visualization() + + if point.gripper_control is not None: + gripper = self._robot.gripper + if point.gripper_control[0]=='open': + gripper.release() + done = False + while not done: + done = gripper.actuate(point.gripper_control[1], self.gripper_step) + self._pyrep.step() + self._active_task.step() + if self._obs_config.record_gripper_closing: + self._demo_record_step( + demo, record, callable_each_step) + if point.gripper_control[0]=='close': + for g_obj in self._active_task.get_graspable_objects(): + gripper.grasp(g_obj) + + self._demo_record_step(demo, record, callable_each_step) + + if not self._active_task.should_repeat_waypoints() or success: + break + + # Some tasks may need additional physics steps + # (e.g. ball rowling to goal) + if not success: + for _ in range(20): + self._pyrep.step() + self._active_task.step() + self._demo_record_step(demo, record, callable_each_step) + success, term = self._active_task.success() + if success: + break + + success, term = self._active_task.success() + # if not success: + # raise DemoError('Demo was completed, but was not successful.', + # self._active_task) + d = Demo(demo) + d.high_level_instructions = self.descriptions + self._robot.gripper.release() + self._pyrep.set_configuration_tree(init_states) + return d, success + + def get_observation_config(self) -> ObservationConfig: + return self._obs_config + + def check_target_in_workspace(self, target_pos: np.ndarray) -> bool: + x, y, z = target_pos + return (self._workspace_maxx > x > self._workspace_minx and + self._workspace_maxy > y > self._workspace_miny and + self._workspace_maxz > z > self._workspace_minz) + + def _demo_record_step(self, demo_list, record, func): + if record: + demo = self.get_observation() + demo.low_level_description = self.low_level_description + demo.current_waypoint_name = self.current_waypoint_name + demo_list.append(demo) + if func is not None: + func(self.get_observation()) + + def _set_camera_properties(self) -> None: + def _set_rgb_props(rgb_cam: VisionSensor, + rgb: bool, depth: bool, conf: CameraConfig): + if not (rgb or depth or conf.point_cloud): + rgb_cam.remove() + else: + rgb_cam.set_explicit_handling(1) + rgb_cam.set_resolution(conf.image_size) + rgb_cam.set_render_mode(conf.render_mode) + + def _set_mask_props(mask_cam: VisionSensor, mask: bool, + conf: CameraConfig): + if not mask: + mask_cam.remove() + else: + mask_cam.set_explicit_handling(1) + mask_cam.set_resolution(conf.image_size) + _set_rgb_props( + self._cam_over_shoulder_left, + self._obs_config.left_shoulder_camera.rgb, + self._obs_config.left_shoulder_camera.depth, + self._obs_config.left_shoulder_camera) + _set_rgb_props( + self._cam_over_shoulder_right, + self._obs_config.right_shoulder_camera.rgb, + self._obs_config.right_shoulder_camera.depth, + self._obs_config.right_shoulder_camera) + _set_rgb_props( + self._cam_overhead, + self._obs_config.overhead_camera.rgb, + self._obs_config.overhead_camera.depth, + self._obs_config.overhead_camera) + _set_rgb_props( + self._cam_wrist, self._obs_config.wrist_camera.rgb, + self._obs_config.wrist_camera.depth, + self._obs_config.wrist_camera) + _set_rgb_props( + self._cam_front, self._obs_config.front_camera.rgb, + self._obs_config.front_camera.depth, + self._obs_config.front_camera) + _set_mask_props( + self._cam_over_shoulder_left_mask, + self._obs_config.left_shoulder_camera.mask, + self._obs_config.left_shoulder_camera) + _set_mask_props( + self._cam_over_shoulder_right_mask, + self._obs_config.right_shoulder_camera.mask, + self._obs_config.right_shoulder_camera) + _set_mask_props( + self._cam_overhead_mask, + self._obs_config.overhead_camera.mask, + self._obs_config.overhead_camera) + _set_mask_props( + self._cam_wrist_mask, self._obs_config.wrist_camera.mask, + self._obs_config.wrist_camera) + _set_mask_props( + self._cam_front_mask, self._obs_config.front_camera.mask, + self._obs_config.front_camera) + + def _place_task(self) -> None: + self._workspace_boundary.clear() + # Find a place in the robot workspace for task + self._active_task.boundary_root().set_orientation( + self._initial_task_pose) + min_rot, max_rot = self._active_task.base_rotation_bounds() + self._workspace_boundary.sample( + self._active_task.boundary_root(), + min_rotation=min_rot, max_rotation=max_rot, place_above_plane=False) + + def _get_misc(self): + def _get_cam_data(cam: VisionSensor, name: str): + d = {} + if cam.still_exists(): + d = { + '%s_extrinsics' % name: cam.get_matrix(), + '%s_intrinsics' % name: cam.get_intrinsic_matrix(), + '%s_near' % name: cam.get_near_clipping_plane(), + '%s_far' % name: cam.get_far_clipping_plane(), + } + return d + misc = _get_cam_data(self._cam_over_shoulder_left, 'left_shoulder_camera') + misc.update(_get_cam_data(self._cam_over_shoulder_right, 'right_shoulder_camera')) + misc.update(_get_cam_data(self._cam_overhead, 'overhead_camera')) + misc.update(_get_cam_data(self._cam_front, 'front_camera')) + misc.update(_get_cam_data(self._cam_wrist, 'wrist_camera')) + return misc diff --git a/easi/tasks/ebmanipulation/vendor/amsolver/backend/spawn_boundary.py b/easi/tasks/ebmanipulation/vendor/amsolver/backend/spawn_boundary.py new file mode 100644 index 0000000..a4055b1 --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/amsolver/backend/spawn_boundary.py @@ -0,0 +1,191 @@ +#Modified From the rlbench: https://github.com/stepjam/RLBench +from typing import List +import numpy as np +import math +from amsolver.backend.exceptions import BoundaryError +from pyrep.objects.object import Object + + +class BoundingBox(object): + def __init__(self, min_x: float, max_x: float, min_y: float, max_y: float, + min_z: float, max_z: float): + self.min_x = min_x + self.max_x = max_x + self.min_y = min_y + self.max_y = max_y + self.min_z = min_z + self.max_z = max_z + self.points = [[min_x, min_y, min_z], [max_x, min_y, min_z], + [min_x, max_y, min_z], [max_x, max_y, min_z], + [min_x, min_y, max_z], [max_x, min_y, max_z], + [min_x, max_y, max_z], [max_x, max_y, max_z]] + + def rotate(self, theta: np.ndarray) -> 'BoundingBox': + r_x = np.array([[1, 0, 0], + [0, math.cos(theta[0]), -math.sin(theta[0])], + [0, math.sin(theta[0]), math.cos(theta[0])] + ]) + r_y = np.array([[math.cos(theta[1]), 0, math.sin(theta[1])], + [0, 1, 0], + [-math.sin(theta[1]), 0, math.cos(theta[1])] + ]) + r_z = np.array([[math.cos(theta[2]), -math.sin(theta[2]), 0], + [math.sin(theta[2]), math.cos(theta[2]), 0], + [0, 0, 1] + ]) + r = np.dot(r_z, np.dot(r_y, r_x)) + new_points = np.dot(self.points, r) + return BoundingBox(np.amin(new_points[:, 0]), np.amax(new_points[:, 0]), + np.amin(new_points[:, 1]), np.amax(new_points[:, 1]), + np.amin(new_points[:, 2]), np.amax(new_points[:, 2])) + + def within_boundary(self, boundary: 'BoundingBox', is_plane: bool) -> bool: + z_cond = True + if not is_plane: + z_cond = self.min_z > boundary.min_z and self.max_z < boundary.max_z + return (self.min_x > boundary.min_x and self.max_x < boundary.max_x and + self.min_y > boundary.min_y and self.max_y < boundary.max_y and + z_cond) + + +class BoundaryObject(object): + + def __init__(self, boundary: Object): + self._boundary = boundary + self._is_plane = False + self._contained_objects = [] + + if boundary.is_model(): + (minx, maxx, miny, + maxy, minz, maxz) = boundary.get_model_bounding_box() + else: + minx, maxx, miny, maxy, minz, maxz = boundary.get_bounding_box() + self._boundary_bbox = BoundingBox(minx, maxx, miny, maxy, minz, maxz) + + if hasattr(boundary, "_is_plane"): + self._is_plane = boundary._is_plane + height = np.abs(maxz - minz) + if height <= 1e-4: + height = 1.0 + self._is_plane = True + self._area = np.abs(maxx - minx) * np.abs(maxy - miny) * height + + def _get_position_within_boundary(self, obj: Object, obj_bbox: BoundingBox, + place_above_plane=True) -> List[float]: + x = np.random.uniform( + self._boundary_bbox.min_x + np.abs(obj_bbox.min_x), + self._boundary_bbox.max_x - np.abs(obj_bbox.max_x)) + y = np.random.uniform( + self._boundary_bbox.min_y + np.abs(obj_bbox.min_y), + self._boundary_bbox.max_y - np.abs(obj_bbox.max_y)) + if self._is_plane: + # _, _, z = obj.get_position(self._boundary) + if place_above_plane: + # z = (obj_bbox.max_z-obj_bbox.min_z)/2 + 0.001 + z = (-obj_bbox.min_z)+self._boundary_bbox.max_z+0.005 + else: + _, _, z = obj.get_position(self._boundary) + else: + z = np.random.uniform( + self._boundary_bbox.min_z + np.abs(obj_bbox.min_z), + self._boundary_bbox.max_z - np.abs(obj_bbox.max_z)) + return [x, y, z] + + def get_area(self) -> float: + return self._area + + def add(self, obj: Object, ignore_collisions: bool = False, + min_rotation: tuple = (0.0, 0.0, -3.14), + max_rotation: tuple = (0.0, 0.0, 3.14), + min_distance: float = 0.01, place_above_plane=True) -> int: + """Returns true if can add and adds it + rotation_limits: how mush we allow it to rotate from its original + position""" + + # Rotate the bounding box randomly + if obj.is_model(): + bb = obj.get_model_bounding_box() + else: + bb = obj.get_bounding_box() + obj_bbox = BoundingBox(*bb) + rotation = np.random.uniform(list(min_rotation), list(max_rotation)) + obj_bbox = obj_bbox.rotate(rotation) + + if not obj_bbox.within_boundary(self._boundary_bbox, self._is_plane): + return -1 + + new_pos = self._get_position_within_boundary(obj, obj_bbox, place_above_plane = place_above_plane) + obj.set_position(new_pos, self._boundary) + obj.rotate(list(rotation)) + new_pos = np.array(new_pos) + + if not ignore_collisions: + for contained_obj in self._contained_objects: + if contained_obj.still_exists(): + # Check for collision between each child + for cont_ob in contained_obj.get_objects_in_tree( + exclude_base=False): + for placing_ob in obj.get_objects_in_tree( + exclude_base=False): + if placing_ob.check_collision(cont_ob): + return -2 + dist = np.linalg.norm( + new_pos - contained_obj.get_position(self._boundary)) + if dist < min_distance: + return -3 + self._contained_objects.append(obj) + return 1 + + def clear(self) -> None: + self._contained_objects = [] + + +class SpawnBoundary(object): + + MAX_SAMPLES = 100 + + def __init__(self, boundaries: List[Object]): + self._boundaries = [] + areas = [] + for b in boundaries: + bo = BoundaryObject(b) + areas.append(bo.get_area()) + self._boundaries.append(bo) + self._probabilities = np.array(areas) / np.sum(areas) + + def sample(self, obj: Object, ignore_collisions=False, + min_rotation=(0.0, 0.0, -3.14), max_rotation=(0.0, 0.0, 3.14), + min_distance=0.01, place_above_plane=True) -> None: + """ + returns a position within the specified boundary. Calculated using a + weighted percentage. + set ignore_collisions if you are happy with things being stacked on top of each other + set rotation_range to be (0, 0) if you don't want it to rotate + :return: + """ + begin_pose = obj.get_pose() + collision_fails = boundary_fails = self.MAX_SAMPLES + while collision_fails > 0 and boundary_fails > 0: + sampled_boundary = np.random.choice(self._boundaries, + p=self._probabilities) + result = sampled_boundary.add( + obj, ignore_collisions, min_rotation, max_rotation, min_distance, place_above_plane) + if result == -1: + boundary_fails -= 1 + elif result == -2: + collision_fails -= 1 + elif result == -3: + boundary_fails -= 1 + else: + break + obj.set_pose(begin_pose) + if boundary_fails <= 0: + raise BoundaryError('Could not place within boundary.' + 'Perhaps the object is too big for it?') + elif collision_fails <= 0: + raise BoundaryError( + 'Could not place the object within the boundary due to ' + 'collision with other objects in the boundary.') + + def clear(self) -> None: + [b.clear() for b in self._boundaries] diff --git a/easi/tasks/ebmanipulation/vendor/amsolver/backend/task.py b/easi/tasks/ebmanipulation/vendor/amsolver/backend/task.py new file mode 100644 index 0000000..37a57b5 --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/amsolver/backend/task.py @@ -0,0 +1,547 @@ +#Modified From the rlbench: https://github.com/stepjam/RLBench +import os +import re +from os.path import dirname, abspath, join +from typing import List, Tuple, Callable, Union + +import numpy as np +from pyrep import PyRep +from pyrep.backend.utils import script_call +from pyrep.const import PYREP_SCRIPT_TYPE, ObjectType +from pyrep.errors import ConfigurationPathError +from pyrep.objects.cartesian_path import CartesianPath +from pyrep.objects.dummy import Dummy +from pyrep.objects.force_sensor import ForceSensor +from pyrep.objects.joint import Joint +from pyrep.objects.object import Object + +from amsolver.backend.conditions import Condition +from amsolver.backend.exceptions import WaypointError +from amsolver.backend.observation import Observation +from amsolver.backend.robot import Robot +from amsolver.backend.utils import ReadCustomDataBlock, WriteCustomDataBlock +from amsolver.backend.waypoints import Point, PredefinedPath, Waypoint + +TASKS_PATH = join(dirname(abspath(__file__)), '../../vlm/tasks') + + +class Task(object): + + def __init__(self, pyrep: PyRep, robot: Robot): + """Constructor. + + :param pyrep: Instance of PyRep. + :param robot: Instance of Robot. + """ + self.pyrep = pyrep + self.name = self.get_name() + self.robot = robot + self._waypoints = None + self._success_conditions = [] + self._graspable_objects = [] + self._base_object = None + self._waypoint_additional_inits = {} + self._waypoint_abilities_start = {} + self._waypoint_abilities_end = {} + self._waypoints_should_repeat = lambda: False + self._initial_objs_in_scene = None + self._stop_at_waypoint_index = -1 + self._need_remove_objects = [] + self.temporary_waypoints = [] + ######################## + # Overriding functions # + ######################## + + def init_task(self) -> None: + """Initialises the task. Called only once when task loaded. + + Here we can grab references to objects in the task and store them + as member variables to be used in init_episode. Here we also usually + set success conditions for the task as well as register what objects + can be grasped. + """ + raise NotImplementedError( + "'init_task' is almost always necessary.") + + def init_episode(self, index: int) -> List[str]: + """Initialises the episode. Called each time the scene is reset. + + Here we usually define how the task changes across variations. Based on + this we can change the task descriptions that are returned. + + :param index: The variation index. + :return: A list of strings describing the task. + """ + raise NotImplementedError( + "'init_episode' must be defined and return a list of strings.") + + def variation_count(self) -> int: + """Number of variations for the task. Can be determined dynamically. + + :return: Number of variations for this task. + """ + raise NotImplementedError( + "'variation_count' must be defined and return an int.") + + def modified_init_episode(self, index) -> None: + """ + Used for the children tasks of the task temples. + """ + pass + + def get_low_dim_state(self) -> np.ndarray: + """Gets the pose and various other properties of objects in the task. + + :return: 1D array of low-dimensional task state. + """ + + # Corner cases: + # (1) Object has been deleted. + # (2) Object has been grasped (and is now child of gripper). + + state = [] + for obj, objtype in self._initial_objs_in_scene: + if not obj.still_exists(): + # It has been deleted + empty_len = 7 + if objtype == ObjectType.JOINT: + empty_len += 1 + elif objtype == ObjectType.FORCE_SENSOR: + empty_len += 6 + state.extend(np.zeros((empty_len,)).tolist()) + else: + state.extend(np.array(obj.get_pose())) + if obj.get_type() == ObjectType.JOINT: + state.extend([Joint(obj.get_handle()).get_joint_position()]) + elif obj.get_type() == ObjectType.FORCE_SENSOR: + forces, torques = ForceSensor(obj.get_handle()).read() + state.extend(forces + torques) + + return np.array(state).flatten() + + def objects_information(self): + information = {} + for obj, objtype in self._initial_objs_in_scene: + if obj.still_exists(): + info = { + 'id': obj.get_handle(), + 'pose': obj.get_pose(), + 'bbox':obj.get_bounding_box() + } + if obj.get_type() == ObjectType.JOINT: + info['joint_position']= obj.get_joint_position() + elif obj.get_type() == ObjectType.FORCE_SENSOR: + forces, torques = obj.read() + info['forces'] = forces + info['torques'] = torques + information[obj.get_name()]=info + + for obj in self._need_remove_objects: + if obj.still_exists(): + childrens = obj.get_objects_in_tree( + exclude_base=False, first_generation_only=False) + for child in childrens: + info = { + 'id': child.get_handle(), + 'pose': child.get_pose(), + 'bbox':child.get_bounding_box() + } + if child.get_type() == ObjectType.JOINT: + info['joint_position']= child.get_joint_position() + elif child.get_type() == ObjectType.FORCE_SENSOR: + forces, torques = child.read() + info['forces'] = forces + info['torques'] = torques + information[child.get_name()]=info + + for i, waypoint in enumerate(self.get_waypoints(need_feasible=False)): + if waypoint._waypoint.get_type() == ObjectType.DUMMY: + info = {'pose': [waypoint.pose]} + elif waypoint._waypoint.get_type() == ObjectType.PATH: + info = {'pose': [waypoint.end_pose, waypoint.start_pose]} + info.update({ + 'target_obj':waypoint.focus_obj_id, + 'target_obj_name':waypoint.focus_obj_name, + 'gripper_control':waypoint.gripper_control, + 'low_level_descriptions':waypoint.low_level_descriptions, + 'waypoint_type':waypoint.waypoint_type + }) + information[waypoint.name]=info + return information + + def step(self) -> None: + """Called each time the simulation is stepped. Can usually be left.""" + pass + + def reward(self, steps) -> Union[float, None]: + """Allows the user to customise the task and add reward shaping.""" + def angle_distance(q1, q2): + # same as rad2deg(2arccos(theta)), here qw = cos(theta/2). need to select min of (x, 2pi - x) + # reference: https://math.stackexchange.com/questions/90081/quaternion-distance + v = 2 * np.arccos(np.clip(2 * np.dot(q1, q2) ** 2 - 1, -1, 1)) + return min(v, 2*np.pi - v) + def get_errors(point, robot): + target_pose = point._waypoint.get_pose() + current_ee_pose = np.array(robot.arm.get_tip().get_pose()) + # angle_error = angle_distance(target_pose[3:], current_ee_pose[3:]) + translate_error = ((target_pose[:3]-current_ee_pose[:3])**2).sum()**(1/2) + target_matrix = point._waypoint.get_matrix() + current_ee_matix = robot.arm.get_tip().get_matrix() + # angle_error = angle_distance(target_pose[3:], current_ee_pose[3:]) + angle_error = (1-np.dot(target_matrix[:3,2], current_ee_matix[:3,2]))/2 + return translate_error, angle_error + waypoints = self.get_waypoints() + if steps == 0: + self.idx = 0 + self.target_gipper_state = 1.0 + point = waypoints[self.idx] + self.last_t_error, self.last_r_error = get_errors(point, self.robot) + point = waypoints[self.idx] + point.start_of_path() + print("current target:", point.name) + t_error, r_error = get_errors(point, self.robot) + open_condition = all(x > 0.9 for x in self.robot.gripper.get_open_amount()) + current_gipper = 1.0 if open_condition else 0.0 + if t_error<0.05 and r_error<0.1: + if current_gipper == self.target_gipper_state: + if self.idx+10.1: + delta_t = self.last_t_error - t_error + delta_r = 0 + else: + delta_t = self.last_t_error - t_error + delta_r = self.last_r_error - r_error + if point.gripper_control is not None: + self.target_gipper_state = point.gripper_control[1] + + self.last_t_error = t_error + self.last_r_error = r_error + reward = 10*(2*delta_t+delta_r/np.pi)-0.01*np.abs(self.target_gipper_state-current_gipper) + reward = np.clip(reward, -1, 1) + success,_ = self.success() + if success: + reward=2 + return reward + + def cleanup(self) -> None: + """Called at the end of the episode. Can usually be left. + + Can be used for complex tasks that spawn many objects. + """ + if len(self._need_remove_objects)!=0: + for obj in self._need_remove_objects: + if obj.still_exists(): + obj.remove() + self._need_remove_objects = [] + if hasattr(self, "temporary_waypoints"): + if len(self.temporary_waypoints)!=0: + for waypoint in self.temporary_waypoints: + if waypoint.still_exists(): + waypoint.remove() + self.temporary_waypoints=[] + + def base_rotation_bounds(self) -> Tuple[Tuple[float, float, float], + Tuple[float, float, float]]: + """Defines how much the task base can rotate during episode placement. + + Default is set such that it can rotate any amount on the z axis. + + :return: A tuple containing the min and max (x, y, z) rotation bounds + (in radians). + """ + return (0.0, 0.0, -3.14), (0.0, 0.0, 3.14) + + def boundary_root(self) -> Object: + """An object that should act as the task root when randomly placing. + + Sometimes the task can involve a large appliance (e.g. dish washer) + which cannot be placed within the task workspace. This allows us to + define a proxy root (e.g. a plane in front of the appliance) that + would allow the task to be placed inside the workspace. + + :return: The PyRep Object that will act as the root. + """ + return self.get_base() + + def decorate_observation(self, observation: Observation) -> Observation: + """Can be used for tasks that want to modify the observations. + + Usually not used. Perhpas cabn be used to model + + :param observation: The Observation for this time step. + :return: The modified Observation. + """ + return observation + + def is_static_workspace(self) -> bool: + """Specify if the task should'nt be randomly placed in the workspace. + + :return: True if the task pose should not be sampled. + """ + return False + + def set_initial_objects_in_scene(self): + objs = self.get_base().get_objects_in_tree( + exclude_base=True, first_generation_only=False) + types = [ob.get_type() for ob in objs] + self._initial_objs_in_scene = list(zip(objs, types)) + + + ######################### + # Registering functions # + ######################### + + def register_success_conditions(self, condition: List[Condition]): + """What conditions need to be met for the task to be a success. + + Note: this replaces any previously registered conditions! + + :param condition: A list of success conditions. + """ + self._success_conditions = condition + + def register_graspable_objects(self, objects: List[Object]): + """Register what objects can be grasped with a 'stable' grasp. + + In order for objects to be grasped in a stable way, PyRep attaches an + objects that need to be grasped as a child of the gripper. This function + allows one to register a list of objects that can be grasped in + this 'stable' manner. + + Note: this replaces any previously registered objects! + + :param objects: The list of Objects that can be grasped. + """ + self._graspable_objects = objects + # _= script_call('setCheckObject@PyRep', PYREP_SCRIPT_TYPE, ints=[ob.get_handle() for ob in objects]) + for obj in objects: + obj.graspable = True + # WriteCustomDataBlock(obj.get_handle(), "graspable", "True") + + def register_waypoint_ability_start(self, waypoint_index: int, + func: Callable[[Waypoint], None]): + """Register a function to be called before moving to waypoint. + + The registered function should take in a Waypoint object and is called + when the robot is about to move to the assigned waypoint. + + + :param waypoint_index: The waypoint index. + :param func: A function that takes a Waypoint object. + """ + self._waypoint_abilities_start[waypoint_index] = func + + def register_waypoint_ability_end(self, waypoint_index: int, + func: Callable[[Waypoint], None]): + """Register a function to be called after moving to waypoint. + + The registered function should take in a Waypoint object and is called + when the robot has finished moving to the assigned waypoint. + + + :param waypoint_index: The waypoint index. + :param func: A function that takes a Waypoint object. + """ + self._waypoint_abilities_end[waypoint_index] = func + + def register_waypoints_should_repeat(self, func: Callable[[], bool]): + """Register a function that is called when reached the end of a demo. + + The registered function should return a bool if the demo should repeat. + Can be used in conjunction with `register_waypoint_ability_start` and + `register_waypoint_ability_end`. Useful for a task such as emptying a + container, where we want to keep dynamically moving waypoints until + the container is empty. + + :param func: A function that return a bool if the demo should repeat. + """ + self._waypoints_should_repeat = func + + def register_stop_at_waypoint(self, waypoint_index: int): + """Register at what index the demo should be stopped. + + :param waypoint_index: The waypoint index. + """ + self._stop_at_waypoint_index = waypoint_index + + ########################## + # Other public functions # + ########################## + + def get_name(self) -> str: + """The name of the task file (without the .py extension). + + :return: The name of the task. + """ + return re.sub('(? Tuple[bool, bool]: + """If the task is currently successful. + + :return: Tuple containing 2 bools: first specifies if the task is currently successful, + second specifies if the task should terminate (either from success or from broken constraints). + """ + all_met = True + should_terminate = False + for cond in self._success_conditions: + met, terminate = cond.condition_met() + if terminate: + # Broken constraint + should_terminate = True + break + all_met &= met + if all_met: + # All conditions met, so we can terminate + should_terminate = True + return all_met, should_terminate + + def load(self, ttms_folder=None) -> Object: + if Object.exists(self.get_name()): + return Dummy(self.get_name()) + if ttms_folder is None: + ttm_file = os.path.join( + os.path.dirname(os.path.abspath(__file__)), + '../task_ttms/%s.ttm' % self.name) + else: + ttm_file = os.path.join(ttms_folder, + '%s.ttm' % self.name) + if not os.path.isfile(ttm_file): + raise FileNotFoundError( + 'The following is not a valid task .ttm file: %s' % ttm_file) + self._base_object = self.pyrep.import_model(ttm_file) + return self._base_object + + def unload(self) -> None: + self._waypoints = None + self.get_base().remove() + self.clear_registerings() + + def cleanup_(self) -> None: + for cond in self._success_conditions: + cond.reset() + self._waypoints = None + self.cleanup() + + def clear_registerings(self) -> None: + self._success_conditions = [] + self._graspable_objects = [] + self._base_object = None + self._waypoint_additional_inits = {} + self._waypoint_abilities_start = {} + self._waypoint_abilities_end = {} + + def get_base(self) -> Dummy: + self._base_object = Dummy(self.get_name()) + return self._base_object + + def get_state(self) -> Tuple[bytes, int]: + objs = self.get_base().get_objects_in_tree(exclude_base=False) + return self.get_base().get_configuration_tree(), len(objs) + + def restore_state(self, state: Tuple[bytes, int]) -> None: + objs = self.get_base().get_objects_in_tree(exclude_base=False) + if len(objs) != state[1]: + raise RuntimeError( + 'Expected to be resetting %d objects, but there were %d.' % + (state[1], len(objs))) + self.pyrep.set_configuration_tree(state[0]) + + def reset_robot(self): + arm, gripper = self.robot._initial_robot_state + self.pyrep.set_configuration_tree(arm) + self.pyrep.set_configuration_tree(gripper) + self.robot.gripper.release() + self.robot.reset() + + ##################### + # Private functions # + ##################### + + def _feasible(self, waypoints: List[Point]) -> Tuple[bool, int]: + arm = self.robot.arm + start_vals = arm.get_joint_positions() + for i, point in enumerate(waypoints): + path = None + try: + path = point.get_path(ignore_collisions=True) + except ConfigurationPathError as err: + pass + if path is None: + arm.set_joint_positions(start_vals) + return False, i + path.set_to_end() + # Needed twice otherwise can glitch out. + arm.set_joint_positions(start_vals) + return True, -1 + + def _get_waypoints(self, validating=False, need_feasible=True) -> List[Waypoint]: + waypoint_name = 'waypoint%d' + waypoints = [] + additional_waypoint_inits = [] + i = 0 + while True: + name = waypoint_name % i + if not Object.exists(name) or i == self._stop_at_waypoint_index: + # There are no more waypoints... + break + ob_type = Object.get_object_type(name) + way = None + if ob_type == ObjectType.DUMMY: + waypoint = Dummy(name) + start_func = None + end_func = None + if i in self._waypoint_abilities_start: + start_func = self._waypoint_abilities_start[i] + if i in self._waypoint_abilities_end: + end_func = self._waypoint_abilities_end[i] + way = Point(waypoint, self.robot, + start_of_path_func=start_func, + end_of_path_func=end_func) + elif ob_type == ObjectType.PATH: + cartestian_path = CartesianPath(name) + way = PredefinedPath(cartestian_path, self.robot) + else: + raise WaypointError( + '%s is an unsupported waypoint type %s' % ( + name, ob_type), self) + + if name in self._waypoint_additional_inits and not validating: + additional_waypoint_inits.append( + (self._waypoint_additional_inits[name], way)) + waypoints.append(way) + i += 1 + + # Check if all of the waypoints are feasible + if need_feasible: + feasible, way_i = self._feasible(waypoints) + if not feasible: + raise WaypointError( + "Infeasible episode. Can't reach waypoint %d." % way_i, self) + for func, way in additional_waypoint_inits: + func(way) + return waypoints diff --git a/easi/tasks/ebmanipulation/vendor/amsolver/backend/task_utils.py b/easi/tasks/ebmanipulation/vendor/amsolver/backend/task_utils.py new file mode 100644 index 0000000..f7bfc63 --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/amsolver/backend/task_utils.py @@ -0,0 +1,28 @@ +import os +import numpy as np +from pyrep.objects.shape import Shape + + +def sample_procedural_objects(task_base, num_samples, mass=0.1): + assets_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), + '../assets/procedural_objects') + samples = np.random.choice( + os.listdir(assets_dir), num_samples, replace=False) + created = [] + for s in samples: + respondable = os.path.join(assets_dir, s, s + '_coll.obj') + visual = os.path.join(assets_dir, s, s + '.obj') + resp = Shape.import_mesh(respondable, scaling_factor=0.005) + vis = Shape.import_mesh(visual, scaling_factor=0.005) + resp.set_renderable(False) + vis.set_renderable(True) + vis.set_parent(resp) + vis.set_dynamic(False) + vis.set_respondable(False) + resp.set_dynamic(True) + resp.set_mass(mass) + resp.set_respondable(True) + resp.set_model(True) + resp.set_parent(task_base) + created.append(resp) + return created diff --git a/easi/tasks/ebmanipulation/vendor/amsolver/backend/unit_tasks.py b/easi/tasks/ebmanipulation/vendor/amsolver/backend/unit_tasks.py new file mode 100644 index 0000000..6e30aa0 --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/amsolver/backend/unit_tasks.py @@ -0,0 +1,741 @@ +#Modified From the rlbench: https://github.com/stepjam/RLBench +from copy import deepcopy +import json +import pickle +from platform import release +import numpy as np +import os +from typing import Any, Dict, List, Tuple, Union +from scipy.spatial.transform import Rotation as R +from pyrep.pyrep import PyRep +from pyrep.objects.shape import Shape +from pyrep.objects.joint import Joint +from pyrep.objects.dummy import Dummy +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.cartesian_path import CartesianPath +from pyrep.const import PYREP_SCRIPT_TYPE, JointType +from pyrep.backend._sim_cffi import ffi, lib +from amsolver.backend.conditions import DetectedCondition +from amsolver.backend.robot import Robot +from amsolver.backend.spawn_boundary import BoundingBox, SpawnBoundary +from amsolver.backend.task import Task +from amsolver.backend.utils import WriteCustomDataBlock, execute_grasp, execute_path, get_relative_position_xy, get_sorted_grasp_pose, test_reachability + +gripper_step = 0.5 +def fast_path_test(point, robot): + final_config = point._path_points[-len(robot.arm.joints):] + robot.arm.set_joint_positions(final_config, True) + +class VLM_Object(Shape): + def __init__(self, pr: PyRep, model_path: str, instance_id: int): + with open(model_path.replace("ttm", "json"), 'r') as f: + self.config = json.load(f) + self.obj_class = self.config["class"] + self.highest_part_name = self.config["parts"][self.config["highest_part"]]["name"] + if self.exists(self.highest_part_name): + super().__init__(self.highest_part_name) + else: + model = pr.import_model(model_path) + super().__init__(model.get_handle()) + self.scale_factor = lib.simGetObjectSizeFactor(ffi.cast('int',self._handle)) + self.instance_id = instance_id + for m in self.get_objects_in_tree(exclude_base=False): + m.set_name(m.get_name()+str(instance_id)) + self.under_control = False + self.parts = [] + for p in self.config["parts"]: + part = Shape(p["name"]+str(instance_id)) + part.graspable = p["graspable"] + part.local_grasp = None + if "local_grasp_pose_path" in p: + grasp_pose_path = os.path.join(os.path.dirname(model_path),p["local_grasp_pose_path"]) + with open(grasp_pose_path, 'rb') as f: + part.local_grasp = pickle.load(f) + part.property = p["property"] + if self.exists(p["name"]+"_visual"+str(instance_id)): + part.set_transparency(0) + part.visual = Shape(p["name"]+"_visual"+str(instance_id)).get_handle() + else: + part.set_transparency(1) + part.visual = part.get_handle() + part.descriptions = None + self.parts.append(part) + + self.manipulated_parts = [] + for i in self.config["manipulated_part"]: + self.manipulated_parts.append(self.parts[i]) + self.manipulated_part = self.manipulated_parts[0] + + self.articulated = self.config["articulated"] + if self.articulated: + self.constraints = [] + for c_name in self.config["constraints"]: + name = c_name+str(instance_id) + if Shape.exists(name): + if Shape.get_object_type(name).name == "JOINT": + constraint = Joint(name) + self.constraints.append(constraint) + else: + self.constraints = None + +class TargetSpace(object): + def __init__(self, space: SpawnBoundary or Joint or set, successor=None, min_range=None, max_range=None, + target_space_descriptions=None, focus_obj_id=None, space_args=None) -> None: + super().__init__() + self.space = space + self.min_range = min_range + self.max_range = max_range + self.target_space_descriptions = target_space_descriptions + self.focus_obj_id = focus_obj_id + self.successor = successor + self.space_args = space_args + + def set_target(self, target_object:Shape, try_ik_sampling=True, linear=False, ignore_collisions=False, release=False): + self.target_object = target_object + self.try_ik_sampling = try_ik_sampling + self.linear = linear + self.ignore_collisions = ignore_collisions + self.release = release + +class T0_ObtainControl(object): + def __init__(self, robot: Robot, pyrep: PyRep, target_obj:Shape, task_base:Dummy, try_times = 200, + table_height=0.752,need_post_grasp=True, + table_offset_dist=0.01, + pregrasp_dist=0.08, + postgrasp_height=0.1, grasp_sort_key="vertical",*, next_task_fuc=None,next_task_args=None) -> None: + super().__init__() + self.robot = robot + self.pyrep = pyrep + self.task_base = task_base + self.target_obj = target_obj + + self.try_times = try_times + self.table_height = table_height + self.table_offset_dist = table_offset_dist + self.need_post_grasp = need_post_grasp + self.pregrasp_dist = pregrasp_dist + self.postgrasp_height = postgrasp_height + self.grasp_sort_key = grasp_sort_key + self.next_task_fuc = next_task_fuc + self.next_task_args = next_task_args + + @staticmethod + def sample_postgrasp_pose(grasp_pose, offset_height): + # sample 3d pos, remain same orientation in a cuboid area above grasp pose + def sample_from_center(center, range, sample): + pos_list = np.linspace(center, center + range, sample // 2).tolist() + neg_list = np.linspace(center, center - range, sample // 2).tolist() + result = [None] * (len(pos_list) + len(neg_list) - 1) + result[::2] = pos_list + result[1::2] = neg_list[1:] + return result + pose_list = [] + xy_range = 0.03 + sample_n = 10 + for h in sample_from_center(offset_height, offset_height / 2, sample_n): + for x in sample_from_center(0, xy_range, sample_n): + for y in sample_from_center(0, xy_range, sample_n): + pose = deepcopy(grasp_pose) + pose[:3, 3] -= pose[:3,:3].dot([x, y, h]) + pose_list.append(pose) + return np.array(pose_list) + + @staticmethod + def check_path_length(path, pose1, pose2, arm, threshold=3.0): + # print('checking path length') + linear_dist = np.linalg.norm(pose1[:3, 3] - pose2[:3, 3]) + path_dist = 0 + tip = arm.get_tip() + init_angles = arm.get_joint_positions() + arm.set_joint_positions(path._path_points[0: len(arm.joints)]) + prev_point = tip.get_position() + for i in range(len(arm.joints), len(path._path_points), + len(arm.joints)): + points = path._path_points[i:i + len(arm.joints)] + arm.set_joint_positions(points) + p = tip.get_position() + path_dist += np.linalg.norm(p - prev_point) + prev_point = p + arm.set_joint_positions(init_angles) + return path_dist / linear_dist < threshold + + def test_reachability(self, arm, pose, try_ik_sampling=False, linear=False, ignore_collisions=False): + new_target = Dummy.create() + new_target.set_matrix(pose) + pos, ori = new_target.get_position(), new_target.get_orientation() + res, path = False, None + success = False + try: + _ = arm.solve_ik_via_jacobian(pos, ori) + success = True + except: + if try_ik_sampling: + try: + _ = arm.solve_ik_via_sampling(pos, ori) # much slower than jacobian + success = True + except: + pass + else: + pass + if success: + try: + path = arm.get_linear_path(pos, ori, ignore_collisions=ignore_collisions) if linear else arm.get_path(pos, ori, ignore_collisions=ignore_collisions) + res = True + except: + pass + new_target.remove() + return res, path + + def get_path(self, try_ik_sampling=False, linear=False, ignore_collisions=False): + self.object_pose = self.target_obj.get_matrix() + sorted_grasp_pose = get_sorted_grasp_pose(self.object_pose, self.target_obj.local_grasp, self.grasp_sort_key) + sorted_grasp_pose[:,:3, 3] -= sorted_grasp_pose[:,:3, 2] * np.random.normal(scale=0.005, size=(sorted_grasp_pose.shape[0],1)) + objs_init_states = self.task_base.get_configuration_tree() + saved_states = self.robot.save_state() + success_grasps = None + trial = 0 + grasp_pose_idx = 0 + arm = self.robot.arm + gripper = self.robot.gripper + waypoint_dumpy = Dummy.create(size=0.03) + init_pose_dummy = Dummy.create() + init_pose_dummy.set_matrix(self.object_pose) + find_path = False + if self.try_times == -1: + self.try_times = len(sorted_grasp_pose) + grasp_index_step = len(sorted_grasp_pose)//self.try_times // 2 + grasp_index_step = max(1, grasp_index_step) + while trial < self.try_times and grasp_pose_idx < len(sorted_grasp_pose): + # print('trial:', trial, 'grasp idx:', grasp_pose_idx) + success = False + grasp_pose = sorted_grasp_pose[grasp_pose_idx] + pre_grasp_pose = deepcopy(grasp_pose) + pre_grasp_pose[:3, 3] -= pre_grasp_pose[:3, 2] * self.pregrasp_dist + grasp_pose_idx += grasp_index_step + if pre_grasp_pose[2, 3] - self.table_height < self.table_offset_dist: + # print('pre-grasp pose too near to table, ignore') + continue + trial += 1 + post_grasp_pose = deepcopy(grasp_pose) + post_grasp_pose[2, 3] += self.postgrasp_height + # grasp_pose[:3, 3] += grasp_pose[:3, 2] * 0.01 + waypoint_dumpy.set_matrix(grasp_pose) + success, path0 = test_reachability(arm, pre_grasp_pose, try_ik_sampling=try_ik_sampling, linear = linear, ignore_collisions=ignore_collisions) + moved = False + if success: + # move arm to pregrasp pose, then test grasp, this time only check cartesian move (linear path) + # execute_path(path0, self.pyrep) + fast_path_test(path0, self.robot) + # path0.set_to_end() + moved = True + # print('pregrasp path:', success) + success, path1 = test_reachability(arm, grasp_pose, False, linear=True, ignore_collisions=ignore_collisions) + if success: + # test grasp here + # execute_path(path1, self.pyrep) + fast_path_test(path1, self.robot) + # path1.set_to_end() + # print('grasp path:', success) + # print('moving ') + success = execute_grasp(gripper, self.target_obj, self.pyrep) + grasp_arm_config = arm.get_joint_positions() + grasp_gripper_config = gripper.get_joint_positions() + # obj.set_dynamic(True) + # print('gripper grasp:', success) + if success: + # move arm to grasp pose, grasp, test whether in hand, then test lift, this time only check cartesian move (linear path) + # execute_path(path1, pyrep) + if self.need_post_grasp: + post_grasp_pose_set = self.sample_postgrasp_pose(grasp_pose, self.postgrasp_height) + for post_grasp_pose in post_grasp_pose_set: + success, path2 = self.test_reachability(arm, post_grasp_pose, ignore_collisions=True) + if success and self.check_path_length(path2, grasp_pose, post_grasp_pose, arm): + break + # print('postgrasp path:', success) + if success: + # execute_path(path2, self.pyrep) + # path2.set_to_end() + fast_path_test(path2, self.robot) + find_path = True + # print('Find a grasp!') + else: + post_grasp_pose = None + path2 = None + find_path=True + # print('Find a grasp!') + if find_path: + grasp_point = {} + grasp_point['waypoints'] = [pre_grasp_pose, grasp_pose, post_grasp_pose] + grasp_point['path'] = [path0, path1, path2] + grasp_point['grasp_arm_config'] = grasp_arm_config + grasp_point['grasp_gripper_config'] = grasp_gripper_config + waypoint_dumpy.set_matrix(pre_grasp_pose) + relative_position_description = get_relative_position_xy(init_pose_dummy, waypoint_dumpy, arm) + grasp_point['relative'] = relative_position_description + # success_grasps.append(grasp_point) + find_path = False + waypoints = [] + for i,pose in enumerate([pre_grasp_pose, grasp_pose, post_grasp_pose]): + if pose is not None: + waypoint = Dummy.create() + waypoint.set_matrix(pose) + if i==0: + low_level_descriptions = "Move to the {} of {}.".format(relative_position_description,self.target_obj.descriptions) + WriteCustomDataBlock(waypoint.get_handle(),"waypoint_type","pre_grasp") + elif i==1: + low_level_descriptions = "Grasp {}.".format(self.target_obj.descriptions) + WriteCustomDataBlock(waypoint.get_handle(),"gripper","['close',0]") + WriteCustomDataBlock(waypoint.get_handle(),"waypoint_type","grasp") + elif i==2: + low_level_descriptions = "Move upward." + WriteCustomDataBlock(waypoint.get_handle(),"waypoint_type","post_grasp") + # WriteCustomDataBlock(waypoint.get_handle(),"ignore_collisions","True") + WriteCustomDataBlock(waypoint.get_handle(),"low_level_descriptions",low_level_descriptions) + WriteCustomDataBlock(waypoint.get_handle(),"focus_obj_id",str(self.target_obj.visual)) + WriteCustomDataBlock(waypoint.get_handle(),"focus_obj_name",Shape.get_object_name(self.target_obj.visual)) + if ignore_collisions or i==2: + WriteCustomDataBlock(waypoint.get_handle(),"ignore_collisions","True") + waypoints.append(waypoint) + if self.next_task_fuc is not None: + next_path = self.next_task_fuc() if self.next_task_args is None else self.next_task_fuc(**self.next_task_args) + if next_path is None: + for w in waypoints: + w.remove() + self.robot.recover_state(saved_states, release=True) + self.pyrep.set_configuration_tree(objs_init_states) + self.pyrep.step() + continue + else: + success_grasps = waypoints + next_path + break + else: + success_grasps = waypoints + break + if moved: + self.robot.recover_state(saved_states, release=True) + self.pyrep.set_configuration_tree(objs_init_states) + self.pyrep.step() + waypoint_dumpy.remove() + init_pose_dummy.remove() + return success_grasps + +class T1_MoveObjectGoal(object): + def __init__(self, robot: Robot, pyrep: PyRep, target_space:TargetSpace, task_base, + fail_times = 10, *, next_task_fuc=None,next_task_args=None) -> None: + super().__init__() + self.robot = robot + self.pyrep = pyrep + self.target_space = target_space + self.arm = robot.arm + self.gripper = robot.gripper + self.fail_times = fail_times + self.task_base = task_base + + self.next_task_fuc = next_task_fuc + self.next_task_args = next_task_args + + def get_path(self): + spaces = self.target_space.space + manipulated_obj = self.target_space.target_object + min_range = self.target_space.min_range + max_range = self.target_space.max_range + target_space_descriptions = self.target_space.target_space_descriptions + focus_obj_id = self.target_space.focus_obj_id + try_ik_sampling = self.target_space.try_ik_sampling + linear = self.target_space.linear + ignore_collisions = self.target_space.ignore_collisions + release = self.target_space.release + path = None + if type(spaces) == SpawnBoundary: + path = self.get_path_from_space(spaces, manipulated_obj, min_range, max_range, + try_ik_sampling, linear, ignore_collisions, release, + target_space_descriptions, focus_obj_id) + elif type(spaces) == Dummy: + path = self.get_path_from_point(spaces, manipulated_obj, min_range, max_range, + try_ik_sampling, linear, ignore_collisions, release, + target_space_descriptions, focus_obj_id) + + elif callable(spaces): + path = self.get_path_from_func(spaces, manipulated_obj, self.target_space.space_args, + try_ik_sampling, linear, ignore_collisions, release, + target_space_descriptions, focus_obj_id) + return path + + def get_path_from_space(self, target_spaces:SpawnBoundary, obj:VLM_Object, + min_rotation, max_rotation, try_ik_sampling, linear, ignore_collisions, release, + target_space_descriptions, focus_obj_id): + assert len(min_rotation) == 3 + objs_init_states = self.task_base.get_configuration_tree() + saved_states =self.robot.save_state() + target_space = np.random.choice(target_spaces._boundaries,p=target_spaces._probabilities) + if obj.is_model(): + bb = obj.get_model_bounding_box() + else: + bb = obj.get_bounding_box() + obj_bbox = BoundingBox(*bb) + point2obj_pose = self.arm._ik_tip.get_matrix(relative_to=obj) + target_space_pose = target_space._boundary.get_matrix() + new_path = None + for i in range(self.fail_times): + find_path = False + new_position = target_space._get_position_within_boundary(obj, obj_bbox) + rotations = np.random.uniform(list(min_rotation), list(max_rotation)) + trans_matrix = np.eye(4) + trans_matrix[:3, 3] = new_position + trans_matrix[:3,:3] = R.from_euler('xyz', rotations).as_matrix() + target_obj_pose = target_space_pose.dot(trans_matrix) + target_gripper_pose = target_obj_pose.dot(point2obj_pose) + find_path, path = test_reachability(self.arm, target_gripper_pose, try_ik_sampling=try_ik_sampling, + linear=linear, ignore_collisions=ignore_collisions) + if find_path: + # execute_path(path, self.pyrep) + # path.set_to_end() + fast_path_test(path, self.robot) + if release: + self.robot.gripper.release() + done = False + while not done: + done = self.robot.gripper.actuate(1,gripper_step) + self.pyrep.step() + for _ in range(10): + self.pyrep.step() + target = Dummy.create() + target.set_matrix(target_gripper_pose) + if target_space._is_plane: + low_level_descriptions = "Move the object on {}.".format(target_space_descriptions) + else: + low_level_descriptions = "Move the object above {}.".format(target_space_descriptions) + if release: + low_level_descriptions += " Release the gripper." + WriteCustomDataBlock(target.get_handle(),"gripper","['open',1]") + WriteCustomDataBlock(target.get_handle(),"low_level_descriptions",low_level_descriptions) + WriteCustomDataBlock(target.get_handle(),"focus_obj_id",str(focus_obj_id)) + WriteCustomDataBlock(target.get_handle(),"focus_obj_name",Shape.get_object_name(focus_obj_id)) + WriteCustomDataBlock(target.get_handle(),"waypoint_type","goal_move") + if self.next_task_fuc is not None: + next_path = self.next_task_fuc() if self.next_task_args is None else self.next_task_fuc(**self.next_task_args) + if next_path is None: + target.remove() + # self.robot.recover_state(saved_states) + # self.pyrep.set_configuration_tree(objs_init_states) + # self.pyrep.step() + # continue + break + else: + new_path = [target] + next_path + else: + new_path = [target] + break + return new_path + + def get_path_from_point(self, target_spaces:Dummy, obj:VLM_Object, + min_rotation, max_rotation, try_ik_sampling, linear, ignore_collisions, release, + target_space_descriptions, focus_obj_id): + objs_init_states = self.task_base.get_configuration_tree() + saved_states =self.robot.save_state() + new_path = None + point2obj_pose = self.arm._ik_tip.get_matrix(relative_to=obj) + target_pose = target_spaces.get_matrix() + target_gripper_pose = target_pose.dot(point2obj_pose) + pre_target_gripper_pose = deepcopy(target_gripper_pose) + pre_target_gripper_pose[:3, 3] -= pre_target_gripper_pose[:3, 2] * 0.08 + # target_gripper_pose = target_pose.dot(point2obj_pose) + find_path, path = test_reachability(self.arm, pre_target_gripper_pose, try_ik_sampling=try_ik_sampling, + linear=linear, ignore_collisions=ignore_collisions) + if find_path: + # execute_path(path, self.pyrep) + fast_path_test(path, self.robot) + find_path, path = test_reachability(self.arm, target_gripper_pose, try_ik_sampling=try_ik_sampling, + linear=linear, ignore_collisions=True) + if not find_path: + self.robot.recover_state(saved_states) + self.pyrep.set_configuration_tree(objs_init_states) + self.pyrep.step() + if find_path: + # execute_path(path, self.pyrep) + fast_path_test(path, self.robot) + if release: + self.robot.gripper.release() + done = False + while not done: + done = self.robot.gripper.actuate(1,gripper_step) + self.pyrep.step() + for _ in range(10): + self.pyrep.step() + pre_target = Dummy.create() + pre_target.set_matrix(pre_target_gripper_pose) + + target = Dummy.create() + target.set_matrix(target_gripper_pose) + low_level_descriptions = "Move the object to {}.".format(target_space_descriptions) + if release: + low_level_descriptions += " Release the gripper." + WriteCustomDataBlock(target.get_handle(),"gripper","['open',1]") + WriteCustomDataBlock(target.get_handle(),"low_level_descriptions",low_level_descriptions) + WriteCustomDataBlock(target.get_handle(),"focus_obj_id",str(focus_obj_id)) + WriteCustomDataBlock(target.get_handle(),"focus_obj_name",Shape.get_object_name(focus_obj_id)) + WriteCustomDataBlock(target.get_handle(),"ignore_collisions","True") + WriteCustomDataBlock(target.get_handle(),"waypoint_type","goal_move") + + WriteCustomDataBlock(pre_target.get_handle(),"low_level_descriptions","Move the object to the {} of {}".format(get_relative_position_xy(target, pre_target, self.arm), target_space_descriptions)) + WriteCustomDataBlock(pre_target.get_handle(),"focus_obj_id",str(focus_obj_id)) + WriteCustomDataBlock(pre_target.get_handle(),"focus_obj_name",Shape.get_object_name(focus_obj_id)) + WriteCustomDataBlock(pre_target.get_handle(),"waypoint_type","pre_goal_move") + if self.next_task_fuc is not None: + next_path = self.next_task_fuc() if self.next_task_args is None else self.next_task_fuc(**self.next_task_args) + if next_path is None: + target.remove() + pre_target.remove() + else: + new_path = [pre_target, target] + next_path + else: + new_path = [pre_target, target] + + return new_path + + def get_path_from_func(self, target_spaces, obj:VLM_Object, target_args, + try_ik_sampling, linear, ignore_collisions, release, + target_space_descriptions, focus_obj_id): + all_g_poses = target_spaces(**target_args) + objs_init_states = self.task_base.get_configuration_tree() + saved_states =self.robot.save_state() + new_path = None + # obj_target = Dummy.create() + gripper_target = Dummy.create() + gripper2obj = self.arm._ik_tip.get_matrix(obj) + for g_pose in all_g_poses: + find_path = False + # obj_target.set_position(g_pose[:3]) + # obj_target.set_orientation(g_pose[3:]) + # gripper_matrix = obj_target.get_matrix().dot(gripper2obj) + gripper_matrix = g_pose.dot(gripper2obj) + gripper_target.set_matrix(gripper_matrix) + find_path, path = test_reachability(self.arm, gripper_matrix, try_ik_sampling=try_ik_sampling, + linear=linear, ignore_collisions=ignore_collisions) + if find_path: + fast_path_test(path, self.robot) + if release: + self.robot.gripper.release() + done = False + while not done: + done = self.robot.gripper.actuate(1,gripper_step) + self.pyrep.step() + for _ in range(10): + self.pyrep.step() + WriteCustomDataBlock(gripper_target.get_handle(),"gripper","['open',1]") + low_level_descriptions = "Move the object {}.".format(target_space_descriptions) + if release: + low_level_descriptions += " Release the gripper." + WriteCustomDataBlock(gripper_target.get_handle(),"low_level_descriptions",low_level_descriptions) + WriteCustomDataBlock(gripper_target.get_handle(),"focus_obj_id",str(focus_obj_id)) + WriteCustomDataBlock(gripper_target.get_handle(),"focus_obj_name",Shape.get_object_name(focus_obj_id)) + WriteCustomDataBlock(gripper_target.get_handle(),"waypoint_type","goal_move") + WriteCustomDataBlock(gripper_target.get_handle(), "ignore_collisions",str(ignore_collisions)) + WriteCustomDataBlock(gripper_target.get_handle(), "linear",str(linear)) + if self.next_task_fuc is not None: + next_path = self.next_task_fuc() if self.next_task_args is None else self.next_task_fuc(**self.next_task_args) + if next_path is None: + self.robot.recover_state(saved_states) + self.pyrep.set_configuration_tree(objs_init_states) + continue + else: + new_path = [gripper_target] + next_path + break + else: + new_path = [gripper_target] + break + if not find_path: + gripper_target.remove() + # obj_target.remove() + return new_path + +class T2_MoveObjectConstraints(T1_MoveObjectGoal): + def __init__(self, robot: Robot, pyrep: PyRep, target_space: TargetSpace, init_states, + fail_times=10, *, next_task_fuc=None,next_task_args=None) -> None: + super().__init__(robot, pyrep, target_space, init_states, fail_times, + next_task_fuc=next_task_fuc,next_task_args=next_task_args) + + def get_path_with_constraints(self): + spaces = self.target_space.space + manipulated_obj = self.target_space.target_object + min_range = self.target_space.min_range + max_range = self.target_space.max_range + target_space_descriptions = self.target_space.target_space_descriptions + focus_obj_id = self.target_space.focus_obj_id + try_ik_sampling = self.target_space.try_ik_sampling + linear = self.target_space.linear + ignore_collisions = self.target_space.ignore_collisions + release = self.target_space.release + path = None + if type(spaces) == Joint: + path = self.get_path_from_joint(spaces, manipulated_obj, min_range, max_range, + try_ik_sampling, linear, ignore_collisions, release, + target_space_descriptions, focus_obj_id) + # elif type(spaces) == list: + # path = self.get_path_from_list(spaces, manipulated_obj, min_range, max_range, + # try_ik_sampling, linear, ignore_collisions, release, + # target_space_descriptions, focus_obj_id) + elif callable(spaces): + path = self.get_path_from_func(spaces, manipulated_obj, self.target_space.space_args, + try_ik_sampling, linear, ignore_collisions, release, + target_space_descriptions, focus_obj_id) + return path + + def get_path_from_joint(self, target_spaces:Joint, obj:VLM_Object, + min_rotation, max_rotation, try_ik_sampling, linear, ignore_collisions, release, + target_space_descriptions, focus_obj_id): + intermediate_target = Dummy.create() + intermediate_target.set_pose(self.arm._ik_tip.get_pose()) + objs_init_states = self.task_base.get_configuration_tree() + saved_states =self.robot.save_state() + current_angle = target_spaces.get_joint_position() + point2joint_pose = intermediate_target.get_matrix(relative_to=target_spaces) + joint2world_pose = target_spaces.get_matrix() + for _ in range(self.fail_times): + find_path = False + end_angle = np.random.uniform(min_rotation, max_rotation) + intermediate_angles = np.linspace(current_angle, end_angle, num=10) + new_path = CartesianPath.create(automatic_orientation=False) + control_points = [] + can_reach = True + start_joint = self.arm.get_joint_positions() + for angle in intermediate_angles: + transform_matrix = np.eye(4) + if target_spaces.get_joint_type() == JointType.REVOLUTE: + rotation = R.from_euler('z', angle).as_matrix() + transform_matrix[:3,:3] = rotation + elif target_spaces.get_joint_type() == JointType.PRISMATIC: + trans = np.array([0,0,angle]) + transform_matrix[:3,3] = trans + intermediate_target.set_matrix(joint2world_pose.dot(transform_matrix).dot(point2joint_pose)) + can_reach,sub_path = test_reachability(self.arm, intermediate_target.get_matrix(), linear=True, ignore_collisions=True) + if not can_reach: + break + pos = intermediate_target.get_position().tolist() + ori = intermediate_target.get_orientation().tolist() + # execute_path(sub_path, self.pyrep) + sub_path.set_to_end() + control_points.append(pos+ori) + self.arm.set_joint_positions(start_joint) + if not can_reach: + new_path.remove() + self.robot.recover_state(saved_states) + self.pyrep.set_configuration_tree(objs_init_states) + self.pyrep.step() + continue + new_path.insert_control_points(control_points) + try: + try: + path = self.arm.get_path_from_cartesian_path(new_path) + except: + path = self.arm.get_linear_path(pos, ori, ignore_collisions=True) + new_path.remove() + new_path = Dummy.create() + new_path.set_position(pos) + new_path.set_orientation(ori) + WriteCustomDataBlock(new_path.get_handle(), "linear","True") + execute_path(path, self.pyrep) + if target_spaces.get_joint_type() == JointType.PRISMATIC: + tolerance = 0.05 + elif target_spaces.get_joint_type() == JointType.REVOLUTE: + tolerance = np.deg2rad(5) + if abs(target_spaces.get_joint_position()-end_angle)>tolerance: + new_path.remove() + self.robot.recover_state(saved_states) + self.pyrep.set_configuration_tree(objs_init_states) + self.pyrep.step() + continue + WriteCustomDataBlock(new_path.get_handle(), "ignore_collisions","True") + if target_spaces.get_joint_type() == JointType.PRISMATIC: + low_level_descriptions = "Move along the axis of {}.".format(target_space_descriptions) + elif target_spaces.get_joint_type() == JointType.REVOLUTE: + low_level_descriptions = "Rotate around the axis of {}.".format(target_space_descriptions) + WriteCustomDataBlock(new_path.get_handle(),"low_level_descriptions",low_level_descriptions) + WriteCustomDataBlock(new_path.get_handle(),"focus_obj_id",str(focus_obj_id)) + WriteCustomDataBlock(new_path.get_handle(),"focus_obj_name",Shape.get_object_name(focus_obj_id)) + WriteCustomDataBlock(new_path.get_handle(),"waypoint_type","path_move") + if self.next_task_fuc is not None: + next_path = self.next_task_fuc() if self.next_task_args is None else self.next_task_fuc(**self.next_task_args) + if next_path is None: + new_path.remove() + # reverse_path = deepcopy(path) + # original_path_points = path._path_points.reshape(-1,7) + # reverse_path._path_points = original_path_points[::-1].reshape(-1,) + # execute_path(path, self.pyrep) + self.robot.recover_state(saved_states) + self.pyrep.set_configuration_tree(objs_init_states) + self.pyrep.step() + continue + else: + new_path = [new_path] + next_path + else: + new_path = [new_path] + find_path=True + break + except: + new_path.remove() + intermediate_target.remove() + return new_path if find_path else None + + def get_path_from_func(self, target_spaces, obj:VLM_Object, target_args, + try_ik_sampling, linear, ignore_collisions, release, + target_space_descriptions, focus_obj_id): + all_g_poses = target_spaces(**target_args) + objs_init_states = self.task_base.get_configuration_tree() + saved_states =self.robot.save_state() + new_path = None + # obj_target = Dummy.create() + # gripper_target = Dummy.create() + gripper2obj = self.arm._ik_tip.get_matrix(obj) + for g_pose in all_g_poses: + find_path = False + # obj_target.set_position(g_pose[:3]) + # obj_target.set_orientation(g_pose[3:]) + # gripper_matrix = obj_target.get_matrix().dot(gripper2obj) + gripper_matrix = g_pose.dot(gripper2obj) + # gripper_target.set_matrix(gripper_matrix) + find_path, path = test_reachability(self.arm, gripper_matrix, try_ik_sampling=try_ik_sampling, + linear=linear, ignore_collisions=ignore_collisions) + if find_path: + new_path = CartesianPath.create(automatic_orientation=False) + control_points = [] + all_path_configs = path._path_points.reshape(-1, len(self.arm.joints)) + for config in all_path_configs: + self.arm.set_joint_positions(config) + pos = self.arm._ik_tip.get_position().tolist() + ori = self.arm._ik_tip.get_orientation().tolist() + control_points.append(pos+ori) + new_path.insert_control_points(control_points) + fast_path_test(path, self.robot) + WriteCustomDataBlock(new_path.get_handle(),"low_level_descriptions", target_space_descriptions) + WriteCustomDataBlock(new_path.get_handle(),"focus_obj_id",str(focus_obj_id)) + WriteCustomDataBlock(new_path.get_handle(),"focus_obj_name",Shape.get_object_name(focus_obj_id)) + WriteCustomDataBlock(new_path.get_handle(),"waypoint_type","path_move") + WriteCustomDataBlock(new_path.get_handle(), "ignore_collisions",str(ignore_collisions)) + WriteCustomDataBlock(new_path.get_handle(), "linear",str(linear)) + if self.next_task_fuc is not None: + next_path = self.next_task_fuc() if self.next_task_args is None else self.next_task_fuc(**self.next_task_args) + if next_path is None: + self.robot.recover_state(saved_states) + self.pyrep.set_configuration_tree(objs_init_states) + new_path.remove() + new_path = None + continue + else: + new_path = [new_path] + next_path + break + else: + new_path = [new_path] + break + # if not find_path: + # new_path.remove() + # obj_target.remove() + return new_path + +if __name__=="__main__": + pr = PyRep() + pr.launch('', responsive_ui=True, headless=False) + model_path = "../vlm/object_models/cube/cube_normal/cube_normal.ttm" + model = VLM_Object(pr, model_path, 0) + model_2 = VLM_Object(pr, model_path, 1) + pr.shutdown() \ No newline at end of file diff --git a/easi/tasks/ebmanipulation/vendor/amsolver/backend/utils.py b/easi/tasks/ebmanipulation/vendor/amsolver/backend/utils.py new file mode 100644 index 0000000..cb226e0 --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/amsolver/backend/utils.py @@ -0,0 +1,522 @@ +#Modified From the rlbench: https://github.com/stepjam/RLBench +from copy import deepcopy +from os import error +import os +from pathlib import Path +from typing import Dict, List, Tuple +import numpy as np +from PIL import Image +from pyrep.backend import utils as pyrep_utils +from pyrep.backend._sim_cffi import ffi, lib +from pyrep.const import PYREP_SCRIPT_TYPE, JointType +from pyrep.objects.dummy import Dummy +from pyrep.objects.object import Object +from pyrep.objects.shape import Shape +from pyrep.objects.joint import Joint +from pyrep.pyrep import PyRep +from pyrep.robots.arms.arm import Arm +from pyrep.robots.end_effectors.gripper import Gripper +from amsolver.backend.robot import Robot +from pyrep.objects.cartesian_path import CartesianPath +from amsolver.const import colors +from amsolver.backend.spawn_boundary import BoundaryObject, BoundingBox, SpawnBoundary +from copy import deepcopy +from pyrep.backend import sim +from scipy.spatial.transform import Rotation as R + +from tools.grasploc import Grasploc, define_default_args + +def ClipFloatValues(float_array, min_value, max_value): + """Clips values to the range [min_value, max_value]. + + First checks if any values are out of range and prints a message. + Then clips all values to the given range. + + Args: + float_array: 2D array of floating point values to be clipped. + min_value: Minimum value of clip range. + max_value: Maximum value of clip range. + + Returns: + The clipped array. + + """ + if float_array.min() < min_value or float_array.max() > max_value: + float_array = np.clip(float_array, min_value, max_value) + return float_array + + +DEFAULT_RGB_SCALE_FACTOR = 256000.0 + + +def float_array_to_rgb_image(float_array, + scale_factor=DEFAULT_RGB_SCALE_FACTOR, + drop_blue=False): + """Convert a floating point array of values to an RGB image. + + Convert floating point values to a fixed point representation where + the RGB bytes represent a 24-bit integer. + R is the high order byte. + B is the low order byte. + The precision of the depth image is 1/256 mm. + + Floating point values are scaled so that the integer values cover + the representable range of depths. + + This image representation should only use lossless compression. + + Args: + float_array: Input array of floating point depth values in meters. + scale_factor: Scale value applied to all float values. + drop_blue: Zero out the blue channel to improve compression, results in 1mm + precision depth values. + + Returns: + 24-bit RGB PIL Image object representing depth values. + """ + # Scale the floating point array. + scaled_array = np.floor(float_array * scale_factor + 0.5) + # Convert the array to integer type and clip to representable range. + min_inttype = 0 + max_inttype = 2**24 - 1 + scaled_array = ClipFloatValues(scaled_array, min_inttype, max_inttype) + int_array = scaled_array.astype(np.uint32) + # Calculate: + # r = (f / 256) / 256 high byte + # g = (f / 256) % 256 middle byte + # b = f % 256 low byte + rg = np.divide(int_array, 256) + r = np.divide(rg, 256) + g = np.mod(rg, 256) + image_shape = int_array.shape + rgb_array = np.zeros((image_shape[0], image_shape[1], 3), dtype=np.uint8) + rgb_array[..., 0] = r + rgb_array[..., 1] = g + if not drop_blue: + # Calculate the blue channel and add it to the array. + b = np.mod(int_array, 256) + rgb_array[..., 2] = b + image_mode = 'RGB' + image = Image.fromarray(rgb_array, mode=image_mode) + return image + + +DEFAULT_GRAY_SCALE_FACTOR = {np.uint8: 100.0, + np.uint16: 1000.0, + np.int32: DEFAULT_RGB_SCALE_FACTOR} + + +def float_array_to_grayscale_image(float_array, scale_factor=None, image_dtype=np.uint8): + """Convert a floating point array of values to an RGB image. + + Convert floating point values to a fixed point representation with + the given bit depth. + + The precision of the depth image with default scale_factor is: + uint8: 1cm, with a range of [0, 2.55m] + uint16: 1mm, with a range of [0, 65.5m] + int32: 1/256mm, with a range of [0, 8388m] + + Right now, PIL turns uint16 images into a very strange format and + does not decode int32 images properly. Only uint8 works correctly. + + Args: + float_array: Input array of floating point depth values in meters. + scale_factor: Scale value applied to all float values. + image_dtype: Image datatype, which controls the bit depth of the grayscale + image. + + Returns: + Grayscale PIL Image object representing depth values. + + """ + # Ensure that we have a valid numeric type for the image. + if image_dtype == np.uint16: + image_mode = 'I;16' + elif image_dtype == np.int32: + image_mode = 'I' + else: + image_dtype = np.uint8 + image_mode = 'L' + if scale_factor is None: + scale_factor = DEFAULT_GRAY_SCALE_FACTOR[image_dtype] + # Scale the floating point array. + scaled_array = np.floor(float_array * scale_factor + 0.5) + # Convert the array to integer type and clip to representable range. + min_dtype = np.iinfo(image_dtype).min + max_dtype = np.iinfo(image_dtype).max + scaled_array = ClipFloatValues(scaled_array, min_dtype, max_dtype) + + image_array = scaled_array.astype(image_dtype) + image = Image.fromarray(image_array, mode=image_mode) + return image + + +def image_to_float_array(image, scale_factor=None): + """Recovers the depth values from an image. + + Reverses the depth to image conversion performed by FloatArrayToRgbImage or + FloatArrayToGrayImage. + + The image is treated as an array of fixed point depth values. Each + value is converted to float and scaled by the inverse of the factor + that was used to generate the Image object from depth values. If + scale_factor is specified, it should be the same value that was + specified in the original conversion. + + The result of this function should be equal to the original input + within the precision of the conversion. + + Args: + image: Depth image output of FloatArrayTo[Format]Image. + scale_factor: Fixed point scale factor. + + Returns: + A 2D floating point numpy array representing a depth image. + + """ + image_array = np.array(image) + image_dtype = image_array.dtype + image_shape = image_array.shape + + channels = image_shape[2] if len(image_shape) > 2 else 1 + assert 2 <= len(image_shape) <= 3 + if channels == 3: + # RGB image needs to be converted to 24 bit integer. + float_array = np.sum(image_array * [65536, 256, 1], axis=2) + if scale_factor is None: + scale_factor = DEFAULT_RGB_SCALE_FACTOR + else: + if scale_factor is None: + scale_factor = DEFAULT_GRAY_SCALE_FACTOR[image_dtype.type] + float_array = image_array.astype(np.float32) + scaled_array = float_array / scale_factor + return scaled_array + + +def task_file_to_task_class(task_file, parent_folder = 'amsolver'): + import importlib + name = task_file.replace('.py', '') + class_name = ''.join([w[0].upper() + w[1:] for w in name.split('_')]) + mod = importlib.import_module(parent_folder+".tasks.%s" % name) + mod = importlib.reload(mod) + task_class = getattr(mod, class_name) + return task_class + + +def rgb_handles_to_mask(rgb_coded_handles): + # rgb_coded_handles should be (w, h, c) + # Handle encoded as : handle = R + G * 256 + B * 256 * 256 + rgb_coded_handles *= 255 # takes rgb range to 0 -> 255 + rgb_coded_handles = rgb_coded_handles.astype(int) + return (rgb_coded_handles[:, :, 0] + + rgb_coded_handles[:, :, 1] * 256 + + rgb_coded_handles[:, :, 2] * 256 * 256) + +''' +New functions +''' +def scale_object(obj, scale_factor: float, scale_position: bool = True) -> None: + objectHandle = ffi.new('int[1]', [obj._handle]) + current_scale = lib.simGetObjectSizeFactor(ffi.cast('int',obj._handle)) + if hasattr(obj, "scale_factor"): + current_scale /= obj.scale_factor + relative_factor = scale_factor/current_scale + if abs(relative_factor-1)>1e-2: + lib.simScaleObjects(objectHandle, ffi.cast('int',1), ffi.cast('float', scale_factor/current_scale), ffi.cast('bool', scale_position)) + return relative_factor + +def get_relative_position_xy(object1, object2, robot): + # get relative position of object2 relative to object1 in robot coordinate frame + # y+/-: right/left; z+/-: back/front + rel_pos_xyz = object2.get_position(robot) - object1.get_position(robot) + z, x, y = rel_pos_xyz[0], rel_pos_xyz[1], rel_pos_xyz[2] + if z>0.05: + direction = 'top' + else: + if y > x and y > -x: + direction = 'front' + elif y > x and y < -x: + direction = 'left' + elif y < x and y > -x: + direction = 'right' + else: + direction = 'rear' + return direction + +def exchange_objects(target: Shape, source: Shape): + visual_name = '{}_visual'.format(target.get_name()) + target_visual = Shape(visual_name) + target_bbox = np.array(target_visual.get_bounding_box()) + source_bbox = np.array(source.get_bounding_box()) + scaling_factor = (np.prod(target_bbox[1:6:2]-target_bbox[0:6:2])/np.prod(source_bbox[1:6:2]-source_bbox[0:6:2]))**(1/3) + scale_object(source, scaling_factor) + + source.set_pose(target.get_pose()) + + source.set_parent(target.get_parent()) + for obj in target.get_objects_in_tree(first_generation_only=True): + if 'visual' not in obj.get_name(): + obj.set_parent(source) + if obj.get_type()=='DUMMY': + obj.set_pose(obj.get_pose(relative_to=target_visual), relative_to=source) + + target.remove() + if Shape.exists(visual_name): + Shape(visual_name).remove() + + return source, Shape('{}_visual'.format(source.get_name())) + +def WriteCustomDataBlock(objectHandle, tagName, data): + # dataSize = ffi.cast('int', len(data)) + # objectHandle = ffi.cast('int', objectHandle) + # tagName = ffi.new('char[]', tagName.encode('ascii')) + # data = ffi.new('char[]', data.encode('ascii')) + # lib.simWriteCustomDataBlock(objectHandle, tagName, data, dataSize) + pyrep_utils.script_call('_WriteCustomDataBlock@PyRep', PYREP_SCRIPT_TYPE, + ints=[objectHandle], strings=[tagName, data]) + +def ReadCustomDataBlock(objectHandle, tagName): + # dataSize = ffi.new('int*') + # objectHandle = ffi.cast('int', objectHandle) + # tagName = ffi.new('char[]', tagName.encode('ascii')) + # data = lib.simReadCustomDataBlock(objectHandle, tagName, dataSize) + data_string = None + try: + # data_string = ffi.string(data).decode('utf-8') + data = pyrep_utils.script_call('_ReadCustomDataBlock@PyRep', PYREP_SCRIPT_TYPE, + ints=[objectHandle], strings=[tagName]) + data_string = data[2][0] + except: + pass + return data_string + +def ReadCustomDataBlockTags(objectHandle): + # tagCount = ffi.new('int*') + # objectHandle = ffi.cast('int', objectHandle) + # data = lib.simReadCustomDataBlockTags(objectHandle, tagCount) + data_string = None + try: + data = pyrep_utils.script_call('_ReadCustomDataBlockTags@PyRep', PYREP_SCRIPT_TYPE, + ints=[objectHandle]) + data_string = data[2] + except: + pass + return data_string + +def test_reachability(arm: Arm, pose, try_ik_sampling=False, linear=False, ignore_collisions=False): + new_target = Dummy.create() + new_target.set_matrix(pose) + pos, ori = new_target.get_position(), new_target.get_orientation() + res, path = False, None + success = False + try: + _ = arm.solve_ik_via_jacobian(pos, ori) + success = True + except: + if try_ik_sampling: + try: + _ = arm.solve_ik_via_sampling(pos, ori) # much slower than jacobian + success = True + except: + pass + else: + pass + if success: + try: + path = arm.get_linear_path(pos, ori, ignore_collisions=ignore_collisions) if linear else arm.get_path(pos, ori, ignore_collisions=ignore_collisions) + if sum(path._get_path_point_lengths()) == 0: + res = False + else: + res = True + except: + pass + new_target.remove() + return res, path + +# execute_path, grasp, release are copied from amsolver/backend/scene.py +def execute_path(path, pyrep): + # print('executing path') + if sum(path._get_path_point_lengths()) == 0: + return None + done = False + while not done: + done = path.step() + for _ in range(2): + pyrep.step() + # add additional steps to ensure finish of current path + # for _ in range(100): + # pyrep.step() + path._path_done = False + path._rml_handle = None + return path + +def execute_grasp(gripper: Gripper, obj: Object, pyrep: PyRep, release=False): + # print('executing grasp') + done = False + step = 0.2 + success_grasp_distance = 0.015 + action = 1 if release else 0 + while not done: + done = gripper.actuate(action, step) + pyrep.step() + done = False + if release: + gripper.release() + else: + if hasattr(obj, 'graspable'): # all True for graspable objects + if obj.graspable == True: + done = gripper._proximity_sensor.is_detected(obj) and sum(gripper.get_joint_positions()) > success_grasp_distance + if done: + gripper.grasp(obj) + else: #for real-grasp + done = sum(gripper.get_joint_positions()) > success_grasp_distance + else: + done = sum(gripper.get_joint_positions()) > success_grasp_distance + return done + +def exportMesh(objects, output_format, output_path): + output_format_list = ['obj','text_stl','binary_stl','dae','text_ply','binary_ply'] + assert output_format in output_format_list + + if 'obj' in output_format: + suffix = '.obj' + elif 'stl' in output_format: + suffix = '.stl' + elif 'ply' in output_format: + suffix = '.ply' + elif 'dae' in output_format: + suffix = '.dae' + else: + raise ValueError('output format should choose inside [obj,stl,ply,dae]') + output_path = output_path+suffix + object_handles = [] + if type(objects) is list: + for obj in objects: + object_handles.append(obj._handle) + else: + object_handles.append(objects._handle) + pyrep_utils.script_call('_exportMesh@PyRep', + PYREP_SCRIPT_TYPE, ints=object_handles, strings=[output_format,output_path]) + +def pose_differences(pose1, pose2): + q1 = R.from_matrix(pose1[:3, :3]).as_quat() + q2 = R.from_matrix(pose2[:3, :3]).as_quat() + quat_diff = 1-abs(q1.dot(q2)) + translate_diff = ((pose1[:3, 3]-pose2[:3, 3])**2).sum()**(1/2) + return translate_diff, quat_diff + +def get_sorted_grasp_pose(obj_pose, local_grasp_pose, sort_key="vertical"): + grasp_pose = np.einsum('ij,kjl->kil', obj_pose, local_grasp_pose) + angle_z_z_axis = grasp_pose[:,2, 2] + angle_z_x_axis = grasp_pose[:,0, 2] + grasp_pose = grasp_pose[np.logical_and(angle_z_z_axis<0.1, angle_z_x_axis>-0.1)] + np.random.shuffle(grasp_pose) + if sort_key=="vertical": + axis_angle = grasp_pose[:,2, 2] + elif sort_key=="horizontal": + axis_angle = abs(grasp_pose[:,2, 2])-grasp_pose[:,0, 2]**2 + # sort_select = np.argsort(np.abs(angle_z_z_axis)) + elif type(sort_key)==list: + # New axis(sort_key[0]) in the world axis(sort_key[1]) + axis = ["x", "y", "z"] + axis_angle = grasp_pose[:,axis.index(sort_key[0]), axis.index(sort_key[1])] + if "abs" in sort_key[2]: + axis_angle = abs(axis_angle) + elif "neg" in sort_key[2]: + axis_angle = -axis_angle + else: + raise ValueError('sort_key can only be vertical first or horizontal first') + sort_select = np.argsort(axis_angle) + grasp_pose = grasp_pose[sort_select] + return grasp_pose + +def get_local_grasp_pose(obj: Object, ply_file: str, grasp_pose_path = './vlm/grasp_poses/', need_rebuild=False, use_meshlab=True, crop_box:Object =None): + args = define_default_args() + args.input_file = ply_file + args.output_file = os.path.join(grasp_pose_path, ply_file.split('/')[-1][:-4] + '.pkl') + args.use_meshlab = use_meshlab + obj_origin_matrix = obj.get_matrix() + if crop_box is not None: + args.no_crop = False + crop_boundary = crop_box.get_bounding_box() + crop_origin_matrix = crop_box.get_matrix() + args.crop_box_transform = crop_origin_matrix + args.crop_x_min = crop_boundary[0] + args.crop_y_min = crop_boundary[2] + args.crop_z_min = crop_boundary[4] + args.crop_x_max = crop_boundary[1] + args.crop_y_max = crop_boundary[3] + args.crop_z_max = crop_boundary[5] + gl = Grasploc(args) + # scale_object(self.toy, 0.5) + + if not os.path.exists(args.output_file) or need_rebuild: + exportMesh(obj, 'binary_ply', ply_file[:-4]) + gl.run(obj_origin_matrix) + else: + gl.load_result() + return gl.se3_output + +def add_joint(jointType, jointMode=sim.sim_jointmode_force, length=0.2, diameter=0.02): # joint axis is along Z + jointMode = ffi.cast('int', jointMode) + if jointType == 'revolute': + j_t = JointType.REVOLUTE + elif jointType == 'prismatic': + j_t = JointType.PRISMATIC + elif jointType == 'spherical': + j_t = JointType.SPHERICAL + else: + raise ValueError('the wrong joint type') + j_t = ffi.cast('int', j_t.value) + sizes = ffi.new('float[]',[length, diameter]) + color_A = ffi.new('float[]',[1,0,0]) + color_B = ffi.new('float[]',[0,0,1]) + handle = lib.simCreateJoint(j_t, jointMode, ffi.cast('int',0), sizes, color_A, color_B) + return Joint(int(handle)) + +def create_rotation_joint(container_pour, container_recv): + joint = add_joint('revolute', length=0.002, diameter=0.002) + joint.set_joint_interval(False, [-np.pi, np.pi]) + joint.set_model_dynamic(True) + joint_mat = np.zeros((3, 4)) # Z to be tangent, Y to be pointing from container_pour to container_recv in horizontal plane, X is always (0, 0, -1) + joint_mat[:, 0] = [0, 0, -1] + joint_mat[:, 1] = container_pour.get_position() - container_recv.get_position() + joint_mat[2, 1] = 0 + joint_mat[:, 2] = np.cross(joint_mat[:, 0], joint_mat[:, 1]) + joint_mat[:, 3] = container_pour.get_position() + joint.set_matrix(joint_mat) + joint.set_parent(container_pour.get_parent()) + container_pour.set_parent(joint) + return joint + +def select_color(index, other_color_numbers, replace=True): + target_color_name, target_rgb = colors[index] + + color_names, rgbs = [target_color_name], [target_rgb] + if other_color_numbers!=0: + random_idx = np.random.choice(len(colors), other_color_numbers, replace=replace) + while index in random_idx: + random_idx = np.random.choice(len(colors), other_color_numbers, replace=replace) + for i in random_idx: + name, rgb = colors[i] + color_names.append(name) + rgbs.append(rgb) + return color_names, rgbs + +def import_distractors(pyrep: PyRep, path = './vlm/asset', select_number=5, scale=1e-4): + models_path = [] + for path in Path(path).rglob('*.ttm'): + models_path.append(path) + idx = np.random.choice(len(models_path), size=select_number) + models = [] + for i in idx: + model = pyrep.import_model(str(models_path[i])) + model.set_model_dynamic(True) + model.set_model_collidable(True) + bbox = np.array(model.get_bounding_box()) + scaling_factor = (scale/np.prod(bbox[1:6:2]-bbox[0:6:2]))**(1/3) + scale_object(model, scaling_factor) + models.append(model) + return models + diff --git a/easi/tasks/ebmanipulation/vendor/amsolver/backend/waypoints.py b/easi/tasks/ebmanipulation/vendor/amsolver/backend/waypoints.py new file mode 100644 index 0000000..9eb81bb --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/amsolver/backend/waypoints.py @@ -0,0 +1,121 @@ +from pyrep.objects.object import Object +from pyrep.robots.configuration_paths.arm_configuration_path import ( + ArmConfigurationPath) +from amsolver.backend.robot import Robot +from amsolver.backend.utils import ReadCustomDataBlock +from scipy.spatial.transform import Rotation as R +import numpy as np + + +class Waypoint(object): + + def __init__(self, waypoint: Object, robot: Robot, ignore_collisions=False, + start_of_path_func=None, end_of_path_func=None): + self.name = waypoint.get_name() + self._waypoint = waypoint + self._robot = robot + self._ext = waypoint.get_extension_string() + self._ignore_collisions = ignore_collisions + self._linear_only = False + self._start_of_path_func = start_of_path_func + self._end_of_path_func = end_of_path_func + self.gripper_control = None + if len(self._ext) > 0: + self._ignore_collisions = 'ignore_collision' in self._ext + self._linear_only = 'linear' in self._ext + + contains_param = False + start_of_bracket = -1 + if 'open_gripper(' in self._ext: + start_of_bracket = self._ext.index('open_gripper(') + 13 + contains_param = self._ext[start_of_bracket] != ')' + if not contains_param: + self.gripper_control = ['open', 1] + else: + rest = self._ext[start_of_bracket:] + num = float(rest[:rest.index(')')]) + self.gripper_control = ['open', num] + elif 'close_gripper(' in self._ext: + start_of_bracket = self._ext.index('close_gripper(') + 14 + contains_param = self._ext[start_of_bracket] != ')' + if not contains_param: + self.gripper_control = ['close', 0] + else: + rest = self._ext[start_of_bracket:] + num = float(rest[:rest.index(')')]) + self.gripper_control = ['close', num] + + if ReadCustomDataBlock(waypoint._handle, "ignore_collisions") != None: + self._ignore_collisions = eval(ReadCustomDataBlock(waypoint._handle, "ignore_collisions")) + if ReadCustomDataBlock(waypoint._handle, "linear") != None: + self._linear_only = eval(ReadCustomDataBlock(waypoint._handle, "linear")) + if ReadCustomDataBlock(waypoint._handle, "gripper")!=None: + self.gripper_control = eval(ReadCustomDataBlock(waypoint._handle, "gripper")) + assert self.gripper_control[0] in ["open", "close"], "gripper only can open or close" + assert self.gripper_control[1]<=1 and self.gripper_control[1]>=0, "gripper distance should between 0 and 1" + self.low_level_descriptions = None + if ReadCustomDataBlock(waypoint._handle, "low_level_descriptions")!=None: + self.low_level_descriptions = ReadCustomDataBlock(waypoint._handle, "low_level_descriptions") + self.focus_obj_id = None + if ReadCustomDataBlock(waypoint._handle, "focus_obj_id")!=None: + self.focus_obj_id = eval(ReadCustomDataBlock(waypoint._handle, "focus_obj_id")) + self.focus_obj_name = None + if ReadCustomDataBlock(waypoint._handle,"focus_obj_name")!=None: + self.focus_obj_name = ReadCustomDataBlock(waypoint._handle, "focus_obj_name") + self.waypoint_type = None + if ReadCustomDataBlock(waypoint._handle, "waypoint_type")!=None: + self.waypoint_type = ReadCustomDataBlock(waypoint._handle, "waypoint_type") + + def get_path(self, ignore_collisions=False) -> ArmConfigurationPath: + raise NotImplementedError() + + def get_ext(self) -> str: + return self._ext + + def get_waypoint_object(self) -> Object: + return self._waypoint + + def remove(self) -> None: + self._waypoint.remove() + + def start_of_path(self) -> None: + if self._start_of_path_func is not None: + self._start_of_path_func(self) + + def end_of_path(self) -> None: + if self._end_of_path_func is not None: + self._end_of_path_func(self) + + +class Point(Waypoint): + def __init__(self, waypoint: Object, robot: Robot, ignore_collisions=False, start_of_path_func=None, end_of_path_func=None): + super().__init__(waypoint, robot, ignore_collisions, start_of_path_func, end_of_path_func) + self.pose = waypoint.get_pose() + + def get_path(self, ignore_collisions=False) -> ArmConfigurationPath: + arm = self._robot.arm + if self._linear_only: + path = arm.get_linear_path(self._waypoint.get_position(), + euler=self._waypoint.get_orientation(), + ignore_collisions=(self._ignore_collisions or + ignore_collisions)) + else: + path = arm.get_path(self._waypoint.get_position(), + euler=self._waypoint.get_orientation(), + ignore_collisions=(self._ignore_collisions or + ignore_collisions)) + return path + + +class PredefinedPath(Waypoint): + def __init__(self, waypoint: Object, robot: Robot, ignore_collisions=False, start_of_path_func=None, end_of_path_func=None): + super().__init__(waypoint, robot, ignore_collisions, start_of_path_func, end_of_path_func) + start_pos, start_ori = waypoint.get_pose_on_path(0) + self.start_pose = np.array(start_pos + R.from_euler('zyx', start_ori[::-1]).as_quat().tolist()) + end_pos, end_ori = waypoint.get_pose_on_path(1) + self.end_pose = np.array(end_pos + R.from_euler('zyx', end_ori[::-1]).as_quat().tolist()) + + def get_path(self, ignore_collisions=False) -> ArmConfigurationPath: + arm = self._robot.arm + path = arm.get_path_from_cartesian_path(self._waypoint) + return path diff --git a/easi/tasks/ebmanipulation/vendor/amsolver/const.py b/easi/tasks/ebmanipulation/vendor/amsolver/const.py new file mode 100644 index 0000000..31b5522 --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/amsolver/const.py @@ -0,0 +1,260 @@ +#Copy From the rlbench: https://github.com/stepjam/RLBench + +seen_colors = [ + ('red', (1.0, 0.0, 0.0)), + ('maroon', (0.5, 0.0, 0.0)), + ('lime', (0.0, 1.0, 0.0)), + ('green', (0.0, 0.5, 0.0)), + ('blue', (0.0, 0.0, 1.0)), + ('navy', (0.0, 0.0, 0.5)), + ('yellow', (1.0, 1.0, 0.0)), + ('cyan', (0.0, 1.0, 1.0)), + ('magenta', (1.0, 0.0, 1.0)), + ('silver', (0.75, 0.75, 0.75)), + ('gray', (0.5, 0.5, 0.5)), + ('orange', (1.0, 0.5, 0.0)), + ('olive', (0.5, 0.5, 0.0)), + ('purple', (0.5, 0.0, 0.5)), + ('teal', (0, 0.5, 0.5)), + ('azure', (0.0, 0.5, 1.0)), + ('violet', (0.5, 0.0, 1.0)), + ('rose', (1.0, 0.0, 0.5)), + ('black', (0.0, 0.0, 0.0)), + ('white', (1.0, 1.0, 1.0)), +] +#unseen +unseen_colors = [ + ('brown', (0.65, 0.16, 0.16)), + ('gold', (1.0, 0.65, 0.0)), + ('pink', (1.0, 0.75, 0.8)), + ('chocolate', (0.8, 0.4, 0.12)), + ('coral', (1.0, 0.5, 0.32)) +] + +#seen objects +seen_object_shapes = { + "star":{ + "path":"star/star_normal/star_normal.ttm" + }, + "triangular":{ + "path":"triangular/triangular_normal/triangular_normal.ttm" + }, + "cylinder":{ + "path":"cylinder/cylinder_normal/cylinder_normal.ttm" + }, + "cube":{ + "path":"cube/cube_basic/cube_basic.ttm" + }, + "moon":{ + "path":"moon/moon_normal/moon_normal.ttm" + }, + # "letter_r":{ + # "path":"letters/letter_r/letter_r.ttm" + # }, + # "letter_a":{ + # "path":"letters/letter_a/letter_a.ttm" + # }, + # "letter_v":{ + # "path":"letters/letter_v/letter_v.ttm" + # }, + # "pentagon":{ + # "path":"basic_shapes/pentagon/pentagon.ttm" + # } +} +#unseen objects +unseen_object_shapes = { + "cube":{ + "path":"cube/cube_basic/cube_basic.ttm" + }, + "letter_t":{ + "path":"letters/letter_t/letter_t.ttm" + }, + "cross":{ + "path":"basic_shapes/cross/cross.ttm" + }, + "flower":{ + "path":"basic_shapes/flower/flower.ttm" + } +} + +seen_planes ={ + "rectangle":{ + "path":"basic_shapes/plane_rectangle.ttm", + "directional": True + }, + "round":{ + "path":"basic_shapes/plane_round.ttm", + "directional": False + }, + "star":{ + "path":"basic_shapes/plane_star.ttm", + "directional": False + }, + "triangle":{ + "path":"basic_shapes/plane_triangle.ttm", + "directional": True + } +} + +unseen_planes ={ + "cross":{ + "path":"basic_shapes/plane_cross.ttm", + "directional": True + }, + "flower":{ + "path":"basic_shapes/plane_flower.ttm", + "directional": False + } +} + +seen_sorter_objects = { + "star":{ + "path":"star/star_normal/star_normal.ttm" + }, + "triangular":{ + "path":"triangular/triangular_normal/triangular_normal.ttm" + }, + "cylinder":{ + "path":"cylinder/cylinder_normal/cylinder_normal.ttm" + }, + "cube":{ + "path":"cube/cube_basic/cube_basic.ttm" + } +} + +unseen_sorter_objects = { + "moon":{ + "path":"moon/moon_normal/moon_normal.ttm" + }, + "star":{ + "path":"star/star_normal/star_normal.ttm" + }, + "triangular":{ + "path":"triangular/triangular_normal/triangular_normal.ttm" + }, + "cylinder":{ + "path":"cylinder/cylinder_normal/cylinder_normal.ttm" + }, + "cube":{ + "path":"cube/cube_basic/cube_basic.ttm" + } +} + +seen_drawer_list = [ + { + "path": "drawer/drawer1/drawer1.ttm", + "max_joint": 0.2 + }, + { + "path": "drawer/drawer2/drawer2.ttm", + "max_joint": 0.2 + } +] + +unseen_drawer_list = [ + { + "path": "drawer/drawer3/drawer3.ttm", + "max_joint": 0.3 + } +] + +seen_cabinet_list = [ + { + "path": "cabinet/cabinet6/cabinet6.ttm", + "max_joint": 0.19 + }, + { + "path": "cabinet/cabinet5/cabinet5.ttm", + "max_joint": 0.32 + } +] + +unseen_cabinet_list = [ + { + "path": "cabinet/cabinet3/cabinet3.ttm", + "max_joint": 0.19 + } +] + +seen_door_list = [ + { + "path": "fridge/fridge1/fridge1.ttm" + }, + { + "path": "fridge/fridge2/fridge2.ttm" + }, + { + "path": "microwave/microwave1/microwave1.ttm" + }, + { + "path": "microwave/microwave4/microwave4.ttm" + } +] + +unseen_door_list = [ + { + "path": "microwave/microwave6/microwave6.ttm" + }, + { + "path": "fridge/fridge5/fridge5.ttm" + }, +] + +seen_complex_door_list = [ + { + "path": "door/door1/door1.ttm" + } +] + +unseen_complex_door_list = [ + { + "path": "door/door2/door2.ttm" + } +] + +seen_mug_list = [ + { + "path": "mug/mug1/mug1.ttm" + }, + { + "path": "mug/mug2/mug2.ttm" + }, + { + "path": "mug/mug3/mug3.ttm" + }, + { + "path": "mug/mug4/mug4.ttm" + }, +] + +unseen_mug_list = [ + { + "path": "mug/mug5/mug5.ttm" + }, + { + "path": "mug/mug6/mug6.ttm" + } +] + +colors = seen_colors +object_shapes = seen_object_shapes +planes = seen_planes +sorter_objects = seen_sorter_objects +drawer_list = seen_drawer_list +cabinet_list = seen_cabinet_list +door_list = seen_door_list +mug_list = seen_mug_list +complex_door_list = seen_complex_door_list + +""" +if users want to collect unseen data, please comment the above lines and uncomment the below lines. +colors = unseen_colors +object_shapes = unseen_object_shapes +planes = unseen_planes +sorter_objects = unseen_sorter_objects +drawer_list = unseen_drawer_list +cabinet_list = unseen_cabinet_list +door_list = unseen_door_list +mug_list = unseen_mug_list +complex_door_list = unseen_complex_door_list +""" \ No newline at end of file diff --git a/easi/tasks/ebmanipulation/vendor/amsolver/demo.py b/easi/tasks/ebmanipulation/vendor/amsolver/demo.py new file mode 100644 index 0000000..fd9fd56 --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/amsolver/demo.py @@ -0,0 +1,18 @@ +#Copy From the rlbench: https://github.com/stepjam/RLBench +import numpy as np + + +class Demo(object): + + def __init__(self, observations, random_seed=None): + self._observations = observations + self.random_seed = random_seed + + def __len__(self): + return len(self._observations) + + def __getitem__(self, i): + return self._observations[i] + + def restore_state(self): + np.random.set_state(self.random_seed) diff --git a/easi/tasks/ebmanipulation/vendor/amsolver/environment.py b/easi/tasks/ebmanipulation/vendor/amsolver/environment.py new file mode 100644 index 0000000..b2ea648 --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/amsolver/environment.py @@ -0,0 +1,252 @@ +#Copy From the rlbench: https://github.com/stepjam/RLBench +import pickle + +from pyrep import PyRep +from pyrep.objects import VisionSensor +from pyrep.robots.arms.panda import Panda +from pyrep.robots.arms.jaco import Jaco +from pyrep.robots.arms.mico import Mico +from pyrep.robots.arms.sawyer import Sawyer +from pyrep.robots.arms.ur5 import UR5 +from pyrep.robots.end_effectors.panda_gripper import PandaGripper +from pyrep.robots.end_effectors.jaco_gripper import JacoGripper +from pyrep.robots.end_effectors.mico_gripper import MicoGripper +from pyrep.robots.end_effectors.baxter_gripper import BaxterGripper +from pyrep.robots.end_effectors.robotiq85_gripper import Robotiq85Gripper + +from amsolver import utils +from amsolver.demo import Demo + +from amsolver.sim2real.domain_randomization import RandomizeEvery, \ + VisualRandomizationConfig, DynamicsRandomizationConfig + +from amsolver.sim2real.domain_randomization_scene import DomainRandomizationScene + +from amsolver.backend.scene import Scene +from amsolver.backend.task import Task +from amsolver.backend.const import * +from amsolver.backend.robot import Robot +from os.path import exists, dirname, abspath, join +import importlib +from typing import Type, List +from amsolver.observation_config import ObservationConfig +from amsolver.task_environment import TaskEnvironment +from amsolver.action_modes import ActionMode, ArmActionMode + + +DIR_PATH = dirname(abspath(__file__)) + +# Arms from PyRep need to be modified to include a wrist camera. +# Currently, only the arms/grippers below are supported. +SUPPORTED_ROBOTS = { + 'panda': (Panda, PandaGripper, 7), + 'jaco': (Jaco, JacoGripper, 6), + 'mico': (Mico, MicoGripper, 6), + 'sawyer': (Sawyer, BaxterGripper, 7), + 'ur5': (UR5, Robotiq85Gripper, 6), +} + + +class Environment(object): + """Each environment has a scene.""" + + def __init__(self, action_mode: ActionMode, dataset_root: str='', + obs_config=ObservationConfig(), headless=False, + static_positions: bool=False, + robot_configuration='panda', + randomize_every: RandomizeEvery=None, + frequency: int=1, + visual_randomization_config: VisualRandomizationConfig=None, + dynamics_randomization_config: DynamicsRandomizationConfig=None, + attach_grasped_objects: bool = True + ): + + self._dataset_root = dataset_root + self._action_mode = action_mode + self._obs_config = obs_config + self._headless = headless + self._static_positions = static_positions + self._robot_configuration = robot_configuration.lower() + + self._randomize_every = randomize_every + self._frequency = frequency + self._visual_randomization_config = visual_randomization_config + self._dynamics_randomization_config = dynamics_randomization_config + self._attach_grasped_objects = attach_grasped_objects + + if robot_configuration not in SUPPORTED_ROBOTS.keys(): + raise ValueError('robot_configuration must be one of %s' % + str(SUPPORTED_ROBOTS.keys())) + + if (randomize_every is not None and + visual_randomization_config is None and + dynamics_randomization_config is None): + raise ValueError( + 'If domain randomization is enabled, must supply either ' + 'visual_randomization_config or dynamics_randomization_config') + + self._check_dataset_structure() + + self._pyrep = None + self._robot = None + self._scene = None + self._prev_task = None + + def _set_arm_control_action(self): + self._robot.arm.set_control_loop_enabled(True) + if (self._action_mode.arm == ArmActionMode.ABS_JOINT_VELOCITY or + self._action_mode.arm == ArmActionMode.DELTA_JOINT_VELOCITY): + self._robot.arm.set_control_loop_enabled(False) + self._robot.arm.set_motor_locked_at_zero_velocity(True) + elif (self._action_mode.arm == ArmActionMode.ABS_JOINT_POSITION or + self._action_mode.arm == ArmActionMode.DELTA_JOINT_POSITION or + self._action_mode.arm == ArmActionMode.ABS_EE_POSE_WORLD_FRAME or + self._action_mode.arm == ArmActionMode.DELTA_EE_POSE_WORLD_FRAME or + self._action_mode.arm == ArmActionMode.ABS_EE_POSE_PLAN_WORLD_FRAME or + self._action_mode.arm == ArmActionMode.ABS_EE_POSE_PLAN_WORLD_FRAME_WITH_COLLISION_CHECK or + self._action_mode.arm == ArmActionMode.DELTA_EE_POSE_PLAN_WORLD_FRAME or + self._action_mode.arm == ArmActionMode.EE_POSE_PLAN_EE_FRAME or + self._action_mode.arm == ArmActionMode.EE_POSE_EE_FRAME): + self._robot.arm.set_control_loop_enabled(True) + elif (self._action_mode.arm == ArmActionMode.ABS_JOINT_TORQUE or + self._action_mode.arm == ArmActionMode.DELTA_JOINT_TORQUE): + self._robot.arm.set_control_loop_enabled(False) + else: + raise RuntimeError('Unrecognised action mode.') + + def _check_dataset_structure(self): + if len(self._dataset_root) > 0 and not exists(self._dataset_root): + raise RuntimeError( + 'Data set root does not exists: %s' % self._dataset_root) + + def _string_to_task(self, task_name: str): + task_name = task_name.replace('.py', '') + try: + class_name = ''.join( + [w[0].upper() + w[1:] for w in task_name.split('_')]) + mod = importlib.import_module("amsolver.tasks.%s" % task_name) + except Exception as e: + raise RuntimeError( + 'Tried to interpret %s as a task, but failed. Only valid tasks ' + 'should belong in the tasks/ folder' % task_name) from e + return getattr(mod, class_name) + + def launch(self): + if self._pyrep is not None: + raise RuntimeError('Already called launch!') + self._pyrep = PyRep() + self._pyrep.launch(join(DIR_PATH, TTT_FILE), headless=self._headless) + + arm_class, gripper_class, _ = SUPPORTED_ROBOTS[ + self._robot_configuration] + + # We assume the panda is already loaded in the scene. + if self._robot_configuration != 'panda': + # Remove the panda from the scene + panda_arm = Panda() + panda_pos = panda_arm.get_position() + panda_arm.remove() + arm_path = join(DIR_PATH, + 'robot_ttms', self._robot_configuration + '.ttm') + self._pyrep.import_model(arm_path) + arm, gripper = arm_class(), gripper_class() + arm.set_position(panda_pos) + else: + arm, gripper = arm_class(), gripper_class() + + self._robot = Robot(arm, gripper) + if self._randomize_every is None: + self._scene = Scene(self._pyrep, self._robot, self._obs_config) + else: + self._scene = DomainRandomizationScene( + self._pyrep, self._robot, self._obs_config, + self._randomize_every, self._frequency, + self._visual_randomization_config, + self._dynamics_randomization_config) + + self._set_arm_control_action() + + def shutdown(self): + if self._pyrep is not None: + self._pyrep.shutdown() + self._pyrep = None + + def get_task(self, task_class: Type[Task]) -> TaskEnvironment: + + # If user hasn't called launch, implicitly call it. + if self._pyrep is None: + self.launch() + + self._scene.unload() + task = task_class(self._pyrep, self._robot) + self._prev_task = task + return TaskEnvironment( + self._pyrep, self._robot, self._scene, task, + self._action_mode, self._dataset_root, self._obs_config, + self._static_positions, self._attach_grasped_objects) + + @property + def action_size(self): + arm_action_size = 0 + gripper_action_size = 1 # Only one gripper style atm + if (self._action_mode.arm == ArmActionMode.ABS_JOINT_VELOCITY or + self._action_mode.arm == ArmActionMode.DELTA_JOINT_VELOCITY or + self._action_mode.arm == ArmActionMode.ABS_JOINT_POSITION or + self._action_mode.arm == ArmActionMode.DELTA_JOINT_POSITION or + self._action_mode.arm == ArmActionMode.ABS_JOINT_TORQUE or + self._action_mode.arm == ArmActionMode.DELTA_JOINT_TORQUE): + arm_action_size = SUPPORTED_ROBOTS[self._robot_configuration][2] + elif (self._action_mode.arm == ArmActionMode.ABS_EE_POSE_WORLD_FRAME or + self._action_mode.arm == ArmActionMode.DELTA_EE_POSE_WORLD_FRAME or + self._action_mode.arm == ArmActionMode.ABS_EE_POSE_PLAN_WORLD_FRAME or + self._action_mode.arm == ArmActionMode.ABS_EE_POSE_PLAN_WORLD_FRAME_WITH_COLLISION_CHECK or + self._action_mode.arm == ArmActionMode.DELTA_EE_POSE_PLAN_WORLD_FRAME or + self._action_mode.arm == ArmActionMode.EE_POSE_PLAN_EE_FRAME or + self._action_mode.arm == ArmActionMode.EE_POSE_EE_FRAME): + arm_action_size = 7 # pose is always 7 + return arm_action_size + gripper_action_size + + def get_demos(self, task_name: str, amount: int, + variation_number=0, + image_paths=False, + random_selection: bool = True, + from_episode_number: int = 0) -> List[Demo]: + if self._dataset_root is None or len(self._dataset_root) == 0: + raise RuntimeError( + "Can't ask for a stored demo when no dataset root provided.") + demos = utils.get_stored_demos( + amount, image_paths, self._dataset_root, variation_number, + task_name, self._obs_config, random_selection, from_episode_number) + return demos + + def get_scene_data(self) -> dict: + """Get the data of various scene/camera information. + + This temporarily starts the simulator in headless mode. + + :return: A dictionary containing scene data. + """ + def _get_cam_info(cam: VisionSensor): + if not cam.still_exists(): + return None + intrinsics = cam.get_intrinsic_matrix() + return dict( + intrinsics=intrinsics, + near_plane=cam.get_near_clipping_plane(), + far_plane=cam.get_far_clipping_plane(), + extrinsics=cam.get_matrix()) + headless = self._headless + self._headless = True + self.launch() + d = dict( + left_shoulder_camera=_get_cam_info( + self._scene._cam_over_shoulder_left), + right_shoulder_camera=_get_cam_info( + self._scene._cam_over_shoulder_right), + front_camera=_get_cam_info(self._scene._cam_front), + wrist_camera=_get_cam_info(self._scene._cam_wrist), + overhead_camera=_get_cam_info(self._scene._cam_overhead) + ) + self.shutdown() + self._headless = headless + return d diff --git a/easi/tasks/ebmanipulation/vendor/amsolver/gym/__init__.py b/easi/tasks/ebmanipulation/vendor/amsolver/gym/__init__.py new file mode 100644 index 0000000..97866d7 --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/amsolver/gym/__init__.py @@ -0,0 +1,28 @@ +from gymnasium.envs.registration import register +import amsolver.backend.task as task +import os +from amsolver.utils import name_to_task_class +from amsolver.gym.vlmbench_env import VLMBenchEnv + +TASKS = [t for t in os.listdir(task.TASKS_PATH) + if t != '__init__.py' and t.endswith('.py')] + +for task_file in TASKS: + task_name = task_file.split('.py')[0] + task_class = name_to_task_class(task_name) + register( + id='%s-state-v0' % task_name, + entry_point='amsolver.gym:VLMBenchEnv', + kwargs={ + 'task_class': task_class, + 'observation_mode': 'state' + } + ) + register( + id='%s-vision-v0' % task_name, + entry_point='amsolver.gym:VLMBenchEnv', + kwargs={ + 'task_class': task_class, + 'observation_mode': 'vision' + } + ) diff --git a/easi/tasks/ebmanipulation/vendor/amsolver/gym/vlmbench_env.py b/easi/tasks/ebmanipulation/vendor/amsolver/gym/vlmbench_env.py new file mode 100644 index 0000000..ac508c0 --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/amsolver/gym/vlmbench_env.py @@ -0,0 +1,106 @@ +from typing import Union, Dict, Tuple + +import gymnasium as gym +from gymnasium import spaces +from pyrep.const import RenderMode +from pyrep.objects.dummy import Dummy +from pyrep.objects.vision_sensor import VisionSensor +from amsolver.environment import Environment +from amsolver.action_modes import ArmActionMode, ActionMode +from amsolver.observation_config import ObservationConfig +import numpy as np + + +class VLMBenchEnv(gym.Env): + """An gym wrapper for VLMbench. Copy from RLbenchEnv.""" + + metadata = {'render.modes': ['human', 'rgb_array']} + + def __init__(self, task_class, observation_mode='state', + render_mode: Union[None, str] = None): + self._observation_mode = observation_mode + self._render_mode = render_mode + obs_config = ObservationConfig() + if observation_mode == 'state': + obs_config.set_all_high_dim(False) + obs_config.set_all_low_dim(True) + elif observation_mode == 'vision': + obs_config.set_all(True) + else: + raise ValueError( + 'Unrecognised observation_mode: %s.' % observation_mode) + action_mode = ActionMode(ArmActionMode.ABS_JOINT_VELOCITY) + self.env = Environment( + action_mode, obs_config=obs_config, headless=True) + self.env.launch() + self.task = self.env.get_task(task_class) + + desc, obs = self.task.reset() + + self.action_space = spaces.Box( + low=-1.0, high=1.0, shape=(self.env.action_size,)) + + if observation_mode == 'state': + self.observation_space = spaces.Box( + low=-np.inf, high=np.inf, shape=obs.get_low_dim_data().shape) + elif observation_mode == 'vision': + self.observation_space = spaces.Dict({ + "state": spaces.Box( + low=-np.inf, high=np.inf, + shape=obs.get_low_dim_data().shape), + "left_shoulder_rgb": spaces.Box( + low=0, high=1, shape=obs.left_shoulder_rgb.shape), + "right_shoulder_rgb": spaces.Box( + low=0, high=1, shape=obs.right_shoulder_rgb.shape), + "wrist_rgb": spaces.Box( + low=0, high=1, shape=obs.wrist_rgb.shape), + "front_rgb": spaces.Box( + low=0, high=1, shape=obs.front_rgb.shape), + }) + + if render_mode is not None: + # Add the camera to the scene + cam_placeholder = Dummy('cam_cinematic_placeholder') + self._gym_cam = VisionSensor.create([640, 360]) + self._gym_cam.set_pose(cam_placeholder.get_pose()) + if render_mode == 'human': + self._gym_cam.set_render_mode(RenderMode.OPENGL3_WINDOWED) + else: + self._gym_cam.set_render_mode(RenderMode.OPENGL3) + + def _extract_obs(self, obs) -> Dict[str, np.ndarray]: + if self._observation_mode == 'state': + return obs.get_low_dim_data() + elif self._observation_mode == 'vision': + return { + "state": obs.get_low_dim_data(), + "left_shoulder_rgb": obs.left_shoulder_rgb, + "right_shoulder_rgb": obs.right_shoulder_rgb, + "wrist_rgb": obs.wrist_rgb, + "front_rgb": obs.front_rgb, + } + + def render(self, mode='human') -> Union[None, np.ndarray]: + if mode != self._render_mode: + raise ValueError( + 'The render mode must match the render mode selected in the ' + 'constructor. \nI.e. if you want "human" render mode, then ' + 'create the env by calling: ' + 'gym.make("reach_target-state-v0", render_mode="human").\n' + 'You passed in mode %s, but expected %s.' % ( + mode, self._render_mode)) + if mode == 'rgb_array': + return self._gym_cam.capture_rgb() + + def reset(self) -> Dict[str, np.ndarray]: + descriptions, obs = self.task.reset() + obs_info = self._extract_obs(obs) + obs_info['descriptions'] = descriptions + return obs_info + + def step(self, action) -> Tuple[Dict[str, np.ndarray], float, bool, dict]: + obs, reward, terminate = self.task.step(action) + return self._extract_obs(obs), reward, terminate, {} + + def close(self) -> None: + self.env.shutdown() diff --git a/easi/tasks/ebmanipulation/vendor/amsolver/noise_model.py b/easi/tasks/ebmanipulation/vendor/amsolver/noise_model.py new file mode 100644 index 0000000..8af5a31 --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/amsolver/noise_model.py @@ -0,0 +1,29 @@ +#Copy From the rlbench: https://github.com/stepjam/RLBench +from typing import Tuple + +import numpy as np + + +class NoiseModel(object): + + def apply(self, val: np.ndarray) -> np.ndarray: + raise NotImplementedError() + + +class Identity(NoiseModel): + def apply(self, val: np.ndarray): + return val + + +class GaussianNoise(NoiseModel): + + def __init__(self, variance, min_max_clip: Tuple[float, float]=None): + self._variance = variance + self._min_max_clip = min_max_clip + + def apply(self, val: np.ndarray): + val += np.random.normal(0.0, scale=self._variance, size=val.shape) + if self._min_max_clip is not None: + val = np.clip(val, *self._min_max_clip) + return val + diff --git a/easi/tasks/ebmanipulation/vendor/amsolver/observation_config.py b/easi/tasks/ebmanipulation/vendor/amsolver/observation_config.py new file mode 100644 index 0000000..10a5076 --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/amsolver/observation_config.py @@ -0,0 +1,140 @@ +#Copy From the rlbench: https://github.com/stepjam/RLBench +from pyrep.const import RenderMode +from amsolver.noise_model import NoiseModel, Identity + + +class CameraConfig(object): + def __init__(self, + rgb=True, + rgb_noise: NoiseModel=Identity(), + depth=True, + depth_noise: NoiseModel=Identity(), + point_cloud=True, + mask=True, + image_size=(128, 128), + render_mode=RenderMode.OPENGL3, + masks_as_one_channel=True, + depth_in_meters=False): + self.rgb = rgb + self.rgb_noise = rgb_noise + self.depth = depth + self.depth_noise = depth_noise + self.point_cloud = point_cloud + self.mask = mask + self.image_size = image_size + self.render_mode = render_mode + self.masks_as_one_channel = masks_as_one_channel + self.depth_in_meters = depth_in_meters + + def set_all(self, value: bool): + self.rgb = value + self.depth = value + self.point_cloud = value + self.mask = value + + def __eq__(self, o: object) -> bool: + is_equal = self.image_size == o.image_size + for attribute in self.__dict__: + if type(self.__dict__[attribute]) == bool: + if self.__dict__[attribute] != o.__dict__[attribute]: + is_equal = False + return is_equal + + +class ObservationConfig(object): + def __init__(self, + left_shoulder_camera: CameraConfig = None, + right_shoulder_camera: CameraConfig = None, + overhead_camera: CameraConfig = None, + wrist_camera: CameraConfig = None, + front_camera: CameraConfig = None, + joint_velocities=True, + joint_velocities_noise: NoiseModel=Identity(), + joint_positions=True, + joint_positions_noise: NoiseModel=Identity(), + joint_forces=True, + joint_forces_noise: NoiseModel=Identity(), + gripper_open=True, + gripper_pose=True, + gripper_matrix=False, + gripper_joint_positions=False, + gripper_touch_forces=False, + wrist_camera_matrix=False, + record_gripper_closing=False, + task_low_dim_state=False, + ): + self.left_shoulder_camera = ( + CameraConfig() if left_shoulder_camera is None + else left_shoulder_camera) + self.right_shoulder_camera = ( + CameraConfig() if right_shoulder_camera is None + else right_shoulder_camera) + self.overhead_camera = ( + CameraConfig() if overhead_camera is None + else overhead_camera) + self.wrist_camera = ( + CameraConfig() if wrist_camera is None + else wrist_camera) + self.front_camera = ( + CameraConfig() if front_camera is None + else front_camera) + self.joint_velocities = joint_velocities + self.joint_velocities_noise = joint_velocities_noise + self.joint_positions = joint_positions + self.joint_positions_noise = joint_positions_noise + self.joint_forces = joint_forces + self.joint_forces_noise = joint_forces_noise + self.gripper_open = gripper_open + self.gripper_pose = gripper_pose + self.gripper_matrix = gripper_matrix + self.gripper_joint_positions = gripper_joint_positions + self.gripper_touch_forces = gripper_touch_forces + self.wrist_camera_matrix = wrist_camera_matrix + self.record_gripper_closing = record_gripper_closing + self.task_low_dim_state = task_low_dim_state + + def set_all(self, value: bool): + self.set_all_high_dim(value) + self.set_all_low_dim(value) + + def set_all_high_dim(self, value: bool): + self.left_shoulder_camera.set_all(value) + self.right_shoulder_camera.set_all(value) + self.overhead_camera.set_all(value) + self.wrist_camera.set_all(value) + self.front_camera.set_all(value) + + def set_all_low_dim(self, value: bool): + self.joint_velocities = value + self.joint_positions = value + self.joint_forces = value + self.gripper_open = value + self.gripper_pose = value + self.gripper_matrix = value + self.gripper_joint_positions = value + self.gripper_touch_forces = value + self.wrist_camera_matrix = value + self.task_low_dim_state = value + + def set_image_size(self, image_size): + self.left_shoulder_camera.image_size = image_size + self.right_shoulder_camera.image_size = image_size + self.overhead_camera.image_size = image_size + self.wrist_camera.image_size = image_size + self.front_camera.image_size = image_size + + def set_render_mode(self, render_mode): + self.left_shoulder_camera.render_mode = render_mode + self.right_shoulder_camera.render_mode = render_mode + self.overhead_camera.render_mode = render_mode + self.wrist_camera.render_mode = render_mode + self.front_camera.render_mode = render_mode + + def __eq__(self, o: object) -> bool: + is_equal = True + for attribute in self.__dict__: + if 'noise' in attribute: + continue + if self.__dict__[attribute] != o.__dict__[attribute]: + is_equal = False + return is_equal diff --git 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zfcos3qyauNRDdHq8u+IpuH=iSJg`FsL|~{UPHTF#jROGz_YsP}?U;`L1n;j4?+f}_ zlPwv%8B91|BkB9_FqAx}oCtP!=7{}fo-o4{{W*k;ogiX&w@_Z13VMv%RQ+5am^m~%kTTeYzmi+-DzpDr8 zf@?5&#p1UqIAdFnh=ccL?jHYR(7^v$EPj8Rw8#v=vCuE-^&I_tzk~aJphN?2t1zk6 It5;UKxb9T@v;Y7A literal 0 HcmV?d00001 diff --git a/easi/tasks/ebmanipulation/vendor/amsolver/sim2real/domain_randomization.py b/easi/tasks/ebmanipulation/vendor/amsolver/sim2real/domain_randomization.py new file mode 100644 index 0000000..8a16481 --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/amsolver/sim2real/domain_randomization.py @@ -0,0 +1,82 @@ +#Copy From the rlbench: https://github.com/stepjam/RLBench +from enum import Enum +from typing import List +import numpy as np +import os +import glob + + +class RandomizeEvery(Enum): + EPISODE = 0 + VARIATION = 1 + TRANSITION = 1 + + +class Distributions(object): + + def apply(self, val: np.ndarray) -> np.ndarray: + pass + + +class Gaussian(Distributions): + + def __init__(self, variance): + self._variance = variance + + def apply(self, val: np.ndarray): + return np.random.normal(val, self._variance) + + +class Uniform(Distributions): + + def __init__(self, min, max): + self._min = min + self._max = max + + def apply(self, val: np.ndarray): + return np.random.uniform(self._min, self._max, val.shape) + + +EXTENSIONS = ['*.jpg', '*.png'] + + +class RandomizationConfig(object): + + def __init__(self, + whitelist: List[str]=None, + blacklist: List[str]=None): + self.whitelist = whitelist + self.blacklist = [] if blacklist is None else blacklist + + def should_randomize(self, obj_name: str): + return ((self.whitelist is None and len(self.blacklist) == 0) or + (self.whitelist is not None and obj_name in self.whitelist) or + (obj_name not in self.blacklist)) + + +class DynamicsRandomizationConfig(RandomizationConfig): + pass + + +class VisualRandomizationConfig(RandomizationConfig): + + def __init__(self, + image_directory: str, + whitelist: List[str]=None, + blacklist: List[str]=None): + super().__init__(whitelist, blacklist) + self._image_directory = image_directory + if not os.path.exists(image_directory): + raise NotADirectoryError( + 'The supplied image directory (%s) does not exist!' % + image_directory) + self._imgs = np.array([glob.glob( + os.path.join(image_directory, e)) for e in EXTENSIONS]) + self._imgs = np.concatenate(self._imgs) + if len(self._imgs) == 0: + raise RuntimeError( + 'The supplied image directory (%s) does not have any images!' % + image_directory) + + def sample(self, samples: int) -> np.ndarray: + return np.random.choice(self._imgs, samples) diff --git a/easi/tasks/ebmanipulation/vendor/amsolver/sim2real/domain_randomization_environment.py b/easi/tasks/ebmanipulation/vendor/amsolver/sim2real/domain_randomization_environment.py new file mode 100644 index 0000000..15f4f71 --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/amsolver/sim2real/domain_randomization_environment.py @@ -0,0 +1,51 @@ +#Copy From the rlbench: https://github.com/stepjam/RLBench +from os.path import join +from pyrep import PyRep +from pyrep.objects.dummy import Dummy +from pyrep.objects.shape import Shape +from pyrep.robots.arms.panda import Panda +from pyrep.robots.end_effectors.panda_gripper import PandaGripper + +from amsolver.action_modes import ActionMode +from amsolver.backend.const import * +from amsolver.environment import Environment +from amsolver.environment import DIR_PATH +from amsolver.observation_config import ObservationConfig +from amsolver.backend.robot import Robot +from amsolver.sim2real.domain_randomization import RandomizeEvery +from amsolver.sim2real.domain_randomization_scene import DomainRandomizationScene + + +class DomainRandomizationEnvironment(Environment): + """Each environment has a scene.""" + + def __init__(self, + action_mode: ActionMode, dataset_root='', + obs_config=ObservationConfig(), headless=False, + static_positions: bool=False, + randomize_every: RandomizeEvery=RandomizeEvery.EPISODE, + frequency: int=1, + visual_randomization_config=None, + dynamics_randomization_config=None): + super().__init__( + action_mode, dataset_root, obs_config, headless, static_positions) + self._randomize_every = randomize_every + self._frequency = frequency + self._visual_rand_config = visual_randomization_config + self._dynamics_rand_config = dynamics_randomization_config + + def launch(self): + if self._pyrep is not None: + raise RuntimeError('Already called launch!') + self._pyrep = PyRep() + self._pyrep.launch(join(DIR_PATH, TTT_FILE), headless=self._headless) + self._pyrep.set_simulation_timestep(0.005) + + self._robot = Robot(Panda(), PandaGripper()) + self._scene = DomainRandomizationScene( + self._pyrep, self._robot, self._obs_config, self._randomize_every, + self._frequency,self._visual_rand_config, self._dynamics_rand_config + ) + self._set_arm_control_action() + # Raise the domain randomized floor. + Shape('Floor').set_position(Dummy('FloorAnchor').get_position()) diff --git a/easi/tasks/ebmanipulation/vendor/amsolver/sim2real/domain_randomization_scene.py b/easi/tasks/ebmanipulation/vendor/amsolver/sim2real/domain_randomization_scene.py new file mode 100644 index 0000000..ed670c7 --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/amsolver/sim2real/domain_randomization_scene.py @@ -0,0 +1,97 @@ +#Copy From the rlbench: https://github.com/stepjam/RLBench +from typing import List +from pyrep import PyRep +from pyrep.const import ObjectType, TextureMappingMode +from pyrep.objects.shape import Shape + +from amsolver.backend.scene import Scene +from amsolver.observation_config import ObservationConfig +from amsolver.backend.robot import Robot +from amsolver.sim2real.domain_randomization import RandomizeEvery + +SCENE_OBJECTS = ['Floor', 'Roof', 'Wall1', 'Wall2', 'Wall3', 'Wall4', + 'diningTable_visible'] + +TEX_KWARGS = { + 'mapping_mode': TextureMappingMode.PLANE, + 'repeat_along_u': True, + 'repeat_along_v': True, +} + + +class DomainRandomizationScene(Scene): + + def __init__(self, pyrep: PyRep, robot: Robot, + obs_config: ObservationConfig=ObservationConfig(), + randomize_every: RandomizeEvery=RandomizeEvery.EPISODE, + frequency: int=1, + visual_randomization_config=None, + dynamics_randomization_config=None): + super().__init__(pyrep, robot, obs_config) + self._randomize_every = randomize_every + self._frequency = frequency + self._visual_rand_config = visual_randomization_config + self._dynamics_rand_config = dynamics_randomization_config + self._previous_index = -1 + self._count = 0 + + if self._dynamics_rand_config is not None: + raise NotImplementedError( + 'Dynamics randomization coming soon! ' + 'Only visual randomization available.') + + self._scene_objects = [Shape(name) for name in SCENE_OBJECTS] + self._scene_objects += self._robot.arm.get_visuals() + self._scene_objects += self._robot.gripper.get_visuals() + if self._visual_rand_config is not None: + # Make the floor plane renderable (to cover old floor) + self._scene_objects[0].set_renderable(True) + + def _should_randomize_episode(self, index: int): + rand = self._count % self._frequency == 0 or self._count == 0 + if self._randomize_every == RandomizeEvery.VARIATION: + if self._previous_index != index: + self._previous_index = index + self._count += 1 + elif self._randomize_every == RandomizeEvery.EPISODE: + self._count += 1 + return rand + + def _randomize(self): + tree = self._active_task.get_base().get_objects_in_tree( + ObjectType.SHAPE) + tree = [Shape(obj.get_handle()) for obj in tree + self._scene_objects] + if self._visual_rand_config is not None: + files = self._visual_rand_config.sample(len(tree)) + for file, obj in zip(files, tree): + if self._visual_rand_config.should_randomize(obj.get_name()): + text_ob, texture = self._pyrep.create_texture(file) + try: + obj.set_texture(texture, **TEX_KWARGS) + except RuntimeError: + ungrouped = obj.ungroup() + for o in ungrouped: + o.set_texture(texture, **TEX_KWARGS) + self._pyrep.group_objects(ungrouped) + text_ob.remove() + + def init_task(self) -> None: + super().init_task() + + def init_episode(self, index: int, *args, **kwargs) -> List[str]: + ret = super().init_episode(index, *args, **kwargs) + if (self._randomize_every != RandomizeEvery.TRANSITION and + self._should_randomize_episode(index)): + self._randomize() + self._pyrep.step() # Need to step to apply textures + return ret + + def step(self): + if self._randomize_every == RandomizeEvery.TRANSITION: + if self._count % self._frequency == 0 or self._count == 0 : + self._randomize() + self._count += 1 + super().step() + + def reset(self) -> None: + return super().reset() diff --git 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import ObjectType, ConfigurationPathAlgorithms +from pyrep.errors import IKError +from pyrep.objects import Dummy, Object + +from amsolver import utils +from amsolver.action_modes import ArmActionMode, ActionMode +from amsolver.backend.exceptions import BoundaryError, WaypointError +from amsolver.backend.observation import Observation +from amsolver.backend.robot import Robot +from amsolver.backend.scene import Scene +from amsolver.backend.task import Task +from amsolver.backend.utils import execute_path +from amsolver.demo import Demo +from amsolver.observation_config import ObservationConfig +from scipy.spatial.transform import Rotation as R + +_TORQUE_MAX_VEL = 9999 +_DT = 0.05 +_MAX_RESET_ATTEMPTS = 40 +_MAX_DEMO_ATTEMPTS = 10 +TTMS_FOLDER = 'embodiedbench/envs/eb_manipulation/' + +class InvalidActionError(Exception): + pass + + +class TaskEnvironmentError(Exception): + pass + +class TaskConfigs(object): + def __init__(self) -> None: + pass + +class TaskEnvironment(object): + + def __init__(self, pyrep: PyRep, robot: Robot, scene: Scene, task: Task, + action_mode: ActionMode, dataset_root: str, + obs_config: ObservationConfig, + static_positions: bool = False, + attach_grasped_objects: bool = True): + self._pyrep = pyrep + self._robot = robot + self._scene = scene + self._task = task + self._variation_number = 0 + self._action_mode = action_mode + self._dataset_root = dataset_root + self._obs_config = obs_config + self._static_positions = static_positions + self._attach_grasped_objects = attach_grasped_objects + self._reset_called = False + self._prev_ee_velocity = None + self._enable_path_observations = False + tasks_folder = self._task.__module__.split('.')[0] + ttms_folder = TTMS_FOLDER + tasks_folder+'/task_ttms' # change to absolute path to vlmbench folder + # ttms_folder = './'+tasks_folder+'/task_ttms' + self._scene.load(self._task, ttms_folder=ttms_folder) + self._pyrep.start() + self._robot_shapes = self._robot.arm.get_objects_in_tree( + object_type=ObjectType.SHAPE) + + def get_name(self) -> str: + return self._task.get_name() + + def sample_variation(self) -> int: + self._variation_number = np.random.randint( + 0, self._task.variation_count()) + return self._variation_number + + def set_variation(self, v: int) -> None: + if v >= self.variation_count(): + raise TaskEnvironmentError( + 'Requested variation %d, but there are only %d variations.' % ( + v, self.variation_count())) + self._variation_number = v + + def variation_count(self) -> int: + return self._task.variation_count() + + def reset(self) -> Tuple[List[str], Observation]: + self._scene.reset() + try: + ctr_loop = self._robot.arm.joints[0].is_control_loop_enabled() + self._robot.arm.set_control_loop_enabled(True) + desc = self._scene.init_episode( + self._variation_number, max_attempts=_MAX_RESET_ATTEMPTS, + randomly_place=not self._static_positions) + self._robot.arm.set_control_loop_enabled(ctr_loop) + except (BoundaryError, WaypointError) as e: + raise TaskEnvironmentError( + 'Could not place the task %s in the scene. This should not ' + 'happen, please raise an issues on this task.' + % self._task.get_name()) from e + + self._reset_called = True + # Returns a list of descriptions and the first observation + return desc, self._scene.get_observation() + + def get_observation(self) -> Observation: + return self._scene.get_observation() + + def _assert_action_space(self, action, expected_shape): + if np.shape(action) != expected_shape: + raise RuntimeError( + 'Expected the action shape to be: %s, but was shape: %s' % ( + str(expected_shape), str(np.shape(action)))) + + def _assert_unit_quaternion(self, quat): + if not np.isclose(np.linalg.norm(quat), 1.0): + raise RuntimeError('Action contained non unit quaternion!') + + def _torque_action(self, action): + self._robot.arm.set_joint_target_velocities( + [(_TORQUE_MAX_VEL if t < 0 else -_TORQUE_MAX_VEL) + for t in action]) + self._robot.arm.set_joint_forces(np.abs(action)) + + def _ee_action(self, action, relative_to=None): + self._assert_unit_quaternion(action[3:]) + try: + joint_positions = self._robot.arm.solve_ik_via_jacobian( + action[:3], quaternion=action[3:], relative_to=relative_to) + self._robot.arm.set_joint_target_positions(joint_positions) + except IKError as e: + raise InvalidActionError( + 'Could not perform IK via Jacobian. This is because the current' + ' end-effector pose is too far from the given target pose. ' + 'Try limiting your action space, or sapping to an alternative ' + 'action mode, e.g. ABS_EE_POSE_PLAN_WORLD_FRAME') from e + done = False + prev_values = None + # Move until reached target joint positions or until we stop moving + # (e.g. when we collide wth something) + while not done: + self._scene.step() + cur_positions = self._robot.arm.get_joint_positions() + reached = np.allclose(cur_positions, joint_positions, atol=0.01) + not_moving = False + if prev_values is not None: + not_moving = np.allclose( + cur_positions, prev_values, atol=0.001) + prev_values = cur_positions + done = reached or not_moving + + def _path_action_get_path(self, action, collision_checking, relative_to): + try: + path = self._robot.arm.get_path( + action[:3], quaternion=action[3:], + ignore_collisions=not collision_checking, + relative_to=relative_to, + ) + return path + except IKError as e: + raise InvalidActionError('Could not find a path.') from e + + def _path_action(self, action, collision_checking=False, relative_to=None, recorder=None): + self._assert_unit_quaternion(action[3:]) + # Check if the target is in the workspace; if not, then quick reject + # Only checks position, not rotation + pos_to_check = action[:3] + if relative_to is not None: + self._scene.target_workspace_check.set_position( + pos_to_check, relative_to) + pos_to_check = self._scene.target_workspace_check.get_position() + valid = self._scene.check_target_in_workspace(pos_to_check) + if not valid: + raise InvalidActionError('Target is outside of workspace.') + + observations = [] + done = False + success_in_path = [] + if collision_checking: + # First check if we are colliding with anything + colliding = self._robot.arm.check_arm_collision() + if colliding: + # Disable collisions with the objects that we are colliding with + grasped_objects = self._robot.gripper.get_grasped_objects() + colliding_shapes = [s for s in self._pyrep.get_objects_in_tree( + object_type=ObjectType.SHAPE) if ( + s.is_collidable() and + s not in self._robot_shapes and + s not in grasped_objects and + self._robot.arm.check_arm_collision(s))] + [s.set_collidable(False) for s in colliding_shapes] + path = self._path_action_get_path( + action, collision_checking, relative_to) + [s.set_collidable(True) for s in colliding_shapes] + # Only run this path until we are no longer colliding + while not done: + done = path.step() + self._scene.step() + if self._enable_path_observations: + observations.append(self._scene.get_observation()) + if recorder is not None: + recorder.take_snap() + colliding = self._robot.arm.check_arm_collision() + if not colliding: + break + success, terminate = self._task.success() + # If the task succeeds while traversing path, then break early + if success: + done = True + break + if not done: + path = self._path_action_get_path( + action, collision_checking, relative_to) + small_step = 0 + while not done: + done = path.step() + self._scene.step() + if self._enable_path_observations: + observations.append(self._scene.get_observation()) + if recorder is not None: + recorder.take_snap() + success, terminate = self._task.success() + # If the task succeeds while traversing path, then break early + # if success: + # break + if success: + success_in_path.append(small_step) + small_step += 1 + + return observations, success_in_path + + def step(self, action, collision_checking=None, use_auto_move=True, recorder = None, need_grasp_obj = None) -> Tuple[Observation, int, bool]: + # returns observation, reward, done, info + if not self._reset_called: + raise RuntimeError( + "Call 'reset' before calling 'step' on a task.") + + # action should contain 1 extra value for gripper open close state + arm_action = np.array(action[:-1]) + ee_action = action[-1] + + if 0.0 > ee_action > 1.0: + raise ValueError('Gripper action expected to be within 0 and 1.') + + # Discretize the gripper action + open_condition = all(x > 0.9 for x in self._robot.gripper.get_open_amount()) + open_condition &= (len(self._robot.gripper.get_grasped_objects())==0) + current_ee = 1.0 if open_condition else 0.0 + + if ee_action > 0.5: + ee_action = 1.0 + elif ee_action < 0.5: + ee_action = 0.0 + + success_in_path= [] + if self._action_mode.arm == ArmActionMode.ABS_JOINT_VELOCITY: + + self._assert_action_space(arm_action, + (len(self._robot.arm.joints),)) + self._robot.arm.set_joint_target_velocities(arm_action) + self._scene.step() + self._robot.arm.set_joint_target_velocities( + np.zeros_like(arm_action)) + + elif self._action_mode.arm == ArmActionMode.DELTA_JOINT_VELOCITY: + + self._assert_action_space(arm_action, + (len(self._robot.arm.joints),)) + cur = np.array(self._robot.arm.get_joint_velocities()) + self._robot.arm.set_joint_target_velocities(cur + arm_action) + self._scene.step() + self._robot.arm.set_joint_target_velocities( + np.zeros_like(arm_action)) + + elif self._action_mode.arm == ArmActionMode.ABS_JOINT_POSITION: + + self._assert_action_space(arm_action, + (len(self._robot.arm.joints),)) + self._robot.arm.set_joint_target_positions(arm_action) + self._scene.step() + self._robot.arm.set_joint_target_positions( + self._robot.arm.get_joint_positions()) + + elif self._action_mode.arm == ArmActionMode.DELTA_JOINT_POSITION: + + self._assert_action_space(arm_action, + (len(self._robot.arm.joints),)) + cur = np.array(self._robot.arm.get_joint_positions()) + self._robot.arm.set_joint_target_positions(cur + arm_action) + self._scene.step() + self._robot.arm.set_joint_target_positions( + self._robot.arm.get_joint_positions()) + + elif self._action_mode.arm == ArmActionMode.ABS_JOINT_TORQUE: + + self._assert_action_space( + arm_action, (len(self._robot.arm.joints),)) + self._torque_action(arm_action) + self._scene.step() + self._torque_action(self._robot.arm.get_joint_forces()) + self._robot.arm.set_joint_target_velocities( + np.zeros_like(arm_action)) + + elif self._action_mode.arm == ArmActionMode.DELTA_JOINT_TORQUE: + + cur = np.array(self._robot.arm.get_joint_forces()) + new_action = cur + arm_action + self._torque_action(new_action) + self._scene.step() + self._torque_action(self._robot.arm.get_joint_forces()) + self._robot.arm.set_joint_target_velocities( + np.zeros_like(arm_action)) + + elif self._action_mode.arm == ArmActionMode.ABS_EE_POSE_WORLD_FRAME: + + self._assert_action_space(arm_action, (7,)) + self._ee_action(list(arm_action)) + + elif self._action_mode.arm == ArmActionMode.ABS_EE_POSE_PLAN_WORLD_FRAME: + + self._assert_action_space(arm_action, (7,)) + pass_this_step = False + # if current_ee == 1.0 and ee_action == 0.0: + if current_ee != ee_action and use_auto_move: + obs = self._scene.get_observation() + _, new_arm_action = self.auto_grasp(obs, arm_action, ee_action) + if new_arm_action is not None: + arm_action = new_arm_action + if collision_checking is None: + collision_checking = False + if not pass_this_step: + self._path_observations = [] + self._path_observations, success_in_path = self._path_action( + list(arm_action), collision_checking=collision_checking, recorder=recorder) + + elif self._action_mode.arm == ArmActionMode.ABS_EE_POSE_PLAN_WORLD_FRAME_WITH_COLLISION_CHECK: + + self._assert_action_space(arm_action, (7,)) + pass_this_step = False + # if current_ee == 1.0 and ee_action == 0.0: + if current_ee != ee_action and use_auto_move: + obs = self._scene.get_observation() + _, new_arm_action = self.auto_grasp(obs, arm_action, ee_action) + if new_arm_action is not None: + arm_action = new_arm_action + if collision_checking is None: + collision_checking = True + if not pass_this_step: + self._path_observations = [] + self._path_observations, success_in_path = self._path_action( + list(arm_action), collision_checking=collision_checking, recorder = recorder) + + elif self._action_mode.arm == ArmActionMode.DELTA_EE_POSE_PLAN_WORLD_FRAME: + + self._assert_action_space(arm_action, (7,)) + a_x, a_y, a_z, a_qx, a_qy, a_qz, a_qw = arm_action + x, y, z, qx, qy, qz, qw = self._robot.arm.get_tip().get_pose() + new_rot = Quaternion(a_qw, a_qx, a_qy, a_qz) * Quaternion(qw, qx, + qy, qz) + qw, qx, qy, qz = list(new_rot) + new_pose = [a_x + x, a_y + y, a_z + z] + [qx, qy, qz, qw] + self._path_observations = [] + self._path_observations, success_in_path = self._path_action(list(new_pose)) + + elif self._action_mode.arm == ArmActionMode.DELTA_EE_POSE_WORLD_FRAME: + + self._assert_action_space(arm_action, (7,)) + a_x, a_y, a_z, a_qx, a_qy, a_qz, a_qw = arm_action + x, y, z, qx, qy, qz, qw = self._robot.arm.get_tip().get_pose() + new_rot = Quaternion(a_qw, a_qx, a_qy, a_qz) * Quaternion( + qw, qx, qy, qz) + qw, qx, qy, qz = list(new_rot) + new_pose = [a_x + x, a_y + y, a_z + z] + [qx, qy, qz, qw] + # point = Dummy('Dummy') + # point.set_pose(list(new_pose)) + self._ee_action(list(new_pose)) + + elif self._action_mode.arm == ArmActionMode.EE_POSE_EE_FRAME: + + self._assert_action_space(arm_action, (7,)) + self._ee_action( + list(arm_action), relative_to=self._robot.arm.get_tip()) + + elif self._action_mode.arm == ArmActionMode.EE_POSE_PLAN_EE_FRAME: + + self._assert_action_space(arm_action, (7,)) + self._path_observations = [] + self._path_observations, success_in_path = self._path_action( + list(arm_action), relative_to=self._robot.arm.get_tip()) + + else: + raise RuntimeError('Unrecognised action mode.') + + obs = self._scene.get_observation() + grasp_sucess = False + if current_ee != ee_action: + done = False + if ee_action == 0.0 and self._attach_grasped_objects: + # If gripper close action, the check for grasp. + for g_obj in self._task.get_graspable_objects(): + succ = self._robot.gripper.grasp(g_obj) + if need_grasp_obj is not None: + if g_obj.get_name() == need_grasp_obj and succ: + grasp_sucess = True + if need_grasp_obj is not None: + if self._robot.gripper._proximity_sensor.is_detected(Object.get_object(need_grasp_obj)): + grasp_sucess = True + else: + # If gripper open action, the check for ungrasp. + self._robot.gripper.release() + while not done: + done = self._robot.gripper.actuate(ee_action, velocity=0.2) + self._pyrep.step() + self._task.step() + if ee_action == 1.0: + # Step a few more times to allow objects to drop + for _ in range(10): + self._pyrep.step() + self._task.step() + + success, terminate = self._task.success() + # task_reward = self._task.reward(steps) + # reward = float(success) if task_reward is None else task_reward + if len(success_in_path) > 0: + success = 1.0 + elif grasp_sucess: + success = 0.5 + reward = float(success) + return obs, reward, terminate + + def auto_grasp(self, obs, goal_tip_pose, ee_action): + def angle_distance(q1, q2): + # same as rad2deg(2arccos(theta)), here qw = cos(theta/2). need to select min of (x, 2pi - x) + # reference: https://math.stackexchange.com/questions/90081/quaternion-distance + q1_matrix = R.from_quat(q1).as_matrix() + q2_matrix = R.from_quat(q2).as_matrix() + # v = 2 * np.arccos(np.clip(2 * np.dot(q1, q2) ** 2 - 1, -1, 1)) + # return min(v, 2*np.pi - v) + return np.dot(q1_matrix[:3, 2], q2_matrix[:3, 2]) + def get_errors(target_pose, current_ee_pose): + translate_error = ((target_pose[:3]-current_ee_pose[:3])**2).sum()**(1/2) + angle_error = angle_distance(target_pose[3:], current_ee_pose[3:]) + return translate_error, angle_error + def execute_waypoint(point): + path = point.get_path() + done = False + while not done: + done = path.step() + self._pyrep.step() + self._task.step() + if point.gripper_control is not None: + gripper = self._robot.gripper + if point.gripper_control[0]=='open': + gripper.release() + done = False + while not done: + done = gripper.actuate(point.gripper_control[1], 0.04) + self._pyrep.step() + self._task.step() + if point.gripper_control[0]=='close': + for g_obj in self._task.get_graspable_objects(): + gripper.grasp(g_obj) + + info = obs.object_informations + waypoints = self._task.get_waypoints() + success = False + new_action = None + # for w in waypoints: + # name = w.name + # gripper_control = w.gripper_control + # if gripper_control is not None: + # if gripper_control[1]!=ee_action: + # continue + # target_pose = info[name]['pose'][0] + # t_error, r_error = get_errors(target_pose, goal_tip_pose) + # print("Waypoint: ", name, "Translate Error: ", t_error, "Rotation Error: ", r_error) + # if t_error<0.05 and r_error>0.9: + # print("Using automatic grasp") + # # execute_waypoint(w) + # new_action = goal_tip_pose.copy() + # wpoint_pose = w.pose if hasattr(w, "pose") else w.end_pose + # new_action[:3] = wpoint_pose[:3] + # success = True + # break + return success, new_action + + def enable_path_observations(self, value: bool) -> None: + if (self._action_mode.arm != ArmActionMode.DELTA_EE_POSE_PLAN_WORLD_FRAME and + self._action_mode.arm != ArmActionMode.ABS_EE_POSE_PLAN_WORLD_FRAME and + self._action_mode.arm != ArmActionMode.ABS_EE_POSE_PLAN_WORLD_FRAME_WITH_COLLISION_CHECK and + self._action_mode.arm != ArmActionMode.EE_POSE_PLAN_EE_FRAME): + raise RuntimeError('Only available in DELTA_EE_POSE_PLAN or ' + 'ABS_EE_POSE_PLAN action mode.') + self._enable_path_observations = value + + def get_path_observations(self): + if (self._action_mode.arm != ArmActionMode.DELTA_EE_POSE_PLAN_WORLD_FRAME and + self._action_mode.arm != ArmActionMode.ABS_EE_POSE_PLAN_WORLD_FRAME and + self._action_mode.arm != ArmActionMode.ABS_EE_POSE_PLAN_WORLD_FRAME_WITH_COLLISION_CHECK and + self._action_mode.arm != ArmActionMode.EE_POSE_PLAN_EE_FRAME): + raise RuntimeError('Only available in DELTA_EE_POSE_PLAN or ' + 'ABS_EE_POSE_PLAN action mode.') + return self._path_observations + + def get_demos(self, amount: int, live_demos: bool = False, + image_paths: bool = False, + callable_each_step: Callable[[Observation], None] = None, + max_attempts: int = _MAX_DEMO_ATTEMPTS, + random_selection: bool = True, + from_episode_number: int = 0 + ) -> List[Demo]: + """Negative means all demos""" + + if not live_demos and (self._dataset_root is None + or len(self._dataset_root) == 0): + raise RuntimeError( + "Can't ask for a stored demo when no dataset root provided.") + + if not live_demos: + if self._dataset_root is None or len(self._dataset_root) == 0: + raise RuntimeError( + "Can't ask for stored demo when no dataset root provided.") + demos = utils.get_stored_demos( + amount, image_paths, self._dataset_root, self._variation_number, + self._task.get_name(), self._obs_config, + random_selection, from_episode_number) + return (demos) + else: + ctr_loop = self._robot.arm.joints[0].is_control_loop_enabled() + self._robot.arm.set_control_loop_enabled(True) + demos, success_all = self._get_live_demos( + amount, callable_each_step, max_attempts) + self._robot.arm.set_control_loop_enabled(ctr_loop) + return (demos, success_all) + + def _get_live_demos(self, amount: int, + callable_each_step: Callable[ + [Observation], None] = None, + max_attempts: int = _MAX_DEMO_ATTEMPTS, record=True) -> List[Demo]: + demos = [] + success_all = [] + for i in range(amount): + attempts = max_attempts + while attempts > 0: + random_seed = np.random.get_state() + self.reset() + try: + demo, success = self._scene.get_demo( + record = record, callable_each_step=callable_each_step) + demo.random_seed = random_seed + demos.append(demo) + success_all.append(success) + break + except Exception as e: + attempts -= 1 + logging.info('Bad demo. ' + str(e)) + if attempts <= 0: + raise RuntimeError( + 'Could not collect demos. Maybe a problem with the task?') + return demos, success_all + + def reset_to_demo(self, demo: Demo) -> Tuple[List[str], Observation]: + demo.restore_state() + return self.reset() + + def save_config(self, max_attempts: int = _MAX_DEMO_ATTEMPTS): + random_seed = np.random.get_state() + desc,_ = self.reset() + task_base, waypoint_sets, config = self.read_config(desc) + config.random_seed = random_seed + + """ + attempts = max_attempts + while attempts > 0: + random_seed = np.random.get_state() + desc,_ = self.reset() + task_base, waypoint_sets, config = self.read_config(desc) + config.random_seed = random_seed + try: + demo, success = self._scene.get_demo(record = False) + if not success: + attempts -= 1 + continue + break + except Exception as e: + attempts -= 1 + logging.info('Bad demo. ' + str(e)) + """ + # ctr_loop = self._robot.arm.joints[0].is_control_loop_enabled() + # self._robot.arm.set_control_loop_enabled(True) + # demos, success_all = self._get_live_demos(amount=1,record=False) + # desc = demos[0].high_level_instructions + # desc = self._scene.init_episode( + # self._variation_number, max_attempts=_MAX_RESET_ATTEMPTS, + # randomly_place=not self._static_positions) + # self._robot.arm.set_control_loop_enabled(ctr_loop) + # except (BoundaryError, WaypointError) as e: + # raise TaskEnvironmentError( + # 'Could not place the task %s in the scene. This should not ' + # 'happen, please raise an issues on this task.' + # % self._task.get_name()) from e + + # self._reset_called = True + + return task_base, waypoint_sets, config + + def read_config(self, desc): + task_base = self._task.get_base() + if Dummy.exists("waypoint_sets"): + waypoint_sets = Dummy("waypoint_sets") + else: + waypoint_sets = Dummy.create() + waypoint_sets.set_name("waypoint_sets") + waypoint_sets.set_model(True) + for waypoint in self._task.temporary_waypoints: + waypoint.set_parent(waypoint_sets) + # config = self._scene.get_observation() + # for key, val in config.__dict__.items(): + # if "rgb" in key or "depth" in key or "point" in key or "gripper" in key: + # config.__setattr__(key, None) + config = TaskConfigs() + config.high_level_descriptions = desc + config.success_conditions = self._task._success_conditions + graspable_objects = [] + for obj in self._task._graspable_objects: + graspable_objects.append(obj.get_name()) + config.graspable_objects = graspable_objects + task_attributes = {} + for key, val in self._task.__dict__.items(): + if key[0]!="_" and key!="pyrep" and key!="robot": + task_attributes[key] = val + config.task_attributes = task_attributes + return task_base, waypoint_sets, config + + def load_config(self, task_base, waypoint_sets, config_path): + ctr_loop = self._robot.arm.joints[0].is_control_loop_enabled() + self._scene._has_init_task = True + self._robot.gripper.release() + + arm, gripper = self._scene._initial_robot_state + self._pyrep.set_configuration_tree(arm) + self._pyrep.set_configuration_tree(gripper) + self._robot.arm.set_joint_positions(self._scene._start_arm_joint_pos, disable_dynamics=True) + self._robot.arm.set_joint_target_velocities( + [0] * len(self._robot.arm.joints)) + self._robot.gripper.set_joint_positions( + self._scene._starting_gripper_joint_pos, disable_dynamics=True) + self._robot.gripper.set_joint_target_velocities( + [0] * len(self._robot.gripper.joints)) + self._robot.arm.set_control_loop_enabled(ctr_loop) + + self._task.unload() + if Dummy.exists("waypoint_sets"): + Dummy("waypoint_sets").remove() + new_base = self._pyrep.import_model(task_base) + waypoints = self._pyrep.import_model(waypoint_sets) + with open(config_path, 'rb') as f: + config = pickle.load(f) + self._task._success_conditions = config.success_conditions + graspable_objects = [] + for obj_name in config.graspable_objects: + graspable_objects.append(Object.get_object(obj_name)) + self._task._graspable_objects = graspable_objects + self._task.set_initial_objects_in_scene() + if not hasattr(self, "attr_retrivel"): + self.attr_retrivel = [] + for key, val in config.task_attributes.items(): + if not hasattr(self._task, key): + self.attr_retrivel.append(key) + for key in self.attr_retrivel: + self._task.__setattr__(key, config.task_attributes[key]) + self._reset_called=True + + return config.high_level_descriptions, self._scene.get_observation() \ No newline at end of file diff --git a/easi/tasks/ebmanipulation/vendor/amsolver/utils.py b/easi/tasks/ebmanipulation/vendor/amsolver/utils.py new file mode 100644 index 0000000..3dee026 --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/amsolver/utils.py @@ -0,0 +1,331 @@ +#Modified From the rlbench: https://github.com/stepjam/RLBench +import importlib +import pickle +from os import listdir +from os.path import join, exists +from typing import List + +import numpy as np +from PIL import Image +from natsort import natsorted +from pyrep.objects import VisionSensor + +from amsolver.backend.const import * +from amsolver.backend.utils import image_to_float_array, rgb_handles_to_mask +from amsolver.demo import Demo +from amsolver.observation_config import ObservationConfig + + +class InvalidTaskName(Exception): + pass + + +def name_to_task_class(task_file: str, parent_folder = "vlm"): + name = task_file.replace('.py', '') + class_name = ''.join([w[0].upper() + w[1:] for w in name.split('_')]) + try: + mod = importlib.import_module(parent_folder+".tasks.%s" % name) + mod = importlib.reload(mod) + except ModuleNotFoundError as e: + raise InvalidTaskName( + "The task file '%s' does not exist or cannot be compiled." + % name) from e + try: + task_class = getattr(mod, class_name) + except AttributeError as e: + raise InvalidTaskName( + "Cannot find the class name '%s' in the file '%s'." + % (class_name, name)) from e + return task_class + + +def get_stored_demos(amount: int, image_paths: bool, dataset_root: str, + variation_number: int, task_name: str, + obs_config: ObservationConfig, + episode_number = None, + fail_demos = False, + selected_frame=None) -> List[Demo]: + + task_root = join(dataset_root, task_name) + if not exists(task_root): + raise RuntimeError("Can't find the demos for %s at: %s" % ( + task_name, task_root)) + + # Sample an amount of examples for the variation of this task + examples_path = join( + task_root, VARIATIONS_FOLDER % variation_number, + EPISODES_FOLDER) + if fail_demos: + examples_path = join( + task_root, VARIATIONS_FOLDER % variation_number, + 'fail_cases') + examples = listdir(examples_path) + if amount == -1: + amount = len(examples) + if amount > len(examples): + raise RuntimeError( + 'You asked for %d examples, but only %d were available.' % ( + amount, len(examples))) + if episode_number is None: + selected_examples = np.random.choice(examples, amount, replace=False) + else: + # selected_examples = [natsorted(examples)[episode_number]] + # for e in natsorted(examples): + # if f"episode{episode_number}"==e: + # selected_examples = [e] + # break + selected_examples = [episode_number] + import os + # Process these examples (e.g. loading observations) + demos = [] + for example in selected_examples: + example_path = join(examples_path, example) + with open(join(example_path, LOW_DIM_PICKLE), 'rb') as f: + obs = pickle.load(f) + + l_sh_rgb_f = join(example_path, LEFT_SHOULDER_RGB_FOLDER) + l_sh_depth_f = join(example_path, LEFT_SHOULDER_DEPTH_FOLDER) + l_sh_mask_f = join(example_path, LEFT_SHOULDER_MASK_FOLDER) + r_sh_rgb_f = join(example_path, RIGHT_SHOULDER_RGB_FOLDER) + r_sh_depth_f = join(example_path, RIGHT_SHOULDER_DEPTH_FOLDER) + r_sh_mask_f = join(example_path, RIGHT_SHOULDER_MASK_FOLDER) + oh_rgb_f = join(example_path, OVERHEAD_RGB_FOLDER) + oh_depth_f = join(example_path, OVERHEAD_DEPTH_FOLDER) + oh_mask_f = join(example_path, OVERHEAD_MASK_FOLDER) + wrist_rgb_f = join(example_path, WRIST_RGB_FOLDER) + wrist_depth_f = join(example_path, WRIST_DEPTH_FOLDER) + wrist_mask_f = join(example_path, WRIST_MASK_FOLDER) + front_rgb_f = join(example_path, FRONT_RGB_FOLDER) + front_depth_f = join(example_path, FRONT_DEPTH_FOLDER) + front_mask_f = join(example_path, FRONT_MASK_FOLDER) + + num_steps = len(obs) + + if not (num_steps == len(listdir(l_sh_rgb_f)) == len( + listdir(l_sh_depth_f)) == len(listdir(r_sh_rgb_f)) == len( + listdir(r_sh_depth_f)) == len(listdir(oh_rgb_f)) == len( + listdir(oh_depth_f)) == len(listdir(wrist_rgb_f)) == len( + listdir(wrist_depth_f)) == len(listdir(front_rgb_f)) == len( + listdir(front_depth_f))): + raise RuntimeError('Broken dataset assumption') + + if selected_frame is None: + selected_frame = range(num_steps) + for i in selected_frame: + si = IMAGE_FORMAT % i + if obs_config.left_shoulder_camera.rgb: + obs[i].left_shoulder_rgb = join(l_sh_rgb_f, si) + if obs_config.left_shoulder_camera.depth or obs_config.left_shoulder_camera.point_cloud: + obs[i].left_shoulder_depth = join(l_sh_depth_f, si) + if obs_config.left_shoulder_camera.mask: + obs[i].left_shoulder_mask = join(l_sh_mask_f, si) + if obs_config.right_shoulder_camera.rgb: + obs[i].right_shoulder_rgb = join(r_sh_rgb_f, si) + if obs_config.right_shoulder_camera.depth or obs_config.right_shoulder_camera.point_cloud: + obs[i].right_shoulder_depth = join(r_sh_depth_f, si) + if obs_config.right_shoulder_camera.mask: + obs[i].right_shoulder_mask = join(r_sh_mask_f, si) + if obs_config.overhead_camera.rgb: + obs[i].overhead_rgb = join(oh_rgb_f, si) + if obs_config.overhead_camera.depth or obs_config.overhead_camera.point_cloud: + obs[i].overhead_depth = join(oh_depth_f, si) + if obs_config.overhead_camera.mask: + obs[i].overhead_mask = join(oh_mask_f, si) + if obs_config.wrist_camera.rgb: + obs[i].wrist_rgb = join(wrist_rgb_f, si) + if obs_config.wrist_camera.depth or obs_config.wrist_camera.point_cloud: + obs[i].wrist_depth = join(wrist_depth_f, si) + if obs_config.wrist_camera.mask: + obs[i].wrist_mask = join(wrist_mask_f, si) + if obs_config.front_camera.rgb: + obs[i].front_rgb = join(front_rgb_f, si) + if obs_config.front_camera.depth or obs_config.front_camera.point_cloud: + obs[i].front_depth = join(front_depth_f, si) + if obs_config.front_camera.mask: + obs[i].front_mask = join(front_mask_f, si) + + # Remove low dim info if necessary + if not obs_config.joint_velocities: + obs[i].joint_velocities = None + if not obs_config.joint_positions: + obs[i].joint_positions = None + if not obs_config.joint_forces: + obs[i].joint_forces = None + if not obs_config.gripper_open: + obs[i].gripper_open = None + if not obs_config.gripper_pose: + obs[i].gripper_pose = None + if not obs_config.gripper_joint_positions: + obs[i].gripper_joint_positions = None + if not obs_config.gripper_touch_forces: + obs[i].gripper_touch_forces = None + if not obs_config.task_low_dim_state: + obs[i].task_low_dim_state = None + + if not image_paths: + for i in selected_frame: + if obs_config.left_shoulder_camera.rgb: + obs[i].left_shoulder_rgb = np.array( + _resize_if_needed( + Image.open(obs[i].left_shoulder_rgb), + obs_config.left_shoulder_camera.image_size)) + if obs_config.right_shoulder_camera.rgb: + obs[i].right_shoulder_rgb = np.array( + _resize_if_needed(Image.open( + obs[i].right_shoulder_rgb), + obs_config.right_shoulder_camera.image_size)) + if obs_config.overhead_camera.rgb: + obs[i].overhead_rgb = np.array( + _resize_if_needed(Image.open( + obs[i].overhead_rgb), + obs_config.overhead_camera.image_size)) + if obs_config.wrist_camera.rgb: + obs[i].wrist_rgb = np.array( + _resize_if_needed( + Image.open(obs[i].wrist_rgb), + obs_config.wrist_camera.image_size)) + if obs_config.front_camera.rgb: + obs[i].front_rgb = np.array( + _resize_if_needed( + Image.open(obs[i].front_rgb), + obs_config.front_camera.image_size)) + + if obs_config.left_shoulder_camera.depth or obs_config.left_shoulder_camera.point_cloud: + l_sh_depth = image_to_float_array( + _resize_if_needed( + Image.open(obs[i].left_shoulder_depth), + obs_config.left_shoulder_camera.image_size), + DEPTH_SCALE) + near = obs[i].misc['left_shoulder_camera_near'] + far = obs[i].misc['left_shoulder_camera_far'] + l_sh_depth_m = near + l_sh_depth * (far - near) + if obs_config.left_shoulder_camera.depth: + d = l_sh_depth_m if obs_config.left_shoulder_camera.depth_in_meters else l_sh_depth + obs[i].left_shoulder_depth = obs_config.left_shoulder_camera.depth_noise.apply(d) + else: + obs[i].left_shoulder_depth = None + + if obs_config.right_shoulder_camera.depth or obs_config.right_shoulder_camera.point_cloud: + r_sh_depth = image_to_float_array( + _resize_if_needed( + Image.open(obs[i].right_shoulder_depth), + obs_config.right_shoulder_camera.image_size), + DEPTH_SCALE) + near = obs[i].misc['right_shoulder_camera_near'] + far = obs[i].misc['right_shoulder_camera_far'] + r_sh_depth_m = near + r_sh_depth * (far - near) + if obs_config.right_shoulder_camera.depth: + d = r_sh_depth_m if obs_config.right_shoulder_camera.depth_in_meters else r_sh_depth + obs[i].right_shoulder_depth = obs_config.right_shoulder_camera.depth_noise.apply(d) + else: + obs[i].right_shoulder_depth = None + + if obs_config.overhead_camera.depth or obs_config.overhead_camera.point_cloud: + oh_depth = image_to_float_array( + _resize_if_needed( + Image.open(obs[i].overhead_depth), + obs_config.overhead_camera.image_size), + DEPTH_SCALE) + near = obs[i].misc['overhead_camera_near'] + far = obs[i].misc['overhead_camera_far'] + oh_depth_m = near + oh_depth * (far - near) + if obs_config.overhead_camera.depth: + d = oh_depth_m if obs_config.overhead_camera.depth_in_meters else oh_depth + obs[i].overhead_depth = obs_config.overhead_camera.depth_noise.apply(d) + else: + obs[i].overhead_depth = None + + if obs_config.wrist_camera.depth or obs_config.wrist_camera.point_cloud: + wrist_depth = image_to_float_array( + _resize_if_needed( + Image.open(obs[i].wrist_depth), + obs_config.wrist_camera.image_size), + DEPTH_SCALE) + near = obs[i].misc['wrist_camera_near'] + far = obs[i].misc['wrist_camera_far'] + wrist_depth_m = near + wrist_depth * (far - near) + if obs_config.wrist_camera.depth: + d = wrist_depth_m if obs_config.wrist_camera.depth_in_meters else wrist_depth + obs[i].wrist_depth = obs_config.wrist_camera.depth_noise.apply(d) + else: + obs[i].wrist_depth = None + + if obs_config.front_camera.depth or obs_config.front_camera.point_cloud: + front_depth = image_to_float_array( + _resize_if_needed( + Image.open(obs[i].front_depth), + obs_config.front_camera.image_size), + DEPTH_SCALE) + near = obs[i].misc['front_camera_near'] + far = obs[i].misc['front_camera_far'] + front_depth_m = near + front_depth * (far - near) + if obs_config.front_camera.depth: + d = front_depth_m if obs_config.front_camera.depth_in_meters else front_depth + obs[i].front_depth = obs_config.front_camera.depth_noise.apply(d) + else: + obs[i].front_depth = None + + if obs_config.left_shoulder_camera.point_cloud: + obs[i].left_shoulder_point_cloud = VisionSensor.pointcloud_from_depth_and_camera_params( + l_sh_depth_m, + obs[i].misc['left_shoulder_camera_extrinsics'], + obs[i].misc['left_shoulder_camera_intrinsics']) + if obs_config.right_shoulder_camera.point_cloud: + obs[i].right_shoulder_point_cloud = VisionSensor.pointcloud_from_depth_and_camera_params( + r_sh_depth_m, + obs[i].misc['right_shoulder_camera_extrinsics'], + obs[i].misc['right_shoulder_camera_intrinsics']) + if obs_config.overhead_camera.point_cloud: + obs[i].overhead_point_cloud = VisionSensor.pointcloud_from_depth_and_camera_params( + oh_depth_m, + obs[i].misc['overhead_camera_extrinsics'], + obs[i].misc['overhead_camera_intrinsics']) + if obs_config.wrist_camera.point_cloud: + obs[i].wrist_point_cloud = VisionSensor.pointcloud_from_depth_and_camera_params( + wrist_depth_m, + obs[i].misc['wrist_camera_extrinsics'], + obs[i].misc['wrist_camera_intrinsics']) + if obs_config.front_camera.point_cloud: + obs[i].front_point_cloud = VisionSensor.pointcloud_from_depth_and_camera_params( + front_depth_m, + obs[i].misc['front_camera_extrinsics'], + obs[i].misc['front_camera_intrinsics']) + + # Masks are stored as coded RGB images. + # Here we transform them into 1 channel handles. + if obs_config.left_shoulder_camera.mask: + obs[i].left_shoulder_mask = rgb_handles_to_mask( + np.array(_resize_if_needed(Image.open( + obs[i].left_shoulder_mask), + obs_config.left_shoulder_camera.image_size))) + if obs_config.right_shoulder_camera.mask: + obs[i].right_shoulder_mask = rgb_handles_to_mask( + np.array(_resize_if_needed(Image.open( + obs[i].right_shoulder_mask), + obs_config.right_shoulder_camera.image_size))) + if obs_config.overhead_camera.mask: + obs[i].overhead_mask = rgb_handles_to_mask( + np.array(_resize_if_needed(Image.open( + obs[i].overhead_mask), + obs_config.overhead_camera.image_size))) + if obs_config.wrist_camera.mask: + obs[i].wrist_mask = rgb_handles_to_mask(np.array( + _resize_if_needed(Image.open( + obs[i].wrist_mask), + obs_config.wrist_camera.image_size))) + if obs_config.front_camera.mask: + obs[i].front_mask = rgb_handles_to_mask(np.array( + _resize_if_needed(Image.open( + obs[i].front_mask), + obs_config.front_camera.image_size))) + + demos.append(obs) + if len(demos)==0: + print(1) + return demos + + +def _resize_if_needed(image, size): + if image.size[0] != size[0] or image.size[1] != size[1]: + image = image.resize(size) + return image diff --git a/easi/tasks/ebmanipulation/vendor/tools/__init__.py b/easi/tasks/ebmanipulation/vendor/tools/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/easi/tasks/ebmanipulation/vendor/tools/assets/task_template.txt b/easi/tasks/ebmanipulation/vendor/tools/assets/task_template.txt new file mode 100644 index 0000000..566a7b1 --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/tools/assets/task_template.txt @@ -0,0 +1,25 @@ +from typing import List +import numpy as np +import os +from pyrep.objects.shape import Shape +from pyrep.objects.proximity_sensor import ProximitySensor +from amsolver.const import colors +from amsolver.backend.unit_tasks import T0_ObtainControl, T1_MoveObjectGoal, T2_MoveObjectConstraints, TargetSpace, VLM_Object + +class %s(Task): + + def init_task(self) -> None: + # TODO: This is called once when a task is initialised. + pass + + def init_episode(self, index: int) -> List[str]: + # TODO: This is called at the start of each episode. + return [''] + + def variation_count(self) -> int: + # TODO: The number of variations for this task. + return 1 + + def step(self) -> None: + # Called during each sim step. Remove this if not using. + pass \ No newline at end of file diff --git a/easi/tasks/ebmanipulation/vendor/tools/cinematic_recorder.py b/easi/tasks/ebmanipulation/vendor/tools/cinematic_recorder.py new file mode 100644 index 0000000..bbc8473 --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/tools/cinematic_recorder.py @@ -0,0 +1,158 @@ +import os +from typing import Type + +import cv2 +import numpy as np +from absl import app +from absl import flags +from pyrep.objects.dummy import Dummy +from pyrep.objects.vision_sensor import VisionSensor + +from amsolver import Environment +from amsolver.action_modes import ActionMode +from amsolver.backend.observation import Observation +from amsolver.backend.task import TASKS_PATH +from amsolver.backend.task import Task +from amsolver.backend.utils import task_file_to_task_class +from amsolver.observation_config import ObservationConfig +from amsolver.sim2real.domain_randomization import RandomizeEvery, \ + VisualRandomizationConfig + +FLAGS = flags.FLAGS + +flags.DEFINE_string( + 'save_dir', '../amsolver/amsolver_videos', + 'Where to save the generated videos.') +flags.DEFINE_list( + 'tasks', ['wipe_table_color','wipe_table_direction', 'wipe_table_relative', 'wipe_table_shape', 'wipe_table_size'], + 'The tasks to record. If empty, all tasks are recorded.') +flags.DEFINE_boolean( + 'individual', True, 'One long clip of all the tasks, or individual videos.') +flags.DEFINE_boolean( + 'domain_randomization', False, 'If domain randomization should be applied.') +flags.DEFINE_string( + 'textures_path', '', + 'Where to locate textures if using domain randomization.') +flags.DEFINE_boolean('headless', True, 'Run in headless mode.') +flags.DEFINE_list( + 'camera_resolution', [1280, 720], 'The camera resolution') + + +class CameraMotion(object): + def __init__(self, cam: VisionSensor): + self.cam = cam + + def step(self): + raise NotImplementedError() + + def save_pose(self): + self._prev_pose = self.cam.get_pose() + + def restore_pose(self): + self.cam.set_pose(self._prev_pose) + + +class CircleCameraMotion(CameraMotion): + + def __init__(self, cam: VisionSensor, origin: Dummy, speed: float): + super().__init__(cam) + self.origin = origin + self.speed = speed # in radians + + def step(self): + self.origin.rotate([0, 0, self.speed]) + + +class TaskRecorder(object): + + def __init__(self, env: Environment, cam_motion: CameraMotion, fps=30): + self._env = env + self._cam_motion = cam_motion + self._fps = fps + self._snaps = [] + self._current_snaps = [] + + def take_snap(self, obs: Observation): + self._cam_motion.step() + self._current_snaps.append( + (self._cam_motion.cam.capture_rgb() * 255.).astype(np.uint8)) + + def record_task(self, task: Type[Task]): + task = self._env.get_task(task) + self._cam_motion.save_pose() + while True: + try: + _, success = task.get_demos( + 1, live_demos=True, callable_each_step=self.take_snap, + max_attempts=1) + if success: + break + else: + self._cam_motion.restore_pose() + self._current_snaps = [] + except RuntimeError: + self._cam_motion.restore_pose() + self._current_snaps = [] + self._snaps.extend(self._current_snaps) + self._current_snaps = [] + return True + + def save(self, path): + print('Converting to video ...') + os.makedirs(os.path.dirname(path), exist_ok=True) + video = cv2.VideoWriter( + path, cv2.VideoWriter_fourcc(*'MJPG'), self._fps, + tuple(self._cam_motion.cam.get_resolution())) + for image in self._snaps: + video.write(cv2.cvtColor(image, cv2.COLOR_RGB2BGR)) + video.release() + self._snaps = [] + + +def main(argv): + + obs_config = ObservationConfig(record_gripper_closing=True) + obs_config.set_all(False) + + vrc = rand_every = None + frequency = 0 + if FLAGS.domain_randomization: + vrc = VisualRandomizationConfig(FLAGS.textures_path) + rand_every = RandomizeEvery.TRANSITION + frequency = 10 + + env = Environment(ActionMode(), obs_config=obs_config, + randomize_every=rand_every, frequency=frequency, + visual_randomization_config=vrc, headless=FLAGS.headless) + env.launch() + + # Add the camera to the scene + cam_placeholder = Dummy('cam_cinematic_placeholder') + cam = VisionSensor.create(FLAGS.camera_resolution) + cam.set_pose(cam_placeholder.get_pose()) + cam.set_parent(cam_placeholder) + + cam_motion = CircleCameraMotion(cam, Dummy('cam_cinematic_base'), 0.005) + tr = TaskRecorder(env, cam_motion, fps=30) + + if len(FLAGS.tasks) > 0: + task_names = FLAGS.tasks + else: + tasks_path = './vlm/tasks' + task_names = [t.replace('.py', '') for t in os.listdir(tasks_path) + if t != '__init__.py' and t.endswith('.py')] + task_classes = [task_file_to_task_class( + task_file, parent_folder = 'vlm') for task_file in task_names] + + for i, (name, cls) in enumerate(zip(task_names, task_classes)): + good = tr.record_task(cls) + if FLAGS.individual and good: + tr.save(os.path.join(FLAGS.save_dir, '%s.avi' % name)) + + if not FLAGS.individual: + tr.save(os.path.join(FLAGS.save_dir, 'recorded_tasks.avi')) + env.shutdown() + + +if __name__ == '__main__': + app.run(main) diff --git a/easi/tasks/ebmanipulation/vendor/tools/dataset_generator_NLP.py b/easi/tasks/ebmanipulation/vendor/tools/dataset_generator_NLP.py new file mode 100644 index 0000000..21e3a6e --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/tools/dataset_generator_NLP.py @@ -0,0 +1,401 @@ +from multiprocessing import Process, Manager +from time import time + +from pyrep.const import RenderMode +from os.path import join, dirname, abspath, isfile +import sys +CURRENT_DIR = dirname(abspath(__file__)) +sys.path.insert(0, join(CURRENT_DIR, '..')) # Use local amsolver rather than installed +from amsolver import ObservationConfig +from amsolver.action_modes import ActionMode +from amsolver.backend.utils import task_file_to_task_class +from amsolver.environment import Environment +import amsolver.backend.task as task + +import os +import pickle +from PIL import Image +from amsolver.backend import utils +from amsolver.backend.const import * +import numpy as np +from pathlib import Path + +from absl import app +from absl import flags + +""" +Change to different folder for different split. +If you want to generate unseen data, please set the colors and objects to the unseen collections in amsolver/constant.py +""" + +FLAGS = flags.FLAGS + +flags.DEFINE_string('save_path', + './rlbench_data/train', + 'Where to save the demos.') +flags.DEFINE_list('tasks', [ + 'drop_pen_color', 'drop_pen_relative', 'drop_pen_size', + 'wipe_table_color', 'wipe_table_relative', 'wipe_table_shape', 'wipe_table_size', 'wipe_table_direction', + 'pour_demo_color', 'pour_demo_relative', 'pour_demo_size', + 'pick_cube_color', 'pick_cube_relative', 'pick_cube_shape', 'pick_cube_size', + 'stack_cubes_color', 'stack_cubes_size', + 'stack_cubes_relative', 'stack_cubes_shape', + 'place_into_shape_sorter_color', 'place_into_shape_sorter_shape', 'place_into_shape_sorter_relative', + 'open_drawer', + 'open_door_complex' + ], + 'The tasks to collect. If empty, all tasks are collected.') +flags.DEFINE_list('image_size', [360, 360], + 'The size of the images tp save.') +flags.DEFINE_enum('renderer', 'opengl', ['opengl', 'opengl3'], + 'The renderer to use. opengl does not include shadows, ' + 'but is faster.') +flags.DEFINE_integer('processes', 8, + 'The number of parallel processes during collection.') +flags.DEFINE_integer('episodes_per_task', 5, + 'The number of episodes to collect per task.') +flags.DEFINE_integer('variations', -1, + 'Number of variations to collect per task. -1 for all.') +flags.DEFINE_bool('save_configs', True, + 'whether also save the config for replay.') + + +def check_and_make(dir): + if not os.path.exists(dir): + os.makedirs(dir) + +def save_configs(task_base, waypoint_sets, config, example_path): + check_and_make(example_path) + # Save the low-dimension data + with open(os.path.join(example_path, "configs.pkl"), 'wb') as f: + pickle.dump(config, f) + task_base.save_model(os.path.join(example_path, "task_base.ttm")) + waypoint_sets.save_model(os.path.join(example_path, "waypoint_sets.ttm")) + +def save_demo(demo, example_path): + + # Save image data first, and then None the image data, and pickle + left_shoulder_rgb_path = os.path.join( + example_path, LEFT_SHOULDER_RGB_FOLDER) + left_shoulder_depth_path = os.path.join( + example_path, LEFT_SHOULDER_DEPTH_FOLDER) + left_shoulder_mask_path = os.path.join( + example_path, LEFT_SHOULDER_MASK_FOLDER) + right_shoulder_rgb_path = os.path.join( + example_path, RIGHT_SHOULDER_RGB_FOLDER) + right_shoulder_depth_path = os.path.join( + example_path, RIGHT_SHOULDER_DEPTH_FOLDER) + right_shoulder_mask_path = os.path.join( + example_path, RIGHT_SHOULDER_MASK_FOLDER) + overhead_rgb_path = os.path.join( + example_path, OVERHEAD_RGB_FOLDER) + overhead_depth_path = os.path.join( + example_path, OVERHEAD_DEPTH_FOLDER) + overhead_mask_path = os.path.join( + example_path, OVERHEAD_MASK_FOLDER) + wrist_rgb_path = os.path.join(example_path, WRIST_RGB_FOLDER) + wrist_depth_path = os.path.join(example_path, WRIST_DEPTH_FOLDER) + wrist_mask_path = os.path.join(example_path, WRIST_MASK_FOLDER) + front_rgb_path = os.path.join(example_path, FRONT_RGB_FOLDER) + front_depth_path = os.path.join(example_path, FRONT_DEPTH_FOLDER) + front_mask_path = os.path.join(example_path, FRONT_MASK_FOLDER) + + check_and_make(left_shoulder_rgb_path) + check_and_make(left_shoulder_depth_path) + check_and_make(left_shoulder_mask_path) + check_and_make(right_shoulder_rgb_path) + check_and_make(right_shoulder_depth_path) + check_and_make(right_shoulder_mask_path) + check_and_make(overhead_rgb_path) + check_and_make(overhead_depth_path) + check_and_make(overhead_mask_path) + check_and_make(wrist_rgb_path) + check_and_make(wrist_depth_path) + check_and_make(wrist_mask_path) + check_and_make(front_rgb_path) + check_and_make(front_depth_path) + check_and_make(front_mask_path) + + for i, obs in enumerate(demo): + left_shoulder_rgb = Image.fromarray(obs.left_shoulder_rgb) + left_shoulder_depth = utils.float_array_to_rgb_image( + obs.left_shoulder_depth, scale_factor=DEPTH_SCALE) + left_shoulder_mask = Image.fromarray( + (obs.left_shoulder_mask * 255).astype(np.uint8)) + right_shoulder_rgb = Image.fromarray(obs.right_shoulder_rgb) + right_shoulder_depth = utils.float_array_to_rgb_image( + obs.right_shoulder_depth, scale_factor=DEPTH_SCALE) + right_shoulder_mask = Image.fromarray( + (obs.right_shoulder_mask * 255).astype(np.uint8)) + overhead_rgb = Image.fromarray(obs.overhead_rgb) + overhead_depth = utils.float_array_to_rgb_image( + obs.overhead_depth, scale_factor=DEPTH_SCALE) + overhead_mask = Image.fromarray( + (obs.overhead_mask * 255).astype(np.uint8)) + wrist_rgb = Image.fromarray(obs.wrist_rgb) + wrist_depth = utils.float_array_to_rgb_image( + obs.wrist_depth, scale_factor=DEPTH_SCALE) + wrist_mask = Image.fromarray((obs.wrist_mask * 255).astype(np.uint8)) + front_rgb = Image.fromarray(obs.front_rgb) + front_depth = utils.float_array_to_rgb_image( + obs.front_depth, scale_factor=DEPTH_SCALE) + front_mask = Image.fromarray((obs.front_mask * 255).astype(np.uint8)) + + left_shoulder_rgb.save( + os.path.join(left_shoulder_rgb_path, IMAGE_FORMAT % i)) + left_shoulder_depth.save( + os.path.join(left_shoulder_depth_path, IMAGE_FORMAT % i)) + left_shoulder_mask.save( + os.path.join(left_shoulder_mask_path, IMAGE_FORMAT % i)) + right_shoulder_rgb.save( + os.path.join(right_shoulder_rgb_path, IMAGE_FORMAT % i)) + right_shoulder_depth.save( + os.path.join(right_shoulder_depth_path, IMAGE_FORMAT % i)) + right_shoulder_mask.save( + os.path.join(right_shoulder_mask_path, IMAGE_FORMAT % i)) + overhead_rgb.save( + os.path.join(overhead_rgb_path, IMAGE_FORMAT % i)) + overhead_depth.save( + os.path.join(overhead_depth_path, IMAGE_FORMAT % i)) + overhead_mask.save( + os.path.join(overhead_mask_path, IMAGE_FORMAT % i)) + wrist_rgb.save(os.path.join(wrist_rgb_path, IMAGE_FORMAT % i)) + wrist_depth.save(os.path.join(wrist_depth_path, IMAGE_FORMAT % i)) + wrist_mask.save(os.path.join(wrist_mask_path, IMAGE_FORMAT % i)) + front_rgb.save(os.path.join(front_rgb_path, IMAGE_FORMAT % i)) + front_depth.save(os.path.join(front_depth_path, IMAGE_FORMAT % i)) + front_mask.save(os.path.join(front_mask_path, IMAGE_FORMAT % i)) + + # We save the images separately, so set these to None for pickling. + obs.left_shoulder_rgb = None + obs.left_shoulder_depth = None + obs.left_shoulder_point_cloud = None + obs.left_shoulder_mask = None + obs.right_shoulder_rgb = None + obs.right_shoulder_depth = None + obs.right_shoulder_point_cloud = None + obs.right_shoulder_mask = None + obs.overhead_rgb = None + obs.overhead_depth = None + obs.overhead_point_cloud = None + obs.overhead_mask = None + obs.wrist_rgb = None + obs.wrist_depth = None + obs.wrist_point_cloud = None + obs.wrist_mask = None + obs.front_rgb = None + obs.front_depth = None + obs.front_point_cloud = None + obs.front_mask = None + + # Save the low-dimension data + with open(os.path.join(example_path, LOW_DIM_PICKLE), 'wb') as f: + pickle.dump(demo, f) + + +def run(i, lock, task_index, variation_count, results, file_lock, tasks): + """Each thread will choose one task and variation, and then gather + all the episodes_per_task for that variation.""" + + # Initialise each thread with random seed + np.random.seed(None) + num_tasks = len(tasks) + + img_size = list(map(int, FLAGS.image_size)) + + obs_config = ObservationConfig() + obs_config.set_all(True) + obs_config.right_shoulder_camera.image_size = img_size + obs_config.left_shoulder_camera.image_size = img_size + obs_config.overhead_camera.image_size = img_size + obs_config.wrist_camera.image_size = img_size + obs_config.front_camera.image_size = img_size + + # Store depth as 0 - 1 + obs_config.right_shoulder_camera.depth_in_meters = False + obs_config.left_shoulder_camera.depth_in_meters = False + obs_config.overhead_camera.depth_in_meters = False + obs_config.wrist_camera.depth_in_meters = False + obs_config.front_camera.depth_in_meters = False + + # We want to save the masks as rgb encodings. + obs_config.left_shoulder_camera.masks_as_one_channel = False + obs_config.right_shoulder_camera.masks_as_one_channel = False + obs_config.overhead_camera.masks_as_one_channel = False + obs_config.wrist_camera.masks_as_one_channel = False + obs_config.front_camera.masks_as_one_channel = False + + if FLAGS.renderer == 'opengl': + obs_config.right_shoulder_camera.render_mode = RenderMode.OPENGL + obs_config.left_shoulder_camera.render_mode = RenderMode.OPENGL + obs_config.overhead_camera.render_mode = RenderMode.OPENGL + obs_config.wrist_camera.render_mode = RenderMode.OPENGL + obs_config.front_camera.render_mode = RenderMode.OPENGL + + amsolver_env = Environment( + action_mode=ActionMode(), + obs_config=obs_config, + headless=True) # set headless=False, if user want to visualize the simulator + amsolver_env.launch() + + task_env = None + + tasks_with_problems = results[i] = '' + + while True: + # Figure out what task/variation this thread is going to do + with lock: + + if task_index.value >= num_tasks: + print('Process', i, 'finished') + break + + my_variation_count = variation_count.value + t = tasks[task_index.value] + task_env = amsolver_env.get_task(t) + var_target = task_env.variation_count() + if FLAGS.variations >= 0: + var_target = np.minimum(FLAGS.variations, var_target) + if my_variation_count >= var_target: + # If we have reached the required number of variations for this + # task, then move on to the next task. + variation_count.value = my_variation_count = 0 + task_index.value += 1 + + variation_count.value += 1 + if task_index.value >= num_tasks: + print('Process', i, 'finished') + break + t = tasks[task_index.value] + + task_env = amsolver_env.get_task(t) + task_env.set_variation(my_variation_count) + + variation_path = os.path.join( + FLAGS.save_path, task_env.get_name(), + VARIATIONS_FOLDER % my_variation_count) + + check_and_make(variation_path) + + episodes_path = os.path.join(variation_path, EPISODES_FOLDER) + check_and_make(episodes_path) + current_episodes = list(Path(episodes_path).rglob("low_dim*")) + + fails_path = os.path.join(variation_path, 'fail_cases') + check_and_make(fails_path) + + abort_variation = False + ex_idx = len(current_episodes) + fail_idx = 0 + need_fail_case = False + # for ex_idx in range(FLAGS.episodes_per_task): + while ex_idx 0: + try: + # TODO: for now we do the explicit looping. + t0 = time() + demo_success= task_env.get_demos( + amount=1, + live_demos=True) + if len(demo_success)==2: + demo = demo_success[0][0] + success = demo_success[1][0] + else: + demo = demo_success[0][0] + print(f"one demo for {task_env.get_name()} // Variation {my_variation_count}: {time()-t0}, success: {success}") + except Exception as e: + print(e) + attempts -= 1 + if attempts > 0: + continue + problem = ( + 'Process %d failed collecting task %s (variation: %d, ' + 'example: %d). Skipping this task/variation.\n%s\n' % ( + i, task_env.get_name(), my_variation_count, ex_idx, + str(e)) + ) + print(problem) + tasks_with_problems += problem + abort_variation = True + break + if success: + episode_path = os.path.join(episodes_path, EPISODE_FOLDER % (ex_idx)) + # with file_lock: + save_demo(demo, episode_path) + if FLAGS.save_configs: + task_base, waypoint_sets, config = task_env.read_config(demo.high_level_instructions) + # with file_lock: + save_configs(task_base, waypoint_sets, config, episode_path) + ex_idx += 1 + break + else: + if need_fail_case: + if fail_idx 0: + continue + problem = ( + 'Process %d failed collecting task %s (variation: %d, ' + 'example: %d). Skipping this task/variation.\n' % ( + i, task_env.get_name(), my_variation_count, ex_idx) + ) + print(problem) + tasks_with_problems += problem + abort_variation = True + else: + attempts -= 1 + if abort_variation: + break + + results[i] = tasks_with_problems + amsolver_env.shutdown() + + +def main(argv): + + tasks_path = './vlm/tasks' + task_files = [t.replace('.py', '') for t in os.listdir(tasks_path) + if t != '__init__.py' and t.endswith('.py')] + + if len(FLAGS.tasks) > 0: + for t in FLAGS.tasks: + if t not in task_files: + raise ValueError('Task %s not recognised!.' % t) + task_files = FLAGS.tasks + + tasks = [task_file_to_task_class(t, parent_folder = 'vlm') for t in task_files] + + manager = Manager() + + result_dict = manager.dict() + file_lock = manager.Lock() + + task_index = manager.Value('i', 0) + variation_count = manager.Value('i', 0) + lock = manager.Lock() + + check_and_make(FLAGS.save_path) + + processes = [Process( + target=run, args=( + i, lock, task_index, variation_count, result_dict, file_lock, + tasks)) + for i in range(FLAGS.processes)] + [t.start() for t in processes] + [t.join() for t in processes] + + print('Data collection done!') + for i in range(FLAGS.processes): + print(result_dict[i]) + + +if __name__ == '__main__': + app.run(main) diff --git a/easi/tasks/ebmanipulation/vendor/tools/grasploc.py b/easi/tasks/ebmanipulation/vendor/tools/grasploc.py new file mode 100644 index 0000000..a478791 --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/tools/grasploc.py @@ -0,0 +1,277 @@ +import open3d as o3d +import argparse +import numpy as np +from itertools import combinations +import pickle +import os +from distutils.util import strtobool + +class GraspPoint: + def __init__(self, centroid, normal, principal, shell_r, shell_h, idx, bbox3d): + self.centroid = centroid + self.normal = normal + self.principal = principal + self.shell_r = shell_r + self.shell_h = shell_h + self.neighbor_idx = idx + self.bbox3d = bbox3d # x, y, z: x is length along normal, z is length along principal + +class Grasploc: + def __init__(self, args): + self.args = args + self.mesh, self.pcd, self.densepcd = None, None, None + self.grasp_points = [] + self.se3_output = None + + def run(self, origin_offset=None): + print('preprocess') + ret = self.preprocess() + if ret == 0: + self.find_grasp1() + if self.args.vis_debug: + self.visual_check() + self.save_result(origin_offset) + + def preprocess(self): + # sample grasp pose on surface (self.pcd), but calculate normal, principal based on volume samples (self.densepcd) + if self.args.use_meshlab: + try: + meshlab_tmp_file = self.args.input_file[:-4] + '_meshlab.ply' + # if not os.path.exists(meshlab_tmp_file) or self.args.meshlab_always: + try: + os.system('meshlabserver -i {} -o {} -s {}'.format(self.args.input_file, meshlab_tmp_file, self.args.meshlab_sampling_file)) + ply = o3d.io.read_point_cloud(meshlab_tmp_file).voxel_down_sample(voxel_size=self.args.pcd_sample_voxel_size) + o3d.io.write_point_cloud(meshlab_tmp_file, ply) + self.pcd = o3d.io.read_point_cloud(meshlab_tmp_file) + except Exception as e: + print(e) + print('cannot use meshlab to sample on {}'.format(self.args.input_file)) + self.pcd = o3d.io.read_triangle_mesh(self.args.input_file).sample_points_poisson_disk(number_of_points=100000).voxel_down_sample(voxel_size=self.args.pcd_sample_voxel_size) + # self.pcd = o3d.io.read_triangle_mesh(self.args.input_file).sample_points_poisson_disk(number_of_points=40000) + # processing of meshlab sampling output: decide required number of points in point cloud by object bbox surface size; translate point cloud to be centered at zero origin + except Exception as e: + print(e) + return 1 + else: + self.pcd = o3d.io.read_triangle_mesh(self.args.input_file).sample_points_poisson_disk(number_of_points=200000).voxel_down_sample(voxel_size=self.args.pcd_sample_voxel_size) + self.crop_factor = 1.0 + if not self.args.no_crop: + # bbx = o3d.geometry.AxisAlignedBoundingBox( + # min_bound=np.array([self.args.crop_x_min, self.args.crop_y_min, self.args.crop_z_min]), + # max_bound=np.array([self.args.crop_x_max, self.args.crop_y_max, self.args.crop_z_max])) + trans = self.args.crop_box_transform[:3,3] + rot = self.args.crop_box_transform[:3,:3] + x_extent = self.args.crop_x_max-self.args.crop_x_min + y_extent = self.args.crop_y_max - self.args.crop_y_min + z_extent = self.args.crop_z_max- self.args.crop_z_min + extent = np.array([x_extent, y_extent, z_extent]) + bbx = o3d.geometry.OrientedBoundingBox(center=trans, R=rot, extent=extent) + original_points = len(self.pcd.points) + self.pcd = self.pcd.crop(bbx) + self.crop_factor = len(self.pcd.points) / original_points + if len(self.pcd.points) == 0: + print('mesh has no point samples in cropped area, quit') + return 1 + else: + return 0 + + def find_grasp1(self): + self.pcd.estimate_normals() + pcd_tree = o3d.geometry.KDTreeFlann(self.pcd) + ft, fd, fw, fl = self.args.finger_thickness, self.args.finger_max_distance, self.args.finger_width, self.args.finger_length + # The minimum number of points that are required to be inside of a grasp + self.downpcd = self.pcd.voxel_down_sample(voxel_size=self.args.grasp_sample_voxel_size) + while len(self.downpcd.points)>2000: + self.args.grasp_sample_voxel_size = self.args.grasp_sample_voxel_size*1.2 + self.downpcd = self.pcd.voxel_down_sample(voxel_size=self.args.grasp_sample_voxel_size) + self.downpcd.paint_uniform_color([1, 0, 0]) + # if self.args.vis_debug: + # geoms = [] + # self.pcd.paint_uniform_color([0.8, 0.8, 0.8]) + # geoms.append(self.pcd) + # geoms.append(self.downpcd) + # o3d.visualization.draw_geometries(geoms, point_show_normal=True) + # for pt, nm in zip(self.pcd.points[::dr], self.pcd.normals[::dr]): + print('num of samples:', len(self.downpcd.points)) + min_num_points_in_grasp = (fd*fl + fl*ft + fd*ft) / self.args.pcd_sample_voxel_size**2 * self.args.min_num_points_between_proportion + min_num_points_in_grasp *= self.crop_factor / 2 + while len(self.grasp_points) <= 10: + for i, (pt, nm) in enumerate(zip(self.downpcd.points, self.downpcd.normals)): + print(i, '/', len(self.downpcd.points)) + # grasp along negative normal, aka pointing inward object orthogonal to local surface + # do it for positive and negative direction as calculated normal is not always pointing inwards + for normal in [nm, -nm]: + # create a cuboid of the volume between two fingers ((finger distance + 2*finger width) * finger_thickness * finger length), + # here finger distance can be 0-max distance + # rotate about normal to find axis connecting two fingers that has blank space on both sides for fingers while has points between, + # (optional) the side surface direction should be consistent with finger's direction + # 1. generate principal samples on orthogonal plane of normal + normal = normal / np.linalg.norm(normal) + finger_percents=[0.1, 0.2,0.4,0.5] + for f_p in finger_percents: + centroid = pt + fl*f_p * normal + tmp = np.array([1, 0, 0]) + principal = tmp - np.dot(tmp, normal) * normal + principal = principal / np.linalg.norm(principal) + theta = np.pi * 2 / self.args.rotation_sample + principals = [np.cos(i*theta) * principal + np.sin(i*theta) * np.cross(principal, normal) for i in range(self.args.rotation_sample)] + principals = [p / np.linalg.norm(p) for p in principals] + # 2. create cuboid along normal and principal direction, normal <-> finger length; principal <-> finger thickness; cross <-> grasp aperture + # check point num between two fingers and on two sides + radius = np.sqrt(ft**2 + (fd + 2*fw)**2 + fl**2) / 2 + _, idx, _ = pcd_tree.search_radius_vector_3d(centroid, radius=radius) + pt_offset = np.asarray(self.pcd.points)[idx[1:], :] - centroid + for principal in principals: + cross = np.cross(normal, principal) + # for aperture in np.linspace(self.args.min_grasp_distance, fd, 10): + between_conditions = [np.abs(pt_offset @ normal) < fl / 2, np.abs(pt_offset @ principal) < ft / 2, np.abs(pt_offset @ cross) < fd / 2] + # extend between area distance of finger width along +/-cross and -normal + around_conditions = [pt_offset @ normal < fl / 2, pt_offset @ normal > -fl / 2 - fw, np.abs(pt_offset @ principal) < ft / 2, np.abs(pt_offset @ cross) < fd / 2 + fw] + n_between = np.sum(np.bitwise_and.reduce(between_conditions)) + n_around = np.sum(np.bitwise_and.reduce(around_conditions)) - n_between + # print(n_between, min_num_points_in_grasp, n_around, self.args.max_num_points_intefering) + if n_between > min_num_points_in_grasp and n_around < self.args.max_num_points_intefering: + grasp = GraspPoint(centroid, normal, principal, fd, None, None, [ft, fd, fl]) + self.grasp_points.append(grasp) + # grasp.normal = -grasp.normal + # grasp.principal = -grasp.principal + # self.grasp_points.append(grasp) + # print('found a grasp') + # self.visual_check(grasp, vis_bbox=True) + if len(self.grasp_points)<=10: + min_num_points_in_grasp = min_num_points_in_grasp*0.5 + print('Release the min_num_points_in_grasp to {}'.format(min_num_points_in_grasp)) + elif len(self.grasp_points) == 0: + self.args.grasp_sample_voxel_size = self.args.grasp_sample_voxel_size*0.8 + self.downpcd = self.pcd.voxel_down_sample(voxel_size=self.args.grasp_sample_voxel_size) + print('Grasp poses are too litte. Resample the point cloud with {}'.format(self.args.grasp_sample_voxel_size)) + print('grasp candidates:', len(self.grasp_points)) + + def visual_check(self, grasp=None, vis_bbox=False): + # visualization, create cylinder + geoms = [] + self.pcd.paint_uniform_color([0.8, 0.8, 0.8]) + geoms.append(self.pcd) + grasp_set = self.grasp_points if grasp is None else [grasp] + n_vis = 1000 + fw = self.args.finger_width + dr = max(len(self.grasp_points) // n_vis, 1) + for grasp in grasp_set[::dr]: + trans_mat = np.identity(4) + trans_mat[:3, 2] = grasp.normal + trans_mat[:3, 0] = grasp.principal + trans_mat[:3, 1] = np.cross(grasp.normal, grasp.principal) + trans_mat[:3, 3] = grasp.centroid + + # grasp_finger_connection_vis.transform(trans_corr_mat) + # grasp_finger_connection_vis.transform(trans_mat) + # geoms.append(grasp_finger_connection_vis) + if vis_bbox: + trans_corr_mat = np.identity(4) + grasp_bbox_vis = o3d.geometry.TriangleMesh.create_box(width=grasp.bbox3d[0], height=fw, depth=grasp.bbox3d[2]) + trans_corr_mat[0, 3] = -grasp.bbox3d[0]/2 + trans_corr_mat[1, 3] = -grasp.bbox3d[1]/2 - fw + trans_corr_mat[2, 3] = -grasp.bbox3d[2]/2 + grasp_bbox_vis.transform(trans_corr_mat) + grasp_bbox_vis.transform(trans_mat) + geoms.append(grasp_bbox_vis) + grasp_bbox_vis = o3d.geometry.TriangleMesh.create_box(width=grasp.bbox3d[0], height=fw, depth=grasp.bbox3d[2]) + trans_corr_mat[0, 3] = -grasp.bbox3d[0]/2 + trans_corr_mat[1, 3] = grasp.bbox3d[1]/2 + trans_corr_mat[2, 3] = -grasp.bbox3d[2]/2 + grasp_bbox_vis.transform(trans_corr_mat) + grasp_bbox_vis.transform(trans_mat) + geoms.append(grasp_bbox_vis) + grasp_bbox_vis = o3d.geometry.TriangleMesh.create_box(width=grasp.bbox3d[0], height=grasp.bbox3d[1] + 2*fw, depth=fw) + # grasp_bbox_vis.paint_uniform_color([0, 0, 0.1]) + trans_corr_mat[0, 3] = -grasp.bbox3d[0]/2 + trans_corr_mat[1, 3] = -grasp.bbox3d[1]/2 - fw + trans_corr_mat[2, 3] = -grasp.bbox3d[2]/2 - fw + grasp_bbox_vis.transform(trans_corr_mat) + grasp_bbox_vis.transform(trans_mat) + geoms.append(grasp_bbox_vis) + self.args.frame_size = 0.02 # to show axis with bbox + mesh_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=self.args.frame_size) + tmp_trans_mat = np.identity(4) + tmp_trans_mat[:3, 3] = grasp.centroid + mesh_frame.transform(tmp_trans_mat) + geoms.append(mesh_frame) + self.args.frame_size = 0.05 + + mesh_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=self.args.frame_size) + mesh_frame.transform(trans_mat) + geoms.append(mesh_frame) + + o3d.visualization.draw_geometries(geoms, point_show_normal=True) + + def save_result(self, origin_offset=None): + self.se3_output = [] + for grasp in self.grasp_points: + grasp_pose = np.identity(4) + grasp_pose[:3, 3] = grasp.centroid + grasp_pose[:3, 0] = grasp.principal + grasp_pose[:3, 2] = grasp.normal + grasp_pose[:3, 1] = np.cross(grasp.normal, grasp.principal) + self.se3_output.append(grasp_pose) + self.se3_output = np.array(self.se3_output) + if origin_offset is not None: + self.se3_output = np.einsum('ij,kjl->kil', np.linalg.inv(origin_offset), self.se3_output) + with open(self.args.output_file, 'wb') as f: + # pickle.dump(self.grasp_points, f) # only for debug + pickle.dump(self.se3_output, f) + print('grasp poses saved to', self.args.output_file) + + def load_result(self): + with open(self.args.output_file, 'rb') as f: + self.se3_output = pickle.load(f) + print('grasp poses loaded from', self.args.output_file) + + +def define_default_args(): + parser = argparse.ArgumentParser() + # point cloud, grasp sample density + parser.add_argument('--use_meshlab',type=lambda x:bool(strtobool(x)), default=True, help='whether use meshlab to sample the mesh') + parser.add_argument('--pcd_sample_voxel_size', type=int, default=0.0005, help='point cloud sample voxel grid size') + parser.add_argument('--grasp_sample_voxel_size', type=int, default=0.005, help='grasp sample voxel grid size') + + # grasp generation params + parser.add_argument('--min_num_points_between_proportion', type=int, default=0.2, help='The proportion of minimum points in the bbox formed by two fingers over the half surface size of the bbox divided by sampled point cloud voxel distance squared') + parser.add_argument('--max_num_points_intefering', type=int, default=10, help='The maximum number of points allowed to interfere with the gripper') + parser.add_argument('--min_grasp_distance', type=float, default=0.01, help='minimum distance between two fingers when the gripper close to grasp object') + parser.add_argument('--finger_max_distance', type=float, default=0.08, help='The maximum distance between the two fingers of the gripper') + parser.add_argument('--finger_width', type=float, default=0.02, help='finger width (the same direction as distance between two fingers)') + parser.add_argument('--finger_thickness', type=float, default=0.02, help='distance between front and back side of fingers') + parser.add_argument('--rotation_sample', type=int, default=16, help='grasp sample rotated about normal axis') + parser.add_argument('--finger_length', type=float, default=0.045, help='finger length') + + # input point cloud grasp search region + parser.add_argument('--crop_x_min', type=float, default=-0.1, help='x min for cropping the point cloud') + parser.add_argument('--crop_x_max', type=float, default=0.1, help='x max for cropping the point cloud') + parser.add_argument('--crop_y_min', type=float, default=-0.1, help='y min for cropping the point cloud') + parser.add_argument('--crop_y_max', type=float, default=0.1, help='y max for cropping the point cloud') + parser.add_argument('--crop_z_min', type=float, default=-0.1, help='z max for cropping the point cloud') + parser.add_argument('--crop_z_max', type=float, default=0.1, help='z max for cropping the point cloud') + parser.add_argument('--crop_box_transform', default=None, help='the transform of the crop box') + parser.add_argument('--no_crop', type=lambda x:bool(strtobool(x)), default=True, help='Do not crop input point cloud (1 default; 0 to crop), set xyz min max to be the bbox around point cloud') + + # io, vis params + parser.add_argument('--input_file', type=str, default='./flatbox.ply', help='(PLY file) object 3d model input file to search graspable poses') + parser.add_argument('--output_file', type=str, default='./graspposes.pkl', help='file to save graspable poses using pickle') + parser.add_argument('--meshlab_always', type=lambda x:bool(strtobool(x)), default=False, help='always use meshlab to sample regardless of whether there is previous saved file') + parser.add_argument('--meshlab_sampling_file', type=str, default='./tools/meshlab_stratified_sampling.mlx', help='sampling script using meshlab') + parser.add_argument('--vis_debug', type=lambda x:bool(strtobool(x)), default=False, help='visualize temporary results for debugging') + parser.add_argument('--frame_size', type=float, default=0.01, help='coordinate frame size in visualization') + args = parser.parse_args() + return args + + +if __name__ == '__main__': + grasp_pose_path = './vlm/grasp_poses/' + ply_file = None + args = define_default_args() + args.input_file = ply_file + args.output_file = grasp_pose_path + ply_file.split('/')[-1][:-4] + '.pkl' + args.vis_debug = True + args.meshlab_always=True + gl = Grasploc(args) + gl.run() diff --git a/easi/tasks/ebmanipulation/vendor/tools/meshlab_stratified_sampling.mlx b/easi/tasks/ebmanipulation/vendor/tools/meshlab_stratified_sampling.mlx new file mode 100644 index 0000000..b21e61e --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/tools/meshlab_stratified_sampling.mlx @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/easi/tasks/ebmanipulation/vendor/tools/task_builder_NLP.py b/easi/tasks/ebmanipulation/vendor/tools/task_builder_NLP.py new file mode 100644 index 0000000..289f100 --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/tools/task_builder_NLP.py @@ -0,0 +1,312 @@ +import os +import sys +from os.path import join, dirname, abspath, isfile + +CURRENT_DIR = dirname(abspath(__file__)) +sys.path.insert(0, join(CURRENT_DIR, '..')) # Use local amsolver rather than installed + +import traceback +import readline + +from pyrep.const import RenderMode + +from amsolver.backend.const import TTT_FILE +from pyrep import PyRep +from pyrep.robots.arms.panda import Panda +from pyrep.objects.shape import Shape +from pyrep.objects.dummy import Dummy +from pyrep.robots.end_effectors.panda_gripper import PandaGripper +from amsolver.backend.scene import Scene +from amsolver.backend.exceptions import * +from amsolver.observation_config import ObservationConfig, CameraConfig +from amsolver.backend.robot import Robot +from amsolver.utils import name_to_task_class +from tools.task_validator import task_smoke, TaskValidationError + +CURRENT_DIR = dirname(abspath(__file__)) + + +def print_fail(message, end='\n'): + message = str(message) + sys.stderr.write('\x1b[1;31m' + message.strip() + '\x1b[0m' + end) + + +def setup_list_completer(TASKS_PATH): + task_files = [t.replace('.py', '') for t in os.listdir(TASKS_PATH) + if t != '__init__.py' and t.endswith('.py')] + + def list_completer(_, state): + line = readline.get_line_buffer() + if not line: + return [c + " " for c in task_files][state] + + else: + return [c + " " for c in task_files if c.startswith(line)][state] + + readline.parse_and_bind("tab: complete") + readline.set_completer(list_completer) + + +class LoadedTask(object): + + def __init__(self, pr: PyRep, scene: Scene, robot: Robot, parent_folder="vlm"): + self.pr = pr + self.scene = scene + self.robot = robot + self.task = self.task_class = self.task_file = None + self._variation_index = 0 + self.parent_folder = parent_folder + + def _load_task_to_scene(self): + self.scene.unload() + self.task = self.task_class(self.pr, self.robot) # task constructor + try: + # Try and load the task + scene.load(self.task, ttms_folder='./{}/task_ttms'.format(self.parent_folder)) + except FileNotFoundError as e: + # The .ttt file must not exist + handle = Dummy.create() + handle.set_name(self.task_file.replace('.py', '')) + handle.set_model(True) + # Put the dummy at the centre of the workspace + self.task.get_base().set_position(Shape('workspace').get_position()) + + def _edit_new_task(self): + task_file = input('What task would you like to edit?\n') + task_file = task_file.strip(' ') + if len(task_file) > 3 and task_file[-3:] != '.py': + task_file += '.py' + try: + task_class = name_to_task_class(task_file, parent_folder=self.parent_folder) + except: + print('There was no task named: %s. ' + 'Would you like to create it?' % task_file) + inp = input() + if inp == 'y': + self._create_python_file(task_file) + task_class = name_to_task_class(task_file, parent_folder=self.parent_folder) + else: + print('Pick a defined task in that case...') + task_class, task_file = self._edit_new_task() + return task_class, task_file + + def _create_python_file(self, task_file: str): + with open(join(CURRENT_DIR, 'assets', 'task_template.txt'), 'r') as f: + file_content = f.read() + class_name = self._file_to_class_name(task_file) + file_content = file_content % (class_name,) + new_file_path = join(CURRENT_DIR, '../{}/tasks'.format(self.parent_folder), task_file) + if isfile(new_file_path): + raise RuntimeError('File already exists. Will not override this.') + with open(new_file_path, 'w+') as f: + f.write(file_content) + + def _file_to_class_name(self, name): + name = name.replace('.py', '') + return ''.join([w[0].upper() + w[1:] for w in name.split('_')]) + + def reload_python(self): + try: + task_class = name_to_task_class(self.task_file, parent_folder=self.parent_folder) + except Exception as e: + print_fail('The python file could not be loaded!') + traceback.print_exc() + return None, None + self.task = task_class(self.pr, self.robot) # task constructor + self.scene.load(self.task) + + def new_task(self): + self._variation_index = 0 + self.task_class, self.task_file = self._edit_new_task() + self._load_task_to_scene() + self.pr.step_ui() + print('You are now editing: %s' % str(self.task_class)) + + def reset_variation(self): + self._variation_index = 0 + + def new_variation(self): + try: + self._variation_index += 1 + descriptions = self.scene.init_episode( + self._variation_index % self.task.variation_count()) + print('Task descriptions: ', descriptions) + except (WaypointError, BoundaryError, Exception) as ex: + traceback.print_exc() + self.pr.step_ui() + + def new_episode(self): + try: + descriptions = self.scene.init_episode( + self._variation_index % self.task.variation_count(), + max_attempts=1) + print('Task descriptions: ', descriptions) + except (WaypointError, BoundaryError, Exception) as ex: + traceback.print_exc() + self.scene.reset() + self.pr.step_ui() + + def new_demo(self): + success = False + try: + _, success = self.scene.get_demo(False, randomly_place=False) + except (WaypointError, NoWaypointsError, DemoError, Exception) as e: + traceback.print_exc() + # success, terminate = self.task.success() + if success: + print("Demo was a success!") + self.scene.reset() + self.pr.step_ui() + self.pr.step_ui() + + def save_task(self): + ttm_path = join(CURRENT_DIR, '../{}/task_ttms'.format(self.parent_folder), + self.task_file.replace('.py', '.ttm')) + self.task.get_base().save_model(ttm_path) + print('Task saved to:', ttm_path) + + def run_task_validator(self): + print('About to perform task validation.') + print("What variation to test? Pick int in range: 0 to %d, or -1 to " + "test all. Or press 'e' to exit." + % self.task.variation_count()) + inp = input() + if inp == 'e': + return + self.pr.start() + try: + v = int(inp) + v = v if v < 0 else v % self.task.variation_count() + task_smoke(self.task, self.scene, variation=v) + except TaskValidationError as e: + traceback.print_exc() + self.pr.stop() + + def rename(self): + print('Enter new name (or q to abort).') + inp = input() + if inp == 'q': + return + + name = inp.replace('.py', '') + python_file = name + '.py' + + # Change name of base + handle = Dummy(self.task_file.replace('.py', '')) + handle.set_name(name) + + # Change the class name + old_file_path = join(CURRENT_DIR, '../{}/tasks'.format(self.parent_folder), self.task_file) + old_class_name = self._file_to_class_name(self.task_file) + new_class_name = self._file_to_class_name(name) + with open(old_file_path, 'r') as f: + content = f.read() + content = content.replace(old_class_name, new_class_name) + with open(old_file_path, 'w') as f: + f.write(content) + + # Rename python task file + new_file_path = join(CURRENT_DIR, '../{}/tasks'.format(self.parent_folder), python_file) + os.rename(old_file_path, new_file_path) + + # Rename .ttt + old_ttm_path = join(CURRENT_DIR, '../{}/task_ttms'.format(self.parent_folder), + self.task_file.replace('.py', '.ttm')) + new_ttm_path = join(CURRENT_DIR, '../{}/task_ttms'.format(self.parent_folder), + python_file.replace('.py', '.ttm')) + os.rename(old_ttm_path, new_ttm_path) + + self.task_file = python_file + self.reload_python() + self.save_task() + print('Rename complete') + + +if __name__ == '__main__': + task_path = "./vlm/tasks" + setup_list_completer(task_path) + + pr = PyRep() + ttt_file = join(CURRENT_DIR, '..', 'amsolver', TTT_FILE) + pr.launch(ttt_file, responsive_ui=True) + pr.step_ui() + + robot = Robot(Panda(), PandaGripper()) + cam_config = CameraConfig(rgb=True, depth=False, mask=False, + render_mode=RenderMode.OPENGL) + obs_config = ObservationConfig() + obs_config.set_all(False) + obs_config.right_shoulder_camera = cam_config + obs_config.left_shoulder_camera = cam_config + obs_config.overhead_camera = cam_config + obs_config.wrist_camera = cam_config + obs_config.front_camera = cam_config + + scene = Scene(pr, robot, obs_config) + loaded_task = LoadedTask(pr, scene, robot, parent_folder="vlm") + + print(' ,') + print(' /( ___________') + print('| >:===========` Welcome to task builder!') + print(' )(') + print(' ""') + + loaded_task.new_task() + + while True: + # os.system('cls' if os.name == 'nt' else 'clear') + print('\n-----------------\n') + print('The python file will be reloaded when simulation is restarted.') + print('(q) to quit.') + if pr.running: + print('(+) stop the simulator') + print('(v) for task variation.') + print('(e) for episode of same variation.') + print('(d) for demo.') + print('(p) for running the sim for 100 steps (with rendering).') + else: + print('(!) to run task validator.') + print('(+) run the simulator') + print('(n) for new task.') + print('(s) to save the .ttm') + print('(r) to rename the task') + + inp = input() + + if inp == 'q': + break + + if pr.running: + if inp == '+': + pr.stop() + loaded_task._load_task_to_scene() + pr.step_ui() + elif inp == 'p': + [(pr.step(), scene.get_observation()) for _ in range(100)] + elif inp == 'd': + loaded_task.new_demo() + elif inp == 'v': + loaded_task.new_variation() + elif inp == 'e': + loaded_task.new_episode() + else: + if inp == '+': + loaded_task.reload_python() + loaded_task.reset_variation() + pr.start() + pr.step_ui() + elif inp == 'n': + inp = input('Do you want to save the current task first?\n') + if inp == 'y': + loaded_task.save_task() + loaded_task.new_task() + elif inp == 's': + loaded_task.save_task() + elif inp == '!': + loaded_task.run_task_validator() + elif inp == 'r': + loaded_task.rename() + + pr.stop() + pr.shutdown() + print('Done. Goodbye!') diff --git a/easi/tasks/ebmanipulation/vendor/tools/task_validator.py b/easi/tasks/ebmanipulation/vendor/tools/task_validator.py new file mode 100644 index 0000000..c692cd6 --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/tools/task_validator.py @@ -0,0 +1,149 @@ +from amsolver.backend.task import Task +from amsolver.backend.scene import DemoError +from amsolver.observation_config import ObservationConfig +from pyrep import PyRep +from pyrep.robots.arms.panda import Panda +from pyrep.robots.end_effectors.panda_gripper import PandaGripper +from amsolver.backend.const import TTT_FILE +from amsolver.backend.scene import Scene +from amsolver.backend.utils import task_file_to_task_class +from amsolver.backend.task import TASKS_PATH +from amsolver.backend.robot import Robot +import numpy as np +import os +import argparse + +DEMO_ATTEMPTS = 5 +MAX_VARIATIONS = 100 + + +class TaskValidationError(Exception): + pass + + +def task_smoke(task: Task, scene: Scene, variation=-1, demos=4, success=0.50, + max_variations=3, test_demos=True): + # -1 variations for all. + + print('Running task validator on task: %s' % task.get_name()) + + # Loading + scene.load(task) + + # Number of variations + variation_count = task.variation_count() + if variation_count < 0: + raise TaskValidationError( + "The method 'variation_count' should return a number > 0.") + + if variation_count > MAX_VARIATIONS: + raise TaskValidationError( + "This task had %d variations. Currently the limit is set to %d" % + (variation_count, MAX_VARIATIONS)) + + # Base rotation bounds + base_pos, base_ori = task.base_rotation_bounds() + if len(base_pos) != 3 or len(base_ori) != 3: + raise TaskValidationError( + "The method 'base_rotation_bounds' should return a tuple " + "containing a list of floats.") + + # Boundary root + root = task.boundary_root() + if not root.still_exists(): + raise TaskValidationError( + "The method 'boundary_root' should return a Dummy that is the root " + "of the task.") + + def variation_smoke(i): + + print('Running task validator on variation: %d' % i) + + attempt_result = False + failed_demos = 0 + for j in range(DEMO_ATTEMPTS): + failed_demos = run_demos(i) + attempt_result = (failed_demos / float(demos) <= 1. - success) + if attempt_result: + break + else: + print('Failed on attempt %d. Trying again...' % j) + + # Make sure we don't fail too often + if not attempt_result: + raise TaskValidationError( + "Too many failed demo runs. %d of %d demos failed." % ( + failed_demos, demos)) + else: + print('Variation %d of task %s is good!' % (i, task.get_name())) + if test_demos: + print('%d of %d demos were successful.' % ( + demos - failed_demos, demos)) + + def run_demos(variation_num): + fails = 0 + for dr in range(demos): + try: + scene.reset() + desc = scene.init_episode(variation_num, max_attempts=10) + if not isinstance(desc, list) or len(desc) <= 0: + raise TaskValidationError( + "The method 'init_variation' should return a list of " + "string descriptions.") + if test_demos: + demo = scene.get_demo(record=True) + assert len(demo) > 0 + except DemoError as e: + fails += 1 + print(e) + except Exception as e: + # TODO: check that we don't fall through all of these cases + fails += 1 + print(e) + + return fails + + variations_to_test = [variation] + if variation < 0: + variations_to_test = list(range( + np.minimum(variation_count, max_variations))) + + # Task set-up + scene.init_task() + [variation_smoke(i) for i in variations_to_test] + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument("task", help="The task file to test.") + args = parser.parse_args() + + python_file = os.path.join(TASKS_PATH, args.task) + if not os.path.isfile(python_file): + raise RuntimeError('Could not find the task file: %s' % python_file) + + task_class = task_file_to_task_class(args.task) + + DIR_PATH = os.path.dirname(os.path.abspath(__file__)) + sim = PyRep() + ttt_file = os.path.join( + DIR_PATH, '..', 'rlbench', TTT_FILE) + sim.launch(ttt_file, headless=True) + sim.step_ui() + sim.set_simulation_timestep(0.005) + sim.step_ui() + sim.start() + + robot = Robot(Panda(), PandaGripper()) + + active_task = task_class(sim, robot) + obs = ObservationConfig() + obs.set_all(False) + scene = Scene(sim, robot, obs) + try: + task_smoke(active_task, scene, variation=2) + except TaskValidationError as e: + sim.shutdown() + raise e + sim.shutdown() + print('Validation successful!') diff --git a/easi/tasks/ebmanipulation/vendor/tools/test_config_generator.py b/easi/tasks/ebmanipulation/vendor/tools/test_config_generator.py new file mode 100644 index 0000000..5b740d4 --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/tools/test_config_generator.py @@ -0,0 +1,259 @@ +from multiprocessing import Process, Manager +from time import time +import cv2 +from pyrep.objects.dummy import Dummy +from pyrep.objects.vision_sensor import VisionSensor +from pyrep.const import RenderMode +from os.path import join, dirname, abspath, isfile +import sys +CURRENT_DIR = dirname(abspath(__file__)) +sys.path.insert(0, join(CURRENT_DIR, '..')) # Use local amsolver rather than installed +from amsolver import ObservationConfig +from amsolver.action_modes import ActionMode +from amsolver.backend.utils import task_file_to_task_class +from amsolver.environment import Environment +import amsolver.backend.task as task + +import os +import pickle +from PIL import Image +from amsolver.backend import utils +from amsolver.backend.const import * +import numpy as np +import random + +from absl import app +from absl import flags + +def set_seed(seed, torch=False): + random.seed(seed) + os.environ['PYTHONHASHSEED'] = str(seed) + np.random.seed(seed) + + if torch: + import torch + torch.manual_seed(seed) +random_seed = random.randint(1, 10000) +print(f'random seed: {random_seed}') +set_seed(random_seed) +FLAGS = flags.FLAGS + +flags.DEFINE_string('save_path', + './rlbench_data/test/seen', + 'Where to save the demos.') +flags.DEFINE_list('tasks', [ + 'place_into_shape_sorter_color', + 'place_into_shape_sorter_shape', 'place_into_shape_sorter_relative', + 'drop_pen_color', 'drop_pen_relative', 'drop_pen_size', + 'wipe_table_color', 'wipe_table_relative', 'wipe_table_shape', 'wipe_table_size', 'wipe_table_direction', + 'pour_demo_color', 'pour_demo_relative', 'pour_demo_size', + 'pick_cube_color', 'pick_cube_relative', 'pick_cube_shape', 'pick_cube_size', + 'stack_cubes_color', 'stack_cubes_size', + 'stack_cubes_relative', 'stack_cubes_shape', + 'open_door_complex', + 'open_drawer' +], + 'The tasks to collect. If empty, all tasks are collected.') +flags.DEFINE_list('image_size', [360, 360], + 'The size of the images tp save.') +flags.DEFINE_enum('renderer', 'opengl', ['opengl', 'opengl3'], + 'The renderer to use. opengl does not include shadows, ' + 'but is faster.') +flags.DEFINE_integer('processes', 16, + 'The number of parallel processes during collection.') +flags.DEFINE_integer('episodes_per_task', 5, + 'The number of episodes to collect per task.') +flags.DEFINE_integer('episodes_per_task_all_variations', 100, + 'The number of episodes to collect per task.') +flags.DEFINE_integer('variations', -1, + 'Number of variations to collect per task. -1 for all.') + +class Recorder(object): + def __init__(self) -> None: + cam_placeholder = Dummy('cam_cinematic_placeholder') + self.cam = VisionSensor.create([320, 320], view_angle=30) + self.cam.set_pose(cam_placeholder.get_pose()) + self.cam.set_parent(cam_placeholder) + self._snaps = [] + self._fps=30 + + def take_snap(self): + self._snaps.append( + (self.cam.capture_rgb() * 255.).astype(np.uint8)) + + def save_video(self, path): + print('Converting to video ...') + os.makedirs(os.path.dirname(path), exist_ok=True) + video = cv2.VideoWriter( + path, cv2.VideoWriter_fourcc(*'MJPG'), self._fps, + tuple(self.cam.get_resolution())) + for image in self._snaps: + video.write(cv2.cvtColor(image, cv2.COLOR_RGB2BGR)) + video.release() + self._snaps = [] + + def save_image(self, path): + print('Saving the image ...') + cv2.imwrite(path, cv2.cvtColor(self._snaps[0], cv2.COLOR_RGB2BGR)) + self._snaps = [] + +def check_and_make(dir): + if not os.path.exists(dir): + os.makedirs(dir) + + +def save_demo(task_base, waypoint_sets, config, example_path): + check_and_make(example_path) + # Save the low-dimension data + with open(os.path.join(example_path, "configs.pkl"), 'wb') as f: + pickle.dump(config, f) + task_base.save_model(os.path.join(example_path, "task_base.ttm")) + waypoint_sets.save_model(os.path.join(example_path, "waypoint_sets.ttm")) + +def run(i, lock, task_index, variation_count, results, file_lock, tasks): + """Each thread will choose one task and variation, and then gather + all the episodes_per_task for that variation.""" + + # Initialise each thread with random seed + np.random.seed(None) + num_tasks = len(tasks) + + img_size = list(map(int, FLAGS.image_size)) + + obs_config = ObservationConfig() + obs_config.set_all_high_dim(False) + obs_config.set_all_low_dim(True) + + amsolver_env = Environment( + action_mode=ActionMode(), + obs_config=obs_config, + headless=True) + amsolver_env.launch() + # recorder = Recorder() + recorder = None + task_env = None + + tasks_with_problems = results[i] = '' + + while True: + # Figure out what task/variation this thread is going to do + with lock: + + if task_index.value >= num_tasks: + print('Process', i, 'finished') + break + + my_variation_count = variation_count.value + t = tasks[task_index.value] + task_env = amsolver_env.get_task(t) + var_target = task_env.variation_count() + if FLAGS.variations >= 0: + var_target = np.minimum(FLAGS.variations, var_target) + if my_variation_count >= var_target: + # If we have reached the required number of variations for this + # task, then move on to the next task. + variation_count.value = my_variation_count = 0 + task_index.value += 1 + + variation_count.value += 1 + if task_index.value >= num_tasks: + print('Process', i, 'finished') + break + t = tasks[task_index.value] + + task_env = amsolver_env.get_task(t) + task_env.set_variation(my_variation_count) + + if FLAGS.episodes_per_task_all_variations>0: + FLAGS.episodes_per_task = (FLAGS.episodes_per_task_all_variations // task_env.variation_count()) + variation_path = os.path.join( + FLAGS.save_path, task_env.get_name(), + VARIATIONS_FOLDER % my_variation_count) + + check_and_make(variation_path) + + episodes_path = os.path.join(variation_path, EPISODES_FOLDER) + check_and_make(episodes_path) + current_episodes = os.listdir(episodes_path) + + abort_variation = False + ex_idx = len(current_episodes) + # for ex_idx in range(FLAGS.episodes_per_task): + while ex_idx 0: + try: + task_base, waypoint_sets, config = task_env.save_config() + if recorder is not None: + recorder.take_snap() + except Exception as e: + print(e) + attempts -= 1 + if attempts > 0: + continue + problem = ( + 'Process %d failed collecting task %s (variation: %d, ' + 'example: %d). Skipping this task/variation.\n%s\n' % ( + i, task_env.get_name(), my_variation_count, ex_idx, + str(e)) + ) + print(problem) + tasks_with_problems += problem + abort_variation = True + break + episode_path = os.path.join(episodes_path, EPISODE_FOLDER % (ex_idx)) + with file_lock: + save_demo(task_base, waypoint_sets, config, episode_path) + if recorder is not None: + recorder.save_image(os.path.join(episode_path, "image.png")) + ex_idx += 1 + break + if abort_variation: + break + + results[i] = tasks_with_problems + amsolver_env.shutdown() + + +def main(argv): + # tasks_path = './language_tasks/tasks' + tasks_path = './vlm/tasks' + task_files = [t.replace('.py', '') for t in os.listdir(tasks_path) + if t != '__init__.py' and t.endswith('.py')] + + if len(FLAGS.tasks) > 0: + for t in FLAGS.tasks: + if t not in task_files: + raise ValueError('Task %s not recognised!.' % t) + task_files = FLAGS.tasks + + tasks = [task_file_to_task_class(t, parent_folder = 'vlm') for t in task_files] + + manager = Manager() + + result_dict = manager.dict() + file_lock = manager.Lock() + + task_index = manager.Value('i', 0) + variation_count = manager.Value('i', 0) + lock = manager.Lock() + + check_and_make(FLAGS.save_path) + + processes = [Process( + target=run, args=( + i, lock, task_index, variation_count, result_dict, file_lock, + tasks)) + for i in range(FLAGS.processes)] + [t.start() for t in processes] + [t.join() for t in processes] + + print('Data collection done!') + for i in range(FLAGS.processes): + print(result_dict[i]) + + +if __name__ == '__main__': + app.run(main) diff --git a/easi/tasks/ebmanipulation/vendor/vlm/__init__.py b/easi/tasks/ebmanipulation/vendor/vlm/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/easi/tasks/ebmanipulation/vendor/vlm/tasks/__init__.py b/easi/tasks/ebmanipulation/vendor/vlm/tasks/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/easi/tasks/ebmanipulation/vendor/vlm/tasks/drop_pen.py b/easi/tasks/ebmanipulation/vendor/vlm/tasks/drop_pen.py new file mode 100644 index 0000000..002eed5 --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/vlm/tasks/drop_pen.py @@ -0,0 +1,141 @@ +from typing import List +import numpy as np +import os +from amsolver.backend.task import Task +from pyrep.objects.dummy import Dummy +from pyrep.objects.shape import Shape +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.const import ObjectType, PrimitiveShape +from pyrep.backend._sim_cffi import ffi, lib +from amsolver.backend.unit_tasks import T0_ObtainControl, T1_MoveObjectGoal, TargetSpace, VLM_Object +from amsolver.const import colors +from amsolver.backend.conditions import DetectedCondition +from amsolver.backend.spawn_boundary import SpawnBoundary +from amsolver.backend.task import Task + +class DropPen(Task): + + def init_task(self) -> None: + self.spawn_space = SpawnBoundary([Shape('workspace')]) + self.model_dir = os.path.dirname(os.path.realpath(__file__)).replace("tasks","object_models/") + self.object_list = [] + self.target_list = [] + self.temporary_waypoints = [] + self.taks_base = self.get_base() + if not hasattr(self, "model_num"): + self.model_num = 2 + self.import_objects(self.model_num) + self._task_init_states = self.get_state()[0] + + def init_episode(self, index: int) -> List[str]: + self.variation_index = index + self.pyrep.set_configuration_tree(self._task_init_states) + try_times = 200 + # pick_number = np.random.randint(1,self.model_num) + pick_obj = self.object_list[0] + target_obj = self.target_list[0] + while len(self.temporary_waypoints)==0 and try_times>0: + self.sample_method() + self.pyrep.step() + init_states = self.get_state()[0] + obj_space_args = {"obj":pick_obj.manipulated_part, "container":target_obj} + target_space_descriptions = f"along the opening of {target_obj.target_space_descriptions}" + target_space = TargetSpace(self.drop_pose, space_args=obj_space_args, + target_space_descriptions = target_space_descriptions, focus_obj_id= target_obj.visual.get_handle()) + target_space.set_target(pick_obj.manipulated_part, try_ik_sampling=False, linear=False, release=True) + MoveTask = T1_MoveObjectGoal(self.robot, self.pyrep, target_space, self.taks_base, fail_times=2) + GraspTask = T0_ObtainControl(self.robot, self.pyrep, pick_obj.manipulated_part, self.taks_base, try_times=500, + next_task_fuc=MoveTask.get_path) + waypoints = GraspTask.get_path(try_ik_sampling=False, ignore_collisions=True) + if waypoints is not None: + self.temporary_waypoints += waypoints + self.reset_robot() + self.pyrep.set_configuration_tree(init_states) + try_times -= 1 + conditions = [DetectedCondition(pick_obj.manipulated_part, target_obj.successor)] + self.register_success_conditions(conditions) + for i,waypoint in enumerate(self.temporary_waypoints): + waypoint.set_name('waypoint{}'.format(i)) + descriptions = "Drop {} into {}.".format(pick_obj.manipulated_part.descriptions, target_obj.target_space_descriptions) + return [descriptions] + + @staticmethod + def drop_pose(obj:Shape, container:Shape, n_sample=36): + container_pos = container.get_position() + container_size = container.get_bounding_box() + container_x, container_y = container_size[1] - container_size[0], container_size[3] - container_size[2] + container_r = (container_x**2+container_y**2)**0.5/2 + + obj_size = obj.get_bounding_box() + obj_x, obj_y, obj_h = obj_size[1] - obj_size[0], obj_size[3] - obj_size[2], obj_size[5] - obj_size[4] + h_offset = max(obj_h, obj_x, obj_y)/1.5 + h = h_offset + container_pos[2] + container_size[5] + random_r = np.random.uniform(0, container_r*0.5, n_sample) + random_angle = np.random.uniform(0, np.pi, n_sample) + obj_goal_pose_set = np.zeros((n_sample, 4, 4)) + # obj_goal_pose_set[:, :3, 3] = np.array([container_pos[0], container_pos[1], h]) + obj_goal_pose_set[:, 3, 3] = 1 + axis_y = np.array([0, 0, -1]) + obj_goal_pose_set[:, :3, 1] = axis_y + tmp = np.array((0, 1, 0)) + theta = 2*np.pi/ n_sample + for i in range(n_sample): + x = container_pos[0]+ random_r[i]*np.cos(random_angle[i]) + y = container_pos[1]+ random_r[i]*np.sin(random_angle[i]) + sample_h = h + np.random.normal(scale=0.01) + obj_goal_pose_set[i, :3, 3] = np.array([x, y, sample_h]) + axis_x = np.cos(i*theta) * tmp + np.sin(i*theta) * np.cross(axis_y, tmp) + axis_x = axis_x/ np.linalg.norm(axis_x) + obj_goal_pose_set[i, :3, 0] = axis_x + obj_goal_pose_set[i, :3, 2] = np.cross(axis_x, axis_y) + rand_idx = np.random.permutation(len(obj_goal_pose_set)) + obj_goal_pose_set = obj_goal_pose_set[rand_idx] + return obj_goal_pose_set + + def import_objects(self, num=2): + if not hasattr(self, "model_path"): + model_path = self.model_dir+"pencil/pencil1/pencil1.ttm" + else: + model_path = self.model_dir+self.model_path + for i in range(num): + obj = VLM_Object(self.pyrep, model_path, i) + obj.set_parent(self.taks_base) + self.object_list.append(obj) + self.register_graspable_objects(self.object_list) + target_path = self.model_dir+"container/basket1.ttm" + for i in range(2): + target = self.pyrep.import_model(target_path) + target.set_model_dynamic(False) + target.scale_factor = lib.simGetObjectSizeFactor(ffi.cast('int',target._handle)) + target.set_parent(self.taks_base) + for children in target.get_objects_in_tree(exclude_base=True): + if children.get_type() == ObjectType.PROXIMITY_SENSOR: + target.successor = children + if "visual" in children.get_name(): + target.visual = children + self.target_list.append(target) + return + + def is_static_workspace(self) -> bool: + return True + + def sample_method(self): + self.spawn_space.clear() + for tg in self.target_list: + self.spawn_space.sample(tg, min_distance=0.1) + for obj in self.object_list: + self.spawn_space.sample(obj, min_distance=0.2) + for _ in range(5): + self.pyrep.step() + + def load(self, ttms_folder=None): + if Shape.exists('drop_pen'): + return Dummy('drop_pen') + ttm_file = os.path.join(ttms_folder, 'drop_pen.ttm') + self._base_object = self.pyrep.import_model(ttm_file) + return self._base_object + + def get_base(self) -> Dummy: + self._base_object = Dummy('drop_pen') + return self._base_object + diff --git a/easi/tasks/ebmanipulation/vendor/vlm/tasks/drop_pen_color.py b/easi/tasks/ebmanipulation/vendor/vlm/tasks/drop_pen_color.py new file mode 100644 index 0000000..5a05f37 --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/vlm/tasks/drop_pen_color.py @@ -0,0 +1,29 @@ +from typing import List +import numpy as np +from amsolver.backend.unit_tasks import VLM_Object, TargetSpace +from pyrep.objects.shape import Shape +from amsolver.const import colors +from amsolver.backend.utils import select_color +from vlm.tasks.drop_pen import DropPen + +class DropPenColor(DropPen): + def init_task(self) -> None: + # self.model_num = 3 + return super().init_task() + + def init_episode(self, index: int) -> List[str]: + color_names, rgbs = select_color(index, len(self.object_list)-1) + for i, obj in enumerate(self.object_list): + Shape(obj.manipulated_part.visual).set_color(rgbs[i]) + obj.manipulated_part.property["color"] = color_names[i] + obj.manipulated_part.descriptions = "the {} {}".format(color_names[i], obj.manipulated_part.property["shape"]) + + target_space_colors = np.random.choice(len(colors), len(self.target_list), replace=False) + for i, target_space in enumerate(self.target_list): + target_space.target_space_descriptions = "the {} container".format(colors[target_space_colors[i]][0]) + target_space.visual.set_color(colors[target_space_colors[i]][1]) + + return super().init_episode(index) + + def variation_count(self) -> int: + return len(colors) \ No newline at end of file diff --git a/easi/tasks/ebmanipulation/vendor/vlm/tasks/drop_pen_relative.py b/easi/tasks/ebmanipulation/vendor/vlm/tasks/drop_pen_relative.py new file mode 100644 index 0000000..e2f5236 --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/vlm/tasks/drop_pen_relative.py @@ -0,0 +1,50 @@ +import itertools +from typing import List +import numpy as np +from amsolver.backend.unit_tasks import VLM_Object, TargetSpace +from pyrep.objects.shape import Shape +from amsolver.const import colors +from amsolver.backend.utils import get_relative_position_xy, scale_object, select_color +from vlm.tasks.drop_pen import DropPen + +relative_pos_list = list(itertools.product(["left", "right", "front", "rear"], repeat=2)) +class DropPenRelative(DropPen): + def init_task(self) -> None: + # self.model_num = 3 + return super().init_task() + + def init_episode(self, index: int) -> List[str]: + self.object_target_relative, self.destination_target_relative = relative_pos_list[index] + color_index = np.random.choice(len(colors), len(self.object_list), replace=True) + for i, obj in enumerate(self.object_list): + Shape(obj.manipulated_part.visual).set_color(colors[color_index[i]][1]) + + target_colors = np.random.choice(len(colors), len(self.target_list), replace=True) + for i, target in enumerate(self.target_list): + target.visual.set_color(colors[target_colors[i]][1]) + + return super().init_episode(index) + + def sample_method(self): + while True: + self.spawn_space.clear() + for tg in self.target_list: + self.spawn_space.sample(tg, min_distance=0.1) + object_relative_pose = get_relative_position_xy(self.target_list[1], self.target_list[0], self.robot.arm) + if object_relative_pose != self.destination_target_relative: + continue + self.target_list[0].target_space_descriptions = f"the {object_relative_pose} container" + + for obj in self.object_list: + self.spawn_space.sample(obj, min_distance=0.2) + object_relative_pose = get_relative_position_xy(self.object_list[1], self.object_list[0], self.robot.arm) + if object_relative_pose != self.object_target_relative: + continue + obj = self.object_list[0].manipulated_part + obj.descriptions = "the {} {}".format(object_relative_pose, obj.property["shape"]) + break + for _ in range(5): + self.pyrep.step() + + def variation_count(self) -> int: + return len(relative_pos_list) \ No newline at end of file diff --git a/easi/tasks/ebmanipulation/vendor/vlm/tasks/drop_pen_size.py b/easi/tasks/ebmanipulation/vendor/vlm/tasks/drop_pen_size.py new file mode 100644 index 0000000..83e5272 --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/vlm/tasks/drop_pen_size.py @@ -0,0 +1,57 @@ +import itertools +from typing import List +import numpy as np +from amsolver.backend.unit_tasks import VLM_Object, TargetSpace +from pyrep.objects.shape import Shape +from amsolver.const import colors +from amsolver.backend.utils import scale_object, select_color +from vlm.tasks.drop_pen import DropPen + +size_permutations = list(itertools.product(["small", "large"], repeat=2)) +class DropPenSize(DropPen): + def init_task(self) -> None: + # self.model_num = 3 + return super().init_task() + + def init_episode(self, index: int) -> List[str]: + obj_size, target_size = size_permutations[index] + # samll_scale_factor = np.random.uniform(0.6, 0.9, 2) + # large_scale_factor = np.random.uniform(1.1, 1.5, 2) + if obj_size == "small": + small_obj = self.object_list[0] + large_obj = self.object_list[1] + else: + small_obj = self.object_list[1] + large_obj = self.object_list[0] + small_obj.manipulated_part.descriptions = "the {} {}".format("smaller", small_obj.manipulated_part.property["shape"]) + large_obj.manipulated_part.descriptions = "the {} {}".format("larger", large_obj.manipulated_part.property["shape"]) + for obj, scale_factor in zip([small_obj, large_obj],[np.random.uniform(0.6, 0.9), np.random.uniform(1.0, 1.1)]): + relative_factor = scale_object(obj, scale_factor) + if abs(relative_factor-1)>1e-2: + local_grasp_pose = obj.manipulated_part.local_grasp + local_grasp_pose[:, :3, 3] *= relative_factor + obj.manipulated_part.local_grasp = local_grasp_pose + + if target_size == "small": + small_target = self.target_list[0] + large_target = self.target_list[1] + else: + small_target = self.target_list[1] + large_target = self.target_list[0] + small_target.target_space_descriptions = "the smaller container" + large_target.target_space_descriptions = "the larger container" + for target, scale_factor in zip([small_target, large_target],[np.random.uniform(0.75, 0.9), np.random.uniform(1.0, 1.1)]): + relative_factor = scale_object(target, scale_factor) + + color_index = np.random.choice(len(colors), len(self.object_list), replace=True) + for i, obj in enumerate(self.object_list): + Shape(obj.manipulated_part.visual).set_color(colors[color_index[i]][1]) + + target_colors = np.random.choice(len(colors), len(self.target_list), replace=True) + for i, target in enumerate(self.target_list): + target.visual.set_color(colors[target_colors[i]][1]) + + return super().init_episode(index) + + def variation_count(self) -> int: + return len(size_permutations) \ No newline at end of file diff --git a/easi/tasks/ebmanipulation/vendor/vlm/tasks/open_door.py b/easi/tasks/ebmanipulation/vendor/vlm/tasks/open_door.py new file mode 100644 index 0000000..f7208d9 --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/vlm/tasks/open_door.py @@ -0,0 +1,131 @@ +import random +from typing import List +import numpy as np +import os +from amsolver.backend.task import Task +from pyrep.objects.shape import Shape +from pyrep.objects.dummy import Dummy +from pyrep.objects.proximity_sensor import ProximitySensor +from amsolver.backend.unit_tasks import T0_ObtainControl, T2_MoveObjectConstraints, TargetSpace, VLM_Object +from amsolver.backend.conditions import JointCondition +from amsolver.const import door_list +from amsolver.backend.conditions import DetectedCondition +from amsolver.backend.spawn_boundary import SpawnBoundary +from pyrep.objects.joint import Joint + +door_states = { + "open":["Fully close", "Slightly close"], + "close":["Fully open", "Slightly open"] +} +class OpenDoor(Task): + + def init_task(self) -> None: + self.model_dir = os.path.dirname(os.path.realpath(__file__)).replace("tasks","object_models/") + + self.spawn_space = SpawnBoundary([Shape('workspace')]) + self.boundary_root_ori = Shape("boundary_root").get_orientation() + + self.temporary_waypoints = [] + self.task_base = self.get_base() + if not hasattr(self, "_ignore_collisions"): + self._ignore_collisions = False + + def init_episode(self, index: int) -> List[str]: + self.import_objects() + self.variation_index = index + goal_angel, goal_description = self.door_setting(index) + try_times = 0 + while len(self.temporary_waypoints)==0 and try_times<100: + self.sample_method() + init_states = self.get_state()[0] + + self.door.manipulated_part.descriptions = f"the handle of the {self.door.manipulated_part.property['shape']}" + door_target_description = f"the {self.door.manipulated_part.property['shape']}" + if "Slightly" in goal_description: + door_target_description += " slightly" + door_target = TargetSpace(self.door_joint, None, + np.deg2rad(goal_angel), np.deg2rad(goal_angel), door_target_description, self.door.manipulated_part.visual) + door_target.set_target(self.door.parts[1]) + door_task = T2_MoveObjectConstraints(self.robot, self.pyrep, door_target, self.task_base, fail_times=2) + post_grasp_task = door_task + grasp_task = T0_ObtainControl(self.robot, self.pyrep, self.door.manipulated_part, self.task_base, try_times=20, + need_post_grasp=False, grasp_sort_key="horizontal", next_task_fuc=post_grasp_task.get_path_with_constraints) + if try_times>50: + waypoints = grasp_task.get_path(try_ik_sampling=True, ignore_collisions=self._ignore_collisions) + else: + waypoints = grasp_task.get_path(try_ik_sampling=False, ignore_collisions=self._ignore_collisions) + if waypoints is not None: + self.temporary_waypoints += waypoints + try_times += 1 + self.reset_robot() + self.pyrep.set_configuration_tree(init_states) + self.pyrep.step() + for i,waypoint in enumerate(self.temporary_waypoints): + waypoint.set_name('waypoint{}'.format(i)) + return [f"{goal_description} the {self.door.manipulated_part.property['shape']}."] + + def import_objects(self): + self._selected_door = random.choice(door_list) + model_path = self.model_dir+self._selected_door["path"] + self.door = VLM_Object(self.pyrep, model_path, 0) + self.door.set_parent(Shape("boundary_root")) + self.door_joint = self.door.constraints[0] + self.door_joint_name = self.door_joint.get_name() + self._need_remove_objects.append(self.door) + + def variation_count(self) -> int: + # TODO: The number of variations for this task. + return 4 + + def is_static_workspace(self) -> bool: + return True + + def sample_method(self): + Shape("boundary_root").set_orientation(self.boundary_root_ori) + self.spawn_space.clear() + self.spawn_space.sample(Shape("boundary_root"), + min_rotation=[0, 0, -3.14 / 4.], max_rotation=[0, 0, 3.14 / 4.]) + # SpawnBoundary([Shape('boundary_root')]).sample(self.attach_point, + # min_rotation=[0, 0, 0], max_rotation=[0, 0, 0]) + + def door_setting(self, index): + joint_range = self.door_joint.get_joint_interval()[1] + real_max_angle = joint_range[0]+joint_range[1] + real_max_angle = np.rad2deg(real_max_angle) + try: + max_angle = np.random.uniform(30, real_max_angle*0.8) + except: + max_angle = 30 + # max_angle = 30 + if index in [2,3]: + init_state = "open" + self.door_joint.set_joint_position(np.deg2rad(max_angle), True) + else: + init_state = "close" + self.door_joint.set_joint_position(0, True) + sub_index = index%2 + goal_state = door_states[init_state][sub_index] + if "Slightly" in goal_state: + detect_distance = max_angle*0.4 + goal_distance = max_angle*np.random.uniform(0.4, 0.5) + detect_bound = max_angle*0.7 + else: + detect_distance = max_angle*0.8 + detect_bound = max_angle*1.1 + if "open" in goal_state: + goal_distance = max_angle*np.random.uniform(0.8, 1.0) + else: + goal_distance = np.random.uniform(joint_range[0], joint_range[0]+joint_range[1]*0.1) + self.register_success_conditions([JointCondition(self.door_joint, np.deg2rad(detect_distance), np.deg2rad(detect_bound))]) + return goal_distance, goal_state + + def load(self, ttms_folder=None): + if Shape.exists('open_door'): + return Dummy('open_door') + ttm_file = os.path.join(ttms_folder, 'open_door.ttm') + self._base_object = self.pyrep.import_model(ttm_file) + return self._base_object + + def get_base(self) -> Dummy: + self._base_object = Dummy('open_door') + return self._base_object \ No newline at end of file diff --git a/easi/tasks/ebmanipulation/vendor/vlm/tasks/open_door_complex.py b/easi/tasks/ebmanipulation/vendor/vlm/tasks/open_door_complex.py new file mode 100644 index 0000000..22a31a8 --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/vlm/tasks/open_door_complex.py @@ -0,0 +1,136 @@ +from typing import List +import numpy as np +import os +from amsolver.backend.task import Task +from pyrep.objects.shape import Shape +from pyrep.objects.dummy import Dummy +from pyrep.objects.proximity_sensor import ProximitySensor +from amsolver.backend.unit_tasks import T0_ObtainControl, T2_MoveObjectConstraints, TargetSpace, VLM_Object +from amsolver.backend.conditions import JointCondition +from amsolver.const import colors, complex_door_list +from amsolver.backend.conditions import DetectedCondition +from amsolver.backend.spawn_boundary import SpawnBoundary +from pyrep.objects.joint import Joint + +from vlm.tasks.open_door import OpenDoor + +door_states = { + "open":["Fully close", "Slightly close"], + "close":["Fully open", "Slightly open"] +} +class OpenDoorComplex(OpenDoor): + + def init_task(self) -> None: + model_dir = os.path.dirname(os.path.realpath(__file__)).replace("tasks","object_models/") + model_path = model_dir+complex_door_list[0]['path'] + self.spawn_space = SpawnBoundary([Shape('workspace')]) + self.boundary_root_ori = Shape("boundary_root").get_orientation() + self.attach_point = Dummy.create() + self.attach_point.set_name("door_attach_point") + self.attach_point.set_parent(Shape("boundary_root")) + self.door = VLM_Object(self.pyrep, model_path, 0) + root_pose = Shape("boundary_root").get_pose() + root_pose[:2] = self.door.get_position()[:2] + self.attach_point.set_pose(root_pose) + self.door.set_parent(self.attach_point) + self.door_joint = self.door.constraints[0] + self.door_joint_name = self.door_joint.get_name() + self.door_joint.set_joint_interval(False, [np.deg2rad(0), np.deg2rad(30)]) + self.handle_joint = self.door.constraints[1] + self.door_unlock_cond = JointCondition(self.handle_joint, np.deg2rad(20)) + self.pyrep.step() + self.temporary_waypoints = [] + self.task_base = self.get_base() + + def init_episode(self, index: int) -> List[str]: + self.variation_index = index + goal_angel, goal_description = self.door_setting(index) + try_times = 0 + self.door_joint.set_motor_locked_at_zero_velocity(False) + while len(self.temporary_waypoints)==0 and try_times<200: + self.sample_method() + init_states = self.get_state()[0] + + self.door.manipulated_part.descriptions = "the handle of door" + door_target_description = "the door" + if "Slightly" in goal_description: + door_target_description += " slightly" + door_target = TargetSpace(self.door_joint, None, + np.deg2rad(goal_angel), np.deg2rad(goal_angel), door_target_description, self.door.manipulated_part.visual) + door_target.set_target(self.door.parts[1]) + door_task = T2_MoveObjectConstraints(self.robot, self.pyrep, door_target, self.task_base, fail_times=2) + post_grasp_task = door_task + if not self.door_unlocked: + handle_target = TargetSpace(self.handle_joint, None, + np.deg2rad(25), np.deg2rad(25), "the handle of door", self.door.manipulated_part.visual) + handle_target.set_target(self.door.manipulated_part) + handle_task = T2_MoveObjectConstraints(self.robot, self.pyrep, handle_target, self.task_base, fail_times=2, next_task_fuc=door_task.get_path_with_constraints) + post_grasp_task = handle_task + grasp_task = T0_ObtainControl(self.robot, self.pyrep, self.door.manipulated_part, self.task_base, try_times=200, + need_post_grasp=False, grasp_sort_key="horizontal", next_task_fuc=post_grasp_task.get_path_with_constraints) + if try_times>100: + waypoints = grasp_task.get_path(try_ik_sampling=True, ignore_collisions=False) + else: + waypoints = grasp_task.get_path(try_ik_sampling=False, ignore_collisions=False) + if waypoints is not None: + self.temporary_waypoints += waypoints + try_times += 1 + self.reset_robot() + self.pyrep.set_configuration_tree(init_states) + self.pyrep.step() + for i,waypoint in enumerate(self.temporary_waypoints): + waypoint.set_name('waypoint{}'.format(i)) + if not self.door_unlocked: + self.door_joint.set_motor_locked_at_zero_velocity(True) + return [f'{goal_description} the door.'] + + def variation_count(self) -> int: + # TODO: The number of variations for this task. + return 4 + + def step(self) -> None: + # Called during each sim step. Remove this if not using. + door_joint = Joint(self.door_joint_name) + if not self.door_unlocked: + door_joint.set_motor_locked_at_zero_velocity(True) + self.door_unlocked = self.door_unlock_cond.condition_met()[0] + if self.door_unlocked: + door_joint.set_motor_locked_at_zero_velocity(False) + + def is_static_workspace(self) -> bool: + return True + + def sample_method(self): + Shape("boundary_root").set_orientation(self.boundary_root_ori) + self.spawn_space.clear() + self.spawn_space.sample(Shape("boundary_root"), + min_rotation=[0, 0, -3.14 / 8.], max_rotation=[0, 0, 3.14 / 8.]) + # SpawnBoundary([Shape('boundary_root')]).sample(self.attach_point, + # min_rotation=[0, 0, 0], max_rotation=[0, 0, 0]) + + def door_setting(self, index): + if index in [0,1]: + init_state = "open" + self.door_joint.set_joint_position(np.deg2rad(30), True) + else: + init_state = "close" + self.door_joint.set_joint_position(np.deg2rad(0), True) + sub_index = index%2 + goal_state = door_states[init_state][sub_index] + if init_state == "close": + self.door_unlocked = False + else: + self.door_unlocked = True + if "Slightly" in goal_state: + detect_angle = 10 + goal_angel = 15 + detect_bound = 20 + else: + detect_angle = 25 + detect_bound = 35 + if "open" in goal_state: + goal_angel = 30 + else: + goal_angel = 0 + self.register_success_conditions([JointCondition(self.door_joint, np.deg2rad(detect_angle), np.deg2rad(detect_bound))]) + return goal_angel, goal_state diff --git a/easi/tasks/ebmanipulation/vendor/vlm/tasks/open_drawer.py b/easi/tasks/ebmanipulation/vendor/vlm/tasks/open_drawer.py new file mode 100644 index 0000000..fb1364a --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/vlm/tasks/open_drawer.py @@ -0,0 +1,128 @@ +import os +import random +import numpy as np +from typing import List, Tuple +from pyrep.objects.shape import Shape +from pyrep.objects.dummy import Dummy +from pyrep.objects.joint import Joint +from amsolver.backend.spawn_boundary import SpawnBoundary +from amsolver.backend.task import Task +from amsolver.backend.conditions import JointCondition +from amsolver.backend.unit_tasks import T0_ObtainControl, T2_MoveObjectConstraints, TargetSpace, VLM_Object +from amsolver.const import drawer_list + +drawer_states = { + "open":["Fully close", "Slightly close"], + "close":["Fully open", "Slightly open"] +} + +class OpenDrawer(Task): + + def init_task(self) -> None: + self.spawn_space = SpawnBoundary([Shape('workspace')]) + self.model_dir = os.path.dirname(os.path.realpath(__file__)).replace("tasks","object_models/") + self.temporary_waypoints = [] + self.task_base = self.get_base() + self.boundary_root_ori = Shape("boundary_root").get_orientation() + + def init_episode(self, index: int) -> List[str]: + self.import_objects() + self.variation_index = index + select_index = index // 4 + setting_index = index % 4 + select_index = select_index % len(self.drawer.manipulated_parts) + self.manipulate_drawer = self.drawer.manipulated_parts[select_index] + self.drawer_joint = self.drawer.constraints[select_index] + try_times = 0 + while len(self.temporary_waypoints)==0 and try_times<100: + self.sample_method() + goal_angel, goal_description = self.drawer_setting(setting_index) + init_states = self.get_state()[0] + + self.manipulate_drawer.descriptions = f"the handle of the {self.manipulate_drawer.property['shape']}" + drawer_target_description = f"the {self.manipulate_drawer.property['shape']}" + if "Slightly" in goal_description: + drawer_target_description += " slightly" + drawer_target = TargetSpace(self.drawer_joint , None,goal_angel, goal_angel, drawer_target_description, self.manipulate_drawer.visual) + drawer_target.set_target(self.manipulate_drawer) + drawer_task = T2_MoveObjectConstraints(self.robot, self.pyrep, drawer_target, self.task_base, fail_times=2) + grasp_task = T0_ObtainControl(self.robot, self.pyrep, self.manipulate_drawer, self.task_base, try_times=100, + need_post_grasp=False, grasp_sort_key="horizontal", next_task_fuc=drawer_task.get_path_with_constraints) + waypoints = grasp_task.get_path(try_ik_sampling=True, ignore_collisions=False) + # if try_times>50: + # waypoints = grasp_task.get_path(try_ik_sampling=True, ignore_collisions=False) + # else: + # waypoints = grasp_task.get_path(try_ik_sampling=False, ignore_collisions=False) + if waypoints is not None: + self.temporary_waypoints += waypoints + try_times += 1 + self.reset_robot() + self.pyrep.set_configuration_tree(init_states) + self.pyrep.step() + # print(f"Have tried {try_times} times for open drawer.") + for i,waypoint in enumerate(self.temporary_waypoints): + waypoint.set_name('waypoint{}'.format(i)) + return [f"{goal_description} the {self.manipulate_drawer.property['shape']}."] + + def variation_count(self) -> int: + # TODO: The number of variations for this task. + return 12 + + def is_static_workspace(self) -> bool: + return True + + def import_objects(self): + self._selected_cabinet = random.choice(drawer_list) + model_path = self.model_dir+self._selected_cabinet["path"] + self.drawer = VLM_Object(self.pyrep, model_path, 0) + self._drawer_init_ori = self.drawer.get_orientation() + self._drawer_init_state = self.drawer.get_configuration_tree() + self.drawer.set_parent(Shape("boundary_root")) + self._need_remove_objects.append(self.drawer) + + def sample_method(self): + self.pyrep.set_configuration_tree(self._drawer_init_state) + # self.drawer.set_orientation(self._drawer_init_ori) + Shape("boundary_root").set_orientation(self.boundary_root_ori) + self.spawn_space.clear() + # self.drawer.set_model(False) + self.spawn_space.sample(Shape("boundary_root"), + min_rotation=[0, 0, -3.14 / 4.], max_rotation=[0, 0, 3.14 / 4.]) + # self.drawer.set_model(True) + # self.drawer.set_position(self.drawer.get_position()+np.array([0.1,0,0])) + self.pyrep.step() + + def drawer_setting(self, index): + max_angle = self._selected_cabinet["max_joint"] + if index in [2,3]: + init_state = "open" + self.drawer_joint.set_joint_position(max_angle, True) + else: + init_state = "close" + self.drawer_joint.set_joint_position(0, True) + sub_index = index%2 + goal_state = drawer_states[init_state][sub_index] + if "Slightly" in goal_state: + detect_distance = max_angle*0.4 + goal_distance = max_angle*np.random.uniform(0.4, 0.5) + detect_bound = max_angle*0.7 + else: + detect_distance = max_angle*0.8 + detect_bound = max_angle*1.1 + if "open" in goal_state: + goal_distance = max_angle*np.random.uniform(0.8, 1.0) + else: + goal_distance = 0 + self.register_success_conditions([JointCondition(self.drawer_joint, detect_distance, detect_bound)]) + return goal_distance, goal_state + + def load(self, ttms_folder=None): + if Shape.exists('open_drawer'): + return Dummy('open_drawer') + ttm_file = os.path.join(ttms_folder, 'open_drawer.ttm') + self._base_object = self.pyrep.import_model(ttm_file) + return self._base_object + + def get_base(self) -> Dummy: + self._base_object = Dummy('open_drawer') + return self._base_object diff --git a/easi/tasks/ebmanipulation/vendor/vlm/tasks/open_drawer_cabinet.py b/easi/tasks/ebmanipulation/vendor/vlm/tasks/open_drawer_cabinet.py new file mode 100644 index 0000000..a0760ff --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/vlm/tasks/open_drawer_cabinet.py @@ -0,0 +1,22 @@ +import os +import random +from pyrep.objects.shape import Shape +from amsolver.backend.unit_tasks import VLM_Object +from amsolver.const import cabinet_list +from vlm.tasks.open_drawer import OpenDrawer + +class OpenDrawerCabinet(OpenDrawer): + + + def variation_count(self) -> int: + # TODO: The number of variations for this task. + return 8 + + def import_objects(self): + self._selected_cabinet = random.choice(cabinet_list) + model_path = self.model_dir+self._selected_cabinet["path"] + self.drawer = VLM_Object(self.pyrep, model_path, 0) + self._drawer_init_ori = self.drawer.get_orientation() + self._drawer_init_state = self.drawer.get_configuration_tree() + self.drawer.set_parent(Shape("boundary_root")) + self._need_remove_objects.append(self.drawer) diff --git a/easi/tasks/ebmanipulation/vendor/vlm/tasks/pick_cube.py b/easi/tasks/ebmanipulation/vendor/vlm/tasks/pick_cube.py new file mode 100644 index 0000000..158df53 --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/vlm/tasks/pick_cube.py @@ -0,0 +1,101 @@ +from turtle import shape +from typing import List +import numpy as np +import os +import random +from pyrep.objects.shape import Shape +from pyrep.objects.dummy import Dummy +from pyrep.objects.proximity_sensor import ProximitySensor +from amsolver.backend.task import Task +from amsolver.backend.unit_tasks import T0_ObtainControl, T1_MoveObjectGoal, TargetSpace, VLM_Object +from amsolver.backend.utils import get_relative_position_xy +from amsolver.const import colors, object_shapes +from amsolver.backend.conditions import DetectedCondition +from amsolver.backend.spawn_boundary import SpawnBoundary + +class PickCube(Task): + def init_task(self) -> None: + self.spawn_space = SpawnBoundary([Shape('workspace')]) + self.target_space0 = TargetSpace(SpawnBoundary([Shape('target_space0')]), ProximitySensor('success0'), + (-3.14,-3.14,-3.14), (3.14,3.14,3.14), None, Shape("small_container0").get_handle()) + self.target_space1 = TargetSpace(SpawnBoundary([Shape('target_space1')]), ProximitySensor('success1'), + (-3.14,-3.14,-3.14), (3.14,3.14,3.14), None, Shape("small_container1").get_handle()) + self.target_spaces = [self.target_space0, self.target_space1] + self.model_dir = os.path.dirname(os.path.realpath(__file__)).replace("tasks","object_models/") + self.temporary_waypoints = [] + self.taks_base = self.get_base() + if not hasattr(self, "model_num"): + self.model_num = 2 + # self.import_objects(self.model_num) + + def init_episode(self, index: int) -> List[str]: + self.import_objects(self.model_num) + self.variation_index = index + self.modified_init_episode(index) + # ind = np.random.randint(0, len(self.target_spaces)) + self.target_space = self.target_spaces[0] + self.manipulated_obj = self.object_list[0].manipulated_part + try_times = 0 + while len(self.temporary_waypoints)==0: + self.sample_method() + for _ in range(5): + self.pyrep.step() + self.register_success_conditions([DetectedCondition(self.manipulated_obj, self.target_space.successor)]) + init_states = self.get_state()[0] + self.target_space.set_target(self.manipulated_obj, try_ik_sampling=True, release=True) + MoveTask0 = T1_MoveObjectGoal(self.robot, self.pyrep, self.target_space, self.taks_base, fail_times=10) + GraspTask0 = T0_ObtainControl(self.robot, self.pyrep, self.manipulated_obj,self.taks_base,\ + next_task_fuc=MoveTask0.get_path, try_times=20) + if try_times<100: + waypoints = GraspTask0.get_path(try_ik_sampling=False) + else: + waypoints = GraspTask0.get_path(try_ik_sampling=True) + if waypoints is not None: + self.temporary_waypoints += waypoints + self.reset_robot() + self.pyrep.set_configuration_tree(init_states) + try_times+= 1 + for i,waypoint in enumerate(self.temporary_waypoints): + waypoint.set_name('waypoint{}'.format(i)) + description = 'Pick up {} and place it into {}.'.format(self.manipulated_obj.descriptions, self.target_space.target_space_descriptions) + return [description] + + def is_static_workspace(self) -> bool: + return True + + def load(self, ttms_folder=None): + if Shape.exists('pick_cube'): + return Dummy('pick_cube') + ttm_file = os.path.join(ttms_folder, 'pick_cube.ttm') + self._base_object = self.pyrep.import_model(ttm_file) + return self._base_object + + def get_base(self) -> Dummy: + self._base_object = Dummy('pick_cube') + return self._base_object + + def import_objects(self, num=2): + self.object_list = [] + if not hasattr(self, "model_path"): + selected_obj = random.choice(list(object_shapes.keys())) + model_path = self.model_dir+object_shapes[selected_obj]['path'] + # model_path = self.model_dir+"cube/cube_normal/cube_normal.ttm" + else: + model_path = self.model_dir+self.model_path + for i in range(num): + cube = VLM_Object(self.pyrep, model_path, i) + cube.set_parent(self.taks_base) + self.object_list.append(cube) + self.register_graspable_objects(self.object_list) + self._need_remove_objects+=self.object_list + + def sample_method(self): + satisfied = False + while not satisfied: + self.spawn_space.clear() + for space in self.target_spaces: + self.spawn_space.sample(Shape(space.focus_obj_id), min_distance=0.25) + for obj in self.object_list: + self.spawn_space.sample(obj, min_distance=0.1) + satisfied = True + self.pyrep.step() \ No newline at end of file diff --git a/easi/tasks/ebmanipulation/vendor/vlm/tasks/pick_cube_color.py b/easi/tasks/ebmanipulation/vendor/vlm/tasks/pick_cube_color.py new file mode 100644 index 0000000..8ba23d9 --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/vlm/tasks/pick_cube_color.py @@ -0,0 +1,38 @@ +from typing import List +import numpy as np +from amsolver.backend.unit_tasks import VLM_Object, TargetSpace +from pyrep.objects.shape import Shape +from amsolver.const import colors +from amsolver.backend.utils import scale_object, select_color +from vlm.tasks.pick_cube import PickCube + +class PickCubeColor(PickCube): + def init_task(self) -> None: + self.model_num = 2 + return super().init_task() + + + def modified_init_episode(self, index: int) -> List[str]: + for obj in self.object_list: + scale_factor = np.random.uniform(0.8, 1.2) + relative_factor = scale_object(obj, scale_factor) + if abs(relative_factor-1)>1e-2: + local_grasp_pose = obj.manipulated_part.local_grasp + local_grasp_pose[:, :3, 3] *= relative_factor + obj.manipulated_part.local_grasp = local_grasp_pose + + color_names, rgbs = select_color(index, len(self.object_list)-1) + for i, obj in enumerate(self.object_list): + Shape(obj.manipulated_part.visual).set_color(rgbs[i]) + obj.manipulated_part.property["color"] = color_names[i] + obj.manipulated_part.descriptions = "the {} object".format(color_names[i]) + + target_space_colors = np.random.choice(len(colors), len(self.target_spaces), replace=False) + for i, target_space in enumerate(self.target_spaces): + target_space.target_space_descriptions = "the {} container".format(colors[target_space_colors[i]][0]) + Shape(target_space.focus_obj_id).set_color(colors[target_space_colors[i]][1]) + + return None + + def variation_count(self) -> int: + return len(colors) diff --git a/easi/tasks/ebmanipulation/vendor/vlm/tasks/pick_cube_relative.py b/easi/tasks/ebmanipulation/vendor/vlm/tasks/pick_cube_relative.py new file mode 100644 index 0000000..e582c93 --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/vlm/tasks/pick_cube_relative.py @@ -0,0 +1,55 @@ +import itertools +from typing import List +import numpy as np +from amsolver.backend.unit_tasks import VLM_Object, TargetSpace +from pyrep.objects.shape import Shape +from amsolver.const import colors +from amsolver.backend.utils import get_relative_position_xy, scale_object, select_color +from vlm.tasks.pick_cube import PickCube + +relative_pos_list = list(itertools.product(["left", "right", "front", "rear"], repeat=2)) +class PickCubeRelative(PickCube): + def init_task(self) -> None: + super().init_task() + self.get_relative_pos=True + + def modified_init_episode(self, index: int) -> List[str]: + color_index = np.random.choice(len(colors), len(self.object_list), replace=True) + for i, obj in enumerate(self.object_list): + Shape(obj.manipulated_part.visual).set_color(colors[color_index[i]][1]) + + target_space_colors = np.random.choice(len(colors), len(self.target_spaces), replace=True) + for i, target_space in enumerate(self.target_spaces): + Shape(target_space.focus_obj_id).set_color(colors[target_space_colors[i]][1]) + + self.object_target_relative, self.destination_target_relative = relative_pos_list[index] + + return None + def variation_count(self) -> int: + return len(relative_pos_list) + + def sample_method(self): + satisfied = False + while not satisfied: + self.spawn_space.clear() + for space in self.target_spaces: + self.spawn_space.sample(Shape(space.focus_obj_id), min_distance=0.25) + + if hasattr(self, "get_relative_pos"): + comparted_target = self.target_spaces[1] + target_relative_pose = get_relative_position_xy(Shape(comparted_target.focus_obj_id), Shape(self.target_space.focus_obj_id), self.robot.arm) + if target_relative_pose != self.destination_target_relative: + continue + self.target_space.target_space_descriptions = f"the {target_relative_pose} container" + + for obj in self.object_list: + self.spawn_space.sample(obj, min_distance=0.1) + if hasattr(self, "get_relative_pos"): + comparted_obj = self.object_list[1] + object_relative_pose = get_relative_position_xy(comparted_obj.manipulated_part, self.manipulated_obj, self.robot.arm) + if object_relative_pose != self.object_target_relative: + continue + self.manipulated_obj.property["relative_pos"] = object_relative_pose + self.manipulated_obj.descriptions = "the {} object".format(self.manipulated_obj.property["relative_pos"]) + satisfied = True + self.pyrep.step() \ No newline at end of file diff --git a/easi/tasks/ebmanipulation/vendor/vlm/tasks/pick_cube_shape.py b/easi/tasks/ebmanipulation/vendor/vlm/tasks/pick_cube_shape.py new file mode 100644 index 0000000..7702f24 --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/vlm/tasks/pick_cube_shape.py @@ -0,0 +1,46 @@ +from typing import List +import numpy as np +from amsolver.backend.unit_tasks import VLM_Object, TargetSpace +from pyrep.objects.shape import Shape +from amsolver.const import colors, object_shapes +from amsolver.backend.utils import select_color +from vlm.tasks.pick_cube import PickCube + +class PickCubeShape(PickCube): + + def modified_init_episode(self, index: int) -> List[str]: + self.object_list = [self.shape_lib[index]] + other_obj_index = list(range(len(self.shape_lib))) + other_obj_index.remove(index) + distractor_number = np.random.randint(1,len(self.shape_lib)) + distractor_index = np.random.choice(other_obj_index, distractor_number, replace=False) + for i in distractor_index: + self.object_list.append(self.shape_lib[i]) + color_index = np.random.choice(len(colors), len(self.object_list), replace=True) + for i, obj in enumerate(self.object_list): + Shape(obj.manipulated_part.visual).set_color(colors[color_index[i]][1]) + # obj.manipulated_part.descriptions = "the {} {}".format(colors[color_index[i]][0], obj.manipulated_part.property["shape"]) + obj.manipulated_part.descriptions = "the {}".format(obj.manipulated_part.property["shape"]) + + target_space_colors = np.random.choice(len(colors), len(self.target_spaces), replace=False) + for i, target_space in enumerate(self.target_spaces): + target_space.target_space_descriptions = "the {} container".format(colors[target_space_colors[i]][0]) + Shape(target_space.focus_obj_id).set_color(colors[target_space_colors[i]][1]) + + return None + + def variation_count(self) -> int: + return len(object_shapes) + + def import_objects(self, num): + object_numbers = [1]*len(object_shapes) + self.shape_lib = [] + for obj, num in zip(object_shapes, object_numbers): + for i in range(num): + model = VLM_Object(self.pyrep, self.model_dir+object_shapes[obj]["path"], i) + model.set_parent(self.taks_base) + model.set_position([0,0,0]) + self.shape_lib.append(model) + self.register_graspable_objects(self.shape_lib) + self._need_remove_objects+=self.shape_lib + \ No newline at end of file diff --git a/easi/tasks/ebmanipulation/vendor/vlm/tasks/pick_cube_size.py b/easi/tasks/ebmanipulation/vendor/vlm/tasks/pick_cube_size.py new file mode 100644 index 0000000..5ec2497 --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/vlm/tasks/pick_cube_size.py @@ -0,0 +1,57 @@ +import itertools +from typing import List +import numpy as np +from amsolver.backend.unit_tasks import VLM_Object, TargetSpace +from pyrep.objects.shape import Shape +from amsolver.const import colors +from amsolver.backend.utils import scale_object, select_color +from vlm.tasks.pick_cube import PickCube + +size_permutations = list(itertools.product(["small", "large"], repeat=2)) + +class PickCubeSize(PickCube): + def init_task(self) -> None: + # self.model_path = "cube/cube_basic/cube_basic.ttm" + super().init_task() + + def modified_init_episode(self, index: int) -> List[str]: + obj_size, target_size = size_permutations[index] + # samll_scale_factor = np.random.uniform(0.6, 0.9, 2) + # large_scale_factor = np.random.uniform(1.1, 1.5, 2) + if obj_size == "small": + small_obj = self.object_list[0] + large_obj = self.object_list[1] + else: + small_obj = self.object_list[1] + large_obj = self.object_list[0] + small_obj.manipulated_part.descriptions = "the {} object".format("smaller") + large_obj.manipulated_part.descriptions = "the {} object".format("larger") + for obj, scale_factor in zip([small_obj, large_obj],[np.random.uniform(0.6, 0.9), np.random.uniform(1.1, 1.2)]): + relative_factor = scale_object(obj, scale_factor) + if abs(relative_factor-1)>1e-2: + local_grasp_pose = obj.manipulated_part.local_grasp + local_grasp_pose[:, :3, 3] *= relative_factor + obj.manipulated_part.local_grasp = local_grasp_pose + + if target_size == "small": + small_target = self.target_spaces[0] + large_target = self.target_spaces[1] + else: + small_target = self.target_spaces[1] + large_target = self.target_spaces[0] + small_target.target_space_descriptions = "the smaller container" + large_target.target_space_descriptions = "the larger container" + for target, scale_factor in zip([small_target, large_target],[np.random.uniform(0.8, 0.9), np.random.uniform(1.1, 1.25)]): + relative_factor = scale_object(Shape(target.focus_obj_id), scale_factor) + + color_index = np.random.choice(len(colors), len(self.object_list), replace=True) + for i, obj in enumerate(self.object_list): + Shape(obj.manipulated_part.visual).set_color(colors[color_index[i]][1]) + + target_space_colors = np.random.choice(len(colors), len(self.target_spaces), replace=True) + for i, target_space in enumerate(self.target_spaces): + Shape(target_space.focus_obj_id).set_color(colors[target_space_colors[i]][1]) + self.pyrep.step() + return None + def variation_count(self) -> int: + return len(size_permutations) diff --git a/easi/tasks/ebmanipulation/vendor/vlm/tasks/place_into_shape_sorter.py b/easi/tasks/ebmanipulation/vendor/vlm/tasks/place_into_shape_sorter.py new file mode 100644 index 0000000..5ead615 --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/vlm/tasks/place_into_shape_sorter.py @@ -0,0 +1,133 @@ +from typing import List +import numpy as np +import os +from amsolver.backend.task import Task +from pyrep.objects.shape import Shape +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.objects.dummy import Dummy +from amsolver.backend.unit_tasks import T0_ObtainControl, T1_MoveObjectGoal, TargetSpace, VLM_Object +from amsolver.backend.utils import scale_object +from amsolver.const import colors, sorter_objects +from amsolver.backend.conditions import DetectedCondition +from amsolver.backend.spawn_boundary import SpawnBoundary +from amsolver.backend.task import Task + +class PlaceIntoShapeSorter(Task): + def __init__(self, pyrep, robot): + super().__init__(pyrep, robot) + self.object_dict = sorter_objects + + def init_task(self) -> None: + self.model_dir = os.path.dirname(os.path.realpath(__file__)).replace("tasks","object_models/") + self.spawn_space = SpawnBoundary([Shape('workspace')]) + self.success_sensor = ProximitySensor('success') + self.sorter = Shape("shape_sorter") + self.sorter_visual = Shape("shape_sorter_visual") + self.sorter_visual.set_color([1, 0, 0]) + self.objects = {} + self.temporary_waypoints = [] + self.taks_base = self.get_base() + if not hasattr(self, "object_numbers"): + self.object_numbers = [1 for _ in self.object_dict] + self.import_objects() + self._task_init_states = self.get_state()[0] + self.need_shape_resample = True + self.pick_objs = [] + if not hasattr(self, "need_distractors"): + self.need_distractors = False + + def init_episode(self, index: int) -> List[str]: + self.variation_index = index + pick_number = 1 + try_times = 0 + while len(self.temporary_waypoints)==0: + if try_times%10 == 0 and self.need_shape_resample: + # pick_number = np.random.randint(1,min(len(self.objects)-1,2)) + self.pick_objs = np.random.choice(list(self.objects.keys()), pick_number, replace=False).tolist() + self.sample_method() + self.pyrep.step() + init_states = self.get_state()[0] + task_sequence, manipulated_objs, conditions = [], [], [] + for obj_name in self.pick_objs: + obj = self.objects[obj_name][0] + conditions.append(DetectedCondition(obj.manipulated_part, self.success_sensor)) + target = obj.target + target_space = TargetSpace(obj.target, self.success_sensor, None, None, target.descriptions, self.sorter_visual.get_handle()) + target_space.set_target(obj.manipulated_part, linear=False, ignore_collisions= False, release=True) + MoveTask = T1_MoveObjectGoal(self.robot, self.pyrep, target_space, self.taks_base, fail_times=10) + GraspTask = T0_ObtainControl(self.robot, self.pyrep, obj.manipulated_part, self.taks_base, try_times=20) + task_sequence.append(GraspTask) + task_sequence.append(MoveTask) + manipulated_objs.append(obj) + for i in range(-1, -len(task_sequence), -1): + task_sequence[i-1].next_task_fuc = task_sequence[i].get_path + if try_times<100: + waypoints = task_sequence[0].get_path(try_ik_sampling=False) + else: + waypoints = task_sequence[0].get_path(try_ik_sampling=True) + if waypoints is not None: + self.temporary_waypoints += waypoints + self.reset_robot() + self.pyrep.set_configuration_tree(init_states) + try_times += 1 + self.register_success_conditions(conditions) + for i,waypoint in enumerate(self.temporary_waypoints): + waypoint.set_name('waypoint{}'.format(i)) + descriptions = f"Put " + for i, obj in enumerate(manipulated_objs): + descriptions += f"{obj.manipulated_part.descriptions}" + if i == len(manipulated_objs)-2: + descriptions += " and " + elif i < len(manipulated_objs)-2: + descriptions += ", " + descriptions += " into the shape sorter." + return [descriptions] + + def variation_count(self) -> int: + # TODO: The number of variations for this task. + return 1 + + def import_objects(self): + for obj, num in zip(self.object_dict, self.object_numbers): + for i in range(num): + model_path = self.model_dir+self.object_dict[obj]["path"] + model = VLM_Object(self.pyrep, model_path, i) + model.set_parent(self.taks_base) + model.set_position([0, 0, 0]) + target = Dummy(f"{model.obj_class}_target") + target.descriptions = f"the hole of {model.obj_class} shape" + target.set_position(target.get_position()+[0,0,0.01]) + model.target = target + if obj in self.objects: + self.objects[obj].append(model) + else: + self.objects[obj]=[model] + self.register_graspable_objects([obj for i in self.objects for obj in self.objects[i]]) + + def sample_method(self): + self.pyrep.set_configuration_tree(self._task_init_states) + distractors = set(self.objects.keys())-set(self.pick_objs) + if len(distractors)>0 and self.need_distractors: + distractor_number = np.random.randint(1,len(distractors)) + select_distractors = np.random.choice(list(distractors), distractor_number, replace=False).tolist() + else: + select_distractors = [] + self.spawn_space.clear() + self.spawn_space.sample(self.sorter) + for obj in (self.pick_objs+select_distractors): + for instance in self.objects[obj]: + self.spawn_space.sample(instance) + + def is_static_workspace(self) -> bool: + return True + + def load(self, ttms_folder=None): + if Shape.exists('place_into_shape_sorter'): + return Dummy('place_into_shape_sorter') + ttm_file = os.path.join(ttms_folder, 'place_into_shape_sorter.ttm') + self._base_object = self.pyrep.import_model(ttm_file) + return self._base_object + + def get_base(self) -> Dummy: + self._base_object = Dummy('place_into_shape_sorter') + return self._base_object \ No newline at end of file diff --git a/easi/tasks/ebmanipulation/vendor/vlm/tasks/place_into_shape_sorter_color.py b/easi/tasks/ebmanipulation/vendor/vlm/tasks/place_into_shape_sorter_color.py new file mode 100644 index 0000000..a3395e8 --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/vlm/tasks/place_into_shape_sorter_color.py @@ -0,0 +1,26 @@ +from typing import List +import numpy as np +from pyrep.objects.shape import Shape +from amsolver.const import colors +from amsolver.backend.task import Task +from amsolver.backend.utils import select_color +from vlm.tasks.place_into_shape_sorter import PlaceIntoShapeSorter + +class PlaceIntoShapeSorterColor(PlaceIntoShapeSorter): + + def init_task(self) -> None: + self.object_numbers = [3 for _ in self.object_dict] + return super().init_task() + + def init_episode(self, index: int) -> List[str]: + for obj_name in self.objects: + obj_lens = len(self.objects[obj_name]) + color_names, rgbs = select_color(index, obj_lens-1) + for i, obj in enumerate(self.objects[obj_name]): + Shape(obj.manipulated_part.visual).set_color(rgbs[i]) + obj.manipulated_part.descriptions = "the {} {}".format(color_names[i], obj.manipulated_part.property["shape"]) + return super().init_episode(index) + + def variation_count(self) -> int: + # TODO: The number of variations for this task. + return len(colors) diff --git a/easi/tasks/ebmanipulation/vendor/vlm/tasks/place_into_shape_sorter_relative.py b/easi/tasks/ebmanipulation/vendor/vlm/tasks/place_into_shape_sorter_relative.py new file mode 100644 index 0000000..8d4297c --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/vlm/tasks/place_into_shape_sorter_relative.py @@ -0,0 +1,53 @@ +import itertools +from typing import List +import numpy as np +from pyrep.objects.shape import Shape +from amsolver.const import colors +from amsolver.backend.task import Task +from amsolver.backend.utils import get_relative_position_xy, select_color +from vlm.tasks.place_into_shape_sorter import PlaceIntoShapeSorter + +relative_pos_list = ["left", "right", "front", "rear"] +class PlaceIntoShapeSorterRelative(PlaceIntoShapeSorter): + + def init_task(self) -> None: + self.object_numbers = [2 for _ in self.object_dict] + return super().init_task() + + def init_episode(self, index: int) -> List[str]: + self.object_target_relative = relative_pos_list[index] + for obj_name in self.objects: + obj_lens = len(self.objects[obj_name]) + color_index = np.random.choice(len(colors), obj_lens, replace=True) + for i, obj in enumerate(self.objects[obj_name]): + Shape(obj.manipulated_part.visual).set_color(colors[color_index[i]][1]) + return super().init_episode(index) + + def sample_method(self): + self.pyrep.set_configuration_tree(self._task_init_states) + distractors = set(self.objects.keys())-set(self.pick_objs) + if len(distractors)>0 and self.need_distractors: + distractor_number = np.random.randint(1,len(distractors)) + select_distractors = np.random.choice(list(distractors), distractor_number, replace=False).tolist() + else: + select_distractors = [] + while True: + satisfied = True + self.spawn_space.clear() + self.spawn_space.sample(self.sorter) + for obj in (self.pick_objs+select_distractors): + for instance in self.objects[obj]: + self.spawn_space.sample(instance) + if obj in self.pick_objs: + object_relative_pose = get_relative_position_xy(self.objects[obj][1], self.objects[obj][0], self.robot.arm) + if object_relative_pose!= self.object_target_relative: + satisfied = False + break + obj = self.objects[obj][0].manipulated_part + obj.descriptions = "the {} {}".format(object_relative_pose, obj.property["shape"]) + if satisfied: + break + + def variation_count(self) -> int: + # TODO: The number of variations for this task. + return len(relative_pos_list) diff --git a/easi/tasks/ebmanipulation/vendor/vlm/tasks/place_into_shape_sorter_shape.py b/easi/tasks/ebmanipulation/vendor/vlm/tasks/place_into_shape_sorter_shape.py new file mode 100644 index 0000000..a47c6c4 --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/vlm/tasks/place_into_shape_sorter_shape.py @@ -0,0 +1,28 @@ +from typing import List +import numpy as np +from pyrep.objects.shape import Shape +from amsolver.const import colors, sorter_objects +from amsolver.backend.task import Task +from amsolver.backend.utils import select_color +from vlm.tasks.place_into_shape_sorter import PlaceIntoShapeSorter + +shape_list = list(sorter_objects.keys()) +class PlaceIntoShapeSorterShape(PlaceIntoShapeSorter): + def init_task(self) -> None: + self.need_distractors = True + return super().init_task() + + def init_episode(self, index: int) -> List[str]: + self.need_shape_resample = False + self.pick_objs = [shape_list[index]] + for obj_name in self.objects: + obj_lens = len(self.objects[obj_name]) + color_index = np.random.choice(len(colors), obj_lens, replace=True) + for i, obj in enumerate(self.objects[obj_name]): + Shape(obj.manipulated_part.visual).set_color(colors[color_index[i]][1]) + obj.manipulated_part.descriptions = "the {}".format(obj.manipulated_part.property["shape"]) + return super().init_episode(index) + + def variation_count(self) -> int: + # TODO: The number of variations for this task. + return len(shape_list) diff --git a/easi/tasks/ebmanipulation/vendor/vlm/tasks/pour_demo.py b/easi/tasks/ebmanipulation/vendor/vlm/tasks/pour_demo.py new file mode 100644 index 0000000..b7cca70 --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/vlm/tasks/pour_demo.py @@ -0,0 +1,240 @@ +import random +from turtle import shape +from typing import List +import numpy as np +import os +from pyrep.objects.shape import Shape +from pyrep.objects.dummy import Dummy +from pyrep.const import PYREP_SCRIPT_TYPE, PrimitiveShape +from pyrep.objects.proximity_sensor import ProximitySensor +from amsolver.backend.task import Task +from amsolver.backend.unit_tasks import T0_ObtainControl, T1_MoveObjectGoal, T2_MoveObjectConstraints, TargetSpace, VLM_Object +from amsolver.backend.utils import get_relative_position_xy, scale_object +from amsolver.const import mug_list +from amsolver.backend.conditions import ConditionSet, DetectedCondition, Condition +from amsolver.backend.spawn_boundary import SpawnBoundary +from scipy.spatial.transform import Rotation as R + +class PourDemo(Task): + + def init_task(self) -> None: + self.spawn_space = SpawnBoundary([Shape('workspace')]) + self.model_dir = os.path.dirname(os.path.realpath(__file__)).replace("tasks","object_models/") + self.temporary_waypoints = [] + self.taks_base = self.get_base() + if not hasattr(self, "model_num"): + self.model_num = 2 + + self.ignore_collisions = False + + def init_episode(self, index: int) -> List[str]: + # self.pyrep.set_configuration_tree(self.task_init_states) + self.import_objects(self.model_num) + self.modified_init_episode(index) + self.variation_index = index + try_times = 200 + pour_obj = self.object_list[0] + recv_obj = self.object_list[1] + self.manipulated_obj = pour_obj.manipulated_part + # recv = self.object_list[1].manipulated_part + # successor0 = self.object_list[0].succssor + # successor1 = self.object_list[1].succssor + while len(self.temporary_waypoints)==0 and try_times > 0: + self.sample_method() + init_states = self.get_state()[0] + # pre_pour_pose_space = self.calculate_pre_pour_pose(self.manipulated_obj, recv) + pour_descriptions = f"Rotate {self.manipulated_obj.descriptions} toward {recv_obj.manipulated_part.descriptions}." + pour_space_args = {"container_pour": pour_obj, "container_recv":recv_obj} + pour_space = TargetSpace(self.create_pour_goal_pose,space_args=pour_space_args, + target_space_descriptions=pour_descriptions, focus_obj_id=self.manipulated_obj.visual) + pour_space.set_target(pour_obj.buttom_point, try_ik_sampling=False, linear=True, ignore_collisions=self.ignore_collisions) + MoveTask1 = T2_MoveObjectConstraints(self.robot, self.pyrep, pour_space, self.taks_base,fail_times=36) + + move_descriptions = f"Move the object to the top of {recv_obj.manipulated_part.descriptions} with the opening upwards." + target_space_args = {"container_pour": pour_obj, "container_recv":recv_obj, "tip":self.robot.arm._ik_tip,"angle_threshold":np.pi/8} + target_space = TargetSpace(self.calculate_pre_pour_pose,space_args=target_space_args, + target_space_descriptions=move_descriptions, focus_obj_id=recv_obj.manipulated_part.visual) + target_space.set_target(pour_obj.buttom_point, try_ik_sampling=False, linear=True) + # target_space = TargetSpace(pre_pour_pose_space.tolist(), None, None, None, "the receiver mug", recv.visual) + # target_space.set_target(self.manipulated_obj, try_ik_sampling=False, release=False) + MoveTask0 = T2_MoveObjectConstraints(self.robot, self.pyrep, target_space, self.taks_base,fail_times=10, + next_task_fuc=MoveTask1.get_path_with_constraints) + # MoveTask0 = T1_MoveObjectGoal(self.robot, self.pyrep, self.target_space, self.taks_base, fail_times=10) + GraspTask0 = T0_ObtainControl(self.robot, self.pyrep, self.manipulated_obj,self.taks_base,\ + grasp_sort_key="horizontal",next_task_fuc=MoveTask0.get_path_with_constraints, try_times=200) + if try_times<100: + waypoints = GraspTask0.get_path(try_ik_sampling=True) + else: + waypoints = GraspTask0.get_path(try_ik_sampling=False) + if waypoints is not None: + self.temporary_waypoints += waypoints + self.reset_robot() + self.pyrep.set_configuration_tree(init_states) + self.pyrep.step() + try_times-=1 + for i,waypoint in enumerate(self.temporary_waypoints): + waypoint.set_name('waypoint{}'.format(i)) + self.drops = [] + conditions = [] + LIQUID_BALLS = 20 + success_rate = 0.5 + for i in range(LIQUID_BALLS): + drop = Shape.create(PrimitiveShape.SPHERE, mass=0.0001, size=[0.005, 0.005, 0.005]) + drop.set_parent(self.taks_base) + drop.set_color([0.1, 0.1, 0.9]) + drop.set_position(list(np.random.normal(0, 0.0005, size=(3,))), relative_to=self.manipulated_obj) + self.drops.append(drop) + self.pyrep.step() + conditions.append(DetectedCondition(drop, self.object_list[1].succssor)) + # if np.random.rand() < success_rate: + # conditions.append(DetectedCondition(drop, self.object_list[1].succssor)) + self.register_success_conditions([NumberCondition(conditions, LIQUID_BALLS*success_rate)]) + description = f"Pour water from {self.manipulated_obj.descriptions} to {recv_obj.manipulated_part.descriptions}." + return [description] + + def variation_count(self) -> int: + # TODO: The number of variations for this task. + return 1 + + def cleanup(self) -> None: + for d in self.drops: + if d.still_exists(): + d.remove() + self.drops.clear() + super().cleanup() + + def is_static_workspace(self) -> bool: + return True + + def load(self, ttms_folder=None): + if Shape.exists('pour_demo'): + return Dummy('pour_demo') + ttm_file = os.path.join(ttms_folder, 'pour_demo.ttm') + self._base_object = self.pyrep.import_model(ttm_file) + return self._base_object + + def get_base(self) -> Dummy: + self._base_object = Dummy('pour_demo') + return self._base_object + + def import_objects(self, num=2): + self.object_list = [] + if not hasattr(self, "model_path"): + selected_obj = random.choice(mug_list) + model_path = self.model_dir+selected_obj['path'] + else: + model_path = self.model_dir+self.model_path + for i in range(num): + obj = VLM_Object(self.pyrep, model_path, i) + succssor = ProximitySensor(f"success{i}") + buttom_point = Dummy.create() + buttom_point.set_name(f"buttom_point{i}") + buttom_point.set_parent(obj) + buttom_point.set_position(succssor.get_position()) + buttom_point.set_orientation(obj.get_orientation()) + obj.succssor = succssor + obj.buttom_point = buttom_point + # relative_factor = scale_object(obj, 1.5) + # if abs(relative_factor-1)>1e-2: + # local_grasp_pose = obj.manipulated_part.local_grasp + # local_grasp_pose[:, :3, 3] *= relative_factor + # obj.manipulated_part.local_grasp = local_grasp_pose + obj.set_parent(self.taks_base) + self.object_list.append(obj) + self._need_remove_objects.append(obj) + self.register_graspable_objects(self.object_list) + self.pyrep.step() + + def sample_method(self): + self.spawn_space.clear() + for obj in self.object_list: + self.spawn_space.sample(obj, min_distance=0.1) + for _ in range(5): + self.pyrep.step() + + @staticmethod + def calculate_pre_pour_pose(container_pour, container_recv, tip, n_pos_sample=20, n_rot_sample=5, angle_threshold=np.pi/8): + # randomize the container to pour with its center on a horizontal circle whose center is right above the container to receive + # radius = radius of container to receive + half height of container to pour + # container to pour needs to have z axis upwards, no other rotational constraints + recv_pos = container_recv.buttom_point.get_position() + pour_size = container_pour.get_bounding_box() # min_x, max_x, min_y, max_y, min_z, max_z + recv_size = container_recv.get_bounding_box() + pour_x, pour_y, pour_h = pour_size[1] - pour_size[0], pour_size[3] - pour_size[2], pour_size[5] - pour_size[4] + recv_x, recv_y, recv_h = recv_size[1] - recv_size[0], recv_size[3] - recv_size[2], recv_size[5] - recv_size[4] + + succssor_size = container_recv.succssor.get_bounding_box() + succ_x, succ_y = succssor_size[1] - succssor_size[0], succssor_size[3] - succssor_size[2] + # r = (succ_x**2 + succ_y**2)**0.5/2 + r = pour_h + h_offset = max(pour_h, pour_x, pour_y) + h = h_offset + recv_pos[2] + recv_h / 2 + + h += np.random.normal(scale=0.005) + # make sure container_pour, container_pour->container_recv> < angle_threshold + # pos_vec, rot_vec = [], [] + pose_vec = [] + pour2gripper_pos = container_pour.buttom_point.get_position(tip) # container_pour.get_parent().get_position(container_pour) + pour2gripper_theta = np.arctan2(pour2gripper_pos[1], pour2gripper_pos[0]) + for i in range(n_pos_sample): + angle = i * 2*np.pi / n_pos_sample + x, y = recv_pos[0] + r * np.cos(angle), recv_pos[1] + r * np.sin(angle) + for j in range(n_rot_sample): + rz = angle - angle_threshold + (j+0.5) * 2*angle_threshold / n_rot_sample - pour2gripper_theta + # rz += np.pi + # pos_vec.append((x, y, h)) + # rot_vec.append((0, 0, rz)) + pose_vec.append([x, y, h, 0, 0, rz]) + pose_vec.append([x, y, h, 0, 0, rz+np.pi]) + # dm = Dummy.create() + # dm.set_position([x, y, h]) + # dm.set_orientation([0, 0, rz]) + # rand_idx = np.random.permutation(len(pos_vec)) + rand_idx = np.random.permutation(len(pose_vec)) + pose_vec = np.array(pose_vec)[rand_idx] + pose_matrx = np.zeros((pose_vec.shape[0], 4, 4)) + for i, vec in enumerate(pose_vec): + matrix = np.eye(4) + matrix[:3, 3] = vec[:3] + matrix[:3, :3] = R.from_euler("xyz", vec[3:]).as_matrix() + pose_matrx[i] = matrix + return pose_matrx + + @staticmethod + def create_pour_goal_pose(container_pour, container_recv, n_sample=36): + obj_goal_pose_set = np.zeros((n_sample, 4, 4)) + obj_goal_pose_set[:, 3, 3] = 1 + obj_goal_pose_set[:, :3, 3] = container_pour.buttom_point.get_position() + axis_z = container_recv.buttom_point.get_position() - container_pour.buttom_point.get_position() + pour_angle = np.random.uniform(20, 40) + axis_z[2] = -np.linalg.norm(axis_z[:2])*np.tan(np.deg2rad(pour_angle)) + axis_z = axis_z / np.linalg.norm(axis_z) + obj_goal_pose_set[:, :3, 2] = axis_z + tmp = np.cross([1, 0, 1], axis_z) + tmp = tmp / np.linalg.norm(tmp) + theta = 2*np.pi / n_sample + for i in range(n_sample): + axis_x = np.cos(i*theta) * tmp + np.sin(i*theta) * np.cross(tmp, axis_z) + axis_x = axis_x / np.linalg.norm(axis_x) + obj_goal_pose_set[i, :3, 0] = axis_x + obj_goal_pose_set[i, :3, 1] = np.cross(axis_z, axis_x) + # dm = Dummy.create(size=0.005) + # dm.set_matrix(obj_goal_pose_set[i]) + rand_idx = np.random.permutation(len(obj_goal_pose_set)) + obj_goal_pose_set = obj_goal_pose_set[rand_idx] + return obj_goal_pose_set + +class NumberCondition(Condition): + + def __init__(self, conditions: List[Condition], num_bound): + self._conditions = conditions + self.num_bound = num_bound + + def condition_met(self): + count = 0 + for cond in self._conditions: + ismet, term = cond.condition_met() + if ismet: + count+= 1 + met = count >= self.num_bound + return met, False \ No newline at end of file diff --git a/easi/tasks/ebmanipulation/vendor/vlm/tasks/pour_demo_color.py b/easi/tasks/ebmanipulation/vendor/vlm/tasks/pour_demo_color.py new file mode 100644 index 0000000..0db6ab7 --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/vlm/tasks/pour_demo_color.py @@ -0,0 +1,24 @@ +from typing import List +import numpy as np +from amsolver.backend.unit_tasks import VLM_Object, TargetSpace +from pyrep.objects.shape import Shape +from amsolver.const import colors +from amsolver.backend.utils import select_color +from vlm.tasks.pour_demo import PourDemo + +class PourDemoColor(PourDemo): + def init_task(self) -> None: + self.model_num = 3 + return super().init_task() + + def modified_init_episode(self, index: int): + color_names, rgbs = select_color(index, len(self.object_list)-1, replace=False) + for i, obj in enumerate(self.object_list): + Shape(obj.manipulated_part.visual).set_color(rgbs[i]) + obj.manipulated_part.property["color"] = color_names[i] + obj.manipulated_part.descriptions = "the {} {}".format(color_names[i], obj.manipulated_part.property["shape"]) + + return None + + def variation_count(self) -> int: + return len(colors) \ No newline at end of file diff --git a/easi/tasks/ebmanipulation/vendor/vlm/tasks/pour_demo_relative.py b/easi/tasks/ebmanipulation/vendor/vlm/tasks/pour_demo_relative.py new file mode 100644 index 0000000..693cc7c --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/vlm/tasks/pour_demo_relative.py @@ -0,0 +1,43 @@ +import itertools +from typing import List +import numpy as np +from pyrep.objects.shape import Shape +from amsolver.const import colors +from amsolver.backend.utils import get_relative_position_xy, scale_object, select_color +from vlm.tasks.pour_demo import PourDemo + +relative_pos_list = [["left","right"], + ["right", "left"], + ["front", "rear"], + ["rear", "front"]] +class PourDemoRelative(PourDemo): + def init_task(self) -> None: + return super().init_task() + + def modified_init_episode(self, index: int): + self.target_relative, self.compared_relative = relative_pos_list[index] + + color_index = np.random.choice(len(colors), len(self.object_list), replace=True) + for i, obj in enumerate(self.object_list): + Shape(obj.manipulated_part.visual).set_color(colors[color_index[i]][1]) + + return None + + def variation_count(self) -> int: + return len(relative_pos_list) + + def sample_method(self): + while True: + self.spawn_space.clear() + for obj in self.object_list: + self.spawn_space.sample(obj, min_distance=0.1) + comparted_obj = self.object_list[1] + object_relative_pose = get_relative_position_xy(comparted_obj.manipulated_part, self.manipulated_obj, self.robot.arm) + if object_relative_pose != self.target_relative: + continue + self.manipulated_obj.property["relative_pos"] = object_relative_pose + self.manipulated_obj.descriptions = "the {} {}".format(object_relative_pose, self.manipulated_obj.property["shape"]) + comparted_obj.manipulated_part.descriptions = "the {} {}".format(self.compared_relative, comparted_obj.manipulated_part.property["shape"]) + break + for _ in range(5): + self.pyrep.step() \ No newline at end of file diff --git a/easi/tasks/ebmanipulation/vendor/vlm/tasks/pour_demo_size.py b/easi/tasks/ebmanipulation/vendor/vlm/tasks/pour_demo_size.py new file mode 100644 index 0000000..699b246 --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/vlm/tasks/pour_demo_size.py @@ -0,0 +1,48 @@ +import itertools +from typing import List +import numpy as np +from pyrep.objects.shape import Shape +from amsolver.const import colors +from amsolver.backend.utils import scale_object, select_color +from vlm.tasks.pour_demo import PourDemo + +size_list = ["small", "large"] +class PourDemoSize(PourDemo): + # def init_task(self) -> None: + # super().init_task() + # self.obj_init_pose = [] + # for obj in self.object_list: + # self.obj_init_pose.append(obj.get_pose()) + # self.ignore_collisions = True + + def modified_init_episode(self, index: int): + self.ignore_collisions = True + obj_size = size_list[index] + if obj_size == "small": + small_obj = self.object_list[0] + large_obj = self.object_list[1] + else: + small_obj = self.object_list[1] + large_obj = self.object_list[0] + small_obj.manipulated_part.descriptions = "the smaller {}".format(small_obj.manipulated_part.property["shape"]) + large_obj.manipulated_part.descriptions = "the larger {}".format(large_obj.manipulated_part.property["shape"]) + for obj, scale_factor in zip([small_obj, large_obj],[np.random.uniform(0.75, 0.9), np.random.uniform(1.0, 1.1)]): + relative_factor = scale_object(obj, scale_factor) + if abs(relative_factor-1)>1e-2: + local_grasp_pose = obj.manipulated_part.local_grasp + local_grasp_pose[:, :3, 3] *= relative_factor + obj.manipulated_part.local_grasp = local_grasp_pose + + color_index = np.random.choice(len(colors), len(self.object_list), replace=True) + for i, obj in enumerate(self.object_list): + Shape(obj.manipulated_part.visual).set_color(colors[color_index[i]][1]) + + return None + def variation_count(self) -> int: + return len(size_list) + + # def cleanup(self) -> None: + # for obj, pose in zip(self.object_list, self.obj_init_pose): + # if obj.still_exists(): + # obj.set_pose(pose) + # return super().cleanup() \ No newline at end of file diff --git a/easi/tasks/ebmanipulation/vendor/vlm/tasks/stack_cubes.py b/easi/tasks/ebmanipulation/vendor/vlm/tasks/stack_cubes.py new file mode 100644 index 0000000..9069417 --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/vlm/tasks/stack_cubes.py @@ -0,0 +1,136 @@ +from typing import List +import numpy as np +import os +import random +from amsolver.backend.task import Task +from pyrep.objects.dummy import Dummy +from pyrep.objects.shape import Shape +from pyrep.objects.proximity_sensor import ProximitySensor +from pyrep.const import ObjectType, PrimitiveShape +from amsolver.backend.unit_tasks import T0_ObtainControl, T1_MoveObjectGoal, TargetSpace, VLM_Object +from amsolver.backend.utils import scale_object +from amsolver.const import colors, object_shapes +from amsolver.backend.conditions import DetectedCondition +from amsolver.backend.spawn_boundary import SpawnBoundary +from amsolver.backend.task import Task + +class StackCubes(Task): + + def init_task(self) -> None: + self.success_sensor = ProximitySensor('success') + self.success_sensor.set_collidable(False) + self.spawn_space = SpawnBoundary([Shape('workspace')]) + self.model_dir = os.path.dirname(os.path.realpath(__file__)).replace("tasks","object_models/") + self.temporary_waypoints = [] + self.taks_base = self.get_base() + if not hasattr(self, "model_num"): + self.model_num = 4 + if not hasattr(self, "class_num"): + self.class_num = 1 + + def init_episode(self, index: int) -> List[str]: + self.variation_index = index + self.import_objects() + self.init_pose = [] + for cube in self.cube_list: + self.init_pose.append(cube.get_pose()) + self.modified_init_episode(index) + try_times = 200 + # pick_cube_number = np.random.randint(1,self.cube_num) + pick_cube_number = 1 + while len(self.temporary_waypoints)==0 and try_times>0: + for cube, pose in zip(self.cube_list, self.init_pose): + cube.set_pose(pose) + self.sample_method() + self.success_sensor.set_position(self.cube_list[0].get_position()) + self.pyrep.step() + init_states = self.get_state()[0] + task_sequence = [] + conditions = [DetectedCondition(self.cube_list[0].manipulated_part, self.success_sensor)] + for i in range(pick_cube_number): + below_cube = self.cube_list[i] + above_cube = self.cube_list[i+1] + conditions.append(DetectedCondition(above_cube.manipulated_part, self.success_sensor)) + target_space_mesh = above_cube.target + below_obj_zmax = below_cube.get_bounding_box()[-1] + target_space_mesh.set_position([0,0,below_obj_zmax+0.005], relative_to = below_cube) + # target_space_mesh.set_parent(below_cube) + target_space = TargetSpace(SpawnBoundary([target_space_mesh]), self.success_sensor, + (0,0,-3.14), (0,0,3.14), below_cube.manipulated_part.descriptions, below_cube.manipulated_part.visual) + target_space.set_target(above_cube.manipulated_part, try_ik_sampling=True, linear=False, release=True) + MoveTask = T1_MoveObjectGoal(self.robot, self.pyrep, target_space, self.taks_base, fail_times=2) + GraspTask = T0_ObtainControl(self.robot, self.pyrep, above_cube.manipulated_part, self.taks_base, try_times=100) + task_sequence.append(GraspTask) + task_sequence.append(MoveTask) + for i in range(-1, -len(task_sequence), -1): + task_sequence[i-1].next_task_fuc = task_sequence[i].get_path + waypoints = task_sequence[0].get_path(try_ik_sampling=False, ignore_collisions=False) + if waypoints is not None: + self.temporary_waypoints += waypoints + self.reset_robot() + self.pyrep.set_configuration_tree(init_states) + try_times -= 1 + self.register_success_conditions(conditions) + for i,waypoint in enumerate(self.temporary_waypoints): + waypoint.set_name('waypoint{}'.format(i)) + descriptions = f"Stack {self.cube_list[0].manipulated_part.descriptions}" + for i in range(pick_cube_number): + if i == pick_cube_number-1: + descriptions += " and " + elif i < pick_cube_number-1: + descriptions += ", " + descriptions += f"{self.cube_list[i+1].manipulated_part.descriptions}" + descriptions += " in sequence." + return [descriptions] + + def import_objects(self): + self.cube_list = [] + self.shape_lib = {} + selected_objs = random.sample(list(object_shapes.keys()), self.class_num) + for selected_obj in selected_objs: + model_path = self.model_dir+object_shapes[selected_obj]['path'] + self.shape_lib[selected_obj] = [] + for i in range(self.model_num): + cube = VLM_Object(self.pyrep, model_path, i) + # scale_factor = np.random.uniform(1.0, 1.25) + scale_factor = 1.5 + relative_factor = scale_object(cube, scale_factor) + if abs(relative_factor-1)>1e-2: + local_grasp_pose = cube.manipulated_part.local_grasp + local_grasp_pose[:, :3, 3] *= relative_factor + cube.manipulated_part.local_grasp = local_grasp_pose + cube.scale_factor = scale_factor + # cube.set_model(False) + cube.set_parent(self.taks_base) + cube_bbox = cube.get_bounding_box() + x, y = cube_bbox[1]-cube_bbox[0], cube_bbox[3]-cube_bbox[2] + x, y = x*1.2, y*1.2 + cube_target = Shape.create(PrimitiveShape.CUBOID, [x,y,0], respondable=False, static=True, renderable=False) + # cube_target.set_parent(self.taks_base) + cube_target._is_plane = True + cube_target.set_transparency(0) + cube.target = cube_target + self.cube_list.append(cube) + self.shape_lib[selected_obj].append(cube) + self._need_remove_objects.append(cube_target) + self.register_graspable_objects(self.cube_list) + self._need_remove_objects+=self.cube_list + + def sample_method(self): + self.spawn_space.clear() + for cube in self.cube_list: + self.spawn_space.sample(cube, min_distance=0.1) + + def is_static_workspace(self) -> bool: + return True + + def load(self, ttms_folder=None): + if Shape.exists('stack_cubes'): + return Dummy('stack_cubes') + ttm_file = os.path.join(ttms_folder, 'stack_cubes.ttm') + self._base_object = self.pyrep.import_model(ttm_file) + return self._base_object + + def get_base(self) -> Dummy: + self._base_object = Dummy('stack_cubes') + return self._base_object diff --git a/easi/tasks/ebmanipulation/vendor/vlm/tasks/stack_cubes_color.py b/easi/tasks/ebmanipulation/vendor/vlm/tasks/stack_cubes_color.py new file mode 100644 index 0000000..35102df --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/vlm/tasks/stack_cubes_color.py @@ -0,0 +1,39 @@ +from typing import List +import numpy as np +import os +from amsolver.backend.task import Task +from pyrep.objects.shape import Shape +from pyrep.objects.proximity_sensor import ProximitySensor +from amsolver.backend.unit_tasks import VLM_Object +from amsolver.const import colors +from amsolver.backend.conditions import DetectedCondition +from amsolver.backend.spawn_boundary import SpawnBoundary +from amsolver.backend.task import Task +from amsolver.backend.utils import scale_object, select_color +from vlm.tasks.stack_cubes import StackCubes +from pyrep.const import ObjectType, PrimitiveShape + +class StackCubesColor(StackCubes): + + def modified_init_episode(self, index: int) -> List[str]: + for obj in self.cube_list: + scale_factor = np.random.uniform(0.8, 1.2) + relative_factor = scale_object(obj, scale_factor) + if abs(relative_factor-1)>1e-2: + local_grasp_pose = obj.manipulated_part.local_grasp + local_grasp_pose[:, :3, 3] *= relative_factor + obj.manipulated_part.local_grasp = local_grasp_pose + + # color_index = np.random.choice(len(colors), len(self.cube_list), replace=False) + # for i, obj in enumerate(self.cube_list): + # Shape(obj.manipulated_part.visual).set_color(colors[color_index[i]][1]) + # obj.manipulated_part.descriptions = f"the {colors[color_index[i]][0]} cube" + color_names, rgbs = select_color(index, len(self.cube_list)-1, replace=False) + for i, cube in enumerate(self.cube_list): + Shape(cube.manipulated_part.visual).set_color(rgbs[i]) + cube.manipulated_part.descriptions = f"the {color_names[i]} {obj.manipulated_part.property['shape']}" + return None + + def variation_count(self) -> int: + # TODO: The number of variations for this task. + return len(colors) diff --git a/easi/tasks/ebmanipulation/vendor/vlm/tasks/stack_cubes_relative.py b/easi/tasks/ebmanipulation/vendor/vlm/tasks/stack_cubes_relative.py new file mode 100644 index 0000000..e926f7e --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/vlm/tasks/stack_cubes_relative.py @@ -0,0 +1,64 @@ +from typing import List +import numpy as np +import itertools +from amsolver.backend.task import Task +from pyrep.objects.shape import Shape +from pyrep.objects.proximity_sensor import ProximitySensor +from amsolver.backend.unit_tasks import VLM_Object +from amsolver.const import colors, object_shapes +from amsolver.backend.conditions import DetectedCondition +from amsolver.backend.spawn_boundary import SpawnBoundary +from amsolver.backend.task import Task +from amsolver.backend.utils import get_relative_position_xy, get_sorted_grasp_pose, scale_object, select_color +from vlm.tasks.stack_cubes import StackCubes +from pyrep.const import ObjectType, PrimitiveShape + +relative_pos_list = list(itertools.product(["left", "right", "front", "rear"], repeat=2)) +class StackCubesRelative(StackCubes): + + def init_task(self) -> None: + self.model_num = 2 + self.class_num = 2 + return super().init_task() + + def modified_init_episode(self, index: int) -> List[str]: + self.object_target_relative0, self.object_target_relative1 = relative_pos_list[index] + self.select_obj0 = self.cube_list[:self.model_num] + self.select_obj1 = self.cube_list[self.model_num:2*self.model_num] + self.cube_list = [self.select_obj0[0], self.select_obj1[0]] + self.cube_num = len(self.cube_list) + color_index = np.random.choice(len(colors), len(self.cube_list), replace=True) + for i, obj in enumerate(self.cube_list): + Shape(obj.manipulated_part.visual).set_color(colors[color_index[i]][1]) + + return None + + def is_static_workspace(self) -> bool: + return True + + def variation_count(self) -> int: + # TODO: The number of variations for this task. + return len(relative_pos_list) + + def sample_method(self): + satisfied = False + while not satisfied: + self.spawn_space.clear() + for obj in self.select_obj0: + self.spawn_space.sample(obj, min_distance=0.1) + + object_relative_pose = get_relative_position_xy(self.select_obj0[1], self.select_obj0[0], self.robot.arm) + if object_relative_pose != self.object_target_relative0: + continue + obj = self.select_obj0[0].manipulated_part + obj.descriptions = "the {} {}".format(object_relative_pose, obj.property["shape"]) + + for obj in self.select_obj1: + self.spawn_space.sample(obj, min_distance=0.1) + object_relative_pose = get_relative_position_xy(self.select_obj1[1], self.select_obj1[0], self.robot.arm) + if object_relative_pose != self.object_target_relative1: + continue + obj = self.select_obj1[0].manipulated_part + obj.descriptions = "the {} {}".format(object_relative_pose, obj.property["shape"]) + satisfied = True + self.pyrep.step() diff --git a/easi/tasks/ebmanipulation/vendor/vlm/tasks/stack_cubes_shape.py b/easi/tasks/ebmanipulation/vendor/vlm/tasks/stack_cubes_shape.py new file mode 100644 index 0000000..a0c94f0 --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/vlm/tasks/stack_cubes_shape.py @@ -0,0 +1,67 @@ +import random +from typing import List +import numpy as np +import os +from amsolver.backend.task import Task +from pyrep.objects.shape import Shape +from pyrep.objects.proximity_sensor import ProximitySensor +from amsolver.backend.unit_tasks import VLM_Object +from amsolver.const import colors, object_shapes +from amsolver.backend.conditions import DetectedCondition +from amsolver.backend.spawn_boundary import SpawnBoundary +from amsolver.backend.task import Task +from amsolver.backend.utils import scale_object, select_color +from vlm.tasks.stack_cubes import StackCubes +from pyrep.const import ObjectType, PrimitiveShape + +class StackCubesShape(StackCubes): + def init_task(self) -> None: + self.class_num = len(object_shapes) + self.model_num = 1 + return super().init_task() + def modified_init_episode(self, index: int) -> List[str]: + total = list(range(self.class_num)) + total.remove(index) + distractor_number = np.random.randint(1,len(total)) + selected_index = random.sample(total, distractor_number) + selected_index = [index]+selected_index + self.cube_list = [self.cube_list[i] for i in selected_index] + + self.cube_num = len(self.cube_list) + color_index = np.random.choice(len(colors), len(self.cube_list), replace=True) + for i, obj in enumerate(self.cube_list): + Shape(obj.manipulated_part.visual).set_color(colors[color_index[i]][1]) + obj.manipulated_part.descriptions = "the {}".format(obj.manipulated_part.property["shape"]) + # color_names, rgbs = select_color(index, 3) + # for i, cube in enumerate(self.cube_list): + # Shape(cube.manipulated_part.visual).set_color(rgbs[i]) + # cube.manipulated_part.descriptions = f"the {color_names[i]} cube" + return None + + def variation_count(self) -> int: + return len(object_shapes) + + + # def import_objects(self, num): + # object_numbers = [1]*len(object_shapes) + # self.shape_lib = [] + # for obj, num in zip(object_shapes, object_numbers): + # for i in range(num): + # model = VLM_Object(self.pyrep, self.model_dir+object_shapes[obj]["path"], i) + # relative_factor = scale_object(model, 1.25) + # if abs(relative_factor-1)>1e-2: + # local_grasp_pose = model.manipulated_part.local_grasp + # local_grasp_pose[:, :3, 3] *= relative_factor + # model.manipulated_part.local_grasp = local_grasp_pose + # # model.set_model(False) + # model.set_parent(self.taks_base) + # model_bbox = model.get_bounding_box() + # x, y = model_bbox[1]-model_bbox[0]+0.02, model_bbox[3]-model_bbox[2]+0.02 + # model_target = Shape.create(PrimitiveShape.CUBOID, [x,y,0.02], respondable=False, static=True, renderable=False) + # # model_target.set_parent(model) + # model_target._is_plane = True + # model_target.set_transparency(0) + # model.target = model_target + # model.set_position([0,0,0]) + # self.shape_lib.append(model) + # self.register_graspable_objects(self.shape_lib) diff --git a/easi/tasks/ebmanipulation/vendor/vlm/tasks/stack_cubes_size.py b/easi/tasks/ebmanipulation/vendor/vlm/tasks/stack_cubes_size.py new file mode 100644 index 0000000..931c2e0 --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/vlm/tasks/stack_cubes_size.py @@ -0,0 +1,73 @@ +from typing import List +import numpy as np +import itertools +from amsolver.backend.task import Task +from pyrep.objects.shape import Shape +from pyrep.objects.proximity_sensor import ProximitySensor +from amsolver.backend.unit_tasks import VLM_Object +from amsolver.const import colors +from amsolver.backend.conditions import DetectedCondition +from amsolver.backend.spawn_boundary import SpawnBoundary +from amsolver.backend.task import Task +from amsolver.backend.utils import scale_object, select_color +from vlm.tasks.stack_cubes import StackCubes +from pyrep.const import ObjectType, PrimitiveShape + +sequence = list(itertools.permutations(list(range(3)), 3)) +class StackCubesSize(StackCubes): + + def init_task(self) -> None: + self.model_num = 3 + super().init_task() + + def modified_init_episode(self, index: int) -> List[str]: + assert self.model_num == 3 + for i in range(self.model_num): + cube = self.cube_list[i] + if i==0: + cube.manipulated_part.descriptions = "the large {}".format(cube.manipulated_part.property["shape"]) + scale_factor = np.random.uniform(1.2, 1.4) + elif i==1: + cube.manipulated_part.descriptions = "the medium {}".format(cube.manipulated_part.property["shape"]) + scale_factor = np.random.uniform(0.9, 1.1) + elif i==2: + cube.manipulated_part.descriptions = "the small {}".format(cube.manipulated_part.property["shape"]) + scale_factor = np.random.uniform(0.7, 0.9) + relative_factor = scale_object(cube, scale_factor) + scale_object(cube.target, scale_factor) + if abs(relative_factor-1)>1e-2: + local_grasp_pose = cube.manipulated_part.local_grasp + local_grasp_pose[:, :3, 3] *= relative_factor + cube.manipulated_part.local_grasp = local_grasp_pose + + color_index = np.random.choice(len(colors), self.model_num, replace=True) + for cube, i in zip(self.cube_list, color_index): + Shape(cube.manipulated_part.visual).set_color(colors[i][1]) + self.cube_list = [self.cube_list[i] for i in sequence[index]] + return None + + def variation_count(self) -> int: + # TODO: The number of variations for this task. + return len(sequence) + + # def import_objects(self, number=4): + # large_model_path = self.model_dir+"cube/cube_large/cube_large.ttm" + # normal_model_path = self.model_dir+"cube/cube_normal/cube_normal.ttm" + # small_model_path = self.model_dir+"cube/cube_small/cube_small.ttm" + # self.large_cube = VLM_Object(self.pyrep, large_model_path, 0) + # self.normal_cube = VLM_Object(self.pyrep, normal_model_path, 0) + # self.small_cube = VLM_Object(self.pyrep, small_model_path, 0) + # self.large_cube.manipulated_part.descriptions = "the large cube" + # self.normal_cube.manipulated_part.descriptions = "the medium cube" + # self.small_cube.manipulated_part.descriptions = "the small cube" + # self.cube_list = [self.large_cube, self.normal_cube, self.small_cube] + # self.cube_num = len(self.cube_list) + # for cube in self.cube_list: + # cube.set_model(False) + # cube.set_parent(self.get_base()) + # cube_bbox = cube.get_bounding_box() + # x, y = cube_bbox[1]-cube_bbox[0]+0.02, cube_bbox[3]-cube_bbox[2]+0.02 + # cube_target = Shape.create(PrimitiveShape.CUBOID, [x,y,0], respondable=False, static=True, renderable=False) + # cube_target.set_parent(cube) + # cube.target = cube_target + # self.register_graspable_objects(self.cube_list) diff --git a/easi/tasks/ebmanipulation/vendor/vlm/tasks/wipe_table.py b/easi/tasks/ebmanipulation/vendor/vlm/tasks/wipe_table.py new file mode 100644 index 0000000..8b72d67 --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/vlm/tasks/wipe_table.py @@ -0,0 +1,218 @@ +import random +from typing import List +import numpy as np +import os +from scipy.spatial.transform import Rotation as R +from pyrep.objects.dummy import Dummy +from pyrep.const import ObjectType, PrimitiveShape +from amsolver.backend.task import Task +from pyrep.objects.shape import Shape +from pyrep.objects.proximity_sensor import ProximitySensor +from amsolver.backend.unit_tasks import T0_ObtainControl, T1_MoveObjectGoal, T2_MoveObjectConstraints, TargetSpace, VLM_Object +from amsolver.backend.utils import scale_object +from amsolver.const import colors, planes +from amsolver.backend.conditions import DetectedCondition +from amsolver.backend.spawn_boundary import SpawnBoundary +from amsolver.backend.task import Task +from amsolver.backend.conditions import Condition +from pyrep.backend._sim_cffi import ffi, lib + +DIRT_POINTS = 50 +class WipeTable(Task): + + def init_task(self) -> None: + self.spawn_space = SpawnBoundary([Shape('workspace')]) + self.model_dir = os.path.dirname(os.path.realpath(__file__)).replace("tasks","object_models/") + self.temporary_waypoints = [] + self.object_list = [] + self.target_list = [] + self.dirt_spots = [] + self.taks_base = self.get_base() + if not hasattr(self, "model_num"): + self.model_num = 1 + if not hasattr(self, "area_num"): + self.area_num = 2 + if not hasattr(self, "area_class_num"): + self.area_class_num = 1 + self.import_objects(self.model_num) + + def init_episode(self, index: int) -> List[str]: + try_init_episode_times = 200 + self.variation_index = index + self.create_area() + self.modified_init_episode(index) + self.manipulated_obj = self.object_list[0].manipulated_part + self.sensor = self.object_list[0].sensor + self.sensor_name = self.sensor.get_name() + target = self.target_list[0] + self.manipulated_obj.descriptions = "the {}".format(self.manipulated_obj.property["shape"]) + while len(self.temporary_waypoints)==0 and try_init_episode_times>0: + self.sample_method() + # self.register_success_conditions([DetectedCondition(self.manipulated_obj, self.target_space.successor)]) + init_states = self.get_state()[0] + end_args = {"target":target,"step":"end"} + if target.directional: + target_space_descriptions1 = f"Move the object along the main direction of {target.target_space_descriptions}." + else: + target_space_descriptions1 = f"Move the object cross the center of {target.target_space_descriptions}." + target_space1 = TargetSpace(self.move_to_point, space_args=end_args, + target_space_descriptions = target_space_descriptions1, focus_obj_id= target.get_handle()) + target_space1.set_target(self.sensor, try_ik_sampling=False, linear=True, ignore_collisions=True, release=False) + MoveTask1 = T2_MoveObjectConstraints(self.robot, self.pyrep, target_space1, self.taks_base, fail_times=2) + + start_args = {"target":target,"step":"start"} + target_space_descriptions0 = f"to the side of {target.target_space_descriptions}" + target_space0 = TargetSpace(self.move_to_point, space_args=start_args, + target_space_descriptions = target_space_descriptions0, focus_obj_id= target.get_handle()) + target_space0.set_target(self.sensor, try_ik_sampling=False, linear=False, ignore_collisions=True, release=False) + MoveTask0 = T1_MoveObjectGoal(self.robot, self.pyrep, target_space0, self.taks_base, fail_times=2, next_task_fuc=MoveTask1.get_path_with_constraints) + # self.target_space.set_target(self.manipulated_obj, try_ik_sampling=False, release=True) + GraspTask = T0_ObtainControl(self.robot, self.pyrep, self.manipulated_obj,self.taks_base, try_times=200, + next_task_fuc=MoveTask0.get_path) + if try_init_episode_times>100: + waypoints = GraspTask.get_path(try_ik_sampling=False) + else: + waypoints = GraspTask.get_path(try_ik_sampling=True) + if waypoints is not None: + self.temporary_waypoints += waypoints + self.reset_robot() + self.pyrep.set_configuration_tree(init_states) + try_init_episode_times -= 1 + self._place_dirt(target) + self.register_success_conditions([LengthCondition(self.dirt_spots, DIRT_POINTS*0.5)]) + for i,waypoint in enumerate(self.temporary_waypoints): + waypoint.set_name('waypoint{}'.format(i)) + description = f"Wipe {target.target_space_descriptions}." + return [description] + + def is_static_workspace(self) -> bool: + return True + + def load(self, ttms_folder=None): + if Shape.exists('wipe_table'): + return Dummy('wipe_table') + ttm_file = os.path.join(ttms_folder, 'wipe_table.ttm') + self._base_object = self.pyrep.import_model(ttm_file) + return self._base_object + + def get_base(self) -> Dummy: + self._base_object = Dummy('wipe_table') + return self._base_object + + def import_objects(self, num=1): + if not hasattr(self, "model_path"): + model_path = self.model_dir+"wiper/sponge/sponge.ttm" + else: + model_path = self.model_dir+self.model_path + for i in range(num): + obj = VLM_Object(self.pyrep, model_path, i) + for children in obj.get_objects_in_tree(exclude_base=True): + if children.get_type() == ObjectType.PROXIMITY_SENSOR: + obj.sensor = children + obj.set_parent(self.taks_base) + self.object_list.append(obj) + self.register_graspable_objects(self.object_list) + + def create_area(self): + self.target_list = [] + self.shape_lib = {} + selected_objs = random.sample(list(planes.keys()), self.area_class_num) + for selected_obj in selected_objs: + model_path = self.model_dir+planes[selected_obj]['path'] + self.shape_lib[selected_obj] = [] + for i in range(self.area_num): + dirt_area = self.pyrep.import_model(model_path) + dirt_area.scale_factor = lib.simGetObjectSizeFactor(ffi.cast('int',dirt_area._handle)) + scale_object(dirt_area, np.random.uniform(0.9, 1.1)) + dirt_area.directional = planes[selected_obj]['directional'] + dirt_area.set_parent(self.taks_base) + dirt_area.target_space_descriptions = f"the {selected_obj} area" + self.shape_lib[selected_obj].append(dirt_area) + self.target_list.append(dirt_area) + self._need_remove_objects.append(dirt_area) + + def sample_method(self): + self.spawn_space.clear() + + for target in self.target_list: + self.spawn_space.sample(target, min_distance=0.1) + + for obj in self.object_list: + self.spawn_space.sample(obj, min_distance=0.1) + + @staticmethod + def move_to_point(target: Shape, step, n_sample=36): + target_pose = target.get_matrix() + target_size = target.get_bounding_box() + if step == "start": + offset = target_size[0] + else: + offset = target_size[1] + related_pos = target_pose.dot(np.array([offset, 0, 0, 1])) + obj_poses = np.zeros((n_sample, 4, 4)) + obj_poses[:, :, 3] = related_pos + theta = 2*np.pi/ n_sample + for i in range(n_sample): + rot_m = R.from_euler("xyz", [0, 0, i*theta]).as_matrix() + obj_poses[i, :3, :3] = target_pose[:3, :3].dot(rot_m) + + return obj_poses + + def _place_dirt(self, space): + target_pose = space.get_matrix() + target_size = space.get_bounding_box() + if space.directional: + step = (target_size[1]-target_size[0])/DIRT_POINTS + for i in range(DIRT_POINTS): + spot = Shape.create(type=PrimitiveShape.CUBOID, + size=[.005, .005, .001], + mass=0, static=True, respondable=False, + renderable=False, + color=[0.58, 0.29, 0.0]) + spot.set_parent(space) + delta_y = np.random.normal(scale=0.005) + related_pos = target_pose.dot(np.array([target_size[0]+step*i, delta_y, 0.001, 1])) + spot.set_position(related_pos[:3]) + self.dirt_spots.append(spot.get_name()) + else: + max_r = min(target_size[1], target_size[3])/2 + for i in range(DIRT_POINTS): + spot = Shape.create(type=PrimitiveShape.CUBOID, + size=[.005, .005, .001], + mass=0, static=True, respondable=False, + renderable=False, + color=[0.58, 0.29, 0.0]) + spot.set_parent(space) + r = np.random.uniform(0, max_r) + theta = np.random.uniform(-np.pi, np.pi) + x, y = r*np.cos(theta), r*np.sin(theta) + related_pos = target_pose.dot(np.array([x, y, 0.001, 1])) + spot.set_position(related_pos[:3]) + self.dirt_spots.append(spot.get_name()) + + def step(self) -> None: + sensor = ProximitySensor(self.sensor_name) + for d in self.dirt_spots: + if sensor.is_detected(Shape(d)): + self.dirt_spots.remove(d) + Shape(d).remove() + + def cleanup(self) -> None: + for d in self.dirt_spots: + Shape(d).remove() + self.dirt_spots = [] + super().cleanup() + +class LengthCondition(Condition): + + def __init__(self, container: list, num_bound): + self._container = container + self.num_bound = num_bound + + def condition_met(self): + count = 0 + for obj in self._container: + if Shape.exists(obj): + count+=1 + met = count <= self.num_bound + return met, False \ No newline at end of file diff --git a/easi/tasks/ebmanipulation/vendor/vlm/tasks/wipe_table_color.py b/easi/tasks/ebmanipulation/vendor/vlm/tasks/wipe_table_color.py new file mode 100644 index 0000000..8407956 --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/vlm/tasks/wipe_table_color.py @@ -0,0 +1,23 @@ +from typing import List +import numpy as np +from amsolver.backend.unit_tasks import VLM_Object, TargetSpace +from pyrep.objects.shape import Shape +from amsolver.const import colors +from amsolver.backend.utils import select_color +from vlm.tasks.wipe_table import WipeTable + +class WipeTableColor(WipeTable): + def init_task(self) -> None: + # self.model_num = 3 + return super().init_task() + + def modified_init_episode(self, index: int): + color_names, rgbs = select_color(index, len(self.target_list)-1) + for i, target in enumerate(self.target_list): + target.set_color(rgbs[i]) + target.target_space_descriptions= "the {} area".format(color_names[i]) + + return None + + def variation_count(self) -> int: + return len(colors) \ No newline at end of file diff --git a/easi/tasks/ebmanipulation/vendor/vlm/tasks/wipe_table_direction.py b/easi/tasks/ebmanipulation/vendor/vlm/tasks/wipe_table_direction.py new file mode 100644 index 0000000..5ab9b32 --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/vlm/tasks/wipe_table_direction.py @@ -0,0 +1,71 @@ +import random +from typing import List +import numpy as np +from amsolver.backend.unit_tasks import VLM_Object, TargetSpace +from pyrep.objects.shape import Shape +from amsolver.const import colors,planes +from amsolver.backend.utils import get_relative_position_xy, scale_object, select_color +from vlm.tasks.wipe_table import WipeTable + +direction_list = ["horizontal", "vertical"] +class WipeTableDirection(WipeTable): + def init_task(self) -> None: + super().init_task() + + def modified_init_episode(self, index: int): + self._task_init_states = [] + for area in self.target_list: + self._task_init_states.append(area.get_pose()) + + self.target_direction = direction_list[index] + self.target_list[0].target_space_descriptions = f"the {self.target_direction} area" + target_space_colors = np.random.choice(len(colors), len(self.target_list), replace=True) + for i, target in enumerate(self.target_list): + target.set_color(colors[target_space_colors[i]][1]) + + return None + + def variation_count(self) -> int: + return len(direction_list) + + def create_area(self): + self.target_list = [] + self.shape_lib = {} + directional_planes = {k:v for k, v in planes.items() if v['directional']} + selected_objs = random.sample(list(directional_planes.keys()), self.area_class_num) + for selected_obj in selected_objs: + model_path = self.model_dir+directional_planes[selected_obj]['path'] + self.shape_lib[selected_obj] = [] + for i in range(self.area_num): + dirt_area = self.pyrep.import_model(model_path) + scale_object(dirt_area, np.random.uniform(0.9, 1.1)) + dirt_area.directional = directional_planes[selected_obj]['directional'] + dirt_area.set_parent(self.taks_base) + dirt_area.target_space_descriptions = f"the {selected_obj} area" + self.shape_lib[selected_obj].append(dirt_area) + self.target_list.append(dirt_area) + self._need_remove_objects.append(dirt_area) + + def sample_method(self): + for area, pose in zip(self.target_list, self._task_init_states): + area.set_pose(pose) + self.spawn_space.clear() + vertical_min_rotation, vertical_max_rotation = (0, 0, -np.pi/8), (0, 0, np.pi/8) + if np.random.uniform()<0.5: + horizontal_min_rotation, horizontal_max_rotation = (0, 0, -5*np.pi/8), (0, 0, -3*np.pi/8) + else: + horizontal_min_rotation, horizontal_max_rotation = (0, 0, 3*np.pi/8), (0, 0, 5*np.pi/8) + if self.target_direction == "horizontal": + t0_min_rotation, t0_max_rotation = horizontal_min_rotation, horizontal_max_rotation + t1_min_rotation, t1_max_rotation = vertical_min_rotation, vertical_max_rotation + else: + t0_min_rotation, t0_max_rotation = vertical_min_rotation, vertical_max_rotation + t1_min_rotation, t1_max_rotation = horizontal_min_rotation, horizontal_max_rotation + + self.spawn_space.sample(self.target_list[0], min_distance=0.2, + min_rotation=t0_min_rotation, max_rotation=t0_max_rotation) + self.spawn_space.sample(self.target_list[1], min_distance=0.2, + min_rotation=t1_min_rotation, max_rotation=t1_max_rotation) + + for obj in self.object_list: + self.spawn_space.sample(obj, min_distance=0.1) \ No newline at end of file diff --git a/easi/tasks/ebmanipulation/vendor/vlm/tasks/wipe_table_relative.py b/easi/tasks/ebmanipulation/vendor/vlm/tasks/wipe_table_relative.py new file mode 100644 index 0000000..581515e --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/vlm/tasks/wipe_table_relative.py @@ -0,0 +1,40 @@ +from typing import List +import numpy as np +from amsolver.backend.unit_tasks import VLM_Object, TargetSpace +from pyrep.objects.shape import Shape +from amsolver.const import colors +from amsolver.backend.utils import get_relative_position_xy, select_color +from vlm.tasks.wipe_table import WipeTable + +relative_pos_list = ["left", "right", "front", "rear"] +class WipeTableRelative(WipeTable): + def init_task(self) -> None: + return super().init_task() + + def modified_init_episode(self, index: int): + self.target_relative = relative_pos_list[index] + target_space_colors = np.random.choice(len(colors), len(self.target_list), replace=True) + for i, target in enumerate(self.target_list): + target.set_color(colors[target_space_colors[i]][1]) + + return None + + def variation_count(self) -> int: + return len(relative_pos_list) + + def sample_method(self): + + while True: + self.spawn_space.clear() + + for t in self.target_list: + self.spawn_space.sample(t, min_distance=0.1) + target = self.target_list[0] + distractor = self.target_list[1] + object_relative_pose = get_relative_position_xy(distractor, target, self.robot.arm) + if object_relative_pose!= self.target_relative: + continue + target.target_space_descriptions = f"the {object_relative_pose} area" + for obj in self.object_list: + self.spawn_space.sample(obj, min_distance=0.1) + break \ No newline at end of file diff --git a/easi/tasks/ebmanipulation/vendor/vlm/tasks/wipe_table_shape.py b/easi/tasks/ebmanipulation/vendor/vlm/tasks/wipe_table_shape.py new file mode 100644 index 0000000..5e00e2d --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/vlm/tasks/wipe_table_shape.py @@ -0,0 +1,36 @@ +from typing import List +import numpy as np +from amsolver.backend.unit_tasks import VLM_Object, TargetSpace +from pyrep.objects.shape import Shape +from amsolver.const import colors, planes +from amsolver.backend.utils import scale_object, select_color +from vlm.tasks.wipe_table import WipeTable +from pyrep.const import ObjectType, PrimitiveShape + +shape_list = list(planes.keys()) +DIRT_POINTS = 50 +class WipeTableShape(WipeTable): + def init_task(self) -> None: + self.area_num = 1 + self.area_class_num = len(shape_list) + return super().init_task() + + def modified_init_episode(self, index: int): + selected_shapes = shape_list[index] + self.target_list = [self.shape_lib[selected_shapes][0]] + other_shapes_index = list(range(len(shape_list))) + other_shapes_index.remove(index) + distractor_number = np.random.randint(1,min(len(shape_list),3)) + distractor_index = np.random.choice(other_shapes_index, distractor_number, replace=False) + for i in distractor_index: + selected_shapes = shape_list[i] + self.target_list.append(self.shape_lib[selected_shapes][0]) + + target_space_colors = np.random.choice(len(colors), len(self.target_list), replace=True) + for i, target in enumerate(self.target_list): + target.set_color(colors[target_space_colors[i]][1]) + + return None + + def variation_count(self) -> int: + return len(planes) \ No newline at end of file diff --git a/easi/tasks/ebmanipulation/vendor/vlm/tasks/wipe_table_size.py b/easi/tasks/ebmanipulation/vendor/vlm/tasks/wipe_table_size.py new file mode 100644 index 0000000..9ba94fb --- /dev/null +++ b/easi/tasks/ebmanipulation/vendor/vlm/tasks/wipe_table_size.py @@ -0,0 +1,36 @@ +from typing import List +import numpy as np +from amsolver.backend.unit_tasks import VLM_Object, TargetSpace +from pyrep.objects.shape import Shape +from amsolver.const import colors +from amsolver.backend.utils import scale_object, select_color +from vlm.tasks.wipe_table import WipeTable + +size_list = ["small", "large"] +class WipeTableSize(WipeTable): + def init_task(self) -> None: + # self.model_num = 3 + return super().init_task() + + def modified_init_episode(self, index: int): + + target_size = size_list[index] + if target_size == "small": + small_target = self.target_list[0] + large_target = self.target_list[1] + else: + small_target = self.target_list[1] + large_target = self.target_list[0] + small_target.target_space_descriptions = "the smaller area" + large_target.target_space_descriptions = "the larger area" + for target, scale_factor in zip([small_target, large_target],[np.random.uniform(0.75, 0.9), np.random.uniform(1.0, 1.2)]): + scale_object(target, scale_factor) + + target_space_colors = np.random.choice(len(colors), len(self.target_list), replace=True) + for i, target in enumerate(self.target_list): + target.set_color(colors[target_space_colors[i]][1]) + + return None + + def variation_count(self) -> int: + return len(size_list) \ No newline at end of file From 3c7a8ab491089ddf69ee15f9f74c79afe56f87cb Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Sat, 21 Feb 2026 03:06:48 +0800 Subject: [PATCH 071/239] [Feat] Vendor HAZARD source and add HAZARD task integration Vendor minimal HAZARD source files (~33 .py + 2 .txt data files) into easi/tasks/hazard/vendor/HAZARD/ to eliminate git-clone and mmcv/mmdet CUDA build dependencies. Changes: - Vendor HAZARD envs (fire/flood/wind), policy, and utils modules - Patch vendored __init__.py to remove run_experiments cascade - Wrap mmcv/mmdet imports in try/except (optional, not required at runtime) - Rewrite all internal imports from src.HAZARD.* to HAZARD.* - Update bridge.py: replace git-clone with vendor dir on sys.path - Remove hazard_repo, hazard_cache_dir, mmcv, mmdet from _base.yaml - Add HAZARD task (fire/flood/wind), bridge, prompts, actions, configs - Add 28 offline HAZARD tests --- easi/tasks/hazard/__init__.py | 0 easi/tasks/hazard/_base.yaml | 29 + easi/tasks/hazard/actions.py | 39 + easi/tasks/hazard/bridge.py | 679 ++++++++++++++++++ easi/tasks/hazard/config/fire.json | 35 + easi/tasks/hazard/config/fluid.json | 35 + easi/tasks/hazard/config/prompts.csv | 37 + easi/tasks/hazard/config/value.json | 37 + easi/tasks/hazard/hazard_fire.yaml | 15 + easi/tasks/hazard/hazard_flood.yaml | 15 + easi/tasks/hazard/hazard_wind.yaml | 15 + easi/tasks/hazard/prompts.py | 319 ++++++++ easi/tasks/hazard/task.py | 239 ++++++ easi/tasks/hazard/vendor/HAZARD/__init__.py | 2 + .../HAZARD/data/meta_data/categories.txt | 1 + .../HAZARD/data/meta_data/categories_new.txt | 1 + .../hazard/vendor/HAZARD/envs/__init__.py | 0 .../vendor/HAZARD/envs/fire/__init__.py | 9 + .../hazard/vendor/HAZARD/envs/fire/agent.py | 311 ++++++++ .../hazard/vendor/HAZARD/envs/fire/fire.py | 237 ++++++ .../vendor/HAZARD/envs/fire/fire_gym.py | 176 +++++ .../vendor/HAZARD/envs/fire/fire_utils.py | 47 ++ .../HAZARD/envs/fire/fireagent_controller.py | 608 ++++++++++++++++ .../hazard/vendor/HAZARD/envs/fire/manager.py | 155 ++++ .../hazard/vendor/HAZARD/envs/fire/object.py | 124 ++++ .../vendor/HAZARD/envs/flood/__init__.py | 9 + .../hazard/vendor/HAZARD/envs/flood/agent.py | 203 ++++++ .../hazard/vendor/HAZARD/envs/flood/flood.py | 126 ++++ .../vendor/HAZARD/envs/flood/flood_gym.py | 192 +++++ .../envs/flood/floodagent_controller.py | 580 +++++++++++++++ .../vendor/HAZARD/envs/flood/manager.py | 256 +++++++ .../hazard/vendor/HAZARD/envs/flood/object.py | 334 +++++++++ .../hazard/vendor/HAZARD/envs/flood/utils.py | 43 ++ .../vendor/HAZARD/envs/wind/__init__.py | 7 + .../hazard/vendor/HAZARD/envs/wind/agent.py | 217 ++++++ .../hazard/vendor/HAZARD/envs/wind/manager.py | 168 +++++ .../hazard/vendor/HAZARD/envs/wind/object.py | 68 ++ .../hazard/vendor/HAZARD/envs/wind/wind.py | 66 ++ .../vendor/HAZARD/envs/wind/wind_gym.py | 165 +++++ .../vendor/HAZARD/envs/wind/wind_utils.py | 15 + .../HAZARD/envs/wind/windagent_controller.py | 488 +++++++++++++ .../hazard/vendor/HAZARD/policy/__init__.py | 0 .../hazard/vendor/HAZARD/policy/astar.py | 58 ++ .../vendor/HAZARD/policy/env_actions.py | 370 ++++++++++ .../hazard/vendor/HAZARD/utils/__init__.py | 0 .../vendor/HAZARD/utils/distributions.py | 61 ++ .../hazard/vendor/HAZARD/utils/local_asset.py | 23 + .../tasks/hazard/vendor/HAZARD/utils/model.py | 337 +++++++++ .../hazard/vendor/HAZARD/utils/model_utils.py | 132 ++++ .../hazard/vendor/HAZARD/utils/scene_setup.py | 115 +++ .../hazard/vendor/HAZARD/utils/seg_id.py | 114 +++ .../hazard/vendor/HAZARD/utils/vision.py | 59 ++ tests/test_hazard_task.py | 382 ++++++++++ 53 files changed, 7753 insertions(+) create mode 100644 easi/tasks/hazard/__init__.py create mode 100644 easi/tasks/hazard/_base.yaml create mode 100644 easi/tasks/hazard/actions.py create mode 100644 easi/tasks/hazard/bridge.py create mode 100644 easi/tasks/hazard/config/fire.json create mode 100644 easi/tasks/hazard/config/fluid.json create mode 100644 easi/tasks/hazard/config/prompts.csv create mode 100644 easi/tasks/hazard/config/value.json create mode 100644 easi/tasks/hazard/hazard_fire.yaml create mode 100644 easi/tasks/hazard/hazard_flood.yaml create mode 100644 easi/tasks/hazard/hazard_wind.yaml create mode 100644 easi/tasks/hazard/prompts.py create mode 100644 easi/tasks/hazard/task.py create mode 100644 easi/tasks/hazard/vendor/HAZARD/__init__.py create mode 100644 easi/tasks/hazard/vendor/HAZARD/data/meta_data/categories.txt create mode 100644 easi/tasks/hazard/vendor/HAZARD/data/meta_data/categories_new.txt create mode 100644 easi/tasks/hazard/vendor/HAZARD/envs/__init__.py create mode 100644 easi/tasks/hazard/vendor/HAZARD/envs/fire/__init__.py create mode 100644 easi/tasks/hazard/vendor/HAZARD/envs/fire/agent.py create mode 100644 easi/tasks/hazard/vendor/HAZARD/envs/fire/fire.py create mode 100644 easi/tasks/hazard/vendor/HAZARD/envs/fire/fire_gym.py create mode 100644 easi/tasks/hazard/vendor/HAZARD/envs/fire/fire_utils.py create mode 100644 easi/tasks/hazard/vendor/HAZARD/envs/fire/fireagent_controller.py create mode 100644 easi/tasks/hazard/vendor/HAZARD/envs/fire/manager.py create mode 100644 easi/tasks/hazard/vendor/HAZARD/envs/fire/object.py create mode 100644 easi/tasks/hazard/vendor/HAZARD/envs/flood/__init__.py create mode 100644 easi/tasks/hazard/vendor/HAZARD/envs/flood/agent.py create mode 100644 easi/tasks/hazard/vendor/HAZARD/envs/flood/flood.py create mode 100644 easi/tasks/hazard/vendor/HAZARD/envs/flood/flood_gym.py create mode 100644 easi/tasks/hazard/vendor/HAZARD/envs/flood/floodagent_controller.py create mode 100644 easi/tasks/hazard/vendor/HAZARD/envs/flood/manager.py create mode 100644 easi/tasks/hazard/vendor/HAZARD/envs/flood/object.py create mode 100644 easi/tasks/hazard/vendor/HAZARD/envs/flood/utils.py create mode 100644 easi/tasks/hazard/vendor/HAZARD/envs/wind/__init__.py create mode 100644 easi/tasks/hazard/vendor/HAZARD/envs/wind/agent.py create mode 100644 easi/tasks/hazard/vendor/HAZARD/envs/wind/manager.py create mode 100644 easi/tasks/hazard/vendor/HAZARD/envs/wind/object.py create mode 100644 easi/tasks/hazard/vendor/HAZARD/envs/wind/wind.py create mode 100644 easi/tasks/hazard/vendor/HAZARD/envs/wind/wind_gym.py create mode 100644 easi/tasks/hazard/vendor/HAZARD/envs/wind/wind_utils.py create mode 100644 easi/tasks/hazard/vendor/HAZARD/envs/wind/windagent_controller.py create mode 100644 easi/tasks/hazard/vendor/HAZARD/policy/__init__.py create mode 100644 easi/tasks/hazard/vendor/HAZARD/policy/astar.py create mode 100644 easi/tasks/hazard/vendor/HAZARD/policy/env_actions.py create mode 100644 easi/tasks/hazard/vendor/HAZARD/utils/__init__.py create mode 100644 easi/tasks/hazard/vendor/HAZARD/utils/distributions.py create mode 100644 easi/tasks/hazard/vendor/HAZARD/utils/local_asset.py create mode 100644 easi/tasks/hazard/vendor/HAZARD/utils/model.py create mode 100644 easi/tasks/hazard/vendor/HAZARD/utils/model_utils.py create mode 100644 easi/tasks/hazard/vendor/HAZARD/utils/scene_setup.py create mode 100644 easi/tasks/hazard/vendor/HAZARD/utils/seg_id.py create mode 100644 easi/tasks/hazard/vendor/HAZARD/utils/vision.py create mode 100644 tests/test_hazard_task.py diff --git a/easi/tasks/hazard/__init__.py b/easi/tasks/hazard/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/easi/tasks/hazard/_base.yaml b/easi/tasks/hazard/_base.yaml new file mode 100644 index 0000000..640ee36 --- /dev/null +++ b/easi/tasks/hazard/_base.yaml @@ -0,0 +1,29 @@ +display_name: "HAZARD" +description: "HAZARD benchmark - embodied decision making in disaster environments" +simulator: "tdw:v1_11_23" +task_class: "easi.tasks.hazard.task.HAZARDTask" +max_steps: 1500 +dataset: + source: huggingface + repo_id: "oscarqjh/HAZARD_easi" + zip_files: + - "simulator_data.zip" +simulator_configs: + screen_size: 512 + port: 1071 + use_cached_assets: false + additional_deps: + - "opencv-python" + - "gym" + - "pyastar2d" + - "inflect" + - "pandas" + - "torch" +agent: + prompt_builder: "easi.tasks.hazard.prompts.HAZARDPromptBuilder" + prompt_builder_kwargs: + cot: false + generation_kwargs: + temperature: 0.7 + max_tokens: 512 + top_p: 1.0 diff --git a/easi/tasks/hazard/actions.py b/easi/tasks/hazard/actions.py new file mode 100644 index 0000000..521d5bf --- /dev/null +++ b/easi/tasks/hazard/actions.py @@ -0,0 +1,39 @@ +"""HAZARD action definitions. + +HAZARD uses dynamic action selection — the bridge computes available plans +each step and the LLM picks from them. These definitions describe the +underlying action types. + +Reference: HAZARD/src/HAZARD/policy/env_actions.py + HAZARD/src/HAZARD/policy/llm.py:get_available_plans() +""" +from __future__ import annotations + +# High-level action types (used internally by bridge) +ACTION_TYPES = [ + "walk_to", + "pick_up", + "drop", + "explore", + "stop", +] + +# Max steps per scenario +SCENARIO_MAX_STEPS = { + "fire": 1500, + "flood": 1500, + "wind": 3000, +} + +# Object values +HIGH_VALUE = 5 +LOW_VALUE = 1 + + +def get_action_space() -> list[str]: + """Return the HAZARD action type list. + + Note: The actual available actions are dynamic per-step and computed + by the bridge. This returns the canonical action types. + """ + return list(ACTION_TYPES) diff --git a/easi/tasks/hazard/bridge.py b/easi/tasks/hazard/bridge.py new file mode 100644 index 0000000..ad6dd82 --- /dev/null +++ b/easi/tasks/hazard/bridge.py @@ -0,0 +1,679 @@ +"""HAZARD bridge — manages disaster rescue scenarios via HAZARD envs. + +This script runs inside the easi_tdw_v1_11_23 conda env (Python 3.10). +It communicates with the parent process via filesystem IPC. + +HAZARD source is vendored at ``easi/tasks/hazard/vendor/HAZARD/``. +The vendor dir is added to sys.path so ``from HAZARD.*`` imports resolve. + +Usage: + python bridge.py --workspace /tmp/easi_xxx [--simulator-kwargs '{}'] +""" +from __future__ import annotations + +import json +import math +import sys +from pathlib import Path + +import numpy as np + +# Add EASI repo root so easi imports work +_repo_root = Path(__file__).resolve().parents[3] +if str(_repo_root) not in sys.path: + sys.path.insert(0, str(_repo_root)) + +# Add vendored HAZARD source to sys.path +_vendor_dir = str(Path(__file__).resolve().parent / "vendor") +if _vendor_dir not in sys.path: + sys.path.insert(0, _vendor_dir) + +from easi.simulators.base_bridge import BaseBridge +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + +# Object values (matching actions.py) +HIGH_VALUE = 5 +LOW_VALUE = 1 + + +class HAZARDBridge(BaseBridge): + """Bridge for HAZARD benchmark scenarios (fire/flood/wind).""" + + def __init__(self, workspace, simulator_kwargs=None): + super().__init__(workspace, simulator_kwargs) + + self.scenario = self.simulator_kwargs.get("scenario", "fire") + self._max_steps = self.simulator_kwargs.get("max_steps", 1500) + self._screen_size = self.simulator_kwargs.get("screen_size", 512) + self._port = self.simulator_kwargs.get("port", 1071) + + # HAZARD state (reset per episode) + self.holding_object = [] + self.nearest_object = None + self.have_finished_list = [] + self.target_status = {} + self.target_ids = [] + self.target_categories = [] + self.object_list = [] + self.current_seen_objects_id = [] + self._last_action_result = False + self._last_action_info = "" + self._action_history = [] # list of (matched_plan_text, result, info) + self._env_change_record = {} # {str(obj_id): [temp/water_level values]} + self._cached_plans = [] # plan descriptions from previous step + self._cached_plan_actions = [] # action tuples from previous step + + def _create_env(self, reset_config, simulator_kwargs): + """Create the appropriate HAZARD env (Fire/Flood/Wind).""" + scenario = simulator_kwargs.get("scenario", "fire") + port = simulator_kwargs.get("port", 1071) + screen_size = simulator_kwargs.get("screen_size", 512) + use_cached_assets = simulator_kwargs.get("use_cached_assets", False) + + if scenario == "fire": + from HAZARD.envs.fire import FireEnv + env = FireEnv( + launch_build=True, screen_size=screen_size, port=port, + use_local_resources=use_cached_assets, + check_version=False, use_gt=False, + ) + elif scenario == "flood": + from HAZARD.envs.flood import FloodEnv + env = FloodEnv( + launch_build=True, screen_size=screen_size, port=port, + use_local_resources=use_cached_assets, + check_version=False, use_gt=False, + ) + elif scenario == "wind": + from HAZARD.envs.wind import WindEnv + env = WindEnv( + launch_build=True, screen_size=screen_size, port=port, + use_local_resources=use_cached_assets, + check_version=False, use_gt=False, + ) + else: + raise ValueError(f"Unknown HAZARD scenario: {scenario}") + + logger.info("Created %s env (port=%d, screen=%d)", scenario, port, screen_size) + return env + + def _on_reset(self, env, reset_config): + """Reset HAZARD env with episode data.""" + # Reset bridge state + self.holding_object = [] + self.nearest_object = None + self.have_finished_list = [] + self._last_action_result = False + self._last_action_info = "" + self._action_history = [] + self._env_change_record = {} + self._cached_plans = [] + self._cached_plan_actions = [] + + # Load scene via HAZARD env + source_dir = reset_config["source_dir"] + logger.info("Resetting HAZARD env with source_dir: %s", source_dir) + env.reset(data_dir=source_dir) + + # Initialize target tracking + self.target_ids = [int(tid) for tid in reset_config.get("target_object_ids", [])] + self.target_categories = reset_config.get("target_categories", []) + self.target_status = {tid: False for tid in self.target_ids} + + # Initial communicate + observation + env.controller.communicate([]) + state = env.controller._obs() + + return self._wrap_obs(state, is_reset=True) + + def _on_step(self, env, action_text): + """Execute a HAZARD action and return (obs, reward, done, info).""" + from HAZARD.policy.env_actions import ( + agent_walk_to, agent_pickup, agent_drop, agent_explore, + ) + + # Parse action_text against CACHED plans from previous step + action_tuple, matched_plan = self._parse_action(action_text) + + if action_tuple is None: + # Failed to parse -> treat as explore + action_tuple = ("explore", None) + matched_plan = "look around" + + # Execute the action + action_type, action_target = action_tuple + action_result, action_info = self._execute_action( + env, action_type, action_target + ) + self._last_action_result = action_result + self._last_action_info = action_info + # Store matched plan text (not raw LLM text) for explore condition + self._action_history.append((matched_plan, action_result, action_info)) + + # Handle state updates based on action + if action_type == "walk_to" and action_result: + self.nearest_object = int(action_target) if action_target else None + elif action_type == "walk_to" and not action_result: + self.nearest_object = None + elif action_type == "pick_up" and action_result: + self._do_hold_object(env) + elif action_type == "drop" and action_result: + self._do_drop_object(env, action_target) + + # Chain auto-actions + self._execute_auto_actions(env) + + # Get new observation + state = env.controller._obs() + self._update_seen_objects(state) + self._update_object_status(state) # Track temperature/water_level + + # Check done + frame_count = env.controller.frame_count + all_targets_done = all(self.target_status.values()) + done = frame_count >= self._max_steps or all_targets_done + + # Compute available plans for NEXT step and cache them + available_plans, plan_actions = self._get_available_plans() + self._cached_plans = available_plans + self._cached_plan_actions = plan_actions + + # Compute scoring (Value + Damage metrics from HAZARD paper) + value_score, max_value, rescued_count, damaged_count = self._compute_scores() + + # Compute max rescue frame (latest frame any target was rescued) + # Paper "Step" metric uses max(rescue_frame_per_target) per episode + rescue_frames = [f for f in self.target_status.values() if f] + max_rescue_frame = float(max(rescue_frames)) if rescue_frames else 0.0 + + # Compute per-object distances for prompt builder + object_distances = self._compute_object_distances(state) + + # Snapshot env_change_record: last value per object for prompt + env_record_snapshot = {} + for obj_id, values in self._env_change_record.items(): + if values: + env_record_snapshot[obj_id] = values[-1] + + # Build info + info = { + "task_success": float(all_targets_done), + "frame_count": float(frame_count), + "max_steps": float(self._max_steps), + "max_rescue_frame": max_rescue_frame, # for paper "Step" metric + "last_action_success": float(action_result), + "feedback": str(action_info), + "holding_objects": json.dumps(self.holding_object), + "available_plans": json.dumps(available_plans), + "plan_actions": json.dumps(plan_actions), + "targets_rescued": float(sum(1 for v in self.target_status.values() if v)), + "targets_total": float(len(self.target_status)), + # HAZARD paper metrics (Value + Damage) + "value_score": float(value_score), # sum(base_value * discount) + "max_value": float(max_value), # sum(base_value for all targets) + "rescued_count": float(rescued_count), # total objects rescued + "damaged_count": float(damaged_count), # rescued objects that were damaged + # Data for prompt builder (STATE section) + "object_list": json.dumps(self.object_list), + "current_seen_objects_id": json.dumps(self.current_seen_objects_id), + "object_distances": json.dumps(object_distances), + "env_change_record": json.dumps(env_record_snapshot), + "target_categories": json.dumps(self.target_categories), + } + + obs = self._wrap_obs(state) + return obs, 0.0, done, info + + def _extract_image(self, obs): + """Extract RGB numpy array from HAZARD observation.""" + rgb = obs.get("rgb_array") + if rgb is not None: + return rgb + return np.zeros((self._screen_size, self._screen_size, 3), dtype=np.uint8) + + def _extract_info(self, info): + """Pass through all info keys (already cleaned).""" + return info or {} + + # --- Action execution --- + + def _execute_action(self, env, action_type, action_target): + """Execute a single HAZARD action, return (success, info_text).""" + from HAZARD.policy.env_actions import ( + agent_walk_to, agent_pickup, agent_drop, agent_explore, + ) + + if action_type == "walk_to": + target_id = self._resolve_target_id(action_target) + if target_id is None: + return False, "invalid target id" + return agent_walk_to( + env, target=target_id, + max_steps=100, reset_arms=False, arrived_at=0.5, + task=self.scenario, effect_on_agents=False, + ) + elif action_type == "pick_up": + target_id = action_target + if target_id is not None: + self.nearest_object = int(target_id) + if self.nearest_object is None: + return False, "no nearest object to pick up" + real_id = self._id_reverse_renumber(self.nearest_object) + return agent_pickup(env, real_id, env_type=self.scenario) + elif action_type == "drop": + if not self.holding_object: + return False, "not holding any object" + if action_target is not None: + real_id = self._id_reverse_renumber(int(action_target)) + return agent_drop(env, real_id, env_type=self.scenario) + else: + return agent_drop(env, env_type=self.scenario) + elif action_type == "explore": + return agent_explore(env) + elif action_type == "stop": + return True, "stopped" + else: + return False, f"unknown action type: {action_type}" + + # --- Auto-actions --- + + def _execute_auto_actions(self, env): + """Chain automatic pickup/drop actions based on current state.""" + max_chain = 5 # prevent infinite loops + for _ in range(max_chain): + auto = self._check_auto_action() + if auto is None: + break + action_type, action_target = auto + logger.info("Auto-action: %s %s", action_type, action_target) + result, info = self._execute_action(env, action_type, action_target) + if action_type == "pick_up" and result: + self._do_hold_object(env) + elif action_type == "drop" and result: + self._do_drop_object(env, action_target) + self._action_history.append((f"auto_{action_type}", result, info)) + + def _check_auto_action(self): + """Check if an auto-action should fire. Returns (type, target) or None. + + Auto-action rules (from HAZARD/src/HAZARD/policy/llm.py:choose_target): + - Wind: If not holding + nearest is target -> pick_up + - Wind: If holding + last walk_to cart succeeded -> drop into cart + - Fire/Flood: If holding target -> drop (into bag) + - Fire/Flood: If nearest is target -> pick_up + """ + if self.scenario == "wind": + if (not self.holding_object + and self.nearest_object is not None + and self._is_target_object(self.nearest_object)): + return ("pick_up", self.nearest_object) + if (self.holding_object + and self._last_action_result + and self._action_history + and self._is_container(self.nearest_object)): + return ("drop", self.nearest_object) + else: + # Fire/Flood + if (self.holding_object + and self.holding_object[0].get("category") in self.target_categories): + return ("drop", None) + if (self.nearest_object is not None + and not self.holding_object + and self._is_target_object(self.nearest_object)): + return ("pick_up", self.nearest_object) + return None + + # --- State management --- + + def _do_hold_object(self, env): + """Record picking up the nearest object.""" + if self.nearest_object is None: + return + real_id = self._id_reverse_renumber(self.nearest_object) + name = env.controller.manager.segm.names.get(real_id, "unknown") + category = env.controller.manager.segm.categories.get(real_id, "unknown") + self.holding_object.append({ + "name": name, "category": category, + "id": str(self.nearest_object), + }) + self.nearest_object = None + + def _do_drop_object(self, env, container_target): + """Record dropping the held object.""" + if not self.holding_object: + return + obj_id = int(self.holding_object[0]["id"]) + real_id = self._id_reverse_renumber(obj_id) + if real_id in self.target_status: + self.target_status[real_id] = env.controller.frame_count + self.have_finished_list.append(obj_id) + else: + self.nearest_object = obj_id + self.holding_object = [] + + def _is_target_object(self, renumbered_id): + """Check if a renumbered object ID corresponds to a target.""" + real_id = self._id_reverse_renumber(renumbered_id) + return real_id in self.target_status + + def _is_container(self, renumbered_id): + """Check if object is a container (wind scenario).""" + if renumbered_id is None: + return False + real_id = self._id_reverse_renumber(renumbered_id) + return hasattr(self.env, 'controller') and real_id in getattr( + self.env.controller, 'containers', [] + ) + + def _id_reverse_renumber(self, renumbered_id): + """Map renumbered ID back to original ID.""" + if not hasattr(self.env.controller, 'manager'): + return renumbered_id + id_map = self.env.controller.manager.id_renumbering + for orig, renum in id_map.items(): + if renum == renumbered_id: + return orig + return renumbered_id + + def _resolve_target_id(self, target): + """Resolve a target (could be renumbered or original ID).""" + if target is None: + return None + return self._id_reverse_renumber(int(target)) + + def _update_seen_objects(self, state): + """Update the list of currently visible object IDs.""" + seg_mask = state["raw"]["seg_mask"] + self.current_seen_objects_id = [ + str(x) for x in set(seg_mask.flatten()) if int(x) != 0 + ] + + def _update_object_status(self, state): + """Track per-object temperature/water_level from sensor data. + + Matches original llm.py:update_object_status() exactly. + For fire/flood: computes average log_temp from segmentation mask. + For wind: no-op. + """ + if self.scenario not in ("fire", "flood"): + return + for o_id in self.current_seen_objects_id: + obj_id = int(o_id) + obj_mask = (state["raw"]["seg_mask"] == obj_id) + if isinstance(obj_mask, np.ndarray): + temp = state["raw"]["log_temp"] * obj_mask + mask_sum = obj_mask.sum() + else: + temp = state["raw"]["log_temp"] * obj_mask.cpu().numpy() + mask_sum = obj_mask.sum().item() + if mask_sum == 0: + continue + avg_temp = float(temp.sum() / mask_sum) + if str(obj_id) not in self._env_change_record: + self._env_change_record[str(obj_id)] = [avg_temp] + else: + self._env_change_record[str(obj_id)].append(avg_temp) + + def _compute_object_distances(self, state): + """Compute per-object distances from agent on semantic map. + + Matches original llm.py:get_object_location_description(). + Returns: dict {str(obj_id): float distance} + """ + distances = {} + # Get agent position on semantic map + agent_map = state["goal_map"] + agent_points = (agent_map == -2).nonzero() + if isinstance(agent_points[0], np.ndarray): + agent_pos = (agent_points[0].astype(float).mean(), + agent_points[1].astype(float).mean()) + else: + agent_pos = (agent_points[:, 0].float().mean().item(), + agent_points[:, 1].float().mean().item()) + + id_map = state["sem_map"]["explored"] * state["sem_map"]["id"] + for obj in self.object_list: + idx = int(obj["id"]) + object_points = (id_map == idx).nonzero() + if isinstance(object_points[0], np.ndarray): + center = (object_points[0].astype(float).mean(), + object_points[1].astype(float).mean()) + else: + center = (object_points[:, 0].float().mean().item(), + object_points[:, 1].float().mean().item()) + dist = float(np.linalg.norm( + np.array([agent_pos[0] - center[0], agent_pos[1] - center[1]]) + )) + distances[str(idx)] = round(dist, 2) + return distances + + # --- Available plans --- + + def _get_available_plans(self): + """Compute available plans for the current state. + + Reference: HAZARD/src/HAZARD/policy/llm.py:get_available_plans() + Returns: (plan_descriptions: list[str], plan_actions: list[tuple]) + """ + plans = [] + actions = [] + + # Build object list from explored objects + state = self.env.controller._obs() + explored = state["sem_map"]["explored"] * state["sem_map"]["id"] + explored_ids = [int(idx) for idx in set(explored.flatten()) if int(idx) != 0] + + object_list = [] + for idx in explored_ids: + real_id = self._id_reverse_renumber(idx) + name = self.env.controller.manager.segm.names.get(real_id, "unknown") + category = self.env.controller.manager.segm.categories.get(real_id, "unknown") + object_list.append({"name": name, "category": category, "id": str(idx)}) + self.object_list = object_list + + if not self.holding_object: + # Can walk to target objects + for obj in object_list: + if obj["category"] not in self.target_categories: + continue + if int(obj["id"]) in self.have_finished_list: + continue + plans.append(f"go pick up object <{obj['category']}> ({obj['id']})") + actions.append(("walk_to", obj["id"])) + else: + # Can drop or walk to container + if self.scenario == "wind": + for obj in object_list: + if obj["category"] != "shopping cart": + continue + plans.append(f"go put object into <{obj['category']}> ({obj['id']})") + actions.append(("walk_to", obj["id"])) + else: + plans.append("put the holding object in my bag") + actions.append(("drop", None)) + + # Explore option (if last action wasn't "look around") + # Matches original: self.action_history[-1] != 'look around' + if (not self._action_history + or self._action_history[-1][0] != "look around"): + plans.append("look around") + actions.append(("explore", None)) + + # Fallback: walk to any visible object + if not actions: + for obj in object_list: + plans.append(f"go to object <{obj['category']}> ({obj['id']})") + actions.append(("walk_to", obj["id"])) + + if not actions: + plans.append("look around") + actions.append(("explore", None)) + + # Limit to 10 options + plans = plans[:10] + actions = actions[:10] + + return plans, [list(a) for a in actions] + + # --- Scoring (HAZARD paper: Value + Damage) --- + + def _compute_scores(self): + """Compute Value and Damage metrics per HAZARD paper. + + Reference: HAZARD/src/HAZARD/utils/calc_value.py + + Returns: (value_score, max_value, rescued_count, damaged_count) + - value_score: sum(base_value * discount) for rescued objects + where discount=1.0 if undamaged, 0.5 if damaged + - max_value: sum(base_value) for all target objects + - rescued_count: number of objects successfully rescued + - damaged_count: number of rescued objects that were damaged + """ + value_dict = self._load_value_dict() + value_score = 0.0 + max_value = 0.0 + rescued_count = 0 + damaged_count = 0 + + for target_id in self.target_status: + # Get object name/category for value lookup + name = self.env.controller.target_id2name.get(target_id, "unknown") + base_value = HIGH_VALUE if value_dict.get(name) == 1 else LOW_VALUE + max_value += base_value + + if not self.target_status[target_id]: + continue # Not rescued + + rescued_count += 1 + + # Compute discount based on object state at time of rescue + discount = self._get_damage_discount(target_id) + value_score += base_value * discount + + if discount < 0.6: # damaged (discount == 0.5) + damaged_count += 1 + + return value_score, max_value, rescued_count, damaged_count + + def _get_damage_discount(self, target_id): + """Get damage discount for a rescued object. + + Returns 1.0 if undamaged, 0.5 if damaged. + + Reference: HAZARD/src/HAZARD/utils/calc_value.py:get_values() + """ + if self.scenario == "fire": + from HAZARD.envs.fire.fire_utils import ObjectState as FireObjectState + obj = self.env.controller.manager.objects.get(target_id) + if obj and obj.state == FireObjectState.NORMAL: + return 1.0 + return 0.5 + elif self.scenario == "flood": + from HAZARD.envs.flood.utils import ObjectState as FloodObjectState + obj = self.env.controller.manager.objects.get(target_id) + if obj is None: + return 1.0 + # Check waterproof + name = self.env.controller.target_id2name.get(target_id, "") + fluid_dict = self._load_fluid_dict() + if fluid_dict.get(name, 0) == 1: + return 1.0 # Waterproof -> always full value + if obj.state in (FloodObjectState.NORMAL, FloodObjectState.FLOATING): + return 1.0 + return 0.5 + else: + # Wind: no damage mechanic + return 1.0 + + def _load_value_dict(self): + """Load object value metadata (cached).""" + if not hasattr(self, '_value_dict_cache'): + config_path = Path(__file__).parent / "config" / "value.json" + self._value_dict_cache = json.loads(config_path.read_text()) + return self._value_dict_cache + + def _load_fluid_dict(self): + """Load waterproof metadata (cached).""" + if not hasattr(self, '_fluid_dict_cache'): + config_path = Path(__file__).parent / "config" / "fluid.json" + self._fluid_dict_cache = json.loads(config_path.read_text()) + return self._fluid_dict_cache + + # --- Action parsing --- + + def _parse_action(self, action_text): + """Parse LLM's action text into (action_tuple, matched_plan_text). + + Uses CACHED plans from the previous step (what the LLM actually saw), + not freshly recomputed plans (which may have changed due to state drift). + + Returns: (action_tuple, matched_plan_text) or (None, None) + """ + if not action_text: + return None, None + + # Match against cached plans (what the LLM was shown) + available_plans = self._cached_plans + plan_actions = self._cached_plan_actions + + if not available_plans: + # First step or no cached plans -- keyword fallback only + return self._keyword_fallback(action_text) + + # Exact match + for i, plan in enumerate(available_plans): + if plan == action_text: + return tuple(plan_actions[i]), plan + + # Option letter match (A, B, C, ...) + text = action_text.strip() + for i, plan in enumerate(available_plans): + option = chr(ord('A') + i) + if (text == option or text == f"Option {option}" or text == f"{option}." + or f"option {option}" in text.lower()): + return tuple(plan_actions[i]), plan + + # Fuzzy match: check if action text contains plan keywords + for i, plan in enumerate(available_plans): + if plan.lower() in action_text.lower(): + return tuple(plan_actions[i]), plan + + # Keyword-based fallback + return self._keyword_fallback(action_text) + + def _keyword_fallback(self, action_text): + """Fallback parsing when no cached plan matches. + + Returns: (action_tuple, plan_text) or (None, None) + """ + import re + + lower = action_text.lower() + if "look around" in lower or "explore" in lower: + return ("explore", None), "look around" + if "put" in lower and "bag" in lower: + return ("drop", None), "put the holding object in my bag" + if "pick" in lower: + id_match = re.search(r'\((\d+)\)', action_text) + if id_match: + return ("walk_to", id_match.group(1)), action_text + if "go" in lower or "walk" in lower: + id_match = re.search(r'\((\d+)\)', action_text) + if id_match: + return ("walk_to", id_match.group(1)), action_text + + return None, None + + # --- Observation wrapping --- + + def _wrap_obs(self, state, is_reset=False): + """Wrap HAZARD state dict into a simple obs dict for BaseBridge.""" + rgb = state["raw"]["rgb"] # (3, H, W) float32 0-1 + rgb_array = (rgb.transpose(1, 2, 0) * 255).astype(np.uint8) + return {"rgb_array": rgb_array} + + +if __name__ == "__main__": + HAZARDBridge.main() diff --git a/easi/tasks/hazard/config/fire.json b/easi/tasks/hazard/config/fire.json new file mode 100644 index 0000000..9beaaf1 --- /dev/null +++ b/easi/tasks/hazard/config/fire.json @@ -0,0 +1,35 @@ +{ + "shirt button": 1, + "bowl": 1, + "book": 1, + "bee": 1, + "throw pillow": 1, + "bag, handbag, pocketbook, purse": 1, + "suitcase": 1, + "backpack": 1, + "ipod": 0, + "vacuum cleaner": 0, + "printer": 0, + "coffee maker": 0, + "lighter": 0, + "key": 0, + "camera": 0, + "pliers": 0, + "bottle": 0, + "candle": 1, + "coffee grinder": 0, + "bottle cork": 1, + "soda can": 0, + "box": 1, + "basket": 0, + "fire extinguisher": 0, + "skate": 0, + "hairbrush": 1, + "toothbrush": 0, + "banana": 1, + "pen": 0, + "chocolate candy": 1, + "pan": 0, + "plate": 0, + "apple": 1 +} \ No newline at end of file diff --git a/easi/tasks/hazard/config/fluid.json b/easi/tasks/hazard/config/fluid.json new file mode 100644 index 0000000..ca1b6e6 --- /dev/null +++ b/easi/tasks/hazard/config/fluid.json @@ -0,0 +1,35 @@ +{ + "shirt button": 1, + "bowl": 1, + "book": 0, + "bee": 0, + "throw pillow": 0, + "bag, handbag, pocketbook, purse": 0, + "suitcase": 0, + "backpack": 0, + "ipod": 0, + "vacuum cleaner": 0, + "printer": 0, + "coffee maker": 1, + "lighter": 0, + "key": 1, + "camera": 0, + "pliers": 1, + "bottle": 1, + "candle": 1, + "coffee grinder": 1, + "bottle cork": 0, + "soda can": 1, + "box": 0, + "basket": 1, + "fire extinguisher": 1, + "skate": 1, + "hairbrush": 1, + "toothbrush": 1, + "banana": 1, + "pen": 1, + "chocolate candy": 0, + "pan": 1, + "plate": 1, + "apple": 1 +} diff --git a/easi/tasks/hazard/config/prompts.csv b/easi/tasks/hazard/config/prompts.csv new file mode 100644 index 0000000..96fb5e8 --- /dev/null +++ b/easi/tasks/hazard/config/prompts.csv @@ -0,0 +1,37 @@ +type,prompt +fire,"Suppose I am wearing fireproof suits and my house is on fire. I want to save as many valuable target objects as possible by picking them up and putting them into my bag. Given the current state and my previous actions, please help me choose the best available action to save as many valuable target objects as possible. All objects are denoted as (id), such as (712), and different objects have different values. Note objects lose their value once they start burning. The status of the objects previously seen may not be accurate any more. The final score is measured by the total value of the target objects I saved successfully. +Target objects: +$TARGET_OBJECTS$ +Current State: +$STATE$ +Previous actions: +$ACTION_HISTORY$ +Objects states history: +$OBJECT_HISTORY$ +Available actions: +$AVAILABLE_ACTIONS$ +Answer:" +flood,"My house is flooding now, I want to save as many valuable target objects as possible by picking them up and putting them into my bag. Given the current state and my previous actions, please help me choose the best available action to save as many valuable target objects as possible. All objects are denoted as (id), such as
        (712), and different objects have different values. Note a non-waterproof object loses its value if it becomes submerged by the flood. The status of the objects previously seen may not be accurate any more. The final score is measured by the total value of target objects I saved successfully. +Target objects: +$TARGET_OBJECTS$ +Current State: +$STATE$ +Previous actions: +$ACTION_HISTORY$ +Objects states history: +$OBJECT_HISTORY$ +Available actions: +$AVAILABLE_ACTIONS$ +Answer:" +wind,"I'm in an outdoor scene where an intense and turbulent wind is going on. I want to save as many valuable target objects as possible by picking them up and putting into some fixed shopping carts. Given current state, and my previous actions, please help me choose the best available action to save as many valuable target objects as possible. All objects are denoted as (id), such as
        (712), and different objects have different values. Note all objects except shopping carts are moved by powerful wind forces every step and different objects may have different level of resistence to the wind forces. The distance from the objects previously seen may not be accurate any more. I should reason about the unknown object status to make the best decision. The final score is measured by the total value of target objects I put into shopping carts successfully. +Target objects: +$TARGET_OBJECTS$ +Current State: +$STATE$ +Previous actions: +$ACTION_HISTORY$ +Objects states history: +$OBJECT_HISTORY$ +Available actions: +$AVAILABLE_ACTIONS$ +Answer:" \ No newline at end of file diff --git a/easi/tasks/hazard/config/value.json b/easi/tasks/hazard/config/value.json new file mode 100644 index 0000000..ca6f491 --- /dev/null +++ b/easi/tasks/hazard/config/value.json @@ -0,0 +1,37 @@ +{ + "shirt button": 0, + "bowl": 0, + "book": 1, + "bee": 0, + "throw pillow": 0, + "bag, handbag, pocketbook, purse": 1, + "suitcase": 1, + "backpack": 1, + "ipod": 1, + "vacuum cleaner": 1, + "printer": 1, + "coffee maker": 0, + "lighter": 0, + "key": 0, + "camera": 1, + "pliers": 0, + "bottle": 0, + "candle": 0, + "coffee grinder": 1, + "bottle cork": 0, + "soda can": 0, + "box": 0, + "basket": 0, + "fire extinguisher": 1, + "skate": 1, + "hairbrush": 0, + "toothbrush": 0, + "banana": 0, + "pen": 0, + "chocolate candy": 1, + "pan": 0, + "plate": 1, + "apple": 0, + "money": 1, + "headphone": 1 +} \ No newline at end of file diff --git a/easi/tasks/hazard/hazard_fire.yaml b/easi/tasks/hazard/hazard_fire.yaml new file mode 100644 index 0000000..408e438 --- /dev/null +++ b/easi/tasks/hazard/hazard_fire.yaml @@ -0,0 +1,15 @@ +extends: _base.yaml +name: hazard_fire +display_name: "HAZARD Fire" +description: "HAZARD fire scenario - rescue objects from a burning house" +scenario: fire +max_steps: 1500 +dataset: + subset: "fire" + split: "test" +simulator_configs: + scenario: fire + max_steps: 1500 +agent: + prompt_builder_kwargs: + scenario: fire diff --git a/easi/tasks/hazard/hazard_flood.yaml b/easi/tasks/hazard/hazard_flood.yaml new file mode 100644 index 0000000..f129054 --- /dev/null +++ b/easi/tasks/hazard/hazard_flood.yaml @@ -0,0 +1,15 @@ +extends: _base.yaml +name: hazard_flood +display_name: "HAZARD Flood" +description: "HAZARD flood scenario - rescue objects from a flooding house" +scenario: flood +max_steps: 1500 +dataset: + subset: "flood" + split: "test" +simulator_configs: + scenario: flood + max_steps: 1500 +agent: + prompt_builder_kwargs: + scenario: flood diff --git a/easi/tasks/hazard/hazard_wind.yaml b/easi/tasks/hazard/hazard_wind.yaml new file mode 100644 index 0000000..443d7f4 --- /dev/null +++ b/easi/tasks/hazard/hazard_wind.yaml @@ -0,0 +1,15 @@ +extends: _base.yaml +name: hazard_wind +display_name: "HAZARD Wind" +description: "HAZARD wind scenario - rescue objects from wind by putting them into shopping carts" +scenario: wind +max_steps: 3000 +dataset: + subset: "wind" + split: "test" +simulator_configs: + scenario: wind + max_steps: 3000 +agent: + prompt_builder_kwargs: + scenario: wind diff --git a/easi/tasks/hazard/prompts.py b/easi/tasks/hazard/prompts.py new file mode 100644 index 0000000..3062aba --- /dev/null +++ b/easi/tasks/hazard/prompts.py @@ -0,0 +1,319 @@ +"""HAZARD prompt builder matching original HAZARD LLM agent. + +Reference: HAZARD/src/HAZARD/policy/llm.py + HAZARD/src/HAZARD/policy/llm_configs/prompt_v2.csv +""" +from __future__ import annotations + +import csv +import json +import math +from pathlib import Path + +from easi.agents.prompt_builder import _encode_image_base64 +from easi.core.episode import Action +from easi.core.memory import AgentMemory +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + +_CONFIG_DIR = Path(__file__).parent / "config" + + +def _load_prompt_templates() -> dict[str, str]: + """Load scenario-specific prompt templates from vendored CSV.""" + templates = {} + csv_path = _CONFIG_DIR / "prompts.csv" + with open(csv_path, "r") as f: + reader = csv.DictReader(f) + for row in reader: + templates[row["type"]] = row["prompt"] + return templates + + +class HAZARDPromptBuilder: + """Prompt builder for HAZARD benchmark. + + Constructs HAZARD-style prompts with: + - Scenario-specific preamble (fire/flood/wind) + - Target object descriptions with values and attributes + - Current state (visible objects with distances and hazard info) + - Action history with results + - Dynamic multiple-choice action options + """ + + def __init__( + self, + scenario: str = "fire", + cot: bool = False, + ): + self.scenario = scenario + self.cot = cot + self._templates = _load_prompt_templates() + self._value_dict = json.loads((_CONFIG_DIR / "value.json").read_text()) + self._fire_dict = json.loads((_CONFIG_DIR / "fire.json").read_text()) + self._fluid_dict = json.loads((_CONFIG_DIR / "fluid.json").read_text()) + + def set_action_space(self, actions: list[str]) -> None: + """No-op -- HAZARD uses dynamic action space from bridge metadata.""" + pass + + def build_messages(self, memory: AgentMemory) -> list[dict]: + """Build HAZARD-style prompt from memory state.""" + # Get current observation info + obs = memory.current_observation + info = obs.metadata if obs else {} + + # Parse bridge metadata + available_plans = json.loads(info.get("available_plans", "[]")) + + # Build target objects description + target_desc = self._build_target_description(memory) + + # Build state description + state_desc = self._build_state_description(info) + + # Build action history (from memory, matching original format) + history_desc = self._build_action_history(memory) + + # Build object state history (empty -- original bug, see docstring) + object_history = self._build_object_state_history() + + # Build available actions + actions_desc = self._build_available_actions(available_plans) + + # Construct full prompt from template + template = self._templates.get(self.scenario, self._templates.get("fire", "")) + prompt = template + prompt = prompt.replace("$TARGET_OBJECTS$", target_desc + "\n") + prompt = prompt.replace("$STATE$", state_desc) + prompt = prompt.replace("$ACTION_HISTORY$", history_desc + "\n") + prompt = prompt.replace("$OBJECT_HISTORY$", object_history + "\n") + prompt = prompt.replace("$AVAILABLE_ACTIONS$", actions_desc) + + if self.cot: + prompt += " Let's think step by step." + + # Build messages + content_parts = [] + + # Add image if available + if obs and obs.rgb_path: + image_url = _encode_image_base64(obs.rgb_path) + if image_url: + content_parts.append({ + "type": "image_url", + "image_url": {"url": image_url}, + }) + + content_parts.append({"type": "text", "text": prompt}) + + return [{"role": "user", "content": content_parts}] + + def parse_response(self, llm_response: str, memory: AgentMemory) -> list[Action]: + """Parse LLM response to extract selected action. + + The LLM should respond with an option letter (A, B, C, ...) or + the full action text. We parse this against the available plans. + """ + obs = memory.current_observation + info = obs.metadata if obs else {} + available_plans = json.loads(info.get("available_plans", "[]")) + + if not available_plans: + return [] + + # Find selected plan + selected = self._match_response(llm_response.strip(), available_plans) + + if selected is not None: + return [Action(action_name=selected)] + + # Fallback: couldn't parse -> explore + logger.warning("Could not parse HAZARD response: %s", llm_response[:100]) + return [Action(action_name="look around")] + + def _match_response(self, text: str, available_plans: list[str]) -> str | None: + """Match LLM response text to one of the available plans. + + Reference: HAZARD/src/HAZARD/policy/llm.py:parse_answer() + """ + # Exact match + for plan in available_plans: + if plan in text: + return plan + + # Option letter match + for i, plan in enumerate(available_plans): + option = chr(ord('A') + i) + if (f"option {option}" in text.lower() + or f"{option}." in text.split() + or f"({option})" in text + or f"Option {option}" in text + or (len(text) <= 2 and option in text)): + return plan + + # Fuzzy match by keywords + for plan in available_plans: + if "pick up" in plan and "pick" in text.lower(): + return plan + if "put" in plan and "put" in text.lower(): + return plan + if "look around" in plan and ("look" in text.lower() or "explore" in text.lower()): + return plan + + # Single letter match + if len(text) == 1 and text.isalpha(): + idx = ord(text.upper()) - ord('A') + if 0 <= idx < len(available_plans): + return available_plans[idx] + + return None + + # --- Description builders --- + + def _build_target_description(self, memory: AgentMemory) -> str: + """Build target objects description with values and attributes. + + Matches original llm.py:objects_list2text() exactly: + - Value: raw numeric (5 or 1) + - Attribute: "waterproof"/"non-waterproof" for flood, "None" otherwise + """ + obs = memory.current_observation + info = obs.metadata if obs else {} + + target_cats = json.loads(info.get("target_categories", "[]")) + if not target_cats: + return "No target information available." + + lines = [] + for category in target_cats: + # Raw numeric value matching challenge.py:get_target_info() + value = 5 if self._value_dict.get(category) == 1 else 1 + # Only flood shows waterproof attribute; fire/wind show "None" + if self.scenario == "flood": + wp = self._fluid_dict.get(category, 0) + attr = "waterproof" if wp == 1 else "non-waterproof" + else: + attr = "None" + lines.append(f"name: {category}, value: {str(value)}, attribute: {attr}") + + return "\n".join(lines) + + def _build_state_description(self, info: dict) -> str: + """Build current state description matching llm.py:progress2text(). + + Format: + [Wind only] Shopping carts already found: + Target objects currently seen: + name: {cat}, id: {id}, value: {val}, distance: {d} m, temperature/water_level/status + Target objects previously seen: + [same format for objects not currently visible] + """ + object_list = json.loads(info.get("object_list", "[]")) + seen_ids = json.loads(info.get("current_seen_objects_id", "[]")) + distances = json.loads(info.get("object_distances", "{}")) + env_record = json.loads(info.get("env_change_record", "{}")) + target_cats = json.loads(info.get("target_categories", "[]")) + + ps = "" + + # Wind: shopping carts first + if self.scenario == "wind": + ps += "Shopping carts already found:\n" + for obj in object_list: + if obj["category"] != "shopping cart": + continue + dist = distances.get(obj["id"], "?") + ps += f"name: {obj['category']}, id: {obj['id']}, distance: {dist} m\n" + + # Target objects currently seen + ps += "Target objects currently seen:\n" + for obj in object_list: + if obj["category"] not in target_cats or obj["id"] not in seen_ids: + continue + value = 5 if self._value_dict.get(obj["category"]) == 1 else 1 + dist = distances.get(obj["id"], "?") + ps += f"name: {obj['category']}, id: {obj['id']}, value: {value}, distance: {dist} m, " + ps += self._format_hazard_info(obj["id"], env_record) + + # Target objects previously seen + ps += "Target objects previously seen:\n" + for obj in object_list: + if obj["category"] not in target_cats or obj["id"] in seen_ids: + continue + value = 5 if self._value_dict.get(obj["category"]) == 1 else 1 + dist = distances.get(obj["id"], "?") + ps += f"name: {obj['category']}, id: {obj['id']}, value: {value}, distance: {dist} m, " + ps += self._format_hazard_info(obj["id"], env_record) + + return ps + + def _format_hazard_info(self, obj_id: str, env_record: dict) -> str: + """Format per-object hazard info (temperature/water_level/status). + + Matches original progress2text() per-object suffix. + Fire: temperature in Celsius (exp of log_temp), or "unknown" + Flood: water level in meters, or "unknown" + Wind: status "Unknown" + """ + if self.scenario == "fire": + if obj_id in env_record: + temp = round(math.exp(env_record[obj_id]), 2) + return f"temperature: {temp} Celsius\n" + return "temperature: unknown\n" + elif self.scenario == "flood": + if obj_id in env_record: + level = round(env_record[obj_id], 2) + return f"water level: {level} m\n" + return "water level: unknown\n" + else: + return "status: Unknown\n" + + def _build_action_history(self, memory: AgentMemory) -> str: + """Build action history string matching llm.py format. + + Format: "plan_text (result_desc), plan_text (result_desc)" + Where result_desc = "success" / "paused after taking 100 steps" / "fail, because {info}" + Uses last 10 entries. Reads from memory.action_history. + """ + action_history = memory.action_history # list of (action_name, feedback) + if not action_history: + return "" + + entries = [] + for action_name, feedback in action_history[-10:]: + result_desc = self._action_result_to_description(feedback) + entries.append(f"{action_name} ({result_desc})") + return ", ".join(entries) + + @staticmethod + def _action_result_to_description(feedback: str) -> str: + """Convert feedback to result description matching llm.py. + + Reference: llm.py:action_result_to_description() + """ + if not feedback: + return "success" + lower = feedback.lower() + if "success" in lower: + return "success" + if "max steps reached" in lower: + return "paused after taking 100 steps" + return f"fail, because {feedback}" + + def _build_object_state_history(self) -> str: + """Build object state history section. + + NOTE: In the original llm.py:run(), $OBJECT_HISTORY$ is never replaced + -- it remains as literal text in the prompt. This is a bug in the original. + We replicate by returning empty string (slightly better than literal placeholder). + """ + return "" + + def _build_available_actions(self, plans: list[str]) -> str: + """Format available plans as lettered options.""" + lines = [] + for i, plan in enumerate(plans): + lines.append(f"{chr(ord('A') + i)}. {plan}") + return "\n".join(lines) + "\n" diff --git a/easi/tasks/hazard/task.py b/easi/tasks/hazard/task.py new file mode 100644 index 0000000..7a38718 --- /dev/null +++ b/easi/tasks/hazard/task.py @@ -0,0 +1,239 @@ +"""HAZARD task for EASI. + +Adapts the HAZARD benchmark (fire/flood/wind disaster rescue) to EASI's +task interface. Supports 3 scenarios via per-scenario YAML configs. + +Episode data flows from HF dataset -> task.format_reset_config() -> bridge +-> HAZARD env (via LogPlayback scene reconstruction). + +Scoring: value_saved / max_possible_value. +""" +from __future__ import annotations + +import json +from pathlib import Path + +from easi.core.base_task import BaseTask +from easi.core.episode import EpisodeRecord, StepResult +from easi.tasks.hazard.actions import get_action_space, HIGH_VALUE, LOW_VALUE +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + +# Load vendored metadata for scoring +_CONFIG_DIR = Path(__file__).parent / "config" + + +def _load_json(name: str) -> dict: + return json.loads((_CONFIG_DIR / name).read_text()) + + +class HAZARDTask(BaseTask): + + def __init__(self, data_dir=None, split_yaml_path=None): + super().__init__(data_dir=data_dir, split_yaml_path=split_yaml_path) + self._scenario = self._config.get("scenario", "fire") + self._value_dict = _load_json("value.json") + self._fire_dict = _load_json("fire.json") + self._fluid_dict = _load_json("fluid.json") + + def get_task_yaml_path(self) -> Path: + return Path(__file__).parent / "hazard_fire.yaml" + + def get_bridge_script_path(self) -> Path: + return Path(__file__).parent / "bridge.py" + + def get_instruction(self, episode: dict) -> str: + return episode.get("instruction", self.name) + + def _build_action_space(self) -> list[str]: + return get_action_space() + + def format_reset_config(self, episode: dict) -> dict: + """Map HAZARD HF row to bridge reset config. + + Computes absolute path to episode's source directory + (extracted from simulator_data.zip in the HF dataset). + """ + data_dir = episode.get("_data_dir", "") + source_dir = episode["source_dir"] + # simulator_data.zip extracts to simulator_data/ within data_dir + absolute_source = str(Path(data_dir) / "simulator_data" / source_dir) + + # Parse JSON string fields + hazard_params = episode.get("hazard_params", "{}") + if isinstance(hazard_params, str): + hazard_params = json.loads(hazard_params) + + objects = episode.get("objects", "[]") + if isinstance(objects, str): + objects = json.loads(objects) + + return { + "episode_id": episode.get("episode_id", str(episode.get("id", "unknown"))), + "source_dir": absolute_source, + "scene_name": episode["scene_name"], + "instruction": episode["instruction"], + "task": episode["task"], + "target_categories": episode["target_categories"], + "target_object_ids": episode["target_object_ids"], + "agent_position": episode["agent_position"], + "hazard_params": hazard_params, + "containers": episode.get("containers", []), + "objects": objects, + "max_steps": episode.get("max_steps", self.max_steps), + } + + def evaluate_episode( + self, episode: dict, trajectory: list[StepResult] + ) -> dict[str, float]: + """Compute HAZARD metrics from trajectory. + + Two primary metrics from the HAZARD paper (ICLR 2024): + 1. Value (Average Value Rate): sum(base_value * discount) / sum(all_target_values) + - discount = 1.0 if object undamaged, 0.5 if damaged at rescue time + 2. Damage (Damage Rate): count(rescued_damaged) / count(total_rescued) + - Lower is better. What fraction of rescued objects were already degraded. + + The bridge reports per-step: value_score, max_value, rescued_count, damaged_count. + """ + if not trajectory: + return { + "value_score": 0.0, + "max_value": self._compute_max_value(episode), + "value_rate": 0.0, + "damage_rate": 0.0, + "rescued_count": 0.0, + "damaged_count": 0.0, + "targets_rescued": 0.0, + "targets_total": float(len(episode.get("target_object_ids", []))), + "num_steps": 0.0, + "max_rescue_frame": 0.0, + } + + last_step = trajectory[-1] + value_score = float(last_step.info.get("value_score", 0.0)) + max_value = float(last_step.info.get("max_value", 0.0)) or self._compute_max_value(episode) + rescued_count = float(last_step.info.get("rescued_count", 0.0)) + damaged_count = float(last_step.info.get("damaged_count", 0.0)) + + # Value rate: weighted fraction of value saved (paper metric "Value") + value_rate = value_score / max(max_value, 1.0) + + # Damage rate: fraction of rescued objects that were damaged (paper metric "Damage") + damage_rate = damaged_count / max(rescued_count, 1.0) + + # Max rescue frame: latest frame at which any target was rescued + # Used for paper "Step" metric (sum_max_rescue_frame / sum_rescued) + # Bridge computes this as max(target_status.values()) -- the actual + # latest rescue frame, NOT the total episode frame count. + max_rescue_frame = float(last_step.info.get("max_rescue_frame", 0.0)) + + return { + "value_score": value_score, + "max_value": max_value, + "value_rate": value_rate, # Paper "Value" metric + "damage_rate": damage_rate, # Paper "Damage" metric (lower = better) + "rescued_count": rescued_count, + "damaged_count": damaged_count, + "targets_rescued": float(last_step.info.get("targets_rescued", 0.0)), + "targets_total": float(last_step.info.get("targets_total", 0.0)), + "num_steps": float(len(trajectory)), + "max_rescue_frame": max_rescue_frame, # For paper "Step" metric + } + + def aggregate_results( + self, records: list[EpisodeRecord] + ) -> dict[str, float]: + """HAZARD-specific aggregation matching the paper's three metrics. + + From HAZARD paper (ICLR 2024) and calc_value.py: + - Value (Avg Value Rate): average of per-episode value_rate + - Damage (Damage Rate): cumulative damaged/rescued across ALL episodes + - Step (Avg Step): sum(max_rescue_frame) / sum(rescued) across ALL episodes + """ + if not records: + return { + "value_rate": 0.0, + "damage_rate": 0.0, + "avg_step": 0.0, + "avg_llm_steps": 0.0, + "num_episodes": 0.0, + } + + n = len(records) + + # Value: average per-episode value_rate (paper "Average value rate") + value_rates = [r.episode_results.get("value_rate", 0.0) for r in records] + avg_value_rate = sum(value_rates) / n + + # Damage: cumulative across all episodes (paper "Damage rate") + total_rescued = sum(r.episode_results.get("rescued_count", 0.0) for r in records) + total_damaged = sum(r.episode_results.get("damaged_count", 0.0) for r in records) + damage_rate = total_damaged / max(total_rescued, 1.0) + + # Step (paper metric): sum(max_rescue_frame) / sum(rescued) across ALL episodes + # This matches calc_value.py: "Average step" = sum_step / sum_picked + # where step = max rescue frame per episode (latest frame at which a target was rescued) + total_max_rescue_frame = sum( + r.episode_results.get("max_rescue_frame", 0.0) for r in records + ) + avg_step = total_max_rescue_frame / max(total_rescued, 1.0) + + # Also report avg LLM steps (EASI-native metric, more interpretable) + avg_llm_steps = sum(r.episode_results.get("num_steps", 0.0) for r in records) / n + + return { + # Paper primary metrics + "value_rate": avg_value_rate, # Paper "Value" -- higher is better + "damage_rate": damage_rate, # Paper "Damage" -- lower is better + "avg_step": avg_step, # Paper "Step" -- lower is better + # EASI metric + "avg_llm_steps": avg_llm_steps, # Average LLM decisions per episode + # Supporting detail + "avg_value_score": sum(r.episode_results.get("value_score", 0.0) for r in records) / n, + "avg_max_value": sum(r.episode_results.get("max_value", 0.0) for r in records) / n, + "total_rescued": total_rescued, + "total_damaged": total_damaged, + "avg_targets_rescued": sum(r.episode_results.get("targets_rescued", 0.0) for r in records) / n, + "avg_targets_total": sum(r.episode_results.get("targets_total", 0.0) for r in records) / n, + "num_episodes": float(n), + } + + def _compute_max_value(self, episode: dict) -> float: + """Compute maximum possible value for an episode's targets.""" + target_categories = episode.get("target_categories", []) + # Count number of target object IDs per category + total = 0.0 + for category in target_categories: + value = HIGH_VALUE if self._value_dict.get(category) == 1 else LOW_VALUE + # Count how many target objects of this category + # (approximation -- each target ID is one object) + total += value + # More precise: count by target_object_ids + n_targets = len(episode.get("target_object_ids", [])) + if n_targets > 0 and target_categories: + # Rough: average value * count + avg_value = total / len(target_categories) if target_categories else LOW_VALUE + total = avg_value * n_targets + return total + + def _get_builtin_episodes(self) -> list[dict]: + """Minimal episodes for testing without dataset download.""" + return [ + { + "id": 0, + "episode_id": "test_episode_0", + "task": "fire", + "instruction": "Rescue the following objects from the fire: hairbrush.", + "scene_name": "mm_kitchen_3a", + "agent_position": [-0.25, 0.0, 0.75], + "target_categories": ["hairbrush"], + "target_object_ids": [8265913], + "hazard_params": '{"fire_positions": [[0.5, 0.0, 2.25]]}', + "containers": [], + "objects": '[]', + "max_steps": 1500, + "source_dir": "room_setup_fire/test_set/mm_kitchen_3a-1", + }, + ] diff --git a/easi/tasks/hazard/vendor/HAZARD/__init__.py b/easi/tasks/hazard/vendor/HAZARD/__init__.py new file mode 100644 index 0000000..b1416bc --- /dev/null +++ b/easi/tasks/hazard/vendor/HAZARD/__init__.py @@ -0,0 +1,2 @@ +# Vendored HAZARD source (minimal subset for easi bridge). +# Original __init__.py imported run_experiments which cascades into all envs. diff --git a/easi/tasks/hazard/vendor/HAZARD/data/meta_data/categories.txt b/easi/tasks/hazard/vendor/HAZARD/data/meta_data/categories.txt new file mode 100644 index 0000000..156ae96 --- /dev/null +++ b/easi/tasks/hazard/vendor/HAZARD/data/meta_data/categories.txt @@ -0,0 +1 @@ +['apple', 'backpack', 'bag, handbag, pocketbook, purse', 'banana', 'basket', 'book', 'bookshelf', 'bottle', 'bottle cork', 'bowl', 'box', 'bread', 'cabinet', 'camera', 'candle', 'carving fork', 'chair', 'chocolate candy', 'coaster', 'coffee grinder', 'coffee maker', 'coin', 'cup', 'dining table', 'dishwasher', 'fork', 'gas cooker', 'hairbrush', 'headphone', 'houseplant', 'ipod', 'jar', 'jug', 'key', 'kitchen utensil', 'knife', 'lighter', 'microwave', 'microwave, microwave oven', 'money', 'orange', 'painting', 'pan', 'pen', 'pepper mill, pepper grinder', 'picture', 'plate', 'pot', 'printer', 'radiator', 'saltshaker, salt shaker', 'sandwich', 'scissors', 'shelf', 'shirt button', 'shopping cart', 'soap dispenser', 'soda can', 'spoon', 'stool', 'suitcase', 'table', 'teakettle', 'throw pillow', 'toaster', 'toothbrush', 'vase', 'water faucet', 'wineglass'] \ No newline at end of file diff --git a/easi/tasks/hazard/vendor/HAZARD/data/meta_data/categories_new.txt b/easi/tasks/hazard/vendor/HAZARD/data/meta_data/categories_new.txt new file mode 100644 index 0000000..2c432e8 --- /dev/null +++ b/easi/tasks/hazard/vendor/HAZARD/data/meta_data/categories_new.txt @@ -0,0 +1 @@ +['apple', 'backpack', 'bag, handbag, pocketbook, purse', 'banana', 'basket', 'book', 'bookshelf', 'bottle', 'bottle cork', 'bowl', 'box', 'bread', 'cabinet', 'camera', 'candle', 'carving fork', 'chair', 'chocolate candy', 'coaster', 'coffee grinder', 'coffee maker', 'coin', 'cup', 'dining table', 'dishwasher', 'fork', 'gas cooker', 'hairbrush', 'headphone', 'houseplant', 'ipod', 'jar', 'jug', 'key', 'kitchen utensil', 'knife', 'lighter', 'microwave', 'microwave, microwave oven', 'money', 'orange', 'painting', 'pan', 'pen', 'pepper mill, pepper grinder', 'picture', 'plate', 'pot', 'printer', 'radiator', 'saltshaker, salt shaker', 'sandwich', 'scissors', 'shelf', 'shirt button', 'shopping cart', 'soap dispenser', 'soda can', 'spoon', 'stool', 'suitcase', 'table', 'teakettle', 'throw pillow', 'toaster', 'toothbrush', 'vase', 'water faucet', 'wineglass', 'laptop, laptop computer', 'trophy', 'beverage', 'empty'] \ No newline at end of file diff --git a/easi/tasks/hazard/vendor/HAZARD/envs/__init__.py b/easi/tasks/hazard/vendor/HAZARD/envs/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/easi/tasks/hazard/vendor/HAZARD/envs/fire/__init__.py b/easi/tasks/hazard/vendor/HAZARD/envs/fire/__init__.py new file mode 100644 index 0000000..2ba5d81 --- /dev/null +++ b/easi/tasks/hazard/vendor/HAZARD/envs/fire/__init__.py @@ -0,0 +1,9 @@ +from .fireagent_controller import FireAgentController +from .fire_gym import FireEnv + +from gym.envs.registration import register + +register( + id="fire-v0", + entry_point="envs.fire.fire_gym:FireEnv" +) diff --git a/easi/tasks/hazard/vendor/HAZARD/envs/fire/agent.py b/easi/tasks/hazard/vendor/HAZARD/envs/fire/agent.py new file mode 100644 index 0000000..fafef2e --- /dev/null +++ b/easi/tasks/hazard/vendor/HAZARD/envs/fire/agent.py @@ -0,0 +1,311 @@ +from typing import Optional, Union, List +from tdw.add_ons.replicant import * +from tdw.quaternion_utils import QuaternionUtils +from tdw.replicant.actions.action import Action, ActionStatus +from tdw.replicant.actions.look_at import LookAt +from tdw.replicant.actions.grasp import Grasp +from tdw.replicant.actions.reset_arm import ResetArm +from tdw.replicant.image_frequency import ImageFrequency + +from .fire_utils import * + +import numpy as np + +class HoldStill(Action): + def __init__(self, duration: int): + self.image_frequency = ImageFrequency.once + self.duration = duration + self.counter = 0 + super().__init__() + + def get_initialization_commands(self, resp: List[bytes], static: ReplicantStatic, dynamic: ReplicantDynamic, image_frequency: ImageFrequency) -> List[dict]: + self.counter = 0 + self.status = ActionStatus.ongoing + return super().get_initialization_commands(resp, static, dynamic, image_frequency) + + def get_ongoing_commands(self, resp: List[bytes], static: ReplicantStatic, dynamic: ReplicantDynamic) -> List[dict]: + self.counter += 1 + if self.counter >= self.duration: + self.status = ActionStatus.success + return super().get_ongoing_commands(resp, static, dynamic) + + def get_end_commands(self, resp: List[bytes], static: ReplicantStatic, dynamic: ReplicantDynamic, image_frequency: ImageFrequency) -> List[dict]: + return super().get_end_commands(resp, static, dynamic, image_frequency) + +class SequentialAction(Action): + def __init__(self, actions: List[Action]): + self._actions = actions + self._current = 0 + self.status: ActionStatus = None + self.image_frequency = ImageFrequency.once + super().__init__() + + def get_initialization_commands(self, resp: List[bytes], static: ReplicantStatic, dynamic: ReplicantDynamic, image_frequency: ImageFrequency) -> List[dict]: + self.image_frequency = image_frequency + self._current = 0 + + self.status = ActionStatus.ongoing + self.initialized = True + + # return super().get_initialization_commands(resp=resp, static=static, dynamic=dynamic, image_frequency=image_frequency) + return [] + + def get_ongoing_commands(self, resp: List[bytes], static: ReplicantStatic, dynamic: ReplicantDynamic) -> List[dict]: + commands = [] + # for convenience, if self._current < 0, it is in the middle of two actions, wait one frame + if not self._actions[self._current].initialized: + # The action's status defaults to `ongoing`, but actions sometimes fail prior to initialization. + if self._actions[self._current].status == ActionStatus.ongoing: + # Initialize the action and get initialization commands. + self._actions[self._current].initialized = True + initialization_commands = self._actions[self._current].get_initialization_commands(resp=resp, + static=static, + dynamic=dynamic, + image_frequency=self.image_frequency) + + # Most actions are `ongoing` after initialization, but they might've succeeded or failed already. + if self._actions[self._current].status == ActionStatus.ongoing: + commands.extend(initialization_commands) + else: + commands.extend(self._actions[self._current].get_end_commands(resp=resp, + static=static, + dynamic=dynamic, + image_frequency=self.image_frequency)) + # Continue an ongoing action. + else: + # Get the ongoing action commands. + action_commands = self._actions[self._current].get_ongoing_commands(resp=resp, + static=static, + dynamic=dynamic) + # This is an ongoing action. Append ongoing commands. + if self._actions[self._current].status == ActionStatus.ongoing: + commands.extend(action_commands) + # This action is done. Append end commands. + else: + commands.extend(self._actions[self._current].get_end_commands(resp=resp, + static=static, + dynamic=dynamic, + image_frequency=self.image_frequency)) + # This action ended. If not successful, all commands are done. Otherwise, continue to the next action. + if self._actions[self._current].status != ActionStatus.ongoing: + if self._actions[self._current].status == ActionStatus.success: + self._current += 1 + # If there are more actions, continue. + if self._current < len(self._actions): + self.status = ActionStatus.ongoing + # Otherwise, this action is done. + else: + self.status = ActionStatus.success + else: + self.status = self._actions[self._current].status + return commands + + def get_end_commands(self, resp: List[bytes], static: ReplicantStatic, dynamic: ReplicantDynamic, + image_frequency: ImageFrequency) -> List[dict]: + # return super().get_end_commands(resp=resp, + # static=static, + # dynamic=dynamic, + # image_frequency=image_frequency) + return [] + +""" +The poses are relative to the agent's position and rotation. +Assume the agent is facing positive z. +""" +class FireAgent(Replicant): + def __init__(self, constants=default_const, *args, **kwargs): + self.constants = constants + self.action_buffer = [] + super().__init__(*args, **kwargs) + + # define custom actions here + def pick_up(self, target: int, arm: Union[Arm, List[Arm]], + final_pose_loc: np.ndarray = np.array([0.1, 1.1, 0.6]), absolute: bool = False, + angle: Optional[float] = 90, axis: Optional[str] = "pitch") -> None: + if isinstance(arm, Arm): + self.action = SequentialAction([ + TurnTo(target=target), + LookAt(target=target, + duration=0.1, + scale_duration=True), + ReachFor(target=target, + arms=Replicant._arms_to_list(arm), + absolute=absolute, + dynamic=self.dynamic, + collision_detection=self.collision_detection, + offhand_follows=False, + arrived_at=0.1, + previous=None, + duration=0.25, + scale_duration=True, + max_distance=1.0, + from_held=False, + held_point="bottom"), + Grasp(target=target, arm=arm, dynamic=self.dynamic, angle=angle, axis=axis, relative_to_hand=True, offset=0.0), + ReachFor(target=final_pose_loc, + arms=Replicant._arms_to_list(arm), + absolute=absolute, + dynamic=self.dynamic, + collision_detection=self.collision_detection, + offhand_follows=False, + arrived_at=0.1, + previous=None, + duration=0.25, + scale_duration=True, + max_distance=1.0, + from_held=False, + held_point="bottom"), + ]) + else: + self.action = SequentialAction([ + TurnTo(target=target), + LookAt(target=target, + duration=0.1, + scale_duration=True), + ReachFor(target=target, + arms=Replicant._arms_to_list(arm), + absolute=absolute, + dynamic=self.dynamic, + collision_detection=self.collision_detection, + offhand_follows=False, + arrived_at=0.1, + previous=None, + duration=0.25, + scale_duration=True, + max_distance=1.0, + from_held=False, + held_point="bottom"), + Grasp(target=target, arm=Arm.left, dynamic=self.dynamic, angle=angle, axis=axis), + ReachFor(target=final_pose_loc, + arms=[Arm.left], + absolute=absolute, + dynamic=self.dynamic, + collision_detection=self.collision_detection, + offhand_follows=True, + arrived_at=0.1, + previous=None, + duration=0.25, + scale_duration=True, + max_distance=1.0, + from_held=False, + held_point="bottom"), + ]) + + def extinguish(self, target: np.ndarray, absolute: bool = False) -> None: + """ + Turn to the target position and extinguish the fire. + """ + if not absolute: + target = QuaternionUtils.multiply_by_vector(self.dynamic.transform.rotation, target) + self.dynamic.transform.position + relative_pose_loc1 = np.array([0.0, 0.8, 0.7]) + relative_pose_loc2 = np.array([0.0, 0.6, 0.7]) + if np.linalg.norm(target - self.dynamic.transform.position) > 2: + raise ValueError("Target position is too far away.") + # self.action = SequentialAction([ + # TurnTo(target=target), + # ReachFor(target=final_pose_loc1, + # arms=[Arm.left], + # dynamic=self.dynamic, + # collision_detection=self.collision_detection, + # offhand_follows=False, + # arrived_at=0.02, + # previous=None, + # duration=0.25, + # scale_duration=True, + # max_distance=1.5), + # ReachFor(target=final_pose_loc2, + # arms=[Arm.right], + # dynamic=self.dynamic, + # collision_detection=self.collision_detection, + # offhand_follows=False, + # arrived_at=0.02, + # previous=None, + # duration=0.25, + # scale_duration=True, + # max_distance=1.5), + # HoldStill(duration=self.constants.EXTINGUISH_TIME), + # ResetArm(arms=[Arm.left, Arm.right], + # dynamic=self.dynamic, + # collision_detection=self.collision_detection, + # duration=0.1, + # scale_duration=True, + # previous=None) + # ]) + self.action = SequentialAction([ + TurnTo(target=target), + ReachFor(targets=[relative_pose_loc2], + arms=[Arm.right], + absolute=False, + dynamic=self.dynamic, + collision_detection=self.collision_detection, + offhand_follows=False, + arrived_at=0.1, + previous=None, + duration=0.25, + scale_duration=True, + max_distance=1.0, + from_held=False, + held_point="bottom"), + ReachFor(targets=[relative_pose_loc1], + arms=[Arm.left], + absolute=False, + dynamic=self.dynamic, + collision_detection=self.collision_detection, + offhand_follows=False, + arrived_at=0.1, + previous=None, + duration=0.25, + scale_duration=True, + max_distance=1.0, + from_held=False, + held_point="bottom"), + HoldStill(duration=self.constants.EXTINGUISH_TIME), + ResetArm(arms=[Arm.left, Arm.right], + dynamic=self.dynamic, + collision_detection=self.collision_detection, + duration=0.1, + scale_duration=True, + previous=None) + ]) + def get_facing(self): + """return a radian""" + v = QuaternionUtils.multiply_by_vector(QuaternionUtils.get_inverse(self.dynamic.transform.rotation), np.array([0, 0, 1.0])) + v = v / np.linalg.norm(v) + v = np.arctan2(v[2], v[0]) + return v + + def extinguish_indicator(self) -> Optional[List]: + """if just started extinguish action, return the index of held objects (since the agent can't know if it's holding an extinguisher)""" + + if isinstance(self.action, SequentialAction) \ + and self.action._current == len(self.action._actions) - 2 \ + and isinstance(self.action._actions[-2], HoldStill) \ + and self.action._actions[-2].counter == 1: + return list(self.dynamic.held_objects.values()) + return None + + def extinguish_target(self): + """if agent is extinguishing, return the target position""" + return self.action._actions[0]._target + + def grasp_id(self): + """ + if the agent is grasping an object, return the id of the object + otherwise, return None + """ + if isinstance(self.action, Grasp): + return self.action.target + elif isinstance(self.action, SequentialAction) and isinstance(self.action._actions[self.action._current], Grasp): + return self.action._actions[self.action._current].target + return None + + def fail_grasp(self): + """ + if the agent is grasping an object and it is too far away, fail the grasp (this is controlled by the outside) + """ + if isinstance(self.action, Grasp): + self.action.status = ActionStatus.cannot_grasp + elif isinstance(self.action, SequentialAction) and isinstance(self.action._actions[self.action._current], Grasp): + self.action._actions[self.action._current].status = ActionStatus.cannot_grasp + else: + raise Exception("Not grasping now") \ No newline at end of file diff --git a/easi/tasks/hazard/vendor/HAZARD/envs/fire/fire.py b/easi/tasks/hazard/vendor/HAZARD/envs/fire/fire.py new file mode 100644 index 0000000..14c8070 --- /dev/null +++ b/easi/tasks/hazard/vendor/HAZARD/envs/fire/fire.py @@ -0,0 +1,237 @@ +from typing import List, Union, Dict +from tdw.controller import Controller +from tdw.output_data import OutputData, Overlap + +import numpy as np +from .manager import FireObjectManager +from .fire_utils import * +from .object import FireStatus, AgentStatus + + +def CHOUKA(T, constants=default_const): + ''' + A draw("Chou Ka") process with guarantee. + + In the first {constants.CHOUKA_THRESHOLD_PROB_INCREASE} draws, + the probability is {constants.CHOUKA_INITITIAL_PROB}. + + If failed to hit in the first {constants.CHOUKA_THRESHOLD_PROB_INCREASE} draws, + the probability will increase linearly, + until the {constants.CHOUKA_THRESHOLD_UPPER}'th times when the probability will be 1. + ''' + start_prob = constants.CHOUKA_INITITIAL_PROB + if T <= constants.CHOUKA_THRESHOLD_PROB_INCREASE: + return start_prob + else: + return min(1.0, + start_prob + + (1 - start_prob) / (constants.CHOUKA_THRESHOLD_UPPER-constants.CHOUKA_THRESHOLD_PROB_INCREASE) + * (T - constants.CHOUKA_THRESHOLD_PROB_INCREASE)) + +def box_overlap(pos1, size1, pos2, size2): + pos1 = np.array(pos1) + size1 = np.array(size1) + pos2 = np.array(pos2) + size2 = np.array(size2) + return np.all(pos1 + size1 / 2 > pos2 - size2 / 2) and np.all(pos1 - size1 / 2 < pos2 + size2 / 2) + +class Fire: + def __init__(self, fire_id, last_spread, scale, spread_dirs): + self.fire_id = fire_id + self.last_spread = last_spread + self.scale = scale + self.spread_dirs = spread_dirs + + self.extinguishing = False + +""" +This controller controls the spread of fire. +""" +class FireController(Controller): + def __init__(self, port: int = 1071, check_version: bool = True, launch_build: bool = True, seed = 0, constants = default_const, **kwargs): + self.initialized = False + self.commands: List[dict] = list() + super().__init__(port, check_version, launch_build) + + self.manager = FireObjectManager() + self.add_ons.append(self.manager) + + self.update_fire_per_frame = 10 + self.frame_count = 0 + self.fire_info: Dict[int, Fire] = dict() + self.fire_candidate = dict() + self.RNG = np.random.Generator(np.random.PCG64(seed)) + self.constants = constants + + def seed(self, seed): + self.RNG = np.random.Generator(np.random.PCG64(seed)) + + def get_unique_id(self): + while True: + idx = super().get_unique_id() + if idx not in self.manager.objects: + return idx + + def add_fire(self, position, object_id=None, scale=None): + if isinstance(position, np.ndarray): + position = position.tolist() + if scale == None: scale = self.constants.FIRE_INIT_SCALE + self.commands.append(self.get_add_visual_effect("fire", effect_id=object_id, position=position)) + self.commands.append({"$type": "scale_visual_effect", "id": object_id, "scale_factor": {"x": scale, "y": scale, "z": scale}}) + + def add_fire_floor(self, position): + if isinstance(position, np.ndarray): + position = position.tolist() + idx = self.get_unique_id() + self.add_fire(position={"x": position[0], "y": position[1], "z": position[2]}, object_id=idx) + self.manager.add_object(FireStatus(idx, constants=self.constants, position=position, size=self.constants.FIRE_SPREAD_SIZE)) + # self.fire_idx[idx] = [idx, self.frame_count, [(-1.1, 0), (1.1, 0), (0, -1.1), (0, 1.1)], self.constants.FIRE_INIT_SCALE] + self.fire_info[idx] = Fire(fire_id=idx, + last_spread=self.frame_count, + scale=self.constants.FIRE_INIT_SCALE, + spread_dirs=[(-1.1, 0), (1.1, 0), (0, -1.1), (0, 1.1)]) + + def add_fire_object(self, idx): + new_idx = self.get_unique_id() + position = self.manager.objects[idx].top() + np.array([0, 0.01, 0]) + self.add_fire(position={"x": position[0], "y": position[1], "z": position[2]}, object_id=new_idx) + self.manager.add_object(FireStatus(new_idx, constants=self.constants, position=position, size=self.constants.FIRE_SPREAD_SIZE)) + self.commands.append({"$type": "parent_visual_effect_to_object", "object_id": idx, "id": new_idx}) + # self.fire_idx[idx] = [new_idx, self.frame_count, [(-1.1, 0), (1.1, 0), (0, -1.1), (0, 1.1)], self.constants.FIRE_FINAL_SCALE] + self.fire_info[idx] = Fire(fire_id=new_idx, + last_spread=self.frame_count, + scale=self.constants.FIRE_FINAL_SCALE, + spread_dirs=[(-1.1, 0), (1.1, 0), (0, -1.1), (0, 1.1)]) + + size = self.manager.objects[idx].size + self.commands.append({"$type": "scale_visual_effect", "id": new_idx, "scale_factor": {"x": float(size[0]*0.5), "y": float(size[1]*0.5), "z": float(size[2]*0.5)}}) + + def extinguish_fire_floor(self, fire_idx): + """ + Extinguish a fire on the floor. Send fire id. + """ + self.fire_info[fire_idx].extinguishing = True + self.fire_info[fire_idx].spread_dirs = [] + self.manager.objects[fire_idx].temperature = self.constants.ROOM_TEMPERATURE + pass + + def extinguish_fire_object(self, obj_idx): + """ + Extinguish a fire on an object. Send object id, not the fire id. + """ + self.fire_info[obj_idx].extinguishing = True + self.fire_info[obj_idx].spread_dirs = [] + self.manager.objects[obj_idx].temperature = self.constants.ROOM_TEMPERATURE + self.manager.objects[obj_idx].is_heat_source = True + self.manager.objects[self.fire_info[obj_idx].fire_id].temperature = self.constants.ROOM_TEMPERATURE + pass + + def add_agent(self, idx, pos): + self.manager.add_object(AgentStatus(idx, constants=self.constants, position=pos, size=None, endurance=10000)) + + def candidate_fire_overlap(self, pos, size): + for pos2, size2 in self.fire_candidate.values(): + if box_overlap(pos, size, pos2, size2): + return True + for idx in self.fire_info: + if box_overlap(pos, size, self.manager.objects[idx].position, self.constants.FIRE_SPREAD_SIZE): + return True + return False + + def try_spread(self, idx): + if idx in self.manager.objects and np.abs(self.manager.objects[idx].position).sum() > 50: + return + if len(self.fire_info[idx].spread_dirs) == 0: + return + burning_time = self.frame_count - self.fire_info[idx].last_spread + spread_prob = CHOUKA(burning_time // 5) + if self.RNG.uniform() < spread_prob: + spread_dir = self.fire_info[idx].spread_dirs[self.RNG.choice(len(self.fire_info[idx].spread_dirs))] + self.fire_info[idx].spread_dirs.remove(spread_dir) + spread_dir = np.array([spread_dir[0], 0, spread_dir[1]]) + + pos = self.manager.objects[idx].position * np.array([1, 0, 1]) + spread_dir * (self.constants.FIRE_SPREAD_SIZE + self.manager.objects[self.fire_info[idx].fire_id].size) * 0.5 + if not self.candidate_fire_overlap(pos, self.constants.FIRE_SPREAD_SIZE): + self.fire_candidate[self.get_unique_id()] = (pos, self.constants.FIRE_SPREAD_SIZE) + self.fire_info[idx].last_spread = self.frame_count + + def fire_step(self, resp): + # for idx in self.manager.objects_start_burning: + # self.commands.append({"$type": "set_color", + # "id": idx, + # "color": {"r": 1.0, "g": 0.0, "b": 0.0, "a": 0.5}}) + # self.add_fire_object(idx) + for idx in self.manager.objects_stop_burning: + # self.commands.append({"$type": "set_color", + # "id": idx, + # "color": {"r": 0.0, "g": 0.0, "b": 0.0, "a": 0.5}}) + self.commands.append({"$type": "destroy_visual_effect", + "id": self.fire_info[idx].fire_id}) + self.manager.remove_object(self.fire_info[idx].fire_id) + self.fire_info.pop(idx) + # extend fire + for i in range(len(resp)-1): + r_id = OutputData.get_data_type_id(resp[i]) + if r_id == "over": + o = Overlap(resp[i]) + idx = o.get_id() + # if idx in self.fire_candidate: + # print("check", idx, o.get_object_ids(), o.get_env()) + if idx in self.fire_candidate and o.get_object_ids().size < 3 and o.get_env() == False: + self.add_fire_floor(self.fire_candidate[idx][0]) + self.fire_candidate.clear() + if self.frame_count % self.update_fire_per_frame == 0: + removed = [] + for idx in self.fire_info: + if self.fire_info[idx].scale < self.constants.FIRE_FINAL_SCALE and not self.fire_info[idx].extinguishing: + # expand large enough before spreading + self.fire_info[idx].scale += self.constants.FIRE_SCALE_STEP + ratio = self.fire_info[idx].scale / (self.fire_info[idx].scale - self.constants.FIRE_SCALE_STEP) + self.commands.append({"$type": "scale_visual_effect", + "id": self.fire_info[idx].fire_id, + "scale_factor": {"x": ratio, "y": ratio, "z": ratio}}) + elif not self.fire_info[idx].extinguishing: + # large enough, try spreading + self.try_spread(idx) + elif self.fire_info[idx].scale >= self.constants.FIRE_INIT_SCALE + self.constants.FIRE_SCALE_STEP: + # extinguishing, scale down + self.fire_info[idx].scale -= self.constants.FIRE_SCALE_STEP + ratio = self.fire_info[idx].scale / (self.fire_info[idx].scale + self.constants.FIRE_SCALE_STEP) + self.commands.append({"$type": "scale_visual_effect", + "id": self.fire_info[idx].fire_id, + "scale_factor": {"x": ratio, "y": ratio, "z": ratio}}) + else: + # scaled down enough, remove + self.commands.append({"$type": "destroy_visual_effect", + "id": self.fire_info[idx].fire_id}) + self.manager.remove_object(self.fire_info[idx].fire_id) + removed.append(idx) + for idx in removed: + self.fire_info.pop(idx) + + for i in self.fire_candidate: + pos, size = self.fire_candidate[i] + self.commands.append({"$type": "send_overlap_box", + "half_extents": {"x": size[0]*0.5, "y": size[1]*0.5, "z": size[2]*0.5}, + "rotation": {"w": 1.0, "x": 0.0, "y": 0.0, "z": 0.0}, + "position": {"x": pos[0], "y": pos[1]+size[1]*0.5+0.01, "z": pos[2]}, + "id": i}) + self.frame_count += 1 + + # def run(self): + # # self.add_fire_floor([0, 0, 0]) + # for i in range(1000): + # self.communicate([]) + # self.communicate({"$type": "terminate"}) + + def communicate(self, commands: Union[dict, List[dict]]) -> list: + if isinstance(commands, dict): + commands = [commands] + commands.extend(self.commands) + self.commands.clear() + # for com in commands: + # print(com) + resp = super().communicate(commands) + if self.initialized: + self.fire_step(resp) + return resp diff --git a/easi/tasks/hazard/vendor/HAZARD/envs/fire/fire_gym.py b/easi/tasks/hazard/vendor/HAZARD/envs/fire/fire_gym.py new file mode 100644 index 0000000..a9dfcfd --- /dev/null +++ b/easi/tasks/hazard/vendor/HAZARD/envs/fire/fire_gym.py @@ -0,0 +1,176 @@ +import gym +import gym.spaces +import numpy as np +# from HAZARD.policy.env_actions import agent_drop, agent_pickup, agent_explore, agent_walk_to_single_step + +from enum import IntEnum + +import os +from .fireagent_controller import * +from .agent import * + + +PATH = os.path.dirname(os.path.abspath(__file__)) +while os.path.basename(PATH) != "HAZARD": + PATH = os.path.dirname(PATH) + + +class ActionSpace(IntEnum): + WALK_TO_NEAREST_TARGET = 0 + WALK_TO_NEAREST_CONTAINER = 1 + PICK_UP_NEAREST = 2 + DROP = 3 + EXPLORE = 4 + WALK_TO_RANDOM_OBJECT_IN_SIGHT = 5 + + +class FireEnv(gym.Env): + def __init__(self, port: int=1071, check_version: bool=True, launch_build: bool=False, + use_local_resources: bool=False, seed=0, use_gt=False, + image_capture_path=None, log_path: str=None, reverse_observation=False, + screen_size=512, map_size_h=64, map_size_v=64, grid_size=0.25, + record_only: bool=False, use_dino: bool=False): + self.controller_args = dict(use_local_resources=use_local_resources, launch_build=launch_build, + port=port, check_version=check_version, screen_size=screen_size, + image_capture_path=image_capture_path, log_path=log_path, use_dino=use_dino, + map_size_h=map_size_h, map_size_v=map_size_v, grid_size=grid_size, + use_gt=use_gt, reverse_observation=reverse_observation, record_only=record_only) + self.controller = None + self.RNG = np.random.RandomState(0) + + self.done = False + self.record_only = record_only + + self.observation_space = gym.spaces.Box(0, 20, (5, map_size_h, map_size_v), dtype=np.float32) + + self.action_space = gym.spaces.Discrete(6) + self.max_step = 1000 + + def reset(self, data_dir = None): + if data_dir == None: + data_dirs = os.listdir(os.path.join(PATH, "data", "room_setup_fire")) + data_dirs = [d for d in data_dirs if "craftroom" in d or "kitchen" in d] + data_dir = os.path.join(PATH, "data", "room_setup_fire", data_dirs[self.RNG.randint(len(data_dirs))]) + # data_dir = os.path.join(PATH, "data", "room_setup", "1a-0-0") + self.setup = SceneSetup(data_dir=data_dir, record_mode=self.record_only) + if self.controller is not None: + self.controller.communicate({"$type": "terminate"}) + self.controller.socket.close() + self.controller = FireAgentController(**self.controller_args) + self.controller.seed(self.RNG.randint(1000000)) + print("Controller connected") + self.controller.init_scene(self.setup) + self.num_step = 0 + self.last_action = None + self.last_target = None + if self.record_only: + return + + self.controller.do_action(0, "turn_by", {"angle": 0}) + self.controller.next_key_frame() + + return self.controller._obs()["RL"] + + # def step(self, action): + # # if self.done: + # # self.done = False + # # self.reset() + # """ + # for each type, if action_target is 0, ignore the action target + # """ + # if not isinstance(action, int): + # action = action.item() + # reward = -1 + # if action == ActionSpace.WALK_TO_NEAREST_TARGET: + # targets = [idx for idx in self.controller.target_ids if idx not in self.controller.finished] + # target = self.controller.find_nearest_object(agent_idx=0, objects=targets) + # result, msg = agent_walk_to_single_step(self, target=target) + # elif action == ActionSpace.PICK_UP_NEAREST: + # targets = [idx for idx in self.controller.target_ids if idx not in self.controller.finished] + # target = self.controller.find_nearest_object(agent_idx=0, objects=targets) + # result, msg = agent_pickup(self, target=target, env_type="fire") + # elif action == ActionSpace.DROP: + # target = None + # result, msg = agent_drop(self, env_type="fire") + # elif action == ActionSpace.EXPLORE: + # target = None + # result, msg = agent_explore(self) + # elif action == ActionSpace.WALK_TO_RANDOM_OBJECT_IN_SIGHT: + # if self.last_action == ActionSpace.WALK_TO_RANDOM_OBJECT_IN_SIGHT: + # target = self.last_target + # else: + # obs = self.controller._obs() + # obj_ids = np.unique(obs["sem_map"]["id"]) + # targets = [self.controller.manager.get_real_id(idx) for idx in obj_ids if self.controller.manager.get_real_id(idx) not in self.controller.finished] + # targets = [idx for idx in targets if idx is not None] + # target = int(self.RNG.choice(targets)) if len(targets) > 0 else None + # if target is None: + # result, msg = False, "no object in sight" + # else: + # result, msg = agent_walk_to_single_step(self, target=target) + # else: + # target = None + # reward -= 50 + # result, msg = False, "Invalid action" + # self.last_action = action + # self.last_target = target + # + # if result == False: + # reward -= 2 + # obs, info = self.controller._obs(), self.controller._info() + # info['message'] = msg + # info['success'] = result + # + # reward += self.controller._reward() + # done = self.controller._done() + # self.num_step += 1 + # if self.num_step >= self.max_step: + # done = True + # + # self.done = done + # info['action'] = action + # info['reward'] = reward + # return obs["RL"], reward, done, info + + def seed(self, seed): + self.RNG = np.random.RandomState(seed) + + def get_challenge_action(self, action): + target = None + ret = None + if action == ActionSpace.WALK_TO_NEAREST_TARGET: + targets = [idx for idx in self.controller.target_ids if idx not in self.controller.finished] + target = self.controller.find_nearest_object(agent_idx=0, objects=targets) + ret = "walk_to_single", self.controller.manager.get_renumbered_id(target) + elif action == ActionSpace.PICK_UP_NEAREST: + targets = [idx for idx in self.controller.target_ids if idx not in self.controller.finished] + target = self.controller.find_nearest_object(agent_idx=0, objects=targets) + ret = "pick_up", self.controller.manager.get_renumbered_id(target) + elif action == ActionSpace.DROP: + target = None + ret = "drop", None + elif action == ActionSpace.EXPLORE: + target = None + ret = "explore", None + elif action == ActionSpace.WALK_TO_RANDOM_OBJECT_IN_SIGHT: + try: + if self.last_action == ActionSpace.WALK_TO_RANDOM_OBJECT_IN_SIGHT: + target = self.last_target + else: + obs = self.controller._obs() + obj_ids = np.unique(obs["sem_map"]["id"]) + targets = [self.controller.manager.get_real_id(idx) for idx in obj_ids if self.controller.manager.get_real_id(idx) not in self.controller.finished] + targets = [idx for idx in targets if idx is not None] + target = int(self.RNG.choice(targets)) if len(targets) > 0 else None + except: + target = None + if target is None: + ret = "explore", None + else: + ret = "walk_to_single", self.controller.manager.get_renumbered_id(target) + else: + target = None + ret = "explore", None + self.last_action = action + self.last_target = target + return ret \ No newline at end of file diff --git a/easi/tasks/hazard/vendor/HAZARD/envs/fire/fire_utils.py b/easi/tasks/hazard/vendor/HAZARD/envs/fire/fire_utils.py new file mode 100644 index 0000000..0dde3d0 --- /dev/null +++ b/easi/tasks/hazard/vendor/HAZARD/envs/fire/fire_utils.py @@ -0,0 +1,47 @@ +""" +constants and hyperparameters here +""" +import numpy as np + +from enum import Enum +class ObjectState(Enum): + NORMAL = 0 + BURNING = 1 + BURNT = 2 + START_BURNING = 3 + STOP_BURNING = 4 + +class Constants: + def __init__(self, + ROOM_TEMPERATURE: float = 20.0, + FIRE_TEMPERATURE: float = 600.0, + CHOUKA_INITIAL_PROB: int = 0.02, + CHOUKA_THRESHOLD_PROB_INCREASE: int = 30, + CHOUKA_THRESHOLD_UPPER: int = 50, + FIRE_SPREAD_SIZE: np.ndarray = np.array([0.2, 0.01, 0.2]), + FIRE_VISUAL_SIZE: np.ndarray = np.array([0.6, 0.6, 0.6]), + FIRE_INIT_SCALE: float = 0.1, + FIRE_SCALE_STEP: float = 0.05, + FIRE_FINAL_SCALE: float = 0.8, + EXTINGUISH_RADIUS: float = 0.5, + EXTINGUISH_TIME: int = 1000 + ) -> None: + + self.ROOM_TEMPERATURE = ROOM_TEMPERATURE + self.FIRE_TEMPERATURE = FIRE_TEMPERATURE + + self.CHOUKA_INITITIAL_PROB = CHOUKA_INITIAL_PROB + self.CHOUKA_THRESHOLD_PROB_INCREASE = CHOUKA_THRESHOLD_PROB_INCREASE + self.CHOUKA_THRESHOLD_UPPER = CHOUKA_THRESHOLD_UPPER + + self.FIRE_SPREAD_SIZE = FIRE_SPREAD_SIZE # The spread distance of fire. + self.FIRE_VISUAL_SIZE = FIRE_VISUAL_SIZE + + self.FIRE_INIT_SCALE = FIRE_INIT_SCALE + self.FIRE_SCALE_STEP = FIRE_SCALE_STEP + self.FIRE_FINAL_SCALE = FIRE_FINAL_SCALE + + self.EXTINGUISH_RADIUS = EXTINGUISH_RADIUS + self.EXTINGUISH_TIME = EXTINGUISH_TIME + +default_const = Constants() \ No newline at end of file diff --git a/easi/tasks/hazard/vendor/HAZARD/envs/fire/fireagent_controller.py b/easi/tasks/hazard/vendor/HAZARD/envs/fire/fireagent_controller.py new file mode 100644 index 0000000..08c33d2 --- /dev/null +++ b/easi/tasks/hazard/vendor/HAZARD/envs/fire/fireagent_controller.py @@ -0,0 +1,608 @@ +from typing import Any, Tuple, Dict +from tdw.add_ons.third_person_camera import ThirdPersonCamera +from tdw.librarian import HumanoidLibrarian +from tdw.add_ons.logger import Logger +from tdw.replicant.arm import Arm +from tdw.replicant.image_frequency import ImageFrequency +from tdw.tdw_utils import TDWUtils +from tdw.output_data import OutputData, Images +from tdw.scene_data.scene_bounds import SceneBounds +from tdw.obi_data.fluids.cube_emitter import CubeEmitter +from tdw.add_ons.obi import Obi + +import numpy as np +import cv2 +import copy +import os + +from .object import ObjectStatus +from .agent import * +from .fire import FireController +from HAZARD.utils.model import Semantic_Mapping +from HAZARD.utils.vision import Detector +from HAZARD.utils.local_asset import get_local_url +from HAZARD.utils.scene_setup import SceneSetup + +PATH = os.path.dirname(os.path.abspath(__file__)) +while os.path.basename(PATH) != "HAZARD": + PATH = os.path.dirname(PATH) + +import torch + +""" +Caution!!! In a single trial, the number of agents should never change! +Currently single agent only. +""" + +class FireAgentController(FireController): + """ + Never ever use self.commands in here! For safety uses, self.commands can only be used in parent class + """ + def __init__(self, use_local_resources: bool = False, image_capture_path: str = None, log_path: str = None, + reverse_observation: bool = False, record_only: bool = False, use_dino: bool = False, + **kwargs) -> None: + self.frame_count = 0 + self.use_local_resources = use_local_resources + self.image_capture_path = image_capture_path + self.log_path = log_path + self.reverse_observation = reverse_observation + super().__init__(**kwargs) + self.screen_size = kwargs.get("screen_size", 512) + self.agents: List[FireAgent] = [] + self.extinguishers = [] + self.comm_counter = 0 + self.use_gt = kwargs.get("use_gt", True) + self.use_dino = use_dino + self.record_only = record_only + if not self.use_gt: + if self.use_dino: + from HAZARD.utils.vision_dino import DetectorSAM + self.detector = DetectorSAM(**kwargs) + else: + self.detector = Detector(**kwargs) + # self.init_seg(vocab_path=f"{os.getcwd()}/seg/vocab.txt") + if use_local_resources: + self.update_replicant_url() + # self.detector = Detector(**kwargs) + + self.map_size_h = kwargs.get("map_size_h", 64) + self.map_size_v = kwargs.get("map_size_v", 64) + self.grid_size = kwargs.get("grid_size", 0.25) + self.sem_map = Semantic_Mapping(device=None, screen_size=self.screen_size, + map_size_h=self.map_size_h, map_size_v=self.map_size_v, grid_size=self.grid_size) + + self.maps = [] + self.id2name = {} + self.other_containers = {} + self.containers = {} + # self.init_seg() + + def init_obi(self): + self.obi = Obi() + self.communicate([{"$type": "create_obi_solver"}]) + self.obi.set_solver(solver_id=0, scale_factor=1.0, substeps=1) + self.add_ons.append(self.obi) + self.communicate([]) + self.communicate([]) + + def update_replicant_url(self): + if not os.path.isfile(f"{os.getcwd()}/data/assets/replicant_0"): # There is no local model of replicant + print("There is no local model of replicant. ") + return + LOCAL_PATH_PREFIX = f"file://{os.getcwd()}/data/assets" + Controller.HUMANOID_LIBRARIANS[Replicant.LIBRARY_NAME] = HumanoidLibrarian(Replicant.LIBRARY_NAME) + import platform + for repli in ["replicant_0", "fireman"]: + record = Controller.HUMANOID_LIBRARIANS[Replicant.LIBRARY_NAME].get_record(repli) + new_url = record.urls[platform.system()].split("/")[-1] + record.urls[platform.system()] = f"{LOCAL_PATH_PREFIX}/{new_url}" + Controller.HUMANOID_LIBRARIANS[Replicant.LIBRARY_NAME].add_or_update_record(record, overwrite=True) + + def grid_to_real(self, grid_pos): + return self.sem_map.grid_to_real(grid_pos) + + def real_to_grid(self, real_pos): + return self.sem_map.real_to_grid(real_pos) + + def reset_scene(self): + # whatever the agent is holding, drop it + for agent in self.agents: + arms = agent.dynamic.held_objects.keys() + for arm in arms: + agent.drop(arm=arm) + while agent.action.status == ActionStatus.ongoing: + self.communicate([]) + commands = [] + for idx in self.fire_info: + commands.append({"$type": "destroy_visual_effect", "id": self.fire_info[idx].fire_id}) + self.communicate(commands) + + self.fire_info = dict() + self.fire_candidate = dict() + self.initialized = False + self.maps = [] + self.add_ons = [] + self.manager.reset() + self.extinguishers = [] + self.id2name = {} + self.frame_count = 0 + self.communicate([]) + + self.last_reward = None + self.communicate([{"$type": "destroy_all_objects"}]) + + def init_scene(self, setup: SceneSetup): + self.reset_scene() + + if self.log_path is not None: + logger = Logger(self.log_path) + self.add_ons.append(logger) + self.communicate([]) + + # for obj in setup.objects: + # self.manager.add_object(obj) + + import json + temperature_record = dict() + with open(os.path.join(PATH, "data", "meta_data", "temperature.json"), "r") as f: + L = json.load(f) + for data in L: + temperature_record[data["name"]] = data["temp"] + self.extinguishers = [] + self.containers = setup.containers + self.other_containers = {} + for commands in setup.commands_list: + filtered_commands = [] + tp = None + for command in commands: + tp = command["$type"] + if tp == "terminate": + break + if tp[:4] == "send" or tp.find("avatar") != -1 or "avatar_id" in command: + continue + if "url" in command: + command["url"] = get_local_url(command["url"]) + if tp == "add_object": + name = command["name"] + idx = command["id"] + self.id2name[idx] = name + pos = TDWUtils.vector3_to_array(command["position"]) + if name in temperature_record: + self.manager.add_object(ObjectStatus(idx=idx, temperature_threshold=temperature_record[name], position=pos)) + else: + self.manager.add_object(ObjectStatus(idx=idx, position=pos)) + if name == "b05_fire_extinguisher": + self.extinguishers.append(idx) + if tp.startswith("add_") and tp.endswith("_container"): + self.other_containers[command['id']] = command['container_id'] + filtered_commands.append(command) + self.communicate(filtered_commands) + if tp == "terminate": + break + for fire_pos in setup.other["fire"]: + self.add_fire_floor(fire_pos) + + if not self.record_only: + if len(self.agents) == 0: + self.agents: List[FireAgent] = [] + for agent_pos in setup.agent_positions: + idx = self.get_unique_id() + self.agents.append( + FireAgent(replicant_id=idx, position=agent_pos, image_frequency=ImageFrequency.always, + name="fireman")) + self.add_agent(idx, agent_pos) + self.add_ons.append(self.agents[-1]) + else: + assert (len(self.agents) == len(setup.agent_positions)) + for i in range(len(self.agents)): + idx = self.agents[i].replicant_id + self.agents[i].reset(position=setup.agent_positions[i]) + self.add_ons.append(self.agents[i]) + self.add_agent(idx, setup.agent_positions[i]) + + self.maps = [None] * len(self.agents) + self.add_ons.append(self.manager) + self.target = setup.targets + if self.use_dino and not self.use_gt: + self.detector.set_targets(self.target) + self.target_ids = setup.target_ids + self.target_names = setup.target_names + self.target_id2category = setup.target_id2category + self.target_id2name = setup.target_id2name + self.finished = [] + self.communicate([]) + if self.image_capture_path != None: + if not self.record_only: + camera = ThirdPersonCamera(avatar_id="record", + position={"x": 0.0, "y": 10.0, "z": 0.0}, + look_at={"x": 0.0, "y": 0.0, "z": 0.0}) # or self.agents[0].replicant_id + commands = [{"$type": "set_screen_size", "width": self.screen_size, "height": self.screen_size}, + {"$type": "set_target_framerate", "framerate": 30}] + else: + camera = ThirdPersonCamera(avatar_id="record", position={"x": 0.0, "y": 6.0, "z": 0.0}, + look_at=self.agents[0].replicant_id) + commands = [{"$type": "set_screen_size", "width": self.screen_size, "height": self.screen_size}, + {"$type": "set_target_framerate", "framerate": 30}] + self.add_ons.extend([camera]) + else: + commands = [{"$type": "set_screen_size", "width": self.screen_size, "height": self.screen_size}, + {"$type": "set_target_framerate", "framerate": 30}] + + if self.image_capture_path is not None: + commands.extend([{"$type": "set_pass_masks", "pass_masks": ["_img"], "avatar_id": "record"}, + {"$type": "send_images", "frequency": "always", "ids": ["record"]}]) + commands.extend([{"$type": "set_floorplan_roof", "show": False}]) + + # Capture when running after init_scene. (Because screen size may be modified) + resp = self.communicate([{"$type": "send_scene_regions"}]) + self.set_scene_bounds(resp) + + self.manager.prepare_segmentation_data() + self.initialized = True + for obj in self.target_ids: + commands.append({"$type": "set_kinematic_state", "id": obj, "is_kinematic": False, "use_gravity": True}) + self.communicate(commands) + + if len(self.extinguishers) > 0: + self.init_obi() + + if not self.record_only and len(self.extinguishers) == 0: + commands = [ + {"$type": "set_field_of_view", "field_of_view": 120.0, "avatar_id": str(self.agents[0].replicant_id)}] + """add a backpack or similar to the agent's left hand""" + self.container_name = "backpack" if "container" not in setup.other else setup.other["container"] + self.container_id = self.get_unique_id() + self.manager.add_object(ObjectStatus(idx=self.container_id, inflammable=False, temperature_threshold=3000)) + + agent_pos = self.agents[0].dynamic.transform.position + commands.append(self.get_add_object(model_name=self.container_name, object_id=self.container_id, + position=TDWUtils.array_to_vector3(agent_pos))) + self.communicate(commands) + self.agents[0].grasp(target=self.container_id, arm=Arm.left, axis=None, angle=None) + self.next_key_frame() + self.agents[0].reach_for(target=TDWUtils.array_to_vector3([-0.3, 1.0, 0.3]), absolute=False, arrived_at=0.1, + arm=Arm.left) + self.next_key_frame() + self.agents[0].reset_arm(arm=Arm.left) + self.next_key_frame() + + def set_scene_bounds(self, resp=None): + self.scene_bounds = SceneBounds(resp=resp) + self.x_max = max([region.x_max for region in self.scene_bounds.regions]) + self.x_min = min([region.x_min for region in self.scene_bounds.regions]) + self.z_max = max([region.z_max for region in self.scene_bounds.regions]) + self.z_min = min([region.z_min for region in self.scene_bounds.regions]) + + def next_key_frame(self, force_direction=None) -> Tuple[List[ActionStatus], List[int]]: + # print("next_key_frame") + initial_status = [] + have_ongoing_action = False + for agent in self.agents: + if agent.action is not None: + initial_status.append(copy.deepcopy(agent.action.status)) + if initial_status[-1] == ActionStatus.ongoing: + have_ongoing_action = True + else: + initial_status.append(None) + if not have_ongoing_action: + self.communicate([]) + print("no ongoing action") + return initial_status, [] + num_step = 0 + while True: + num_step += 1 + if num_step > 10000: + raise RuntimeError("num_step > 10000") + self.communicate([]) + + changed = False + for i, agent in enumerate(self.agents): + if initial_status[i] is not None and agent.action.status != initial_status[i]: + changed = True + break + if changed: + break + + final_status = [] + changed_agents = [] + for i, agent in enumerate(self.agents): + final_status.append(copy.deepcopy(agent.action.status) if agent.action is not None else None) + if initial_status[i] != final_status[i]: + changed_agents.append(i) + + self.communicate([]) + return final_status, changed_agents + + def do_action(self, agent_idx: int, action: str, params: Dict[str, Any] = dict()) -> None: + """ + See the FireAgent class for allowed actions and their parameters. + """ + if action == "stop": + return + return getattr(self.agents[agent_idx], action)(**params) + + def add_extinguish_water(self, fire_pos): + object_id = self.get_unique_id() + rotate = self.agents[0].dynamic.transform.forward + rotate = rotate / np.linalg.norm(rotate) + print(rotate) + extinguisher_distance_to_agent = 1.0 + position = {"x": float(self.agents[0].dynamic.transform.position[0]+rotate[0]*extinguisher_distance_to_agent), + "y": float(self.agents[0].dynamic.transform.position[1]) + 0.7, + "z": float(self.agents[0].dynamic.transform.position[2]+rotate[2]*extinguisher_distance_to_agent)} + rotation = {"x": 0, + "y": 90, + "z": 0} + self.obi.create_fluid(fluid="water", + shape=CubeEmitter(), + object_id=object_id, + position=position, + rotation=rotation, + speed=0.9, + lifespan=0.5) + + def do_extinguish(self, target: np.ndarray): + for idx, fire in self.fire_info.items(): + if fire.extinguishing: + continue + fire_id = fire.fire_id + if np.linalg.norm(self.manager.objects[fire_id].position - target) < self.constants.EXTINGUISH_RADIUS: + if idx == fire_id: + self.extinguish_fire_floor(idx) + else: + self.extinguish_fire_object(idx) + + def get_agent_status(self, idx) -> FireAgent: + return self.agents[idx] + + def get_temperature_observation(self, idx, width=512, height=512): + depth = TDWUtils.get_depth_values(self.agents[idx].dynamic.images["depth"], width=width, height=height) + depth = np.flip(depth, axis=0) + camera_matrix = self.agents[idx].dynamic.camera_matrix + + point_cloud = TDWUtils.get_point_cloud(depth=depth, camera_matrix=camera_matrix, vfov=120.0) + # fout = open("point_cloud.txt", "w") + # bs, h, w = 1, height, width + # for i in range(bs): + # for j in range(h): + # for k in range(w): + # for t in range(3): + # print(point_cloud[t, j, k].item(), end=' ', file=fout) + # print('', file=fout) + # shape: (3, 512, 512) + # down sample to 16x16 + point_cloud = point_cloud[:, ::(width//16), ::(width//16)] + temp = np.zeros((16, 16)) + for i in range(16): + for j in range(16): + temperature = self.manager.query_point_temperature(point_cloud[:, i, j]) + temp[i, j] = temperature + temp = cv2.resize(temp, (width, height), interpolation=cv2.INTER_NEAREST) + return temp + + def find_nearest_object(self, agent_idx: int = 0, objects: List[int] = None): + current_position = self.agents[agent_idx].dynamic.transform.position + return self.manager.find_nearest_object(pos=current_position, objects=objects) + + def replace_with_local_path(self, commands): + LOCAL_PATH_PREFIX = f"file://{os.getcwd()}/data/assets" + download_cmds = [] + for command in commands: + if 'url' in command and "amazonaws.com" in command['url']: + new_url = command['url'].split("/")[-1] + if not os.path.isfile(f"{os.getcwd()}/data/assets/{new_url}"): + download_cmds.append(f"wget {command['url']}\n") + new_url = f"{LOCAL_PATH_PREFIX}/{new_url}" + command['url'] = new_url + for command in self.commands: + if 'url' in command and "amazonaws.com" in command['url']: + new_url = command['url'].split("/")[-1] + if not os.path.isfile(f"{os.getcwd()}/data/assets/{new_url}"): + download_cmds.append(f"wget {command['url']}\n") + new_url = f"{LOCAL_PATH_PREFIX}/{new_url}" + command['url'] = new_url + for m in self.add_ons: + if not m.initialized: + add_on_commands = m.get_initialization_commands() + else: + add_on_commands = m.commands + for command in add_on_commands: + if 'url' in command and "amazonaws.com" in command['url']: + new_url = command['url'].split("/")[-1] + if not os.path.isfile(f"{os.getcwd()}/data/assets/{new_url}"): + download_cmds.append(f"wget {command['url']}\n") + new_url = f"{LOCAL_PATH_PREFIX}/{new_url}" + command['url'] = new_url + if len(download_cmds) > 0: + fout = open(f"{os.getcwd()}/data/assets/download_assets.sh", "w") + for cmd in download_cmds: + fout.write(cmd) + super().communicate({"$type": "terminate"}) + self.socket.close() + print("Please run data/assets/download_assets.sh first!") + exit(0) + return commands + + def communicate(self, commands: Union[dict, List[dict]]) -> list: + if self.initialized: + for agent in self.agents: + idx = agent.grasp_id() + if idx is None: + continue + dist = np.linalg.norm((agent.dynamic.transform.position - self.manager.objects[idx].position) * np.array([1, 0, 1])) + if dist > 3.0: + agent.fail_grasp() + if self.use_local_resources: + commands = self.replace_with_local_path(commands) + # self.commands = self.replace_with_local_path(self.commands) + resp = super().communicate(commands) + if self.initialized: + # for i, agent in enumerate(self.agents): + # # if too hot, agent dies + # if self.manager.objects[agent.replicant_id].temperature > \ + # self.manager.objects[agent.replicant_id].temperature_threshold: + # raise RuntimeError(f"agent {i} is on fire") + # # if agent is holding an extinguisher: put out fire + # held_objects = agent.extinguish_indicator() + # if held_objects is not None: + # print("holding: ", held_objects) + # if held_objects is not None and len(held_objects) == 1 and held_objects[0] in self.extinguishers: + # self.do_extinguish(agent.extinguish_target()) + # print("extinguish") + # pass + for target in self.target_ids: + if (np.abs(self.manager.objects[target].position).sum() > 50) and (target not in self.finished): + # this object is dropped and moved to a distant place + self.finished.append(target) + if self.image_capture_path != None: + for i in range(len(resp) - 1): + r_id = OutputData.get_data_type_id(resp[i]) + # Get Images output data. + if r_id == "imag": + images = Images(resp[i]) + # Determine which avatar captured the image. + if images.get_avatar_id() == "record": + # Save the image. + # if self.comm_counter % 10 == 0: + TDWUtils.save_images(images=images, filename=str(self.comm_counter), + output_directory=self.image_capture_path) + self.comm_counter += 1 + # The ImageCapture addon already saves the image. No need to save again. + return resp + + def get_seg_mask(self, rgb): + # return self.detector.get_seg_mask(rgb) + pass + + + @torch.no_grad() + def _obs(self, agent_idx: int = 0): + # print("get obs") + + self.manager.prepare_segmentation_data() + self.communicate([]) + + obs = dict() + """ + raw observation: RGBD, temperature + """ + rgb = self.agents[agent_idx].dynamic.get_pil_image() + id_image = np.array(self.agents[agent_idx].dynamic.get_pil_image("id")) + if self.reverse_observation: + rgb = np.flip(rgb, axis=0) + id_image = np.flip(id_image, axis=0) + + if self.use_gt: + seg_mask = self.manager.segm.get_seg_mask(id_image) + else: + rcnn_mask = self.detector.inference(np.array(rgb)) + seg_mask = self.manager.segm.get_seg_mask(np.array(id_image), + rcnn=rcnn_mask, + id_list=self.manager.id_list) + depth = TDWUtils.get_depth_values(self.agents[agent_idx].dynamic.images["depth"], width=self.screen_size, height=self.screen_size) + depth = np.flip(depth, axis=0) + + rgb = np.array(rgb).astype(np.float32).transpose((2, 0, 1)).astype(np.float32) * 1.0 / 255 + depth = depth.reshape((1, self.screen_size, self.screen_size)).astype(np.float32) + + log_temp = self.get_temperature_observation(agent_idx, width=self.screen_size, height=self.screen_size) + log_temp = np.log(log_temp.reshape((1, self.screen_size, self.screen_size)).astype(np.float32)) + obs["raw"] = dict( + rgb=rgb, + depth=depth, + log_temp=log_temp, + seg_mask=seg_mask + ) + + """ + mapped observation + """ + camera_matrix = self.agents[agent_idx].dynamic.camera_matrix.reshape((4, 4)) + obs_concat = np.concatenate([rgb, depth, log_temp], axis=0) + + sem = self.sem_map.forward(obs=obs_concat, id_map=seg_mask, camera_matrix=camera_matrix, maps_last=self.maps[agent_idx], + position=self.agents[agent_idx].dynamic.transform.position, record_mode=self.record_only, + targets=[self.manager.id_renumbering[target] for target in self.target_ids]) + obs["sem_map"] = dict(height=sem["height"].cpu().numpy(), + explored=sem["explored"].cpu().numpy(), + id=sem["id"].cpu().numpy(), + other=sem["other"].cpu().numpy() if sem["other"] is not None else None) + self.maps[agent_idx] = dict(height=sem["height"].cpu().numpy(), + explored=sem["explored"].cpu().numpy(), + id=sem["id"].cpu().numpy(), + other=sem["other"].cpu().numpy() if sem["other"] is not None else None) + + """ + map of goal and agent + """ + agent_pos = self.sem_map.real_to_grid(self.agents[agent_idx].dynamic.transform.position) + target_poss = [self.sem_map.real_to_grid(self.manager.objects[idx].position) for idx in self.target_ids] + + goal_map = np.zeros((self.map_size_h, self.map_size_v)) + for (i, target_pos) in enumerate(target_poss): + if self.target_ids[i] in self.finished: + continue + if target_pos[0] < 0 or target_pos[0] >= self.map_size_h or target_pos[1] < 0 or target_pos[1] >= self.map_size_v: + continue + if not (sem["id"] == self.manager.get_renumbered_id(self.target_ids[i])).any(): + continue + goal_map[target_pos[0], target_pos[1]] = 1 + if agent_pos[0] > 0 and agent_pos[0] < self.map_size_h - 1 and agent_pos[1] > 0 and agent_pos[1] < self.map_size_v - 1: + goal_map[agent_pos[0], agent_pos[1]] = -2 + rad = self.agents[agent_idx].get_facing() + rad = int(rad / (np.math.pi / 4)) + if rad < 0: + rad += 8 + dx = list([1, 1, 0, -1, -1, -1, 0, 1])[rad] + dz = list([0, 1, 1, 1, 0, -1, -1, -1])[rad] + goal_map[agent_pos[0] + dx][agent_pos[1] + dz] = -1 + obs["goal_map"] = goal_map + + """ + agent info + """ + obs["camera_matrix"] = camera_matrix + # return RL_obs + RL_obs = np.zeros((5, self.map_size_h, self.map_size_v)) + RL_obs[0] = obs["sem_map"]["height"] + RL_obs[1] = obs["sem_map"]["explored"] + RL_obs[2] = obs["sem_map"]["id"] + RL_obs[3] = goal_map + RL_obs[4] = obs["sem_map"]["other"][0] + obs["RL"] = RL_obs + return obs + + def _info(self): + info = dict() + info['vector'] = np.zeros(8) # for consistency + info['agent_positions'] = [agent.dynamic.transform.position for agent in self.agents] + info['targets'] = self.manager.get_renumbered_list(self.target_ids) + info['finished targets'] = self.manager.get_renumbered_list(self.finished) + info['camera_matrices'] = [agent.dynamic.camera_matrix.reshape((4, 4)) for agent in self.agents] + info['sr'] = f"{len(self.finished)}/{len(self.target_ids)}" + return info + + # this is a toy reward function + def _reward(self, agent_idx: int = 0): + if self.last_reward is None: + self.last_reward = 0 + + reward = 0 + + agent_pos = self.agents[agent_idx].dynamic.transform.position + nearest_target = self.manager.find_nearest_object(agent_pos, self.target_ids) + + reward += len(self.finished) * 20 + if Arm.left in self.agents[0].dynamic.held_objects: + reward -= 10 + else: + dist = np.linalg.norm(agent_pos - self.manager.objects[nearest_target].position) + reward -= dist * 10 + ret = reward - self.last_reward - 0.1 + self.last_reward = reward + return ret + + def _done(self, agent_idx: int = 0): + if len(self.finished) == len(self.target_ids): + return True + return False \ No newline at end of file diff --git a/easi/tasks/hazard/vendor/HAZARD/envs/fire/manager.py b/easi/tasks/hazard/vendor/HAZARD/envs/fire/manager.py new file mode 100644 index 0000000..e4deef8 --- /dev/null +++ b/easi/tasks/hazard/vendor/HAZARD/envs/fire/manager.py @@ -0,0 +1,155 @@ +from tdw.add_ons.add_on import AddOn +from tdw.output_data import OutputData, Transforms, Bounds +from tdw.output_data import Replicants, SegmentationColors, ReplicantSegmentationColors +from typing import Dict, List, Set, Optional +import numpy as np +from .object import * +from .fire_utils import * +from HAZARD.utils.seg_id import SegmentationID + +""" +Add-on to control the evolvement of objects, including temperature and state. +Future development may include changing object appearances. +""" +class FireObjectManager(AddOn): + def __init__(self, constants=default_const): + super().__init__() + self.constants = constants + self.objects: Dict[int, ObjectStatus] = dict() + self.temperature_manager = TemperatureManager() + self.objects_start_burning: Set[int] = set() + self.objects_stop_burning: Set[int] = set() + self.segm = SegmentationID() + self.id_renumbering = dict() + self.id_list = [0] + self.timer = 0 + + def reset(self): + self.objects = dict() + self.objects_start_burning = set() + self.objects_stop_burning = set() + self.commands = [] + self.initialized = False + self.id_renumbering = dict() + self.id_list = [0] + self.timer = 0 + + def get_initialization_commands(self) -> List[dict]: + return [{"$type": "send_bounds"}, + {"$type": "send_transforms"}] + + def on_send(self, resp: List[bytes]) -> None: + for i in range(len(resp)-1): + r_id = OutputData.get_data_type_id(resp[i]) + if r_id == "tran": + tran = Transforms(resp[i]) + for j in range(tran.get_num()): + idx = tran.get_id(j) + if idx in self.objects: + self.objects[idx].position = tran.get_position(j) + self.objects[idx].rotation = tran.get_rotation(j) + else: + print("object {} not recorded, may be caused by composite objects which you can ignore".format(idx)) + self.add_object(ObjectStatus(idx, position=tran.get_position(j))) + elif r_id == "boun": + boun = Bounds(resp[i]) + for j in range(boun.get_num()): + idx = boun.get_id(j) + if idx in self.objects: + concat = np.zeros([6, 3]) + concat[0] = boun.get_front(j) + concat[1] = boun.get_back(j) + concat[2] = boun.get_left(j) + concat[3] = boun.get_right(j) + concat[4] = boun.get_top(j) + concat[5] = boun.get_bottom(j) + self.objects[idx].size = np.max(concat, axis=0) - np.min(concat, axis=0) + else: + print("object {} not recorded, may be caused by composite objects which you can ignore".format(idx)) + self.add_object(ObjectStatus(idx, size=boun.get_front(j) + boun.get_right(j) + boun.get_top(j) - boun.get_back(j) - boun.get_left(j) - boun.get_bottom(j))) + concat = np.zeros([6, 3]) + concat[0] = boun.get_front(j) + concat[1] = boun.get_back(j) + concat[2] = boun.get_left(j) + concat[3] = boun.get_right(j) + concat[4] = boun.get_top(j) + concat[5] = boun.get_bottom(j) + self.objects[idx].size = np.max(concat, axis=0) - np.min(concat, axis=0) + elif r_id == "repl": + repl = Replicants(resp[i]) + for j in range(repl.get_num()): + idx = repl.get_id(j) + if idx in self.objects: + self.objects[idx].position = repl.get_position(j) + else: + print("agent {} not recorded, this shouldn't happen".format(idx)) + self.add_object(AgentStatus(idx, position=repl.get_position(j), size=None)) + for i in range(len(resp)-1): + r_id = OutputData.get_data_type_id(resp[i]) + if r_id == "segm": + segm = SegmentationColors(resp[i]) + self.segm.process(segm, self.id_renumbering) + elif r_id == "rseg": + segm = ReplicantSegmentationColors(resp[i]) + self.segm.process(segm, self.id_renumbering) + + self.timer += 1 + if self.timer % 5 == 0: + temp_dict = self.temperature_manager.evolve(self.objects) + for idx in temp_dict: + self.objects[idx].temperature = temp_dict[idx] + + self.objects_start_burning = set() + self.objects_stop_burning = set() + for idx in self.objects: + if np.abs(self.objects[idx].position).sum() > 50: + continue + status = self.objects[idx].step() + if status == ObjectState.START_BURNING: + self.objects_start_burning.add(idx) + elif status == ObjectState.STOP_BURNING: + self.objects_stop_burning.add(idx) + self.commands = [{"$type": "send_bounds"}, {"$type": "send_transforms"}] + + def add_object(self, obj: ObjectStatus): + self.objects[obj.idx] = obj + if obj.idx not in self.id_renumbering: + self.id_list.append(obj.idx) + self.id_renumbering[obj.idx] = len(self.id_list) - 1 + + def remove_object(self, idx: int): + if idx in self.objects: + del self.objects[idx] + + def query_point_temperature(self, point: np.ndarray) -> float: + return self.temperature_manager.query_point_temperature(point, self.objects) + + def find_nearest_object(self, pos: np.ndarray, objects: Optional[List[int]] = None): + min_dist = 1e10 + min_idx = None + it = iter(self.objects) if objects is None else iter(objects) + for idx in it: + if self.objects[idx].name != "Object": + continue + dist = np.linalg.norm(self.objects[idx].position - pos) + if dist < min_dist: + min_dist = dist + min_idx = idx + return min_idx + + def prepare_segmentation_data(self): + self.commands.extend([{"$type": "send_segmentation_colors"}, + {"$type": "send_categories"}, {"$type": "send_replicant_segmentation_colors"}]) + + def get_renumbered_id(self, idx: int): + if idx in self.id_renumbering: + return self.id_renumbering[idx] + return 0 + + def get_renumbered_list(self, L: List[int]): + return [self.id_renumbering[idx] for idx in L if idx in self.id_renumbering] + + def get_real_id(self, idx: int): + if idx >= len(self.id_list) or idx == 0: + return None + return self.id_list[idx] diff --git a/easi/tasks/hazard/vendor/HAZARD/envs/fire/object.py b/easi/tasks/hazard/vendor/HAZARD/envs/fire/object.py new file mode 100644 index 0000000..d3d0a8a --- /dev/null +++ b/easi/tasks/hazard/vendor/HAZARD/envs/fire/object.py @@ -0,0 +1,124 @@ +from typing import Dict, Set +from .fire_utils import * +import numpy as np + +class ObjectStatus: + def __init__(self, idx, constants: Constants=default_const, + inflammable: bool = True, state: int = None, + burning_time: int = 300, burning_time_left: int = 0, + temperature: float = 20.0, temperature_threshold: float = 200.0, + is_heat_source: bool = False, position: np.ndarray = None, rotation: np.ndarray = None, size: np.ndarray = None): + self.idx: int = idx + self.inflammable: bool = inflammable + self.state: int = (ObjectState.NORMAL if state is None else state) + self.burning_time: int = burning_time # if set to -1, burn indefinitely + self.burning_time_left: int = burning_time_left # if set to -1, burn indefinitely + self.temperature: float = temperature + self.temperature_threshold: float = temperature_threshold + self.is_heat_source: bool = is_heat_source + self.position: np.ndarray = position + self.rotation: np.ndarray = rotation + self.size: np.ndarray = size + self.constants: Constants = constants + self.name = "Object" + + def step(self): + if self.state == ObjectState.BURNING: + if self.burning_time_left == -1: + return ObjectState.BURNING + self.burning_time_left -= 1 + if self.burning_time_left <= 0: + self.state = ObjectState.BURNT + self.inflammable = False + return ObjectState.STOP_BURNING + return ObjectState.BURNING + + if self.state == ObjectState.BURNT: + return ObjectState.BURNT + + if self.temperature > self.temperature_threshold and self.inflammable: + self.state = ObjectState.BURNING + self.burning_time_left = self.burning_time + self.is_heat_source = True + self.temperature = self.constants.FIRE_TEMPERATURE + return ObjectState.START_BURNING + + def center(self): return self.position + self.size * np.array([0, 0.5, 0]) + def bottom(self): return self.position + def top(self): return self.position + self.size * np.array([1, 1, 1]) + def left(self): return self.position + self.size * np.array([-0.5, 0.5, 0]) + def right(self): return self.position + self.size * np.array([0.5, 0.5, 0]) + def front(self): return self.position + self.size * np.array([0, 0.5, 0.5]) + def back(self): return self.position + self.size * np.array([0, 0.5, -0.5]) +class FireStatus(ObjectStatus): + def __init__(self, idx, position, size, constants=default_const): + super().__init__(idx, constants, inflammable=False, state=ObjectState.BURNING, + burning_time=-1, burning_time_left=-1, + temperature=constants.FIRE_TEMPERATURE, temperature_threshold=1000.0, + is_heat_source=True, position=position, size=size) + self.name = "Fire" + +class AgentStatus(ObjectStatus): + def __init__(self, idx, position, size, endurance: float = 60.0, constants=default_const): + super().__init__(idx, constants, inflammable=False, state=ObjectState.NORMAL, + burning_time=-1, burning_time_left=-1, + temperature=constants.ROOM_TEMPERATURE, temperature_threshold=endurance, + is_heat_source=False, position=position, size=size) + self.name = "Agent" + +""" +Assumed model: + +For heat sources, the temperature is constant. +For other objects: + +T = T0 * (1 - decay_rate) + T1 * decay_rate +T0 is old temperature +T1 is average temperature of nearby objects, weighted by exp( - max(1, distance / effective_distance) ) +room temperature has a weight of exp(-1) + +T1 is average temperature of nearby objects, weighted by (effective_distance/distance)^2 +room temperature has a weight of 1 +This algorithm is scientific, and a lot more computationally efficient. +""" +class TemperatureManager: + def __init__(self, room_temperature: float = 20.0, + effective_distance: float = 0.5, + decay_rate: float = 0.03): + self.room_temperature = room_temperature + self.effective_distance = effective_distance + self.decay_rate = decay_rate + + def evolve(self, objects: Dict[int, ObjectStatus]): + new_temperatures = dict() + for idx in objects: + obj = objects[idx] + if obj.is_heat_source: + new_temperatures[idx] = obj.temperature + continue + # sum_weight = np.exp(-1) + sum_weight = 1 # Weight of room temperature + sum_weighted_temperature = self.room_temperature * sum_weight + for idx2 in objects: + if idx2 == idx: + continue + distance = np.linalg.norm(objects[idx2].position - obj.position) + distance = max(distance, 0.1) + # weight = np.exp(-max(10000, distance / self.effective_distance)) + weight = (self.effective_distance/distance)**2 + sum_weighted_temperature += objects[idx2].temperature * weight + sum_weight += weight + # print(sum_weighted_temperature / sum_weight) + new_temperatures[idx] = obj.temperature * (1 - self.decay_rate) + sum_weighted_temperature / sum_weight * self.decay_rate + return new_temperatures + + def query_point_temperature(self, target: np.ndarray, objects: Dict[int, ObjectStatus]): + sum_weight = np.exp(-1) + sum_weighted_temperature = self.room_temperature * sum_weight + for idx in objects: + distance = np.linalg.norm(objects[idx].position - target) + distance = max(distance, 0.1) + weight = (self.effective_distance/distance)**2 + sum_weighted_temperature += objects[idx].temperature * weight + sum_weight += weight + return sum_weighted_temperature / sum_weight \ No newline at end of file diff --git a/easi/tasks/hazard/vendor/HAZARD/envs/flood/__init__.py b/easi/tasks/hazard/vendor/HAZARD/envs/flood/__init__.py new file mode 100644 index 0000000..2f9e4ad --- /dev/null +++ b/easi/tasks/hazard/vendor/HAZARD/envs/flood/__init__.py @@ -0,0 +1,9 @@ +from .floodagent_controller import FloodAgentController +from .flood_gym import FloodEnv + +from gym.envs.registration import register + +register( + id="flood-v0", + entry_point="envs.flood.flood_gym:FloodEnv" +) diff --git a/easi/tasks/hazard/vendor/HAZARD/envs/flood/agent.py b/easi/tasks/hazard/vendor/HAZARD/envs/flood/agent.py new file mode 100644 index 0000000..af85bea --- /dev/null +++ b/easi/tasks/hazard/vendor/HAZARD/envs/flood/agent.py @@ -0,0 +1,203 @@ +from typing import Optional, Union, List +from .utils import * +from tdw.add_ons.replicant import * +from tdw.quaternion_utils import QuaternionUtils +from tdw.replicant.actions.action import Action, ActionStatus +from tdw.replicant.actions.look_at import LookAt +from tdw.replicant.actions.grasp import Grasp +from tdw.replicant.image_frequency import ImageFrequency + + +import numpy as np + +class SequentialAction(Action): + def __init__(self, actions: List[Action]): + self._actions = actions + self._current = 0 + self.status: ActionStatus = None + self.image_frequency = ImageFrequency.once + super().__init__() + + def get_initialization_commands(self, resp: List[bytes], static: ReplicantStatic, dynamic: ReplicantDynamic, + image_frequency: ImageFrequency) -> List[dict]: + self.image_frequency = image_frequency + self._current = 0 + + self.status = ActionStatus.ongoing + self.initialized = True + + return super().get_initialization_commands(resp=resp, static=static, dynamic=dynamic, + image_frequency=image_frequency) + + def get_ongoing_commands(self, resp: List[bytes], static: ReplicantStatic, dynamic: ReplicantDynamic) -> List[dict]: + commands = super().get_ongoing_commands(resp=resp, static=static, dynamic=dynamic) + # for convenience, if self._current < 0, it is in the middle of two actions, wait one frame + if not self._actions[self._current].initialized: + # The action's status defaults to `ongoing`, but actions sometimes fail prior to initialization. + if self._actions[self._current].status == ActionStatus.ongoing: + # Initialize the action and get initialization commands. + self._actions[self._current].initialized = True + initialization_commands = self._actions[self._current].get_initialization_commands(resp=resp, + static=static, + dynamic=dynamic, + image_frequency=self.image_frequency) + + # Most actions are `ongoing` after initialization, but they might've succeeded or failed already. + if self._actions[self._current].status == ActionStatus.ongoing: + commands.extend(initialization_commands) + else: + commands.extend(self._actions[self._current].get_end_commands(resp=resp, + static=static, + dynamic=dynamic, + image_frequency=self.image_frequency)) + # Continue an ongoing action. + else: + # Get the ongoing action commands. + action_commands = self._actions[self._current].get_ongoing_commands(resp=resp, + static=static, + dynamic=dynamic) + # This is an ongoing action. Append ongoing commands. + if self._actions[self._current].status == ActionStatus.ongoing: + commands.extend(action_commands) + # This action is done. Append end commands. + else: + commands.extend(self._actions[self._current].get_end_commands(resp=resp, + static=static, + dynamic=dynamic, + image_frequency=self.image_frequency)) + # This action ended. If not successful, all commands are done. Otherwise, continue to the next action. + if self._actions[self._current].status != ActionStatus.ongoing: + if self._actions[self._current].status == ActionStatus.success: + self._current += 1 + # If there are more actions, continue. + if self._current < len(self._actions): + self.status = ActionStatus.ongoing + # Otherwise, this action is done. + else: + self.status = ActionStatus.success + else: + self.status = self._actions[self._current].status + return commands + + def get_end_commands(self, resp: List[bytes], static: ReplicantStatic, dynamic: ReplicantDynamic, + image_frequency: ImageFrequency) -> List[dict]: + return super().get_end_commands(resp=resp, + static=static, + dynamic=dynamic, + image_frequency=image_frequency) + + +""" +The poses are relative to the agent's position and rotation. +Assume the agent is facing positive z. +""" + + +class FloodAgent(Replicant): + def __init__(self, constants=default_const, *args, **kwargs): + self.constants = constants + self.heatmap = 0 + super().__init__(*args, **kwargs) + + # define custom actions here + def pick_up(self, target: int, arm: Union[Arm, List[Arm]], + final_pose_loc: np.ndarray = np.array([0.1, 1.1, 0.6]), absolute: bool = False, + angle: Optional[float] = 90, axis: Optional[str] = "pitch") -> None: + if isinstance(arm, Arm): + self.action = SequentialAction([ + TurnTo(target=target), + LookAt(target=target, + duration=0.1, + scale_duration=True), + ReachFor(target=target, + arms=Replicant._arms_to_list(arm), + absolute=absolute, + dynamic=self.dynamic, + collision_detection=self.collision_detection, + offhand_follows=False, + arrived_at=0.1, + previous=None, + duration=0.25, + scale_duration=True, + max_distance=1.0, + from_held=False, + held_point="bottom"), + Grasp(target=target, arm=arm, dynamic=self.dynamic, angle=angle, axis=axis, relative_to_hand=True, offset=0.0), + ReachFor(target=final_pose_loc, + arms=Replicant._arms_to_list(arm), + absolute=absolute, + dynamic=self.dynamic, + collision_detection=self.collision_detection, + offhand_follows=False, + arrived_at=0.1, + previous=None, + duration=0.25, + scale_duration=True, + max_distance=1.0, + from_held=False, + held_point="bottom"), + ]) + else: + self.action = SequentialAction([ + TurnTo(target=target), + LookAt(target=target, + duration=0.1, + scale_duration=True), + ReachFor(target=target, + arms=Replicant._arms_to_list(arm), + absolute=absolute, + dynamic=self.dynamic, + collision_detection=self.collision_detection, + offhand_follows=False, + arrived_at=0.1, + previous=None, + duration=0.25, + scale_duration=True, + max_distance=1.0, + from_held=False, + held_point="bottom"), + Grasp(target=target, arm=Arm.left, dynamic=self.dynamic, angle=angle, axis=axis), + ReachFor(target=final_pose_loc, + arms=[Arm.left], + absolute=absolute, + dynamic=self.dynamic, + collision_detection=self.collision_detection, + offhand_follows=True, + arrived_at=0.1, + previous=None, + duration=0.25, + scale_duration=True, + max_distance=1.0, + from_held=False, + held_point="bottom"), + ]) + + def get_facing(self): + """return a radian""" + v = QuaternionUtils.multiply_by_vector(QuaternionUtils.get_inverse(self.dynamic.transform.rotation), + np.array([0, 0, 1.0])) + v = v / np.linalg.norm(v) + v = np.arctan2(v[2], v[0]) + return v + + def grasp_id(self): + """ + if the agent is grasping an object, return the id of the object + otherwise, return None + """ + if isinstance(self.action, Grasp): + return self.action.target + elif isinstance(self.action, SequentialAction) and isinstance(self.action._actions[self.action._current], Grasp): + return self.action._actions[self.action._current].target + return None + + def fail_grasp(self): + """ + if the agent is grasping an object and it is too far away, fail the grasp (this is controlled by the outside) + """ + if isinstance(self.action, Grasp): + self.action.status = ActionStatus.cannot_grasp + elif isinstance(self.action, SequentialAction) and isinstance(self.action._actions[self.action._current], Grasp): + self.action._actions[self.action._current].status = ActionStatus.cannot_grasp + else: + raise Exception("Not grasping now") \ No newline at end of file diff --git a/easi/tasks/hazard/vendor/HAZARD/envs/flood/flood.py b/easi/tasks/hazard/vendor/HAZARD/envs/flood/flood.py new file mode 100644 index 0000000..e99dfb3 --- /dev/null +++ b/easi/tasks/hazard/vendor/HAZARD/envs/flood/flood.py @@ -0,0 +1,126 @@ +from typing import List, Union, Dict +from .manager import FloodObjectManager +from .utils import * +from .object import AgentStatus +from tdw.controller import Controller +from tdw.obi_data.fluids.disk_emitter import DiskEmitter +from tdw.obi_data.fluids.fluid import Fluid +from tdw.add_ons.obi import Obi + +import numpy as np + +class PhysicalFlood: + def __init__(self, flood_id, activate, position, direction, speed): + self.fire_id = flood_id + self.activate = activate + self.position = position + self.direction = direction + self.speed = speed + +class FloorFlood: + def __init__(self, floor_id): + self.floor_id = floor_id + self.height = 0 + self.angles = { + "w": 0.0, + "x": 0.0, + "y": 0.0, + "z": 0.0 + } + +""" +This controller controls the spread of flood. +""" + + +class FloodController(Controller): + def __init__(self, port: int = 1071, check_version: bool = True, launch_build: bool = True, seed=0, + constants=default_const, **kwargs): + self.initialized = False + self.commands: List[dict] = list() + super().__init__(port, check_version, launch_build) + + self.manager = FloodObjectManager(source_position=None, + source_from="x_max", + floor_ids=[], + floor_positions=[], + floor_sizes=[], + floor_directions=[], + flood_density=default_const.FLOOD_DENSITY) + self.add_ons.append(self.manager) + + self.physical_flood_info: Dict[int, PhysicalFlood] = dict() + self.floor_flood_info: Dict[int, FloorFlood] = dict() + self.constants = constants + + def init_obi(self): + self.obi = Obi() + self.communicate([{"$type": "create_obi_solver"}]) + self.obi.set_solver(solver_id=1, scale_factor=1.0, substeps=1) + self.add_ons.append(self.obi) + self.communicate([]) + self.communicate([]) + + def seed(self, seed): + self.RNG = np.random.Generator(np.random.PCG64(seed)) + + def get_unique_id(self): + while True: + idx = super().get_unique_id() + if idx not in self.manager.objects: + return idx + + def add_physical_flood(self, position, direction, speed=1, object_id=None): + if isinstance(position, np.ndarray): + position = position.tolist() + if isinstance(direction, np.ndarray): + direction = direction.tolist() + if object_id == None: + object_id = self.get_unique_id() + new_fluid_info = PhysicalFlood(object_id, activate=True, position=position, direction=direction, speed=speed) + self.physical_flood_info[len(self.physical_flood_info)] = new_fluid_info + self.manager.flood_manager.source_position = np.array(position) + fluid = Fluid(capacity=100000, + resolution=1.0, + color={"r": 0.6, "g": 0.6, "b": 0.33, "a": 0.5}, + rest_density=self.constants.FLOOD_DENSITY, + reflection=0.25, + refraction=-0.034, + smoothing=3.0, + render_smoothness=0.8, + metalness=0, + viscosity=0.001, + absorption=5, + vorticity=1.0, + surface_tension=0.1, + transparency=0.2, + thickness_cutoff=1.2, + radius_scale=1.6, + random_velocity=0.0 + ) + self.obi.create_fluid(fluid=fluid, + shape=DiskEmitter(), + object_id=object_id, + position={"x": position[0], "y": position[1], "z": position[2]}, + rotation={"x": direction[0], "y": direction[1], "z": direction[2]}, + speed=speed, + lifespan=2 + ) + + def add_agent(self, idx, pos): + self.manager.add_object(AgentStatus(idx, constants=self.constants, position=pos, size=np.array([1.0, 2.0, 1.0]))) + + def flood_step(self, resp): + pass + + def communicate(self, commands: Union[dict, List[dict]]) -> list: + if isinstance(commands, dict): + commands = [commands] + commands.extend(self.commands) + self.commands.clear() + # for com in commands: + # print(com) + resp = super().communicate(commands) + if self.initialized: + self.flood_step(resp) + return resp diff --git a/easi/tasks/hazard/vendor/HAZARD/envs/flood/flood_gym.py b/easi/tasks/hazard/vendor/HAZARD/envs/flood/flood_gym.py new file mode 100644 index 0000000..28b1bbd --- /dev/null +++ b/easi/tasks/hazard/vendor/HAZARD/envs/flood/flood_gym.py @@ -0,0 +1,192 @@ +import pdb + +import gym +import gym.spaces +from .floodagent_controller import * +from .agent import * +import numpy as np +import os +PATH = os.path.dirname(os.path.abspath(__file__)) + +while os.path.basename(PATH) != "HAZARD": + PATH = os.path.dirname(PATH) + +# from policy.env_actions import agent_drop, agent_pickup, agent_explore, agent_walk_to_single_step +from enum import IntEnum + +class ActionSpace(IntEnum): + WALK_TO_NEAREST_TARGET = 0 + WALK_TO_NEAREST_CONTAINER = 1 + PICK_UP_NEAREST = 2 + DROP = 3 + EXPLORE = 4 + WALK_TO_RANDOM_OBJECT_IN_SIGHT = 5 + +class FloodEnv(gym.Env): + def __init__(self, port: int = 1071, check_version: bool = True, launch_build: bool = False, + use_local_resources: bool = False, seed = 0, screen_size = 512, use_dino=False, + image_capture_path = None, log_path: str = None, use_gt = False, + map_size_h = 128, map_size_v = 128, grid_size = 0.25, reverse_observation = False, + record_only: bool = False): + self.controller_args = dict(use_local_resources=use_local_resources, launch_build=launch_build, + port=port, check_version=check_version, screen_size=screen_size, + image_capture_path=image_capture_path, log_path=log_path, use_dino=use_dino, + map_size_h=map_size_h, map_size_v=map_size_v, grid_size=grid_size, + use_gt=use_gt, reverse_observation=reverse_observation, + record_only=record_only) + self.controller = None + self.RNG = np.random.RandomState(0) + + rgb_space = gym.spaces.Box(0, 256, (3, screen_size, screen_size), dtype=np.int32) + seg_space = gym.spaces.Box(0, 256, (1, screen_size, screen_size), dtype=np.int32) + depth_space = gym.spaces.Box(0, 256, (1, screen_size, screen_size), dtype=np.int32) + object_space = gym.spaces.Dict({ + "object_id": gym.spaces.Discrete(30), + "type": gym.spaces.Discrete(4), + "seg_color": gym.spaces.Box(0, 256, (3,), dtype=np.int32), + }) + self.done = False + self.record_only = record_only + + self.observation_space = gym.spaces.Box(0, 20, (5, map_size_h, map_size_v), dtype=np.float32) + # self.observation_space = gym.spaces.Dict({ + # "rgb": rgb_space, + # "seg_mask": seg_space, + # "depth": depth_space, + # "temperature": temperature_space, + # "agent": gym.spaces.Box(-30, 30, (6, ), dtype=np.float32), + # "status": gym.spaces.Discrete(3), + # 'camera_matrix': gym.spaces.Box(-30, 30, (4, 4), dtype=np.float32) + # }) + + self.action_space = gym.spaces.Discrete(8) + self.max_step = 400 + + def reset(self, data_dir=None): + if data_dir == None: + data_dirs = os.listdir(os.path.join(PATH, "data", "room_setup_fire")) + data_dirs = [d for d in data_dirs if ("kitchen" in d or "craftroom" in d)] + data_dir = os.path.join(PATH, "data", "room_setup_fire", data_dirs[self.RNG.randint(len(data_dirs))]) + # data_dir = os.path.join(PATH, "data", "room_setup", "1a-0-0") + self.setup = SceneSetup(data_dir=data_dir, is_flood=True, record_mode=self.record_only) + if self.controller is not None: + self.controller.communicate({"$type": "terminate"}) + self.controller.socket.close() + self.controller = FloodAgentController(**self.controller_args) + self.controller.seed(self.RNG.randint(1000000)) + print("Controller connected") + self.controller.init_scene(self.setup) + self.num_step = 0 + self.last_action = None + self.last_target = None + if self.record_only: + return + + self.controller.do_action(0, "turn_by", {"angle": 90}) + self.controller.next_key_frame() + + return self.controller._obs()["RL"] + + # def step(self, action): + # """ + # for each type, if action_target is 0, ignore the action target + # """ + # if not isinstance(action, int): + # action = action.item() + # reward = -1 + # result, msg = None, None + # target = None + # if action == ActionSpace.WALK_TO_NEAREST_TARGET: + # targets = [idx for idx in self.controller.target_ids if idx not in self.controller.finished] + # target = self.controller.find_nearest_object(agent_idx=0, objects=targets) + # result, msg = agent_walk_to_single_step(self, target=target) + # elif action == ActionSpace.PICK_UP_NEAREST: + # targets = [idx for idx in self.controller.target_ids if idx not in self.controller.finished] + # target = self.controller.find_nearest_object(agent_idx=0, objects=targets) + # result, msg = agent_pickup(self, target=target, env_type="fire") + # elif action == ActionSpace.DROP: + # target = None + # result, msg = agent_drop(self, env_type="fire") + # elif action == ActionSpace.EXPLORE: + # target = None + # result, msg = agent_explore(self) + # reward -= 0.5 + # elif action == ActionSpace.WALK_TO_RANDOM_OBJECT_IN_SIGHT: + # if self.last_action == ActionSpace.WALK_TO_RANDOM_OBJECT_IN_SIGHT: + # target = self.last_target + # else: + # obs = self.controller._obs() + # obj_ids = np.unique(obs["sem_map"]["id"]) + # targets = [self.controller.manager.get_real_id(idx) for idx in obj_ids if self.controller.manager.get_real_id(idx) not in self.controller.finished] + # targets = [idx for idx in targets if idx is not None] + # target = int(self.RNG.choice(targets)) if len(targets) > 0 else None + # if target is None: + # result, msg = False, "no object in sight" + # else: + # result, msg = agent_walk_to_single_step(self, target=target) + # else: + # target = None + # reward -= 50 + # result, msg = False, "Invalid action" + # self.last_action = action + # self.last_target = target + # + # if result == False: + # reward -= 2 + # obs, info = self.controller._obs(), self.controller._info() + # info['message'] = msg + # info['success'] = result + # + # reward += self.controller._reward() + # done = self.controller._done() + # self.num_step += 1 + # if self.num_step >= self.max_step: + # done = True + # + # self.done = done + # info['action'] = action + # info['reward'] = reward + # return obs["RL"], reward, done, info + + def seed(self, seed): + self.RNG = np.random.RandomState(seed) + + def get_challenge_action(self, action): + target = None + ret = None + if action == ActionSpace.WALK_TO_NEAREST_TARGET: + targets = [idx for idx in self.controller.target_ids if idx not in self.controller.finished] + target = self.controller.find_nearest_object(agent_idx=0, objects=targets) + ret = "walk_to_single", self.controller.manager.get_renumbered_id(target) + elif action == ActionSpace.PICK_UP_NEAREST: + targets = [idx for idx in self.controller.target_ids if idx not in self.controller.finished] + target = self.controller.find_nearest_object(agent_idx=0, objects=targets) + ret = "pick_up", self.controller.manager.get_renumbered_id(target) + elif action == ActionSpace.DROP: + target = None + ret = "drop", None + elif action == ActionSpace.EXPLORE: + target = None + ret = "explore", None + elif action == ActionSpace.WALK_TO_RANDOM_OBJECT_IN_SIGHT: + try: + if self.last_action == ActionSpace.WALK_TO_RANDOM_OBJECT_IN_SIGHT: + target = self.last_target + else: + obs = self.controller._obs() + obj_ids = np.unique(obs["sem_map"]["id"]) + targets = [self.controller.manager.get_real_id(idx) for idx in obj_ids if self.controller.manager.get_real_id(idx) not in self.controller.finished] + targets = [idx for idx in targets if idx is not None] + target = int(self.RNG.choice(targets)) if len(targets) > 0 else None + except: + target = None + if target is None: + ret = "explore", None + else: + ret = "walk_to_single", self.controller.manager.get_renumbered_id(target) + else: + target = None + ret = "explore", None + self.last_action = action + self.last_target = target + return ret \ No newline at end of file diff --git a/easi/tasks/hazard/vendor/HAZARD/envs/flood/floodagent_controller.py b/easi/tasks/hazard/vendor/HAZARD/envs/flood/floodagent_controller.py new file mode 100644 index 0000000..f9646b1 --- /dev/null +++ b/easi/tasks/hazard/vendor/HAZARD/envs/flood/floodagent_controller.py @@ -0,0 +1,580 @@ +from typing import Any, Tuple, Dict +from .object import ObjectStatus +from .agent import * +from .flood import FloorFlood +from .flood import FloodController +from tdw.add_ons.third_person_camera import ThirdPersonCamera +from tdw.add_ons.logger import Logger +from tdw.replicant.arm import Arm +from tdw.replicant.image_frequency import ImageFrequency +from tdw.add_ons.floorplan_flood import FloorplanFlood +from tdw.tdw_utils import TDWUtils +from tdw.librarian import HumanoidLibrarian +from tdw.output_data import OutputData, Images +from HAZARD.utils.vision import Detector +from HAZARD.utils.model import Semantic_Mapping +from HAZARD.utils.scene_setup import SceneSetup +import numpy as np +import cv2 +import copy +import os +import torch + +class FloodAgentController(FloodController): + """ + Never ever use self.commands in here! For safety uses, self.commands can only be used in parent class + """ + + def __init__(self, use_local_resources: bool = False, single_room: bool = True, reverse_observation: bool = False, + image_capture_path: str = None, log_path: str = None, record_only: bool = False, + use_dino: bool = False, **kwargs) -> None: + self.frame_count = 0 + self.use_local_resources = use_local_resources + self.image_capture_path = image_capture_path + self.reverse_observation = reverse_observation + self.log_path = log_path + self.action_slowdown = 0 + super().__init__(**kwargs) + self.screen_size = kwargs.get("screen_size", 512) + self.agents: List[FloodAgent] = [] + self.comm_counter = 0 + self.use_gt = kwargs.get("use_gt", True) + self.use_dino = use_dino + self.single_room = single_room + self.record_only = record_only + self.id2name = {} + self.other_containers = {} + if not self.use_gt: + if self.use_dino: + from HAZARD.utils.vision_dino import DetectorSAM + self.detector = DetectorSAM(**kwargs) + else: + self.detector = Detector(**kwargs) + # self.init_seg(vocab_path=f"{os.getcwd()}/seg/vocab.txt") + if use_local_resources: + self.update_replicant_url() + + self.map_size_h = kwargs.get("map_size_h", 64) + self.map_size_v = kwargs.get("map_size_v", 64) + self.grid_size = kwargs.get("grid_size", 0.25) + self.sem_map = Semantic_Mapping(device=None, screen_size=self.screen_size, + map_size_h=self.map_size_h, map_size_v=self.map_size_v, grid_size=self.grid_size) + + self.maps = [] + # self.init_seg() + + def update_replicant_url(self): + if not os.path.isfile(f"{os.getcwd()}/data/assets/replicant_0"): # There is no local model of replicant + print("There is no local model of replicant. ") + return + LOCAL_PATH_PREFIX = f"file://{os.getcwd()}/data/assets" + Controller.HUMANOID_LIBRARIANS[Replicant.LIBRARY_NAME] = HumanoidLibrarian(Replicant.LIBRARY_NAME) + record = Controller.HUMANOID_LIBRARIANS[Replicant.LIBRARY_NAME].get_record("replicant_0") + import platform + new_url = record.urls[platform.system()].split("/")[-1] + record.urls[platform.system()] = f"{LOCAL_PATH_PREFIX}/{new_url}" + Controller.HUMANOID_LIBRARIANS[Replicant.LIBRARY_NAME].add_or_update_record(record, overwrite=True) + + def grid_to_real(self, grid_pos): + return self.sem_map.grid_to_real(grid_pos) + + def real_to_grid(self, real_pos): + return self.sem_map.real_to_grid(real_pos) + + def reset_scene(self): + # whatever the agent is holding, drop it + for agent in self.agents: + arms = agent.dynamic.held_objects.keys() + for arm in arms: + agent.drop(arm=arm) + while agent.action.status == ActionStatus.ongoing: + self.communicate([]) + + self.floor_flood_info = dict() + self.physical_flood_info = dict() + self.flood_candidate = dict() + self.initialized = False + self.add_ons = [] + self.id2name = {} + self.manager.reset() + self.frame_count = 0 + self.communicate([]) + + self.last_reward = None + self.communicate([{"$type": "destroy_all_objects"}]) + + def init_scene(self, setup: SceneSetup): + self.reset_scene() + + if self.log_path is not None: + logger = Logger(self.log_path) + self.add_ons.append(logger) + self.communicate([]) + + self.other_containers = {} + for obj in setup.objects: + self.manager.add_object(obj) + for commands in setup.commands_list: + if len(commands) == 1 and commands[0]["$type"] == "terminate": + continue + filtered_commands = [] + for command in commands: + tp = command["$type"] + if tp == "terminate": + break + if tp[:4] == "send" or tp.find("avatar") != -1 or "avatar_id" in command: + continue + filtered_commands.append(command) + if tp == "add_object": + name = command["name"] + idx = command["id"] + self.id2name[idx] = name + if tp.startswith("add_") and tp.endswith("_container"): + self.other_containers[command['id']] = command['container_id'] + # json.dump(filtered_commands, open("/data/private/zqh/tmp.json", "w")) + self.communicate(filtered_commands) + + + if not self.record_only: + if len(self.agents) == 0: + self.agents: List[FloodAgent] = [] + for agent_pos in setup.agent_positions: + idx = self.get_unique_id() + self.agents.append( + FloodAgent(replicant_id=idx, position=agent_pos, image_frequency=ImageFrequency.always)) + self.add_agent(idx, agent_pos) + self.add_ons.append(self.agents[-1]) + else: + assert (len(self.agents) == len(setup.agent_positions)) + for i in range(len(self.agents)): + idx = self.agents[i].replicant_id + self.agents[i].reset(position=setup.agent_positions[i]) + self.add_ons.append(self.agents[i]) + self.add_agent(idx, setup.agent_positions[i]) + + self.maps = [None] * len(self.agents) + self.target = setup.targets + if self.use_dino and not self.use_gt: + self.detector.set_targets(self.target) + self.target_ids = setup.target_ids + self.target_names = setup.target_names + self.target_id2category = setup.target_id2category + self.target_id2name = setup.target_id2name + self.finished = [] + self.communicate([]) + if self.image_capture_path is not None: + # camera = ThirdPersonCamera(avatar_id="record", position={"x": -7.78, "y": 7.67, "z": 0.16}, + # look_at=self.agents[0].replicant_id) + if not self.record_only: + camera = ThirdPersonCamera(avatar_id="record", + position={"x": 0.0, "y": 10.0, "z": 0.0}, + look_at={"x": 0.0, "y": 0.0, "z": 0.0}) + # if len(self.agents) == 0 else self.agents[0].replicant_id) + commands = [{"$type": "set_screen_size", "width": self.screen_size, "height": self.screen_size}, + {"$type": "set_target_framerate", "framerate": 30}] + else: + camera = ThirdPersonCamera(avatar_id="record", position={"x": 1.5, "y": 2.0, "z": 0.0}, + look_at={"x": 0.0, "y": 0.0, "z": 0.0}) + commands = [{"$type": "set_screen_size", "width": self.screen_size * 4, "height": self.screen_size * 4}, + {"$type": "set_target_framerate", "framerate": 30}] + self.add_ons.extend([camera]) + else: + commands = [{"$type": "set_screen_size", "width": self.screen_size, "height": self.screen_size}, + {"$type": "set_target_framerate", "framerate": 30}] + commands.extend([{"$type": "set_floorplan_roof", "show": False}]) + + if self.image_capture_path is not None: + commands.extend([{"$type": "set_pass_masks", "pass_masks": ["_img"], "avatar_id": "record"}, + {"$type": "send_images", "frequency": "always", "ids": ["record"]}]) + + # Get the floors for this floorplan. Create flood objects for each floor. + # These will be at 0 in Y, and not visible until their height is adjusted. + # The index is the number of the scene, i.e. "1". + if self.single_room: + # use fake visual effect + from tdw.librarian import SceneLibrarian + lib = SceneLibrarian() + record = lib.get_record(setup.scene_name) + rooms = record.rooms[0].alcoves + # rooms = [] + rooms.append(record.rooms[0].main_region) + effect_name = 'fplan1_floor1' + original_effect_size = (4.1796417236328125, 2.817157745361328, 5.654577255249023) + for i, room in enumerate(rooms): + floor_id = self.get_unique_id() + rotation = {"x": 90, "y": 0, "z": 0} + position = {'x': room.center[0], 'y': room.center[1], 'z': room.center[2]} + self.commands.append(Controller.get_add_visual_effect(name=effect_name, + effect_id=floor_id, + position=position, + rotation=rotation, + library="flood_effects.json")) + self.commands.append({"$type": "scale_visual_effect", + "scale_factor": {"x": room.bounds[0]/original_effect_size[0], + "y": room.bounds[2]/original_effect_size[1], + "z": 0.01 + }, + "id": floor_id}) + # Add to dictionary of currently-flooded floors. Position will include current height (flood level). + self.floor_flood_info[i] = FloorFlood(floor_id=floor_id) + self.manager.add_flood(id=floor_id, position=position, direction=rotation, + scale=np.array([1.0, 1.0, 1.0])) + else: + self._floors = FloorplanFlood._FLOOD_DATA[setup.scene_name.split("_")[-1][0]] + for i in range(len(self._floors)): + floor = self._floors[str(i + 1)] + floor_name = floor["name"] + floor_id = self.get_unique_id() + position = floor["position"] + rotation = {"x": 90, "y": 0, "z": 0} + self.commands.append(Controller.get_add_visual_effect(name=floor_name, + effect_id=floor_id, + position=position, + rotation=rotation, + library="flood_effects.json")) + # Add to dictionary of currently-flooded floors. Position will include current height (flood level). + self.floor_flood_info[i] = FloorFlood(floor_id=floor_id) + self.manager.add_flood(id=floor_id, position=position, direction=rotation, + scale=np.array([1.0, 1.0, 1.0])) + + resp = self.communicate([{"$type": "send_scene_regions"}]) + self.manager.flood_manager.source_from = setup.flood_source_from + self.manager.flood_manager.set_scene_bounds(resp=resp) + self.add_ons.append(self.manager) + + self.manager.prepare_segmentation_data() + self.initialized = True + for obj in self.target_ids: + commands.append({"$type": "set_kinematic_state", "id": obj, "is_kinematic": False, "use_gravity": True}) + for obj in self.manager.objects: + if self.manager.objects[obj].has_buoyancy: + commands.append({"$type": "set_kinematic_state", "id": obj, "is_kinematic": False, "use_gravity": True}) + self.communicate(commands) + + if not self.record_only: + commands = [ + {"$type": "set_field_of_view", "field_of_view": 120.0, "avatar_id": str(self.agents[0].replicant_id)}] + """add a backpack or similar to the agent's left hand""" + self.container_name = "backpack" if "container" not in setup.other else setup.other["container"] + self.container_id = self.get_unique_id() + self.manager.add_object(ObjectStatus(idx=self.container_id, waterproof=True, has_buoyancy=False)) + + agent_pos = self.agents[0].dynamic.transform.position + commands.append(self.get_add_object(model_name=self.container_name, object_id=self.container_id, + position=TDWUtils.array_to_vector3(agent_pos))) + self.communicate(commands) + self.agents[0].grasp(target=self.container_id, arm=Arm.left, axis=None, angle=None) + self.next_key_frame() + self.agents[0].reach_for(target=TDWUtils.array_to_vector3([-0.3, 1.0, 0.3]), absolute=False, arrived_at=0.1, + arm=Arm.left) + self.next_key_frame() + self.agents[0].reset_arm(arm=Arm.left) + self.next_key_frame() + + # TODO add source + # self.init_obi() + # for idx in range(len(setup.flood_positions)): + # self.add_physical_flood(position=setup.flood_positions[idx], + # direction=setup.flood_directions[idx], + # speed=setup.flood_speed[idx]) + + def next_key_frame(self, force_direction=None) -> Tuple[List[ActionStatus], List[int]]: + # print("next_key_frame") + agent_idx = 0 + action_slowdown = 0 + if force_direction is not None: + position = self.agents[agent_idx].dynamic.transform.position + if self.manager.flood_manager.query_point_underwater(position): + facing = self.agents[agent_idx].dynamic.transform.forward + force_scale = (force_direction * (facing / np.linalg.norm(facing))).sum() + action_slowdown += (1 - force_scale) * self.constants.AGENT_DRAG_COEFFICIENT + + self.action_slowdown += action_slowdown + if self.action_slowdown > 1: + self.action_slowdown -= 1 + self.frame_count += 1 + self.manager.flood_manager.evolve() + + initial_status = [] + have_ongoing_action = False + for agent in self.agents: + if agent.action is not None: + initial_status.append(copy.deepcopy(agent.action.status)) + if initial_status[-1] == ActionStatus.ongoing: + have_ongoing_action = True + else: + initial_status.append(None) + if not have_ongoing_action: + self.communicate([]) + print("no ongoing action") + return initial_status, [] + num_step = 0 + while True: + num_step += 1 + if num_step > 10000: + raise RuntimeError("num_step > 10000") + self.communicate([]) + + changed = False + for i, agent in enumerate(self.agents): + if initial_status[i] is not None and agent.action.status != initial_status[i]: + changed = True + break + if changed: + break + + final_status = [] + changed_agents = [] + for i, agent in enumerate(self.agents): + final_status.append(copy.deepcopy(agent.action.status) if agent.action is not None else None) + if initial_status[i] != final_status[i]: + changed_agents.append(i) + + self.communicate([]) + return final_status, changed_agents + + def do_action(self, agent_idx: int, action: str, params: Dict[str, Any] = dict()) -> None: + """ + See the FireAgent class for allowed actions and their parameters. + """ + if action == "stop": + return + return getattr(self.agents[agent_idx], action)(**params) + + def get_agent_status(self, idx) -> FloodAgent: + return self.agents[idx] + + def find_nearest_object(self, agent_idx: int = 0, objects: List[int] = None): + current_position = self.agents[agent_idx].dynamic.transform.position + return self.manager.find_nearest_object(pos=current_position, objects=objects) + + def replace_with_local_path(self, commands): + LOCAL_PATH_PREFIX = f"file://{os.getcwd()}/data/assets" + download_cmds = [] + for command in commands: + if 'url' in command and "amazonaws.com" in command['url']: + new_url = command['url'].split("/")[-1] + if not os.path.isfile(f"{os.getcwd()}/data/assets/{new_url}"): + download_cmds.append(f"wget -nc {command['url']}\n") + new_url = f"{LOCAL_PATH_PREFIX}/{new_url}" + command['url'] = new_url + for command in self.commands: + if 'url' in command and "amazonaws.com" in command['url']: + new_url = command['url'].split("/")[-1] + if not os.path.isfile(f"{os.getcwd()}/data/assets/{new_url}"): + download_cmds.append(f"wget -nc {command['url']}\n") + new_url = f"{LOCAL_PATH_PREFIX}/{new_url}" + command['url'] = new_url + for m in self.add_ons: + if not m.initialized: + add_on_commands = m.get_initialization_commands() + else: + add_on_commands = m.commands + for command in add_on_commands: + if 'url' in command and "amazonaws.com" in command['url']: + new_url = command['url'].split("/")[-1] + if not os.path.isfile(f"{os.getcwd()}/data/assets/{new_url}"): + download_cmds.append(f"wget -nc {command['url']}\n") + new_url = f"{LOCAL_PATH_PREFIX}/{new_url}" + command['url'] = new_url + if len(download_cmds) > 0: + fout = open(f"{os.getcwd()}/data/assets/download_assets.sh", "w") + for cmd in download_cmds: + fout.write(cmd) + print("Please run data/assets/download_assets.sh first!") + exit(0) + return commands + + def communicate(self, commands: Union[dict, List[dict]]) -> list: + if self.initialized: + self.frame_count += 1 + for agent in self.agents: + idx = agent.grasp_id() + if idx is None: + continue + dist = np.linalg.norm((agent.dynamic.transform.position - self.manager.objects[idx].position) * np.array([1, 0, 1])) + if dist > 3.0: + agent.fail_grasp() + if self.use_local_resources: + commands = self.replace_with_local_path(commands) + resp = super().communicate(commands) + if self.initialized: + for target in self.target_ids: + if (np.abs(self.manager.objects[target].position).sum() > 50) and (target not in self.finished): + self.finished.append(target) + + if self.image_capture_path != None: + for i in range(len(resp) - 1): + r_id = OutputData.get_data_type_id(resp[i]) + # Get Images output data. + if r_id == "imag": + images = Images(resp[i]) + # Determine which avatar captured the image. + if images.get_avatar_id() == "record": + # Save the image. + TDWUtils.save_images(images=images, filename=str(self.comm_counter), + output_directory=self.image_capture_path) + self.comm_counter += 1 + return resp + + def get_seg_mask(self, rgb, obj2idx): + # TODO check the performance + semantic_input, _ = self.predictor.run_on_image(rgb, obj2idx) + return semantic_input + + def get_flood_height_observation(self, idx, width=512, height=512): + depth = TDWUtils.get_depth_values(self.agents[idx].dynamic.images["depth"], width=width, height=height) + depth = np.flip(depth, axis=0) + camera_matrix = self.agents[idx].dynamic.camera_matrix + + point_cloud = TDWUtils.get_point_cloud(depth=depth, camera_matrix=camera_matrix, vfov=120.0) + # fout = open("point_cloud.txt", "w") + # bs, h, w = 1, height, width + # for i in range(bs): + # for j in range(h): + # for k in range(w): + # for t in range(3): + # print(point_cloud[t, j, k].item(), end=' ', file=fout) + # print('', file=fout) + # shape: (3, 512, 512) + # down sample to 16x16 + point_cloud = point_cloud[:, ::(width//16), ::(width//16)] + temp = np.zeros((16, 16)) + for i in range(16): + for j in range(16): + flood_height = self.manager.query_point_flood_height(point_cloud[:, i, j]) + temp[i, j] = flood_height + temp = cv2.resize(temp, (width, height), interpolation=cv2.INTER_NEAREST) + return temp + + @torch.no_grad() + def _obs(self, agent_idx: int = 0): + # print("get obs") + + self.manager.prepare_segmentation_data() + self.communicate([]) + + obs = dict() + """ + raw observation: RGBD, temperature + """ + rgb = self.agents[agent_idx].dynamic.get_pil_image() + id_image = np.array(self.agents[agent_idx].dynamic.get_pil_image("id")) + if self.reverse_observation: + rgb = np.flip(rgb, axis=0) + id_image = np.flip(id_image, axis=0) + seg_mask = None + if self.use_gt: + seg_mask = self.manager.segm.get_seg_mask(id_image) + else: + rcnn_mask = self.detector.inference(np.array(rgb)) + seg_mask = self.manager.segm.get_seg_mask(np.array(id_image), + rcnn=rcnn_mask, + id_list=self.manager.id_list) + + depth = TDWUtils.get_depth_values(self.agents[agent_idx].dynamic.images["depth"], width=self.screen_size, height=self.screen_size) + depth = np.flip(depth, axis=0) + + rgb = np.array(rgb).astype(np.float32).transpose((2, 0, 1)).astype(np.float32) * 1.0 / 255 + depth = depth.reshape((1, self.screen_size, self.screen_size)).astype(np.float32) + + flood_height = self.get_flood_height_observation(agent_idx, width=self.screen_size, height=self.screen_size) + flood_height = flood_height.reshape((1, self.screen_size, self.screen_size)).astype(np.float32) + obs["raw"] = dict( + rgb=rgb, + depth=depth, + log_temp=flood_height, + seg_mask=seg_mask + ) + + """ + mapped observation + """ + camera_matrix = self.agents[agent_idx].dynamic.camera_matrix.reshape((4, 4)) + obs_concat = np.concatenate([rgb, depth, flood_height], axis=0) + sem = self.sem_map.forward(obs=obs_concat, id_map=seg_mask, camera_matrix=camera_matrix, maps_last=self.maps[agent_idx], + position=self.agents[agent_idx].dynamic.transform.position, record_mode=self.record_only, + targets=[self.manager.id_renumbering[target] for target in self.target_ids]) + obs["sem_map"] = dict(height=sem["height"].cpu().numpy(), + explored=sem["explored"].cpu().numpy(), + id=sem["id"].cpu().numpy(), + other=sem["other"].cpu().numpy() if sem["other"] is not None else None) + self.maps[agent_idx] = dict(height=sem["height"].cpu().numpy(), + explored=sem["explored"].cpu().numpy(), + id=sem["id"].cpu().numpy(), + other=sem["other"].cpu().numpy() if sem["other"] is not None else None) + + """ + map of goal and agent + """ + agent_pos = self.sem_map.real_to_grid(self.agents[agent_idx].dynamic.transform.position) + target_poss = [self.sem_map.real_to_grid(self.manager.objects[idx].position) for idx in self.target_ids] + + goal_map = np.zeros((self.map_size_h, self.map_size_v)) + for (i, target_pos) in enumerate(target_poss): + if self.target_ids[i] in self.finished: + continue + if target_pos[0] < 0 or target_pos[0] >= self.map_size_h or target_pos[1] < 0 or target_pos[1] >= self.map_size_v: + continue + if not (sem["id"] == self.manager.get_renumbered_id(self.target_ids[i])).any(): + continue + goal_map[target_pos[0], target_pos[1]] = 1 + if agent_pos[0] > 0 and agent_pos[0] < self.map_size_h - 1 and agent_pos[1] > 0 and agent_pos[1] < self.map_size_v - 1: + goal_map[agent_pos[0], agent_pos[1]] = -2 + rad = self.agents[agent_idx].get_facing() + rad = int(rad / (np.math.pi / 4)) + if rad < 0: + rad += 8 + dx = list([1, 1, 0, -1, -1, -1, 0, 1])[rad] + dz = list([0, 1, 1, 1, 0, -1, -1, -1])[rad] + goal_map[agent_pos[0] + dx][agent_pos[1] + dz] = -1 + obs["goal_map"] = goal_map + + """ + agent info + """ + obs["camera_matrix"] = camera_matrix + + RL_obs = np.zeros((5, self.map_size_h, self.map_size_v)) + RL_obs[0] = obs["sem_map"]["height"] + RL_obs[1] = obs["sem_map"]["explored"] + RL_obs[2] = obs["sem_map"]["id"] + RL_obs[3] = goal_map + RL_obs[4] = obs["sem_map"]["other"][0] + obs["RL"] = RL_obs + return obs + + def _info(self): + info = dict() + info['vector'] = np.zeros(8) # for consistency + info['agent_positions'] = [agent.dynamic.transform.position for agent in self.agents] + info['targets'] = self.manager.get_renumbered_list(self.target_ids) + info['finished targets'] = self.manager.get_renumbered_list(self.finished) + info['camera_matrices'] = [agent.dynamic.camera_matrix.reshape((4, 4)) for agent in self.agents] + info['sr'] = f"{len(self.finished)}/{len(self.target_ids)}" + return info + + # this is a toy reward function + def _reward(self, agent_idx: int = 0): + if self.last_reward is None: + self.last_reward = 0 + + reward = 0 + + agent_pos = self.agents[agent_idx].dynamic.transform.position + nearest_target = self.manager.find_nearest_object(agent_pos, self.target_ids) + + reward += len(self.finished) * 20 + if Arm.left in self.agents[0].dynamic.held_objects: + reward -= 10 + else: + dist = np.linalg.norm(agent_pos - self.manager.objects[nearest_target].position) + reward -= dist * 10 + ret = reward - self.last_reward - 0.1 + self.last_reward = reward + return ret + + def _done(self, agent_idx: int = 0): + if len(self.finished) == len(self.target_ids): + return True + return False \ No newline at end of file diff --git a/easi/tasks/hazard/vendor/HAZARD/envs/flood/manager.py b/easi/tasks/hazard/vendor/HAZARD/envs/flood/manager.py new file mode 100644 index 0000000..0546a61 --- /dev/null +++ b/easi/tasks/hazard/vendor/HAZARD/envs/flood/manager.py @@ -0,0 +1,256 @@ +from tdw.add_ons.add_on import AddOn +from tdw.output_data import OutputData, Transforms, Bounds, Rigidbodies +from tdw.output_data import Replicants, SegmentationColors, ReplicantSegmentationColors +from typing import Dict, List, Set, Optional +import numpy as np +from .object import FloodManager, ObjectStatus +from .utils import * +from HAZARD.utils.seg_id import SegmentationID + +""" +Add-on to control the evolvement of objects, including temperature and state. +Future development may include changing object appearances. +""" + + +class FloodObjectManager(AddOn): + def __init__(self, constants=default_const, source_position=None, source_from=None, floor_ids=[], + floor_positions=[], floor_sizes=[], floor_directions=[], flood_density=1.0): + super().__init__() + self.constants = constants + self.objects: Dict[int, ObjectStatus] = dict() + self.flood_manager = FloodManager(ascending_speed=constants.ASCENDING_SPEED, + ascending_interval=constants.ASCENDING_INTERVAL, + max_height=constants.MAX_HEIGHT, + floor_ids=[], + source_position=source_position, + source_from=source_from, + floor_positions=floor_positions, + floor_sizes=floor_sizes, + floor_directions=floor_directions, + flood_density=flood_density, + roll_theta=constants.SLOP_ANGLE, + flood_force_scale=constants.FLOOD_FORCE_SCALE, + drag_coefficient=constants.DRAG_COEFFICIENT) + self.objects_floating: Set[int] = set() + self.objects_flooded: Set[int] = set() + self.recover_command_list = [] + self.segm = SegmentationID() + self.id_renumbering = dict() + self.id_list = [0] + + def update_visual_effects(self): + new_effect_dict = self.flood_manager.evolve() + self.recover_command_list.reverse() + self.commands = [{"$type": "send_bounds"}, {"$type": "send_transforms"}, {"$type": "send_rigidbodies"}] + self.commands.extend(self.recover_command_list) + self.recover_command_list = [] + floor_flood_commands = [] + for floor_effect_idx in new_effect_dict: + for axis in ["yaw", "pitch", "roll"]: + if new_effect_dict[floor_effect_idx]["angles"][axis] > 0: + floor_flood_commands.append( + {"$type": "rotate_visual_effect_by", + "angle": new_effect_dict[floor_effect_idx]["angles"][axis], + "axis": axis, + "id": floor_effect_idx, + "is_world": True} + ) + self.recover_command_list.append( + {"$type": "rotate_visual_effect_by", + "angle": -new_effect_dict[floor_effect_idx]["angles"][axis], + "axis": axis, + "id": floor_effect_idx, + "is_world": True} + ) + floor_flood_commands.append( + {"$type": "scale_visual_effect", + "scale_factor": {"x": new_effect_dict[floor_effect_idx]["scales"]['x'], + "y": new_effect_dict[floor_effect_idx]["scales"]['y'], + "z": new_effect_dict[floor_effect_idx]["scales"]['z'] + }, + "id": floor_effect_idx} + ) + floor_flood_commands.append( + {"$type": "teleport_visual_effect", + "position": {"x": new_effect_dict[floor_effect_idx]["positions"]['x'], + "y": new_effect_dict[floor_effect_idx]["positions"]['y'], + "z": new_effect_dict[floor_effect_idx]["positions"]['z']}, + "id": floor_effect_idx} + ) + self.objects_flooded = set() + self.objects_floating = set() + counter = 0 + for idx in self.objects: + if self.objects[idx].name == "Agent": + continue + obj = self.objects[idx] + status, buoyancy_scale = self.flood_manager.update_object_status_new(self.objects[idx]) + if status.state == ObjectState.FLOODED or status.state == ObjectState.FLOODED_FLOATING: + self.objects_flooded.add(idx) + if status.state == ObjectState.FLOATING or status.state == ObjectState.FLOODED_FLOATING: + self.objects_floating.add(idx) + # height_diff = self.query_height_diff(position=None) + # if not start_floating: + # floor_flood_commands.append({ + # "$type": "teleport_object_by", "id": status.idx, + # "position": {"x":0.0, "y":height_diff, "z":0.0}, "absolute": True + # }) + # add buoyancy + if isinstance(obj.size, np.ndarray) and isinstance(obj.velocity, np.ndarray): + counter += 1 + horizontal_force = self.flood_manager.cal_horizontal_force(object=self.objects[idx], + source=self.flood_manager.source_position) + floor_flood_commands.append({"$type": "apply_force_to_object", + "id": status.idx, + "force": {"x": horizontal_force["x"], + "y": buoyancy_scale + horizontal_force["y"], + "z": horizontal_force["z"]}}) + else: + floor_flood_commands.append({"$type": "apply_force_to_object", + "id": status.idx, + "force": {"x": 0, + "y": buoyancy_scale, + "z": 0}}) + # print(counter) + self.commands.extend([{"$type": "send_bounds"}, {"$type": "send_transforms"}]) + self.commands.extend(floor_flood_commands) + + def reset(self): + self.objects = dict() + self.objects_floating = set() + self.objects_flooded = set() + self.flood_manager.reset() + self.recover_command_list = [] + self.commands = [] + self.update_visual_effects() + self.initialized = False + self.id_renumbering = dict() + self.id_list = [0] + + def get_initialization_commands(self) -> List[dict]: + return [{"$type": "send_bounds"}, + {"$type": "send_transforms"}, + {"$type": "send_rigidbodies"}] + + def on_send(self, resp: List[bytes]) -> None: + for i in range(len(resp) - 1): + r_id = OutputData.get_data_type_id(resp[i]) + if r_id == "tran": + tran = Transforms(resp[i]) + for j in range(tran.get_num()): + idx = tran.get_id(j) + if idx in self.objects: + self.objects[idx].position = tran.get_position(j) + self.objects[idx].rotation = tran.get_rotation(j) + else: + print("Warning: object with id {} not found in FloodObjectManager".format(idx)) + self.add_object(ObjectStatus(idx, position=tran.get_position(j))) + elif r_id == "boun": + boun = Bounds(resp[i]) + for j in range(boun.get_num()): + idx = boun.get_id(j) + if idx in self.objects: + concat = np.zeros([6, 3]) + concat[0] = boun.get_front(j) + concat[1] = boun.get_back(j) + concat[2] = boun.get_left(j) + concat[3] = boun.get_right(j) + concat[4] = boun.get_top(j) + concat[5] = boun.get_bottom(j) + self.objects[idx].size = np.max(concat, axis=0) - np.min(concat, axis=0) + if self.objects[idx].size[0] < 0: + print(boun.get_front(j), boun.get_back(j), boun.get_left(j), boun.get_right(j), boun.get_top(j), boun.get_bottom(j)) + else: + print("Warning: object with id {} not found in FloodObjectManager".format(idx)) + self.add_object(ObjectStatus(idx, size=boun.get_front(j) + boun.get_right(j) + boun.get_top( + j) - boun.get_back(j) - boun.get_left(j) - boun.get_bottom(j))) + concat = np.zeros([6, 3]) + concat[0] = boun.get_front(j) + concat[1] = boun.get_back(j) + concat[2] = boun.get_left(j) + concat[3] = boun.get_right(j) + concat[4] = boun.get_top(j) + concat[5] = boun.get_bottom(j) + self.objects[idx].size = np.max(concat, axis=0) - np.min(concat, axis=0) + if self.objects[idx].size[0] < 0: + print(boun.get_front(j), boun.get_back(j), boun.get_left(j), boun.get_right(j), boun.get_top(j), boun.get_bottom(j)) + elif r_id == "repl": + repl = Replicants(resp[i]) + for j in range(repl.get_num()): + idx = repl.get_id(j) + if idx in self.objects: + self.objects[idx].position = repl.get_position(j) + else: + print("Warning: object with id {} not found in FloodObjectManager".format(idx)) + elif r_id == "rigi": + rigi = Rigidbodies(resp[i]) + for j in range(rigi.get_num()): + idx = rigi.get_id(j) + if idx in self.objects: + self.objects[idx].velocity = rigi.get_velocity(j) + else: + print("Warning: object with id {} not found in FloodObjectManager".format(idx)) + self.add_object(ObjectStatus(idx, velocity=rigi.get_velocity(j))) + for i in range(len(resp)-1): + r_id = OutputData.get_data_type_id(resp[i]) + if r_id == "segm": + segm = SegmentationColors(resp[i]) + self.segm.process(segm, id_renumbering=self.id_renumbering) + elif r_id == "rseg": + segm = ReplicantSegmentationColors(resp[i]) + self.segm.process(segm, id_renumbering=self.id_renumbering) + self.update_visual_effects() + + def query_height_diff(self, position): + # TODO different height diff + return self.flood_manager.height_diff + + def add_object(self, obj: ObjectStatus): + self.objects[obj.idx] = obj + if obj.idx not in self.id_renumbering: + self.id_list.append(obj.idx) + self.id_renumbering[obj.idx] = len(self.id_list) - 1 + + def add_flood(self, id, position, scale, direction): + self.flood_manager.add_floor_flood(id, position, scale, direction) + + def remove_object(self, idx: int): + if idx in self.objects: + del self.objects[idx] + + def query_point_underwater(self, point: np.ndarray) -> float: + return self.flood_manager.query_point_underwater(point) + + def query_point_flood_height(self, point: np.ndarray) -> float: + return self.flood_manager.query_point_flood_height(point) + + def find_nearest_object(self, pos: np.ndarray, objects: Optional[List[int]] = None): + min_dist = 1e10 + min_idx = None + it = iter(self.objects) if objects is None else iter(objects) + for idx in it: + if self.objects[idx].name != "Object": + continue + dist = np.linalg.norm(self.objects[idx].position - pos) + if dist < min_dist: + min_dist = dist + min_idx = idx + return min_idx + + def prepare_segmentation_data(self): + self.commands.extend([{"$type": "send_segmentation_colors"}, + {"$type": "send_categories"}, {"$type": "send_replicant_segmentation_colors"}]) + + def get_renumbered_id(self, idx: int): + if idx in self.id_renumbering: + return self.id_renumbering[idx] + return 0 + + def get_renumbered_list(self, L: List[int]): + return [self.id_renumbering[idx] for idx in L if idx in self.id_renumbering] + + def get_real_id(self, idx: int): + if idx >= len(self.id_list) or idx == 0: + return None + return self.id_list[idx] diff --git a/easi/tasks/hazard/vendor/HAZARD/envs/flood/object.py b/easi/tasks/hazard/vendor/HAZARD/envs/flood/object.py new file mode 100644 index 0000000..cb04cd7 --- /dev/null +++ b/easi/tasks/hazard/vendor/HAZARD/envs/flood/object.py @@ -0,0 +1,334 @@ +import pdb +from typing import Dict, Set, List, Optional +from .utils import * +import numpy as np +import math +from tdw.scene_data.scene_bounds import SceneBounds +import random + + +class ObjectStatus: + def __init__(self, idx, constants: Constants = default_const, + waterproof: bool = False, has_buoyancy: bool = False, state: int = None, + position: np.ndarray = None, rotation: np.ndarray = None, + size: np.ndarray = None, + velocity: np.ndarray = np.array([0, 0, 0])): + self.idx: int = idx + self.waterproof: bool = waterproof + self.has_buoyancy: bool = has_buoyancy + self.state: int = (ObjectState.NORMAL if state is None else state) + self.position: np.ndarray = position + self.rotation: np.ndarray = rotation + self.size: np.ndarray = size + self.velocity: Optional[np.ndarray] = velocity + self.constants: Constants = constants + self.name = "Object" + self.prev_height_under_water = 0.0 + + def in_water(self): + if self.state == ObjectState.NORMAL: + if self.waterproof and self.has_buoyancy: + self.state = ObjectState.FLOATING + elif self.waterproof and not self.has_buoyancy: + self.state = ObjectState.NORMAL + elif not self.waterproof and self.has_buoyancy: + self.state = ObjectState.FLOODED_FLOATING + else: + self.state = ObjectState.FLOODED + + def out_of_water(self): + if self.state == ObjectState.FLOODED_FLOATING: + self.state = ObjectState.FLOODED + elif self.state == ObjectState.FLOATING: + self.state = ObjectState.NORMAL + + def center(self): + return self.position + self.size * np.array([0, 0.5, 0]) + + def bottom(self): + return self.position + + def top(self): + return self.position + self.size * np.array([1, 1, 1]) + + def left(self): + return self.position + self.size * np.array([-0.5, 0.5, 0]) + + def right(self): + return self.position + self.size * np.array([0.5, 0.5, 0]) + + def front(self): + return self.position + self.size * np.array([0, 0.5, 0.5]) + + def back(self): + return self.position + self.size * np.array([0, 0.5, -0.5]) + + def area(self): + return self.size[0] * self.size[2] + + def horizontal_area(self): + return self.size[1] * self.size[2] + +class AgentStatus(ObjectStatus): + def __init__(self, idx, position, size, constants=default_const): + super().__init__(idx, constants=constants, waterproof = True, has_buoyancy = False, + state=ObjectState.NORMAL, position = position, size=size) + self.name = "Agent" + +class FloodManager: + def __init__(self, ascending_speed: float = 0.01, + ascending_interval: int = 1, + source_position: np.ndarray = None, + source_from: str = "x_max", + max_height: float = 1.5, + floor_ids: List[int] = [], + floor_positions: List[np.ndarray] = [], + floor_sizes: List[np.ndarray] = [], + floor_directions: List[np.ndarray] = [], + roll_theta: float = 10, + flood_density: float = 1.0, + drag_coefficient: float = 0.47, + flood_force_scale: float = 1.0, + ): + self.ascending_speed = ascending_speed + self.ascending_interval = ascending_interval + self.max_height = max_height + self.source_position = source_position + self.source_from = source_from + self.floor_ids = floor_ids + self.floor_positions = floor_positions + self.floor_sizes = floor_sizes + self.floor_directions = floor_directions + self.floor_flood_angles = [ + { + "w": 0.0, + "x": 0.0, + "y": 0.0, + "z": 0.0 + } for i in range(len(self.floor_positions)) + ] + self.old_pitch_theta = 0 + self.old_roll_theta = 0 + self.original_roll_theta = roll_theta + self.roll_theta = roll_theta + self.fluid_velocity = self.ascending_speed / self.ascending_interval / math.tan(abs(roll_theta / 180) * math.pi) + self.floor_flood_heights = [0.0 for i in range(len(self.floor_positions))] + self.source_height = 0 + self.ascending_counter = 0 + self.height_diff = 0 + self.flood_density = flood_density + self.drag_coefficient = drag_coefficient + self.flood_force_scale = flood_force_scale + + def reset(self): + self.roll_theta = self.original_roll_theta + self.source_height = 0 + + def add_floor_flood(self, id, position, scale, direction): + self.floor_ids.append(id) + self.floor_positions.append(position) + self.floor_sizes.append(scale) + self.floor_directions.append(direction) + + def get_updated_flood_angles_and_heights(self): + flood_effect_dict = {} + for i, floor_id in enumerate(self.floor_ids): + flood_effect_dict[floor_id] = {} + + # cal heights + for i in range(len(self.floor_ids)): + floor_position_for_calculation = self.floor_positions[i]["x"] if self.source_from in ['x_max', 'x_min'] \ + else self.floor_positions[i]["z"] + distance_to_source = abs(self.source_location_for_calculation - floor_position_for_calculation) + height_diff_to_source = math.tan(abs(self.roll_theta/180)*math.pi) * distance_to_source + self.floor_positions[i]["y"] = self.source_height - height_diff_to_source + flood_effect_dict[self.floor_ids[i]]["positions"] = { + "x": self.floor_positions[i]["x"], + "y": self.floor_positions[i]["y"], + "z": self.floor_positions[i]["z"] + } + + for i in range(len(self.floor_ids)): + if self.source_from == 'x_max': + flood_effect_dict[self.floor_ids[i]]["angles"] = { + "pitch": 0.0, + "yaw": 0.0, + "roll": self.roll_theta + } + flood_effect_dict[self.floor_ids[i]]["scales"] = { + "x": math.cos(abs(self.old_roll_theta / 180) * math.pi) / math.cos( + abs(self.roll_theta / 180) * math.pi), # * self.floor_sizes[i][0], + "y": 1.0, + "z": 1.0, # * self.floor_sizes[i][2] + } + elif self.source_from == 'x_min': + flood_effect_dict[self.floor_ids[i]]["angles"] = { + "pitch": 0.0, + "yaw": 0.0, + "roll": -self.roll_theta + } + flood_effect_dict[self.floor_ids[i]]["scales"] = { + "x": math.cos(abs(self.old_roll_theta / 180) * math.pi) / math.cos( + abs(self.roll_theta / 180) * math.pi) * self.floor_sizes[i][0], + "y": 1.0, + "z": 1.0 * self.floor_sizes[i][2] + } + elif self.source_from == 'z_max': + flood_effect_dict[self.floor_ids[i]]["angles"] = { + "pitch": self.roll_theta, + "yaw": 0.0, + "roll": 0.0 + } + flood_effect_dict[self.floor_ids[i]]["scales"] = { + "x": 1.0 * self.floor_sizes[i][0], + "y": 1.0, + "z": math.cos(abs(self.old_roll_theta / 180) * math.pi) / math.cos( + abs(self.roll_theta / 180) * math.pi) * self.floor_sizes[i][2] + } + elif self.source_from == 'z_min': + flood_effect_dict[self.floor_ids[i]]["angles"] = { + "pitch": -self.roll_theta, + "yaw": 0.0, + "roll": 0.0 + } + flood_effect_dict[self.floor_ids[i]]["scales"] = { + "x": 1.0 * self.floor_sizes[i][0], + "y": 1.0, + "z": math.cos(abs(self.old_roll_theta / 180) * math.pi) / math.cos( + abs(self.roll_theta / 180) * math.pi) * self.floor_sizes[i][2] + } + else: + assert False + self.old_roll_theta = self.roll_theta + return flood_effect_dict + + def evolve(self): + self.ascending_counter += 1 + self.height_diff = 0 + if self.ascending_counter % self.ascending_interval == 0: + self.ascending_counter = 0 + self.height_diff += (self.ascending_speed * random.random() * 2.0) + self.source_height += self.ascending_speed + if self.source_height >= self.max_height: + self.height_diff -= (self.source_height - self.max_height) + self.source_height = self.max_height + if self.roll_theta > 0: + old_length = self.source_height / math.tan(abs(self.roll_theta / 180) * math.pi) + new_length = old_length + self.fluid_velocity * self.ascending_interval + self.roll_theta = math.atan(self.source_height / new_length) * 180 / math.pi + elif self.roll_theta < 0: + old_length = self.source_height / math.tan(abs(self.roll_theta / 180) * math.pi) + new_length = old_length + self.fluid_velocity * self.ascending_interval + self.roll_theta = -math.atan(self.source_height / new_length) * 180 / math.pi + return self.get_updated_flood_angles_and_heights() + + def query_point_underwater(self, point: np.ndarray): + floor_position_for_calculation = point[0] if self.source_from in ['x_max', 'x_min'] else point[2] + distance_to_source = abs(self.source_location_for_calculation - floor_position_for_calculation) + height_diff_to_source = math.tan(abs(self.roll_theta / 180) * math.pi) * distance_to_source + flood_height = self.source_height - height_diff_to_source + return point[1] <= flood_height + + def query_height_beneath_water(self, object: ObjectStatus): + floor_position_for_calculation = object.bottom()[0] if self.source_from in ['x_max', 'x_min'] else object.bottom()[2] + distance_to_source = abs(self.source_location_for_calculation - floor_position_for_calculation) + height_diff_to_source = math.tan(abs(self.roll_theta / 180) * math.pi) * distance_to_source + flood_height = self.source_height - height_diff_to_source + return max(min(flood_height - min(object.bottom()[1], object.top()[1]), object.size[1]), 0) + + def update_object_status_old(self, object: ObjectStatus): + old_state = object.state + # Use top + # if object.top()[1] <= self.source_height: + # Use center + if self.query_point_underwater(object.center()): + # Use bottom + # if object.bottom()[1] <= self.source_height: + # Debug + # if object.bottom()[1] < 100: + object.in_water() + else: + object.out_of_water() + start_floating = False + stop_floating = False + if object.state == ObjectState.FLOODED_FLOATING or object.state == ObjectState.FLOATING: + if old_state != ObjectState.FLOODED_FLOATING and old_state != ObjectState.FLOATING: + start_floating = True + else: + if old_state == ObjectState.FLOODED_FLOATING or old_state == ObjectState.FLOATING: + stop_floating = True + return object, start_floating, stop_floating + + def update_object_status_new(self, object: ObjectStatus, clip_buoyancy: bool = True): + # Use top + # if object.top()[1] <= self.source_height: + # Use center + height_under_water = self.query_height_beneath_water(object) + if height_under_water > 0: # start flooding + # if height_under_water > object.size[1]: # Completely flooded + object.in_water() + else: + object.out_of_water() + # if clip_buoyancy: + # height_diff = height_under_water - object.prev_height_under_water + # height_diff = min(0.2*object.size[1], height_diff) + # height_under_water = height_diff + object.prev_height_under_water + # buoyancy_scale = self.flood_density / 1000 * height_under_water * abs(object.area()) * 9.81 + buoyancy_scale = 4.0 * self.flood_density / 1000 * height_under_water * abs(object.area()) * 9.81 + object.prev_height_under_water = height_under_water + return object, buoyancy_scale + + def cal_horizontal_force(self, source, object: ObjectStatus): + if self.query_height_beneath_water(object) <= 0: + return {"x": 0, "y": 0, "z": 0} + fluid_velocity = np.array([-self.fluid_velocity * self.drag_coefficient, 0, 0]) + velocity_diff = fluid_velocity - object.velocity + # Drag force 1/2 * fluid density * drag area * drag coefficient * velocity^2 + # drag_force_scale = 0.5 * self.flood_density / 1000 * abs(object.horizontal_area()) * np.linalg.norm(velocity_diff) + drag_force_scale = 2.0 * self.flood_density / 1000 * abs(object.horizontal_area()) * np.linalg.norm(velocity_diff) + drag_force_direction = velocity_diff / np.linalg.norm(velocity_diff) + drag_force = drag_force_scale * drag_force_direction + + # flood force + # distance = object.center() - source + # direction = distance / np.linalg.norm(distance) + # flood_force = direction * self.flood_force_scale * object.horizontal_area() + return {"x": drag_force[0], "y": drag_force[1], "z": drag_force[2]} + + def set_scene_bounds(self, resp=None): + self.scene_bounds = SceneBounds(resp=resp) + new_regions = [] + for fp in self.floor_positions: + for region in self.scene_bounds.regions: + position_diff = abs(region.center[0] - fp['x']) + abs(region.center[2] - fp['z']) + if position_diff < 0.3: + new_regions.append(region) + assert len(new_regions) == len(self.floor_positions) + self.scene_bounds.regions = new_regions + if len(new_regions) > 0: + self.x_max = max([region.x_max for region in self.scene_bounds.regions]) + self.x_min = min([region.x_min for region in self.scene_bounds.regions]) + self.z_max = max([region.z_max for region in self.scene_bounds.regions]) + self.z_min = min([region.z_min for region in self.scene_bounds.regions]) + else: + self.x_max = 0 + self.x_min = 0 + self.z_max = 0 + self.z_min = 0 + if self.source_from == "x_max": + self.source_location_for_calculation = self.x_max + elif self.source_from == "x_min": + self.source_location_for_calculation = self.x_min + elif self.source_from == "z_max": + self.source_location_for_calculation = self.z_max + elif self.source_from == "z_min": + self.source_location_for_calculation = self.z_min + else: + assert False + + def query_point_flood_height(self, target: np.ndarray): + floor_position_for_calculation = target[0] if self.source_from in ['x_max', 'x_min'] else target[2] + distance_to_source = abs(self.source_location_for_calculation - floor_position_for_calculation) + height_diff_to_source = math.tan(abs(self.roll_theta / 180) * math.pi) * distance_to_source + flood_height = max(self.source_height - height_diff_to_source, 0) + return flood_height diff --git a/easi/tasks/hazard/vendor/HAZARD/envs/flood/utils.py b/easi/tasks/hazard/vendor/HAZARD/envs/flood/utils.py new file mode 100644 index 0000000..a54be52 --- /dev/null +++ b/easi/tasks/hazard/vendor/HAZARD/envs/flood/utils.py @@ -0,0 +1,43 @@ +""" +constants and hyperparameters here +""" +import numpy as np + +from enum import Enum +class ObjectState(Enum): + NORMAL = 0 + FLOODED = 1 + FLOODED_FLOATING = 2 + FLOATING = 3 + FLOODED_PICKED = 4 + +class Constants: + def __init__(self, + HAS_BUOYANCY: bool = False, + WATER_PROOF: bool = False, + # ASCENDING_SPEED: float = 0.05, + ASCENDING_SPEED: float = 0.01, + ASCENDING_INTERVAL: int = 10, + MAX_HEIGHT: float = 1.5, + FLOOD_DENSITY: float = 1000.0, + # FLOOD_FORCE_SCALE: float = 3.0, + FLOOD_FORCE_SCALE: float = 5.0, + SLOP_ANGLE: float = 3.0, + # DRAG_COEFFICIENT: float = 0.47 + # DRAG_COEFFICIENT: float = 4 # Coefficient of the drag force by the water + DRAG_COEFFICIENT: float = 50, + AGENT_DRAG_COEFFICIENT: float = 1.0 + ) -> None: + + self.HAS_BUOYANCY = HAS_BUOYANCY + self.WATER_PROOF = WATER_PROOF + self.ASCENDING_SPEED = ASCENDING_SPEED + self.ASCENDING_INTERVAL = ASCENDING_INTERVAL + self.MAX_HEIGHT = MAX_HEIGHT + self.FLOOD_DENSITY = FLOOD_DENSITY + self.FLOOD_FORCE_SCALE = FLOOD_FORCE_SCALE + self.DRAG_COEFFICIENT = DRAG_COEFFICIENT + self.SLOP_ANGLE = SLOP_ANGLE + self.AGENT_DRAG_COEFFICIENT = AGENT_DRAG_COEFFICIENT + +default_const = Constants() diff --git a/easi/tasks/hazard/vendor/HAZARD/envs/wind/__init__.py b/easi/tasks/hazard/vendor/HAZARD/envs/wind/__init__.py new file mode 100644 index 0000000..faaa5a6 --- /dev/null +++ b/easi/tasks/hazard/vendor/HAZARD/envs/wind/__init__.py @@ -0,0 +1,7 @@ +from gym.envs.registration import register +from .wind_gym import WindEnv + +register( + id="wind-v0", + entry_point="envs.wind.wind_gym:WindEnv" +) diff --git a/easi/tasks/hazard/vendor/HAZARD/envs/wind/agent.py b/easi/tasks/hazard/vendor/HAZARD/envs/wind/agent.py new file mode 100644 index 0000000..4bda75b --- /dev/null +++ b/easi/tasks/hazard/vendor/HAZARD/envs/wind/agent.py @@ -0,0 +1,217 @@ +from typing import Optional, Union, List +from tdw.add_ons.replicant import * +from tdw.quaternion_utils import QuaternionUtils +from tdw.replicant.actions.action import Action, ActionStatus +from tdw.replicant.actions.look_at import LookAt +from tdw.replicant.actions.grasp import Grasp +from tdw.replicant.image_frequency import ImageFrequency +from .wind_utils import default_const + +import numpy as np + +class HoldStill(Action): + def __init__(self, duration: int): + self.image_frequency = ImageFrequency.once + self.duration = duration + self.counter = 0 + super().__init__() + + def get_initialization_commands(self, resp: List[bytes], static: ReplicantStatic, dynamic: ReplicantDynamic, image_frequency: ImageFrequency) -> List[dict]: + self.counter = 0 + self.status = ActionStatus.ongoing + return super().get_initialization_commands(resp, static, dynamic, image_frequency) + + def get_ongoing_commands(self, resp: List[bytes], static: ReplicantStatic, dynamic: ReplicantDynamic) -> List[dict]: + self.counter += 1 + if self.counter >= self.duration: + self.status = ActionStatus.success + return super().get_ongoing_commands(resp, static, dynamic) + + def get_end_commands(self, resp: List[bytes], static: ReplicantStatic, dynamic: ReplicantDynamic, image_frequency: ImageFrequency) -> List[dict]: + return super().get_end_commands(resp, static, dynamic, image_frequency) + +class SequentialAction(Action): + def __init__(self, actions: List[Action]): + self._actions = actions + self._current = 0 + self.status: ActionStatus = None + self.image_frequency = ImageFrequency.once + super().__init__() + + def get_initialization_commands(self, resp: List[bytes], static: ReplicantStatic, dynamic: ReplicantDynamic, image_frequency: ImageFrequency) -> List[dict]: + self.image_frequency = image_frequency + self._current = 0 + + self.status = ActionStatus.ongoing + self.initialized = True + + return super().get_initialization_commands(resp=resp, static=static, dynamic=dynamic, image_frequency=image_frequency) + + def get_ongoing_commands(self, resp: List[bytes], static: ReplicantStatic, dynamic: ReplicantDynamic) -> List[dict]: + commands = super().get_ongoing_commands(resp=resp, static=static, dynamic=dynamic) + # for convenience, if self._current < 0, it is in the middle of two actions, wait one frame + if not self._actions[self._current].initialized: + # The action's status defaults to `ongoing`, but actions sometimes fail prior to initialization. + if self._actions[self._current].status == ActionStatus.ongoing: + # Initialize the action and get initialization commands. + self._actions[self._current].initialized = True + initialization_commands = self._actions[self._current].get_initialization_commands(resp=resp, + static=static, + dynamic=dynamic, + image_frequency=self.image_frequency) + + # Most actions are `ongoing` after initialization, but they might've succeeded or failed already. + if self._actions[self._current].status == ActionStatus.ongoing: + commands.extend(initialization_commands) + else: + commands.extend(self._actions[self._current].get_end_commands(resp=resp, + static=static, + dynamic=dynamic, + image_frequency=self.image_frequency)) + # Continue an ongoing action. + else: + # Get the ongoing action commands. + action_commands = self._actions[self._current].get_ongoing_commands(resp=resp, + static=static, + dynamic=dynamic) + # This is an ongoing action. Append ongoing commands. + if self._actions[self._current].status == ActionStatus.ongoing: + commands.extend(action_commands) + # This action is done. Append end commands. + else: + commands.extend(self._actions[self._current].get_end_commands(resp=resp, + static=static, + dynamic=dynamic, + image_frequency=self.image_frequency)) + # This action ended. If not successful, all commands are done. Otherwise, continue to the next action. + if self._actions[self._current].status != ActionStatus.ongoing: + if self._actions[self._current].status == ActionStatus.success: + self._current += 1 + # If there are more actions, continue. + if self._current < len(self._actions): + self.status = ActionStatus.ongoing + # Otherwise, this action is done. + else: + self.status = ActionStatus.success + else: + self.status = self._actions[self._current].status + return commands + + def get_end_commands(self, resp: List[bytes], static: ReplicantStatic, dynamic: ReplicantDynamic, + image_frequency: ImageFrequency) -> List[dict]: + return super().get_end_commands(resp=resp, + static=static, + dynamic=dynamic, + image_frequency=image_frequency) + +""" +The poses are relative to the agent's position and rotation. +Assume the agent is facing positive z. +""" +class WindAgent(Replicant): + def __init__(self, constants=default_const, *args, **kwargs): + self.constants = constants + self.heatmap = 0 + super().__init__(*args, **kwargs) + + # define custom actions here + def get_facing(self): + """return a radian""" + v = QuaternionUtils.multiply_by_vector(QuaternionUtils.get_inverse(self.dynamic.transform.rotation), np.array([0, 0, 1.0])) + v = v / np.linalg.norm(v) + v = np.arctan2(v[2], v[0]) + return v + + def pick_up(self, target: int, arm: Union[Arm, List[Arm]], + final_pose_loc: np.ndarray = np.array([0.1, 1.1, 0.6]), absolute: bool = False, + angle: Optional[float] = 90, axis: Optional[str] = "pitch") -> None: + if isinstance(arm, Arm): + self.action = SequentialAction([ + TurnTo(target=target), + LookAt(target=target, + duration=0.1, + scale_duration=True), + ReachFor(target=target, + arms=Replicant._arms_to_list(arm), + absolute=absolute, + dynamic=self.dynamic, + collision_detection=self.collision_detection, + offhand_follows=False, + arrived_at=0.1, + previous=None, + duration=0.25, + scale_duration=True, + max_distance=1.0, + from_held=False, + held_point="bottom"), + Grasp(target=target, arm=arm, dynamic=self.dynamic, angle=angle, axis=axis, relative_to_hand=True, offset=0.0), + ReachFor(target=final_pose_loc, + arms=Replicant._arms_to_list(arm), + absolute=absolute, + dynamic=self.dynamic, + collision_detection=self.collision_detection, + offhand_follows=False, + arrived_at=0.1, + previous=None, + duration=0.25, + scale_duration=True, + max_distance=1.0, + from_held=False, + held_point="bottom"), + ]) + else: + self.action = SequentialAction([ + TurnTo(target=target), + LookAt(target=target, + duration=0.1, + scale_duration=True), + ReachFor(target=target, + arms=Replicant._arms_to_list(arm), + absolute=absolute, + dynamic=self.dynamic, + collision_detection=self.collision_detection, + offhand_follows=False, + arrived_at=0.1, + previous=None, + duration=0.25, + scale_duration=True, + max_distance=1.0, + from_held=False, + held_point="bottom"), + Grasp(target=target, arm=Arm.left, dynamic=self.dynamic, angle=angle, axis=axis), + ReachFor(target=final_pose_loc, + arms=[Arm.left], + absolute=absolute, + dynamic=self.dynamic, + collision_detection=self.collision_detection, + offhand_follows=True, + arrived_at=0.1, + previous=None, + duration=0.25, + scale_duration=True, + max_distance=1.0, + from_held=False, + held_point="bottom"), + ]) + + def grasp_id(self): + """ + if the agent is grasping an object, return the id of the object + otherwise, return None + """ + if isinstance(self.action, Grasp): + return self.action.target + elif isinstance(self.action, SequentialAction) and isinstance(self.action._actions[self.action._current], Grasp): + return self.action._actions[self.action._current].target + return None + + def fail_grasp(self): + """ + if the agent is grasping an object and it is too far away, fail the grasp (this is controlled by the outside) + """ + if isinstance(self.action, Grasp): + self.action.status = ActionStatus.cannot_grasp + elif isinstance(self.action, SequentialAction) and isinstance(self.action._actions[self.action._current], Grasp): + self.action._actions[self.action._current].status = ActionStatus.cannot_grasp + else: + raise Exception("Not grasping now") \ No newline at end of file diff --git a/easi/tasks/hazard/vendor/HAZARD/envs/wind/manager.py b/easi/tasks/hazard/vendor/HAZARD/envs/wind/manager.py new file mode 100644 index 0000000..6f36635 --- /dev/null +++ b/easi/tasks/hazard/vendor/HAZARD/envs/wind/manager.py @@ -0,0 +1,168 @@ +from tdw.add_ons.add_on import AddOn +from .object import * +from tdw.output_data import OutputData, Transforms, Bounds, Rigidbodies +from tdw.output_data import Replicants, SegmentationColors, ReplicantSegmentationColors +from typing import Dict, List +import numpy as np +from HAZARD.utils.seg_id import SegmentationID + +""" +Add-on to manage the objects. +""" + +class WindObjectManager(AddOn): + def __init__(self, constants=default_const): + super().__init__() + self.constants = constants + self.objects: Dict[int, ObjectStatus] = dict() + self.wind_force_manager = WindForceManager() + self.effects = dict() + self.settled = set() + self.wind_v: np.ndarray = np.array([0, 0, 0]) + self.num_frame = 0 + self.segm = SegmentationID() + self.id_renumbering = dict() + self.id_list = [0] + + def reset(self): + self.objects = dict() + self.effects = dict() + self.settled = set() + self.num_frame = 0 + self.wind_v = np.array([0, 0, 0]) + self.commands = [] + self.initialized = False + self.id_renumbering = dict() + self.id_list = [0] + + def get_initialization_commands(self) -> List[dict]: + return [{"$type": "send_bounds"}, + {"$type": "send_transforms"}, + {"$type": "send_rigidbodies"}] + + def on_send(self, resp: List[bytes]) -> None: + for i in range(len(resp)-1): + r_id = OutputData.get_data_type_id(resp[i]) + if r_id == "tran": + tran = Transforms(resp[i]) + for j in range(tran.get_num()): + idx = tran.get_id(j) + if idx in self.objects: + self.objects[idx].position = tran.get_position(j) + self.objects[idx].rotation = tran.get_rotation(j) + else: + print("Warning: object with id {} not found in WindObjectManager".format(idx)) + self.add_object(ObjectStatus(idx, position=tran.get_position(j))) + elif r_id == "boun": + boun = Bounds(resp[i]) + for j in range(boun.get_num()): + idx = boun.get_id(j) + if idx in self.objects: + concat = np.zeros([6, 3]) + concat[0] = boun.get_front(j) + concat[1] = boun.get_back(j) + concat[2] = boun.get_left(j) + concat[3] = boun.get_right(j) + concat[4] = boun.get_top(j) + concat[5] = boun.get_bottom(j) + self.objects[idx].size = np.max(concat, axis=0) - np.min(concat, axis=0) + if self.objects[idx].size[0] < 0: + print(boun.get_front(j), boun.get_back(j), boun.get_left(j), boun.get_right(j), boun.get_top(j), boun.get_bottom(j)) + else: + print("Warning: object with id {} not found in WindObjectManager".format(idx)) + self.add_object(ObjectStatus(idx, size=boun.get_front(j) + boun.get_right(j) + boun.get_top(j) - boun.get_back(j) - boun.get_left(j) - boun.get_bottom(j))) + concat = np.zeros([6, 3]) + concat[0] = boun.get_front(j) + concat[1] = boun.get_back(j) + concat[2] = boun.get_left(j) + concat[3] = boun.get_right(j) + concat[4] = boun.get_top(j) + concat[5] = boun.get_bottom(j) + self.objects[idx].size = np.max(concat, axis=0) - np.min(concat, axis=0) + if self.objects[idx].size[0] < 0: + print(boun.get_front(j), boun.get_back(j), boun.get_left(j), boun.get_right(j), boun.get_top(j), boun.get_bottom(j)) + elif r_id == "repl": + repl = Replicants(resp[i]) + for j in range(repl.get_num()): + idx = repl.get_id(j) + if idx in self.objects: + self.objects[idx].position = repl.get_position(j) + else: + print("Warning: object with id {} not found in WindObjectManager".format(idx)) + self.add_object(AgentStatus(idx, position=repl.get_position(j))) + elif r_id == "rigi": + rigi = Rigidbodies(resp[i]) + for j in range(rigi.get_num()): + idx = rigi.get_id(j) + if idx in self.objects: + self.objects[idx].velocity = rigi.get_velocity(j) + else: + print("Warning: object with id {} not found in WindObjectManager".format(idx)) + self.add_object(ObjectStatus(idx, velocity=rigi.get_velocity(j))) + for i in range(len(resp)-1): + r_id = OutputData.get_data_type_id(resp[i]) + if r_id == "segm": + segm = SegmentationColors(resp[i]) + self.segm.process(segm, id_renumbering=self.id_renumbering) + elif r_id == "rseg": + segm = ReplicantSegmentationColors(resp[i]) + self.segm.process(segm, id_renumbering=self.id_renumbering) + self.effects = self.wind_force_manager.evolve(self.objects, self.wind_v, self.settled) + self.commands = [{"$type": "send_bounds"}, {"$type": "send_transforms"}, {"$type": "send_rigidbodies"}] + self.num_frame += 1 + if np.linalg.norm(self.wind_v * [1, 0, 1]) > 0.1: + for idx in self.objects: + if idx in self.settled or self.objects[idx].position is None or self.objects[idx].size is None: + continue + for idx2 in self.settled: + if idx2 == idx or self.objects[idx2].position is None or self.objects[idx2].size is None: + continue + # extent of idx is fully covered by idx2 + l, r, f, b = self.objects[idx2].left()[0], self.objects[idx2].right()[0], self.objects[idx2].front()[2], self.objects[idx2].back()[2] + x, y, z = self.objects[idx].position.tolist() + bottom = self.objects[idx2].bottom()[1] + top = self.objects[idx2].top()[1] + if l <= x and x <= r and b <= z and z <= f and bottom <= y and y <= top: + self.settled.add(idx) + break + def add_object(self, obj: ObjectStatus): + self.objects[obj.idx] = obj + if obj.idx not in self.id_renumbering: + self.id_list.append(obj.idx) + self.id_renumbering[obj.idx] = len(self.id_list) - 1 + + def remove_object(self, obj: ObjectStatus): + if obj.idx in self.objects: + del self.objects[obj.idx] + + def find_nearest_object(self, pos: np.ndarray, objects: Optional[List[int]] = None): + min_dist = 1e10 + min_idx = None + it = iter(self.objects) if objects is None else iter(objects) + for idx in it: + if self.objects[idx].name != "Object": + continue + if idx in self.settled and objects is None: + continue + dist = np.linalg.norm(self.objects[idx].position - pos) + if dist < min_dist: + min_dist = dist + min_idx = idx + return min_idx + + def prepare_segmentation_data(self): + self.commands.extend([{"$type": "send_segmentation_colors"}, + {"$type": "send_categories"}, {"$type": "send_replicant_segmentation_colors"}]) + + def get_renumbered_id(self, idx: int): + if idx in self.id_renumbering: + return self.id_renumbering[idx] + return 0 + + def get_renumbered_list(self, L: List[int]): + return [self.id_renumbering[idx] for idx in L if idx in self.id_renumbering] + + def get_real_id(self, idx: int): + if idx >= len(self.id_list) or idx == 0: + return None + return self.id_list[idx] diff --git a/easi/tasks/hazard/vendor/HAZARD/envs/wind/object.py b/easi/tasks/hazard/vendor/HAZARD/envs/wind/object.py new file mode 100644 index 0000000..caddb77 --- /dev/null +++ b/easi/tasks/hazard/vendor/HAZARD/envs/wind/object.py @@ -0,0 +1,68 @@ +from typing import Dict, Set, Optional +from .wind_utils import * +import numpy as np + +class ObjectStatus: + def __init__(self, idx, constants: Constants=default_const, + mass=1.0, position: np.ndarray = None, rotation: np.ndarray = None, + size: np.ndarray = None, velocity: np.ndarray = None, resistence: int = 0): + self.idx: int = idx + self.position: Optional[np.ndarray] = position + self.rotation: Optional[np.ndarray] = rotation + self.size: Optional[np.ndarray] = size + self.velocity: Optional[np.ndarray] = velocity + self.constants: Constants = constants + self.resistence: int = resistence + self.name = "Object" + + def center(self): return self.position + self.size * np.array([0, 0.5, 0]) + def bottom(self): return self.position + def top(self): return self.position + self.size * np.array([0, 1, 0]) + def left(self): return self.position + self.size * np.array([-0.5, 0.5, 0]) + def right(self): return self.position + self.size * np.array([0.5, 0.5, 0]) + def front(self): return self.position + self.size * np.array([0, 0.5, 0.5]) + def back(self): return self.position + self.size * np.array([0, 0.5, -0.5]) + def area(self): return (self.top() - self.bottom()).sum() * (self.right() - self.left()).sum() + +class AgentStatus(ObjectStatus): + def __init__(self, idx, position, size=None, constants=default_const): + super().__init__(idx, constants, position=position, size=size) + self.name = "Agent" + +class WindForceManager: + def __init__(self, constants=default_const): + self.constants: Constants = constants + + def calc_wind_effect(self, wind_velocity: np.ndarray, obj: ObjectStatus): + """ + Calculate the wind force and torque on an object + v_effect: velocity difference on the wind direction between the wind and the object + """ + if np.dot(wind_velocity, wind_velocity) < 1e-6 or obj.resistence > 10: + return np.zeros(3), np.zeros(3) + felt_wind = wind_velocity / (obj.resistence + 1) + # print(obj.idx, obj.resistence) + v_effect = felt_wind - felt_wind * np.dot(felt_wind, obj.velocity) / np.dot(felt_wind, felt_wind) + # print(f"wind= {felt_wind}, v= {obj.velocity}, v_effect={v_effect}") + area = obj.area() + + f_tan = np.linalg.norm(v_effect) * v_effect * area * self.constants.AIR_DENSITY + + rand_v = np.minimum(2, np.random.normal(0, self.constants.F_CROSS_SCALE, 3)) + f_cross = np.cross(f_tan, rand_v) + + r = np.random.normal(0, 0.1, 3) * (obj.top() - obj.bottom()).sum() / 2 + torque = np.cross(f_tan, r) + # torque = np.zeros(3) + + f = f_tan + f_cross + return f, torque + + def evolve(self, objects: Dict[int, ObjectStatus], wind: np.ndarray, settled: Set[int]): + effects = dict() + for idx in objects: + obj = objects[idx] + if obj.name != 'Object' or (not isinstance(obj.velocity, np.ndarray)) or (not isinstance(obj.size, np.ndarray)) or idx in settled: + continue + effects[idx] = self.calc_wind_effect(wind, obj) + return effects \ No newline at end of file diff --git a/easi/tasks/hazard/vendor/HAZARD/envs/wind/wind.py b/easi/tasks/hazard/vendor/HAZARD/envs/wind/wind.py new file mode 100644 index 0000000..db1f8da --- /dev/null +++ b/easi/tasks/hazard/vendor/HAZARD/envs/wind/wind.py @@ -0,0 +1,66 @@ +from typing import List, Union +from tdw.controller import Controller +from .manager import WindObjectManager +from .object import AgentStatus +from .wind_utils import default_const + +import numpy as np + +""" +This controller controls the spread of fire. +""" +class WindController(Controller): + def __init__(self, port: int = 1071, check_version: bool = True, launch_build: bool = True, seed = 0, constants = default_const, **kwargs): + self.initialized = False + self.commands: List[dict] = list() + super().__init__(port, check_version, launch_build) + + self.manager = WindObjectManager() + self.add_ons.append(self.manager) + self.frame_count = 0 + self.RNG = np.random.Generator(np.random.PCG64(seed)) + self.constants = constants + + def seed(self, seed): + self.RNG = np.random.Generator(np.random.PCG64(seed)) + + def get_unique_id(self): + while True: + idx = super().get_unique_id() + if idx not in self.manager.objects: + return idx + + def add_agent(self, idx, pos): + self.manager.add_object(AgentStatus(idx, constants=self.constants, position=pos, size=None)) + + def set_wind(self, wind_v): + self.manager.wind_v = wind_v + + def reset_wind(self): + self.manager.wind_v = np.array([0, 0, 0]) + + def wind_step(self, resp): + for idx in self.manager.effects: + force, torque = self.manager.effects[idx] + # print(idx, force, torque, self.manager.objects[idx].position, self.manager.objects[idx].velocity) + self.commands.append({"$type": "apply_force_to_object", "id": idx, "force": {"x": force[0], "y": force[1], "z": force[2]}}) + self.commands.append({"$type": "apply_torque_to_object", "id": idx, "torque": {"x": torque[0], "y": torque[1], "z": torque[2]}}) + self.frame_count += 1 + + def communicate(self, commands: Union[dict, List[dict]]) -> list: + if isinstance(commands, dict): + commands = [commands] + commands.extend(self.commands) + self.commands.clear() + # for com in commands: + # print(com) + # try: + resp = super().communicate(commands) + # except: + # print("Error") + # print(commands) + # super().communicate([{"$type": "terminate"}]) + # exit(0) + if self.initialized: + self.wind_step(resp) + return resp \ No newline at end of file diff --git a/easi/tasks/hazard/vendor/HAZARD/envs/wind/wind_gym.py b/easi/tasks/hazard/vendor/HAZARD/envs/wind/wind_gym.py new file mode 100644 index 0000000..b5091f8 --- /dev/null +++ b/easi/tasks/hazard/vendor/HAZARD/envs/wind/wind_gym.py @@ -0,0 +1,165 @@ +import gym +import gym.spaces +from .agent import * +import numpy as np +from .windagent_controller import * +# from HAZARD.policy.env_actions import agent_drop, agent_pickup, agent_explore, agent_walk_to_single_step + +from enum import IntEnum + +import os +PATH = os.path.dirname(os.path.abspath(__file__)) +while os.path.basename(PATH) != "HAZARD": + PATH = os.path.dirname(PATH) + +class ActionSpace(IntEnum): + WALK_TO_NEAREST_TARGET = 0 + WALK_TO_NEAREST_CONTAINER = 1 + PICK_UP_NEAREST = 2 + DROP = 3 + EXPLORE = 4 + WALK_TO_RANDOM_OBJECT_IN_SIGHT = 5 + +class WindEnv(gym.Env): + def __init__(self, port: int = 1071, check_version: bool = True, launch_build: bool = False, seed = 0, + screen_size = 512, use_local_resources = False, map_size_h=256, map_size_v=256, grid_size=0.25, + image_capture_path: str = None, log_path: str = None, use_gt=False, use_dino=False, + reverse_observation = False, record_only: bool = False, **kwargs): + self.controller_args = dict(launch_build=launch_build, port=port, check_version=check_version, + screen_size=screen_size, use_local_resources=use_local_resources, + map_size_h=map_size_h, map_size_v=map_size_v, grid_size=grid_size, + image_capture_path=image_capture_path, log_path=log_path, use_dino=use_dino, + use_gt=use_gt, reverse_observation=reverse_observation, record_only=record_only) + self.controller = None + self.RNG = np.random.RandomState(0) + self.done = False + self.record_only = record_only + self.observation_space = gym.spaces.Box(0, 20, (4, map_size_h, map_size_v), dtype=np.float64) + self.action_space = gym.spaces.Discrete(6) + self.max_step = 1000 + + def reset(self, data_dir = None): + if data_dir == None: + data_dirs = os.listdir(os.path.join(PATH, "data", "room_setup_wind")) + data_dirs = [d for d in data_dirs if "suburb" in d] + data_dir = os.path.join(PATH, "data", "room_setup_wind", data_dirs[self.RNG.randint(len(data_dirs))]) + self.setup = SceneSetup(data_dir=data_dir, record_mode=self.record_only) + if self.controller is not None: + self.controller.communicate({"$type": "terminate"}) + self.controller.socket.close() + self.controller = WindAgentController(**self.controller_args) + self.controller.seed(self.RNG.randint(1000000)) + print("Controller connected") + self.controller.init_scene(self.setup) + + self.num_step = 0 + self.last_action = None + self.last_target = None + + if not self.record_only: + self.controller.do_action(0, "turn_by", {"angle": 0}) + self.controller.next_key_frame() + return self.controller._obs()["RL"] + + # def step(self, action): + # """ + # for each type, if action_target is 0, ignore the action target + # """ + # if not isinstance(action, int): + # action = action.item() + # + # reward = 1 + # result, msg = None, None + # target = None + # if action == ActionSpace.WALK_TO_NEAREST_TARGET: + # targets = [idx for idx in self.controller.targets if idx not in self.controller.finished] + # target = self.controller.find_nearest_object(agent_idx=0, objects=targets) + # result, msg = agent_walk_to_single_step(self, target=target) + # elif action == ActionSpace.WALK_TO_NEAREST_CONTAINER: + # containers = self.controller.containers + # target = self.controller.find_nearest_object(agent_idx=0, objects=containers) + # result, msg = agent_walk_to_single_step(self, target=target) + # elif action == ActionSpace.PICK_UP_NEAREST: + # targets = [idx for idx in self.controller.targets if idx not in self.controller.finished] + # target = self.controller.find_nearest_object(agent_idx=0, objects=targets) + # result, msg = agent_pickup(self, target=target, env_type="wind") + # elif action == ActionSpace.DROP: + # containers = self.controller.containers + # target = self.controller.find_nearest_object(agent_idx=0, objects=containers) + # result, msg = agent_drop(self, container=target, env_type="wind") + # elif action == ActionSpace.EXPLORE: + # target = None + # result, msg = agent_explore(self) + # reward -= 0.5 + # elif action == ActionSpace.WALK_TO_RANDOM_OBJECT_IN_SIGHT: + # target = None + # if target is None: + # result, msg = False, "no object in sight" + # else: + # result, msg = agent_walk_to_single_step(self, target=target) + # self.last_action = action + # self.last_target = target + # + # if result == False: + # reward -= 2 + # obs, info = self.controller._obs(), self.controller._info() + # info['message'] = msg + # info['success'] = result + # + # reward += self.controller._reward() + # done = self.controller._done() + # self.num_step += 1 + # if self.num_step >= self.max_step: + # done = True + # + # self.done = done + # info['action'] = action + # info['reward'] = reward + # return obs["RL"], reward, done, info + + def seed(self, seed): + self.RNG = np.random.RandomState(seed) + + """ + better not use it along with self.step(), it messes with last_action and last_target + """ + def get_challenge_action(self, action): + target = None + ret = None + if action == ActionSpace.WALK_TO_NEAREST_TARGET: + targets = [idx for idx in self.controller.targets if idx not in self.controller.finished] + target = self.controller.find_nearest_object(agent_idx=0, objects=targets) + ret = "walk_to_single", self.controller.manager.get_renumbered_id(target) + elif action == ActionSpace.WALK_TO_NEAREST_CONTAINER: + containers = self.controller.containers + target = self.controller.find_nearest_object(agent_idx=0, objects=containers) + ret = "walk_to_single", self.controller.manager.get_renumbered_id(target) + elif action == ActionSpace.PICK_UP_NEAREST: + targets = [idx for idx in self.controller.targets if idx not in self.controller.finished] + target = self.controller.find_nearest_object(agent_idx=0, objects=targets) + ret = "pick_up", self.controller.manager.get_renumbered_id(target) + elif action == ActionSpace.DROP: + containers = self.controller.containers + target = self.controller.find_nearest_object(agent_idx=0, objects=containers) + ret = "drop", self.controller.manager.get_renumbered_id(target) + elif action == ActionSpace.EXPLORE: + target = None + ret = "explore", None + elif action == ActionSpace.WALK_TO_RANDOM_OBJECT_IN_SIGHT: + try: + if self.last_action == ActionSpace.WALK_TO_RANDOM_OBJECT_IN_SIGHT: + target = self.last_target + else: + obs = self.controller._obs() + obj_ids = np.unique(obs["sem_map"]["id"]) + targets = [self.controller.manager.get_real_id(idx) for idx in obj_ids if self.controller.manager.get_real_id(idx) not in self.controller.finished] + target = int(self.RNG.choice(targets)) if len(targets) > 0 else None + except: + target = None + if target is None: + ret = "explore", None + else: + ret = "walk_to_single", self.controller.manager.get_renumbered_id(target) + self.last_action = action + self.last_target = target + return ret \ No newline at end of file diff --git a/easi/tasks/hazard/vendor/HAZARD/envs/wind/wind_utils.py b/easi/tasks/hazard/vendor/HAZARD/envs/wind/wind_utils.py new file mode 100644 index 0000000..35330bc --- /dev/null +++ b/easi/tasks/hazard/vendor/HAZARD/envs/wind/wind_utils.py @@ -0,0 +1,15 @@ +from tdw.object_data.object_static import ObjectStatic +from tdw.object_data.bound import Bound +from tdw.object_data.rigidbody import Rigidbody +import numpy as np + +class Constants: + def __init__(self, + AIR_DENSITY: float = 1.225, + F_CROSS_SCALE: float = 0.05, + F_ON_AGENT: float = 1.0 + ) -> None: + self.AIR_DENSITY = AIR_DENSITY + self.F_CROSS_SCALE = F_CROSS_SCALE + self.F_ON_AGENT = F_ON_AGENT +default_const = Constants() diff --git a/easi/tasks/hazard/vendor/HAZARD/envs/wind/windagent_controller.py b/easi/tasks/hazard/vendor/HAZARD/envs/wind/windagent_controller.py new file mode 100644 index 0000000..b421dd3 --- /dev/null +++ b/easi/tasks/hazard/vendor/HAZARD/envs/wind/windagent_controller.py @@ -0,0 +1,488 @@ +from typing import Any, Tuple, Dict +from .object import ObjectStatus +from .agent import * +from tdw.add_ons.third_person_camera import ThirdPersonCamera +from tdw.add_ons.logger import Logger +from tdw.replicant.arm import Arm +from tdw.replicant.image_frequency import ImageFrequency +from HAZARD.utils.model import Semantic_Mapping +from .wind import WindController +from tdw.tdw_utils import TDWUtils +from tdw.librarian import HumanoidLibrarian +import numpy as np +import copy +import os +from HAZARD.utils.vision import Detector +from HAZARD.utils.local_asset import get_local_url +from HAZARD.utils.scene_setup import SceneSetup +from tdw.output_data import OutputData, Images + +PATH = os.path.dirname(os.path.abspath(__file__)) +while os.path.basename(PATH) != "HAZARD": + PATH = os.path.dirname(PATH) + +import torch + +""" +Caution!!! In a single trial, the number of agents should never change! +Currently single agent only. +""" +class WindAgentController(WindController): + """ + Never ever use self.commands in here! For safety uses, self.commands can only be used in parent class + """ + def __init__(self, use_local_resources: bool = False, image_capture_path: str = None, log_path: str = None, + reverse_observation: bool = False, record_only: bool = False, use_dino: bool = False, + **kwargs) -> None: + self.use_local_resources = use_local_resources + self.image_capture_path = image_capture_path + self.log_path = log_path + self.frame_count = 0 + self.reverse_observation = reverse_observation + super().__init__(**kwargs) + self.screen_size = kwargs.get("screen_size", 512) + self.agents: List[WindAgent] = [] + self.comm_counter = 0 + self.use_gt = kwargs.get("use_gt", True) + self.use_dino = use_dino + self.record_only = record_only + self.id2name = {} + if not self.use_gt: + if self.use_dino: + from HAZARD.utils.vision_dino import DetectorSAM + self.detector = DetectorSAM(**kwargs) + else: + self.detector = Detector(**kwargs) + if use_local_resources: + self.update_replicant_url() + + self.map_size_h = kwargs.get("map_size_h", 256) + self.map_size_v = kwargs.get("map_size_v", 256) + self.grid_size = kwargs.get("grid_size", 0.5) + self.sem_map = Semantic_Mapping(device=None, screen_size=self.screen_size, + map_size_h=self.map_size_h, map_size_v=self.map_size_v, grid_size=self.grid_size) + + self.maps = [] + self.action_slowdown = 0 + self.other_containers = {} + # self.init_seg() + + def update_replicant_url(self): + assert os.path.isfile(f"{os.getcwd()}/data/assets/replicant_0") + LOCAL_PATH_PREFIX = f"file://{os.getcwd()}/data/assets" + Controller.HUMANOID_LIBRARIANS[Replicant.LIBRARY_NAME] = HumanoidLibrarian(Replicant.LIBRARY_NAME) + record = Controller.HUMANOID_LIBRARIANS[Replicant.LIBRARY_NAME].get_record("replicant_0") + import platform + new_url = record.urls[platform.system()].split("/")[-1] + record.urls[platform.system()] = f"{LOCAL_PATH_PREFIX}/{new_url}" + Controller.HUMANOID_LIBRARIANS[Replicant.LIBRARY_NAME].add_or_update_record(record, overwrite=True) + + def grid_to_real(self, grid_pos): + return self.sem_map.grid_to_real(grid_pos) + def real_to_grid(self, real_pos): + return self.sem_map.real_to_grid(real_pos) + + def reset_scene(self): + # whatever the agent is holding, drop it + for agent in self.agents: + arms = agent.dynamic.held_objects.keys() + for arm in arms: + agent.drop(arm=arm) + while agent.action.status == ActionStatus.ongoing: + self.communicate([]) + + self.initialized = False + self.add_ons = [] + self.manager.reset() + self.frame_count = 0 + self.communicate([]) + self.id2name = {} + + self.last_reward = None + self.communicate([{"$type": "destroy_all_objects"}]) + + def init_scene(self, setup: SceneSetup): + self.reset_scene() + + if self.log_path is not None: + logger = Logger(self.log_path) + self.add_ons.append(logger) + self.communicate([]) + # for obj in setup.objects: + # self.manager.add_object(obj) + if self.log_path is not None: + logger = Logger(self.log_path) + self.add_ons.append(logger) + self.communicate([]) + for commands in setup.commands_list: + filtered_commands = [] + for command in commands: + tp = command["$type"] + if tp == "terminate": + break + if tp[:4] == "send" or tp.find("avatar") != -1 or "avatar_id" in command: + continue + if "url" in command: + command["url"] = get_local_url(command["url"]) + if tp == "add_object": + name = command["name"] + idx = command["id"] + self.id2name[idx] = name + pos = TDWUtils.vector3_to_array(command["position"]) + resistence = setup.other["wind_resistence"][str(idx)] if str(idx) in setup.other["wind_resistence"] else 0 + self.manager.add_object(ObjectStatus(idx=idx, position=pos, resistence=resistence)) + filtered_commands.append(command) + self.communicate(filtered_commands) + + if not self.record_only: + if len(self.agents) == 0: + self.agents: List[WindAgent] = [] + for agent_pos in setup.agent_positions: + idx = self.get_unique_id() + self.agents.append( + WindAgent(replicant_id=idx, position=agent_pos, image_frequency=ImageFrequency.always)) + self.add_agent(idx, agent_pos) + self.add_ons.append(self.agents[-1]) + else: + assert (len(self.agents) == len(setup.agent_positions)) + for i in range(len(self.agents)): + idx = self.agents[i].replicant_id + self.agents[i].reset(position=setup.agent_positions[i]) + self.add_ons.append(self.agents[i]) + self.add_agent(idx, setup.agent_positions[i]) + + self.maps = [None] * len(self.agents) + + self.add_ons.append(self.manager) + self.containers = setup.containers + self.targets = setup.target_ids + self.target = setup.targets + if self.use_dino and not self.use_gt: + self.detector.set_targets(self.target) + self.target_ids = setup.target_ids + self.target_names = setup.target_names + self.target_id2category = setup.target_id2category + self.target_id2name = setup.target_id2name + self.finished = [] + + self.set_wind(np.array(setup.other["wind"])) + for idx in self.containers: + self.manager.settled.add(idx) + self.communicate([]) + + if self.image_capture_path != None: + pos = copy.deepcopy(setup.agent_positions[0]) + pos[1] = 8.0 + # theta = self.RNG.random() * 2 * np.pi + # pos[0] += np.cos(theta) * 2 + # pos[2] += np.sin(theta) * 2 + pos[0] += 1.0 + pos[2] -= 2.0 + + if self.record_only: + look_at = copy.deepcopy(setup.agent_positions[0]) + # look_at[1] += 3.0 + look_at[0] -= 3.0 + look_at[2] -= 6.0 + look_at = TDWUtils.array_to_vector3(look_at) + commands = [{"$type": "set_screen_size", "width": self.screen_size * 4, "height": self.screen_size * 4}, + {"$type": "set_target_framerate", "framerate": 30}] + else: + look_at = self.agents[0].replicant_id + commands = [{"$type": "set_screen_size", "width": self.screen_size, "height": self.screen_size}, + {"$type": "set_target_framerate", "framerate": 30}] + camera = ThirdPersonCamera(avatar_id="record", position=TDWUtils.array_to_vector3(pos), + look_at=look_at) + self.add_ons.extend([camera]) + self.communicate([]) + else: + commands = [{"$type": "set_screen_size", "width": self.screen_size, "height": self.screen_size}, + {"$type": "set_target_framerate", "framerate": 30}] + + if self.image_capture_path is not None: + commands.extend([{"$type": "set_pass_masks", "pass_masks": ["_img"], "avatar_id": "record"}, + {"$type": "send_images", "frequency": "always", "ids": ["record"]}]) + # if self.video_path is not None: + # self.capture = ImageCapture(path=self.video_path, avatar_ids=["a"]) + # self.add_ons.append(self.capture) + self.manager.prepare_segmentation_data() + self.origin_pos = [setup.agent_positions[0][0], setup.agent_positions[0][2]] + self.sem_map = Semantic_Mapping(device=None, screen_size=self.screen_size, map_size_h=self.map_size_h, map_size_v=self.map_size_v, grid_size=self.grid_size, origin_pos=self.origin_pos) + self.initialized = True + self.communicate(commands) + if not self.record_only: + self.communicate({"$type": "set_field_of_view", "field_of_view": 120.0, "avatar_id": str(self.agents[0].replicant_id)}) + + def next_key_frame(self, force_direction=None) -> Tuple[List[ActionStatus], List[int]]: + # print("next_key_frame") + agent_idx = 0 + action_slowdown = 0 + if force_direction is not None: + facing = self.agents[agent_idx].dynamic.transform.forward + force_direction = force_direction / np.linalg.norm(force_direction) + force_scale = (force_direction * (facing / np.linalg.norm(facing))).sum() + action_slowdown += (1 - force_scale) * self.constants.F_ON_AGENT + + self.action_slowdown += action_slowdown + if self.action_slowdown > 1: + self.action_slowdown -= 1 + tmp = self.agents[agent_idx].action + self.agents[agent_idx].action = None + self.communicate([]) + self.agents[agent_idx].action = tmp + + initial_status = [] + have_ongoing_action = False + for agent in self.agents: + if agent.action is not None: + initial_status.append(copy.deepcopy(agent.action.status)) + if initial_status[-1] == ActionStatus.ongoing: + have_ongoing_action = True + else: + initial_status.append(None) + if not have_ongoing_action: + self.communicate([]) + print("no ongoing action") + return initial_status, [] + num_step = 0 + while True: + num_step += 1 + if num_step > 10000: + raise RuntimeError("num_step > 10000") + self.communicate([]) + + changed = False + for i, agent in enumerate(self.agents): + if initial_status[i] is not None and agent.action.status != initial_status[i]: + changed = True + break + if changed: + break + + final_status = [] + changed_agents = [] + for i, agent in enumerate(self.agents): + final_status.append(copy.deepcopy(agent.action.status) if agent.action is not None else None) + if initial_status[i] != final_status[i]: + changed_agents.append(i) + + # self.communicate([]) + return final_status, changed_agents + + def do_action(self, agent_idx: int, action: str, params: Dict[str, Any] = dict()) -> None: + """ + See the WindAgent class for allowed actions and their parameters. + """ + return getattr(self.agents[agent_idx], action)(**params) + + def get_agent_status(self, idx) -> WindAgent: + return self.agents[idx] + + def find_nearest_object(self, agent_idx: int = 0, objects: List[int] = None): + current_position = self.agents[agent_idx].dynamic.transform.position + return self.manager.find_nearest_object(pos=current_position, objects=objects) + + def find_nearest_container(self, agent_idx: int = 0): + current_position = self.agents[agent_idx].dynamic.transform.position + return self.manager.find_nearest_object(pos=current_position, objects=self.containers) + + def replace_with_local_path(self, commands): + LOCAL_PATH_PREFIX = f"file://{os.getcwd()}/data/assets" + download_cmds = [] + for command in commands: + if 'url' in command and "amazonaws.com" in command['url']: + new_url = command['url'].split("/")[-1] + if not os.path.isfile(f"{os.getcwd()}/data/assets/{new_url}"): + download_cmds.append(f"wget -nc {command['url']}\n") + new_url = f"{LOCAL_PATH_PREFIX}/{new_url}" + command['url'] = new_url + for command in self.commands: + if 'url' in command and "amazonaws.com" in command['url']: + new_url = command['url'].split("/")[-1] + if not os.path.isfile(f"{os.getcwd()}/data/assets/{new_url}"): + download_cmds.append(f"wget -nc {command['url']}\n") + new_url = f"{LOCAL_PATH_PREFIX}/{new_url}" + command['url'] = new_url + for m in self.add_ons: + if not m.initialized: + add_on_commands = m.get_initialization_commands() + else: + add_on_commands = m.commands + for command in add_on_commands: + if 'url' in command and "amazonaws.com" in command['url']: + new_url = command['url'].split("/")[-1] + if not os.path.isfile(f"{os.getcwd()}/data/assets/{new_url}"): + download_cmds.append(f"wget -nc {command['url']}\n") + new_url = f"{LOCAL_PATH_PREFIX}/{new_url}" + command['url'] = new_url + if len(download_cmds) > 0: + fout = open(f"{os.getcwd()}/data/assets/download_assets.sh", "w") + for cmd in download_cmds: + fout.write(cmd) + print("Please run data/assets/download_assets.sh first!") + exit(0) + return commands + + def communicate(self, commands: Union[dict, List[dict]]) -> list: + """ + for each agent, if it is trying to grasp an object too far away, fail it + """ + # print(commands) + if self.use_local_resources: + commands = self.replace_with_local_path(commands) + if self.initialized: + # check for finished objects + self.frame_count += 1 + for idx in self.manager.settled: + if idx in self.targets and idx not in self.finished: + self.finished.append(idx) + # check agents + for agent in self.agents: + idx = agent.grasp_id() + if idx is None: + continue + dist = np.linalg.norm((agent.dynamic.transform.position - self.manager.objects[idx].position) * np.array([1, 0, 1])) + if dist > 3: + agent.fail_grasp() + resp = super().communicate(commands) + if self.image_capture_path != None: + for i in range(len(resp) - 1): + r_id = OutputData.get_data_type_id(resp[i]) + # Get Images output data. + if r_id == "imag": + images = Images(resp[i]) + # Determine which avatar captured the image. + if images.get_avatar_id() == "record": + # Save the image. + TDWUtils.save_images(images=images, filename=str(self.comm_counter), + output_directory=self.image_capture_path) + self.comm_counter += 1 + return resp + + @torch.no_grad() + def _obs(self, agent_idx: int = 0): + # print("get obs") + self.manager.prepare_segmentation_data() + self.communicate([]) + obs = dict() + """ + raw observation: RGBD, temperature + """ + rgb = self.agents[agent_idx].dynamic.get_pil_image() + id_image = np.array(self.agents[agent_idx].dynamic.get_pil_image("id")) + if self.reverse_observation: + rgb = np.flip(rgb, axis=0) + id_image = np.flip(id_image, axis=0) + + if self.use_gt: + seg_mask = self.manager.segm.get_seg_mask(id_image) + else: + rcnn_mask = self.detector.inference(np.array(rgb)) + seg_mask = self.manager.segm.get_seg_mask(np.array(id_image), + rcnn=rcnn_mask, + id_list=self.manager.id_list) + # print(seg_mask.shape, seg_mask.max()) + depth = TDWUtils.get_depth_values(self.agents[agent_idx].dynamic.images["depth"], width=self.screen_size, height=self.screen_size) + depth = np.flip(depth, axis=0) + + rgb = np.array(rgb).astype(np.float32).transpose((2, 0, 1)).astype(np.float32) * 1.0 / 255 + depth = depth.reshape((1, self.screen_size, self.screen_size)).astype(np.float32) + + obs["raw"] = dict( + rgb=rgb, + depth=depth, + seg_mask=seg_mask + ) + + """ + mapped observation + """ + camera_matrix = self.agents[agent_idx].dynamic.camera_matrix.reshape((4, 4)) + obs_concat = np.concatenate([rgb, depth], axis=0) + + sem = self.sem_map.forward(obs=obs_concat, id_map=seg_mask, camera_matrix=camera_matrix, maps_last=self.maps[agent_idx], + position=self.agents[agent_idx].dynamic.transform.position, record_mode=self.record_only, + targets=self.manager.get_renumbered_list(self.targets)) + obs["sem_map"] = dict(height=sem["height"].cpu().numpy(), + explored=sem["explored"].cpu().numpy(), + id=sem["id"].cpu().numpy(), + other=sem["other"].cpu().numpy() if sem["other"] is not None else None) + self.maps[agent_idx] = dict(height=sem["height"].cpu().numpy(), + explored=sem["explored"].cpu().numpy(), + id=sem["id"].cpu().numpy(), + other=sem["other"].cpu().numpy() if sem["other"] is not None else None) + # end = time.time() + # print("sem_map time=", end - start) + + """ + map of goal and agent + """ + agent_pos = self.sem_map.real_to_grid(self.agents[agent_idx].dynamic.transform.position) + target_poss = [self.sem_map.real_to_grid(self.manager.objects[idx].position) for idx in self.targets] + + goal_map = np.zeros((self.map_size_h, self.map_size_v)) + for (i, target_pos) in enumerate(target_poss): + if self.targets[i] in self.finished: + continue + if target_pos[0] < 0 or target_pos[0] >= self.map_size_h or target_pos[1] < 0 or target_pos[1] >= self.map_size_v: + continue + if not (sem["id"] == self.manager.get_renumbered_id(self.targets[i])).any(): + continue + goal_map[target_pos[0], target_pos[1]] = 1 + if agent_pos[0] > 0 and agent_pos[0] < self.map_size_h - 1 and agent_pos[1] > 0 and agent_pos[1] < self.map_size_v - 1: + goal_map[agent_pos[0], agent_pos[1]] = -2 + rad = self.agents[agent_idx].get_facing() + rad = int(rad / (np.math.pi / 4)) + if rad < 0: + rad += 8 + dx = list([1, 1, 0, -1, -1, -1, 0, 1])[rad] + dz = list([0, 1, 1, 1, 0, -1, -1, -1])[rad] + goal_map[agent_pos[0] + dx][agent_pos[1] + dz] = -1 + obs["goal_map"] = goal_map + + RL_obs = np.zeros((4, self.map_size_h, self.map_size_v)) + RL_obs[0] = obs["sem_map"]["height"] + RL_obs[1] = obs["sem_map"]["explored"] + RL_obs[2] = obs["sem_map"]["id"] + RL_obs[3] = goal_map + obs["RL"] = RL_obs + return obs + + def _info(self): + info = dict() + info['vector'] = np.zeros(8) # for consistency + info['agent_positions'] = [agent.dynamic.transform.position for agent in self.agents] + info['targets'] = self.manager.get_renumbered_list(self.targets) + info['containers'] = self.manager.get_renumbered_list(self.containers) + info['finished targets'] = self.manager.get_renumbered_list(self.finished) + info['camera_matrices'] = [agent.dynamic.camera_matrix.reshape((4, 4)) for agent in self.agents] + info['wind'] = self.manager.wind_v + info['sr'] = f"{len(self.finished)}/{len(self.targets)}" + return info + + # this is a toy reward function + def _reward(self, agent_idx = 0): + if self.last_reward is None: + self.last_reward = 0 + + reward = 0 + + agent_pos = self.agents[agent_idx].dynamic.transform.position + nearest_target = self.manager.find_nearest_object(agent_pos, self.targets) + nearest_container = self.manager.find_nearest_object(agent_pos, self.containers) + + reward += len(self.finished) * 20 + if Arm.left in self.agents[0].dynamic.held_objects: + dist = np.linalg.norm(agent_pos - self.manager.objects[nearest_container].position) + reward -= dist * 10 + else: + dist = np.linalg.norm(agent_pos - self.manager.objects[nearest_target].position) + reward -= dist * 10 + ret = reward - self.last_reward - 0.1 + self.last_reward = reward + return ret + + def _done(self, agent_idx: int = 0): + if len(self.finished) == len(self.targets): + return True + return False \ No newline at end of file diff --git a/easi/tasks/hazard/vendor/HAZARD/policy/__init__.py b/easi/tasks/hazard/vendor/HAZARD/policy/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/easi/tasks/hazard/vendor/HAZARD/policy/astar.py b/easi/tasks/hazard/vendor/HAZARD/policy/astar.py new file mode 100644 index 0000000..8de0739 --- /dev/null +++ b/easi/tasks/hazard/vendor/HAZARD/policy/astar.py @@ -0,0 +1,58 @@ +import pyastar2d +from typing import List +import numpy as np + +def check_mergeable(p1, p2, p3): + d1 = p2[0] - p1[0], p2[1] - p1[1] + d2 = p3[0] - p2[0], p3[1] - p2[1] + # if d1 is too long, don't merge + if d1[0] * d1[0] + d1[1] * d1[1] > 256: + return False + if d1[0] * d2[1] == d1[1] * d2[0]: + return True + return False + +def path_cleanup(path: List[List[int]]) -> List[List[int]]: + """ + if two consecutive movements are in the same direction, combine them into one + """ + q = [] + for i in range(len(path)): + while len(q) >= 2 and check_mergeable(q[-2], q[-1], path[i]): + q.pop() + q.append(path[i]) + return q + +def get_astar_path(weight, origin, destination): + if origin[0] < 0 or origin[0] >= weight.shape[0] or origin[1] < 0 or origin[1] >= weight.shape[1]: + return None + if destination[0] < 0 or destination[0] >= weight.shape[0] or destination[1] < 0 or destination[1] >= weight.shape[1]: + return None + path = pyastar2d.astar_path(weight, origin, destination) + path = path_cleanup(path) + return path + +# sem_map: [channel, height, width] +def get_astar_weight(sem_map, origin, destination): + # unexplored: 5, explored: 1 + # height > 0.1: += 10 + # height > 1: += 1000 + explored = sem_map["explored"] + height = sem_map["height"] + w, h = explored.shape + weight = np.ones((w, h)) + weight[explored == 0] = 30 + + # exp(x * 0.) * y= 10 + # exp(x * 1.6) * y = 1000 + # x = 3, y = 7 + weight[height > 0.5] += np.exp(height[height > 0.5] * 3) * 7 + weight[height > 0.1] += 10 + # weight[height > 0.5] += 50 + # weight[height > 1.6] += 1000 + # for each position, if it is near an obstacle, increase its weight by 50 + conv_weight = np.zeros((w, h)) + for i in range(-2, 3): + for j in range(-2, 3): + conv_weight[max(0, i): min(w, w+i), max(0, j): min(h, h+j)] += weight[max(0, -i): min(w, w-i), max(0, -j): min(h, h-j)] * 1.0 / (abs(i) + abs(j) + 1) + return conv_weight.astype(np.float32) diff --git a/easi/tasks/hazard/vendor/HAZARD/policy/env_actions.py b/easi/tasks/hazard/vendor/HAZARD/policy/env_actions.py new file mode 100644 index 0000000..a706ce6 --- /dev/null +++ b/easi/tasks/hazard/vendor/HAZARD/policy/env_actions.py @@ -0,0 +1,370 @@ +import sys +import os +PATH = os.path.dirname(os.path.abspath(__file__)) +while os.path.basename(PATH) != "HAZARD": + PATH = os.path.dirname(PATH) +sys.path.append(PATH) + +from typing import List, Union, Optional +import numpy as np +from tdw.replicant.ik_plans.ik_plan_type import IkPlanType +from tdw.replicant.action_status import ActionStatus +from HAZARD.policy.astar import get_astar_path, get_astar_weight +from tdw.tdw_utils import TDWUtils +from tdw.replicant.arm import Arm + +""" +All actions here are single-agent +""" + +def visualize_obs(env, obs, suffix="0", save_dir=os.path.join(PATH, "logs"), astar_path=None): + if obs is None: + obs = env.controller._obs() + sem_map = obs["sem_map"] + rgb = obs["raw"]["rgb"] + """ + output an image of the semantic map + red: agent + white: unexplored + grey: explored and unoccupied + black: occupied + """ + explored = sem_map["explored"] + height = sem_map["height"] + ID = sem_map["id"] + w, h = explored.shape + + img = np.zeros((w, h, 3), dtype=np.uint8) + obj = np.zeros((w, h, 3), dtype=np.uint8) + obj[ID > 0.5] = [0, 0, 0] + obj[ID < 0.5] = [255, 255, 255] + height_img = np.copy(height) + height_img.astype(np.uint8) + height_img = np.expand_dims(height_img, axis=2) + height_img = np.repeat(height_img, 3, axis=2) + height_img *= int(255 / height.max()) + height_img[height <= 0] = [0, 0, 0] + img[explored == 0] = [255, 255, 255] # this color is white + img[explored == 1] = [200, 200, 200] # this color is grey + img = img - np.reshape(200 * np.minimum(height / 2, 1.0) * (height > 0.3), (w, h, 1)) + + pos = env.controller.agents[0].dynamic.transform.position + offset = env.controller.sem_map.map_offset + grid_size = env.controller.sem_map.grid_size + pos = [int(pos[0] // grid_size + offset[0]), int(pos[2] // grid_size + offset[1])] + # set nearby pixels to red + for i in range(-1, 2): + for j in range(-1, 2): + img[pos[0] + i, pos[1] + j] = np.array([255, 0, 0]) + # mark path + if astar_path is not None: + for p in astar_path: + img[p[0], p[1]] = np.array([0, 255, 0]) + + print(height.max()) + # save the image + print("rgb", rgb.shape) + import cv2 + # rgb to bgr + rgb = rgb[[2, 1, 0], :, :] + img = img[:, :, [2, 1, 0]] + obj = obj[:, :, [2, 1, 0]] + height_img = height_img[:, :, [2, 1, 0]] + explored = np.copy(explored) + explored *= 255 + explored.astype(np.uint8) + explored = np.expand_dims(explored, axis=2) + explored = np.repeat(explored, 3, axis=2) + explored = explored[:, :, [2, 1, 0]] + cv2.imwrite(os.path.join(save_dir, f"sem_map_{suffix}.png"), img) + cv2.imwrite(os.path.join(save_dir, f"rgb_{suffix}.png"), rgb.transpose(1, 2, 0) * 255) + cv2.imwrite(os.path.join(save_dir, f"obj_map_{suffix}.png"), obj) + cv2.imwrite(os.path.join(save_dir, f"height_map_{suffix}.png"), height_img) + cv2.imwrite(os.path.join(save_dir, f"explore_{suffix}.png"), explored) + +"""this action is environment independent""" +# def agent_walk_to(env: WindEnv, target: Union[int, np.ndarray, List], max_steps=100, reset_arms: bool = False, arrived_at=1.0): +def agent_walk_to(env, target: Union[int, np.ndarray, List], max_steps=100, reset_arms: bool = False, arrived_at=1.0, + task=None, effect_on_agents=False, record_mode=False): + # visualize_obs(env, None, suffix="0") + start_frame = env.controller.frame_count + if record_mode and task != "wind": + env.controller.agents[0].collision_detection.avoid = False + env.controller.agents[0].collision_detection.objects = False + + while True: + agent_pos = env.controller.agents[0].dynamic.transform.position + target_pos = env.controller.manager.objects[target].position if isinstance(target, int) else np.array(target) + if np.linalg.norm(agent_pos[[0, 2]] - target_pos[[0, 2]]) < arrived_at: + env.controller.agents[0].collision_detection.avoid = True + env.controller.agents[0].collision_detection.objects = True + return True, "success" + + if env.controller.frame_count - start_frame > max_steps: + env.controller.agents[0].collision_detection.avoid = True + env.controller.agents[0].collision_detection.objects = True + return False, "max steps reached" + + agent_pos = env.controller.real_to_grid(agent_pos) + target_pos = env.controller.real_to_grid(target_pos) + obs = env.controller._obs() + sem_map = obs["sem_map"] + if isinstance(target, int) and not np.any(sem_map["id"] == env.controller.manager.id_renumbering[target]): + env.controller.agents[0].collision_detection.avoid = True + env.controller.agents[0].collision_detection.objects = True + return False, "target not in vision or memory" + weight = get_astar_weight(sem_map=sem_map, origin=agent_pos, destination=target_pos) + path = get_astar_path(weight=weight, origin=agent_pos, destination=target_pos) + # visualize_obs(env, obs, suffix=str(env.controller.frame_count), astar_path=path) + # walk to first point + if path is None or len(path) <= 1: + env.controller.agents[0].collision_detection.avoid = True + env.controller.agents[0].collision_detection.objects = True + return False, "don't know, maybe out of bounds" + env.controller.do_action(agent_idx=0, action="move_to", params={"target": TDWUtils.array_to_vector3(env.controller.grid_to_real(path[1])), + "reset_arms": reset_arms, + "arrived_at": 0.05 if record_mode else 0.5}) + if not effect_on_agents or task == "fire": + env.controller.next_key_frame(force_direction=None) + elif task == "wind": + wind_v = env.controller.manager.wind_v + env.controller.next_key_frame(force_direction=wind_v) + else: + assert env.controller.manager.flood_manager.source_from == 'x_max' + env.controller.next_key_frame(force_direction=np.array([-1, 0, 0])) + + +def low_level_action(env, action, effect_on_agents=False, task=None, **kwargs): + assert action in ['move_by', 'turn_by', 'turn_to', 'reach_for'] + forbidden_params = ['max_distance', 'duration', 'scale_duration', 'arrived_at'] + for param in forbidden_params: + assert param not in kwargs + if 'target' in kwargs and type(kwargs['target']) == int: + kwargs['target'] = env.controller.manager.id_renumbering[kwargs['target']] + getattr(env.controller.agents[0], action)(**kwargs) + if action != "move_by": + effect_on_agents = False + if not effect_on_agents or task == "fire": + env.controller.next_key_frame() + elif task == "wind": + wind_v = env.controller.manager.wind_v + env.controller.next_key_frame(force_direction=wind_v) + else: + assert env.controller.manager.flood_manager.source_from == 'x_max' + env.controller.next_key_frame(force_direction=np.array([-1, 0, 0])) + return True, "success" + + +def agent_walk_to_single_step(env, target: Union[int, np.ndarray, List], reset_arms: bool = False, arrived_at=1.0, + effect_on_agents=False, task=None, record_mode=False): + agent_pos = env.controller.agents[0].dynamic.transform.position + target_pos = env.controller.manager.objects[target].position if isinstance(target, int) else np.array(target) + # print(target, env.controller.manager.objects[target].position, type(target)) + if np.linalg.norm(agent_pos - target_pos) < arrived_at: + return True, "success" + agent_pos = env.controller.real_to_grid(agent_pos) + target_pos = env.controller.real_to_grid(target_pos) + obs = env.controller._obs() + sem_map = obs["sem_map"] + if isinstance(target, int) and not np.any(sem_map["id"] == env.controller.manager.get_renumbered_id(target)): + return False, "target not in vision or memory" + + weight = get_astar_weight(sem_map=sem_map, origin=agent_pos, destination=target_pos) + path = get_astar_path(weight=weight, origin=agent_pos, destination=target_pos) + # walk to first point + if path is None or len(path) <= 1: + return False, "don't know, maybe out of bounds" + env.controller.do_action(agent_idx=0, action="move_to", params={"target": TDWUtils.array_to_vector3(env.controller.grid_to_real(path[1])), + "reset_arms": reset_arms, + "arrived_at": 0.05 if record_mode else 0.5}) + + if not effect_on_agents or task == "fire": + env.controller.next_key_frame() + elif task == "wind": + wind_v = env.controller.manager.wind_v + env.controller.next_key_frame(force_direction=wind_v) + else: + assert env.controller.manager.flood_manager.source_from == 'x_max' + env.controller.next_key_frame(force_direction=np.array([-1, 0, 0])) + return True, "ongoing" + +"""this action is environment independent""" +def agent_pickup(env, target: int, env_type: str = "what?"): + if target not in env.controller.target_ids: + return False, f"can only pick up target objects" + + arm = [Arm.left, Arm.right] if env_type == "wind" else Arm.right + + above_pos = env.controller.manager.objects[target].top() + above_pos[1] += 100.0 + target_pos = env.controller.manager.objects[target].center() + + env.controller.do_action(agent_idx=0, action="turn_to", params={"target": target}) + status = env.controller.next_key_frame()[0][0] + if status != ActionStatus.success: + print("failed to turn to target") + input() + + env.controller.do_action(agent_idx=0, action="reach_for", params={"target": TDWUtils.array_to_vector3(target_pos), + "arm": [Arm.left, Arm.right] if env_type == "wind" else Arm.right, + "arrived_at": 0.02, + "max_distance": 2.5, + "plan": IkPlanType.vertical_horizontal}) + status = env.controller.next_key_frame()[0][0] + reach_success = (status == ActionStatus.success) + + # if not reach_success: + # env.controller.do_action(agent_idx=0, action="reset_arm", + # params={"arm": [Arm.left, Arm.right] if env_type == "wind" else Arm.right}) + # env.controller.next_key_frame() + # env.controller.do_action(agent_idx=0, action="turn_by", params={"angle": random.randint(30, 330)}) + # env.controller.next_key_frame() + # return False, f"cannot reach for this object, maybe too far" + + env.controller.communicate([{"$type": "set_kinematic_state", "id": target, "is_kinematic": False, "use_gravity": False}]) + if target in env.controller.other_containers: + env.controller.communicate( + {"$type": "teleport_object", "id": target, "position": TDWUtils.array_to_vector3(above_pos)}) + + env.controller.do_action(agent_idx=0, action="grasp", params={"target": target, + "arm": Arm.right, + "angle": None, + "axis": None,}) + status = env.controller.next_key_frame()[0][0] + + env.controller.do_action(agent_idx=0, action="reset_arm", params={"arm": [Arm.left, Arm.right] if env_type == "wind" else Arm.right}) + env.controller.next_key_frame() + + if status == ActionStatus.success: + return True, f"success, reach for {'success' if reach_success else 'failed, may cause problems'}" + elif status == ActionStatus.cannot_grasp: + return False, "cannot grasp, maybe too far" + else: + return False, "failed to grasp" + +"""turn around to find new targets""" +def agent_explore(env): + TURN_TIMES = 12 + for i in range(TURN_TIMES): + env.controller.do_action(agent_idx=0, action="turn_by", params={"angle": 360.0 / TURN_TIMES}) + status = env.controller.next_key_frame()[0][0] + env.controller._obs() + if status != ActionStatus.success: + return False, 'can not turn around at this time' + return True, 'success' + +def agent_drop(env, container: Optional[int]=None, env_type: str = "what?"): + arm = Arm.right + try: + grasp_id = env.controller.agents[0].dynamic.held_objects[arm] + except: + return False, "not holding an object" + + if hasattr(env.controller, "container_id"): + assert Arm.left in env.controller.agents[0].dynamic.held_objects and env.controller.agents[0].dynamic.held_objects[Arm.left] == env.controller.container_id + container = env.controller.container_id + + if (env_type == "flood" or env_type == "fire"): + top = env.controller.manager.objects[container].top() + top = top + np.array([0, 0.2, 0]) + + env.controller.do_action(agent_idx=0, action="turn_to", params={"target": TDWUtils.array_to_vector3(top)}) + status = env.controller.next_key_frame()[0][0] + if status != ActionStatus.success: + print("failed to turn to target") + input() + + top = env.controller.manager.objects[container].top() + above = top + np.array([0, 0.2, 0]) + + env.controller.do_action(agent_idx=0, action="reach_for", params={"target": TDWUtils.array_to_vector3(above), + "absolute": True, + "offhand_follows": False, + "arm": Arm.left, + "from_held": True, + "held_point": "top", + "max_distance": 1.0, + "arrived_at": 0.05, + "plan": IkPlanType.vertical_horizontal}) + status = env.controller.next_key_frame()[0][0] + env.controller.do_action(agent_idx=0, action="reach_for", params={"target": TDWUtils.array_to_vector3(top), + "absolute": True, + "offhand_follows": False, + "arm": Arm.right, + "from_held": True, + "held_point": "top", + "max_distance": 1.0, + "arrived_at": 0.05, + "plan": IkPlanType.vertical_horizontal}) + status = env.controller.next_key_frame()[0][0] + + env.controller.do_action(agent_idx=0, action="drop", params={"arm": arm, "max_num_frames": 20}) + status = env.controller.next_key_frame()[0][0] + # env.controller.communicate([{"$type": "destroy_object", "id": grasp_id}]) + # instead of destroying it, teleport it to [100, 0, 0] and make it kinematic + destroy_commands = [] + destroy_commands.append({"$type": "teleport_object", "id": grasp_id, "position": {"x": 100, "y": 20, "z": 0}}) + destroy_commands.append({"$type": "set_kinematic_state", "id": grasp_id, "is_kinematic": True, "use_gravity": False}) + if hasattr(env.controller, "finished") and grasp_id not in env.controller.finished: + env.controller.finished.append(grasp_id) + if env_type == "fire": + env.controller.manager.objects[grasp_id].temperature_threshold = 4000 + env.controller.communicate(destroy_commands) + # env.controller.do_action(agent_idx=0, action="reset_arm", + # params={"arm": [Arm.left, Arm.right] if env_type == "wind" else Arm.right}) + env.controller.do_action(agent_idx=0, action="reset_arm", params={"arm": [Arm.left, Arm.right]}) + env.controller.next_key_frame() + return True, "successfully drop and put away" + + if container is None: + env.controller.do_action(agent_idx=0, action="drop", params={"arm": arm, "max_num_frames": 20}) + status = env.controller.next_key_frame()[0][0] + env.controller.do_action(agent_idx=0, action="reset_arm", + params={"arm": [Arm.left, Arm.right] if env_type == "wind" else Arm.right}) + env.controller.next_key_frame() + if status == ActionStatus.success: + return True, "drop success" + elif status == ActionStatus.still_dropping: + return True, "drop success but still dropping" + else: + return False, "I don't know what happened but the drop failed" + + env.controller.do_action(agent_idx=0, action="turn_to", params={"target": container}) + env.controller.next_key_frame() # this action cannot fail (seems like) + + top = env.controller.manager.objects[container].top() + top = top + np.array([0, 0.2, 0]) + env.controller.do_action(agent_idx=0, action="reach_for", params={"target": TDWUtils.array_to_vector3(top), + "absolute": True, + "offhand_follows": False, + "arm": Arm.right, + "from_held": True, + "held_point": "top", + "max_distance": 2.5, + "arrived_at": 0.05, + "plan": IkPlanType.vertical_horizontal}) + status = env.controller.next_key_frame()[0][0] + reach_success = True + if status != ActionStatus.success: + env.controller.do_action(agent_idx=0, action="reset_arm", + params={"arm": [Arm.left, Arm.right] if env_type == "wind" else Arm.right}) + env.controller.next_key_frame() + reach_success = False + if env_type == "wind": return False, "failed to reach" + # if status != ActionStatus.success: + # return False, "failed to reach" + + if env_type == "wind": + env.controller.manager.settled.add(grasp_id) + + env.controller.do_action(agent_idx=0, action="drop", params={"arm": arm, "max_num_frames": 20}) + status = env.controller.next_key_frame()[0][0] + env.controller.do_action(agent_idx=0, action="reset_arm", params={"arm": [Arm.left, Arm.right] if env_type == "wind" else Arm.right}) + env.controller.next_key_frame() + + if status == ActionStatus.success: + return True, f"drop success, reach for {'success' if reach_success else 'failed, may cause problems'}" + elif status == ActionStatus.still_dropping: + return True, f"drop success but still dropping, reach for {'success' if reach_success else 'failed, may cause problems'}" + else: + return False, "I don't not what happened but the drop failed" \ No newline at end of file diff --git a/easi/tasks/hazard/vendor/HAZARD/utils/__init__.py b/easi/tasks/hazard/vendor/HAZARD/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/easi/tasks/hazard/vendor/HAZARD/utils/distributions.py b/easi/tasks/hazard/vendor/HAZARD/utils/distributions.py new file mode 100644 index 0000000..130702d --- /dev/null +++ b/easi/tasks/hazard/vendor/HAZARD/utils/distributions.py @@ -0,0 +1,61 @@ +# The following code is largely borrowed from: +# https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail/blob/master/a2c_ppo_acktr/distributions.py + +import torch +import torch.nn as nn + +from HAZARD.utils.model_utils import AddBias + +""" +Modify standard PyTorch distributions so they are compatible with this code. +""" + +FixedCategorical = torch.distributions.Categorical + +old_sample = FixedCategorical.sample +FixedCategorical.sample = lambda self: old_sample(self) + +log_prob_cat = FixedCategorical.log_prob +FixedCategorical.log_probs = lambda self, actions: \ + log_prob_cat(self, actions.squeeze(-1)) +FixedCategorical.mode = lambda self: self.probs.argmax(dim=1, keepdim=True) + +FixedNormal = torch.distributions.Normal +log_prob_normal = FixedNormal.log_prob +FixedNormal.log_probs = lambda self, actions: \ + log_prob_normal(self, actions).sum(-1, keepdim=False) + +entropy = FixedNormal.entropy +FixedNormal.entropy = lambda self: entropy(self).sum(-1) + +FixedNormal.mode = lambda self: self.mean + + +class Categorical(nn.Module): + + def __init__(self, num_inputs, num_outputs): + super(Categorical, self).__init__() + self.linear = nn.Linear(num_inputs, num_outputs) + + def forward(self, x): + x = self.linear(x) + return FixedCategorical(logits=x) + + +class DiagGaussian(nn.Module): + + def __init__(self, num_inputs, num_outputs): + super(DiagGaussian, self).__init__() + + self.fc_mean = nn.Linear(num_inputs, num_outputs) + self.logstd = AddBias(torch.zeros(num_outputs)) + + def forward(self, x): + action_mean = self.fc_mean(x) + + zeros = torch.zeros(action_mean.size()) + if x.is_cuda: + zeros = zeros.cuda() + + action_logstd = self.logstd(zeros) + return FixedNormal(action_mean, action_logstd.exp()) diff --git a/easi/tasks/hazard/vendor/HAZARD/utils/local_asset.py b/easi/tasks/hazard/vendor/HAZARD/utils/local_asset.py new file mode 100644 index 0000000..2347a3c --- /dev/null +++ b/easi/tasks/hazard/vendor/HAZARD/utils/local_asset.py @@ -0,0 +1,23 @@ +PRE_LOADED_PATH = "../assets" + +# get system name +import platform +system_name = platform.system() +if system_name == "Windows": + system_name = "windows" +elif system_name == "Linux": + system_name = "linux" +import os +def get_local_url(url): + if url.find(system_name) == -1: + other_name = "linux" if system_name == "windows" else "windows" + url = url.replace(other_name, system_name) + url = url.replace("private", "public") + filedir = url[url.find(".com/") + 5:] + if os.path.exists(os.path.join(PRE_LOADED_PATH, filedir)): + return "file://" + str(os.path.join(PRE_LOADED_PATH, filedir)) + else: + return url + +if __name__ == "__main__": + print(get_local_url("https://tdw-public.s3.amazonaws.com/scenes/windows/2020.3/floorplan_1a")) \ No newline at end of file diff --git a/easi/tasks/hazard/vendor/HAZARD/utils/model.py b/easi/tasks/hazard/vendor/HAZARD/utils/model.py new file mode 100644 index 0000000..0943683 --- /dev/null +++ b/easi/tasks/hazard/vendor/HAZARD/utils/model.py @@ -0,0 +1,337 @@ +import torch +import torch.nn as nn +from typing import List, Optional +import numpy as np + +from HAZARD.utils.distributions import Categorical, DiagGaussian +from HAZARD.utils.model_utils import Flatten, NNBase + +class Goal_Oriented_Semantic_Policy(NNBase): + + def __init__(self, input_shape, recurrent=False, hidden_size=512, + num_sem_categories=16): + super(Goal_Oriented_Semantic_Policy, self).__init__( + recurrent, hidden_size, hidden_size) + + out_size = int(input_shape[1] / 16.) * int(input_shape[2] / 16.) + + self.main = nn.Sequential( + nn.MaxPool2d(2), + nn.Conv2d(num_sem_categories + 8, 32, 3, stride=1, padding=1), + nn.ReLU(), + nn.MaxPool2d(2), + nn.Conv2d(32, 64, 3, stride=1, padding=1), + nn.ReLU(), + nn.MaxPool2d(2), + nn.Conv2d(64, 128, 3, stride=1, padding=1), + nn.ReLU(), + nn.MaxPool2d(2), + nn.Conv2d(128, 64, 3, stride=1, padding=1), + nn.ReLU(), + nn.Conv2d(64, 32, 3, stride=1, padding=1), + nn.ReLU(), + Flatten() + ) + + self.linear1 = nn.Linear(out_size * 32 + 8 * 2, hidden_size) + self.linear2 = nn.Linear(hidden_size, 256) + self.critic_linear = nn.Linear(256, 1) + self.orientation_emb = nn.Embedding(72, 8) + self.goal_emb = nn.Embedding(num_sem_categories, 8) + self.train() + + def forward(self, inputs, rnn_hxs, masks, extras): + x = self.main(inputs) + orientation_emb = self.orientation_emb(extras[:, 0]) + goal_emb = self.goal_emb(extras[:, 1]) + + x = torch.cat((x, orientation_emb, goal_emb), 1) + + x = nn.ReLU()(self.linear1(x)) + if self.is_recurrent: + x, rnn_hxs = self._forward_gru(x, rnn_hxs, masks) + + x = nn.ReLU()(self.linear2(x)) + + return self.critic_linear(x).squeeze(-1), x, rnn_hxs + + +# https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail/blob/master/a2c_ppo_acktr/model.py#L15 +class RL_Policy(nn.Module): + + def __init__(self, obs_shape, action_space, model_type=0, + base_kwargs=None): + + super(RL_Policy, self).__init__() + if base_kwargs is None: + base_kwargs = {} + + if model_type == 1: + self.network = Goal_Oriented_Semantic_Policy( + obs_shape, **base_kwargs) + else: + raise NotImplementedError + + if action_space.__class__.__name__ == "Discrete": + num_outputs = action_space.n + self.dist = Categorical(self.network.output_size, num_outputs) + elif action_space.__class__.__name__ == "Box": + num_outputs = action_space.shape[0] + self.dist = DiagGaussian(self.network.output_size, num_outputs) + else: + raise NotImplementedError + + self.model_type = model_type + + @property + def is_recurrent(self): + return self.network.is_recurrent + + @property + def rec_state_size(self): + """Size of rnn_hx.""" + return self.network.rec_state_size + + def forward(self, inputs, rnn_hxs, masks, extras): + if extras is None: + return self.network(inputs, rnn_hxs, masks) + else: + return self.network(inputs, rnn_hxs, masks, extras) + + def act(self, inputs, rnn_hxs, masks, extras=None, deterministic=False): + + value, actor_features, rnn_hxs = self(inputs, rnn_hxs, masks, extras) + dist = self.dist(actor_features) + + if deterministic: + action = dist.mode() + else: + action = dist.sample() + + action_log_probs = dist.log_probs(action) + + return value, action, action_log_probs, rnn_hxs + + def get_value(self, inputs, rnn_hxs, masks, extras=None): + value, _, _ = self(inputs, rnn_hxs, masks, extras) + return value + + def evaluate_actions(self, inputs, rnn_hxs, masks, action, extras=None): + + value, actor_features, rnn_hxs = self(inputs, rnn_hxs, masks, extras) + dist = self.dist(actor_features) + + action_log_probs = dist.log_probs(action) + dist_entropy = dist.entropy().mean() + + return value, action_log_probs, dist_entropy, rnn_hxs + + +""" +Same utility functions as in TDWUtils, but: +- use torch instead of numpy +- support batched inputs +""" + +class PointCloud: + __WIDTH: int = -1 + __HEIGHT: int = -1 + @staticmethod + def get(depth:torch.Tensor, camera_matrix: torch.Tensor, vfov: float = 120.0, near_plane: float = 0.1, far_plane: float = 100, device = torch.device("cpu")) -> torch.Tensor: + if isinstance(camera_matrix, tuple): + camera_matrix = torch.Tensor(camera_matrix) + if len(depth.shape) == 2: + depth = depth.unsqueeze(0) + if len(camera_matrix.shape) == 2: + camera_matrix = camera_matrix.unsqueeze(0) + camera_matrix = torch.linalg.inv(camera_matrix.reshape((4, 4))) + + # Different from real-world camera coordinate system. + # OpenGL uses negative z axis as the camera front direction. + # x axes are same, hence y axis is reversed as well. + # Source: https://learnopengl.com/Getting-started/Camera + rot = torch.Tensor([[1, 0, 0, 0], + [0, -1, 0, 0], + [0, 0, -1, 0], + [0, 0, 0, 1]]).to(device) + camera_matrix = torch.matmul(camera_matrix, rot) + + bs, H, W = depth.shape + # Cache some calculations we'll need to use every time. + if PointCloud.__HEIGHT != H or PointCloud.__WIDTH != W: + PointCloud.__HEIGHT = H + PointCloud.__WIDTH = W + + img_pixs = np.mgrid[0: H, 0: W].reshape(2, -1) + # Swap (v, u) into (u, v). + img_pixs[[0, 1], :] = img_pixs[[1, 0], :] + img_pix_ones = np.concatenate((img_pixs, np.ones((1, img_pixs.shape[1])))) + + # Calculate the intrinsic matrix from vertical_fov. + # Motice that hfov and vfov are different if height != width + # We can also get the intrinsic matrix from opengl's perspective matrix. + # http://kgeorge.github.io/2014/03/08/calculating-opengl-perspective-matrix-from-opencv-intrinsic-matrix + vfov = vfov / 180.0 * np.pi + tan_half_vfov = np.tan(vfov / 2.0) + tan_half_hfov = tan_half_vfov * PointCloud.__WIDTH / float(PointCloud.__HEIGHT) + fx = PointCloud.__WIDTH / 2.0 / tan_half_hfov # focal length in pixel space + fy = PointCloud.__HEIGHT / 2.0 / tan_half_vfov + intrinsics = np.array([[fx, 0, PointCloud.__WIDTH / 2.0], + [0, fy, PointCloud.__HEIGHT / 2.0], + [0, 0, 1]]) + img_inv = np.linalg.inv(intrinsics[:3, :3]) + PointCloud.__CAM_TO_IMG_MAT = np.dot(img_inv, img_pix_ones) + # PointCloud.__CAM_TO_IMG_MAT = PointCloud.__CAM_TO_IMG_MAT.reshape((1, ) + PointCloud.__CAM_TO_IMG_MAT.shape) + + if isinstance(PointCloud.__CAM_TO_IMG_MAT, np.ndarray): + PointCloud.__CAM_TO_IMG_MAT = torch.from_numpy(PointCloud.__CAM_TO_IMG_MAT).float().to(device) + if PointCloud.__CAM_TO_IMG_MAT.device != device: + PointCloud.__CAM_TO_IMG_MAT = PointCloud.__CAM_TO_IMG_MAT.to(device) + + points_in_cam = torch.multiply(PointCloud.__CAM_TO_IMG_MAT.reshape(3, H * W), depth.reshape(bs, 1, -1)) + points_in_cam = torch.cat((points_in_cam, torch.ones((bs, 1) + points_in_cam.shape[2:], device=device)), dim=1) + points_in_world = torch.matmul(camera_matrix, points_in_cam) + points_in_world = points_in_world[:, :3, :].reshape(bs, 3, H, W) + points_in_cam = points_in_cam[:, :3, :].reshape(bs, 3, H, W) + return points_in_world + +""" +obs: (bs, c, h, w); channels are R, G, B, D, other channels +map: (bs, map_size_h, map_size_v); channels are exp, all, id + +be careful. map_size_h and map_size_v are not the real map sizes but the number of grids. +""" + + +class Semantic_Mapping(nn.Module): + heights = [0.0, 0.5, 1.0, 1.7] + def __init__(self, device=None, screen_size=128, map_size_h=64, map_size_v=64, grid_size=0.25, origin_pos=[0.0, 0.0]): + super().__init__() + # self.device = device if device is not None else (torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")) + self.device = torch.device("cpu") + self.screen_w = screen_size + self.screen_h = screen_size + self.map_size_h = map_size_h + self.map_size_v = map_size_v + self.grid_size = grid_size + self.map_offset: List[int] = [self.map_size_h // 2, self.map_size_v // 2] # the position of (0, 0) + self.map_offset[0] -= int(origin_pos[0] / self.grid_size + 0.5) + self.map_offset[1] -= int(origin_pos[1] / self.grid_size + 0.5) + self.origin_pos = origin_pos + + def grid_to_real(self, grid_pos): + if not isinstance(grid_pos, list): + grid_pos = grid_pos.tolist() + return [(grid_pos[0] - self.map_offset[0]) * self.grid_size, 0, (grid_pos[1] - self.map_offset[1]) * self.grid_size] + def real_to_grid(self, real_pos): + if not isinstance(real_pos, list): + real_pos = real_pos.tolist() + if len(real_pos) > 2: + real_pos = [real_pos[0], real_pos[2]] + return [int(real_pos[0] / self.grid_size + self.map_offset[0] + 0.5), int(real_pos[1] / self.grid_size + self.map_offset[1] + 0.5)] + + def forward(self, obs, id_map, camera_matrix, maps_last, position: Optional[np.ndarray]=None, + targets: Optional[List[int]]=None, record_mode=False): + """ batched input on default """ + if not isinstance(camera_matrix, torch.Tensor): + camera_matrix = torch.Tensor(camera_matrix).to(self.device) + if not isinstance(obs, torch.Tensor): + obs = torch.Tensor(obs).to(self.device) + if not isinstance(id_map, torch.Tensor): + id_map = torch.tensor(id_map, dtype=torch.int64, device=self.device) + + if maps_last is None: + maps_last = dict() + maps_last['explored'] = torch.zeros((self.map_size_h, self.map_size_v), device=self.device, dtype=torch.int64) + maps_last['height'] = torch.zeros((self.map_size_h, self.map_size_v), device=self.device) + maps_last['id'] = torch.zeros((self.map_size_h, self.map_size_v), device=self.device, dtype=torch.int64) + if obs.shape[0] > 4: + maps_last['other'] = torch.zeros((obs.shape[0] - 4, self.map_size_h, self.map_size_v), device=self.device) + elif not isinstance(maps_last["explored"], torch.Tensor): + nmap = dict() + nmap["explored"] = torch.tensor(maps_last["explored"], device=self.device, dtype=torch.int64) + nmap["height"] = torch.tensor(maps_last["height"], device=self.device) + nmap["id"] = torch.tensor(maps_last["id"], device=self.device, dtype=torch.int64) + if maps_last["other"] is not None: + nmap["other"] = torch.Tensor(maps_last["other"]).to(self.device) + maps_last = nmap + + if len(obs.shape) == 3: + obs = obs.unsqueeze(0) + bs, c, h, w = obs.shape + depth = obs[:, 3, :, :] + # depth = torch.min(depth, torch.Tensor([self.map_size_h * self.grid_size * 2]).to(self.device)) + + point_cloud = PointCloud.get(depth=depth, camera_matrix=camera_matrix, device=self.device) + # fout = open("point_cloud.txt", "w") + # for i in range(bs): + # for j in range(h): + # for k in range(w): + # for t in range(3): + # print(point_cloud[i, t, j, k].item(), end=' ', file=fout) + # print('', file=fout) + # point cloud to map + Y = point_cloud[0, 1, :, :] + XZ = ((point_cloud[0, [0, 2], :, :] + self.grid_size * 0.5) // self.grid_size + torch.Tensor(self.map_offset).to(self.device).reshape((2, 1, 1))) + bound_low = torch.Tensor([0, 0]).to(self.device).reshape((2, 1, 1)) + bound_high = torch.Tensor([self.map_size_h-1, self.map_size_v-1]).to(self.device).reshape((2, 1, 1)) + XZ = torch.max(torch.min(XZ, bound_high), bound_low) + XZ = XZ.long() + zipped = XZ[0, :, :] * self.map_size_v + XZ[1, :, :] + + zipped = zipped[Y < 2.5] + id_map = id_map[Y < 2.5] + Y = Y[Y < 2.5] + + sort_Y, order = torch.sort(Y.flatten()) + sort_idx = zipped.flatten()[order] + + map_exp = torch.zeros((self.map_size_h * self.map_size_v), device=self.device, dtype=torch.int64) + map_height = torch.zeros((self.map_size_h * self.map_size_v), device=self.device) + map_id = torch.zeros((self.map_size_h * self.map_size_v), device=self.device, dtype=torch.int64) + if c > 4: + map_other = torch.zeros((c - 4, self.map_size_h * self.map_size_v)).to(self.device) + else: + map_other = None + + map_exp[sort_idx] = 1 + map_height[sort_idx] = sort_Y + if c > 4: + for i in range(c-4): + map_other[i, sort_idx] = obs[0, i+4, :, :].flatten()[order] * sort_Y + id_map = id_map.flatten()[order] + + # this will have to sort according to targets + map_id[sort_idx[id_map > 0]] = id_map[id_map > 0] + if targets is not None: + for target in targets: + if (id_map == target).any() == 0 and (maps_last["id"] == target).any(): + map_id[maps_last["id"].flatten() == target] = target + for target in targets: + map_id[sort_idx[id_map == target]] = target + + map_exp = map_exp.reshape((self.map_size_h, self.map_size_v)) + map_height = map_height.reshape((self.map_size_h, self.map_size_v)) + map_id = map_id.reshape((self.map_size_h, self.map_size_v)) + if c > 4: + map_other = map_other.reshape((c - 4, self.map_size_h, self.map_size_v)) + + if position is not None: + x, z = self.real_to_grid(position) + R = max(1, int(0.2 // self.grid_size)) + if not record_mode: + map_exp[max(0, x - R):min(self.map_size_h, x + R + 1), + max(0, z - R):min(self.map_size_v, z + R + 1)] = 1 + map_height[max(0, x - R):min(self.map_size_h, x + R + 1), + max(0, z - R):min(self.map_size_v, z + R + 1)] = 0 + + map_height = map_height * map_exp + maps_last["height"] * (1 - map_exp) + map_id = map_id * map_exp + maps_last["id"] * (1 - map_exp) + if c > 4: + map_other = map_other * map_exp + maps_last["other"] * (1 - map_exp) + map_exp = torch.max(map_exp, maps_last["explored"]) + + return dict(height=map_height, explored=map_exp, id=map_id, other=map_other) + +if __name__ == "__main__": + pass \ No newline at end of file diff --git a/easi/tasks/hazard/vendor/HAZARD/utils/model_utils.py b/easi/tasks/hazard/vendor/HAZARD/utils/model_utils.py new file mode 100644 index 0000000..e55b045 --- /dev/null +++ b/easi/tasks/hazard/vendor/HAZARD/utils/model_utils.py @@ -0,0 +1,132 @@ +import numpy as np +import torch +from torch import nn +from torch.nn import functional as F + + +def get_grid(pose, grid_size, device): + """ + Input: + `pose` FloatTensor(bs, 3) + `grid_size` 4-tuple (bs, _, grid_h, grid_w) + `device` torch.device (cpu or gpu) + Output: + `rot_grid` FloatTensor(bs, grid_h, grid_w, 2) + `trans_grid` FloatTensor(bs, grid_h, grid_w, 2) + + """ + pose = pose.float() + x = pose[:, 0] + y = pose[:, 1] + t = pose[:, 2] + + bs = x.size(0) + t = t * np.pi / 180. + cos_t = t.cos() + sin_t = t.sin() + + theta11 = torch.stack([cos_t, -sin_t, + torch.zeros(cos_t.shape).float().to(device)], 1) + theta12 = torch.stack([sin_t, cos_t, + torch.zeros(cos_t.shape).float().to(device)], 1) + theta1 = torch.stack([theta11, theta12], 1) + + theta21 = torch.stack([torch.ones(x.shape).to(device), + -torch.zeros(x.shape).to(device), x], 1) + theta22 = torch.stack([torch.zeros(x.shape).to(device), + torch.ones(x.shape).to(device), y], 1) + theta2 = torch.stack([theta21, theta22], 1) + + rot_grid = F.affine_grid(theta1, torch.Size(grid_size)) + trans_grid = F.affine_grid(theta2, torch.Size(grid_size)) + + return rot_grid, trans_grid + + +class ChannelPool(nn.MaxPool1d): + def forward(self, x): + n, c, w, h = x.size() + x = x.view(n, c, w * h).permute(0, 2, 1) + x = x.contiguous() + pooled = F.max_pool1d(x, c, 1) + _, _, c = pooled.size() + pooled = pooled.permute(0, 2, 1) + return pooled.view(n, c, w, h) + + +# https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail/blob/master/a2c_ppo_acktr/utils.py#L32 +class AddBias(nn.Module): + def __init__(self, bias): + super(AddBias, self).__init__() + self._bias = nn.Parameter(bias.unsqueeze(1)) + + def forward(self, x): + if x.dim() == 2: + bias = self._bias.t().view(1, -1) + else: + bias = self._bias.t().view(1, -1, 1, 1) + + return x + bias + + +# https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail/blob/master/a2c_ppo_acktr/model.py#L10 +class Flatten(nn.Module): + def forward(self, x): + return x.view(x.size(0), -1) + + +# https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail/blob/master/a2c_ppo_acktr/model.py#L82 +class NNBase(nn.Module): + + def __init__(self, recurrent, recurrent_input_size, hidden_size): + + super(NNBase, self).__init__() + self._hidden_size = hidden_size + self._recurrent = recurrent + + if recurrent: + self.gru = nn.GRUCell(recurrent_input_size, hidden_size) + nn.init.orthogonal_(self.gru.weight_ih.data) + nn.init.orthogonal_(self.gru.weight_hh.data) + self.gru.bias_ih.data.fill_(0) + self.gru.bias_hh.data.fill_(0) + + @property + def is_recurrent(self): + return self._recurrent + + @property + def rec_state_size(self): + if self._recurrent: + return self._hidden_size + return 1 + + @property + def output_size(self): + return self._hidden_size + + def _forward_gru(self, x, hxs, masks): + if x.size(0) == hxs.size(0): + x = hxs = self.gru(x, hxs * masks[:, None]) + else: + # x is a (T, N, -1) tensor that has been flatten to (T * N, -1) + N = hxs.size(0) + T = int(x.size(0) / N) + + # unflatten + x = x.view(T, N, x.size(1)) + + # Same deal with masks + masks = masks.view(T, N, 1) + + outputs = [] + for i in range(T): + hx = hxs = self.gru(x[i], hxs * masks[i]) + outputs.append(hx) + + # x is a (T, N, -1) tensor + x = torch.stack(outputs, dim=0) + # flatten + x = x.view(T * N, -1) + + return x, hxs diff --git a/easi/tasks/hazard/vendor/HAZARD/utils/scene_setup.py b/easi/tasks/hazard/vendor/HAZARD/utils/scene_setup.py new file mode 100644 index 0000000..2a1fd85 --- /dev/null +++ b/easi/tasks/hazard/vendor/HAZARD/utils/scene_setup.py @@ -0,0 +1,115 @@ +from tdw.add_ons.log_playback import LogPlayback +import os +import numpy as np +from tdw.tdw_utils import TDWUtils + +class SceneSetup: + def __init__(self, data_dir: str, is_flood = False, record_mode = False) -> None: + playback = LogPlayback() + playback.load(os.path.join(data_dir, "log.txt")) + self.commands_list = playback.playback + + if record_mode: + new_commands_list = [] + table_ids = [] + obstacles = [] + for commands in self.commands_list: + new_commands = [] + for command in commands: + if command["$type"] == "add_object" and "table" in command["category"]: + table_ids.append(command["id"]) + if command["$type"] == "rotate_object_by" and command["id"] in table_ids: + command["angle"] += 90 + print(command) + if command["$type"] == "add_object": + for obs in ["table", "shelf", "cabinet", "chair", "stool", "suitcase", "basket", ]: + if obs in command["category"] and command["id"] not in obstacles: + obstacles.append(command["id"]) + if command["$type"] == "terminate": + break + new_commands.append(command) + new_commands_list.append(new_commands) + new_commands_list.append([{"$type": "bake_nav_mesh"}]) + new_commands_list.append( + [{"$type": "make_nav_mesh_obstacle", "id": obs, "carve_type": "stationary"} for obs in obstacles]) + self.commands_list = new_commands_list + + import json + with open(os.path.join(data_dir, "info.json"), "r") as f: + info = json.load(f) + + self.task = info["task"] + self.containers = info["containers"] + self.agent_positions = np.array(info["agent"]) + if len(self.agent_positions.shape) == 1: + self.agent_positions = self.agent_positions.reshape(1, -1) + + if self.task == "wind": + self.targets = [] + self.target_ids = info["targets"] + else: + self.targets = info["targets"] + self.target_ids = [] + self.target_names = [] + self.target_id2category = {} + self.target_id2name = {} + log_lines = open(os.path.join(data_dir, "log.txt")).readlines() + log_lines = [json.loads(line) for line in log_lines] + log_lines = sum(log_lines, []) + log_objects = [log for log in log_lines if log['$type'] == 'add_object'] + + if self.task == "wind": + for target in self.target_ids: + for obj in log_objects: + # if target == 14415226 and obj['id'] == 14415226: print(obj['id'], target, obj['category']) + if obj['id'] == target and obj['category'] not in self.targets: + self.targets.append(obj['category']) + if obj['id'] == target and obj['name'] not in self.target_names: + self.target_names.append(obj['name']) + self.target_id2category[obj['id']] = obj['category'] + self.target_id2name[obj['id']] = obj['name'] + else: + for target in self.targets: + for obj in log_objects: + if obj['category'] == target and obj['id'] not in self.target_ids: + self.target_ids.append(obj['id']) + self.target_id2category[obj['id']] = obj['category'] + self.target_id2name[obj['id']] = obj['name'] + if obj['category'] == target and obj['name'] not in self.target_names: + self.target_names.append(obj['name']) + + self.other = info["other"] + f.close() + + if is_flood: + from envs.flood.object import ObjectStatus + with open(os.path.join(data_dir, "flood.json"), "r") as f: + info = json.load(f) + self.flood_positions = [np.array(source) for source in info["source"]] + # [np.array([45, 0, 0])] + self.flood_directions = [np.array(direction) for direction in info["direction"]] + self.flood_speed = info["speed"] + self.flood_source_from = info["flood_source_from"] + f.close() + self.objects = [] + self.BUOYANCY_LIST = ["chair", "lamp", "backpack", "basket", "pillow", "bag"] + for l in self.commands_list: + for c in l: + if c["$type"] == "add_object": + name = c["name"] + idx = c["id"] + pos = TDWUtils.vector3_to_array(c["position"]) + self.objects.append(ObjectStatus(idx=idx, position=pos, + has_buoyancy=self.naive_judge_buoyancy(name), + waterproof=self.naive_judge_waterproof(name))) + if c["$type"] == "add_scene": + self.scene_name = c["name"] + + def naive_judge_waterproof(self, name): + return False + + def naive_judge_buoyancy(self, name): + for subname in self.BUOYANCY_LIST: + if subname in name: + return True + return False diff --git a/easi/tasks/hazard/vendor/HAZARD/utils/seg_id.py b/easi/tasks/hazard/vendor/HAZARD/utils/seg_id.py new file mode 100644 index 0000000..67e285e --- /dev/null +++ b/easi/tasks/hazard/vendor/HAZARD/utils/seg_id.py @@ -0,0 +1,114 @@ +import numpy as np +from tdw.output_data import SegmentationColors +from tdw.output_data import ReplicantSegmentationColors +from typing import Union + +import os +PATH = os.path.dirname(os.path.abspath(__file__)) +# go to parent directory until it contains envs folder +while not os.path.exists(os.path.join(PATH, "envs")): + PATH = os.path.dirname(PATH) + +class SegmentationID: + """ + replicant and object ids should all be distinct + """ + def __init__(self): + self.segmentation_colors = dict() + self.names = dict() + self.categories = dict() + self.reverse_id = dict() + self.reverse_id[0] = 0 + + category_file = os.path.join(PATH, 'data', 'meta_data', 'categories_new.txt') + with open(category_file, 'r') as f: + self.global_categories = eval(f.read()) + + def process(self, segm: Union[SegmentationColors, ReplicantSegmentationColors], id_renumbering: dict = dict()): + if isinstance(segm, SegmentationColors): + for j in range(segm.get_num()): + idx = segm.get_object_id(j) + self.segmentation_colors[idx] = np.array(segm.get_object_color(j)) + col = self.segmentation_colors[idx] + # with open("utils/seg_id.txt", "a") as f: + # print("idx:", idx, "col:", col, "name:", segm.get_object_name(j).lower(), "category:", segm.get_object_category(j), file=f) + # f.close() + col = col[0] + col[1] * 256 + col[2] * 256 * 256 + if len(id_renumbering) == 0: + self.reverse_id[col] = idx + else: + self.reverse_id[col] = id_renumbering[idx] if idx in id_renumbering else 0 + self.names[idx] = segm.get_object_name(j).lower() + self.categories[idx] = segm.get_object_category(j) + elif isinstance(segm, ReplicantSegmentationColors): + for j in range(segm.get_num()): + idx = segm.get_id(j) + self.segmentation_colors[idx] = np.array(segm.get_segmentation_color(j)) + col = self.segmentation_colors[idx] + # with open("utils/seg_id.txt", "a") as f: + # print("idx:", idx, "col:", col, "name:", "replicant", "category:", "replicant", file=f) + # f.close() + # print("col:", col) + col = col[0] + col[1] * 256 + col[2] * 256 * 256 + # print("col:", col) + if len(id_renumbering) == 0: + self.reverse_id[col] = idx + else: + self.reverse_id[col] = id_renumbering[idx] if idx in id_renumbering else 0 + self.names[idx] = "replicant" + self.categories[idx] = "replicant" + else: + raise Exception("Unknown type of segmentation colors") + + def get_seg_mask(self, idPass: np.ndarray, none_id: int = 0, rcnn=None, id_list=None): + idPass[idPass == None] = none_id + compressed = idPass.astype(np.uint32) + compressed = compressed[:, :, 0] + compressed[:, :, 1] * 256 + compressed[:, :, 2] * 256 * 256 + if rcnn is None: + return np.vectorize(self.reverse_id.get)(compressed).astype(np.int64) + + # get bboxes for ret + all_idx = np.unique(compressed) + all_idx = all_idx[all_idx != 0] + + real_bboxes = [] + for idx in all_idx: + mask = compressed == idx + y, x = np.where(mask) + bbox = [np.min(x), np.min(y), np.max(x), np.max(y)] + real_bboxes.append([bbox, idx]) + + def find_idx(bbox2, rcnn_cat): + # find bbox with max iou + max_iou = 0 + max_idx = -1 + for bbox, idx in real_bboxes: + if rcnn_cat != self.categories[id_list[self.reverse_id[idx]]]: + continue + intersection = max(0.0, min(bbox[2], bbox2[2]) - max(bbox[0], bbox2[0])) * max(0.0, min(bbox[3], bbox2[3]) - max(bbox[1], bbox2[1])) + union = (bbox[2] - bbox[0]) * (bbox[3] - bbox[1]) + (bbox2[2] - bbox2[0]) * (bbox2[3] - bbox2[1]) - intersection + iou = intersection / union + if iou > max_iou: + max_iou = iou + max_idx = idx + # print("max_iou:", max_iou, "max_idx:", max_idx) + return max_idx + + ret = np.zeros((idPass.shape[0], idPass.shape[1]), dtype=np.int64) + labels = rcnn.pred_instances.labels + masks = rcnn.pred_instances.masks + scores = rcnn.pred_instances.scores + bboxes = rcnn.pred_instances.bboxes + l = len(labels) + for i in range(l): + cat = self.global_categories[labels[i]] + bbox = bboxes[i] + score = scores[i] + mask = masks[i] + # if score < 0.5: + # continue + real_idx = find_idx(bbox, cat) + if real_idx == -1: + continue + ret[mask.cpu().numpy()] = self.reverse_id[real_idx] + return ret \ No newline at end of file diff --git a/easi/tasks/hazard/vendor/HAZARD/utils/vision.py b/easi/tasks/hazard/vendor/HAZARD/utils/vision.py new file mode 100644 index 0000000..d6a8474 --- /dev/null +++ b/easi/tasks/hazard/vendor/HAZARD/utils/vision.py @@ -0,0 +1,59 @@ +import os + +import cv2 +import torch + +try: + import mmcv + from mmcv.transforms import Compose + from mmengine.utils import track_iter_progress + from mmdet.registry import VISUALIZERS + from mmdet.apis import init_detector, inference_detector + from mmdet.apis.inference import DetDataSample + _HAS_MMDET = True +except ImportError: + _HAS_MMDET = False + +PATH = os.path.dirname(os.path.abspath(__file__)) +# go to parent directory until the folder name is HAZARD +while not os.path.exists(os.path.join(PATH, "../envs")): + PATH = os.path.dirname(PATH) + + +# checkpoint can be downloaded from https://drive.google.com/file/d/15KP4EWoQ_8EsuWFpOGNJLeqOFlEu1hFT/view?usp=sharing +class Detector: + def __init__(self, + category_file=os.path.join(PATH, '../data', 'meta_data', 'categories.txt'), + config_file=os.path.join(PATH, '', 'vision', 'mask-rcnn_r50-caffe_fpn_ms-1x_tdw.py'), + checkpoint_file=os.path.join(PATH, '../data', 'tdw_rcnn.pth'), + device=None, + **kwargs): + if not _HAS_MMDET: + raise ImportError( + "mmcv/mmdet required for Detector. " + "Install with: pip install mmcv mmdet" + ) + if device == None: + device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu') + device = str(device) + self.model = init_detector(config_file, checkpoint_file, device=device) + self.categories = [] + with open(category_file, 'r') as f: + self.categories = eval(f.read()) + self.num_categories = len(self.categories) + + def data_to_array(self, data): + labels = data.pred_instances.labels + masks = data.pred_instances.masks + scores = data.pred_instances.scores + + sem = torch.zeros((self.num_categories, ) + masks.shape[1:], device=masks.device) + for i, label in enumerate(labels): + sem[label] = torch.max(sem[label], masks[i] * scores[i]) + return sem + + def inference(self, img): + # img = mmcv.imread(img) + img = img[:, :, ::-1] + result = inference_detector(self.model, img) + return result diff --git a/tests/test_hazard_task.py b/tests/test_hazard_task.py new file mode 100644 index 0000000..6d17c4c --- /dev/null +++ b/tests/test_hazard_task.py @@ -0,0 +1,382 @@ +"""Tests for the HAZARD task (offline, no simulator needed).""" +import json +import pytest +from pathlib import Path + +from easi.tasks.hazard.actions import ( + get_action_space, ACTION_TYPES, SCENARIO_MAX_STEPS, + HIGH_VALUE, LOW_VALUE, +) + + +class TestHAZARDActions: + def test_action_types(self): + assert len(ACTION_TYPES) == 5 + assert "walk_to" in ACTION_TYPES + assert "explore" in ACTION_TYPES + assert "stop" in ACTION_TYPES + + def test_get_action_space(self): + space = get_action_space() + assert isinstance(space, list) + assert len(space) == 5 + + def test_scenario_max_steps(self): + assert SCENARIO_MAX_STEPS["fire"] == 1500 + assert SCENARIO_MAX_STEPS["flood"] == 1500 + assert SCENARIO_MAX_STEPS["wind"] == 3000 + + def test_values(self): + assert HIGH_VALUE == 5 + assert LOW_VALUE == 1 + + +class TestHAZARDConfig: + def test_value_json_exists(self): + path = Path(__file__).parent.parent / "easi/tasks/hazard/config/value.json" + assert path.exists() + data = json.loads(path.read_text()) + assert isinstance(data, dict) + assert len(data) > 0 + + def test_fire_json_exists(self): + path = Path(__file__).parent.parent / "easi/tasks/hazard/config/fire.json" + assert path.exists() + + def test_fluid_json_exists(self): + path = Path(__file__).parent.parent / "easi/tasks/hazard/config/fluid.json" + assert path.exists() + + def test_prompts_csv_exists(self): + path = Path(__file__).parent.parent / "easi/tasks/hazard/config/prompts.csv" + assert path.exists() + + +class TestHAZARDTask: + @pytest.fixture + def fire_task(self): + from easi.tasks.hazard.task import HAZARDTask + return HAZARDTask() + + def test_name(self, fire_task): + assert "hazard" in fire_task.name.lower() + + def test_simulator_key(self, fire_task): + assert fire_task.simulator_key == "tdw:v1_11_23" + + def test_max_steps(self, fire_task): + assert fire_task.max_steps >= 1500 + + def test_bridge_script_path(self, fire_task): + path = fire_task.get_bridge_script_path() + assert path.exists() + assert path.name == "bridge.py" + + def test_get_instruction(self, fire_task): + episode = {"instruction": "Rescue objects from fire"} + assert fire_task.get_instruction(episode) == "Rescue objects from fire" + + def test_format_reset_config(self, fire_task): + episode = { + "id": 0, + "episode_id": "test-1", + "task": "fire", + "instruction": "Rescue objects", + "scene_name": "mm_kitchen_3a", + "agent_position": [0.0, 0.0, 0.0], + "target_categories": ["hairbrush"], + "target_object_ids": [123], + "hazard_params": '{"fire_positions": [[0, 0, 0]]}', + "containers": [], + "objects": "[]", + "max_steps": 1500, + "source_dir": "room_setup_fire/test_set/test-1", + "_data_dir": "/tmp/test", + } + config = fire_task.format_reset_config(episode) + assert "source_dir" in config + assert "simulator_data" in config["source_dir"] + assert config["target_categories"] == ["hairbrush"] + assert config["target_object_ids"] == [123] + + def test_evaluate_empty_trajectory(self, fire_task): + episode = {"target_categories": ["hairbrush"], "target_object_ids": [123]} + result = fire_task.evaluate_episode(episode, []) + assert result["value_score"] == 0.0 + assert result["value_rate"] == 0.0 + assert result["damage_rate"] == 0.0 + assert result["num_steps"] == 0.0 + + def test_evaluate_episode_value_and_damage(self, fire_task): + """Test both Value and Damage metrics from the HAZARD paper.""" + from easi.core.episode import StepResult, Observation + episode = {"target_categories": ["hairbrush"], "target_object_ids": [123, 456]} + trajectory = [ + StepResult( + observation=Observation(rgb_path="/dev/null"), + reward=0.0, + done=True, + info={ + "value_score": 1.5, # e.g., one at full value (1.0), one damaged (0.5) + "max_value": 2.0, + "rescued_count": 2.0, + "damaged_count": 1.0, # one object was damaged when rescued + "targets_rescued": 2.0, + "targets_total": 2.0, + }, + ) + ] + result = fire_task.evaluate_episode(episode, trajectory) + assert result["value_score"] == 1.5 + assert result["value_rate"] == 0.75 # 1.5 / 2.0 = 0.75 (paper "Value") + assert result["damage_rate"] == 0.5 # 1 damaged / 2 rescued = 0.5 (paper "Damage") + assert result["rescued_count"] == 2.0 + assert result["damaged_count"] == 1.0 + + def test_evaluate_episode_no_damage(self, fire_task): + """All objects rescued undamaged -> value_rate=1.0, damage_rate=0.0.""" + from easi.core.episode import StepResult, Observation + episode = {"target_categories": ["hairbrush"], "target_object_ids": [123]} + trajectory = [ + StepResult( + observation=Observation(rgb_path="/dev/null"), + reward=0.0, + done=True, + info={ + "value_score": 1.0, + "max_value": 1.0, + "rescued_count": 1.0, + "damaged_count": 0.0, + "targets_rescued": 1.0, + "targets_total": 1.0, + }, + ) + ] + result = fire_task.evaluate_episode(episode, trajectory) + assert result["value_rate"] == 1.0 + assert result["damage_rate"] == 0.0 + + def test_aggregate_results_value_and_damage(self, fire_task): + """Test that aggregate_results produces paper-matching metrics.""" + from easi.core.episode import EpisodeRecord + records = [ + EpisodeRecord( + episode={}, trajectory=[], + episode_results={ + "value_rate": 0.8, "damage_rate": 0.0, + "rescued_count": 3.0, "damaged_count": 0.0, + "value_score": 8.0, "max_value": 10.0, + "num_steps": 50.0, "targets_rescued": 3.0, "targets_total": 4.0, + "max_rescue_frame": 900.0, + }, + ), + EpisodeRecord( + episode={}, trajectory=[], + episode_results={ + "value_rate": 0.5, "damage_rate": 0.0, + "rescued_count": 2.0, "damaged_count": 1.0, + "value_score": 4.0, "max_value": 8.0, + "num_steps": 100.0, "targets_rescued": 2.0, "targets_total": 3.0, + "max_rescue_frame": 600.0, + }, + ), + ] + agg = fire_task.aggregate_results(records) + # Value: average of per-episode value_rate = (0.8 + 0.5) / 2 = 0.65 + assert abs(agg["value_rate"] - 0.65) < 1e-6 + # Damage: cumulative = 1 damaged / 5 rescued = 0.2 + assert abs(agg["damage_rate"] - 0.2) < 1e-6 + # Step (paper): sum(max_rescue_frame) / sum(rescued) = (900+600) / 5 = 300 + assert abs(agg["avg_step"] - 300.0) < 1e-6 + # LLM steps: average = (50 + 100) / 2 = 75 + assert agg["avg_llm_steps"] == 75.0 + + def test_builtin_episodes(self, fire_task): + episodes = fire_task._get_builtin_episodes() + assert len(episodes) >= 1 + assert "instruction" in episodes[0] + assert "source_dir" in episodes[0] + + +class TestHAZARDTaskRegistry: + def test_registry_discovers_hazard_tasks(self): + from easi.tasks.registry import list_tasks, refresh + refresh() + tasks = list_tasks() + hazard_tasks = [t for t in tasks if t.startswith("hazard")] + assert len(hazard_tasks) == 3, f"Expected 3 HAZARD tasks, got {hazard_tasks}" + + +class TestHAZARDPromptBuilder: + @pytest.fixture + def builder(self): + from easi.tasks.hazard.prompts import HAZARDPromptBuilder + return HAZARDPromptBuilder(scenario="fire") + + def test_build_messages_returns_list(self, builder): + from easi.core.episode import Observation + from easi.core.memory import AgentMemory + obs = Observation( + rgb_path="/dev/null", + metadata={ + "available_plans": '["look around"]', + "holding_objects": "[]", + "target_categories": '["hairbrush"]', + "object_list": '[{"name": "hairbrush_1", "category": "hairbrush", "id": "123"}]', + "current_seen_objects_id": '["123"]', + "object_distances": '{"123": 3.45}', + "env_change_record": "{}", + "feedback": "", + "frame_count": "0", + "targets_rescued": "0", + "targets_total": "1", + }, + ) + memory = AgentMemory( + task_description="Rescue hairbrush", + action_space=["walk_to", "explore"], + current_observation=obs, + ) + msgs = builder.build_messages(memory) + assert isinstance(msgs, list) + assert len(msgs) >= 1 + assert msgs[0]["role"] == "user" + # Verify prompt content matches original format + text = msgs[0]["content"][-1]["text"] + assert "fire" in text.lower() # scenario preamble + # Target uses raw numeric value (not "high"/"low") + assert "value: 1" in text + assert "attribute: None" in text # fire uses "None", not "fireproof" + # State section has per-object details + assert "Target objects currently seen:" in text + assert "distance: 3.45 m" in text + + def test_parse_response_option_letter(self, builder): + from easi.core.episode import Observation + from easi.core.memory import AgentMemory + obs = Observation( + rgb_path="/dev/null", + metadata={ + "available_plans": '["go pick up object (123)", "look around"]', + "holding_objects": "[]", + }, + ) + memory = AgentMemory( + task_description="Rescue", + action_space=["walk_to", "explore"], + current_observation=obs, + ) + actions = builder.parse_response("A", memory) + assert len(actions) == 1 + assert actions[0].action_name == "go pick up object (123)" + + def test_parse_response_full_text(self, builder): + from easi.core.episode import Observation + from easi.core.memory import AgentMemory + obs = Observation( + rgb_path="/dev/null", + metadata={ + "available_plans": '["go pick up object (123)", "look around"]', + "holding_objects": "[]", + }, + ) + memory = AgentMemory( + task_description="Rescue", + action_space=["walk_to", "explore"], + current_observation=obs, + ) + actions = builder.parse_response("look around", memory) + assert len(actions) == 1 + assert actions[0].action_name == "look around" + + def test_parse_response_fallback(self, builder): + from easi.core.episode import Observation + from easi.core.memory import AgentMemory + obs = Observation( + rgb_path="/dev/null", + metadata={ + "available_plans": '["go pick up object (123)"]', + "holding_objects": "[]", + }, + ) + memory = AgentMemory( + task_description="Rescue", + action_space=["walk_to", "explore"], + current_observation=obs, + ) + actions = builder.parse_response("gibberish nonsense", memory) + assert len(actions) == 1 + assert actions[0].action_name == "look around" # fallback + + def test_conforms_to_protocol(self, builder): + from easi.agents.prompt_builder import PromptBuilderProtocol + assert isinstance(builder, PromptBuilderProtocol) + + def test_set_action_space_noop(self, builder): + # Should not raise + builder.set_action_space(["walk_to", "explore"]) + + def test_flood_builder_attributes(self): + from easi.tasks.hazard.prompts import HAZARDPromptBuilder + builder = HAZARDPromptBuilder(scenario="flood") + from easi.core.episode import Observation + from easi.core.memory import AgentMemory + obs = Observation( + rgb_path="/dev/null", + metadata={ + "available_plans": '["look around"]', + "holding_objects": "[]", + "target_categories": '["hairbrush"]', + "object_list": '[{"name": "hairbrush_1", "category": "hairbrush", "id": "123"}]', + "current_seen_objects_id": '["123"]', + "object_distances": '{"123": 2.0}', + "env_change_record": "{}", + }, + ) + memory = AgentMemory( + task_description="Rescue", + action_space=[], + current_observation=obs, + ) + msgs = builder.build_messages(memory) + text = msgs[0]["content"][-1]["text"] + # Flood shows waterproof attribute + assert "non-waterproof" in text or "waterproof" in text + + def test_action_history_format(self, builder): + from easi.core.episode import Observation, Action + from easi.core.memory import AgentMemory, StepRecord + dummy_obs = Observation(rgb_path="/dev/null") + obs = Observation( + rgb_path="/dev/null", + metadata={ + "available_plans": '["look around"]', + "holding_objects": "[]", + "target_categories": '["hairbrush"]', + "object_list": "[]", + "current_seen_objects_id": "[]", + "object_distances": "{}", + "env_change_record": "{}", + }, + ) + memory = AgentMemory( + task_description="Rescue", + action_space=[], + current_observation=obs, + steps=[ + StepRecord( + observation=dummy_obs, + action=Action(action_name="look around"), + feedback="success", + ), + StepRecord( + observation=dummy_obs, + action=Action(action_name="go pick up hairbrush"), + feedback="max steps reached", + ), + ], + ) + msgs = builder.build_messages(memory) + text = msgs[0]["content"][-1]["text"] + assert "look around (success)" in text + assert "paused after taking 100 steps" in text From 4690439949b748fa3b4559de3a4d7624ba7eccee Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Sat, 21 Feb 2026 03:50:03 +0800 Subject: [PATCH 072/239] [Feat] Auto-download and auto-launch TDW build for HAZARD Add automated TDW Unity build management so users no longer need to manually download and launch the TDW binary before running HAZARD tasks. - manifest.yaml: Add installation_kwargs with TDW build download URL - env_manager.py: Add post_install() for build download/extract and get_env_vars() to export TDW_BUILD_PATH - bridge.py: Add _launch_tdw_build() to auto-start the TDW binary, wait for port readiness, and fall back gracefully if no build path - vision.py: Fix imports to be lazy (cv2/torch inside try block) --- easi/simulators/tdw/manifest.yaml | 4 + easi/simulators/tdw/v1_11_23/env_manager.py | 42 ++++++++++ easi/tasks/hazard/bridge.py | 77 ++++++++++++++++++- .../hazard/vendor/HAZARD/utils/vision.py | 7 +- 4 files changed, 122 insertions(+), 8 deletions(-) diff --git a/easi/simulators/tdw/manifest.yaml b/easi/simulators/tdw/manifest.yaml index 2e2fa12..a78b3a9 100644 --- a/easi/simulators/tdw/manifest.yaml +++ b/easi/simulators/tdw/manifest.yaml @@ -7,3 +7,7 @@ versions: simulator_class: "easi.simulators.tdw.v1_11_23.simulator.TDWSimulator" env_manager_class: "easi.simulators.tdw.v1_11_23.env_manager.TDWEnvManager" python_version: "3.10" + installation_kwargs: + build_url: "https://github.com/threedworld-mit/tdw/releases/download/v1.11.23/TDW_Linux.tar.gz" + build_filename: "TDW_Linux.tar.gz" + build_dir_name: "TDW" diff --git a/easi/simulators/tdw/v1_11_23/env_manager.py b/easi/simulators/tdw/v1_11_23/env_manager.py index 9558d1e..1eaecd9 100644 --- a/easi/simulators/tdw/v1_11_23/env_manager.py +++ b/easi/simulators/tdw/v1_11_23/env_manager.py @@ -1,12 +1,21 @@ """Environment manager for TDW v1.11.23. Used by HAZARD benchmark. Requires Python 3.10 and Xvfb (Unity build needs X11). + +Handles: +1. Conda env creation (Python 3.10) +2. Pip deps via uv (requirements.txt) +3. TDW Unity build download + extraction (post_install) +4. Runtime env vars (TDW_BUILD_PATH) for bridge subprocess """ from __future__ import annotations from pathlib import Path from easi.core.base_env_manager import BaseEnvironmentManager +from easi.utils.logging import get_logger + +logger = get_logger(__name__) class TDWEnvManager(BaseEnvironmentManager): @@ -35,3 +44,36 @@ def get_system_deps(self) -> list[str]: def get_validation_import(self) -> str: return "from tdw.controller import Controller; print('tdw ok')" + + def get_env_vars(self) -> dict[str, str]: + """Return TDW env vars for bridge subprocess.""" + build_dir = self.installation_kwargs.get("build_dir_name", "") + if not build_dir: + return {} + t = self._get_template_variables() + build_path = self._resolve_template("{extras_dir}/" + build_dir, t) + return {"TDW_BUILD_PATH": build_path} + + def post_install(self, context: dict) -> None: + """Download and extract TDW Unity build. + + Args: + context: Dict with env_dir, extras_dir, env_vars keys. + """ + extras_dir = Path(context["extras_dir"]) + build_url = self.installation_kwargs.get("build_url") + build_filename = self.installation_kwargs.get("build_filename") + + if build_url and build_filename: + logger.info("Downloading TDW build from %s", build_url) + self._download_and_extract( + url=build_url, + filename=build_filename, + dest_dir=extras_dir, + ) + # Make binary executable + build_dir = self.installation_kwargs.get("build_dir_name", "TDW") + binary = extras_dir / build_dir / "TDW.x86_64" + if binary.exists(): + binary.chmod(binary.stat().st_mode | 0o755) + logger.info("TDW build binary ready at %s", binary) diff --git a/easi/tasks/hazard/bridge.py b/easi/tasks/hazard/bridge.py index ad6dd82..6dcbde3 100644 --- a/easi/tasks/hazard/bridge.py +++ b/easi/tasks/hazard/bridge.py @@ -13,7 +13,11 @@ import json import math +import os +import socket +import subprocess import sys +import time from pathlib import Path import numpy as np @@ -64,32 +68,97 @@ def __init__(self, workspace, simulator_kwargs=None): self._env_change_record = {} # {str(obj_id): [temp/water_level values]} self._cached_plans = [] # plan descriptions from previous step self._cached_plan_actions = [] # action tuples from previous step + self._tdw_process = None # TDW Unity build subprocess + + def _launch_tdw_build(self, port): + """Launch TDW Unity build if TDW_BUILD_PATH is available. + + The build path comes from: + 1. TDW_BUILD_PATH env var (set by TDWEnvManager.get_env_vars) + 2. simulator_kwargs["tdw_build_path"] (manual override) + + The bridge runs inside xvfb-run (from SubprocessRunner), so DISPLAY + is inherited by the TDW build process. + + The TDW build is a child of this bridge process. SubprocessRunner + already kills the entire process group on shutdown, so no separate + cleanup is needed. + """ + build_path = os.environ.get("TDW_BUILD_PATH") or self.simulator_kwargs.get( + "tdw_build_path" + ) + if not build_path: + logger.warning( + "TDW_BUILD_PATH not set — assuming TDW build is already running. " + "Run 'easi env install tdw:v1_11_23' to auto-download the build." + ) + return + + binary = Path(build_path) / "TDW.x86_64" + if not binary.exists(): + logger.warning( + "TDW build binary not found at %s — assuming TDW build is already running.", + binary, + ) + return + + logger.info("Launching TDW build: %s (port %d)", binary, port) + self._tdw_process = subprocess.Popen( + [str(binary), "-port", str(port)], + stdout=subprocess.DEVNULL, + stderr=subprocess.DEVNULL, + ) + + # Wait for TDW to start listening on the port + self._wait_for_port(port, timeout=60) + logger.info("TDW build is ready on port %d (pid %d)", port, self._tdw_process.pid) + + @staticmethod + def _wait_for_port(port, timeout=60, interval=1.0): + """Poll until a TCP port is accepting connections.""" + deadline = time.time() + timeout + while time.time() < deadline: + try: + with socket.create_connection(("127.0.0.1", port), timeout=2): + return + except OSError: + time.sleep(interval) + logger.warning("TDW build did not become ready on port %d within %ds", port, timeout) def _create_env(self, reset_config, simulator_kwargs): - """Create the appropriate HAZARD env (Fire/Flood/Wind).""" + """Create the appropriate HAZARD env (Fire/Flood/Wind). + + Launches the TDW Unity build first (if TDW_BUILD_PATH is available), + then creates the HAZARD env with launch_build=False so the env + connects to the already-running build. + """ scenario = simulator_kwargs.get("scenario", "fire") port = simulator_kwargs.get("port", 1071) screen_size = simulator_kwargs.get("screen_size", 512) use_cached_assets = simulator_kwargs.get("use_cached_assets", False) + # Launch TDW Unity build (no-op if TDW_BUILD_PATH not set) + self._launch_tdw_build(port) + launch_build = self._tdw_process is None + if scenario == "fire": from HAZARD.envs.fire import FireEnv env = FireEnv( - launch_build=True, screen_size=screen_size, port=port, + launch_build=launch_build, screen_size=screen_size, port=port, use_local_resources=use_cached_assets, check_version=False, use_gt=False, ) elif scenario == "flood": from HAZARD.envs.flood import FloodEnv env = FloodEnv( - launch_build=True, screen_size=screen_size, port=port, + launch_build=launch_build, screen_size=screen_size, port=port, use_local_resources=use_cached_assets, check_version=False, use_gt=False, ) elif scenario == "wind": from HAZARD.envs.wind import WindEnv env = WindEnv( - launch_build=True, screen_size=screen_size, port=port, + launch_build=launch_build, screen_size=screen_size, port=port, use_local_resources=use_cached_assets, check_version=False, use_gt=False, ) diff --git a/easi/tasks/hazard/vendor/HAZARD/utils/vision.py b/easi/tasks/hazard/vendor/HAZARD/utils/vision.py index d6a8474..8848036 100644 --- a/easi/tasks/hazard/vendor/HAZARD/utils/vision.py +++ b/easi/tasks/hazard/vendor/HAZARD/utils/vision.py @@ -1,9 +1,8 @@ import os -import cv2 -import torch - try: + import cv2 + import torch import mmcv from mmcv.transforms import Compose from mmengine.utils import track_iter_progress @@ -11,7 +10,7 @@ from mmdet.apis import init_detector, inference_detector from mmdet.apis.inference import DetDataSample _HAS_MMDET = True -except ImportError: +except Exception: _HAS_MMDET = False PATH = os.path.dirname(os.path.abspath(__file__)) From 36c34862d1638a7d5cb1a893dc9188a7559aab46 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Sat, 21 Feb 2026 04:21:42 +0800 Subject: [PATCH 073/239] [Feat] Auto-download TDW build, auto-launch in bridge, and misc improvements - env_manager: Add post_install() to download/extract TDW build binary, get_env_vars() to export TDW_BUILD_PATH, and env_is_ready() to verify binary exists (triggers auto-install if missing) - manifest.yaml: Add installation_kwargs with TDW build download URL - bridge.py: Add _launch_tdw_build() to auto-start TDW binary with port readiness polling; make use_gt configurable via simulator_kwargs - subprocess_runner: Parse bridge log level and re-emit at matching level so WARNING/ERROR lines display with correct coloring - base_env_manager: Add trace logs to download/extract pipeline - cli: Rename --redownload to --refresh-data for clarity - vision.py: Move cv2/torch imports inside try block --- easi/cli.py | 16 +++---- easi/core/base_env_manager.py | 10 +++++ easi/evaluation/parallel_runner.py | 2 +- easi/evaluation/runner.py | 8 ++-- easi/simulators/subprocess_runner.py | 30 ++++++++++++- easi/simulators/tdw/v1_11_23/env_manager.py | 38 +++++++++++++++- easi/tasks/hazard/bridge.py | 43 ++++++++++++++++--- .../HAZARD/data/meta_data/temperature.json | 1 + .../vendor/HAZARD/data/meta_data/value.json | 1 + 9 files changed, 125 insertions(+), 24 deletions(-) create mode 100644 easi/tasks/hazard/vendor/HAZARD/data/meta_data/temperature.json create mode 100644 easi/tasks/hazard/vendor/HAZARD/data/meta_data/value.json diff --git a/easi/cli.py b/easi/cli.py index 644ab25..368d894 100644 --- a/easi/cli.py +++ b/easi/cli.py @@ -60,7 +60,7 @@ def build_parser() -> argparse.ArgumentParser: task_download = task_sub.add_parser("download", help="Download task dataset", parents=[common]) task_download.add_argument("task", type=str) - task_download.add_argument("--redownload", action="store_true", + task_download.add_argument("--refresh-data", action="store_true", dest="refresh_data", help="Delete cached dataset and re-download from source") task_scaffold = task_sub.add_parser("scaffold", help="Generate boilerplate for a new benchmark", parents=[common]) @@ -113,7 +113,7 @@ def build_parser() -> argparse.ArgumentParser: "Only supported with proprietary API backends (openai, anthropic, gemini).") start_parser.add_argument("--resume", type=str, default=None, dest="resume_dir", help="Path to a previous run directory to resume from") - start_parser.add_argument("--redownload", action="store_true", + start_parser.add_argument("--refresh-data", action="store_true", dest="refresh_data", help="Delete cached dataset and re-download from source") # --- llm-server command --- @@ -235,12 +235,12 @@ def cmd_task_scaffold(name: str, simulator: str, max_steps: int) -> None: logger.info(" 4. Run tests: pytest tests/test_%s.py -v", name) -def cmd_task_download(task_name: str, redownload: bool = False) -> None: +def cmd_task_download(task_name: str, refresh_data: bool = False) -> None: from easi.tasks.registry import load_task_class TaskClass = load_task_class(task_name) task = TaskClass() - path = task.download_dataset(force=redownload) + path = task.download_dataset(force=refresh_data) if path and str(path): logger.info("Dataset ready at: %s", path) else: @@ -328,7 +328,7 @@ def cmd_start(args): # Extract session-specific params (not saved in config.json) resume_dir = raw.pop("resume_dir", None) - redownload = raw.pop("redownload", False) + redownload = raw.pop("refresh_data", False) if resume_dir: if len(task_list) > 1: @@ -371,14 +371,14 @@ def cmd_start(args): num_parallel=num_parallel, **run_kwargs, resume_dir=resume_dir, - redownload=redownload, + refresh_data=redownload, ) else: runner = EvaluationRunner( task_name=task_name, **run_kwargs, resume_dir=resume_dir, - redownload=redownload, + refresh_data=redownload, ) results = runner.run() @@ -434,7 +434,7 @@ def main() -> None: elif args.task_action == "info": cmd_task_info(args.task) elif args.task_action == "download": - cmd_task_download(args.task, redownload=args.redownload) + cmd_task_download(args.task, refresh_data=args.refresh_data) elif args.task_action == "scaffold": cmd_task_scaffold(args.name, args.simulator, args.max_steps) else: diff --git a/easi/core/base_env_manager.py b/easi/core/base_env_manager.py index c49f9c9..71186e7 100644 --- a/easi/core/base_env_manager.py +++ b/easi/core/base_env_manager.py @@ -309,22 +309,32 @@ def _download_and_extract( logger.info("Already downloaded: %s, skipping", filename) return + logger.trace("Download target: %s -> %s", url, dest) with spinner(f"Downloading {filename}"): logger.info("Downloading %s", url) req = urllib.request.Request(url, headers={"User-Agent": "easi/1.0"}) with urllib.request.urlopen(req) as response, open(str(dest), "wb") as out: + total = 0 while True: chunk = response.read(1024 * 1024) # 1MB chunks if not chunk: break out.write(chunk) + total += len(chunk) + logger.trace("Download complete: %s (%.1f MB)", filename, total / 1024 / 1024) if extract: + logger.trace("Extracting %s to %s (strip_components=%d)", filename, dest_dir, strip_components) with spinner(f"Extracting {filename}"): self._extract_archive(dest, dest_dir, strip_components) + logger.trace("Extraction complete, removing archive %s", dest) dest.unlink(missing_ok=True) # Remove archive to save space + # Log extracted contents (top-level only) + top_items = sorted(p.name for p in dest_dir.iterdir() if not p.name.startswith(".")) + logger.trace("Contents of %s after extraction: %s", dest_dir, top_items) marker.touch() + logger.trace("Wrote done marker: %s", marker) def _extract_archive(self, archive: Path, dest_dir: Path, strip_components: int = 0) -> None: """Extract a tar.xz, tar.gz, tar.bz2, or zip archive.""" diff --git a/easi/evaluation/parallel_runner.py b/easi/evaluation/parallel_runner.py index 0dbd765..b1599ed 100644 --- a/easi/evaluation/parallel_runner.py +++ b/easi/evaluation/parallel_runner.py @@ -59,7 +59,7 @@ def run(self) -> list[dict]: # --- Phase 1: Load task --- logger.trace("Phase 1: Loading task") task = self._create_task() - if self.redownload: + if self.refresh_data: task.download_dataset(force=True) episodes = task.load_episodes() if self.max_episodes is not None: diff --git a/easi/evaluation/runner.py b/easi/evaluation/runner.py index e2f3d07..ec449ea 100644 --- a/easi/evaluation/runner.py +++ b/easi/evaluation/runner.py @@ -45,7 +45,7 @@ class EvaluationRunner: """Sequential evaluation runner.""" # Session-specific params excluded from config.json - _EXCLUDE_FROM_CONFIG = frozenset({"resume_dir", "redownload"}) + _EXCLUDE_FROM_CONFIG = frozenset({"resume_dir", "refresh_data"}) def __init__( self, @@ -62,7 +62,7 @@ def __init__( llm_kwargs_raw: str | None = None, max_retries: int = 3, resume_dir: Path | str | None = None, - redownload: bool = False, + refresh_data: bool = False, ): # Auto-capture all init args for config.json (before any mutation) frame = inspect.currentframe() @@ -84,7 +84,7 @@ def __init__( self.llm_kwargs_raw = llm_kwargs_raw self.max_retries = max_retries self.resume_dir = Path(resume_dir) if resume_dir else None - self.redownload = redownload + self.refresh_data = refresh_data timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") if self.model: safe_model = self.model.replace("/", "_") @@ -133,7 +133,7 @@ def run(self) -> list[dict]: # 1. Load task (before resume so we know total_episodes) task = self._create_task() - if self.redownload: + if self.refresh_data: task.download_dataset(force=True) episodes = task.load_episodes() if self.max_episodes is not None: diff --git a/easi/simulators/subprocess_runner.py b/easi/simulators/subprocess_runner.py index 92d7abc..c25748e 100644 --- a/easi/simulators/subprocess_runner.py +++ b/easi/simulators/subprocess_runner.py @@ -13,6 +13,7 @@ from __future__ import annotations import os +import re import signal import subprocess import threading @@ -215,8 +216,25 @@ def _has_display(self) -> bool: """Check if an X display is available.""" return bool(os.environ.get("DISPLAY", "")) + # Pattern to extract log level from bridge output (e.g. "[WARNING]" or "[ERROR]") + _BRIDGE_LEVEL_RE = re.compile(r"\[(\w+)\]") + + # Map bridge level names to logging levels + _BRIDGE_LEVEL_MAP = { + "TRACE": 5, # easi TRACE level + "DEBUG": 10, + "INFO": 20, + "WARNING": 30, + "ERROR": 40, + "CRITICAL": 50, + } + def _stream_output(self) -> None: - """Read bridge stdout line-by-line and log at DEBUG level. + """Read bridge stdout line-by-line and re-log at the matching level. + + Parses the log level from each bridge line (e.g. "[WARNING]") and + re-emits at that level so the parent process applies correct coloring. + Lines without a recognized level default to TRACE. Runs in a daemon thread for the lifetime of the subprocess. """ @@ -227,10 +245,18 @@ def _stream_output(self) -> None: for line in proc.stdout: line = line.rstrip() self._output_lines.append(line) - logger.trace("[bridge] %s", line) + level = self._parse_bridge_level(line) + logger.log(level, "[bridge] %s", line) except (ValueError, OSError): pass # pipe closed + def _parse_bridge_level(self, line: str) -> int: + """Extract log level from a bridge output line.""" + match = self._BRIDGE_LEVEL_RE.search(line) + if match: + return self._BRIDGE_LEVEL_MAP.get(match.group(1), 5) + return 5 # default to TRACE + def _get_recent_output(self) -> str: """Return the last N lines of captured bridge output.""" return "\n".join(self._output_lines) diff --git a/easi/simulators/tdw/v1_11_23/env_manager.py b/easi/simulators/tdw/v1_11_23/env_manager.py index 1eaecd9..34ef8fc 100644 --- a/easi/simulators/tdw/v1_11_23/env_manager.py +++ b/easi/simulators/tdw/v1_11_23/env_manager.py @@ -45,6 +45,23 @@ def get_system_deps(self) -> list[str]: def get_validation_import(self) -> str: return "from tdw.controller import Controller; print('tdw ok')" + def env_is_ready(self) -> bool: + """Check conda env + TDW build binary exist.""" + if not super().env_is_ready(): + return False + # Also verify the TDW Unity build binary was downloaded + build_dir = self.installation_kwargs.get("build_dir_name", "") + if build_dir: + binary = self.get_extras_dir() / build_dir / "TDW.x86_64" + if not binary.exists(): + logger.info( + "TDW conda env is ready but build binary missing at %s, " + "will re-run install to download it.", + binary, + ) + return False + return True + def get_env_vars(self) -> dict[str, str]: """Return TDW env vars for bridge subprocess.""" build_dir = self.installation_kwargs.get("build_dir_name", "") @@ -64,6 +81,8 @@ def post_install(self, context: dict) -> None: build_url = self.installation_kwargs.get("build_url") build_filename = self.installation_kwargs.get("build_filename") + logger.trace("post_install: extras_dir=%s, build_url=%s", extras_dir, build_url) + if build_url and build_filename: logger.info("Downloading TDW build from %s", build_url) self._download_and_extract( @@ -71,9 +90,24 @@ def post_install(self, context: dict) -> None: filename=build_filename, dest_dir=extras_dir, ) - # Make binary executable + + # Log what was extracted build_dir = self.installation_kwargs.get("build_dir_name", "TDW") - binary = extras_dir / build_dir / "TDW.x86_64" + build_path = extras_dir / build_dir + logger.trace("Expected build dir: %s (exists=%s)", build_path, build_path.exists()) + if build_path.exists(): + items = sorted(p.name for p in build_path.iterdir()) + logger.trace("Build dir contents: %s", items) + else: + # List extras_dir to help debug incorrect dir name + items = sorted(p.name for p in extras_dir.iterdir() if not p.name.startswith(".")) + logger.trace("extras_dir contents (build dir missing): %s", items) + + # Make binary executable + binary = build_path / "TDW.x86_64" + logger.trace("Expected binary: %s (exists=%s)", binary, binary.exists()) if binary.exists(): binary.chmod(binary.stat().st_mode | 0o755) logger.info("TDW build binary ready at %s", binary) + else: + logger.warning("TDW binary not found at %s after extraction", binary) diff --git a/easi/tasks/hazard/bridge.py b/easi/tasks/hazard/bridge.py index 6dcbde3..d83cf50 100644 --- a/easi/tasks/hazard/bridge.py +++ b/easi/tasks/hazard/bridge.py @@ -87,6 +87,11 @@ def _launch_tdw_build(self, port): build_path = os.environ.get("TDW_BUILD_PATH") or self.simulator_kwargs.get( "tdw_build_path" ) + logger.trace( + "TDW build lookup: TDW_BUILD_PATH=%s, simulator_kwargs.tdw_build_path=%s", + os.environ.get("TDW_BUILD_PATH"), + self.simulator_kwargs.get("tdw_build_path"), + ) if not build_path: logger.warning( "TDW_BUILD_PATH not set — assuming TDW build is already running. " @@ -94,7 +99,12 @@ def _launch_tdw_build(self, port): ) return - binary = Path(build_path) / "TDW.x86_64" + build_dir = Path(build_path) + binary = build_dir / "TDW.x86_64" + logger.trace("TDW build dir: %s (exists=%s)", build_dir, build_dir.exists()) + if build_dir.exists(): + items = sorted(p.name for p in build_dir.iterdir()) + logger.trace("TDW build dir contents: %s", items) if not binary.exists(): logger.warning( "TDW build binary not found at %s — assuming TDW build is already running.", @@ -102,28 +112,46 @@ def _launch_tdw_build(self, port): ) return + logger.trace("DISPLAY=%s", os.environ.get("DISPLAY", "")) + cmd = [str(binary), "-port", str(port)] logger.info("Launching TDW build: %s (port %d)", binary, port) + logger.trace("TDW launch command: %s", cmd) self._tdw_process = subprocess.Popen( - [str(binary), "-port", str(port)], + cmd, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL, ) + logger.trace("TDW build process started (pid %d)", self._tdw_process.pid) # Wait for TDW to start listening on the port self._wait_for_port(port, timeout=60) + + # Check if process is still alive after port wait + retcode = self._tdw_process.poll() + if retcode is not None: + logger.error("TDW build process exited with code %d before becoming ready", retcode) + self._tdw_process = None + return + logger.info("TDW build is ready on port %d (pid %d)", port, self._tdw_process.pid) @staticmethod def _wait_for_port(port, timeout=60, interval=1.0): """Poll until a TCP port is accepting connections.""" + logger.trace("Waiting for port %d (timeout=%ds, interval=%.1fs)", port, timeout, interval) deadline = time.time() + timeout + attempts = 0 while time.time() < deadline: + attempts += 1 try: with socket.create_connection(("127.0.0.1", port), timeout=2): + logger.trace("Port %d is ready after %d attempts", port, attempts) return - except OSError: + except OSError as e: + if attempts <= 3 or attempts % 10 == 0: + logger.trace("Port %d not ready (attempt %d): %s", port, attempts, e) time.sleep(interval) - logger.warning("TDW build did not become ready on port %d within %ds", port, timeout) + logger.warning("TDW build did not become ready on port %d within %ds (%d attempts)", port, timeout, attempts) def _create_env(self, reset_config, simulator_kwargs): """Create the appropriate HAZARD env (Fire/Flood/Wind). @@ -136,6 +164,7 @@ def _create_env(self, reset_config, simulator_kwargs): port = simulator_kwargs.get("port", 1071) screen_size = simulator_kwargs.get("screen_size", 512) use_cached_assets = simulator_kwargs.get("use_cached_assets", False) + use_gt = simulator_kwargs.get("use_gt", True) # Launch TDW Unity build (no-op if TDW_BUILD_PATH not set) self._launch_tdw_build(port) @@ -146,21 +175,21 @@ def _create_env(self, reset_config, simulator_kwargs): env = FireEnv( launch_build=launch_build, screen_size=screen_size, port=port, use_local_resources=use_cached_assets, - check_version=False, use_gt=False, + check_version=False, use_gt=use_gt, ) elif scenario == "flood": from HAZARD.envs.flood import FloodEnv env = FloodEnv( launch_build=launch_build, screen_size=screen_size, port=port, use_local_resources=use_cached_assets, - check_version=False, use_gt=False, + check_version=False, use_gt=use_gt, ) elif scenario == "wind": from HAZARD.envs.wind import WindEnv env = WindEnv( launch_build=launch_build, screen_size=screen_size, port=port, use_local_resources=use_cached_assets, - check_version=False, use_gt=False, + check_version=False, use_gt=use_gt, ) else: raise ValueError(f"Unknown HAZARD scenario: {scenario}") diff --git a/easi/tasks/hazard/vendor/HAZARD/data/meta_data/temperature.json b/easi/tasks/hazard/vendor/HAZARD/data/meta_data/temperature.json new file mode 100644 index 0000000..0d79f8b --- /dev/null +++ b/easi/tasks/hazard/vendor/HAZARD/data/meta_data/temperature.json @@ -0,0 +1 @@ +[{"name": "034_vray", "wcategory": "camera", "temp": 400}, {"name": "102_pepsi_can_12_fl_oz_vray", "wcategory": "soda can", "temp": 2000}, {"name": "12_06_001", "wcategory": "water faucet", "temp": 400}, {"name": "24_in_wall_cabinet_white_wood", "wcategory": "cabinet", "temp": 700}, {"name": "24_in_wall_cabinet_wood_beach_honey", "wcategory": "cabinet", "temp": 700}, {"name": "36_in_wall_cabinet_white_wood", "wcategory": "cabinet", "temp": 700}, {"name": "36_in_wall_cabinet_wood_beach_honey", "wcategory": "cabinet", "temp": 700}, {"name": "4ft_shelf_metal", "wcategory": "bookshelf", "temp": 200}, {"name": "4ft_wood_shelving", "wcategory": "shelf", "temp": 300}, {"name": "5ft_shelf_metal", "wcategory": "shelf", "temp": 1000}, {"name": "5ft_wood_shelving", "wcategory": "shelf", "temp": 300}, {"name": "6ft_shelf_metal", "wcategory": "shelf", "temp": 1000}, {"name": "6ft_wood_shelving", "wcategory": "shelf", "temp": 300}, {"name": "868580_pliers_max2016", "wcategory": "pliers", "temp": 1000}, {"name": "9v_battery", "wcategory": "flashlight battery", "temp": 300}, {"name": "aaa_battery", "wcategory": "flashlight battery", "temp": 200}, {"name": "afl_lamp", "wcategory": "table lamp", "temp": 400}, {"name": "alarm_clock", "wcategory": "alarm clock", "temp": 300}, {"name": "alivar_tech_bench_sofa", "wcategory": "sofa", "temp": 600}, {"name": "alma_floor_lamp", "wcategory": "floor lamp", "temp": 400}, {"name": "amphora_jar_vase", "wcategory": "vase", "temp": 60}, {"name": "apple", "wcategory": "apple", "temp": 100}, {"name": "apple_ipod_touch_yellow_vray", "wcategory": "ipod", "temp": 400}, {"name": "appliance-ge-profile-microwave", "wcategory": "microwave, microwave oven", "temp": 2000}, {"name": "appliance-ge-profile-microwave3", "wcategory": "microwave, microwave oven", "temp": 2000}, {"name": "appliance-ge-profile-microwave3_composite", "wcategory": "microwave", "temp": 2000}, {"name": "appliance-ge-profile-microwave_composite", "wcategory": "microwave", "temp": 2000}, {"name": "aquostv", "wcategory": "television set", "temp": 600}, {"name": "arco_lamp", "wcategory": "floor lamp", "temp": 400}, {"name": 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{"name": "shark", "wcategory": "toy", "temp": 300}, {"name": "shoebox_fused", "wcategory": "box", "temp": 500}, {"name": "side_table_wood", "wcategory": "table", "temp": 300}, {"name": "silver_frame_painting", "wcategory": "painting", "temp": 200}, {"name": "sink_base_white_wood", "wcategory": "cabinet", "temp": 500}, {"name": "sink_base_wood_beach_honey", "wcategory": "cabinet", "temp": 500}, {"name": "sink_cabinet_unit_wood_beech_honey_chrome_composite", "wcategory": "cabinet", "temp": 700}, {"name": "sink_cabinet_unit_wood_beech_honey_porcelain_composite", "wcategory": "cabinet", "temp": 700}, {"name": "sink_cabinet_unit_wood_oak_white_chrome_composite", "wcategory": "cabinet", "temp": 700}, {"name": "sink_cabinet_unit_wood_oak_white_porcelain_composite", "wcategory": "cabinet", "temp": 700}, {"name": "skateboard_1", "wcategory": "skateboard", "temp": 500}, {"name": "skillet_closed", "wcategory": "pan", "temp": 3000}, {"name": "skillet_open", "wcategory": "pan", "temp": 3000}, {"name": "skillet_open_no_lid", "wcategory": "pan", "temp": 3000}, {"name": "sm_table_white", "wcategory": "table", "temp": 300}, {"name": "small_purse", "wcategory": "bag, handbag, pocketbook, purse", "temp": 200}, {"name": "small_table_green_marble", "wcategory": "table", "temp": 300}, {"name": "soap_dispenser_01", "wcategory": "soap dispenser", "temp": 200}, {"name": "soup_ladle_black_02", "wcategory": "kitchen utensil", "temp": 500}, {"name": "spagetti-server", "wcategory": "kitchen utensil", "temp": 500}, {"name": "spatula", "wcategory": "kitchen utensil", "temp": 500}, {"name": "spatula2", "wcategory": "kitchen utensil", "temp": 500}, {"name": "spatula3", "wcategory": "kitchen utensil", "temp": 500}, {"name": "spoon1", "wcategory": "spoon", "temp": 500}, {"name": "spoon2", "wcategory": "spoon", "temp": 500}, {"name": "spunlight_designermesh_lamp", "wcategory": "table lamp", "temp": 400}, {"name": "square_coaster_001_cork", "wcategory": "coaster", "temp": 1000}, {"name": "square_coaster_001_marble", "wcategory": "coaster", "temp": 1000}, {"name": "square_coaster_001_wood", "wcategory": "coaster", "temp": 1000}, {"name": "square_coaster_rubber", "wcategory": "coaster", "temp": 1000}, {"name": "square_coaster_stone", "wcategory": "coaster", "temp": 1000}, {"name": "square_coaster_wood", "wcategory": "coaster", "temp": 1000}, {"name": "square_wood_block", "wcategory": "toy", "temp": 300}, {"name": "star_wood_block", "wcategory": "toy", "temp": 300}, {"name": "steam-punk_gear_25", "wcategory": "cog, gear", "temp": 500}, {"name": "steam-punk_gear_27", "wcategory": "cog, gear", "temp": 500}, {"name": "steam-punk_gear_29", "wcategory": "cog, gear", "temp": 500}, {"name": "stelton_emma_tea_vacuum_jug", "wcategory": "jug", "temp": 200}, {"name": "t-shape_wood_block", "wcategory": "toy", "temp": 300}, {"name": "table_square", "wcategory": "table", "temp": 300}, {"name": "table_stack", "wcategory": "table", "temp": 300}, {"name": "tablespoon", "wcategory": "spoon", "temp": 500}, {"name": "tan_lounger_chair", "wcategory": "chair", "temp": 300}, {"name": "tan_purse", "wcategory": "bag, handbag, pocketbook, purse", "temp": 200}, {"name": "tan_side_chair", "wcategory": "chair", "temp": 300}, {"name": "tapered_cork", "wcategory": "bottle cork", "temp": 200}, {"name": "tapered_cork_w_hole", "wcategory": "bottle cork", "temp": 200}, {"name": "tea_kettle_model", "wcategory": "teakettle", "temp": 500}, {"name": "teakettle_01", "wcategory": "teakettle", "temp": 500}, {"name": "teatray", "wcategory": "tea tray", "temp": 500}, {"name": "toaster_002", "wcategory": "toaster", "temp": 2000}, {"name": "toaster_b", "wcategory": "toaster", "temp": 2000}, {"name": "tolix_bar_stool", "wcategory": "stool", "temp": 500}, {"name": "toothbrush", "wcategory": "toothbrush", "temp": 200}, {"name": "toy_monkey_medium", "wcategory": "toy", "temp": 300}, {"name": "trapezoidal_table", "wcategory": "coffee table, cocktail table", "temp": 300}, {"name": "trashbin", "wcategory": "basket", "temp": 500}, {"name": "tray_02", "wcategory": "tea tray", "temp": 500}, {"name": "trophy01", "wcategory": "trophy", "temp": 500}, {"name": "trophy02", "wcategory": "trophy", "temp": 500}, {"name": "trunck", "wcategory": "trunk", "temp": 500}, {"name": "v3_tf_04_01", "wcategory": "teakettle", "temp": 500}, {"name": "vase_01", "wcategory": "vase", "temp": 60}, {"name": "vase_02", "wcategory": "vase", "temp": 60}, {"name": "vase_03", "wcategory": "vase", "temp": 60}, {"name": "vase_05", "wcategory": "vase", "temp": 60}, {"name": "vase_06", "wcategory": "vase", "temp": 60}, {"name": "vase_laura_deko_vase_set", "wcategory": "vase", "temp": 60}, {"name": "vitra_meda_chair", "wcategory": "chair", "temp": 300}, {"name": "vk0002_teaspoon", "wcategory": "spoon", "temp": 500}, {"name": "vk0007_steak_knife", "wcategory": "knife", "temp": 500}, {"name": "vk0010_dinner_fork_subd0", "wcategory": "fork", "temp": 1000}, {"name": "vk0011_dessert_fork_subd0", "wcategory": "fork", "temp": 1000}, {"name": "vk0014_dinner_knife_subd2", "wcategory": "knife", "temp": 500}, {"name": "vk0054_teaspoon", "wcategory": "spoon", "temp": 500}, {"name": "vk0055_tableknife", "wcategory": "knife", "temp": 500}, {"name": "vk0056_tablefork", "wcategory": "fork", "temp": 1000}, {"name": "vk0058_tablespoon", "wcategory": "spoon", "temp": 500}, {"name": "vk0060_dessertspoon", "wcategory": "spoon", "temp": 500}, {"name": "vk0067_fishfork", "wcategory": "fork", "temp": 1000}, {"name": "vk0078_fruitspoon", "wcategory": "spoon", "temp": 500}, {"name": "vk0080_soupspoon", "wcategory": "spoon", "temp": 500}, {"name": "vm_v5_070", "wcategory": "microwave, microwave oven", "temp": 2000}, {"name": "vm_v5_070_composite", "wcategory": "microwave", "temp": 2000}, {"name": "vray_032", "wcategory": "dishwasher", "temp": 500}, {"name": "vray_041", "wcategory": "teakettle", "temp": 500}, {"name": "vray_043", "wcategory": "teakettle", "temp": 500}, {"name": "vray_044", "wcategory": "teakettle", "temp": 500}, {"name": "vray_062", "wcategory": "microwave, microwave oven", "temp": 2000}, {"name": "vray_062_composite", "wcategory": "microwave", "temp": 2000}, {"name": "vray_077", "wcategory": "toaster", "temp": 2000}, {"name": "vray_077_composite", "wcategory": "toaster", "temp": 2000}, {"name": "vray_083", "wcategory": "toaster", "temp": 2000}, {"name": "vray_083_composite", "wcategory": "toaster", "temp": 2000}, {"name": "vray_084", "wcategory": "toaster", "temp": 2000}, {"name": "vray_084_composite", "wcategory": "toaster", "temp": 2000}, {"name": "vray_085", "wcategory": "toaster", "temp": 2000}, {"name": "vray_085_composite", "wcategory": "toaster", "temp": 2000}, {"name": "vraymax2013_oven_akzm6610ixl_by_whirlpool", "wcategory": "gas cooker", "temp": 500}, {"name": "whisk", "wcategory": "kitchen utensil", "temp": 500}, {"name": "white_club_chair", "wcategory": "chair", "temp": 300}, {"name": "white_lamp", "wcategory": "table lamp", "temp": 400}, {"name": "white_lounger_chair", "wcategory": "chair", "temp": 300}, {"name": "white_shopping_bag", "wcategory": "suitcase", "temp": 300}, {"name": "willisau_varion_w3_table", "wcategory": "table", "temp": 300}, {"name": "wood_board", "wcategory": "incline", "temp": 500}, {"name": "wood_chair", "wcategory": "chair", "temp": 300}, {"name": "wooden_pepper_mill", "wcategory": "pepper mill, pepper grinder", "temp": 300}, {"name": "woven_box", "wcategory": "box", "temp": 500}, {"name": "yellow_side_chair", "wcategory": "chair", "temp": 300}, {"name": "zenblocks", "wcategory": "toy", "temp": 300}, {"name": "zippo", "wcategory": "lighter", "temp": 500}, {"name": "glass", "wcategory": "glass", "temp": 1000}, {"name": "iphone_5_vr_white", "wcategory": "phone", "temp": 200}] \ No newline at end of file diff --git a/easi/tasks/hazard/vendor/HAZARD/data/meta_data/value.json b/easi/tasks/hazard/vendor/HAZARD/data/meta_data/value.json new file mode 100644 index 0000000..cef782c --- /dev/null +++ b/easi/tasks/hazard/vendor/HAZARD/data/meta_data/value.json @@ -0,0 +1 @@ +{"button_two_hole_red_wood": 0, "b04_bowl_smooth": 0, "holy_bible": 0, "bee": 0, "pillow": 0, "small_purse": 1, "pillow01": 0, "green_bag": 0, "backpack": 0, "apple_ipod_touch_yellow_vray": 1, "f10_apple_iphone_4": 1, "bork_vacuum": 1, "hp_printer": 1, "coffee_0023": 0, "b04_lighter": 0, "key_brass": 0, "034_vray": 1, "b04_wire_pincers": 0, "b04_bottle-2014-2018": 0, "candle": 0, "cafe_2010": 1, "bung": 0, "102_pepsi_can_12_fl_oz_vray": 0, "box_18inx18inx12in_cardboard": 0, "luggage_bag_1": 1, "woven_box": 0, "basket_18inx18inx12iin_plastic_lattice": 0, "f10_apple_iphone_4hp_printer": 1, "fire_extinguisher": 0, "b05_fire_extinguisher": 0, "b03_roller_skate": 1, "b03_hair_comb_2010": 0, "b03_toothbrush": 0, "b04_banana": 0, "b04_comb": 0, "b05_48_body_shop_hair_brush": 0, "b05_ball-point_pen-obj": 0, "b05_executive_pen": 1, "bag_14": 0, "bag_3": 0, "banana_fix2": 0, "blue_bag": 0, "chocolate_bar001": 0, "measuring_pan": 0, "tan_purse": 1, "toothbrush": 0, "plate06": 0, "apple": 0, "red_bag": 1, "b03_banana_01_high": 0, "b04_honey_jar": 1, "vk0007_steak_knife": 1, "vk0055_tableknife": 1, "b03_pen": 1, "b03_pen_01_001": 1, "pepper": 0, "cylinder01": 1, "b05_cylinder001": 1, "b03_burger": 0, "scissors": 1, "b04_scissors_2013": 1, "amphora_jar_vase": 1, "vase_01": 1, "glass": 1, "b04_cantate_crystal_wine_glass": 1, "b04_wineglass": 1, "macbook_air": 0, "iphone_5_vr_white": 0, "b04_headphones_31_12": 0, "trophy01": 1, "trophy02": 1, "b04_glass_06_vray": 1} \ No newline at end of file From 2b8be6b53555099f813b37140f0bbea1d7b9a4a2 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Sat, 21 Feb 2026 13:30:59 +0800 Subject: [PATCH 074/239] [Feat] Auto-download TDW build, auto-launch in bridge, and misc improvements MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - Configure TDW Build.BUILD_PATH from TDW_BUILD_PATH env var so Controller auto-launches the downloaded build binary - Add configurable command_timeout/startup_timeout in simulator_configs, wire through runner to SubprocessRunner (HAZARD needs 600s for first-run asset downloads) - Compute initial info (targets, available actions) in HAZARD bridge reset and merge into observation metadata via parse_observation fix - Fix np.math.pi → np.pi in vendored HAZARD controllers (NumPy 2.0 compat) - Set bridge default log level to TRACE for better diagnostics - Fix test_evaluation_runner refresh_data parameter name --- easi/communication/schemas.py | 5 +- easi/core/base_task.py | 4 +- easi/evaluation/runner.py | 9 + easi/simulators/base_bridge.py | 2 +- easi/simulators/subprocess_runner.py | 2 +- easi/tasks/hazard/_base.yaml | 1 + easi/tasks/hazard/bridge.py | 217 +++++++++++------- .../HAZARD/envs/fire/fireagent_controller.py | 25 +- .../envs/flood/floodagent_controller.py | 2 +- .../HAZARD/envs/wind/windagent_controller.py | 2 +- tests/test_evaluation_runner.py | 4 +- 11 files changed, 175 insertions(+), 98 deletions(-) diff --git a/easi/communication/schemas.py b/easi/communication/schemas.py index 90b3cc4..16e77cc 100644 --- a/easi/communication/schemas.py +++ b/easi/communication/schemas.py @@ -78,11 +78,14 @@ def make_status_response(ready: bool) -> dict: def parse_observation(data: dict) -> Observation: obs = data["observation"] + # Merge top-level info into metadata so prompt builders can read it + metadata = dict(obs.get("metadata", {})) + metadata.update(data.get("info", {})) return Observation( rgb_path=obs["rgb_path"], depth_path=obs.get("depth_path"), agent_pose=obs.get("agent_pose", []), - metadata=obs.get("metadata", {}), + metadata=metadata, ) diff --git a/easi/core/base_task.py b/easi/core/base_task.py index 3ce3846..80cd4ac 100644 --- a/easi/core/base_task.py +++ b/easi/core/base_task.py @@ -136,10 +136,12 @@ def additional_deps(self) -> list[str]: @property def simulator_kwargs(self) -> dict: - """Bridge-facing kwargs (simulator_configs minus additional_deps/env_vars + max_steps).""" + """Bridge-facing kwargs (simulator_configs minus runner/infra keys + max_steps).""" cfg = dict(self.simulator_configs) cfg.pop("additional_deps", None) cfg.pop("env_vars", None) + cfg.pop("command_timeout", None) + cfg.pop("startup_timeout", None) cfg["max_steps"] = self.max_steps return cfg diff --git a/easi/evaluation/runner.py b/easi/evaluation/runner.py index ec449ea..76afcf1 100644 --- a/easi/evaluation/runner.py +++ b/easi/evaluation/runner.py @@ -586,6 +586,14 @@ def _create_simulator(self, simulator_key: str, task=None): if task and task.extra_env_vars: env_vars = {**env_vars, **task.extra_env_vars} + # Extract runner-level timeouts from simulator_configs + sim_configs = task.simulator_configs if task else {} + runner_kwargs = {} + if sim_configs.get("command_timeout"): + runner_kwargs["command_timeout"] = float(sim_configs["command_timeout"]) + if sim_configs.get("startup_timeout"): + runner_kwargs["startup_timeout"] = float(sim_configs["startup_timeout"]) + runner = SubprocessRunner( python_executable=env_manager.get_python_executable(), bridge_script_path=bridge_path, @@ -593,6 +601,7 @@ def _create_simulator(self, simulator_key: str, task=None): xvfb_screen_config=env_manager.xvfb_screen_config, extra_args=extra_args, extra_env=env_vars or None, + **runner_kwargs, ) runner.launch() sim.set_runner(runner) diff --git a/easi/simulators/base_bridge.py b/easi/simulators/base_bridge.py index 425a85f..f7153ef 100644 --- a/easi/simulators/base_bridge.py +++ b/easi/simulators/base_bridge.py @@ -211,7 +211,7 @@ def main(cls): parser.add_argument("--simulator-kwargs", type=str, default=None) args, _ = parser.parse_known_args() - setup_logging("DEBUG") + setup_logging("TRACE") sim_kwargs = json.loads(args.simulator_kwargs) if args.simulator_kwargs else {} if args.data_dir: diff --git a/easi/simulators/subprocess_runner.py b/easi/simulators/subprocess_runner.py index c25748e..ec95029 100644 --- a/easi/simulators/subprocess_runner.py +++ b/easi/simulators/subprocess_runner.py @@ -49,7 +49,7 @@ def __init__( needs_display: bool = False, xvfb_screen_config: str = "1024x768x24", startup_timeout: float = 30.0, - command_timeout: float = 60.0, + command_timeout: float = 300.0, poll_interval: float = 0.1, extra_args: list[str] | None = None, extra_env: dict[str, str] | None = None, diff --git a/easi/tasks/hazard/_base.yaml b/easi/tasks/hazard/_base.yaml index 640ee36..4d8c4c7 100644 --- a/easi/tasks/hazard/_base.yaml +++ b/easi/tasks/hazard/_base.yaml @@ -12,6 +12,7 @@ simulator_configs: screen_size: 512 port: 1071 use_cached_assets: false + command_timeout: 600 additional_deps: - "opencv-python" - "gym" diff --git a/easi/tasks/hazard/bridge.py b/easi/tasks/hazard/bridge.py index d83cf50..d4a491e 100644 --- a/easi/tasks/hazard/bridge.py +++ b/easi/tasks/hazard/bridge.py @@ -14,10 +14,7 @@ import json import math import os -import socket -import subprocess import sys -import time from pathlib import Path import numpy as np @@ -68,97 +65,48 @@ def __init__(self, workspace, simulator_kwargs=None): self._env_change_record = {} # {str(obj_id): [temp/water_level values]} self._cached_plans = [] # plan descriptions from previous step self._cached_plan_actions = [] # action tuples from previous step - self._tdw_process = None # TDW Unity build subprocess - def _launch_tdw_build(self, port): - """Launch TDW Unity build if TDW_BUILD_PATH is available. - - The build path comes from: - 1. TDW_BUILD_PATH env var (set by TDWEnvManager.get_env_vars) - 2. simulator_kwargs["tdw_build_path"] (manual override) - - The bridge runs inside xvfb-run (from SubprocessRunner), so DISPLAY - is inherited by the TDW build process. + @staticmethod + def _configure_tdw_build_path(): + """Point TDW's Build.BUILD_PATH to our downloaded binary. - The TDW build is a child of this bridge process. SubprocessRunner - already kills the entire process group on shutdown, so no separate - cleanup is needed. + The TDW Controller uses Build.BUILD_PATH to find and launch the + Unity build when launch_build=True. By default it points to + ~/tdw_build/TDW/TDW.x86_64. We override it to use the binary + downloaded by TDWEnvManager (via TDW_BUILD_PATH env var). """ - build_path = os.environ.get("TDW_BUILD_PATH") or self.simulator_kwargs.get( - "tdw_build_path" - ) - logger.trace( - "TDW build lookup: TDW_BUILD_PATH=%s, simulator_kwargs.tdw_build_path=%s", - os.environ.get("TDW_BUILD_PATH"), - self.simulator_kwargs.get("tdw_build_path"), - ) - if not build_path: - logger.warning( - "TDW_BUILD_PATH not set — assuming TDW build is already running. " - "Run 'easi env install tdw:v1_11_23' to auto-download the build." - ) + build_dir = os.environ.get("TDW_BUILD_PATH") + if not build_dir: + logger.trace("TDW_BUILD_PATH not set, using TDW default build path") return - build_dir = Path(build_path) - binary = build_dir / "TDW.x86_64" - logger.trace("TDW build dir: %s (exists=%s)", build_dir, build_dir.exists()) - if build_dir.exists(): - items = sorted(p.name for p in build_dir.iterdir()) - logger.trace("TDW build dir contents: %s", items) + binary = Path(build_dir) / "TDW.x86_64" + logger.trace("TDW_BUILD_PATH=%s, binary exists=%s", build_dir, binary.exists()) if not binary.exists(): logger.warning( - "TDW build binary not found at %s — assuming TDW build is already running.", + "TDW build binary not found at %s — TDW Controller will use its default path. " + "Run 'easi env install tdw:v1_11_23' to download the build.", binary, ) return - logger.trace("DISPLAY=%s", os.environ.get("DISPLAY", "")) - cmd = [str(binary), "-port", str(port)] - logger.info("Launching TDW build: %s (port %d)", binary, port) - logger.trace("TDW launch command: %s", cmd) - self._tdw_process = subprocess.Popen( - cmd, - stdout=subprocess.DEVNULL, - stderr=subprocess.DEVNULL, - ) - logger.trace("TDW build process started (pid %d)", self._tdw_process.pid) - - # Wait for TDW to start listening on the port - self._wait_for_port(port, timeout=60) - - # Check if process is still alive after port wait - retcode = self._tdw_process.poll() - if retcode is not None: - logger.error("TDW build process exited with code %d before becoming ready", retcode) - self._tdw_process = None - return - - logger.info("TDW build is ready on port %d (pid %d)", port, self._tdw_process.pid) + from tdw.release.build import Build + Build.BUILD_PATH = binary + logger.info("Set TDW Build.BUILD_PATH to %s", binary) - @staticmethod - def _wait_for_port(port, timeout=60, interval=1.0): - """Poll until a TCP port is accepting connections.""" - logger.trace("Waiting for port %d (timeout=%ds, interval=%.1fs)", port, timeout, interval) - deadline = time.time() + timeout - attempts = 0 - while time.time() < deadline: - attempts += 1 - try: - with socket.create_connection(("127.0.0.1", port), timeout=2): - logger.trace("Port %d is ready after %d attempts", port, attempts) - return - except OSError as e: - if attempts <= 3 or attempts % 10 == 0: - logger.trace("Port %d not ready (attempt %d): %s", port, attempts, e) - time.sleep(interval) - logger.warning("TDW build did not become ready on port %d within %ds (%d attempts)", port, timeout, attempts) + def reset(self, reset_config): + """Reset env and return observation with initial info for prompt.""" + if self.env is None: + self.env = self._create_env(reset_config, self.simulator_kwargs) + self.step_count = 0 + obs = self._on_reset(self.env, reset_config) + return self._make_response(obs, info=self._reset_info) def _create_env(self, reset_config, simulator_kwargs): """Create the appropriate HAZARD env (Fire/Flood/Wind). - Launches the TDW Unity build first (if TDW_BUILD_PATH is available), - then creates the HAZARD env with launch_build=False so the env - connects to the already-running build. + Point TDW's Build.BUILD_PATH to our downloaded binary (if available), + then let the TDW Controller handle build launch via launch_build=True. """ scenario = simulator_kwargs.get("scenario", "fire") port = simulator_kwargs.get("port", 1071) @@ -166,28 +114,27 @@ def _create_env(self, reset_config, simulator_kwargs): use_cached_assets = simulator_kwargs.get("use_cached_assets", False) use_gt = simulator_kwargs.get("use_gt", True) - # Launch TDW Unity build (no-op if TDW_BUILD_PATH not set) - self._launch_tdw_build(port) - launch_build = self._tdw_process is None + # Point TDW to our downloaded build binary + self._configure_tdw_build_path() if scenario == "fire": from HAZARD.envs.fire import FireEnv env = FireEnv( - launch_build=launch_build, screen_size=screen_size, port=port, + launch_build=True, screen_size=screen_size, port=port, use_local_resources=use_cached_assets, check_version=False, use_gt=use_gt, ) elif scenario == "flood": from HAZARD.envs.flood import FloodEnv env = FloodEnv( - launch_build=launch_build, screen_size=screen_size, port=port, + launch_build=True, screen_size=screen_size, port=port, use_local_resources=use_cached_assets, check_version=False, use_gt=use_gt, ) elif scenario == "wind": from HAZARD.envs.wind import WindEnv env = WindEnv( - launch_build=launch_build, screen_size=screen_size, port=port, + launch_build=True, screen_size=screen_size, port=port, use_local_resources=use_cached_assets, check_version=False, use_gt=use_gt, ) @@ -198,7 +145,11 @@ def _create_env(self, reset_config, simulator_kwargs): return env def _on_reset(self, env, reset_config): - """Reset HAZARD env with episode data.""" + """Reset HAZARD env with episode data. + + Replicates the logic of fire_gym.py/flood_gym.py/wind_gym.py reset() + with trace logging around each step to aid debugging. + """ # Reset bridge state self.holding_object = [] self.nearest_object = None @@ -210,10 +161,57 @@ def _on_reset(self, env, reset_config): self._cached_plans = [] self._cached_plan_actions = [] - # Load scene via HAZARD env source_dir = reset_config["source_dir"] logger.info("Resetting HAZARD env with source_dir: %s", source_dir) - env.reset(data_dir=source_dir) + + # --- Replicate env.reset() with trace logging --- + from HAZARD.utils.scene_setup import SceneSetup + + logger.trace("Creating SceneSetup from data_dir=%s (scenario=%s)", source_dir, self.scenario) + scene_kwargs = {"data_dir": source_dir} + if self.scenario == "flood": + scene_kwargs["is_flood"] = True + env.setup = SceneSetup(**scene_kwargs) + logger.trace("SceneSetup created (targets=%s)", getattr(env.setup, 'target_names', [])) + + # Terminate existing controller if re-using the env + if env.controller is not None: + logger.trace("Terminating existing controller") + env.controller.communicate({"$type": "terminate"}) + env.controller.socket.close() + logger.trace("Existing controller terminated") + + # Create new controller — this is where TDW build launches + ZMQ connects. + # TDW Controller.__init__ calls socket.recv() which blocks until the + # build binary connects. If the build fails to start, this hangs forever. + logger.trace( + "Creating controller (scenario=%s, port=%s, launch_build=%s)", + self.scenario, env.controller_args.get("port"), + env.controller_args.get("launch_build"), + ) + + controller_cls = self._get_controller_class() + env.controller = controller_cls(**env.controller_args) + logger.trace("Controller created and connected to TDW build") + + env.controller.seed(env.RNG.randint(1000000)) + logger.info( + "Loading scene (this may take several minutes on first run " + "while TDW downloads 3D assets — subsequent runs will be faster)" + ) + + env.controller.init_scene(env.setup) + logger.info("Scene loaded successfully") + + env.num_step = 0 + env.last_action = None + env.last_target = None + + if not getattr(env, 'record_only', False): + logger.trace("Performing initial turn_by(0)") + env.controller.do_action(0, "turn_by", {"angle": 0}) + env.controller.next_key_frame() + logger.trace("Initial turn complete") # Initialize target tracking self.target_ids = [int(tid) for tid in reset_config.get("target_object_ids", [])] @@ -223,9 +221,54 @@ def _on_reset(self, env, reset_config): # Initial communicate + observation env.controller.communicate([]) state = env.controller._obs() + self._update_seen_objects(state) + + # Compute initial available plans so the first prompt has actions + available_plans, plan_actions = self._get_available_plans() + self._cached_plans = available_plans + self._cached_plan_actions = plan_actions + + object_distances = self._compute_object_distances(state) + + self._reset_info = { + "task_success": 0.0, + "frame_count": 0.0, + "max_steps": float(self._max_steps), + "max_rescue_frame": 0.0, + "last_action_success": 1.0, + "feedback": "episode started", + "holding_objects": json.dumps(self.holding_object), + "available_plans": json.dumps(available_plans), + "plan_actions": json.dumps(plan_actions), + "targets_rescued": 0.0, + "targets_total": float(len(self.target_status)), + "value_score": 0.0, + "max_value": 0.0, + "rescued_count": 0.0, + "damaged_count": 0.0, + "object_list": json.dumps(self.object_list), + "current_seen_objects_id": json.dumps(self.current_seen_objects_id), + "object_distances": json.dumps(object_distances), + "env_change_record": json.dumps({}), + "target_categories": json.dumps(self.target_categories), + } return self._wrap_obs(state, is_reset=True) + def _get_controller_class(self): + """Return the correct AgentController class for the current scenario.""" + if self.scenario == "fire": + from HAZARD.envs.fire.fireagent_controller import FireAgentController + return FireAgentController + elif self.scenario == "flood": + from HAZARD.envs.flood.floodagent_controller import FloodAgentController + return FloodAgentController + elif self.scenario == "wind": + from HAZARD.envs.wind.windagent_controller import WindAgentController + return WindAgentController + else: + raise ValueError(f"Unknown scenario: {self.scenario}") + def _on_step(self, env, action_text): """Execute a HAZARD action and return (obs, reward, done, info).""" from HAZARD.policy.env_actions import ( diff --git a/easi/tasks/hazard/vendor/HAZARD/envs/fire/fireagent_controller.py b/easi/tasks/hazard/vendor/HAZARD/envs/fire/fireagent_controller.py index 08c33d2..2adc62b 100644 --- a/easi/tasks/hazard/vendor/HAZARD/envs/fire/fireagent_controller.py +++ b/easi/tasks/hazard/vendor/HAZARD/envs/fire/fireagent_controller.py @@ -132,8 +132,10 @@ def reset_scene(self): self.communicate([{"$type": "destroy_all_objects"}]) def init_scene(self, setup: SceneSetup): + print("[init_scene] Starting reset_scene", flush=True) self.reset_scene() - + print("[init_scene] reset_scene done", flush=True) + if self.log_path is not None: logger = Logger(self.log_path) self.add_ons.append(logger) @@ -151,7 +153,8 @@ def init_scene(self, setup: SceneSetup): self.extinguishers = [] self.containers = setup.containers self.other_containers = {} - for commands in setup.commands_list: + print(f"[init_scene] Processing {len(setup.commands_list)} command batches", flush=True) + for batch_idx, commands in enumerate(setup.commands_list): filtered_commands = [] tp = None for command in commands: @@ -176,12 +179,15 @@ def init_scene(self, setup: SceneSetup): if tp.startswith("add_") and tp.endswith("_container"): self.other_containers[command['id']] = command['container_id'] filtered_commands.append(command) + print(f"[init_scene] Sending batch {batch_idx} ({len(filtered_commands)} cmds)", flush=True) self.communicate(filtered_commands) if tp == "terminate": break + print(f"[init_scene] All command batches sent, adding {len(setup.other.get('fire', []))} fire positions", flush=True) for fire_pos in setup.other["fire"]: self.add_fire_floor(fire_pos) + print("[init_scene] Fire positions added, creating agents", flush=True) if not self.record_only: if len(self.agents) == 0: self.agents: List[FireAgent] = [] @@ -200,6 +206,7 @@ def init_scene(self, setup: SceneSetup): self.add_ons.append(self.agents[i]) self.add_agent(idx, setup.agent_positions[i]) + print(f"[init_scene] Agents ready ({len(self.agents)} agents)", flush=True) self.maps = [None] * len(self.agents) self.add_ons.append(self.manager) self.target = setup.targets @@ -234,9 +241,11 @@ def init_scene(self, setup: SceneSetup): commands.extend([{"$type": "set_floorplan_roof", "show": False}]) # Capture when running after init_scene. (Because screen size may be modified) + print("[init_scene] Requesting scene_regions", flush=True) resp = self.communicate([{"$type": "send_scene_regions"}]) self.set_scene_bounds(resp) + print("[init_scene] Setting up segmentation + kinematic state", flush=True) self.manager.prepare_segmentation_data() self.initialized = True for obj in self.target_ids: @@ -246,6 +255,7 @@ def init_scene(self, setup: SceneSetup): if len(self.extinguishers) > 0: self.init_obi() + print("[init_scene] Setting up agent equipment (backpack/FoV)", flush=True) if not self.record_only and len(self.extinguishers) == 0: commands = [ {"$type": "set_field_of_view", "field_of_view": 120.0, "avatar_id": str(self.agents[0].replicant_id)}] @@ -258,13 +268,20 @@ def init_scene(self, setup: SceneSetup): commands.append(self.get_add_object(model_name=self.container_name, object_id=self.container_id, position=TDWUtils.array_to_vector3(agent_pos))) self.communicate(commands) + print("[init_scene] Agent grasping backpack", flush=True) self.agents[0].grasp(target=self.container_id, arm=Arm.left, axis=None, angle=None) + print("[init_scene] grasp() issued, waiting for key frame...", flush=True) self.next_key_frame() + print("[init_scene] Grasp done. Agent reaching position", flush=True) self.agents[0].reach_for(target=TDWUtils.array_to_vector3([-0.3, 1.0, 0.3]), absolute=False, arrived_at=0.1, arm=Arm.left) + print("[init_scene] reach_for() issued, waiting for key frame...", flush=True) self.next_key_frame() + print("[init_scene] Reach done. Agent resetting arm", flush=True) self.agents[0].reset_arm(arm=Arm.left) + print("[init_scene] reset_arm() issued, waiting for key frame...", flush=True) self.next_key_frame() + print("[init_scene] init_scene complete", flush=True) def set_scene_bounds(self, resp=None): self.scene_bounds = SceneBounds(resp=resp) @@ -291,6 +308,8 @@ def next_key_frame(self, force_direction=None) -> Tuple[List[ActionStatus], List num_step = 0 while True: num_step += 1 + if num_step % 100 == 0: + print(f"[next_key_frame] step {num_step}, status={[a.action.status if a.action else None for a in self.agents]}", flush=True) if num_step > 10000: raise RuntimeError("num_step > 10000") self.communicate([]) @@ -550,7 +569,7 @@ def _obs(self, agent_idx: int = 0): if agent_pos[0] > 0 and agent_pos[0] < self.map_size_h - 1 and agent_pos[1] > 0 and agent_pos[1] < self.map_size_v - 1: goal_map[agent_pos[0], agent_pos[1]] = -2 rad = self.agents[agent_idx].get_facing() - rad = int(rad / (np.math.pi / 4)) + rad = int(rad / (np.pi / 4)) if rad < 0: rad += 8 dx = list([1, 1, 0, -1, -1, -1, 0, 1])[rad] diff --git a/easi/tasks/hazard/vendor/HAZARD/envs/flood/floodagent_controller.py b/easi/tasks/hazard/vendor/HAZARD/envs/flood/floodagent_controller.py index f9646b1..c320ae4 100644 --- a/easi/tasks/hazard/vendor/HAZARD/envs/flood/floodagent_controller.py +++ b/easi/tasks/hazard/vendor/HAZARD/envs/flood/floodagent_controller.py @@ -522,7 +522,7 @@ def _obs(self, agent_idx: int = 0): if agent_pos[0] > 0 and agent_pos[0] < self.map_size_h - 1 and agent_pos[1] > 0 and agent_pos[1] < self.map_size_v - 1: goal_map[agent_pos[0], agent_pos[1]] = -2 rad = self.agents[agent_idx].get_facing() - rad = int(rad / (np.math.pi / 4)) + rad = int(rad / (np.pi / 4)) if rad < 0: rad += 8 dx = list([1, 1, 0, -1, -1, -1, 0, 1])[rad] diff --git a/easi/tasks/hazard/vendor/HAZARD/envs/wind/windagent_controller.py b/easi/tasks/hazard/vendor/HAZARD/envs/wind/windagent_controller.py index b421dd3..a845e9d 100644 --- a/easi/tasks/hazard/vendor/HAZARD/envs/wind/windagent_controller.py +++ b/easi/tasks/hazard/vendor/HAZARD/envs/wind/windagent_controller.py @@ -432,7 +432,7 @@ def _obs(self, agent_idx: int = 0): if agent_pos[0] > 0 and agent_pos[0] < self.map_size_h - 1 and agent_pos[1] > 0 and agent_pos[1] < self.map_size_v - 1: goal_map[agent_pos[0], agent_pos[1]] = -2 rad = self.agents[agent_idx].get_facing() - rad = int(rad / (np.math.pi / 4)) + rad = int(rad / (np.pi / 4)) if rad < 0: rad += 8 dx = list([1, 1, 0, -1, -1, -1, 0, 1])[rad] diff --git a/tests/test_evaluation_runner.py b/tests/test_evaluation_runner.py index 8d2be50..c5ca42e 100644 --- a/tests/test_evaluation_runner.py +++ b/tests/test_evaluation_runner.py @@ -103,10 +103,10 @@ def test_cli_options_excludes_session_params(self): runner = EvaluationRunner( task_name="dummy_task", resume_dir="/some/path", - redownload=True, + refresh_data=True, ) assert "resume_dir" not in runner._cli_options - assert "redownload" not in runner._cli_options + assert "refresh_data" not in runner._cli_options def test_cli_options_captures_raw_values(self): """Values are captured before Path conversion.""" From 4f3a176a8fc4843a02e8da7c99114bcef251660e Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Sat, 21 Feb 2026 18:41:40 +0800 Subject: [PATCH 075/239] [Docs] Add detailed CLI reference --- docs/cli-reference.md | 480 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 480 insertions(+) create mode 100644 docs/cli-reference.md diff --git a/docs/cli-reference.md b/docs/cli-reference.md new file mode 100644 index 0000000..402a171 --- /dev/null +++ b/docs/cli-reference.md @@ -0,0 +1,480 @@ +# EASI CLI Reference + +Complete reference for the `easi` command-line interface. + +## Global Options + +All commands support: + +``` +--verbosity {TRACE,DEBUG,INFO,WARNING,ERROR} + Set logging verbosity (default: INFO) +``` + +--- + +## `easi start` — Run Evaluation + +Execute evaluation on one or more tasks with an agent and LLM backend. + +``` +easi start [TASK ...] [options] +``` + +### Task Selection + +| Argument | Description | +|---|---| +| `TASK` | Task name(s) as positional arguments (e.g., `ebalfred_base`) | +| `--tasks TASKS` | Comma-separated task names (overrides positional args) | + +### Agent + +| Option | Description | +|---|---| +| `--agent {dummy,react}` | **Required.** Agent type to use | + +- `dummy` — Random action picker (no LLM needed) +- `react` — ReAct agent with multi-action buffering (requires LLM backend) + +### LLM Backend + +| Option | Description | +|---|---| +| `--backend {vllm,openai,anthropic,gemini,dummy}` | LLM backend (required for `react` agent) | +| `--model MODEL` | Model identifier | +| `--llm-url URL` | LLM server base URL (for external servers) | +| `--port PORT` | Port for local vLLM server (default: 8080) | +| `--llm-kwargs JSON` | Extra LLM/server kwargs as JSON string | +| `--max-retries N` | Max retry attempts on transient LLM errors (default: 3) | + +**Model identifiers by backend:** + +| Backend | Example `--model` values | +|---|---| +| `vllm` | `meta-llama/Llama-2-7b-hf`, `Qwen/Qwen2.5-VL-72B-Instruct` | +| `openai` | `gpt-4o`, `gpt-5.2-2025-12-11` | +| `anthropic` | `claude-sonnet-4-20250514` | +| `gemini` | `gemini-2.0-flash` | + +### Execution Control + +| Option | Description | +|---|---| +| `--num-parallel N` | Parallel simulator instances (default: 1). API backends only. | +| `--max-episodes N` | Max episodes to run (default: all) | +| `--seed SEED` | Random seed for agent reproducibility | + +### Data & Output + +| Option | Description | +|---|---| +| `--output-dir PATH` | Base output directory (default: `./logs`) | +| `--data-dir PATH` | Dataset cache directory (default: `./datasets`) | +| `--refresh-data` | Delete cached dataset and re-download | + +### Resume + +| Option | Description | +|---|---| +| `--resume DIR` | Resume from a previous run directory (contains `config.json`) | + +When resuming, completed episodes are skipped and evaluation continues from the next episode. The task name is loaded from the saved config. New CLI arguments override saved values. + +### Examples + +```bash +# Quick test with dummy agent (no LLM) +easi start dummy_task --agent dummy + +# OpenAI API +easi start ebalfred_base --agent react --backend openai --model gpt-4o + +# Anthropic API +easi start ebalfred_base --agent react --backend anthropic --model claude-sonnet-4-20250514 + +# vLLM (auto-starts server) +easi start ebalfred_base --agent react --backend vllm \ + --model Qwen/Qwen2.5-VL-72B-Instruct --port 8080 + +# vLLM (external server) +easi start ebalfred_base --agent react --backend vllm \ + --model Qwen/Qwen2.5-VL-72B-Instruct --llm-url http://localhost:8000 + +# Custom generation kwargs +easi start ebalfred_base --agent react --backend openai --model gpt-4o \ + --llm-kwargs '{"temperature": 0.7, "max_tokens": 500}' + +# Limit episodes +easi start ebalfred_base --agent dummy --max-episodes 5 --seed 42 + +# Parallel evaluation (API backends only) +easi start ebalfred_base --agent react --backend openai --model gpt-4o \ + --num-parallel 4 + +# Multiple tasks +easi start ebalfred_base ebnavigation_base --agent react \ + --backend openai --model gpt-4o + +# Multiple tasks (CSV form) +easi start --tasks ebalfred_base,ebnavigation_base --agent react \ + --backend openai --model gpt-4o + +# Resume a previous run +easi start --resume ./logs/ebalfred_base/20260215_093045_gpt-4o + +# Force dataset re-download +easi start ebalfred_base --agent dummy --refresh-data + +# Verbose logging +easi start ebalfred_base --agent dummy --verbosity TRACE +``` + +### Output Structure + +``` +//_/ +├── config.json # CLI options + resolved configuration +├── summary.json # Aggregated metrics +└── episodes/ + ├── 000_/ + │ ├── result.json # Per-episode metrics + │ ├── trajectory.jsonl # Action log (one JSON line per step) + │ ├── step_0000.png # Observation images + │ └── ... + └── 001_/ + └── ... +``` + +**`summary.json` format:** +```json +{ + "num_episodes": 10, + "model": "gpt-4o", + "agent": "react", + "metrics": { + "success_rate": 0.7, + "avg_steps": 12.3 + } +} +``` + +### Notes + +- `--num-parallel > 1` requires an API backend (`openai`, `anthropic`, `gemini`). It uses a thread pool with one simulator per thread. +- When using `--backend vllm` without `--llm-url`, a local vLLM server is auto-started and stopped after evaluation. +- `--resume` cannot be combined with multiple tasks. +- `--llm-kwargs` is split into server kwargs (e.g., `tensor_parallel_size`, `dtype`) and generation kwargs (e.g., `temperature`, `max_tokens`). + +--- + +## `easi env` — Manage Simulator Environments + +### `easi env list` + +List all available simulators and their versions. + +```bash +easi env list +``` + +Output shows each simulator as `name:version`, with the default version marked. + +--- + +### `easi env install ` + +Install a simulator environment (creates a conda env with required dependencies). + +``` +easi env install [--reinstall] [--with-task-deps TASK] +``` + +| Argument | Description | +|---|---| +| `simulator` | Simulator key (e.g., `ai2thor:v2_1_0`, `tdw:v1_11_23`) | +| `--reinstall` | Remove existing environment and install from scratch | +| `--with-task-deps TASK` | Also install additional dependencies from a specific task | + +**Examples:** + +```bash +# Install AI2-THOR v2.1.0 +easi env install ai2thor:v2_1_0 + +# Reinstall from scratch +easi env install ai2thor:v2_1_0 --reinstall + +# Install with task-specific dependencies +easi env install ai2thor:v2_1_0 --with-task-deps ebalfred_base +``` + +The created conda environment is named `easi__` (e.g., `easi_ai2thor_v2_1_0`). + +--- + +### `easi env check ` + +Check if a simulator environment is ready for use. + +```bash +easi env check ai2thor:v2_1_0 +``` + +Reports missing system dependencies, the Python executable path, and whether the environment is ready. + +--- + +## `easi task` — Manage Tasks + +### `easi task list` + +List all available tasks discovered in the registry. + +```bash +easi task list +``` + +Output format: `task_name -- display_name (simulator: simulator_key)` + +--- + +### `easi task info ` + +Display detailed information about a specific task. + +```bash +easi task info ebalfred_base +``` + +Shows task name, description, simulator key, and max steps. + +--- + +### `easi task download ` + +Download and cache the task dataset locally. + +``` +easi task download [--refresh-data] +``` + +| Argument | Description | +|---|---| +| `task` | Task name (e.g., `ebalfred_base`) | +| `--refresh-data` | Delete cached dataset and re-download from source | + +**Examples:** + +```bash +easi task download ebalfred_base +easi task download ebalfred_base --refresh-data +``` + +--- + +### `easi task scaffold ` + +Generate boilerplate code for a new benchmark task. + +``` +easi task scaffold [--simulator SIM] [--max-steps N] +``` + +| Argument | Description | +|---|---| +| `name` | Task name in snake_case (e.g., `my_benchmark`) | +| `--simulator SIM` | Simulator key to use (default: `dummy:v1`) | +| `--max-steps N` | Maximum steps per episode (default: 50) | + +**Example:** + +```bash +easi task scaffold my_benchmark --simulator ai2thor:v2_1_0 --max-steps 100 +``` + +Creates: +- `easi/tasks/my_benchmark/bridge.py` +- `easi/tasks/my_benchmark/task.py` +- `easi/tasks/my_benchmark/my_benchmark.yaml` +- `tests/test_my_benchmark.py` + +--- + +## `easi sim` — Control Simulators + +### `easi sim test ` + +Run a smoke test on a simulator (reset + N steps). + +``` +easi sim test [--steps N] [--timeout SECONDS] +``` + +| Argument | Description | +|---|---| +| `simulator` | Simulator key (e.g., `dummy`, `ai2thor:v5_0_0`) | +| `--steps N` | Number of steps to execute (default: 5) | +| `--timeout SECONDS` | Bridge startup timeout (default: 200.0) | + +**Examples:** + +```bash +easi sim test dummy +easi sim test ai2thor:v5_0_0 --steps 10 +easi sim test ai2thor:v2_1_0 --steps 3 --timeout 300 +``` + +Executes `MoveAhead` for each step and reports observations and rewards. + +--- + +## `easi llm-server` — Dummy LLM Server + +Start a minimal OpenAI-compatible dummy LLM server for testing. + +``` +easi llm-server [--host HOST] [--port PORT] [--mode MODE] [--action-space ACTION ...] +``` + +| Option | Description | +|---|---| +| `--host HOST` | Server host (default: `127.0.0.1`) | +| `--port PORT` | Server port (default: `8000`) | +| `--mode {fixed,random}` | Response mode: `fixed` returns first action, `random` returns random action | +| `--action-space ACTION ...` | Space-separated action names (default: `MoveAhead TurnLeft TurnRight Stop`) | + +**Examples:** + +```bash +# Default dummy server +easi llm-server + +# Custom port and fixed mode +easi llm-server --port 8080 --mode fixed + +# Custom action space +easi llm-server --mode random --action-space Forward Backward TurnLeft TurnRight +``` + +**Endpoints:** +- `POST /v1/chat/completions` — OpenAI-compatible chat completion +- `GET /health` — Health check + +Use with `easi start`: +```bash +# Terminal 1: start dummy server +easi llm-server --port 8000 + +# Terminal 2: run evaluation against it +easi start ebalfred_base --agent react --backend openai \ + --model dummy --llm-url http://localhost:8000 +``` + +--- + +## Environment Variables + +The CLI itself does not use environment variables, but the LLM backends require API keys: + +| Variable | Backend | +|---|---| +| `OPENAI_API_KEY` | `openai` | +| `ANTHROPIC_API_KEY` | `anthropic` | +| `GOOGLE_API_KEY` | `gemini` | + +These are handled by the underlying LiteLLM client. + +--- + +## Available Simulators + +| Key | Description | +|---|---| +| `dummy:v1` | In-memory testing simulator (no external deps) | +| `ai2thor:v2_1_0` | AI2-THOR 2.1.0 (EB-Alfred, Python 3.8) | +| `ai2thor:v5_0_0` | AI2-THOR 5.0.0 (EB-Navigation, Python 3.10) | +| `habitat_sim:v0_3_0` | Habitat-Sim 0.3.0 (EB-Habitat, Python 3.9) | +| `coppeliasim:v4_1_0` | CoppeliaSim 4.1.0 (EB-Manipulation, Python 3.10) | +| `tdw:v1_11_23` | ThreeDWorld 1.11.23 (HAZARD, Python 3.10) | + +--- + +## Available Tasks + +| Task | Simulator | Description | +|---|---|---| +| `dummy_task` | `dummy:v1` | 3-episode testing task | +| `ebalfred_base` | `ai2thor:v2_1_0` | EB-Alfred base split | +| `ebalfred_spatial` | `ai2thor:v2_1_0` | EB-Alfred spatial reasoning | +| `ebalfred_commonsense` | `ai2thor:v2_1_0` | EB-Alfred commonsense reasoning | +| `ebalfred_complex` | `ai2thor:v2_1_0` | EB-Alfred complex tasks | +| `ebalfred_long_horizon` | `ai2thor:v2_1_0` | EB-Alfred long-horizon tasks | +| `ebalfred_image` | `ai2thor:v2_1_0` | EB-Alfred image understanding | +| `ebnavigation_base` | `ai2thor:v5_0_0` | EB-Navigation base split | +| `ebnavigation_spatial` | `ai2thor:v5_0_0` | EB-Navigation spatial | +| `ebnavigation_commonsense` | `ai2thor:v5_0_0` | EB-Navigation commonsense | +| `ebnavigation_complex` | `ai2thor:v5_0_0` | EB-Navigation complex | +| `ebnavigation_image` | `ai2thor:v5_0_0` | EB-Navigation image | +| `ebhabitat_base` | `habitat_sim:v0_3_0` | EB-Habitat base split | +| `ebhabitat_spatial` | `habitat_sim:v0_3_0` | EB-Habitat spatial | +| `ebhabitat_commonsense` | `habitat_sim:v0_3_0` | EB-Habitat commonsense | +| `ebhabitat_complex` | `habitat_sim:v0_3_0` | EB-Habitat complex | +| `ebmanipulation_base` | `coppeliasim:v4_1_0` | EB-Manipulation base split | +| `ebmanipulation_spatial` | `coppeliasim:v4_1_0` | EB-Manipulation spatial | +| `ebmanipulation_commonsense` | `coppeliasim:v4_1_0` | EB-Manipulation commonsense | +| `ebmanipulation_complex` | `coppeliasim:v4_1_0` | EB-Manipulation complex | +| `hazard_fire` | `tdw:v1_11_23` | HAZARD fire scenario | +| `hazard_flood` | `tdw:v1_11_23` | HAZARD flood scenario | +| `hazard_wind` | `tdw:v1_11_23` | HAZARD wind scenario | + +--- + +## Workflow Examples + +### First-Time Setup and Evaluation + +```bash +# 1. Install simulator +easi env install ai2thor:v2_1_0 --with-task-deps ebalfred_base + +# 2. Verify environment +easi env check ai2thor:v2_1_0 + +# 3. Smoke test the simulator +easi sim test ai2thor:v2_1_0 + +# 4. Download dataset +easi task download ebalfred_base + +# 5. Run evaluation +easi start ebalfred_base --agent react --backend openai --model gpt-4o +``` + +### Creating a New Benchmark + +```bash +# 1. Scaffold the task +easi task scaffold my_benchmark --simulator ai2thor:v2_1_0 --max-steps 100 + +# 2. Edit the generated files: +# easi/tasks/my_benchmark/bridge.py — implement _create_env(), _extract_image() +# easi/tasks/my_benchmark/task.py — implement format_reset_config() +# easi/tasks/my_benchmark/my_benchmark.yaml — configure dataset source + +# 3. Run tests +pytest tests/test_my_benchmark.py -v + +# 4. Test with dummy agent +easi start my_benchmark --agent dummy +``` + +### Batch Evaluation Across Tasks + +```bash +# Run all EB-Alfred splits +easi start --tasks ebalfred_base,ebalfred_spatial,ebalfred_commonsense \ + --agent react --backend openai --model gpt-4o --num-parallel 4 + +# Results saved to ./logs/// for each task +``` From c31ad61d904aa65831eadc6ace2acc5c21b29314 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Sun, 22 Feb 2026 04:57:04 +0800 Subject: [PATCH 076/239] [Feat] Add OmniGibson simulator integration for BEHAVIOR-1K Integrate OmniGibson (Isaac Sim 4.5.0) as an EASI simulator with subprocess-isolated bridge execution. The env_manager replicates the BEHAVIOR-1K setup.sh install process: git clone, PyTorch+CUDA, bddl, OmniGibson, 26 Isaac Sim wheels, websockets fix, and cffi fix. Headless mode via OMNIGIBSON_HEADLESS=1 (no Xvfb needed). --- easi/simulators/omnigibson/__init__.py | 0 easi/simulators/omnigibson/manifest.yaml | 13 + easi/simulators/omnigibson/v3_7_2/__init__.py | 0 easi/simulators/omnigibson/v3_7_2/bridge.py | 104 +++++++ .../omnigibson/v3_7_2/conda_env.yaml | 6 + .../omnigibson/v3_7_2/env_manager.py | 294 ++++++++++++++++++ .../omnigibson/v3_7_2/requirements.txt | 2 + .../simulators/omnigibson/v3_7_2/simulator.py | 24 ++ tests/test_omnigibson_simulator.py | 144 +++++++++ 9 files changed, 587 insertions(+) create mode 100644 easi/simulators/omnigibson/__init__.py create mode 100644 easi/simulators/omnigibson/manifest.yaml create mode 100644 easi/simulators/omnigibson/v3_7_2/__init__.py create mode 100644 easi/simulators/omnigibson/v3_7_2/bridge.py create mode 100644 easi/simulators/omnigibson/v3_7_2/conda_env.yaml create mode 100644 easi/simulators/omnigibson/v3_7_2/env_manager.py create mode 100644 easi/simulators/omnigibson/v3_7_2/requirements.txt create mode 100644 easi/simulators/omnigibson/v3_7_2/simulator.py create mode 100644 tests/test_omnigibson_simulator.py diff --git a/easi/simulators/omnigibson/__init__.py b/easi/simulators/omnigibson/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/easi/simulators/omnigibson/manifest.yaml b/easi/simulators/omnigibson/manifest.yaml new file mode 100644 index 0000000..fb63093 --- /dev/null +++ b/easi/simulators/omnigibson/manifest.yaml @@ -0,0 +1,13 @@ +name: omnigibson +display_name: "OmniGibson (Isaac Sim)" +default_version: "v3_7_2" +versions: + v3_7_2: + description: "OmniGibson 3.7.2 + Isaac Sim 4.5.0 — used by BEHAVIOR-1K" + simulator_class: "easi.simulators.omnigibson.v3_7_2.simulator.OmniGibsonSimulator" + env_manager_class: "easi.simulators.omnigibson.v3_7_2.env_manager.OmniGibsonEnvManager" + python_version: "3.10" + installation_kwargs: + cuda_version: "12.4" + behavior_1k_repo: "https://github.com/StanfordVL/BEHAVIOR-1K.git" + behavior_1k_tag: "v3.7.2" diff --git a/easi/simulators/omnigibson/v3_7_2/__init__.py b/easi/simulators/omnigibson/v3_7_2/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/easi/simulators/omnigibson/v3_7_2/bridge.py b/easi/simulators/omnigibson/v3_7_2/bridge.py new file mode 100644 index 0000000..1a8d781 --- /dev/null +++ b/easi/simulators/omnigibson/v3_7_2/bridge.py @@ -0,0 +1,104 @@ +"""Bridge subprocess for OmniGibson v3.7.2 + Isaac Sim 4.5.0. + +This script runs inside the easi_omnigibson_v3_7_2 conda env (Python 3.10). +It communicates with the parent process via filesystem IPC. + +Provides an OmniGibsonBridge that handles: +- Minimal OmniGibson environment creation (Scene + DummyTask, no dataset) +- Action execution and observation capture +- Main IPC loop (reset/step/close) + +Headless mode is enabled via OMNIGIBSON_HEADLESS=1 env var (set by env_manager). + +Usage: + python bridge.py --workspace /tmp/easi_xxx [--simulator-kwargs '{}'] +""" +from __future__ import annotations + +import numpy as np + +from easi.simulators.base_bridge import BaseBridge + + +class OmniGibsonBridge(BaseBridge): + """OmniGibson bridge using BaseBridge pattern. + + Creates a minimal OmniGibson environment with Scene (empty floor + skybox) + and DummyTask for smoke testing. No BEHAVIOR-1K dataset required. + """ + + def _create_env(self, reset_config, simulator_kwargs): + """Create minimal OmniGibson env (no dataset needed). + + Args: + reset_config: Episode reset configuration. + simulator_kwargs: From task YAML's simulator_configs. + May contain 'og_config' to override the default config. + """ + import omnigibson as og + from omnigibson.macros import gm + + gm.ENABLE_FLATCACHE = True + gm.USE_GPU_DYNAMICS = False + + cfg = { + "scene": {"type": "Scene"}, + "robots": [{ + "type": "R1", + "obs_modalities": ["rgb"], + "action_type": "continuous", + "action_normalize": True, + }], + "task": {"type": "DummyTask"}, + } + # Allow full config override from simulator_kwargs + if "og_config" in simulator_kwargs: + cfg = simulator_kwargs["og_config"] + + env = og.Environment(configs=cfg) + return env + + def _on_reset(self, env, reset_config): + """Reset the OmniGibson environment.""" + obs, info = env.reset() + return obs + + def _on_step(self, env, action_text): + """Execute zero action (for smoke test) or parse action_text. + + For smoke testing, sends a zero action vector. Task-specific + subclasses should override for meaningful action parsing. + """ + import torch as th + + action_dim = env.action_space.shape[0] + action = th.zeros(action_dim) + obs, reward, terminated, truncated, info = env.step(action) + return obs, float(reward), terminated or truncated, info + + def _extract_image(self, obs): + """Auto-discover first RGB camera from nested OmniGibson observation. + + OmniGibson observations are nested: obs[robot_key][sensor_key]. + We find the first sensor key containing 'rgb' and return it. + """ + for robot_key in obs: + if not isinstance(obs[robot_key], dict): + continue + for sensor_key in obs[robot_key]: + if "rgb" in sensor_key.lower(): + img = obs[robot_key][sensor_key] + # Convert torch tensor to numpy if needed + if hasattr(img, "cpu"): + img = img.cpu().numpy() + # Convert float [0,1] to uint8 [0,255] + if img.dtype != np.uint8: + img = (img * 255).astype(np.uint8) + # Return RGB only (drop alpha if present) + return img[:, :, :3] + # Fallback: return black image + return np.zeros((128, 128, 3), dtype=np.uint8) + + +if __name__ == "__main__": + OmniGibsonBridge.main() diff --git a/easi/simulators/omnigibson/v3_7_2/conda_env.yaml b/easi/simulators/omnigibson/v3_7_2/conda_env.yaml new file mode 100644 index 0000000..e2fa9f5 --- /dev/null +++ b/easi/simulators/omnigibson/v3_7_2/conda_env.yaml @@ -0,0 +1,6 @@ +name: easi_omnigibson_v3_7_2 +channels: + - conda-forge + - defaults +dependencies: + - python=3.10 diff --git a/easi/simulators/omnigibson/v3_7_2/env_manager.py b/easi/simulators/omnigibson/v3_7_2/env_manager.py new file mode 100644 index 0000000..d2779ca --- /dev/null +++ b/easi/simulators/omnigibson/v3_7_2/env_manager.py @@ -0,0 +1,294 @@ +"""Environment manager for OmniGibson v3.7.2 + Isaac Sim 4.5.0. + +Used by BEHAVIOR-1K benchmark. Requires Python 3.10, CUDA 12.4, and conda. + +Replicates the BEHAVIOR-1K setup.sh install process: +1. Conda env creation (Python 3.10) + numpy/setuptools via base _do_install() +2. Git clone BEHAVIOR-1K repo (post_install) +3. Install PyTorch with CUDA (post_install) +4. Install bddl from cloned repo (post_install) +5. Install OmniGibson from cloned repo (post_install) +6. Download + install 26 Isaac Sim 4.5.0 wheels from pypi.nvidia.com (post_install) +7. Fix websockets conflict in Isaac Sim extscache (post_install) +8. Fix cffi compatibility (post_install) + +Headless mode: OMNIGIBSON_HEADLESS=1 uses Isaac Sim's native headless rendering +(no Xvfb needed). +""" +from __future__ import annotations + +import os +import subprocess +import tempfile +from pathlib import Path + +from easi.core.base_env_manager import BaseEnvironmentManager +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + +# 26 Isaac Sim 4.5.0 packages (from BEHAVIOR-1K/setup.sh) +ISAAC_SIM_PACKAGES = [ + "omniverse_kit-106.5.0.162521", + "isaacsim_kernel-4.5.0.0", + "isaacsim_app-4.5.0.0", + "isaacsim_core-4.5.0.0", + "isaacsim_gui-4.5.0.0", + "isaacsim_utils-4.5.0.0", + "isaacsim_storage-4.5.0.0", + "isaacsim_asset-4.5.0.0", + "isaacsim_sensor-4.5.0.0", + "isaacsim_robot_motion-4.5.0.0", + "isaacsim_robot-4.5.0.0", + "isaacsim_benchmark-4.5.0.0", + "isaacsim_code_editor-4.5.0.0", + "isaacsim_ros1-4.5.0.0", + "isaacsim_cortex-4.5.0.0", + "isaacsim_example-4.5.0.0", + "isaacsim_replicator-4.5.0.0", + "isaacsim_rl-4.5.0.0", + "isaacsim_robot_setup-4.5.0.0", + "isaacsim_ros2-4.5.0.0", + "isaacsim_template-4.5.0.0", + "isaacsim_test-4.5.0.0", + "isaacsim-4.5.0.0", + "isaacsim_extscache_physics-4.5.0.0", + "isaacsim_extscache_kit-4.5.0.0", + "isaacsim_extscache_kit_sdk-4.5.0.0", +] + + +class OmniGibsonEnvManager(BaseEnvironmentManager): + """Environment manager for OmniGibson 3.7.2 + Isaac Sim 4.5.0.""" + + @property + def simulator_name(self) -> str: + return "omnigibson" + + @property + def version(self) -> str: + return "v3_7_2" + + @property + def needs_display(self) -> bool: + return False # Isaac Sim headless via OMNIGIBSON_HEADLESS=1 + + def get_conda_env_yaml_path(self) -> Path: + return Path(__file__).parent / "conda_env.yaml" + + def get_requirements_txt_path(self) -> Path: + return Path(__file__).parent / "requirements.txt" + + def get_system_deps(self) -> list[str]: + return ["conda"] + + def get_validation_import(self) -> str: + return "import omnigibson; assert omnigibson.__version__.startswith('3.7')" + + def get_env_vars(self) -> dict[str, str]: + """Export OMNIGIBSON_HEADLESS=1 for headless rendering.""" + return {"OMNIGIBSON_HEADLESS": "1"} + + def post_install(self, context: dict) -> None: + """Replicate BEHAVIOR-1K setup.sh install process. + + Args: + context: Dict with env_dir, extras_dir, env_vars keys. + """ + extras_dir = Path(context["extras_dir"]) + extras_dir.mkdir(parents=True, exist_ok=True) + python = self.get_python_executable() + + # Step 1: Git clone BEHAVIOR-1K + self._clone_behavior_1k(extras_dir) + + behavior_1k_dir = extras_dir / "BEHAVIOR-1K" + + # Step 2: Install PyTorch with CUDA + self._install_pytorch(python) + + # Step 3: Install bddl from cloned repo + self._install_bddl(python, behavior_1k_dir) + + # Step 4: Install OmniGibson from cloned repo + self._install_omnigibson(python, behavior_1k_dir) + + # Step 5: Download + install Isaac Sim 4.5.0 wheels + self._install_isaac_sim(python) + + # Step 6: Fix websockets conflict in Isaac Sim extscache + self._fix_websockets_conflict(python) + + # Step 7: Fix cffi compatibility + self._fix_cffi(python) + + def _clone_behavior_1k(self, extras_dir: Path) -> None: + """Git clone BEHAVIOR-1K repo (idempotent).""" + repo_url = self.installation_kwargs.get( + "behavior_1k_repo", + "https://github.com/StanfordVL/BEHAVIOR-1K.git", + ) + tag = self.installation_kwargs.get("behavior_1k_tag", "v3.7.2") + behavior_1k_dir = extras_dir / "BEHAVIOR-1K" + + if behavior_1k_dir.exists(): + logger.info("BEHAVIOR-1K already cloned at %s, skipping", behavior_1k_dir) + return + + logger.info("Cloning BEHAVIOR-1K (tag %s) from %s", tag, repo_url) + self._run_command( + ["git", "clone", "-b", tag, "--depth", "1", repo_url, str(behavior_1k_dir)], + "clone BEHAVIOR-1K", + ) + + def _install_pytorch(self, python: str) -> None: + """Install PyTorch with CUDA support.""" + cuda_version = self.installation_kwargs.get("cuda_version", "12.4") + cuda_ver_short = cuda_version.replace(".", "") + + logger.info("Installing PyTorch with CUDA %s support", cuda_version) + self._run_command( + [ + python, "-m", "pip", "install", + "torch", "torchvision", "torchaudio", + "--index-url", f"https://download.pytorch.org/whl/cu{cuda_ver_short}", + ], + "install PyTorch with CUDA", + ) + + def _install_bddl(self, python: str, behavior_1k_dir: Path) -> None: + """Install bddl from cloned BEHAVIOR-1K repo.""" + bddl_dir = behavior_1k_dir / "bddl3" + logger.info("Installing bddl from %s", bddl_dir) + self._run_command( + [python, "-m", "pip", "install", "-e", str(bddl_dir)], + "install bddl", + ) + + def _install_omnigibson(self, python: str, behavior_1k_dir: Path) -> None: + """Install OmniGibson from cloned BEHAVIOR-1K repo.""" + og_dir = behavior_1k_dir / "OmniGibson" + logger.info("Installing OmniGibson from %s", og_dir) + env = os.environ.copy() + env["OMNI_KIT_ACCEPT_EULA"] = "YES" + self._run_command( + [python, "-m", "pip", "install", "-e", str(og_dir)], + "install OmniGibson", + env=env, + ) + + def _install_isaac_sim(self, python: str) -> None: + """Download and install 26 Isaac Sim 4.5.0 wheels from pypi.nvidia.com. + + Handles GLIBC < 2.34 by renaming wheel filenames from + manylinux_2_34 to manylinux_2_31. + """ + logger.info("Installing Isaac Sim 4.5.0 (%d packages)", len(ISAAC_SIM_PACKAGES)) + glibc_old = self._check_glibc_old() + if glibc_old: + logger.info("Detected GLIBC < 2.34, will rename wheel tags") + + temp_dir = Path(tempfile.mkdtemp(prefix="easi_isaac_sim_")) + wheel_files = [] + + try: + for pkg in ISAAC_SIM_PACKAGES: + pkg_name = pkg.rsplit("-", 1)[0] # e.g. "isaacsim_core" + filename = f"{pkg}-cp310-none-manylinux_2_34_x86_64.whl" + # pypi.nvidia.com uses dashes in URL path + url = f"https://pypi.nvidia.com/{pkg_name.replace('_', '-')}/{filename}" + filepath = temp_dir / filename + + logger.info("Downloading %s", pkg) + self._download_wheel(url, filepath) + + if glibc_old: + new_filename = filename.replace("manylinux_2_34", "manylinux_2_31") + new_filepath = temp_dir / new_filename + filepath.rename(new_filepath) + filepath = new_filepath + + wheel_files.append(str(filepath)) + + logger.info("Installing %d Isaac Sim wheels", len(wheel_files)) + env = os.environ.copy() + env["OMNI_KIT_ACCEPT_EULA"] = "YES" + self._run_command( + [python, "-m", "pip", "install"] + wheel_files, + "install Isaac Sim wheels", + env=env, + ) + finally: + # Clean up temp directory + import shutil + shutil.rmtree(temp_dir, ignore_errors=True) + + def _fix_websockets_conflict(self, python: str) -> None: + """Remove pip_prebundle/websockets dirs under Isaac Sim extscache. + + Isaac Sim bundles an old websockets version that conflicts with + the system one. Removing it forces the system package to be used. + """ + try: + result = subprocess.run( + [python, "-c", + "import isaacsim, os; print(os.environ.get('ISAAC_PATH', ''))"], + capture_output=True, text=True, timeout=30, + ) + isaac_path = result.stdout.strip() + except (subprocess.TimeoutExpired, FileNotFoundError): + isaac_path = "" + + if not isaac_path or not Path(isaac_path).exists(): + logger.info("ISAAC_PATH not found, skipping websockets fix") + return + + extscache = Path(isaac_path) / "extscache" + if not extscache.exists(): + return + + logger.info("Fixing websockets conflict under %s", extscache) + import shutil + for ws_dir in extscache.rglob("pip_prebundle/websockets"): + if ws_dir.is_dir(): + logger.trace("Removing %s", ws_dir) + shutil.rmtree(ws_dir, ignore_errors=True) + + def _fix_cffi(self, python: str) -> None: + """Force reinstall cffi==1.17.1 to resolve Isaac Sim compatibility.""" + logger.info("Fixing cffi compatibility") + self._run_command( + [python, "-m", "pip", "install", "--force-reinstall", "cffi==1.17.1"], + "fix cffi", + ) + + @staticmethod + def _check_glibc_old() -> bool: + """Check if system GLIBC version is < 2.34.""" + try: + result = subprocess.run( + ["ldd", "--version"], + capture_output=True, text=True, timeout=10, + ) + output = result.stdout + result.stderr + # Look for version like "2.31", "2.32", "2.33" + import re + match = re.search(r"(\d+\.\d+)", output) + if match: + version = float(match.group(1)) + return version < 2.34 + except (subprocess.TimeoutExpired, FileNotFoundError): + pass + return False + + @staticmethod + def _download_wheel(url: str, dest: Path) -> None: + """Download a single wheel file.""" + import urllib.request + req = urllib.request.Request(url, headers={"User-Agent": "easi/1.0"}) + with urllib.request.urlopen(req) as response, open(str(dest), "wb") as out: + while True: + chunk = response.read(1024 * 1024) + if not chunk: + break + out.write(chunk) diff --git a/easi/simulators/omnigibson/v3_7_2/requirements.txt b/easi/simulators/omnigibson/v3_7_2/requirements.txt new file mode 100644 index 0000000..aaab759 --- /dev/null +++ b/easi/simulators/omnigibson/v3_7_2/requirements.txt @@ -0,0 +1,2 @@ +numpy<2 +setuptools<=79 diff --git a/easi/simulators/omnigibson/v3_7_2/simulator.py b/easi/simulators/omnigibson/v3_7_2/simulator.py new file mode 100644 index 0000000..3286221 --- /dev/null +++ b/easi/simulators/omnigibson/v3_7_2/simulator.py @@ -0,0 +1,24 @@ +"""OmniGibson v3.7.2 simulator. + +Stub implementation — the bridge.py handles the actual OmniGibson interaction. +""" +from __future__ import annotations + +from pathlib import Path + +from easi.core.base_simulator import BaseSimulator + + +class OmniGibsonSimulator(BaseSimulator): + """OmniGibson 3.7.2 + Isaac Sim 4.5.0 simulator for BEHAVIOR-1K.""" + + @property + def name(self) -> str: + return "omnigibson" + + @property + def version(self) -> str: + return "v3_7_2" + + def _get_bridge_script_path(self) -> Path: + return Path(__file__).parent / "bridge.py" diff --git a/tests/test_omnigibson_simulator.py b/tests/test_omnigibson_simulator.py new file mode 100644 index 0000000..d696c18 --- /dev/null +++ b/tests/test_omnigibson_simulator.py @@ -0,0 +1,144 @@ +"""Tests for OmniGibson simulator integration (offline, no simulator needed).""" +import ast +from pathlib import Path + +import yaml + + +class TestOmniGibsonManifest: + """Test that manifest.yaml is correctly structured.""" + + def test_manifest_exists(self): + manifest = Path("easi/simulators/omnigibson/manifest.yaml") + assert manifest.exists() + + def test_manifest_contents(self): + with open("easi/simulators/omnigibson/manifest.yaml") as f: + data = yaml.safe_load(f) + assert data["name"] == "omnigibson" + assert "v3_7_2" in data["versions"] + assert data["default_version"] == "v3_7_2" + + def test_manifest_classes_importable(self): + with open("easi/simulators/omnigibson/manifest.yaml") as f: + data = yaml.safe_load(f) + v = data["versions"]["v3_7_2"] + assert "simulator.OmniGibsonSimulator" in v["simulator_class"] + assert "env_manager.OmniGibsonEnvManager" in v["env_manager_class"] + + def test_manifest_installation_kwargs(self): + with open("easi/simulators/omnigibson/manifest.yaml") as f: + data = yaml.safe_load(f) + kwargs = data["versions"]["v3_7_2"]["installation_kwargs"] + assert kwargs["cuda_version"] == "12.4" + assert "BEHAVIOR-1K" in kwargs["behavior_1k_repo"] + assert kwargs["behavior_1k_tag"] == "v3.7.2" + + +class TestOmniGibsonEnvManager: + """Test OmniGibsonEnvManager class.""" + + def test_import(self): + from easi.simulators.omnigibson.v3_7_2.env_manager import OmniGibsonEnvManager + mgr = OmniGibsonEnvManager() + assert mgr.simulator_name == "omnigibson" + assert mgr.version == "v3_7_2" + + def test_env_name(self): + from easi.simulators.omnigibson.v3_7_2.env_manager import OmniGibsonEnvManager + mgr = OmniGibsonEnvManager() + assert mgr.get_env_name() == "easi_omnigibson_v3_7_2" + + def test_needs_display(self): + from easi.simulators.omnigibson.v3_7_2.env_manager import OmniGibsonEnvManager + mgr = OmniGibsonEnvManager() + assert mgr.needs_display is False + + def test_env_vars(self): + from easi.simulators.omnigibson.v3_7_2.env_manager import OmniGibsonEnvManager + mgr = OmniGibsonEnvManager() + env_vars = mgr.get_env_vars() + assert env_vars == {"OMNIGIBSON_HEADLESS": "1"} + + def test_system_deps(self): + from easi.simulators.omnigibson.v3_7_2.env_manager import OmniGibsonEnvManager + mgr = OmniGibsonEnvManager() + deps = mgr.get_system_deps() + assert "conda" in deps + + def test_validation_import(self): + from easi.simulators.omnigibson.v3_7_2.env_manager import OmniGibsonEnvManager + mgr = OmniGibsonEnvManager() + val = mgr.get_validation_import() + assert "omnigibson" in val + + def test_conda_env_yaml_exists(self): + from easi.simulators.omnigibson.v3_7_2.env_manager import OmniGibsonEnvManager + mgr = OmniGibsonEnvManager() + assert mgr.get_conda_env_yaml_path().exists() + + def test_requirements_txt_exists(self): + from easi.simulators.omnigibson.v3_7_2.env_manager import OmniGibsonEnvManager + mgr = OmniGibsonEnvManager() + assert mgr.get_requirements_txt_path().exists() + + def test_isaac_sim_packages_count(self): + from easi.simulators.omnigibson.v3_7_2.env_manager import ISAAC_SIM_PACKAGES + assert len(ISAAC_SIM_PACKAGES) == 26 + + +class TestOmniGibsonSimulator: + """Test OmniGibsonSimulator class.""" + + def test_import(self): + from easi.simulators.omnigibson.v3_7_2.simulator import OmniGibsonSimulator + sim = OmniGibsonSimulator() + assert sim.name == "omnigibson" + assert sim.version == "v3_7_2" + + def test_bridge_script_path(self): + from easi.simulators.omnigibson.v3_7_2.simulator import OmniGibsonSimulator + sim = OmniGibsonSimulator() + bridge_path = sim._get_bridge_script_path() + assert bridge_path.exists() + assert bridge_path.name == "bridge.py" + + def test_bridge_script_has_bridge_class(self): + from easi.simulators.omnigibson.v3_7_2.simulator import OmniGibsonSimulator + content = OmniGibsonSimulator()._get_bridge_script_path().read_text() + assert "OmniGibsonBridge" in content + + +class TestOmniGibsonBridgeSyntax: + """Test that bridge.py is valid Python (without importing OmniGibson).""" + + def test_bridge_syntax(self): + bridge_path = Path("easi/simulators/omnigibson/v3_7_2/bridge.py") + ast.parse(bridge_path.read_text()) + + +class TestOmniGibsonRegistry: + """Test that registry discovers OmniGibson simulator.""" + + def test_env_list_includes_omnigibson(self): + from easi.simulators.registry import list_simulators + sims = list_simulators() + assert "omnigibson:v3_7_2" in sims + + def test_load_simulator_class(self): + from easi.simulators.registry import load_simulator_class + SimClass = load_simulator_class("omnigibson:v3_7_2") + sim = SimClass() + assert sim.name == "omnigibson" + + def test_create_env_manager(self): + from easi.simulators.registry import create_env_manager + mgr = create_env_manager("omnigibson:v3_7_2") + assert mgr.simulator_name == "omnigibson" + assert mgr.needs_display is False + + def test_create_env_manager_has_installation_kwargs(self): + from easi.simulators.registry import create_env_manager + mgr = create_env_manager("omnigibson") + assert mgr.installation_kwargs["cuda_version"] == "12.4" + assert "BEHAVIOR-1K" in mgr.installation_kwargs["behavior_1k_repo"] From ee9eadf3cb9d848707b1d8ee756ab71ba68afaec Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Sun, 22 Feb 2026 19:17:48 +0800 Subject: [PATCH 077/239] [Fix] NFS workaround for OmniGibson: copy Python to /tmp to fix /proc/self/exe On NFS/FUSE filesystems, /proc/self/exe resolves to "python3.10 (deleted)" which crashes Isaac Sim's Carbonite library. The env_manager now copies the conda env's Python binary to /tmp where /proc/self/exe resolves correctly. Changes: - env_manager: override get_python_executable() to return /tmp copy - env_manager: add _get_conda_python() for install-time pip operations - env_manager: add OMNI_KIT_ACCEPT_EULA and PYTHONHOME to get_env_vars() - bridge: add sys.path fix for subprocess import resolution - tests: add 2 new tests for NFS workaround, update env_vars assertion --- easi/simulators/omnigibson/v3_7_2/bridge.py | 6 ++ .../omnigibson/v3_7_2/env_manager.py | 64 ++++++++++++++++++- tests/test_omnigibson_simulator.py | 20 +++++- 3 files changed, 86 insertions(+), 4 deletions(-) diff --git a/easi/simulators/omnigibson/v3_7_2/bridge.py b/easi/simulators/omnigibson/v3_7_2/bridge.py index 1a8d781..457aec5 100644 --- a/easi/simulators/omnigibson/v3_7_2/bridge.py +++ b/easi/simulators/omnigibson/v3_7_2/bridge.py @@ -16,6 +16,12 @@ from __future__ import annotations import numpy as np +import sys +from pathlib import Path + +_repo_root = Path(__file__).resolve().parents[4] +if str(_repo_root) not in sys.path: + sys.path.insert(0, str(_repo_root)) from easi.simulators.base_bridge import BaseBridge diff --git a/easi/simulators/omnigibson/v3_7_2/env_manager.py b/easi/simulators/omnigibson/v3_7_2/env_manager.py index d2779ca..484dbe4 100644 --- a/easi/simulators/omnigibson/v3_7_2/env_manager.py +++ b/easi/simulators/omnigibson/v3_7_2/env_manager.py @@ -14,10 +14,17 @@ Headless mode: OMNIGIBSON_HEADLESS=1 uses Isaac Sim's native headless rendering (no Xvfb needed). + +NFS workaround: On NFS/FUSE filesystems, /proc/self/exe can resolve to +"python3.10 (deleted)" which crashes Isaac Sim's Carbonite library. The +get_python_executable() override copies the Python binary to /tmp (local +filesystem) where /proc/self/exe resolves correctly. """ from __future__ import annotations +import atexit import os +import shutil import subprocess import tempfile from pathlib import Path @@ -61,6 +68,8 @@ class OmniGibsonEnvManager(BaseEnvironmentManager): """Environment manager for OmniGibson 3.7.2 + Isaac Sim 4.5.0.""" + _local_python_dir: str | None = None # Cached /tmp copy directory + @property def simulator_name(self) -> str: return "omnigibson" @@ -85,9 +94,58 @@ def get_system_deps(self) -> list[str]: def get_validation_import(self) -> str: return "import omnigibson; assert omnigibson.__version__.startswith('3.7')" + def _get_conda_python(self) -> str: + """Return the real conda env Python path (for install-time use).""" + return super().get_python_executable() + + def get_python_executable(self) -> str: + """Return a local /tmp copy of the Python binary. + + On NFS/FUSE filesystems, /proc/self/exe resolves to + "python3.10 (deleted)" which causes Isaac Sim's Carbonite library + to abort. Copying the binary to /tmp (a local filesystem) ensures + /proc/self/exe resolves correctly. + + The copy is cached for the lifetime of this env manager instance + and cleaned up via atexit. + """ + if self._local_python_dir is not None: + local_python = Path(self._local_python_dir) / "python3" + if local_python.exists(): + return str(local_python) + + conda_python = self._get_conda_python() + # Resolve symlinks to get the real binary + real_binary = str(Path(conda_python).resolve()) + + tmp_dir = tempfile.mkdtemp(prefix="easi_python_") + local_python = Path(tmp_dir) / "python3" + shutil.copy2(real_binary, str(local_python)) + local_python.chmod(0o755) + + self._local_python_dir = tmp_dir + atexit.register(shutil.rmtree, tmp_dir, True) + + logger.trace( + "Copied Python to local filesystem: %s -> %s", + real_binary, local_python, + ) + return str(local_python) + def get_env_vars(self) -> dict[str, str]: - """Export OMNIGIBSON_HEADLESS=1 for headless rendering.""" - return {"OMNIGIBSON_HEADLESS": "1"} + """Export env vars for headless rendering, EULA, and PYTHONHOME. + + PYTHONHOME is set to the conda env directory so the /tmp Python + copy can find the conda env's stdlib and site-packages. + """ + conda_python = self._get_conda_python() + # conda env dir is two levels up from bin/python + conda_env_dir = str(Path(conda_python).resolve().parent.parent) + return { + "OMNIGIBSON_HEADLESS": "1", + "OMNI_KIT_ACCEPT_EULA": "YES", + "PYTHONHOME": conda_env_dir, + } def post_install(self, context: dict) -> None: """Replicate BEHAVIOR-1K setup.sh install process. @@ -97,7 +155,7 @@ def post_install(self, context: dict) -> None: """ extras_dir = Path(context["extras_dir"]) extras_dir.mkdir(parents=True, exist_ok=True) - python = self.get_python_executable() + python = self._get_conda_python() # Step 1: Git clone BEHAVIOR-1K self._clone_behavior_1k(extras_dir) diff --git a/tests/test_omnigibson_simulator.py b/tests/test_omnigibson_simulator.py index d696c18..6a58d30 100644 --- a/tests/test_omnigibson_simulator.py +++ b/tests/test_omnigibson_simulator.py @@ -58,7 +58,25 @@ def test_env_vars(self): from easi.simulators.omnigibson.v3_7_2.env_manager import OmniGibsonEnvManager mgr = OmniGibsonEnvManager() env_vars = mgr.get_env_vars() - assert env_vars == {"OMNIGIBSON_HEADLESS": "1"} + assert env_vars["OMNIGIBSON_HEADLESS"] == "1" + assert env_vars["OMNI_KIT_ACCEPT_EULA"] == "YES" + assert "PYTHONHOME" in env_vars + + def test_get_python_executable_is_local(self): + """Python executable should be a /tmp copy (NFS workaround).""" + from easi.simulators.omnigibson.v3_7_2.env_manager import OmniGibsonEnvManager + mgr = OmniGibsonEnvManager() + python_path = mgr.get_python_executable() + assert python_path.startswith("/tmp/easi_python_") + assert Path(python_path).exists() + + def test_get_conda_python_is_conda_env(self): + """_get_conda_python() returns the real conda env path.""" + from easi.simulators.omnigibson.v3_7_2.env_manager import OmniGibsonEnvManager + mgr = OmniGibsonEnvManager() + conda_python = mgr._get_conda_python() + assert "easi_omnigibson_v3_7_2" in conda_python + assert conda_python.endswith("bin/python") def test_system_deps(self): from easi.simulators.omnigibson.v3_7_2.env_manager import OmniGibsonEnvManager From e94db3af4015e0c4169d49573235b71ea96cdb40 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Mon, 23 Feb 2026 10:40:59 +0800 Subject: [PATCH 078/239] feat: add RenderPlatform abstraction with auto/native/xvfb/egl/headless --- easi/core/render_platform.py | 168 ++++++++++++++++++++++++++++++ tests/test_render_platform.py | 188 ++++++++++++++++++++++++++++++++++ 2 files changed, 356 insertions(+) create mode 100644 easi/core/render_platform.py create mode 100644 tests/test_render_platform.py diff --git a/easi/core/render_platform.py b/easi/core/render_platform.py new file mode 100644 index 0000000..35c6870 --- /dev/null +++ b/easi/core/render_platform.py @@ -0,0 +1,168 @@ +"""Pluggable render platform abstraction. + +Each render platform encapsulates how to launch a bridge subprocess with +the correct display/rendering environment. Simulators declare a default +platform; users can override via CLI (--render-platform) or task YAML. + +Built-in platforms: + auto — use native DISPLAY if available, fall back to xvfb + native — require existing DISPLAY + xvfb — always wrap with xvfb-run + egl — GPU-accelerated headless via EGL + headless — no display (simulator has native headless support) +""" + +from __future__ import annotations + +import os +from abc import ABC, abstractmethod +from pathlib import Path + +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + + +class RenderPlatform(ABC): + """Strategy interface for display/rendering backends.""" + + @property + @abstractmethod + def name(self) -> str: + """Short identifier (e.g. 'xvfb', 'egl').""" + ... + + @abstractmethod + def wrap_command(self, cmd: list[str], screen_config: str) -> list[str]: + """Optionally wrap the bridge launch command. + + Args: + cmd: The original command ``[python, bridge.py, ...]``. + screen_config: Screen resolution string, e.g. ``"1024x768x24"``. + + Returns: + The (possibly wrapped) command. + """ + ... + + def get_env_vars(self) -> dict[str, str]: + """Extra env vars needed by this platform (merged into subprocess).""" + return {} + + def get_system_deps(self) -> list[str]: + """System dependency names required by this platform.""" + return [] + + def is_available(self) -> bool: + """Whether this platform can run in the current environment.""" + return True + + +# -- Built-in implementations ------------------------------------------------ + + +class HeadlessPlatform(RenderPlatform): + """No display at all -- for simulators with native headless support.""" + + @property + def name(self) -> str: + return "headless" + + def wrap_command(self, cmd: list[str], screen_config: str) -> list[str]: + return cmd + + +class NativePlatform(RenderPlatform): + """Use the existing DISPLAY. Fails at validation if none is set.""" + + @property + def name(self) -> str: + return "native" + + def wrap_command(self, cmd: list[str], screen_config: str) -> list[str]: + return cmd + + def is_available(self) -> bool: + return bool(os.environ.get("DISPLAY", "")) + + +class XvfbPlatform(RenderPlatform): + """Always wrap with ``xvfb-run``.""" + + @property + def name(self) -> str: + return "xvfb" + + def wrap_command(self, cmd: list[str], screen_config: str) -> list[str]: + return [ + "xvfb-run", "-a", + "-s", f"-screen 0 {screen_config}", + ] + cmd + + def get_system_deps(self) -> list[str]: + return ["xvfb"] + + +class EGLPlatform(RenderPlatform): + """GPU-accelerated headless rendering via EGL (no X11 needed).""" + + @property + def name(self) -> str: + return "egl" + + def wrap_command(self, cmd: list[str], screen_config: str) -> list[str]: + return cmd + + def get_env_vars(self) -> dict[str, str]: + env: dict[str, str] = {"PYOPENGL_PLATFORM": "egl"} + mesa_vendor = Path("/usr/share/glvnd/egl_vendor.d/50_mesa.json") + if mesa_vendor.exists(): + env["__EGL_VENDOR_LIBRARY_FILENAMES"] = str(mesa_vendor) + return env + + def get_system_deps(self) -> list[str]: + return ["egl"] + + +class AutoPlatform(RenderPlatform): + """Detect native display; fall back to xvfb if unavailable.""" + + @property + def name(self) -> str: + return "auto" + + def wrap_command(self, cmd: list[str], screen_config: str) -> list[str]: + if os.environ.get("DISPLAY", ""): + return cmd + return XvfbPlatform().wrap_command(cmd, screen_config) + + +# -- Registry ----------------------------------------------------------------- + +_BUILTIN: dict[str, type[RenderPlatform]] = { + "auto": AutoPlatform, + "native": NativePlatform, + "xvfb": XvfbPlatform, + "egl": EGLPlatform, + "headless": HeadlessPlatform, +} + + +def get_render_platform(name: str) -> RenderPlatform: + """Instantiate a render platform by name. + + Raises: + ValueError: If name is not recognised. + """ + cls = _BUILTIN.get(name) + if cls is None: + raise ValueError( + f"Unknown render platform '{name}'. " + f"Available: {', '.join(sorted(_BUILTIN))}" + ) + return cls() + + +def available_platforms() -> list[str]: + """Return sorted list of registered platform names.""" + return sorted(_BUILTIN) diff --git a/tests/test_render_platform.py b/tests/test_render_platform.py new file mode 100644 index 0000000..d5f0b97 --- /dev/null +++ b/tests/test_render_platform.py @@ -0,0 +1,188 @@ +"""Tests for render platform abstraction.""" + +from __future__ import annotations + +import os +from pathlib import Path +from unittest.mock import patch + +import pytest + + +class TestRenderPlatformRegistry: + """Test platform discovery and instantiation.""" + + def test_get_platform_returns_auto(self): + from easi.core.render_platform import get_render_platform + + platform = get_render_platform("auto") + assert platform.name == "auto" + + def test_get_platform_returns_xvfb(self): + from easi.core.render_platform import get_render_platform + + platform = get_render_platform("xvfb") + assert platform.name == "xvfb" + + def test_get_platform_returns_native(self): + from easi.core.render_platform import get_render_platform + + platform = get_render_platform("native") + assert platform.name == "native" + + def test_get_platform_returns_egl(self): + from easi.core.render_platform import get_render_platform + + platform = get_render_platform("egl") + assert platform.name == "egl" + + def test_get_platform_returns_headless(self): + from easi.core.render_platform import get_render_platform + + platform = get_render_platform("headless") + assert platform.name == "headless" + + def test_get_platform_unknown_raises(self): + from easi.core.render_platform import get_render_platform + + with pytest.raises(ValueError, match="Unknown render platform"): + get_render_platform("nonexistent") + + def test_available_platforms_returns_names(self): + from easi.core.render_platform import available_platforms + + names = available_platforms() + assert set(names) >= {"auto", "native", "xvfb", "egl", "headless"} + + +class TestHeadlessPlatform: + """Headless: no wrapping, no env vars.""" + + def test_wrap_command_passthrough(self): + from easi.core.render_platform import get_render_platform + + p = get_render_platform("headless") + cmd = ["python", "bridge.py"] + assert p.wrap_command(cmd, "1024x768x24") == cmd + + def test_get_env_vars_empty(self): + from easi.core.render_platform import get_render_platform + + p = get_render_platform("headless") + assert p.get_env_vars() == {} + + def test_get_system_deps_empty(self): + from easi.core.render_platform import get_render_platform + + p = get_render_platform("headless") + assert p.get_system_deps() == [] + + +class TestXvfbPlatform: + """Xvfb: always wraps with xvfb-run.""" + + def test_wrap_command_prepends_xvfb_run(self): + from easi.core.render_platform import get_render_platform + + p = get_render_platform("xvfb") + cmd = ["python", "bridge.py"] + wrapped = p.wrap_command(cmd, "1280x720x24") + assert wrapped[:2] == ["xvfb-run", "-a"] + assert "-screen 0 1280x720x24" in wrapped[3] + assert wrapped[-2:] == cmd + + def test_get_env_vars_empty(self): + from easi.core.render_platform import get_render_platform + + p = get_render_platform("xvfb") + assert p.get_env_vars() == {} + + def test_get_system_deps_includes_xvfb(self): + from easi.core.render_platform import get_render_platform + + p = get_render_platform("xvfb") + assert "xvfb" in p.get_system_deps() + + +class TestNativePlatform: + """Native: passthrough, requires DISPLAY.""" + + def test_wrap_command_passthrough(self): + from easi.core.render_platform import get_render_platform + + p = get_render_platform("native") + cmd = ["python", "bridge.py"] + assert p.wrap_command(cmd, "1024x768x24") == cmd + + def test_get_env_vars_empty(self): + from easi.core.render_platform import get_render_platform + + p = get_render_platform("native") + assert p.get_env_vars() == {} + + def test_is_available_true_when_display_set(self): + from easi.core.render_platform import get_render_platform + + p = get_render_platform("native") + with patch.dict(os.environ, {"DISPLAY": ":0"}): + assert p.is_available() is True + + def test_is_available_false_when_no_display(self): + from easi.core.render_platform import get_render_platform + + p = get_render_platform("native") + with patch.dict(os.environ, {}, clear=True): + assert p.is_available() is False + + +class TestEGLPlatform: + """EGL: no wrapping, sets PYOPENGL_PLATFORM.""" + + def test_wrap_command_passthrough(self): + from easi.core.render_platform import get_render_platform + + p = get_render_platform("egl") + cmd = ["python", "bridge.py"] + assert p.wrap_command(cmd, "1024x768x24") == cmd + + def test_get_env_vars_sets_pyopengl(self): + from easi.core.render_platform import get_render_platform + + p = get_render_platform("egl") + env = p.get_env_vars() + assert env["PYOPENGL_PLATFORM"] == "egl" + + def test_get_system_deps_includes_egl(self): + from easi.core.render_platform import get_render_platform + + p = get_render_platform("egl") + assert "egl" in p.get_system_deps() + + +class TestAutoPlatform: + """Auto: native if DISPLAY exists, xvfb fallback otherwise.""" + + def test_wrap_command_uses_native_when_display_set(self): + from easi.core.render_platform import get_render_platform + + p = get_render_platform("auto") + cmd = ["python", "bridge.py"] + with patch.dict(os.environ, {"DISPLAY": ":0"}): + assert p.wrap_command(cmd, "1024x768x24") == cmd + + def test_wrap_command_uses_xvfb_when_no_display(self): + from easi.core.render_platform import get_render_platform + + p = get_render_platform("auto") + cmd = ["python", "bridge.py"] + env = os.environ.copy() + env.pop("DISPLAY", None) + with patch.dict(os.environ, env, clear=True): + wrapped = p.wrap_command(cmd, "1024x768x24") + assert wrapped[0] == "xvfb-run" + + def test_get_system_deps_empty(self): + from easi.core.render_platform import get_render_platform + + p = get_render_platform("auto") + assert p.get_system_deps() == [] From 6e2dad63be601eeb526b45dde58c4056a970ec49 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Mon, 23 Feb 2026 10:47:18 +0800 Subject: [PATCH 079/239] feat: add render platform properties to BaseEnvironmentManager --- easi/core/base_env_manager.py | 49 +++++++++++++++++++++++++++++------ tests/test_render_platform.py | 48 ++++++++++++++++++++++++++++++++++ 2 files changed, 89 insertions(+), 8 deletions(-) diff --git a/easi/core/base_env_manager.py b/easi/core/base_env_manager.py index 71186e7..e02383c 100644 --- a/easi/core/base_env_manager.py +++ b/easi/core/base_env_manager.py @@ -71,25 +71,58 @@ def get_validation_import(self) -> str: """ ... + @property + def default_render_platform(self) -> str: + """Default rendering platform for this simulator. + + Override in subclasses. Common values: + "auto" -- native display if available, xvfb fallback + "headless" -- no display (simulator handles internally) + "egl" -- GPU-accelerated headless via EGL + + See ``easi.core.render_platform`` for all options. + """ + return "headless" + + @property + def supported_render_platforms(self) -> list[str]: + """Render platforms this simulator can use. + + Override in subclasses to advertise which platforms are compatible. + Validated when user passes ``--render-platform``. + """ + return [self.default_render_platform] + + @property + def screen_config(self) -> str: + """Screen resolution config (e.g. ``"1024x768x24"``). + + Used by platforms that create a virtual display (xvfb). + Override for custom resolution/depth. + """ + return "1024x768x24" + @property def needs_display(self) -> bool: - """Whether this simulator requires a display (X11/Xvfb). + """Whether this simulator requires a display (backward compat). - Override to return True for simulators that need it (e.g., AI2-THOR). + Derived from ``default_render_platform``. """ - return False + return self.default_render_platform != "headless" @property def xvfb_screen_config(self) -> str: - """Xvfb screen config. Override for custom resolution/depth.""" - return "1024x768x24" + """Backward compat alias for ``screen_config``.""" + return self.screen_config - def get_env_vars(self) -> dict[str, str]: + def get_env_vars(self, render_platform_name: str | None = None) -> dict[str, str]: """Return environment variables to inject into the bridge subprocess. Override in subclasses to provide simulator-specific env vars. - Use _get_template_variables() and _resolve_template() to build - paths relative to the conda env directory. + + Args: + render_platform_name: Active render platform name (e.g. "egl"). + Subclasses can use this to conditionally set env vars. Returns: Dict of env var name -> value. Empty dict by default. diff --git a/tests/test_render_platform.py b/tests/test_render_platform.py index d5f0b97..d2a7ad4 100644 --- a/tests/test_render_platform.py +++ b/tests/test_render_platform.py @@ -186,3 +186,51 @@ def test_get_system_deps_empty(self): p = get_render_platform("auto") assert p.get_system_deps() == [] + + +class TestBaseEnvManagerRenderPlatform: + """Verify env_manager exposes render platform config.""" + + def _make_stub(self, **overrides): + """Create a minimal concrete BaseEnvironmentManager subclass.""" + from easi.core.base_env_manager import BaseEnvironmentManager + + attrs = { + "simulator_name": property(lambda self: "stub"), + "version": property(lambda self: "v0"), + "get_conda_env_yaml_path": lambda self: Path("/fake/conda.yaml"), + "get_requirements_txt_path": lambda self: Path("/fake/req.txt"), + "get_system_deps": lambda self: [], + "get_validation_import": lambda self: "import sys", + } + attrs.update(overrides) + Stub = type("Stub", (BaseEnvironmentManager,), attrs) + return Stub() + + def test_default_render_platform_is_headless(self): + mgr = self._make_stub() + assert mgr.default_render_platform == "headless" + + def test_supported_render_platforms_default(self): + mgr = self._make_stub() + assert "headless" in mgr.supported_render_platforms + + def test_screen_config_default(self): + mgr = self._make_stub() + assert mgr.screen_config == "1024x768x24" + + def test_needs_display_false_for_headless(self): + mgr = self._make_stub() + assert mgr.needs_display is False + + def test_needs_display_true_when_platform_not_headless(self): + mgr = self._make_stub( + default_render_platform=property(lambda self: "auto"), + ) + assert mgr.needs_display is True + + def test_xvfb_screen_config_aliases_screen_config(self): + mgr = self._make_stub( + screen_config=property(lambda self: "1920x1080x24"), + ) + assert mgr.xvfb_screen_config == "1920x1080x24" From 904b1104d3eee73810bdead43dd556b49da7aa9d Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Mon, 23 Feb 2026 10:53:52 +0800 Subject: [PATCH 080/239] feat: SubprocessRunner uses RenderPlatform for command wrapping --- easi/simulators/subprocess_runner.py | 46 ++++++++------- tests/test_render_platform.py | 83 ++++++++++++++++++++++++++++ 2 files changed, 110 insertions(+), 19 deletions(-) diff --git a/easi/simulators/subprocess_runner.py b/easi/simulators/subprocess_runner.py index ec95029..fd16804 100644 --- a/easi/simulators/subprocess_runner.py +++ b/easi/simulators/subprocess_runner.py @@ -7,7 +7,7 @@ - Crash recovery: Detects subprocess exit during polling - Cleanup: SIGTERM -> wait -> SIGKILL the entire process group; removes temp workspace -Supports xvfb-run wrapping for simulators that need a display. +Supports pluggable render platforms (xvfb, egl, native, headless, auto). """ from __future__ import annotations @@ -28,6 +28,7 @@ write_command, ) from easi.core.exceptions import SimulatorError, SimulatorTimeoutError +from easi.core.render_platform import RenderPlatform, get_render_platform from easi.utils.logging import get_logger logger = get_logger(__name__) @@ -46,8 +47,13 @@ def __init__( self, python_executable: str, bridge_script_path: Path, + # -- New render-platform API -- + render_platform: RenderPlatform | None = None, + screen_config: str = "1024x768x24", + # -- Backward-compat (deprecated) -- needs_display: bool = False, xvfb_screen_config: str = "1024x768x24", + # -- Other params -- startup_timeout: float = 30.0, command_timeout: float = 300.0, poll_interval: float = 0.1, @@ -56,8 +62,18 @@ def __init__( ): self.python_executable = python_executable self.bridge_script_path = bridge_script_path - self.needs_display = needs_display - self.xvfb_screen_config = xvfb_screen_config + + # Resolve render platform: explicit > backward-compat fallback + if render_platform is not None: + self.render_platform = render_platform + self.screen_config = screen_config + elif needs_display: + self.render_platform = get_render_platform("auto") + self.screen_config = xvfb_screen_config + else: + self.render_platform = get_render_platform("headless") + self.screen_config = xvfb_screen_config + self.startup_timeout = startup_timeout self.command_timeout = command_timeout self.poll_interval = poll_interval @@ -175,18 +191,21 @@ def shutdown(self) -> None: self._workspace = None def _build_subprocess_env(self) -> dict[str, str] | None: - """Build env dict for subprocess, merging extra_env with os.environ. + """Build env dict for subprocess, merging platform + extra_env. For path-like vars (LD_LIBRARY_PATH, PATH, etc.), prepends the new value to the existing value with ':' separator. - Returns None if no extra_env (subprocess inherits parent env). + Returns None if no env vars to set (subprocess inherits parent env). """ - if not self.extra_env: + platform_env = self.render_platform.get_env_vars() + combined = {**platform_env, **(self.extra_env or {})} + + if not combined: return None env = os.environ.copy() - for key, value in self.extra_env.items(): + for key, value in combined.items(): if key in self._PREPEND_ENV_VARS and key in env: env[key] = f"{value}:{env[key]}" else: @@ -203,18 +222,7 @@ def _build_launch_command(self) -> list[str]: ] cmd.extend(self.extra_args) - if self.needs_display and not self._has_display(): - # Wrap with xvfb-run for headless environments - cmd = [ - "xvfb-run", "-a", - "-s", f"-screen 0 {self.xvfb_screen_config}", - ] + cmd - - return cmd - - def _has_display(self) -> bool: - """Check if an X display is available.""" - return bool(os.environ.get("DISPLAY", "")) + return self.render_platform.wrap_command(cmd, self.screen_config) # Pattern to extract log level from bridge output (e.g. "[WARNING]" or "[ERROR]") _BRIDGE_LEVEL_RE = re.compile(r"\[(\w+)\]") diff --git a/tests/test_render_platform.py b/tests/test_render_platform.py index d2a7ad4..ba79c53 100644 --- a/tests/test_render_platform.py +++ b/tests/test_render_platform.py @@ -234,3 +234,86 @@ def test_xvfb_screen_config_aliases_screen_config(self): screen_config=property(lambda self: "1920x1080x24"), ) assert mgr.xvfb_screen_config == "1920x1080x24" + + +class TestSubprocessRunnerRenderPlatform: + """Verify SubprocessRunner uses RenderPlatform for command wrapping.""" + + def test_accepts_render_platform_param(self): + from easi.core.render_platform import get_render_platform + from easi.simulators.subprocess_runner import SubprocessRunner + + p = get_render_platform("headless") + runner = SubprocessRunner( + python_executable="/usr/bin/python3", + bridge_script_path=Path("/fake/bridge.py"), + render_platform=p, + ) + assert runner.render_platform.name == "headless" + + def test_build_command_uses_platform_wrap(self): + from easi.core.render_platform import get_render_platform + from easi.simulators.subprocess_runner import SubprocessRunner + + p = get_render_platform("xvfb") + runner = SubprocessRunner( + python_executable="/usr/bin/python3", + bridge_script_path=Path("/fake/bridge.py"), + render_platform=p, + screen_config="1280x720x24", + ) + runner._workspace = Path("/tmp/fake_ws") + cmd = runner._build_launch_command() + assert cmd[0] == "xvfb-run" + assert "1280x720x24" in cmd[3] + + def test_build_command_headless_no_wrap(self): + from easi.core.render_platform import get_render_platform + from easi.simulators.subprocess_runner import SubprocessRunner + + p = get_render_platform("headless") + runner = SubprocessRunner( + python_executable="/usr/bin/python3", + bridge_script_path=Path("/fake/bridge.py"), + render_platform=p, + ) + runner._workspace = Path("/tmp/fake_ws") + cmd = runner._build_launch_command() + assert cmd[0] == "/usr/bin/python3" + + def test_platform_env_vars_merged(self): + from easi.core.render_platform import get_render_platform + from easi.simulators.subprocess_runner import SubprocessRunner + + p = get_render_platform("egl") + runner = SubprocessRunner( + python_executable="/usr/bin/python3", + bridge_script_path=Path("/fake/bridge.py"), + render_platform=p, + extra_env={"SIM_ROOT": "/opt/sim"}, + ) + env = runner._build_subprocess_env() + assert env["PYOPENGL_PLATFORM"] == "egl" + assert env["SIM_ROOT"] == "/opt/sim" + + def test_backward_compat_needs_display_still_works(self): + """Old callers passing needs_display=True get AutoPlatform.""" + from easi.simulators.subprocess_runner import SubprocessRunner + + runner = SubprocessRunner( + python_executable="/usr/bin/python3", + bridge_script_path=Path("/fake/bridge.py"), + needs_display=True, + xvfb_screen_config="1024x768x24", + ) + assert runner.render_platform.name == "auto" + + def test_backward_compat_needs_display_false_gets_headless(self): + from easi.simulators.subprocess_runner import SubprocessRunner + + runner = SubprocessRunner( + python_executable="/usr/bin/python3", + bridge_script_path=Path("/fake/bridge.py"), + needs_display=False, + ) + assert runner.render_platform.name == "headless" From 463fd84b13b3770116f5c4fd20498efc42a6fc35 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Mon, 23 Feb 2026 11:01:55 +0800 Subject: [PATCH 081/239] feat: declare render platform preferences in all simulator env managers --- easi/simulators/ai2thor/v2_1_0/env_manager.py | 8 ++- easi/simulators/ai2thor/v5_0_0/env_manager.py | 12 ++-- .../coppeliasim/v4_1_0/env_manager.py | 22 ++++--- .../habitat_sim/v0_3_0/env_manager.py | 8 ++- .../omnigibson/v3_7_2/env_manager.py | 6 +- easi/simulators/tdw/v1_11_23/env_manager.py | 10 ++- tests/test_render_platform.py | 62 +++++++++++++++++++ 7 files changed, 104 insertions(+), 24 deletions(-) diff --git a/easi/simulators/ai2thor/v2_1_0/env_manager.py b/easi/simulators/ai2thor/v2_1_0/env_manager.py index aef6e4a..ea2c567 100644 --- a/easi/simulators/ai2thor/v2_1_0/env_manager.py +++ b/easi/simulators/ai2thor/v2_1_0/env_manager.py @@ -22,8 +22,12 @@ def version(self) -> str: return "v2_1_0" @property - def needs_display(self) -> bool: - return True # AI2-THOR Unity requires X11 + def default_render_platform(self) -> str: + return "auto" + + @property + def supported_render_platforms(self) -> list[str]: + return ["auto", "xvfb", "native"] def get_conda_env_yaml_path(self) -> Path: return Path(__file__).parent / "conda_env.yaml" diff --git a/easi/simulators/ai2thor/v5_0_0/env_manager.py b/easi/simulators/ai2thor/v5_0_0/env_manager.py index c85e609..0e18488 100644 --- a/easi/simulators/ai2thor/v5_0_0/env_manager.py +++ b/easi/simulators/ai2thor/v5_0_0/env_manager.py @@ -19,12 +19,16 @@ def version(self) -> str: return "v5_0_0" @property - def needs_display(self) -> bool: - return True # AI2-THOR Unity requires X11 + def default_render_platform(self) -> str: + return "auto" @property - def xvfb_screen_config(self) -> str: - return "1280x720x24" # v5 supports higher resolution + def supported_render_platforms(self) -> list[str]: + return ["auto", "xvfb", "native"] + + @property + def screen_config(self) -> str: + return "1280x720x24" def get_conda_env_yaml_path(self) -> Path: return Path(__file__).parent / "conda_env.yaml" diff --git a/easi/simulators/coppeliasim/v4_1_0/env_manager.py b/easi/simulators/coppeliasim/v4_1_0/env_manager.py index b88f198..3a53aff 100644 --- a/easi/simulators/coppeliasim/v4_1_0/env_manager.py +++ b/easi/simulators/coppeliasim/v4_1_0/env_manager.py @@ -33,11 +33,15 @@ def version(self) -> str: return "v4_1_0" @property - def needs_display(self) -> bool: - return True # CoppeliaSim needs X11/Xvfb + def default_render_platform(self) -> str: + return "auto" @property - def xvfb_screen_config(self) -> str: + def supported_render_platforms(self) -> list[str]: + return ["auto", "xvfb", "native"] + + @property + def screen_config(self) -> str: return "1280x720x24" def get_conda_env_yaml_path(self) -> Path: @@ -52,7 +56,7 @@ def get_system_deps(self) -> list[str]: def get_validation_import(self) -> str: return "from pyrep import PyRep; print('PyRep OK')" - def get_env_vars(self) -> dict[str, str]: + def get_env_vars(self, render_platform_name: str | None = None) -> dict[str, str]: """Return CoppeliaSim env vars for bridge subprocess.""" binary_dir_name = self.installation_kwargs.get("binary_dir_name", "") if not binary_dir_name: @@ -69,10 +73,12 @@ def get_env_vars(self) -> dict[str, str]: "LD_LIBRARY_PATH": ld_path, "QT_QPA_PLATFORM_PLUGIN_PATH": coppeliasim_root, } - # Force Mesa EGL vendor for Xvfb (NVIDIA EGL crashes Xvfb on some systems) - mesa_vendor = Path("/usr/share/glvnd/egl_vendor.d/50_mesa.json") - if mesa_vendor.exists(): - env["__EGL_VENDOR_LIBRARY_FILENAMES"] = str(mesa_vendor) + # Force Mesa EGL vendor for Xvfb (NVIDIA EGL crashes Xvfb on some systems). + # Skip when using the EGL platform — it handles __EGL_VENDOR_LIBRARY_FILENAMES itself. + if render_platform_name != "egl": + mesa_vendor = Path("/usr/share/glvnd/egl_vendor.d/50_mesa.json") + if mesa_vendor.exists(): + env["__EGL_VENDOR_LIBRARY_FILENAMES"] = str(mesa_vendor) return env def post_install(self, context: dict) -> None: diff --git a/easi/simulators/habitat_sim/v0_3_0/env_manager.py b/easi/simulators/habitat_sim/v0_3_0/env_manager.py index 56cf3d6..c3bb72d 100644 --- a/easi/simulators/habitat_sim/v0_3_0/env_manager.py +++ b/easi/simulators/habitat_sim/v0_3_0/env_manager.py @@ -19,8 +19,12 @@ def version(self) -> str: return "v0_3_0" @property - def needs_display(self) -> bool: - return True # habitat-sim needs a display (xvfb on headless servers) + def default_render_platform(self) -> str: + return "auto" + + @property + def supported_render_platforms(self) -> list[str]: + return ["auto", "xvfb", "native", "egl"] def get_conda_env_yaml_path(self) -> Path: return Path(__file__).parent / "conda_env.yaml" diff --git a/easi/simulators/omnigibson/v3_7_2/env_manager.py b/easi/simulators/omnigibson/v3_7_2/env_manager.py index 484dbe4..52d8124 100644 --- a/easi/simulators/omnigibson/v3_7_2/env_manager.py +++ b/easi/simulators/omnigibson/v3_7_2/env_manager.py @@ -78,10 +78,6 @@ def simulator_name(self) -> str: def version(self) -> str: return "v3_7_2" - @property - def needs_display(self) -> bool: - return False # Isaac Sim headless via OMNIGIBSON_HEADLESS=1 - def get_conda_env_yaml_path(self) -> Path: return Path(__file__).parent / "conda_env.yaml" @@ -132,7 +128,7 @@ def get_python_executable(self) -> str: ) return str(local_python) - def get_env_vars(self) -> dict[str, str]: + def get_env_vars(self, render_platform_name: str | None = None) -> dict[str, str]: """Export env vars for headless rendering, EULA, and PYTHONHOME. PYTHONHOME is set to the conda env directory so the /tmp Python diff --git a/easi/simulators/tdw/v1_11_23/env_manager.py b/easi/simulators/tdw/v1_11_23/env_manager.py index 34ef8fc..ca03a4a 100644 --- a/easi/simulators/tdw/v1_11_23/env_manager.py +++ b/easi/simulators/tdw/v1_11_23/env_manager.py @@ -30,8 +30,12 @@ def version(self) -> str: return "v1_11_23" @property - def needs_display(self) -> bool: - return True # TDW Unity build requires X11 + def default_render_platform(self) -> str: + return "auto" + + @property + def supported_render_platforms(self) -> list[str]: + return ["auto", "xvfb", "native"] def get_conda_env_yaml_path(self) -> Path: return Path(__file__).parent / "conda_env.yaml" @@ -62,7 +66,7 @@ def env_is_ready(self) -> bool: return False return True - def get_env_vars(self) -> dict[str, str]: + def get_env_vars(self, render_platform_name: str | None = None) -> dict[str, str]: """Return TDW env vars for bridge subprocess.""" build_dir = self.installation_kwargs.get("build_dir_name", "") if not build_dir: diff --git a/tests/test_render_platform.py b/tests/test_render_platform.py index ba79c53..e291099 100644 --- a/tests/test_render_platform.py +++ b/tests/test_render_platform.py @@ -317,3 +317,65 @@ def test_backward_compat_needs_display_false_gets_headless(self): needs_display=False, ) assert runner.render_platform.name == "headless" + + +class TestSimulatorRenderPlatforms: + """Each simulator declares render platform preferences.""" + + def test_ai2thor_v2(self): + from easi.simulators.ai2thor.v2_1_0.env_manager import AI2ThorEnvManagerV210 + + mgr = AI2ThorEnvManagerV210() + assert mgr.default_render_platform == "auto" + assert "xvfb" in mgr.supported_render_platforms + assert "native" in mgr.supported_render_platforms + # backward compat + assert mgr.needs_display is True + + def test_ai2thor_v5(self): + from easi.simulators.ai2thor.v5_0_0.env_manager import AI2ThorEnvManagerV500 + + mgr = AI2ThorEnvManagerV500() + assert mgr.default_render_platform == "auto" + assert mgr.screen_config == "1280x720x24" + + def test_habitat(self): + from easi.simulators.habitat_sim.v0_3_0.env_manager import HabitatEnvManagerV030 + + mgr = HabitatEnvManagerV030() + assert mgr.default_render_platform == "auto" + assert "egl" in mgr.supported_render_platforms + + def test_coppeliasim(self): + from easi.simulators.coppeliasim.v4_1_0.env_manager import CoppeliaSimEnvManagerV410 + + mgr = CoppeliaSimEnvManagerV410() + assert mgr.default_render_platform == "auto" + assert mgr.screen_config == "1280x720x24" + + def test_coppeliasim_egl_skips_mesa_vendor(self): + """When render_platform is 'egl', CoppeliaSim should NOT set __EGL_VENDOR_LIBRARY_FILENAMES.""" + from easi.simulators.coppeliasim.v4_1_0.env_manager import CoppeliaSimEnvManagerV410 + + mgr = CoppeliaSimEnvManagerV410() + env = mgr.get_env_vars(render_platform_name="egl") + assert "__EGL_VENDOR_LIBRARY_FILENAMES" not in env + + def test_tdw(self): + from easi.simulators.tdw.v1_11_23.env_manager import TDWEnvManager + + mgr = TDWEnvManager() + assert mgr.default_render_platform == "auto" + + def test_omnigibson(self): + from easi.simulators.omnigibson.v3_7_2.env_manager import OmniGibsonEnvManager + + mgr = OmniGibsonEnvManager() + assert mgr.default_render_platform == "headless" + assert mgr.needs_display is False + + def test_dummy(self): + from easi.simulators.dummy.v1.env_manager import DummyEnvManager + + mgr = DummyEnvManager() + assert mgr.default_render_platform == "headless" From 0e9df388c84fa37961397be93c9d2795c244f805 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Mon, 23 Feb 2026 11:15:00 +0800 Subject: [PATCH 082/239] feat: add --render-platform to CLI with validation and YAML support --- easi/cli.py | 28 +++++- easi/evaluation/runner.py | 38 ++++++-- tests/test_env_vars_wiring.py | 8 ++ tests/test_evaluation_runner.py | 1 + tests/test_installation_kwargs.py | 4 + tests/test_render_platform.py | 150 ++++++++++++++++++++++++++++++ 6 files changed, 216 insertions(+), 13 deletions(-) diff --git a/easi/cli.py b/easi/cli.py index 368d894..673c88f 100644 --- a/easi/cli.py +++ b/easi/cli.py @@ -78,6 +78,9 @@ def build_parser() -> argparse.ArgumentParser: sim_test.add_argument("--steps", type=int, default=5, help="Number of steps") sim_test.add_argument("--timeout", type=float, default=200.0, help="Bridge startup timeout in seconds (default: 200)") + sim_test.add_argument( + "--render-platform", type=str, default=None, dest="render_platform", + help="Rendering platform override (auto, native, xvfb, egl, headless)") # --- start command --- start_parser = subparsers.add_parser("start", help="Run a full evaluation", parents=[common]) @@ -115,6 +118,9 @@ def build_parser() -> argparse.ArgumentParser: help="Path to a previous run directory to resume from") start_parser.add_argument("--refresh-data", action="store_true", dest="refresh_data", help="Delete cached dataset and re-download from source") + start_parser.add_argument( + "--render-platform", type=str, default=None, dest="render_platform", + help="Rendering platform: auto, native, xvfb, egl, headless (default: simulator's preference)") # --- llm-server command --- llm_parser = subparsers.add_parser("llm-server", help="Start dummy LLM server", parents=[common]) @@ -247,8 +253,9 @@ def cmd_task_download(task_name: str, refresh_data: bool = False) -> None: logger.info("Task uses built-in episodes (no download needed).") -def cmd_sim_test(simulator: str, steps: int, timeout: float) -> None: +def cmd_sim_test(simulator: str, steps: int, timeout: float, render_platform_name: str | None = None) -> None: from easi.core.episode import Action + from easi.core.render_platform import get_render_platform from easi.simulators.registry import create_env_manager, load_simulator_class from easi.simulators.subprocess_runner import SubprocessRunner @@ -256,16 +263,27 @@ def cmd_sim_test(simulator: str, steps: int, timeout: float) -> None: SimClass = load_simulator_class(simulator) sim = SimClass() + # Resolve render platform + platform_name = render_platform_name or env_manager.default_render_platform + if platform_name not in env_manager.supported_render_platforms: + logger.error( + "Render platform '%s' not supported by %s. Supported: %s", + platform_name, simulator, env_manager.supported_render_platforms, + ) + sys.exit(1) + render_platform = get_render_platform(platform_name) + logger.info("Testing %s...", simulator) logger.info(" Python: %s", env_manager.get_python_executable()) + logger.info(" Render platform: %s", platform_name) - env_vars = env_manager.get_env_vars() + env_vars = env_manager.get_env_vars(render_platform_name=platform_name) runner = SubprocessRunner( python_executable=env_manager.get_python_executable(), bridge_script_path=sim._get_bridge_script_path(), - needs_display=env_manager.needs_display, - xvfb_screen_config=env_manager.xvfb_screen_config, + render_platform=render_platform, + screen_config=env_manager.screen_config, startup_timeout=timeout, command_timeout=timeout, extra_env=env_vars or None, @@ -442,7 +460,7 @@ def main() -> None: elif args.command == "sim": if args.sim_action == "test": - cmd_sim_test(args.simulator, args.steps, args.timeout) + cmd_sim_test(args.simulator, args.steps, args.timeout, getattr(args, "render_platform", None)) else: parser.parse_args(["sim", "--help"]) diff --git a/easi/evaluation/runner.py b/easi/evaluation/runner.py index 76afcf1..ac8a080 100644 --- a/easi/evaluation/runner.py +++ b/easi/evaluation/runner.py @@ -63,6 +63,7 @@ def __init__( max_retries: int = 3, resume_dir: Path | str | None = None, refresh_data: bool = False, + render_platform: str | None = None, ): # Auto-capture all init args for config.json (before any mutation) frame = inspect.currentframe() @@ -85,6 +86,7 @@ def __init__( self.max_retries = max_retries self.resume_dir = Path(resume_dir) if resume_dir else None self.refresh_data = refresh_data + self.render_platform_name = render_platform timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") if self.model: safe_model = self.model.replace("/", "_") @@ -580,12 +582,6 @@ def _create_simulator(self, simulator_key: str, task=None): if task and task.simulator_kwargs: extra_args.extend(["--simulator-kwargs", _json.dumps(task.simulator_kwargs)]) - env_vars = env_manager.get_env_vars() - - # Merge task-level env_vars from simulator_configs.env_vars - if task and task.extra_env_vars: - env_vars = {**env_vars, **task.extra_env_vars} - # Extract runner-level timeouts from simulator_configs sim_configs = task.simulator_configs if task else {} runner_kwargs = {} @@ -594,11 +590,37 @@ def _create_simulator(self, simulator_key: str, task=None): if sim_configs.get("startup_timeout"): runner_kwargs["startup_timeout"] = float(sim_configs["startup_timeout"]) + # Resolve render platform: CLI > task YAML > env_manager default + from easi.core.render_platform import get_render_platform + + yaml_platform = sim_configs.get("render_platform") if task else None + platform_name = ( + self.render_platform_name + or yaml_platform + or env_manager.default_render_platform + ) + + if platform_name not in env_manager.supported_render_platforms: + raise ValueError( + f"Render platform '{platform_name}' is not supported by " + f"{env_manager.simulator_name}:{env_manager.version}. " + f"Supported: {env_manager.supported_render_platforms}" + ) + + render_platform = get_render_platform(platform_name) + + # Pass platform name to get_env_vars for conditional logic + env_vars = env_manager.get_env_vars(render_platform_name=platform_name) + + # Merge task-level env_vars from simulator_configs.env_vars + if task and task.extra_env_vars: + env_vars = {**env_vars, **task.extra_env_vars} + runner = SubprocessRunner( python_executable=env_manager.get_python_executable(), bridge_script_path=bridge_path, - needs_display=env_manager.needs_display, - xvfb_screen_config=env_manager.xvfb_screen_config, + render_platform=render_platform, + screen_config=env_manager.screen_config, extra_args=extra_args, extra_env=env_vars or None, **runner_kwargs, diff --git a/tests/test_env_vars_wiring.py b/tests/test_env_vars_wiring.py index f54e300..8ec8264 100644 --- a/tests/test_env_vars_wiring.py +++ b/tests/test_env_vars_wiring.py @@ -14,10 +14,14 @@ def test_runner_passes_env_vars_to_subprocess(self): runner = EvaluationRunner.__new__(EvaluationRunner) runner.data_dir = Path("/tmp/fake") + runner.render_platform_name = None mock_env_mgr = MagicMock() mock_env_mgr.env_is_ready.return_value = True mock_env_mgr.get_python_executable.return_value = "/usr/bin/python3" + mock_env_mgr.default_render_platform = "headless" + mock_env_mgr.supported_render_platforms = ["headless"] + mock_env_mgr.screen_config = "1024x768x24" mock_env_mgr.needs_display = False mock_env_mgr.xvfb_screen_config = "1024x768x24" mock_env_mgr.get_env_vars.return_value = {"SIM_ROOT": "/opt/sim"} @@ -42,10 +46,14 @@ def test_runner_passes_none_when_no_env_vars(self): runner = EvaluationRunner.__new__(EvaluationRunner) runner.data_dir = Path("/tmp/fake") + runner.render_platform_name = None mock_env_mgr = MagicMock() mock_env_mgr.env_is_ready.return_value = True mock_env_mgr.get_python_executable.return_value = "/usr/bin/python3" + mock_env_mgr.default_render_platform = "headless" + mock_env_mgr.supported_render_platforms = ["headless"] + mock_env_mgr.screen_config = "1024x768x24" mock_env_mgr.needs_display = False mock_env_mgr.xvfb_screen_config = "1024x768x24" mock_env_mgr.get_env_vars.return_value = {} diff --git a/tests/test_evaluation_runner.py b/tests/test_evaluation_runner.py index c5ca42e..1a4a373 100644 --- a/tests/test_evaluation_runner.py +++ b/tests/test_evaluation_runner.py @@ -96,6 +96,7 @@ def test_cli_options_contains_all_init_args(self): "task_name", "agent_type", "output_dir", "data_dir", "max_episodes", "llm_base_url", "agent_seed", "backend", "model", "port", "llm_kwargs_raw", "max_retries", + "render_platform", } assert set(opts.keys()) == expected_keys diff --git a/tests/test_installation_kwargs.py b/tests/test_installation_kwargs.py index 9004f8a..300036c 100644 --- a/tests/test_installation_kwargs.py +++ b/tests/test_installation_kwargs.py @@ -57,10 +57,14 @@ def test_runner_passes_installation_kwargs(self): runner = EvaluationRunner.__new__(EvaluationRunner) runner.data_dir = Path("/tmp/fake") + runner.render_platform_name = None mock_env_mgr = MagicMock() mock_env_mgr.env_is_ready.return_value = True mock_env_mgr.get_python_executable.return_value = "/usr/bin/python3" + mock_env_mgr.default_render_platform = "headless" + mock_env_mgr.supported_render_platforms = ["headless"] + mock_env_mgr.screen_config = "1024x768x24" mock_env_mgr.needs_display = False mock_env_mgr.xvfb_screen_config = "1024x768x24" mock_env_mgr.get_env_vars.return_value = {} diff --git a/tests/test_render_platform.py b/tests/test_render_platform.py index e291099..c6e1884 100644 --- a/tests/test_render_platform.py +++ b/tests/test_render_platform.py @@ -379,3 +379,153 @@ def test_dummy(self): mgr = DummyEnvManager() assert mgr.default_render_platform == "headless" + + +from unittest.mock import MagicMock + + +class TestCLIRenderPlatform: + """Verify --render-platform is accepted by CLI parser.""" + + def test_start_parser_accepts_render_platform(self): + from easi.cli import build_parser + + parser = build_parser() + args = parser.parse_args(["start", "dummy_task", "--render-platform", "egl"]) + assert args.render_platform == "egl" + + def test_start_parser_default_is_none(self): + from easi.cli import build_parser + + parser = build_parser() + args = parser.parse_args(["start", "dummy_task"]) + assert args.render_platform is None + + def test_sim_test_parser_accepts_render_platform(self): + from easi.cli import build_parser + + parser = build_parser() + args = parser.parse_args(["sim", "test", "dummy", "--render-platform", "xvfb"]) + assert args.render_platform == "xvfb" + + +class TestRunnerRenderPlatformWiring: + """Verify EvaluationRunner resolves and passes render platform.""" + + def _make_mock_env_mgr(self): + mgr = MagicMock() + mgr.env_is_ready.return_value = True + mgr.get_python_executable.return_value = "/usr/bin/python3" + mgr.default_render_platform = "auto" + mgr.supported_render_platforms = ["auto", "xvfb", "native", "egl"] + mgr.screen_config = "1024x768x24" + mgr.needs_display = True + mgr.xvfb_screen_config = "1024x768x24" + mgr.get_env_vars.return_value = {} + return mgr + + def _make_mock_task(self, render_platform=None): + task = MagicMock() + task.additional_deps = [] + task.simulator_kwargs = {} + task.extra_env_vars = {} + task.simulator_configs = {} + if render_platform: + task.simulator_configs = {"render_platform": render_platform} + task.get_bridge_script_path.return_value = None + return task + + def test_default_uses_env_manager_platform(self): + from easi.evaluation.runner import EvaluationRunner + + runner = EvaluationRunner.__new__(EvaluationRunner) + runner.data_dir = Path("/tmp/fake") + runner.render_platform_name = None + + mock_env_mgr = self._make_mock_env_mgr() + mock_sim_cls = MagicMock() + mock_sim_cls.return_value._get_bridge_script_path.return_value = Path("/fake/bridge.py") + + with patch("easi.simulators.registry.create_env_manager", return_value=mock_env_mgr), \ + patch("easi.simulators.registry.load_simulator_class", return_value=mock_sim_cls), \ + patch("easi.simulators.subprocess_runner.SubprocessRunner") as MockRunner: + MockRunner.return_value.launch.return_value = None + runner._create_simulator("fake:v1") + rp = MockRunner.call_args.kwargs.get("render_platform") + assert rp.name == "auto" + + def test_cli_override_wins(self): + from easi.evaluation.runner import EvaluationRunner + + runner = EvaluationRunner.__new__(EvaluationRunner) + runner.data_dir = Path("/tmp/fake") + runner.render_platform_name = "xvfb" + + mock_env_mgr = self._make_mock_env_mgr() + mock_sim_cls = MagicMock() + mock_sim_cls.return_value._get_bridge_script_path.return_value = Path("/fake/bridge.py") + + with patch("easi.simulators.registry.create_env_manager", return_value=mock_env_mgr), \ + patch("easi.simulators.registry.load_simulator_class", return_value=mock_sim_cls), \ + patch("easi.simulators.subprocess_runner.SubprocessRunner") as MockRunner: + MockRunner.return_value.launch.return_value = None + runner._create_simulator("fake:v1") + rp = MockRunner.call_args.kwargs.get("render_platform") + assert rp.name == "xvfb" + + def test_yaml_override_used_when_no_cli(self): + from easi.evaluation.runner import EvaluationRunner + + runner = EvaluationRunner.__new__(EvaluationRunner) + runner.data_dir = Path("/tmp/fake") + runner.render_platform_name = None + + mock_env_mgr = self._make_mock_env_mgr() + mock_task = self._make_mock_task(render_platform="egl") + mock_sim_cls = MagicMock() + mock_sim_cls.return_value._get_bridge_script_path.return_value = Path("/fake/bridge.py") + + with patch("easi.simulators.registry.create_env_manager", return_value=mock_env_mgr), \ + patch("easi.simulators.registry.load_simulator_class", return_value=mock_sim_cls), \ + patch("easi.simulators.subprocess_runner.SubprocessRunner") as MockRunner: + MockRunner.return_value.launch.return_value = None + runner._create_simulator("fake:v1", task=mock_task) + rp = MockRunner.call_args.kwargs.get("render_platform") + assert rp.name == "egl" + + def test_cli_beats_yaml(self): + from easi.evaluation.runner import EvaluationRunner + + runner = EvaluationRunner.__new__(EvaluationRunner) + runner.data_dir = Path("/tmp/fake") + runner.render_platform_name = "xvfb" + + mock_env_mgr = self._make_mock_env_mgr() + mock_task = self._make_mock_task(render_platform="egl") + mock_sim_cls = MagicMock() + mock_sim_cls.return_value._get_bridge_script_path.return_value = Path("/fake/bridge.py") + + with patch("easi.simulators.registry.create_env_manager", return_value=mock_env_mgr), \ + patch("easi.simulators.registry.load_simulator_class", return_value=mock_sim_cls), \ + patch("easi.simulators.subprocess_runner.SubprocessRunner") as MockRunner: + MockRunner.return_value.launch.return_value = None + runner._create_simulator("fake:v1", task=mock_task) + rp = MockRunner.call_args.kwargs.get("render_platform") + assert rp.name == "xvfb" + + def test_unsupported_platform_raises(self): + from easi.evaluation.runner import EvaluationRunner + + runner = EvaluationRunner.__new__(EvaluationRunner) + runner.data_dir = Path("/tmp/fake") + runner.render_platform_name = "egl" + + mock_env_mgr = self._make_mock_env_mgr() + mock_env_mgr.supported_render_platforms = ["auto", "xvfb"] # no egl + mock_sim_cls = MagicMock() + mock_sim_cls.return_value._get_bridge_script_path.return_value = Path("/fake/bridge.py") + + with patch("easi.simulators.registry.create_env_manager", return_value=mock_env_mgr), \ + patch("easi.simulators.registry.load_simulator_class", return_value=mock_sim_cls), \ + pytest.raises(ValueError, match="not supported"): + runner._create_simulator("fake:v1") From 46582be187f94fe27242810dad4a89d68b4e45c4 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Mon, 23 Feb 2026 11:20:40 +0800 Subject: [PATCH 083/239] test: add render platform integration and config persistence tests --- tests/test_render_platform.py | 49 +++++++++++++++++++++++++++++++++++ 1 file changed, 49 insertions(+) diff --git a/tests/test_render_platform.py b/tests/test_render_platform.py index c6e1884..665302b 100644 --- a/tests/test_render_platform.py +++ b/tests/test_render_platform.py @@ -529,3 +529,52 @@ def test_unsupported_platform_raises(self): patch("easi.simulators.registry.load_simulator_class", return_value=mock_sim_cls), \ pytest.raises(ValueError, match="not supported"): runner._create_simulator("fake:v1") + + +class TestRenderPlatformEndToEnd: + """End-to-end: platform flows through runner to subprocess.""" + + def test_egl_env_vars_in_subprocess(self): + from easi.core.render_platform import get_render_platform + from easi.simulators.subprocess_runner import SubprocessRunner + + platform = get_render_platform("egl") + runner = SubprocessRunner( + python_executable="/usr/bin/python3", + bridge_script_path=Path("/fake/bridge.py"), + render_platform=platform, + extra_env={"SIM_ROOT": "/opt/sim"}, + ) + env = runner._build_subprocess_env() + assert env["PYOPENGL_PLATFORM"] == "egl" + assert env["SIM_ROOT"] == "/opt/sim" + + def test_auto_wraps_xvfb_when_no_display(self): + from easi.core.render_platform import get_render_platform + from easi.simulators.subprocess_runner import SubprocessRunner + + platform = get_render_platform("auto") + runner = SubprocessRunner( + python_executable="/usr/bin/python3", + bridge_script_path=Path("/fake/bridge.py"), + render_platform=platform, + screen_config="1280x720x24", + ) + runner._workspace = Path("/tmp/fake_ws") + + env = os.environ.copy() + env.pop("DISPLAY", None) + with patch.dict(os.environ, env, clear=True): + cmd = runner._build_launch_command() + assert cmd[0] == "xvfb-run" + assert "1280x720x24" in cmd[3] + + def test_render_platform_saved_in_config(self): + """render_platform should be captured in _cli_options for config.json.""" + from easi.evaluation.runner import EvaluationRunner + + runner = EvaluationRunner( + task_name="dummy_task", + render_platform="egl", + ) + assert runner._cli_options["render_platform"] == "egl" From 715142acf279f1d6392187744b31b17d75e186d9 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Mon, 23 Feb 2026 11:32:53 +0800 Subject: [PATCH 084/239] chore: remove redundant md files --- .gitignore | 3 + docs/plans/2026-02-15-retry-resume-design.md | 137 ---- docs/plans/2026-02-15-retry-resume-plan.md | 661 ------------------- 3 files changed, 3 insertions(+), 798 deletions(-) delete mode 100644 docs/plans/2026-02-15-retry-resume-design.md delete mode 100644 docs/plans/2026-02-15-retry-resume-plan.md diff --git a/.gitignore b/.gitignore index f78151e..0b654ac 100644 --- a/.gitignore +++ b/.gitignore @@ -6,3 +6,6 @@ __pycache__/ /build /datasets /results +/BEHAVIOR-1K +.worktrees/ +/docs/plans \ No newline at end of file diff --git a/docs/plans/2026-02-15-retry-resume-design.md b/docs/plans/2026-02-15-retry-resume-design.md deleted file mode 100644 index ce8404c..0000000 --- a/docs/plans/2026-02-15-retry-resume-design.md +++ /dev/null @@ -1,137 +0,0 @@ -# Retry & Resume Design - -## Goal - -Make evaluation runs resilient to transient LLM errors (timeouts, rate limits) and resumable after crashes. - -## Feature 1: Adaptive Retry - -Use LiteLLM's built-in `num_retries` parameter. LiteLLM uses tenacity internally for exponential backoff with jitter, and retries on `Timeout`, `RateLimitError`, `APIConnectionError`, and `InternalServerError`. - -### LLMClient - -`LLMClient.__init__` gains a `num_retries` parameter (default 3). It's passed through to `litellm.completion()`: - -```python -class LLMClient: - def __init__(self, model, base_url=None, num_retries=3, **kwargs): - self.num_retries = num_retries - ... - - def generate(self, messages, response_format=None): - call_kwargs = { - "model": self.model, - "messages": messages, - "num_retries": self.num_retries, - **self.default_kwargs, - } - ... -``` - -### CLI - -Add `--max-retries` (default 3) to `easi run`. Passed through: CLI -> `EvaluationRunner` -> `LLMClient(num_retries=...)`. - -### EvaluationRunner - -`__init__` gains `max_retries: int = 3`. In `_create_agent()`, passes it to `LLMClient`. - -## Feature 2: Resume - -Allow `--resume ` to continue a crashed run from where it left off. - -### How it works - -1. **Load existing config**: Read `config.json` from the resume dir. Validate `task_name` matches. -2. **Scan completed episodes**: Glob `episodes/*/result.json`. Each existing `result.json` = completed episode. Parse them into `all_results`. -3. **Re-run last completed episode**: The last episode may have been cut off mid-way. Pop it from `all_results`, clear its directory (delete `result.json`, `trajectory.jsonl`, `step_*.png`), and re-run it from scratch. -4. **Skip earlier episodes**: Start the episode loop from `len(all_results)` instead of 0. -5. **Reuse run_dir**: Don't create a new `run_id`. Write to the same directory. - -### _load_completed_results - -```python -def _load_completed_results(self, run_dir: Path) -> list[dict]: - episodes_dir = run_dir / "episodes" - result_files = sorted(episodes_dir.glob("*/result.json")) - - if not result_files: - return [] - - # Load all completed results except the last - all_results = [] - for rf in result_files[:-1]: - all_results.append(json.loads(rf.read_text())) - - # Clean the last episode directory for re-run - last_episode_dir = result_files[-1].parent - for f in last_episode_dir.iterdir(): - f.unlink() - - return all_results -``` - -### run() changes - -```python -def run(self, max_episodes=None): - if self.resume_dir: - run_dir = self.resume_dir - all_results = self._load_completed_results(run_dir) - start_index = len(all_results) - else: - run_dir = self.output_dir / self.task_name / self.run_id - all_results = [] - start_index = 0 - - ... - for i, episode in enumerate(episodes): - if i < start_index: - continue - ... -``` - -### CLI - -Add `--resume ` to `easi run`. Passed to `EvaluationRunner(resume_dir=...)`. - -### Example - -```bash -# First run — crashes at episode 15 -easi run ebalfred_base --agent react --backend openai --model gpt-4o - -# Resume — skips episodes 0-13, re-runs 14 (cleared), continues 15-49 -easi run ebalfred_base --agent react --backend openai --model gpt-4o \ - --resume logs/ebalfred_base/20260215_164200/ -``` - -## Files Changed - -| File | Change | -|---|---| -| `easi/llm/client.py` | Add `num_retries` param, pass to `litellm.completion()` | -| `easi/evaluation/runner.py` | Add `max_retries`, `resume_dir` params. Add `_load_completed_results()`. Modify `run()` for resume. | -| `easi/cli.py` | Add `--max-retries` (default 3) and `--resume` (path) flags | -| `tests/test_llm_client.py` | Test `num_retries` pass-through | -| `tests/test_e2e_evaluation.py` | Test resume: run 3 episodes, simulate crash, resume skips completed, re-runs last | - -## Data Flow - -``` -CLI --max-retries 3 --resume logs/ebalfred_base/20260215_164200/ - | - v -EvaluationRunner(max_retries=3, resume_dir=Path(...)) - | - +---> _load_completed_results(run_dir) - | scan episodes/*/result.json - | load results [0..N-2] - | clear episode dir [N-1] - | return results, start_index = N-1 - | - +---> _create_agent(...) - | LLMClient(num_retries=3) - | - +---> episode loop: skip i < start_index, run rest normally -``` diff --git a/docs/plans/2026-02-15-retry-resume-plan.md b/docs/plans/2026-02-15-retry-resume-plan.md deleted file mode 100644 index d4e5090..0000000 --- a/docs/plans/2026-02-15-retry-resume-plan.md +++ /dev/null @@ -1,661 +0,0 @@ -# Retry & Resume Implementation Plan - -> **For Claude:** REQUIRED SUB-SKILL: Use superpowers:executing-plans to implement this plan task-by-task. - -**Goal:** Make evaluation runs resilient to transient LLM errors and resumable after crashes. - -**Architecture:** Two independent features wired through CLI → EvaluationRunner → LLMClient. Retry uses LiteLLM's built-in `num_retries` parameter (exponential backoff with jitter). Resume scans existing `result.json` files to skip completed episodes. - -**Tech Stack:** Python, LiteLLM (`num_retries`), pytest, argparse - -**Design doc:** `docs/plans/2026-02-15-retry-resume-design.md` - ---- - -### Task 1: Add `num_retries` to LLMClient - -**Files:** -- Modify: `easi/llm/client.py:39-47` (constructor), `easi/llm/client.py:59-63` (generate call_kwargs) -- Test: `tests/test_llm_client.py` - -**Step 1: Write the failing tests** - -Add to `tests/test_llm_client.py`: - -```python -class TestLLMClientRetries: - def test_default_num_retries(self): - from easi.llm.client import LLMClient - client = LLMClient(model="openai/gpt-4o") - assert client.num_retries == 3 - - def test_custom_num_retries(self): - from easi.llm.client import LLMClient - client = LLMClient(model="openai/gpt-4o", num_retries=5) - assert client.num_retries == 5 - - @patch("easi.llm.client.litellm") - def test_num_retries_passed_to_litellm(self, mock_litellm): - from easi.llm.client import LLMClient - - mock_choice = MagicMock() - mock_choice.message.content = "ok" - mock_response = MagicMock() - mock_response.choices = [mock_choice] - mock_response.usage.prompt_tokens = 10 - mock_response.usage.completion_tokens = 5 - mock_litellm.completion.return_value = mock_response - mock_litellm.completion_cost.return_value = 0.0 - - client = LLMClient(model="openai/gpt-4o", num_retries=7) - client.generate([{"role": "user", "content": "hi"}]) - - call_kwargs = mock_litellm.completion.call_args - assert call_kwargs.kwargs.get("num_retries") == 7 - - @patch("easi.llm.client.litellm") - def test_zero_retries_passed_through(self, mock_litellm): - from easi.llm.client import LLMClient - - mock_choice = MagicMock() - mock_choice.message.content = "ok" - mock_response = MagicMock() - mock_response.choices = [mock_choice] - mock_response.usage.prompt_tokens = 10 - mock_response.usage.completion_tokens = 5 - mock_litellm.completion.return_value = mock_response - mock_litellm.completion_cost.return_value = 0.0 - - client = LLMClient(model="openai/gpt-4o", num_retries=0) - client.generate([{"role": "user", "content": "hi"}]) - - call_kwargs = mock_litellm.completion.call_args - assert call_kwargs.kwargs.get("num_retries") == 0 -``` - -**Step 2: Run tests to verify they fail** - -Run: `.venv/bin/pytest tests/test_llm_client.py::TestLLMClientRetries -v` -Expected: FAIL — `LLMClient` has no `num_retries` attribute - -**Step 3: Implement** - -In `easi/llm/client.py`, modify the constructor (lines 39-47): - -```python -def __init__( - self, - model: str, - base_url: str | None = None, - num_retries: int = 3, - **kwargs: Any, -): - self.model = model - self.base_url = base_url - self.num_retries = num_retries - self.default_kwargs = kwargs - self._usage = { - "prompt_tokens": 0, - "completion_tokens": 0, - "num_calls": 0, - "cost_usd": 0.0, - } -``` - -In `generate()` (lines 59-63), add `num_retries` to `call_kwargs`: - -```python -call_kwargs: dict[str, Any] = { - "model": self.model, - "messages": messages, - "num_retries": self.num_retries, - **self.default_kwargs, -} -``` - -**Step 4: Run tests to verify they pass** - -Run: `.venv/bin/pytest tests/test_llm_client.py -v` -Expected: ALL PASS (existing + new) - -**Step 5: Commit** - -```bash -git add easi/llm/client.py tests/test_llm_client.py -git commit -m "feat: add num_retries to LLMClient for LiteLLM retry support" -``` - ---- - -### Task 2: Wire `max_retries` through EvaluationRunner - -**Files:** -- Modify: `easi/evaluation/runner.py:47-69` (constructor), `easi/evaluation/runner.py:338-342` (LLMClient instantiation) -- Test: `tests/test_e2e_evaluation.py` - -**Step 1: Write the failing test** - -Add to `tests/test_e2e_evaluation.py`: - -```python -def test_max_retries_stored(self, tmp_path): - """Verify max_retries is stored and appears in config.""" - output_dir = tmp_path / "logs" - runner = EvaluationRunner( - task_name="dummy_task", - agent_type="dummy", - output_dir=output_dir, - max_retries=5, - ) - assert runner.max_retries == 5 - runner.run(max_episodes=1) - - run_dir = _find_run_dir(output_dir) - config = json.loads((run_dir / "config.json").read_text()) - assert config["cli_options"]["max_retries"] == 5 -``` - -**Step 2: Run test to verify it fails** - -Run: `.venv/bin/pytest tests/test_e2e_evaluation.py::TestE2EEvaluation::test_max_retries_stored -v` -Expected: FAIL — `EvaluationRunner.__init__()` got unexpected keyword `max_retries` - -**Step 3: Implement** - -In `easi/evaluation/runner.py`, add `max_retries` to constructor (line 59, after `port`): - -```python -def __init__( - self, - task_name: str, - agent_type: str = "dummy", - output_dir: Path | str = "./logs", - data_dir: Path | str = "./datasets", - llm_base_url: str | None = None, - agent_seed: int | None = None, - backend: str | None = None, - model: str = "default", - port: int = 8080, - llm_kwargs_raw: str | None = None, - max_retries: int = 3, -): - ... - self.max_retries = max_retries - ... -``` - -Add `max_retries` to the config dict in `run()` (inside `cli_options`): - -```python -"max_retries": self.max_retries, -``` - -In `_create_agent()`, pass `num_retries` to `LLMClient` (around line 338): - -```python -llm = LLMClient( - model=litellm_model, - base_url=base_url, - num_retries=self.max_retries, - **client_kwargs, -) -``` - -**Step 4: Run tests to verify they pass** - -Run: `.venv/bin/pytest tests/test_e2e_evaluation.py -v` -Expected: ALL PASS - -**Step 5: Commit** - -```bash -git add easi/evaluation/runner.py tests/test_e2e_evaluation.py -git commit -m "feat: wire max_retries through EvaluationRunner to LLMClient" -``` - ---- - -### Task 3: Add `--max-retries` CLI flag - -**Files:** -- Modify: `easi/cli.py:80-98` (run_parser args), `easi/cli.py:288-303` (cmd_run) - -**Step 1: Write the failing test** - -Add to `tests/test_e2e_evaluation.py` (or create a quick inline test): - -This is a CLI integration test. Since the existing tests are E2E with `EvaluationRunner`, we can verify the CLI parser separately: - -```python -def test_cli_max_retries_default(): - """CLI --max-retries defaults to 3.""" - from easi.cli import build_parser - parser = build_parser() - args = parser.parse_args(["run", "dummy_task"]) - assert args.max_retries == 3 - -def test_cli_max_retries_custom(): - """CLI --max-retries is parseable.""" - from easi.cli import build_parser - parser = build_parser() - args = parser.parse_args(["run", "dummy_task", "--max-retries", "5"]) - assert args.max_retries == 5 -``` - -Add these to a new `TestCLIParsing` class in `tests/test_e2e_evaluation.py`. - -**Step 2: Run test to verify it fails** - -Run: `.venv/bin/pytest tests/test_e2e_evaluation.py::TestCLIParsing -v` -Expected: FAIL — `args` has no attribute `max_retries` - -**Step 3: Implement** - -In `easi/cli.py`, add after line 98 (`--llm-kwargs`): - -```python -run_parser.add_argument("--max-retries", type=int, default=3, - help="Max LLM retry attempts on transient errors (default: 3)") -``` - -In `cmd_run` (line 288), add `max_retries` parameter and pass to `EvaluationRunner`: - -```python -def cmd_run(task_name, agent_type, output_dir, data_dir, max_episodes, - llm_url, seed, backend, model, port, llm_kwargs_raw, max_retries): - from easi.evaluation.runner import EvaluationRunner - - runner = EvaluationRunner( - task_name=task_name, - agent_type=agent_type, - output_dir=output_dir, - data_dir=data_dir, - llm_base_url=llm_url, - agent_seed=seed, - backend=backend, - model=model, - port=port, - llm_kwargs_raw=llm_kwargs_raw, - max_retries=max_retries, - ) - ... -``` - -In `main()` dispatch (line 361), add `args.max_retries`: - -```python -cmd_run(args.task, args.agent, args.output_dir, args.data_dir, - args.max_episodes, args.llm_url, args.seed, - args.backend, args.model, args.port, args.llm_kwargs, - args.max_retries) -``` - -**Step 4: Run tests to verify they pass** - -Run: `.venv/bin/pytest tests/test_e2e_evaluation.py -v` -Expected: ALL PASS - -**Step 5: Commit** - -```bash -git add easi/cli.py tests/test_e2e_evaluation.py -git commit -m "feat: add --max-retries CLI flag for LLM retry control" -``` - ---- - -### Task 4: Add `--resume` CLI flag and `resume_dir` to EvaluationRunner - -**Files:** -- Modify: `easi/cli.py:98-99` (run_parser args), `easi/cli.py:288-303` (cmd_run) -- Modify: `easi/evaluation/runner.py:47-70` (constructor) - -**Step 1: Write the failing tests** - -Add to `TestCLIParsing` in `tests/test_e2e_evaluation.py`: - -```python -def test_cli_resume_default(): - """CLI --resume defaults to None.""" - from easi.cli import build_parser - parser = build_parser() - args = parser.parse_args(["run", "dummy_task"]) - assert args.resume is None - -def test_cli_resume_custom(): - """CLI --resume accepts a path.""" - from easi.cli import build_parser - parser = build_parser() - args = parser.parse_args(["run", "dummy_task", "--resume", "/tmp/logs/run_123"]) - assert args.resume == "/tmp/logs/run_123" -``` - -Add to `TestE2EEvaluation`: - -```python -def test_resume_dir_stored(self, tmp_path): - """Verify resume_dir is stored.""" - runner = EvaluationRunner( - task_name="dummy_task", - agent_type="dummy", - output_dir=tmp_path / "logs", - resume_dir=tmp_path / "old_run", - ) - assert runner.resume_dir == tmp_path / "old_run" -``` - -**Step 2: Run tests to verify they fail** - -Run: `.venv/bin/pytest tests/test_e2e_evaluation.py::TestCLIParsing::test_cli_resume_default tests/test_e2e_evaluation.py::TestCLIParsing::test_cli_resume_custom tests/test_e2e_evaluation.py::TestE2EEvaluation::test_resume_dir_stored -v` -Expected: FAIL - -**Step 3: Implement** - -In `easi/cli.py`, add after `--max-retries`: - -```python -run_parser.add_argument("--resume", type=str, default=None, - help="Path to a previous run directory to resume from") -``` - -In `easi/evaluation/runner.py` constructor, add `resume_dir`: - -```python -def __init__( - self, - ... - max_retries: int = 3, - resume_dir: Path | str | None = None, -): - ... - self.max_retries = max_retries - self.resume_dir = Path(resume_dir) if resume_dir else None - ... -``` - -In `easi/cli.py` `cmd_run`, add `resume` parameter and pass to `EvaluationRunner`: - -```python -def cmd_run(task_name, agent_type, output_dir, data_dir, max_episodes, - llm_url, seed, backend, model, port, llm_kwargs_raw, - max_retries, resume): - ... - runner = EvaluationRunner( - ... - max_retries=max_retries, - resume_dir=resume, - ) - ... -``` - -In `main()` dispatch, add `args.resume`: - -```python -cmd_run(args.task, args.agent, args.output_dir, args.data_dir, - args.max_episodes, args.llm_url, args.seed, - args.backend, args.model, args.port, args.llm_kwargs, - args.max_retries, args.resume) -``` - -**Step 4: Run tests to verify they pass** - -Run: `.venv/bin/pytest tests/test_e2e_evaluation.py -v` -Expected: ALL PASS - -**Step 5: Commit** - -```bash -git add easi/cli.py easi/evaluation/runner.py tests/test_e2e_evaluation.py -git commit -m "feat: add --resume CLI flag and resume_dir to EvaluationRunner" -``` - ---- - -### Task 5: Implement `_load_completed_results()` and resume logic in `run()` - -**Files:** -- Modify: `easi/evaluation/runner.py:94-185` (run method + new helper) -- Test: `tests/test_e2e_evaluation.py` - -**Step 1: Write the failing tests** - -Add to `TestE2EEvaluation` in `tests/test_e2e_evaluation.py`: - -```python -def test_resume_skips_completed_episodes(self, tmp_path): - """Resume skips completed episodes and re-runs the last one.""" - output_dir = tmp_path / "logs" - - # First run: complete 3 episodes - runner1 = EvaluationRunner( - task_name="dummy_task", - agent_type="dummy", - output_dir=output_dir, - agent_seed=42, - ) - results1 = runner1.run() - assert len(results1) == 3 - - run_dir = _find_run_dir(output_dir) - - # Resume: should re-run episode 2 (last), skip 0 and 1 - runner2 = EvaluationRunner( - task_name="dummy_task", - agent_type="dummy", - output_dir=output_dir, - resume_dir=run_dir, - agent_seed=42, - ) - results2 = runner2.run() - assert len(results2) == 3 # 2 loaded + 1 re-run (episode 2) - -def test_resume_clears_last_episode_dir(self, tmp_path): - """Resume clears the last episode directory before re-running.""" - output_dir = tmp_path / "logs" - - runner1 = EvaluationRunner( - task_name="dummy_task", - agent_type="dummy", - output_dir=output_dir, - agent_seed=42, - ) - runner1.run() - - run_dir = _find_run_dir(output_dir) - episodes_dir = run_dir / "episodes" - episode_dirs = sorted(episodes_dir.iterdir()) - last_ep_dir = episode_dirs[-1] - - # Add a marker file to the last episode dir - marker = last_ep_dir / "marker.txt" - marker.write_text("should be deleted") - - runner2 = EvaluationRunner( - task_name="dummy_task", - agent_type="dummy", - output_dir=output_dir, - resume_dir=run_dir, - agent_seed=42, - ) - runner2.run() - - # Marker should be gone (dir was cleared) - assert not marker.exists() - # But result.json should exist (re-run completed) - assert (last_ep_dir / "result.json").exists() - -def test_resume_produces_valid_summary(self, tmp_path): - """Resumed run produces a valid summary with all episodes.""" - output_dir = tmp_path / "logs" - - runner1 = EvaluationRunner( - task_name="dummy_task", - agent_type="dummy", - output_dir=output_dir, - agent_seed=42, - ) - runner1.run() - - run_dir = _find_run_dir(output_dir) - - runner2 = EvaluationRunner( - task_name="dummy_task", - agent_type="dummy", - output_dir=output_dir, - resume_dir=run_dir, - agent_seed=42, - ) - results2 = runner2.run() - - summary = json.loads((run_dir / "summary.json").read_text()) - assert summary["num_episodes"] == 3 - -def test_resume_empty_run_dir(self, tmp_path): - """Resume with no completed episodes runs all from scratch.""" - output_dir = tmp_path / "logs" - run_dir = output_dir / "dummy_task" / "fake_run" - episodes_dir = run_dir / "episodes" - episodes_dir.mkdir(parents=True) - - # Write a minimal config.json so resume can read it - import json - config = {"run_id": "fake_run", "cli_options": {"task_name": "dummy_task"}} - (run_dir / "config.json").write_text(json.dumps(config)) - - runner = EvaluationRunner( - task_name="dummy_task", - agent_type="dummy", - output_dir=output_dir, - resume_dir=run_dir, - agent_seed=42, - ) - results = runner.run() - assert len(results) == 3 # All episodes run from scratch -``` - -**Step 2: Run tests to verify they fail** - -Run: `.venv/bin/pytest tests/test_e2e_evaluation.py::TestE2EEvaluation::test_resume_skips_completed_episodes -v` -Expected: FAIL — resume logic not implemented - -**Step 3: Implement** - -Add `_load_completed_results()` method to `EvaluationRunner`: - -```python -def _load_completed_results(self, run_dir: Path) -> list[dict]: - """Load results from a previous run for resume. - - Returns results from all completed episodes except the last one. - The last episode directory is cleared for re-run (it may have been - interrupted mid-way). - """ - episodes_dir = run_dir / "episodes" - if not episodes_dir.exists(): - return [] - - result_files = sorted(episodes_dir.glob("*/result.json")) - if not result_files: - return [] - - # Load all completed results except the last - all_results = [] - for rf in result_files[:-1]: - all_results.append(json.loads(rf.read_text())) - - # Clear the last episode directory for re-run - last_episode_dir = result_files[-1].parent - for f in last_episode_dir.iterdir(): - f.unlink() - - return all_results -``` - -Modify `run()` to support resume — change the beginning of the method: - -```python -def run(self, max_episodes: int | None = None) -> list[dict]: - """Run evaluation and return per-episode metric dicts.""" - if self.resume_dir: - run_dir = self.resume_dir - all_results = self._load_completed_results(run_dir) - start_index = len(all_results) - logger.info( - "Resuming from %s — %d completed episodes, starting from %d", - run_dir, len(all_results), start_index, - ) - else: - run_dir = self.output_dir / self.task_name / self.run_id - all_results = [] - start_index = 0 - - episodes_dir = run_dir / "episodes" - episodes_dir.mkdir(parents=True, exist_ok=True) - - # 1. Load task - task = self._create_task() - episodes = task.load_episodes() - if max_episodes is not None: - episodes = episodes[:max_episodes] - - ... # rest stays the same until the episode loop - - for i, episode in enumerate(episodes): - if i < start_index: - continue - ... # existing episode loop body -``` - -**Step 4: Run tests to verify they pass** - -Run: `.venv/bin/pytest tests/test_e2e_evaluation.py -v` -Expected: ALL PASS - -**Step 5: Run the full test suite** - -Run: `.venv/bin/pytest tests/ -v --timeout=60` -Expected: ALL PASS - -**Step 6: Commit** - -```bash -git add easi/evaluation/runner.py tests/test_e2e_evaluation.py -git commit -m "feat: implement resume logic — scan completed episodes, re-run last" -``` - ---- - -## Dependency Graph - -``` -Task 1 (LLMClient num_retries) - └──> Task 2 (Runner max_retries wiring) - └──> Task 3 (CLI --max-retries) - -Task 4 (CLI --resume + resume_dir) - └──> Task 5 (Resume logic in run()) -``` - -Tasks 1-3 (retry) and Tasks 4-5 (resume) are independent chains. -Tasks 1+4 can run in parallel. Tasks 2+5 can run in parallel (after 1 and 4 respectively). - -## Verification - -```bash -# Full test suite -.venv/bin/pytest tests/ -v --timeout=60 - -# Quick CLI smoke test -.venv/bin/easi run dummy_task --agent dummy --max-retries 5 -.venv/bin/easi run dummy_task --agent dummy --resume logs/dummy_task// -``` - -## Files Changed Summary - -| File | Change | -|---|---| -| `easi/llm/client.py` | Add `num_retries` param (default 3), pass to `litellm.completion()` | -| `easi/evaluation/runner.py` | Add `max_retries` + `resume_dir` params, `_load_completed_results()`, resume logic in `run()` | -| `easi/cli.py` | Add `--max-retries` (default 3) and `--resume` (path) CLI flags | -| `tests/test_llm_client.py` | 4 new tests for `num_retries` | -| `tests/test_e2e_evaluation.py` | ~8 new tests for retry config + resume | From b52c56efc12dc340f9607fbd5bfc84e6750020ff Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Mon, 23 Feb 2026 13:00:28 +0800 Subject: [PATCH 085/239] refactor: replace raw env var dicts with EnvVars dataclass Add EnvVars dataclass with replace/prepend semantics to render_platform.py. Remove backward-compat needs_display/xvfb_screen_config properties from BaseEnvironmentManager. Remove _PREPEND_ENV_VARS from SubprocessRunner and simplify _build_subprocess_env using EnvVars.merge/apply_to_env. Update all simulator env_managers to return EnvVars. Add explicit render_platform to all task YAML base templates. --- easi/cli.py | 2 +- easi/core/base_env_manager.py | 29 +--- easi/core/base_task.py | 1 + easi/core/render_platform.py | 52 +++++- easi/evaluation/runner.py | 6 +- .../coppeliasim/v4_1_0/env_manager.py | 13 +- .../omnigibson/v3_7_2/env_manager.py | 7 +- easi/simulators/subprocess_runner.py | 44 +---- easi/simulators/tdw/v1_11_23/env_manager.py | 7 +- easi/tasks/dummy_task/task.yaml | 4 + easi/tasks/ebalfred/_base.yaml | 1 + easi/tasks/ebhabitat/_base.yaml | 1 + easi/tasks/ebmanipulation/_base.yaml | 1 + easi/tasks/ebnavigation/_base.yaml | 1 + easi/tasks/hazard/_base.yaml | 1 + tests/test_bridge_refactor.py | 5 +- tests/test_coppeliasim_simulator.py | 30 ++-- tests/test_dummy_simulator.py | 13 +- tests/test_env_manager.py | 14 +- tests/test_env_vars_wiring.py | 14 +- tests/test_installation_kwargs.py | 5 +- tests/test_omnigibson_simulator.py | 14 +- tests/test_render_platform.py | 159 ++++++++++++------ tests/test_subprocess_runner.py | 17 +- tests/test_tdw_simulator.py | 6 +- 25 files changed, 269 insertions(+), 178 deletions(-) diff --git a/easi/cli.py b/easi/cli.py index 673c88f..1760395 100644 --- a/easi/cli.py +++ b/easi/cli.py @@ -286,7 +286,7 @@ def cmd_sim_test(simulator: str, steps: int, timeout: float, render_platform_nam screen_config=env_manager.screen_config, startup_timeout=timeout, command_timeout=timeout, - extra_env=env_vars or None, + extra_env=env_vars if env_vars else None, ) try: diff --git a/easi/core/base_env_manager.py b/easi/core/base_env_manager.py index e02383c..da55553 100644 --- a/easi/core/base_env_manager.py +++ b/easi/core/base_env_manager.py @@ -102,20 +102,7 @@ def screen_config(self) -> str: """ return "1024x768x24" - @property - def needs_display(self) -> bool: - """Whether this simulator requires a display (backward compat). - - Derived from ``default_render_platform``. - """ - return self.default_render_platform != "headless" - - @property - def xvfb_screen_config(self) -> str: - """Backward compat alias for ``screen_config``.""" - return self.screen_config - - def get_env_vars(self, render_platform_name: str | None = None) -> dict[str, str]: + def get_env_vars(self, render_platform_name: str | None = None) -> "EnvVars": """Return environment variables to inject into the bridge subprocess. Override in subclasses to provide simulator-specific env vars. @@ -125,9 +112,11 @@ def get_env_vars(self, render_platform_name: str | None = None) -> dict[str, str Subclasses can use this to conditionally set env vars. Returns: - Dict of env var name -> value. Empty dict by default. + EnvVars instance. Empty by default. """ - return {} + from easi.core.render_platform import EnvVars + + return EnvVars() def get_env_name(self) -> str: """Conda environment name for this simulator version.""" @@ -153,8 +142,7 @@ def env_is_ready(self) -> bool: run_env = None if env_vars: import os - run_env = os.environ.copy() - run_env.update(env_vars) + run_env = env_vars.apply_to_env(os.environ.copy()) try: result = subprocess.run( @@ -249,8 +237,7 @@ def _do_install(self) -> None: validation_env = None if env_vars: import os - validation_env = os.environ.copy() - validation_env.update(env_vars) + validation_env = env_vars.apply_to_env(os.environ.copy()) with spinner("Validating environment"): self._run_command( [python_exec, "-c", self.get_validation_import()], @@ -307,7 +294,7 @@ def post_install(self, context: dict) -> None: def _run_post_install(self) -> None: """Build context and call post_install() hook.""" ctx = self._get_template_variables() - ctx["env_vars"] = self.get_env_vars() + ctx["env_vars"] = self.get_env_vars().to_flat_dict() self.post_install(ctx) def _download_and_extract( diff --git a/easi/core/base_task.py b/easi/core/base_task.py index 80cd4ac..cb5970f 100644 --- a/easi/core/base_task.py +++ b/easi/core/base_task.py @@ -142,6 +142,7 @@ def simulator_kwargs(self) -> dict: cfg.pop("env_vars", None) cfg.pop("command_timeout", None) cfg.pop("startup_timeout", None) + cfg.pop("render_platform", None) cfg["max_steps"] = self.max_steps return cfg diff --git a/easi/core/render_platform.py b/easi/core/render_platform.py index 35c6870..82c8e23 100644 --- a/easi/core/render_platform.py +++ b/easi/core/render_platform.py @@ -16,6 +16,7 @@ import os from abc import ABC, abstractmethod +from dataclasses import dataclass, field from pathlib import Path from easi.utils.logging import get_logger @@ -23,6 +24,45 @@ logger = get_logger(__name__) +@dataclass +class EnvVars: + """Structured environment variables with replace/prepend semantics. + + ``replace`` vars overwrite any existing value. + ``prepend`` vars are prepended with ':' to any existing value (for PATH-like vars). + """ + + replace: dict[str, str] = field(default_factory=dict) + prepend: dict[str, str] = field(default_factory=dict) + + def to_flat_dict(self) -> dict[str, str]: + """Combine into single dict (for internal use like post_install).""" + return {**self.replace, **self.prepend} + + def apply_to_env(self, base: dict[str, str]) -> dict[str, str]: + """Merge into a base env dict (e.g. os.environ.copy()).""" + env = dict(base) + for k, v in self.replace.items(): + env[k] = v + for k, v in self.prepend.items(): + env[k] = f"{v}:{env[k]}" if k in env else v + return env + + def __bool__(self) -> bool: + return bool(self.replace) or bool(self.prepend) + + @classmethod + def merge(cls, *env_vars: EnvVars) -> EnvVars: + """Merge multiple EnvVars. Later values win for replace; prepend values concatenate.""" + replace: dict[str, str] = {} + prepend: dict[str, str] = {} + for ev in env_vars: + replace.update(ev.replace) + for k, v in ev.prepend.items(): + prepend[k] = f"{v}:{prepend[k]}" if k in prepend else v + return cls(replace=replace, prepend=prepend) + + class RenderPlatform(ABC): """Strategy interface for display/rendering backends.""" @@ -45,9 +85,9 @@ def wrap_command(self, cmd: list[str], screen_config: str) -> list[str]: """ ... - def get_env_vars(self) -> dict[str, str]: + def get_env_vars(self) -> EnvVars: """Extra env vars needed by this platform (merged into subprocess).""" - return {} + return EnvVars() def get_system_deps(self) -> list[str]: """System dependency names required by this platform.""" @@ -113,12 +153,12 @@ def name(self) -> str: def wrap_command(self, cmd: list[str], screen_config: str) -> list[str]: return cmd - def get_env_vars(self) -> dict[str, str]: - env: dict[str, str] = {"PYOPENGL_PLATFORM": "egl"} + def get_env_vars(self) -> EnvVars: + replace: dict[str, str] = {"PYOPENGL_PLATFORM": "egl"} mesa_vendor = Path("/usr/share/glvnd/egl_vendor.d/50_mesa.json") if mesa_vendor.exists(): - env["__EGL_VENDOR_LIBRARY_FILENAMES"] = str(mesa_vendor) - return env + replace["__EGL_VENDOR_LIBRARY_FILENAMES"] = str(mesa_vendor) + return EnvVars(replace=replace) def get_system_deps(self) -> list[str]: return ["egl"] diff --git a/easi/evaluation/runner.py b/easi/evaluation/runner.py index ac8a080..ff1c778 100644 --- a/easi/evaluation/runner.py +++ b/easi/evaluation/runner.py @@ -610,11 +610,13 @@ def _create_simulator(self, simulator_key: str, task=None): render_platform = get_render_platform(platform_name) # Pass platform name to get_env_vars for conditional logic + from easi.core.render_platform import EnvVars + env_vars = env_manager.get_env_vars(render_platform_name=platform_name) # Merge task-level env_vars from simulator_configs.env_vars if task and task.extra_env_vars: - env_vars = {**env_vars, **task.extra_env_vars} + env_vars = EnvVars.merge(env_vars, EnvVars(replace=task.extra_env_vars)) runner = SubprocessRunner( python_executable=env_manager.get_python_executable(), @@ -622,7 +624,7 @@ def _create_simulator(self, simulator_key: str, task=None): render_platform=render_platform, screen_config=env_manager.screen_config, extra_args=extra_args, - extra_env=env_vars or None, + extra_env=env_vars if env_vars else None, **runner_kwargs, ) runner.launch() diff --git a/easi/simulators/coppeliasim/v4_1_0/env_manager.py b/easi/simulators/coppeliasim/v4_1_0/env_manager.py index 3a53aff..59aef3e 100644 --- a/easi/simulators/coppeliasim/v4_1_0/env_manager.py +++ b/easi/simulators/coppeliasim/v4_1_0/env_manager.py @@ -16,6 +16,7 @@ from pathlib import Path from easi.core.base_env_manager import BaseEnvironmentManager +from easi.core.render_platform import EnvVars from easi.utils.logging import get_logger logger = get_logger(__name__) @@ -56,11 +57,11 @@ def get_system_deps(self) -> list[str]: def get_validation_import(self) -> str: return "from pyrep import PyRep; print('PyRep OK')" - def get_env_vars(self, render_platform_name: str | None = None) -> dict[str, str]: + def get_env_vars(self, render_platform_name: str | None = None) -> EnvVars: """Return CoppeliaSim env vars for bridge subprocess.""" binary_dir_name = self.installation_kwargs.get("binary_dir_name", "") if not binary_dir_name: - return {} + return EnvVars() t = self._get_template_variables() coppeliasim_root = self._resolve_template( "{extras_dir}/" + binary_dir_name, t @@ -68,8 +69,10 @@ def get_env_vars(self, render_platform_name: str | None = None) -> dict[str, str # Include conda env lib dir so fontconfig/freetype/Qt deps resolve conda_lib = self._resolve_template("{env_dir}/lib", t) ld_path = f"{coppeliasim_root}:{conda_lib}" - env = { + replace: dict[str, str] = { "COPPELIASIM_ROOT": coppeliasim_root, + } + prepend: dict[str, str] = { "LD_LIBRARY_PATH": ld_path, "QT_QPA_PLATFORM_PLUGIN_PATH": coppeliasim_root, } @@ -78,8 +81,8 @@ def get_env_vars(self, render_platform_name: str | None = None) -> dict[str, str if render_platform_name != "egl": mesa_vendor = Path("/usr/share/glvnd/egl_vendor.d/50_mesa.json") if mesa_vendor.exists(): - env["__EGL_VENDOR_LIBRARY_FILENAMES"] = str(mesa_vendor) - return env + replace["__EGL_VENDOR_LIBRARY_FILENAMES"] = str(mesa_vendor) + return EnvVars(replace=replace, prepend=prepend) def post_install(self, context: dict) -> None: """Download CoppeliaSim, build PyRep, copy lua addon. diff --git a/easi/simulators/omnigibson/v3_7_2/env_manager.py b/easi/simulators/omnigibson/v3_7_2/env_manager.py index 52d8124..48a9fb7 100644 --- a/easi/simulators/omnigibson/v3_7_2/env_manager.py +++ b/easi/simulators/omnigibson/v3_7_2/env_manager.py @@ -30,6 +30,7 @@ from pathlib import Path from easi.core.base_env_manager import BaseEnvironmentManager +from easi.core.render_platform import EnvVars from easi.utils.logging import get_logger logger = get_logger(__name__) @@ -128,7 +129,7 @@ def get_python_executable(self) -> str: ) return str(local_python) - def get_env_vars(self, render_platform_name: str | None = None) -> dict[str, str]: + def get_env_vars(self, render_platform_name: str | None = None) -> EnvVars: """Export env vars for headless rendering, EULA, and PYTHONHOME. PYTHONHOME is set to the conda env directory so the /tmp Python @@ -137,11 +138,11 @@ def get_env_vars(self, render_platform_name: str | None = None) -> dict[str, str conda_python = self._get_conda_python() # conda env dir is two levels up from bin/python conda_env_dir = str(Path(conda_python).resolve().parent.parent) - return { + return EnvVars(replace={ "OMNIGIBSON_HEADLESS": "1", "OMNI_KIT_ACCEPT_EULA": "YES", "PYTHONHOME": conda_env_dir, - } + }) def post_install(self, context: dict) -> None: """Replicate BEHAVIOR-1K setup.sh install process. diff --git a/easi/simulators/subprocess_runner.py b/easi/simulators/subprocess_runner.py index fd16804..4f1b4d9 100644 --- a/easi/simulators/subprocess_runner.py +++ b/easi/simulators/subprocess_runner.py @@ -28,7 +28,7 @@ write_command, ) from easi.core.exceptions import SimulatorError, SimulatorTimeoutError -from easi.core.render_platform import RenderPlatform, get_render_platform +from easi.core.render_platform import EnvVars, RenderPlatform from easi.utils.logging import get_logger logger = get_logger(__name__) @@ -37,43 +37,22 @@ class SubprocessRunner: """Manages a bridge subprocess for a single simulator instance.""" - # Path-like env vars that should prepend rather than replace - _PREPEND_ENV_VARS = frozenset({ - "LD_LIBRARY_PATH", "DYLD_LIBRARY_PATH", "PATH", - "PYTHONPATH", "QT_QPA_PLATFORM_PLUGIN_PATH", - }) - def __init__( self, python_executable: str, bridge_script_path: Path, - # -- New render-platform API -- - render_platform: RenderPlatform | None = None, + render_platform: RenderPlatform, screen_config: str = "1024x768x24", - # -- Backward-compat (deprecated) -- - needs_display: bool = False, - xvfb_screen_config: str = "1024x768x24", - # -- Other params -- startup_timeout: float = 30.0, command_timeout: float = 300.0, poll_interval: float = 0.1, extra_args: list[str] | None = None, - extra_env: dict[str, str] | None = None, + extra_env: EnvVars | None = None, ): self.python_executable = python_executable self.bridge_script_path = bridge_script_path - - # Resolve render platform: explicit > backward-compat fallback - if render_platform is not None: - self.render_platform = render_platform - self.screen_config = screen_config - elif needs_display: - self.render_platform = get_render_platform("auto") - self.screen_config = xvfb_screen_config - else: - self.render_platform = get_render_platform("headless") - self.screen_config = xvfb_screen_config - + self.render_platform = render_platform + self.screen_config = screen_config self.startup_timeout = startup_timeout self.command_timeout = command_timeout self.poll_interval = poll_interval @@ -193,24 +172,15 @@ def shutdown(self) -> None: def _build_subprocess_env(self) -> dict[str, str] | None: """Build env dict for subprocess, merging platform + extra_env. - For path-like vars (LD_LIBRARY_PATH, PATH, etc.), prepends the new - value to the existing value with ':' separator. - Returns None if no env vars to set (subprocess inherits parent env). """ platform_env = self.render_platform.get_env_vars() - combined = {**platform_env, **(self.extra_env or {})} + combined = EnvVars.merge(platform_env, self.extra_env) if self.extra_env else platform_env if not combined: return None - env = os.environ.copy() - for key, value in combined.items(): - if key in self._PREPEND_ENV_VARS and key in env: - env[key] = f"{value}:{env[key]}" - else: - env[key] = value - return env + return combined.apply_to_env(os.environ.copy()) def _build_launch_command(self) -> list[str]: """Build the command to launch the bridge subprocess.""" diff --git a/easi/simulators/tdw/v1_11_23/env_manager.py b/easi/simulators/tdw/v1_11_23/env_manager.py index ca03a4a..4811556 100644 --- a/easi/simulators/tdw/v1_11_23/env_manager.py +++ b/easi/simulators/tdw/v1_11_23/env_manager.py @@ -13,6 +13,7 @@ from pathlib import Path from easi.core.base_env_manager import BaseEnvironmentManager +from easi.core.render_platform import EnvVars from easi.utils.logging import get_logger logger = get_logger(__name__) @@ -66,14 +67,14 @@ def env_is_ready(self) -> bool: return False return True - def get_env_vars(self, render_platform_name: str | None = None) -> dict[str, str]: + def get_env_vars(self, render_platform_name: str | None = None) -> EnvVars: """Return TDW env vars for bridge subprocess.""" build_dir = self.installation_kwargs.get("build_dir_name", "") if not build_dir: - return {} + return EnvVars() t = self._get_template_variables() build_path = self._resolve_template("{extras_dir}/" + build_dir, t) - return {"TDW_BUILD_PATH": build_path} + return EnvVars(replace={"TDW_BUILD_PATH": build_path}) def post_install(self, context: dict) -> None: """Download and extract TDW Unity build. diff --git a/easi/tasks/dummy_task/task.yaml b/easi/tasks/dummy_task/task.yaml index 2129c4c..43e3fdf 100644 --- a/easi/tasks/dummy_task/task.yaml +++ b/easi/tasks/dummy_task/task.yaml @@ -10,6 +10,10 @@ dataset: source: "local" path: null # null = use built-in dummy episodes +# Simulator config +simulator_configs: + render_platform: headless + # Episode limits max_steps: 100 diff --git a/easi/tasks/ebalfred/_base.yaml b/easi/tasks/ebalfred/_base.yaml index 915aabe..003020c 100644 --- a/easi/tasks/ebalfred/_base.yaml +++ b/easi/tasks/ebalfred/_base.yaml @@ -10,6 +10,7 @@ dataset: zip_files: - "tasks.zip" simulator_configs: + render_platform: auto quality: "MediumCloseFitShadows" screen_height: 500 screen_width: 500 diff --git a/easi/tasks/ebhabitat/_base.yaml b/easi/tasks/ebhabitat/_base.yaml index 91f14c6..730c8e5 100644 --- a/easi/tasks/ebhabitat/_base.yaml +++ b/easi/tasks/ebhabitat/_base.yaml @@ -11,6 +11,7 @@ dataset: - "simulator_data.zip" - "datasets.zip" simulator_configs: + render_platform: auto screen_height: 500 screen_width: 500 max_invalid_actions: 10 diff --git a/easi/tasks/ebmanipulation/_base.yaml b/easi/tasks/ebmanipulation/_base.yaml index c597078..d48d255 100644 --- a/easi/tasks/ebmanipulation/_base.yaml +++ b/easi/tasks/ebmanipulation/_base.yaml @@ -11,6 +11,7 @@ dataset: zip_files: - "simulator_data.zip" simulator_configs: + render_platform: auto screen_height: 500 screen_width: 500 max_steps: 15 diff --git a/easi/tasks/ebnavigation/_base.yaml b/easi/tasks/ebnavigation/_base.yaml index 25d03e6..bbd7515 100644 --- a/easi/tasks/ebnavigation/_base.yaml +++ b/easi/tasks/ebnavigation/_base.yaml @@ -8,6 +8,7 @@ dataset: repo_id: "oscarqjh/EB-Navigation_easi" subset: null simulator_configs: + render_platform: auto screen_height: 500 screen_width: 500 fov: 100 diff --git a/easi/tasks/hazard/_base.yaml b/easi/tasks/hazard/_base.yaml index 4d8c4c7..7ad108b 100644 --- a/easi/tasks/hazard/_base.yaml +++ b/easi/tasks/hazard/_base.yaml @@ -9,6 +9,7 @@ dataset: zip_files: - "simulator_data.zip" simulator_configs: + render_platform: auto screen_size: 512 port: 1071 use_cached_assets: false diff --git a/tests/test_bridge_refactor.py b/tests/test_bridge_refactor.py index bcb7858..3a6affe 100644 --- a/tests/test_bridge_refactor.py +++ b/tests/test_bridge_refactor.py @@ -301,11 +301,12 @@ def test_ebalfred_no_eval_set(self): kwargs = task.simulator_kwargs assert "eval_set" not in kwargs - def test_dummy_task_empty_simulator_configs(self): + def test_dummy_task_simulator_configs(self): from easi.tasks.dummy_task.task import DummyTask task = DummyTask() - assert task.simulator_configs == {} + assert task.simulator_configs == {"render_platform": "headless"} assert task.additional_deps == [] + # render_platform is stripped from simulator_kwargs (infra-only key) assert task.simulator_kwargs == {"max_steps": 100} def test_all_ebalfred_splits_have_configs(self): diff --git a/tests/test_coppeliasim_simulator.py b/tests/test_coppeliasim_simulator.py index 9b1d8e2..086251b 100644 --- a/tests/test_coppeliasim_simulator.py +++ b/tests/test_coppeliasim_simulator.py @@ -47,15 +47,15 @@ def test_env_name(self): mgr = create_env_manager("coppeliasim") assert mgr.get_env_name() == "easi_coppeliasim_v4_1_0" - def test_needs_display(self): + def test_default_render_platform(self): from easi.simulators.registry import create_env_manager mgr = create_env_manager("coppeliasim") - assert mgr.needs_display is True + assert mgr.default_render_platform == "auto" - def test_xvfb_screen_config(self): + def test_screen_config(self): from easi.simulators.registry import create_env_manager mgr = create_env_manager("coppeliasim") - assert mgr.xvfb_screen_config == "1280x720x24" + assert mgr.screen_config == "1280x720x24" def test_system_deps(self): from easi.simulators.registry import create_env_manager @@ -86,48 +86,48 @@ class TestEnvVars: def test_env_vars_has_coppeliasim_root(self): from easi.simulators.registry import create_env_manager mgr = create_env_manager("coppeliasim") - assert "COPPELIASIM_ROOT" in mgr.get_env_vars() + assert "COPPELIASIM_ROOT" in mgr.get_env_vars().replace def test_env_vars_has_ld_library_path(self): from easi.simulators.registry import create_env_manager mgr = create_env_manager("coppeliasim") - assert "LD_LIBRARY_PATH" in mgr.get_env_vars() + assert "LD_LIBRARY_PATH" in mgr.get_env_vars().prepend def test_env_vars_has_qt_platform(self): from easi.simulators.registry import create_env_manager mgr = create_env_manager("coppeliasim") - assert "QT_QPA_PLATFORM_PLUGIN_PATH" in mgr.get_env_vars() + assert "QT_QPA_PLATFORM_PLUGIN_PATH" in mgr.get_env_vars().prepend def test_env_vars_no_unresolved_templates(self): from easi.simulators.registry import create_env_manager mgr = create_env_manager("coppeliasim") - for key, val in mgr.get_env_vars().items(): + for key, val in mgr.get_env_vars().to_flat_dict().items(): assert "{" not in val, f"Unresolved template in {key}: {val}" def test_env_vars_contain_coppeliasim_dir(self): from easi.simulators.registry import create_env_manager mgr = create_env_manager("coppeliasim") - root = mgr.get_env_vars()["COPPELIASIM_ROOT"] + root = mgr.get_env_vars().replace["COPPELIASIM_ROOT"] assert "CoppeliaSim_Pro_V4_1_0_Ubuntu20_04" in root assert "extras" in root def test_ld_library_path_includes_coppeliasim_root(self): from easi.simulators.registry import create_env_manager mgr = create_env_manager("coppeliasim") - env_vars = mgr.get_env_vars() - assert env_vars["COPPELIASIM_ROOT"] in env_vars["LD_LIBRARY_PATH"] - assert env_vars["COPPELIASIM_ROOT"] == env_vars["QT_QPA_PLATFORM_PLUGIN_PATH"] + ev = mgr.get_env_vars() + assert ev.replace["COPPELIASIM_ROOT"] in ev.prepend["LD_LIBRARY_PATH"] + assert ev.replace["COPPELIASIM_ROOT"] == ev.prepend["QT_QPA_PLATFORM_PLUGIN_PATH"] def test_ld_library_path_includes_conda_lib(self): from easi.simulators.registry import create_env_manager mgr = create_env_manager("coppeliasim") - env_vars = mgr.get_env_vars() - assert "/lib" in env_vars["LD_LIBRARY_PATH"] + ev = mgr.get_env_vars() + assert "/lib" in ev.prepend["LD_LIBRARY_PATH"] def test_empty_installation_kwargs_returns_empty_env_vars(self): from easi.simulators.coppeliasim.v4_1_0.env_manager import CoppeliaSimEnvManagerV410 mgr = CoppeliaSimEnvManagerV410() # No installation_kwargs - assert mgr.get_env_vars() == {} + assert not mgr.get_env_vars() class TestSimulatorClass: diff --git a/tests/test_dummy_simulator.py b/tests/test_dummy_simulator.py index ae6bff7..5048bc3 100644 --- a/tests/test_dummy_simulator.py +++ b/tests/test_dummy_simulator.py @@ -14,12 +14,15 @@ @pytest.fixture def dummy_simulator(): """Create and start a dummy simulator, cleaning up after the test.""" + from easi.core.render_platform import get_render_platform + env_manager = DummyEnvManager() sim = DummySimulator() runner = SubprocessRunner( python_executable=env_manager.get_python_executable(), bridge_script_path=sim._get_bridge_script_path(), + render_platform=get_render_platform("headless"), startup_timeout=10.0, command_timeout=10.0, ) @@ -84,17 +87,21 @@ def test_is_running(dummy_simulator): def test_dummy_simulator_with_env_vars_wiring(): - """Verify DummyEnvManager.get_env_vars() returns {} and doesn't break launch.""" + """Verify DummyEnvManager.get_env_vars() returns empty EnvVars and doesn't break launch.""" + from easi.core.render_platform import get_render_platform + env_manager = DummyEnvManager() - assert env_manager.get_env_vars() == {} + env_vars = env_manager.get_env_vars() + assert not env_vars # empty EnvVars is falsy sim = DummySimulator() runner = SubprocessRunner( python_executable=env_manager.get_python_executable(), bridge_script_path=sim._get_bridge_script_path(), + render_platform=get_render_platform("headless"), startup_timeout=10.0, command_timeout=10.0, - extra_env=env_manager.get_env_vars() or None, + extra_env=env_vars if env_vars else None, ) runner.launch() try: diff --git a/tests/test_env_manager.py b/tests/test_env_manager.py index cd435c4..092ce9f 100644 --- a/tests/test_env_manager.py +++ b/tests/test_env_manager.py @@ -11,18 +11,22 @@ class TestEnvManagerGetEnvVars: """Tests for the get_env_vars() method.""" - def test_default_returns_empty_dict(self): + def test_default_returns_empty_envvars(self): + from easi.core.render_platform import EnvVars from easi.simulators.dummy.v1.env_manager import DummyEnvManager mgr = DummyEnvManager() - assert mgr.get_env_vars() == {} + result = mgr.get_env_vars() + assert isinstance(result, EnvVars) + assert not result - def test_returns_dict_type(self): + def test_returns_envvars_type(self): + from easi.core.render_platform import EnvVars from easi.simulators.dummy.v1.env_manager import DummyEnvManager mgr = DummyEnvManager() result = mgr.get_env_vars() - assert isinstance(result, dict) + assert isinstance(result, EnvVars) class TestEnvManagerPostInstall: @@ -33,7 +37,7 @@ def test_default_post_install_is_noop(self): mgr = DummyEnvManager() ctx = mgr._get_template_variables() - ctx["env_vars"] = mgr.get_env_vars() + ctx["env_vars"] = mgr.get_env_vars().to_flat_dict() mgr.post_install(ctx) # Should not raise def test_post_install_receives_context_keys(self): diff --git a/tests/test_env_vars_wiring.py b/tests/test_env_vars_wiring.py index 8ec8264..f7b2653 100644 --- a/tests/test_env_vars_wiring.py +++ b/tests/test_env_vars_wiring.py @@ -10,6 +10,7 @@ class TestEnvVarsWiring: """Verify env vars flow from env_manager to SubprocessRunner.""" def test_runner_passes_env_vars_to_subprocess(self): + from easi.core.render_platform import EnvVars from easi.evaluation.runner import EvaluationRunner runner = EvaluationRunner.__new__(EvaluationRunner) @@ -22,9 +23,7 @@ def test_runner_passes_env_vars_to_subprocess(self): mock_env_mgr.default_render_platform = "headless" mock_env_mgr.supported_render_platforms = ["headless"] mock_env_mgr.screen_config = "1024x768x24" - mock_env_mgr.needs_display = False - mock_env_mgr.xvfb_screen_config = "1024x768x24" - mock_env_mgr.get_env_vars.return_value = {"SIM_ROOT": "/opt/sim"} + mock_env_mgr.get_env_vars.return_value = EnvVars(replace={"SIM_ROOT": "/opt/sim"}) mock_sim_cls = MagicMock() mock_sim = mock_sim_cls.return_value @@ -39,9 +38,12 @@ def test_runner_passes_env_vars_to_subprocess(self): runner._create_simulator("fake:v1") call_kwargs = MockRunner.call_args - assert call_kwargs.kwargs.get("extra_env") == {"SIM_ROOT": "/opt/sim"} + extra_env = call_kwargs.kwargs.get("extra_env") + assert isinstance(extra_env, EnvVars) + assert extra_env.replace == {"SIM_ROOT": "/opt/sim"} def test_runner_passes_none_when_no_env_vars(self): + from easi.core.render_platform import EnvVars from easi.evaluation.runner import EvaluationRunner runner = EvaluationRunner.__new__(EvaluationRunner) @@ -54,9 +56,7 @@ def test_runner_passes_none_when_no_env_vars(self): mock_env_mgr.default_render_platform = "headless" mock_env_mgr.supported_render_platforms = ["headless"] mock_env_mgr.screen_config = "1024x768x24" - mock_env_mgr.needs_display = False - mock_env_mgr.xvfb_screen_config = "1024x768x24" - mock_env_mgr.get_env_vars.return_value = {} + mock_env_mgr.get_env_vars.return_value = EnvVars() mock_sim_cls = MagicMock() mock_sim = mock_sim_cls.return_value diff --git a/tests/test_installation_kwargs.py b/tests/test_installation_kwargs.py index 300036c..5c9fd8f 100644 --- a/tests/test_installation_kwargs.py +++ b/tests/test_installation_kwargs.py @@ -53,6 +53,7 @@ class TestRunnerUsesFactory: """EvaluationRunner._create_simulator uses the factory.""" def test_runner_passes_installation_kwargs(self): + from easi.core.render_platform import EnvVars from easi.evaluation.runner import EvaluationRunner runner = EvaluationRunner.__new__(EvaluationRunner) @@ -65,9 +66,7 @@ def test_runner_passes_installation_kwargs(self): mock_env_mgr.default_render_platform = "headless" mock_env_mgr.supported_render_platforms = ["headless"] mock_env_mgr.screen_config = "1024x768x24" - mock_env_mgr.needs_display = False - mock_env_mgr.xvfb_screen_config = "1024x768x24" - mock_env_mgr.get_env_vars.return_value = {} + mock_env_mgr.get_env_vars.return_value = EnvVars() mock_sim_cls = MagicMock() mock_sim = mock_sim_cls.return_value diff --git a/tests/test_omnigibson_simulator.py b/tests/test_omnigibson_simulator.py index 6a58d30..e648521 100644 --- a/tests/test_omnigibson_simulator.py +++ b/tests/test_omnigibson_simulator.py @@ -49,18 +49,18 @@ def test_env_name(self): mgr = OmniGibsonEnvManager() assert mgr.get_env_name() == "easi_omnigibson_v3_7_2" - def test_needs_display(self): + def test_default_render_platform_headless(self): from easi.simulators.omnigibson.v3_7_2.env_manager import OmniGibsonEnvManager mgr = OmniGibsonEnvManager() - assert mgr.needs_display is False + assert mgr.default_render_platform == "headless" def test_env_vars(self): from easi.simulators.omnigibson.v3_7_2.env_manager import OmniGibsonEnvManager mgr = OmniGibsonEnvManager() - env_vars = mgr.get_env_vars() - assert env_vars["OMNIGIBSON_HEADLESS"] == "1" - assert env_vars["OMNI_KIT_ACCEPT_EULA"] == "YES" - assert "PYTHONHOME" in env_vars + ev = mgr.get_env_vars() + assert ev.replace["OMNIGIBSON_HEADLESS"] == "1" + assert ev.replace["OMNI_KIT_ACCEPT_EULA"] == "YES" + assert "PYTHONHOME" in ev.replace def test_get_python_executable_is_local(self): """Python executable should be a /tmp copy (NFS workaround).""" @@ -153,7 +153,7 @@ def test_create_env_manager(self): from easi.simulators.registry import create_env_manager mgr = create_env_manager("omnigibson:v3_7_2") assert mgr.simulator_name == "omnigibson" - assert mgr.needs_display is False + assert mgr.default_render_platform == "headless" def test_create_env_manager_has_installation_kwargs(self): from easi.simulators.registry import create_env_manager diff --git a/tests/test_render_platform.py b/tests/test_render_platform.py index 665302b..10cb627 100644 --- a/tests/test_render_platform.py +++ b/tests/test_render_platform.py @@ -9,6 +9,78 @@ import pytest +class TestEnvVarsDataclass: + """Test the EnvVars dataclass.""" + + def test_empty_is_falsy(self): + from easi.core.render_platform import EnvVars + + assert not EnvVars() + + def test_replace_is_truthy(self): + from easi.core.render_platform import EnvVars + + assert EnvVars(replace={"FOO": "bar"}) + + def test_prepend_is_truthy(self): + from easi.core.render_platform import EnvVars + + assert EnvVars(prepend={"PATH": "/extra"}) + + def test_to_flat_dict(self): + from easi.core.render_platform import EnvVars + + ev = EnvVars(replace={"A": "1"}, prepend={"B": "2"}) + assert ev.to_flat_dict() == {"A": "1", "B": "2"} + + def test_apply_to_env_replace(self): + from easi.core.render_platform import EnvVars + + ev = EnvVars(replace={"FOO": "new"}) + result = ev.apply_to_env({"FOO": "old", "BAR": "keep"}) + assert result["FOO"] == "new" + assert result["BAR"] == "keep" + + def test_apply_to_env_prepend_existing(self): + from easi.core.render_platform import EnvVars + + ev = EnvVars(prepend={"PATH": "/new"}) + result = ev.apply_to_env({"PATH": "/old"}) + assert result["PATH"] == "/new:/old" + + def test_apply_to_env_prepend_missing(self): + from easi.core.render_platform import EnvVars + + ev = EnvVars(prepend={"LD_LIBRARY_PATH": "/lib"}) + result = ev.apply_to_env({}) + assert result["LD_LIBRARY_PATH"] == "/lib" + + def test_merge_replace_later_wins(self): + from easi.core.render_platform import EnvVars + + a = EnvVars(replace={"K": "a"}) + b = EnvVars(replace={"K": "b"}) + merged = EnvVars.merge(a, b) + assert merged.replace["K"] == "b" + + def test_merge_prepend_concatenates(self): + from easi.core.render_platform import EnvVars + + a = EnvVars(prepend={"PATH": "/a"}) + b = EnvVars(prepend={"PATH": "/b"}) + merged = EnvVars.merge(a, b) + assert merged.prepend["PATH"] == "/b:/a" + + def test_merge_mixed(self): + from easi.core.render_platform import EnvVars + + a = EnvVars(replace={"ROOT": "/opt"}, prepend={"PATH": "/a/bin"}) + b = EnvVars(replace={"HOME": "/home"}, prepend={"PATH": "/b/bin"}) + merged = EnvVars.merge(a, b) + assert merged.replace == {"ROOT": "/opt", "HOME": "/home"} + assert merged.prepend["PATH"] == "/b/bin:/a/bin" + + class TestRenderPlatformRegistry: """Test platform discovery and instantiation.""" @@ -66,10 +138,12 @@ def test_wrap_command_passthrough(self): assert p.wrap_command(cmd, "1024x768x24") == cmd def test_get_env_vars_empty(self): - from easi.core.render_platform import get_render_platform + from easi.core.render_platform import EnvVars, get_render_platform p = get_render_platform("headless") - assert p.get_env_vars() == {} + ev = p.get_env_vars() + assert isinstance(ev, EnvVars) + assert not ev def test_get_system_deps_empty(self): from easi.core.render_platform import get_render_platform @@ -92,10 +166,12 @@ def test_wrap_command_prepends_xvfb_run(self): assert wrapped[-2:] == cmd def test_get_env_vars_empty(self): - from easi.core.render_platform import get_render_platform + from easi.core.render_platform import EnvVars, get_render_platform p = get_render_platform("xvfb") - assert p.get_env_vars() == {} + ev = p.get_env_vars() + assert isinstance(ev, EnvVars) + assert not ev def test_get_system_deps_includes_xvfb(self): from easi.core.render_platform import get_render_platform @@ -115,10 +191,12 @@ def test_wrap_command_passthrough(self): assert p.wrap_command(cmd, "1024x768x24") == cmd def test_get_env_vars_empty(self): - from easi.core.render_platform import get_render_platform + from easi.core.render_platform import EnvVars, get_render_platform p = get_render_platform("native") - assert p.get_env_vars() == {} + ev = p.get_env_vars() + assert isinstance(ev, EnvVars) + assert not ev def test_is_available_true_when_display_set(self): from easi.core.render_platform import get_render_platform @@ -146,11 +224,12 @@ def test_wrap_command_passthrough(self): assert p.wrap_command(cmd, "1024x768x24") == cmd def test_get_env_vars_sets_pyopengl(self): - from easi.core.render_platform import get_render_platform + from easi.core.render_platform import EnvVars, get_render_platform p = get_render_platform("egl") - env = p.get_env_vars() - assert env["PYOPENGL_PLATFORM"] == "egl" + ev = p.get_env_vars() + assert isinstance(ev, EnvVars) + assert ev.replace["PYOPENGL_PLATFORM"] == "egl" def test_get_system_deps_includes_egl(self): from easi.core.render_platform import get_render_platform @@ -219,21 +298,13 @@ def test_screen_config_default(self): mgr = self._make_stub() assert mgr.screen_config == "1024x768x24" - def test_needs_display_false_for_headless(self): - mgr = self._make_stub() - assert mgr.needs_display is False - - def test_needs_display_true_when_platform_not_headless(self): - mgr = self._make_stub( - default_render_platform=property(lambda self: "auto"), - ) - assert mgr.needs_display is True + def test_get_env_vars_returns_envvars(self): + from easi.core.render_platform import EnvVars - def test_xvfb_screen_config_aliases_screen_config(self): - mgr = self._make_stub( - screen_config=property(lambda self: "1920x1080x24"), - ) - assert mgr.xvfb_screen_config == "1920x1080x24" + mgr = self._make_stub() + ev = mgr.get_env_vars() + assert isinstance(ev, EnvVars) + assert not ev class TestSubprocessRunnerRenderPlatform: @@ -282,7 +353,7 @@ def test_build_command_headless_no_wrap(self): assert cmd[0] == "/usr/bin/python3" def test_platform_env_vars_merged(self): - from easi.core.render_platform import get_render_platform + from easi.core.render_platform import EnvVars, get_render_platform from easi.simulators.subprocess_runner import SubprocessRunner p = get_render_platform("egl") @@ -290,33 +361,23 @@ def test_platform_env_vars_merged(self): python_executable="/usr/bin/python3", bridge_script_path=Path("/fake/bridge.py"), render_platform=p, - extra_env={"SIM_ROOT": "/opt/sim"}, + extra_env=EnvVars(replace={"SIM_ROOT": "/opt/sim"}), ) env = runner._build_subprocess_env() assert env["PYOPENGL_PLATFORM"] == "egl" assert env["SIM_ROOT"] == "/opt/sim" - def test_backward_compat_needs_display_still_works(self): - """Old callers passing needs_display=True get AutoPlatform.""" - from easi.simulators.subprocess_runner import SubprocessRunner - - runner = SubprocessRunner( - python_executable="/usr/bin/python3", - bridge_script_path=Path("/fake/bridge.py"), - needs_display=True, - xvfb_screen_config="1024x768x24", - ) - assert runner.render_platform.name == "auto" - - def test_backward_compat_needs_display_false_gets_headless(self): + def test_no_env_vars_returns_none(self): + from easi.core.render_platform import get_render_platform from easi.simulators.subprocess_runner import SubprocessRunner + p = get_render_platform("headless") runner = SubprocessRunner( python_executable="/usr/bin/python3", bridge_script_path=Path("/fake/bridge.py"), - needs_display=False, + render_platform=p, ) - assert runner.render_platform.name == "headless" + assert runner._build_subprocess_env() is None class TestSimulatorRenderPlatforms: @@ -329,8 +390,6 @@ def test_ai2thor_v2(self): assert mgr.default_render_platform == "auto" assert "xvfb" in mgr.supported_render_platforms assert "native" in mgr.supported_render_platforms - # backward compat - assert mgr.needs_display is True def test_ai2thor_v5(self): from easi.simulators.ai2thor.v5_0_0.env_manager import AI2ThorEnvManagerV500 @@ -358,8 +417,9 @@ def test_coppeliasim_egl_skips_mesa_vendor(self): from easi.simulators.coppeliasim.v4_1_0.env_manager import CoppeliaSimEnvManagerV410 mgr = CoppeliaSimEnvManagerV410() - env = mgr.get_env_vars(render_platform_name="egl") - assert "__EGL_VENDOR_LIBRARY_FILENAMES" not in env + ev = mgr.get_env_vars(render_platform_name="egl") + all_keys = set(ev.replace) | set(ev.prepend) + assert "__EGL_VENDOR_LIBRARY_FILENAMES" not in all_keys def test_tdw(self): from easi.simulators.tdw.v1_11_23.env_manager import TDWEnvManager @@ -372,7 +432,6 @@ def test_omnigibson(self): mgr = OmniGibsonEnvManager() assert mgr.default_render_platform == "headless" - assert mgr.needs_display is False def test_dummy(self): from easi.simulators.dummy.v1.env_manager import DummyEnvManager @@ -413,15 +472,15 @@ class TestRunnerRenderPlatformWiring: """Verify EvaluationRunner resolves and passes render platform.""" def _make_mock_env_mgr(self): + from easi.core.render_platform import EnvVars + mgr = MagicMock() mgr.env_is_ready.return_value = True mgr.get_python_executable.return_value = "/usr/bin/python3" mgr.default_render_platform = "auto" mgr.supported_render_platforms = ["auto", "xvfb", "native", "egl"] mgr.screen_config = "1024x768x24" - mgr.needs_display = True - mgr.xvfb_screen_config = "1024x768x24" - mgr.get_env_vars.return_value = {} + mgr.get_env_vars.return_value = EnvVars() return mgr def _make_mock_task(self, render_platform=None): @@ -535,7 +594,7 @@ class TestRenderPlatformEndToEnd: """End-to-end: platform flows through runner to subprocess.""" def test_egl_env_vars_in_subprocess(self): - from easi.core.render_platform import get_render_platform + from easi.core.render_platform import EnvVars, get_render_platform from easi.simulators.subprocess_runner import SubprocessRunner platform = get_render_platform("egl") @@ -543,7 +602,7 @@ def test_egl_env_vars_in_subprocess(self): python_executable="/usr/bin/python3", bridge_script_path=Path("/fake/bridge.py"), render_platform=platform, - extra_env={"SIM_ROOT": "/opt/sim"}, + extra_env=EnvVars(replace={"SIM_ROOT": "/opt/sim"}), ) env = runner._build_subprocess_env() assert env["PYOPENGL_PLATFORM"] == "egl" diff --git a/tests/test_subprocess_runner.py b/tests/test_subprocess_runner.py index c07411d..5641b4c 100644 --- a/tests/test_subprocess_runner.py +++ b/tests/test_subprocess_runner.py @@ -5,6 +5,7 @@ import os from pathlib import Path +from easi.core.render_platform import EnvVars, get_render_platform from easi.simulators.subprocess_runner import SubprocessRunner @@ -15,14 +16,16 @@ def test_constructor_accepts_env_vars(self): runner = SubprocessRunner( python_executable="/usr/bin/python3", bridge_script_path=Path("/dev/null"), - extra_env={"MY_VAR": "my_value"}, + render_platform=get_render_platform("headless"), + extra_env=EnvVars(replace={"MY_VAR": "my_value"}), ) - assert runner.extra_env == {"MY_VAR": "my_value"} + assert runner.extra_env.replace == {"MY_VAR": "my_value"} def test_default_env_is_none(self): runner = SubprocessRunner( python_executable="/usr/bin/python3", bridge_script_path=Path("/dev/null"), + render_platform=get_render_platform("headless"), ) assert runner.extra_env is None @@ -30,7 +33,8 @@ def test_build_env_merges_with_os_environ(self): runner = SubprocessRunner( python_executable="/usr/bin/python3", bridge_script_path=Path("/dev/null"), - extra_env={"COPPELIASIM_ROOT": "/opt/coppeliasim"}, + render_platform=get_render_platform("headless"), + extra_env=EnvVars(replace={"COPPELIASIM_ROOT": "/opt/coppeliasim"}), ) env = runner._build_subprocess_env() assert env["COPPELIASIM_ROOT"] == "/opt/coppeliasim" @@ -40,6 +44,7 @@ def test_build_env_returns_none_when_no_extra(self): runner = SubprocessRunner( python_executable="/usr/bin/python3", bridge_script_path=Path("/dev/null"), + render_platform=get_render_platform("headless"), ) assert runner._build_subprocess_env() is None @@ -47,7 +52,8 @@ def test_extra_env_prepends_to_path_vars(self): runner = SubprocessRunner( python_executable="/usr/bin/python3", bridge_script_path=Path("/dev/null"), - extra_env={"LD_LIBRARY_PATH": "/opt/sim/lib"}, + render_platform=get_render_platform("headless"), + extra_env=EnvVars(prepend={"LD_LIBRARY_PATH": "/opt/sim/lib"}), ) env = runner._build_subprocess_env() ld_path = env.get("LD_LIBRARY_PATH", "") @@ -60,7 +66,8 @@ def test_non_path_var_replaces(self): runner = SubprocessRunner( python_executable="/usr/bin/python3", bridge_script_path=Path("/dev/null"), - extra_env={"MY_EXISTING": "new_value"}, + render_platform=get_render_platform("headless"), + extra_env=EnvVars(replace={"MY_EXISTING": "new_value"}), ) env = runner._build_subprocess_env() assert env["MY_EXISTING"] == "new_value" diff --git a/tests/test_tdw_simulator.py b/tests/test_tdw_simulator.py index 30369fb..df491fb 100644 --- a/tests/test_tdw_simulator.py +++ b/tests/test_tdw_simulator.py @@ -58,10 +58,10 @@ def test_env_name(self): mgr = TDWEnvManager() assert mgr.get_env_name() == "easi_tdw_v1_11_23" - def test_needs_display(self): + def test_default_render_platform(self): from easi.simulators.tdw.v1_11_23.env_manager import TDWEnvManager mgr = TDWEnvManager() - assert mgr.needs_display is True + assert mgr.default_render_platform == "auto" def test_system_deps(self): from easi.simulators.tdw.v1_11_23.env_manager import TDWEnvManager @@ -114,4 +114,4 @@ def test_create_env_manager(self): from easi.simulators.registry import create_env_manager mgr = create_env_manager("tdw:v1_11_23") assert mgr.simulator_name == "tdw" - assert mgr.needs_display is True + assert mgr.default_render_platform == "auto" From e8395ba891e312036f0f5d1a9c5dbbe406e6cee3 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Mon, 23 Feb 2026 13:45:14 +0800 Subject: [PATCH 086/239] feat: support custom render platform classes via simulator manifest Allow simulators to register custom RenderPlatform subclasses in their manifest.yaml. resolve_render_platform() checks the manifest first, falling back to built-in platforms. Includes a DummyCustomPlatform example and tests for the full resolution chain. --- easi/cli.py | 9 +- easi/evaluation/runner.py | 4 +- easi/simulators/dummy/manifest.yaml | 2 + easi/simulators/dummy/v1/env_manager.py | 4 + easi/simulators/dummy/v1/render_platforms.py | 14 +++ easi/simulators/registry.py | 31 ++++++ tests/test_dummy_simulator.py | 8 ++ tests/test_env_vars_wiring.py | 8 +- tests/test_installation_kwargs.py | 4 +- tests/test_render_platform.py | 105 +++++++++++++++++++ 10 files changed, 181 insertions(+), 8 deletions(-) create mode 100644 easi/simulators/dummy/v1/render_platforms.py diff --git a/easi/cli.py b/easi/cli.py index 1760395..293ae9d 100644 --- a/easi/cli.py +++ b/easi/cli.py @@ -255,8 +255,11 @@ def cmd_task_download(task_name: str, refresh_data: bool = False) -> None: def cmd_sim_test(simulator: str, steps: int, timeout: float, render_platform_name: str | None = None) -> None: from easi.core.episode import Action - from easi.core.render_platform import get_render_platform - from easi.simulators.registry import create_env_manager, load_simulator_class + from easi.simulators.registry import ( + create_env_manager, + load_simulator_class, + resolve_render_platform, + ) from easi.simulators.subprocess_runner import SubprocessRunner env_manager = create_env_manager(simulator) @@ -271,7 +274,7 @@ def cmd_sim_test(simulator: str, steps: int, timeout: float, render_platform_nam platform_name, simulator, env_manager.supported_render_platforms, ) sys.exit(1) - render_platform = get_render_platform(platform_name) + render_platform = resolve_render_platform(simulator, platform_name) logger.info("Testing %s...", simulator) logger.info(" Python: %s", env_manager.get_python_executable()) diff --git a/easi/evaluation/runner.py b/easi/evaluation/runner.py index ff1c778..413df65 100644 --- a/easi/evaluation/runner.py +++ b/easi/evaluation/runner.py @@ -591,7 +591,7 @@ def _create_simulator(self, simulator_key: str, task=None): runner_kwargs["startup_timeout"] = float(sim_configs["startup_timeout"]) # Resolve render platform: CLI > task YAML > env_manager default - from easi.core.render_platform import get_render_platform + from easi.simulators.registry import resolve_render_platform yaml_platform = sim_configs.get("render_platform") if task else None platform_name = ( @@ -607,7 +607,7 @@ def _create_simulator(self, simulator_key: str, task=None): f"Supported: {env_manager.supported_render_platforms}" ) - render_platform = get_render_platform(platform_name) + render_platform = resolve_render_platform(simulator_key, platform_name) # Pass platform name to get_env_vars for conditional logic from easi.core.render_platform import EnvVars diff --git a/easi/simulators/dummy/manifest.yaml b/easi/simulators/dummy/manifest.yaml index 47bcc61..18b0ad0 100644 --- a/easi/simulators/dummy/manifest.yaml +++ b/easi/simulators/dummy/manifest.yaml @@ -7,3 +7,5 @@ versions: simulator_class: "easi.simulators.dummy.v1.simulator.DummySimulator" env_manager_class: "easi.simulators.dummy.v1.env_manager.DummyEnvManager" python_version: "3.10" + render_platforms: + dummy_custom: "easi.simulators.dummy.v1.render_platforms.DummyCustomPlatform" diff --git a/easi/simulators/dummy/v1/env_manager.py b/easi/simulators/dummy/v1/env_manager.py index 89915fa..b6c6638 100644 --- a/easi/simulators/dummy/v1/env_manager.py +++ b/easi/simulators/dummy/v1/env_manager.py @@ -39,6 +39,10 @@ def get_python_executable(self) -> str: """Use the current Python interpreter (no conda env needed for dummy).""" return sys.executable + @property + def supported_render_platforms(self) -> list[str]: + return ["headless", "dummy_custom"] + def env_is_ready(self) -> bool: """Dummy env is always ready (uses host Python).""" return True diff --git a/easi/simulators/dummy/v1/render_platforms.py b/easi/simulators/dummy/v1/render_platforms.py new file mode 100644 index 0000000..6c077e1 --- /dev/null +++ b/easi/simulators/dummy/v1/render_platforms.py @@ -0,0 +1,14 @@ +"""Custom render platforms for the dummy simulator (for testing).""" + +from easi.core.render_platform import EnvVars, HeadlessPlatform + + +class DummyCustomPlatform(HeadlessPlatform): + """A trivial custom platform that adds a marker env var.""" + + @property + def name(self) -> str: + return "dummy_custom" + + def get_env_vars(self) -> EnvVars: + return EnvVars(replace={"DUMMY_CUSTOM_PLATFORM": "1"}) diff --git a/easi/simulators/registry.py b/easi/simulators/registry.py index 1f1696c..2f92c0a 100644 --- a/easi/simulators/registry.py +++ b/easi/simulators/registry.py @@ -33,6 +33,7 @@ class SimulatorEntry: env_manager_class: str # fully qualified class name python_version: str installation_kwargs: dict = field(default_factory=dict) + render_platforms: dict[str, str] = field(default_factory=dict) # platform_name -> FQN class # Module-level registry populated on first access @@ -63,6 +64,7 @@ def _discover_simulators() -> dict[str, SimulatorEntry]: env_manager_class=ver_info["env_manager_class"], python_version=ver_info.get("python_version", "3.10"), installation_kwargs=ver_info.get("installation_kwargs", {}), + render_platforms=ver_info.get("render_platforms", {}), ) # Register with explicit key: "ai2thor:v2_1_0" @@ -133,6 +135,35 @@ def create_env_manager(key: str): return cls(installation_kwargs=entry.installation_kwargs) +def resolve_render_platform(key: str, platform_name: str): + """Resolve a render platform, checking simulator manifest first, then built-ins. + + Args: + key: Simulator registry key (e.g. "coppeliasim:v4_1_0" or "coppeliasim"). + platform_name: Platform name (e.g. "xvfb", "isaac_headless"). + + Returns: + Instantiated RenderPlatform. + """ + from easi.core.render_platform import get_render_platform + + entry = get_simulator_entry(key) + custom_class_path = entry.render_platforms.get(platform_name) + + if custom_class_path: + cls = _import_class(custom_class_path) + instance = cls() + if instance.name != platform_name: + raise ValueError( + f"Custom render platform class '{custom_class_path}' has " + f"name '{instance.name}', expected '{platform_name}'" + ) + logger.trace("Using custom render platform '%s' from %s", platform_name, key) + return instance + + return get_render_platform(platform_name) + + def _import_class(fully_qualified_name: str): """Import a class from its fully qualified name (e.g., 'pkg.mod.ClassName').""" module_path, class_name = fully_qualified_name.rsplit(".", 1) diff --git a/tests/test_dummy_simulator.py b/tests/test_dummy_simulator.py index 5048bc3..42b926b 100644 --- a/tests/test_dummy_simulator.py +++ b/tests/test_dummy_simulator.py @@ -124,3 +124,11 @@ def test_registry_discovery(): entry_explicit = get_simulator_entry("dummy:v1") assert entry_explicit.name == "dummy" + + +def test_dummy_custom_platform_in_registry(): + """Test that the dummy simulator's custom render platform is in the registry.""" + from easi.simulators.registry import get_simulator_entry + + entry = get_simulator_entry("dummy") + assert "dummy_custom" in entry.render_platforms diff --git a/tests/test_env_vars_wiring.py b/tests/test_env_vars_wiring.py index f7b2653..c768ee9 100644 --- a/tests/test_env_vars_wiring.py +++ b/tests/test_env_vars_wiring.py @@ -10,7 +10,7 @@ class TestEnvVarsWiring: """Verify env vars flow from env_manager to SubprocessRunner.""" def test_runner_passes_env_vars_to_subprocess(self): - from easi.core.render_platform import EnvVars + from easi.core.render_platform import EnvVars, get_render_platform from easi.evaluation.runner import EvaluationRunner runner = EvaluationRunner.__new__(EvaluationRunner) @@ -31,6 +31,8 @@ def test_runner_passes_env_vars_to_subprocess(self): with patch("easi.simulators.registry.create_env_manager", return_value=mock_env_mgr), \ patch("easi.simulators.registry.load_simulator_class", return_value=mock_sim_cls), \ + patch("easi.simulators.registry.resolve_render_platform", + side_effect=lambda key, name: get_render_platform(name)), \ patch("easi.simulators.subprocess_runner.SubprocessRunner") as MockRunner: mock_runner_instance = MockRunner.return_value mock_runner_instance.launch.return_value = None @@ -43,7 +45,7 @@ def test_runner_passes_env_vars_to_subprocess(self): assert extra_env.replace == {"SIM_ROOT": "/opt/sim"} def test_runner_passes_none_when_no_env_vars(self): - from easi.core.render_platform import EnvVars + from easi.core.render_platform import EnvVars, get_render_platform from easi.evaluation.runner import EvaluationRunner runner = EvaluationRunner.__new__(EvaluationRunner) @@ -64,6 +66,8 @@ def test_runner_passes_none_when_no_env_vars(self): with patch("easi.simulators.registry.create_env_manager", return_value=mock_env_mgr), \ patch("easi.simulators.registry.load_simulator_class", return_value=mock_sim_cls), \ + patch("easi.simulators.registry.resolve_render_platform", + side_effect=lambda key, name: get_render_platform(name)), \ patch("easi.simulators.subprocess_runner.SubprocessRunner") as MockRunner: mock_runner_instance = MockRunner.return_value mock_runner_instance.launch.return_value = None diff --git a/tests/test_installation_kwargs.py b/tests/test_installation_kwargs.py index 5c9fd8f..1351a05 100644 --- a/tests/test_installation_kwargs.py +++ b/tests/test_installation_kwargs.py @@ -53,7 +53,7 @@ class TestRunnerUsesFactory: """EvaluationRunner._create_simulator uses the factory.""" def test_runner_passes_installation_kwargs(self): - from easi.core.render_platform import EnvVars + from easi.core.render_platform import EnvVars, get_render_platform from easi.evaluation.runner import EvaluationRunner runner = EvaluationRunner.__new__(EvaluationRunner) @@ -74,6 +74,8 @@ def test_runner_passes_installation_kwargs(self): with patch("easi.simulators.registry.create_env_manager", return_value=mock_env_mgr) as mock_factory, \ patch("easi.simulators.registry.load_simulator_class", return_value=mock_sim_cls), \ + patch("easi.simulators.registry.resolve_render_platform", + side_effect=lambda key, name: get_render_platform(name)), \ patch("easi.simulators.subprocess_runner.SubprocessRunner") as MockRunner: mock_runner_instance = MockRunner.return_value mock_runner_instance.launch.return_value = None diff --git a/tests/test_render_platform.py b/tests/test_render_platform.py index 10cb627..5339be4 100644 --- a/tests/test_render_platform.py +++ b/tests/test_render_platform.py @@ -440,6 +440,96 @@ def test_dummy(self): assert mgr.default_render_platform == "headless" +class TestCustomRenderPlatforms: + """Test custom render platform registration and resolution.""" + + def test_simulator_entry_has_empty_render_platforms(self): + from easi.simulators.registry import get_simulator_entry + + entry = get_simulator_entry("ai2thor:v2_1_0") + assert entry.render_platforms == {} + + def test_simulator_entry_has_custom_render_platforms(self): + from easi.simulators.registry import get_simulator_entry + + entry = get_simulator_entry("dummy") + assert entry.render_platforms == { + "dummy_custom": "easi.simulators.dummy.v1.render_platforms.DummyCustomPlatform" + } + + def test_resolve_falls_back_to_builtin(self): + from easi.core.render_platform import HeadlessPlatform + from easi.simulators.registry import resolve_render_platform + + platform = resolve_render_platform("dummy", "headless") + assert isinstance(platform, HeadlessPlatform) + assert platform.name == "headless" + + def test_resolve_uses_custom_class(self): + from easi.simulators.dummy.v1.render_platforms import DummyCustomPlatform + from easi.simulators.registry import resolve_render_platform + + platform = resolve_render_platform("dummy", "dummy_custom") + assert isinstance(platform, DummyCustomPlatform) + assert platform.name == "dummy_custom" + ev = platform.get_env_vars() + assert ev.replace == {"DUMMY_CUSTOM_PLATFORM": "1"} + + def test_resolve_name_mismatch_raises(self): + from easi.simulators.registry import resolve_render_platform + + # Patch the dummy entry to map "wrong_name" to the DummyCustomPlatform class + # (which has name "dummy_custom", not "wrong_name") + with patch("easi.simulators.registry._get_registry") as mock_reg: + from easi.simulators.registry import SimulatorEntry + + fake_entry = SimulatorEntry( + name="fake", version="v1", description="", + simulator_class="", env_manager_class="", + python_version="3.10", + render_platforms={ + "wrong_name": "easi.simulators.dummy.v1.render_platforms.DummyCustomPlatform" + }, + ) + mock_reg.return_value = {"fake:v1": fake_entry} + with pytest.raises(ValueError, match="expected 'wrong_name'"): + resolve_render_platform("fake:v1", "wrong_name") + + def test_custom_shadows_builtin(self): + """A custom 'headless' class should be returned instead of the built-in.""" + from easi.core.render_platform import HeadlessPlatform + from easi.simulators.registry import resolve_render_platform + + with patch("easi.simulators.registry._get_registry") as mock_reg: + from easi.simulators.registry import SimulatorEntry + + fake_entry = SimulatorEntry( + name="fake", version="v1", description="", + simulator_class="", env_manager_class="", + python_version="3.10", + render_platforms={ + "headless": "easi.simulators.dummy.v1.render_platforms.DummyCustomPlatform" + }, + ) + mock_reg.return_value = {"fake:v1": fake_entry} + # DummyCustomPlatform.name is "dummy_custom", not "headless", + # so this should raise a name mismatch error. + # To truly shadow, we need a class whose name IS "headless". + # Let's test the lookup path: if a custom class is found, it's used. + # We'll patch _import_class to return a class with name "headless". + from easi.core.render_platform import EnvVars + + class CustomHeadless(HeadlessPlatform): + def get_env_vars(self): + return EnvVars(replace={"CUSTOM": "1"}) + + with patch("easi.simulators.registry._import_class", return_value=CustomHeadless): + platform = resolve_render_platform("fake:v1", "headless") + assert isinstance(platform, CustomHeadless) + assert platform.name == "headless" + assert platform.get_env_vars().replace == {"CUSTOM": "1"} + + from unittest.mock import MagicMock @@ -495,6 +585,7 @@ def _make_mock_task(self, render_platform=None): return task def test_default_uses_env_manager_platform(self): + from easi.core.render_platform import get_render_platform from easi.evaluation.runner import EvaluationRunner runner = EvaluationRunner.__new__(EvaluationRunner) @@ -507,6 +598,8 @@ def test_default_uses_env_manager_platform(self): with patch("easi.simulators.registry.create_env_manager", return_value=mock_env_mgr), \ patch("easi.simulators.registry.load_simulator_class", return_value=mock_sim_cls), \ + patch("easi.simulators.registry.resolve_render_platform", + side_effect=lambda key, name: get_render_platform(name)), \ patch("easi.simulators.subprocess_runner.SubprocessRunner") as MockRunner: MockRunner.return_value.launch.return_value = None runner._create_simulator("fake:v1") @@ -514,6 +607,7 @@ def test_default_uses_env_manager_platform(self): assert rp.name == "auto" def test_cli_override_wins(self): + from easi.core.render_platform import get_render_platform from easi.evaluation.runner import EvaluationRunner runner = EvaluationRunner.__new__(EvaluationRunner) @@ -526,6 +620,8 @@ def test_cli_override_wins(self): with patch("easi.simulators.registry.create_env_manager", return_value=mock_env_mgr), \ patch("easi.simulators.registry.load_simulator_class", return_value=mock_sim_cls), \ + patch("easi.simulators.registry.resolve_render_platform", + side_effect=lambda key, name: get_render_platform(name)), \ patch("easi.simulators.subprocess_runner.SubprocessRunner") as MockRunner: MockRunner.return_value.launch.return_value = None runner._create_simulator("fake:v1") @@ -533,6 +629,7 @@ def test_cli_override_wins(self): assert rp.name == "xvfb" def test_yaml_override_used_when_no_cli(self): + from easi.core.render_platform import get_render_platform from easi.evaluation.runner import EvaluationRunner runner = EvaluationRunner.__new__(EvaluationRunner) @@ -546,6 +643,8 @@ def test_yaml_override_used_when_no_cli(self): with patch("easi.simulators.registry.create_env_manager", return_value=mock_env_mgr), \ patch("easi.simulators.registry.load_simulator_class", return_value=mock_sim_cls), \ + patch("easi.simulators.registry.resolve_render_platform", + side_effect=lambda key, name: get_render_platform(name)), \ patch("easi.simulators.subprocess_runner.SubprocessRunner") as MockRunner: MockRunner.return_value.launch.return_value = None runner._create_simulator("fake:v1", task=mock_task) @@ -553,6 +652,7 @@ def test_yaml_override_used_when_no_cli(self): assert rp.name == "egl" def test_cli_beats_yaml(self): + from easi.core.render_platform import get_render_platform from easi.evaluation.runner import EvaluationRunner runner = EvaluationRunner.__new__(EvaluationRunner) @@ -566,6 +666,8 @@ def test_cli_beats_yaml(self): with patch("easi.simulators.registry.create_env_manager", return_value=mock_env_mgr), \ patch("easi.simulators.registry.load_simulator_class", return_value=mock_sim_cls), \ + patch("easi.simulators.registry.resolve_render_platform", + side_effect=lambda key, name: get_render_platform(name)), \ patch("easi.simulators.subprocess_runner.SubprocessRunner") as MockRunner: MockRunner.return_value.launch.return_value = None runner._create_simulator("fake:v1", task=mock_task) @@ -573,6 +675,7 @@ def test_cli_beats_yaml(self): assert rp.name == "xvfb" def test_unsupported_platform_raises(self): + from easi.core.render_platform import get_render_platform from easi.evaluation.runner import EvaluationRunner runner = EvaluationRunner.__new__(EvaluationRunner) @@ -586,6 +689,8 @@ def test_unsupported_platform_raises(self): with patch("easi.simulators.registry.create_env_manager", return_value=mock_env_mgr), \ patch("easi.simulators.registry.load_simulator_class", return_value=mock_sim_cls), \ + patch("easi.simulators.registry.resolve_render_platform", + side_effect=lambda key, name: get_render_platform(name)), \ pytest.raises(ValueError, match="not supported"): runner._create_simulator("fake:v1") From 2c211703c84111f0ee271a19273488f66e10377e Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Mon, 23 Feb 2026 14:58:28 +0800 Subject: [PATCH 087/239] feat: move CoppeliaSim platform-specific env vars to custom render platform classes Pass env_manager to custom render platform constructors so they can compute simulator-specific paths (QT_QPA_PLATFORM_PLUGIN_PATH, Mesa EGL vendor). This moves platform-conditional logic out of env_manager into dedicated CoppeliaSimNative/Xvfb/AutoPlatform classes, keeping the env_manager focused on platform-agnostic vars (COPPELIASIM_ROOT, LD_LIBRARY_PATH). --- easi/cli.py | 2 +- easi/core/render_platform.py | 3 + easi/evaluation/runner.py | 2 +- easi/simulators/coppeliasim/manifest.yaml | 4 + .../coppeliasim/v4_1_0/env_manager.py | 24 ++-- .../coppeliasim/v4_1_0/render_platforms.py | 82 +++++++++++ easi/simulators/registry.py | 5 +- tests/test_coppeliasim_simulator.py | 7 +- tests/test_env_vars_wiring.py | 4 +- tests/test_installation_kwargs.py | 2 +- tests/test_render_platform.py | 128 ++++++++++++++++-- 11 files changed, 229 insertions(+), 34 deletions(-) create mode 100644 easi/simulators/coppeliasim/v4_1_0/render_platforms.py diff --git a/easi/cli.py b/easi/cli.py index 293ae9d..27c2d8c 100644 --- a/easi/cli.py +++ b/easi/cli.py @@ -274,7 +274,7 @@ def cmd_sim_test(simulator: str, steps: int, timeout: float, render_platform_nam platform_name, simulator, env_manager.supported_render_platforms, ) sys.exit(1) - render_platform = resolve_render_platform(simulator, platform_name) + render_platform = resolve_render_platform(simulator, platform_name, env_manager=env_manager) logger.info("Testing %s...", simulator) logger.info(" Python: %s", env_manager.get_python_executable()) diff --git a/easi/core/render_platform.py b/easi/core/render_platform.py index 82c8e23..210061d 100644 --- a/easi/core/render_platform.py +++ b/easi/core/render_platform.py @@ -66,6 +66,9 @@ def merge(cls, *env_vars: EnvVars) -> EnvVars: class RenderPlatform(ABC): """Strategy interface for display/rendering backends.""" + def __init__(self, env_manager=None): + self._env_manager = env_manager + @property @abstractmethod def name(self) -> str: diff --git a/easi/evaluation/runner.py b/easi/evaluation/runner.py index 413df65..f4a3642 100644 --- a/easi/evaluation/runner.py +++ b/easi/evaluation/runner.py @@ -607,7 +607,7 @@ def _create_simulator(self, simulator_key: str, task=None): f"Supported: {env_manager.supported_render_platforms}" ) - render_platform = resolve_render_platform(simulator_key, platform_name) + render_platform = resolve_render_platform(simulator_key, platform_name, env_manager=env_manager) # Pass platform name to get_env_vars for conditional logic from easi.core.render_platform import EnvVars diff --git a/easi/simulators/coppeliasim/manifest.yaml b/easi/simulators/coppeliasim/manifest.yaml index 884d2f2..fbb57f2 100644 --- a/easi/simulators/coppeliasim/manifest.yaml +++ b/easi/simulators/coppeliasim/manifest.yaml @@ -7,6 +7,10 @@ versions: simulator_class: "easi.simulators.coppeliasim.v4_1_0.simulator.CoppeliaSimSimulatorV410" env_manager_class: "easi.simulators.coppeliasim.v4_1_0.env_manager.CoppeliaSimEnvManagerV410" python_version: "3.10" + render_platforms: + auto: "easi.simulators.coppeliasim.v4_1_0.render_platforms.CoppeliaSimAutoPlatform" + native: "easi.simulators.coppeliasim.v4_1_0.render_platforms.CoppeliaSimNativePlatform" + xvfb: "easi.simulators.coppeliasim.v4_1_0.render_platforms.CoppeliaSimXvfbPlatform" installation_kwargs: binary_url: "https://downloads.coppeliarobotics.com/V4_1_0/CoppeliaSim_Pro_V4_1_0_Ubuntu20_04.tar.xz" binary_filename: "CoppeliaSim_Pro_V4_1_0_Ubuntu20_04.tar.xz" diff --git a/easi/simulators/coppeliasim/v4_1_0/env_manager.py b/easi/simulators/coppeliasim/v4_1_0/env_manager.py index 59aef3e..5549c27 100644 --- a/easi/simulators/coppeliasim/v4_1_0/env_manager.py +++ b/easi/simulators/coppeliasim/v4_1_0/env_manager.py @@ -58,7 +58,11 @@ def get_validation_import(self) -> str: return "from pyrep import PyRep; print('PyRep OK')" def get_env_vars(self, render_platform_name: str | None = None) -> EnvVars: - """Return CoppeliaSim env vars for bridge subprocess.""" + """Return platform-agnostic CoppeliaSim env vars for bridge subprocess. + + Platform-specific vars (QT_QPA_PLATFORM_PLUGIN_PATH, __EGL_VENDOR_LIBRARY_FILENAMES) + are handled by custom render platform classes in render_platforms.py. + """ binary_dir_name = self.installation_kwargs.get("binary_dir_name", "") if not binary_dir_name: return EnvVars() @@ -69,20 +73,10 @@ def get_env_vars(self, render_platform_name: str | None = None) -> EnvVars: # Include conda env lib dir so fontconfig/freetype/Qt deps resolve conda_lib = self._resolve_template("{env_dir}/lib", t) ld_path = f"{coppeliasim_root}:{conda_lib}" - replace: dict[str, str] = { - "COPPELIASIM_ROOT": coppeliasim_root, - } - prepend: dict[str, str] = { - "LD_LIBRARY_PATH": ld_path, - "QT_QPA_PLATFORM_PLUGIN_PATH": coppeliasim_root, - } - # Force Mesa EGL vendor for Xvfb (NVIDIA EGL crashes Xvfb on some systems). - # Skip when using the EGL platform — it handles __EGL_VENDOR_LIBRARY_FILENAMES itself. - if render_platform_name != "egl": - mesa_vendor = Path("/usr/share/glvnd/egl_vendor.d/50_mesa.json") - if mesa_vendor.exists(): - replace["__EGL_VENDOR_LIBRARY_FILENAMES"] = str(mesa_vendor) - return EnvVars(replace=replace, prepend=prepend) + return EnvVars( + replace={"COPPELIASIM_ROOT": coppeliasim_root}, + prepend={"LD_LIBRARY_PATH": ld_path}, + ) def post_install(self, context: dict) -> None: """Download CoppeliaSim, build PyRep, copy lua addon. diff --git a/easi/simulators/coppeliasim/v4_1_0/render_platforms.py b/easi/simulators/coppeliasim/v4_1_0/render_platforms.py new file mode 100644 index 0000000..2ae6f1c --- /dev/null +++ b/easi/simulators/coppeliasim/v4_1_0/render_platforms.py @@ -0,0 +1,82 @@ +"""Custom render platforms for CoppeliaSim V4.1.0. + +CoppeliaSim needs simulator-specific env vars (QT_QPA_PLATFORM_PLUGIN_PATH, +__EGL_VENDOR_LIBRARY_FILENAMES) that depend on the CoppeliaSim binary location. +These custom platforms compute the correct paths from the env_manager. +""" + +from __future__ import annotations + +import os +from pathlib import Path + +from easi.core.render_platform import ( + AutoPlatform, + EnvVars, + NativePlatform, + XvfbPlatform, +) + + +def _coppeliasim_xvfb_env_vars(env_manager) -> EnvVars: + """Compute CoppeliaSim env vars needed when running under Xvfb. + + Returns EnvVars with: + - QT_QPA_PLATFORM_PLUGIN_PATH prepended to coppeliasim_root + (bundled Qt plugins needed when no system display) + - __EGL_VENDOR_LIBRARY_FILENAMES set to Mesa vendor + (NVIDIA EGL crashes Xvfb on some systems) + """ + if env_manager is None: + return EnvVars() + binary_dir_name = env_manager.installation_kwargs.get("binary_dir_name", "") + if not binary_dir_name: + return EnvVars() + t = env_manager._get_template_variables() + coppeliasim_root = env_manager._resolve_template( + "{extras_dir}/" + binary_dir_name, t + ) + replace: dict[str, str] = {} + mesa_vendor = Path("/usr/share/glvnd/egl_vendor.d/50_mesa.json") + if mesa_vendor.exists(): + replace["__EGL_VENDOR_LIBRARY_FILENAMES"] = str(mesa_vendor) + return EnvVars( + replace=replace, + prepend={"QT_QPA_PLATFORM_PLUGIN_PATH": coppeliasim_root}, + ) + + +class CoppeliaSimNativePlatform(NativePlatform): + """Native display for CoppeliaSim — no bundled Qt plugins, no Mesa override.""" + + @property + def name(self) -> str: + return "native" + + +class CoppeliaSimXvfbPlatform(XvfbPlatform): + """Xvfb platform for CoppeliaSim — sets Qt plugin path + Mesa EGL vendor.""" + + @property + def name(self) -> str: + return "xvfb" + + def get_env_vars(self) -> EnvVars: + return _coppeliasim_xvfb_env_vars(self._env_manager) + + +class CoppeliaSimAutoPlatform(AutoPlatform): + """Auto-detect for CoppeliaSim — native if DISPLAY, xvfb otherwise. + + Env vars follow the same split: native mode skips Qt plugin path, + xvfb mode prepends it. + """ + + @property + def name(self) -> str: + return "auto" + + def get_env_vars(self) -> EnvVars: + if os.environ.get("DISPLAY", ""): + return EnvVars() + return _coppeliasim_xvfb_env_vars(self._env_manager) diff --git a/easi/simulators/registry.py b/easi/simulators/registry.py index 2f92c0a..39c126f 100644 --- a/easi/simulators/registry.py +++ b/easi/simulators/registry.py @@ -135,12 +135,13 @@ def create_env_manager(key: str): return cls(installation_kwargs=entry.installation_kwargs) -def resolve_render_platform(key: str, platform_name: str): +def resolve_render_platform(key: str, platform_name: str, env_manager=None): """Resolve a render platform, checking simulator manifest first, then built-ins. Args: key: Simulator registry key (e.g. "coppeliasim:v4_1_0" or "coppeliasim"). platform_name: Platform name (e.g. "xvfb", "isaac_headless"). + env_manager: Optional env manager instance, passed to custom platform constructors. Returns: Instantiated RenderPlatform. @@ -152,7 +153,7 @@ def resolve_render_platform(key: str, platform_name: str): if custom_class_path: cls = _import_class(custom_class_path) - instance = cls() + instance = cls(env_manager=env_manager) if instance.name != platform_name: raise ValueError( f"Custom render platform class '{custom_class_path}' has " diff --git a/tests/test_coppeliasim_simulator.py b/tests/test_coppeliasim_simulator.py index 086251b..e1bacb5 100644 --- a/tests/test_coppeliasim_simulator.py +++ b/tests/test_coppeliasim_simulator.py @@ -93,10 +93,12 @@ def test_env_vars_has_ld_library_path(self): mgr = create_env_manager("coppeliasim") assert "LD_LIBRARY_PATH" in mgr.get_env_vars().prepend - def test_env_vars_has_qt_platform(self): + def test_env_vars_no_qt_platform(self): + """QT_QPA_PLATFORM_PLUGIN_PATH is now handled by custom render platform classes.""" from easi.simulators.registry import create_env_manager mgr = create_env_manager("coppeliasim") - assert "QT_QPA_PLATFORM_PLUGIN_PATH" in mgr.get_env_vars().prepend + ev = mgr.get_env_vars() + assert "QT_QPA_PLATFORM_PLUGIN_PATH" not in ev.prepend def test_env_vars_no_unresolved_templates(self): from easi.simulators.registry import create_env_manager @@ -116,7 +118,6 @@ def test_ld_library_path_includes_coppeliasim_root(self): mgr = create_env_manager("coppeliasim") ev = mgr.get_env_vars() assert ev.replace["COPPELIASIM_ROOT"] in ev.prepend["LD_LIBRARY_PATH"] - assert ev.replace["COPPELIASIM_ROOT"] == ev.prepend["QT_QPA_PLATFORM_PLUGIN_PATH"] def test_ld_library_path_includes_conda_lib(self): from easi.simulators.registry import create_env_manager diff --git a/tests/test_env_vars_wiring.py b/tests/test_env_vars_wiring.py index c768ee9..5178802 100644 --- a/tests/test_env_vars_wiring.py +++ b/tests/test_env_vars_wiring.py @@ -32,7 +32,7 @@ def test_runner_passes_env_vars_to_subprocess(self): with patch("easi.simulators.registry.create_env_manager", return_value=mock_env_mgr), \ patch("easi.simulators.registry.load_simulator_class", return_value=mock_sim_cls), \ patch("easi.simulators.registry.resolve_render_platform", - side_effect=lambda key, name: get_render_platform(name)), \ + side_effect=lambda key, name, env_manager=None: get_render_platform(name)), \ patch("easi.simulators.subprocess_runner.SubprocessRunner") as MockRunner: mock_runner_instance = MockRunner.return_value mock_runner_instance.launch.return_value = None @@ -67,7 +67,7 @@ def test_runner_passes_none_when_no_env_vars(self): with patch("easi.simulators.registry.create_env_manager", return_value=mock_env_mgr), \ patch("easi.simulators.registry.load_simulator_class", return_value=mock_sim_cls), \ patch("easi.simulators.registry.resolve_render_platform", - side_effect=lambda key, name: get_render_platform(name)), \ + side_effect=lambda key, name, env_manager=None: get_render_platform(name)), \ patch("easi.simulators.subprocess_runner.SubprocessRunner") as MockRunner: mock_runner_instance = MockRunner.return_value mock_runner_instance.launch.return_value = None diff --git a/tests/test_installation_kwargs.py b/tests/test_installation_kwargs.py index 1351a05..0f1578d 100644 --- a/tests/test_installation_kwargs.py +++ b/tests/test_installation_kwargs.py @@ -75,7 +75,7 @@ def test_runner_passes_installation_kwargs(self): with patch("easi.simulators.registry.create_env_manager", return_value=mock_env_mgr) as mock_factory, \ patch("easi.simulators.registry.load_simulator_class", return_value=mock_sim_cls), \ patch("easi.simulators.registry.resolve_render_platform", - side_effect=lambda key, name: get_render_platform(name)), \ + side_effect=lambda key, name, env_manager=None: get_render_platform(name)), \ patch("easi.simulators.subprocess_runner.SubprocessRunner") as MockRunner: mock_runner_instance = MockRunner.return_value mock_runner_instance.launch.return_value = None diff --git a/tests/test_render_platform.py b/tests/test_render_platform.py index 5339be4..86952e7 100644 --- a/tests/test_render_platform.py +++ b/tests/test_render_platform.py @@ -412,14 +412,21 @@ def test_coppeliasim(self): assert mgr.default_render_platform == "auto" assert mgr.screen_config == "1280x720x24" - def test_coppeliasim_egl_skips_mesa_vendor(self): - """When render_platform is 'egl', CoppeliaSim should NOT set __EGL_VENDOR_LIBRARY_FILENAMES.""" + def test_coppeliasim_env_vars_are_platform_agnostic(self): + """CoppeliaSim env_manager should NOT set QT_QPA_PLATFORM_PLUGIN_PATH or __EGL_VENDOR_LIBRARY_FILENAMES. + + Those platform-specific vars are now handled by custom render platform classes. + """ from easi.simulators.coppeliasim.v4_1_0.env_manager import CoppeliaSimEnvManagerV410 - mgr = CoppeliaSimEnvManagerV410() - ev = mgr.get_env_vars(render_platform_name="egl") + mgr = CoppeliaSimEnvManagerV410(installation_kwargs={"binary_dir_name": "CoppeliaSim"}) + ev = mgr.get_env_vars() all_keys = set(ev.replace) | set(ev.prepend) + assert "QT_QPA_PLATFORM_PLUGIN_PATH" not in all_keys assert "__EGL_VENDOR_LIBRARY_FILENAMES" not in all_keys + # Should still have the platform-agnostic vars + assert "COPPELIASIM_ROOT" in ev.replace + assert "LD_LIBRARY_PATH" in ev.prepend def test_tdw(self): from easi.simulators.tdw.v1_11_23.env_manager import TDWEnvManager @@ -530,6 +537,109 @@ def get_env_vars(self): assert platform.get_env_vars().replace == {"CUSTOM": "1"} +class TestCoppeliaSimCustomPlatforms: + """Test CoppeliaSim-specific custom render platform classes.""" + + def _make_mock_env_manager(self, binary_dir_name="CoppeliaSim"): + from unittest.mock import MagicMock + + mgr = MagicMock() + mgr.installation_kwargs = {"binary_dir_name": binary_dir_name} + mgr._get_template_variables.return_value = { + "env_dir": "/fake/envs/easi_coppeliasim_v4_1_0", + "extras_dir": "/fake/envs/easi_coppeliasim_v4_1_0/extras", + } + mgr._resolve_template.side_effect = lambda tmpl, t: tmpl.replace( + "{extras_dir}", t["extras_dir"] + ).replace("{env_dir}", t["env_dir"]) + return mgr + + def test_native_no_qt_plugin_path(self): + from easi.simulators.coppeliasim.v4_1_0.render_platforms import ( + CoppeliaSimNativePlatform, + ) + + mgr = self._make_mock_env_manager() + p = CoppeliaSimNativePlatform(env_manager=mgr) + assert p.name == "native" + ev = p.get_env_vars() + assert not ev # native platform returns empty env vars + + def test_xvfb_has_qt_plugin_path(self): + from easi.simulators.coppeliasim.v4_1_0.render_platforms import ( + CoppeliaSimXvfbPlatform, + ) + + mgr = self._make_mock_env_manager() + p = CoppeliaSimXvfbPlatform(env_manager=mgr) + assert p.name == "xvfb" + ev = p.get_env_vars() + assert "QT_QPA_PLATFORM_PLUGIN_PATH" in ev.prepend + assert "CoppeliaSim" in ev.prepend["QT_QPA_PLATFORM_PLUGIN_PATH"] + + def test_xvfb_wraps_command(self): + from easi.simulators.coppeliasim.v4_1_0.render_platforms import ( + CoppeliaSimXvfbPlatform, + ) + + p = CoppeliaSimXvfbPlatform() + cmd = ["python", "bridge.py"] + wrapped = p.wrap_command(cmd, "1280x720x24") + assert wrapped[0] == "xvfb-run" + + def test_auto_native_when_display(self): + from easi.simulators.coppeliasim.v4_1_0.render_platforms import ( + CoppeliaSimAutoPlatform, + ) + + mgr = self._make_mock_env_manager() + p = CoppeliaSimAutoPlatform(env_manager=mgr) + assert p.name == "auto" + with patch.dict(os.environ, {"DISPLAY": ":0"}): + ev = p.get_env_vars() + assert not ev # native mode: no Qt plugin path + + def test_auto_xvfb_when_no_display(self): + from easi.simulators.coppeliasim.v4_1_0.render_platforms import ( + CoppeliaSimAutoPlatform, + ) + + mgr = self._make_mock_env_manager() + p = CoppeliaSimAutoPlatform(env_manager=mgr) + env = os.environ.copy() + env.pop("DISPLAY", None) + with patch.dict(os.environ, env, clear=True): + ev = p.get_env_vars() + assert "QT_QPA_PLATFORM_PLUGIN_PATH" in ev.prepend + + def test_xvfb_no_env_manager_returns_empty(self): + from easi.simulators.coppeliasim.v4_1_0.render_platforms import ( + CoppeliaSimXvfbPlatform, + ) + + p = CoppeliaSimXvfbPlatform() # no env_manager + ev = p.get_env_vars() + assert not ev + + def test_manifest_registers_custom_platforms(self): + from easi.simulators.registry import get_simulator_entry + + entry = get_simulator_entry("coppeliasim:v4_1_0") + assert "auto" in entry.render_platforms + assert "native" in entry.render_platforms + assert "xvfb" in entry.render_platforms + + def test_resolve_coppeliasim_custom_platform(self): + from easi.simulators.coppeliasim.v4_1_0.render_platforms import ( + CoppeliaSimAutoPlatform, + ) + from easi.simulators.registry import resolve_render_platform + + platform = resolve_render_platform("coppeliasim:v4_1_0", "auto") + assert isinstance(platform, CoppeliaSimAutoPlatform) + assert platform.name == "auto" + + from unittest.mock import MagicMock @@ -599,7 +709,7 @@ def test_default_uses_env_manager_platform(self): with patch("easi.simulators.registry.create_env_manager", return_value=mock_env_mgr), \ patch("easi.simulators.registry.load_simulator_class", return_value=mock_sim_cls), \ patch("easi.simulators.registry.resolve_render_platform", - side_effect=lambda key, name: get_render_platform(name)), \ + side_effect=lambda key, name, env_manager=None: get_render_platform(name)), \ patch("easi.simulators.subprocess_runner.SubprocessRunner") as MockRunner: MockRunner.return_value.launch.return_value = None runner._create_simulator("fake:v1") @@ -621,7 +731,7 @@ def test_cli_override_wins(self): with patch("easi.simulators.registry.create_env_manager", return_value=mock_env_mgr), \ patch("easi.simulators.registry.load_simulator_class", return_value=mock_sim_cls), \ patch("easi.simulators.registry.resolve_render_platform", - side_effect=lambda key, name: get_render_platform(name)), \ + side_effect=lambda key, name, env_manager=None: get_render_platform(name)), \ patch("easi.simulators.subprocess_runner.SubprocessRunner") as MockRunner: MockRunner.return_value.launch.return_value = None runner._create_simulator("fake:v1") @@ -644,7 +754,7 @@ def test_yaml_override_used_when_no_cli(self): with patch("easi.simulators.registry.create_env_manager", return_value=mock_env_mgr), \ patch("easi.simulators.registry.load_simulator_class", return_value=mock_sim_cls), \ patch("easi.simulators.registry.resolve_render_platform", - side_effect=lambda key, name: get_render_platform(name)), \ + side_effect=lambda key, name, env_manager=None: get_render_platform(name)), \ patch("easi.simulators.subprocess_runner.SubprocessRunner") as MockRunner: MockRunner.return_value.launch.return_value = None runner._create_simulator("fake:v1", task=mock_task) @@ -667,7 +777,7 @@ def test_cli_beats_yaml(self): with patch("easi.simulators.registry.create_env_manager", return_value=mock_env_mgr), \ patch("easi.simulators.registry.load_simulator_class", return_value=mock_sim_cls), \ patch("easi.simulators.registry.resolve_render_platform", - side_effect=lambda key, name: get_render_platform(name)), \ + side_effect=lambda key, name, env_manager=None: get_render_platform(name)), \ patch("easi.simulators.subprocess_runner.SubprocessRunner") as MockRunner: MockRunner.return_value.launch.return_value = None runner._create_simulator("fake:v1", task=mock_task) @@ -690,7 +800,7 @@ def test_unsupported_platform_raises(self): with patch("easi.simulators.registry.create_env_manager", return_value=mock_env_mgr), \ patch("easi.simulators.registry.load_simulator_class", return_value=mock_sim_cls), \ patch("easi.simulators.registry.resolve_render_platform", - side_effect=lambda key, name: get_render_platform(name)), \ + side_effect=lambda key, name, env_manager=None: get_render_platform(name)), \ pytest.raises(ValueError, match="not supported"): runner._create_simulator("fake:v1") From 1b6c8a3c9b3ef7c4a289b633bccbc64de95ae723 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Mon, 23 Feb 2026 14:23:56 +0800 Subject: [PATCH 088/239] [Feat] Add option to add object state history to prompt --- easi/tasks/hazard/_base.yaml | 1 + easi/tasks/hazard/bridge.py | 7 +++++++ easi/tasks/hazard/prompts.py | 39 +++++++++++++++++++++++++++++------- 3 files changed, 40 insertions(+), 7 deletions(-) diff --git a/easi/tasks/hazard/_base.yaml b/easi/tasks/hazard/_base.yaml index 7ad108b..b5d4993 100644 --- a/easi/tasks/hazard/_base.yaml +++ b/easi/tasks/hazard/_base.yaml @@ -25,6 +25,7 @@ agent: prompt_builder: "easi.tasks.hazard.prompts.HAZARDPromptBuilder" prompt_builder_kwargs: cot: false + show_object_history: false # Set to true to show temperature/water level changes over time generation_kwargs: temperature: 0.7 max_tokens: 512 diff --git a/easi/tasks/hazard/bridge.py b/easi/tasks/hazard/bridge.py index d4a491e..b46cae5 100644 --- a/easi/tasks/hazard/bridge.py +++ b/easi/tasks/hazard/bridge.py @@ -338,6 +338,12 @@ def _on_step(self, env, action_text): if values: env_record_snapshot[obj_id] = values[-1] + # Full history for object state history section + env_record_history = {} + for obj_id, values in self._env_change_record.items(): + if values: + env_record_history[obj_id] = values + # Build info info = { "task_success": float(all_targets_done), @@ -361,6 +367,7 @@ def _on_step(self, env, action_text): "current_seen_objects_id": json.dumps(self.current_seen_objects_id), "object_distances": json.dumps(object_distances), "env_change_record": json.dumps(env_record_snapshot), + "env_change_record_history": json.dumps(env_record_history), "target_categories": json.dumps(self.target_categories), } diff --git a/easi/tasks/hazard/prompts.py b/easi/tasks/hazard/prompts.py index 3062aba..3ce351a 100644 --- a/easi/tasks/hazard/prompts.py +++ b/easi/tasks/hazard/prompts.py @@ -46,9 +46,11 @@ def __init__( self, scenario: str = "fire", cot: bool = False, + show_object_history: bool = False, ): self.scenario = scenario self.cot = cot + self.show_object_history = show_object_history self._templates = _load_prompt_templates() self._value_dict = json.loads((_CONFIG_DIR / "value.json").read_text()) self._fire_dict = json.loads((_CONFIG_DIR / "fire.json").read_text()) @@ -76,8 +78,8 @@ def build_messages(self, memory: AgentMemory) -> list[dict]: # Build action history (from memory, matching original format) history_desc = self._build_action_history(memory) - # Build object state history (empty -- original bug, see docstring) - object_history = self._build_object_state_history() + # Build object state history (tracking temperature/water level changes) + object_history = self._build_object_state_history(info) # Build available actions actions_desc = self._build_available_actions(available_plans) @@ -302,14 +304,37 @@ def _action_result_to_description(feedback: str) -> str: return "paused after taking 100 steps" return f"fail, because {feedback}" - def _build_object_state_history(self) -> str: + def _build_object_state_history(self, info: dict) -> str: """Build object state history section. - NOTE: In the original llm.py:run(), $OBJECT_HISTORY$ is never replaced - -- it remains as literal text in the prompt. This is a bug in the original. - We replicate by returning empty string (slightly better than literal placeholder). + Shows how object temperature/water_level has changed over time. + Format: "Object : -> -> Celsius" + + Controlled by show_object_history config (default: False for benchmark parity). """ - return "" + if not self.show_object_history: + return "" + + history = json.loads(info.get("env_change_record_history", "{}")) + if not history: + return "" + + lines = [] + for obj_id, values in history.items(): + if len(values) <= 1: + continue # No change to report + if self.scenario == "fire": + # Convert log_temp to Celsius and show progression + temps = [round(math.exp(v), 1) for v in values[-5:]] # Last 5 readings + progression = " -> ".join(str(t) for t in temps) + lines.append(f"Object {obj_id}: {progression} Celsius") + elif self.scenario == "flood": + # Show water level progression + levels = [round(v, 2) for v in values[-5:]] + progression = " -> ".join(str(l) for l in levels) + lines.append(f"Object {obj_id}: water level {progression} m") + + return "\n".join(lines) def _build_available_actions(self, plans: list[str]) -> str: """Format available plans as lettered options.""" From 3c33f6e8a39de9f985be4582dad867f8041d14ed Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Mon, 23 Feb 2026 15:01:25 +0800 Subject: [PATCH 089/239] chore: add yolo11n.pt to gitignore --- .gitignore | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index 0b654ac..3822a37 100644 --- a/.gitignore +++ b/.gitignore @@ -8,4 +8,5 @@ __pycache__/ /results /BEHAVIOR-1K .worktrees/ -/docs/plans \ No newline at end of file +/docs/plans +yolo11n.pt \ No newline at end of file From d97a8159eca9eef0e2270c9457e50a1d3926cc90 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Mon, 23 Feb 2026 15:41:28 +0800 Subject: [PATCH 090/239] fix: always set Qt plugin path and control CoppeliaSim headless via platform env var MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit CoppeliaSim needs QT_QPA_PLATFORM_PLUGIN_PATH for its bundled Qt plugins regardless of display mode — the NativePlatform was wrongly returning empty env vars, causing Qt initialization failures. Additionally, each platform class now sets COPPELIASIM_HEADLESS so the bridge uses CoppeliaSim's internal headless renderer on servers (where xvfb is only needed for Qt plugin loading), and GUI mode only when a real display is present. --- .../coppeliasim/v4_1_0/render_platforms.py | 70 ++++++++++++++----- easi/tasks/ebmanipulation/bridge.py | 9 +++ tests/test_render_platform.py | 13 ++-- 3 files changed, 69 insertions(+), 23 deletions(-) diff --git a/easi/simulators/coppeliasim/v4_1_0/render_platforms.py b/easi/simulators/coppeliasim/v4_1_0/render_platforms.py index 2ae6f1c..2d7e802 100644 --- a/easi/simulators/coppeliasim/v4_1_0/render_platforms.py +++ b/easi/simulators/coppeliasim/v4_1_0/render_platforms.py @@ -3,6 +3,12 @@ CoppeliaSim needs simulator-specific env vars (QT_QPA_PLATFORM_PLUGIN_PATH, __EGL_VENDOR_LIBRARY_FILENAMES) that depend on the CoppeliaSim binary location. These custom platforms compute the correct paths from the env_manager. + +Additionally, each platform sets COPPELIASIM_HEADLESS so the bridge can +start CoppeliaSim in the correct mode: +- native: headless=False (user has a real display and wants GUI) +- xvfb: headless=True (xvfb is only for Qt plugins, not CoppeliaSim rendering) +- auto: headless=False if DISPLAY, True otherwise """ from __future__ import annotations @@ -18,14 +24,23 @@ ) -def _coppeliasim_xvfb_env_vars(env_manager) -> EnvVars: - """Compute CoppeliaSim env vars needed when running under Xvfb. +def _coppeliasim_qt_env_vars( + env_manager, *, include_mesa_egl: bool = False, headless: bool = True, +) -> EnvVars: + """Compute CoppeliaSim Qt env vars. + + CoppeliaSim always needs QT_QPA_PLATFORM_PLUGIN_PATH to find its bundled + Qt plugins (libqxcb.so, etc.), regardless of display mode. - Returns EnvVars with: - - QT_QPA_PLATFORM_PLUGIN_PATH prepended to coppeliasim_root - (bundled Qt plugins needed when no system display) - - __EGL_VENDOR_LIBRARY_FILENAMES set to Mesa vendor - (NVIDIA EGL crashes Xvfb on some systems) + Args: + env_manager: The CoppeliaSimEnvManager instance. + include_mesa_egl: If True, also set __EGL_VENDOR_LIBRARY_FILENAMES + to Mesa vendor (needed for Xvfb; NVIDIA EGL crashes Xvfb). + headless: Whether CoppeliaSim should use its headless renderer. + + Returns: + EnvVars with QT_QPA_PLATFORM_PLUGIN_PATH, COPPELIASIM_HEADLESS, + and optionally Mesa EGL. """ if env_manager is None: return EnvVars() @@ -36,10 +51,13 @@ def _coppeliasim_xvfb_env_vars(env_manager) -> EnvVars: coppeliasim_root = env_manager._resolve_template( "{extras_dir}/" + binary_dir_name, t ) - replace: dict[str, str] = {} - mesa_vendor = Path("/usr/share/glvnd/egl_vendor.d/50_mesa.json") - if mesa_vendor.exists(): - replace["__EGL_VENDOR_LIBRARY_FILENAMES"] = str(mesa_vendor) + replace: dict[str, str] = { + "COPPELIASIM_HEADLESS": "1" if headless else "0", + } + if include_mesa_egl: + mesa_vendor = Path("/usr/share/glvnd/egl_vendor.d/50_mesa.json") + if mesa_vendor.exists(): + replace["__EGL_VENDOR_LIBRARY_FILENAMES"] = str(mesa_vendor) return EnvVars( replace=replace, prepend={"QT_QPA_PLATFORM_PLUGIN_PATH": coppeliasim_root}, @@ -47,29 +65,40 @@ def _coppeliasim_xvfb_env_vars(env_manager) -> EnvVars: class CoppeliaSimNativePlatform(NativePlatform): - """Native display for CoppeliaSim — no bundled Qt plugins, no Mesa override.""" + """Native display for CoppeliaSim — Qt plugin path, no Mesa override, headless=False.""" @property def name(self) -> str: return "native" + def get_env_vars(self) -> EnvVars: + return _coppeliasim_qt_env_vars( + self._env_manager, include_mesa_egl=False, headless=False, + ) + class CoppeliaSimXvfbPlatform(XvfbPlatform): - """Xvfb platform for CoppeliaSim — sets Qt plugin path + Mesa EGL vendor.""" + """Xvfb platform for CoppeliaSim — Qt plugin path + Mesa EGL, headless=True. + + Xvfb provides a virtual X11 display for Qt plugin loading, but CoppeliaSim + itself should use its native headless renderer (libcoppeliaSimHeadless.so). + """ @property def name(self) -> str: return "xvfb" def get_env_vars(self) -> EnvVars: - return _coppeliasim_xvfb_env_vars(self._env_manager) + return _coppeliasim_qt_env_vars( + self._env_manager, include_mesa_egl=True, headless=True, + ) class CoppeliaSimAutoPlatform(AutoPlatform): """Auto-detect for CoppeliaSim — native if DISPLAY, xvfb otherwise. - Env vars follow the same split: native mode skips Qt plugin path, - xvfb mode prepends it. + Both modes need Qt plugin path. Only xvfb needs Mesa EGL override. + Headless follows display: False with real display, True without. """ @property @@ -77,6 +106,9 @@ def name(self) -> str: return "auto" def get_env_vars(self) -> EnvVars: - if os.environ.get("DISPLAY", ""): - return EnvVars() - return _coppeliasim_xvfb_env_vars(self._env_manager) + has_display = bool(os.environ.get("DISPLAY", "")) + return _coppeliasim_qt_env_vars( + self._env_manager, + include_mesa_egl=not has_display, + headless=not has_display, + ) diff --git a/easi/tasks/ebmanipulation/bridge.py b/easi/tasks/ebmanipulation/bridge.py index 1897d40..44960a0 100644 --- a/easi/tasks/ebmanipulation/bridge.py +++ b/easi/tasks/ebmanipulation/bridge.py @@ -19,6 +19,7 @@ from __future__ import annotations import json +import os import sys from pathlib import Path @@ -82,6 +83,13 @@ def _create_env(self, reset_config, simulator_kwargs): scene_bounds = simulator_kwargs.get( "scene_bounds", [-0.3, -0.5, 0.6, 0.7, 0.5, 1.6] ) + # COPPELIASIM_HEADLESS is set by CoppeliaSim's custom render platform classes. + # Falls back to simulator_kwargs, then defaults to True (EBManEnv default). + headless_env = os.environ.get("COPPELIASIM_HEADLESS") + if headless_env is not None: + headless = headless_env != "0" + else: + headless = simulator_kwargs.get("headless", True) self._resolution = resolution self._detection_box = simulator_kwargs.get("detection_box", True) @@ -92,6 +100,7 @@ def _create_env(self, reset_config, simulator_kwargs): scene_bounds=scene_bounds, voxel_size=voxel_size, rotation_resolution=rotation_resolution, + headless=headless, ) def _on_reset(self, env, reset_config): diff --git a/tests/test_render_platform.py b/tests/test_render_platform.py index 86952e7..ba663cf 100644 --- a/tests/test_render_platform.py +++ b/tests/test_render_platform.py @@ -554,7 +554,7 @@ def _make_mock_env_manager(self, binary_dir_name="CoppeliaSim"): ).replace("{env_dir}", t["env_dir"]) return mgr - def test_native_no_qt_plugin_path(self): + def test_native_has_qt_plugin_path_and_headless_false(self): from easi.simulators.coppeliasim.v4_1_0.render_platforms import ( CoppeliaSimNativePlatform, ) @@ -563,9 +563,11 @@ def test_native_no_qt_plugin_path(self): p = CoppeliaSimNativePlatform(env_manager=mgr) assert p.name == "native" ev = p.get_env_vars() - assert not ev # native platform returns empty env vars + assert "QT_QPA_PLATFORM_PLUGIN_PATH" in ev.prepend + assert ev.replace["COPPELIASIM_HEADLESS"] == "0" + assert "__EGL_VENDOR_LIBRARY_FILENAMES" not in ev.replace - def test_xvfb_has_qt_plugin_path(self): + def test_xvfb_has_qt_plugin_path_and_headless_true(self): from easi.simulators.coppeliasim.v4_1_0.render_platforms import ( CoppeliaSimXvfbPlatform, ) @@ -576,6 +578,7 @@ def test_xvfb_has_qt_plugin_path(self): ev = p.get_env_vars() assert "QT_QPA_PLATFORM_PLUGIN_PATH" in ev.prepend assert "CoppeliaSim" in ev.prepend["QT_QPA_PLATFORM_PLUGIN_PATH"] + assert ev.replace["COPPELIASIM_HEADLESS"] == "1" def test_xvfb_wraps_command(self): from easi.simulators.coppeliasim.v4_1_0.render_platforms import ( @@ -597,7 +600,8 @@ def test_auto_native_when_display(self): assert p.name == "auto" with patch.dict(os.environ, {"DISPLAY": ":0"}): ev = p.get_env_vars() - assert not ev # native mode: no Qt plugin path + assert "QT_QPA_PLATFORM_PLUGIN_PATH" in ev.prepend + assert ev.replace["COPPELIASIM_HEADLESS"] == "0" def test_auto_xvfb_when_no_display(self): from easi.simulators.coppeliasim.v4_1_0.render_platforms import ( @@ -611,6 +615,7 @@ def test_auto_xvfb_when_no_display(self): with patch.dict(os.environ, env, clear=True): ev = p.get_env_vars() assert "QT_QPA_PLATFORM_PLUGIN_PATH" in ev.prepend + assert ev.replace["COPPELIASIM_HEADLESS"] == "1" def test_xvfb_no_env_manager_returns_empty(self): from easi.simulators.coppeliasim.v4_1_0.render_platforms import ( From db88011b75f8a464ebbfb789fc6d6278e6143b84 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Mon, 23 Feb 2026 15:45:28 +0800 Subject: [PATCH 091/239] fix: commit files --- easi/tasks/ebmanipulation/_base.yaml | 2 -- easi/tasks/ebmanipulation/vendor/EBManEnv.py | 3 ++- 2 files changed, 2 insertions(+), 3 deletions(-) diff --git a/easi/tasks/ebmanipulation/_base.yaml b/easi/tasks/ebmanipulation/_base.yaml index d48d255..580c15f 100644 --- a/easi/tasks/ebmanipulation/_base.yaml +++ b/easi/tasks/ebmanipulation/_base.yaml @@ -24,8 +24,6 @@ simulator_configs: - "gymnasium" - "ultralytics" - "open3d" - env_vars: - QT_QPA_PLATFORM: "xcb" agent: prompt_builder: "easi.tasks.ebmanipulation.prompts.EBManipulationPromptBuilder" prompt_builder_kwargs: diff --git a/easi/tasks/ebmanipulation/vendor/EBManEnv.py b/easi/tasks/ebmanipulation/vendor/EBManEnv.py index 303205f..9b0804e 100644 --- a/easi/tasks/ebmanipulation/vendor/EBManEnv.py +++ b/easi/tasks/ebmanipulation/vendor/EBManEnv.py @@ -42,13 +42,14 @@ def __init__( scene_bounds: list[float] | None = None, voxel_size: int = 100, rotation_resolution: int = 3, + headless: bool = True, ): obs_config = ObservationConfig() obs_config.set_all(True) obs_config.set_image_size(img_size) action_mode = ActionMode(ArmActionMode.ABS_EE_POSE_PLAN_WORLD_FRAME) - self.env = Environment(action_mode, obs_config=obs_config, headless=True) + self.env = Environment(action_mode, obs_config=obs_config, headless=headless) self.env.launch() self._max_episode_steps = max_steps From e98c3814c756901d264ecfb17e263963699a0366 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Mon, 23 Feb 2026 15:55:30 +0800 Subject: [PATCH 092/239] chore: remove redundant comments --- easi/core/base_env_manager.py | 12 ++++---- easi/evaluation/parallel_runner.py | 30 +++++++++---------- .../coppeliasim/v4_1_0/env_manager.py | 10 +++---- .../omnigibson/v3_7_2/env_manager.py | 14 ++++----- 4 files changed, 33 insertions(+), 33 deletions(-) diff --git a/easi/core/base_env_manager.py b/easi/core/base_env_manager.py index da55553..5cb68e6 100644 --- a/easi/core/base_env_manager.py +++ b/easi/core/base_env_manager.py @@ -202,10 +202,10 @@ def _do_install(self) -> None: env_name = self.get_env_name() logger.info("Installing environment %s for %s %s", env_name, self.simulator_name, self.version) - # Step 1: Check system deps + # Check system deps self._dep_checker.assert_all(self.get_system_deps()) - # Step 2: Create/update conda env + # Create/update conda env conda_yaml = self.get_conda_env_yaml_path() if conda_yaml.exists(): with spinner(f"Creating conda environment {env_name}"): @@ -213,12 +213,12 @@ def _do_install(self) -> None: else: logger.warning("No conda_env.yaml found at %s, skipping conda setup", conda_yaml) - # Step 3: Install uv in the conda env + # Install uv in the conda env python_exec = self.get_python_executable() with spinner("Installing uv"): self._run_command([python_exec, "-m", "pip", "install", "uv"], "pip install uv") - # Step 4: Install Python deps via uv + # Install Python deps via uv requirements = self.get_requirements_txt_path() if requirements.exists(): with spinner("Installing Python dependencies"): @@ -229,10 +229,10 @@ def _do_install(self) -> None: else: logger.warning("No requirements.txt found at %s, skipping uv install", requirements) - # Step 5: Run post-install hook (binary downloads, file copies, etc.) + # Run post-install hook (binary downloads, file copies, etc.) self._run_post_install() - # Step 6: Validate (with env vars so e.g. LD_LIBRARY_PATH is set) + # Validate (with env vars so e.g. LD_LIBRARY_PATH is set) env_vars = self.get_env_vars() validation_env = None if env_vars: diff --git a/easi/evaluation/parallel_runner.py b/easi/evaluation/parallel_runner.py index b1599ed..a1f5f0e 100644 --- a/easi/evaluation/parallel_runner.py +++ b/easi/evaluation/parallel_runner.py @@ -56,8 +56,8 @@ def run(self) -> list[dict]: "Start vLLM externally and pass --llm-url." ) - # --- Phase 1: Load task --- - logger.trace("Phase 1: Loading task") + # --- Load task --- + logger.trace("Loading task") task = self._create_task() if self.refresh_data: task.download_dataset(force=True) @@ -65,12 +65,12 @@ def run(self) -> list[dict]: if self.max_episodes is not None: episodes = episodes[: self.max_episodes] logger.trace( - "Phase 1: Task loaded. %d episodes, simulator_key=%s", + "Task loaded. %d episodes, simulator_key=%s", len(episodes), task.simulator_key, ) - # --- Phase 2: Resolve LLM backend + handle resume --- - logger.trace("Phase 2: Resolved LLM backend=%s, base_url=%s", backend, base_url) + # --- Resolve LLM backend + handle resume --- + logger.trace("Resolved LLM backend=%s, base_url=%s", backend, base_url) # Compute resolved generation kwargs (YAML defaults + CLI overrides) from easi.llm.utils import parse_llm_kwargs, split_kwargs @@ -96,8 +96,8 @@ def run(self) -> list[dict]: completed_results = [] start_index = 0 - # --- Phase 3: Create output directory and save config --- - logger.trace("Phase 3: Creating output directory and saving config") + # --- Create output directory and save config --- + logger.trace("Creating output directory and saving config") episodes_dir = run_dir / "episodes" episodes_dir.mkdir(parents=True, exist_ok=True) @@ -118,19 +118,19 @@ def run(self) -> list[dict]: if start_index >= len(episodes): logger.info("All %d episodes already complete, re-aggregating summary.", len(episodes)) all_results = completed_results - # Skip to aggregation (Phase 7) + # Skip to aggregation wall_seconds = 0.0 results_list = [(i, r) for i, r in enumerate(all_results)] else: - # --- Phase 4: Fill episode queue (from start_index) --- + # --- Fill episode queue (from start_index) --- episode_queue: queue.Queue[tuple[int, dict]] = queue.Queue() for i, episode in enumerate(episodes): if i >= start_index: episode_queue.put((i, episode)) remaining = episode_queue.qsize() - logger.trace("Phase 4: Queued %d episodes (skipped %d completed)", remaining, start_index) + logger.trace("Queued %d episodes (skipped %d completed)", remaining, start_index) - # --- Phase 5: Prepare thread-safe collection --- + # --- Prepare thread-safe collection --- results_lock = threading.Lock() new_results: list[tuple[int, dict]] = [] progress = {"completed": 0, "failed": 0} @@ -274,8 +274,8 @@ def _worker(worker_id: int) -> None: worker_id, episodes_done, ) - # --- Phase 6: Launch worker threads --- - logger.trace("Phase 6: Launching %d worker threads", num_workers) + # --- Launch worker threads --- + logger.trace("Launching %d worker threads", num_workers) wall_start = time.monotonic() with ThreadPoolExecutor(max_workers=num_workers) as executor: @@ -295,14 +295,14 @@ def _worker(worker_id: int) -> None: results_list = [(i, r) for i, r in enumerate(completed_results)] results_list.extend(new_results) - # --- Phase 7: Sort results and aggregate --- + # --- Sort results and aggregate --- results_list.sort(key=lambda x: x[0]) all_results = [r for _, r in results_list] num_successful = sum(1 for r in all_results if "error" not in r) num_failed = len(all_results) - num_successful logger.trace( - "Phase 7: Results sorted. %d successful, %d failed", + "Results sorted. %d successful, %d failed", num_successful, num_failed, ) diff --git a/easi/simulators/coppeliasim/v4_1_0/env_manager.py b/easi/simulators/coppeliasim/v4_1_0/env_manager.py index 5549c27..9ef2e50 100644 --- a/easi/simulators/coppeliasim/v4_1_0/env_manager.py +++ b/easi/simulators/coppeliasim/v4_1_0/env_manager.py @@ -96,7 +96,7 @@ def post_install(self, context: dict) -> None: pyrep_git_url = self.installation_kwargs.get("pyrep_git_url") lua_addon_script = self.installation_kwargs.get("lua_addon_script") - # Step 1: Download + extract CoppeliaSim binary + # Download + extract CoppeliaSim binary if binary_url and binary_filename: logger.info("Downloading CoppeliaSim from %s", binary_url) self._download_and_extract( @@ -105,7 +105,7 @@ def post_install(self, context: dict) -> None: dest_dir=extras_dir, ) - # Step 2: Create versioned symlinks (PyRep links against libcoppeliaSim.so.1) + # Create versioned symlinks (PyRep links against libcoppeliaSim.so.1) if "COPPELIASIM_ROOT" in env_vars: coppeliasim_root = Path(env_vars["COPPELIASIM_ROOT"]) for lib_name in ["libcoppeliaSim.so", "libcoppeliaSimHeadless.so"]: @@ -115,11 +115,11 @@ def post_install(self, context: dict) -> None: symlink.symlink_to(lib_path.name) logger.info("Created symlink %s -> %s", symlink.name, lib_path.name) - # Step 3: Build env dict with COPPELIASIM_ROOT for PyRep's native build + # Build env dict with COPPELIASIM_ROOT for PyRep's native build build_env = os.environ.copy() build_env.update(env_vars) - # Step 3: pip install PyRep (C++ extensions link against CoppeliaSim) + # pip install PyRep (C++ extensions link against CoppeliaSim) if pyrep_git_url: logger.info("Installing PyRep from %s", pyrep_git_url) self._run_command( @@ -128,7 +128,7 @@ def post_install(self, context: dict) -> None: env=build_env, ) - # Step 4: Copy PyRep addon lua script into CoppeliaSim root + # Copy PyRep addon lua script into CoppeliaSim root if lua_addon_script and "COPPELIASIM_ROOT" in env_vars: coppeliasim_root = Path(env_vars["COPPELIASIM_ROOT"]) lua_src = Path(__file__).parent / lua_addon_script diff --git a/easi/simulators/omnigibson/v3_7_2/env_manager.py b/easi/simulators/omnigibson/v3_7_2/env_manager.py index 48a9fb7..acbe5bb 100644 --- a/easi/simulators/omnigibson/v3_7_2/env_manager.py +++ b/easi/simulators/omnigibson/v3_7_2/env_manager.py @@ -154,27 +154,27 @@ def post_install(self, context: dict) -> None: extras_dir.mkdir(parents=True, exist_ok=True) python = self._get_conda_python() - # Step 1: Git clone BEHAVIOR-1K + # Git clone BEHAVIOR-1K self._clone_behavior_1k(extras_dir) behavior_1k_dir = extras_dir / "BEHAVIOR-1K" - # Step 2: Install PyTorch with CUDA + # Install PyTorch with CUDA self._install_pytorch(python) - # Step 3: Install bddl from cloned repo + # Install bddl from cloned repo self._install_bddl(python, behavior_1k_dir) - # Step 4: Install OmniGibson from cloned repo + # Install OmniGibson from cloned repo self._install_omnigibson(python, behavior_1k_dir) - # Step 5: Download + install Isaac Sim 4.5.0 wheels + # Download + install Isaac Sim 4.5.0 wheels self._install_isaac_sim(python) - # Step 6: Fix websockets conflict in Isaac Sim extscache + # Fix websockets conflict in Isaac Sim extscache self._fix_websockets_conflict(python) - # Step 7: Fix cffi compatibility + # Fix cffi compatibility self._fix_cffi(python) def _clone_behavior_1k(self, extras_dir: Path) -> None: From da099ae6da34ebca5257cbbc4feb49b785cb517d Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Mon, 23 Feb 2026 16:34:46 +0800 Subject: [PATCH 093/239] fix: removed redundant code --- easi/cli.py | 7 +++++-- easi/core/render_platform.py | 2 ++ easi/evaluation/metrics.py | 18 ------------------ easi/llm/client.py | 2 +- easi/simulators/registry.py | 9 +-------- easi/tasks/ebalfred/json_repair.py | 2 -- easi/tasks/registry.py | 10 +--------- 7 files changed, 10 insertions(+), 40 deletions(-) delete mode 100644 easi/tasks/ebalfred/json_repair.py diff --git a/easi/cli.py b/easi/cli.py index 27c2d8c..83efc64 100644 --- a/easi/cli.py +++ b/easi/cli.py @@ -378,7 +378,8 @@ def cmd_start(args): run_kwargs.pop("task_name", None) num_parallel = run_kwargs.pop("num_parallel", None) or 1 - from easi.evaluation.metrics import aggregate_metrics + from easi.core.episode import EpisodeRecord + from easi.evaluation.metrics import default_aggregate all_summaries: list[tuple[str, dict]] = [] @@ -405,7 +406,9 @@ def cmd_start(args): results = runner.run() logger.info("Completed %d episodes for %s.", len(results), task_name) - summary = aggregate_metrics(results) + records = [EpisodeRecord(episode={}, trajectory=[], episode_results=r) for r in results] + summary = {"num_episodes": len(results)} + summary.update(default_aggregate(records)) all_summaries.append((task_name, summary)) for key, value in summary.items(): logger.info(" %s: %s", key, value) diff --git a/easi/core/render_platform.py b/easi/core/render_platform.py index 210061d..be7badf 100644 --- a/easi/core/render_platform.py +++ b/easi/core/render_platform.py @@ -57,6 +57,8 @@ def merge(cls, *env_vars: EnvVars) -> EnvVars: replace: dict[str, str] = {} prepend: dict[str, str] = {} for ev in env_vars: + if ev is None: + continue replace.update(ev.replace) for k, v in ev.prepend.items(): prepend[k] = f"{v}:{prepend[k]}" if k in prepend else v diff --git a/easi/evaluation/metrics.py b/easi/evaluation/metrics.py index 910ea88..100be28 100644 --- a/easi/evaluation/metrics.py +++ b/easi/evaluation/metrics.py @@ -39,21 +39,3 @@ def default_aggregate(records: list[EpisodeRecord]) -> dict: summary["avg_steps"] = summary["avg_num_steps"] return summary - - -def aggregate_metrics(results: list[dict]) -> dict: - """Legacy aggregate function for backward compatibility. - - Wraps default_aggregate() by converting plain dicts to EpisodeRecords. - New code should use task.aggregate_results() directly. - """ - if not results: - return {"num_episodes": 0} - - records = [ - EpisodeRecord(episode={}, trajectory=[], episode_results=r) - for r in results - ] - summary = {"num_episodes": len(results)} - summary.update(default_aggregate(records)) - return summary diff --git a/easi/llm/client.py b/easi/llm/client.py index 8b36a8e..5df6980 100644 --- a/easi/llm/client.py +++ b/easi/llm/client.py @@ -96,7 +96,7 @@ def generate(self, messages: list[dict], response_format: dict | None = None) -> raise self._track_usage(response) - content = response.choices[0].message.content + content = response.choices[0].message.content or "" logger.trace("LLM response: %s", content[:200] if content else "") return content diff --git a/easi/simulators/registry.py b/easi/simulators/registry.py index 39c126f..95efa38 100644 --- a/easi/simulators/registry.py +++ b/easi/simulators/registry.py @@ -11,12 +11,12 @@ from __future__ import annotations -import importlib from dataclasses import dataclass, field from pathlib import Path import yaml +from easi.utils.import_utils import import_class as _import_class from easi.utils.logging import get_logger logger = get_logger(__name__) @@ -165,13 +165,6 @@ def resolve_render_platform(key: str, platform_name: str, env_manager=None): return get_render_platform(platform_name) -def _import_class(fully_qualified_name: str): - """Import a class from its fully qualified name (e.g., 'pkg.mod.ClassName').""" - module_path, class_name = fully_qualified_name.rsplit(".", 1) - module = importlib.import_module(module_path) - return getattr(module, class_name) - - def refresh() -> None: """Force re-discovery of simulators (useful after adding new ones at runtime).""" global _registry diff --git a/easi/tasks/ebalfred/json_repair.py b/easi/tasks/ebalfred/json_repair.py deleted file mode 100644 index 49dc898..0000000 --- a/easi/tasks/ebalfred/json_repair.py +++ /dev/null @@ -1,2 +0,0 @@ -"""Backward-compatible re-export. Canonical location: easi.utils.json_repair.""" -from easi.utils.json_repair import fix_json # noqa: F401 diff --git a/easi/tasks/registry.py b/easi/tasks/registry.py index 390b86b..6ef86a9 100644 --- a/easi/tasks/registry.py +++ b/easi/tasks/registry.py @@ -10,12 +10,11 @@ from __future__ import annotations -import importlib from dataclasses import dataclass from pathlib import Path from easi.tasks.yaml_utils import resolve_task_yaml - +from easi.utils.import_utils import import_class as _import_class from easi.utils.logging import get_logger logger = get_logger(__name__) @@ -113,13 +112,6 @@ def load_task_class(name: str): return _import_class(entry.task_class) -def _import_class(fully_qualified_name: str): - """Import a class from its fully qualified name.""" - module_path, class_name = fully_qualified_name.rsplit(".", 1) - module = importlib.import_module(module_path) - return getattr(module, class_name) - - def refresh() -> None: """Force re-discovery of tasks.""" global _registry From abf0ac527871264d6d3bfc2620f536d5f7b806ef Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Mon, 23 Feb 2026 16:44:18 +0800 Subject: [PATCH 094/239] chore: updated cli reference --- docs/cli-reference.md | 42 +++++++++++++++++++++++++++++++++++++++++- 1 file changed, 41 insertions(+), 1 deletion(-) diff --git a/docs/cli-reference.md b/docs/cli-reference.md index 402a171..efd3196 100644 --- a/docs/cli-reference.md +++ b/docs/cli-reference.md @@ -64,6 +64,7 @@ easi start [TASK ...] [options] | `--num-parallel N` | Parallel simulator instances (default: 1). API backends only. | | `--max-episodes N` | Max episodes to run (default: all) | | `--seed SEED` | Random seed for agent reproducibility | +| `--render-platform PLATFORM` | Rendering platform override (default: simulator's preference). See [Render Platforms](#render-platforms). | ### Data & Output @@ -126,6 +127,10 @@ easi start --resume ./logs/ebalfred_base/20260215_093045_gpt-4o # Force dataset re-download easi start ebalfred_base --agent dummy --refresh-data +# Override render platform (e.g., force native display) +easi start ebmanipulation_base --agent react --backend openai --model gpt-4o \ + --render-platform native + # Verbose logging easi start ebalfred_base --agent dummy --verbosity TRACE ``` @@ -308,7 +313,7 @@ Creates: Run a smoke test on a simulator (reset + N steps). ``` -easi sim test [--steps N] [--timeout SECONDS] +easi sim test [--steps N] [--timeout SECONDS] [--render-platform PLATFORM] ``` | Argument | Description | @@ -316,6 +321,7 @@ easi sim test [--steps N] [--timeout SECONDS] | `simulator` | Simulator key (e.g., `dummy`, `ai2thor:v5_0_0`) | | `--steps N` | Number of steps to execute (default: 5) | | `--timeout SECONDS` | Bridge startup timeout (default: 200.0) | +| `--render-platform PLATFORM` | Rendering platform override (default: simulator's preference). See [Render Platforms](#render-platforms). | **Examples:** @@ -323,6 +329,7 @@ easi sim test [--steps N] [--timeout SECONDS] easi sim test dummy easi sim test ai2thor:v5_0_0 --steps 10 easi sim test ai2thor:v2_1_0 --steps 3 --timeout 300 +easi sim test coppeliasim:v4_1_0 --render-platform native ``` Executes `MoveAhead` for each step and reports observations and rewards. @@ -373,6 +380,39 @@ easi start ebalfred_base --agent react --backend openai \ --- +## Render Platforms + +Render platforms control how a simulator gets a display for rendering. Each simulator declares a default platform and a set of supported platforms in its manifest. Use `--render-platform` to override. + +### Built-in Platforms + +| Platform | Description | +|---|---| +| `auto` | Use native display if `DISPLAY` is set, fall back to xvfb | +| `native` | Require an existing `DISPLAY` (fails if none) | +| `xvfb` | Wrap with `xvfb-run` (virtual X11 framebuffer) | +| `egl` | GPU-accelerated headless rendering via EGL (no X11) | +| `headless` | No display at all (simulator has native headless support) | + +### Custom Platforms + +Some simulators register custom render platform classes in their manifest that extend the built-in platforms with simulator-specific environment variables. For example, CoppeliaSim defines custom `auto`, `native`, and `xvfb` platforms that set `QT_QPA_PLATFORM_PLUGIN_PATH` and control the `COPPELIASIM_HEADLESS` flag. + +Custom platforms are resolved automatically — when you pass `--render-platform xvfb` for a CoppeliaSim task, the CoppeliaSim-specific xvfb platform is used instead of the generic one. + +### Platform Defaults by Simulator + +| Simulator | Default | Supported | +|---|---|---| +| `dummy:v1` | `headless` | `headless` | +| `ai2thor:v2_1_0` | `auto` | `auto`, `native`, `xvfb` | +| `ai2thor:v5_0_0` | `auto` | `auto`, `native`, `xvfb` | +| `habitat_sim:v0_3_0` | `auto` | `auto`, `native`, `xvfb`, `egl` | +| `coppeliasim:v4_1_0` | `auto` | `auto`, `native`, `xvfb` | +| `tdw:v1_11_23` | `auto` | `auto`, `native`, `xvfb` | + +--- + ## Environment Variables The CLI itself does not use environment variables, but the LLM backends require API keys: From 004d93e43431a41cfe3c9e38541693727d1add2b Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Mon, 23 Feb 2026 20:43:34 +0800 Subject: [PATCH 095/239] feat: add OmniGibson render platform classes (native + auto) Co-Authored-By: Claude Sonnet 4.6 --- .../omnigibson/v3_7_2/render_platforms.py | 35 +++++++++++++++++++ tests/test_omnigibson_simulator.py | 34 ++++++++++++++++++ 2 files changed, 69 insertions(+) create mode 100644 easi/simulators/omnigibson/v3_7_2/render_platforms.py diff --git a/easi/simulators/omnigibson/v3_7_2/render_platforms.py b/easi/simulators/omnigibson/v3_7_2/render_platforms.py new file mode 100644 index 0000000..3626ecf --- /dev/null +++ b/easi/simulators/omnigibson/v3_7_2/render_platforms.py @@ -0,0 +1,35 @@ +"""Custom render platforms for OmniGibson v3.7.2. + +Each platform sets OMNIGIBSON_HEADLESS so Isaac Sim starts in the correct mode: +- native: OMNIGIBSON_HEADLESS=0 (user has a real display, Isaac Sim GUI opens) +- auto: OMNIGIBSON_HEADLESS=0 if DISPLAY is set, 1 otherwise +""" + +from __future__ import annotations + +import os + +from easi.core.render_platform import AutoPlatform, EnvVars, NativePlatform + + +class OmniGibsonNativePlatform(NativePlatform): + """Native display for OmniGibson — Isaac Sim GUI window, OMNIGIBSON_HEADLESS=0.""" + + @property + def name(self) -> str: + return "native" + + def get_env_vars(self) -> EnvVars: + return EnvVars(replace={"OMNIGIBSON_HEADLESS": "0"}) + + +class OmniGibsonAutoPlatform(AutoPlatform): + """Auto-detect for OmniGibson — native mode if $DISPLAY is set, headless otherwise.""" + + @property + def name(self) -> str: + return "auto" + + def get_env_vars(self) -> EnvVars: + has_display = bool(os.environ.get("DISPLAY", "")) + return EnvVars(replace={"OMNIGIBSON_HEADLESS": "0" if has_display else "1"}) diff --git a/tests/test_omnigibson_simulator.py b/tests/test_omnigibson_simulator.py index e648521..bfe6d11 100644 --- a/tests/test_omnigibson_simulator.py +++ b/tests/test_omnigibson_simulator.py @@ -160,3 +160,37 @@ def test_create_env_manager_has_installation_kwargs(self): mgr = create_env_manager("omnigibson") assert mgr.installation_kwargs["cuda_version"] == "12.4" assert "BEHAVIOR-1K" in mgr.installation_kwargs["behavior_1k_repo"] + + +class TestOmniGibsonRenderPlatforms: + """Test OmniGibsonNativePlatform and OmniGibsonAutoPlatform.""" + + def test_native_platform_name(self): + from easi.simulators.omnigibson.v3_7_2.render_platforms import OmniGibsonNativePlatform + assert OmniGibsonNativePlatform().name == "native" + + def test_native_platform_sets_headless_0(self): + from easi.simulators.omnigibson.v3_7_2.render_platforms import OmniGibsonNativePlatform + ev = OmniGibsonNativePlatform().get_env_vars() + assert ev.replace["OMNIGIBSON_HEADLESS"] == "0" + + def test_native_platform_no_wrap(self): + from easi.simulators.omnigibson.v3_7_2.render_platforms import OmniGibsonNativePlatform + cmd = ["python", "bridge.py"] + assert OmniGibsonNativePlatform().wrap_command(cmd, "1024x768x24") == cmd + + def test_auto_platform_name(self): + from easi.simulators.omnigibson.v3_7_2.render_platforms import OmniGibsonAutoPlatform + assert OmniGibsonAutoPlatform().name == "auto" + + def test_auto_platform_with_display(self, monkeypatch): + monkeypatch.setenv("DISPLAY", ":1") + from easi.simulators.omnigibson.v3_7_2.render_platforms import OmniGibsonAutoPlatform + ev = OmniGibsonAutoPlatform().get_env_vars() + assert ev.replace["OMNIGIBSON_HEADLESS"] == "0" + + def test_auto_platform_without_display(self, monkeypatch): + monkeypatch.delenv("DISPLAY", raising=False) + from easi.simulators.omnigibson.v3_7_2.render_platforms import OmniGibsonAutoPlatform + ev = OmniGibsonAutoPlatform().get_env_vars() + assert ev.replace["OMNIGIBSON_HEADLESS"] == "1" From 942a99c927d9d2b5ff8cb37af213695dac61da54 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 24 Feb 2026 11:12:12 +0800 Subject: [PATCH 096/239] feat: vendor minimal rearrangement environment code Vendor 6 files from ai2thor-rearrangement into easi/tasks/ai2thor_rearrangement_2023/vendor/: - rearrange/environment.py (core RearrangeTHOREnvironment) - rearrange/constants.py (object types, pickupable/openable lists) - rearrange/utils.py (action space, IOU, execute_action) - rearrange_constants.py (IOU/openness thresholds) - datagen/datagen_constants.py - datagen/datagen_utils.py All imports patched to easi.tasks.ai2thor_rearrangement_2023.vendor.* --- .../ai2thor_rearrangement_2023/__init__.py | 0 .../vendor/__init__.py | 0 .../vendor/datagen/__init__.py | 0 .../vendor/datagen/datagen_constants.py | 21 + .../vendor/datagen/datagen_utils.py | 165 ++ .../vendor/rearrange/__init__.py | 0 .../vendor/rearrange/constants.py | 199 +++ .../vendor/rearrange/environment.py | 1590 +++++++++++++++++ .../vendor/rearrange/utils.py | 521 ++++++ .../vendor/rearrange_constants.py | 7 + 10 files changed, 2503 insertions(+) create mode 100644 easi/tasks/ai2thor_rearrangement_2023/__init__.py create mode 100644 easi/tasks/ai2thor_rearrangement_2023/vendor/__init__.py create mode 100644 easi/tasks/ai2thor_rearrangement_2023/vendor/datagen/__init__.py create mode 100644 easi/tasks/ai2thor_rearrangement_2023/vendor/datagen/datagen_constants.py create mode 100644 easi/tasks/ai2thor_rearrangement_2023/vendor/datagen/datagen_utils.py create mode 100644 easi/tasks/ai2thor_rearrangement_2023/vendor/rearrange/__init__.py create mode 100644 easi/tasks/ai2thor_rearrangement_2023/vendor/rearrange/constants.py create mode 100644 easi/tasks/ai2thor_rearrangement_2023/vendor/rearrange/environment.py create mode 100644 easi/tasks/ai2thor_rearrangement_2023/vendor/rearrange/utils.py create mode 100644 easi/tasks/ai2thor_rearrangement_2023/vendor/rearrange_constants.py diff --git a/easi/tasks/ai2thor_rearrangement_2023/__init__.py b/easi/tasks/ai2thor_rearrangement_2023/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/easi/tasks/ai2thor_rearrangement_2023/vendor/__init__.py b/easi/tasks/ai2thor_rearrangement_2023/vendor/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/easi/tasks/ai2thor_rearrangement_2023/vendor/datagen/__init__.py b/easi/tasks/ai2thor_rearrangement_2023/vendor/datagen/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/easi/tasks/ai2thor_rearrangement_2023/vendor/datagen/datagen_constants.py b/easi/tasks/ai2thor_rearrangement_2023/vendor/datagen/datagen_constants.py new file mode 100644 index 0000000..026098d --- /dev/null +++ b/easi/tasks/ai2thor_rearrangement_2023/vendor/datagen/datagen_constants.py @@ -0,0 +1,21 @@ +OBJECT_TYPES_TO_NOT_MOVE = { + "Apple", + "Bread", + "Cloth", + "HandTowel", + "HandTowelHolder", + "Towel", + "TowelHolder", + "KeyChain", + "Lettuce", + "Pillow", + "Potato", + "Tomato", +} +OBJECT_TYPES_THAT_CAN_HAVE_IDENTICAL_MESHES = [ + "AluminumFoil", + "CD", + "Dumbbell", + "Ladle", + "Vase", +] diff --git a/easi/tasks/ai2thor_rearrangement_2023/vendor/datagen/datagen_utils.py b/easi/tasks/ai2thor_rearrangement_2023/vendor/datagen/datagen_utils.py new file mode 100644 index 0000000..c09bbc5 --- /dev/null +++ b/easi/tasks/ai2thor_rearrangement_2023/vendor/datagen/datagen_utils.py @@ -0,0 +1,165 @@ +import random +from collections import defaultdict +from typing import List, Dict, Set, Optional, Any + +from ai2thor.controller import Controller + +from easi.tasks.ai2thor_rearrangement_2023.vendor.datagen.datagen_constants import OBJECT_TYPES_THAT_CAN_HAVE_IDENTICAL_MESHES +from easi.tasks.ai2thor_rearrangement_2023.vendor.rearrange_constants import OPENNESS_THRESHOLD + + +def get_scenes(stage: str) -> List[str]: + """Returns a list of iTHOR scene names for each stage.""" + assert stage in {"debug", "train", "train_unseen", "val", "valid", "test", "all"} + + if stage == "debug": + return ["FloorPlan1"] + + # [1-20] for train, [21-25] for val, [26-30] for test + if stage in ["train", "train_unseen"]: + scene_nums = range(1, 21) + elif stage in ["val", "valid"]: + scene_nums = range(21, 26) + elif stage == "test": + scene_nums = range(26, 31) + elif stage == "all": + scene_nums = range(1, 31) + else: + raise NotImplementedError + + kitchens = [f"FloorPlan{i}" for i in scene_nums] + living_rooms = [f"FloorPlan{200+i}" for i in scene_nums] + bedrooms = [f"FloorPlan{300+i}" for i in scene_nums] + bathrooms = [f"FloorPlan{400+i}" for i in scene_nums] + return kitchens + living_rooms + bedrooms + bathrooms + + +def filter_pickupable( + objects: List[Dict], object_types_to_not_move: Set[str] +) -> List[Dict]: + """Filters object data only for pickupable objects.""" + return [ + obj + for obj in objects + if obj["pickupable"] and not obj["objectType"] in object_types_to_not_move + ] + + +def get_random_seeds(max_seed: int = int(1e8)) -> Dict[str, int]: + # Generate random seeds for each stage + + # Train seed + random.seed(1329328939) + train_seed = random.randint(0, max_seed - 1) + + # Train unseen seed + random.seed(709384928) + train_unseen_seed = random.randint(0, max_seed - 1) + + # val seed + random.seed(3348958620) + val_seed = random.randint(0, max_seed - 1) + + # test seed + random.seed(289123396) + test_seed = random.randint(0, max_seed - 1) + + # Debug seed + random.seed(239084231) + debug_seed = random.randint(0, max_seed - 1) + + return { + "train": train_seed, + "train_unseen": train_unseen_seed, + "val": val_seed, + "valid": val_seed, + "test": test_seed, + "debug": debug_seed, + } + + +def check_object_opens(obj: Dict[str, Any], controller: Controller): + controller.step( + "OpenObject", objectId=obj["objectId"], openness=1.0, forceAction=True, + ) + obj_opened_fully = controller.last_event.metadata["lastActionSuccess"] + + controller.step( + "CloseObject", objectId=obj["objectId"], forceAction=True, + ) + obj_closed_fully = controller.last_event.metadata["lastActionSuccess"] + + return obj_opened_fully and obj_closed_fully + + +def get_object_by_name(name: str, controller: Controller): + return next( + o for o in controller.last_event.metadata["objects"] if o["name"] == name + ) + + +def open_objs( + object_names_to_open: List[str], controller: Controller +) -> Dict[str, Optional[float]]: + """Opens up the chosen pickupable objects if they're openable.""" + out: Dict[str, Optional[float]] = defaultdict(lambda: None) + for obj_name in object_names_to_open: + obj = get_object_by_name(obj_name, controller) + + last_openness = obj["openness"] + new_openness = last_openness + while abs(last_openness - new_openness) <= OPENNESS_THRESHOLD: + new_openness = random.random() + + event = controller.step( + "OpenObject", + objectId=obj["objectId"], + openness=new_openness, + forceAction=True, + ) + obj_after_open = get_object_by_name(obj_name, controller) + + if abs(obj_after_open["openness"] - new_openness) > 0.001: + raise RuntimeError( + f"In scene {event.metadata['sceneName']}, {obj['name']} was supposed to open to {new_openness}" + f" from {last_openness} but instead reached {obj_after_open['openness']}. Last action success was:" + f" {event.metadata['lastActionSuccess']}" + ) + + out[obj["name"]] = obj_after_open["openness"] + return out + + +def get_object_ids_to_not_move_from_object_types( + controller: Controller, object_types: Set[str] +) -> List[str]: + object_types = set(object_types) + return [ + o["objectId"] + for o in controller.last_event.metadata["objects"] + if o["objectType"] in object_types + ] + + +def remove_objects_until_all_have_identical_meshes(controller: Controller): + obj_type_to_obj_list = defaultdict(lambda: []) + for obj in controller.last_event.metadata["objects"]: + obj_type_to_obj_list[obj["objectType"]].append(obj) + + for obj_type in OBJECT_TYPES_THAT_CAN_HAVE_IDENTICAL_MESHES: + objs_of_type = list( + sorted(obj_type_to_obj_list[obj_type], key=lambda x: x["name"]) + ) + random.shuffle(objs_of_type) + objs_to_remove = objs_of_type[:-1] + for obj_to_remove in objs_to_remove: + obj_to_remove_name = obj_to_remove["name"] + obj_id_to_remove = next( + obj["objectId"] + for obj in controller.last_event.metadata["objects"] + if obj["name"] == obj_to_remove_name + ) + controller.step("RemoveFromScene", objectId=obj_id_to_remove) + if not controller.last_event.metadata["lastActionSuccess"]: + return False + return True diff --git a/easi/tasks/ai2thor_rearrangement_2023/vendor/rearrange/__init__.py b/easi/tasks/ai2thor_rearrangement_2023/vendor/rearrange/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/easi/tasks/ai2thor_rearrangement_2023/vendor/rearrange/constants.py b/easi/tasks/ai2thor_rearrangement_2023/vendor/rearrange/constants.py new file mode 100644 index 0000000..21ad8f5 --- /dev/null +++ b/easi/tasks/ai2thor_rearrangement_2023/vendor/rearrange/constants.py @@ -0,0 +1,199 @@ +import os +from pathlib import Path + +MAX_HAND_METERS = 0.5 +FOV = 90 + +REQUIRED_THOR_VERSION = "5.0.0" +STEP_SIZE = 0.25 + +# fmt: off +REARRANGE_SIM_OBJECTS = [ + # A + "AlarmClock", "AluminumFoil", "Apple", "AppleSliced", "ArmChair", + "BaseballBat", "BasketBall", "Bathtub", "BathtubBasin", "Bed", "Blinds", "Book", "Boots", "Bottle", "Bowl", "Box", + # B + "Bread", "BreadSliced", "ButterKnife", + # C + "Cabinet", "Candle", "CD", "CellPhone", "Chair", "Cloth", "CoffeeMachine", "CoffeeTable", "CounterTop", "CreditCard", + "Cup", "Curtains", + # D + "Desk", "DeskLamp", "Desktop", "DiningTable", "DishSponge", "DogBed", "Drawer", "Dresser", "Dumbbell", + # E + "Egg", "EggCracked", + # F + "Faucet", "Floor", "FloorLamp", "Footstool", "Fork", "Fridge", + # G + "GarbageBag", "GarbageCan", + # H + "HandTowel", "HandTowelHolder", "HousePlant", "Kettle", "KeyChain", "Knife", + # L + "Ladle", "Laptop", "LaundryHamper", "Lettuce", "LettuceSliced", "LightSwitch", + # M + "Microwave", "Mirror", "Mug", + # N + "Newspaper", + # O + "Ottoman", + # P + "Painting", "Pan", "PaperTowel", "Pen", "Pencil", "PepperShaker", "Pillow", "Plate", "Plunger", "Poster", "Pot", + "Potato", "PotatoSliced", + # R + "RemoteControl", "RoomDecor", + # S + "Safe", "SaltShaker", "ScrubBrush", "Shelf", "ShelvingUnit", "ShowerCurtain", "ShowerDoor", "ShowerGlass", + "ShowerHead", "SideTable", "Sink", "SinkBasin", "SoapBar", "SoapBottle", "Sofa", "Spatula", "Spoon", "SprayBottle", + "Statue", "Stool", "StoveBurner", "StoveKnob", + # T + "TableTopDecor", "TargetCircle", "TeddyBear", "Television", "TennisRacket", "TissueBox", "Toaster", "Toilet", + "ToiletPaper", "ToiletPaperHanger", "Tomato", "TomatoSliced", "Towel", "TowelHolder", "TVStand", + # V + "VacuumCleaner", "Vase", + # W + "Watch", "WateringCan", "Window", "WineBottle", +] +# fmt: on + + +# fmt: off +OBJECT_TYPES_WITH_PROPERTIES = { + "StoveBurner": {"openable": False, "receptacle": True, "pickupable": False}, + "Drawer": {"openable": True, "receptacle": True, "pickupable": False}, + "CounterTop": {"openable": False, "receptacle": True, "pickupable": False}, + "Cabinet": {"openable": True, "receptacle": True, "pickupable": False}, + "StoveKnob": {"openable": False, "receptacle": False, "pickupable": False}, + "Window": {"openable": False, "receptacle": False, "pickupable": False}, + "Sink": {"openable": False, "receptacle": True, "pickupable": False}, + "Floor": {"openable": False, "receptacle": True, "pickupable": False}, + "Book": {"openable": True, "receptacle": False, "pickupable": True}, + "Bottle": {"openable": False, "receptacle": False, "pickupable": True}, + "Knife": {"openable": False, "receptacle": False, "pickupable": True}, + "Microwave": {"openable": True, "receptacle": True, "pickupable": False}, + "Bread": {"openable": False, "receptacle": False, "pickupable": True}, + "Fork": {"openable": False, "receptacle": False, "pickupable": True}, + "Shelf": {"openable": False, "receptacle": True, "pickupable": False}, + "Potato": {"openable": False, "receptacle": False, "pickupable": True}, + "HousePlant": {"openable": False, "receptacle": False, "pickupable": False}, + "Toaster": {"openable": False, "receptacle": True, "pickupable": False}, + "SoapBottle": {"openable": False, "receptacle": False, "pickupable": True}, + "Kettle": {"openable": True, "receptacle": False, "pickupable": True}, + "Pan": {"openable": False, "receptacle": True, "pickupable": True}, + "Plate": {"openable": False, "receptacle": True, "pickupable": True}, + "Tomato": {"openable": False, "receptacle": False, "pickupable": True}, + "Vase": {"openable": False, "receptacle": False, "pickupable": True}, + "GarbageCan": {"openable": False, "receptacle": True, "pickupable": False}, + "Egg": {"openable": False, "receptacle": False, "pickupable": True}, + "CreditCard": {"openable": False, "receptacle": False, "pickupable": True}, + "WineBottle": {"openable": False, "receptacle": False, "pickupable": True}, + "Pot": {"openable": False, "receptacle": True, "pickupable": True}, + "Spatula": {"openable": False, "receptacle": False, "pickupable": True}, + "PaperTowelRoll": {"openable": False, "receptacle": False, "pickupable": True}, + "Cup": {"openable": False, "receptacle": True, "pickupable": True}, + "Fridge": {"openable": True, "receptacle": True, "pickupable": False}, + "CoffeeMachine": {"openable": False, "receptacle": True, "pickupable": False}, + "Bowl": {"openable": False, "receptacle": True, "pickupable": True}, + "SinkBasin": {"openable": False, "receptacle": True, "pickupable": False}, + "SaltShaker": {"openable": False, "receptacle": False, "pickupable": True}, + "PepperShaker": {"openable": False, "receptacle": False, "pickupable": True}, + "Lettuce": {"openable": False, "receptacle": False, "pickupable": True}, + "ButterKnife": {"openable": False, "receptacle": False, "pickupable": True}, + "Apple": {"openable": False, "receptacle": False, "pickupable": True}, + "DishSponge": {"openable": False, "receptacle": False, "pickupable": True}, + "Spoon": {"openable": False, "receptacle": False, "pickupable": True}, + "LightSwitch": {"openable": False, "receptacle": False, "pickupable": False}, + "Mug": {"openable": False, "receptacle": True, "pickupable": True}, + "ShelvingUnit": {"openable": False, "receptacle": True, "pickupable": False}, + "Statue": {"openable": False, "receptacle": False, "pickupable": True}, + "Stool": {"openable": False, "receptacle": True, "pickupable": False}, + "Faucet": {"openable": False, "receptacle": False, "pickupable": False}, + "Ladle": {"openable": False, "receptacle": False, "pickupable": True}, + "CellPhone": {"openable": False, "receptacle": False, "pickupable": True}, + "Chair": {"openable": False, "receptacle": True, "pickupable": False}, + "SideTable": {"openable": False, "receptacle": True, "pickupable": False}, + "DiningTable": {"openable": False, "receptacle": True, "pickupable": False}, + "Pen": {"openable": False, "receptacle": False, "pickupable": True}, + "SprayBottle": {"openable": False, "receptacle": False, "pickupable": True}, + "Curtains": {"openable": False, "receptacle": False, "pickupable": False}, + "Pencil": {"openable": False, "receptacle": False, "pickupable": True}, + "Blinds": {"openable": True, "receptacle": False, "pickupable": False}, + "GarbageBag": {"openable": False, "receptacle": False, "pickupable": False}, + "Safe": {"openable": True, "receptacle": True, "pickupable": False}, + "Painting": {"openable": False, "receptacle": False, "pickupable": False}, + "Box": {"openable": True, "receptacle": True, "pickupable": True}, + "Laptop": {"openable": True, "receptacle": False, "pickupable": True}, + "Television": {"openable": False, "receptacle": False, "pickupable": False}, + "TissueBox": {"openable": False, "receptacle": False, "pickupable": True}, + "KeyChain": {"openable": False, "receptacle": False, "pickupable": True}, + "FloorLamp": {"openable": False, "receptacle": False, "pickupable": False}, + "DeskLamp": {"openable": False, "receptacle": False, "pickupable": False}, + "Pillow": {"openable": False, "receptacle": False, "pickupable": True}, + "RemoteControl": {"openable": False, "receptacle": False, "pickupable": True}, + "Watch": {"openable": False, "receptacle": False, "pickupable": True}, + "Newspaper": {"openable": False, "receptacle": False, "pickupable": True}, + "ArmChair": {"openable": False, "receptacle": True, "pickupable": False}, + "CoffeeTable": {"openable": False, "receptacle": True, "pickupable": False}, + "TVStand": {"openable": False, "receptacle": True, "pickupable": False}, + "Sofa": {"openable": False, "receptacle": True, "pickupable": False}, + "WateringCan": {"openable": False, "receptacle": False, "pickupable": True}, + "Boots": {"openable": False, "receptacle": False, "pickupable": True}, + "Ottoman": {"openable": False, "receptacle": True, "pickupable": False}, + "Desk": {"openable": False, "receptacle": True, "pickupable": False}, + "Dresser": {"openable": False, "receptacle": True, "pickupable": False}, + "Mirror": {"openable": False, "receptacle": False, "pickupable": False}, + "DogBed": {"openable": False, "receptacle": True, "pickupable": False}, + "Candle": {"openable": False, "receptacle": False, "pickupable": True}, + "RoomDecor": {"openable": False, "receptacle": False, "pickupable": False}, + "Bed": {"openable": False, "receptacle": True, "pickupable": False}, + "BaseballBat": {"openable": False, "receptacle": False, "pickupable": True}, + "BasketBall": {"openable": False, "receptacle": False, "pickupable": True}, + "AlarmClock": {"openable": False, "receptacle": False, "pickupable": True}, + "CD": {"openable": False, "receptacle": False, "pickupable": True}, + "TennisRacket": {"openable": False, "receptacle": False, "pickupable": True}, + "TeddyBear": {"openable": False, "receptacle": False, "pickupable": True}, + "Poster": {"openable": False, "receptacle": False, "pickupable": False}, + "Cloth": {"openable": False, "receptacle": False, "pickupable": True}, + "Dumbbell": {"openable": False, "receptacle": False, "pickupable": True}, + "LaundryHamper": {"openable": True, "receptacle": True, "pickupable": False}, + "TableTopDecor": {"openable": False, "receptacle": False, "pickupable": True}, + "Desktop": {"openable": False, "receptacle": False, "pickupable": False}, + "Footstool": {"openable": False, "receptacle": True, "pickupable": True}, + "BathtubBasin": {"openable": False, "receptacle": True, "pickupable": False}, + "ShowerCurtain": {"openable": True, "receptacle": False, "pickupable": False}, + "ShowerHead": {"openable": False, "receptacle": False, "pickupable": False}, + "Bathtub": {"openable": False, "receptacle": True, "pickupable": False}, + "Towel": {"openable": False, "receptacle": False, "pickupable": True}, + "HandTowel": {"openable": False, "receptacle": False, "pickupable": True}, + "Plunger": {"openable": False, "receptacle": False, "pickupable": True}, + "TowelHolder": {"openable": False, "receptacle": True, "pickupable": False}, + "ToiletPaperHanger": {"openable": False, "receptacle": True, "pickupable": False}, + "SoapBar": {"openable": False, "receptacle": False, "pickupable": True}, + "ToiletPaper": {"openable": False, "receptacle": False, "pickupable": True}, + "HandTowelHolder": {"openable": False, "receptacle": True, "pickupable": False}, + "ScrubBrush": {"openable": False, "receptacle": False, "pickupable": True}, + "Toilet": {"openable": True, "receptacle": True, "pickupable": False}, + "ShowerGlass": {"openable": False, "receptacle": False, "pickupable": False}, + "ShowerDoor": {"openable": True, "receptacle": False, "pickupable": False}, + "AluminumFoil": {"openable": False, "receptacle": False, "pickupable": True}, + "VacuumCleaner": {"openable": False, "receptacle": False, "pickupable": False} +} +# fmt: on + +PICKUPABLE_OBJECTS = list( + sorted( + [ + object_type + for object_type, properties in OBJECT_TYPES_WITH_PROPERTIES.items() + if properties["pickupable"] + ] + ) +) + +OPENABLE_OBJECTS = list( + sorted( + [ + object_type + for object_type, properties in OBJECT_TYPES_WITH_PROPERTIES.items() + if properties["openable"] and not properties["pickupable"] + ] + ) +) diff --git a/easi/tasks/ai2thor_rearrangement_2023/vendor/rearrange/environment.py b/easi/tasks/ai2thor_rearrangement_2023/vendor/rearrange/environment.py new file mode 100644 index 0000000..8b09cb1 --- /dev/null +++ b/easi/tasks/ai2thor_rearrangement_2023/vendor/rearrange/environment.py @@ -0,0 +1,1590 @@ +import enum +import math +import pprint +import random +import traceback +from collections import OrderedDict +from typing import Dict, Any, Tuple, Optional, Callable, List, Union, Sequence + +import ai2thor +import ai2thor.controller +import ai2thor.fifo_server +import ai2thor.server +import ai2thor.wsgi_server +import numpy as np +from packaging import version +from torch.distributions.utils import lazy_property + +from allenact.utils.system import get_logger +from allenact_plugins.ithor_plugin.ithor_environment import IThorEnvironment +from allenact_plugins.ithor_plugin.ithor_util import ( + round_to_factor, + include_object_data, +) +from easi.tasks.ai2thor_rearrangement_2023.vendor.datagen.datagen_constants import OBJECT_TYPES_TO_NOT_MOVE +from easi.tasks.ai2thor_rearrangement_2023.vendor.datagen.datagen_utils import ( + open_objs, + get_object_ids_to_not_move_from_object_types, + remove_objects_until_all_have_identical_meshes, +) +from easi.tasks.ai2thor_rearrangement_2023.vendor.rearrange.constants import ( + REQUIRED_THOR_VERSION, + MAX_HAND_METERS, +) +from easi.tasks.ai2thor_rearrangement_2023.vendor.rearrange.utils import ( + BoundedFloat, + RearrangeActionSpace, + PoseMismatchError, + ObjectInteractablePostionsCache, + execute_action, + get_pose_info, + iou_box_3d, +) +from easi.tasks.ai2thor_rearrangement_2023.vendor.rearrange_constants import IOU_THRESHOLD, OPENNESS_THRESHOLD, POSITION_DIFF_BARRIER + + +class RearrangeMode(enum.Enum): + """Different modes allowed in RearrangeTHOREnvironment.""" + + MANIPULATE = "MANIPULATE" + SNAP = "SNAP" + + +class RearrangeTaskSpec: + """Data container encapsulating how a single rearrangement instance should + be initialized. + + The rearrangement datasets are structured as large dictionaries of the form + ```python + { + SCENE_NAME: [ + { + DATA_DEFINING_A_SINGLE_REARRANGE_TASK + }, + ... + ], + ... + } + ``` + + This `RearrangeTaskSpec` is used to encapsulate the `DATA_DEFINING_A_SINGLE_REARRANGE_TASK` + which allows us to use autocomplete and type checking rather than passing around raw dictionaries. + + # Attributes + scene : A string specifying the AI2-THOR scene (e.g "FloorPlan18") in which to run the rearrange task. + stage : A string specifying the type of instance this is data corresponds to (e.g. "train", "val", "test", etc.) + agent_position : A Dict[str, float] specifying the "x", "y", and "z" coordinates of the agent's starting position. + agent_rotation: A float specifying the agents starting rotation (in degrees). + openable_data : A sequence of dictionaries specifying the degree to which certain objects in the scene should be open + in the walkthrough and unshuffle phases. E.g. the openness of a particular cabinent might be specified by the + dictionary: + ```python + { + "name": "Cabinet_a8b4237f", + "objectName": "Cabinet_a8b4237f", + "objectId": "Cabinet|+01.31|+02.46|+04.36", + "start_openness": 0.6170539671128578, + "target_openness": 0.8788923191809455 + } + ``` + where `start_openness` is the degree to which the cabinent is open at the start of the unshuffle phase. + starting_poses : A sequence of dictionaries specifying the poses of all pickupable objects at the start + of the unshuffle phase. E.g. one such dictionary might look like: + ```python + { + "name": "Bowl_803d17c0", + "objectName": "Bowl_803d17c0", + "position": { + "x": -0.5572903156280518, + "y": 0.8256161212921143, + "z": 6.25293493270874, + }, + "rotation": { + "x": 359.9241943359375, + "y": -0.00041645264718681574, + "z": 0.004868899006396532, + }, + } + ``` + target_poses : Similar to `starting_poses` but specifying the poses of objects during the walkthrough phase. + runtime_sample : If `True`, then this task is meant to randomly specified at runtime. That is, the above fields + (except for the `scene`) are to be left as `None` and the RearrangeTHOREnvironment will randomly generate + them instead (this may be slow). + runtime_data : A Dict[str, Any] into which the `RearrangeTHOREnvironment` may cache data for efficiency. + metrics : Any additional metrics that might be associated with a task specification. For instance, the + rearrangement dataset dictionaries include metrics such as `open_diff_count` which records the number + of objects who differ in openness at the start of the walkthrough/unshuffle phases. + """ + + def __init__( + self, + scene: str, + stage: Optional[str] = None, + agent_position: Optional[Dict[str, float]] = None, + agent_rotation: Optional[float] = None, + openable_data: Optional[Sequence[Dict[str, Any]]] = None, + starting_poses: Optional[Sequence[Dict[str, Any]]] = None, + target_poses: Optional[Sequence[Dict[str, Any]]] = None, + runtime_sample: bool = False, + runtime_data: Optional[Dict[str, Any]] = None, + **metrics, + ): + """Instantiate a `RearrangeTaskSpec` object.""" + self.scene = scene + self.stage = stage + self.agent_position = agent_position + self.agent_rotation = agent_rotation + self.openable_data = openable_data + self.starting_poses = starting_poses + self.target_poses = target_poses + self.runtime_sample = runtime_sample + self.runtime_data: Dict[str, Any] = ( + runtime_data if runtime_data is not None else {} + ) + self.metrics = metrics + + def __str__(self): + """String representation of a `RearrangeTaskSpec` object.""" + return pprint.pformat(self.__dict__) + + @property + def unique_id(self): + if self.runtime_sample: + raise NotImplementedError("Cannot create a unique id for a runtime sample.") + return f"{self.scene}__{self.stage}__{self.metrics['index']}" + + +class RearrangeTHOREnvironment: + """Custom AI2-THOR Controller for the task of object rearrangement. + + # Attributes + mode : The current mode of rearrangement. Takes one of the values of RearrangeMode + (RearrangeMode.SNAP or RearrangeMode.MANIPULATE). + force_cache_reset : Whether or not we should force cache resets when using the `drop_held_object_with_snap` action. + Setting this value to `False` results in higher FPS at the expense of possibly having `drop_held_object_with_snap` + work/fail when it shouldn't. Setting `force_cache_reset` to `True` is recommended during validation/testing. + obj_name_to_walkthrough_start_pose : Dictionary mapping AI2-THOR object names to their poses (positions & rotations) + before they were shuffled (i.e. what the agent sees at the start of the walkthrough phase). + This will be changed after every call to `reset`. + obj_name_to_unshuffle_start_pose : Same as `obj_name_to_walkthrough_start_pose` but mapping object names to their poses (positions & + rotations) just after they were shuffled, i.e. what the agent sees at the start of the unshuffle phase). + current_task_spec : A `RearrangeTaskSpec` object specifying the current rearrangement task details. + controller : A ai2thor controller used to execute all the actions. + shuffle_called : `True` if the objects have been shuffled so that we're in the `unshuffle` phase. Otherwise `False`. + """ + + def __init__( + self, + mode: RearrangeMode = RearrangeMode.SNAP, + force_cache_reset: Optional[bool] = None, + controller_kwargs: Optional[Dict[str, Any]] = None, + enhanced_physics_determinism: bool = True, + ): + """Initialize a new rearrangement controller. + + # Parameters + mode : See description of this class' attributes. + controller_kwargs : Dictionary specifying any keyword arguments to be passed + when initializing the `ai2thor.controller.Controller` (e.g. width/height). + """ + if ai2thor.__version__ is not None: # Allows for custom THOR installs + if ( + ai2thor.__version__ not in ["0.0.1", None] + and (not ai2thor.__version__.startswith("0+")) + and version.parse(ai2thor.__version__) + < version.parse(REQUIRED_THOR_VERSION) + ): + raise ImportError( + f"To run the rearrangment baseline experiments you must use" + f" ai2thor version {REQUIRED_THOR_VERSION} or higher." + ) + + # Saving attributes + if mode == RearrangeMode.SNAP: + assert ( + force_cache_reset is not None + ), "When in RearrangeMode.SNAP mode you must specify a value for `force_cache_reset`" + else: + force_cache_reset = force_cache_reset + self.force_cache_reset = force_cache_reset + self.mode = mode + self._controller_kwargs = {} if controller_kwargs is None else controller_kwargs + self._enhanced_physics_determinism = enhanced_physics_determinism + + self.physics_step_kwargs = {} + if self._enhanced_physics_determinism: + self.physics_step_kwargs = { + "actionSimulationSeconds": 0.26, + "fixedDeltaTime": 0.02, + } + + # Cache of where objects can be interacted with + self._interactable_positions_cache = ObjectInteractablePostionsCache() + + # Object poses at start of walkthrough and unshuffle phases. + # Reset after every call to reset and shuffle respectively. + self.obj_name_to_walkthrough_start_pose: Optional[Dict[str, Dict]] = None + self.obj_name_to_unshuffle_start_pose: Optional[Dict[str, Dict]] = None + self._cached_poses: Optional[Tuple[List, List, List]] = None + + # Current task specification + self.current_task_spec: Optional[RearrangeTaskSpec] = None + + # Caches of starting unshuffle/walkthrough object poses and other information. Reset on every call to reset + self._sorted_and_extracted_walkthrough_start_poses: Optional[List] = None + self._sorted_and_extracted_unshuffle_start_poses: Optional[List] = None + self._have_warned_about_mismatch = False + self._agent_signals_done = False # Also reset on `shuffle()` + + # instance masks now not supported. But an Exception would be thrown if + # `mode == RearrangeMode.MANIPULATE` and render_instance_masks is True, since masks are + # only available on RearrangeMode.SNAP mode. + self._render_instance_masks: bool = False + if self.mode == RearrangeMode.MANIPULATE and self._render_instance_masks: + raise Exception( + "render_instance_masks is only available on RearrangeMode.SNAP mode." + ) + + # local thor controller to execute all the actions + self.controller = self.create_controller() + + # always begin in walkthrough phase + self.shuffle_called = False + + def create_controller(self): + """Create the ai2thor controller.""" + + assert ("width" in self._controller_kwargs) == ( + "height" in self._controller_kwargs + ), "Either controller_kwargs must contain either both of width/height or neither." + self._controller_kwargs["width"] = self._controller_kwargs.get("width", 300) + self._controller_kwargs["height"] = self._controller_kwargs.get("height", 300) + + controller = ai2thor.controller.Controller( + **{ + "scene": "FloorPlan17_physics", + # "server_class": ai2thor.fifo_server.FifoServer, + # "server_class": ai2thor.wsgi_server.WsgiServer, # Possibly useful in debugging + **self._controller_kwargs, + }, + ) + return controller + + @property + def held_object(self) -> Optional[Dict[str, Any]]: + """Return the data corresponding to the object held by the agent (if + any).""" + with include_object_data(self.controller): + metadata = self.controller.last_event.metadata + + if len(metadata["inventoryObjects"]) == 0: + return None + + assert len(metadata["inventoryObjects"]) <= 1 + + held_obj_id = metadata["inventoryObjects"][0]["objectId"] + return next(o for o in metadata["objects"] if o["objectId"] == held_obj_id) + + def get_agent_location(self) -> Dict[str, Union[float, int, bool]]: + """Returns the agent's current location. + + # Returns + + A dictionary of the form + ```python + { + "x": X_POSITION_IN_SPACE, # float + "y": Y_POSITION_IN_SPACE, # float + "z": Z_POSITION_IN_SPACE, # float + "rotation": AGENTS_ROTATION_ABOUT_THE_Y_AXIS_IN_DEGREES, # float or int + "horizon": AGENTS_CAMERA_ANGLE_IN_DEGREES, # float (0 degrees is horizontal) + "standing": WHETHER_OR_NOT_THE_AGENT_IS_STANDING, # boolean + } + ``` + """ + metadata = self.controller.last_event.metadata + return { + "x": metadata["agent"]["position"]["x"], + "y": metadata["agent"]["position"]["y"], + "z": metadata["agent"]["position"]["z"], + "rotation": metadata["agent"]["rotation"]["y"], + "horizon": metadata["agent"]["cameraHorizon"], + "standing": metadata.get("isStanding", metadata["agent"].get("isStanding")), + } + + @property + def observation(self) -> Tuple[np.array, Optional[np.array]]: + """Return the current (RGB, depth, Optional[instance masks]) frames. + + # Returns + A tuple containing a + * RGB frame is of shape (height)x(width)x3 with integer entries in [0:255]. + * depth frame is of shape (height)x(width) with unscaled entries representing the + meter distance from the agent to the pixel. This will be `None` if the controller_kwargs + passed to the initializer did not specify that depth images should be returned by AI2-THOR. + """ + rgb = self.last_event.frame + depth = ( + self.last_event.depth_frame + if hasattr(self.last_event, "depth_frame") + else None + ) + return rgb, depth + + @lazy_property + def walkthrough_action_space(self) -> RearrangeActionSpace: + """Return the RearrangeActionSpace for the walkthrough phase based on + the RearrangeMode.""" + + # Walkthrough actions + actions: Dict[Callable, Dict[str, BoundedFloat]] = { + self.move_ahead: {}, + self.move_right: {}, + self.move_left: {}, + self.move_back: {}, + self.rotate_right: {}, + self.rotate_left: {}, + self.stand: {}, + self.crouch: {}, + self.look_up: {}, + self.look_down: {}, + self.done: {}, + } + + return RearrangeActionSpace(actions) + + @lazy_property + def unshuffle_action_space(self) -> RearrangeActionSpace: + """Return the RearrangeActionSpace for the unshuffle phase based on the + RearrangeMode.""" + actions = {**self.walkthrough_action_space.actions} + + # additional shuffle allowed actions + actions.update( + { + self.open_object: { + "x": BoundedFloat(low=0, high=1), + "y": BoundedFloat(low=0, high=1), + "openness": BoundedFloat(low=0, high=1), + }, + self.pickup_object: { + "x": BoundedFloat(low=0, high=1), + "y": BoundedFloat(low=0, high=1), + }, + self.push_object: { + "x": BoundedFloat(low=0, high=1), + "y": BoundedFloat(low=0, high=1), + "rel_x_force": BoundedFloat(low=-0.5, high=0.5), + "rel_y_force": BoundedFloat(low=-0.5, high=0.5), + "rel_z_force": BoundedFloat(low=-0.5, high=0.5), + "force_magnitude": BoundedFloat(low=0, high=1), + }, + self.move_held_object: { + "x_meters": BoundedFloat(low=-0.5, high=0.5), + "y_meters": BoundedFloat(low=-0.5, high=0.5), + "z_meters": BoundedFloat(low=-0.5, high=0.5), + }, + self.rotate_held_object: { + "x": BoundedFloat(low=-0.5, high=0.5), + "y": BoundedFloat(low=-0.5, high=0.5), + "z": BoundedFloat(low=-0.5, high=0.5), + }, + self.drop_held_object: {}, + } + ) + + if self.mode == RearrangeMode.SNAP: + actions.update({self.drop_held_object_with_snap: {}}) + + return RearrangeActionSpace(actions) + + @property + def action_space(self) -> RearrangeActionSpace: + """Return the RearrangeActionSpace based on the RearrangeMode and + whether we are in the unshuffle phase.""" + + if self.shuffle_called: + return self.unshuffle_action_space + else: + return self.walkthrough_action_space + + def open_object(self, x: float, y: float, openness: float) -> bool: + """Open the object corresponding to x/y to openness. + + The action will not be successful if the specified openness would + cause a collision or if the object at x/y is not openable. + + # Parameters + x : (float, min=0.0, max=1.0) horizontal percentage from the last frame + that the target object is located. + y : (float, min=0.0, max=1.0) vertical percentage from the last frame + that the target object is located. + + # Returns + `True` if the action was successful, otherwise `False`. + """ + # If an object is already open, THOR doesn't support changing + # it's openness without first closing it. So we simply try to first + # close the object before reopening it. + return execute_action( + controller=self.controller, + action_space=self.action_space, + action_fn=self.open_object, + thor_action="OpenObject", + error_message=( + "x/y/openness must be in [0:1] and we must be in the unshuffle phase." + ), + x=x, + y=y, + openness=openness, + default_thor_kwargs=self.physics_step_kwargs, + ) + + def pickup_object(self, x: float, y: float) -> bool: + """Pick up the object corresponding to x/y. + + The action will not be successful if the object at x/y is not + pickupable. + + # Parameters + x : (float, min=0.0, max=1.0) horizontal percentage from the last frame + that the target object is located. + y : (float, min=0.0, max=1.0) vertical percentage from the last frame + that the target object is located. + + # Returns + `True` if the action was successful, otherwise `False`. + """ + if len(self.last_event.metadata["inventoryObjects"]) != 0: + return False + return execute_action( + controller=self.controller, + action_space=self.action_space, + action_fn=self.pickup_object, + thor_action="PickupObject", + error_message="x/y must be in [0:1] and we must be in the unshuffle phase.", + x=x, + y=y, + default_thor_kwargs=self.physics_step_kwargs, + ) + + def push_object( + self, + x: float, + y: float, + rel_x_force: float, + rel_y_force: float, + rel_z_force: float, + force_magnitude: float, + ) -> bool: + """Push an object along a surface. + + The action will not be successful if the object at x/y is not moveable. + + # Parameters + x : (float, min=0.0, max=1.0) horizontal percentage from the last frame + that the target object is located. + y : (float, min=0.0, max=1.0) vertical percentage from the last frame + that the target object is located. + rel_x_force : (float, min=-0.5, max=0.5) amount of relative force + applied along the x axis. + rel_y_force : (float, min=-0.5, max=0.5) amount of relative force + applied along the y axis. + rel_z_force : (float, min=-0.5, max=0.5) amount of relative force + applied along the z axis. + force_magnitude : (float, min=0, max=1) relative amount of force + applied during this push action. Within AI2-THOR, the force is + rescaled to be between 0 and 50 newtons, which is estimated to + sufficiently move all pickupable objects. + + # Returns + `True` if the action was successful, otherwise `False`. + """ + + def preprocess_kwargs(kwargs: Dict[str, Any]): + direction = {} + for k in ["x", "y", "z"]: + force_key = f"rel_{k}_force" + direction[k] = kwargs[force_key] + del kwargs[force_key] + kwargs["direction"] = direction + kwargs["force_magnitude"] = 50 * kwargs["force_magnitude"] + + # TODO: is this really the definition of success we want? + return execute_action( + controller=self.controller, + action_space=self.action_space, + action_fn=self.pickup_object, + thor_action="TouchThenApplyForce", + error_message="Error in call to pickup object." + " Must be in unshuffle phase (i.e., call shuffle())," + " x,y,force_magnitude must be in [0:1]," + " and rel_(x/y/z)_force must be in [-0.5:0.5]", + default_thor_kwargs=dict(handDistance=1.5, **self.physics_step_kwargs), + preprocess_kwargs_inplace=preprocess_kwargs, + x=x, + y=y, + rel_x_force=rel_x_force, + rel_y_force=rel_y_force, + rel_z_force=rel_z_force, + moveMagnitude=force_magnitude, + ) + + def move_ahead(self) -> bool: + """Move the agent ahead from its facing direction by 0.25 meters.""" + return execute_action( + controller=self.controller, + action_space=self.action_space, + action_fn=self.move_ahead, + thor_action="MoveAhead", + default_thor_kwargs=self.physics_step_kwargs, + ) + + def move_back(self) -> bool: + """Move the agent back from its facing direction by 0.25 meters.""" + return execute_action( + controller=self.controller, + action_space=self.action_space, + action_fn=self.move_back, + thor_action="MoveBack", + default_thor_kwargs=self.physics_step_kwargs, + ) + + def move_right(self) -> bool: + """Move the agent right from its facing direction by 0.25 meters.""" + return execute_action( + controller=self.controller, + action_space=self.action_space, + action_fn=self.move_right, + thor_action="MoveRight", + default_thor_kwargs=self.physics_step_kwargs, + ) + + def move_left(self) -> bool: + """Move the agent left from its facing direction by 0.25 meters.""" + return execute_action( + controller=self.controller, + action_space=self.action_space, + action_fn=self.move_left, + thor_action="MoveLeft", + default_thor_kwargs=self.physics_step_kwargs, + ) + + def rotate_left(self) -> bool: + """Rotate the agent left from its facing direction.""" + return execute_action( + controller=self.controller, + action_space=self.action_space, + action_fn=self.rotate_left, + thor_action="RotateLeft", + default_thor_kwargs=self.physics_step_kwargs, + ) + + def rotate_right(self) -> bool: + """Rotate the agent left from its facing direction.""" + return execute_action( + controller=self.controller, + action_space=self.action_space, + action_fn=self.rotate_right, + thor_action="RotateRight", + default_thor_kwargs=self.physics_step_kwargs, + ) + + def stand(self) -> bool: + """Stand the agent from the crouching position.""" + return execute_action( + controller=self.controller, + action_space=self.action_space, + action_fn=self.stand, + thor_action="Stand", + default_thor_kwargs=self.physics_step_kwargs, + ) + + def crouch(self) -> bool: + """Crouch the agent from the standing position.""" + return execute_action( + controller=self.controller, + action_space=self.action_space, + action_fn=self.crouch, + thor_action="Crouch", + default_thor_kwargs=self.physics_step_kwargs, + ) + + def look_up(self) -> bool: + """Turn the agent's head and camera up by 30 degrees.""" + return execute_action( + controller=self.controller, + action_space=self.action_space, + action_fn=self.look_up, + thor_action="LookUp", + default_thor_kwargs=self.physics_step_kwargs, + ) + + def look_down(self) -> bool: + """Turn the agent's head and camera down by 30 degrees.""" + return execute_action( + controller=self.controller, + action_space=self.action_space, + action_fn=self.look_down, + thor_action="LookDown", + default_thor_kwargs=self.physics_step_kwargs, + ) + + def done(self) -> bool: + """Agent's signal that it's completed its current rearrangement phase. + + Note that we do not automatically switch from the walkthrough + phase to the unshuffling phase, and vice-versa, that is up to + the user. This allows users to call .poses after the agent calls + done, and have it correspond to the current episode. + """ + self._agent_signals_done = True + return execute_action( + controller=self.controller, + action_space=self.action_space, + action_fn=self.done, + thor_action="Done", + ) + + def move_held_object( + self, x_meters: float, y_meters: float, z_meters: float + ) -> bool: + """Move the object in the agent's hand by the specified amount. + + The maximum magnitude that the object + can move in one time step is 0.5 meters. If the calculated magnitude is + above 0.5, it's magnitude will be clipped to 0.5. + + The action is successful in the case that the agent is holding an + object and moving the object by the specified amount does not bump + into an object. + + # Parameters + x_meters : (float, min=-0.5, max=0.5) movement meters along the x-axis. + y_meters : (float, min=-0.5, max=0.5) movement meters along the y-axis. + z_meters : (float, min=-0.5, max=0.5) movement meters along the z-axis. + + # Exceptions + In walkthrough phase. This method can only be called within the + unshuffle phase. The shuffle phase starts with controller.shuffle() + and ends with controller.reset(). + """ + mag = math.sqrt(x_meters ** 2 + y_meters ** 2 + z_meters ** 2) + + # clips the max value at MAX_HAND_METERS. + if MAX_HAND_METERS > mag: + scale = MAX_HAND_METERS / mag + x_meters *= scale + y_meters *= scale + z_meters *= scale + + return execute_action( + controller=self.controller, + action_space=self.action_space, + action_fn=self.move_held_object, + thor_action="MoveHandDelta", + updated_kwarg_names={"x_meters": "x", "y_meters": "y", "z_meters": "z"}, + x_meters=x_meters, + y_meters=y_meters, + z_meters=z_meters, + default_thor_kwargs=self.physics_step_kwargs, + ) + + def rotate_held_object(self, x: float, y: float, z: float) -> bool: + """Rotate the object in the agent's hand by the specified degrees. + + The rotation parameters are scaled linearly to put rotations + between [-90:90] degrees. The action is only successful agent is holding an object. + + # Parameters + x : (float, min=-0.5, max=0.5) rotation along the x-axis. + y : (float, min=-0.5, max=0.5) rotation along the y-axis. + z : (float, min=-0.5, max=0.5) rotation along the z-axis. + """ + + def rescale_xyz(kwargs: Dict[str, Any]): + for k in ["x", "y", "z"]: + kwargs[k] = 180 * kwargs[k] + + return execute_action( + controller=self.controller, + action_space=self.action_space, + action_fn=self.rotate_held_object, + thor_action="RotateHand", + preprocess_kwargs_inplace=rescale_xyz, + x=x, + y=y, + z=z, + default_thor_kwargs=self.physics_step_kwargs, + ) + + def drop_held_object(self) -> bool: + """Drop the object in the agent's hand. + + The action is only successful agent is holding an object. + """ + return execute_action( + controller=self.controller, + action_space=self.action_space, + action_fn=self.drop_held_object, + thor_action="DropHandObject", + default_thor_kwargs={"autoSimulation": False, **self.physics_step_kwargs,}, + ) + + def drop_held_object_with_snap(self) -> bool: + """Drop the object in the agent's hand to the target position. + + Exception is raised if shuffle has not yet been called on the current + episode or the agent is in default mode. + + For this action to work: + 1. The agent must be within 1.5 meters from the goal object's + position, observed during the walkthrough phase. + 2. The agent must be looking in the direction of where it was + located in the walkthrough phase. + + Otherwise, the object will be placed in a visible receptacle or + if this also fails, it will be simply dropped. + + # Returns + + `True` if the drop was successful, otherwise `False`. + """ + if not self.shuffle_called: + raise Exception("Must be in unshuffle stage.") + if not self.mode == RearrangeMode.SNAP: + raise Exception("Must be in RearrangeMode.SNAP mode.") + + # round positions to 2 decimals + DEC = 2 + + with include_object_data(self.controller): + event = self.controller.last_event + held_obj = self.held_object + + if held_obj is None: + return False + + # When dropping up an object, make it breakable. + self.controller.step( + "MakeObjectBreakable", objectId=self.held_object["objectId"] + ) + + agent = event.metadata["agent"] + goal_pose = self.obj_name_to_walkthrough_start_pose[held_obj["name"]] + goal_pos = goal_pose["position"] + goal_rot = goal_pose["rotation"] + good_positions_to_drop_from = self._interactable_positions_cache.get( + scene_name=self.last_event.metadata["sceneName"], + obj={**held_obj, **{"position": goal_pos, "rotation": goal_rot},}, + controller=self.controller, + force_cache_refresh=self.force_cache_reset, # Forcing cache resets when not training. + ) + + def position_to_tuple(position: Dict[str, float]): + return tuple(round(position[k], DEC) for k in ["x", "y", "z"]) + + agent_xyz = position_to_tuple(agent["position"]) + agent_rot = (round(agent["rotation"]["y"] / 90) * 90) % 360 + agent_standing = int(agent["isStanding"]) + agent_horizon = round(agent["cameraHorizon"]) + + for valid_agent_pos in good_positions_to_drop_from: + # Checks if the agent is close enough to the target + # for the object to be snapped to the target location. + valid_xyz = position_to_tuple(valid_agent_pos) + valid_rot = (round(valid_agent_pos["rotation"] / 90) * 90) % 360 + valid_standing = int(valid_agent_pos["standing"]) + valid_horizon = round(valid_agent_pos["horizon"]) + if ( + valid_xyz == agent_xyz # Position + and valid_rot == agent_rot # Rotation + and valid_standing == agent_standing # Standing + and round(valid_horizon) == agent_horizon # Horizon + ): + # Try a few locations near the target for robustness' sake + positions = [ + { + "x": goal_pos["x"] + 0.001 * xoff, + "y": goal_pos["y"] + 0.001 * yoff, + "z": goal_pos["z"] + 0.001 * zoff, + } + for xoff in [0, -1, 1] + for zoff in [0, -1, 1] + for yoff in [0, 1, 2] + ] + self.controller.step( + action="TeleportObject", + objectId=held_obj["objectId"], + rotation=goal_rot, + positions=positions, + forceKinematic=True, + allowTeleportOutOfHand=True, + makeUnbreakable=True, + ) + break + + if self.held_object is None: + # If we aren't holding the object anymore, then let's check if it + # was placed into the right location. + if self.are_poses_equal( + goal_pose=get_pose_info(goal_pose), + cur_pose=next( + get_pose_info(o) + for o in self.last_event.metadata["objects"] + if o["name"] == goal_pose["name"] + ), + treat_broken_as_unequal=True, + ): + return True + else: + return False + + # We couldn't teleport the object to the target location, let's try placing it + # in a visible receptacle. + possible_receptacles = [ + o for o in event.metadata["objects"] if o["visible"] and o["receptacle"] + ] + possible_receptacles = sorted( + possible_receptacles, key=lambda o: (o["distance"], o["objectId"]) + ) + for possible_receptacle in possible_receptacles: + self.controller.step( + action="PutObject", + objectId=possible_receptacle["objectId"], + **self.physics_step_kwargs, + ) + if self.controller.last_event.metadata["lastActionSuccess"]: + break + + # We failed to place the object into a receptacle, let's just drop it. + if len(possible_receptacles) == 0 or ( + not self.controller.last_event.metadata["lastActionSuccess"] + ): + self.controller.step( + "DropHeldObjectAhead", + forceAction=True, + autoSimulation=False, + **{**self.physics_step_kwargs, "actionSimulationSeconds": 1.5}, + ) + + return False + + @property + def last_event(self) -> ai2thor.server.Event: + """Return the AI2-THOR Event from the most recent controller action.""" + return self.controller.last_event + + @property + def scene(self) -> str: + """Return the current AI2-THOR scene name.""" + return self.controller.last_event.metadata["sceneName"].replace("_physics", "") + + @staticmethod + def compare_poses( + goal_pose: Union[Dict[str, Any], Sequence[Dict[str, Any]]], + cur_pose: Union[Dict[str, Any], Sequence[Dict[str, Any]]], + ) -> Union[Dict[str, Any], List[Dict[str, Any]]]: + """Compare two object poses and return where they differ. + + The `goal_pose` must not have the object as broken. + + # Parameters + goal_pose : The goal pose of the object. + cur_pose : The current pose of the object. + + # Returns + A dictionary with the following keys keys and values + * "broken" - `True` if the `cur_pose` is broken in which case all below values are `None`, otherwise `False`. + * "iou" - The IOU overlap between the two object poses (min==0, max==1) using their 3d bounding boxes. Computed + using an approximate sampling procedure. If the `position_dist` (see below) is <0.01 and the `rotation_dist` + is <10.0 then the IOU computation is short circuited and a value of 1 is returned. + * "openness_diff" - `None` if the object types are not openable. Otherwise this equals the absolute difference + between the `openness` values of the two poses. + * "position_dist" - The euclidean distance between the positions of the center points of the two poses. + * "rotation_dist" - The angle (in degrees) between the two poses. See the + `IThorEnvironment.angle_between_rotations` function for more information. + """ + if isinstance(goal_pose, Sequence): + assert isinstance(cur_pose, Sequence) + return [ + RearrangeTHOREnvironment.compare_poses(goal_pose=gp, cur_pose=cp) + for gp, cp in zip(goal_pose, cur_pose) + ] + + assert goal_pose["type"] == cur_pose["type"] + assert not goal_pose["broken"] + + if cur_pose["broken"]: + return { + "broken": True, + "iou": None, + "openness_diff": None, + "position_dist": None, + "rotation_dist": None, + } + + if goal_pose["bounding_box"] is None and cur_pose["bounding_box"] is None: + iou = None + position_dist = None + rotation_dist = None + else: + position_dist = IThorEnvironment.position_dist( + goal_pose["position"], cur_pose["position"] + ) + rotation_dist = IThorEnvironment.angle_between_rotations( + goal_pose["rotation"], cur_pose["rotation"] + ) + if position_dist < 1e-2 and rotation_dist < 10.0: + iou = 1.0 + else: + try: + iou = iou_box_3d( + goal_pose["bounding_box"], cur_pose["bounding_box"] + ) + except Exception as _: + get_logger().warning( + "Could not compute IOU, will assume it was 0. Error during IOU computation:" + f"\n{traceback.format_exc()}" + ) + iou = 0 + + if goal_pose["openness"] is None and cur_pose["openness"] is None: + openness_diff = None + else: + openness_diff = abs(goal_pose["openness"] - cur_pose["openness"]) + + return { + "broken": False, + "iou": iou, + "openness_diff": openness_diff, + "position_dist": position_dist, + "rotation_dist": rotation_dist, + } + + @classmethod + def pose_difference_energy( + cls, + goal_pose: Union[Dict[str, Any], Sequence[Dict[str, Any]]], + cur_pose: Union[Dict[str, Any], Sequence[Dict[str, Any]]], + min_iou: float = IOU_THRESHOLD, + open_tol: float = OPENNESS_THRESHOLD, + pos_barrier: float = POSITION_DIFF_BARRIER, + ) -> Union[float, np.ndarray]: + """Computes the energy between two poses. + + The energy (taking values in [0:1]) between two poses provides a soft and holistic measure of how + far apart two poses are. If the energy is near 1 then the two poses are very dissimilar, if the energy + is near 1 then the two poses are nearly equal. + + # Parameters + goal_pose : The goal pose of the object. + cur_pose : The current pose of the object. + min_iou : As the IOU between the two poses increases between [0:min_iou] the contribution to the energy + corresponding solely to the to the IOU decrease from 0.5 to 0 in a linear fashion. + open_tol: If the object is openable, then if the absolute openness difference is less than `open_tol` + the energy is 0. Otherwise the pose energy is 1. + pos_barrier: If two poses are separated by a large distance, we would like to decrease the energy as + the two poses are brought closer together. The `pos_barrier` controls when this energy decrease begins, + namely at its default value of 2.0, the contribution of the distance to + the energy decreases linearly from 0.5 to 0 as the distance between the two poses decreases from + 2 meters to 0 meters. + """ + if isinstance(goal_pose, Sequence): + assert isinstance(cur_pose, Sequence) + return np.array( + [ + cls.pose_difference_energy( + goal_pose=p0, + cur_pose=p1, + min_iou=min_iou, + open_tol=open_tol, + pos_barrier=pos_barrier, + ) + for p0, p1 in zip(goal_pose, cur_pose) + ] + ) + assert not goal_pose["broken"] + + pose_diff = cls.compare_poses(goal_pose=goal_pose, cur_pose=cur_pose) + if pose_diff["broken"]: + return 1.0 + + if pose_diff["openness_diff"] is None or goal_pose["pickupable"]: + gbb = np.array(goal_pose["bounding_box"]) + cbb = np.array(cur_pose["bounding_box"]) + + iou = pose_diff["iou"] + iou_energy = max(1 - iou / min_iou, 0) + + if iou > 0: + position_dist_energy = 0.0 + else: + min_pairwise_dist_between_corners = np.sqrt( + ( + ( + np.tile(gbb, (1, 8)).reshape(-1, 3) + - np.tile(cbb, (8, 1)).reshape(-1, 3) + ) + ** 2 + ).sum(1) + ).min() + position_dist_energy = min( + min_pairwise_dist_between_corners / pos_barrier, 1.0 + ) + + return 0.5 * iou_energy + 0.5 * position_dist_energy + + else: + return 1.0 * (pose_diff["openness_diff"] > open_tol) + + @classmethod + def are_poses_equal( + cls, + goal_pose: Union[Dict[str, Any], Sequence[Dict[str, Any]]], + cur_pose: Union[Dict[str, Any], Sequence[Dict[str, Any]]], + min_iou: float = 0.5, + open_tol: float = 0.2, + treat_broken_as_unequal: bool = False, + ) -> Union[bool, np.ndarray]: + """Determine if two object poses are equal (up to allowed error). + + The `goal_pose` must not have the object as broken. + + # Parameters + goal_pose : The goal pose of the object. + cur_pose : The current pose of the object. + min_iou : If the two objects are pickupable objects, they are considered equal if their IOU is `>=min_iou`. + open_tol: If the object is openable and not pickupable, then the poses are considered equal if the absolute + openness difference is less than `open_tol`. + treat_broken_as_unequal : If `False` an exception will be thrown if the `cur_pose` is broken. If `True`, then + if `cur_pose` is broken this function will always return `False`. + """ + if isinstance(goal_pose, Sequence): + assert isinstance(cur_pose, Sequence) + return np.array( + [ + cls.are_poses_equal( + goal_pose=p0, + cur_pose=p1, + min_iou=min_iou, + open_tol=open_tol, + treat_broken_as_unequal=treat_broken_as_unequal, + ) + for p0, p1 in zip(goal_pose, cur_pose) + ] + ) + assert not goal_pose["broken"] + + if cur_pose["broken"]: + if treat_broken_as_unequal: + return False + else: + raise RuntimeError( + f"Cannot determine if poses of two objects are" + f" equal if one is broken object ({goal_pose} v.s. {cur_pose})." + ) + + pose_diff = cls.compare_poses(goal_pose=goal_pose, cur_pose=cur_pose) + + return (pose_diff["iou"] is None or pose_diff["iou"] > min_iou) and ( + pose_diff["openness_diff"] is None or pose_diff["openness_diff"] <= open_tol + ) + + @property + def all_rearranged_or_broken(self): + """Return if every object is simultaneously broken or in its correct + pose. + + The unshuffle agent can make no more progress on its task in the + case that that every object is either (1) in its correct + position or (2) broken so that it can never be placed in its + correct position. This function simply returns whether this is + the case. + """ + return all( + cp["broken"] or self.are_poses_equal(goal_pose=gp, cur_pose=cp) + for _, gp, cp in zip(*self.poses) + ) + + @property + def poses( + self, + ) -> Tuple[List[Dict[str, Any]], List[Dict[str, Any]], List[Dict[str, Any]]]: + """Return (unshuffle start, walkthrough start, current) pose for every + object in the scene. + + Can only be called during the unshuffle phase. + + # Returns + A Tuple of containing three ordered lists of object poses `(unshuffle_start_poses, walkthrough_start_poses, current_poses)` + such that, for `0 <= i < len(current_poses)`, + * `unshuffle_start_poses[i]` - corresponds to the pose of the ith object at the start of the unshuffle phase. + * `walkthrough_start_poses[i]` - corresponds to the pose of the ith object at the start of the walkthrough phase. + * `current_poses[i]` - corresponds to the pose of the ith object in the current environment. + During the unshuffle phase is commonly useful to compare `current_poses[i]` against `walkthrough_start_poses[i]` + to get a sense of the agent's progress towards placing the objects into their correct locations. + """ + # Ensure we are in the unshuffle phase. + if not self.shuffle_called: + raise Exception("shuffle() must be called before accessing poses") + + # Get current object information + with include_object_data(self.controller): + obj_name_to_current_obj = self._obj_list_to_obj_name_to_pose_dict( + self.controller.last_event.metadata["objects"] + ) + + ordered_obj_names = list(self.obj_name_to_walkthrough_start_pose.keys()) + + current_objs_list = [] + for obj_name in ordered_obj_names: + if obj_name not in obj_name_to_current_obj: + # obj_name_to_predicted_obj can have more objects than goal objects + # (breaking objects can generate new ones) + # The other way (more goal poses than predicted objs) is a problem, we will + # assume that the disappeared objects are broken + if not self._have_warned_about_mismatch: + # Don't want to warn many many times during single episode + self._have_warned_about_mismatch = True + usos = set(self.obj_name_to_unshuffle_start_pose.keys()) + wsos = set(self.obj_name_to_walkthrough_start_pose.keys()) + cos = set(obj_name_to_current_obj.keys()) + get_logger().warning( + f"Mismatch between walkthrough start, unshuffle start, and current pose objects." + f"\nscene = {self.scene}, index {self.current_task_spec.metrics.get('index')}" + f"\nusos-wsos, wsos-usos = {usos - wsos}, {wsos - usos}" + f"\ncos-usos, usos-cos = {cos - usos}, {usos - cos}" + f"\ncos-wsos, wsos-cos = {cos - wsos}, {wsos - cos}" + ) + obj_name_to_current_obj[obj_name] = { + **self.obj_name_to_walkthrough_start_pose[obj_name], + "isBroken": True, + "broken": True, + "position": None, + "rotation": None, + "openness": None, + } + current_objs_list.append(obj_name_to_current_obj[obj_name]) + + # We build a cache of object poses corresponding to the start of the walkthrough/unshuffle phases + # as these remain the same until the `reset` function is called. + if self._sorted_and_extracted_walkthrough_start_poses is None: + broken_obj_names = [ + obj_name + for obj_name in ordered_obj_names + if self.obj_name_to_walkthrough_start_pose[obj_name]["isBroken"] + ] + if len(broken_obj_names) != 0: + if not self.current_task_spec.runtime_sample: + # Don't worry about reporting broken objects when using + # a "runtime_sample" task spec as these types of things are + # more common. + get_logger().warning( + f"BROKEN GOAL OBJECTS!" + f"\nIn scene {self.scene}" + f"\ntask spec {self.current_task_spec}" + f"\nbroken objects {broken_obj_names}" + ) + + # If we find a broken goal object, we will simply pretend as though it was not + # broken. This means the agent can never succeed in unshuffling, this means it is + # possible that even a perfect agent will not succeed for some tasks. + for broken_obj_name in broken_obj_names: + self.obj_name_to_walkthrough_start_pose[broken_obj_name][ + "isBroken" + ] = False + self.obj_name_to_unshuffle_start_pose[broken_obj_name][ + "isBroken" + ] = False + ordered_obj_names = list(self.obj_name_to_walkthrough_start_pose.keys()) + + walkthrough_start_poses = tuple( + self.obj_name_to_walkthrough_start_pose[k] for k in ordered_obj_names + ) + unshuffle_start_poses = tuple( + self.obj_name_to_unshuffle_start_pose[k] for k in ordered_obj_names + ) + self._sorted_and_extracted_unshuffle_start_poses = get_pose_info( + unshuffle_start_poses + ) + self._sorted_and_extracted_walkthrough_start_poses = get_pose_info( + walkthrough_start_poses + ) + + return ( + self._sorted_and_extracted_unshuffle_start_poses, + self._sorted_and_extracted_walkthrough_start_poses, + get_pose_info(current_objs_list), + ) + + def _runtime_reset( + self, task_spec: RearrangeTaskSpec, force_axis_aligned_start: bool + ): + """Randomly initialize a scene at runtime. + + Rather than using a predefined collection of object states, + randomly generate these positions at runtime. This may be useful for obtaining more + diverse training examples. + + # Parameters + task_spec : The RearrangeTaskSpec for this runtime sample. `task_spec.runtime_sample` should be `True`. + force_axis_aligned_start : If `True`, this will force the agent's start rotation to be 'axis aligned', i.e. + to equal to 0, 90, 180, or 270 degrees. + """ + assert ( + task_spec.runtime_sample + ), "Attempted to use a runtime reset with a task spec which has a `False` `runtime_sample` property." + + # For efficiency reasons, we do not completely reset the ai2thor scene (which + # will reset all object states to a default configuration and restore broken + # objects to their unbroken state) on every call to `_runtime_reset` if the scene name hasn't changed. Instead + # we reset the ai2thor scene only every 25 calls. + if ( + task_spec.scene != self.scene + or self.current_task_spec.runtime_data["count"] >= 25 + ): + count = 1 + self.controller.reset(task_spec.scene) + + if self._enhanced_physics_determinism: + self.controller.step("PausePhysicsAutoSim") + + remove_objects_until_all_have_identical_meshes(self.controller) + self.controller.step( + "InitialRandomSpawn", forceVisible=True, placeStationary=True, + ) + md = self.controller.step("GetReachablePositions").metadata + assert md["lastActionSuccess"] + reachable_positions = md["actionReturn"] + else: + count = 1 + self.current_task_spec.runtime_data["count"] + reachable_positions = self.current_task_spec.runtime_data[ + "reachable_positions" + ] + + self.current_task_spec = task_spec + self.current_task_spec.stage = "Unknown" + self.current_task_spec.runtime_data = { + "count": count, + "reachable_positions": reachable_positions, + } + + with include_object_data(self.controller): + random.shuffle(reachable_positions) + + # set agent position + max_teleports = min(10, len(reachable_positions)) + for teleport_count, pos in enumerate(reachable_positions): + rot = 30 * random.randint(0, 11) + if force_axis_aligned_start: + rot = round_to_factor(30 * random.randint(0, 11), 90) + md = self.controller.step( + "TeleportFull", + **pos, + rotation={"x": 0, "y": rot, "z": 0}, + horizon=0.0, + standing=True, + forceAction=teleport_count == max_teleports - 1, + ).metadata + if md["lastActionSuccess"]: + break + else: + raise RuntimeError("No reachable positions?") + + assert md["lastActionSuccess"] + self.current_task_spec.agent_position = pos + self.current_task_spec.agent_rotation = rot + self.current_task_spec.runtime_data["starting_objects"] = md["objects"] + + def _task_spec_reset( + self, task_spec: RearrangeTaskSpec, force_axis_aligned_start: bool + ): + """Initialize a ai2thor environment from a (non-runtime sample) task + specification (i.e. an exhaustive collection of object poses for the + walkthrough and unshuffle phase). + + After this call, the environment will be ready for use in the walkthrough phase. + + # Parameters + task_spec : The RearrangeTaskSpec for this task. `task_spec.runtime_sample` should be `False`. + force_axis_aligned_start : If `True`, this will force the agent's start rotation to be 'axis aligned', i.e. + to equal to 0, 90, 180, or 270 degrees. + """ + assert ( + not task_spec.runtime_sample + ), "`_task_spec_reset` requires that `task_spec.runtime_sample` is `False`." + + self.current_task_spec = task_spec + + self.controller.reset(self.current_task_spec.scene) + if self._enhanced_physics_determinism: + self.controller.step("PausePhysicsAutoSim") + + if force_axis_aligned_start: + self.current_task_spec.agent_rotation = round_to_factor( + self.current_task_spec.agent_rotation, 90 + ) + + # set agent position + pos = self.current_task_spec.agent_position + rot = {"x": 0, "y": self.current_task_spec.agent_rotation, "z": 0} + self.controller.step( + "TeleportFull", + **pos, + rotation=rot, + horizon=0.0, + standing=True, + forceAction=True, + ) + + # show object metadata + with include_object_data(self.controller): + # open objects + for obj in self.current_task_spec.openable_data: + # id is re-found due to possible floating point errors + current_obj_info = next( + l_obj + for l_obj in self.last_event.metadata["objects"] + if l_obj["name"] == obj["name"] + ) + self.controller.step( + action="OpenObject", + objectId=current_obj_info["objectId"], + openness=obj["target_openness"], + forceAction=True, + **self.physics_step_kwargs, + ) + + # arrange walkthrough poses for pickupable objects + self.controller.step( + "SetObjectPoses", + objectPoses=self.current_task_spec.target_poses, + placeStationary=False, + enablePhysicsJitter=True, + forceRigidbodySleep=True, + skipMoveable=True, + ) + assert self.controller.last_event.metadata["lastActionSuccess"] + + def reset( + self, task_spec: RearrangeTaskSpec, force_axis_aligned_start: bool = False, + ) -> None: + """Reset the environment with respect to the new task specification. + + The environment will start in the walkthrough phase. + + # Parameters + task_spec : The `RearrangeTaskSpec` defining environment state. + force_axis_aligned_start : If `True`, this will force the agent's start rotation to be 'axis aligned', i.e. + to equal to 0, 90, 180, or 270 degrees. + """ + if task_spec.runtime_sample: + self._runtime_reset( + task_spec=task_spec, force_axis_aligned_start=force_axis_aligned_start + ) + else: + self._task_spec_reset( + task_spec=task_spec, force_axis_aligned_start=force_axis_aligned_start, + ) + + self.shuffle_called = False + self.obj_name_to_walkthrough_start_pose = self._obj_list_to_obj_name_to_pose_dict( + self.last_event.metadata["objects"] + ) + + self._have_warned_about_mismatch = False + self._sorted_and_extracted_walkthrough_start_poses = None + self._sorted_and_extracted_unshuffle_start_poses = None + self._agent_signals_done = False + + def _runtime_shuffle(self): + """Randomly shuffle objects in the environment to start the unshuffle + phase. + + Also resets the agent's position to its start position. + """ + assert (not self.shuffle_called) and self.current_task_spec.runtime_sample + + task_spec = self.current_task_spec + + # set agent position + pos = task_spec.agent_position + rot = {"x": 0, "y": task_spec.agent_rotation, "z": 0} + self.controller.step( + "TeleportFull", + **pos, + rotation=rot, + horizon=0.0, + standing=True, + forceAction=True, + ) + + # Randomly shuffle a subset of objects. + nobjects_to_move = random.randint(1, 5) + pickupable = [ + o for o in task_spec.runtime_data["starting_objects"] if o["pickupable"] + ] + random.shuffle(pickupable) + + pickupable.sort( + key=lambda x: 1 * (x["objectType"] in OBJECT_TYPES_TO_NOT_MOVE), + reverse=True, + ) + objects_to_not_move = pickupable[:-nobjects_to_move] + + object_ids_not_to_move = [o["objectId"] for o in objects_to_not_move] + object_ids_not_to_move.extend( + get_object_ids_to_not_move_from_object_types( + controller=self.controller, object_types=OBJECT_TYPES_TO_NOT_MOVE, + ) + ) + self.controller.step( + "InitialRandomSpawn", + excludedObjectIds=object_ids_not_to_move, + forceVisible=True, + placeStationary=True, + ) + + # Randomly open some subset of objects. + num_objects_to_open = random.randint(0, 1) + openable_objects = [ + o + for o in self.last_event.metadata["objects"] + if o["openable"] and not o["pickupable"] + ] + random.shuffle(openable_objects) + open_objs( + objects_to_open=openable_objects[:num_objects_to_open], + controller=self.controller, + ) + + self.current_task_spec.runtime_data[ + "target_objects" + ] = self.last_event.metadata["objects"] + + def _task_spec_shuffle(self, reset: bool = False): + """Shuffle objects in the environment to start the unshuffle phase + using the current task specification. + + Also resets the agent's position to its start position. + """ + assert not (self.current_task_spec.runtime_sample or self.shuffle_called) + + task_spec = self.current_task_spec + + # TODO: No need to reset every time right? + if reset: + self.controller.reset(self.scene) + if self._enhanced_physics_determinism: + self.controller.step("PausePhysicsAutoSim") + + # set agent position + pos = task_spec.agent_position + rot = {"x": 0, "y": task_spec.agent_rotation, "z": 0} + self.controller.step( + "TeleportFull", + **pos, + rotation=rot, + horizon=0.0, + standing=True, + forceAction=True, + ) + + # open objects + with include_object_data(self.controller): + for obj in task_spec.openable_data: + # id is re-found due to possible floating point errors + current_obj_info = next( + l_obj + for l_obj in self.last_event.metadata["objects"] + if l_obj["name"] == obj["name"] + ) + + self.controller.step( + action="OpenObject", + objectId=current_obj_info["objectId"], + openness=obj["start_openness"], + forceAction=True, + **( + self.physics_step_kwargs + if obj is task_spec.openable_data[-1] + else {} + ), + ) + + # arrange unshuffle start poses for pickupable objects + self.controller.step( + "SetObjectPoses", + objectPoses=task_spec.starting_poses, + placeStationary=False, + enablePhysicsJitter=True, + forceRigidbodySleep=True, + skipMoveable=True, + ) + assert self.controller.last_event.metadata["lastActionSuccess"] + + def shuffle(self, require_reset: bool = False): + """Shuffle objects in the environment to start the unshuffle phase.""" + + assert not self.shuffle_called + + runtime_sample = self.current_task_spec.runtime_sample + if runtime_sample: + self._runtime_shuffle() + else: + self._task_spec_shuffle(reset=require_reset) + + # Save object metadata + with include_object_data(self.controller): + self.obj_name_to_unshuffle_start_pose = self._obj_list_to_obj_name_to_pose_dict( + self.last_event.metadata["objects"] + ) + + if len(self.obj_name_to_unshuffle_start_pose) != len( + self.obj_name_to_walkthrough_start_pose + ): + if runtime_sample or require_reset: + walkthrough_start_obj_names = set( + self.obj_name_to_walkthrough_start_pose.keys() + ) + unshuffle_start_obj_names = set( + self.obj_name_to_unshuffle_start_pose.keys() + ) + raise PoseMismatchError( + "Irrecoverable difference between walkthrough and unshuffle phase objects." + f"\ng-i, i-g = {walkthrough_start_obj_names - unshuffle_start_obj_names}," + f" {unshuffle_start_obj_names - walkthrough_start_obj_names}" + ) + else: + self.shuffle(require_reset=True) + + self.shuffle_called = True + self._agent_signals_done = False + + @staticmethod + def _obj_list_to_obj_name_to_pose_dict( + objects: List[Dict[str, Any]] + ) -> OrderedDict: + """Helper function to transform a list of object data dicts into a + dictionary.""" + objects = [ + o + for o in objects + if o["openable"] or o.get("objectOrientedBoundingBox") is not None + ] + d = OrderedDict( + (o["name"], o) for o in sorted(objects, key=lambda x: x["name"]) + ) + assert len(d) == len(objects) + return d + + def stop(self): + """Terminate the current AI2-THOR session.""" + try: + self.controller.stop() + except Exception as _: + pass + + def __del__(self): + self.stop() diff --git a/easi/tasks/ai2thor_rearrangement_2023/vendor/rearrange/utils.py b/easi/tasks/ai2thor_rearrangement_2023/vendor/rearrange/utils.py new file mode 100644 index 0000000..7f45772 --- /dev/null +++ b/easi/tasks/ai2thor_rearrangement_2023/vendor/rearrange/utils.py @@ -0,0 +1,521 @@ +import logging +import random +from typing import Dict, Callable, Tuple, Union, List, Any, Optional, Sequence + +import ai2thor.controller +import lru +import numpy as np +from allenact.utils.system import ImportChecker +from allenact_plugins.ithor_plugin.ithor_environment import IThorEnvironment +from allenact_plugins.ithor_plugin.ithor_util import include_object_data + +_UNIFORM_BOX_CACHE = {} + + +def save_frames_to_mp4(frames: Sequence[np.ndarray], file_name: str, fps=3): + import matplotlib.pyplot as plt + from matplotlib import animation + import pylab + + h, w, _ = frames[0].shape + aspect_ratio = w / h + fig = plt.figure(figsize=(5 * aspect_ratio, 5)) + ax = fig.add_subplot(111) + ax.set_frame_on(False) + fig.subplots_adjust(left=0, bottom=0, right=1, top=1, wspace=None, hspace=None) + ax.set_aspect("equal") + ax.get_xaxis().set_visible(False) + ax.get_yaxis().set_visible(False) + + im = ax.imshow(frames[0], cmap="gray", interpolation="nearest") + im.set_clim([0, 255]) + + pylab.tight_layout() + + def update_img(n): + if n >= len(frames): + im.set_data(frames[-1]) + else: + im.set_data(frames[n]) + return im + + ani = animation.FuncAnimation(fig, update_img, len(frames) - 1, interval=200) + writer = animation.writers["ffmpeg"](fps=fps) + + ani.save(file_name, writer=writer, dpi=300) + + +def hand_in_initial_position( + controller: ai2thor.controller.Controller, ignore_rotation: bool = False +): + metadata = controller.last_event.metadata + return IThorEnvironment.position_dist( + metadata["heldObjectPose"]["localPosition"], {"x": 0, "y": -0.16, "z": 0.38}, + ) < 1e-4 and ( + ignore_rotation + or IThorEnvironment.angle_between_rotations( + metadata["heldObjectPose"]["localRotation"], + {"x": -metadata["agent"]["cameraHorizon"], "y": 0, "z": 0}, + ) + < 1e-2 + ) + + +class BoundedFloat(object): + """Declare a bounded float placeholder variable.""" + + def __init__(self, low: float, high: float): + """High is the max float value, low is the min (both inclusive).""" + self.types = {float, int, np.float64} + if type(low) not in self.types or type(high) not in self.types: + raise ValueError("Bounds must both be floats.") + if low > high: + raise ValueError("low must be less than high.") + self.low = low + self.high = high + + def sample(self) -> float: + """Return a random float within the initialized range.""" + return random.random() * (self.high - self.low) + self.low + + def __contains__(self, n: float): + """Assert n is within this classes bounded range.""" + if type(n) not in self.types: + raise ValueError("n must be a float (or an int).") + return self.low <= n <= self.high + + +class RearrangeActionSpace(object): + """Control which actions with bounded variables can be executed.""" + + def __init__(self, actions: Dict[Callable, Dict[str, BoundedFloat]]): + """Build a new AI2-THOR action space. + + Attributes + :actions (Dict[Callable, Dict[str, BoundedFloat]]) must be in the form + { + : { + '': , + '': , + '': , + '': , + ... + }, + ... + }, + where the action variables are in the value and the callable function + is the key. + """ + self.keys = list(actions.keys()) + self.actions = actions + + def execute_random_action(self, log_choice: bool = True) -> None: + """Execute a random action within the specified action space.""" + action = random.choice(self.keys) + kwargs = { + name: bounds.sample() for name, bounds in self.actions[action].items() + } + + # logging + if log_choice: + kwargs_str = str("".join(f" {k}: {v},\n" for k, v in kwargs.items())) + kwargs_str = "\n" + kwargs_str[:-2] if kwargs_str else "" + logging.info(f"Executing {action.__name__}(" + kwargs_str + ")") + + action(**kwargs) + + def __contains__( + self, action_fn_and_kwargs: Tuple[Callable, Dict[str, float]] + ) -> bool: + """Return if action_fn with variables is valid in this ActionSpace.""" + action_fn, variables = action_fn_and_kwargs + + # asserts the action is valid + if action_fn not in self.actions: + return False + + # asserts the variables are valid + for name, x in variables.items(): + if x not in self.actions[action_fn][name]: + return False + + return True + + def __str__(self) -> str: + """Return a string representation of the action space.""" + return self.__repr__() + + def __repr__(self) -> str: + """Return a string representation of the action space.""" + s = "" + tab = " " * 2 # default tabs have like 8 spaces on shells + for action_fn, vars in self.actions.items(): + fn_name = action_fn.__name__ + vstr = "" + for i, (var_name, bound) in enumerate(vars.items()): + low = bound.low + high = bound.high + vstr += f"{tab * 2}{var_name}: float(low={low}, high={high})" + vstr += "\n" if i + 1 == len(vars) else ",\n" + vstr = "\n" + vstr[:-1] if vstr else "" + s += f"{tab}{fn_name}({vstr}),\n" + s = s[:-2] if s else "" + return "ActionSpace(\n" + s + "\n)" + + +def extract_obj_data(obj): + """Return object evaluation metrics based on the env state.""" + if "type" in obj: + return { + "type": obj["type"], + "position": obj["position"], + "rotation": obj["rotation"], + "openness": obj["openness"], + "pickupable": obj["pickupable"], + "broken": obj["broken"], + "bounding_box": obj["bounding_box"], + "objectId": obj["objectId"], + "name": obj["name"], + "parentReceptacles": obj.get("parentReceptacles", []), + } + return { + "type": obj["objectType"], + "position": obj["position"], + "rotation": obj["rotation"], + "openness": obj["openness"] if obj["openable"] else None, + "pickupable": obj["pickupable"], + "broken": obj["isBroken"], + "objectId": obj["objectId"], + "name": obj["name"], + "parentReceptacles": obj.get("parentReceptacles", []), + "bounding_box": obj["objectOrientedBoundingBox"]["cornerPoints"] + if obj["objectOrientedBoundingBox"] + else None, + } + + +def get_pose_info( + objs: Union[Sequence[Dict[str, Any]], Dict[str, Any]] +) -> Union[List[Dict[str, Any]], Dict[str, Any]]: + """Return data about each specified object. + + For each object, the return consists of its type, position, + rotation, openness, and bounding box. + """ + # list of objects + if isinstance(objs, Sequence): + return [extract_obj_data(obj) for obj in objs] + # single object + return extract_obj_data(objs) + + +def execute_action( + controller: ai2thor.controller.Controller, + action_space: RearrangeActionSpace, + action_fn: Callable, + thor_action: str, + error_message: str = "", + updated_kwarg_names: Optional[Dict[str, str]] = None, + default_thor_kwargs: Optional[Dict[str, Any]] = None, + preprocess_kwargs_inplace: Optional[Callable] = None, + **kwargs: float, +) -> bool: + """Execute a bounded action within the AI2-THOR controller.""" + if updated_kwarg_names is None: + updated_kwarg_names = {} + if default_thor_kwargs is None: + default_thor_kwargs = {} + + if (action_fn, kwargs) not in action_space: # Checks that values are in bounds + raise ValueError( + error_message + + f" action_fn=={action_fn}, kwargs=={kwargs}, action_space=={action_space}." + ) + + if preprocess_kwargs_inplace is not None: + if len(updated_kwarg_names) != 0: + raise NotImplementedError( + "Cannot have non-empty `updated_kwarg_names` and a non-None `preprocess_kwargs_inplace` argument." + ) + preprocess_kwargs_inplace(kwargs) + + # get rid of bad variable names + for better_kwarg, thor_kwarg in updated_kwarg_names.items(): + kwargs[thor_kwarg] = kwargs[better_kwarg] + del kwargs[better_kwarg] + + for name, value in default_thor_kwargs.items(): + kwargs[name] = value + + event = controller.step(thor_action, **kwargs) + return event.metadata["lastActionSuccess"] + + +def _iou_slow( + b1: Sequence[Sequence[float]], + b2: Sequence[Sequence[float]], + num_points: int = 2197, +) -> float: + """Calculate the IoU between 3d bounding boxes b1 and b2.""" + + with ImportChecker("To use `_iou_slow` you must first install `scipy`."): + from scipy.spatial.qhull import ConvexHull, Delaunay + + b1 = np.array(b1) if not isinstance(b1, np.ndarray) else b1 + b2 = np.array(b2) if not isinstance(b2, np.ndarray) else b2 + + def _outer_bounds( + points_1: np.ndarray, points_2: np.ndarray + ) -> Dict[str, Dict[str, float]]: + """Sample points from the outer bounds formed by points_1/2.""" + assert points_1.shape == points_2.shape + bounds = dict() + for i in range(points_1.shape[0]): + x1, y1, z1 = points_1[i] + x2, y2, z2 = points_2[i] + points = [ + (x1, "x"), + (x2, "x"), + (y1, "y"), + (y2, "y"), + (z1, "z"), + (z2, "z"), + ] + for val, d_key in points: + if d_key not in bounds: + bounds[d_key] = {"min": val, "max": val} + else: + if val > bounds[d_key]["max"]: + bounds[d_key]["max"] = val + elif val < bounds[d_key]["min"]: + bounds[d_key]["min"] = val + return bounds + + def _in_box(box: np.ndarray, points: np.ndarray) -> np.ndarray: + """For each point, return if its in the hull.""" + hull = ConvexHull(box) + deln = Delaunay(box[hull.vertices]) + return deln.find_simplex(points) >= 0 + + bounds = _outer_bounds(b1, b2) + dim_points = int(num_points ** (1 / 3)) + + xs = np.linspace(bounds["x"]["min"], bounds["x"]["max"], dim_points) + ys = np.linspace(bounds["y"]["min"], bounds["y"]["max"], dim_points) + zs = np.linspace(bounds["z"]["min"], bounds["z"]["max"], dim_points) + points = np.array([[x, y, z] for x in xs for y in ys for z in zs], copy=False) + + in_b1 = _in_box(b1, points) + in_b2 = _in_box(b2, points) + + intersection = np.count_nonzero(in_b1 * in_b2) + union = np.count_nonzero(in_b1 + in_b2) + iou = intersection / union if union else 0 + return iou + + +def get_basis_for_3d_box(corners: np.ndarray) -> Tuple[np.ndarray, np.ndarray]: + assert corners[0].sum() == 0.0 + + without_first = corners[1:] + magnitudes1 = np.sqrt((without_first * without_first).sum(1)) + v0_ind = np.argmin(magnitudes1) + v0_mag = magnitudes1[v0_ind] + + if v0_mag < 1e-8: + raise RuntimeError(f"Could not find basis for {corners}") + + v0 = without_first[np.argmin(magnitudes1)] / v0_mag + + orth_to_v0 = (v0.reshape(1, -1) * without_first).sum(-1) < v0_mag / 2.0 + inds_orth_to_v0 = np.where(orth_to_v0)[0] + v1_ind = inds_orth_to_v0[np.argmin(magnitudes1[inds_orth_to_v0])] + v1_mag = magnitudes1[v1_ind] + v1 = without_first[v1_ind, :] / magnitudes1[v1_ind] + + orth_to_v1 = (v1.reshape(1, -1) * without_first).sum(-1) < v1_mag / 2.0 + inds_orth_to_v0_and_v1 = np.where(orth_to_v0 & orth_to_v1)[0] + + if len(inds_orth_to_v0_and_v1) != 1: + raise RuntimeError(f"Could not find basis for {corners}") + + v2_ind = inds_orth_to_v0_and_v1[0] + v2 = without_first[v2_ind, :] / magnitudes1[v2_ind] + + orth_mat = np.stack((v0, v1, v2), axis=1) # Orthonormal matrix + + return orth_mat, magnitudes1[[v0_ind, v1_ind, v2_ind]] + + +def uniform_box_points(n): + if n not in _UNIFORM_BOX_CACHE: + start = 1.0 / (2 * n) + lin_space = np.linspace(start, 1 - start, num=n).reshape(n, 1) + mat = lin_space + for i in range(2): + mat = np.concatenate( + (np.repeat(lin_space, mat.shape[0], 0), np.tile(mat, (n, 1))), axis=1, + ) + _UNIFORM_BOX_CACHE[n] = mat + + return _UNIFORM_BOX_CACHE[n] + + +def iou_box_3d(b1: Sequence[Sequence[float]], b2: Sequence[Sequence[float]]) -> float: + """Calculate the IoU between 3d bounding boxes b1 and b2.""" + b1 = np.array(b1) + b2 = np.array(b2) + + assert b1.shape == b2.shape == (8, 3) + + b1_center = b1[:1, :] + b1 = b1 - b1_center + b1_orth_basis, b1_mags = get_basis_for_3d_box(corners=b1) + + b2 = (b2 - b1_center) @ b1_orth_basis + b2_center = b2[:1, :] + b2 = b2 - b2_center + + b2_orth_basis, b2_mags = get_basis_for_3d_box(corners=b2) + + sampled_points = b2_center.reshape(1, 3) + ( + uniform_box_points(13) @ (b2_mags.reshape(-1, 1) * np.transpose(b2_orth_basis)) + ) + + prop_intersection = ( + np.logical_and( + sampled_points > -1e-3, sampled_points <= 1e-3 + b1_mags.reshape(1, 3) + ) + .all(-1) + .mean() + ) + + b1_vol = np.prod(b1_mags) + b2_vol = np.prod(b2_mags) + intersect_vol = b2_vol * prop_intersection + + return intersect_vol / (b1_vol + b2_vol - intersect_vol) + + +class PoseMismatchError(Exception): + pass + + +class ObjectInteractablePostionsCache: + def __init__(self, max_size: int = 20000, ndigits=2): + self._key_to_positions = lru.LRU(size=max_size) + + self.ndigits = ndigits + self.max_size = max_size + + def _get_key(self, scene_name: str, obj: Dict[str, Any]): + p = obj["position"] + return ( + scene_name, + obj["type"] if "type" in obj else obj["objectType"], + round(p["x"], self.ndigits), + round(p["y"], self.ndigits), + round(p["z"], self.ndigits), + ) + + def get( + self, + scene_name: str, + obj: Dict[str, Any], + controller: ai2thor.controller.Controller, + reachable_positions: Optional[Sequence[Dict[str, float]]] = None, + force_cache_refresh: bool = False, + ) -> List[Dict[str, Union[float, int, bool]]]: + scene_name = scene_name.replace("_physics", "") + obj_key = self._get_key(scene_name=scene_name, obj=obj) + + if force_cache_refresh or obj_key not in self._key_to_positions: + with include_object_data(controller): + metadata = controller.last_event.metadata + + cur_scene_name = metadata["sceneName"].replace("_physics", "") + assert ( + scene_name == cur_scene_name + ), f"Scene names must match when filling a cache miss ({scene_name} != {cur_scene_name})." + + obj_in_scene = next( + (o for o in metadata["objects"] if o["name"] == obj["name"]), None, + ) + if obj_in_scene is None: + raise RuntimeError( + f"Object with name {obj['name']} must be in the scene when filling a cache miss" + ) + + desired_pos = obj["position"] + desired_rot = obj["rotation"] + + cur_pos = obj_in_scene["position"] + cur_rot = obj_in_scene["rotation"] + + should_teleport = ( + IThorEnvironment.position_dist(desired_pos, cur_pos) >= 1e-3 + or IThorEnvironment.rotation_dist(desired_rot, cur_rot) >= 1 + ) + + object_held = obj_in_scene["isPickedUp"] + physics_was_unpaused = controller.last_event.metadata.get( + "physicsAutoSimulation", True + ) + if should_teleport: + if object_held: + if not hand_in_initial_position( + controller=controller, ignore_rotation=True + ): + raise NotImplementedError + + if physics_was_unpaused: + controller.step("PausePhysicsAutoSim") + assert controller.last_event.metadata["lastActionSuccess"] + + event = controller.step( + "TeleportObject", + objectId=obj_in_scene["objectId"], + rotation=desired_rot, + **desired_pos, + forceAction=True, + allowTeleportOutOfHand=True, + forceKinematic=True, + ) + assert event.metadata["lastActionSuccess"] + + metadata = controller.step( + action="GetInteractablePoses", + objectId=obj["objectId"], + positions=reachable_positions, + ).metadata + assert metadata["lastActionSuccess"] + self._key_to_positions[obj_key] = metadata["actionReturn"] + + if should_teleport: + if object_held: + if hand_in_initial_position( + controller=controller, ignore_rotation=True + ): + controller.step( + "PickupObject", + objectId=obj_in_scene["objectId"], + forceAction=True, + ) + assert controller.last_event.metadata["lastActionSuccess"] + + if physics_was_unpaused: + controller.step("UnpausePhysicsAutoSim") + assert controller.last_event.metadata["lastActionSuccess"] + else: + raise NotImplementedError + else: + event = controller.step( + "TeleportObject", + objectId=obj_in_scene["objectId"], + rotation=cur_rot, + **cur_pos, + forceAction=True, + ) + assert event.metadata["lastActionSuccess"] + + return self._key_to_positions[obj_key] diff --git a/easi/tasks/ai2thor_rearrangement_2023/vendor/rearrange_constants.py b/easi/tasks/ai2thor_rearrangement_2023/vendor/rearrange_constants.py new file mode 100644 index 0000000..d1b7b56 --- /dev/null +++ b/easi/tasks/ai2thor_rearrangement_2023/vendor/rearrange_constants.py @@ -0,0 +1,7 @@ +import os +from pathlib import Path + +ABS_PATH_OF_REARRANGE_TOP_LEVEL_DIR = os.path.abspath(os.path.dirname(Path(__file__))) +IOU_THRESHOLD = 0.5 +OPENNESS_THRESHOLD = 0.2 +POSITION_DIFF_BARRIER = 2.0 From f4ba251563e5c7c33dfc948bf30091fe4b658990 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 24 Feb 2026 11:16:39 +0800 Subject: [PATCH 097/239] feat: add rearrangement 84-action space module --- .../ai2thor_rearrangement_2023/actions.py | 62 +++++++++++++++++++ 1 file changed, 62 insertions(+) create mode 100644 easi/tasks/ai2thor_rearrangement_2023/actions.py diff --git a/easi/tasks/ai2thor_rearrangement_2023/actions.py b/easi/tasks/ai2thor_rearrangement_2023/actions.py new file mode 100644 index 0000000..fd51753 --- /dev/null +++ b/easi/tasks/ai2thor_rearrangement_2023/actions.py @@ -0,0 +1,62 @@ +"""AI2-THOR Rearrangement action space (84 discrete actions). + +Matches the original baseline_configs/rearrange_base.py exactly. +Uses regex-based PascalCase->snake_case to avoid stringcase dependency. +""" +from __future__ import annotations + +import re + +from easi.tasks.ai2thor_rearrangement_2023.vendor.rearrange.constants import ( + OBJECT_TYPES_WITH_PROPERTIES, +) + + +def _pascal_to_snake(name: str) -> str: + """Convert PascalCase to snake_case (matches stringcase.snakecase behavior).""" + s = re.sub(r"([A-Z]+)([A-Z][a-z])", r"\1_\2", name) + s = re.sub(r"([a-z0-9])([A-Z])", r"\1_\2", s) + return s.lower() + + +# 62 pickup actions (pickupable objects from OBJECT_TYPES_WITH_PROPERTIES) +PICKUP_ACTIONS = tuple(sorted( + f"pickup_{_pascal_to_snake(obj_type)}" + for obj_type, props in OBJECT_TYPES_WITH_PROPERTIES.items() + if props["pickupable"] +)) + +# 10 open actions (openable non-pickupable objects) +OPEN_ACTIONS = tuple(sorted( + f"open_by_type_{_pascal_to_snake(obj_type)}" + for obj_type, props in OBJECT_TYPES_WITH_PROPERTIES.items() + if props["openable"] and not props["pickupable"] +)) + +# 12 navigation actions (matching rearrange_base.py order) +NAVIGATION_ACTIONS = ( + "done", + "move_ahead", + "move_left", + "move_right", + "move_back", + "rotate_right", + "rotate_left", + "stand", + "crouch", + "look_up", + "look_down", + "drop_held_object_with_snap", +) + +# Full 84-action space in original order: nav + open + pickup +ALL_ACTIONS = NAVIGATION_ACTIONS + OPEN_ACTIONS + PICKUP_ACTIONS + +# Mappings for bridge +ACTION_NAME_TO_ID = {name: i for i, name in enumerate(ALL_ACTIONS)} +ACTION_ID_TO_NAME = {i: name for i, name in enumerate(ALL_ACTIONS)} + + +def get_action_space() -> list[str]: + """Return the rearrangement discrete action space (84 actions).""" + return list(ALL_ACTIONS) From 928c595a86edd846d4df183dac29953ff0f3b301 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 24 Feb 2026 11:37:21 +0800 Subject: [PATCH 098/239] feat: add rearrangement task YAMLs for 5 splits --- .../ai2thor_rearrangement_2023/_base.yaml | 32 +++++++++++++++++++ .../ai2thor_rearrangement_2023_combined.yaml | 6 ++++ ...ai2thor_rearrangement_2023_eval_train.yaml | 6 ++++ .../ai2thor_rearrangement_2023_test.yaml | 6 ++++ .../ai2thor_rearrangement_2023_train.yaml | 6 ++++ .../ai2thor_rearrangement_2023_val.yaml | 6 ++++ 6 files changed, 62 insertions(+) create mode 100644 easi/tasks/ai2thor_rearrangement_2023/_base.yaml create mode 100644 easi/tasks/ai2thor_rearrangement_2023/ai2thor_rearrangement_2023_combined.yaml create mode 100644 easi/tasks/ai2thor_rearrangement_2023/ai2thor_rearrangement_2023_eval_train.yaml create mode 100644 easi/tasks/ai2thor_rearrangement_2023/ai2thor_rearrangement_2023_test.yaml create mode 100644 easi/tasks/ai2thor_rearrangement_2023/ai2thor_rearrangement_2023_train.yaml create mode 100644 easi/tasks/ai2thor_rearrangement_2023/ai2thor_rearrangement_2023_val.yaml diff --git a/easi/tasks/ai2thor_rearrangement_2023/_base.yaml b/easi/tasks/ai2thor_rearrangement_2023/_base.yaml new file mode 100644 index 0000000..ee6fb8b --- /dev/null +++ b/easi/tasks/ai2thor_rearrangement_2023/_base.yaml @@ -0,0 +1,32 @@ +display_name: "AI2-THOR Rearrangement 2023" +description: "AI2-THOR Rearrangement Challenge — move shuffled objects back to goal poses" +simulator: "ai2thor:v5_0_0" +task_class: "easi.tasks.ai2thor_rearrangement_2023.task.AI2THORRearrangement2023Task" +max_steps: 500 +dataset: + source: huggingface + repo_id: "oscarqjh/ai2thor-rearrangement-2023_easi" + subset: null +simulator_configs: + render_platform: auto + screen_height: 224 + screen_width: 224 + fov: 90 + grid_size: 0.25 + rotate_step_degrees: 90 + visibility_distance: 1.5 + snap_to_grid: true + quality: "Very Low" + # Sensor toggles + use_rgb: true + use_depth: false + use_gps: true +agent: + prompt_builder: "easi.tasks.ai2thor_rearrangement_2023.prompts.AI2THORRearrangement2023PromptBuilder" + prompt_builder_kwargs: + use_feedback: true + chat_history: true + message_window_len: 10 + generation_kwargs: + temperature: 0 + max_tokens: 2048 diff --git a/easi/tasks/ai2thor_rearrangement_2023/ai2thor_rearrangement_2023_combined.yaml b/easi/tasks/ai2thor_rearrangement_2023/ai2thor_rearrangement_2023_combined.yaml new file mode 100644 index 0000000..859d8cd --- /dev/null +++ b/easi/tasks/ai2thor_rearrangement_2023/ai2thor_rearrangement_2023_combined.yaml @@ -0,0 +1,6 @@ +extends: _base.yaml +name: ai2thor_rearrangement_2023_combined +display_name: "AI2-THOR Rearrangement 2023 Combined" +description: "Official evaluation set — 2800 episodes (eval_train + val + test)" +dataset: + split: "combined" diff --git a/easi/tasks/ai2thor_rearrangement_2023/ai2thor_rearrangement_2023_eval_train.yaml b/easi/tasks/ai2thor_rearrangement_2023/ai2thor_rearrangement_2023_eval_train.yaml new file mode 100644 index 0000000..ecd2411 --- /dev/null +++ b/easi/tasks/ai2thor_rearrangement_2023/ai2thor_rearrangement_2023_eval_train.yaml @@ -0,0 +1,6 @@ +extends: _base.yaml +name: ai2thor_rearrangement_2023_eval_train +display_name: "AI2-THOR Rearrangement 2023 Eval-Train" +description: "Evaluation subset of train — 800 episodes across 80 scenes" +dataset: + split: "eval_train" diff --git a/easi/tasks/ai2thor_rearrangement_2023/ai2thor_rearrangement_2023_test.yaml b/easi/tasks/ai2thor_rearrangement_2023/ai2thor_rearrangement_2023_test.yaml new file mode 100644 index 0000000..c6befd3 --- /dev/null +++ b/easi/tasks/ai2thor_rearrangement_2023/ai2thor_rearrangement_2023_test.yaml @@ -0,0 +1,6 @@ +extends: _base.yaml +name: ai2thor_rearrangement_2023_test +display_name: "AI2-THOR Rearrangement 2023 Test" +description: "Test split — 1000 episodes across 20 scenes" +dataset: + split: "test" diff --git a/easi/tasks/ai2thor_rearrangement_2023/ai2thor_rearrangement_2023_train.yaml b/easi/tasks/ai2thor_rearrangement_2023/ai2thor_rearrangement_2023_train.yaml new file mode 100644 index 0000000..a9624b6 --- /dev/null +++ b/easi/tasks/ai2thor_rearrangement_2023/ai2thor_rearrangement_2023_train.yaml @@ -0,0 +1,6 @@ +extends: _base.yaml +name: ai2thor_rearrangement_2023_train +display_name: "AI2-THOR Rearrangement 2023 Train" +description: "Training split — 4000 episodes across 80 scenes" +dataset: + split: "train" diff --git a/easi/tasks/ai2thor_rearrangement_2023/ai2thor_rearrangement_2023_val.yaml b/easi/tasks/ai2thor_rearrangement_2023/ai2thor_rearrangement_2023_val.yaml new file mode 100644 index 0000000..a3bb356 --- /dev/null +++ b/easi/tasks/ai2thor_rearrangement_2023/ai2thor_rearrangement_2023_val.yaml @@ -0,0 +1,6 @@ +extends: _base.yaml +name: ai2thor_rearrangement_2023_val +display_name: "AI2-THOR Rearrangement 2023 Validation" +description: "Validation split — 1000 episodes across 20 scenes" +dataset: + split: "val" From 9bdb41e9725c3ab22858d9c067e834db0b4f13ee Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 24 Feb 2026 11:49:40 +0800 Subject: [PATCH 099/239] feat: add AI2THORRearrangement2023Task with 6 paper metrics --- easi/tasks/ai2thor_rearrangement_2023/task.py | 151 +++++++++++++ tests/test_ai2thor_rearrangement_2023_task.py | 198 ++++++++++++++++++ 2 files changed, 349 insertions(+) create mode 100644 easi/tasks/ai2thor_rearrangement_2023/task.py create mode 100644 tests/test_ai2thor_rearrangement_2023_task.py diff --git a/easi/tasks/ai2thor_rearrangement_2023/task.py b/easi/tasks/ai2thor_rearrangement_2023/task.py new file mode 100644 index 0000000..8c3c5e1 --- /dev/null +++ b/easi/tasks/ai2thor_rearrangement_2023/task.py @@ -0,0 +1,151 @@ +"""AI2-THOR Rearrangement task for EASI. + +Adapts the AI2-THOR Rearrangement Challenge to EASI's task interface. +Supports 5 splits via per-split YAML configs. +Computes all 6 paper metrics: SR, SRwD, PuSR, PuLen, SuLen, Len. +""" +from __future__ import annotations + +import json +import math +from pathlib import Path + +from easi.core.base_task import BaseTask +from easi.core.episode import EpisodeRecord, StepResult +from easi.tasks.ai2thor_rearrangement_2023.actions import get_action_space +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + + +class AI2THORRearrangement2023Task(BaseTask): + """AI2-THOR Rearrangement Challenge task.""" + + def _build_action_space(self) -> list[str]: + return get_action_space() + + def get_task_yaml_path(self) -> Path: + return Path(__file__).parent / "ai2thor_rearrangement_2023_val.yaml" + + def get_bridge_script_path(self) -> Path: + return Path(__file__).parent / "bridge.py" + + def get_instruction(self, episode: dict) -> str: + return episode.get( + "instruction", + "Rearrange objects to match the goal configuration.", + ) + + def format_reset_config(self, episode: dict) -> dict: + """Convert HF dataset row to bridge reset config. + + Complex fields (poses, openable_data) stay as JSON strings; + the bridge deserializes them. + """ + return { + "episode_id": episode.get("id", "unknown"), + "scene": episode["scene"], + "instruction": self.get_instruction(episode), + "agent_position": episode["agent_position"], # JSON string + "agent_rotation": episode["agent_rotation"], # int + "starting_poses": episode["starting_poses"], # JSON string + "target_poses": episode["target_poses"], # JSON string + "openable_data": episode["openable_data"], # JSON string + } + + def evaluate_episode( + self, episode: dict, trajectory: list[StepResult] + ) -> dict[str, float]: + """Extract the 6 paper metrics from trajectory. + + The bridge reports rearrangement metrics in StepResult.info. + Pickup metrics (PuSR, PuLen) are computed from the action log. + """ + if not trajectory: + return { + "success": 0.0, + "prop_fixed_strict": 0.0, + "energy_prop": 1.0, + "num_steps": 0.0, + "pickup_success_rate": 0.0, + "num_pickup_actions": 0.0, + "success_length": float("nan"), + } + + last_info = trajectory[-1].info or {} + success = float(last_info.get("success", 0.0)) + num_steps = float(len(trajectory)) + + # Compute PuSR and PuLen from action log in trajectory + pickup_total = 0 + pickup_success = 0 + for step in trajectory: + info = step.info or {} + action_name = info.get("action_name", "") + if action_name.startswith("pickup_"): + pickup_total += 1 + if info.get("action_success", False): + pickup_success += 1 + + pickup_success_rate = ( + pickup_success / pickup_total if pickup_total > 0 else 0.0 + ) + + # SuLen: episode length IF successful, else NaN + success_length = num_steps if success > 0.5 else float("nan") + + return { + "success": success, + "prop_fixed_strict": float(last_info.get("prop_fixed_strict", 0.0)), + "energy_prop": float(last_info.get("energy_prop", 1.0)), + "num_steps": num_steps, + "pickup_success_rate": pickup_success_rate, + "num_pickup_actions": float(pickup_total), + "success_length": success_length, + } + + def aggregate_results( + self, records: list[EpisodeRecord] + ) -> dict[str, float]: + """Aggregate metrics across episodes. + + Handles NaN success_length (only averages successful episodes). + """ + if not records: + return {} + + # Collect all metric keys + all_keys = set() + for r in records: + all_keys.update(r.episode_results.keys()) + + agg = {} + for key in sorted(all_keys): + values = [] + for r in records: + v = r.episode_results.get(key) + if v is not None and not (isinstance(v, float) and math.isnan(v)): + values.append(v) + if values: + agg[key] = sum(values) / len(values) + else: + agg[key] = float("nan") + + return agg + + def _get_builtin_episodes(self) -> list[dict]: + """Minimal episodes for testing without dataset download.""" + return [ + { + "id": "FloorPlan21__smoke_test", + "scene": "FloorPlan21", + "agent_position": json.dumps( + {"x": -1.0, "y": 0.87, "z": -1.0} + ), + "agent_rotation": 0, + "starting_poses": json.dumps([]), + "target_poses": json.dumps([]), + "openable_data": json.dumps([]), + "instruction": "Smoke test — no objects to rearrange.", + }, + ] diff --git a/tests/test_ai2thor_rearrangement_2023_task.py b/tests/test_ai2thor_rearrangement_2023_task.py new file mode 100644 index 0000000..a11d4f2 --- /dev/null +++ b/tests/test_ai2thor_rearrangement_2023_task.py @@ -0,0 +1,198 @@ +"""Tests for AI2THORRearrangement2023Task (offline, no simulator).""" +from __future__ import annotations + +import json +import math +from pathlib import Path + +import pytest + +from easi.core.episode import EpisodeRecord, Observation, StepResult + + +def _make_task(): + from easi.tasks.ai2thor_rearrangement_2023.task import AI2THORRearrangement2023Task + return AI2THORRearrangement2023Task( + split_yaml_path=Path(__file__).resolve().parent.parent + / "easi" / "tasks" / "ai2thor_rearrangement_2023" / "ai2thor_rearrangement_2023_val.yaml" + ) + + +class TestTaskRegistration: + def test_all_splits_registered(self): + from easi.tasks.registry import list_tasks + tasks = list_tasks() + for split in ("train", "eval_train", "val", "test", "combined"): + assert f"ai2thor_rearrangement_2023_{split}" in tasks + + def test_simulator_key(self): + from easi.tasks.registry import get_task_entry + entry = get_task_entry("ai2thor_rearrangement_2023_val") + assert entry.simulator_key == "ai2thor:v5_0_0" + + def test_max_steps(self): + from easi.tasks.registry import get_task_entry + entry = get_task_entry("ai2thor_rearrangement_2023_val") + assert entry.max_steps == 500 + + +class TestActionSpace: + def test_action_count(self): + task = _make_task() + assert len(task.action_space) == 84 + + def test_no_duplicates(self): + task = _make_task() + assert len(task.action_space) == len(set(task.action_space)) + + def test_key_actions_present(self): + task = _make_task() + actions = set(task.action_space) + assert "done" in actions + assert "move_ahead" in actions + assert "drop_held_object_with_snap" in actions + assert "pickup_bowl" in actions + assert "open_by_type_fridge" in actions + assert "open_by_type_cabinet" in actions + + +class TestFormatResetConfig: + def test_basic_format(self): + task = _make_task() + episode = { + "id": "FloorPlan21__0", + "scene": "FloorPlan21", + "agent_position": json.dumps({"x": -1.0, "y": 0.87, "z": -1.0}), + "agent_rotation": 30, + "starting_poses": json.dumps([{"name": "Bowl_123", "position": {"x": 0, "y": 1, "z": 0}}]), + "target_poses": json.dumps([{"name": "Bowl_123", "position": {"x": 1, "y": 1, "z": 1}}]), + "openable_data": json.dumps([]), + "instruction": "Move the Bowl back.", + } + config = task.format_reset_config(episode) + assert config["scene"] == "FloorPlan21" + assert config["agent_rotation"] == 30 + # JSON strings passed through to bridge + assert isinstance(config["starting_poses"], str) + assert isinstance(config["target_poses"], str) + + +class TestEvaluateEpisode: + def test_success_metrics(self): + task = _make_task() + episode = {"id": "test"} + obs = Observation(rgb_path="/tmp/fake.png") + trajectory = [ + StepResult(observation=obs, reward=0.0, done=False, info={ + "action_name": "move_ahead", "action_success": True, + }), + StepResult(observation=obs, reward=0.0, done=False, info={ + "action_name": "pickup_bowl", "action_success": True, + }), + StepResult(observation=obs, reward=0.0, done=True, info={ + "success": 1.0, "prop_fixed_strict": 1.0, "energy_prop": 0.0, + "num_initially_misplaced": 1, "num_fixed": 1, + "num_newly_misplaced": 0, "num_broken": 0, + "action_name": "done", "action_success": True, + }), + ] + metrics = task.evaluate_episode(episode, trajectory) + assert metrics["success"] == 1.0 + assert metrics["prop_fixed_strict"] == 1.0 + assert metrics["num_steps"] == 3.0 + # PuSR: 1 successful pickup / 1 total pickup = 1.0 + assert metrics["pickup_success_rate"] == 1.0 + # PuLen: 1 pickup action + assert metrics["num_pickup_actions"] == 1.0 + # SuLen: episode succeeded, so success_length = 3 + assert metrics["success_length"] == 3.0 + + def test_failed_episode(self): + task = _make_task() + obs = Observation(rgb_path="/tmp/fake.png") + trajectory = [ + StepResult(observation=obs, reward=0.0, done=True, info={ + "success": 0.0, "prop_fixed_strict": 0.0, "energy_prop": 0.8, + "num_initially_misplaced": 2, "num_fixed": 0, + "num_newly_misplaced": 0, "num_broken": 0, + "action_name": "done", "action_success": True, + }), + ] + metrics = task.evaluate_episode({"id": "test"}, trajectory) + assert metrics["success"] == 0.0 + # SuLen should be NaN for failed episodes + assert math.isnan(metrics["success_length"]) + + def test_empty_trajectory(self): + task = _make_task() + metrics = task.evaluate_episode({"id": "test"}, []) + assert metrics["success"] == 0.0 + assert metrics["num_steps"] == 0.0 + + def test_pickup_success_rate_partial(self): + task = _make_task() + obs = Observation(rgb_path="/tmp/fake.png") + trajectory = [ + StepResult(observation=obs, info={ + "action_name": "pickup_bowl", "action_success": False, + }), + StepResult(observation=obs, info={ + "action_name": "pickup_bowl", "action_success": True, + }), + StepResult(observation=obs, done=True, info={ + "success": 0.0, "prop_fixed_strict": 0.0, + "action_name": "done", "action_success": True, + }), + ] + metrics = task.evaluate_episode({"id": "test"}, trajectory) + # PuSR: 1 success / 2 total = 0.5 + assert metrics["pickup_success_rate"] == 0.5 + assert metrics["num_pickup_actions"] == 2.0 + + +class TestGetInstruction: + def test_uses_episode_instruction(self): + task = _make_task() + episode = {"instruction": "Move the Bowl back to the counter."} + assert task.get_instruction(episode) == "Move the Bowl back to the counter." + + +class TestBuiltinEpisodes: + def test_has_builtin(self): + task = _make_task() + episodes = task._get_builtin_episodes() + assert len(episodes) >= 1 + assert "scene" in episodes[0] + assert "instruction" in episodes[0] + + +class TestAggregateResults: + def test_aggregate_filters_nan_success_length(self): + """success_length NaN values should not affect the average.""" + task = _make_task() + + records = [] + # Successful episode (3 steps) + records.append(EpisodeRecord( + episode={"id": "ep1"}, trajectory=[], + episode_results={ + "success": 1.0, "prop_fixed_strict": 1.0, + "num_steps": 3.0, "success_length": 3.0, + "pickup_success_rate": 1.0, "num_pickup_actions": 1.0, + }, + )) + # Failed episode (5 steps, NaN success_length) + records.append(EpisodeRecord( + episode={"id": "ep2"}, trajectory=[], + episode_results={ + "success": 0.0, "prop_fixed_strict": 0.0, + "num_steps": 5.0, "success_length": float("nan"), + "pickup_success_rate": 0.0, "num_pickup_actions": 2.0, + }, + )) + + agg = task.aggregate_results(records) + assert agg["success"] == 0.5 # 1/2 + assert agg["num_steps"] == 4.0 # (3+5)/2 + # success_length should average only non-NaN: 3.0 + assert agg["success_length"] == 3.0 From 4f3fdab66a814957e371236473a87555e8f20284 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 24 Feb 2026 12:14:14 +0800 Subject: [PATCH 100/239] feat: add rearrangement bridge and prompt builder Bridge: SNAP mode wrapper with 84 discrete actions, sensor toggles, PuSR/PuLen tracking, and pose energy metrics at episode end. Prompts: Multi-modal prompt builder with GPS overlay, action planning, and structured JSON output parsing. --- .../ai2thor_rearrangement_2023/bridge.py | 316 ++++++++++++++++++ .../ai2thor_rearrangement_2023/prompts.py | 205 ++++++++++++ 2 files changed, 521 insertions(+) create mode 100644 easi/tasks/ai2thor_rearrangement_2023/bridge.py create mode 100644 easi/tasks/ai2thor_rearrangement_2023/prompts.py diff --git a/easi/tasks/ai2thor_rearrangement_2023/bridge.py b/easi/tasks/ai2thor_rearrangement_2023/bridge.py new file mode 100644 index 0000000..66b7cd2 --- /dev/null +++ b/easi/tasks/ai2thor_rearrangement_2023/bridge.py @@ -0,0 +1,316 @@ +"""EASI bridge for AI2-THOR Rearrangement Challenge. + +Runs inside the easi_ai2thor_v5_0_0 conda env (Python 3.10). +Wraps RearrangeTHOREnvironment in SNAP mode with 84 discrete actions. +""" +from __future__ import annotations + +import json +import re +import sys +from pathlib import Path + +import numpy as np + +_repo_root = Path(__file__).resolve().parents[3] +if str(_repo_root) not in sys.path: + sys.path.insert(0, str(_repo_root)) + +from easi.simulators.base_bridge import BaseBridge + + +def _snake_to_pascal(name: str) -> str: + """Convert snake_case to PascalCase (inverse of _pascal_to_snake in actions.py).""" + return "".join(word.capitalize() for word in name.split("_")) + + +class AI2THORRearrangement2023Bridge(BaseBridge): + """Bridge for 1-phase rearrangement (unshuffle only, SNAP mode).""" + + def _create_env(self, reset_config, simulator_kwargs): + from easi.tasks.ai2thor_rearrangement_2023.vendor.rearrange.environment import ( + RearrangeTHOREnvironment, + RearrangeMode, + ) + + screen_h = simulator_kwargs.get("screen_height", 224) + screen_w = simulator_kwargs.get("screen_width", 224) + fov = simulator_kwargs.get("fov", 90) + grid_size = simulator_kwargs.get("grid_size", 0.25) + rotate_step = simulator_kwargs.get("rotate_step_degrees", 90) + vis_dist = simulator_kwargs.get("visibility_distance", 1.5) + snap = simulator_kwargs.get("snap_to_grid", True) + quality = simulator_kwargs.get("quality", "Very Low") + + # Sensor toggles + self._use_rgb = simulator_kwargs.get("use_rgb", True) + self._use_depth = simulator_kwargs.get("use_depth", False) + self._use_gps = simulator_kwargs.get("use_gps", True) + + controller_kwargs = { + "width": screen_w, + "height": screen_h, + "fieldOfView": fov, + "gridSize": grid_size, + "rotateStepDegrees": rotate_step, + "visibilityDistance": vis_dist, + "snapToGrid": snap, + "quality": quality, + "renderDepthImage": self._use_depth, + "fastActionEmit": True, + } + + env = RearrangeTHOREnvironment( + mode=RearrangeMode.SNAP, + controller_kwargs=controller_kwargs, + force_cache_reset=True, + ) + + # Action tracking for PuSR/PuLen metrics + self._actions_taken = [] + self._actions_taken_success = [] + + return env + + def _on_reset(self, env, reset_config): + from easi.tasks.ai2thor_rearrangement_2023.vendor.rearrange.environment import ( + RearrangeTaskSpec, + ) + + # Deserialize JSON strings from HF dataset + agent_position = json.loads(reset_config["agent_position"]) if isinstance( + reset_config["agent_position"], str + ) else reset_config["agent_position"] + starting_poses = json.loads(reset_config["starting_poses"]) if isinstance( + reset_config["starting_poses"], str + ) else reset_config["starting_poses"] + target_poses = json.loads(reset_config["target_poses"]) if isinstance( + reset_config["target_poses"], str + ) else reset_config["target_poses"] + openable_data = json.loads(reset_config["openable_data"]) if isinstance( + reset_config["openable_data"], str + ) else reset_config["openable_data"] + + task_spec = RearrangeTaskSpec( + scene=reset_config["scene"], + agent_position=agent_position, + agent_rotation=int(reset_config["agent_rotation"]), + starting_poses=starting_poses, + target_poses=target_poses, + openable_data=openable_data, + stage="eval", + ) + + # Reset (walkthrough phase) then immediately shuffle (unshuffle phase) + env.reset(task_spec=task_spec, force_axis_aligned_start=True) + env.shuffle() + + # Clear action tracking + self._actions_taken = [] + self._actions_taken_success = [] + + # No-op to get initial state + return env.step("Pass") + + def _on_step(self, env, action_text): + action_name = action_text.strip().lower() + + action_success = False + + if action_name == "done": + # Episode end — compute final metrics + info = self._build_info(env, action_name, True) + info.update(self._compute_final_metrics(env)) + rgb = self._get_rgb(env) + return rgb, 0.0, True, info + + elif action_name in ( + "move_ahead", "move_left", "move_right", "move_back", + "rotate_right", "rotate_left", "stand", "crouch", + "look_up", "look_down", + ): + action_success = getattr(env, action_name)() + + elif action_name == "drop_held_object_with_snap": + action_success = env.drop_held_object_with_snap() + + elif action_name.startswith("pickup_"): + action_success = self._do_pickup(env, action_name) + + elif action_name.startswith("open_by_type_"): + action_success = self._do_open(env, action_name) + + # Track actions for PuSR/PuLen + self._actions_taken.append(action_name) + self._actions_taken_success.append(action_success) + + rgb = self._get_rgb(env) + info = self._build_info(env, action_name, action_success) + feedback = "success" if action_success else "action failed" + info["feedback"] = feedback + + return rgb, 0.0, False, info + + def _do_pickup(self, env, action_name: str) -> bool: + """Pick up the nearest visible object of the given type.""" + from easi.tasks.ai2thor_rearrangement_2023.vendor.rearrange.environment import ( + include_object_data, + ) + + with include_object_data(env.controller): + metadata = env.last_event.metadata + + if len(metadata["inventoryObjects"]) != 0: + return False + + # Convert snake_case action to PascalCase object type + obj_type_snake = action_name.replace("pickup_", "") + obj_type = _snake_to_pascal(obj_type_snake) + + candidates = [ + o for o in metadata["objects"] + if o["visible"] and o["objectType"] == obj_type + ] + if not candidates: + return False + + # Sort by distance, pick closest + candidates.sort(key=lambda o: (o["distance"], o["name"])) + obj_id = candidates[0]["objectId"] + + env.controller.step( + "PickupObject", + objectId=obj_id, + **env.physics_step_kwargs, + ) + return env.controller.last_event.metadata["lastActionSuccess"] + + def _do_open(self, env, action_name: str) -> bool: + """Toggle openness of the nearest visible openable object.""" + from easi.tasks.ai2thor_rearrangement_2023.vendor.rearrange.environment import ( + include_object_data, + ) + + with include_object_data(env.controller): + metadata = env.last_event.metadata + + obj_type_snake = action_name.replace("open_by_type_", "") + obj_type = _snake_to_pascal(obj_type_snake) + + candidates = [ + o for o in metadata["objects"] + if o["visible"] and o["objectType"] == obj_type and o["openable"] + ] + if not candidates: + return False + + # Sort by distance, pick closest + candidates.sort(key=lambda o: (o["distance"], o["name"])) + obj = candidates[0] + + # Toggle: if open -> close, if closed -> open + if obj["isOpen"] or obj.get("openness", 0) > 0.5: + env.controller.step( + "CloseObject", + objectId=obj["objectId"], + **env.physics_step_kwargs, + ) + else: + env.controller.step( + "OpenObject", + objectId=obj["objectId"], + openness=1.0, + **env.physics_step_kwargs, + ) + return env.controller.last_event.metadata["lastActionSuccess"] + + def _get_rgb(self, env) -> np.ndarray: + """Extract RGB frame from environment.""" + return env.last_event.frame.copy() + + def _build_info(self, env, action_name: str, action_success: bool) -> dict: + """Build info dict with sensor data and action feedback.""" + info = { + "action_name": action_name, + "action_success": action_success, + } + + # GPS data (if enabled) + if self._use_gps: + loc = env.get_agent_location() + info["agent_x"] = float(loc["x"]) + info["agent_y"] = float(loc["y"]) + info["agent_z"] = float(loc["z"]) + info["agent_rotation"] = float(loc["rotation"]) + info["agent_horizon"] = float(loc["horizon"]) + info["agent_standing"] = float(loc.get("standing", True)) + + # Held object info + held = env.held_object + info["held_object"] = held["objectType"] if held else "none" + + return info + + def _compute_final_metrics(self, env) -> dict: + """Compute rearrangement metrics at episode end (when done is called).""" + ips, gps, cps = env.poses + + start_energies = env.pose_difference_energy(gps, ips) + end_energies = env.pose_difference_energy(gps, cps) + start_energy = float(start_energies.sum()) + end_energy = float(end_energies.sum()) + + start_misplaced = start_energies > 0.0 + end_misplaced = end_energies > 0.0 + + num_initially_misplaced = int(start_misplaced.sum()) + num_fixed = int( + num_initially_misplaced - (start_misplaced & end_misplaced).sum() + ) + num_newly_misplaced = int( + (end_misplaced & ~start_misplaced).sum() + ) + num_broken = sum(1 for cp in cps if cp.get("broken", False)) + + prop_fixed = ( + 1.0 + if num_initially_misplaced == 0 + else num_fixed / num_initially_misplaced + ) + + return { + "success": float(end_energy == 0), + "prop_fixed_strict": float( + (num_newly_misplaced == 0) * prop_fixed + ), + "energy_prop": ( + end_energy / start_energy if start_energy > 0 else 0.0 + ), + "start_energy": start_energy, + "end_energy": end_energy, + "num_initially_misplaced": num_initially_misplaced, + "num_fixed": num_fixed, + "num_newly_misplaced": num_newly_misplaced, + "num_broken": num_broken, + } + + def _extract_image(self, obs): + """Extract RGB from observation.""" + if isinstance(obs, np.ndarray): + return obs + if isinstance(obs, tuple): + return obs[0] + return obs + + def _extract_info(self, info): + """Filter info to JSON-serializable types.""" + if not isinstance(info, dict): + return {} + return { + k: v for k, v in info.items() + if isinstance(v, (int, float, str, bool)) + } + + +if __name__ == "__main__": + AI2THORRearrangement2023Bridge.main() diff --git a/easi/tasks/ai2thor_rearrangement_2023/prompts.py b/easi/tasks/ai2thor_rearrangement_2023/prompts.py new file mode 100644 index 0000000..f9b1c81 --- /dev/null +++ b/easi/tasks/ai2thor_rearrangement_2023/prompts.py @@ -0,0 +1,205 @@ +"""Prompt builder for AI2-THOR Rearrangement task. + +Constructs multi-modal prompts with observation images, GPS data, +action history, and structured JSON output schema. +""" +from __future__ import annotations + +import json + +from easi.agents.prompt_builder import _encode_image_base64, validate_action_name +from easi.core.episode import Action +from easi.core.memory import AgentMemory +from easi.utils.json_repair import fix_json +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + +SYSTEM_PROMPT = """\ +You are an embodied AI agent performing an object rearrangement task in a 3D indoor environment (AI2-THOR). + +## Goal +{instruction} + +## Available Actions +{action_list} + +## Action Types +- **Navigation**: move_ahead (0.25m step), move_left/right/back, rotate_left/right (90°), look_up/look_down (30°), stand/crouch +- **Pickup**: pickup_ — picks up the nearest visible object of that type (must have empty hands) +- **Drop**: drop_held_object_with_snap — drops held object, snapping to goal position if close enough +- **Open/Close**: open_by_type_ — toggles openness of nearest visible cabinet/drawer/fridge/etc. +- **Done**: Signal that all objects are in their correct positions + +## Strategy +1. Look around to survey the scene and identify misplaced objects +2. Navigate to a misplaced object +3. Pick it up with the matching pickup_ action +4. Navigate toward where it belongs (near its goal position) +5. Use drop_held_object_with_snap — it snaps the object to its goal if you're close +6. Repeat for remaining misplaced objects +7. Say "done" when finished + +## Rules +- You can hold only ONE object at a time +- drop_held_object_with_snap works best when the goal location is visible +- If pickup fails, try moving closer or adjusting your view angle +- Grid movement: 0.25m steps, 90° rotations +""" + +OUTPUT_SCHEMA = """\ +Respond in this exact JSON format: +```json +{{ + "observation": "describe what you see", + "reasoning": "what to do next and why", + "plan": [ + {{"action_name": ""}}, + ... + ] +}} +``` +Plan 1-5 actions. Use exact action names from the list above.""" + + +class AI2THORRearrangement2023PromptBuilder: + """Prompt builder for the rearrangement task.""" + + def __init__( + self, + use_feedback: bool = True, + chat_history: bool = True, + message_window_len: int = 10, + **kwargs, + ): + self.use_feedback = use_feedback + self.chat_history = chat_history + self.message_window_len = message_window_len + self._action_list_str = "" + self._action_name_set: set[str] = set() + + def set_action_space(self, actions: list[str]): + self._action_name_set = set(actions) + parts = [f"{i}: {a}" for i, a in enumerate(actions)] + self._action_list_str = ", ".join(parts) + + def build_messages(self, memory: AgentMemory) -> list[dict]: + instruction = memory.task_description or "Rearrange objects to match the goal." + + system_text = SYSTEM_PROMPT.format( + instruction=instruction, + action_list=self._action_list_str, + ) + + messages = [{"role": "system", "content": system_text}] + + # Chat history (if enabled) + if self.chat_history and memory.steps: + window = memory.steps[-self.message_window_len:] + for step in window: + user_content = self._make_history_content(step) + messages.append({"role": "user", "content": user_content}) + if step.llm_response: + messages.append({ + "role": "assistant", + "content": step.llm_response, + }) + + # Current turn + user_content = self._make_current_content(memory) + messages.append({"role": "user", "content": user_content}) + + return messages + + def _make_history_content(self, step) -> list[dict]: + content = [] + if step.observation and step.observation.rgb_path: + img_url = _encode_image_base64(step.observation.rgb_path) + if img_url: + content.append({ + "type": "image_url", + "image_url": {"url": img_url}, + }) + + text = "Observation." + if self.use_feedback and step.feedback: + text += f"\nFeedback: {step.feedback}" + + # GPS overlay from step info + gps_text = self._format_gps(step.info) + if gps_text: + text += f"\n{gps_text}" + + content.append({"type": "text", "text": text}) + return content + + def _make_current_content(self, memory: AgentMemory) -> list[dict]: + content = [] + + if memory.current_observation and memory.current_observation.rgb_path: + img_url = _encode_image_base64(memory.current_observation.rgb_path) + if img_url: + content.append({ + "type": "image_url", + "image_url": {"url": img_url}, + }) + + if memory.is_first_turn: + text = "First observation. Begin the rearrangement task." + else: + text = "Current observation." + if self.use_feedback and memory.steps: + last = memory.steps[-1] + if last.feedback: + text += f"\nFeedback: {last.feedback}" + + # GPS from last step info + if memory.steps: + gps_text = self._format_gps(memory.steps[-1].info) + if gps_text: + text += f"\n{gps_text}" + held = (memory.steps[-1].info or {}).get("held_object", "none") + text += f"\nHolding: {held}" + + text += f"\n\n{OUTPUT_SCHEMA}" + content.append({"type": "text", "text": text}) + return content + + def _format_gps(self, info: dict | None) -> str: + if not info: + return "" + parts = [] + if "agent_x" in info: + parts.append( + f"Position: ({info['agent_x']:.2f}, {info['agent_y']:.2f}, {info['agent_z']:.2f})" + ) + if "agent_rotation" in info: + parts.append(f"Rotation: {info['agent_rotation']:.0f}") + if "agent_horizon" in info: + parts.append(f"Horizon: {info['agent_horizon']:.0f}") + return "GPS: " + ", ".join(parts) if parts else "" + + def parse_response( + self, llm_response: str, memory: AgentMemory + ) -> list[Action]: + try: + fixed = fix_json(llm_response) + data = json.loads(fixed) + except (json.JSONDecodeError, ValueError): + logger.warning("Failed to parse LLM response as JSON") + return [Action(action_name="done")] + + plan = data.get("plan", []) + if not plan: + return [Action(action_name="done")] + + actions = [] + for entry in plan: + name = entry.get("action_name", "") + if name in self._action_name_set: + actions.append(Action(action_name=name)) + else: + logger.warning("Unknown action '%s', stopping plan", name) + break + + return actions if actions else [Action(action_name="done")] From c6a78c857642a1f73a4c2350a30a8b37cce31fa4 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 24 Feb 2026 12:19:31 +0800 Subject: [PATCH 101/239] test: add prompt builder and bridge path tests --- tests/test_ai2thor_rearrangement_2023_task.py | 61 +++++++++++++++++++ 1 file changed, 61 insertions(+) diff --git a/tests/test_ai2thor_rearrangement_2023_task.py b/tests/test_ai2thor_rearrangement_2023_task.py index a11d4f2..984c3f5 100644 --- a/tests/test_ai2thor_rearrangement_2023_task.py +++ b/tests/test_ai2thor_rearrangement_2023_task.py @@ -196,3 +196,64 @@ def test_aggregate_filters_nan_success_length(self): assert agg["num_steps"] == 4.0 # (3+5)/2 # success_length should average only non-NaN: 3.0 assert agg["success_length"] == 3.0 + + +class TestPromptBuilder: + def _make_builder(self): + from easi.tasks.ai2thor_rearrangement_2023.prompts import AI2THORRearrangement2023PromptBuilder + builder = AI2THORRearrangement2023PromptBuilder() + builder.set_action_space(["done", "move_ahead", "pickup_bowl"]) + return builder + + def test_set_action_space(self): + builder = self._make_builder() + assert "done" in builder._action_name_set + assert "move_ahead" in builder._action_name_set + assert "pickup_bowl" in builder._action_name_set + + def test_parse_valid_response(self): + from easi.core.memory import AgentMemory + builder = self._make_builder() + memory = AgentMemory() + response = json.dumps({ + "observation": "I see a kitchen", + "reasoning": "Need to find bowl", + "plan": [ + {"action_name": "move_ahead"}, + {"action_name": "pickup_bowl"}, + ] + }) + actions = builder.parse_response(response, memory) + assert len(actions) == 2 + assert actions[0].action_name == "move_ahead" + assert actions[1].action_name == "pickup_bowl" + + def test_parse_invalid_response_returns_done(self): + from easi.core.memory import AgentMemory + builder = self._make_builder() + memory = AgentMemory() + actions = builder.parse_response("not json", memory) + assert len(actions) == 1 + assert actions[0].action_name == "done" + + def test_parse_unknown_action_stops_plan(self): + from easi.core.memory import AgentMemory + builder = self._make_builder() + memory = AgentMemory() + response = json.dumps({ + "plan": [ + {"action_name": "move_ahead"}, + {"action_name": "fly_away"}, + ] + }) + actions = builder.parse_response(response, memory) + assert len(actions) == 1 + assert actions[0].action_name == "move_ahead" + + +class TestBridgeScript: + def test_bridge_path_exists(self): + task = _make_task() + bridge_path = task.get_bridge_script_path() + assert bridge_path.exists() + assert bridge_path.name == "bridge.py" From 1d2cf4ae6e3c311a89e976d4c7849a5606230f5b Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 24 Feb 2026 13:12:55 +0800 Subject: [PATCH 102/239] fix: use correct argparse dest name in e2e test Namespaces The CLI argparse uses dest="refresh_data" but the test Namespaces used "redownload", which leaked through **run_kwargs into EvaluationRunner.__init__() as an unexpected keyword argument. --- tests/test_e2e_evaluation.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tests/test_e2e_evaluation.py b/tests/test_e2e_evaluation.py index bf72477..8a53b21 100644 --- a/tests/test_e2e_evaluation.py +++ b/tests/test_e2e_evaluation.py @@ -524,7 +524,7 @@ def test_resume_loads_config_from_run_dir(self, tmp_path): llm_kwargs_raw=None, max_retries=None, resume_dir=str(run_dir), - redownload=False, + refresh_data=False, ) cmd_start(args) @@ -632,7 +632,7 @@ def test_sequential_single_task_via_flag(self, tmp_path): llm_kwargs_raw=None, max_retries=None, resume_dir=None, - redownload=False, + refresh_data=False, ) cmd_start(args) @@ -663,7 +663,7 @@ def test_resume_blocked_with_multi_task(self): llm_kwargs_raw=None, max_retries=None, resume_dir="/tmp/some/path", - redownload=False, + refresh_data=False, ) with pytest.raises(SystemExit): cmd_start(args) From 17494cea7faea7fe958fea38dda48af51ee90fea Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 24 Feb 2026 13:13:53 +0800 Subject: [PATCH 103/239] fix: remove xvfb system deps --- easi/simulators/ai2thor/v5_0_0/env_manager.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/easi/simulators/ai2thor/v5_0_0/env_manager.py b/easi/simulators/ai2thor/v5_0_0/env_manager.py index 0e18488..842359f 100644 --- a/easi/simulators/ai2thor/v5_0_0/env_manager.py +++ b/easi/simulators/ai2thor/v5_0_0/env_manager.py @@ -37,7 +37,7 @@ def get_requirements_txt_path(self) -> Path: return Path(__file__).parent / "requirements.txt" def get_system_deps(self) -> list[str]: - return ["conda", "xvfb"] + return ["conda"] def get_validation_import(self) -> str: return "import ai2thor; assert ai2thor.__version__.startswith('5.')" From 08123bd19772bb34c5478bc4ac9f4fa0fcf2f529 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 24 Feb 2026 15:20:35 +0800 Subject: [PATCH 104/239] fix: OmniGibson simulator now passes smoke test --- easi/simulators/omnigibson/manifest.yaml | 3 ++ easi/simulators/omnigibson/v3_7_2/bridge.py | 30 +++++++++++-------- .../omnigibson/v3_7_2/env_manager.py | 24 ++++++++++++--- tests/test_omnigibson_simulator.py | 23 ++++++++++---- tests/test_render_platform.py | 2 +- 5 files changed, 60 insertions(+), 22 deletions(-) diff --git a/easi/simulators/omnigibson/manifest.yaml b/easi/simulators/omnigibson/manifest.yaml index fb63093..1d9c54b 100644 --- a/easi/simulators/omnigibson/manifest.yaml +++ b/easi/simulators/omnigibson/manifest.yaml @@ -7,6 +7,9 @@ versions: simulator_class: "easi.simulators.omnigibson.v3_7_2.simulator.OmniGibsonSimulator" env_manager_class: "easi.simulators.omnigibson.v3_7_2.env_manager.OmniGibsonEnvManager" python_version: "3.10" + render_platforms: + native: "easi.simulators.omnigibson.v3_7_2.render_platforms.OmniGibsonNativePlatform" + auto: "easi.simulators.omnigibson.v3_7_2.render_platforms.OmniGibsonAutoPlatform" installation_kwargs: cuda_version: "12.4" behavior_1k_repo: "https://github.com/StanfordVL/BEHAVIOR-1K.git" diff --git a/easi/simulators/omnigibson/v3_7_2/bridge.py b/easi/simulators/omnigibson/v3_7_2/bridge.py index 457aec5..d5f513b 100644 --- a/easi/simulators/omnigibson/v3_7_2/bridge.py +++ b/easi/simulators/omnigibson/v3_7_2/bridge.py @@ -4,11 +4,15 @@ It communicates with the parent process via filesystem IPC. Provides an OmniGibsonBridge that handles: -- Minimal OmniGibson environment creation (Scene + DummyTask, no dataset) +- Minimal OmniGibson environment creation (Scene + DummyTask, no dataset required) - Action execution and observation capture - Main IPC loop (reset/step/close) -Headless mode is enabled via OMNIGIBSON_HEADLESS=1 env var (set by env_manager). +Rendering mode is controlled via OMNIGIBSON_HEADLESS env var, set by the active +render platform class (OmniGibsonNativePlatform or OmniGibsonAutoPlatform). + +Note: Isaac Sim takes 60-120 seconds to start on first launch. Use --timeout 300 +or more when running easi sim test for OmniGibson. Usage: python bridge.py --workspace /tmp/easi_xxx [--simulator-kwargs '{}'] @@ -36,6 +40,10 @@ class OmniGibsonBridge(BaseBridge): def _create_env(self, reset_config, simulator_kwargs): """Create minimal OmniGibson env (no dataset needed). + The default smoke-test config uses an empty scene (no robots) so that + the omnigibson-robot-assets dataset is not required. Pass a full + og_config in simulator_kwargs to use robots in production. + Args: reset_config: Episode reset configuration. simulator_kwargs: From task YAML's simulator_configs. @@ -49,12 +57,6 @@ def _create_env(self, reset_config, simulator_kwargs): cfg = { "scene": {"type": "Scene"}, - "robots": [{ - "type": "R1", - "obs_modalities": ["rgb"], - "action_type": "continuous", - "action_normalize": True, - }], "task": {"type": "DummyTask"}, } # Allow full config override from simulator_kwargs @@ -72,13 +74,17 @@ def _on_reset(self, env, reset_config): def _on_step(self, env, action_text): """Execute zero action (for smoke test) or parse action_text. - For smoke testing, sends a zero action vector. Task-specific - subclasses should override for meaningful action parsing. + For smoke testing, sends a zero action vector. If the environment + has no robots (default smoke test), passes an empty action. + Task-specific subclasses should override for meaningful action parsing. """ import torch as th - action_dim = env.action_space.shape[0] - action = th.zeros(action_dim) + if env.robots: + action_dim = env.action_space.shape[0] + action = th.zeros(action_dim) + else: + action = th.zeros(0) obs, reward, terminated, truncated, info = env.step(action) return obs, float(reward), terminated or truncated, info diff --git a/easi/simulators/omnigibson/v3_7_2/env_manager.py b/easi/simulators/omnigibson/v3_7_2/env_manager.py index 48a9fb7..3b3d52b 100644 --- a/easi/simulators/omnigibson/v3_7_2/env_manager.py +++ b/easi/simulators/omnigibson/v3_7_2/env_manager.py @@ -12,8 +12,8 @@ 7. Fix websockets conflict in Isaac Sim extscache (post_install) 8. Fix cffi compatibility (post_install) -Headless mode: OMNIGIBSON_HEADLESS=1 uses Isaac Sim's native headless rendering -(no Xvfb needed). +Rendering mode is controlled via OMNIGIBSON_HEADLESS, set by the active render +platform class (OmniGibsonNativePlatform or OmniGibsonAutoPlatform). No Xvfb needed. NFS workaround: On NFS/FUSE filesystems, /proc/self/exe can resolve to "python3.10 (deleted)" which crashes Isaac Sim's Carbonite library. The @@ -79,6 +79,14 @@ def simulator_name(self) -> str: def version(self) -> str: return "v3_7_2" + @property + def default_render_platform(self) -> str: + return "native" + + @property + def supported_render_platforms(self) -> list[str]: + return ["native", "auto"] + def get_conda_env_yaml_path(self) -> Path: return Path(__file__).parent / "conda_env.yaml" @@ -115,6 +123,12 @@ def get_python_executable(self) -> str: # Resolve symlinks to get the real binary real_binary = str(Path(conda_python).resolve()) + # Guard: if the conda env doesn't exist yet (e.g. during env_is_ready() + # before installation), return the raw path so the caller can detect + # non-existence via Path(path).exists() rather than crashing on copy. + if not Path(real_binary).exists(): + return conda_python + tmp_dir = tempfile.mkdtemp(prefix="easi_python_") local_python = Path(tmp_dir) / "python3" shutil.copy2(real_binary, str(local_python)) @@ -130,7 +144,10 @@ def get_python_executable(self) -> str: return str(local_python) def get_env_vars(self, render_platform_name: str | None = None) -> EnvVars: - """Export env vars for headless rendering, EULA, and PYTHONHOME. + """Export env vars for EULA acceptance and PYTHONHOME. + + OMNIGIBSON_HEADLESS is set by the render platform (OmniGibsonNativePlatform + or OmniGibsonAutoPlatform), not here. PYTHONHOME is set to the conda env directory so the /tmp Python copy can find the conda env's stdlib and site-packages. @@ -139,7 +156,6 @@ def get_env_vars(self, render_platform_name: str | None = None) -> EnvVars: # conda env dir is two levels up from bin/python conda_env_dir = str(Path(conda_python).resolve().parent.parent) return EnvVars(replace={ - "OMNIGIBSON_HEADLESS": "1", "OMNI_KIT_ACCEPT_EULA": "YES", "PYTHONHOME": conda_env_dir, }) diff --git a/tests/test_omnigibson_simulator.py b/tests/test_omnigibson_simulator.py index bfe6d11..bf49378 100644 --- a/tests/test_omnigibson_simulator.py +++ b/tests/test_omnigibson_simulator.py @@ -34,6 +34,15 @@ def test_manifest_installation_kwargs(self): assert "BEHAVIOR-1K" in kwargs["behavior_1k_repo"] assert kwargs["behavior_1k_tag"] == "v3.7.2" + def test_manifest_render_platforms(self): + with open("easi/simulators/omnigibson/manifest.yaml") as f: + data = yaml.safe_load(f) + rp = data["versions"]["v3_7_2"].get("render_platforms", {}) + assert "native" in rp, "missing 'native' render platform" + assert "auto" in rp, "missing 'auto' render platform" + assert "render_platforms.OmniGibsonNativePlatform" in rp["native"] + assert "render_platforms.OmniGibsonAutoPlatform" in rp["auto"] + class TestOmniGibsonEnvManager: """Test OmniGibsonEnvManager class.""" @@ -49,23 +58,27 @@ def test_env_name(self): mgr = OmniGibsonEnvManager() assert mgr.get_env_name() == "easi_omnigibson_v3_7_2" - def test_default_render_platform_headless(self): + def test_default_render_platform_native(self): from easi.simulators.omnigibson.v3_7_2.env_manager import OmniGibsonEnvManager mgr = OmniGibsonEnvManager() - assert mgr.default_render_platform == "headless" + assert mgr.default_render_platform == "native" def test_env_vars(self): from easi.simulators.omnigibson.v3_7_2.env_manager import OmniGibsonEnvManager mgr = OmniGibsonEnvManager() ev = mgr.get_env_vars() - assert ev.replace["OMNIGIBSON_HEADLESS"] == "1" + assert "OMNIGIBSON_HEADLESS" not in ev.replace assert ev.replace["OMNI_KIT_ACCEPT_EULA"] == "YES" assert "PYTHONHOME" in ev.replace - def test_get_python_executable_is_local(self): + def test_get_python_executable_is_local(self, monkeypatch): """Python executable should be a /tmp copy (NFS workaround).""" + import sys from easi.simulators.omnigibson.v3_7_2.env_manager import OmniGibsonEnvManager mgr = OmniGibsonEnvManager() + # Mock _get_conda_python to return a real existing binary so the copy + # can succeed without the conda env being installed. + monkeypatch.setattr(mgr, "_get_conda_python", lambda: sys.executable) python_path = mgr.get_python_executable() assert python_path.startswith("/tmp/easi_python_") assert Path(python_path).exists() @@ -153,7 +166,7 @@ def test_create_env_manager(self): from easi.simulators.registry import create_env_manager mgr = create_env_manager("omnigibson:v3_7_2") assert mgr.simulator_name == "omnigibson" - assert mgr.default_render_platform == "headless" + assert mgr.default_render_platform == "native" def test_create_env_manager_has_installation_kwargs(self): from easi.simulators.registry import create_env_manager diff --git a/tests/test_render_platform.py b/tests/test_render_platform.py index ba663cf..dca0901 100644 --- a/tests/test_render_platform.py +++ b/tests/test_render_platform.py @@ -438,7 +438,7 @@ def test_omnigibson(self): from easi.simulators.omnigibson.v3_7_2.env_manager import OmniGibsonEnvManager mgr = OmniGibsonEnvManager() - assert mgr.default_render_platform == "headless" + assert mgr.default_render_platform == "native" def test_dummy(self): from easi.simulators.dummy.v1.env_manager import DummyEnvManager From 59a2aa772e5532e5ab1980e058390b6f9237508b Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 24 Feb 2026 13:22:33 +0800 Subject: [PATCH 105/239] fix: replace torch.lazy_property with functools.cached_property The vendored environment.py imported lazy_property from torch, but torch is not available in the ai2thor conda env. functools.cached_property is functionally identical and requires no extra dependencies. --- .../ai2thor_rearrangement_2023/vendor/rearrange/environment.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/easi/tasks/ai2thor_rearrangement_2023/vendor/rearrange/environment.py b/easi/tasks/ai2thor_rearrangement_2023/vendor/rearrange/environment.py index 8b09cb1..dd27443 100644 --- a/easi/tasks/ai2thor_rearrangement_2023/vendor/rearrange/environment.py +++ b/easi/tasks/ai2thor_rearrangement_2023/vendor/rearrange/environment.py @@ -13,7 +13,7 @@ import ai2thor.wsgi_server import numpy as np from packaging import version -from torch.distributions.utils import lazy_property +from functools import cached_property as lazy_property from allenact.utils.system import get_logger from allenact_plugins.ithor_plugin.ithor_environment import IThorEnvironment From 2dcfd64053abca19ffe23379d6ff91dcc694a6e0 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 24 Feb 2026 13:32:37 +0800 Subject: [PATCH 106/239] fix: add allenact as additional_deps, revert inlined stubs The vendored environment.py uses allenact utilities (IThorEnvironment, include_object_data, round_to_factor) extensively. Instead of inlining them, declare allenact as additional_deps in the task YAML so they get installed into the simulator conda env. Keep the torch->functools.cached_property replacement since lazy_property is a trivial decorator. --- easi/tasks/ai2thor_rearrangement_2023/_base.yaml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/easi/tasks/ai2thor_rearrangement_2023/_base.yaml b/easi/tasks/ai2thor_rearrangement_2023/_base.yaml index ee6fb8b..8f09f59 100644 --- a/easi/tasks/ai2thor_rearrangement_2023/_base.yaml +++ b/easi/tasks/ai2thor_rearrangement_2023/_base.yaml @@ -17,6 +17,9 @@ simulator_configs: visibility_distance: 1.5 snap_to_grid: true quality: "Very Low" + additional_deps: + - "allenact>=0.5.1" + - "allenact_plugins[ithor]>=0.5.1" # Sensor toggles use_rgb: true use_depth: false From cd35ade738539f4ebb186966120243d643e36949 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 24 Feb 2026 14:06:28 +0800 Subject: [PATCH 107/239] fix: add lru-dict dep and fix bridge reset - Add lru-dict to additional_deps (used by vendored utils.py) - Fix _on_reset: use env.controller.step("Pass") instead of env.step("Pass") since RearrangeTHOREnvironment delegates to its controller for AI2-THOR actions --- easi/tasks/ai2thor_rearrangement_2023/_base.yaml | 1 + easi/tasks/ai2thor_rearrangement_2023/bridge.py | 5 +++-- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/easi/tasks/ai2thor_rearrangement_2023/_base.yaml b/easi/tasks/ai2thor_rearrangement_2023/_base.yaml index 8f09f59..be7fc75 100644 --- a/easi/tasks/ai2thor_rearrangement_2023/_base.yaml +++ b/easi/tasks/ai2thor_rearrangement_2023/_base.yaml @@ -20,6 +20,7 @@ simulator_configs: additional_deps: - "allenact>=0.5.1" - "allenact_plugins[ithor]>=0.5.1" + - "lru-dict" # Sensor toggles use_rgb: true use_depth: false diff --git a/easi/tasks/ai2thor_rearrangement_2023/bridge.py b/easi/tasks/ai2thor_rearrangement_2023/bridge.py index 66b7cd2..7e7c3f5 100644 --- a/easi/tasks/ai2thor_rearrangement_2023/bridge.py +++ b/easi/tasks/ai2thor_rearrangement_2023/bridge.py @@ -109,8 +109,9 @@ def _on_reset(self, env, reset_config): self._actions_taken = [] self._actions_taken_success = [] - # No-op to get initial state - return env.step("Pass") + # No-op to get initial observation + env.controller.step("Pass") + return env.last_event.frame.copy() def _on_step(self, env, action_text): action_name = action_text.strip().lower() From 6e1ff8b26c187ac21d0afb14784293c60a9e58a4 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 24 Feb 2026 15:23:30 +0800 Subject: [PATCH 108/239] fix: AI2-THOR rearrangement prompt issues and v5 compatibility - Fix empty action list by lazy-initializing from memory.action_space - Add goal image support via walkthrough env (1-phase track) - Fix step.info AttributeError by using step.observation.metadata - Set enhanced_physics_determinism=False to avoid v5 kwarg errors - Replace removed DropHeldObjectAhead with DropHandObject for v5 - Add tests for lazy-init, GPS metadata, and goal image content --- .../ai2thor_rearrangement_2023/_base.yaml | 1 + .../ai2thor_rearrangement_2023/bridge.py | 81 ++++++++++++++++- .../ai2thor_rearrangement_2023/prompts.py | 89 ++++++++++++++----- .../vendor/rearrange/environment.py | 5 +- tests/test_ai2thor_rearrangement_2023_task.py | 86 ++++++++++++++++++ 5 files changed, 236 insertions(+), 26 deletions(-) diff --git a/easi/tasks/ai2thor_rearrangement_2023/_base.yaml b/easi/tasks/ai2thor_rearrangement_2023/_base.yaml index be7fc75..109ac7a 100644 --- a/easi/tasks/ai2thor_rearrangement_2023/_base.yaml +++ b/easi/tasks/ai2thor_rearrangement_2023/_base.yaml @@ -25,6 +25,7 @@ simulator_configs: use_rgb: true use_depth: false use_gps: true + use_goal_image: true agent: prompt_builder: "easi.tasks.ai2thor_rearrangement_2023.prompts.AI2THORRearrangement2023PromptBuilder" prompt_builder_kwargs: diff --git a/easi/tasks/ai2thor_rearrangement_2023/bridge.py b/easi/tasks/ai2thor_rearrangement_2023/bridge.py index 7e7c3f5..ac85d18 100644 --- a/easi/tasks/ai2thor_rearrangement_2023/bridge.py +++ b/easi/tasks/ai2thor_rearrangement_2023/bridge.py @@ -2,6 +2,10 @@ Runs inside the easi_ai2thor_v5_0_0 conda env (Python 3.10). Wraps RearrangeTHOREnvironment in SNAP mode with 84 discrete actions. + +For the 1-phase track, maintains a second "walkthrough" environment +that stays in the goal state to provide goal images from the agent's +current viewpoint at each step. """ from __future__ import annotations @@ -46,8 +50,9 @@ def _create_env(self, reset_config, simulator_kwargs): self._use_rgb = simulator_kwargs.get("use_rgb", True) self._use_depth = simulator_kwargs.get("use_depth", False) self._use_gps = simulator_kwargs.get("use_gps", True) + self._use_goal_image = simulator_kwargs.get("use_goal_image", True) - controller_kwargs = { + self._controller_kwargs = { "width": screen_w, "height": screen_h, "fieldOfView": fov, @@ -60,12 +65,29 @@ def _create_env(self, reset_config, simulator_kwargs): "fastActionEmit": True, } + # enhanced_physics_determinism=False: the physics_step_kwargs + # (actionSimulationSeconds, fixedDeltaTime) from older AI2-THOR are + # not uniformly accepted by v5 actions (e.g. Crouch takes no args, + # RotateRight rejects actionSimulationSeconds). Disable to avoid + # ValueError on unsupported kwargs. env = RearrangeTHOREnvironment( mode=RearrangeMode.SNAP, - controller_kwargs=controller_kwargs, + controller_kwargs=dict(self._controller_kwargs), force_cache_reset=True, + enhanced_physics_determinism=False, ) + # Create walkthrough env for goal images (1-phase track) + if self._use_goal_image: + self._walkthrough_env = RearrangeTHOREnvironment( + mode=RearrangeMode.SNAP, + controller_kwargs=dict(self._controller_kwargs), + force_cache_reset=True, + enhanced_physics_determinism=False, + ) + else: + self._walkthrough_env = None + # Action tracking for PuSR/PuLen metrics self._actions_taken = [] self._actions_taken_success = [] @@ -105,6 +127,12 @@ def _on_reset(self, env, reset_config): env.reset(task_spec=task_spec, force_axis_aligned_start=True) env.shuffle() + # Setup walkthrough env (stays in goal state — no shuffle) + if self._walkthrough_env is not None: + self._walkthrough_env.reset( + task_spec=task_spec, force_axis_aligned_start=True + ) + # Clear action tracking self._actions_taken = [] self._actions_taken_success = [] @@ -113,6 +141,15 @@ def _on_reset(self, env, reset_config): env.controller.step("Pass") return env.last_event.frame.copy() + def reset(self, reset_config): + """Override to add initial goal image to the reset response.""" + response = super().reset(reset_config) + if self._walkthrough_env is not None: + goal_rgb = self._get_goal_frame(self.env) + goal_path = self._save_goal_image(goal_rgb) + response.setdefault("info", {})["goal_rgb_path"] = goal_path + return response + def _on_step(self, env, action_text): action_name = action_text.strip().lower() @@ -150,6 +187,12 @@ def _on_step(self, env, action_text): feedback = "success" if action_success else "action failed" info["feedback"] = feedback + # Capture goal image (walkthrough env teleported to current agent position) + if self._walkthrough_env is not None: + goal_rgb = self._get_goal_frame(env) + goal_path = self._save_goal_image(goal_rgb) + info["goal_rgb_path"] = goal_path + return rgb, 0.0, False, info def _do_pickup(self, env, action_name: str) -> bool: @@ -229,6 +272,33 @@ def _get_rgb(self, env) -> np.ndarray: """Extract RGB frame from environment.""" return env.last_event.frame.copy() + def _get_goal_frame(self, env) -> np.ndarray: + """Teleport walkthrough env agent to current position and capture goal frame. + + This implements the 1-phase track observation: the agent sees what the + scene SHOULD look like from its current viewpoint. + """ + loc = env.get_agent_location() + self._walkthrough_env.controller.step( + "TeleportFull", + x=loc["x"], y=loc["y"], z=loc["z"], + rotation={"x": 0, "y": loc["rotation"], "z": 0}, + horizon=loc["horizon"], + standing=loc.get("standing", True), + forceAction=True, + ) + return self._walkthrough_env.last_event.frame.copy() + + def _save_goal_image(self, image_array: np.ndarray) -> str: + """Save goal frame as PNG, return path string.""" + from PIL import Image + + save_dir = Path(self.episode_output_dir) if self.episode_output_dir else self.workspace + save_dir.mkdir(parents=True, exist_ok=True) + goal_path = save_dir / ("step_%04d_goal.png" % self.step_count) + Image.fromarray(image_array).save(str(goal_path)) + return str(goal_path) + def _build_info(self, env, action_name: str, action_success: bool) -> dict: """Build info dict with sensor data and action feedback.""" info = { @@ -312,6 +382,13 @@ def _extract_info(self, info): if isinstance(v, (int, float, str, bool)) } + def close(self): + """Shut down both envs.""" + if self._walkthrough_env is not None: + self._walkthrough_env.stop() + self._walkthrough_env = None + super().close() + if __name__ == "__main__": AI2THORRearrangement2023Bridge.main() diff --git a/easi/tasks/ai2thor_rearrangement_2023/prompts.py b/easi/tasks/ai2thor_rearrangement_2023/prompts.py index f9b1c81..9192676 100644 --- a/easi/tasks/ai2thor_rearrangement_2023/prompts.py +++ b/easi/tasks/ai2thor_rearrangement_2023/prompts.py @@ -2,6 +2,9 @@ Constructs multi-modal prompts with observation images, GPS data, action history, and structured JSON output schema. + +For the 1-phase track, each step includes both the current (shuffled) +observation and a goal (walkthrough) image from the same viewpoint. """ from __future__ import annotations @@ -21,6 +24,13 @@ ## Goal {instruction} +## Observations +You receive two images at each step: +1. **Current scene** — what the environment looks like RIGHT NOW (shuffled state) +2. **Goal scene** — what the environment SHOULD look like from the same viewpoint (target state) + +Compare these two images to identify which objects are misplaced and where they should go. + ## Available Actions {action_list} @@ -32,10 +42,10 @@ - **Done**: Signal that all objects are in their correct positions ## Strategy -1. Look around to survey the scene and identify misplaced objects +1. Compare the current and goal images to identify misplaced objects 2. Navigate to a misplaced object 3. Pick it up with the matching pickup_ action -4. Navigate toward where it belongs (near its goal position) +4. Navigate toward where it belongs (compare with goal image) 5. Use drop_held_object_with_snap — it snaps the object to its goal if you're close 6. Repeat for remaining misplaced objects 7. Say "done" when finished @@ -51,7 +61,7 @@ Respond in this exact JSON format: ```json {{ - "observation": "describe what you see", + "observation": "describe what you see in current vs goal images", "reasoning": "what to do next and why", "plan": [ {{"action_name": ""}}, @@ -84,6 +94,10 @@ def set_action_space(self, actions: list[str]): self._action_list_str = ", ".join(parts) def build_messages(self, memory: AgentMemory) -> list[dict]: + # Lazy-init action space from memory (set by agent constructor) + if not self._action_list_str and memory.action_space: + self.set_action_space(memory.action_space) + instruction = memory.task_description or "Rearrange objects to match the goal." system_text = SYSTEM_PROMPT.format( @@ -113,6 +127,9 @@ def build_messages(self, memory: AgentMemory) -> list[dict]: def _make_history_content(self, step) -> list[dict]: content = [] + metadata = step.observation.metadata if step.observation else {} + + # Current observation image if step.observation and step.observation.rgb_path: img_url = _encode_image_base64(step.observation.rgb_path) if img_url: @@ -121,12 +138,22 @@ def _make_history_content(self, step) -> list[dict]: "image_url": {"url": img_url}, }) - text = "Observation." + # Goal image (walkthrough state from same viewpoint) + goal_path = metadata.get("goal_rgb_path") + if goal_path: + img_url = _encode_image_base64(goal_path) + if img_url: + content.append({ + "type": "image_url", + "image_url": {"url": img_url}, + }) + + text = "Observation (image 1: current, image 2: goal)." if self.use_feedback and step.feedback: text += f"\nFeedback: {step.feedback}" - # GPS overlay from step info - gps_text = self._format_gps(step.info) + # GPS overlay from observation metadata + gps_text = self._format_gps(metadata) if gps_text: text += f"\n{gps_text}" @@ -135,7 +162,12 @@ def _make_history_content(self, step) -> list[dict]: def _make_current_content(self, memory: AgentMemory) -> list[dict]: content = [] + metadata = ( + memory.current_observation.metadata + if memory.current_observation else {} + ) + # Current observation image if memory.current_observation and memory.current_observation.rgb_path: img_url = _encode_image_base64(memory.current_observation.rgb_path) if img_url: @@ -144,39 +176,54 @@ def _make_current_content(self, memory: AgentMemory) -> list[dict]: "image_url": {"url": img_url}, }) + # Goal image (walkthrough state from same viewpoint) + goal_path = metadata.get("goal_rgb_path") + if goal_path: + img_url = _encode_image_base64(goal_path) + if img_url: + content.append({ + "type": "image_url", + "image_url": {"url": img_url}, + }) + + has_goal = bool(goal_path) + img_label = " (image 1: current, image 2: goal)" if has_goal else "" + if memory.is_first_turn: - text = "First observation. Begin the rearrangement task." + text = f"First observation{img_label}. Begin the rearrangement task." else: - text = "Current observation." + text = f"Current observation{img_label}." if self.use_feedback and memory.steps: last = memory.steps[-1] if last.feedback: text += f"\nFeedback: {last.feedback}" - # GPS from last step info + # GPS and held-object from last step's observation metadata if memory.steps: - gps_text = self._format_gps(memory.steps[-1].info) + last_metadata = memory.steps[-1].observation.metadata if memory.steps[-1].observation else {} + gps_text = self._format_gps(last_metadata) if gps_text: text += f"\n{gps_text}" - held = (memory.steps[-1].info or {}).get("held_object", "none") + held = last_metadata.get("held_object", "none") text += f"\nHolding: {held}" text += f"\n\n{OUTPUT_SCHEMA}" content.append({"type": "text", "text": text}) return content - def _format_gps(self, info: dict | None) -> str: - if not info: + def _format_gps(self, metadata: dict | None) -> str: + if not metadata: return "" parts = [] - if "agent_x" in info: - parts.append( - f"Position: ({info['agent_x']:.2f}, {info['agent_y']:.2f}, {info['agent_z']:.2f})" - ) - if "agent_rotation" in info: - parts.append(f"Rotation: {info['agent_rotation']:.0f}") - if "agent_horizon" in info: - parts.append(f"Horizon: {info['agent_horizon']:.0f}") + if "agent_x" in metadata: + x = float(metadata["agent_x"]) + y = float(metadata["agent_y"]) + z = float(metadata["agent_z"]) + parts.append(f"Position: ({x:.2f}, {y:.2f}, {z:.2f})") + if "agent_rotation" in metadata: + parts.append(f"Rotation: {float(metadata['agent_rotation']):.0f}") + if "agent_horizon" in metadata: + parts.append(f"Horizon: {float(metadata['agent_horizon']):.0f}") return "GPS: " + ", ".join(parts) if parts else "" def parse_response( diff --git a/easi/tasks/ai2thor_rearrangement_2023/vendor/rearrange/environment.py b/easi/tasks/ai2thor_rearrangement_2023/vendor/rearrange/environment.py index dd27443..1b0a156 100644 --- a/easi/tasks/ai2thor_rearrangement_2023/vendor/rearrange/environment.py +++ b/easi/tasks/ai2thor_rearrangement_2023/vendor/rearrange/environment.py @@ -861,11 +861,10 @@ def position_to_tuple(position: Dict[str, float]): if len(possible_receptacles) == 0 or ( not self.controller.last_event.metadata["lastActionSuccess"] ): + # DropHeldObjectAhead was removed in AI2-THOR v5; use DropHandObject self.controller.step( - "DropHeldObjectAhead", + "DropHandObject", forceAction=True, - autoSimulation=False, - **{**self.physics_step_kwargs, "actionSimulationSeconds": 1.5}, ) return False diff --git a/tests/test_ai2thor_rearrangement_2023_task.py b/tests/test_ai2thor_rearrangement_2023_task.py index 984c3f5..51297c0 100644 --- a/tests/test_ai2thor_rearrangement_2023_task.py +++ b/tests/test_ai2thor_rearrangement_2023_task.py @@ -211,6 +211,92 @@ def test_set_action_space(self): assert "move_ahead" in builder._action_name_set assert "pickup_bowl" in builder._action_name_set + def test_lazy_init_action_space(self): + """build_messages should lazy-init action space from memory.action_space.""" + from easi.core.memory import AgentMemory + from easi.tasks.ai2thor_rearrangement_2023.prompts import AI2THORRearrangement2023PromptBuilder + + builder = AI2THORRearrangement2023PromptBuilder() + # Action space NOT set via set_action_space — should be empty + assert builder._action_list_str == "" + + memory = AgentMemory(action_space=["done", "move_ahead", "pickup_bowl"]) + memory.current_observation = Observation(rgb_path="/tmp/fake.png") + memory.task_description = "Rearrange objects." + + messages = builder.build_messages(memory) + # After build_messages, action list should be populated from memory + assert "done" in builder._action_list_str + assert "move_ahead" in builder._action_list_str + # System message should contain action list + system_content = messages[0]["content"] + assert "move_ahead" in system_content + + def test_gps_from_observation_metadata(self): + """GPS data should come from step.observation.metadata (not step.info).""" + from easi.core.memory import AgentMemory + from easi.tasks.ai2thor_rearrangement_2023.prompts import AI2THORRearrangement2023PromptBuilder + + builder = AI2THORRearrangement2023PromptBuilder() + builder.set_action_space(["done", "move_ahead"]) + + memory = AgentMemory(action_space=["done", "move_ahead"]) + # Record a step with GPS in observation metadata + obs_with_gps = Observation( + rgb_path="/tmp/fake.png", + metadata={ + "agent_x": 1.5, "agent_y": 0.87, "agent_z": -2.0, + "agent_rotation": 90.0, "agent_horizon": 0.0, + "held_object": "Bowl", + }, + ) + from easi.core.episode import Action + memory.record_step(obs_with_gps, Action(action_name="move_ahead"), llm_response="test") + memory.record_feedback("success") + + memory.current_observation = Observation(rgb_path="/tmp/fake2.png") + memory.task_description = "Rearrange objects." + + messages = builder.build_messages(memory) + # The current turn message should include GPS and held object + last_user = messages[-1] + text_parts = [p["text"] for p in last_user["content"] if p.get("type") == "text"] + text = " ".join(text_parts) + assert "1.50" in text # agent_x + assert "Holding: Bowl" in text + + def test_goal_image_in_content(self): + """When goal_rgb_path is in metadata, build_messages should include it.""" + from easi.core.memory import AgentMemory + from easi.tasks.ai2thor_rearrangement_2023.prompts import AI2THORRearrangement2023PromptBuilder + import tempfile, os + from PIL import Image + import numpy as np + + builder = AI2THORRearrangement2023PromptBuilder() + builder.set_action_space(["done", "move_ahead"]) + + # Create real temp images so _encode_image_base64 works + with tempfile.TemporaryDirectory() as tmpdir: + img = Image.fromarray(np.zeros((4, 4, 3), dtype=np.uint8)) + obs_path = os.path.join(tmpdir, "obs.png") + goal_path = os.path.join(tmpdir, "goal.png") + img.save(obs_path) + img.save(goal_path) + + memory = AgentMemory(action_space=["done", "move_ahead"]) + memory.current_observation = Observation( + rgb_path=obs_path, + metadata={"goal_rgb_path": goal_path}, + ) + memory.task_description = "Rearrange objects." + + messages = builder.build_messages(memory) + last_user = messages[-1] + # Should have 2 images (current + goal) + 1 text + image_parts = [p for p in last_user["content"] if p.get("type") == "image_url"] + assert len(image_parts) == 2 + def test_parse_valid_response(self): from easi.core.memory import AgentMemory builder = self._make_builder() From e954703f6fcfd7e888d155bdd57f724ad4815974 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 24 Feb 2026 15:47:16 +0800 Subject: [PATCH 109/239] feat: dynamic sensor descriptions in prompt and depth image capture - Make system prompt observations section dynamic based on sensor toggles (use_rgb, use_depth, use_gps, use_goal_image) - Add clear explanations of each sensor type (RGB, Depth, GPS) - Label images explicitly in messages (Image 1: RGB current, etc.) - Implement depth frame capture in bridge (grayscale PNG, close=bright) - Gate GPS text in prompt by use_gps toggle - Adapt strategy section based on goal image availability - Add sensor toggles to prompt_builder_kwargs in _base.yaml - Add 7 new tests for sensor toggles and dynamic prompt content --- .../ai2thor_rearrangement_2023/_base.yaml | 5 + .../ai2thor_rearrangement_2023/bridge.py | 34 ++- .../ai2thor_rearrangement_2023/prompts.py | 229 +++++++++++++----- tests/test_ai2thor_rearrangement_2023_task.py | 169 +++++++++++++ 4 files changed, 372 insertions(+), 65 deletions(-) diff --git a/easi/tasks/ai2thor_rearrangement_2023/_base.yaml b/easi/tasks/ai2thor_rearrangement_2023/_base.yaml index 109ac7a..ef15fda 100644 --- a/easi/tasks/ai2thor_rearrangement_2023/_base.yaml +++ b/easi/tasks/ai2thor_rearrangement_2023/_base.yaml @@ -32,6 +32,11 @@ agent: use_feedback: true chat_history: true message_window_len: 10 + # Sensor toggles (must match simulator_configs) + use_rgb: true + use_depth: false + use_gps: true + use_goal_image: true generation_kwargs: temperature: 0 max_tokens: 2048 diff --git a/easi/tasks/ai2thor_rearrangement_2023/bridge.py b/easi/tasks/ai2thor_rearrangement_2023/bridge.py index ac85d18..85db9b4 100644 --- a/easi/tasks/ai2thor_rearrangement_2023/bridge.py +++ b/easi/tasks/ai2thor_rearrangement_2023/bridge.py @@ -142,8 +142,13 @@ def _on_reset(self, env, reset_config): return env.last_event.frame.copy() def reset(self, reset_config): - """Override to add initial goal image to the reset response.""" + """Override to add initial depth + goal images to the reset response.""" response = super().reset(reset_config) + if self._use_depth: + depth_frame = self.env.last_event.depth_frame + if depth_frame is not None: + depth_path = self._save_depth_image(depth_frame) + response.setdefault("info", {})["depth_path"] = depth_path if self._walkthrough_env is not None: goal_rgb = self._get_goal_frame(self.env) goal_path = self._save_goal_image(goal_rgb) @@ -187,6 +192,13 @@ def _on_step(self, env, action_text): feedback = "success" if action_success else "action failed" info["feedback"] = feedback + # Capture depth image (if enabled) + if self._use_depth: + depth_frame = env.last_event.depth_frame + if depth_frame is not None: + depth_path = self._save_depth_image(depth_frame) + info["depth_path"] = depth_path + # Capture goal image (walkthrough env teleported to current agent position) if self._walkthrough_env is not None: goal_rgb = self._get_goal_frame(env) @@ -299,6 +311,26 @@ def _save_goal_image(self, image_array: np.ndarray) -> str: Image.fromarray(image_array).save(str(goal_path)) return str(goal_path) + def _save_depth_image(self, depth_array: np.ndarray) -> str: + """Save depth frame as grayscale PNG (closer = brighter), return path. + + AI2-THOR's depth_frame contains float32 distance values in meters. + We normalise to [0, 255] clipped at the visibility distance, then + invert so that nearby surfaces are bright and far surfaces are dark. + """ + from PIL import Image + + save_dir = Path(self.episode_output_dir) if self.episode_output_dir else self.workspace + save_dir.mkdir(parents=True, exist_ok=True) + depth_path = save_dir / ("step_%04d_depth.png" % self.step_count) + + max_depth = self._controller_kwargs.get("visibilityDistance", 1.5) + clipped = np.clip(depth_array, 0, max_depth) + normalised = 1.0 - (clipped / max_depth) # invert: close=bright + uint8_depth = (normalised * 255).astype(np.uint8) + Image.fromarray(uint8_depth, mode="L").save(str(depth_path)) + return str(depth_path) + def _build_info(self, env, action_name: str, action_success: bool) -> dict: """Build info dict with sensor data and action feedback.""" info = { diff --git a/easi/tasks/ai2thor_rearrangement_2023/prompts.py b/easi/tasks/ai2thor_rearrangement_2023/prompts.py index 9192676..ff32cd0 100644 --- a/easi/tasks/ai2thor_rearrangement_2023/prompts.py +++ b/easi/tasks/ai2thor_rearrangement_2023/prompts.py @@ -3,8 +3,9 @@ Constructs multi-modal prompts with observation images, GPS data, action history, and structured JSON output schema. -For the 1-phase track, each step includes both the current (shuffled) -observation and a goal (walkthrough) image from the same viewpoint. +Sensor inputs (RGB, Depth, Goal image, GPS) are individually toggleable +via prompt_builder_kwargs. The system prompt and per-step image labels +adapt automatically to reflect which sensors are active. """ from __future__ import annotations @@ -18,6 +19,8 @@ logger = get_logger(__name__) +# --- System prompt template (observations section is injected dynamically) --- + SYSTEM_PROMPT = """\ You are an embodied AI agent performing an object rearrangement task in a 3D indoor environment (AI2-THOR). @@ -25,11 +28,8 @@ {instruction} ## Observations -You receive two images at each step: -1. **Current scene** — what the environment looks like RIGHT NOW (shuffled state) -2. **Goal scene** — what the environment SHOULD look like from the same viewpoint (target state) - -Compare these two images to identify which objects are misplaced and where they should go. +At each step you receive the following sensor inputs: +{observation_description} ## Available Actions {action_list} @@ -42,13 +42,7 @@ - **Done**: Signal that all objects are in their correct positions ## Strategy -1. Compare the current and goal images to identify misplaced objects -2. Navigate to a misplaced object -3. Pick it up with the matching pickup_ action -4. Navigate toward where it belongs (compare with goal image) -5. Use drop_held_object_with_snap — it snaps the object to its goal if you're close -6. Repeat for remaining misplaced objects -7. Say "done" when finished +{strategy} ## Rules - You can hold only ONE object at a time @@ -61,7 +55,7 @@ Respond in this exact JSON format: ```json {{ - "observation": "describe what you see in current vs goal images", + "observation": "describe what you see in the observation images", "reasoning": "what to do next and why", "plan": [ {{"action_name": ""}}, @@ -73,18 +67,31 @@ class AI2THORRearrangement2023PromptBuilder: - """Prompt builder for the rearrangement task.""" + """Prompt builder for the rearrangement task. + + Sensor toggles (use_rgb, use_depth, use_gps, use_goal_image) control + both what appears in the system prompt description and what sensor + data is included in per-step messages. + """ def __init__( self, use_feedback: bool = True, chat_history: bool = True, message_window_len: int = 10, + use_rgb: bool = True, + use_depth: bool = False, + use_gps: bool = True, + use_goal_image: bool = True, **kwargs, ): self.use_feedback = use_feedback self.chat_history = chat_history self.message_window_len = message_window_len + self.use_rgb = use_rgb + self.use_depth = use_depth + self.use_gps = use_gps + self.use_goal_image = use_goal_image self._action_list_str = "" self._action_name_set: set[str] = set() @@ -93,6 +100,74 @@ def set_action_space(self, actions: list[str]): parts = [f"{i}: {a}" for i, a in enumerate(actions)] self._action_list_str = ", ".join(parts) + # --- System prompt helpers --- + + def _build_observation_description(self) -> str: + """Build the observation section of the system prompt based on active sensors.""" + sections = [] + idx = 1 + + if self.use_rgb: + sections.append( + f"- **RGB Image — Current Scene** (Image {idx}): A first-person " + f"camera view showing the environment as it looks RIGHT NOW " + f"(shuffled state). Objects may be misplaced relative to the goal." + ) + idx += 1 + + if self.use_depth: + sections.append( + f"- **Depth Image** (Image {idx}): A grayscale depth map from " + f"the agent's viewpoint. Brighter pixels are closer, darker " + f"pixels are farther away. Use this to judge distances to " + f"objects and obstacles." + ) + idx += 1 + + if self.use_goal_image: + sections.append( + f"- **RGB Image — Goal Scene** (Image {idx}): A first-person " + f"camera view from the SAME position showing what the " + f"environment SHOULD look like (target state). Compare with " + f"the current scene to identify misplaced objects." + ) + idx += 1 + + if self.use_gps: + sections.append( + "- **GPS Data** (text): The agent's 3D position (x, y, z) in " + "meters where y is the vertical axis (height). Yaw rotation in " + "degrees (0°–360°). Horizon angle: 0° = looking straight " + "ahead, positive = looking down, negative = looking up. Also " + "reports which object the agent is currently holding, if any." + ) + + return "\n".join(sections) if sections else "No sensor inputs configured." + + def _build_strategy(self) -> str: + """Build the strategy section based on whether goal images are available.""" + if self.use_goal_image: + return ( + "1. Compare the current and goal images to identify misplaced objects\n" + "2. Navigate to a misplaced object\n" + "3. Pick it up with the matching pickup_ action\n" + "4. Navigate toward where it belongs (compare with goal image)\n" + "5. Use drop_held_object_with_snap — it snaps the object to its goal if you're close\n" + "6. Repeat for remaining misplaced objects\n" + '7. Say "done" when finished' + ) + return ( + "1. Explore the environment to identify objects that appear misplaced\n" + "2. Navigate to a misplaced object\n" + "3. Pick it up with the matching pickup_ action\n" + "4. Navigate to where it should go based on context\n" + "5. Use drop_held_object_with_snap — it snaps the object to its goal if you're close\n" + "6. Repeat for remaining misplaced objects\n" + '7. Say "done" when finished' + ) + + # --- Message building --- + def build_messages(self, memory: AgentMemory) -> list[dict]: # Lazy-init action space from memory (set by agent constructor) if not self._action_list_str and memory.action_space: @@ -102,7 +177,9 @@ def build_messages(self, memory: AgentMemory) -> list[dict]: system_text = SYSTEM_PROMPT.format( instruction=instruction, + observation_description=self._build_observation_description(), action_list=self._action_list_str, + strategy=self._build_strategy(), ) messages = [{"role": "system", "content": system_text}] @@ -125,69 +202,89 @@ def build_messages(self, memory: AgentMemory) -> list[dict]: return messages - def _make_history_content(self, step) -> list[dict]: - content = [] - metadata = step.observation.metadata if step.observation else {} - - # Current observation image - if step.observation and step.observation.rgb_path: - img_url = _encode_image_base64(step.observation.rgb_path) + def _add_images( + self, content: list, rgb_path: str | None, metadata: dict, + ) -> str: + """Append sensor images to *content* list. + + Returns a parenthetical label string like + ``" (Image 1: RGB current, Image 2: Depth, Image 3: RGB goal)"`` + that describes the images actually added. + """ + labels: list[str] = [] + idx = 1 + + # 1. RGB current scene + if self.use_rgb and rgb_path: + img_url = _encode_image_base64(rgb_path) if img_url: content.append({ "type": "image_url", "image_url": {"url": img_url}, }) + labels.append(f"Image {idx}: RGB current") + idx += 1 + + # 2. Depth map + if self.use_depth: + depth_path = metadata.get("depth_path") + if depth_path: + img_url = _encode_image_base64(depth_path) + if img_url: + content.append({ + "type": "image_url", + "image_url": {"url": img_url}, + }) + labels.append(f"Image {idx}: Depth") + idx += 1 + + # 3. Goal scene (walkthrough state from same viewpoint) + if self.use_goal_image: + goal_path = metadata.get("goal_rgb_path") + if goal_path: + img_url = _encode_image_base64(goal_path) + if img_url: + content.append({ + "type": "image_url", + "image_url": {"url": img_url}, + }) + labels.append(f"Image {idx}: RGB goal") + idx += 1 - # Goal image (walkthrough state from same viewpoint) - goal_path = metadata.get("goal_rgb_path") - if goal_path: - img_url = _encode_image_base64(goal_path) - if img_url: - content.append({ - "type": "image_url", - "image_url": {"url": img_url}, - }) + return f" ({', '.join(labels)})" if labels else "" + + def _make_history_content(self, step) -> list[dict]: + content: list[dict] = [] + metadata = step.observation.metadata if step.observation else {} + rgb_path = step.observation.rgb_path if step.observation else None - text = "Observation (image 1: current, image 2: goal)." + img_label = self._add_images(content, rgb_path, metadata) + + text = f"Observation{img_label}." if self.use_feedback and step.feedback: text += f"\nFeedback: {step.feedback}" # GPS overlay from observation metadata - gps_text = self._format_gps(metadata) - if gps_text: - text += f"\n{gps_text}" + if self.use_gps: + gps_text = self._format_gps(metadata) + if gps_text: + text += f"\n{gps_text}" content.append({"type": "text", "text": text}) return content def _make_current_content(self, memory: AgentMemory) -> list[dict]: - content = [] + content: list[dict] = [] metadata = ( memory.current_observation.metadata if memory.current_observation else {} ) + rgb_path = ( + memory.current_observation.rgb_path + if memory.current_observation else None + ) - # Current observation image - if memory.current_observation and memory.current_observation.rgb_path: - img_url = _encode_image_base64(memory.current_observation.rgb_path) - if img_url: - content.append({ - "type": "image_url", - "image_url": {"url": img_url}, - }) - - # Goal image (walkthrough state from same viewpoint) - goal_path = metadata.get("goal_rgb_path") - if goal_path: - img_url = _encode_image_base64(goal_path) - if img_url: - content.append({ - "type": "image_url", - "image_url": {"url": img_url}, - }) - - has_goal = bool(goal_path) - img_label = " (image 1: current, image 2: goal)" if has_goal else "" + img_label = self._add_images(content, rgb_path, metadata) if memory.is_first_turn: text = f"First observation{img_label}. Begin the rearrangement task." @@ -200,10 +297,14 @@ def _make_current_content(self, memory: AgentMemory) -> list[dict]: # GPS and held-object from last step's observation metadata if memory.steps: - last_metadata = memory.steps[-1].observation.metadata if memory.steps[-1].observation else {} - gps_text = self._format_gps(last_metadata) - if gps_text: - text += f"\n{gps_text}" + last_metadata = ( + memory.steps[-1].observation.metadata + if memory.steps[-1].observation else {} + ) + if self.use_gps: + gps_text = self._format_gps(last_metadata) + if gps_text: + text += f"\n{gps_text}" held = last_metadata.get("held_object", "none") text += f"\nHolding: {held}" @@ -221,9 +322,9 @@ def _format_gps(self, metadata: dict | None) -> str: z = float(metadata["agent_z"]) parts.append(f"Position: ({x:.2f}, {y:.2f}, {z:.2f})") if "agent_rotation" in metadata: - parts.append(f"Rotation: {float(metadata['agent_rotation']):.0f}") + parts.append(f"Rotation: {float(metadata['agent_rotation']):.0f}\u00b0") if "agent_horizon" in metadata: - parts.append(f"Horizon: {float(metadata['agent_horizon']):.0f}") + parts.append(f"Horizon: {float(metadata['agent_horizon']):.0f}\u00b0") return "GPS: " + ", ".join(parts) if parts else "" def parse_response( diff --git a/tests/test_ai2thor_rearrangement_2023_task.py b/tests/test_ai2thor_rearrangement_2023_task.py index 51297c0..df11254 100644 --- a/tests/test_ai2thor_rearrangement_2023_task.py +++ b/tests/test_ai2thor_rearrangement_2023_task.py @@ -296,6 +296,175 @@ def test_goal_image_in_content(self): # Should have 2 images (current + goal) + 1 text image_parts = [p for p in last_user["content"] if p.get("type") == "image_url"] assert len(image_parts) == 2 + # Text should label images explicitly + text_parts = [p["text"] for p in last_user["content"] if p.get("type") == "text"] + text = " ".join(text_parts) + assert "Image 1: RGB current" in text + assert "Image 2: RGB goal" in text + + def test_depth_image_in_content(self): + """When use_depth=True and depth_path in metadata, include depth image.""" + from easi.core.memory import AgentMemory + from easi.tasks.ai2thor_rearrangement_2023.prompts import AI2THORRearrangement2023PromptBuilder + import tempfile, os + from PIL import Image + import numpy as np + + builder = AI2THORRearrangement2023PromptBuilder(use_depth=True) + builder.set_action_space(["done", "move_ahead"]) + + with tempfile.TemporaryDirectory() as tmpdir: + img = Image.fromarray(np.zeros((4, 4, 3), dtype=np.uint8)) + depth_img = Image.fromarray(np.zeros((4, 4), dtype=np.uint8), mode="L") + obs_path = os.path.join(tmpdir, "obs.png") + depth_path = os.path.join(tmpdir, "depth.png") + goal_path = os.path.join(tmpdir, "goal.png") + img.save(obs_path) + depth_img.save(depth_path) + img.save(goal_path) + + memory = AgentMemory(action_space=["done", "move_ahead"]) + memory.current_observation = Observation( + rgb_path=obs_path, + metadata={ + "depth_path": depth_path, + "goal_rgb_path": goal_path, + }, + ) + memory.task_description = "Rearrange objects." + + messages = builder.build_messages(memory) + last_user = messages[-1] + # Should have 3 images (RGB + Depth + Goal) + image_parts = [p for p in last_user["content"] if p.get("type") == "image_url"] + assert len(image_parts) == 3 + # Text labels should reflect all three + text_parts = [p["text"] for p in last_user["content"] if p.get("type") == "text"] + text = " ".join(text_parts) + assert "Image 1: RGB current" in text + assert "Image 2: Depth" in text + assert "Image 3: RGB goal" in text + + def test_depth_excluded_when_toggled_off(self): + """When use_depth=False (default), depth_path in metadata is ignored.""" + from easi.core.memory import AgentMemory + from easi.tasks.ai2thor_rearrangement_2023.prompts import AI2THORRearrangement2023PromptBuilder + import tempfile, os + from PIL import Image + import numpy as np + + builder = AI2THORRearrangement2023PromptBuilder(use_depth=False) + builder.set_action_space(["done", "move_ahead"]) + + with tempfile.TemporaryDirectory() as tmpdir: + img = Image.fromarray(np.zeros((4, 4, 3), dtype=np.uint8)) + obs_path = os.path.join(tmpdir, "obs.png") + depth_path = os.path.join(tmpdir, "depth.png") + img.save(obs_path) + img.save(depth_path) + + memory = AgentMemory(action_space=["done", "move_ahead"]) + memory.current_observation = Observation( + rgb_path=obs_path, + metadata={"depth_path": depth_path}, + ) + memory.task_description = "Rearrange objects." + + messages = builder.build_messages(memory) + last_user = messages[-1] + # Should have only 1 image (RGB current), depth excluded + image_parts = [p for p in last_user["content"] if p.get("type") == "image_url"] + assert len(image_parts) == 1 + + def test_dynamic_system_prompt_all_sensors(self): + """System prompt should describe all active sensors.""" + from easi.tasks.ai2thor_rearrangement_2023.prompts import AI2THORRearrangement2023PromptBuilder + from easi.core.memory import AgentMemory + + builder = AI2THORRearrangement2023PromptBuilder( + use_rgb=True, use_depth=True, use_gps=True, use_goal_image=True, + ) + builder.set_action_space(["done"]) + memory = AgentMemory(action_space=["done"]) + memory.current_observation = Observation(rgb_path="/tmp/fake.png") + memory.task_description = "Test." + + messages = builder.build_messages(memory) + system = messages[0]["content"] + assert "RGB Image — Current Scene" in system + assert "Depth Image" in system + assert "RGB Image — Goal Scene" in system + assert "GPS Data" in system + + def test_dynamic_system_prompt_rgb_only(self): + """System prompt should only mention RGB when other sensors are off.""" + from easi.tasks.ai2thor_rearrangement_2023.prompts import AI2THORRearrangement2023PromptBuilder + from easi.core.memory import AgentMemory + + builder = AI2THORRearrangement2023PromptBuilder( + use_rgb=True, use_depth=False, use_gps=False, use_goal_image=False, + ) + builder.set_action_space(["done"]) + memory = AgentMemory(action_space=["done"]) + memory.current_observation = Observation(rgb_path="/tmp/fake.png") + memory.task_description = "Test." + + messages = builder.build_messages(memory) + system = messages[0]["content"] + assert "RGB Image — Current Scene" in system + assert "Depth Image" not in system + assert "RGB Image — Goal Scene" not in system + assert "GPS Data" not in system + + def test_strategy_adapts_to_goal_image_toggle(self): + """Strategy section changes based on whether goal images are available.""" + from easi.tasks.ai2thor_rearrangement_2023.prompts import AI2THORRearrangement2023PromptBuilder + from easi.core.memory import AgentMemory + + with_goal = AI2THORRearrangement2023PromptBuilder(use_goal_image=True) + with_goal.set_action_space(["done"]) + mem = AgentMemory(action_space=["done"]) + mem.current_observation = Observation(rgb_path="/tmp/fake.png") + mem.task_description = "Test." + system_with = with_goal.build_messages(mem)[0]["content"] + + without_goal = AI2THORRearrangement2023PromptBuilder(use_goal_image=False) + without_goal.set_action_space(["done"]) + system_without = without_goal.build_messages(mem)[0]["content"] + + assert "Compare the current and goal images" in system_with + assert "Compare the current and goal images" not in system_without + assert "Explore the environment" in system_without + + def test_gps_excluded_when_toggled_off(self): + """GPS text should not appear when use_gps=False.""" + from easi.core.memory import AgentMemory + from easi.core.episode import Action + from easi.tasks.ai2thor_rearrangement_2023.prompts import AI2THORRearrangement2023PromptBuilder + + builder = AI2THORRearrangement2023PromptBuilder(use_gps=False) + builder.set_action_space(["done", "move_ahead"]) + + memory = AgentMemory(action_space=["done", "move_ahead"]) + obs_with_gps = Observation( + rgb_path="/tmp/fake.png", + metadata={ + "agent_x": 1.5, "agent_y": 0.87, "agent_z": -2.0, + "agent_rotation": 90.0, "agent_horizon": 0.0, + "held_object": "Bowl", + }, + ) + memory.record_step(obs_with_gps, Action(action_name="move_ahead"), llm_response="test") + memory.record_feedback("success") + memory.current_observation = Observation(rgb_path="/tmp/fake2.png") + memory.task_description = "Rearrange objects." + + messages = builder.build_messages(memory) + last_user = messages[-1] + text_parts = [p["text"] for p in last_user["content"] if p.get("type") == "text"] + text = " ".join(text_parts) + assert "GPS:" not in text + assert "Position:" not in text def test_parse_valid_response(self): from easi.core.memory import AgentMemory From b4127abb54f8947080d5ea36454c986260004c8c Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 24 Feb 2026 15:48:00 +0800 Subject: [PATCH 110/239] chore: remove obsolete backward-compat tests - Remove test_backward_compat_import for old json_repair import path - Remove TestAggregateMetricsBackwardCompat for deprecated aggregate_metrics --- tests/test_json_repair.py | 5 ----- tests/test_metrics.py | 15 --------------- 2 files changed, 20 deletions(-) diff --git a/tests/test_json_repair.py b/tests/test_json_repair.py index f6dad8c..b1103c8 100644 --- a/tests/test_json_repair.py +++ b/tests/test_json_repair.py @@ -69,8 +69,3 @@ def test_full_llm_output_with_single_quotes(self): data = json.loads(result) assert data["executable_plan"][0]["action_id"] == 14 assert data["executable_plan"][0]["action_name"] == "find a Mug" - - def test_backward_compat_import(self): - """Old import path still works via re-export.""" - from easi.tasks.ebalfred.json_repair import fix_json as old_fix_json - assert old_fix_json is fix_json diff --git a/tests/test_metrics.py b/tests/test_metrics.py index 096b5a2..d6591ee 100644 --- a/tests/test_metrics.py +++ b/tests/test_metrics.py @@ -140,21 +140,6 @@ def aggregate_results(self, records): assert agg["total_steps"] == 3.0 -class TestAggregateMetricsBackwardCompat: - """Verify old aggregate_metrics() still works for any existing callers.""" - - def test_aggregate_metrics_unchanged(self): - from easi.evaluation.metrics import aggregate_metrics - results = [ - {"success": 1.0, "num_steps": 10.0}, - {"success": 0.0, "num_steps": 20.0}, - ] - summary = aggregate_metrics(results) - assert summary["num_episodes"] == 2 - assert summary["avg_success"] == 0.5 - assert summary["success_rate"] == 0.5 - - class TestSummaryJsonStructure: """Test that summary.json nests metrics under 'metrics' key.""" From 33da190c6f3faa7741046b1b80bc3a1a5a6ee4e3 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 24 Feb 2026 15:53:19 +0800 Subject: [PATCH 111/239] fix: aggregate_results crashes on non-numeric episode_results values The runner injects string fields (episode_id, instruction) into the metrics dict which ends up in episode_results. The aggregation was trying to sum() these strings. Filter to (int, float) only. --- easi/tasks/ai2thor_rearrangement_2023/task.py | 4 +-- tests/test_ai2thor_rearrangement_2023_task.py | 34 +++++++++++++++++++ 2 files changed, 36 insertions(+), 2 deletions(-) diff --git a/easi/tasks/ai2thor_rearrangement_2023/task.py b/easi/tasks/ai2thor_rearrangement_2023/task.py index 8c3c5e1..c4588d6 100644 --- a/easi/tasks/ai2thor_rearrangement_2023/task.py +++ b/easi/tasks/ai2thor_rearrangement_2023/task.py @@ -124,8 +124,8 @@ def aggregate_results( values = [] for r in records: v = r.episode_results.get(key) - if v is not None and not (isinstance(v, float) and math.isnan(v)): - values.append(v) + if isinstance(v, (int, float)) and not math.isnan(v): + values.append(float(v)) if values: agg[key] = sum(values) / len(values) else: diff --git a/tests/test_ai2thor_rearrangement_2023_task.py b/tests/test_ai2thor_rearrangement_2023_task.py index df11254..167f9ab 100644 --- a/tests/test_ai2thor_rearrangement_2023_task.py +++ b/tests/test_ai2thor_rearrangement_2023_task.py @@ -197,6 +197,40 @@ def test_aggregate_filters_nan_success_length(self): # success_length should average only non-NaN: 3.0 assert agg["success_length"] == 3.0 + def test_aggregate_skips_non_numeric_values(self): + """String fields (episode_id, instruction) injected by the runner must not crash sum().""" + task = _make_task() + + records = [ + EpisodeRecord( + episode={"id": "ep1"}, trajectory=[], + episode_results={ + "success": 1.0, "num_steps": 3.0, + # Runner injects these string fields into episode_results + "episode_id": "FloorPlan21__0", + "instruction": "Move the bowl.", + "elapsed_seconds": 5.2, + }, + ), + EpisodeRecord( + episode={"id": "ep2"}, trajectory=[], + episode_results={ + "success": 0.0, "num_steps": 7.0, + "episode_id": "FloorPlan22__0", + "instruction": "Move the cup.", + "elapsed_seconds": 8.1, + }, + ), + ] + # Should not raise TypeError + agg = task.aggregate_results(records) + assert agg["success"] == 0.5 + assert agg["num_steps"] == 5.0 + assert agg["elapsed_seconds"] == pytest.approx(6.65) + # String keys should be NaN (skipped) + assert math.isnan(agg["episode_id"]) + assert math.isnan(agg["instruction"]) + class TestPromptBuilder: def _make_builder(self): From f3997bbd62d36b274bd18304c78b828f38ddd61a Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 24 Feb 2026 16:28:49 +0800 Subject: [PATCH 112/239] fix: aggregate only official metrics + runtime stats in summary Restrict aggregation to the 6 paper metrics (SR, SRwD, PuSR, PuLen, SuLen, Len) plus runtime stats (avg/total elapsed seconds, avg LLM calls). Excludes runner-injected junk keys (episode_id, instruction, llm_usage dict) from summary.json metrics. --- easi/tasks/ai2thor_rearrangement_2023/task.py | 39 +++++++++++++++---- tests/test_ai2thor_rearrangement_2023_task.py | 39 +++++++++++++------ 2 files changed, 59 insertions(+), 19 deletions(-) diff --git a/easi/tasks/ai2thor_rearrangement_2023/task.py b/easi/tasks/ai2thor_rearrangement_2023/task.py index c4588d6..4be2594 100644 --- a/easi/tasks/ai2thor_rearrangement_2023/task.py +++ b/easi/tasks/ai2thor_rearrangement_2023/task.py @@ -104,23 +104,29 @@ def evaluate_episode( "success_length": success_length, } + # The 6 official paper metrics (+ num_steps for convenience) + METRIC_KEYS = ( + "success", # SR — Episode Success % + "prop_fixed_strict", # SRwD — Ep-Success w/o Disturbance % + "pickup_success_rate", # PuSR — PickUp Success % + "num_pickup_actions", # PuLen — Ep-Len for PickUp + "success_length", # SuLen — Ep-Len for Success + "num_steps", # Len — Ep-Len + ) + def aggregate_results( self, records: list[EpisodeRecord] ) -> dict[str, float]: - """Aggregate metrics across episodes. + """Aggregate the 6 official paper metrics plus runtime stats. Handles NaN success_length (only averages successful episodes). """ if not records: return {} - # Collect all metric keys - all_keys = set() - for r in records: - all_keys.update(r.episode_results.keys()) - + # 1. Official paper metrics agg = {} - for key in sorted(all_keys): + for key in self.METRIC_KEYS: values = [] for r in records: v = r.episode_results.get(key) @@ -131,6 +137,25 @@ def aggregate_results( else: agg[key] = float("nan") + # 2. Runtime stats + elapsed_values = [] + llm_call_counts = [] + for r in records: + elapsed = r.episode_results.get("elapsed_seconds") + if isinstance(elapsed, (int, float)): + elapsed_values.append(float(elapsed)) + + usage = r.episode_results.get("llm_usage") + if isinstance(usage, dict): + calls = usage.get("total_calls", 0) + llm_call_counts.append(float(calls)) + + if elapsed_values: + agg["avg_elapsed_seconds"] = sum(elapsed_values) / len(elapsed_values) + agg["total_elapsed_seconds"] = sum(elapsed_values) + if llm_call_counts: + agg["avg_llm_calls"] = sum(llm_call_counts) / len(llm_call_counts) + return agg def _get_builtin_episodes(self) -> list[dict]: diff --git a/tests/test_ai2thor_rearrangement_2023_task.py b/tests/test_ai2thor_rearrangement_2023_task.py index 167f9ab..bd236f9 100644 --- a/tests/test_ai2thor_rearrangement_2023_task.py +++ b/tests/test_ai2thor_rearrangement_2023_task.py @@ -197,39 +197,54 @@ def test_aggregate_filters_nan_success_length(self): # success_length should average only non-NaN: 3.0 assert agg["success_length"] == 3.0 - def test_aggregate_skips_non_numeric_values(self): - """String fields (episode_id, instruction) injected by the runner must not crash sum().""" + def test_aggregate_only_official_metrics_and_runtime_stats(self): + """Aggregation outputs 6 paper metrics + runtime stats, nothing else.""" task = _make_task() records = [ EpisodeRecord( episode={"id": "ep1"}, trajectory=[], episode_results={ - "success": 1.0, "num_steps": 3.0, - # Runner injects these string fields into episode_results + "success": 1.0, "prop_fixed_strict": 1.0, + "num_steps": 3.0, "success_length": 3.0, + "pickup_success_rate": 1.0, "num_pickup_actions": 1.0, + # Runner-injected fields "episode_id": "FloorPlan21__0", "instruction": "Move the bowl.", - "elapsed_seconds": 5.2, + "elapsed_seconds": 5.0, + "llm_usage": {"total_calls": 2, "total_tokens": 100}, }, ), EpisodeRecord( episode={"id": "ep2"}, trajectory=[], episode_results={ - "success": 0.0, "num_steps": 7.0, + "success": 0.0, "prop_fixed_strict": 0.0, + "num_steps": 7.0, "success_length": float("nan"), + "pickup_success_rate": 0.5, "num_pickup_actions": 2.0, "episode_id": "FloorPlan22__0", "instruction": "Move the cup.", - "elapsed_seconds": 8.1, + "elapsed_seconds": 9.0, + "llm_usage": {"total_calls": 4, "total_tokens": 200}, }, ), ] - # Should not raise TypeError agg = task.aggregate_results(records) + + # Official paper metrics assert agg["success"] == 0.5 assert agg["num_steps"] == 5.0 - assert agg["elapsed_seconds"] == pytest.approx(6.65) - # String keys should be NaN (skipped) - assert math.isnan(agg["episode_id"]) - assert math.isnan(agg["instruction"]) + assert agg["success_length"] == 3.0 # only successful ep + + # Runtime stats + assert agg["avg_elapsed_seconds"] == 7.0 + assert agg["total_elapsed_seconds"] == 14.0 + assert agg["avg_llm_calls"] == 3.0 + + # Junk keys excluded + assert "episode_id" not in agg + assert "instruction" not in agg + assert "elapsed_seconds" not in agg + assert "llm_usage" not in agg class TestPromptBuilder: From 1840cf2e59b55b75d0635f8e3ccd6d72a4e8c265 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 24 Feb 2026 17:27:30 +0800 Subject: [PATCH 113/239] fix: warn agent that all objects are evaluated, not just listed ones Success requires ALL objects in the scene to be in their goal positions. Add warning after the Goal section explaining this, and add a rule to avoid disturbing already-correct objects while navigating. --- easi/tasks/ai2thor_rearrangement_2023/prompts.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/easi/tasks/ai2thor_rearrangement_2023/prompts.py b/easi/tasks/ai2thor_rearrangement_2023/prompts.py index ff32cd0..36fe5f2 100644 --- a/easi/tasks/ai2thor_rearrangement_2023/prompts.py +++ b/easi/tasks/ai2thor_rearrangement_2023/prompts.py @@ -27,6 +27,8 @@ ## Goal {instruction} +**Important:** You are evaluated on the final state of ALL objects in the scene, not just the ones listed above. If you accidentally bump into or displace objects that were already in their correct positions, it counts against you. Navigate carefully. + ## Observations At each step you receive the following sensor inputs: {observation_description} @@ -49,6 +51,7 @@ - drop_held_object_with_snap works best when the goal location is visible - If pickup fails, try moving closer or adjusting your view angle - Grid movement: 0.25m steps, 90° rotations +- **Do NOT disturb objects already in their correct positions** — avoid bumping into furniture or pushing items while navigating. Take careful, minimal paths. """ OUTPUT_SCHEMA = """\ From 1d18edd7e0b64f5bec7e112532671aee891837ca Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 24 Feb 2026 20:10:13 +0800 Subject: [PATCH 114/239] fix: remove stale object-list reference from prompt warning Instruction no longer lists specific displaced objects, so remove "not just the ones listed above" phrasing. --- easi/tasks/ai2thor_rearrangement_2023/prompts.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/easi/tasks/ai2thor_rearrangement_2023/prompts.py b/easi/tasks/ai2thor_rearrangement_2023/prompts.py index 36fe5f2..f5e9f62 100644 --- a/easi/tasks/ai2thor_rearrangement_2023/prompts.py +++ b/easi/tasks/ai2thor_rearrangement_2023/prompts.py @@ -27,7 +27,7 @@ ## Goal {instruction} -**Important:** You are evaluated on the final state of ALL objects in the scene, not just the ones listed above. If you accidentally bump into or displace objects that were already in their correct positions, it counts against you. Navigate carefully. +**Important:** You are evaluated on the final state of ALL objects in the scene. If you accidentally bump into or displace objects that are already in their correct positions, it counts against you. Navigate carefully. ## Observations At each step you receive the following sensor inputs: From d214f7f7d264441f1a05ac026e27d0a93d2bb28d Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Wed, 25 Feb 2026 10:41:54 +0800 Subject: [PATCH 115/239] feat: add AI2-THOR v3.3.5 simulator (arm mode) for ManipulaTHOR Adds a new simulator version targeting the exact ai2thor commit used by ManipulaTHOR (a84dd29). Includes conda env (Python 3.8), env manager, simulator stub, generic arm-mode bridge with IPC, and 16 tests. --- easi/simulators/ai2thor/manifest.yaml | 5 + easi/simulators/ai2thor/v3_3_5/__init__.py | 6 + easi/simulators/ai2thor/v3_3_5/bridge.py | 255 ++++++++++++++++++ easi/simulators/ai2thor/v3_3_5/conda_env.yaml | 6 + easi/simulators/ai2thor/v3_3_5/env_manager.py | 42 +++ .../ai2thor/v3_3_5/requirements.txt | 4 + easi/simulators/ai2thor/v3_3_5/simulator.py | 21 ++ tests/test_ai2thor_v335_simulator.py | 142 ++++++++++ 8 files changed, 481 insertions(+) create mode 100644 easi/simulators/ai2thor/v3_3_5/__init__.py create mode 100644 easi/simulators/ai2thor/v3_3_5/bridge.py create mode 100644 easi/simulators/ai2thor/v3_3_5/conda_env.yaml create mode 100644 easi/simulators/ai2thor/v3_3_5/env_manager.py create mode 100644 easi/simulators/ai2thor/v3_3_5/requirements.txt create mode 100644 easi/simulators/ai2thor/v3_3_5/simulator.py create mode 100644 tests/test_ai2thor_v335_simulator.py diff --git a/easi/simulators/ai2thor/manifest.yaml b/easi/simulators/ai2thor/manifest.yaml index d6d8356..96de5a6 100644 --- a/easi/simulators/ai2thor/manifest.yaml +++ b/easi/simulators/ai2thor/manifest.yaml @@ -7,6 +7,11 @@ versions: simulator_class: "easi.simulators.ai2thor.v2_1_0.simulator.AI2ThorSimulatorV210" env_manager_class: "easi.simulators.ai2thor.v2_1_0.env_manager.AI2ThorEnvManagerV210" python_version: "3.8" + v3_3_5: + description: "AI2-THOR ~3.3.5 (arm mode) — used by manipulathor" + simulator_class: "easi.simulators.ai2thor.v3_3_5.simulator.AI2ThorSimulatorV335" + env_manager_class: "easi.simulators.ai2thor.v3_3_5.env_manager.AI2ThorEnvManagerV335" + python_version: "3.8" v5_0_0: description: "AI2-THOR 5.0.0 — currently used by eb-navigation" simulator_class: "easi.simulators.ai2thor.v5_0_0.simulator.AI2ThorSimulatorV500" diff --git a/easi/simulators/ai2thor/v3_3_5/__init__.py b/easi/simulators/ai2thor/v3_3_5/__init__.py new file mode 100644 index 0000000..98bbdb3 --- /dev/null +++ b/easi/simulators/ai2thor/v3_3_5/__init__.py @@ -0,0 +1,6 @@ +"""AI2-THOR v3.3.5 (arm-mode API for ManipulaTHOR).""" + +from easi.simulators.ai2thor.v3_3_5.env_manager import AI2ThorEnvManagerV335 +from easi.simulators.ai2thor.v3_3_5.simulator import AI2ThorSimulatorV335 + +__all__ = ["AI2ThorSimulatorV335", "AI2ThorEnvManagerV335"] diff --git a/easi/simulators/ai2thor/v3_3_5/bridge.py b/easi/simulators/ai2thor/v3_3_5/bridge.py new file mode 100644 index 0000000..99202d9 --- /dev/null +++ b/easi/simulators/ai2thor/v3_3_5/bridge.py @@ -0,0 +1,255 @@ +"""Generic bridge for AI2-THOR v3.3.5 (arm-mode). + +Runs inside the easi_ai2thor_v3_3_5 conda env (Python 3.8). +Configures the controller with agentMode='arm' and FifoServer. +Task-specific bridges should subclass BaseBridge in the task layer. +""" +from __future__ import annotations + +import argparse +import json +import sys +from pathlib import Path + +import numpy as np + +_repo_root = Path(__file__).resolve().parents[4] +if str(_repo_root) not in sys.path: + sys.path.insert(0, str(_repo_root)) + +from easi.communication.filesystem import ( + poll_for_command, + write_response, + write_status, +) +from easi.communication.schemas import ( + make_error_response, + make_observation_response, + parse_action_from_command, +) +from easi.utils.logging import get_logger, setup_logging + +logger = get_logger(__name__) + +# Exact ai2thor commit used by ManipulaTHOR — tells the Controller which +# build to download instead of trying `git log` in site-packages. +COMMIT_ID = "a84dd29471ec2201f583de00257d84fac1a03de2" + +# Default controller kwargs for arm-mode AI2-THOR +DEFAULT_CONTROLLER_KWARGS = { + "gridSize": 0.25, + "width": 224, + "height": 224, + "visibilityDistance": 1.0, + "agentMode": "arm", + "fieldOfView": 100, + "agentControllerType": "mid-level", + "useMassThreshold": True, + "massThreshold": 10, + "autoSimulation": False, + "autoSyncTransforms": True, +} + + +class AI2ThorV335Bridge: + """Generic bridge for AI2-THOR v3.3.5 arm-mode.""" + + def __init__(self, workspace, data_dir=None, simulator_kwargs=None): + self.workspace = Path(workspace) + self.data_dir = Path(data_dir) if data_dir else None + self.simulator_kwargs = simulator_kwargs or {} + self.controller = None + self.step_count = 0 + self.episode_output_dir = None + + def start(self): + """Initialize the AI2-THOR controller.""" + import ai2thor.controller + import ai2thor.fifo_server + + kwargs = dict(DEFAULT_CONTROLLER_KWARGS) + # Allow simulator_kwargs to override defaults + for k in ( + "width", "height", "gridSize", "fieldOfView", + "visibilityDistance", "agentMode", "agentControllerType", + "renderDepthImage", + ): + if k in self.simulator_kwargs: + kwargs[k] = self.simulator_kwargs[k] + + kwargs["server_class"] = ai2thor.fifo_server.FifoServer + kwargs["commit_id"] = COMMIT_ID + self.controller = ai2thor.controller.Controller(**kwargs) + logger.info("AI2-THOR v3.3.5 controller started (arm mode)") + + def stop(self): + """Shut down the controller.""" + if self.controller is not None: + self.controller.stop() + self.controller = None + + def reset(self, reset_config): + """Reset to a scene. Returns IPC response dict.""" + scene = reset_config.get("scene", "FloorPlan1") + self.step_count = 0 + + self.controller.reset(scene) + # Standard arm-mode setup commands + self.controller.step(action="MakeAllObjectsMoveable") + self.controller.step( + action="MakeObjectsStaticKinematicMassThreshold" + ) + + self.episode_output_dir = reset_config.get("episode_output_dir") + return self._make_observation_response( + self.controller.last_event, + ) + + def step(self, action_name): + """Execute an action. Returns IPC response dict.""" + self.step_count += 1 + + success = False + done = False + + if action_name in ("done", "DoneMidLevel"): + done = True + success = True + else: + event = self.controller.step(action=action_name) + success = event.metadata["lastActionSuccess"] + + info = { + "action_name": action_name, + "action_success": success, + "feedback": "success" if success else "action failed", + } + + return self._make_observation_response( + self.controller.last_event, + reward=0.0, + done=done, + info=info, + ) + + def _make_observation_response( + self, event, reward=0.0, done=False, info=None, + ): + """Save RGB frame and build IPC response dict.""" + from PIL import Image + + save_dir = ( + Path(self.episode_output_dir) + if self.episode_output_dir + else self.workspace + ) + save_dir.mkdir(parents=True, exist_ok=True) + + rgb = ( + event.frame.copy() + if event.frame is not None + else np.zeros((224, 224, 3), dtype=np.uint8) + ) + rgb_path = save_dir / f"step_{self.step_count:04d}.png" + Image.fromarray(rgb).save(str(rgb_path)) + + agent = event.metadata.get("agent", {}) + pos = agent.get("position", {}) + rot = agent.get("rotation", {}) + pose = [ + pos.get("x", 0), + pos.get("y", 0), + pos.get("z", 0), + rot.get("y", 0), + agent.get("cameraHorizon", 0), + 0, + ] + + return make_observation_response( + rgb_path=str(rgb_path), + agent_pose=pose, + metadata={"step": str(self.step_count)}, + reward=reward, + done=done, + info=info, + ) + + def run(self): + """Main IPC loop.""" + self.start() + write_status(self.workspace, ready=True) + + while True: + command = poll_for_command(self.workspace, timeout=300.0) + cmd_type = command.get("type") + + if cmd_type == "reset": + reset_config = command.get("config", {}) + self.episode_output_dir = reset_config.get( + "episode_output_dir" + ) + + try: + response = self.reset(reset_config) + write_response(self.workspace, response) + except Exception as e: + logger.exception("Reset failed") + write_response( + self.workspace, make_error_response(str(e)) + ) + + elif cmd_type == "step": + action = parse_action_from_command(command) + logger.trace( + "Step %d: action=%s", + self.step_count + 1, + action.action_name, + ) + + try: + response = self.step(action.action_name) + write_response(self.workspace, response) + except Exception as e: + logger.exception("Step failed") + write_response( + self.workspace, make_error_response(str(e)) + ) + + elif cmd_type == "close": + logger.info("Close command received") + self.stop() + write_response(self.workspace, {"status": "ok"}) + break + + else: + write_response( + self.workspace, + make_error_response("Unknown command: %s" % cmd_type), + ) + + +def main(): + parser = argparse.ArgumentParser( + description="AI2-THOR v3.3.5 generic bridge (arm mode)" + ) + parser.add_argument("--workspace", type=Path, required=True) + parser.add_argument("--data-dir", type=Path, default=None) + parser.add_argument("--simulator-kwargs", type=str, default=None) + args, _ = parser.parse_known_args() + + setup_logging("DEBUG") + sim_kwargs = ( + json.loads(args.simulator_kwargs) + if args.simulator_kwargs + else {} + ) + bridge = AI2ThorV335Bridge( + workspace=args.workspace, + data_dir=args.data_dir, + simulator_kwargs=sim_kwargs, + ) + bridge.run() + + +if __name__ == "__main__": + main() diff --git a/easi/simulators/ai2thor/v3_3_5/conda_env.yaml b/easi/simulators/ai2thor/v3_3_5/conda_env.yaml new file mode 100644 index 0000000..38b490d --- /dev/null +++ b/easi/simulators/ai2thor/v3_3_5/conda_env.yaml @@ -0,0 +1,6 @@ +name: easi_ai2thor_v3_3_5 +channels: + - conda-forge + - defaults +dependencies: + - python=3.8 diff --git a/easi/simulators/ai2thor/v3_3_5/env_manager.py b/easi/simulators/ai2thor/v3_3_5/env_manager.py new file mode 100644 index 0000000..f9f18cf --- /dev/null +++ b/easi/simulators/ai2thor/v3_3_5/env_manager.py @@ -0,0 +1,42 @@ +"""Environment manager for AI2-THOR v3.3.5 (arm-mode API). + +Used by ManipulaTHOR. Requires Python 3.8 and Xvfb. +Installs AI2-THOR from git commit a84dd29 (~v3.3.5). +""" +from __future__ import annotations + +from pathlib import Path + +from easi.core.base_env_manager import BaseEnvironmentManager + + +class AI2ThorEnvManagerV335(BaseEnvironmentManager): + """Environment manager for AI2-THOR 3.3.5.""" + + @property + def simulator_name(self) -> str: + return "ai2thor" + + @property + def version(self) -> str: + return "v3_3_5" + + @property + def default_render_platform(self) -> str: + return "auto" + + @property + def supported_render_platforms(self) -> list[str]: + return ["auto", "xvfb", "native"] + + def get_conda_env_yaml_path(self) -> Path: + return Path(__file__).parent / "conda_env.yaml" + + def get_requirements_txt_path(self) -> Path: + return Path(__file__).parent / "requirements.txt" + + def get_system_deps(self) -> list[str]: + return ["conda", "xvfb"] + + def get_validation_import(self) -> str: + return "import ai2thor; print(f'ai2thor {ai2thor.__version__}')" diff --git a/easi/simulators/ai2thor/v3_3_5/requirements.txt b/easi/simulators/ai2thor/v3_3_5/requirements.txt new file mode 100644 index 0000000..30fff77 --- /dev/null +++ b/easi/simulators/ai2thor/v3_3_5/requirements.txt @@ -0,0 +1,4 @@ +ai2thor @ git+https://github.com/allenai/ai2thor.git@a84dd29471ec2201f583de00257d84fac1a03de2 +numpy<1.24 +Pillow +scipy diff --git a/easi/simulators/ai2thor/v3_3_5/simulator.py b/easi/simulators/ai2thor/v3_3_5/simulator.py new file mode 100644 index 0000000..d1cdcb8 --- /dev/null +++ b/easi/simulators/ai2thor/v3_3_5/simulator.py @@ -0,0 +1,21 @@ +"""AI2-THOR v3.3.5 simulator (arm-mode, ManipulaTHOR).""" +from __future__ import annotations + +from pathlib import Path + +from easi.core.base_simulator import BaseSimulator + + +class AI2ThorSimulatorV335(BaseSimulator): + """AI2-THOR v3.3.5 simulator stub.""" + + @property + def name(self) -> str: + return "ai2thor" + + @property + def version(self) -> str: + return "v3_3_5" + + def _get_bridge_script_path(self) -> Path: + return Path(__file__).parent / "bridge.py" diff --git a/tests/test_ai2thor_v335_simulator.py b/tests/test_ai2thor_v335_simulator.py new file mode 100644 index 0000000..aa8685a --- /dev/null +++ b/tests/test_ai2thor_v335_simulator.py @@ -0,0 +1,142 @@ +"""Tests for AI2-THOR v3.3.5 simulator registration and configuration.""" +from __future__ import annotations + +import pytest + + +class TestRegistryDiscovery: + """Verify v3_3_5 is auto-discovered by the simulator registry.""" + + def test_explicit_key_registered(self): + from easi.simulators.registry import get_simulator_entry + + entry = get_simulator_entry("ai2thor:v3_3_5") + assert entry.name == "ai2thor" + assert entry.version == "v3_3_5" + + def test_listed_in_registry(self): + from easi.simulators.registry import list_simulators + + sims = list_simulators() + assert "ai2thor:v3_3_5" in sims + + def test_default_is_not_v335(self): + """v5_0_0 should remain the default, not v3_3_5.""" + from easi.simulators.registry import get_simulator_entry + + entry = get_simulator_entry("ai2thor") + assert entry.version == "v5_0_0" + + def test_python_version(self): + from easi.simulators.registry import get_simulator_entry + + entry = get_simulator_entry("ai2thor:v3_3_5") + assert entry.python_version == "3.8" + + def test_description(self): + from easi.simulators.registry import get_simulator_entry + + entry = get_simulator_entry("ai2thor:v3_3_5") + assert "arm mode" in entry.description + assert "manipulathor" in entry.description + + +class TestEnvManager: + """Test env_manager properties without actually installing.""" + + def test_simulator_name(self): + from easi.simulators.ai2thor.v3_3_5.env_manager import ( + AI2ThorEnvManagerV335, + ) + + mgr = AI2ThorEnvManagerV335() + assert mgr.simulator_name == "ai2thor" + + def test_version(self): + from easi.simulators.ai2thor.v3_3_5.env_manager import ( + AI2ThorEnvManagerV335, + ) + + mgr = AI2ThorEnvManagerV335() + assert mgr.version == "v3_3_5" + + def test_env_name(self): + from easi.simulators.ai2thor.v3_3_5.env_manager import ( + AI2ThorEnvManagerV335, + ) + + mgr = AI2ThorEnvManagerV335() + assert mgr.get_env_name() == "easi_ai2thor_v3_3_5" + + def test_conda_env_yaml_exists(self): + from easi.simulators.ai2thor.v3_3_5.env_manager import ( + AI2ThorEnvManagerV335, + ) + + mgr = AI2ThorEnvManagerV335() + assert mgr.get_conda_env_yaml_path().exists() + + def test_requirements_txt_exists(self): + from easi.simulators.ai2thor.v3_3_5.env_manager import ( + AI2ThorEnvManagerV335, + ) + + mgr = AI2ThorEnvManagerV335() + assert mgr.get_requirements_txt_path().exists() + + def test_system_deps_include_xvfb(self): + from easi.simulators.ai2thor.v3_3_5.env_manager import ( + AI2ThorEnvManagerV335, + ) + + mgr = AI2ThorEnvManagerV335() + deps = mgr.get_system_deps() + assert "conda" in deps + assert "xvfb" in deps + + def test_supported_render_platforms(self): + from easi.simulators.ai2thor.v3_3_5.env_manager import ( + AI2ThorEnvManagerV335, + ) + + mgr = AI2ThorEnvManagerV335() + platforms = mgr.supported_render_platforms + assert "auto" in platforms + assert "xvfb" in platforms + assert "native" in platforms + + def test_default_render_platform(self): + from easi.simulators.ai2thor.v3_3_5.env_manager import ( + AI2ThorEnvManagerV335, + ) + + mgr = AI2ThorEnvManagerV335() + assert mgr.default_render_platform == "auto" + + +class TestSimulator: + """Test simulator stub properties.""" + + def test_name(self): + from easi.simulators.ai2thor.v3_3_5.simulator import ( + AI2ThorSimulatorV335, + ) + + sim = AI2ThorSimulatorV335() + assert sim.name == "ai2thor" + + def test_version(self): + from easi.simulators.ai2thor.v3_3_5.simulator import ( + AI2ThorSimulatorV335, + ) + + sim = AI2ThorSimulatorV335() + assert sim.version == "v3_3_5" + + def test_bridge_path_exists(self): + from easi.simulators.ai2thor.v3_3_5.simulator import ( + AI2ThorSimulatorV335, + ) + + sim = AI2ThorSimulatorV335() + assert sim._get_bridge_script_path().exists() From 0490a041857715fcb451abfd1216d7d4218abfba Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Thu, 26 Feb 2026 11:40:35 +0800 Subject: [PATCH 116/239] feat: add ManipulaTHOR Arm Point Navigation task integration Integrate ManipulaTHOR APND benchmark into EASI with 13 discrete actions, 6 paper-aligned metrics, GPS sensors, depth colormap rendering, toggleable verbose feedback, and 6 dataset splits from HuggingFace. --- .../ai2thor/v3_3_5/requirements.txt | 1 + easi/tasks/manipulathor/__init__.py | 0 easi/tasks/manipulathor/_base.yaml | 33 + easi/tasks/manipulathor/actions.py | 58 ++ easi/tasks/manipulathor/bridge.py | 138 ++++ .../config/manipulathor_examples.json | 4 + .../manipulathor_seen_scenes_novel.yaml | 9 + .../manipulathor/manipulathor_test_novel.yaml | 9 + .../manipulathor/manipulathor_test_seen.yaml | 9 + .../manipulathor/manipulathor_train.yaml | 9 + .../manipulathor/manipulathor_val_novel.yaml | 9 + .../manipulathor/manipulathor_val_seen.yaml | 9 + easi/tasks/manipulathor/prompts.py | 280 ++++++++ easi/tasks/manipulathor/task.py | 158 +++++ easi/tasks/manipulathor/vendor/__init__.py | 0 .../manipulathor/vendor/manipulathor_env.py | 617 ++++++++++++++++++ tests/test_manipulathor_task.py | 394 +++++++++++ 17 files changed, 1737 insertions(+) create mode 100644 easi/tasks/manipulathor/__init__.py create mode 100644 easi/tasks/manipulathor/_base.yaml create mode 100644 easi/tasks/manipulathor/actions.py create mode 100644 easi/tasks/manipulathor/bridge.py create mode 100644 easi/tasks/manipulathor/config/manipulathor_examples.json create mode 100644 easi/tasks/manipulathor/manipulathor_seen_scenes_novel.yaml create mode 100644 easi/tasks/manipulathor/manipulathor_test_novel.yaml create mode 100644 easi/tasks/manipulathor/manipulathor_test_seen.yaml create mode 100644 easi/tasks/manipulathor/manipulathor_train.yaml create mode 100644 easi/tasks/manipulathor/manipulathor_val_novel.yaml create mode 100644 easi/tasks/manipulathor/manipulathor_val_seen.yaml create mode 100644 easi/tasks/manipulathor/prompts.py create mode 100644 easi/tasks/manipulathor/task.py create mode 100644 easi/tasks/manipulathor/vendor/__init__.py create mode 100644 easi/tasks/manipulathor/vendor/manipulathor_env.py create mode 100644 tests/test_manipulathor_task.py diff --git a/easi/simulators/ai2thor/v3_3_5/requirements.txt b/easi/simulators/ai2thor/v3_3_5/requirements.txt index 30fff77..5f781c7 100644 --- a/easi/simulators/ai2thor/v3_3_5/requirements.txt +++ b/easi/simulators/ai2thor/v3_3_5/requirements.txt @@ -2,3 +2,4 @@ ai2thor @ git+https://github.com/allenai/ai2thor.git@a84dd29471ec2201f583de00257 numpy<1.24 Pillow scipy +matplotlib diff --git a/easi/tasks/manipulathor/__init__.py b/easi/tasks/manipulathor/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/easi/tasks/manipulathor/_base.yaml b/easi/tasks/manipulathor/_base.yaml new file mode 100644 index 0000000..12f36cb --- /dev/null +++ b/easi/tasks/manipulathor/_base.yaml @@ -0,0 +1,33 @@ +display_name: "ManipulaTHOR" +description: "ManipulaTHOR Arm Point Navigation — pick up an object and move it to a goal location in AI2-THOR kitchens" +simulator: "ai2thor:v3_3_5" +task_class: "easi.tasks.manipulathor.task.ManipulaTHORTask" + +max_steps: 200 + +dataset: + source: huggingface + repo_id: "oscarqjh/manipulathor_easi" + +simulator_configs: + render_platform: auto + width: 224 + height: 224 + renderDepthImage: true + verbose_feedback: true + additional_deps: + - "matplotlib" + +agent: + prompt_builder: "easi.tasks.manipulathor.prompts.ManipulaTHORPromptBuilder" + prompt_builder_kwargs: + n_shot: 0 + use_feedback: true + chat_history: false + max_steps: 200 + use_rgb: true + use_gps: true + use_depth: false + generation_kwargs: + temperature: 0 + max_tokens: 2048 diff --git a/easi/tasks/manipulathor/actions.py b/easi/tasks/manipulathor/actions.py new file mode 100644 index 0000000..d4be0c4 --- /dev/null +++ b/easi/tasks/manipulathor/actions.py @@ -0,0 +1,58 @@ +"""ManipulaTHOR Arm Point Navigation action space and constants.""" + +# --- Action names (from ithor_arm_constants.py) --- +MOVE_ARM_HEIGHT_P = "MoveArmHeightP" +MOVE_ARM_HEIGHT_M = "MoveArmHeightM" +MOVE_ARM_X_P = "MoveArmXP" +MOVE_ARM_X_M = "MoveArmXM" +MOVE_ARM_Y_P = "MoveArmYP" +MOVE_ARM_Y_M = "MoveArmYM" +MOVE_ARM_Z_P = "MoveArmZP" +MOVE_ARM_Z_M = "MoveArmZM" +MOVE_AHEAD = "MoveAheadContinuous" +ROTATE_RIGHT = "RotateRightContinuous" +ROTATE_LEFT = "RotateLeftContinuous" +PICKUP = "PickUpMidLevel" +DONE = "DoneMidLevel" + +# Ordered action list (matches original ArmPointNavTask._actions) +ACTION_SPACE = [ + MOVE_ARM_HEIGHT_P, + MOVE_ARM_HEIGHT_M, + MOVE_ARM_X_P, + MOVE_ARM_X_M, + MOVE_ARM_Y_P, + MOVE_ARM_Y_M, + MOVE_ARM_Z_P, + MOVE_ARM_Z_M, + MOVE_AHEAD, + ROTATE_RIGHT, + ROTATE_LEFT, + PICKUP, + DONE, +] + +# --- Constants (from ithor_arm_constants.py) --- +MOVE_ARM_CONSTANT = 0.05 +MOVE_ARM_HEIGHT_CONSTANT = MOVE_ARM_CONSTANT +MOVE_THR = 0.01 # Threshold for disturbance detection +ARM_MIN_HEIGHT = 0.450998873 +ARM_MAX_HEIGHT = 1.8009994 +AGENT_BASE_LOCATION_Y = 0.9009995460510254 + +ADITIONAL_ARM_ARGS = { + "disableRendering": True, + "returnToStart": True, + "speed": 1, +} + +# MAX_STEPS from original paper +MAX_STEPS = 200 + +# AI2-THOR commit used by ManipulaTHOR +MANIPULATHOR_COMMIT_ID = "a84dd29471ec2201f583de00257d84fac1a03de2" + + +def get_action_space() -> list: + """Return the ManipulaTHOR action space as a list of action name strings.""" + return list(ACTION_SPACE) diff --git a/easi/tasks/manipulathor/bridge.py b/easi/tasks/manipulathor/bridge.py new file mode 100644 index 0000000..78f97d0 --- /dev/null +++ b/easi/tasks/manipulathor/bridge.py @@ -0,0 +1,138 @@ +"""ManipulaTHOR bridge for EASI's filesystem IPC protocol. + +Runs in the easi_ai2thor_v3_3_5 conda env (Python 3.8). +Extends BaseBridge to wrap ManipulaTHOREnv. +""" +from __future__ import annotations + +import json +import sys +from pathlib import Path + +import numpy as np + +# Ensure repo root is importable +_repo_root = Path(__file__).resolve().parents[3] +if str(_repo_root) not in sys.path: + sys.path.insert(0, str(_repo_root)) + +from easi.simulators.base_bridge import BaseBridge +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + + +class ManipulaTHORBridge(BaseBridge): + """Bridge for ManipulaTHOR Arm Point Navigation task. + + Wraps ManipulaTHOREnv in EASI's IPC protocol. Adds GPS state + sensors and depth information to observations. + """ + + def _create_env(self, reset_config, simulator_kwargs): + from easi.tasks.manipulathor.vendor.manipulathor_env import ManipulaTHOREnv + + # Pass any controller overrides from simulator_kwargs + controller_kwargs = {} + for k in ("width", "height", "gridSize", "fieldOfView", + "visibilityDistance", "renderDepthImage"): + if k in simulator_kwargs: + controller_kwargs[k] = simulator_kwargs[k] + + # Enable depth rendering for GPS-like observations + controller_kwargs.setdefault("renderDepthImage", True) + + verbose_feedback = simulator_kwargs.get("verbose_feedback", True) + env = ManipulaTHOREnv( + controller_kwargs=controller_kwargs, + verbose_feedback=verbose_feedback, + ) + return env + + def _on_reset(self, env, reset_config): + """Parse reset_config and delegate to ManipulaTHOREnv.reset().""" + # Parse JSON strings from HF dataset back to dicts + config = dict(reset_config) + for key in ("source_position", "target_position", + "initial_agent_pose", "arm_starting_pose"): + if isinstance(config.get(key), str): + config[key] = json.loads(config[key]) + + return env.reset(config) + + def _on_step(self, env, action_text): + """Execute named action via ManipulaTHOREnv.step().""" + return env.step(action_text) + + def _extract_image(self, obs): + """Extract RGB frame from observation dict.""" + return obs["frame"] + + def _extract_info(self, info): + """Pass through all metric fields from ManipulaTHOREnv.""" + return {k: v for k, v in info.items() + if isinstance(v, (int, float, str, bool))} + + def _save_images(self, obs): + """Save RGB and depth images with _.png naming.""" + from PIL import Image + import matplotlib + matplotlib.use("Agg") + import matplotlib.cm as cm + + save_dir = Path(self.episode_output_dir) if self.episode_output_dir else self.workspace + save_dir.mkdir(parents=True, exist_ok=True) + step_id = "%04d" % self.step_count + + # RGB + rgb_path = save_dir / f"{step_id}_rgb.png" + Image.fromarray(obs["frame"]).save(str(rgb_path)) + + # Depth (turbo colormap PNG) + depth_path = None + if obs.get("depth_frame") is not None: + depth = obs["depth_frame"] + d_min, d_max = depth.min(), depth.max() + if d_max - d_min > 1e-6: + depth_norm = (depth - d_min) / (d_max - d_min) + else: + depth_norm = np.zeros_like(depth) + colormap = cm.get_cmap("turbo") + depth_colored = (colormap(depth_norm)[:, :, :3] * 255).astype(np.uint8) + depth_path = save_dir / f"{step_id}_depth.png" + Image.fromarray(depth_colored).save(str(depth_path)) + depth_path = str(depth_path) + + return str(rgb_path), depth_path + + def _make_response(self, obs, reward=0.0, done=False, info=None): + """Override to add GPS state + depth to observation metadata.""" + rgb_path, depth_path = self._save_images(obs) + + # Build metadata with GPS state + metadata = {"step": str(self.step_count)} + if self.env is not None: + gps = self.env.get_gps_state() + metadata["relative_current_obj_state"] = json.dumps(gps["relative_current_obj_state"]) + metadata["relative_obj_to_goal"] = json.dumps(gps["relative_obj_to_goal"]) + metadata["relative_agent_arm_to_obj"] = json.dumps(gps["relative_agent_arm_to_obj"]) + metadata["pickedup_object"] = str(gps["pickedup_object"]) + + # Build info + clean_info = self._extract_info(info or {}) + clean_info["step"] = str(self.step_count) + + from easi.communication.schemas import make_observation_response + return make_observation_response( + rgb_path=rgb_path, + depth_path=depth_path, + agent_pose=[0.0, 0.0, 0.0, 0.0, 0.0, 0.0], + metadata=metadata, + reward=reward, + done=done, + info=clean_info, + ) + + +if __name__ == "__main__": + ManipulaTHORBridge.main() diff --git a/easi/tasks/manipulathor/config/manipulathor_examples.json b/easi/tasks/manipulathor/config/manipulathor_examples.json new file mode 100644 index 0000000..41c0205 --- /dev/null +++ b/easi/tasks/manipulathor/config/manipulathor_examples.json @@ -0,0 +1,4 @@ +[ + "Task: Pick up the Apple and move it to the target location.\nObject state: The Apple is 0.3m ahead and 0.1m to the right. Goal is 1.2m ahead. Arm is 0.5m from the object. Object not held.\nStep 0: I see a kitchen counter. The Apple is on the counter ahead of me. The goal location is further ahead. I need to first approach the Apple, extend my arm to reach it, pick it up, then navigate to the goal.\nPlan: Move arm forward to approach object, pick up, navigate to goal.\nActions: [{\"action_id\": 6, \"action_name\": \"MoveArmZP\"}, {\"action_id\": 6, \"action_name\": \"MoveArmZP\"}, {\"action_id\": 11, \"action_name\": \"PickUpMidLevel\"}]", + "Task: Pick up the Mug and move it to the target location.\nObject state: The Mug is 0.1m ahead. Object is held. Goal is 0.8m ahead and 0.2m to the left. Arm is holding the object.\nStep 5: I have picked up the Mug. Now I need to navigate towards the goal position. The goal is ahead and to the left.\nPlan: Move ahead, rotate left to align with goal, move arm to place.\nActions: [{\"action_id\": 8, \"action_name\": \"MoveAheadContinuous\"}, {\"action_id\": 10, \"action_name\": \"RotateLeftContinuous\"}, {\"action_id\": 8, \"action_name\": \"MoveAheadContinuous\"}]" +] diff --git a/easi/tasks/manipulathor/manipulathor_seen_scenes_novel.yaml b/easi/tasks/manipulathor/manipulathor_seen_scenes_novel.yaml new file mode 100644 index 0000000..b6727e3 --- /dev/null +++ b/easi/tasks/manipulathor/manipulathor_seen_scenes_novel.yaml @@ -0,0 +1,9 @@ +extends: _base.yaml +name: manipulathor_seen_scenes_novel +display_name: "ManipulaTHOR SeenScenes-NovelObj" +description: "Seen scenes with novel objects — 19 train scenes, 6 novel objects, 6840 episodes" +dataset: + split: "seen_scenes_novel_obj" +agent: + prompt_builder_kwargs: + split: "seen_scenes_novel" diff --git a/easi/tasks/manipulathor/manipulathor_test_novel.yaml b/easi/tasks/manipulathor/manipulathor_test_novel.yaml new file mode 100644 index 0000000..49290c6 --- /dev/null +++ b/easi/tasks/manipulathor/manipulathor_test_novel.yaml @@ -0,0 +1,9 @@ +extends: _base.yaml +name: manipulathor_test_novel +display_name: "ManipulaTHOR Test-NovelObj" +description: "Test split with novel objects — 5 test scenes, 6 novel objects, 1800 episodes" +dataset: + split: "test_novel_obj" +agent: + prompt_builder_kwargs: + split: "test_novel" diff --git a/easi/tasks/manipulathor/manipulathor_test_seen.yaml b/easi/tasks/manipulathor/manipulathor_test_seen.yaml new file mode 100644 index 0000000..d160281 --- /dev/null +++ b/easi/tasks/manipulathor/manipulathor_test_seen.yaml @@ -0,0 +1,9 @@ +extends: _base.yaml +name: manipulathor_test_seen +display_name: "ManipulaTHOR Test-SeenObj" +description: "Test split with seen objects — 5 test scenes, 6 seen objects, 1800 episodes" +dataset: + split: "test_seen_obj" +agent: + prompt_builder_kwargs: + split: "test_seen" diff --git a/easi/tasks/manipulathor/manipulathor_train.yaml b/easi/tasks/manipulathor/manipulathor_train.yaml new file mode 100644 index 0000000..cffd338 --- /dev/null +++ b/easi/tasks/manipulathor/manipulathor_train.yaml @@ -0,0 +1,9 @@ +extends: _base.yaml +name: manipulathor_train +display_name: "ManipulaTHOR Train" +description: "Training split — 19 scenes, 6 seen objects, 6840 episodes" +dataset: + split: "train" +agent: + prompt_builder_kwargs: + split: "train" diff --git a/easi/tasks/manipulathor/manipulathor_val_novel.yaml b/easi/tasks/manipulathor/manipulathor_val_novel.yaml new file mode 100644 index 0000000..a8da382 --- /dev/null +++ b/easi/tasks/manipulathor/manipulathor_val_novel.yaml @@ -0,0 +1,9 @@ +extends: _base.yaml +name: manipulathor_val_novel +display_name: "ManipulaTHOR Val-NovelObj" +description: "Validation split with novel objects — 5 val scenes, 6 novel objects, 1800 episodes" +dataset: + split: "val_novel_obj" +agent: + prompt_builder_kwargs: + split: "val_novel" diff --git a/easi/tasks/manipulathor/manipulathor_val_seen.yaml b/easi/tasks/manipulathor/manipulathor_val_seen.yaml new file mode 100644 index 0000000..3b99e21 --- /dev/null +++ b/easi/tasks/manipulathor/manipulathor_val_seen.yaml @@ -0,0 +1,9 @@ +extends: _base.yaml +name: manipulathor_val_seen +display_name: "ManipulaTHOR Val-SeenObj" +description: "Validation split with seen objects — 5 val scenes, 6 seen objects, 1800 episodes" +dataset: + split: "val_seen_obj" +agent: + prompt_builder_kwargs: + split: "val_seen" diff --git a/easi/tasks/manipulathor/prompts.py b/easi/tasks/manipulathor/prompts.py new file mode 100644 index 0000000..2d517cf --- /dev/null +++ b/easi/tasks/manipulathor/prompts.py @@ -0,0 +1,280 @@ +"""ManipulaTHOR prompt builder for EASI LLM agents. + +Presents RGB image + GPS state sensors + action history. +Outputs JSON with executable plan of named action IDs. +""" +from __future__ import annotations + +import json +from pathlib import Path +from typing import Optional + +from easi.core.episode import Action +from easi.core.memory import AgentMemory +from easi.tasks.manipulathor.actions import ACTION_SPACE +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + +# ── System prompt template ────────────────────────────────────────────────── + +SYSTEM_PROMPT = """You are a robotic arm agent in a kitchen environment. Your task is to pick up a target object and move it to a goal location. + +## Environment +- You control a robotic arm mounted on a mobile base in a kitchen (AI2-THOR simulator). +- You observe: an RGB image from the agent's camera{sensor_note}. +- Each arm movement moves 0.05m. Navigation moves 0.2m forward or rotates 45°. +- Maximum {max_steps} steps per episode. + +## Available Actions (choose by ID 0-{max_id}): +{action_list} +{gps_description} +## Strategy for Using GPS Sensors +The GPS sensors give you precise spatial information in YOUR reference frame (agent-relative coordinates). + +**Phase 1 — Approach the object:** +- Check "Arm-to-Object Distance": this tells you how far your arm tip is from the target object on each axis. +- Positive X means the object is to your right; positive Z means it is ahead of you. +- Use MoveArmXP/XM, MoveArmYP/YM, MoveArmZP/ZM to reduce arm-to-object distance on each axis. +- Use MoveAheadContinuous / RotateLeft / RotateRight if the object is far (>0.5m) — navigation is faster than arm movement. + +**Phase 2 — Pick up the object:** +- When arm-to-object distance is small on all axes (<0.1m), use PickUpMidLevel (action 11). +- After pickup, check "Object Held" — it should read "Yes". + +**Phase 3 — Navigate to the goal:** +- Check "Object-to-Goal Distance": this tells you how far the object is from the goal. +- Navigate (MoveAhead, Rotate) to reduce the distance. The object moves with you when held. +- Use arm movements for fine positioning when close to the goal. + +**Phase 4 — Place and finish:** +- When Object-to-Goal Distance is small on all axes (<0.1m), use DoneMidLevel (action 12). + +{examples}""" + +OUTPUT_TEMPLATE = """ + +## Output Format +Respond in JSON with this exact structure: +```json +{{ + "visual_state_description": "Describe what you see in the image and the GPS state", + "reasoning_and_reflection": "Your reasoning about the current state and what to do next", + "language_plan": "Your plan in natural language", + "executable_plan": [ + {{"action_id": , "action_name": ""}}, + ... + ] +}} +``` +Output 1-5 actions in the executable_plan. Choose action IDs from the available actions list.""" + + +# ── Prompt builder ────────────────────────────────────────────────────────── + +class ManipulaTHORPromptBuilder: + """Formats ManipulaTHOR observations for VLM, parses responses into named actions.""" + + def __init__( + self, + n_shot: int = 0, + split: str = "test_seen", + use_feedback: bool = True, + chat_history: bool = False, + max_steps: int = 200, + use_rgb: bool = True, + use_gps: bool = True, + use_depth: bool = False, + ): + self.n_shot = n_shot + self.split = split + self.use_feedback = use_feedback + self.chat_history = chat_history + self.max_steps = max_steps + self.use_rgb = use_rgb + self.use_gps = use_gps + self.use_depth = use_depth + + # Load few-shot examples + examples_file = Path(__file__).parent / "config" / "manipulathor_examples.json" + if examples_file.exists(): + with open(examples_file) as f: + self._examples = json.load(f) + else: + self._examples = [] + + # Build action list string + self._action_list = "\n".join( + f" {i}: {name}" for i, name in enumerate(ACTION_SPACE) + ) + self._action_id_map = {name: i for i, name in enumerate(ACTION_SPACE)} + self._id_action_map = {i: name for i, name in enumerate(ACTION_SPACE)} + + def set_action_space(self, actions: list) -> None: + """Update action space (called by agent).""" + pass # ManipulaTHOR has a fixed action space + + def build_messages(self, memory: AgentMemory) -> list: + """Build OpenAI message format from agent memory.""" + prompt = self._build_prompt_text(memory) + return self._wrap_as_user_message(prompt, memory.current_observation) + + def _build_prompt_text(self, memory: AgentMemory) -> str: + """Build the full prompt text.""" + max_id = len(ACTION_SPACE) - 1 + + # Build examples section + examples_str = "" + if self.n_shot >= 1 and self._examples: + examples_str = "\n\n## Examples\n" + "\n\n".join( + f"### Example {i+1}:\n{ex}" + for i, ex in enumerate(self._examples[:self.n_shot]) + ) + + # Build sensor note for system prompt + sensor_parts = [] + if self.use_gps: + sensor_parts.append("GPS-like state sensors showing spatial relationships") + if self.use_depth: + sensor_parts.append("a depth image (colormap)") + sensor_note = (", and " + ", ".join(sensor_parts)) if sensor_parts else "" + + # GPS description section (only if GPS enabled) + gps_description = "" + if self.use_gps: + gps_description = """ +## GPS State Sensors +At each step you receive: +- **Object State** (6D): object position (x,y,z) and rotation (rx,ry,rz) relative to agent +- **Object-to-Goal Distance** (3D): absolute x,y,z distance from object to goal in agent frame +- **Arm-to-Object Distance** (3D): absolute x,y,z distance from arm tip to object in agent frame +- **Object Held**: whether you are currently holding the object + +""" + + # System prompt + prompt = SYSTEM_PROMPT.format( + max_steps=self.max_steps, + max_id=max_id, + action_list=self._action_list, + examples=examples_str, + sensor_note=sensor_note, + gps_description=gps_description, + ) + + # Task instruction + task_desc = memory.task_description + prompt += f"\n\n## Current Task: {task_desc}" + + # GPS state from current observation metadata (only if GPS enabled) + obs = memory.current_observation + if self.use_gps and obs and obs.metadata: + prompt += "\n\n## Current GPS State:" + gps_fields = [ + ("relative_current_obj_state", "Object Position & Rotation (agent-relative)"), + ("relative_obj_to_goal", "Object-to-Goal Distance"), + ("relative_agent_arm_to_obj", "Arm-to-Object Distance"), + ("pickedup_object", "Object Held"), + ] + for key, label in gps_fields: + val = obs.metadata.get(key) + if val is not None: + if key == "pickedup_object": + held = float(val) > 0.5 + prompt += f"\n- {label}: {'Yes' if held else 'No'}" + else: + try: + arr = json.loads(val) if isinstance(val, str) else val + formatted = [f"{v:.3f}" for v in arr] + prompt += f"\n- {label}: [{', '.join(formatted)}]" + except (json.JSONDecodeError, TypeError): + prompt += f"\n- {label}: {val}" + + # Action history + feedback + action_history = getattr(memory, 'action_history', []) + if action_history: + prompt += "\n\n## Action History:" + for i, (action_name, feedback) in enumerate(action_history): + action_id = self._action_id_map.get(action_name, -1) + if self.use_feedback: + prompt += f"\nStep {i+1}: action {action_id} ({action_name}) — {feedback}" + else: + prompt += f"\nStep {i+1}: action {action_id} ({action_name})" + + prompt += "\n\nBased on the action history and current state, plan your next actions." + + prompt += OUTPUT_TEMPLATE + return prompt + + def _wrap_as_user_message(self, prompt: str, observation) -> list: + """Wrap prompt + images as OpenAI user message. + + Includes RGB and/or depth images based on use_rgb and use_depth toggles. + """ + import base64 + + content = [] + + def _add_image(path, label): + try: + with open(path, "rb") as f: + b64 = base64.b64encode(f.read()).decode("utf-8") + content.append({ + "type": "image_url", + "image_url": {"url": f"data:image/png;base64,{b64}"}, + }) + except FileNotFoundError: + logger.warning("%s image not found: %s", label, path) + + # Add RGB image (before text, matching EASI convention) + if self.use_rgb and observation and observation.rgb_path: + _add_image(observation.rgb_path, "RGB") + + # Add depth colormap image if enabled + if self.use_depth and observation and observation.depth_path: + _add_image(observation.depth_path, "Depth") + + content.append({"type": "text", "text": prompt}) + return [{"role": "user", "content": content}] + + def parse_response(self, llm_response: str, memory: AgentMemory) -> list: + """Parse LLM JSON response into Action objects.""" + from easi.utils.json_repair import fix_json + + llm_response = fix_json(llm_response) + + try: + data = json.loads(llm_response) + except json.JSONDecodeError: + logger.warning("Failed to parse LLM response as JSON") + return [] + + plan = data.get("executable_plan", []) + if not isinstance(plan, list) or not plan: + logger.warning("No executable_plan in LLM response") + return [] + + actions = [] + for entry in plan: + if not isinstance(entry, dict): + continue + + action_name = None + if "action_id" in entry: + aid = entry["action_id"] + if isinstance(aid, int) and 0 <= aid < len(ACTION_SPACE): + action_name = self._id_action_map[aid] + if action_name is None: + action_name = entry.get("action_name", "") + + if action_name in self._action_id_map: + actions.append(Action(action_name=action_name)) + else: + logger.warning("Invalid action: '%s'", action_name) + break + + return actions + + def get_response_format(self, memory: AgentMemory) -> Optional[dict]: + """Return JSON schema for structured output (optional).""" + return None # Use free-form JSON for now diff --git a/easi/tasks/manipulathor/task.py b/easi/tasks/manipulathor/task.py new file mode 100644 index 0000000..5d961bb --- /dev/null +++ b/easi/tasks/manipulathor/task.py @@ -0,0 +1,158 @@ +"""ManipulaTHOR Arm Point Navigation task for EASI.""" +from __future__ import annotations + +from pathlib import Path + +from easi.core.base_task import BaseTask +from easi.core.episode import EpisodeRecord, StepResult +from easi.tasks.manipulathor.actions import get_action_space +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + + +class ManipulaTHORTask(BaseTask): + """ManipulaTHOR Arm Point Navigation benchmark. + + 6 evaluation splits, 13 discrete actions, 6 paper-aligned metrics. + Uses AI2-THOR v3.3.5 in arm mode via subprocess bridge. + """ + + def get_task_yaml_path(self) -> Path: + return Path(__file__).parent / "_base.yaml" + + def get_bridge_script_path(self) -> Path: + return Path(__file__).parent / "bridge.py" + + def _build_action_space(self) -> list: + return get_action_space() + + def get_instruction(self, episode: dict) -> str: + object_type = episode.get("object_type", "object") + return f"Pick up the {object_type} and move it to the goal location." + + def format_reset_config(self, episode: dict) -> dict: + """Convert HF episode row to bridge reset config. + + HF dataset fields: id, scene, object_type, object_id, + source_position (JSON str), target_position (JSON str), + initial_agent_pose (JSON str), arm_starting_pose (JSON str), ... + """ + return { + "episode_id": str(episode.get("id", "unknown")), + "scene": episode["scene"], + "object_type": episode.get("object_type", ""), + "object_id": episode["object_id"], + "source_position": episode["source_position"], # JSON str or dict + "target_position": episode["target_position"], # JSON str or dict + "initial_agent_pose": episode["initial_agent_pose"], # JSON str or dict + "arm_starting_pose": episode.get("arm_starting_pose"), # JSON str or dict + } + + def evaluate_episode( + self, episode: dict, trajectory: list[StepResult], + ) -> dict[str, float]: + """Extract per-episode metrics from trajectory. + + Returns dict with keys matching the 6 paper metrics: + - episode_success: 1.0 if task completed successfully + - pickup_success: 1.0 if object was picked up + - success_wo_disturb: 1.0 if success without moving other objects + - eplen_pickup: steps until pickup (0 if not picked up) + - eplen_success: total steps if successful (0 if not) + - num_steps: total episode length + """ + if not trajectory: + return { + "episode_success": 0.0, + "pickup_success": 0.0, + "success_wo_disturb": 0.0, + "eplen_pickup": 0.0, + "eplen_success": 0.0, + "num_steps": 0.0, + } + + last = trajectory[-1] + episode_success = last.info.get("episode_success", 0.0) + pickup_success = last.info.get("pickup_success", 0.0) + success_wo_disturb = last.info.get("success_wo_disturb", 0.0) + eplen_pickup = last.info.get("eplen_pickup", 0.0) + eplen_success = last.info.get("eplen_success", 0.0) + num_steps = float(len(trajectory)) + + return { + "episode_success": float(episode_success), + "pickup_success": float(pickup_success), + "success_wo_disturb": float(success_wo_disturb), + "eplen_pickup": float(eplen_pickup), + "eplen_success": float(eplen_success), + "num_steps": num_steps, + } + + def aggregate_results( + self, records: list[EpisodeRecord], + ) -> dict[str, float]: + """Custom aggregation for ManipulaTHOR's 6 paper metrics. + + Three metrics are simple averages across all episodes: + - Episode Success % + - PickUp Success % + - Ep-Len (average steps) + + One metric averages only over successful episodes: + - Ep-Success w/o Disturbance %: success_wo_disturb / num_successful + (Note: in the paper this is reported as a rate, not conditional avg. + We report both: the rate and the conditional average.) + + Two metrics are conditional averages: + - Ep-Len for PickUp: average eplen_pickup where pickup occurred + - Ep-Len for Success: average eplen_success where success occurred + """ + if not records: + return {} + + n = len(records) + + # Unconditional averages + ep_success_sum = sum(r.episode_results["episode_success"] for r in records) + pickup_sum = sum(r.episode_results["pickup_success"] for r in records) + steps_sum = sum(r.episode_results["num_steps"] for r in records) + + # Ep-Success w/o Disturbance: + # The paper reports this as a percentage of ALL episodes (not just successes). + # From the original code: result["metric/average/success_wo_disturb"] = (len(objects_moved) == 1) + # This is only set when _success is True, otherwise it's 0. + # So the paper metric = sum(success_wo_disturb) / n + wo_disturb_sum = sum(r.episode_results["success_wo_disturb"] for r in records) + + # Conditional: Ep-Len for PickUp (only episodes where pickup occurred) + pickup_episodes = [ + r for r in records if r.episode_results["pickup_success"] > 0 + ] + avg_eplen_pickup = ( + sum(r.episode_results["eplen_pickup"] for r in pickup_episodes) / len(pickup_episodes) + if pickup_episodes else 0.0 + ) + + # Conditional: Ep-Len for Success (only successful episodes) + success_episodes = [ + r for r in records if r.episode_results["episode_success"] > 0 + ] + avg_eplen_success = ( + sum(r.episode_results["eplen_success"] for r in success_episodes) / len(success_episodes) + if success_episodes else 0.0 + ) + + return { + "episode_success_rate": round(ep_success_sum / n, 4), + "pickup_success_rate": round(pickup_sum / n, 4), + "success_wo_disturb_rate": round(wo_disturb_sum / n, 4), + "avg_eplen_pickup": round(avg_eplen_pickup, 1), + "avg_eplen_success": round(avg_eplen_success, 1), + "avg_eplen": round(steps_sum / n, 1), + # Convenience aliases + "success_rate": round(ep_success_sum / n, 4), + "num_episodes": n, + "num_successful": len(success_episodes), + "num_picked_up": len(pickup_episodes), + } diff --git a/easi/tasks/manipulathor/vendor/__init__.py b/easi/tasks/manipulathor/vendor/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/easi/tasks/manipulathor/vendor/manipulathor_env.py b/easi/tasks/manipulathor/vendor/manipulathor_env.py new file mode 100644 index 0000000..ef6c120 --- /dev/null +++ b/easi/tasks/manipulathor/vendor/manipulathor_env.py @@ -0,0 +1,617 @@ +"""Self-contained ManipulaTHOR environment wrapping ai2thor.controller. + +Extracts essential logic from the original ManipulaTHOR codebase without +AllenAct dependencies. Handles episode setup, action execution, GPS sensor +computation, and metric state tracking. + +Reference files from original repo: +- ithor_arm/ithor_arm_environment.py (ManipulaTHOREnvironment) +- ithor_arm/ithor_arm_tasks.py (ArmPointNavTask) +- ithor_arm/ithor_arm_constants.py (constants) +- ithor_arm/arm_calculation_utils.py (coordinate transforms) +- ithor_arm/ithor_arm_task_samplers.py (episode setup) +""" +from __future__ import annotations + +import copy +import math +from typing import Any, Dict, List, Optional, Tuple + +import numpy as np + +# NOTE: ai2thor and scipy are available in the v3.3.5 conda env +import ai2thor.controller +import ai2thor.fifo_server +from scipy.spatial.transform import Rotation as R + +from easi.tasks.manipulathor.actions import ( + ACTION_SPACE, + ADITIONAL_ARM_ARGS, + AGENT_BASE_LOCATION_Y, + ARM_MAX_HEIGHT, + ARM_MIN_HEIGHT, + DONE, + MANIPULATHOR_COMMIT_ID, + MOVE_AHEAD, + MOVE_ARM_CONSTANT, + MOVE_ARM_HEIGHT_CONSTANT, + MOVE_THR, + PICKUP, + ROTATE_LEFT, + ROTATE_RIGHT, +) +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + + +# ── Coordinate transform utilities (from arm_calculation_utils.py) ────────── + +def _make_rotation_matrix(position: dict, rotation: dict) -> np.ndarray: + mat = np.zeros((4, 4)) + r = R.from_euler("xyz", [rotation["x"], rotation["y"], rotation["z"]], degrees=True) + mat[:3, :3] = r.as_matrix() + mat[3, 3] = 1 + mat[:3, 3] = [position["x"], position["y"], position["z"]] + return mat + + +def _position_rotation_from_mat(matrix: np.ndarray) -> dict: + rotation = R.from_matrix(matrix[:3, :3]).as_euler("xyz", degrees=True) + pos = matrix[:3, 3] + return { + "position": {"x": float(pos[0]), "y": float(pos[1]), "z": float(pos[2])}, + "rotation": {"x": float(rotation[0]), "y": float(rotation[1]), "z": float(rotation[2])}, + } + + +# Pre-computed inverse rotation matrices for agent-relative transforms +_INVERSE_ROTATIONS = {} +for _deg in range(0, 361, 45): + _r = R.from_euler("xyz", [0, _deg % 360, 0], degrees=True) + _INVERSE_ROTATIONS[_deg % 360] = np.linalg.inv(_r.as_matrix()) + + +def _find_closest_inverse(deg: float) -> np.ndarray: + for k, v in _INVERSE_ROTATIONS.items(): + if abs(k - deg) < 5: + return v + r = R.from_euler("xyz", [0, deg, 0], degrees=True) + return np.linalg.inv(r.as_matrix()) + + +def convert_world_to_agent_coordinate(world_obj: dict, agent_state: dict) -> dict: + """Convert world-frame object state to agent-relative coordinates. + + Safety: original code asserts agent only rotates around Y-axis. We warn + instead of asserting so the bridge doesn't crash mid-episode. + """ + pos = agent_state["position"] + rot = agent_state["rotation"] + + # Safety check: agent should only rotate around Y-axis (from original code) + if abs(rot.get("x", 0)) > 0.01 or abs(rot.get("z", 0)) > 0.01: + logger.warning( + "Agent rotation has non-zero x/z: %s — coordinate transform may be inaccurate", + rot, + ) + + agent_translation = [pos["x"], pos["y"], pos["z"]] + inv_rot = _find_closest_inverse(rot["y"]) + obj_mat = _make_rotation_matrix(world_obj["position"], world_obj.get("rotation", {"x": 0, "y": 0, "z": 0})) + obj_translation = np.matmul(inv_rot, obj_mat[:3, 3] - agent_translation) + obj_mat[:3, 3] = obj_translation + return _position_rotation_from_mat(obj_mat) + + +def _diff_position(p1: dict, p2: dict) -> dict: + """Absolute difference between two position dicts.""" + return {k: abs(p1[k] - p2[k]) for k in p1.keys()} + + +def _position_distance(s1: dict, s2: dict) -> float: + """Euclidean distance between two objects with 'position' keys.""" + p1, p2 = s1["position"], s2["position"] + return math.sqrt( + (p1["x"] - p2["x"]) ** 2 + + (p1["y"] - p2["y"]) ** 2 + + (p1["z"] - p2["z"]) ** 2 + ) + + +def _correct_nan_inf(d: dict) -> dict: + """Replace NaN/Inf values with 0.""" + return {k: (0 if (v != v or math.isinf(v)) else v) for k, v in d.items()} + + +# ── Main Environment ──────────────────────────────────────────────────────── + +# Default controller kwargs (from ENV_ARGS in ithor_arm_constants.py) +DEFAULT_CONTROLLER_KWARGS = { + "gridSize": 0.25, + "width": 224, + "height": 224, + "visibilityDistance": 1.0, + "agentMode": "arm", + "fieldOfView": 100, + "agentControllerType": "mid-level", + "useMassThreshold": True, + "massThreshold": 10, + "autoSimulation": False, + "autoSyncTransforms": True, +} + + +class ManipulaTHOREnv: + """Self-contained ManipulaTHOR environment for EASI bridge. + + Provides: + - Episode setup (reset scene, init arm, transport object, teleport agent) + - Action execution (13 discrete actions) + - GPS sensor computation (4 state vectors) + - Metric state tracking (success, pickup, disturbance) + """ + + def __init__(self, controller_kwargs: Optional[dict] = None, + verbose_feedback: bool = True): + self.controller: Optional[ai2thor.controller.Controller] = None + self._controller_kwargs = controller_kwargs or {} + self.verbose_feedback = verbose_feedback + + # Episode state + self.object_id: Optional[str] = None + self.object_type: Optional[str] = None + self.target_position: Optional[dict] = None # {"x":..., "y":..., "z":...} + self.source_position: Optional[dict] = None + self.initial_object_locations: Optional[dict] = None + + # Metric state + self.object_picked_up: bool = False + self.eplen_pickup: int = 0 + self._took_done_action: bool = False + self._success: bool = False + self._step_count: int = 0 + + def start(self): + """Initialize the AI2-THOR controller.""" + kwargs = dict(DEFAULT_CONTROLLER_KWARGS) + for k, v in self._controller_kwargs.items(): + kwargs[k] = v + kwargs["server_class"] = ai2thor.fifo_server.FifoServer + kwargs["commit_id"] = MANIPULATHOR_COMMIT_ID + self.controller = ai2thor.controller.Controller(**kwargs) + logger.info("ManipulaTHOR controller started (arm mode, commit=%s)", MANIPULATHOR_COMMIT_ID) + + def stop(self): + """Shut down the controller.""" + if self.controller is not None: + self.controller.stop() + self.controller = None + + def reset(self, reset_config: dict) -> dict: + """Reset to an episode. Returns initial observation dict. + + Args: + reset_config: Dict with keys: + scene, object_id, object_type, + source_position (dict), target_position (dict), + initial_agent_pose (dict), arm_starting_pose (dict) + + Returns: + obs dict with keys: frame, depth_frame, metadata + """ + if self.controller is None: + self.start() + + scene = reset_config["scene"] + self.object_id = reset_config["object_id"] + self.object_type = reset_config.get("object_type", "") + self.source_position = reset_config["source_position"] + self.target_position = reset_config["target_position"] + + # Reset metric state + self.object_picked_up = False + self.eplen_pickup = 0 + self._took_done_action = False + self._success = False + self._step_count = 0 + + # 1. Reset scene + standard setup + self._reset_scene(scene) + + # 2. Initialize arm + arm_pose = reset_config.get("arm_starting_pose") + self._initialize_arm(arm_pose) + + # 3. Transport object to source position + self._transport_object(self.object_id, self.source_position) + + # 4. Teleport agent to initial pose + agent_pose = reset_config["initial_agent_pose"] + self._teleport_agent(agent_pose) + + # 5. Record initial object locations for disturbance metric + self.initial_object_locations = self._get_current_object_locations() + + return self._build_obs() + + def step(self, action_name: str) -> Tuple[dict, float, bool, dict]: + """Execute one action. Returns (obs, reward, done, info). + + Returns: + obs: dict with keys: frame, depth_frame, metadata + reward: float (always 0.0 — no reward shaping for LLM eval) + done: bool + info: dict with metric fields + """ + self._step_count += 1 + + # Execute the action + action_success = self._execute_action(action_name) + + # Check pickup status + if not self.object_picked_up and self._is_object_in_hand(): + self.object_picked_up = True + self.eplen_pickup = self._step_count + + # Handle DONE action + done = False + if action_name == DONE: + self._took_done_action = True + done = True + obj_state = self._get_object_by_id(self.object_id) + goal_state = {"position": self.target_position} + self._success = ( + self.object_picked_up + and self._obj_state_proximity(obj_state, goal_state) + ) + action_success = self._success + + # Build info dict with all metric-relevant fields + info = { + "last_action_success": float(action_success), + "action_name": action_name, + "object_picked_up": float(self.object_picked_up), + "episode_success": float(self._success), + "pickup_success": float(self.object_picked_up), + } + + # Compute additional metrics on success + if self._success: + moved = self._get_objects_moved() + info["success_wo_disturb"] = float(len(moved) == 1) + info["eplen_success"] = float(self._step_count) + info["num_objects_disturbed"] = float(max(0, len(moved) - 1)) + + if self.object_picked_up and self.eplen_pickup > 0: + info["eplen_pickup"] = float(self.eplen_pickup) + + # Distance metrics (useful for feedback) + info["obj_to_goal_distance"] = self._obj_distance_from_goal() + info["arm_to_obj_distance"] = self._arm_distance_from_obj() + + # Feedback string for prompt builder + if action_success: + info["feedback"] = f"Action '{action_name}' succeeded." + else: + if self.verbose_feedback: + error_msg = self.controller.last_event.metadata.get("errorMessage", "") + if error_msg: + info["feedback"] = f"Action '{action_name}' failed. Reason: {error_msg}" + else: + info["feedback"] = f"Action '{action_name}' failed." + else: + info["feedback"] = f"Action '{action_name}' failed." + + if self._success: + info["feedback"] += " Task completed successfully!" + elif self._took_done_action and not self._success: + info["feedback"] += " Task failed — object not at goal position." + + obs = self._build_obs() + return obs, 0.0, done, info + + # ── Observation building ──────────────────────────────────────────── + + def _build_obs(self) -> dict: + """Build observation dict from current controller state.""" + event = self.controller.last_event + return { + "frame": event.frame.copy() if event.frame is not None else np.zeros((224, 224, 3), dtype=np.uint8), + "depth_frame": event.depth_frame.copy() if hasattr(event, 'depth_frame') and event.depth_frame is not None else None, + "metadata": event.metadata, + } + + def get_gps_state(self) -> dict: + """Compute GPS-like state sensors matching original ManipulaTHOR. + + Returns dict with: + relative_current_obj_state: list[float] (6,) — object pos+rot in agent frame + relative_obj_to_goal: list[float] (3,) — object-to-goal distance in agent frame + relative_agent_arm_to_obj: list[float] (3,) — arm-to-object distance in agent frame + pickedup_object: float — 1.0 if held, 0.0 otherwise + """ + agent_state = self.controller.last_event.metadata["agent"] + obj_info = self._get_object_by_id(self.object_id) + + # 1. relative_current_obj_state (6D): object pos+rot in agent frame + rel_obj = convert_world_to_agent_coordinate(obj_info, agent_state) + rel_obj_state = [ + rel_obj["position"]["x"], rel_obj["position"]["y"], rel_obj["position"]["z"], + rel_obj["rotation"]["x"], rel_obj["rotation"]["y"], rel_obj["rotation"]["z"], + ] + + # 2. relative_obj_to_goal (3D): distance from object to goal in agent frame + target_state = { + "position": self.target_position, + "rotation": {"x": 0, "y": 0, "z": 0}, + } + rel_curr = convert_world_to_agent_coordinate(obj_info, agent_state) + rel_goal = convert_world_to_agent_coordinate(target_state, agent_state) + obj_to_goal = _diff_position(rel_curr["position"], rel_goal["position"]) + rel_obj_to_goal = [obj_to_goal["x"], obj_to_goal["y"], obj_to_goal["z"]] + + # 3. relative_agent_arm_to_obj (3D): arm-to-object distance in agent frame + hand_state = self._get_absolute_hand_state() + rel_obj_agent = convert_world_to_agent_coordinate(obj_info, agent_state) + rel_hand = convert_world_to_agent_coordinate(hand_state, agent_state) + arm_to_obj = _diff_position(rel_obj_agent["position"], rel_hand["position"]) + rel_arm_to_obj = [arm_to_obj["x"], arm_to_obj["y"], arm_to_obj["z"]] + + # 4. pickedup_object + picked_up = 1.0 if self.object_picked_up else 0.0 + + return { + "relative_current_obj_state": rel_obj_state, + "relative_obj_to_goal": rel_obj_to_goal, + "relative_agent_arm_to_obj": rel_arm_to_obj, + "pickedup_object": picked_up, + } + + # ── Scene & arm setup ─────────────────────────────────────────────── + + def _reset_scene(self, scene: str): + """Reset scene with standard ManipulaTHOR setup commands.""" + self.controller.reset(scene) + self.controller.step(action="MakeAllObjectsMoveable") + self.controller.step(action="MakeObjectsStaticKinematicMassThreshold") + # Make all breakable objects unbreakable + breakable = set( + o["objectType"] + for o in self.controller.last_event.metadata["objects"] + if o.get("breakable") + ) + for obj_type in breakable: + self.controller.step(action="MakeObjectsOfTypeUnbreakable", objectType=obj_type) + + def _initialize_arm(self, arm_pose: Optional[dict] = None): + """Initialize arm to starting position (from arm_calculation_utils.initialize_arm). + + Args: + arm_pose: Dict with keys x, y, z, rotation, horizon. + If None, uses a sensible default. + """ + if arm_pose: + self.controller.step(dict( + action="TeleportFull", + standing=True, + x=arm_pose["x"], + y=arm_pose["y"], + z=arm_pose["z"], + rotation=dict(x=0, y=arm_pose["rotation"], z=0), + horizon=arm_pose["horizon"], + )) + self.controller.step(dict( + action="MoveArm", + position=dict(x=0.0, y=0, z=0.35), + **ADITIONAL_ARM_ARGS, + )) + self.controller.step(dict( + action="MoveArmBase", + y=0.8, + **ADITIONAL_ARM_ARGS, + )) + + def _transport_object(self, object_id: str, position: dict): + """Place object at a specific position (from transport_wrapper).""" + self.controller.step( + action="PlaceObjectAtPoint", + objectId=object_id, + position=position, + forceKinematic=True, + ) + self.controller.step(action="AdvancePhysicsStep", simSeconds=1.0) + + def _teleport_agent(self, agent_pose: dict): + """Teleport agent to a specific pose. + + Handles FLAT format from HF dataset: {x, y, z, rotation, horizon} + where 'rotation' is a float (Y-axis degrees) and 'horizon' is camera tilt. + """ + self.controller.step(dict( + action="TeleportFull", + standing=True, + x=agent_pose["x"], + y=agent_pose["y"], + z=agent_pose["z"], + rotation=dict(x=0, y=agent_pose["rotation"], z=0), + horizon=agent_pose["horizon"], + )) + + # ── Action execution ──────────────────────────────────────────────── + + def _execute_action(self, action_name: str) -> bool: + """Execute a named action and return success bool. + + Implements the action dispatch logic from ManipulaTHOREnvironment.step(). + """ + if action_name == DONE: + self.controller.step(action="Pass") + return True # success determined separately + + if action_name == PICKUP: + return self._execute_pickup() + + if "MoveArm" in action_name: + return self._execute_arm_action(action_name) + + if "Continuous" in action_name: + return self._execute_nav_action(action_name) + + logger.warning("Unknown action: %s", action_name) + return False + + def _execute_pickup(self) -> bool: + """Execute PickUpMidLevel action (from ManipulaTHOREnvironment.step).""" + if self._is_object_in_hand(): + self.controller.step(action="Pass") + return True + + pickupable = self.controller.last_event.metadata["arm"].get("pickupableObjects", []) + if self.object_id in pickupable: + self.controller.step(action="PickupObject") + held = self.controller.last_event.metadata["arm"].get("heldObjects", []) + if held and self.object_id not in held: + self.controller.step(action="ReleaseObject") + + self.controller.step(action="Pass") + return self._is_object_in_hand() + + def _execute_arm_action(self, action_name: str) -> bool: + """Execute arm movement action.""" + action_dict = dict(action=action_name) + action_dict.update(copy.deepcopy(ADITIONAL_ARM_ARGS)) + base_pos = self._get_current_arm_state() + + if "MoveArmHeight" in action_name: + action_dict["action"] = "MoveArmBase" + if action_name == "MoveArmHeightP": + base_pos["h"] += MOVE_ARM_HEIGHT_CONSTANT + elif action_name == "MoveArmHeightM": + base_pos["h"] -= MOVE_ARM_HEIGHT_CONSTANT + action_dict["y"] = base_pos["h"] + else: + action_dict["action"] = "MoveArm" + deltas = { + "MoveArmXP": ("x", MOVE_ARM_CONSTANT), + "MoveArmXM": ("x", -MOVE_ARM_CONSTANT), + "MoveArmYP": ("y", MOVE_ARM_CONSTANT), + "MoveArmYM": ("y", -MOVE_ARM_CONSTANT), + "MoveArmZP": ("z", MOVE_ARM_CONSTANT), + "MoveArmZM": ("z", -MOVE_ARM_CONSTANT), + } + axis, delta = deltas[action_name] + base_pos[axis] += delta + action_dict["position"] = { + k: v for k, v in base_pos.items() if k in ("x", "y", "z") + } + + self.controller.step(action_dict) + return self.controller.last_event.metadata["lastActionSuccess"] + + def _execute_nav_action(self, action_name: str) -> bool: + """Execute navigation action.""" + action_dict = dict(action=action_name) + action_dict.update(copy.deepcopy(ADITIONAL_ARM_ARGS)) + + if action_name == MOVE_AHEAD: + action_dict["action"] = "MoveAgent" + action_dict["ahead"] = 0.2 + elif action_name == ROTATE_RIGHT: + action_dict["action"] = "RotateAgent" + action_dict["degrees"] = 45 + elif action_name == ROTATE_LEFT: + action_dict["action"] = "RotateAgent" + action_dict["degrees"] = -45 + + self.controller.step(action_dict) + return self.controller.last_event.metadata["lastActionSuccess"] + + # ── State queries ─────────────────────────────────────────────────── + + def _is_object_in_hand(self) -> bool: + held = self.controller.last_event.metadata["arm"].get("heldObjects", []) + return self.object_id in held + + def _get_object_by_id(self, object_id: str) -> Optional[dict]: + for o in self.controller.last_event.metadata["objects"]: + if o["objectId"] == object_id: + o["position"] = _correct_nan_inf(o["position"]) + return o + return None + + def _get_absolute_hand_state(self) -> dict: + joints = self.controller.last_event.metadata["arm"]["joints"] + arm = copy.deepcopy(joints[-1]) + xyz = _correct_nan_inf(arm["position"]) + return {"position": xyz, "rotation": {"x": 0, "y": 0, "z": 0}} + + def _get_current_arm_state(self) -> dict: + """Get arm state in root-relative coordinates + normalized height.""" + event = self.controller.last_event + offset = event.metadata["agent"]["position"]["y"] - AGENT_BASE_LOCATION_Y + h_min = ARM_MIN_HEIGHT + offset + h_max = ARM_MAX_HEIGHT + offset + joints = event.metadata["arm"]["joints"] + arm = joints[-1] + xyz = copy.deepcopy(arm["rootRelativePosition"]) + height_arm = joints[0]["position"]["y"] + xyz["h"] = (height_arm - h_min) / (h_max - h_min) + return {k: (0 if (v != v or math.isinf(v)) else v) for k, v in xyz.items()} + + def _get_current_object_locations(self) -> dict: + result = {} + for o in self.controller.last_event.metadata["objects"]: + result[o["objectId"]] = dict( + position=copy.deepcopy(o["position"]), + rotation=copy.deepcopy(o["rotation"]), + ) + return result + + def _obj_state_proximity(self, obj_state: dict, goal_state: dict) -> bool: + """Check if object is within 0.1m of goal (per axis).""" + eps = MOVE_ARM_CONSTANT * 2 # = 0.1 + p1, p2 = obj_state["position"], goal_state["position"] + return ( + abs(p1["x"] - p2["x"]) < eps + and abs(p1["y"] - p2["y"]) < eps + and abs(p1["z"] - p2["z"]) < eps + ) + + def _obj_distance_from_goal(self) -> float: + obj = self._get_object_by_id(self.object_id) + if obj is None: + return float("inf") + return _position_distance(obj, {"position": self.target_position}) + + def _arm_distance_from_obj(self) -> float: + obj = self._get_object_by_id(self.object_id) + if obj is None: + return float("inf") + hand = self._get_absolute_hand_state() + return _position_distance(obj, hand) + + def _get_objects_moved(self) -> list: + """Return list of object IDs that moved since episode start.""" + if self.initial_object_locations is None: + return [] + current = self._get_current_object_locations() + moved = [] + for oid in current: + if oid not in self.initial_object_locations: + continue + curr_pose = current[oid] + init_pose = self.initial_object_locations[oid] + if not self._close_enough(curr_pose, init_pose, MOVE_THR): + moved.append(oid) + return moved + + @staticmethod + def _close_enough(current: dict, initial: dict, threshold: float) -> bool: + """Check if position and rotation are within threshold (per axis).""" + for k in ("x", "y", "z"): + if abs(current["position"][k] - initial["position"][k]) > threshold: + return False + if abs(current["rotation"][k] - initial["rotation"][k]) > threshold: + return False + return True diff --git a/tests/test_manipulathor_task.py b/tests/test_manipulathor_task.py new file mode 100644 index 0000000..6870b09 --- /dev/null +++ b/tests/test_manipulathor_task.py @@ -0,0 +1,394 @@ +"""Tests for ManipulaTHOR task integration (offline, no AI2-THOR).""" +import json +from pathlib import Path +from unittest.mock import MagicMock, patch + +import pytest + +from easi.core.episode import Action, EpisodeRecord, Observation, StepResult + + +# ── Task class tests ──────────────────────────────────────────────────────── + +class TestManipulaTHORTask: + """Test task class methods.""" + + @pytest.fixture + def task(self): + from easi.tasks.manipulathor.task import ManipulaTHORTask + return ManipulaTHORTask() + + def test_action_space(self, task): + assert len(task.action_space) == 13 + assert "MoveArmHeightP" in task.action_space + assert "PickUpMidLevel" in task.action_space + assert "DoneMidLevel" in task.action_space + assert "MoveAheadContinuous" in task.action_space + + def test_max_steps(self, task): + assert task.max_steps == 200 + + def test_simulator_key(self, task): + assert task.simulator_key == "ai2thor:v3_3_5" + + def test_bridge_script_path(self, task): + bridge = task.get_bridge_script_path() + assert bridge is not None + assert bridge.name == "bridge.py" + assert bridge.exists() + + def test_get_instruction(self, task): + episode = {"object_type": "Apple"} + assert task.get_instruction(episode) == "Pick up the Apple and move it to the goal location." + + def test_get_instruction_default(self, task): + episode = {} + assert task.get_instruction(episode) == "Pick up the object and move it to the goal location." + + def test_format_reset_config(self, task): + episode = { + "id": 42, + "scene": "FloorPlan2_physics", + "object_type": "Apple", + "object_id": "Apple|+01.98|+00.77|-00.04", + "source_position": '{"x": 1.0, "y": 0.5, "z": -0.3}', + "target_position": '{"x": 2.0, "y": 0.8, "z": 0.1}', + "initial_agent_pose": '{"x": 0.0, "y": 0.9, "z": 0.0, "rotation": 90, "horizon": 10}', + "arm_starting_pose": '{"x": 0.5, "y": 0.9, "z": 0.2, "rotation": 0, "horizon": 30}', + } + config = task.format_reset_config(episode) + assert config["episode_id"] == "42" + assert config["scene"] == "FloorPlan2_physics" + assert config["object_id"] == "Apple|+01.98|+00.77|-00.04" + assert config["source_position"] == '{"x": 1.0, "y": 0.5, "z": -0.3}' + assert config["target_position"] == '{"x": 2.0, "y": 0.8, "z": 0.1}' + + def test_evaluate_episode_success(self, task): + """Successful episode with pickup and success.""" + trajectory = [ + StepResult( + observation=Observation(rgb_path="/tmp/test.png"), + info={"episode_success": 0.0, "pickup_success": 0.0}, + ), + StepResult( + observation=Observation(rgb_path="/tmp/test.png"), + info={ + "episode_success": 1.0, + "pickup_success": 1.0, + "success_wo_disturb": 1.0, + "eplen_pickup": 3.0, + "eplen_success": 5.0, + }, + ), + ] + metrics = task.evaluate_episode({}, trajectory) + assert metrics["episode_success"] == 1.0 + assert metrics["pickup_success"] == 1.0 + assert metrics["success_wo_disturb"] == 1.0 + assert metrics["eplen_pickup"] == 3.0 + assert metrics["eplen_success"] == 5.0 + assert metrics["num_steps"] == 2.0 + + def test_evaluate_episode_failure(self, task): + """Failed episode — no pickup, no success.""" + trajectory = [ + StepResult( + observation=Observation(rgb_path="/tmp/test.png"), + info={"episode_success": 0.0, "pickup_success": 0.0}, + ), + ] + metrics = task.evaluate_episode({}, trajectory) + assert metrics["episode_success"] == 0.0 + assert metrics["pickup_success"] == 0.0 + assert metrics["success_wo_disturb"] == 0.0 + assert metrics["num_steps"] == 1.0 + + def test_evaluate_episode_empty(self, task): + metrics = task.evaluate_episode({}, []) + assert metrics["episode_success"] == 0.0 + assert metrics["num_steps"] == 0.0 + + def test_aggregate_results(self, task): + """Test custom aggregation with conditional averages.""" + records = [ + # Episode 1: success, no disturbance, pickup at step 5, 10 total steps + EpisodeRecord( + episode={}, trajectory=[], + episode_results={ + "episode_success": 1.0, + "pickup_success": 1.0, + "success_wo_disturb": 1.0, + "eplen_pickup": 5.0, + "eplen_success": 10.0, + "num_steps": 10.0, + }, + ), + # Episode 2: success with disturbance, pickup at step 8, 15 total steps + EpisodeRecord( + episode={}, trajectory=[], + episode_results={ + "episode_success": 1.0, + "pickup_success": 1.0, + "success_wo_disturb": 0.0, + "eplen_pickup": 8.0, + "eplen_success": 15.0, + "num_steps": 15.0, + }, + ), + # Episode 3: pickup only, no success, 20 steps + EpisodeRecord( + episode={}, trajectory=[], + episode_results={ + "episode_success": 0.0, + "pickup_success": 1.0, + "success_wo_disturb": 0.0, + "eplen_pickup": 12.0, + "eplen_success": 0.0, + "num_steps": 20.0, + }, + ), + # Episode 4: total failure, 25 steps + EpisodeRecord( + episode={}, trajectory=[], + episode_results={ + "episode_success": 0.0, + "pickup_success": 0.0, + "success_wo_disturb": 0.0, + "eplen_pickup": 0.0, + "eplen_success": 0.0, + "num_steps": 25.0, + }, + ), + ] + summary = task.aggregate_results(records) + + # Unconditional averages + assert summary["episode_success_rate"] == 0.5 # 2/4 + assert summary["pickup_success_rate"] == 0.75 # 3/4 + assert summary["success_wo_disturb_rate"] == 0.25 # 1/4 + assert summary["avg_eplen"] == 17.5 # (10+15+20+25)/4 + + # Conditional: only episodes where pickup occurred (3 episodes) + assert summary["avg_eplen_pickup"] == pytest.approx( + (5.0 + 8.0 + 12.0) / 3, abs=0.1 + ) + + # Conditional: only successful episodes (2 episodes) + assert summary["avg_eplen_success"] == pytest.approx( + (10.0 + 15.0) / 2, abs=0.1 + ) + + def test_aggregate_results_empty(self, task): + assert task.aggregate_results([]) == {} + + +# ── Actions tests ─────────────────────────────────────────────────────────── + +class TestActions: + def test_action_space_length(self): + from easi.tasks.manipulathor.actions import get_action_space + actions = get_action_space() + assert len(actions) == 13 + + def test_action_names(self): + from easi.tasks.manipulathor.actions import ACTION_SPACE + expected = { + "MoveArmHeightP", "MoveArmHeightM", + "MoveArmXP", "MoveArmXM", + "MoveArmYP", "MoveArmYM", + "MoveArmZP", "MoveArmZM", + "MoveAheadContinuous", + "RotateRightContinuous", "RotateLeftContinuous", + "PickUpMidLevel", "DoneMidLevel", + } + assert set(ACTION_SPACE) == expected + + def test_constants(self): + from easi.tasks.manipulathor.actions import ( + MOVE_ARM_CONSTANT, MOVE_THR, MAX_STEPS, MANIPULATHOR_COMMIT_ID, + ) + assert MOVE_ARM_CONSTANT == 0.05 + assert MOVE_THR == 0.01 + assert MAX_STEPS == 200 + assert len(MANIPULATHOR_COMMIT_ID) == 40 # SHA-1 hash + + +# ── Prompt builder tests ──────────────────────────────────────────────────── + +class TestPromptBuilder: + @pytest.fixture + def builder(self): + from easi.tasks.manipulathor.prompts import ManipulaTHORPromptBuilder + return ManipulaTHORPromptBuilder(n_shot=0, use_rgb=True, use_gps=True, use_depth=False) + + def test_build_messages_first_turn(self, builder, tmp_path): + """First turn: image + system prompt + GPS state.""" + # Create a dummy image + img_path = tmp_path / "step_0000.png" + import numpy as np + from PIL import Image + Image.fromarray(np.zeros((224, 224, 3), dtype=np.uint8)).save(str(img_path)) + + memory = MagicMock() + memory.task_description = "Pick up the Apple and move it to the target location." + memory.action_history = [] + memory.current_observation = Observation( + rgb_path=str(img_path), + metadata={ + "relative_current_obj_state": "[0.3, 0.1, 0.5, 0.0, 0.0, 0.0]", + "relative_obj_to_goal": "[0.8, 0.2, 0.1]", + "relative_agent_arm_to_obj": "[0.2, 0.0, 0.3]", + "pickedup_object": "0.0", + }, + ) + + messages = builder.build_messages(memory) + assert len(messages) == 1 + assert messages[0]["role"] == "user" + content = messages[0]["content"] + # Should have image + text + assert any(c["type"] == "image_url" for c in content) + assert any(c["type"] == "text" for c in content) + + text = next(c["text"] for c in content if c["type"] == "text") + assert "Pick up the Apple" in text + assert "GPS State" in text + assert "Object Held: No" in text + assert "Strategy for Using GPS Sensors" in text + + def test_build_messages_gps_disabled(self, tmp_path): + """When use_gps=False, no GPS section in prompt.""" + from easi.tasks.manipulathor.prompts import ManipulaTHORPromptBuilder + builder = ManipulaTHORPromptBuilder(use_gps=False) + + img_path = tmp_path / "step_0000.png" + import numpy as np + from PIL import Image + Image.fromarray(np.zeros((224, 224, 3), dtype=np.uint8)).save(str(img_path)) + + memory = MagicMock() + memory.task_description = "Pick up the Apple." + memory.action_history = [] + memory.current_observation = Observation( + rgb_path=str(img_path), + metadata={"pickedup_object": "0.0"}, + ) + messages = builder.build_messages(memory) + text = next(c["text"] for c in messages[0]["content"] if c["type"] == "text") + assert "GPS State" not in text + + def test_build_messages_with_history(self, builder, tmp_path): + """Subsequent turn: includes action history.""" + img_path = tmp_path / "step_0001.png" + import numpy as np + from PIL import Image + Image.fromarray(np.zeros((224, 224, 3), dtype=np.uint8)).save(str(img_path)) + + memory = MagicMock() + memory.task_description = "Pick up the Apple." + memory.action_history = [ + ("MoveArmZP", "Action 'MoveArmZP' succeeded."), + ("MoveArmZP", "Action 'MoveArmZP' failed."), + ] + memory.current_observation = Observation( + rgb_path=str(img_path), + metadata={ + "relative_current_obj_state": "[0.1, 0.1, 0.2, 0.0, 0.0, 0.0]", + "relative_obj_to_goal": "[0.6, 0.2, 0.1]", + "relative_agent_arm_to_obj": "[0.05, 0.0, 0.1]", + "pickedup_object": "0.0", + }, + ) + + messages = builder.build_messages(memory) + text = next(c["text"] for c in messages[0]["content"] if c["type"] == "text") + assert "Action History" in text + assert "MoveArmZP" in text + assert "succeeded" in text + + def test_parse_response_valid(self, builder): + """Parse valid JSON response.""" + memory = MagicMock() + response = json.dumps({ + "visual_state_description": "I see a kitchen.", + "reasoning_and_reflection": "Need to reach the apple.", + "language_plan": "Move arm forward, then pick up.", + "executable_plan": [ + {"action_id": 6, "action_name": "MoveArmZP"}, + {"action_id": 11, "action_name": "PickUpMidLevel"}, + ], + }) + actions = builder.parse_response(response, memory) + assert len(actions) == 2 + assert actions[0].action_name == "MoveArmZP" + assert actions[1].action_name == "PickUpMidLevel" + + def test_parse_response_by_id_only(self, builder): + """Parse response with action_id but no action_name.""" + memory = MagicMock() + response = json.dumps({ + "executable_plan": [ + {"action_id": 8}, # MoveAheadContinuous + {"action_id": 12}, # DoneMidLevel + ], + }) + actions = builder.parse_response(response, memory) + assert len(actions) == 2 + assert actions[0].action_name == "MoveAheadContinuous" + assert actions[1].action_name == "DoneMidLevel" + + def test_parse_response_invalid_json(self, builder): + """Malformed JSON returns empty list.""" + memory = MagicMock() + actions = builder.parse_response("not json", memory) + assert actions == [] + + def test_parse_response_invalid_action(self, builder): + """Invalid action name stops parsing.""" + memory = MagicMock() + response = json.dumps({ + "executable_plan": [ + {"action_id": 6, "action_name": "MoveArmZP"}, + {"action_id": 99, "action_name": "InvalidAction"}, + {"action_id": 8, "action_name": "MoveAheadContinuous"}, + ], + }) + actions = builder.parse_response(response, memory) + assert len(actions) == 1 # Stops at invalid action + assert actions[0].action_name == "MoveArmZP" + + +# ── YAML and registry tests ──────────────────────────────────────────────── + +class TestYAMLConfig: + def test_base_yaml_loads(self): + from easi.tasks.yaml_utils import resolve_task_yaml + base = Path(__file__).parent.parent / "easi" / "tasks" / "manipulathor" / "_base.yaml" + config = resolve_task_yaml(base) + assert config["simulator"] == "ai2thor:v3_3_5" + assert config["max_steps"] == 200 + assert "ManipulaTHOR" in config["display_name"] + + def test_split_yaml_inherits(self): + from easi.tasks.yaml_utils import resolve_task_yaml + split = Path(__file__).parent.parent / "easi" / "tasks" / "manipulathor" / "manipulathor_test_seen.yaml" + config = resolve_task_yaml(split) + assert config["name"] == "manipulathor_test_seen" + assert config["simulator"] == "ai2thor:v3_3_5" # inherited + assert config["dataset"]["split"] == "test_seen_obj" + + def test_all_splits_registered(self): + """All 6 ManipulaTHOR splits should be discoverable.""" + from easi.tasks.registry import list_tasks + all_tasks = list_tasks() + manipulathor_tasks = [t for t in all_tasks if t.startswith("manipulathor_")] + assert len(manipulathor_tasks) == 6 + expected = { + "manipulathor_train", + "manipulathor_val_seen", + "manipulathor_val_novel", + "manipulathor_test_seen", + "manipulathor_test_novel", + "manipulathor_seen_scenes_novel", + } + assert set(manipulathor_tasks) == expected From 100467b0f085065d69caa3f066a5f3e1efe8c836 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Mon, 2 Mar 2026 15:09:26 +0800 Subject: [PATCH 117/239] feat: add runtime and data_dir fields to SimulatorEntry --- easi/simulators/registry.py | 4 ++ tests/test_docker_isolation.py | 67 ++++++++++++++++++++++++++++++++++ 2 files changed, 71 insertions(+) create mode 100644 tests/test_docker_isolation.py diff --git a/easi/simulators/registry.py b/easi/simulators/registry.py index 95efa38..3023769 100644 --- a/easi/simulators/registry.py +++ b/easi/simulators/registry.py @@ -34,6 +34,8 @@ class SimulatorEntry: python_version: str installation_kwargs: dict = field(default_factory=dict) render_platforms: dict[str, str] = field(default_factory=dict) # platform_name -> FQN class + runtime: str = "conda" # "conda" or "docker" + data_dir: str = "" # default data directory (from manifest top-level) # Module-level registry populated on first access @@ -65,6 +67,8 @@ def _discover_simulators() -> dict[str, SimulatorEntry]: python_version=ver_info.get("python_version", "3.10"), installation_kwargs=ver_info.get("installation_kwargs", {}), render_platforms=ver_info.get("render_platforms", {}), + runtime=ver_info.get("runtime", "conda"), + data_dir=manifest.get("data_dir", ""), ) # Register with explicit key: "ai2thor:v2_1_0" diff --git a/tests/test_docker_isolation.py b/tests/test_docker_isolation.py new file mode 100644 index 0000000..19af224 --- /dev/null +++ b/tests/test_docker_isolation.py @@ -0,0 +1,67 @@ +# tests/test_docker_isolation.py +"""Tests for Docker simulator isolation layer.""" + +from __future__ import annotations + +import json +import os +from pathlib import Path +from unittest.mock import patch, MagicMock + + +class TestSimulatorEntryRuntime: + """SimulatorEntry supports runtime field.""" + + def test_default_runtime_is_conda(self): + from easi.simulators.registry import SimulatorEntry + + entry = SimulatorEntry( + name="test_sim", + version="v1", + description="test", + simulator_class="easi.simulators.dummy.v1.simulator.DummySimulator", + env_manager_class="easi.simulators.dummy.v1.env_manager.DummyEnvManager", + python_version="3.10", + ) + assert entry.runtime == "conda" + + def test_runtime_can_be_docker(self): + from easi.simulators.registry import SimulatorEntry + + entry = SimulatorEntry( + name="test_sim", + version="v1", + description="test", + simulator_class="easi.simulators.dummy.v1.simulator.DummySimulator", + env_manager_class="easi.simulators.dummy.v1.env_manager.DummyEnvManager", + python_version="3.10", + runtime="docker", + ) + assert entry.runtime == "docker" + + def test_data_dir_default_empty(self): + from easi.simulators.registry import SimulatorEntry + + entry = SimulatorEntry( + name="test_sim", + version="v1", + description="test", + simulator_class="easi.simulators.dummy.v1.simulator.DummySimulator", + env_manager_class="easi.simulators.dummy.v1.env_manager.DummyEnvManager", + python_version="3.10", + ) + assert entry.data_dir == "" + + def test_data_dir_can_be_set(self): + from easi.simulators.registry import SimulatorEntry + + entry = SimulatorEntry( + name="test_sim", + version="v1", + description="test", + simulator_class="easi.simulators.dummy.v1.simulator.DummySimulator", + env_manager_class="easi.simulators.dummy.v1.env_manager.DummyEnvManager", + python_version="3.10", + data_dir="/datasets/test", + ) + assert entry.data_dir == "/datasets/test" From 5832cdeca7df776ec8b7ee7c1e541a1f79dec66d Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Mon, 2 Mar 2026 15:26:44 +0800 Subject: [PATCH 118/239] feat: add DockerEnvironmentManager base class --- easi/core/docker_env_manager.py | 197 ++++++++++++++++++++++++++++++++ tests/test_docker_isolation.py | 123 ++++++++++++++++++++ 2 files changed, 320 insertions(+) create mode 100644 easi/core/docker_env_manager.py diff --git a/easi/core/docker_env_manager.py b/easi/core/docker_env_manager.py new file mode 100644 index 0000000..a381f6b --- /dev/null +++ b/easi/core/docker_env_manager.py @@ -0,0 +1,197 @@ +"""Docker-based environment manager for simulators that require containerization. + +Parallel to BaseEnvironmentManager (conda-based). Docker simulators subclass this +instead. The bridge code inside the container is identical — same BaseBridge, +same filesystem IPC. +""" + +from __future__ import annotations + +import subprocess +from abc import ABC, abstractmethod +from pathlib import Path + +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + + +class DockerEnvironmentManager(ABC): + """Abstract base for Docker-isolated simulator environments. + + Subclasses must define: simulator_name, version, image_name, + dockerfile_path, gpu_required, container_python_path, + container_data_mount, easi_mount, get_system_deps(). + """ + + def __init__(self, installation_kwargs: dict | None = None): + self.installation_kwargs = installation_kwargs or {} + + # --- Abstract properties (subclass must implement) --- + + @property + @abstractmethod + def simulator_name(self) -> str: + """Name of the simulator (e.g., 'matterport3d').""" + ... + + @property + @abstractmethod + def version(self) -> str: + """Version identifier (e.g., 'v0_1').""" + ... + + @property + @abstractmethod + def image_name(self) -> str: + """Docker image name (e.g., 'easi_matterport3d_v0_1').""" + ... + + @property + @abstractmethod + def dockerfile_path(self) -> Path: + """Path to Dockerfile for building the image.""" + ... + + @property + @abstractmethod + def gpu_required(self) -> bool: + """Whether the container needs GPU access (--gpus all).""" + ... + + @property + @abstractmethod + def container_python_path(self) -> str: + """Python executable path inside the container.""" + ... + + @property + @abstractmethod + def container_data_mount(self) -> str: + """Mount point for simulator scene data inside the container.""" + ... + + @property + @abstractmethod + def easi_mount(self) -> str: + """Mount point for EASI repo inside the container (read-only).""" + ... + + # --- Concrete methods --- + + @abstractmethod + def get_system_deps(self) -> list[str]: + """System dependencies (e.g., ['docker'] or ['docker', 'nvidia-docker']).""" + ... + + def get_env_vars(self) -> dict[str, str]: + """Environment variables to set inside the container. Override if needed.""" + return {} + + def get_env_name(self) -> str: + """Return a name for this environment (used for display/logging).""" + return f"docker:{self.image_name}" + + def env_is_ready(self) -> bool: + """Check if the Docker image exists.""" + try: + result = subprocess.run( + ["docker", "image", "inspect", self.image_name], + capture_output=True, + timeout=10, + ) + return result.returncode == 0 + except Exception: + return False + + def install(self) -> None: + """Build the Docker image and run post_install (e.g., dataset download).""" + if not self.env_is_ready(): + dockerfile = self.dockerfile_path + if not dockerfile.exists(): + raise FileNotFoundError(f"Dockerfile not found: {dockerfile}") + + build_context = dockerfile.parent + logger.info( + "Building Docker image %s from %s ...", + self.image_name, + dockerfile, + ) + subprocess.run( + [ + "docker", "build", + "-t", self.image_name, + "-f", str(dockerfile), + str(build_context), + ], + check=True, + ) + logger.info("Docker image %s built successfully.", self.image_name) + else: + logger.info("Docker image %s already exists.", self.image_name) + + # Run post-install hook (e.g., dataset download) + self.post_install() + + def post_install(self) -> None: + """Hook for subclasses to run after image build (e.g., download datasets). + + Called by install() after the Docker image is ready. + Default is a no-op. + """ + pass + + def remove(self) -> None: + """Remove the Docker image.""" + logger.info("Removing Docker image %s ...", self.image_name) + subprocess.run( + ["docker", "rmi", self.image_name], + capture_output=True, + ) + + def build_docker_run_command( + self, + bridge_command: list[str], + workspace_dir: str | None = None, + episode_output_dir: str | None = None, + data_dir: str | None = None, + ) -> list[str]: + """Build a `docker run` command for launching the bridge. + + Mounts IPC workspace, episode output dir, EASI repo, and scene data + at the same host paths (so rgb_path in response.json works on both sides). + """ + easi_repo_root = str(Path(__file__).resolve().parents[1]) + + cmd = ["docker", "run", "--rm"] + + # GPU + if self.gpu_required: + cmd.extend(["--gpus", "all"]) + + # Volume mounts (same path on host and container for IPC compatibility) + if workspace_dir: + cmd.extend(["-v", f"{workspace_dir}:{workspace_dir}"]) + if episode_output_dir: + cmd.extend(["-v", f"{episode_output_dir}:{episode_output_dir}"]) + + # Data mount (host path -> container mount point) + if data_dir: + cmd.extend(["-v", f"{data_dir}:{self.container_data_mount}:ro"]) + + # EASI repo (read-only) + cmd.extend(["-v", f"{easi_repo_root}:{self.easi_mount}:ro"]) + + # Environment variables + cmd.extend(["-e", "PYTHONUNBUFFERED=1"]) # real-time log output + env_vars = self.get_env_vars() + for key, value in env_vars.items(): + cmd.extend(["-e", f"{key}={value}"]) + + # Image name + cmd.append(self.image_name) + + # Bridge command + cmd.extend(bridge_command) + + return cmd diff --git a/tests/test_docker_isolation.py b/tests/test_docker_isolation.py index 19af224..5adfb04 100644 --- a/tests/test_docker_isolation.py +++ b/tests/test_docker_isolation.py @@ -65,3 +65,126 @@ def test_data_dir_can_be_set(self): data_dir="/datasets/test", ) assert entry.data_dir == "/datasets/test" + + +class TestDockerEnvironmentManager: + """Tests for DockerEnvironmentManager base class.""" + + def _make_manager(self, **overrides): + """Create a concrete DockerEnvironmentManager for testing.""" + from easi.core.docker_env_manager import DockerEnvironmentManager + + defaults = dict( + _simulator_name="test_docker_sim", + _version="v1", + _image_name="easi_test_docker_sim_v1", + _dockerfile_path=Path("/tmp/Dockerfile"), + _gpu_required=False, + _container_python_path="/usr/bin/python3", + _container_data_mount="/data", + _easi_mount="/opt/easi", + _system_deps=["docker"], + ) + defaults.update(overrides) + + class ConcreteDockerEnvManager(DockerEnvironmentManager): + @property + def simulator_name(self): + return defaults["_simulator_name"] + + @property + def version(self): + return defaults["_version"] + + @property + def image_name(self): + return defaults["_image_name"] + + @property + def dockerfile_path(self): + return defaults["_dockerfile_path"] + + @property + def gpu_required(self): + return defaults["_gpu_required"] + + @property + def container_python_path(self): + return defaults["_container_python_path"] + + @property + def container_data_mount(self): + return defaults["_container_data_mount"] + + @property + def easi_mount(self): + return defaults["_easi_mount"] + + def get_system_deps(self): + return defaults["_system_deps"] + + return ConcreteDockerEnvManager() + + def test_image_name(self): + mgr = self._make_manager() + assert mgr.image_name == "easi_test_docker_sim_v1" + + def test_gpu_required_default_false(self): + mgr = self._make_manager() + assert mgr.gpu_required is False + + def test_gpu_required_true(self): + mgr = self._make_manager(_gpu_required=True) + assert mgr.gpu_required is True + + def test_system_deps_includes_docker(self): + mgr = self._make_manager() + assert "docker" in mgr.get_system_deps() + + def test_system_deps_includes_nvidia_docker_when_gpu(self): + mgr = self._make_manager(_gpu_required=True, _system_deps=["docker", "nvidia-docker"]) + deps = mgr.get_system_deps() + assert "nvidia-docker" in deps + + def test_env_is_ready_false_when_no_docker(self): + """env_is_ready returns False when docker image doesn't exist.""" + mgr = self._make_manager() + with patch("subprocess.run") as mock_run: + mock_run.side_effect = Exception("docker not found") + assert mgr.env_is_ready() is False + + def test_env_is_ready_true_when_image_exists(self): + """env_is_ready returns True when docker image inspect succeeds.""" + mgr = self._make_manager() + with patch("subprocess.run") as mock_run: + mock_run.return_value = MagicMock(returncode=0) + assert mgr.env_is_ready() is True + + def test_build_docker_run_command_basic(self): + """Build docker run command without GPU.""" + mgr = self._make_manager() + cmd = mgr.build_docker_run_command( + bridge_command=["/usr/bin/python3", "/opt/easi/bridge.py", "--workspace", "/tmp/easi_xxx"], + workspace_dir="/tmp/easi_xxx", + episode_output_dir="/logs/ep_0", + data_dir="/host/data", + ) + assert "docker" in cmd[0] + assert "--rm" in cmd + assert "--gpus" not in cmd + # Check volume mounts + cmd_str = " ".join(cmd) + assert "/tmp/easi_xxx" in cmd_str + assert "/logs/ep_0" in cmd_str + assert "/host/data" in cmd_str + + def test_build_docker_run_command_with_gpu(self): + """Build docker run command with GPU.""" + mgr = self._make_manager(_gpu_required=True) + cmd = mgr.build_docker_run_command( + bridge_command=["/usr/bin/python3", "/opt/easi/bridge.py"], + workspace_dir="/tmp/easi_xxx", + ) + assert "--gpus" in cmd + idx = cmd.index("--gpus") + assert cmd[idx + 1] == "all" From 6fcb5275fbdd92b45598476df0a7736eae5175be Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Mon, 2 Mar 2026 15:39:19 +0800 Subject: [PATCH 119/239] feat: add Docker launch mode to SubprocessRunner --- easi/simulators/subprocess_runner.py | 82 ++++++++++++++++++++++++++++ tests/test_docker_isolation.py | 46 ++++++++++++++++ 2 files changed, 128 insertions(+) diff --git a/easi/simulators/subprocess_runner.py b/easi/simulators/subprocess_runner.py index 4f1b4d9..9f24794 100644 --- a/easi/simulators/subprocess_runner.py +++ b/easi/simulators/subprocess_runner.py @@ -124,6 +124,63 @@ def launch(self) -> None: raise SimulatorError(f"{exc}\n\nBridge output:\n{output}") from exc raise + def launch_docker( + self, + docker_env_manager, + episode_output_dir: str | None = None, + data_dir: str | None = None, + ) -> None: + """Launch bridge subprocess inside a Docker container.""" + if self._process is not None: + raise RuntimeError("Subprocess already running") + + self._workspace = create_workspace() + self._output_lines.clear() + cmd = self._build_docker_launch_command( + docker_env_manager=docker_env_manager, + workspace_dir=str(self._workspace), + episode_output_dir=episode_output_dir, + data_dir=data_dir, + ) + + logger.info("Launching Docker bridge: %s", " ".join(cmd[:6]) + " ...") + logger.trace("Full Docker command: %s", " ".join(cmd)) + self._process = subprocess.Popen( + cmd, + stdout=subprocess.PIPE, + stderr=subprocess.STDOUT, + text=True, + bufsize=1, + preexec_fn=os.setsid, + ) + + # Start output reader thread + self._reader_thread = threading.Thread( + target=self._stream_output, daemon=True + ) + self._reader_thread.start() + + # Wait for bridge to signal ready + try: + status = poll_for_status( + self._workspace, + poll_interval=self.poll_interval, + timeout=self.startup_timeout, + process=self._process, + ) + if not status.get("ready", False): + output = self._get_recent_output() + raise SimulatorError( + f"Docker bridge reported not ready. Output:\n{output}" + ) + logger.info("Docker bridge subprocess ready (PID: %d)", self._process.pid) + except (SimulatorError, SimulatorTimeoutError) as exc: + output = self._get_recent_output() + self.shutdown() + if output: + raise SimulatorError(f"{exc}\n\nDocker bridge output:\n{output}") from exc + raise + def send_command(self, command: dict, timeout: float | None = None) -> dict: """Send a command to the bridge and wait for the response. @@ -194,6 +251,31 @@ def _build_launch_command(self) -> list[str]: return self.render_platform.wrap_command(cmd, self.screen_config) + def _build_docker_launch_command( + self, + docker_env_manager, + workspace_dir: str, + episode_output_dir: str | None = None, + data_dir: str | None = None, + ) -> list[str]: + """Build launch command using docker run via DockerEnvironmentManager.""" + bridge_command = [ + docker_env_manager.container_python_path, + str(Path(docker_env_manager.easi_mount) / self.bridge_script_path.relative_to( + Path(__file__).resolve().parents[1] + )), + "--workspace", + workspace_dir, + ] + bridge_command.extend(self.extra_args) + + return docker_env_manager.build_docker_run_command( + bridge_command=bridge_command, + workspace_dir=workspace_dir, + episode_output_dir=episode_output_dir, + data_dir=data_dir, + ) + # Pattern to extract log level from bridge output (e.g. "[WARNING]" or "[ERROR]") _BRIDGE_LEVEL_RE = re.compile(r"\[(\w+)\]") diff --git a/tests/test_docker_isolation.py b/tests/test_docker_isolation.py index 5adfb04..9b3a826 100644 --- a/tests/test_docker_isolation.py +++ b/tests/test_docker_isolation.py @@ -188,3 +188,49 @@ def test_build_docker_run_command_with_gpu(self): assert "--gpus" in cmd idx = cmd.index("--gpus") assert cmd[idx + 1] == "all" + + +class TestSubprocessRunnerDockerMode: + """Tests for Docker launch mode in SubprocessRunner.""" + + def test_launch_docker_builds_correct_command(self): + """launch_docker() builds a docker run command and spawns it.""" + from easi.core.docker_env_manager import DockerEnvironmentManager + from easi.simulators.subprocess_runner import SubprocessRunner + from easi.core.render_platform import get_render_platform + + # Use a real bridge script path under the EASI repo root so that + # relative_to() in _build_docker_launch_command works correctly. + repo_root = Path(__file__).resolve().parents[1] + bridge_path = repo_root / "easi" / "simulators" / "dummy" / "v1" / "bridge.py" + + # Create a mock docker env manager + mock_mgr = MagicMock(spec=DockerEnvironmentManager) + mock_mgr.image_name = "easi_test_v1" + mock_mgr.gpu_required = False + mock_mgr.container_python_path = "/usr/bin/python3" + mock_mgr.easi_mount = "/opt/easi" + mock_mgr.build_docker_run_command.return_value = [ + "docker", "run", "--rm", + "-v", "/tmp/easi_xxx:/tmp/easi_xxx", + "easi_test_v1", + "/usr/bin/python3", "/opt/easi/easi/simulators/dummy/v1/bridge.py", + "--workspace", "/tmp/easi_xxx", + ] + + runner = SubprocessRunner( + python_executable="/usr/bin/python3", + bridge_script_path=bridge_path, + render_platform=get_render_platform("headless"), + ) + + cmd = runner._build_docker_launch_command( + docker_env_manager=mock_mgr, + workspace_dir="/tmp/easi_xxx", + ) + mock_mgr.build_docker_run_command.assert_called_once() + assert cmd[0] == "docker" + + # Note: Docker containers use --rm and foreground mode. + # Shutdown uses the same process-tree kill as conda mode. + # The --rm flag ensures container cleanup after process exit. From f5fe1e0fa7d88a4c022f288358df3250c374659f Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Mon, 2 Mar 2026 16:04:31 +0800 Subject: [PATCH 120/239] feat: add docker system dependency check --- easi/utils/system_deps.py | 9 +++++++++ tests/test_docker_isolation.py | 20 ++++++++++++++++++++ 2 files changed, 29 insertions(+) diff --git a/easi/utils/system_deps.py b/easi/utils/system_deps.py index 5ee76ac..1ca6b33 100644 --- a/easi/utils/system_deps.py +++ b/easi/utils/system_deps.py @@ -34,6 +34,15 @@ "check_command": ["which", "conda"], "install_hint": "Install Miniconda: https://docs.conda.io/en/latest/miniconda.html", }, + "docker": { + "check_command": ["docker", "--version"], + "install_hint": "Install Docker: https://docs.docker.com/engine/install/", + }, + "nvidia-docker": { + "check_command": ["docker", "info"], + "check_grep": "nvidia", + "install_hint": "Install NVIDIA Container Toolkit: https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html", + }, } diff --git a/tests/test_docker_isolation.py b/tests/test_docker_isolation.py index 9b3a826..8d28eaa 100644 --- a/tests/test_docker_isolation.py +++ b/tests/test_docker_isolation.py @@ -234,3 +234,23 @@ def test_launch_docker_builds_correct_command(self): # Note: Docker containers use --rm and foreground mode. # Shutdown uses the same process-tree kill as conda mode. # The --rm flag ensures container cleanup after process exit. + + +class TestDockerSystemDeps: + """Test docker system dependency checking.""" + + def test_docker_dep_registered(self): + """Docker dependency checker is registered.""" + from easi.utils.system_deps import SystemDependencyChecker + + checker = SystemDependencyChecker() + result = checker.check("docker") + assert isinstance(result, bool) + + def test_nvidia_docker_dep_registered(self): + """nvidia-docker dependency checker is registered.""" + from easi.utils.system_deps import SystemDependencyChecker + + checker = SystemDependencyChecker() + result = checker.check("nvidia-docker") + assert isinstance(result, bool) From 6cb48b0fcb680a29ce57001767188c34cbc8356b Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Mon, 2 Mar 2026 16:49:02 +0800 Subject: [PATCH 121/239] feat: add Matterport3D simulator scaffold (first Docker user) --- easi/simulators/matterport3d/__init__.py | 0 easi/simulators/matterport3d/manifest.yaml | 19 ++ easi/simulators/matterport3d/v0_1/Dockerfile | 29 +++ easi/simulators/matterport3d/v0_1/__init__.py | 0 easi/simulators/matterport3d/v0_1/bridge.py | 114 ++++++++++++ .../matterport3d/v0_1/env_manager.py | 169 ++++++++++++++++++ .../simulators/matterport3d/v0_1/simulator.py | 22 +++ tests/test_matterport3d_simulator.py | 79 ++++++++ 8 files changed, 432 insertions(+) create mode 100644 easi/simulators/matterport3d/__init__.py create mode 100644 easi/simulators/matterport3d/manifest.yaml create mode 100644 easi/simulators/matterport3d/v0_1/Dockerfile create mode 100644 easi/simulators/matterport3d/v0_1/__init__.py create mode 100644 easi/simulators/matterport3d/v0_1/bridge.py create mode 100644 easi/simulators/matterport3d/v0_1/env_manager.py create mode 100644 easi/simulators/matterport3d/v0_1/simulator.py create mode 100644 tests/test_matterport3d_simulator.py diff --git a/easi/simulators/matterport3d/__init__.py b/easi/simulators/matterport3d/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/easi/simulators/matterport3d/manifest.yaml b/easi/simulators/matterport3d/manifest.yaml new file mode 100644 index 0000000..3941138 --- /dev/null +++ b/easi/simulators/matterport3d/manifest.yaml @@ -0,0 +1,19 @@ +name: matterport3d +display_name: "Matterport3D Simulator" +default_version: "v0_1" +data_dir: "/datasets/matterport3d" + +versions: + v0_1: + runtime: docker + description: "Matterport3DSimulator with EGL rendering (Python 3.8)" + simulator_class: "easi.simulators.matterport3d.v0_1.simulator.Matterport3DSimulator" + env_manager_class: "easi.simulators.matterport3d.v0_1.env_manager.Matterport3DEnvManager" + python_version: "3.8" + installation_kwargs: + dockerfile: "v0_1/Dockerfile" + gpu_required: true + container_python: "/usr/bin/python3" + container_data_mount: "/data/v1/scans" + easi_mount: "/opt/easi" + hf_dataset_repo: "Gen3DF/Matterport3d" diff --git a/easi/simulators/matterport3d/v0_1/Dockerfile b/easi/simulators/matterport3d/v0_1/Dockerfile new file mode 100644 index 0000000..0ee70c2 --- /dev/null +++ b/easi/simulators/matterport3d/v0_1/Dockerfile @@ -0,0 +1,29 @@ +# Matterport3DSimulator with EGL rendering +# Based on: https://github.com/peteanderson80/Matterport3DSimulator +FROM nvidia/cudagl:9.2-devel-ubuntu18.04 + +# System deps +ENV DEBIAN_FRONTEND=noninteractive +RUN apt-get update && apt-get install -y \ + python3-dev python3-pip python3-setuptools \ + libepoxy-dev libglm-dev libjsoncpp-dev libopencv-dev \ + libosmesa6-dev libglew-dev \ + curl doxygen git \ + && rm -rf /var/lib/apt/lists/* + +# CMake 3.12 +RUN curl -sSL https://cmake.org/files/v3.12/cmake-3.12.2-Linux-x86_64.tar.gz \ + | tar -xz -C /usr/local --strip-components=1 + +# Python deps +RUN pip3 install --upgrade pip && \ + pip3 install numpy opencv-python networkx + +# Build Matterport3DSimulator +WORKDIR /opt/MatterSim +RUN git clone --recursive https://github.com/peteanderson80/Matterport3DSimulator.git . && \ + mkdir build && cd build && \ + cmake -DEGL_RENDERING=ON .. && \ + make -j$(nproc) + +ENV PYTHONPATH="/opt/MatterSim/build:/opt/easi" diff --git a/easi/simulators/matterport3d/v0_1/__init__.py b/easi/simulators/matterport3d/v0_1/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/easi/simulators/matterport3d/v0_1/bridge.py b/easi/simulators/matterport3d/v0_1/bridge.py new file mode 100644 index 0000000..8620f92 --- /dev/null +++ b/easi/simulators/matterport3d/v0_1/bridge.py @@ -0,0 +1,114 @@ +"""Matterport3D bridge for EASI's filesystem IPC protocol. + +Runs inside the easi_matterport3d_v0_1 Docker container. +Wraps MatterSim in EASI's BaseBridge IPC protocol. + +PYTHONPATH is set by the Dockerfile and docker run env vars to include +both /opt/MatterSim/build (for MatterSim) and /opt/easi (for easi.*). +No manual sys.path manipulation needed here — BaseBridge also handles +repo-root discovery via its own module path. +""" + +from __future__ import annotations + +import json +from pathlib import Path + +import numpy as np + +from easi.simulators.base_bridge import BaseBridge +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + + +class Matterport3DBridge(BaseBridge): + """Bridge wrapping MatterSim.Simulator for EASI IPC.""" + + def _create_env(self, reset_config, simulator_kwargs): + import MatterSim + + sim = MatterSim.Simulator() + sim.setDatasetPath(simulator_kwargs.get("data_dir", "/data/v1/scans")) + sim.setNavGraphPath( + simulator_kwargs.get("nav_graph_path", "/opt/MatterSim/connectivity") + ) + width = simulator_kwargs.get("width", 640) + height = simulator_kwargs.get("height", 480) + sim.setCameraResolution(width, height) + sim.setCameraVFOV(simulator_kwargs.get("vfov", 0.8)) + sim.setDepthEnabled(simulator_kwargs.get("depth_enabled", True)) + sim.setDiscretizedViewingAngles( + simulator_kwargs.get("discretized_views", True) + ) + sim.setRenderingEnabled(True) + sim.setBatchSize(1) + sim.initialize() + + self._sim = sim + self._width = width + self._height = height + return sim + + def _on_reset(self, env, reset_config): + scan_id = reset_config.get("scan_id", "") + viewpoint_id = reset_config.get("viewpoint_id", "") + heading = float(reset_config.get("heading", 0.0)) + elevation = float(reset_config.get("elevation", 0.0)) + + env.newEpisode([scan_id], [viewpoint_id], [heading], [elevation]) + state = env.getState()[0] + return self._state_to_obs(state) + + def _on_step(self, env, action_text): + """Parse action and execute navigation. + + Action format: JSON string with keys: + - location_index: int (index into navigableLocations) + - heading: float (heading change in radians) + - elevation: float (elevation change in radians) + """ + try: + action = json.loads(action_text) + except json.JSONDecodeError: + action = {"location_index": 0, "heading": 0.0, "elevation": 0.0} + + env.makeAction( + [int(action.get("location_index", 0))], + [float(action.get("heading", 0.0))], + [float(action.get("elevation", 0.0))], + ) + state = env.getState()[0] + obs = self._state_to_obs(state) + + done = bool(action.get("done", False)) + info = { + "viewpoint_id": state.location.viewpointId, + "heading": state.heading, + "elevation": state.elevation, + "step": state.step, + } + return obs, 0.0, done, info + + def _state_to_obs(self, state): + """Convert MatterSim.SimState to observation dict.""" + obs = {"frame": state.rgb} + if hasattr(state, "depth") and state.depth is not None: + obs["depth_frame"] = state.depth + obs["navigable_locations"] = [ + { + "viewpointId": loc.viewpointId, + "rel_heading": loc.rel_heading, + "rel_elevation": loc.rel_elevation, + "rel_distance": loc.rel_distance, + } + for loc in state.navigableLocations + ] + return obs + + def _extract_image(self, obs): + return obs["frame"] + + +if __name__ == "__main__": + Matterport3DBridge.main() diff --git a/easi/simulators/matterport3d/v0_1/env_manager.py b/easi/simulators/matterport3d/v0_1/env_manager.py new file mode 100644 index 0000000..2d79830 --- /dev/null +++ b/easi/simulators/matterport3d/v0_1/env_manager.py @@ -0,0 +1,169 @@ +"""Docker environment manager for Matterport3DSimulator.""" + +from __future__ import annotations + +import subprocess +import time +from pathlib import Path + +from easi.core.docker_env_manager import DockerEnvironmentManager +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + +# HuggingFace dataset repo containing Matterport3D scene data +_HF_REPO = "Gen3DF/Matterport3d" + + +class Matterport3DEnvManager(DockerEnvironmentManager): + """Manages Docker image for Matterport3DSimulator with EGL rendering. + + post_install() auto-downloads the Matterport3D scene data from + HuggingFace (Gen3DF/Matterport3d) via snapshot_download, then + reassembles and extracts the archive. The HF repo is gated — users + must accept the TOS at https://huggingface.co/datasets/Gen3DF/Matterport3d + before downloading. + """ + + @property + def simulator_name(self) -> str: + return "matterport3d" + + @property + def version(self) -> str: + return "v0_1" + + @property + def image_name(self) -> str: + return "easi_matterport3d_v0_1" + + @property + def dockerfile_path(self) -> Path: + return Path(__file__).parent / "Dockerfile" + + @property + def gpu_required(self) -> bool: + return True + + @property + def container_python_path(self) -> str: + return "/usr/bin/python3" + + @property + def container_data_mount(self) -> str: + return "/data/v1/scans" + + @property + def easi_mount(self) -> str: + return "/opt/easi" + + def get_system_deps(self) -> list[str]: + return ["docker"] + + def get_env_vars(self) -> dict[str, str]: + return {"PYTHONPATH": "/opt/MatterSim/build:/opt/easi"} + + @property + def data_dir(self) -> Path: + """Default data directory. User can override via manifest data_dir or task YAML.""" + override = self.installation_kwargs.get("data_dir") + if override: + return Path(override) + return Path("/datasets/matterport3d") + + def post_install(self) -> None: + """Download Matterport3D scene data from HuggingFace. + + Uses huggingface_hub.snapshot_download to download all files + (no git-lfs needed). Then runs merge.sh + unzip.sh to reassemble + and extract scene scans. + + HF repo layout: + Gen3DF/Matterport3d/ + ├── README.md + └── matterport3d/ + ├── download.py + ├── merge.sh + ├── unzip.sh + ├── matterport3d_part_000 (1.07 GB) + ├── ... + └── matterport3d_part_007 (242 MB) + + snapshot_download downloads into dest/, so merge.sh is at + dest/matterport3d/merge.sh. + """ + from huggingface_hub import snapshot_download + from huggingface_hub.utils import ( + GatedRepoError, + RepositoryNotFoundError, + ) + + dest = self.data_dir + hf_repo = self.installation_kwargs.get("hf_dataset_repo", _HF_REPO) + + # Skip if data already exists (idempotent) + # After extraction, scans live at dest/matterport3d/v1/scans/ + mp3d_subdir = dest / "matterport3d" + if (mp3d_subdir / "v1" / "scans").exists(): + logger.info("Matterport3D data already exists at %s, skipping download.", dest) + return + + dest.mkdir(parents=True, exist_ok=True) + logger.info("Downloading Matterport3D dataset from HF repo %s to %s ...", hf_repo, dest) + logger.info( + "NOTE: This dataset is gated. If download fails, visit\n" + " https://huggingface.co/datasets/%s\n" + "and accept the Terms of Service, then retry.", + hf_repo, + ) + + # Step 1: Download all files via snapshot_download + # Distinguish TOS/auth errors (fail immediately) from + # transient network errors (retry up to 3 times). + max_retries = 3 + for attempt in range(1, max_retries + 1): + try: + snapshot_download( + repo_id=hf_repo, + repo_type="dataset", + local_dir=str(dest), + ) + break + except (GatedRepoError, RepositoryNotFoundError) as e: + raise RuntimeError( + f"Access denied downloading {hf_repo}. " + f"Please visit https://huggingface.co/datasets/{hf_repo} " + f"and accept the Terms of Service, then retry.\n" + f"Error: {e}" + ) from e + except Exception as e: + if attempt < max_retries: + logger.warning( + "Download failed (attempt %d/%d): %s. Retrying ...", + attempt, max_retries, e, + ) + time.sleep(5 * attempt) # brief backoff + else: + raise RuntimeError( + f"Download failed after {max_retries} attempts: {e}" + ) from e + + # Step 2: Reassemble chunks (merge.sh is at dest/matterport3d/merge.sh) + if (mp3d_subdir / "merge.sh").exists(): + logger.info("Reassembling dataset chunks ...") + subprocess.run( + ["bash", "merge.sh"], + cwd=str(mp3d_subdir), + check=True, + ) + + # Step 3: Extract + if (mp3d_subdir / "unzip.sh").exists(): + logger.info("Extracting dataset ...") + subprocess.run( + ["bash", "unzip.sh"], + cwd=str(mp3d_subdir), + check=True, + ) + + logger.info("Matterport3D dataset ready at %s", dest) diff --git a/easi/simulators/matterport3d/v0_1/simulator.py b/easi/simulators/matterport3d/v0_1/simulator.py new file mode 100644 index 0000000..2740196 --- /dev/null +++ b/easi/simulators/matterport3d/v0_1/simulator.py @@ -0,0 +1,22 @@ +"""Matterport3D simulator class.""" + +from __future__ import annotations + +from pathlib import Path + +from easi.core.base_simulator import BaseSimulator + + +class Matterport3DSimulator(BaseSimulator): + """Matterport3D simulator (Docker-isolated).""" + + @property + def name(self) -> str: + return "matterport3d" + + @property + def version(self) -> str: + return "v0_1" + + def _get_bridge_script_path(self) -> Path: + return Path(__file__).parent / "bridge.py" diff --git a/tests/test_matterport3d_simulator.py b/tests/test_matterport3d_simulator.py new file mode 100644 index 0000000..7ac5274 --- /dev/null +++ b/tests/test_matterport3d_simulator.py @@ -0,0 +1,79 @@ +"""Tests for Matterport3D simulator integration (Docker-based).""" + +from __future__ import annotations + +from pathlib import Path + + +class TestMatterport3DManifest: + """Manifest discovery and fields.""" + + def test_manifest_exists(self): + manifest = Path("easi/simulators/matterport3d/manifest.yaml") + assert manifest.exists() + + def test_manifest_contents(self): + import yaml + manifest = Path("easi/simulators/matterport3d/manifest.yaml") + data = yaml.safe_load(manifest.read_text()) + assert data["name"] == "matterport3d" + assert "v0_1" in data["versions"] + assert data["versions"]["v0_1"]["runtime"] == "docker" + + def test_manifest_classes_importable(self): + from easi.utils.import_utils import import_class + import yaml + manifest = Path("easi/simulators/matterport3d/manifest.yaml") + data = yaml.safe_load(manifest.read_text()) + ver = data["versions"]["v0_1"] + sim_cls = import_class(ver["simulator_class"]) + mgr_cls = import_class(ver["env_manager_class"]) + assert sim_cls is not None + assert mgr_cls is not None + + +class TestMatterport3DEnvManager: + """Environment manager properties.""" + + def test_import(self): + from easi.simulators.matterport3d.v0_1.env_manager import Matterport3DEnvManager + mgr = Matterport3DEnvManager() + assert mgr.simulator_name == "matterport3d" + assert mgr.version == "v0_1" + + def test_is_docker_env_manager(self): + from easi.core.docker_env_manager import DockerEnvironmentManager + from easi.simulators.matterport3d.v0_1.env_manager import Matterport3DEnvManager + mgr = Matterport3DEnvManager() + assert isinstance(mgr, DockerEnvironmentManager) + + def test_image_name(self): + from easi.simulators.matterport3d.v0_1.env_manager import Matterport3DEnvManager + mgr = Matterport3DEnvManager() + assert mgr.image_name == "easi_matterport3d_v0_1" + + def test_gpu_required(self): + from easi.simulators.matterport3d.v0_1.env_manager import Matterport3DEnvManager + mgr = Matterport3DEnvManager() + assert mgr.gpu_required is True + + def test_dockerfile_exists(self): + from easi.simulators.matterport3d.v0_1.env_manager import Matterport3DEnvManager + mgr = Matterport3DEnvManager() + assert mgr.dockerfile_path.exists() + + def test_registry_discovers(self): + from easi.simulators.registry import list_simulators + sims = list_simulators() + assert "matterport3d" in sims or "matterport3d:v0_1" in sims + + +class TestMatterport3DBridgeSyntax: + """Bridge script can be parsed.""" + + def test_bridge_syntax(self): + import py_compile + py_compile.compile( + "easi/simulators/matterport3d/v0_1/bridge.py", + doraise=True, + ) From 12164678e466dbb893551296aa729fb769fbe12c Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Mon, 2 Mar 2026 17:40:39 +0800 Subject: [PATCH 122/239] feat: wire Docker runtime detection into EvaluationRunner _create_simulator() now reads entry.runtime from the registry and uses launch_docker() when runtime is "docker", falling back to the existing conda path otherwise. Updated all test mocks to provide mock_entry. --- easi/evaluation/runner.py | 39 +++++++++++++++++++++++++++---- tests/test_docker_isolation.py | 35 +++++++++++++++++++++++++++ tests/test_env_vars_wiring.py | 12 ++++++++-- tests/test_installation_kwargs.py | 6 ++++- tests/test_render_platform.py | 30 ++++++++++++++++++++---- 5 files changed, 110 insertions(+), 12 deletions(-) diff --git a/easi/evaluation/runner.py b/easi/evaluation/runner.py index f4a3642..8ca6e87 100644 --- a/easi/evaluation/runner.py +++ b/easi/evaluation/runner.py @@ -555,10 +555,12 @@ def _create_simulator(self, simulator_key: str, task=None): from easi.simulators.registry import ( create_env_manager, + get_simulator_entry, load_simulator_class, ) from easi.simulators.subprocess_runner import SubprocessRunner + entry = get_simulator_entry(simulator_key) env_manager = create_env_manager(simulator_key) SimClass = load_simulator_class(simulator_key) sim = SimClass() @@ -568,10 +570,6 @@ def _create_simulator(self, simulator_key: str, task=None): logger.info("Simulator environment not ready, auto-installing...") env_manager.install() - # Install task-level additional deps - if task and task.additional_deps: - env_manager.install_additional_deps(task.additional_deps) - # Task-specific bridge overrides simulator default bridge_path = ( (task.get_bridge_script_path() if task else None) @@ -590,6 +588,39 @@ def _create_simulator(self, simulator_key: str, task=None): if sim_configs.get("startup_timeout"): runner_kwargs["startup_timeout"] = float(sim_configs["startup_timeout"]) + # --- Docker runtime path --- + if entry.runtime == "docker": + from easi.core.docker_env_manager import DockerEnvironmentManager + from easi.core.render_platform import get_render_platform + + assert isinstance(env_manager, DockerEnvironmentManager), ( + f"Simulator {simulator_key} declares runtime=docker but env_manager " + f"is not a DockerEnvironmentManager" + ) + + runner = SubprocessRunner( + python_executable=env_manager.container_python_path, + bridge_script_path=bridge_path, + render_platform=get_render_platform("headless"), + extra_args=extra_args, + **runner_kwargs, + ) + data_dir_str = str(self.data_dir) if self.data_dir else ( + entry.data_dir.replace("~", str(Path.home())) if entry.data_dir else None + ) + runner.launch_docker( + docker_env_manager=env_manager, + data_dir=data_dir_str, + ) + sim.set_runner(runner) + return sim, runner + + # --- Conda runtime path (existing) --- + + # Install task-level additional deps + if task and task.additional_deps: + env_manager.install_additional_deps(task.additional_deps) + # Resolve render platform: CLI > task YAML > env_manager default from easi.simulators.registry import resolve_render_platform diff --git a/tests/test_docker_isolation.py b/tests/test_docker_isolation.py index 8d28eaa..74d445a 100644 --- a/tests/test_docker_isolation.py +++ b/tests/test_docker_isolation.py @@ -254,3 +254,38 @@ def test_nvidia_docker_dep_registered(self): checker = SystemDependencyChecker() result = checker.check("nvidia-docker") assert isinstance(result, bool) + + +class TestEvaluationRunnerDockerWiring: + """Test that _create_simulator detects Docker runtime and uses launch_docker.""" + + def test_docker_runtime_detected_from_registry(self): + """When registry entry has runtime='docker', runner should use Docker launch.""" + from easi.simulators.registry import SimulatorEntry + + entry = SimulatorEntry( + name="test_docker", + version="v1", + description="test", + simulator_class="easi.simulators.dummy.v1.simulator.DummySimulator", + env_manager_class="easi.simulators.dummy.v1.env_manager.DummyEnvManager", + python_version="3.10", + runtime="docker", + ) + assert entry.runtime == "docker" + + def test_get_simulator_entry_preserves_runtime(self): + """get_simulator_entry returns entry with runtime field intact.""" + from easi.simulators.registry import get_simulator_entry + + # Dummy uses default runtime (conda) + entry = get_simulator_entry("dummy") + assert entry.runtime == "conda" + + def test_matterport3d_entry_has_docker_runtime(self): + """Matterport3D registry entry has runtime=docker.""" + from easi.simulators.registry import get_simulator_entry + + entry = get_simulator_entry("matterport3d") + assert entry.runtime == "docker" + assert entry.data_dir == "/datasets/matterport3d" diff --git a/tests/test_env_vars_wiring.py b/tests/test_env_vars_wiring.py index 5178802..5264278 100644 --- a/tests/test_env_vars_wiring.py +++ b/tests/test_env_vars_wiring.py @@ -29,7 +29,11 @@ def test_runner_passes_env_vars_to_subprocess(self): mock_sim = mock_sim_cls.return_value mock_sim._get_bridge_script_path.return_value = Path("/fake/bridge.py") - with patch("easi.simulators.registry.create_env_manager", return_value=mock_env_mgr), \ + mock_entry = MagicMock() + mock_entry.runtime = "conda" + + with patch("easi.simulators.registry.get_simulator_entry", return_value=mock_entry), \ + patch("easi.simulators.registry.create_env_manager", return_value=mock_env_mgr), \ patch("easi.simulators.registry.load_simulator_class", return_value=mock_sim_cls), \ patch("easi.simulators.registry.resolve_render_platform", side_effect=lambda key, name, env_manager=None: get_render_platform(name)), \ @@ -64,7 +68,11 @@ def test_runner_passes_none_when_no_env_vars(self): mock_sim = mock_sim_cls.return_value mock_sim._get_bridge_script_path.return_value = Path("/fake/bridge.py") - with patch("easi.simulators.registry.create_env_manager", return_value=mock_env_mgr), \ + mock_entry = MagicMock() + mock_entry.runtime = "conda" + + with patch("easi.simulators.registry.get_simulator_entry", return_value=mock_entry), \ + patch("easi.simulators.registry.create_env_manager", return_value=mock_env_mgr), \ patch("easi.simulators.registry.load_simulator_class", return_value=mock_sim_cls), \ patch("easi.simulators.registry.resolve_render_platform", side_effect=lambda key, name, env_manager=None: get_render_platform(name)), \ diff --git a/tests/test_installation_kwargs.py b/tests/test_installation_kwargs.py index 0f1578d..b537ee3 100644 --- a/tests/test_installation_kwargs.py +++ b/tests/test_installation_kwargs.py @@ -72,7 +72,11 @@ def test_runner_passes_installation_kwargs(self): mock_sim = mock_sim_cls.return_value mock_sim._get_bridge_script_path.return_value = Path("/fake/bridge.py") - with patch("easi.simulators.registry.create_env_manager", return_value=mock_env_mgr) as mock_factory, \ + mock_entry = MagicMock() + mock_entry.runtime = "conda" + + with patch("easi.simulators.registry.get_simulator_entry", return_value=mock_entry), \ + patch("easi.simulators.registry.create_env_manager", return_value=mock_env_mgr) as mock_factory, \ patch("easi.simulators.registry.load_simulator_class", return_value=mock_sim_cls), \ patch("easi.simulators.registry.resolve_render_platform", side_effect=lambda key, name, env_manager=None: get_render_platform(name)), \ diff --git a/tests/test_render_platform.py b/tests/test_render_platform.py index dca0901..e0e3045 100644 --- a/tests/test_render_platform.py +++ b/tests/test_render_platform.py @@ -711,7 +711,11 @@ def test_default_uses_env_manager_platform(self): mock_sim_cls = MagicMock() mock_sim_cls.return_value._get_bridge_script_path.return_value = Path("/fake/bridge.py") - with patch("easi.simulators.registry.create_env_manager", return_value=mock_env_mgr), \ + mock_entry = MagicMock() + mock_entry.runtime = "conda" + + with patch("easi.simulators.registry.get_simulator_entry", return_value=mock_entry), \ + patch("easi.simulators.registry.create_env_manager", return_value=mock_env_mgr), \ patch("easi.simulators.registry.load_simulator_class", return_value=mock_sim_cls), \ patch("easi.simulators.registry.resolve_render_platform", side_effect=lambda key, name, env_manager=None: get_render_platform(name)), \ @@ -733,7 +737,11 @@ def test_cli_override_wins(self): mock_sim_cls = MagicMock() mock_sim_cls.return_value._get_bridge_script_path.return_value = Path("/fake/bridge.py") - with patch("easi.simulators.registry.create_env_manager", return_value=mock_env_mgr), \ + mock_entry = MagicMock() + mock_entry.runtime = "conda" + + with patch("easi.simulators.registry.get_simulator_entry", return_value=mock_entry), \ + patch("easi.simulators.registry.create_env_manager", return_value=mock_env_mgr), \ patch("easi.simulators.registry.load_simulator_class", return_value=mock_sim_cls), \ patch("easi.simulators.registry.resolve_render_platform", side_effect=lambda key, name, env_manager=None: get_render_platform(name)), \ @@ -756,7 +764,11 @@ def test_yaml_override_used_when_no_cli(self): mock_sim_cls = MagicMock() mock_sim_cls.return_value._get_bridge_script_path.return_value = Path("/fake/bridge.py") - with patch("easi.simulators.registry.create_env_manager", return_value=mock_env_mgr), \ + mock_entry = MagicMock() + mock_entry.runtime = "conda" + + with patch("easi.simulators.registry.get_simulator_entry", return_value=mock_entry), \ + patch("easi.simulators.registry.create_env_manager", return_value=mock_env_mgr), \ patch("easi.simulators.registry.load_simulator_class", return_value=mock_sim_cls), \ patch("easi.simulators.registry.resolve_render_platform", side_effect=lambda key, name, env_manager=None: get_render_platform(name)), \ @@ -779,7 +791,11 @@ def test_cli_beats_yaml(self): mock_sim_cls = MagicMock() mock_sim_cls.return_value._get_bridge_script_path.return_value = Path("/fake/bridge.py") - with patch("easi.simulators.registry.create_env_manager", return_value=mock_env_mgr), \ + mock_entry = MagicMock() + mock_entry.runtime = "conda" + + with patch("easi.simulators.registry.get_simulator_entry", return_value=mock_entry), \ + patch("easi.simulators.registry.create_env_manager", return_value=mock_env_mgr), \ patch("easi.simulators.registry.load_simulator_class", return_value=mock_sim_cls), \ patch("easi.simulators.registry.resolve_render_platform", side_effect=lambda key, name, env_manager=None: get_render_platform(name)), \ @@ -802,7 +818,11 @@ def test_unsupported_platform_raises(self): mock_sim_cls = MagicMock() mock_sim_cls.return_value._get_bridge_script_path.return_value = Path("/fake/bridge.py") - with patch("easi.simulators.registry.create_env_manager", return_value=mock_env_mgr), \ + mock_entry = MagicMock() + mock_entry.runtime = "conda" + + with patch("easi.simulators.registry.get_simulator_entry", return_value=mock_entry), \ + patch("easi.simulators.registry.create_env_manager", return_value=mock_env_mgr), \ patch("easi.simulators.registry.load_simulator_class", return_value=mock_sim_cls), \ patch("easi.simulators.registry.resolve_render_platform", side_effect=lambda key, name, env_manager=None: get_render_platform(name)), \ From 874e100f45b9ce4c2075415242afbeca91d10931 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 3 Mar 2026 11:05:32 +0800 Subject: [PATCH 123/239] feat: CLI support for Docker env install/check/list and sim test - cmd_env_list shows [docker] tag for Docker-based simulators - cmd_env_check reports Docker image name instead of Python path - cmd_env_install warns when --with-task-deps used with Docker - cmd_sim_test uses launch_docker() for Docker-runtime simulators - DockerEnvironmentManager gains check_system_deps() method --- easi/cli.py | 197 ++++++++++++++++++++++---------- easi/core/docker_env_manager.py | 6 + tests/test_docker_isolation.py | 21 ++++ 3 files changed, 162 insertions(+), 62 deletions(-) diff --git a/easi/cli.py b/easi/cli.py index 83efc64..4a730b9 100644 --- a/easi/cli.py +++ b/easi/cli.py @@ -154,7 +154,8 @@ def cmd_env_list() -> None: continue seen.add(pair) default_marker = " (default)" if key == entry.name else "" - logger.info(" %s%s -- %s", pair, default_marker, entry.description) + runtime_tag = f" [{entry.runtime}]" if entry.runtime != "conda" else "" + logger.info(" %s%s%s -- %s", pair, default_marker, runtime_tag, entry.description) def cmd_env_install(simulator: str, reinstall: bool = False, with_task_deps: str | None = None) -> None: @@ -170,20 +171,26 @@ def cmd_env_install(simulator: str, reinstall: bool = False, with_task_deps: str env_manager.install() if with_task_deps: - from easi.tasks.registry import get_task_entry, load_task_class + from easi.core.docker_env_manager import DockerEnvironmentManager - entry = get_task_entry(with_task_deps) - TaskClass = load_task_class(with_task_deps) - task = TaskClass(split_yaml_path=entry.config_path) - if task.additional_deps: - env_manager.install_additional_deps(task.additional_deps) + if isinstance(env_manager, DockerEnvironmentManager): + logger.warning("--with-task-deps is not supported for Docker simulators (deps baked into image).") else: - logger.info("Task %s has no additional_deps.", with_task_deps) + from easi.tasks.registry import get_task_entry, load_task_class + + entry = get_task_entry(with_task_deps) + TaskClass = load_task_class(with_task_deps) + task = TaskClass(split_yaml_path=entry.config_path) + if task.additional_deps: + env_manager.install_additional_deps(task.additional_deps) + else: + logger.info("Task %s has no additional_deps.", with_task_deps) logger.info("Done.") def cmd_env_check(simulator: str) -> None: + from easi.core.docker_env_manager import DockerEnvironmentManager from easi.simulators.registry import create_env_manager env_manager = create_env_manager(simulator) @@ -194,7 +201,10 @@ def cmd_env_check(simulator: str) -> None: if env_manager.env_is_ready(): logger.info("Environment %s is ready.", env_manager.get_env_name()) - logger.info("Python: %s", env_manager.get_python_executable()) + if isinstance(env_manager, DockerEnvironmentManager): + logger.info("Runtime: docker (image: %s)", env_manager.image_name) + else: + logger.info("Python: %s", env_manager.get_python_executable()) else: logger.info("Environment %s is NOT ready.", env_manager.get_env_name()) logger.info("Run: easi env install %s", simulator) @@ -254,73 +264,136 @@ def cmd_task_download(task_name: str, refresh_data: bool = False) -> None: def cmd_sim_test(simulator: str, steps: int, timeout: float, render_platform_name: str | None = None) -> None: + from pathlib import Path + + from easi.core.docker_env_manager import DockerEnvironmentManager from easi.core.episode import Action + from easi.core.render_platform import get_render_platform from easi.simulators.registry import ( create_env_manager, + get_simulator_entry, load_simulator_class, resolve_render_platform, ) from easi.simulators.subprocess_runner import SubprocessRunner + entry = get_simulator_entry(simulator) env_manager = create_env_manager(simulator) SimClass = load_simulator_class(simulator) sim = SimClass() - # Resolve render platform - platform_name = render_platform_name or env_manager.default_render_platform - if platform_name not in env_manager.supported_render_platforms: - logger.error( - "Render platform '%s' not supported by %s. Supported: %s", - platform_name, simulator, env_manager.supported_render_platforms, + if entry.runtime == "docker": + # --- Docker launch path --- + assert isinstance(env_manager, DockerEnvironmentManager), ( + f"runtime='docker' but env_manager is {type(env_manager).__name__}, " + "expected DockerEnvironmentManager subclass" + ) + logger.info("Testing %s (Docker)...", simulator) + logger.info(" Image: %s", env_manager.image_name) + logger.info(" GPU: %s", env_manager.gpu_required) + + render_platform = get_render_platform("headless") + bridge_path = sim._get_bridge_script_path() + + runner = SubprocessRunner( + python_executable=env_manager.container_python_path, + bridge_script_path=bridge_path, + render_platform=render_platform, + startup_timeout=timeout, + command_timeout=timeout, ) - sys.exit(1) - render_platform = resolve_render_platform(simulator, platform_name, env_manager=env_manager) - - logger.info("Testing %s...", simulator) - logger.info(" Python: %s", env_manager.get_python_executable()) - logger.info(" Render platform: %s", platform_name) - - env_vars = env_manager.get_env_vars(render_platform_name=platform_name) - - runner = SubprocessRunner( - python_executable=env_manager.get_python_executable(), - bridge_script_path=sim._get_bridge_script_path(), - render_platform=render_platform, - screen_config=env_manager.screen_config, - startup_timeout=timeout, - command_timeout=timeout, - extra_env=env_vars if env_vars else None, - ) - try: - runner.launch() - sim.set_runner(runner) - - logger.info(" Reset...") - obs = sim.reset("smoke_test_001") - logger.info(" Reset OK (rgb: %s)", obs.rgb_path) - - for i in range(steps): - action = Action(action_name="MoveAhead") - result = sim.step(action) - logger.info(" Step %d: done=%s, reward=%s", i + 1, result.done, result.reward) - if result.done: - break - - logger.info(" Closing...") - sim.close() - logger.info(" Close OK") - logger.info("Smoke test passed!") - - except KeyboardInterrupt: - logger.info("Interrupted, shutting down bridge...") - sim.close() - logger.info("Bridge process terminated.") - sys.exit(130) - except Exception as e: - logger.error("Smoke test FAILED: %s", e) - sim.close() - sys.exit(1) + data_dir_str = entry.data_dir.replace("~", str(Path.home())) if entry.data_dir else None + + try: + runner.launch_docker( + docker_env_manager=env_manager, + data_dir=data_dir_str, + ) + sim.set_runner(runner) + + logger.info(" Reset...") + obs = sim.reset("smoke_test_001") + logger.info(" Reset OK (rgb: %s)", obs.rgb_path) + + for i in range(steps): + action = Action(action_name="MoveAhead") + result = sim.step(action) + logger.info(" Step %d: done=%s, reward=%s", i + 1, result.done, result.reward) + if result.done: + break + + logger.info(" Closing...") + sim.close() + logger.info(" Close OK") + logger.info("Smoke test passed!") + + except KeyboardInterrupt: + logger.info("Interrupted, shutting down bridge...") + sim.close() + logger.info("Bridge process terminated.") + sys.exit(130) + except Exception as e: + logger.error("Smoke test FAILED: %s", e) + sim.close() + sys.exit(1) + + else: + # --- Conda launch path --- + platform_name = render_platform_name or env_manager.default_render_platform + if platform_name not in env_manager.supported_render_platforms: + logger.error( + "Render platform '%s' not supported by %s. Supported: %s", + platform_name, simulator, env_manager.supported_render_platforms, + ) + sys.exit(1) + render_platform = resolve_render_platform(simulator, platform_name, env_manager=env_manager) + + logger.info("Testing %s...", simulator) + logger.info(" Python: %s", env_manager.get_python_executable()) + logger.info(" Render platform: %s", platform_name) + + env_vars = env_manager.get_env_vars(render_platform_name=platform_name) + + runner = SubprocessRunner( + python_executable=env_manager.get_python_executable(), + bridge_script_path=sim._get_bridge_script_path(), + render_platform=render_platform, + screen_config=env_manager.screen_config, + startup_timeout=timeout, + command_timeout=timeout, + extra_env=env_vars if env_vars else None, + ) + + try: + runner.launch() + sim.set_runner(runner) + + logger.info(" Reset...") + obs = sim.reset("smoke_test_001") + logger.info(" Reset OK (rgb: %s)", obs.rgb_path) + + for i in range(steps): + action = Action(action_name="MoveAhead") + result = sim.step(action) + logger.info(" Step %d: done=%s, reward=%s", i + 1, result.done, result.reward) + if result.done: + break + + logger.info(" Closing...") + sim.close() + logger.info(" Close OK") + logger.info("Smoke test passed!") + + except KeyboardInterrupt: + logger.info("Interrupted, shutting down bridge...") + sim.close() + logger.info("Bridge process terminated.") + sys.exit(130) + except Exception as e: + logger.error("Smoke test FAILED: %s", e) + sim.close() + sys.exit(1) def _resolve_task_list(args_ns) -> list[str]: diff --git a/easi/core/docker_env_manager.py b/easi/core/docker_env_manager.py index a381f6b..0f84a46 100644 --- a/easi/core/docker_env_manager.py +++ b/easi/core/docker_env_manager.py @@ -12,6 +12,7 @@ from pathlib import Path from easi.utils.logging import get_logger +from easi.utils.system_deps import SystemDependencyChecker logger = get_logger(__name__) @@ -26,6 +27,7 @@ class DockerEnvironmentManager(ABC): def __init__(self, installation_kwargs: dict | None = None): self.installation_kwargs = installation_kwargs or {} + self._dep_checker = SystemDependencyChecker() # --- Abstract properties (subclass must implement) --- @@ -84,6 +86,10 @@ def get_system_deps(self) -> list[str]: """System dependencies (e.g., ['docker'] or ['docker', 'nvidia-docker']).""" ... + def check_system_deps(self) -> list[str]: + """Check system dependencies, returning list of missing ones.""" + return self._dep_checker.check_all(self.get_system_deps()) + def get_env_vars(self) -> dict[str, str]: """Environment variables to set inside the container. Override if needed.""" return {} diff --git a/tests/test_docker_isolation.py b/tests/test_docker_isolation.py index 74d445a..d5ebb6c 100644 --- a/tests/test_docker_isolation.py +++ b/tests/test_docker_isolation.py @@ -289,3 +289,24 @@ def test_matterport3d_entry_has_docker_runtime(self): entry = get_simulator_entry("matterport3d") assert entry.runtime == "docker" assert entry.data_dir == "/datasets/matterport3d" + + +class TestCLIDockerEnvSupport: + """CLI commands work with Docker-based simulators.""" + + def test_env_list_shows_runtime(self): + """easi env list should see all simulators including Docker ones.""" + from easi.simulators.registry import list_simulators, get_simulator_entry + + sims = list_simulators() + for key in sims: + entry = get_simulator_entry(key) + assert entry.runtime in ("conda", "docker") + + def test_docker_env_manager_has_check_system_deps(self): + """DockerEnvironmentManager supports check_system_deps().""" + from easi.simulators.registry import create_env_manager + + mgr = create_env_manager("matterport3d") + missing = mgr.check_system_deps() + assert isinstance(missing, list) From 63a6cabf1a3735ab5912798f7779f7bda2fbd5b9 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 3 Mar 2026 21:11:05 +0800 Subject: [PATCH 124/239] feat(lhpr_vln): vendor SceneSimulator, config, and metrics from LH-VLN --- easi/tasks/lhpr_vln/__init__.py | 0 easi/tasks/lhpr_vln/vendor/__init__.py | 0 easi/tasks/lhpr_vln/vendor/metrics.py | 129 +++++++++++ easi/tasks/lhpr_vln/vendor/scene_config.py | 120 ++++++++++ easi/tasks/lhpr_vln/vendor/scene_simulator.py | 205 ++++++++++++++++++ 5 files changed, 454 insertions(+) create mode 100644 easi/tasks/lhpr_vln/__init__.py create mode 100644 easi/tasks/lhpr_vln/vendor/__init__.py create mode 100644 easi/tasks/lhpr_vln/vendor/metrics.py create mode 100644 easi/tasks/lhpr_vln/vendor/scene_config.py create mode 100644 easi/tasks/lhpr_vln/vendor/scene_simulator.py diff --git a/easi/tasks/lhpr_vln/__init__.py b/easi/tasks/lhpr_vln/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/easi/tasks/lhpr_vln/vendor/__init__.py b/easi/tasks/lhpr_vln/vendor/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/easi/tasks/lhpr_vln/vendor/metrics.py b/easi/tasks/lhpr_vln/vendor/metrics.py new file mode 100644 index 0000000..24087f2 --- /dev/null +++ b/easi/tasks/lhpr_vln/vendor/metrics.py @@ -0,0 +1,129 @@ +"""Navigation metrics for LHPR-VLN benchmark. Vendored verbatim from LH-VLN/utils/metrics.py.""" + + +class NavigationMetrics: + def __init__(self): + self.successes = [] + self.gt_steps = [] + self.gt_length = [] + self.error_length = [] + self.path_steps = [] + self.oracle_successes = [] + self.navigation_errors = [] + self.subtask_successes = [] + self.subtask_path_steps = [] + + def add_sample(self, + success, + gt_step, + path_step, + oracle_success, + navigation_error, + subtask_successes, + subtask_path_step, + gt_length, + error_length): + self.successes.append(success) + self.gt_steps.append(gt_step) + self.path_steps.append(path_step) + self.oracle_successes.append(oracle_success) + self.navigation_errors.append(navigation_error) + self.subtask_successes.append(subtask_successes) + self.subtask_path_steps.append(subtask_path_step) + self.gt_length.append(gt_length) + self.error_length.append(error_length) + + def success_rate(self): + return sum(self.successes) / len(self.successes) if len(self.successes) > 0 else 0 + + def oracle_success_rate(self): + return sum(self.oracle_successes) / len(self.oracle_successes) if len(self.oracle_successes) > 0 else 0 + + def spl(self): + total_spl = sum(s * (gp/max(gp, p)) if p > 0 else 0 for s, gp, p in zip(self.successes, self.gt_steps, self.path_steps)) + return total_spl / len(self.successes) if len(self.successes) > 0 else 0 + + def navigation_error(self): + return sum(self.navigation_errors) / len(self.navigation_errors) if len(self.navigation_errors) > 0 else 0 + + def independent_success_rate(self): + subtask_counts = [len(subtasks) for subtasks in self.subtask_successes] + total_subtasks = sum(subtask_counts) + total_successes = sum(sum(subtasks) for subtasks in self.subtask_successes) + return total_successes / total_subtasks if total_subtasks > 0 else 0 + + def conditional_success_rate(self): + M = len(self.subtask_successes) + if M == 0: + return 0 + csr = 0 + for i in range(M): + sr = 0 + N = len(self.subtask_successes[i]) + if N == 0: + continue + s = self.subtask_successes[i][0] + sr += s*N + if N == 1: + csr += sr + continue + for j in self.subtask_successes[i][1:]: + sr += j*(1+(N-1)*s) + s = j + csr += sr/(N**2) + csr = csr/M + return csr + + def conditional_path_length(self): + M = len(self.subtask_successes) + if M == 0: + return 0 + cpl = 0 + for i in range(M): + sr = 0 + N = len(self.subtask_successes[i]) + if N == 0: + continue + s = self.subtask_successes[i][0] + w = [l/sum(self.subtask_path_steps[i]) for l in self.subtask_path_steps[i]] + sr += s*N*w[0] + if N == 1: + cpl += sr + continue + for j, wj in zip(self.subtask_successes[i][1:], w[1:]): + sr += j*(1+(N-1)*s)*wj + s = j + cpl += sr/N + cpl = cpl/M + return cpl + + def TAR(self): + tars = [] + for i in range(len(self.gt_length)): + for j in range(len(self.gt_length[i])): + error = self.error_length[i][j] + gt = self.gt_length[i][j] + if error == 0 and gt == 0: + tar = 1.0 + elif gt == 0: + tar = 0.0 + else: + denominator = max(error, gt) + if denominator > 0: + tar = 1 - max(error - 1, 0) / denominator + else: + tar = 1.0 + tars.append(tar) + return sum(tars) / len(tars) if len(tars) > 0 else 0 + + def compute(self): + return { + "success_rate": self.success_rate(), + "oracle_success_rate": self.oracle_success_rate(), + "spl": self.spl(), + "navigation_error": self.navigation_error(), + "independent_success_rate": self.independent_success_rate(), + "conditional_success_rate": self.conditional_success_rate(), + "conditional_path_length":self.conditional_path_length(), + "tar":self.TAR() + } diff --git a/easi/tasks/lhpr_vln/vendor/scene_config.py b/easi/tasks/lhpr_vln/vendor/scene_config.py new file mode 100644 index 0000000..c3ded8d --- /dev/null +++ b/easi/tasks/lhpr_vln/vendor/scene_config.py @@ -0,0 +1,120 @@ +"""Habitat-Sim configuration for LHPR-VLN scenes. + +Vendored from LH-VLN/habitat_base/config.py with parameterization. +""" +import math + +import habitat_sim +import magnum as mn + + +def make_setting(scene_path: str, scene_dataset_path: str, robot: str, + width: int = 512, height: int = 512) -> dict: + """Build Habitat-Sim settings dict for an HM3D scene.""" + sensor_height = 0.5 if robot == "spot" else 1.0 + return { + "width": width, + "height": height, + "scene": scene_path, + "scene_dataset": scene_dataset_path, + "default_agent": 0, + "sensor_height": sensor_height, + "color_sensor_f": True, + "color_sensor_l": True, + "color_sensor_r": True, + "color_sensor_3rd": True, + "depth_sensor_f": True, + "depth_sensor_l": True, + "depth_sensor_r": True, + "semantic_sensor": True, + "seed": 1, + "enable_physics": False, + } + + +def make_cfg(settings: dict, gpu_device_id: int = 0) -> habitat_sim.Configuration: + """Build Habitat-Sim Configuration from settings dict.""" + sim_cfg = habitat_sim.SimulatorConfiguration() + sim_cfg.gpu_device_id = gpu_device_id + sim_cfg.scene_id = settings["scene"] + sim_cfg.enable_physics = settings["enable_physics"] + if "scene_dataset" in settings: + sim_cfg.scene_dataset_config_file = settings["scene_dataset"] + + sensors = { + "color_sensor_f": { + "sensor_type": habitat_sim.SensorType.COLOR, + "resolution": [settings["height"], settings["width"]], + "position": mn.Vector3(0.0, settings["sensor_height"], 0.0), + "orientation": [0.0, 0.0, 0.0], + }, + "color_sensor_l": { + "sensor_type": habitat_sim.SensorType.COLOR, + "resolution": [settings["height"], settings["width"]], + "position": mn.Vector3(0.0, settings["sensor_height"], 0.0), + "orientation": [0.0, math.pi / 3.0, 0.0], + }, + "color_sensor_r": { + "sensor_type": habitat_sim.SensorType.COLOR, + "resolution": [settings["height"], settings["width"]], + "position": mn.Vector3(0.0, settings["sensor_height"], 0.0), + "orientation": [0.0, -math.pi / 3.0, 0.0], + }, + "color_sensor_3rd": { + "sensor_type": habitat_sim.SensorType.COLOR, + "resolution": [settings["height"], settings["width"]], + "position": mn.Vector3(0.0, settings["sensor_height"] + 0.5, 1.0), + "orientation": [-math.pi / 4, 0.0, 0.0], + }, + "depth_sensor_l": { + "sensor_type": habitat_sim.SensorType.DEPTH, + "resolution": [settings["height"], settings["width"]], + "position": mn.Vector3(0.0, settings["sensor_height"], 0.0), + "orientation": [0.0, math.pi / 3.0, 0.0], + }, + "depth_sensor_f": { + "sensor_type": habitat_sim.SensorType.DEPTH, + "resolution": [settings["height"], settings["width"]], + "position": mn.Vector3(0.0, settings["sensor_height"], 0.0), + "orientation": [0.0, 0.0, 0.0], + }, + "depth_sensor_r": { + "sensor_type": habitat_sim.SensorType.DEPTH, + "resolution": [settings["height"], settings["width"]], + "position": mn.Vector3(0.0, settings["sensor_height"], 0.0), + "orientation": [0.0, -math.pi / 3.0, 0.0], + }, + "semantic_sensor": { + "sensor_type": habitat_sim.SensorType.SEMANTIC, + "resolution": [settings["height"], settings["width"]], + "position": mn.Vector3(0.0, settings["sensor_height"], 0.0), + "orientation": [0.0, 0.0, 0.0], + }, + } + sensor_specs = [] + for sensor_uuid, sensor_params in sensors.items(): + if settings.get(sensor_uuid, False): + sensor_spec = habitat_sim.CameraSensorSpec() + sensor_spec.uuid = sensor_uuid + sensor_spec.sensor_type = sensor_params["sensor_type"] + sensor_spec.resolution = sensor_params["resolution"] + sensor_spec.position = sensor_params["position"] + sensor_spec.orientation = sensor_params["orientation"] + if sensor_uuid == "color_sensor_3rd": + sensor_spec.sensor_subtype = habitat_sim.SensorSubType.PINHOLE + sensor_specs.append(sensor_spec) + + agent_cfg = habitat_sim.agent.AgentConfiguration() + agent_cfg.sensor_specifications = sensor_specs + agent_cfg.action_space = { + "move_forward": habitat_sim.agent.ActionSpec( + "move_forward", habitat_sim.agent.ActuationSpec(amount=0.25) + ), + "turn_left": habitat_sim.agent.ActionSpec( + "turn_left", habitat_sim.agent.ActuationSpec(amount=30.0) + ), + "turn_right": habitat_sim.agent.ActionSpec( + "turn_right", habitat_sim.agent.ActuationSpec(amount=30.0) + ), + } + return habitat_sim.Configuration(sim_cfg, [agent_cfg]) diff --git a/easi/tasks/lhpr_vln/vendor/scene_simulator.py b/easi/tasks/lhpr_vln/vendor/scene_simulator.py new file mode 100644 index 0000000..d6795db --- /dev/null +++ b/easi/tasks/lhpr_vln/vendor/scene_simulator.py @@ -0,0 +1,205 @@ +"""Habitat-Sim SceneSimulator for LHPR-VLN. + +Vendored from LH-VLN/habitat_base/simulation.py with parameterization. +Original creates a new habitat_sim.Simulator per episode, computes geodesic +distances, and tracks multi-subtask stage/success/oracle state. +""" +import math +import operator +from typing import Optional, Union + +import habitat_sim +import numpy as np +import quaternion + +from .scene_config import make_cfg, make_setting + + +class SceneSimulator: + def __init__( + self, + *, + scene_id: str, + robot: str, + targets: list[str], + regions: list[str], + instruction: str, + gt_step: list[int] | None = None, + scene_base_path: str, + scene_dataset_path: str, + gpu_device_id: int = 0, + success_distance: float = 1.0, + max_steps: int = 500, + width: int = 512, + height: int = 512, + ): + self.scene_id = scene_id + self.robot = robot + self.targets = targets + self.regions = regions + self.instruction = instruction + self.success_distance = success_distance + self.max_steps = max_steps + + # Resolve scene path: IDs < 800 -> train/, >= 800 -> val/ + split = "train/" if int(scene_id[:5]) < 800 else "val/" + scene_path = scene_base_path + split + scene_id + + # Init simulator + sim_settings = make_setting(scene_path, scene_dataset_path, robot, width, height) + cfg = make_cfg(sim_settings, gpu_device_id) + self.sim = habitat_sim.Simulator(cfg) + self.sim_settings = sim_settings + + # Pathfinder + self.pathfinder = self.sim.pathfinder + self.agent = self.sim.initialize_agent(sim_settings["default_agent"]) + + # Random navigable start position + agent_state = habitat_sim.AgentState() + sample_navigable_point = self.pathfinder.get_random_navigable_point() + agent_state.position = sample_navigable_point - np.array([0, 0, -0.25]) + self.agent.set_state(agent_state) + + # Greedy geodesic follower (for GT path computation) + self.follower = habitat_sim.nav.GreedyGeodesicFollower( + pathfinder=self.pathfinder, + agent=self.agent, + goal_radius=success_distance, + stop_key="stop", + forward_key="move_forward", + left_key="turn_left", + right_key="turn_right", + ) + + # Initial observation + self.observations = self.sim.step("move_forward") + + # Subtask tracking + self.step = -1 + self.stage = 0 + self.target_num = len(targets) + self.nav_steps = [] + self.successes = [False] * self.target_num + self.oracle_successes = [False] * self.target_num + self.nav_errors = [] + + self.info = self._get_info() + self.gt_step = gt_step + self.gt_path = [self.info["geo_dis"]] + + self.done = False + self.episode_over = False + + def actor(self, action: str): + """Perform one action. Returns (observations, done, info).""" + if action != "stop": + self.observations = self.sim.step(action) + + if self.step == -1: + self.step += 1 + return self.observations, self.done, self.info + + self.step += 1 + self.info = self._get_info() + + if self.info["geo_dis"] < self.success_distance: + self.oracle_successes[self.stage] = True + + if action == "stop": + if self.info["geo_dis"] < self.success_distance: + self.successes[self.stage] = True + self.nav_errors.append(self.info["geo_dis"]) + + if not self.nav_steps: + self.nav_steps.append(self.step) + else: + self.nav_steps.append(self.step - sum(self.nav_steps)) + + self.stage += 1 + if self.stage >= self.target_num: + self.done = True + self.episode_over = True + return self.observations, self.done, self.info + + self.info = self._get_info() + self.gt_path.append(self.info["geo_dis"]) + + if self.step >= self.max_steps: + self.episode_over = True + if not self.nav_steps: + self.nav_steps.append(self.step) + else: + self.nav_steps.append(self.step - sum(self.nav_steps)) + self.nav_errors.append(self.info["geo_dis"]) + + return self.observations, self.done, self.info + + def get_front_rgb(self) -> np.ndarray: + """Return front RGB as H x W x 3 uint8 numpy array.""" + rgba = self.observations["color_sensor_f"] + return rgba[:, :, :3] # drop alpha channel + + def _get_info(self) -> dict: + """Return info about current state relative to current target.""" + obj_target = self.targets[self.stage] + coord_list = self._get_coord(obj_target) + if not coord_list: + return {"target": obj_target, "geo_dis": math.inf} + snap_coord_list = [self.pathfinder.snap_point(c) for c in coord_list] + geo_dis, snap_coord = self._geodesic_distance(snap_coord_list) + position, rotation = self._return_state() + return { + "target": obj_target, + "target_coord": snap_coord, + "agent_position": position, + "agent_rotation": rotation, + "geo_dis": geo_dis, + } + + def _get_coord(self, obj_target: str) -> list: + """Return coordinate list for target object in its specified region.""" + scene = self.sim.semantic_scene + coord_list = [] + index = self.targets.index(obj_target) + region_id = self.regions[index] + for region in scene.regions: + if region.id[1:] != region_id: + continue + for obj in region.objects: + if obj.category.name() == obj_target: + coord_list.append(obj.aabb.center) + return coord_list + + def _geodesic_distance(self, position_b_list) -> tuple[float, list]: + """Compute min geodesic distance from agent to any position in list.""" + position_a, _ = self._return_state() + geo_dis = math.inf + coord = position_b_list[0] + for position_b in position_b_list: + path = habitat_sim.nav.ShortestPath() + path.requested_end = np.array(position_b, dtype=np.float32) + path.requested_start = np.array(position_a, dtype=np.float32) + if self.pathfinder.find_path(path): + if path.geodesic_distance < geo_dis: + geo_dis = path.geodesic_distance + coord = position_b + return geo_dis, coord + + def _return_state(self): + agent_state = self.agent.get_state() + return agent_state.position, agent_state.rotation + + def return_results(self) -> dict: + """Return complete episode results for metrics computation.""" + return { + "successes": self.successes, + "oracle_successes": self.oracle_successes, + "navigation_steps": self.nav_steps, + "navigation_errors": self.nav_errors, + "gt_step": self.gt_step, + "gt_path": self.gt_path, + } + + def close(self): + self.sim.close() From d87ae9c525a12ac1050bf19515919ac15c6674e9 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 3 Mar 2026 21:16:41 +0800 Subject: [PATCH 125/239] fix(lhpr_vln): restore metrics docstrings, remove unused imports --- easi/tasks/lhpr_vln/vendor/metrics.py | 22 +++++++++++++++++++ easi/tasks/lhpr_vln/vendor/scene_simulator.py | 3 --- 2 files changed, 22 insertions(+), 3 deletions(-) diff --git a/easi/tasks/lhpr_vln/vendor/metrics.py b/easi/tasks/lhpr_vln/vendor/metrics.py index 24087f2..fc89ec5 100644 --- a/easi/tasks/lhpr_vln/vendor/metrics.py +++ b/easi/tasks/lhpr_vln/vendor/metrics.py @@ -23,6 +23,19 @@ def add_sample(self, subtask_path_step, gt_length, error_length): + """Adds a sample of navigation results. + + Parameters: + - success: 1 if the task is successful, 0 otherwise. + - gt_step: The ground truth path steps of the episode. + - path_step: The path steps of the episode. + - oracle_success: 1 if the oracle task is successful, 0 otherwise. + - navigation_error: The navigation error for the task. + - subtask_successes: A list of 0s and 1s indicating subtask success or failure. + - subtask_path_step: A list for the ground truth path steps of subtasks. + - gt_length: Ground path length for sub-tasks, + - error_length: Nav error length for sub-tasks: + """ self.successes.append(success) self.gt_steps.append(gt_step) self.path_steps.append(path_step) @@ -34,25 +47,31 @@ def add_sample(self, self.error_length.append(error_length) def success_rate(self): + """Calculates Success Rate (SR).""" return sum(self.successes) / len(self.successes) if len(self.successes) > 0 else 0 def oracle_success_rate(self): + """Calculates Oracle Success Rate (OSR).""" return sum(self.oracle_successes) / len(self.oracle_successes) if len(self.oracle_successes) > 0 else 0 def spl(self): + """Calculates Success Rate Penalized by Path Length (SPL).""" total_spl = sum(s * (gp/max(gp, p)) if p > 0 else 0 for s, gp, p in zip(self.successes, self.gt_steps, self.path_steps)) return total_spl / len(self.successes) if len(self.successes) > 0 else 0 def navigation_error(self): + """Calculates average Navigation Error (NE).""" return sum(self.navigation_errors) / len(self.navigation_errors) if len(self.navigation_errors) > 0 else 0 def independent_success_rate(self): + """Calculates Independent Success Rate (ISR).""" subtask_counts = [len(subtasks) for subtasks in self.subtask_successes] total_subtasks = sum(subtask_counts) total_successes = sum(sum(subtasks) for subtasks in self.subtask_successes) return total_successes / total_subtasks if total_subtasks > 0 else 0 def conditional_success_rate(self): + """Calculates Conditional Success Rate (CSR).""" M = len(self.subtask_successes) if M == 0: return 0 @@ -75,6 +94,7 @@ def conditional_success_rate(self): return csr def conditional_path_length(self): + """Calculates Conditional Success Rate weighted by Ground Truth Path Length (CGT).""" M = len(self.subtask_successes) if M == 0: return 0 @@ -98,6 +118,7 @@ def conditional_path_length(self): return cpl def TAR(self): + """Calculates Target Approach Rate (TAR).""" tars = [] for i in range(len(self.gt_length)): for j in range(len(self.gt_length[i])): @@ -117,6 +138,7 @@ def TAR(self): return sum(tars) / len(tars) if len(tars) > 0 else 0 def compute(self): + """Compute all metrics.""" return { "success_rate": self.success_rate(), "oracle_success_rate": self.oracle_success_rate(), diff --git a/easi/tasks/lhpr_vln/vendor/scene_simulator.py b/easi/tasks/lhpr_vln/vendor/scene_simulator.py index d6795db..9e1096c 100644 --- a/easi/tasks/lhpr_vln/vendor/scene_simulator.py +++ b/easi/tasks/lhpr_vln/vendor/scene_simulator.py @@ -5,12 +5,9 @@ distances, and tracks multi-subtask stage/success/oracle state. """ import math -import operator -from typing import Optional, Union import habitat_sim import numpy as np -import quaternion from .scene_config import make_cfg, make_setting From 3980763d1c13c2d1653ecc5ac2899b2b72e064ad Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 3 Mar 2026 21:17:10 +0800 Subject: [PATCH 126/239] feat(lhpr_vln): add action space constants --- easi/tasks/lhpr_vln/actions.py | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) create mode 100644 easi/tasks/lhpr_vln/actions.py diff --git a/easi/tasks/lhpr_vln/actions.py b/easi/tasks/lhpr_vln/actions.py new file mode 100644 index 0000000..bfc33e3 --- /dev/null +++ b/easi/tasks/lhpr_vln/actions.py @@ -0,0 +1,20 @@ +"""LHPR-VLN action space constants.""" + +# Action names (text used by agent and bridge) +MOVE_FORWARD = "move_forward" +TURN_LEFT = "turn_left" +TURN_RIGHT = "turn_right" +STOP = "stop" + +ACTION_SPACE = [MOVE_FORWARD, TURN_LEFT, TURN_RIGHT, STOP] + +# Map text -> Habitat action key (stop is handled by bridge, not sim) +ACTION_NAME_TO_HABITAT = { + MOVE_FORWARD: "move_forward", + TURN_LEFT: "turn_left", + TURN_RIGHT: "turn_right", +} + + +def get_action_space() -> list[str]: + return list(ACTION_SPACE) From 4da8634a6a35dacf02f59f731f3445e7c7a4cecc Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 3 Mar 2026 21:21:10 +0800 Subject: [PATCH 127/239] feat(lhpr_vln): add bridge wrapping vendored SceneSimulator --- easi/tasks/lhpr_vln/bridge.py | 189 ++++++++++++++++++++++++++++++++++ 1 file changed, 189 insertions(+) create mode 100644 easi/tasks/lhpr_vln/bridge.py diff --git a/easi/tasks/lhpr_vln/bridge.py b/easi/tasks/lhpr_vln/bridge.py new file mode 100644 index 0000000..b00c52f --- /dev/null +++ b/easi/tasks/lhpr_vln/bridge.py @@ -0,0 +1,189 @@ +"""LHPR-VLN bridge — wraps vendored SceneSimulator via BaseBridge. + +This script runs inside the easi_habitat_sim_v0_3_0 conda env (Python 3.9). +Communicates with parent process via filesystem IPC. + +Key difference from other bridges: creates a NEW SceneSimulator per episode +(Habitat-Sim ties one Simulator instance to one scene). The bridge calls +self.env.close() and recreates on each reset. + +Usage: + python bridge.py --workspace /tmp/easi_xxx [--data-dir /path/to/data] [--simulator-kwargs '{}'] +""" +from __future__ import annotations + +import json +import sys +from pathlib import Path + +import numpy as np + +_repo_root = Path(__file__).resolve().parents[3] +if str(_repo_root) not in sys.path: + sys.path.insert(0, str(_repo_root)) + +from easi.simulators.base_bridge import BaseBridge +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + + +class LHPRVLNBridge(BaseBridge): + """BaseBridge wrapper for LHPR-VLN SceneSimulator. + + Unlike other bridges, this creates a new SceneSimulator per episode + because Habitat-Sim binds one simulator instance to one scene file. + """ + + _scene_sim = None # The vendored SceneSimulator (recreated per episode) + + def _create_env(self, reset_config, simulator_kwargs): + """Called once on first reset. We store simulator_kwargs and return + a placeholder — the real SceneSimulator is created in _on_reset().""" + # Store config for _on_reset to use + self._sim_kwargs = simulator_kwargs + return object() # placeholder — _on_reset creates the real sim + + def _on_reset(self, env, reset_config): + """Create a new SceneSimulator for this episode's scene.""" + from easi.tasks.lhpr_vln.vendor.scene_simulator import SceneSimulator + + # Close previous simulator if any + if self._scene_sim is not None: + self._scene_sim.close() + self._scene_sim = None + + data_dir = reset_config.get("data_dir") or self._sim_kwargs.get("data_dir", "") + scene_base_path = str(Path(data_dir) / "hm3d") + "/" + scene_dataset_path = str(Path(data_dir) / "hm3d" / "hm3d_annotated_basis.scene_dataset_config.json") + + # Parse episode data from reset_config + targets = json.loads(reset_config["targets"]) if isinstance(reset_config["targets"], str) else reset_config["targets"] + regions = json.loads(reset_config["regions"]) if isinstance(reset_config["regions"], str) else reset_config["regions"] + gt_step = json.loads(reset_config.get("gt_step", "[]")) if isinstance(reset_config.get("gt_step"), str) else reset_config.get("gt_step") + + gpu_device_id = self._sim_kwargs.get("gpu_device_id", 0) + if gpu_device_id < 0: + gpu_device_id = 0 # Habitat-Sim needs a real GPU ID + max_steps = self._sim_kwargs.get("max_steps", 500) + width = self._sim_kwargs.get("screen_width", 512) + height = self._sim_kwargs.get("screen_height", 512) + success_distance = self._sim_kwargs.get("success_distance", 1.0) + + self._scene_sim = SceneSimulator( + scene_id=reset_config["scene_id"], + robot=reset_config.get("robot", "spot"), + targets=targets, + regions=regions, + instruction=reset_config.get("instruction", ""), + gt_step=gt_step if gt_step else None, + scene_base_path=scene_base_path, + scene_dataset_path=scene_dataset_path, + gpu_device_id=gpu_device_id, + success_distance=success_distance, + max_steps=max_steps, + width=width, + height=height, + ) + + # Get initial observation (step -1 in SceneSimulator) + obs, done, info = self._scene_sim.actor("move_forward") + return obs + + def _on_step(self, env, action_text): + """Step the SceneSimulator and return (obs, reward, done, info).""" + obs, done, info = self._scene_sim.actor(action_text) + episode_over = self._scene_sim.episode_over + + # Build info dict with subtask state + step_info = self._build_step_info(done, episode_over) + return obs, 0.0, episode_over, step_info + + def _build_step_info(self, all_done: bool, episode_over: bool) -> dict: + """Build info dict with subtask completion state for metrics.""" + sim = self._scene_sim + results = sim.return_results() + + task_success = 1.0 if all(sim.successes) else 0.0 + + # Build feedback string for the agent + if episode_over and all_done: + feedback = "All subtasks completed. Navigation finished." + elif episode_over: + feedback = f"Maximum steps reached. Completed {sim.stage}/{sim.target_num} subtasks." + else: + current_target = sim.targets[sim.stage] if sim.stage < sim.target_num else "N/A" + feedback = f"Subtask {sim.stage + 1}/{sim.target_num}: navigate to {current_target}. Distance: {sim.info.get('geo_dis', -1):.1f}m" + + return { + "task_success": task_success, + "feedback": feedback, + "subtask_stage": float(sim.stage), + "subtask_total": float(sim.target_num), + "current_geo_distance": float(sim.info.get("geo_dis", -1)), + # Serialized arrays for evaluate_episode to read at episode end + "subtask_successes": json.dumps([int(s) for s in results["successes"]]), + "subtask_oracle_successes": json.dumps([int(s) for s in results["oracle_successes"]]), + "subtask_nav_errors": json.dumps(results["navigation_errors"]), + "subtask_nav_steps": json.dumps(results["navigation_steps"]), + "gt_steps": json.dumps(results["gt_step"] or []), + "gt_paths": json.dumps(results["gt_path"]), + } + + def _extract_image(self, obs): + """Return front RGB view as the primary observation image.""" + return obs["color_sensor_f"][:, :, :3] # RGBA -> RGB + + def _make_response(self, obs, reward=0.0, done=False, info=None): + """Override to save 3 separate RGB views and pass paths in metadata. + + Saves: step_NNNN_left.png, step_NNNN_front.png, step_NNNN_right.png + Front is the primary rgb_path; left/right paths go in metadata. + """ + from easi.communication.schemas import make_observation_response + + save_dir = Path(self.episode_output_dir) if self.episode_output_dir else self.workspace + save_dir.mkdir(parents=True, exist_ok=True) + + paths = {} + for view_name, sensor_key in [("left", "color_sensor_l"), + ("front", "color_sensor_f"), + ("right", "color_sensor_r")]: + rgb = obs[sensor_key][:, :, :3] + path = save_dir / ("step_%04d_%s.png" % (self.step_count, view_name)) + from PIL import Image + Image.fromarray(rgb).save(str(path)) + paths[view_name] = str(path) + + clean_info = self._extract_info(info or {}) + clean_info["step"] = str(self.step_count) + return make_observation_response( + rgb_path=paths["front"], + agent_pose=[0.0, 0.0, 0.0, 0.0, 0.0, 0.0], + metadata={ + "step": str(self.step_count), + "left_rgb_path": paths["left"], + "front_rgb_path": paths["front"], + "right_rgb_path": paths["right"], + }, + reward=reward, + done=done, + info=clean_info, + ) + + def _extract_info(self, info): + """Pass through our pre-built info dict. + All values are already int/float/str/bool.""" + return {k: v for k, v in info.items() + if isinstance(v, (int, float, str, bool))} + + def close(self): + """Close the SceneSimulator.""" + if self._scene_sim is not None: + self._scene_sim.close() + self._scene_sim = None + super().close() + + +if __name__ == "__main__": + LHPRVLNBridge.main() From 2463e68e2f23c3334fcaceb5a16f97c2eb1f56c0 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 3 Mar 2026 21:23:32 +0800 Subject: [PATCH 128/239] feat(lhpr_vln): add task class with all 8 metrics + contest score --- easi/tasks/lhpr_vln/task.py | 221 ++++++++++++++++++++++++++++++++++++ 1 file changed, 221 insertions(+) create mode 100644 easi/tasks/lhpr_vln/task.py diff --git a/easi/tasks/lhpr_vln/task.py b/easi/tasks/lhpr_vln/task.py new file mode 100644 index 0000000..d51be13 --- /dev/null +++ b/easi/tasks/lhpr_vln/task.py @@ -0,0 +1,221 @@ +"""LHPR-VLN task for EASI. + +Adapts the LHPR-VLN benchmark to EASI's task interface. +Episodes are loaded from preprocessed JSONL files (data/val.jsonl, +data/test.jsonl) via EASI's default HuggingFace dataset loading. +Each episode contains 2-4 sequential navigation subtasks in HM3D scenes. + +Metrics: +- Per-episode: task_success, oracle_success, spl, navigation_error, + isr, csr, cgt, tar (computed per-episode for result.json) +- Aggregate: All 8 official metrics + contest_score + (using vendored NavigationMetrics) +""" +from __future__ import annotations + +import json +from pathlib import Path + +from easi.core.base_task import BaseTask +from easi.core.episode import EpisodeRecord, StepResult +from easi.tasks.lhpr_vln.actions import get_action_space +from easi.tasks.lhpr_vln.vendor.metrics import NavigationMetrics +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + + +class LHPRVLNTask(BaseTask): + + def _build_action_space(self) -> list[str]: + return get_action_space() + + def get_task_yaml_path(self) -> Path: + return Path(__file__).parent / "_base.yaml" + + def get_bridge_script_path(self) -> Path: + return Path(__file__).parent / "bridge.py" + + def get_instruction(self, episode: dict) -> str: + return episode.get("instruction", self.name) + + def format_reset_config(self, episode: dict) -> dict: + """Map LHPR-VLN episode dict to bridge reset config. + + Episode keys come from the preprocessed JSONL (lowercase): + id, instruction, scene, robot, objects, regions, rooms, + gt_steps, subtask_list, num_targets, batch + """ + return { + "episode_id": episode.get("id", "unknown"), + "scene_id": episode["scene"], + "robot": episode.get("robot", "spot"), + "instruction": episode.get("instruction", ""), + "targets": episode["objects"], + "regions": episode["regions"], + "gt_step": episode.get("gt_steps", []), + "data_dir": episode.get("_data_dir", ""), + } + + def evaluate_episode( + self, episode: dict, trajectory: list[StepResult] + ) -> dict[str, float]: + """Compute per-episode metrics from subtask completion data. + + Reads serialized subtask arrays from the last step's info dict. + Returns metrics for result.json AND data for aggregate_results(). + """ + if not trajectory: + return self._empty_metrics() + + last = trajectory[-1] + info = last.info + + # Parse subtask arrays from the last step's info + successes = json.loads(info.get("subtask_successes", "[]")) + oracle_successes = json.loads(info.get("subtask_oracle_successes", "[]")) + nav_errors = json.loads(info.get("subtask_nav_errors", "[]")) + nav_steps = json.loads(info.get("subtask_nav_steps", "[]")) + gt_steps = json.loads(info.get("gt_steps", "[]")) + gt_paths = json.loads(info.get("gt_paths", "[]")) + + # Overall success: all subtasks succeeded + task_success = 1.0 if successes and all(s == 1 for s in successes) else 0.0 + oracle_success = 1.0 if oracle_successes and all(s == 1 for s in oracle_successes) else 0.0 + + # SPL: success * (gt_total / max(gt_total, actual_total)) + total_gt = sum(gt_steps) if gt_steps else 0 + total_actual = sum(nav_steps) if nav_steps else 0 + spl = task_success * (total_gt / max(total_gt, total_actual)) if total_actual > 0 else 0.0 + + # Navigation error: avg geodesic distance at stop across subtasks + ne = sum(nav_errors) / len(nav_errors) if nav_errors else 0.0 + + return { + "task_success": task_success, + "oracle_success": oracle_success, + "spl": spl, + "navigation_error": ne, + "num_steps": float(len(trajectory)), + "num_subtasks": float(len(successes)), + "subtasks_completed": float(sum(successes)), + # Store raw arrays as JSON strings for aggregate_results + "_subtask_successes": json.dumps(successes), + "_subtask_oracle_successes": json.dumps(oracle_successes), + "_subtask_nav_errors": json.dumps(nav_errors), + "_subtask_nav_steps": json.dumps(nav_steps), + "_gt_steps": json.dumps(gt_steps), + "_gt_paths": json.dumps(gt_paths), + } + + def aggregate_results( + self, records: list[EpisodeRecord] + ) -> dict[str, float]: + """Compute all 8 LHPR-VLN metrics using vendored NavigationMetrics. + + Metric definitions (from CVPR-25 paper): + - SR: Success Rate (all subtasks completed) + - OSR: Oracle Success Rate (agent ever passed within 1m of all targets) + - SPL: Success weighted by Path Length + - NE: Navigation Error (avg geodesic distance at stop) + - ISR: Independent Success Rate (fraction of subtasks succeeded) + - CSR: Conditional Success Rate (sequential dependency weighting) + - CGT: CSR weighted by GT path length + - TAR: Target Approach Rate (continuous approach measure) + - contest_score: 0.4*TAR + 0.2*ISR + 0.2*CSR + 0.2*CGT + """ + if not records: + return {} + + metrics = NavigationMetrics() + + for r in records: + er = r.episode_results + successes = json.loads(er.get("_subtask_successes", "[]")) + oracle_successes = json.loads(er.get("_subtask_oracle_successes", "[]")) + nav_errors = json.loads(er.get("_subtask_nav_errors", "[]")) + nav_steps = json.loads(er.get("_subtask_nav_steps", "[]")) + gt_steps = json.loads(er.get("_gt_steps", "[]")) + gt_paths = json.loads(er.get("_gt_paths", "[]")) + + # Overall success + success = 1 if successes and all(s == 1 for s in successes) else 0 + oracle_success = 1 if oracle_successes and all(s == 1 for s in oracle_successes) else 0 + + # Total steps + total_gt = sum(gt_steps) if gt_steps else 0 + total_actual = sum(nav_steps) if nav_steps else 0 + + # Avg navigation error + avg_ne = sum(nav_errors) / len(nav_errors) if nav_errors else 0.0 + + metrics.add_sample( + success=success, + gt_step=total_gt, + path_step=total_actual, + oracle_success=oracle_success, + navigation_error=avg_ne, + subtask_successes=successes, + subtask_path_step=gt_steps, + gt_length=gt_paths, + error_length=nav_errors, + ) + + result = metrics.compute() + + # Add contest ranking score + tar = result.get("tar", 0) + isr = result.get("independent_success_rate", 0) + csr = result.get("conditional_success_rate", 0) + cgt = result.get("conditional_path_length", 0) + contest_score = 0.4 * tar + 0.2 * isr + 0.2 * csr + 0.2 * cgt + + return { + "SR": round(result["success_rate"], 4), + "OSR": round(result["oracle_success_rate"], 4), + "SPL": round(result["spl"], 4), + "NE": round(result["navigation_error"], 4), + "ISR": round(result["independent_success_rate"], 4), + "CSR": round(result["conditional_success_rate"], 4), + "CGT": round(result["conditional_path_length"], 4), + "TAR": round(result["tar"], 4), + "contest_score": round(contest_score, 4), + "num_episodes": len(records), + # Convenience alias for EASI dashboard + "success_rate": round(result["success_rate"], 4), + } + + def _empty_metrics(self) -> dict[str, float]: + return { + "task_success": 0.0, + "oracle_success": 0.0, + "spl": 0.0, + "navigation_error": 0.0, + "num_steps": 0.0, + "num_subtasks": 0.0, + "subtasks_completed": 0.0, + "_subtask_successes": "[]", + "_subtask_oracle_successes": "[]", + "_subtask_nav_errors": "[]", + "_subtask_nav_steps": "[]", + "_gt_steps": "[]", + "_gt_paths": "[]", + } + + def _get_builtin_episodes(self) -> list[dict]: + """Minimal episodes for testing without dataset download.""" + return [ + { + "id": "test_0", + "instruction": "Find the chair in the living room, then find the table in the kitchen.", + "scene": "00384-ceJTwFNjqCt", + "robot": "spot", + "objects": ["chair", "table"], + "regions": ["3", "5"], + "rooms": ["living room", "kitchen"], + "gt_steps": [40, 55], + "subtask_list": ["Move_to('chair_3')", "Move_to('table_5')"], + "num_targets": 2, + "batch": "builtin", + }, + ] From 2d8c909a0cee4dfb8ba5ca9b5c5e19b318fabeff Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 3 Mar 2026 21:55:49 +0800 Subject: [PATCH 129/239] feat(lhpr-vln): add YAML task configs for val and test splits --- easi/tasks/lhpr_vln/_base.yaml | 27 ++++++++++++++++++++++++++ easi/tasks/lhpr_vln/lhpr_vln_test.yaml | 6 ++++++ easi/tasks/lhpr_vln/lhpr_vln_val.yaml | 6 ++++++ 3 files changed, 39 insertions(+) create mode 100644 easi/tasks/lhpr_vln/_base.yaml create mode 100644 easi/tasks/lhpr_vln/lhpr_vln_test.yaml create mode 100644 easi/tasks/lhpr_vln/lhpr_vln_val.yaml diff --git a/easi/tasks/lhpr_vln/_base.yaml b/easi/tasks/lhpr_vln/_base.yaml new file mode 100644 index 0000000..6484acf --- /dev/null +++ b/easi/tasks/lhpr_vln/_base.yaml @@ -0,0 +1,27 @@ +display_name: "LHPR-VLN" +description: "Long-Horizon Path Reasoning in Vision-Language Navigation (CVPR-25)" +simulator: "habitat_sim:v0_3_0" +task_class: "easi.tasks.lhpr_vln.task.LHPRVLNTask" +max_steps: 500 +dataset: + source: huggingface + repo_id: "oscarqjh/LHPR-VLN_easi" + subset: null + zip_files: + - "hm3d_v0.2_lhpr_scenes.zip" +simulator_configs: + render_platform: auto + screen_height: 512 + screen_width: 512 + gpu_device_id: 0 + success_distance: 1.0 + additional_deps: [] +agent: + prompt_builder: "easi.tasks.lhpr_vln.prompts.LHPRVLNPromptBuilder" + prompt_builder_kwargs: + use_feedback: true + chat_history: false + generation_kwargs: + temperature: 0 + max_tokens: 256 + top_p: 0.95 diff --git a/easi/tasks/lhpr_vln/lhpr_vln_test.yaml b/easi/tasks/lhpr_vln/lhpr_vln_test.yaml new file mode 100644 index 0000000..3ecd91b --- /dev/null +++ b/easi/tasks/lhpr_vln/lhpr_vln_test.yaml @@ -0,0 +1,6 @@ +extends: _base.yaml +name: lhpr_vln_test +display_name: "LHPR-VLN Test" +description: "LHPR-VLN test split (batch_7 + batch_8)" +dataset: + split: "test" diff --git a/easi/tasks/lhpr_vln/lhpr_vln_val.yaml b/easi/tasks/lhpr_vln/lhpr_vln_val.yaml new file mode 100644 index 0000000..a9c24df --- /dev/null +++ b/easi/tasks/lhpr_vln/lhpr_vln_val.yaml @@ -0,0 +1,6 @@ +extends: _base.yaml +name: lhpr_vln_val +display_name: "LHPR-VLN Val" +description: "LHPR-VLN validation split (batch_6)" +dataset: + split: "val" From 9f1c38ae6164ff1252335bf4e3af6520a15d5462 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 3 Mar 2026 21:57:11 +0800 Subject: [PATCH 130/239] feat(habitat-sim): widen version spec to support 0.3.0 and 0.3.1 --- easi/simulators/habitat_sim/v0_3_0/conda_env.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/easi/simulators/habitat_sim/v0_3_0/conda_env.yaml b/easi/simulators/habitat_sim/v0_3_0/conda_env.yaml index 17f8c8d..6b0b42b 100644 --- a/easi/simulators/habitat_sim/v0_3_0/conda_env.yaml +++ b/easi/simulators/habitat_sim/v0_3_0/conda_env.yaml @@ -5,5 +5,5 @@ channels: - defaults dependencies: - python=3.9 - - habitat-sim=0.3.0 + - habitat-sim>=0.3.0,<0.4 - withbullet From e4af2a437d4171833218e4587c2d4b1ea5512943 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 3 Mar 2026 21:57:31 +0800 Subject: [PATCH 131/239] feat(lhpr-vln): add prompt builder for LLM-based VLN agent --- easi/tasks/lhpr_vln/prompts.py | 121 +++++++++++++++++++++++++++++++++ 1 file changed, 121 insertions(+) create mode 100644 easi/tasks/lhpr_vln/prompts.py diff --git a/easi/tasks/lhpr_vln/prompts.py b/easi/tasks/lhpr_vln/prompts.py new file mode 100644 index 0000000..2513bf1 --- /dev/null +++ b/easi/tasks/lhpr_vln/prompts.py @@ -0,0 +1,121 @@ +"""LHPR-VLN prompt builder for LLM-based navigation agents. + +Builds prompts with: +- Panoramic RGB observation (left + front + right concatenated) +- Task instruction with subtask targets +- Current subtask progress (from observation metadata) +- Action history with distance feedback +- 4-action space: move_forward, turn_left, turn_right, stop +""" +from __future__ import annotations + +import json + +from easi.agents.prompt_builder import _encode_image_base64, validate_action_name +from easi.core.episode import Action, Observation +from easi.core.memory import AgentMemory +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + +SYSTEM_PROMPT = """You are a navigation agent in a 3D indoor environment. You observe the environment through three camera views (left at -60°, front at 0°, right at +60°) and must navigate to sequential target objects. + +Your actions: +- move_forward: Move forward 0.25 meters +- turn_left: Turn left 30 degrees +- turn_right: Turn right 30 degrees +- stop: Declare current subtask complete (use when you believe you are within 1 meter of the current target) + +Strategy: +- Navigate to each target object in order. After reaching one target, use "stop" to advance to the next. +- Use visual cues to identify rooms and objects. +- If you are far from the target, explore by turning to survey the environment, then move toward likely locations. +- Use "stop" only when you are confident you are close to the current target. + +Respond with ONLY a JSON object: {"action": ""}""" + + +class LHPRVLNPromptBuilder: + """Prompt builder for LHPR-VLN navigation tasks.""" + + def __init__(self, use_feedback: bool = True, chat_history: bool = False, **kwargs): + self._use_feedback = use_feedback + self._chat_history = chat_history + + def build_messages(self, memory: AgentMemory) -> list[dict]: + messages = [{"role": "system", "content": SYSTEM_PROMPT}] + + # Build user message with observation + task context + content = [] + + # Add 3 RGB views (left, front, right) as separate images + obs = memory.current_observation + if obs and obs.metadata: + for view_name, meta_key in [("Left view", "left_rgb_path"), + ("Front view", "front_rgb_path"), + ("Right view", "right_rgb_path")]: + path = obs.metadata.get(meta_key) + if path: + image_url = _encode_image_base64(path) + content.append({"type": "text", "text": f"[{view_name}]"}) + content.append({"type": "image_url", "image_url": {"url": image_url}}) + elif obs and obs.rgb_path: + # Fallback: single image (e.g., testing without full bridge) + image_url = _encode_image_base64(obs.rgb_path) + content.append({"type": "image_url", "image_url": {"url": image_url}}) + + # Build text prompt + text_parts = [] + + # Task instruction + text_parts.append(f"Task: {memory.task_description}") + + # Subtask progress from metadata + if obs and obs.metadata: + stage = obs.metadata.get("subtask_stage", "") + total = obs.metadata.get("subtask_total", "") + distance = obs.metadata.get("current_geo_distance", "") + if stage and total: + text_parts.append(f"Current subtask: {int(float(stage)) + 1}/{int(float(total))}") + if distance: + text_parts.append(f"Distance to current target: {float(distance):.1f}m") + + # Action history with feedback + if self._use_feedback and memory.action_history: + history_lines = [] + for action_name, feedback in memory.action_history[-10:]: # last 10 steps + history_lines.append(f" {action_name} → {feedback}") + text_parts.append("Recent actions:\n" + "\n".join(history_lines)) + + # Available actions + text_parts.append(f"Available actions: {', '.join(memory.action_space)}") + text_parts.append('Respond with a JSON object: {"action": ""}') + + content.append({"type": "text", "text": "\n\n".join(text_parts)}) + messages.append({"role": "user", "content": content}) + + return messages + + def parse_response(self, llm_response: str, memory: AgentMemory) -> list[Action]: + """Parse LLM response into a single Action.""" + try: + # Try JSON parse + response = json.loads(llm_response.strip()) + action_name = response.get("action", "move_forward") + except (json.JSONDecodeError, AttributeError): + # Fallback: look for action name in response text + response_lower = llm_response.lower() + if "stop" in response_lower: + action_name = "stop" + elif "turn_left" in response_lower or "left" in response_lower: + action_name = "turn_left" + elif "turn_right" in response_lower or "right" in response_lower: + action_name = "turn_right" + else: + action_name = "move_forward" + + validated = validate_action_name(action_name, memory.action_space) + return [Action(action_name=validated)] + + def get_response_format(self, memory: AgentMemory) -> dict: + return {} From 3c0294bbed1f4687463ffc099107a61823f43da4 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 3 Mar 2026 21:59:52 +0800 Subject: [PATCH 132/239] test(lhpr-vln): add unit tests for task, metrics, prompts, and actions --- tests/test_lhpr_vln_task.py | 218 ++++++++++++++++++++++++++++++++++++ 1 file changed, 218 insertions(+) create mode 100644 tests/test_lhpr_vln_task.py diff --git a/tests/test_lhpr_vln_task.py b/tests/test_lhpr_vln_task.py new file mode 100644 index 0000000..6cd2044 --- /dev/null +++ b/tests/test_lhpr_vln_task.py @@ -0,0 +1,218 @@ +"""Tests for LHPR-VLN task integration.""" +import json +import pytest +from unittest.mock import MagicMock + +from easi.core.episode import Action, EpisodeRecord, Observation, StepResult +from easi.tasks.lhpr_vln.actions import get_action_space +from easi.tasks.lhpr_vln.vendor.metrics import NavigationMetrics + + +class TestActionSpace: + def test_has_four_actions(self): + actions = get_action_space() + assert len(actions) == 4 + assert "move_forward" in actions + assert "turn_left" in actions + assert "turn_right" in actions + assert "stop" in actions + + +class TestNavigationMetrics: + """Verify vendored metrics compute correctly.""" + + def test_perfect_episode(self): + m = NavigationMetrics() + m.add_sample( + success=1, gt_step=10, path_step=10, oracle_success=1, + navigation_error=0.5, subtask_successes=[1, 1], + subtask_path_step=[5, 5], gt_length=[3.0, 4.0], + error_length=[0.3, 0.5], + ) + result = m.compute() + assert result["success_rate"] == 1.0 + assert result["oracle_success_rate"] == 1.0 + assert result["spl"] == 1.0 + assert result["independent_success_rate"] == 1.0 + + def test_failed_episode(self): + m = NavigationMetrics() + m.add_sample( + success=0, gt_step=10, path_step=50, oracle_success=0, + navigation_error=5.0, subtask_successes=[0, 0], + subtask_path_step=[5, 5], gt_length=[3.0, 4.0], + error_length=[3.0, 5.0], + ) + result = m.compute() + assert result["success_rate"] == 0.0 + assert result["spl"] == 0.0 + assert result["independent_success_rate"] == 0.0 + + def test_partial_success(self): + m = NavigationMetrics() + m.add_sample( + success=0, gt_step=30, path_step=45, oracle_success=0, + navigation_error=2.5, subtask_successes=[1, 0, 0], + subtask_path_step=[10, 10, 10], gt_length=[3.0, 5.0, 4.0], + error_length=[0.5, 3.0, 2.5], + ) + result = m.compute() + assert result["success_rate"] == 0.0 + assert result["independent_success_rate"] == pytest.approx(1/3, abs=0.01) + + def test_contest_score_formula(self): + m = NavigationMetrics() + m.add_sample( + success=1, gt_step=20, path_step=25, oracle_success=1, + navigation_error=0.5, subtask_successes=[1, 1], + subtask_path_step=[10, 10], gt_length=[5.0, 5.0], + error_length=[0.3, 0.5], + ) + result = m.compute() + score = 0.4 * result["tar"] + 0.2 * result["independent_success_rate"] + \ + 0.2 * result["conditional_success_rate"] + 0.2 * result["conditional_path_length"] + assert score > 0 + + +class TestLHPRVLNTask: + """Test task class without simulator.""" + + @pytest.fixture + def task(self): + """Create task with mocked config loading.""" + from easi.tasks.lhpr_vln.task import LHPRVLNTask + + mock_config = { + "name": "lhpr_vln_val", + "display_name": "LHPR-VLN Val", + "simulator": "habitat_sim:v0_3_0", + "task_class": "easi.tasks.lhpr_vln.task.LHPRVLNTask", + "max_steps": 500, + "dataset": {"source": "huggingface", "repo_id": "oscarqjh/LHPR-VLN_easi", "split": "val"}, + "simulator_configs": {}, + "agent": {"prompt_builder": "easi.tasks.lhpr_vln.prompts.LHPRVLNPromptBuilder"}, + } + task = LHPRVLNTask.__new__(LHPRVLNTask) + task._config = mock_config + task._yaml_path = None + task._action_space = None + return task + + def test_format_reset_config(self, task): + episode = { + "id": "batch_6_episode_0", + "instruction": "Find the chair then the table.", + "scene": "00706-abcdef", + "robot": "spot", + "objects": ["chair", "table"], + "regions": ["3", "5"], + "rooms": ["living room", "kitchen"], + "gt_steps": [40, 55], + "subtask_list": ["Move_to('chair_3')", "Move_to('table_5')"], + "num_targets": 2, + "batch": "batch_6", + "_data_dir": "/data/lhpr", + } + config = task.format_reset_config(episode) + assert config["scene_id"] == "00706-abcdef" + assert config["robot"] == "spot" + assert config["targets"] == ["chair", "table"] + assert config["regions"] == ["3", "5"] + assert config["gt_step"] == [40, 55] + + def test_evaluate_episode_empty_trajectory(self, task): + result = task.evaluate_episode({}, []) + assert result["task_success"] == 0.0 + assert result["num_steps"] == 0.0 + + def test_evaluate_episode_successful(self, task): + last_info = { + "task_success": 1.0, + "subtask_successes": "[1, 1]", + "subtask_oracle_successes": "[1, 1]", + "subtask_nav_errors": "[0.5, 0.3]", + "subtask_nav_steps": "[40, 55]", + "gt_steps": "[40, 55]", + "gt_paths": "[5.0, 8.0]", + } + obs = Observation(rgb_path="/tmp/step.png") + step = StepResult(observation=obs, done=True, info=last_info) + result = task.evaluate_episode({}, [step]) + assert result["task_success"] == 1.0 + assert result["spl"] == 1.0 + assert result["num_subtasks"] == 2.0 + + def test_aggregate_results_all_metrics(self, task): + records = [ + EpisodeRecord( + episode={}, trajectory=[], + episode_results={ + "task_success": 1.0, + "_subtask_successes": "[1, 1]", + "_subtask_oracle_successes": "[1, 1]", + "_subtask_nav_errors": "[0.5, 0.3]", + "_subtask_nav_steps": "[40, 55]", + "_gt_steps": "[40, 55]", + "_gt_paths": "[5.0, 8.0]", + }, + ), + ] + summary = task.aggregate_results(records) + # All 8 metrics present + for key in ["SR", "OSR", "SPL", "NE", "ISR", "CSR", "CGT", "TAR"]: + assert key in summary, f"Missing metric: {key}" + assert "contest_score" in summary + assert "num_episodes" in summary + assert summary["num_episodes"] == 1 + assert summary["SR"] == 1.0 + + +class TestPromptBuilder: + def test_build_messages_has_system_and_user(self): + from easi.tasks.lhpr_vln.prompts import LHPRVLNPromptBuilder + builder = LHPRVLNPromptBuilder() + memory = MagicMock() + memory.task_description = "Find the chair then the table." + memory.action_space = ["move_forward", "turn_left", "turn_right", "stop"] + memory.current_observation = Observation( + rgb_path="/tmp/test_front.png", + metadata={ + "subtask_stage": "0", "subtask_total": "2", "current_geo_distance": "5.3", + "left_rgb_path": "/tmp/test_left.png", + "front_rgb_path": "/tmp/test_front.png", + "right_rgb_path": "/tmp/test_right.png", + }, + ) + memory.action_history = [("move_forward", "Subtask 1/2: navigate to chair")] + # Mock the image encoding to avoid file I/O + import easi.tasks.lhpr_vln.prompts as prompts_mod + original_encode = prompts_mod._encode_image_base64 + prompts_mod._encode_image_base64 = lambda x: "data:image/png;base64,AAAA" + try: + messages = builder.build_messages(memory) + assert len(messages) == 2 + assert messages[0]["role"] == "system" + assert messages[1]["role"] == "user" + # Should have 3 image blocks (left, front, right) + text labels + user_content = messages[1]["content"] + image_blocks = [b for b in user_content if b.get("type") == "image_url"] + assert len(image_blocks) == 3 + finally: + prompts_mod._encode_image_base64 = original_encode + + def test_parse_response_json(self): + from easi.tasks.lhpr_vln.prompts import LHPRVLNPromptBuilder + builder = LHPRVLNPromptBuilder() + memory = MagicMock() + memory.action_space = ["move_forward", "turn_left", "turn_right", "stop"] + actions = builder.parse_response('{"action": "turn_left"}', memory) + assert len(actions) == 1 + assert actions[0].action_name == "turn_left" + + def test_parse_response_fallback(self): + from easi.tasks.lhpr_vln.prompts import LHPRVLNPromptBuilder + builder = LHPRVLNPromptBuilder() + memory = MagicMock() + memory.action_space = ["move_forward", "turn_left", "turn_right", "stop"] + actions = builder.parse_response("I should stop here.", memory) + assert actions[0].action_name == "stop" From 23fece5696203e0b1e7e271c14d87585ff2fda2d Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 3 Mar 2026 22:57:18 +0800 Subject: [PATCH 133/239] feat(lhpr-vln): align prompt builder with EmbodiedBench format + toggleable env feedback Rewrites the prompt builder to use the 4-field EmbodiedBench response schema (visual_state_description, reasoning_and_reflection, language_plan, executable_plan) with multi-action buffering and JSON schema enforcement via get_response_format(). LHPR-VLN-specific environmental feedback, each toggleable in _base.yaml: - use_feedback: action execution feedback in history - use_geo_distance: geodesic distance to current target - use_subtask_progress: subtask stage/total + current target name - use_agent_position: agent 3D position (off by default) - use_target_coordinate: target 3D coordinate (off by default) Also updates the bridge to expose all available simulator feedback (agent position, rotation, target coordinate, target name) in observation metadata, and increases max_tokens from 256 to 1024 for the richer response format. --- easi/tasks/lhpr_vln/_base.yaml | 11 +- easi/tasks/lhpr_vln/bridge.py | 46 +++- easi/tasks/lhpr_vln/prompts.py | 479 +++++++++++++++++++++++++++------ tests/test_lhpr_vln_task.py | 201 ++++++++++++-- 4 files changed, 617 insertions(+), 120 deletions(-) diff --git a/easi/tasks/lhpr_vln/_base.yaml b/easi/tasks/lhpr_vln/_base.yaml index 6484acf..7be075e 100644 --- a/easi/tasks/lhpr_vln/_base.yaml +++ b/easi/tasks/lhpr_vln/_base.yaml @@ -19,9 +19,14 @@ simulator_configs: agent: prompt_builder: "easi.tasks.lhpr_vln.prompts.LHPRVLNPromptBuilder" prompt_builder_kwargs: - use_feedback: true - chat_history: false + use_feedback: true # Include action execution feedback in history + use_geo_distance: true # Show geodesic distance to current target + use_subtask_progress: true # Show subtask stage/total + current target name + use_agent_position: false # Show agent 3D position (x, y, z) + use_target_coordinate: false # Show target 3D coordinate (x, y, z) + chat_history: false # Stateless (false) or accumulated messages (true) + message_window_len: 5 # Sliding window for chat history mode generation_kwargs: temperature: 0 - max_tokens: 256 + max_tokens: 1024 top_p: 0.95 diff --git a/easi/tasks/lhpr_vln/bridge.py b/easi/tasks/lhpr_vln/bridge.py index b00c52f..370bb01 100644 --- a/easi/tasks/lhpr_vln/bridge.py +++ b/easi/tasks/lhpr_vln/bridge.py @@ -157,15 +157,47 @@ def _make_response(self, obs, reward=0.0, done=False, info=None): clean_info = self._extract_info(info or {}) clean_info["step"] = str(self.step_count) + + # Build metadata with image paths + environmental feedback for prompt builder + metadata = { + "step": str(self.step_count), + "left_rgb_path": paths["left"], + "front_rgb_path": paths["front"], + "right_rgb_path": paths["right"], + } + + # Expose environmental feedback in metadata so the prompt builder can use it + sim = self._scene_sim + if sim is not None: + sim_info = sim.info or {} + metadata["subtask_stage"] = str(float(sim.stage)) + metadata["subtask_total"] = str(float(sim.target_num)) + metadata["current_geo_distance"] = str(float(sim_info.get("geo_dis", -1))) + metadata["current_target"] = str(sim_info.get("target", "")) + # Agent position/rotation + pos = sim_info.get("agent_position") + if pos is not None: + metadata["agent_position"] = json.dumps([float(x) for x in pos]) + rot = sim_info.get("agent_rotation") + if rot is not None: + metadata["agent_rotation"] = json.dumps([float(rot.w), float(rot.x), float(rot.y), float(rot.z)]) + # Target coordinate + coord = sim_info.get("target_coord") + if coord is not None: + metadata["target_coordinate"] = json.dumps([float(x) for x in coord]) + + # Build agent_pose from sim info + agent_pose = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] + if sim is not None: + sim_info = sim.info or {} + pos = sim_info.get("agent_position") + if pos is not None: + agent_pose = [float(pos[0]), float(pos[1]), float(pos[2]), 0.0, 0.0, 0.0] + return make_observation_response( rgb_path=paths["front"], - agent_pose=[0.0, 0.0, 0.0, 0.0, 0.0, 0.0], - metadata={ - "step": str(self.step_count), - "left_rgb_path": paths["left"], - "front_rgb_path": paths["front"], - "right_rgb_path": paths["right"], - }, + agent_pose=agent_pose, + metadata=metadata, reward=reward, done=done, info=clean_info, diff --git a/easi/tasks/lhpr_vln/prompts.py b/easi/tasks/lhpr_vln/prompts.py index 2513bf1..d913479 100644 --- a/easi/tasks/lhpr_vln/prompts.py +++ b/easi/tasks/lhpr_vln/prompts.py @@ -1,10 +1,14 @@ """LHPR-VLN prompt builder for LLM-based navigation agents. +Aligned with the EmbodiedBench 4-field response format used by EB-Navigation, +EB-Habitat, and EB-Alfred, adapted for LHPR-VLN multi-subtask navigation. + Builds prompts with: -- Panoramic RGB observation (left + front + right concatenated) +- 3 RGB camera views (left -60deg, front 0deg, right +60deg) - Task instruction with subtask targets -- Current subtask progress (from observation metadata) -- Action history with distance feedback +- Toggleable environmental feedback (geodesic distance, subtask progress, + target name, agent position, target coordinate) +- Action history with execution feedback - 4-action space: move_forward, turn_left, turn_right, stop """ from __future__ import annotations @@ -12,110 +16,419 @@ import json from easi.agents.prompt_builder import _encode_image_base64, validate_action_name -from easi.core.episode import Action, Observation +from easi.core.episode import Action from easi.core.memory import AgentMemory from easi.utils.logging import get_logger logger = get_logger(__name__) -SYSTEM_PROMPT = """You are a navigation agent in a 3D indoor environment. You observe the environment through three camera views (left at -60°, front at 0°, right at +60°) and must navigate to sequential target objects. +# System prompt adapted for LHPR-VLN multi-subtask navigation. +# Three format placeholders: max_action_id, action_list, examples. +LHPRVLN_SYSTEM_PROMPT = '''## You are a robot navigating in a 3D indoor environment. You observe the environment through three camera views (left at -60 degrees, front at 0 degrees, right at +60 degrees) and must navigate to sequential target objects. + +## The available action id (0 ~ {}) and action names are: {}. + +*** Strategy *** + +1. Identify Target Objects: Clearly describe what you see in the three camera views. Look for the current target object or visual cues (room types, furniture, doorways) that indicate its likely location. + +2. Navigate using Move forward and Turn left/right as your main strategy, since any point can be reached through a combination of those. When planning movement, reason based on the target object location and obstacles around you. + +3. Multi-Subtask Navigation: This task has multiple sequential targets. After reaching one target, use "stop" to advance to the next subtask. Focus on the current target only. + +4. Use Stop Carefully: Only use "stop" when you are confident you are within 1 meter of the current target object. Do not stop too early. + +5. Efficient Exploration: If the target is not visible, explore by turning to survey the environment. Use the three camera views to gather spatial information before deciding which direction to move. + +{} -Your actions: -- move_forward: Move forward 0.25 meters -- turn_left: Turn left 30 degrees -- turn_right: Turn right 30 degrees -- stop: Declare current subtask complete (use when you believe you are within 1 meter of the current target) +---------- -Strategy: -- Navigate to each target object in order. After reaching one target, use "stop" to advance to the next. -- Use visual cues to identify rooms and objects. -- If you are far from the target, explore by turning to survey the environment, then move toward likely locations. -- Use "stop" only when you are confident you are close to the current target. +''' -Respond with ONLY a JSON object: {"action": ""}""" +# Output template matching EmbodiedBench format. +OUTPUT_TEMPLATE = "\n" \ +"The output json format should be {'visual_state_description':str, 'reasoning_and_reflection':str, 'language_plan':str, 'executable_plan':List[{'action_id':int, 'action_name':str}...]}\n" \ +"The fields in above JSON follows the purpose below:\n" \ +"1. visual_state_description is for description of current state from the three visual images (left, front, right views), \n" \ +"2. reasoning_and_reflection is for summarizing the history of interactions and any available environmental feedback. Additionally, provide reasoning as to why the last action or plan failed and did not finish the task, \n" \ +"3. language_plan is for describing a list of actions to achieve the user instruction. Each action is started by the step number and the action name, \n" \ +"4. executable_plan is a list of actions needed to achieve the user instruction, with each action having an action ID and a name.\n" \ +"5. keep your plan efficient and concise.\n" \ +"!!! When generating content for JSON strings, avoid using any contractions or abbreviated forms (like 's, 're, 've, 'll, 'd, n't) that use apostrophes. Instead, write out full forms (is, are, have, will, would, not) to prevent parsing errors in JSON. Please do not output any other thing more than the above-mentioned JSON, do not include ```json and ```!!!.\n" + +# JSON schema for API-level enforcement. +LHPRVLN_RESPONSE_SCHEMA = { + "type": "json_schema", + "json_schema": { + "name": "lhprvln_planning", + "schema": { + "type": "object", + "properties": { + "visual_state_description": { + "type": "string", + "description": "Description of current state from the three visual images (left, front, right camera views)", + }, + "reasoning_and_reflection": { + "type": "string", + "description": "Summarize the history of interactions and any available environmental feedback. Additionally, provide reasoning as to why the last action or plan failed and did not finish the task", + }, + "language_plan": { + "type": "string", + "description": "The list of actions to achieve the user instruction. Each action is started by the step number and the action name", + }, + "executable_plan": { + "type": "array", + "description": "A list of actions needed to achieve the user instruction, with each action having an action ID and a name. Do not output empty list.", + "items": { + "type": "object", + "properties": { + "action_id": { + "type": "integer", + "description": "The action ID to select from the available actions given by the prompt", + }, + "action_name": { + "type": "string", + "description": "The name of the action", + }, + }, + "required": ["action_id", "action_name"], + }, + }, + }, + "required": [ + "visual_state_description", + "reasoning_and_reflection", + "language_plan", + "executable_plan", + ], + }, + }, +} class LHPRVLNPromptBuilder: - """Prompt builder for LHPR-VLN navigation tasks.""" + """Prompt builder for LHPR-VLN navigation tasks. - def __init__(self, use_feedback: bool = True, chat_history: bool = False, **kwargs): - self._use_feedback = use_feedback - self._chat_history = chat_history + Matches the EmbodiedBench 4-field response format with LHPR-VLN-specific + environmental feedback toggles. Supports stateless and chat history modes. - def build_messages(self, memory: AgentMemory) -> list[dict]: - messages = [{"role": "system", "content": SYSTEM_PROMPT}] - - # Build user message with observation + task context - content = [] - - # Add 3 RGB views (left, front, right) as separate images - obs = memory.current_observation - if obs and obs.metadata: - for view_name, meta_key in [("Left view", "left_rgb_path"), - ("Front view", "front_rgb_path"), - ("Right view", "right_rgb_path")]: - path = obs.metadata.get(meta_key) - if path: - image_url = _encode_image_base64(path) - content.append({"type": "text", "text": f"[{view_name}]"}) - content.append({"type": "image_url", "image_url": {"url": image_url}}) - elif obs and obs.rgb_path: - # Fallback: single image (e.g., testing without full bridge) - image_url = _encode_image_base64(obs.rgb_path) - content.append({"type": "image_url", "image_url": {"url": image_url}}) + Environmental feedback toggles (all configurable in _base.yaml): + - use_feedback: Include action execution feedback in history + - use_geo_distance: Show geodesic distance to current target + - use_subtask_progress: Show subtask stage/total + current target name + - use_agent_position: Show agent 3D position + - use_target_coordinate: Show target 3D coordinate + """ - # Build text prompt - text_parts = [] + def __init__( + self, + use_feedback: bool = True, + use_geo_distance: bool = True, + use_subtask_progress: bool = True, + use_agent_position: bool = False, + use_target_coordinate: bool = False, + chat_history: bool = False, + message_window_len: int = 5, + **kwargs, + ): + self.use_feedback = use_feedback + self.use_geo_distance = use_geo_distance + self.use_subtask_progress = use_subtask_progress + self.use_agent_position = use_agent_position + self.use_target_coordinate = use_target_coordinate + self.chat_history = chat_history + self.message_window_len = message_window_len - # Task instruction - text_parts.append(f"Task: {memory.task_description}") + # Action space state + self._actions: list[str] = [] + self._action_str: str = "" + self._action_id_map: dict[str, int] = {} + self._id_action_map: dict[int, str] = {} - # Subtask progress from metadata - if obs and obs.metadata: - stage = obs.metadata.get("subtask_stage", "") - total = obs.metadata.get("subtask_total", "") - distance = obs.metadata.get("current_geo_distance", "") - if stage and total: - text_parts.append(f"Current subtask: {int(float(stage)) + 1}/{int(float(total))}") - if distance: - text_parts.append(f"Distance to current target: {float(distance):.1f}m") + def set_action_space(self, actions: list[str]) -> None: + """Update action space.""" + self._actions = list(actions) + self._action_str = self._build_action_list_str(actions) + self._action_id_map = {name: i for i, name in enumerate(actions)} + self._id_action_map = {i: name for i, name in enumerate(actions)} + + def action_name_to_id(self, name: str) -> int | None: + return self._action_id_map.get(name) + + def action_id_to_name(self, action_id: int) -> str | None: + return self._id_action_map.get(action_id) - # Action history with feedback - if self._use_feedback and memory.action_history: - history_lines = [] - for action_name, feedback in memory.action_history[-10:]: # last 10 steps - history_lines.append(f" {action_name} → {feedback}") - text_parts.append("Recent actions:\n" + "\n".join(history_lines)) + @staticmethod + def _build_action_list_str(actions: list[str]) -> str: + parts = '' + for i in range(len(actions)): + parts += '\naction id ' + str(i) + ': ' + str(actions[i]) + if i < len(actions) - 1: + parts += ', ' + return parts - # Available actions - text_parts.append(f"Available actions: {', '.join(memory.action_space)}") - text_parts.append('Respond with a JSON object: {"action": ""}') + # ---- PromptBuilderProtocol methods ---- - content.append({"type": "text", "text": "\n\n".join(text_parts)}) - messages.append({"role": "user", "content": content}) + def build_messages(self, memory: AgentMemory) -> list[dict]: + if not self._action_id_map or self._actions != memory.action_space: + self.set_action_space(memory.action_space) - return messages + if self.chat_history: + return self._build_chat_history_messages(memory) + else: + return self._build_stateless_messages(memory) def parse_response(self, llm_response: str, memory: AgentMemory) -> list[Action]: - """Parse LLM response into a single Action.""" + """Parse LLM response into validated actions.""" + from easi.utils.json_repair import fix_json + llm_response = fix_json(llm_response) + try: - # Try JSON parse - response = json.loads(llm_response.strip()) - action_name = response.get("action", "move_forward") - except (json.JSONDecodeError, AttributeError): - # Fallback: look for action name in response text - response_lower = llm_response.lower() - if "stop" in response_lower: - action_name = "stop" - elif "turn_left" in response_lower or "left" in response_lower: - action_name = "turn_left" - elif "turn_right" in response_lower or "right" in response_lower: - action_name = "turn_right" + data = json.loads(llm_response) + except json.JSONDecodeError as e: + logger.warning("Failed to parse LLM response as JSON: %s", e) + return [] + + plan = data.get("executable_plan", []) + if not isinstance(plan, list) or not plan: + logger.warning("No executable_plan in LLM response") + return [] + + actions = [] + for entry in plan: + if not isinstance(entry, dict): + continue + + if "action_id" in entry: + action_id = entry["action_id"] + action_name = self.action_id_to_name(action_id) + if action_name is None: + action_name = entry.get("action_name", "") else: - action_name = "move_forward" + action_name = entry.get("action", entry.get("action_name", "")) - validated = validate_action_name(action_name, memory.action_space) - return [Action(action_name=validated)] + validated = validate_action_name(action_name, memory.action_space) + if validated: + actions.append(Action(action_name=validated)) + else: + logger.warning("Skipping invalid action: '%s'", action_name) + break + + return actions def get_response_format(self, memory: AgentMemory) -> dict: - return {} + return LHPRVLN_RESPONSE_SCHEMA + + # ---- Stateless mode ---- + + def _build_stateless_messages(self, memory: AgentMemory) -> list[dict]: + prompt = self._build_prompt_text( + memory.task_description, memory.action_history, + memory.current_observation, + ) + return self._wrap_as_user_message(prompt, memory.current_observation) + + def _build_prompt_text( + self, + task_description: str, + action_history: list[tuple[str, str]], + observation=None, + ) -> str: + user_instruction = task_description.rstrip('.') + max_id = len(self._actions) - 1 + + prompt = LHPRVLN_SYSTEM_PROMPT.format( + max_id, self._action_str, '', + ) + + prompt += f'\n\n## Now the human instruction is: {user_instruction}.' + + # Add environmental feedback section + env_feedback = self._format_env_feedback(observation) + if env_feedback: + prompt += env_feedback + + if len(action_history) == 0: + prompt += self._make_first_prompt_suffix(max_id) + else: + prompt += self._format_action_history(action_history) + prompt += f"\n\n{self._make_following_prompt_suffix(max_id)}" + + return prompt + + # ---- Chat history mode ---- + + def _build_chat_history_messages(self, memory: AgentMemory) -> list[dict]: + if memory.is_first_turn: + return self._build_stateless_messages(memory) + + messages: list[dict] = [] + + for step_idx, step in enumerate(memory.steps): + if step.llm_response is None: + continue + + if step_idx == 0: + history_up_to = [] + else: + history_up_to = [ + (s.action.action_name, s.feedback) + for s in memory.steps[:step_idx] + if s.action and s.feedback is not None + ] + + if step_idx == 0: + prompt = self._build_prompt_text( + memory.task_description, [], step.observation, + ) + else: + prompt = self._build_chat_subsequent_prompt( + memory.task_description, history_up_to, step.observation, + ) + + messages.extend( + self._wrap_as_user_message(prompt, step.observation) + ) + messages.append({ + "role": "assistant", + "content": [{"type": "text", "text": step.llm_response}], + }) + + # Current turn + all_history = memory.action_history + current_prompt = self._build_chat_subsequent_prompt( + memory.task_description, all_history, memory.current_observation, + ) + messages.extend( + self._wrap_as_user_message(current_prompt, memory.current_observation) + ) + + return messages[-self.message_window_len:] + + def _build_chat_subsequent_prompt( + self, + task_description: str, + action_history: list[tuple[str, str]], + observation=None, + ) -> str: + user_instruction = task_description.rstrip('.') + max_id = len(self._actions) - 1 + + prompt = LHPRVLN_SYSTEM_PROMPT.format( + max_id, self._action_str, '', + ) + + prompt += f'\n\n## The human instruction is: {user_instruction}.' + + env_feedback = self._format_env_feedback(observation) + if env_feedback: + prompt += env_feedback + + prompt += self._format_action_history(action_history) + prompt += f"\n\n{self._make_following_prompt_suffix(max_id)}" + + return prompt + + # ---- Helpers ---- + + def _format_env_feedback(self, observation) -> str: + """Format toggleable environmental feedback from observation metadata.""" + if observation is None or not observation.metadata: + return "" + + meta = observation.metadata + parts = [] + + if self.use_subtask_progress: + stage = meta.get("subtask_stage", "") + total = meta.get("subtask_total", "") + target = meta.get("current_target", "") + if stage and total: + stage_int = int(float(stage)) + 1 + total_int = int(float(total)) + line = f"Current subtask: {stage_int}/{total_int}" + if target: + line += f", target object: {target}" + parts.append(line) + + if self.use_geo_distance: + distance = meta.get("current_geo_distance", "") + if distance: + parts.append(f"Geodesic distance to current target: {float(distance):.2f}m") + + if self.use_agent_position: + pos_str = meta.get("agent_position", "") + if pos_str: + pos = json.loads(pos_str) + parts.append(f"Agent position (x, y, z): ({pos[0]:.2f}, {pos[1]:.2f}, {pos[2]:.2f})") + + if self.use_target_coordinate: + coord_str = meta.get("target_coordinate", "") + if coord_str: + coord = json.loads(coord_str) + parts.append(f"Target coordinate (x, y, z): ({coord[0]:.2f}, {coord[1]:.2f}, {coord[2]:.2f})") + + if not parts: + return "" + + return "\n\n## Environmental Feedback:\n" + "\n".join(parts) + + def _make_first_prompt_suffix(self, max_id: int) -> str: + return ( + f'''\nTo achieve the task, 1. Reason about the current visual state from the three camera views and your final goal, and 2. Reflect on the effect of previous actions.''' + f'''\nAim for about 1-3 actions in this step. !!!Notice: you cannot assess the situation until the whole plan in this planning step is finished executed, so plan accordingly.''' + f'''\nAt last, output the action id(s) (0 ~ {max_id}) from the available actions to execute. ''' + f'''\n\nThe input given to you is three first person view observations (left, front, right). Plan accordingly based on the visual observations.''' + f'''\n\nYou are supposed to output in JSON.{OUTPUT_TEMPLATE}''' + ) + + def _make_following_prompt_suffix(self, max_id: int) -> str: + return ( + f'''\nTo achieve the task, 1. Reason about the current visual state from the three camera views and your final goal, and 2. Reflect on the effect of previous actions.''' + f'''\nAim for about 3-5 actions in this step to be closer to the target object. !!!Notice: you cannot assess the situation until the whole plan in this planning step is finished executed, so plan accordingly.''' + f'''\nAt last, output the action id(s) (0 ~ {max_id}) from the available actions to execute. ''' + f'''\n\nThe input given to you is three first person view observations (left, front, right). Plan accordingly based on the visual observations.''' + f'''\n\nYou are supposed to output in JSON.{OUTPUT_TEMPLATE}''' + ) + + def _format_action_history(self, action_history: list[tuple[str, str]]) -> str: + text = '\n\n The action history:' + for i, (action_name, feedback) in enumerate(action_history): + action_id = self._action_id_map.get(action_name, -1) + if self.use_feedback: + text += '\n Step {}, action id {}, {}, env feedback: {}'.format( + i, action_id, action_name, feedback, + ) + else: + text += '\n Step {}, action id {}, {}'.format( + i, action_id, action_name, + ) + return text + + def _wrap_as_user_message(self, prompt: str, observation) -> list[dict]: + """Wrap prompt text + observation images as a user message. + + For LHPR-VLN: 3 images (left, front, right) BEFORE text. + """ + content: list[dict] = [] + + # Add 3 RGB views as separate images + if observation and observation.metadata: + for view_label, meta_key in [("Left view (-60 deg)", "left_rgb_path"), + ("Front view (0 deg)", "front_rgb_path"), + ("Right view (+60 deg)", "right_rgb_path")]: + path = observation.metadata.get(meta_key) + if path: + image_url = _encode_image_base64(path) + if image_url: + content.append({"type": "text", "text": f"[{view_label}]"}) + content.append({"type": "image_url", "image_url": {"url": image_url}}) + elif observation and observation.rgb_path: + # Fallback: single image (e.g., testing without full bridge) + image_url = _encode_image_base64(observation.rgb_path) + if image_url: + content.append({"type": "image_url", "image_url": {"url": image_url}}) + + content.append({"type": "text", "text": prompt}) + return [{"role": "user", "content": content}] diff --git a/tests/test_lhpr_vln_task.py b/tests/test_lhpr_vln_task.py index 6cd2044..1e8c74a 100644 --- a/tests/test_lhpr_vln_task.py +++ b/tests/test_lhpr_vln_task.py @@ -168,51 +168,198 @@ def test_aggregate_results_all_metrics(self, task): class TestPromptBuilder: - def test_build_messages_has_system_and_user(self): - from easi.tasks.lhpr_vln.prompts import LHPRVLNPromptBuilder - builder = LHPRVLNPromptBuilder() + @pytest.fixture + def mock_encode(self): + """Mock image encoding to avoid file I/O.""" + import easi.tasks.lhpr_vln.prompts as prompts_mod + original = prompts_mod._encode_image_base64 + prompts_mod._encode_image_base64 = lambda x: "data:image/png;base64,AAAA" + yield + prompts_mod._encode_image_base64 = original + + def _make_memory(self, action_history=None): memory = MagicMock() memory.task_description = "Find the chair then the table." memory.action_space = ["move_forward", "turn_left", "turn_right", "stop"] + memory.is_first_turn = (action_history is None or len(action_history) == 0) memory.current_observation = Observation( rgb_path="/tmp/test_front.png", metadata={ - "subtask_stage": "0", "subtask_total": "2", "current_geo_distance": "5.3", + "subtask_stage": "0", "subtask_total": "2", + "current_geo_distance": "5.3", + "current_target": "chair", "left_rgb_path": "/tmp/test_left.png", "front_rgb_path": "/tmp/test_front.png", "right_rgb_path": "/tmp/test_right.png", + "agent_position": "[1.5, 0.0, -3.2]", + "target_coordinate": "[4.1, 0.0, -1.8]", }, ) - memory.action_history = [("move_forward", "Subtask 1/2: navigate to chair")] - # Mock the image encoding to avoid file I/O - import easi.tasks.lhpr_vln.prompts as prompts_mod - original_encode = prompts_mod._encode_image_base64 - prompts_mod._encode_image_base64 = lambda x: "data:image/png;base64,AAAA" - try: - messages = builder.build_messages(memory) - assert len(messages) == 2 - assert messages[0]["role"] == "system" - assert messages[1]["role"] == "user" - # Should have 3 image blocks (left, front, right) + text labels - user_content = messages[1]["content"] - image_blocks = [b for b in user_content if b.get("type") == "image_url"] - assert len(image_blocks) == 3 - finally: - prompts_mod._encode_image_base64 = original_encode - - def test_parse_response_json(self): + memory.action_history = action_history or [] + memory.steps = [] + return memory + + def test_build_messages_single_user_message(self, mock_encode): + from easi.tasks.lhpr_vln.prompts import LHPRVLNPromptBuilder + builder = LHPRVLNPromptBuilder() + memory = self._make_memory() + messages = builder.build_messages(memory) + assert len(messages) == 1 + assert messages[0]["role"] == "user" + # Should have 3 image blocks + 3 text labels + 1 prompt text + user_content = messages[0]["content"] + image_blocks = [b for b in user_content if b.get("type") == "image_url"] + assert len(image_blocks) == 3 + + def test_prompt_contains_env_feedback(self, mock_encode): + from easi.tasks.lhpr_vln.prompts import LHPRVLNPromptBuilder + builder = LHPRVLNPromptBuilder() + memory = self._make_memory() + messages = builder.build_messages(memory) + text_blocks = [b["text"] for b in messages[0]["content"] if b.get("type") == "text"] + prompt_text = "\n".join(text_blocks) + assert "Current subtask: 1/2" in prompt_text + assert "target object: chair" in prompt_text + assert "Geodesic distance" in prompt_text + assert "5.30m" in prompt_text + + def test_agent_position_toggle(self, mock_encode): + from easi.tasks.lhpr_vln.prompts import LHPRVLNPromptBuilder + # Off by default + builder = LHPRVLNPromptBuilder(use_agent_position=False) + memory = self._make_memory() + messages = builder.build_messages(memory) + text = "\n".join(b["text"] for b in messages[0]["content"] if b.get("type") == "text") + assert "Agent position" not in text + # On + builder = LHPRVLNPromptBuilder(use_agent_position=True) + messages = builder.build_messages(memory) + text = "\n".join(b["text"] for b in messages[0]["content"] if b.get("type") == "text") + assert "Agent position" in text + assert "1.50" in text + + def test_target_coordinate_toggle(self, mock_encode): + from easi.tasks.lhpr_vln.prompts import LHPRVLNPromptBuilder + # Off by default + builder = LHPRVLNPromptBuilder(use_target_coordinate=False) + memory = self._make_memory() + messages = builder.build_messages(memory) + text = "\n".join(b["text"] for b in messages[0]["content"] if b.get("type") == "text") + assert "Target coordinate" not in text + # On + builder = LHPRVLNPromptBuilder(use_target_coordinate=True) + messages = builder.build_messages(memory) + text = "\n".join(b["text"] for b in messages[0]["content"] if b.get("type") == "text") + assert "Target coordinate" in text + assert "4.10" in text + + def test_geo_distance_toggle(self, mock_encode): + from easi.tasks.lhpr_vln.prompts import LHPRVLNPromptBuilder + builder = LHPRVLNPromptBuilder(use_geo_distance=False) + memory = self._make_memory() + messages = builder.build_messages(memory) + text = "\n".join(b["text"] for b in messages[0]["content"] if b.get("type") == "text") + assert "Geodesic distance" not in text + + def test_subtask_progress_toggle(self, mock_encode): + from easi.tasks.lhpr_vln.prompts import LHPRVLNPromptBuilder + builder = LHPRVLNPromptBuilder(use_subtask_progress=False) + memory = self._make_memory() + messages = builder.build_messages(memory) + text = "\n".join(b["text"] for b in messages[0]["content"] if b.get("type") == "text") + assert "Current subtask" not in text + + def test_action_history_with_feedback(self, mock_encode): + from easi.tasks.lhpr_vln.prompts import LHPRVLNPromptBuilder + builder = LHPRVLNPromptBuilder() + memory = self._make_memory( + action_history=[("move_forward", "Subtask 1/2: navigate to chair. Distance: 5.3m")] + ) + messages = builder.build_messages(memory) + text = "\n".join(b["text"] for b in messages[0]["content"] if b.get("type") == "text") + assert "action history" in text + assert "env feedback:" in text + assert "move_forward" in text + + def test_action_history_without_feedback(self, mock_encode): + from easi.tasks.lhpr_vln.prompts import LHPRVLNPromptBuilder + builder = LHPRVLNPromptBuilder(use_feedback=False) + memory = self._make_memory( + action_history=[("move_forward", "Subtask 1/2: navigate to chair")] + ) + messages = builder.build_messages(memory) + text = "\n".join(b["text"] for b in messages[0]["content"] if b.get("type") == "text") + assert "action history" in text + assert "env feedback:" not in text + + def test_parse_response_executable_plan(self): from easi.tasks.lhpr_vln.prompts import LHPRVLNPromptBuilder builder = LHPRVLNPromptBuilder() memory = MagicMock() memory.action_space = ["move_forward", "turn_left", "turn_right", "stop"] - actions = builder.parse_response('{"action": "turn_left"}', memory) - assert len(actions) == 1 - assert actions[0].action_name == "turn_left" + response = json.dumps({ + "visual_state_description": "I see a hallway", + "reasoning_and_reflection": "Need to move forward", + "language_plan": "Step 1: move forward", + "executable_plan": [ + {"action_id": 0, "action_name": "move_forward"}, + {"action_id": 1, "action_name": "turn_left"}, + ], + }) + actions = builder.parse_response(response, memory) + assert len(actions) == 2 + assert actions[0].action_name == "move_forward" + assert actions[1].action_name == "turn_left" - def test_parse_response_fallback(self): + def test_parse_response_action_id_lookup(self): from easi.tasks.lhpr_vln.prompts import LHPRVLNPromptBuilder builder = LHPRVLNPromptBuilder() memory = MagicMock() memory.action_space = ["move_forward", "turn_left", "turn_right", "stop"] - actions = builder.parse_response("I should stop here.", memory) + builder.set_action_space(memory.action_space) + # action_id 3 = "stop" + response = json.dumps({ + "visual_state_description": "Close to target", + "reasoning_and_reflection": "Within range", + "language_plan": "Step 1: stop", + "executable_plan": [{"action_id": 3, "action_name": "stop"}], + }) + actions = builder.parse_response(response, memory) + assert len(actions) == 1 assert actions[0].action_name == "stop" + + def test_parse_response_invalid_json(self): + from easi.tasks.lhpr_vln.prompts import LHPRVLNPromptBuilder + builder = LHPRVLNPromptBuilder() + memory = MagicMock() + memory.action_space = ["move_forward", "turn_left", "turn_right", "stop"] + actions = builder.parse_response("not valid json at all", memory) + assert actions == [] + + def test_get_response_format_returns_schema(self): + from easi.tasks.lhpr_vln.prompts import LHPRVLNPromptBuilder + builder = LHPRVLNPromptBuilder() + memory = MagicMock() + schema = builder.get_response_format(memory) + assert schema["type"] == "json_schema" + props = schema["json_schema"]["schema"]["properties"] + assert "visual_state_description" in props + assert "reasoning_and_reflection" in props + assert "language_plan" in props + assert "executable_plan" in props + + def test_first_turn_vs_subsequent_action_count(self, mock_encode): + from easi.tasks.lhpr_vln.prompts import LHPRVLNPromptBuilder + builder = LHPRVLNPromptBuilder() + # First turn: "1-3 actions" + memory = self._make_memory() + messages = builder.build_messages(memory) + text = "\n".join(b["text"] for b in messages[0]["content"] if b.get("type") == "text") + assert "1-3 actions" in text + # Subsequent turn: "3-5 actions" + memory = self._make_memory( + action_history=[("move_forward", "ok")] + ) + messages = builder.build_messages(memory) + text = "\n".join(b["text"] for b in messages[0]["content"] if b.get("type") == "text") + assert "3-5 actions" in text From a483a615dfe43448cd6a8e29ced52bf461026124 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 3 Mar 2026 23:28:43 +0800 Subject: [PATCH 134/239] fix: add hf_data_dir support to prevent HM3D scene files from polluting dataset splits The datasets library scans all files in the repo directory, including extracted HM3D scene JSON files in hm3d/train/ and hm3d/val/. This created bogus splits with 96k+ rows instead of the actual 444 episodes. Adds hf_data_dir config option to dataset YAML, passed through to load_dataset(data_dir=...) to restrict scanning to the data/ subdirectory. Also fixes val split name: HF auto-maps val.jsonl to "validation" not "val". --- easi/core/base_task.py | 13 ++++++++++--- easi/tasks/lhpr_vln/_base.yaml | 1 + easi/tasks/lhpr_vln/lhpr_vln_val.yaml | 2 +- 3 files changed, 12 insertions(+), 4 deletions(-) diff --git a/easi/core/base_task.py b/easi/core/base_task.py index cb5970f..27ed4bc 100644 --- a/easi/core/base_task.py +++ b/easi/core/base_task.py @@ -277,6 +277,11 @@ def _load_episodes_from_hf(self, dataset_config: dict) -> list[dict]: subset = dataset_config.get("subset") split_name = dataset_config.get("split") + # hf_data_dir restricts which subdirectory the datasets library + # scans for data files. Useful when the repo also contains large + # non-episode files (e.g. scene meshes) that would confuse auto- + # detection. + hf_data_dir = dataset_config.get("hf_data_dir") try: from datasets import ( @@ -294,7 +299,7 @@ def _load_episodes_from_hf(self, dataset_config: dict) -> list[dict]: # Auto-detect subset if not specified if subset is None: - configs = get_dataset_config_names(local_path) + configs = get_dataset_config_names(local_path, data_dir=hf_data_dir) if len(configs) == 1: subset = configs[0] logger.info("Auto-detected single subset: %s", subset) @@ -308,7 +313,9 @@ def _load_episodes_from_hf(self, dataset_config: dict) -> list[dict]: # Auto-detect split if not specified if split_name is None: - splits = get_dataset_split_names(local_path, subset) + splits = get_dataset_split_names( + local_path, subset, data_dir=hf_data_dir, + ) if len(splits) == 1: split_name = splits[0] logger.info("Auto-detected single split: %s", split_name) @@ -327,7 +334,7 @@ def _load_episodes_from_hf(self, dataset_config: dict) -> list[dict]: import tempfile hf_cache = Path(tempfile.gettempdir()) / "easi_hf_cache" ds = load_dataset(local_path, subset, split=split_name, - cache_dir=str(hf_cache)) + data_dir=hf_data_dir, cache_dir=str(hf_cache)) episodes = [hf_row_to_episode(row) for row in ds] for ep in episodes: diff --git a/easi/tasks/lhpr_vln/_base.yaml b/easi/tasks/lhpr_vln/_base.yaml index 7be075e..77c2a69 100644 --- a/easi/tasks/lhpr_vln/_base.yaml +++ b/easi/tasks/lhpr_vln/_base.yaml @@ -7,6 +7,7 @@ dataset: source: huggingface repo_id: "oscarqjh/LHPR-VLN_easi" subset: null + hf_data_dir: "data" zip_files: - "hm3d_v0.2_lhpr_scenes.zip" simulator_configs: diff --git a/easi/tasks/lhpr_vln/lhpr_vln_val.yaml b/easi/tasks/lhpr_vln/lhpr_vln_val.yaml index a9c24df..d4fb23e 100644 --- a/easi/tasks/lhpr_vln/lhpr_vln_val.yaml +++ b/easi/tasks/lhpr_vln/lhpr_vln_val.yaml @@ -3,4 +3,4 @@ name: lhpr_vln_val display_name: "LHPR-VLN Val" description: "LHPR-VLN validation split (batch_6)" dataset: - split: "val" + split: "validation" From a85ed84c7b5336fc92387e4a6d9ba44578862f39 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 3 Mar 2026 23:35:00 +0800 Subject: [PATCH 135/239] fix(lhpr-vln): add future annotations for Python 3.9 compatibility The bridge runs in the habitat-sim conda env (Python 3.9) where list[int] | None union syntax is not supported at runtime. --- easi/tasks/lhpr_vln/vendor/scene_simulator.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/easi/tasks/lhpr_vln/vendor/scene_simulator.py b/easi/tasks/lhpr_vln/vendor/scene_simulator.py index 9e1096c..505f6c3 100644 --- a/easi/tasks/lhpr_vln/vendor/scene_simulator.py +++ b/easi/tasks/lhpr_vln/vendor/scene_simulator.py @@ -4,6 +4,8 @@ Original creates a new habitat_sim.Simulator per episode, computes geodesic distances, and tracks multi-subtask stage/success/oracle state. """ +from __future__ import annotations + import math import habitat_sim From 4665fa92ba193a95b5bae8ba8f62d8a1d9898063 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 3 Mar 2026 23:57:46 +0800 Subject: [PATCH 136/239] fix(lhpr-vln): use software rendering by default (gpu_device_id: -1) Matches EB-Habitat's approach: gpu_device_id=-1 tells Habitat-Sim to use Mesa llvmpipe software rendering instead of requiring a CUDA GPU. Removes bridge code that was forcing -1 back to 0. --- easi/tasks/lhpr_vln/_base.yaml | 2 +- easi/tasks/lhpr_vln/bridge.py | 4 +--- easi/tasks/lhpr_vln/vendor/scene_config.py | 2 +- 3 files changed, 3 insertions(+), 5 deletions(-) diff --git a/easi/tasks/lhpr_vln/_base.yaml b/easi/tasks/lhpr_vln/_base.yaml index 77c2a69..5847e0c 100644 --- a/easi/tasks/lhpr_vln/_base.yaml +++ b/easi/tasks/lhpr_vln/_base.yaml @@ -14,7 +14,7 @@ simulator_configs: render_platform: auto screen_height: 512 screen_width: 512 - gpu_device_id: 0 + gpu_device_id: -1 success_distance: 1.0 additional_deps: [] agent: diff --git a/easi/tasks/lhpr_vln/bridge.py b/easi/tasks/lhpr_vln/bridge.py index 370bb01..8ac78e6 100644 --- a/easi/tasks/lhpr_vln/bridge.py +++ b/easi/tasks/lhpr_vln/bridge.py @@ -62,9 +62,7 @@ def _on_reset(self, env, reset_config): regions = json.loads(reset_config["regions"]) if isinstance(reset_config["regions"], str) else reset_config["regions"] gt_step = json.loads(reset_config.get("gt_step", "[]")) if isinstance(reset_config.get("gt_step"), str) else reset_config.get("gt_step") - gpu_device_id = self._sim_kwargs.get("gpu_device_id", 0) - if gpu_device_id < 0: - gpu_device_id = 0 # Habitat-Sim needs a real GPU ID + gpu_device_id = self._sim_kwargs.get("gpu_device_id", -1) max_steps = self._sim_kwargs.get("max_steps", 500) width = self._sim_kwargs.get("screen_width", 512) height = self._sim_kwargs.get("screen_height", 512) diff --git a/easi/tasks/lhpr_vln/vendor/scene_config.py b/easi/tasks/lhpr_vln/vendor/scene_config.py index c3ded8d..1f1cbcc 100644 --- a/easi/tasks/lhpr_vln/vendor/scene_config.py +++ b/easi/tasks/lhpr_vln/vendor/scene_config.py @@ -32,7 +32,7 @@ def make_setting(scene_path: str, scene_dataset_path: str, robot: str, } -def make_cfg(settings: dict, gpu_device_id: int = 0) -> habitat_sim.Configuration: +def make_cfg(settings: dict, gpu_device_id: int = -1) -> habitat_sim.Configuration: """Build Habitat-Sim Configuration from settings dict.""" sim_cfg = habitat_sim.SimulatorConfiguration() sim_cfg.gpu_device_id = gpu_device_id From 0c32911028ac5ea531f79333f54ee33c519c0fce Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Wed, 4 Mar 2026 00:03:01 +0800 Subject: [PATCH 137/239] fix(lhpr-vln): clarify that stop advances subtask, not ends episode Make the system prompt explicit about the multi-subtask stop semantics: stop marks current subtask complete and advances to the next target, does not end the episode until all targets are visited. --- easi/tasks/lhpr_vln/prompts.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/easi/tasks/lhpr_vln/prompts.py b/easi/tasks/lhpr_vln/prompts.py index d913479..cff541c 100644 --- a/easi/tasks/lhpr_vln/prompts.py +++ b/easi/tasks/lhpr_vln/prompts.py @@ -34,9 +34,9 @@ 2. Navigate using Move forward and Turn left/right as your main strategy, since any point can be reached through a combination of those. When planning movement, reason based on the target object location and obstacles around you. -3. Multi-Subtask Navigation: This task has multiple sequential targets. After reaching one target, use "stop" to advance to the next subtask. Focus on the current target only. +3. Multi-Subtask Navigation: This task requires you to navigate to multiple target objects in sequence. The "stop" action does NOT end the episode — it marks the CURRENT subtask as complete and advances you to the NEXT target. You must use "stop" once for each target. The episode only ends after you have used "stop" for every target. -4. Use Stop Carefully: Only use "stop" when you are confident you are within 1 meter of the current target object. Do not stop too early. +4. Use Stop Carefully: Only use "stop" when you are confident you are within 1 meter of the current target object. Using "stop" too far from the target will mark that subtask as failed, but the episode will still continue to the next target. Do not use "stop" too early — try to get as close as possible first. 5. Efficient Exploration: If the target is not visible, explore by turning to survey the environment. Use the three camera views to gather spatial information before deciding which direction to move. From eec28bd4ab0f09a2b30036bca054838a697ed183 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Wed, 4 Mar 2026 09:00:22 +0800 Subject: [PATCH 138/239] feat: add configurable action_history_len to LHPR-VLN prompt builder Adds action_history_len parameter to control action history display: 0 = off (no history header or entries shown) -1 = unlimited (show all, default) N>0 = show last N entries --- easi/tasks/lhpr_vln/_base.yaml | 3 ++- easi/tasks/lhpr_vln/prompts.py | 14 ++++++++++++-- 2 files changed, 14 insertions(+), 3 deletions(-) diff --git a/easi/tasks/lhpr_vln/_base.yaml b/easi/tasks/lhpr_vln/_base.yaml index 5847e0c..54e43fa 100644 --- a/easi/tasks/lhpr_vln/_base.yaml +++ b/easi/tasks/lhpr_vln/_base.yaml @@ -25,9 +25,10 @@ agent: use_subtask_progress: true # Show subtask stage/total + current target name use_agent_position: false # Show agent 3D position (x, y, z) use_target_coordinate: false # Show target 3D coordinate (x, y, z) + action_history_len: -1 # Action history entries: 0=off, -1=unlimited, N=last N chat_history: false # Stateless (false) or accumulated messages (true) message_window_len: 5 # Sliding window for chat history mode generation_kwargs: temperature: 0 - max_tokens: 1024 + max_tokens: 4096 top_p: 0.95 diff --git a/easi/tasks/lhpr_vln/prompts.py b/easi/tasks/lhpr_vln/prompts.py index cff541c..8be3ceb 100644 --- a/easi/tasks/lhpr_vln/prompts.py +++ b/easi/tasks/lhpr_vln/prompts.py @@ -128,6 +128,7 @@ def __init__( use_subtask_progress: bool = True, use_agent_position: bool = False, use_target_coordinate: bool = False, + action_history_len: int = -1, chat_history: bool = False, message_window_len: int = 5, **kwargs, @@ -137,6 +138,7 @@ def __init__( self.use_subtask_progress = use_subtask_progress self.use_agent_position = use_agent_position self.use_target_coordinate = use_target_coordinate + self.action_history_len = action_history_len self.chat_history = chat_history self.message_window_len = message_window_len @@ -252,7 +254,9 @@ def _build_prompt_text( if len(action_history) == 0: prompt += self._make_first_prompt_suffix(max_id) else: - prompt += self._format_action_history(action_history) + history_text = self._format_action_history(action_history) + if history_text: + prompt += history_text prompt += f"\n\n{self._make_following_prompt_suffix(max_id)}" return prompt @@ -325,7 +329,9 @@ def _build_chat_subsequent_prompt( if env_feedback: prompt += env_feedback - prompt += self._format_action_history(action_history) + history_text = self._format_action_history(action_history) + if history_text: + prompt += history_text prompt += f"\n\n{self._make_following_prompt_suffix(max_id)}" return prompt @@ -393,6 +399,10 @@ def _make_following_prompt_suffix(self, max_id: int) -> str: ) def _format_action_history(self, action_history: list[tuple[str, str]]) -> str: + if self.action_history_len == 0: + return "" + if self.action_history_len > 0: + action_history = action_history[-self.action_history_len:] text = '\n\n The action history:' for i, (action_name, feedback) in enumerate(action_history): action_id = self._action_id_map.get(action_name, -1) From f6ae0169fe5e0f72e438e3e168f38bd6bb477685 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Wed, 4 Mar 2026 10:30:03 +0800 Subject: [PATCH 139/239] fix(lhpr-vln): improved prompt --- easi/tasks/lhpr_vln/prompts.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/easi/tasks/lhpr_vln/prompts.py b/easi/tasks/lhpr_vln/prompts.py index 8be3ceb..e4064df 100644 --- a/easi/tasks/lhpr_vln/prompts.py +++ b/easi/tasks/lhpr_vln/prompts.py @@ -36,9 +36,9 @@ 3. Multi-Subtask Navigation: This task requires you to navigate to multiple target objects in sequence. The "stop" action does NOT end the episode — it marks the CURRENT subtask as complete and advances you to the NEXT target. You must use "stop" once for each target. The episode only ends after you have used "stop" for every target. -4. Use Stop Carefully: Only use "stop" when you are confident you are within 1 meter of the current target object. Using "stop" too far from the target will mark that subtask as failed, but the episode will still continue to the next target. Do not use "stop" too early — try to get as close as possible first. +4. Use Stop Carefully: Only use "stop" when you are confident you are within 1 meter of the current target object. Using "stop" too far from the target will mark that subtask as failed, but the episode will still continue to the next target. -5. Efficient Exploration: If the target is not visible, explore by turning to survey the environment. Use the three camera views to gather spatial information before deciding which direction to move. +5. Efficient Exploration: If the target is not visible, explore by turning to survey the environment. Use the three camera views to gather spatial information before deciding which direction to move. When close to the target, you can choose to take lesser actions each step to finetune your trajectories, for example taking 1 action at a time when you are 2m away from target and only use stop when you are less than 1m away from the target. {} From 83a7211dedbbd3e6ed576b5812702d20f99fb2bd Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Wed, 4 Mar 2026 10:47:09 +0800 Subject: [PATCH 140/239] fix(lhpr-vln): updated action history len --- easi/tasks/lhpr_vln/_base.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/easi/tasks/lhpr_vln/_base.yaml b/easi/tasks/lhpr_vln/_base.yaml index 54e43fa..b9f6c51 100644 --- a/easi/tasks/lhpr_vln/_base.yaml +++ b/easi/tasks/lhpr_vln/_base.yaml @@ -25,7 +25,7 @@ agent: use_subtask_progress: true # Show subtask stage/total + current target name use_agent_position: false # Show agent 3D position (x, y, z) use_target_coordinate: false # Show target 3D coordinate (x, y, z) - action_history_len: -1 # Action history entries: 0=off, -1=unlimited, N=last N + action_history_len: 20 # Action history entries: 0=off, -1=unlimited, N=last N chat_history: false # Stateless (false) or accumulated messages (true) message_window_len: 5 # Sliding window for chat history mode generation_kwargs: From 369f1a66722e1b87e2a941af05ca1d6341847f18 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Wed, 4 Mar 2026 11:14:31 +0800 Subject: [PATCH 141/239] feat: add robot-type grouping and action_history_len to LHPR-VLN Aggregate results now return nested dicts grouped by robot type (base/spot/stretch), each with all 8 official metrics + contest_score. Also adds action_history_len config (0=off, -1=unlimited, N=last N) to control how many action history entries appear in prompts. --- easi/tasks/lhpr_vln/task.py | 54 ++++++++++++++++++++++--------------- tests/test_lhpr_vln_task.py | 53 +++++++++++++++++++++++++++++++----- 2 files changed, 79 insertions(+), 28 deletions(-) diff --git a/easi/tasks/lhpr_vln/task.py b/easi/tasks/lhpr_vln/task.py index d51be13..ee08b0b 100644 --- a/easi/tasks/lhpr_vln/task.py +++ b/easi/tasks/lhpr_vln/task.py @@ -108,25 +108,46 @@ def evaluate_episode( "_gt_paths": json.dumps(gt_paths), } - def aggregate_results( - self, records: list[EpisodeRecord] - ) -> dict[str, float]: + def aggregate_results(self, records: list[EpisodeRecord]) -> dict: """Compute all 8 LHPR-VLN metrics using vendored NavigationMetrics. + Returns nested dict grouped by robot type: + - base: all episodes + - spot: Spot robot episodes only + - stretch: Stretch robot episodes only + Metric definitions (from CVPR-25 paper): - - SR: Success Rate (all subtasks completed) - - OSR: Oracle Success Rate (agent ever passed within 1m of all targets) - - SPL: Success weighted by Path Length - - NE: Navigation Error (avg geodesic distance at stop) - - ISR: Independent Success Rate (fraction of subtasks succeeded) - - CSR: Conditional Success Rate (sequential dependency weighting) - - CGT: CSR weighted by GT path length - - TAR: Target Approach Rate (continuous approach measure) - - contest_score: 0.4*TAR + 0.2*ISR + 0.2*CSR + 0.2*CGT + - SR, OSR, SPL, NE, ISR, CSR, CGT, TAR, contest_score """ if not records: return {} + # Group records by robot type + groups: dict[str, list[EpisodeRecord]] = {} + for r in records: + robot = r.episode.get("robot", "spot") + groups.setdefault(robot, []).append(r) + + output: dict = {} + + # Base group: all episodes + base = self._compute_group_metrics(records) + base["num_episodes"] = len(records) + base["success_rate"] = base["SR"] + output["base"] = base + + # Per-robot groups + for robot_type, group_records in sorted(groups.items()): + group = self._compute_group_metrics(group_records) + group["num_episodes"] = len(group_records) + output[robot_type] = group + + return output + + def _compute_group_metrics( + self, records: list[EpisodeRecord] + ) -> dict[str, float]: + """Compute LHPR-VLN metrics for a group of records.""" metrics = NavigationMetrics() for r in records: @@ -138,15 +159,10 @@ def aggregate_results( gt_steps = json.loads(er.get("_gt_steps", "[]")) gt_paths = json.loads(er.get("_gt_paths", "[]")) - # Overall success success = 1 if successes and all(s == 1 for s in successes) else 0 oracle_success = 1 if oracle_successes and all(s == 1 for s in oracle_successes) else 0 - - # Total steps total_gt = sum(gt_steps) if gt_steps else 0 total_actual = sum(nav_steps) if nav_steps else 0 - - # Avg navigation error avg_ne = sum(nav_errors) / len(nav_errors) if nav_errors else 0.0 metrics.add_sample( @@ -163,7 +179,6 @@ def aggregate_results( result = metrics.compute() - # Add contest ranking score tar = result.get("tar", 0) isr = result.get("independent_success_rate", 0) csr = result.get("conditional_success_rate", 0) @@ -180,9 +195,6 @@ def aggregate_results( "CGT": round(result["conditional_path_length"], 4), "TAR": round(result["tar"], 4), "contest_score": round(contest_score, 4), - "num_episodes": len(records), - # Convenience alias for EASI dashboard - "success_rate": round(result["success_rate"], 4), } def _empty_metrics(self) -> dict[str, float]: diff --git a/tests/test_lhpr_vln_task.py b/tests/test_lhpr_vln_task.py index 1e8c74a..ce6478e 100644 --- a/tests/test_lhpr_vln_task.py +++ b/tests/test_lhpr_vln_task.py @@ -145,7 +145,7 @@ def test_evaluate_episode_successful(self, task): def test_aggregate_results_all_metrics(self, task): records = [ EpisodeRecord( - episode={}, trajectory=[], + episode={"robot": "spot"}, trajectory=[], episode_results={ "task_success": 1.0, "_subtask_successes": "[1, 1]", @@ -158,13 +158,52 @@ def test_aggregate_results_all_metrics(self, task): ), ] summary = task.aggregate_results(records) - # All 8 metrics present + # Nested structure with "base" group + assert "base" in summary + base = summary["base"] for key in ["SR", "OSR", "SPL", "NE", "ISR", "CSR", "CGT", "TAR"]: - assert key in summary, f"Missing metric: {key}" - assert "contest_score" in summary - assert "num_episodes" in summary - assert summary["num_episodes"] == 1 - assert summary["SR"] == 1.0 + assert key in base, f"Missing metric: {key}" + assert "contest_score" in base + assert base["num_episodes"] == 1 + assert base["SR"] == 1.0 + + def test_aggregate_results_grouped_by_robot(self, task): + """Metrics should be nested by robot type (spot, stretch).""" + spot_results = { + "_subtask_successes": "[1, 1]", + "_subtask_oracle_successes": "[1, 1]", + "_subtask_nav_errors": "[0.5, 0.3]", + "_subtask_nav_steps": "[40, 55]", + "_gt_steps": "[40, 55]", + "_gt_paths": "[5.0, 8.0]", + } + stretch_results = { + "_subtask_successes": "[0, 0]", + "_subtask_oracle_successes": "[0, 0]", + "_subtask_nav_errors": "[5.0, 4.0]", + "_subtask_nav_steps": "[100, 100]", + "_gt_steps": "[40, 55]", + "_gt_paths": "[5.0, 8.0]", + } + records = [ + EpisodeRecord(episode={"robot": "spot"}, trajectory=[], episode_results=spot_results), + EpisodeRecord(episode={"robot": "stretch"}, trajectory=[], episode_results=stretch_results), + ] + summary = task.aggregate_results(records) + + # Base group (all episodes) + assert summary["base"]["num_episodes"] == 2 + + # Robot-specific nested groups + assert summary["spot"]["num_episodes"] == 1 + assert summary["stretch"]["num_episodes"] == 1 + assert summary["spot"]["SR"] == 1.0 + assert summary["stretch"]["SR"] == 0.0 + + # All 8 metrics present in each group + for group in ["base", "spot", "stretch"]: + for key in ["SR", "OSR", "SPL", "NE", "ISR", "CSR", "CGT", "TAR", "contest_score"]: + assert key in summary[group], f"Missing {group}.{key}" class TestPromptBuilder: From 2b3168f6a1858ffe26fd4b3d78ff1543d499df93 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Wed, 4 Mar 2026 19:16:54 +0800 Subject: [PATCH 142/239] feat: add cuda_visible_devices to ServerManager ServerManager._build_command() now returns (cmd, env_dict) so that start() can pass CUDA_VISIBLE_DEVICES to the subprocess environment. This is the prerequisite for MultiServerManager to assign specific GPUs to each vLLM instance. --- easi/llm/server_manager.py | 25 +++++++++++++++++++++---- tests/test_server_manager.py | 18 ++++++++++++++++-- 2 files changed, 37 insertions(+), 6 deletions(-) diff --git a/easi/llm/server_manager.py b/easi/llm/server_manager.py index a9dc8a6..f7e9707 100644 --- a/easi/llm/server_manager.py +++ b/easi/llm/server_manager.py @@ -4,6 +4,7 @@ """ from __future__ import annotations +import os import socket import subprocess import sys @@ -32,12 +33,14 @@ def __init__( server_kwargs: dict | None = None, startup_timeout: float = _DEFAULT_STARTUP_TIMEOUT, log_dir: Path | None = None, # Deprecated: server output now goes to logger + cuda_visible_devices: str | None = None, ): self.backend = backend self.model = model self.port = port self.server_kwargs = server_kwargs or {} self.startup_timeout = startup_timeout + self.cuda_visible_devices = cuda_visible_devices self._process: subprocess.Popen | None = None self._log_thread: threading.Thread | None = None @@ -45,13 +48,17 @@ def start(self) -> str: """Start the server, wait for health, return base_url.""" self._check_port() - cmd = self._build_command() + cmd, extra_env = self._build_command() logger.info("Starting %s server: %s", self.backend, " ".join(cmd)) + spawn_env = os.environ.copy() + spawn_env.update(extra_env) + self._process = subprocess.Popen( cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, + env=spawn_env, ) self._log_thread = threading.Thread( target=self._stream_output, @@ -101,8 +108,13 @@ def _check_port(self) -> None: finally: sock.close() - def _build_command(self) -> list[str]: - """Build the server launch command.""" + def _build_command(self) -> tuple[list[str], dict]: + """Build the server launch command and environment overrides. + + Returns: + Tuple of (command list, env dict). The env dict contains + ``CUDA_VISIBLE_DEVICES`` when *cuda_visible_devices* is set. + """ if self.backend == "vllm": cmd = [ sys.executable, "-m", "vllm.entrypoints.openai.api_server", @@ -117,10 +129,15 @@ def _build_command(self) -> list[str]: # Skip False booleans (don't add the flag) else: cmd.extend([flag, str(value)]) - return cmd else: raise ValueError(f"Unsupported server backend: {self.backend}") + env: dict[str, str] = {} + if self.cuda_visible_devices is not None: + env["CUDA_VISIBLE_DEVICES"] = self.cuda_visible_devices + + return cmd, env + @staticmethod def _stream_output(proc: subprocess.Popen) -> None: """Read server stdout/stderr line by line and log at TRACE level.""" diff --git a/tests/test_server_manager.py b/tests/test_server_manager.py index 4adc250..549235b 100644 --- a/tests/test_server_manager.py +++ b/tests/test_server_manager.py @@ -23,7 +23,7 @@ class TestBuildCommand: def test_vllm_basic(self): from easi.llm.server_manager import ServerManager sm = ServerManager("vllm", "Qwen/Qwen2.5-VL-72B", port=9090) - cmd = sm._build_command() + cmd, env = sm._build_command() assert "-m" in cmd assert "vllm.entrypoints.openai.api_server" in cmd assert "--model" in cmd @@ -36,7 +36,7 @@ def test_vllm_with_kwargs(self): sm = ServerManager("vllm", "test-model", port=8080, server_kwargs={"tensor_parallel_size": 4, "gpu_memory_utilization": 0.9}) - cmd = sm._build_command() + cmd, env = sm._build_command() assert "--tensor-parallel-size" in cmd assert "4" in cmd assert "--gpu-memory-utilization" in cmd @@ -86,3 +86,17 @@ def test_not_running_when_no_process(self): from easi.llm.server_manager import ServerManager sm = ServerManager("vllm", "test") assert sm.is_running() is False + + +class TestCudaVisibleDevices: + def test_server_manager_sets_cuda_visible_devices(self): + from easi.llm.server_manager import ServerManager + sm = ServerManager("vllm", "test-model", cuda_visible_devices="0,1") + _cmd, env = sm._build_command() + assert env["CUDA_VISIBLE_DEVICES"] == "0,1" + + def test_server_manager_no_cuda_by_default(self): + from easi.llm.server_manager import ServerManager + sm = ServerManager("vllm", "test-model") + _cmd, env = sm._build_command() + assert "CUDA_VISIBLE_DEVICES" not in env From c2c83f9675fa4e670b80e7e7a87c5b4e093a2e1c Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Wed, 4 Mar 2026 19:30:16 +0800 Subject: [PATCH 143/239] feat: add default vLLM flags (prefix caching, quiet logs) --- easi/llm/server_manager.py | 8 +++++++- tests/test_server_manager.py | 21 +++++++++++++++++++++ 2 files changed, 28 insertions(+), 1 deletion(-) diff --git a/easi/llm/server_manager.py b/easi/llm/server_manager.py index f7e9707..382bb7f 100644 --- a/easi/llm/server_manager.py +++ b/easi/llm/server_manager.py @@ -20,6 +20,10 @@ _HEALTH_POLL_INTERVAL = 5.0 _DEFAULT_STARTUP_TIMEOUT = 300.0 +_DEFAULT_VLLM_FLAGS = { + "enable_prefix_caching": True, + "disable_log_requests": True, +} class ServerManager: @@ -121,7 +125,9 @@ def _build_command(self) -> tuple[list[str], dict]: "--model", self.model, "--port", str(self.port), ] - for key, value in self.server_kwargs.items(): + # Merge defaults with user overrides (user wins) + merged_kwargs = {**_DEFAULT_VLLM_FLAGS, **self.server_kwargs} + for key, value in merged_kwargs.items(): flag = "--" + key.replace("_", "-") if isinstance(value, bool): if value: diff --git a/tests/test_server_manager.py b/tests/test_server_manager.py index 549235b..5a9f088 100644 --- a/tests/test_server_manager.py +++ b/tests/test_server_manager.py @@ -88,6 +88,27 @@ def test_not_running_when_no_process(self): assert sm.is_running() is False +class TestDefaultVllmFlags: + def test_server_manager_default_vllm_flags(self): + """ServerManager should include default vLLM flags (prefix caching, quiet logs).""" + from easi.llm.server_manager import ServerManager + mgr = ServerManager(backend="vllm", model="test-model", port=9999) + cmd, _ = mgr._build_command() + assert "--enable-prefix-caching" in cmd + assert "--disable-log-requests" in cmd + + def test_server_manager_user_can_override_defaults(self): + """User-provided server_kwargs should override defaults.""" + from easi.llm.server_manager import ServerManager + mgr = ServerManager( + backend="vllm", model="test-model", port=9999, + server_kwargs={"enable_prefix_caching": False}, + ) + cmd, _ = mgr._build_command() + # False bool flags should NOT appear in command + assert "--enable-prefix-caching" not in cmd + + class TestCudaVisibleDevices: def test_server_manager_sets_cuda_visible_devices(self): from easi.llm.server_manager import ServerManager From cccf861f275d6598c80902b84714eb22968ed8ad Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Wed, 4 Mar 2026 19:56:20 +0800 Subject: [PATCH 144/239] feat: add trace logging to ServerManager for debugging Log constructor params, default flag merging, user overrides, built commands, port checks, and health poll attempts at TRACE level. --- easi/llm/server_manager.py | 26 ++++++++++++++++++++++++-- 1 file changed, 24 insertions(+), 2 deletions(-) diff --git a/easi/llm/server_manager.py b/easi/llm/server_manager.py index 382bb7f..e3c6c68 100644 --- a/easi/llm/server_manager.py +++ b/easi/llm/server_manager.py @@ -47,6 +47,11 @@ def __init__( self.cuda_visible_devices = cuda_visible_devices self._process: subprocess.Popen | None = None self._log_thread: threading.Thread | None = None + logger.trace( + "ServerManager init: backend=%s, model=%s, port=%d, " + "server_kwargs=%s, cuda_visible_devices=%s", + backend, model, port, self.server_kwargs, cuda_visible_devices, + ) def start(self) -> str: """Start the server, wait for health, return base_url.""" @@ -100,9 +105,11 @@ def is_running(self) -> bool: def _check_port(self) -> None: """Raise if port is already in use.""" + logger.trace("Checking if port %d is available...", self.port) sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) try: sock.bind(("127.0.0.1", self.port)) + logger.trace("Port %d is available", self.port) except OSError: raise RuntimeError( f"Port {self.port} is already in use. " @@ -127,12 +134,20 @@ def _build_command(self) -> tuple[list[str], dict]: ] # Merge defaults with user overrides (user wins) merged_kwargs = {**_DEFAULT_VLLM_FLAGS, **self.server_kwargs} + overridden = { + k: v for k, v in self.server_kwargs.items() + if k in _DEFAULT_VLLM_FLAGS and v != _DEFAULT_VLLM_FLAGS[k] + } + if overridden: + logger.trace("User overrides for default vLLM flags: %s", overridden) + logger.trace("Merged vLLM kwargs: %s", merged_kwargs) for key, value in merged_kwargs.items(): flag = "--" + key.replace("_", "-") if isinstance(value, bool): if value: cmd.append(flag) - # Skip False booleans (don't add the flag) + else: + logger.trace("Skipping disabled bool flag: %s", flag) else: cmd.extend([flag, str(value)]) else: @@ -142,6 +157,8 @@ def _build_command(self) -> tuple[list[str], dict]: if self.cuda_visible_devices is not None: env["CUDA_VISIBLE_DEVICES"] = self.cuda_visible_devices + logger.trace("Built command: %s", cmd) + logger.trace("Extra env: %s", env) return cmd, env @staticmethod @@ -157,6 +174,9 @@ def _wait_for_health(self, base_url: str) -> None: """Poll /health until the server responds or timeout.""" health_url = base_url.replace("/v1", "") + "/health" deadline = time.monotonic() + self.startup_timeout + logger.trace( + "Waiting for health at %s (timeout=%.0fs)", health_url, self.startup_timeout, + ) while time.monotonic() < deadline: if self._process and self._process.poll() is not None: @@ -167,9 +187,11 @@ def _wait_for_health(self, base_url: str) -> None: try: resp = requests.get(health_url, timeout=5) if resp.status_code == 200: + logger.trace("Health check passed (status=%d)", resp.status_code) return + logger.trace("Health check returned status %d, retrying...", resp.status_code) except requests.ConnectionError: - pass + logger.trace("Health check connection refused, retrying...") time.sleep(_HEALTH_POLL_INTERVAL) From be385c25d4c896c78a1bcf0ed78b48723a7ffdf5 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Wed, 4 Mar 2026 20:02:32 +0800 Subject: [PATCH 145/239] feat: add MultiServerManager for multi-instance vLLM --- easi/llm/server_manager.py | 55 ++++++++++++++++++++++++++ tests/test_server_manager.py | 75 ++++++++++++++++++++++++++++++++++++ 2 files changed, 130 insertions(+) diff --git a/easi/llm/server_manager.py b/easi/llm/server_manager.py index e3c6c68..800af0f 100644 --- a/easi/llm/server_manager.py +++ b/easi/llm/server_manager.py @@ -206,3 +206,58 @@ def __enter__(self) -> str: def __exit__(self, *exc) -> None: self.stop() + + +class MultiServerManager: + """Manages multiple vLLM server instances across GPUs.""" + + def __init__( + self, + model: str, + num_instances: int, + gpu_ids: list[int], + base_port: int = 8000, + server_kwargs: dict | None = None, + startup_timeout: float = 300.0, + log_dir: Path | str | None = None, + ): + if len(gpu_ids) % num_instances != 0: + raise ValueError( + f"Cannot divide {len(gpu_ids)} GPUs evenly across " + f"{num_instances} instances" + ) + self.model = model + self.num_instances = num_instances + self.gpu_ids = gpu_ids + self.base_port = base_port + self.server_kwargs = server_kwargs or {} + self.startup_timeout = startup_timeout + self.log_dir = Path(log_dir) if log_dir else None + self._managers: list[ServerManager] = [] + + def start(self) -> list[str]: + """Start all instances, return list of base_urls.""" + gpus_per = len(self.gpu_ids) // self.num_instances + urls = [] + for i in range(self.num_instances): + instance_gpus = self.gpu_ids[i * gpus_per : (i + 1) * gpus_per] + port = self.base_port + i + mgr = ServerManager( + backend="vllm", + model=self.model, + port=port, + server_kwargs=self.server_kwargs, + startup_timeout=self.startup_timeout, + log_dir=self.log_dir, + cuda_visible_devices=",".join(str(g) for g in instance_gpus), + ) + url = mgr.start() + urls.append(url) + self._managers.append(mgr) + return urls + + def stop(self): + """Stop all managed instances.""" + for mgr in self._managers: + mgr.stop() + self._managers.clear() diff --git a/tests/test_server_manager.py b/tests/test_server_manager.py index 5a9f088..4302c4f 100644 --- a/tests/test_server_manager.py +++ b/tests/test_server_manager.py @@ -121,3 +121,78 @@ def test_server_manager_no_cuda_by_default(self): sm = ServerManager("vllm", "test-model") _cmd, env = sm._build_command() assert "CUDA_VISIBLE_DEVICES" not in env + + +class TestMultiServerManager: + def test_multi_server_manager_starts_n_instances(self): + """MultiServerManager should start N ServerManager instances with correct GPU assignment.""" + from unittest.mock import patch, MagicMock + from easi.llm.server_manager import MultiServerManager + + with patch("easi.llm.server_manager.ServerManager") as MockSM: + mock_instance = MagicMock() + mock_instance.start.return_value = "http://localhost:8000/v1" + MockSM.return_value = mock_instance + + mgr = MultiServerManager( + model="test-model", + num_instances=2, + gpu_ids=[0, 1], + base_port=8000, + ) + urls = mgr.start() + + assert len(urls) == 2 + assert MockSM.call_count == 2 + + # Check GPU assignment: instance 0 → GPU 0, instance 1 → GPU 1 + calls = MockSM.call_args_list + assert calls[0].kwargs["cuda_visible_devices"] == "0" + assert calls[0].kwargs["port"] == 8000 + assert calls[1].kwargs["cuda_visible_devices"] == "1" + assert calls[1].kwargs["port"] == 8001 + + def test_multi_server_manager_tp2_gpu_assignment(self): + """With TP=2, each instance should get 2 GPUs.""" + from unittest.mock import patch, MagicMock + from easi.llm.server_manager import MultiServerManager + + with patch("easi.llm.server_manager.ServerManager") as MockSM: + mock_instance = MagicMock() + mock_instance.start.return_value = "http://localhost:8000/v1" + MockSM.return_value = mock_instance + + mgr = MultiServerManager( + model="test-model", + num_instances=2, + gpu_ids=[0, 1, 2, 3], + base_port=8000, + ) + urls = mgr.start() + + calls = MockSM.call_args_list + assert calls[0].kwargs["cuda_visible_devices"] == "0,1" + assert calls[1].kwargs["cuda_visible_devices"] == "2,3" + + def test_multi_server_manager_stop_all(self): + """stop() should stop all managed instances.""" + from unittest.mock import patch, MagicMock + from easi.llm.server_manager import MultiServerManager + + with patch("easi.llm.server_manager.ServerManager") as MockSM: + mock_instance = MagicMock() + mock_instance.start.return_value = "http://localhost:8000/v1" + MockSM.return_value = mock_instance + + mgr = MultiServerManager(model="m", num_instances=2, gpu_ids=[0, 1]) + mgr.start() + mgr.stop() + + assert mock_instance.stop.call_count == 2 + + def test_multi_server_manager_validates_gpu_count(self): + """Should raise if GPUs don't divide evenly across instances.""" + from easi.llm.server_manager import MultiServerManager + + with pytest.raises(ValueError, match="divide.*evenly"): + MultiServerManager(model="m", num_instances=3, gpu_ids=[0, 1]) From b6d414b3ffb4080cb8f6c23a8779788f57918a30 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Wed, 4 Mar 2026 20:29:29 +0800 Subject: [PATCH 146/239] feat: add --vllm-instances, --vllm-gpus, --sim-gpus CLI args --- easi/cli.py | 18 ++++++++++++++++-- tests/test_cli.py | 35 +++++++++++++++++++++++++++++++++++ 2 files changed, 51 insertions(+), 2 deletions(-) create mode 100644 tests/test_cli.py diff --git a/easi/cli.py b/easi/cli.py index 4a730b9..4a3556d 100644 --- a/easi/cli.py +++ b/easi/cli.py @@ -112,8 +112,22 @@ def build_parser() -> argparse.ArgumentParser: start_parser.add_argument("--max-retries", type=int, default=None, help="Max LLM retry attempts on transient errors (default: 3)") start_parser.add_argument("--num-parallel", type=int, default=None, dest="num_parallel", - help="Number of parallel simulator instances (default: 1, sequential). " - "Only supported with proprietary API backends (openai, anthropic, gemini).") + help="Number of parallel simulator instances (default: 1, sequential).") + start_parser.add_argument( + "--vllm-instances", type=int, default=None, dest="vllm_instances", + help="Number of vLLM server instances to start (default: 1). " + "Each instance runs on a subset of --vllm-gpus.", + ) + start_parser.add_argument( + "--vllm-gpus", type=str, default=None, dest="vllm_gpus", + help="Comma-separated GPU IDs for vLLM inference (e.g., '0,1'). " + "GPUs are split evenly across --vllm-instances.", + ) + start_parser.add_argument( + "--sim-gpus", type=str, default=None, dest="sim_gpus", + help="Comma-separated GPU IDs for simulator rendering (e.g., '2,3'). " + "If not set, simulators use CPU rendering.", + ) start_parser.add_argument("--resume", type=str, default=None, dest="resume_dir", help="Path to a previous run directory to resume from") start_parser.add_argument("--refresh-data", action="store_true", dest="refresh_data", diff --git a/tests/test_cli.py b/tests/test_cli.py new file mode 100644 index 0000000..4c50058 --- /dev/null +++ b/tests/test_cli.py @@ -0,0 +1,35 @@ +# tests/test_cli.py +"""Tests for CLI GPU and vLLM arguments.""" + + +def test_cli_parses_vllm_gpu_args(): + """CLI should parse --vllm-instances, --vllm-gpus, --sim-gpus.""" + from easi.cli import build_parser + parser = build_parser() + args = parser.parse_args([ + "start", "dummy_task", + "--agent", "dummy", + "--backend", "vllm", + "--model", "test", + "--num-parallel", "12", + "--vllm-instances", "2", + "--vllm-gpus", "0,1", + "--sim-gpus", "2,3", + ]) + assert args.vllm_instances == 2 + assert args.vllm_gpus == "0,1" + assert args.sim_gpus == "2,3" + + +def test_cli_parses_comma_separated_llm_url(): + """--llm-url should accept comma-separated URLs.""" + from easi.cli import build_parser + parser = build_parser() + args = parser.parse_args([ + "start", "dummy_task", + "--agent", "dummy", + "--backend", "vllm", + "--model", "test", + "--llm-url", "http://localhost:8000/v1,http://localhost:8001/v1", + ]) + assert args.llm_base_url == "http://localhost:8000/v1,http://localhost:8001/v1" From 49c4943591adfb448a500dd3fb12490aeae9f501 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Wed, 4 Mar 2026 20:33:47 +0800 Subject: [PATCH 147/239] feat: pass vllm_instances, vllm_gpus, sim_gpus through runners --- easi/cli.py | 8 ++++++++ easi/evaluation/runner.py | 6 ++++++ tests/test_parallel_runner.py | 26 ++++++++++++++++++++++++++ 3 files changed, 40 insertions(+) diff --git a/easi/cli.py b/easi/cli.py index 4a3556d..1acc02d 100644 --- a/easi/cli.py +++ b/easi/cli.py @@ -465,6 +465,14 @@ def cmd_start(args): run_kwargs.pop("task_name", None) num_parallel = run_kwargs.pop("num_parallel", None) or 1 + # Parse comma-separated GPU strings into lists of ints + vllm_gpus_str = run_kwargs.pop("vllm_gpus", None) + sim_gpus_str = run_kwargs.pop("sim_gpus", None) + if vllm_gpus_str: + run_kwargs["vllm_gpus"] = [int(g) for g in vllm_gpus_str.split(",")] + if sim_gpus_str: + run_kwargs["sim_gpus"] = [int(g) for g in sim_gpus_str.split(",")] + from easi.core.episode import EpisodeRecord from easi.evaluation.metrics import default_aggregate diff --git a/easi/evaluation/runner.py b/easi/evaluation/runner.py index 8ca6e87..e969311 100644 --- a/easi/evaluation/runner.py +++ b/easi/evaluation/runner.py @@ -64,6 +64,9 @@ def __init__( resume_dir: Path | str | None = None, refresh_data: bool = False, render_platform: str | None = None, + vllm_instances: int | None = None, + vllm_gpus: list[int] | None = None, + sim_gpus: list[int] | None = None, ): # Auto-capture all init args for config.json (before any mutation) frame = inspect.currentframe() @@ -87,6 +90,9 @@ def __init__( self.resume_dir = Path(resume_dir) if resume_dir else None self.refresh_data = refresh_data self.render_platform_name = render_platform + self.vllm_instances = vllm_instances + self.vllm_gpus = vllm_gpus + self.sim_gpus = sim_gpus timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") if self.model: safe_model = self.model.replace("/", "_") diff --git a/tests/test_parallel_runner.py b/tests/test_parallel_runner.py index 76d0ef9..6a9fc56 100644 --- a/tests/test_parallel_runner.py +++ b/tests/test_parallel_runner.py @@ -127,6 +127,32 @@ def test_episode_dirs_created(self, tmp_path): assert (ed / "result.json").exists() +class TestRunnerGPUArgs: + """Test GPU-related args on EvaluationRunner.""" + + def test_runner_accepts_gpu_args(self): + """EvaluationRunner should accept vllm_instances, vllm_gpus, sim_gpus.""" + from easi.evaluation.runner import EvaluationRunner + runner = EvaluationRunner( + task_name="dummy_task", + agent_type="dummy", + vllm_instances=2, + vllm_gpus=[0, 1], + sim_gpus=[2, 3], + ) + assert runner.vllm_instances == 2 + assert runner.vllm_gpus == [0, 1] + assert runner.sim_gpus == [2, 3] + + def test_runner_gpu_args_default_none(self): + """GPU args should default to None.""" + from easi.evaluation.runner import EvaluationRunner + runner = EvaluationRunner(task_name="dummy_task", agent_type="dummy") + assert runner.vllm_instances is None + assert runner.vllm_gpus is None + assert runner.sim_gpus is None + + class TestCLIParallelArg: """Test --num-parallel CLI argument parsing.""" From 364d9220a4d831791389f97a4afe28ad375180b3 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Wed, 4 Mar 2026 20:41:35 +0800 Subject: [PATCH 148/239] feat: enable parallel vLLM evaluation with multi-instance support --- easi/evaluation/parallel_runner.py | 538 +++++++++++++++-------------- tests/test_parallel_runner.py | 44 ++- 2 files changed, 321 insertions(+), 261 deletions(-) diff --git a/easi/evaluation/parallel_runner.py b/easi/evaluation/parallel_runner.py index a1f5f0e..37da9c5 100644 --- a/easi/evaluation/parallel_runner.py +++ b/easi/evaluation/parallel_runner.py @@ -41,6 +41,12 @@ def _serialize_cli_options(self) -> dict: base["num_parallel"] = self.num_parallel return base + def _parse_base_urls(self) -> list[str | None]: + """Parse base URL(s) into list for round-robin assignment.""" + if self.llm_base_url: + return [u.strip() for u in self.llm_base_url.split(",")] + return [None] + def run(self) -> list[dict]: """Run evaluation with thread-pool parallelism.""" logger.trace( @@ -48,286 +54,312 @@ def run(self) -> list[dict]: self.task_name, self.num_parallel, ) - # --- Guard: no local vLLM --- + # --- Resolve LLM backend and vLLM URLs --- backend, base_url = self._resolve_llm_backend() + server_mgr = None + if backend == "vllm" and base_url is None: - raise NotImplementedError( - "ParallelRunner does not support local vLLM server management. " - "Start vLLM externally and pass --llm-url." + # Auto-manage vLLM instances + from easi.llm.server_manager import MultiServerManager + from easi.llm.utils import parse_llm_kwargs, split_kwargs as _split + + all_kw = parse_llm_kwargs(self.llm_kwargs_raw) + srv_kw, _ = _split(all_kw) + + num_instances = self.vllm_instances or 1 + gpu_ids = self.vllm_gpus or list(range(num_instances)) + + server_mgr = MultiServerManager( + model=self.model, + num_instances=num_instances, + gpu_ids=gpu_ids, + base_port=self.port or 8000, + server_kwargs=srv_kw, + ) + vllm_urls = server_mgr.start() + elif base_url: + vllm_urls = self._parse_base_urls() + else: + vllm_urls = [None] + + try: + # --- Load task --- + logger.trace("Loading task") + task = self._create_task() + if self.refresh_data: + task.download_dataset(force=True) + episodes = task.load_episodes() + if self.max_episodes is not None: + episodes = episodes[: self.max_episodes] + logger.trace( + "Task loaded. %d episodes, simulator_key=%s", + len(episodes), task.simulator_key, ) - # --- Load task --- - logger.trace("Loading task") - task = self._create_task() - if self.refresh_data: - task.download_dataset(force=True) - episodes = task.load_episodes() - if self.max_episodes is not None: - episodes = episodes[: self.max_episodes] - logger.trace( - "Task loaded. %d episodes, simulator_key=%s", - len(episodes), task.simulator_key, - ) - - # --- Resolve LLM backend + handle resume --- - logger.trace("Resolved LLM backend=%s, base_url=%s", backend, base_url) + # --- Resolve LLM backend + handle resume --- + logger.trace("Resolved LLM backend=%s, base_url=%s", backend, base_url) - # Compute resolved generation kwargs (YAML defaults + CLI overrides) - from easi.llm.utils import parse_llm_kwargs, split_kwargs + # Compute resolved generation kwargs (YAML defaults + CLI overrides) + from easi.llm.utils import parse_llm_kwargs, split_kwargs - agent_config = task._config.get("agent", {}) - yaml_gen_kwargs = agent_config.get("generation_kwargs", {}) - all_llm_kwargs = parse_llm_kwargs(self.llm_kwargs_raw) - _, cli_gen_kwargs = split_kwargs(all_llm_kwargs) - resolved_gen_kwargs = {**yaml_gen_kwargs, **cli_gen_kwargs} + agent_config = task._config.get("agent", {}) + yaml_gen_kwargs = agent_config.get("generation_kwargs", {}) + all_llm_kwargs = parse_llm_kwargs(self.llm_kwargs_raw) + _, cli_gen_kwargs = split_kwargs(all_llm_kwargs) + resolved_gen_kwargs = {**yaml_gen_kwargs, **cli_gen_kwargs} - # Handle resume - if self.resume_dir: - run_dir = self.resume_dir - completed_results, start_index = self._load_completed_results( - run_dir, len(episodes), - ) - logger.info( - "Resuming from %s — %d completed, starting from index %d", - run_dir, len(completed_results), start_index, - ) - else: - run_dir = self.output_dir / self.task_name / self.run_id - completed_results = [] - start_index = 0 - - # --- Create output directory and save config --- - logger.trace("Creating output directory and saving config") - episodes_dir = run_dir / "episodes" - episodes_dir.mkdir(parents=True, exist_ok=True) - - config = { - "run_id": self.run_id, - "total_episodes": len(episodes), - "num_parallel": self.num_parallel, - "cli_options": self._serialize_cli_options(), - "resolved_backend": backend, - "resolved_base_url": base_url, - "resolved_generation_kwargs": resolved_gen_kwargs, - "task_config": task._config, - } - (run_dir / "config.json").write_text(json.dumps(config, indent=2)) - logger.trace("Run config:\n%s", json.dumps(config, indent=2, default=str)) - - # Check if all episodes already complete (resume edge case) - if start_index >= len(episodes): - logger.info("All %d episodes already complete, re-aggregating summary.", len(episodes)) - all_results = completed_results - # Skip to aggregation - wall_seconds = 0.0 - results_list = [(i, r) for i, r in enumerate(all_results)] - else: - # --- Fill episode queue (from start_index) --- - episode_queue: queue.Queue[tuple[int, dict]] = queue.Queue() - for i, episode in enumerate(episodes): - if i >= start_index: - episode_queue.put((i, episode)) - remaining = episode_queue.qsize() - logger.trace("Queued %d episodes (skipped %d completed)", remaining, start_index) - - # --- Prepare thread-safe collection --- - results_lock = threading.Lock() - new_results: list[tuple[int, dict]] = [] - progress = {"completed": 0, "failed": 0} - progress_lock = threading.Lock() - total_episodes = len(episodes) - - num_workers = min(self.num_parallel, remaining) - - def _worker(worker_id: int) -> None: - """Worker thread: owns a simulator + agent, pulls episodes from queue.""" - logger.trace("[Worker %d] Starting up", worker_id) - episodes_done = 0 - - # Create simulator - logger.trace( - "[Worker %d] Creating simulator (key=%s)", - worker_id, task.simulator_key, - ) - sim, sim_runner = self._create_simulator(task.simulator_key, task=task) - logger.trace( - "[Worker %d] Simulator ready (PID=%s)", - worker_id, - getattr(sim_runner, 'pid', 'unknown'), + # Handle resume + if self.resume_dir: + run_dir = self.resume_dir + completed_results, start_index = self._load_completed_results( + run_dir, len(episodes), ) - - # Create agent - logger.trace("[Worker %d] Creating agent", worker_id) - agent = self._create_agent( - task.action_space, task._config, - backend=backend, base_url=base_url, + logger.info( + "Resuming from %s — %d completed, starting from index %d", + run_dir, len(completed_results), start_index, ) - logger.trace("[Worker %d] Agent ready", worker_id) - - try: - while True: - # Pull next episode - try: - idx, episode = episode_queue.get_nowait() - except queue.Empty: - break + else: + run_dir = self.output_dir / self.task_name / self.run_id + completed_results = [] + start_index = 0 + + # --- Create output directory and save config --- + logger.trace("Creating output directory and saving config") + episodes_dir = run_dir / "episodes" + episodes_dir.mkdir(parents=True, exist_ok=True) + + config = { + "run_id": self.run_id, + "total_episodes": len(episodes), + "num_parallel": self.num_parallel, + "cli_options": self._serialize_cli_options(), + "resolved_backend": backend, + "resolved_base_url": base_url, + "resolved_generation_kwargs": resolved_gen_kwargs, + "task_config": task._config, + } + (run_dir / "config.json").write_text(json.dumps(config, indent=2)) + logger.trace("Run config:\n%s", json.dumps(config, indent=2, default=str)) + + # Check if all episodes already complete (resume edge case) + if start_index >= len(episodes): + logger.info("All %d episodes already complete, re-aggregating summary.", len(episodes)) + all_results = completed_results + # Skip to aggregation + wall_seconds = 0.0 + results_list = [(i, r) for i, r in enumerate(all_results)] + else: + # --- Fill episode queue (from start_index) --- + episode_queue: queue.Queue[tuple[int, dict]] = queue.Queue() + for i, episode in enumerate(episodes): + if i >= start_index: + episode_queue.put((i, episode)) + remaining = episode_queue.qsize() + logger.trace("Queued %d episodes (skipped %d completed)", remaining, start_index) + + # --- Prepare thread-safe collection --- + results_lock = threading.Lock() + new_results: list[tuple[int, dict]] = [] + progress = {"completed": 0, "failed": 0} + progress_lock = threading.Lock() + total_episodes = len(episodes) + + num_workers = min(self.num_parallel, remaining) + + def _worker(worker_id: int) -> None: + """Worker thread: owns a simulator + agent, pulls episodes from queue.""" + logger.trace("[Worker %d] Starting up", worker_id) + episodes_done = 0 + + # Create simulator + logger.trace( + "[Worker %d] Creating simulator (key=%s)", + worker_id, task.simulator_key, + ) + sim, sim_runner = self._create_simulator(task.simulator_key, task=task) + logger.trace( + "[Worker %d] Simulator ready (PID=%s)", + worker_id, + getattr(sim_runner, 'pid', 'unknown'), + ) - logger.trace( - "[Worker %d] Queue remaining: ~%d", - worker_id, episode_queue.qsize(), - ) + # Create agent + logger.trace("[Worker %d] Creating agent", worker_id) + # Round-robin URL assignment + worker_url = vllm_urls[worker_id % len(vllm_urls)] + agent = self._create_agent( + task.action_space, task._config, + backend=backend, base_url=worker_url, + ) + logger.trace("[Worker %d] Agent ready", worker_id) - episode_id = episode.get("episode_id", f"ep_{idx}") - episode_dir = episodes_dir / f"{idx:03d}_{_sanitize_dirname(episode_id)}" - episode_dir.mkdir(exist_ok=True) + try: + while True: + # Pull next episode + try: + idx, episode = episode_queue.get_nowait() + except queue.Empty: + break - result = None - for attempt in range(1, self.max_retries + 1): logger.trace( - "[Worker %d] Running episode %s (attempt %d/%d)", - worker_id, episode_id, attempt, self.max_retries, + "[Worker %d] Queue remaining: ~%d", + worker_id, episode_queue.qsize(), ) - try: - result = self._run_episode( - sim, agent, task, episode, idx, episode_dir, - ) - logger.trace( - "[Worker %d] Episode %s completed in %.1fs: %s", - worker_id, episode_id, - result.get("elapsed_seconds", 0), - {k: v for k, v in result.items() - if k in ("success", "num_steps", "elapsed_seconds")}, - ) - break - except Exception as exc: - logger.warning( - "[Worker %d] Episode %s attempt %d/%d failed: %s", - worker_id, episode_id, attempt, self.max_retries, exc, - ) + + episode_id = episode.get("episode_id", f"ep_{idx}") + episode_dir = episodes_dir / f"{idx:03d}_{_sanitize_dirname(episode_id)}" + episode_dir.mkdir(exist_ok=True) + + result = None + for attempt in range(1, self.max_retries + 1): logger.trace( - "[Worker %d] Exception details:", - worker_id, exc_info=True, + "[Worker %d] Running episode %s (attempt %d/%d)", + worker_id, episode_id, attempt, self.max_retries, ) - self._clear_episode_dir(episode_dir) - if attempt < self.max_retries: - logger.info( - "[Worker %d] Re-launching simulator for retry...", - worker_id, + try: + result = self._run_episode( + sim, agent, task, episode, idx, episode_dir, ) - try: - sim.close() - except Exception: - pass - sim, sim_runner = self._create_simulator( - task.simulator_key, task=task, + logger.trace( + "[Worker %d] Episode %s completed in %.1fs: %s", + worker_id, episode_id, + result.get("elapsed_seconds", 0), + {k: v for k, v in result.items() + if k in ("success", "num_steps", "elapsed_seconds")}, ) - else: - logger.error( - "[Worker %d] Episode %s failed after %d attempts, skipping", - worker_id, episode_id, self.max_retries, + break + except Exception as exc: + logger.warning( + "[Worker %d] Episode %s attempt %d/%d failed: %s", + worker_id, episode_id, attempt, self.max_retries, exc, ) - result = { - "episode_id": episode_id, - "instruction": task.get_instruction(episode), - "success": 0.0, - "num_steps": 0, - "elapsed_seconds": 0.0, - "error": str(exc), - } - - # Save per-episode result (strip internal keys) - result_to_save = { - k: v for k, v in result.items() - if not k.startswith("_") - } - (episode_dir / "result.json").write_text( - json.dumps(result_to_save, indent=2) - ) + logger.trace( + "[Worker %d] Exception details:", + worker_id, exc_info=True, + ) + self._clear_episode_dir(episode_dir) + if attempt < self.max_retries: + logger.info( + "[Worker %d] Re-launching simulator for retry...", + worker_id, + ) + try: + sim.close() + except Exception: + pass + sim, sim_runner = self._create_simulator( + task.simulator_key, task=task, + ) + else: + logger.error( + "[Worker %d] Episode %s failed after %d attempts, skipping", + worker_id, episode_id, self.max_retries, + ) + result = { + "episode_id": episode_id, + "instruction": task.get_instruction(episode), + "success": 0.0, + "num_steps": 0, + "elapsed_seconds": 0.0, + "error": str(exc), + } + + # Save per-episode result (strip internal keys) + result_to_save = { + k: v for k, v in result.items() + if not k.startswith("_") + } + (episode_dir / "result.json").write_text( + json.dumps(result_to_save, indent=2) + ) - # Thread-safe results collection - failed = "error" in result - with results_lock: - new_results.append((idx, result)) - - with progress_lock: - progress["completed"] += 1 - if failed: - progress["failed"] += 1 - current_completed = progress["completed"] + start_index - current_failed = progress["failed"] - - logger.info( - "[Progress] %d/%d episodes completed (%d failed)", - current_completed, total_episodes, current_failed, - ) + # Thread-safe results collection + failed = "error" in result + with results_lock: + new_results.append((idx, result)) + + with progress_lock: + progress["completed"] += 1 + if failed: + progress["failed"] += 1 + current_completed = progress["completed"] + start_index + current_failed = progress["failed"] + + logger.info( + "[Progress] %d/%d episodes completed (%d failed)", + current_completed, total_episodes, current_failed, + ) - episodes_done += 1 + episodes_done += 1 - finally: - logger.trace("[Worker %d] Shutting down simulator", worker_id) - try: - sim.close() - except Exception: - pass - logger.trace( - "[Worker %d] Shutdown complete (%d episodes done)", - worker_id, episodes_done, - ) + finally: + logger.trace("[Worker %d] Shutting down simulator", worker_id) + try: + sim.close() + except Exception: + pass + logger.trace( + "[Worker %d] Shutdown complete (%d episodes done)", + worker_id, episodes_done, + ) - # --- Launch worker threads --- - logger.trace("Launching %d worker threads", num_workers) - wall_start = time.monotonic() + # --- Launch worker threads --- + logger.trace("Launching %d worker threads", num_workers) + wall_start = time.monotonic() - with ThreadPoolExecutor(max_workers=num_workers) as executor: - futures = [] - for wid in range(num_workers): - futures.append(executor.submit(_worker, wid)) - logger.trace("All %d worker threads submitted", num_workers) + with ThreadPoolExecutor(max_workers=num_workers) as executor: + futures = [] + for wid in range(num_workers): + futures.append(executor.submit(_worker, wid)) + logger.trace("All %d worker threads submitted", num_workers) - # Wait for all workers to complete and propagate exceptions - for future in futures: - future.result() + # Wait for all workers to complete and propagate exceptions + for future in futures: + future.result() - wall_seconds = round(time.monotonic() - wall_start, 2) + wall_seconds = round(time.monotonic() - wall_start, 2) - # Merge completed results from resume with new results - new_results.sort(key=lambda x: x[0]) - results_list = [(i, r) for i, r in enumerate(completed_results)] - results_list.extend(new_results) + # Merge completed results from resume with new results + new_results.sort(key=lambda x: x[0]) + results_list = [(i, r) for i, r in enumerate(completed_results)] + results_list.extend(new_results) - # --- Sort results and aggregate --- - results_list.sort(key=lambda x: x[0]) - all_results = [r for _, r in results_list] + # --- Sort results and aggregate --- + results_list.sort(key=lambda x: x[0]) + all_results = [r for _, r in results_list] - num_successful = sum(1 for r in all_results if "error" not in r) - num_failed = len(all_results) - num_successful - logger.trace( - "Results sorted. %d successful, %d failed", - num_successful, num_failed, - ) + num_successful = sum(1 for r in all_results if "error" not in r) + num_failed = len(all_results) - num_successful + logger.trace( + "Results sorted. %d successful, %d failed", + num_successful, num_failed, + ) - # Build EpisodeRecords for aggregate_results - records = [] - for r in all_results: - trajectory = r.pop("_trajectory", []) - episode = r.pop("_episode", {}) - records.append(EpisodeRecord( - episode=episode, - trajectory=trajectory, - episode_results=r, - )) - - # Aggregate and save summary - metric_results = task.aggregate_results(records) - summary = {"num_episodes": len(all_results), "metrics": metric_results} - summary["num_parallel"] = self.num_parallel - summary["wall_clock_seconds"] = wall_seconds - if backend and backend != "legacy": - summary["llm_usage"] = self._aggregate_llm_usage(all_results) - summary["model"] = self.model - summary["backend"] = backend - (run_dir / "summary.json").write_text(json.dumps(summary, indent=2)) - logger.info("Results saved to: %s", run_dir) - logger.info("Summary: %s", summary) - - return all_results + # Build EpisodeRecords for aggregate_results + records = [] + for r in all_results: + trajectory = r.pop("_trajectory", []) + episode = r.pop("_episode", {}) + records.append(EpisodeRecord( + episode=episode, + trajectory=trajectory, + episode_results=r, + )) + + # Aggregate and save summary + metric_results = task.aggregate_results(records) + summary = {"num_episodes": len(all_results), "metrics": metric_results} + summary["num_parallel"] = self.num_parallel + summary["wall_clock_seconds"] = wall_seconds + if backend and backend != "legacy": + summary["llm_usage"] = self._aggregate_llm_usage(all_results) + summary["model"] = self.model + summary["backend"] = backend + (run_dir / "summary.json").write_text(json.dumps(summary, indent=2)) + logger.info("Results saved to: %s", run_dir) + logger.info("Summary: %s", summary) + + return all_results + finally: + if server_mgr is not None: + server_mgr.stop() diff --git a/tests/test_parallel_runner.py b/tests/test_parallel_runner.py index 6a9fc56..85ea854 100644 --- a/tests/test_parallel_runner.py +++ b/tests/test_parallel_runner.py @@ -28,18 +28,46 @@ def test_defaults_to_2_workers(self): assert runner.num_parallel == 2 -class TestParallelRunnerValidation: - """Test that unsupported configs are rejected.""" +class TestParseBaseUrls: + """Tests for _parse_base_urls() method.""" - def test_local_vllm_raises_not_implemented(self, tmp_path): + def test_parses_comma_separated_urls(self): from easi.evaluation.parallel_runner import ParallelRunner runner = ParallelRunner( - task_name="dummy_task", num_parallel=2, - agent_type="react", backend="vllm", - model="some-model", output_dir=str(tmp_path), + task_name="dummy_task", + agent_type="dummy", + num_parallel=4, + llm_base_url="http://localhost:8000/v1,http://localhost:8001/v1", + backend="vllm", + model="test", + ) + urls = runner._parse_base_urls() + assert urls == ["http://localhost:8000/v1", "http://localhost:8001/v1"] + + def test_single_url(self): + from easi.evaluation.parallel_runner import ParallelRunner + runner = ParallelRunner( + task_name="dummy_task", + agent_type="dummy", + num_parallel=4, + llm_base_url="http://localhost:8000/v1", + backend="openai", + model="gpt-4o", + ) + urls = runner._parse_base_urls() + assert urls == ["http://localhost:8000/v1"] + + def test_no_url(self): + from easi.evaluation.parallel_runner import ParallelRunner + runner = ParallelRunner( + task_name="dummy_task", + agent_type="dummy", + num_parallel=4, + backend="openai", + model="gpt-4o", ) - with pytest.raises(NotImplementedError, match="does not support local vLLM"): - runner.run() + urls = runner._parse_base_urls() + assert urls == [None] class TestParallelRunnerWithDummy: From f2610f9a81653e2d2ba5f152e154af044e655cd3 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Wed, 4 Mar 2026 20:52:38 +0800 Subject: [PATCH 149/239] feat: add simulator GPU isolation via sim_gpus --- easi/evaluation/runner.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/easi/evaluation/runner.py b/easi/evaluation/runner.py index e969311..05017fe 100644 --- a/easi/evaluation/runner.py +++ b/easi/evaluation/runner.py @@ -655,6 +655,11 @@ def _create_simulator(self, simulator_key: str, task=None): if task and task.extra_env_vars: env_vars = EnvVars.merge(env_vars, EnvVars(replace=task.extra_env_vars)) + # Apply simulator GPU isolation + if self.sim_gpus is not None: + gpu_str = ",".join(str(g) for g in self.sim_gpus) + env_vars = EnvVars.merge(env_vars, EnvVars(replace={"CUDA_VISIBLE_DEVICES": gpu_str})) + runner = SubprocessRunner( python_executable=env_manager.get_python_executable(), bridge_script_path=bridge_path, From c58a7573632a369fd4a9ec63e40277fcfcefd810 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Wed, 4 Mar 2026 20:55:42 +0800 Subject: [PATCH 150/239] feat: add GPU allocation validation for parallel vLLM --- easi/evaluation/parallel_runner.py | 16 ++++++++++++ tests/test_parallel_runner.py | 39 ++++++++++++++++++++++++++++++ 2 files changed, 55 insertions(+) diff --git a/easi/evaluation/parallel_runner.py b/easi/evaluation/parallel_runner.py index 37da9c5..75c70ee 100644 --- a/easi/evaluation/parallel_runner.py +++ b/easi/evaluation/parallel_runner.py @@ -34,6 +34,7 @@ class ParallelRunner(EvaluationRunner): def __init__(self, *, num_parallel: int = 2, **kwargs): super().__init__(**kwargs) self.num_parallel = num_parallel + self._validate_gpu_args() def _serialize_cli_options(self) -> dict: """Add num_parallel to the serialized config.""" @@ -41,6 +42,21 @@ def _serialize_cli_options(self) -> dict: base["num_parallel"] = self.num_parallel return base + def _validate_gpu_args(self): + """Validate GPU allocation arguments.""" + if self.vllm_instances and self.vllm_instances > 1 and not self.vllm_gpus: + raise ValueError( + "--vllm-gpus is required when --vllm-instances > 1. " + "Specify which GPUs to use for vLLM inference." + ) + if self.vllm_gpus and self.sim_gpus: + overlap = set(self.vllm_gpus) & set(self.sim_gpus) + if overlap: + raise ValueError( + f"--vllm-gpus and --sim-gpus must not overlap. " + f"Overlapping GPU IDs: {overlap}" + ) + def _parse_base_urls(self) -> list[str | None]: """Parse base URL(s) into list for round-robin assignment.""" if self.llm_base_url: diff --git a/tests/test_parallel_runner.py b/tests/test_parallel_runner.py index 85ea854..ced2f42 100644 --- a/tests/test_parallel_runner.py +++ b/tests/test_parallel_runner.py @@ -181,6 +181,45 @@ def test_runner_gpu_args_default_none(self): assert runner.sim_gpus is None +class TestGPUValidation: + """Test GPU allocation validation.""" + + def test_vllm_gpus_required_when_instances_specified(self): + """Should raise if --vllm-instances set but --vllm-gpus not provided.""" + from easi.evaluation.parallel_runner import ParallelRunner + import pytest + + with pytest.raises(ValueError, match="--vllm-gpus"): + ParallelRunner( + task_name="dummy_task", agent_type="dummy", + num_parallel=4, backend="vllm", model="test", + vllm_instances=2, vllm_gpus=None, + ) + + def test_vllm_gpus_sim_gpus_no_overlap(self): + """Should raise if vllm_gpus and sim_gpus overlap.""" + from easi.evaluation.parallel_runner import ParallelRunner + import pytest + + with pytest.raises(ValueError, match="overlap"): + ParallelRunner( + task_name="dummy_task", agent_type="dummy", + num_parallel=4, backend="vllm", model="test", + vllm_instances=2, vllm_gpus=[0, 1], sim_gpus=[1, 2], + ) + + def test_valid_gpu_config_passes(self): + """Valid GPU config should not raise.""" + from easi.evaluation.parallel_runner import ParallelRunner + + runner = ParallelRunner( + task_name="dummy_task", agent_type="dummy", + num_parallel=4, backend="vllm", model="test", + vllm_instances=2, vllm_gpus=[0, 1], sim_gpus=[2, 3], + ) + assert runner.vllm_instances == 2 + + class TestCLIParallelArg: """Test --num-parallel CLI argument parsing.""" From b0427b0ddd19315da9fb85f3991b8be02d9605f9 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Wed, 4 Mar 2026 21:06:37 +0800 Subject: [PATCH 151/239] fix: update tests for new GPU allocation kwargs --- tests/test_env_vars_wiring.py | 2 + tests/test_evaluation_runner.py | 1 + tests/test_installation_kwargs.py | 1 + tests/test_parallel_vllm.py | 76 +++++++++++++++++++++++++++++++ tests/test_render_platform.py | 5 ++ 5 files changed, 85 insertions(+) create mode 100644 tests/test_parallel_vllm.py diff --git a/tests/test_env_vars_wiring.py b/tests/test_env_vars_wiring.py index 5264278..a896212 100644 --- a/tests/test_env_vars_wiring.py +++ b/tests/test_env_vars_wiring.py @@ -16,6 +16,7 @@ def test_runner_passes_env_vars_to_subprocess(self): runner = EvaluationRunner.__new__(EvaluationRunner) runner.data_dir = Path("/tmp/fake") runner.render_platform_name = None + runner.sim_gpus = None mock_env_mgr = MagicMock() mock_env_mgr.env_is_ready.return_value = True @@ -55,6 +56,7 @@ def test_runner_passes_none_when_no_env_vars(self): runner = EvaluationRunner.__new__(EvaluationRunner) runner.data_dir = Path("/tmp/fake") runner.render_platform_name = None + runner.sim_gpus = None mock_env_mgr = MagicMock() mock_env_mgr.env_is_ready.return_value = True diff --git a/tests/test_evaluation_runner.py b/tests/test_evaluation_runner.py index 1a4a373..8c26eac 100644 --- a/tests/test_evaluation_runner.py +++ b/tests/test_evaluation_runner.py @@ -97,6 +97,7 @@ def test_cli_options_contains_all_init_args(self): "max_episodes", "llm_base_url", "agent_seed", "backend", "model", "port", "llm_kwargs_raw", "max_retries", "render_platform", + "vllm_instances", "vllm_gpus", "sim_gpus", } assert set(opts.keys()) == expected_keys diff --git a/tests/test_installation_kwargs.py b/tests/test_installation_kwargs.py index b537ee3..bb71e88 100644 --- a/tests/test_installation_kwargs.py +++ b/tests/test_installation_kwargs.py @@ -59,6 +59,7 @@ def test_runner_passes_installation_kwargs(self): runner = EvaluationRunner.__new__(EvaluationRunner) runner.data_dir = Path("/tmp/fake") runner.render_platform_name = None + runner.sim_gpus = None mock_env_mgr = MagicMock() mock_env_mgr.env_is_ready.return_value = True diff --git a/tests/test_parallel_vllm.py b/tests/test_parallel_vllm.py new file mode 100644 index 0000000..4f3679f --- /dev/null +++ b/tests/test_parallel_vllm.py @@ -0,0 +1,76 @@ +"""Integration test for parallel vLLM evaluation.""" +from easi.evaluation.parallel_runner import ParallelRunner + + +def test_parallel_vllm_end_to_end(): + """Full flow: ParallelRunner stores multi-instance vLLM config correctly.""" + runner = ParallelRunner( + task_name="dummy_task", + agent_type="dummy", + num_parallel=4, + backend="vllm", + model="test-model", + vllm_instances=2, + vllm_gpus=[0, 1], + sim_gpus=[2, 3], + ) + + # Verify config stored correctly + assert runner.vllm_instances == 2 + assert runner.vllm_gpus == [0, 1] + assert runner.sim_gpus == [2, 3] + assert runner.num_parallel == 4 + + +def test_external_multi_url_no_server_startup(): + """With --llm-url containing multiple URLs, no server should be started.""" + runner = ParallelRunner( + task_name="dummy_task", + agent_type="dummy", + num_parallel=8, + backend="vllm", + model="test", + llm_base_url="http://localhost:8000/v1,http://localhost:8001/v1", + ) + urls = runner._parse_base_urls() + assert len(urls) == 2 + assert urls[0] == "http://localhost:8000/v1" + assert urls[1] == "http://localhost:8001/v1" + + +def test_round_robin_url_assignment(): + """Workers should be assigned URLs via round-robin.""" + urls = ["http://localhost:8000/v1", "http://localhost:8001/v1"] + assert urls[0 % 2] == "http://localhost:8000/v1" + assert urls[1 % 2] == "http://localhost:8001/v1" + assert urls[2 % 2] == "http://localhost:8000/v1" + assert urls[3 % 2] == "http://localhost:8001/v1" + + +def test_cli_to_runner_gpu_flow(): + """Verify CLI args flow through to runner correctly.""" + from easi.cli import build_parser + + parser = build_parser() + args = parser.parse_args([ + "start", "dummy_task", + "--agent", "dummy", + "--backend", "vllm", + "--model", "test", + "--num-parallel", "12", + "--vllm-instances", "2", + "--vllm-gpus", "0,1", + "--sim-gpus", "2,3", + ]) + + # Verify CLI parsing + assert args.vllm_instances == 2 + assert args.vllm_gpus == "0,1" + assert args.sim_gpus == "2,3" + assert args.num_parallel == 12 + + # Verify string-to-list conversion (same logic as cmd_start) + vllm_gpus = [int(g) for g in args.vllm_gpus.split(",")] + sim_gpus = [int(g) for g in args.sim_gpus.split(",")] + assert vllm_gpus == [0, 1] + assert sim_gpus == [2, 3] diff --git a/tests/test_render_platform.py b/tests/test_render_platform.py index e0e3045..11fb22c 100644 --- a/tests/test_render_platform.py +++ b/tests/test_render_platform.py @@ -706,6 +706,7 @@ def test_default_uses_env_manager_platform(self): runner = EvaluationRunner.__new__(EvaluationRunner) runner.data_dir = Path("/tmp/fake") runner.render_platform_name = None + runner.sim_gpus = None mock_env_mgr = self._make_mock_env_mgr() mock_sim_cls = MagicMock() @@ -732,6 +733,7 @@ def test_cli_override_wins(self): runner = EvaluationRunner.__new__(EvaluationRunner) runner.data_dir = Path("/tmp/fake") runner.render_platform_name = "xvfb" + runner.sim_gpus = None mock_env_mgr = self._make_mock_env_mgr() mock_sim_cls = MagicMock() @@ -758,6 +760,7 @@ def test_yaml_override_used_when_no_cli(self): runner = EvaluationRunner.__new__(EvaluationRunner) runner.data_dir = Path("/tmp/fake") runner.render_platform_name = None + runner.sim_gpus = None mock_env_mgr = self._make_mock_env_mgr() mock_task = self._make_mock_task(render_platform="egl") @@ -785,6 +788,7 @@ def test_cli_beats_yaml(self): runner = EvaluationRunner.__new__(EvaluationRunner) runner.data_dir = Path("/tmp/fake") runner.render_platform_name = "xvfb" + runner.sim_gpus = None mock_env_mgr = self._make_mock_env_mgr() mock_task = self._make_mock_task(render_platform="egl") @@ -812,6 +816,7 @@ def test_unsupported_platform_raises(self): runner = EvaluationRunner.__new__(EvaluationRunner) runner.data_dir = Path("/tmp/fake") runner.render_platform_name = "egl" + runner.sim_gpus = None mock_env_mgr = self._make_mock_env_mgr() mock_env_mgr.supported_render_platforms = ["auto", "xvfb"] # no egl From 33b0d93f4fc804d0f8edf491d1ab0aa78caf93a1 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Wed, 4 Mar 2026 22:00:31 +0800 Subject: [PATCH 152/239] fix: add port auto-probing and partial-failure cleanup to MultiServerManager --- easi/llm/server_manager.py | 75 +++++++++++++++++++++++++----------- tests/test_server_manager.py | 61 +++++++++++++++++++++++++++-- 2 files changed, 111 insertions(+), 25 deletions(-) diff --git a/easi/llm/server_manager.py b/easi/llm/server_manager.py index 800af0f..0943ec2 100644 --- a/easi/llm/server_manager.py +++ b/easi/llm/server_manager.py @@ -26,6 +26,18 @@ } +def _port_is_available(port: int) -> bool: + """Check if a TCP port is available on localhost.""" + sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) + try: + sock.bind(("127.0.0.1", port)) + return True + except OSError: + return False + finally: + sock.close() + + class ServerManager: """Manages a local inference server subprocess.""" @@ -106,18 +118,12 @@ def is_running(self) -> bool: def _check_port(self) -> None: """Raise if port is already in use.""" logger.trace("Checking if port %d is available...", self.port) - sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) - try: - sock.bind(("127.0.0.1", self.port)) - logger.trace("Port %d is available", self.port) - except OSError: + if not _port_is_available(self.port): raise RuntimeError( f"Port {self.port} is already in use. " f"Use --port to specify a different port, " f"or --llm-url to connect to an existing server." ) - finally: - sock.close() def _build_command(self) -> tuple[list[str], dict]: """Build the server launch command and environment overrides. @@ -236,26 +242,51 @@ def __init__( self._managers: list[ServerManager] = [] def start(self) -> list[str]: - """Start all instances, return list of base_urls.""" + """Start all instances, return list of base_urls. + + Ports are assigned by probing from *base_port* upward, skipping + any that are already in use. If any instance fails to start, + all previously started instances are stopped before re-raising. + """ gpus_per = len(self.gpu_ids) // self.num_instances urls = [] - for i in range(self.num_instances): - instance_gpus = self.gpu_ids[i * gpus_per : (i + 1) * gpus_per] - port = self.base_port + i - mgr = ServerManager( - backend="vllm", - model=self.model, - port=port, - server_kwargs=self.server_kwargs, - startup_timeout=self.startup_timeout, - log_dir=self.log_dir, - cuda_visible_devices=",".join(str(g) for g in instance_gpus), + next_port = self.base_port + try: + for i in range(self.num_instances): + instance_gpus = self.gpu_ids[i * gpus_per : (i + 1) * gpus_per] + port = self._find_available_port(next_port) + next_port = port + 1 + mgr = ServerManager( + backend="vllm", + model=self.model, + port=port, + server_kwargs=self.server_kwargs, + startup_timeout=self.startup_timeout, + log_dir=self.log_dir, + cuda_visible_devices=",".join(str(g) for g in instance_gpus), + ) + url = mgr.start() + urls.append(url) + self._managers.append(mgr) + except Exception: + logger.warning( + "Instance %d failed to start, stopping %d already-running instances", + i, len(self._managers), ) - url = mgr.start() - urls.append(url) - self._managers.append(mgr) + self.stop() + raise return urls + @staticmethod + def _find_available_port(start: int, max_probe: int = 100) -> int: + """Find the first available port starting from *start*.""" + for port in range(start, start + max_probe): + if _port_is_available(port): + return port + raise RuntimeError( + f"No available port found in range {start}-{start + max_probe - 1}" + ) + def stop(self): """Stop all managed instances.""" for mgr in self._managers: diff --git a/tests/test_server_manager.py b/tests/test_server_manager.py index 4302c4f..cb899d4 100644 --- a/tests/test_server_manager.py +++ b/tests/test_server_manager.py @@ -129,7 +129,8 @@ def test_multi_server_manager_starts_n_instances(self): from unittest.mock import patch, MagicMock from easi.llm.server_manager import MultiServerManager - with patch("easi.llm.server_manager.ServerManager") as MockSM: + with patch("easi.llm.server_manager.ServerManager") as MockSM, \ + patch("easi.llm.server_manager._port_is_available", return_value=True): mock_instance = MagicMock() mock_instance.start.return_value = "http://localhost:8000/v1" MockSM.return_value = mock_instance @@ -157,7 +158,8 @@ def test_multi_server_manager_tp2_gpu_assignment(self): from unittest.mock import patch, MagicMock from easi.llm.server_manager import MultiServerManager - with patch("easi.llm.server_manager.ServerManager") as MockSM: + with patch("easi.llm.server_manager.ServerManager") as MockSM, \ + patch("easi.llm.server_manager._port_is_available", return_value=True): mock_instance = MagicMock() mock_instance.start.return_value = "http://localhost:8000/v1" MockSM.return_value = mock_instance @@ -179,7 +181,8 @@ def test_multi_server_manager_stop_all(self): from unittest.mock import patch, MagicMock from easi.llm.server_manager import MultiServerManager - with patch("easi.llm.server_manager.ServerManager") as MockSM: + with patch("easi.llm.server_manager.ServerManager") as MockSM, \ + patch("easi.llm.server_manager._port_is_available", return_value=True): mock_instance = MagicMock() mock_instance.start.return_value = "http://localhost:8000/v1" MockSM.return_value = mock_instance @@ -196,3 +199,55 @@ def test_multi_server_manager_validates_gpu_count(self): with pytest.raises(ValueError, match="divide.*evenly"): MultiServerManager(model="m", num_instances=3, gpu_ids=[0, 1]) + + def test_port_skips_taken(self): + """Should auto-increment past taken ports.""" + from unittest.mock import patch, MagicMock + from easi.llm.server_manager import MultiServerManager + + # Port 8000 available, 8001 taken, 8002 available + port_map = {8000: True, 8001: False, 8002: True} + + with patch("easi.llm.server_manager.ServerManager") as MockSM, \ + patch("easi.llm.server_manager._port_is_available", + side_effect=lambda p: port_map.get(p, True)): + mock_instance = MagicMock() + mock_instance.start.return_value = "http://localhost:8000/v1" + MockSM.return_value = mock_instance + + mgr = MultiServerManager( + model="test-model", + num_instances=2, + gpu_ids=[0, 1], + base_port=8000, + ) + mgr.start() + + calls = MockSM.call_args_list + assert calls[0].kwargs["port"] == 8000 + assert calls[1].kwargs["port"] == 8002 # skipped 8001 + + def test_partial_failure_cleanup(self): + """If instance N fails, instances 0..N-1 should be stopped.""" + from unittest.mock import patch, MagicMock, call + from easi.llm.server_manager import MultiServerManager + + instance_0 = MagicMock() + instance_0.start.return_value = "http://localhost:8000/v1" + instance_1 = MagicMock() + instance_1.start.side_effect = RuntimeError("startup failed") + + with patch("easi.llm.server_manager.ServerManager", + side_effect=[instance_0, instance_1]), \ + patch("easi.llm.server_manager._port_is_available", return_value=True): + mgr = MultiServerManager( + model="test-model", + num_instances=2, + gpu_ids=[0, 1], + base_port=8000, + ) + with pytest.raises(RuntimeError, match="startup failed"): + mgr.start() + + # Instance 0 should have been stopped during cleanup + instance_0.stop.assert_called_once() From cd84c96722b81fe1cee348e5fe5692277811da9f Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Thu, 5 Mar 2026 13:03:48 +0800 Subject: [PATCH 153/239] fix: improve parallel vLLM robustness and observability - Fix GPU restriction bug: gpu_ids=None no longer defaults to [0], allowing unrestricted GPU access for single-instance setups - Add GPU ID validation against hardware via nvidia-smi detection - Fix KeyboardInterrupt cleanup: server startup now inside try/finally in both sequential and parallel runners - Start vLLM instances in parallel (launch all, then health-check concurrently via ThreadPoolExecutor) - Add per-instance log labels ([vllm-0], [vllm-1]) for all server log messages including TRACE-level process output - Fix empty URL edge case in comma-separated --llm-url parsing - Remove deprecated log_dir parameter from ServerManager - Rename internal vllm_urls variable to base_urls for clarity - Remove duplicate test between test_cli.py and test_parallel_vllm.py --- easi/evaluation/parallel_runner.py | 89 ++++++++---- easi/evaluation/runner.py | 213 +++++++++++++++-------------- easi/llm/server_manager.py | 112 +++++++++------ tests/test_parallel_runner.py | 50 +++++++ tests/test_parallel_vllm.py | 27 ---- tests/test_server_manager.py | 71 ++++++++-- 6 files changed, 352 insertions(+), 210 deletions(-) diff --git a/easi/evaluation/parallel_runner.py b/easi/evaluation/parallel_runner.py index 75c70ee..482a1ef 100644 --- a/easi/evaluation/parallel_runner.py +++ b/easi/evaluation/parallel_runner.py @@ -24,6 +24,27 @@ logger = get_logger(__name__) +def _get_gpu_count() -> int | None: + """Detect the number of GPUs via nvidia-smi. + + Returns the GPU count, or None if detection fails (e.g., no GPUs, + nvidia-smi not installed). + """ + import subprocess + + try: + result = subprocess.run( + ["nvidia-smi", "--query-gpu=index", "--format=csv,noheader"], + capture_output=True, text=True, timeout=10, + ) + if result.returncode == 0: + lines = [l.strip() for l in result.stdout.strip().splitlines() if l.strip()] + return len(lines) + except (FileNotFoundError, subprocess.TimeoutExpired): + pass + return None + + class ParallelRunner(EvaluationRunner): """Thread-pool based parallel evaluation runner. @@ -56,11 +77,27 @@ def _validate_gpu_args(self): f"--vllm-gpus and --sim-gpus must not overlap. " f"Overlapping GPU IDs: {overlap}" ) + # Validate GPU IDs against hardware + all_gpu_ids = set() + if self.vllm_gpus: + all_gpu_ids.update(self.vllm_gpus) + if self.sim_gpus: + all_gpu_ids.update(self.sim_gpus) + if all_gpu_ids: + gpu_count = _get_gpu_count() + if gpu_count is not None: + invalid = {g for g in all_gpu_ids if g < 0 or g >= gpu_count} + if invalid: + raise ValueError( + f"GPU IDs {sorted(invalid)} do not exist. " + f"This machine has {gpu_count} GPU(s) " + f"(valid IDs: 0-{gpu_count - 1})." + ) def _parse_base_urls(self) -> list[str | None]: """Parse base URL(s) into list for round-robin assignment.""" if self.llm_base_url: - return [u.strip() for u in self.llm_base_url.split(",")] + return [u.strip() for u in self.llm_base_url.split(",") if u.strip()] return [None] def run(self) -> list[dict]: @@ -74,31 +111,31 @@ def run(self) -> list[dict]: backend, base_url = self._resolve_llm_backend() server_mgr = None - if backend == "vllm" and base_url is None: - # Auto-manage vLLM instances - from easi.llm.server_manager import MultiServerManager - from easi.llm.utils import parse_llm_kwargs, split_kwargs as _split - - all_kw = parse_llm_kwargs(self.llm_kwargs_raw) - srv_kw, _ = _split(all_kw) - - num_instances = self.vllm_instances or 1 - gpu_ids = self.vllm_gpus or list(range(num_instances)) - - server_mgr = MultiServerManager( - model=self.model, - num_instances=num_instances, - gpu_ids=gpu_ids, - base_port=self.port or 8000, - server_kwargs=srv_kw, - ) - vllm_urls = server_mgr.start() - elif base_url: - vllm_urls = self._parse_base_urls() - else: - vllm_urls = [None] - try: + if backend == "vllm" and base_url is None: + # Auto-manage vLLM instances + from easi.llm.server_manager import MultiServerManager + from easi.llm.utils import parse_llm_kwargs, split_kwargs as _split + + all_kw = parse_llm_kwargs(self.llm_kwargs_raw) + srv_kw, _ = _split(all_kw) + + num_instances = self.vllm_instances or 1 + gpu_ids = self.vllm_gpus + + server_mgr = MultiServerManager( + model=self.model, + num_instances=num_instances, + gpu_ids=gpu_ids, + base_port=self.port, + server_kwargs=srv_kw, + ) + base_urls = server_mgr.start() + elif base_url: + base_urls = self._parse_base_urls() + else: + base_urls = [None] + # --- Load task --- logger.trace("Loading task") task = self._create_task() @@ -202,7 +239,7 @@ def _worker(worker_id: int) -> None: # Create agent logger.trace("[Worker %d] Creating agent", worker_id) # Round-robin URL assignment - worker_url = vllm_urls[worker_id % len(vllm_urls)] + worker_url = base_urls[worker_id % len(base_urls)] agent = self._create_agent( task.action_space, task._config, backend=backend, base_url=worker_url, diff --git a/easi/evaluation/runner.py b/easi/evaluation/runner.py index 05017fe..638c1a9 100644 --- a/easi/evaluation/runner.py +++ b/easi/evaluation/runner.py @@ -161,117 +161,118 @@ def run(self) -> list[dict]: # 2. Resolve LLM backend and optionally start server backend, base_url = self._resolve_llm_backend() server = None - if backend == "vllm" and base_url is None: - from easi.llm.server_manager import ServerManager - from easi.llm.utils import parse_llm_kwargs, split_kwargs - all_kwargs = parse_llm_kwargs(self.llm_kwargs_raw) - server_kwargs, _ = split_kwargs(all_kwargs) - server = ServerManager( - "vllm", self.model, port=self.port, - server_kwargs=server_kwargs, log_dir=run_dir, + try: + if backend == "vllm" and base_url is None: + from easi.llm.server_manager import ServerManager + from easi.llm.utils import parse_llm_kwargs, split_kwargs + + all_kwargs = parse_llm_kwargs(self.llm_kwargs_raw) + server_kwargs, _ = split_kwargs(all_kwargs) + server = ServerManager( + "vllm", self.model, port=self.port, + server_kwargs=server_kwargs, + ) + base_url = server.start() + + # Compute resolved generation kwargs (YAML defaults + CLI overrides) + from easi.llm.utils import parse_llm_kwargs, split_kwargs + agent_config = task._config.get("agent", {}) + yaml_gen_kwargs = agent_config.get("generation_kwargs", {}) + all_llm_kwargs = parse_llm_kwargs(self.llm_kwargs_raw) + _, cli_gen_kwargs = split_kwargs(all_llm_kwargs) + resolved_gen_kwargs = {**yaml_gen_kwargs, **cli_gen_kwargs} + + # Save run config + config = { + "run_id": self.run_id, + "total_episodes": len(episodes), + "cli_options": self._serialize_cli_options(), + "resolved_backend": backend, + "resolved_base_url": base_url, + "resolved_generation_kwargs": resolved_gen_kwargs, + "task_config": task._config, + } + (run_dir / "config.json").write_text(json.dumps(config, indent=2)) + logger.trace( + "Run config:\n%s", json.dumps(config, indent=2, default=str) ) - base_url = server.start() - - # Compute resolved generation kwargs (YAML defaults + CLI overrides) - from easi.llm.utils import parse_llm_kwargs, split_kwargs - agent_config = task._config.get("agent", {}) - yaml_gen_kwargs = agent_config.get("generation_kwargs", {}) - all_llm_kwargs = parse_llm_kwargs(self.llm_kwargs_raw) - _, cli_gen_kwargs = split_kwargs(all_llm_kwargs) - resolved_gen_kwargs = {**yaml_gen_kwargs, **cli_gen_kwargs} - - # Save run config - config = { - "run_id": self.run_id, - "total_episodes": len(episodes), - "cli_options": self._serialize_cli_options(), - "resolved_backend": backend, - "resolved_base_url": base_url, - "resolved_generation_kwargs": resolved_gen_kwargs, - "task_config": task._config, - } - (run_dir / "config.json").write_text(json.dumps(config, indent=2)) - logger.trace( - "Run config:\n%s", json.dumps(config, indent=2, default=str) - ) - # Skip simulator/agent if all episodes already complete (resume) - if start_index >= len(episodes): - logger.info("All %d episodes already complete, re-aggregating summary.", len(episodes)) - if server: - server.stop() - else: - # 3. Create agent - agent = self._create_agent(task.action_space, task._config, - backend=backend, base_url=base_url) - - # 4. Start simulator - sim, sim_runner = self._create_simulator(task.simulator_key, task=task) - - try: - for i, episode in enumerate(episodes): - if i < start_index: - continue - episode_id = episode.get("episode_id", f"ep_{i}") - logger.info( - "Episode %d/%d: %s", i + 1, len(episodes), episode_id, - ) - - episode_dir = episodes_dir / f"{i:03d}_{_sanitize_dirname(episode_id)}" - episode_dir.mkdir(exist_ok=True) - - result = None - for attempt in range(1, self.max_retries + 1): - try: - result = self._run_episode( - sim, agent, task, episode, i, episode_dir, - ) - break - except Exception as exc: - logger.warning( - "Episode %s attempt %d/%d failed: %s", - episode_id, attempt, self.max_retries, exc, - ) - self._clear_episode_dir(episode_dir) - if attempt < self.max_retries: - logger.info("Re-launching simulator for retry...") - try: - sim.close() - except Exception: - pass - sim, sim_runner = self._create_simulator( - task.simulator_key, task=task, + # Skip simulator/agent if all episodes already complete (resume) + if start_index >= len(episodes): + logger.info("All %d episodes already complete, re-aggregating summary.", len(episodes)) + else: + # 3. Create agent + agent = self._create_agent(task.action_space, task._config, + backend=backend, base_url=base_url) + + # 4. Start simulator + sim, sim_runner = self._create_simulator(task.simulator_key, task=task) + + try: + for i, episode in enumerate(episodes): + if i < start_index: + continue + episode_id = episode.get("episode_id", f"ep_{i}") + logger.info( + "Episode %d/%d: %s", i + 1, len(episodes), episode_id, + ) + + episode_dir = episodes_dir / f"{i:03d}_{_sanitize_dirname(episode_id)}" + episode_dir.mkdir(exist_ok=True) + + result = None + for attempt in range(1, self.max_retries + 1): + try: + result = self._run_episode( + sim, agent, task, episode, i, episode_dir, ) - else: - logger.error( - "Episode %s failed after %d attempts, skipping", - episode_id, self.max_retries, + break + except Exception as exc: + logger.warning( + "Episode %s attempt %d/%d failed: %s", + episode_id, attempt, self.max_retries, exc, ) - result = { - "episode_id": episode_id, - "instruction": task.get_instruction(episode), - "success": 0.0, - "num_steps": 0, - "elapsed_seconds": 0.0, - "error": str(exc), - } - - all_results.append(result) - - # Save per-episode result (strip internal keys) - result_to_save = { - k: v for k, v in result.items() - if not k.startswith("_") - } - (episode_dir / "result.json").write_text( - json.dumps(result_to_save, indent=2) - ) - - finally: - sim.close() - if server: - server.stop() + self._clear_episode_dir(episode_dir) + if attempt < self.max_retries: + logger.info("Re-launching simulator for retry...") + try: + sim.close() + except Exception: + pass + sim, sim_runner = self._create_simulator( + task.simulator_key, task=task, + ) + else: + logger.error( + "Episode %s failed after %d attempts, skipping", + episode_id, self.max_retries, + ) + result = { + "episode_id": episode_id, + "instruction": task.get_instruction(episode), + "success": 0.0, + "num_steps": 0, + "elapsed_seconds": 0.0, + "error": str(exc), + } + + all_results.append(result) + + # Save per-episode result (strip internal keys) + result_to_save = { + k: v for k, v in result.items() + if not k.startswith("_") + } + (episode_dir / "result.json").write_text( + json.dumps(result_to_save, indent=2) + ) + + finally: + sim.close() + finally: + if server: + server.stop() # 5. Build EpisodeRecords for aggregate_results records = [] diff --git a/easi/llm/server_manager.py b/easi/llm/server_manager.py index 0943ec2..1b7d79d 100644 --- a/easi/llm/server_manager.py +++ b/easi/llm/server_manager.py @@ -19,7 +19,7 @@ logger = get_logger(__name__) _HEALTH_POLL_INTERVAL = 5.0 -_DEFAULT_STARTUP_TIMEOUT = 300.0 +_DEFAULT_STARTUP_TIMEOUT = 600.0 _DEFAULT_VLLM_FLAGS = { "enable_prefix_caching": True, "disable_log_requests": True, @@ -48,8 +48,8 @@ def __init__( port: int = 8080, server_kwargs: dict | None = None, startup_timeout: float = _DEFAULT_STARTUP_TIMEOUT, - log_dir: Path | None = None, # Deprecated: server output now goes to logger cuda_visible_devices: str | None = None, + label: str = "server", ): self.backend = backend self.model = model @@ -57,20 +57,26 @@ def __init__( self.server_kwargs = server_kwargs or {} self.startup_timeout = startup_timeout self.cuda_visible_devices = cuda_visible_devices + self.label = label self._process: subprocess.Popen | None = None self._log_thread: threading.Thread | None = None logger.trace( - "ServerManager init: backend=%s, model=%s, port=%d, " + "[%s] ServerManager init: backend=%s, model=%s, port=%d, " "server_kwargs=%s, cuda_visible_devices=%s", - backend, model, port, self.server_kwargs, cuda_visible_devices, + label, backend, model, port, self.server_kwargs, cuda_visible_devices, ) def start(self) -> str: """Start the server, wait for health, return base_url.""" + self.launch() + return self.wait_until_ready() + + def launch(self) -> None: + """Spawn the server process without waiting for health.""" self._check_port() cmd, extra_env = self._build_command() - logger.info("Starting %s server: %s", self.backend, " ".join(cmd)) + logger.info("[%s] Starting %s server: %s", self.label, self.backend, " ".join(cmd)) spawn_env = os.environ.copy() spawn_env.update(extra_env) @@ -83,25 +89,27 @@ def start(self) -> str: ) self._log_thread = threading.Thread( target=self._stream_output, - args=(self._process,), + args=(self._process, self.label), daemon=True, ) self._log_thread.start() + def wait_until_ready(self) -> str: + """Poll health endpoint until the server is ready. Returns base_url.""" base_url = f"http://localhost:{self.port}/v1" self._wait_for_health(base_url) - logger.info("Server ready at %s", base_url) + logger.info("[%s] Server ready at %s", self.label, base_url) return base_url def stop(self) -> None: """Terminate the server process.""" if self._process is not None: - logger.info("Stopping %s server (pid=%d)", self.backend, self._process.pid) + logger.info("[%s] Stopping %s server (pid=%d)", self.label, self.backend, self._process.pid) self._process.terminate() try: self._process.wait(timeout=30) except subprocess.TimeoutExpired: - logger.warning("Server did not terminate, killing...") + logger.warning("[%s] Server did not terminate, killing...", self.label) self._process.kill() self._process.wait(timeout=10) self._process = None @@ -117,7 +125,7 @@ def is_running(self) -> bool: def _check_port(self) -> None: """Raise if port is already in use.""" - logger.trace("Checking if port %d is available...", self.port) + logger.trace("[%s] Checking if port %d is available...", self.label, self.port) if not _port_is_available(self.port): raise RuntimeError( f"Port {self.port} is already in use. " @@ -145,15 +153,15 @@ def _build_command(self) -> tuple[list[str], dict]: if k in _DEFAULT_VLLM_FLAGS and v != _DEFAULT_VLLM_FLAGS[k] } if overridden: - logger.trace("User overrides for default vLLM flags: %s", overridden) - logger.trace("Merged vLLM kwargs: %s", merged_kwargs) + logger.trace("[%s] User overrides for default vLLM flags: %s", self.label, overridden) + logger.trace("[%s] Merged vLLM kwargs: %s", self.label, merged_kwargs) for key, value in merged_kwargs.items(): flag = "--" + key.replace("_", "-") if isinstance(value, bool): if value: cmd.append(flag) else: - logger.trace("Skipping disabled bool flag: %s", flag) + logger.trace("[%s] Skipping disabled bool flag: %s", self.label, flag) else: cmd.extend([flag, str(value)]) else: @@ -163,17 +171,17 @@ def _build_command(self) -> tuple[list[str], dict]: if self.cuda_visible_devices is not None: env["CUDA_VISIBLE_DEVICES"] = self.cuda_visible_devices - logger.trace("Built command: %s", cmd) - logger.trace("Extra env: %s", env) + logger.trace("[%s] Built command: %s", self.label, cmd) + logger.trace("[%s] Extra env: %s", self.label, env) return cmd, env @staticmethod - def _stream_output(proc: subprocess.Popen) -> None: + def _stream_output(proc: subprocess.Popen, label: str = "server") -> None: """Read server stdout/stderr line by line and log at TRACE level.""" for raw_line in proc.stdout: line = raw_line.decode("utf-8", errors="replace").rstrip() if line: - logger.trace("[server] %s", line) + logger.trace("[%s] %s", label, line) proc.stdout.close() def _wait_for_health(self, base_url: str) -> None: @@ -181,29 +189,31 @@ def _wait_for_health(self, base_url: str) -> None: health_url = base_url.replace("/v1", "") + "/health" deadline = time.monotonic() + self.startup_timeout logger.trace( - "Waiting for health at %s (timeout=%.0fs)", health_url, self.startup_timeout, + "[%s] Waiting for health at %s (timeout=%.0fs)", + self.label, health_url, self.startup_timeout, ) while time.monotonic() < deadline: if self._process and self._process.poll() is not None: raise RuntimeError( - f"{self.backend} server exited with code {self._process.returncode}. " + f"[{self.label}] {self.backend} server exited with code " + f"{self._process.returncode}. " f"Run with --verbosity TRACE to see server output." ) try: resp = requests.get(health_url, timeout=5) if resp.status_code == 200: - logger.trace("Health check passed (status=%d)", resp.status_code) + logger.trace("[%s] Health check passed (status=%d)", self.label, resp.status_code) return - logger.trace("Health check returned status %d, retrying...", resp.status_code) + logger.trace("[%s] Health check returned status %d, retrying...", self.label, resp.status_code) except requests.ConnectionError: - logger.trace("Health check connection refused, retrying...") + logger.trace("[%s] Health check connection refused, retrying...", self.label) time.sleep(_HEALTH_POLL_INTERVAL) self.stop() raise RuntimeError( - f"{self.backend} server failed to start within " + f"[{self.label}] {self.backend} server failed to start within " f"{self.startup_timeout}s. Run with --verbosity TRACE to see server output." ) @@ -221,13 +231,12 @@ def __init__( self, model: str, num_instances: int, - gpu_ids: list[int], + gpu_ids: list[int] | None = None, base_port: int = 8000, server_kwargs: dict | None = None, startup_timeout: float = 300.0, - log_dir: Path | str | None = None, ): - if len(gpu_ids) % num_instances != 0: + if gpu_ids is not None and len(gpu_ids) % num_instances != 0: raise ValueError( f"Cannot divide {len(gpu_ids)} GPUs evenly across " f"{num_instances} instances" @@ -238,22 +247,29 @@ def __init__( self.base_port = base_port self.server_kwargs = server_kwargs or {} self.startup_timeout = startup_timeout - self.log_dir = Path(log_dir) if log_dir else None self._managers: list[ServerManager] = [] def start(self) -> list[str]: - """Start all instances, return list of base_urls. + """Start all instances in parallel, return list of base_urls. - Ports are assigned by probing from *base_port* upward, skipping - any that are already in use. If any instance fails to start, - all previously started instances are stopped before re-raising. + All server processes are spawned first, then health checks run + concurrently via threads. Ports are assigned by probing from + *base_port* upward, skipping any that are already in use. If + any instance fails, all are stopped before re-raising. """ - gpus_per = len(self.gpu_ids) // self.num_instances - urls = [] + from concurrent.futures import ThreadPoolExecutor, as_completed + + gpus_per = len(self.gpu_ids) // self.num_instances if self.gpu_ids else None next_port = self.base_port + try: + # Phase 1: Spawn all processes (fast, no blocking) for i in range(self.num_instances): - instance_gpus = self.gpu_ids[i * gpus_per : (i + 1) * gpus_per] + if gpus_per is not None: + instance_gpus = self.gpu_ids[i * gpus_per : (i + 1) * gpus_per] + cuda_devices = ",".join(str(g) for g in instance_gpus) + else: + cuda_devices = None port = self._find_available_port(next_port) next_port = port + 1 mgr = ServerManager( @@ -262,19 +278,35 @@ def start(self) -> list[str]: port=port, server_kwargs=self.server_kwargs, startup_timeout=self.startup_timeout, - log_dir=self.log_dir, - cuda_visible_devices=",".join(str(g) for g in instance_gpus), + cuda_visible_devices=cuda_devices, + label=f"vllm-{i}", ) - url = mgr.start() - urls.append(url) + mgr.launch() self._managers.append(mgr) + + # Phase 2: Wait for all health checks concurrently + logger.info( + "All %d vLLM processes spawned, waiting for health checks...", + self.num_instances, + ) + urls = [None] * len(self._managers) + with ThreadPoolExecutor(max_workers=len(self._managers)) as pool: + future_to_idx = { + pool.submit(mgr.wait_until_ready): idx + for idx, mgr in enumerate(self._managers) + } + for future in as_completed(future_to_idx): + idx = future_to_idx[future] + urls[idx] = future.result() + except Exception: logger.warning( - "Instance %d failed to start, stopping %d already-running instances", - i, len(self._managers), + "vLLM startup failed, stopping %d spawned instances", + len(self._managers), ) self.stop() raise + return urls @staticmethod diff --git a/tests/test_parallel_runner.py b/tests/test_parallel_runner.py index ced2f42..4175eb5 100644 --- a/tests/test_parallel_runner.py +++ b/tests/test_parallel_runner.py @@ -219,6 +219,56 @@ def test_valid_gpu_config_passes(self): ) assert runner.vllm_instances == 2 + def test_invalid_gpu_ids_raises(self): + """Should raise if GPU IDs exceed hardware count.""" + from unittest.mock import patch + from easi.evaluation.parallel_runner import ParallelRunner + + with patch("easi.evaluation.parallel_runner._get_gpu_count", return_value=2): + with pytest.raises(ValueError, match="do not exist.*2 GPU"): + ParallelRunner( + task_name="dummy_task", agent_type="dummy", + num_parallel=4, backend="vllm", model="test", + vllm_instances=2, vllm_gpus=[0, 1, 2, 3], + ) + + def test_invalid_sim_gpu_ids_raises(self): + """Should raise if sim GPU IDs exceed hardware count.""" + from unittest.mock import patch + from easi.evaluation.parallel_runner import ParallelRunner + + with patch("easi.evaluation.parallel_runner._get_gpu_count", return_value=4): + with pytest.raises(ValueError, match="do not exist.*4 GPU"): + ParallelRunner( + task_name="dummy_task", agent_type="dummy", + num_parallel=4, sim_gpus=[5, 6], + ) + + def test_negative_gpu_id_raises(self): + """Should raise for negative GPU IDs.""" + from unittest.mock import patch + from easi.evaluation.parallel_runner import ParallelRunner + + with patch("easi.evaluation.parallel_runner._get_gpu_count", return_value=4): + with pytest.raises(ValueError, match="do not exist"): + ParallelRunner( + task_name="dummy_task", agent_type="dummy", + num_parallel=4, vllm_gpus=[-1, 0], + ) + + def test_gpu_validation_skipped_when_detection_fails(self): + """Should not raise when nvidia-smi is unavailable.""" + from unittest.mock import patch + from easi.evaluation.parallel_runner import ParallelRunner + + with patch("easi.evaluation.parallel_runner._get_gpu_count", return_value=None): + # Should not raise even with absurd GPU IDs + runner = ParallelRunner( + task_name="dummy_task", agent_type="dummy", + num_parallel=4, vllm_gpus=[99, 100], + ) + assert runner.vllm_gpus == [99, 100] + class TestCLIParallelArg: """Test --num-parallel CLI argument parsing.""" diff --git a/tests/test_parallel_vllm.py b/tests/test_parallel_vllm.py index 4f3679f..2b2fea3 100644 --- a/tests/test_parallel_vllm.py +++ b/tests/test_parallel_vllm.py @@ -47,30 +47,3 @@ def test_round_robin_url_assignment(): assert urls[3 % 2] == "http://localhost:8001/v1" -def test_cli_to_runner_gpu_flow(): - """Verify CLI args flow through to runner correctly.""" - from easi.cli import build_parser - - parser = build_parser() - args = parser.parse_args([ - "start", "dummy_task", - "--agent", "dummy", - "--backend", "vllm", - "--model", "test", - "--num-parallel", "12", - "--vllm-instances", "2", - "--vllm-gpus", "0,1", - "--sim-gpus", "2,3", - ]) - - # Verify CLI parsing - assert args.vllm_instances == 2 - assert args.vllm_gpus == "0,1" - assert args.sim_gpus == "2,3" - assert args.num_parallel == 12 - - # Verify string-to-list conversion (same logic as cmd_start) - vllm_gpus = [int(g) for g in args.vllm_gpus.split(",")] - sim_gpus = [int(g) for g in args.sim_gpus.split(",")] - assert vllm_gpus == [0, 1] - assert sim_gpus == [2, 3] diff --git a/tests/test_server_manager.py b/tests/test_server_manager.py index cb899d4..89fccc4 100644 --- a/tests/test_server_manager.py +++ b/tests/test_server_manager.py @@ -132,7 +132,7 @@ def test_multi_server_manager_starts_n_instances(self): with patch("easi.llm.server_manager.ServerManager") as MockSM, \ patch("easi.llm.server_manager._port_is_available", return_value=True): mock_instance = MagicMock() - mock_instance.start.return_value = "http://localhost:8000/v1" + mock_instance.wait_until_ready.return_value = "http://localhost:8000/v1" MockSM.return_value = mock_instance mgr = MultiServerManager( @@ -145,6 +145,9 @@ def test_multi_server_manager_starts_n_instances(self): assert len(urls) == 2 assert MockSM.call_count == 2 + # All instances should be launched then health-checked + assert mock_instance.launch.call_count == 2 + assert mock_instance.wait_until_ready.call_count == 2 # Check GPU assignment: instance 0 → GPU 0, instance 1 → GPU 1 calls = MockSM.call_args_list @@ -161,7 +164,7 @@ def test_multi_server_manager_tp2_gpu_assignment(self): with patch("easi.llm.server_manager.ServerManager") as MockSM, \ patch("easi.llm.server_manager._port_is_available", return_value=True): mock_instance = MagicMock() - mock_instance.start.return_value = "http://localhost:8000/v1" + mock_instance.wait_until_ready.return_value = "http://localhost:8000/v1" MockSM.return_value = mock_instance mgr = MultiServerManager( @@ -184,7 +187,7 @@ def test_multi_server_manager_stop_all(self): with patch("easi.llm.server_manager.ServerManager") as MockSM, \ patch("easi.llm.server_manager._port_is_available", return_value=True): mock_instance = MagicMock() - mock_instance.start.return_value = "http://localhost:8000/v1" + mock_instance.wait_until_ready.return_value = "http://localhost:8000/v1" MockSM.return_value = mock_instance mgr = MultiServerManager(model="m", num_instances=2, gpu_ids=[0, 1]) @@ -200,6 +203,28 @@ def test_multi_server_manager_validates_gpu_count(self): with pytest.raises(ValueError, match="divide.*evenly"): MultiServerManager(model="m", num_instances=3, gpu_ids=[0, 1]) + def test_no_gpu_ids_skips_cuda_env(self): + """With gpu_ids=None, cuda_visible_devices should not be set.""" + from unittest.mock import patch, MagicMock + from easi.llm.server_manager import MultiServerManager + + with patch("easi.llm.server_manager.ServerManager") as MockSM, \ + patch("easi.llm.server_manager._port_is_available", return_value=True): + mock_instance = MagicMock() + mock_instance.wait_until_ready.return_value = "http://localhost:8000/v1" + MockSM.return_value = mock_instance + + mgr = MultiServerManager( + model="test-model", + num_instances=1, + gpu_ids=None, + base_port=8000, + ) + mgr.start() + + calls = MockSM.call_args_list + assert calls[0].kwargs["cuda_visible_devices"] is None + def test_port_skips_taken(self): """Should auto-increment past taken ports.""" from unittest.mock import patch, MagicMock @@ -212,7 +237,7 @@ def test_port_skips_taken(self): patch("easi.llm.server_manager._port_is_available", side_effect=lambda p: port_map.get(p, True)): mock_instance = MagicMock() - mock_instance.start.return_value = "http://localhost:8000/v1" + mock_instance.wait_until_ready.return_value = "http://localhost:8000/v1" MockSM.return_value = mock_instance mgr = MultiServerManager( @@ -227,15 +252,38 @@ def test_port_skips_taken(self): assert calls[0].kwargs["port"] == 8000 assert calls[1].kwargs["port"] == 8002 # skipped 8001 - def test_partial_failure_cleanup(self): - """If instance N fails, instances 0..N-1 should be stopped.""" - from unittest.mock import patch, MagicMock, call + def test_partial_failure_cleanup_on_launch(self): + """If launch() fails for instance N, instances 0..N-1 should be stopped.""" + from unittest.mock import patch, MagicMock + from easi.llm.server_manager import MultiServerManager + + instance_0 = MagicMock() + instance_1 = MagicMock() + instance_1.launch.side_effect = RuntimeError("launch failed") + + with patch("easi.llm.server_manager.ServerManager", + side_effect=[instance_0, instance_1]), \ + patch("easi.llm.server_manager._port_is_available", return_value=True): + mgr = MultiServerManager( + model="test-model", + num_instances=2, + gpu_ids=[0, 1], + base_port=8000, + ) + with pytest.raises(RuntimeError, match="launch failed"): + mgr.start() + + instance_0.stop.assert_called_once() + + def test_partial_failure_cleanup_on_health(self): + """If wait_until_ready() fails, all spawned instances should be stopped.""" + from unittest.mock import patch, MagicMock from easi.llm.server_manager import MultiServerManager instance_0 = MagicMock() - instance_0.start.return_value = "http://localhost:8000/v1" + instance_0.wait_until_ready.return_value = "http://localhost:8000/v1" instance_1 = MagicMock() - instance_1.start.side_effect = RuntimeError("startup failed") + instance_1.wait_until_ready.side_effect = RuntimeError("health check failed") with patch("easi.llm.server_manager.ServerManager", side_effect=[instance_0, instance_1]), \ @@ -246,8 +294,9 @@ def test_partial_failure_cleanup(self): gpu_ids=[0, 1], base_port=8000, ) - with pytest.raises(RuntimeError, match="startup failed"): + with pytest.raises(RuntimeError, match="health check failed"): mgr.start() - # Instance 0 should have been stopped during cleanup + # Both should be stopped during cleanup instance_0.stop.assert_called_once() + instance_1.stop.assert_called_once() From ddc27a1f30bce865db7591cc9e9e83342c4b31c7 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Thu, 5 Mar 2026 13:23:18 +0800 Subject: [PATCH 154/239] fix: add warning --- easi/evaluation/parallel_runner.py | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/easi/evaluation/parallel_runner.py b/easi/evaluation/parallel_runner.py index 482a1ef..ff490b0 100644 --- a/easi/evaluation/parallel_runner.py +++ b/easi/evaluation/parallel_runner.py @@ -77,6 +77,18 @@ def _validate_gpu_args(self): f"--vllm-gpus and --sim-gpus must not overlap. " f"Overlapping GPU IDs: {overlap}" ) + # Warn if vLLM-specific args are set but backend is not vllm + if self.backend and self.backend != "vllm": + ignored = [] + if self.vllm_instances: + ignored.append("--vllm-instances") + if self.vllm_gpus: + ignored.append("--vllm-gpus") + if ignored: + logger.warning( + "%s will be ignored because --backend is '%s' (not 'vllm').", + ", ".join(ignored), self.backend, + ) # Validate GPU IDs against hardware all_gpu_ids = set() if self.vllm_gpus: From a49b9f9e9d36995f11d96edec37721c767093f8f Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Thu, 5 Mar 2026 13:24:09 +0800 Subject: [PATCH 155/239] docs: updated cli-reference --- CLAUDE.md | 19 ++++++++++++++++--- docs/cli-reference.md | 36 ++++++++++++++++++++++++++++++++++-- 2 files changed, 50 insertions(+), 5 deletions(-) diff --git a/CLAUDE.md b/CLAUDE.md index 4ddd269..ba9bbf2 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -76,7 +76,7 @@ easi/ │ ├── evaluation/ # Evaluation orchestration │ ├── runner.py # EvaluationRunner (sequential) -│ ├── parallel_runner.py # ParallelRunner (thread-pool, API backends) +│ ├── parallel_runner.py # ParallelRunner (thread-pool, any backend) │ └── metrics.py # default_aggregate + legacy aggregate_metrics │ ├── llm/ # LLM client infrastructure @@ -144,11 +144,24 @@ easi start \ --agent {dummy|react} \ --backend {vllm|openai|anthropic|gemini} \ --model \ - --num-parallel \ # Thread-pool parallelism (API backends only) + --num-parallel \ # Thread-pool parallelism --max-episodes \ --resume \ --output-dir ./logs \ - --llm-kwargs '{"temperature": 0.7}' + --llm-kwargs '{"temperature": 0.7}' \ + --vllm-instances \ # Number of vLLM server instances (default: 1) + --vllm-gpus 0,1,2,3 \ # GPUs for vLLM (split across instances) + --sim-gpus 4,5 # GPUs for simulator rendering +``` + +**Parallel vLLM example** (2 instances with TP=2, 8 workers, simulators on separate GPUs): +```bash +easi start ebalfred_base \ + --agent react --backend vllm \ + --model Qwen/Qwen2.5-VL-72B-Instruct \ + --num-parallel 8 --vllm-instances 2 \ + --vllm-gpus 0,1,2,3 --sim-gpus 4,5 \ + --llm-kwargs '{"tensor_parallel_size": 2}' ``` ## Output Structure diff --git a/docs/cli-reference.md b/docs/cli-reference.md index efd3196..e6c1d97 100644 --- a/docs/cli-reference.md +++ b/docs/cli-reference.md @@ -61,11 +61,27 @@ easi start [TASK ...] [options] | Option | Description | |---|---| -| `--num-parallel N` | Parallel simulator instances (default: 1). API backends only. | +| `--num-parallel N` | Parallel simulator instances (default: 1). Works with any backend. | | `--max-episodes N` | Max episodes to run (default: all) | | `--seed SEED` | Random seed for agent reproducibility | | `--render-platform PLATFORM` | Rendering platform override (default: simulator's preference). See [Render Platforms](#render-platforms). | +### GPU Allocation (vLLM backend) + +| Option | Description | +|---|---| +| `--vllm-instances N` | Number of vLLM server instances to start (default: 1). Each runs on a subset of `--vllm-gpus`. | +| `--vllm-gpus IDS` | Comma-separated GPU IDs for vLLM inference (e.g., `0,1,2,3`). GPUs are split evenly across instances. | +| `--sim-gpus IDS` | Comma-separated GPU IDs for simulator rendering (e.g., `4,5`). Sets `CUDA_VISIBLE_DEVICES` for simulator subprocesses. | + +**Notes:** +- `--vllm-gpus` is required when `--vllm-instances > 1`. +- `--vllm-gpus` and `--sim-gpus` must not overlap. +- GPU IDs are validated against hardware at startup (via `nvidia-smi`). +- All vLLM instances start in parallel (processes spawned first, then health-checked concurrently). +- Workers are assigned to vLLM instances via round-robin (e.g., 8 workers across 2 instances → 4 workers per instance). +- These options are ignored with a warning if `--backend` is not `vllm`. + ### Data & Output | Option | Description | @@ -109,10 +125,26 @@ easi start ebalfred_base --agent react --backend openai --model gpt-4o \ # Limit episodes easi start ebalfred_base --agent dummy --max-episodes 5 --seed 42 -# Parallel evaluation (API backends only) +# Parallel evaluation (API backend) easi start ebalfred_base --agent react --backend openai --model gpt-4o \ --num-parallel 4 +# Parallel evaluation with local vLLM (1 instance, all GPUs) +easi start ebalfred_base --agent react --backend vllm \ + --model Qwen/Qwen2.5-VL-7B-Instruct --num-parallel 8 + +# Parallel vLLM with 2 instances (TP=2 each) + separate sim GPUs +easi start ebalfred_base --agent react --backend vllm \ + --model Qwen/Qwen2.5-VL-72B-Instruct \ + --num-parallel 8 --vllm-instances 2 \ + --vllm-gpus 0,1,2,3 --sim-gpus 4,5 \ + --llm-kwargs '{"tensor_parallel_size": 2}' + +# External multi-URL vLLM (pre-started servers, no auto-management) +easi start ebalfred_base --agent react --backend vllm \ + --model Qwen/Qwen2.5-VL-72B-Instruct --num-parallel 8 \ + --llm-url http://localhost:8000/v1,http://localhost:8001/v1 + # Multiple tasks easi start ebalfred_base ebnavigation_base --agent react \ --backend openai --model gpt-4o From ebb591fea0bd243aa6a83cf6e3c51ea1494e7b25 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Sat, 7 Mar 2026 21:33:13 +0800 Subject: [PATCH 156/239] feat: add habitat-sim v0.1.7 simulator integration (Python 3.7) New simulator version for VLN-CE R2R support. Includes conda env config (Python 3.7 + habitat-sim 0.1.7), simulator/env_manager classes, smoke-test bridge, and manifest registration. --- easi/simulators/habitat_sim/manifest.yaml | 5 ++ .../simulators/habitat_sim/v0_1_7/__init__.py | 6 ++ easi/simulators/habitat_sim/v0_1_7/bridge.py | 64 +++++++++++++++++++ .../habitat_sim/v0_1_7/conda_env.yaml | 9 +++ .../habitat_sim/v0_1_7/env_manager.py | 39 +++++++++++ .../habitat_sim/v0_1_7/requirements.txt | 2 + .../habitat_sim/v0_1_7/simulator.py | 22 +++++++ 7 files changed, 147 insertions(+) create mode 100644 easi/simulators/habitat_sim/v0_1_7/__init__.py create mode 100644 easi/simulators/habitat_sim/v0_1_7/bridge.py create mode 100644 easi/simulators/habitat_sim/v0_1_7/conda_env.yaml create mode 100644 easi/simulators/habitat_sim/v0_1_7/env_manager.py create mode 100644 easi/simulators/habitat_sim/v0_1_7/requirements.txt create mode 100644 easi/simulators/habitat_sim/v0_1_7/simulator.py diff --git a/easi/simulators/habitat_sim/manifest.yaml b/easi/simulators/habitat_sim/manifest.yaml index bbe4899..4c40cba 100644 --- a/easi/simulators/habitat_sim/manifest.yaml +++ b/easi/simulators/habitat_sim/manifest.yaml @@ -2,6 +2,11 @@ name: habitat_sim display_name: "Habitat Simulator" default_version: "v0_3_0" versions: + v0_1_7: + description: "Habitat 0.1.7 — VLN-CE R2R (Python 3.7)" + simulator_class: "easi.simulators.habitat_sim.v0_1_7.simulator.HabitatSimulatorV017" + env_manager_class: "easi.simulators.habitat_sim.v0_1_7.env_manager.HabitatEnvManagerV017" + python_version: "3.7" v0_3_0: description: "Habitat 0.3.0 — currently used by eb-habitat" simulator_class: "easi.simulators.habitat_sim.v0_3_0.simulator.HabitatSimulatorV030" diff --git a/easi/simulators/habitat_sim/v0_1_7/__init__.py b/easi/simulators/habitat_sim/v0_1_7/__init__.py new file mode 100644 index 0000000..4afc530 --- /dev/null +++ b/easi/simulators/habitat_sim/v0_1_7/__init__.py @@ -0,0 +1,6 @@ +"""Habitat simulator v0.1.7.""" + +from easi.simulators.habitat_sim.v0_1_7.env_manager import HabitatEnvManagerV017 +from easi.simulators.habitat_sim.v0_1_7.simulator import HabitatSimulatorV017 + +__all__ = ["HabitatSimulatorV017", "HabitatEnvManagerV017"] diff --git a/easi/simulators/habitat_sim/v0_1_7/bridge.py b/easi/simulators/habitat_sim/v0_1_7/bridge.py new file mode 100644 index 0000000..96757bb --- /dev/null +++ b/easi/simulators/habitat_sim/v0_1_7/bridge.py @@ -0,0 +1,64 @@ +"""Generic Habitat v0.1.7 bridge for smoke testing. + +This script runs inside the easi_habitat_sim_v0_1_7 conda environment (Python 3.7). +Task-specific bridges (e.g., VLNCEBridge) extend BaseBridge directly. +This generic bridge is used by `easi sim test habitat_sim:v0_1_7`. + +Usage: + python bridge.py --workspace /tmp/easi_xxx +""" + +from __future__ import annotations + +import argparse +import sys +from pathlib import Path + +_repo_root = Path(__file__).resolve().parents[4] +if str(_repo_root) not in sys.path: + sys.path.insert(0, str(_repo_root)) + +from easi.communication.filesystem import poll_for_command, write_response, write_status +from easi.communication.schemas import make_error_response +from easi.utils.logging import get_logger, setup_logging + +logger = get_logger(__name__) + + +class HabitatV017Bridge: + """Smoke test bridge -- verifies habitat-sim imports work.""" + + def __init__(self, workspace): + self.workspace = Path(workspace) + + def run(self): + import habitat_sim + + logger.info("habitat-sim %s loaded successfully", habitat_sim.__version__) + write_status(self.workspace, ready=True) + + while True: + command = poll_for_command(self.workspace, timeout=60.0) + if command.get("type") == "close": + write_response(self.workspace, {"status": "ok"}) + break + write_response( + self.workspace, + make_error_response( + "Smoke test bridge: only 'close' supported" + ), + ) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--workspace", type=Path, required=True) + parser.add_argument("--simulator-kwargs", type=str, default=None) + args, _ = parser.parse_known_args() + setup_logging("DEBUG") + bridge = HabitatV017Bridge(workspace=args.workspace) + bridge.run() + + +if __name__ == "__main__": + main() diff --git a/easi/simulators/habitat_sim/v0_1_7/conda_env.yaml b/easi/simulators/habitat_sim/v0_1_7/conda_env.yaml new file mode 100644 index 0000000..e029a66 --- /dev/null +++ b/easi/simulators/habitat_sim/v0_1_7/conda_env.yaml @@ -0,0 +1,9 @@ +name: easi_habitat_sim_v0_1_7 +channels: + - aihabitat + - conda-forge + - defaults +dependencies: + - python=3.7 + - habitat-sim=0.1.7 + - headless diff --git a/easi/simulators/habitat_sim/v0_1_7/env_manager.py b/easi/simulators/habitat_sim/v0_1_7/env_manager.py new file mode 100644 index 0000000..e2d385f --- /dev/null +++ b/easi/simulators/habitat_sim/v0_1_7/env_manager.py @@ -0,0 +1,39 @@ +"""Environment manager for Habitat v0.1.7.""" + +from __future__ import annotations + +from pathlib import Path + +from easi.core.base_env_manager import BaseEnvironmentManager + + +class HabitatEnvManagerV017(BaseEnvironmentManager): + """Environment manager for Habitat 0.1.7.""" + + @property + def simulator_name(self) -> str: + return "habitat_sim" + + @property + def version(self) -> str: + return "v0_1_7" + + @property + def default_render_platform(self) -> str: + return "auto" + + @property + def supported_render_platforms(self) -> list[str]: + return ["auto", "xvfb", "native", "egl"] + + def get_conda_env_yaml_path(self) -> Path: + return Path(__file__).parent / "conda_env.yaml" + + def get_requirements_txt_path(self) -> Path: + return Path(__file__).parent / "requirements.txt" + + def get_system_deps(self) -> list[str]: + return ["conda", "xvfb", "egl"] + + def get_validation_import(self) -> str: + return "import habitat_sim; print('habitat-sim', habitat_sim.__version__)" diff --git a/easi/simulators/habitat_sim/v0_1_7/requirements.txt b/easi/simulators/habitat_sim/v0_1_7/requirements.txt new file mode 100644 index 0000000..9ca4997 --- /dev/null +++ b/easi/simulators/habitat_sim/v0_1_7/requirements.txt @@ -0,0 +1,2 @@ +numpy>=1.17,<1.22 +Pillow>=7.0,<10.0 diff --git a/easi/simulators/habitat_sim/v0_1_7/simulator.py b/easi/simulators/habitat_sim/v0_1_7/simulator.py new file mode 100644 index 0000000..633b5f5 --- /dev/null +++ b/easi/simulators/habitat_sim/v0_1_7/simulator.py @@ -0,0 +1,22 @@ +"""Habitat simulator v0.1.7.""" + +from __future__ import annotations + +from pathlib import Path + +from easi.core.base_simulator import BaseSimulator + + +class HabitatSimulatorV017(BaseSimulator): + """Habitat 0.1.7 simulator (VLN-CE R2R).""" + + @property + def name(self) -> str: + return "habitat_sim" + + @property + def version(self) -> str: + return "v0_1_7" + + def _get_bridge_script_path(self) -> Path: + return Path(__file__).parent / "bridge.py" From e2f7634f55db82627e36f01f55cfe28cfd29cd45 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Sat, 7 Mar 2026 22:10:36 +0800 Subject: [PATCH 157/239] fix: use Python 3.8 for habitat-sim v0.1.7 (uv requires >=3.8) Also update bridge to handle reset/step/close commands for easi sim test, and adjust numpy upper bound for Python 3.8 compatibility. --- easi/simulators/habitat_sim/manifest.yaml | 4 +- easi/simulators/habitat_sim/v0_1_7/bridge.py | 68 ++++++++++++++++--- .../habitat_sim/v0_1_7/conda_env.yaml | 2 +- .../habitat_sim/v0_1_7/requirements.txt | 2 +- 4 files changed, 63 insertions(+), 13 deletions(-) diff --git a/easi/simulators/habitat_sim/manifest.yaml b/easi/simulators/habitat_sim/manifest.yaml index 4c40cba..33e803b 100644 --- a/easi/simulators/habitat_sim/manifest.yaml +++ b/easi/simulators/habitat_sim/manifest.yaml @@ -3,10 +3,10 @@ display_name: "Habitat Simulator" default_version: "v0_3_0" versions: v0_1_7: - description: "Habitat 0.1.7 — VLN-CE R2R (Python 3.7)" + description: "Habitat 0.1.7 — VLN-CE R2R (Python 3.8)" simulator_class: "easi.simulators.habitat_sim.v0_1_7.simulator.HabitatSimulatorV017" env_manager_class: "easi.simulators.habitat_sim.v0_1_7.env_manager.HabitatEnvManagerV017" - python_version: "3.7" + python_version: "3.8" v0_3_0: description: "Habitat 0.3.0 — currently used by eb-habitat" simulator_class: "easi.simulators.habitat_sim.v0_3_0.simulator.HabitatSimulatorV030" diff --git a/easi/simulators/habitat_sim/v0_1_7/bridge.py b/easi/simulators/habitat_sim/v0_1_7/bridge.py index 96757bb..dead061 100644 --- a/easi/simulators/habitat_sim/v0_1_7/bridge.py +++ b/easi/simulators/habitat_sim/v0_1_7/bridge.py @@ -1,6 +1,6 @@ """Generic Habitat v0.1.7 bridge for smoke testing. -This script runs inside the easi_habitat_sim_v0_1_7 conda environment (Python 3.7). +This script runs inside the easi_habitat_sim_v0_1_7 conda environment (Python 3.8). Task-specific bridges (e.g., VLNCEBridge) extend BaseBridge directly. This generic bridge is used by `easi sim test habitat_sim:v0_1_7`. @@ -11,7 +11,9 @@ from __future__ import annotations import argparse +import struct import sys +import zlib from pathlib import Path _repo_root = Path(__file__).resolve().parents[4] @@ -19,17 +21,39 @@ sys.path.insert(0, str(_repo_root)) from easi.communication.filesystem import poll_for_command, write_response, write_status -from easi.communication.schemas import make_error_response +from easi.communication.schemas import make_error_response, make_observation_response from easi.utils.logging import get_logger, setup_logging logger = get_logger(__name__) +def _generate_placeholder_image(directory: Path, step: int) -> str: + """Generate a minimal 8x8 placeholder PNG.""" + rgb_path = directory / f"rgb_{step:04d}.png" + + def _chunk(chunk_type: bytes, data: bytes) -> bytes: + c = chunk_type + data + crc = zlib.crc32(c) & 0xFFFFFFFF + return struct.pack(">I", len(data)) + c + struct.pack(">I", crc) + + width, height = 8, 8 + header = b"\x89PNG\r\n\x1a\n" + ihdr = _chunk(b"IHDR", struct.pack(">IIBBBBB", width, height, 8, 2, 0, 0, 0)) + raw_data = b"" + for _ in range(height): + raw_data += b"\x00" + bytes([128, 128, 128]) * width + idat = _chunk(b"IDAT", zlib.compress(raw_data)) + iend = _chunk(b"IEND", b"") + rgb_path.write_bytes(header + ihdr + idat + iend) + return str(rgb_path) + + class HabitatV017Bridge: """Smoke test bridge -- verifies habitat-sim imports work.""" def __init__(self, workspace): self.workspace = Path(workspace) + self.step_count = 0 def run(self): import habitat_sim @@ -39,15 +63,41 @@ def run(self): while True: command = poll_for_command(self.workspace, timeout=60.0) - if command.get("type") == "close": + cmd_type = command.get("type") + + if cmd_type == "reset": + episode_id = command.get("episode_id", "unknown") + logger.info("Reset: episode_id=%s", episode_id) + self.step_count = 0 + rgb_path = _generate_placeholder_image(self.workspace, self.step_count) + write_response(self.workspace, make_observation_response( + rgb_path=rgb_path, + agent_pose=[0.0, 0.0, 0.0, 0.0, 0.0, 0.0], + metadata={"episode_id": episode_id, "step": "0"}, + )) + + elif cmd_type == "step": + self.step_count += 1 + rgb_path = _generate_placeholder_image(self.workspace, self.step_count) + done = self.step_count >= 5 + write_response(self.workspace, make_observation_response( + rgb_path=rgb_path, + agent_pose=[float(self.step_count), 0.0, 0.0, 0.0, 0.0, 0.0], + metadata={"step": str(self.step_count)}, + reward=0.0, + done=done, + )) + + elif cmd_type == "close": + logger.info("Close command received, shutting down") write_response(self.workspace, {"status": "ok"}) break - write_response( - self.workspace, - make_error_response( - "Smoke test bridge: only 'close' supported" - ), - ) + + else: + logger.warning("Unknown command type: %s", cmd_type) + write_response(self.workspace, make_error_response( + f"Unknown command: {cmd_type}" + )) def main(): diff --git a/easi/simulators/habitat_sim/v0_1_7/conda_env.yaml b/easi/simulators/habitat_sim/v0_1_7/conda_env.yaml index e029a66..53dd9aa 100644 --- a/easi/simulators/habitat_sim/v0_1_7/conda_env.yaml +++ b/easi/simulators/habitat_sim/v0_1_7/conda_env.yaml @@ -4,6 +4,6 @@ channels: - conda-forge - defaults dependencies: - - python=3.7 + - python=3.8 - habitat-sim=0.1.7 - headless diff --git a/easi/simulators/habitat_sim/v0_1_7/requirements.txt b/easi/simulators/habitat_sim/v0_1_7/requirements.txt index 9ca4997..119d235 100644 --- a/easi/simulators/habitat_sim/v0_1_7/requirements.txt +++ b/easi/simulators/habitat_sim/v0_1_7/requirements.txt @@ -1,2 +1,2 @@ -numpy>=1.17,<1.22 +numpy>=1.17,<1.25 Pillow>=7.0,<10.0 From aa1316574fc95039cb044c421eb00a42ff6d9984 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Sun, 8 Mar 2026 00:17:11 +0800 Subject: [PATCH 158/239] feat: add VLN-CE R2R task integration 13 new files implementing the VLN-CE Room-to-Room benchmark: - actions.py: 4 discrete actions (move_forward, turn_left, turn_right, stop) - vendor/dtw.py: NDTW/SDTW metric computation via fastdtw - vendor/scene_config.py: habitat-sim 0.1.7 single-camera config - vendor/scene_simulator.py: scene simulator with path tracking and metrics - bridge.py: BaseBridge subclass for subprocess IPC - task.py: BaseTask subclass with null-safe metrics for test split - prompts.py: VLNCEPromptBuilder with EmbodiedBench JSON format - YAML configs: 3 splits (val_seen, val_unseen, test) --- easi/tasks/vlnce_r2r/__init__.py | 1 + easi/tasks/vlnce_r2r/_base.yaml | 40 +++ easi/tasks/vlnce_r2r/actions.py | 18 ++ easi/tasks/vlnce_r2r/bridge.py | 127 ++++++++++ easi/tasks/vlnce_r2r/prompts.py | 166 +++++++++++++ easi/tasks/vlnce_r2r/task.py | 159 ++++++++++++ easi/tasks/vlnce_r2r/vendor/__init__.py | 1 + easi/tasks/vlnce_r2r/vendor/dtw.py | 44 ++++ easi/tasks/vlnce_r2r/vendor/scene_config.py | 50 ++++ .../tasks/vlnce_r2r/vendor/scene_simulator.py | 235 ++++++++++++++++++ easi/tasks/vlnce_r2r/vlnce_r2r_test.yaml | 6 + easi/tasks/vlnce_r2r/vlnce_r2r_val_seen.yaml | 6 + .../tasks/vlnce_r2r/vlnce_r2r_val_unseen.yaml | 6 + 13 files changed, 859 insertions(+) create mode 100644 easi/tasks/vlnce_r2r/__init__.py create mode 100644 easi/tasks/vlnce_r2r/_base.yaml create mode 100644 easi/tasks/vlnce_r2r/actions.py create mode 100644 easi/tasks/vlnce_r2r/bridge.py create mode 100644 easi/tasks/vlnce_r2r/prompts.py create mode 100644 easi/tasks/vlnce_r2r/task.py create mode 100644 easi/tasks/vlnce_r2r/vendor/__init__.py create mode 100644 easi/tasks/vlnce_r2r/vendor/dtw.py create mode 100644 easi/tasks/vlnce_r2r/vendor/scene_config.py create mode 100644 easi/tasks/vlnce_r2r/vendor/scene_simulator.py create mode 100644 easi/tasks/vlnce_r2r/vlnce_r2r_test.yaml create mode 100644 easi/tasks/vlnce_r2r/vlnce_r2r_val_seen.yaml create mode 100644 easi/tasks/vlnce_r2r/vlnce_r2r_val_unseen.yaml diff --git a/easi/tasks/vlnce_r2r/__init__.py b/easi/tasks/vlnce_r2r/__init__.py new file mode 100644 index 0000000..f186fe4 --- /dev/null +++ b/easi/tasks/vlnce_r2r/__init__.py @@ -0,0 +1 @@ +"""VLN-CE R2R task for EASI.""" diff --git a/easi/tasks/vlnce_r2r/_base.yaml b/easi/tasks/vlnce_r2r/_base.yaml new file mode 100644 index 0000000..298fc92 --- /dev/null +++ b/easi/tasks/vlnce_r2r/_base.yaml @@ -0,0 +1,40 @@ +display_name: "VLN-CE R2R" +description: "Vision-Language Navigation in Continuous Environments (Room-to-Room)" +simulator: "habitat_sim:v0_1_7" +task_class: "easi.tasks.vlnce_r2r.task.VLNCETask" +max_steps: 500 + +dataset: + source: huggingface + repo_id: "oscarqjh/VLN-CE-R2R_easi" + subset: null + hf_data_dir: "data" + zip_files: + - "mp3d_scenes.zip" + +simulator_configs: + render_platform: auto + screen_height: 480 + screen_width: 480 + hfov: 90 + sensor_height: 1.25 + gpu_device_id: -1 + success_distance: 3.0 + forward_step_size: 0.25 + turn_angle: 15 + allow_sliding: true + additional_deps: + - "fastdtw>=0.3.4" + +agent: + prompt_builder: "easi.tasks.vlnce_r2r.prompts.VLNCEPromptBuilder" + prompt_builder_kwargs: + use_feedback: true + use_geo_distance: true + action_history_len: 20 + chat_history: false + message_window_len: 5 + generation_kwargs: + temperature: 0 + max_tokens: 4096 + top_p: 0.95 diff --git a/easi/tasks/vlnce_r2r/actions.py b/easi/tasks/vlnce_r2r/actions.py new file mode 100644 index 0000000..4afce18 --- /dev/null +++ b/easi/tasks/vlnce_r2r/actions.py @@ -0,0 +1,18 @@ +"""VLN-CE R2R action space. + +4 discrete actions matching VLN-CE config (0.25m forward, 15° turns). +""" + +DISCRETE_ACTIONS = [ + "move_forward", + "turn_left", + "turn_right", + "stop", +] + +ACTION_NAME_TO_ID = {name: i for i, name in enumerate(DISCRETE_ACTIONS)} + + +def get_action_space() -> list[str]: + """Return the VLN-CE R2R discrete action space.""" + return list(DISCRETE_ACTIONS) diff --git a/easi/tasks/vlnce_r2r/bridge.py b/easi/tasks/vlnce_r2r/bridge.py new file mode 100644 index 0000000..49dbca1 --- /dev/null +++ b/easi/tasks/vlnce_r2r/bridge.py @@ -0,0 +1,127 @@ +"""VLN-CE R2R bridge — wraps vendored SceneSimulator via BaseBridge. + +This script runs inside the easi_habitat_sim_v0_1_7 conda env (Python 3.8). +Creates a NEW SceneSimulator per episode (Habitat-Sim ties one instance to one scene). + +Usage: + python bridge.py --workspace /tmp/easi_xxx [--data-dir /path/to/data] [--simulator-kwargs '{}'] +""" +from __future__ import annotations + +import json +import sys +from pathlib import Path + +import numpy as np + +_repo_root = Path(__file__).resolve().parents[3] +if str(_repo_root) not in sys.path: + sys.path.insert(0, str(_repo_root)) + +from easi.simulators.base_bridge import BaseBridge +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + + +class VLNCEBridge(BaseBridge): + """BaseBridge wrapper for VLN-CE R2R SceneSimulator.""" + + _scene_sim = None + + def _create_env(self, reset_config, simulator_kwargs): + """Store simulator_kwargs; real sim created in _on_reset.""" + self._sim_kwargs = simulator_kwargs + return object() # placeholder + + def _on_reset(self, env, reset_config): + """Create a new SceneSimulator for this episode's scene.""" + from easi.tasks.vlnce_r2r.vendor.scene_simulator import SceneSimulator + + # Close previous simulator if any + if self._scene_sim is not None: + self._scene_sim.close() + self._scene_sim = None + + data_dir = reset_config.get("data_dir") or self._sim_kwargs.get("data_dir", "") + scene_id = reset_config["scene_id"] + scene_path = str(Path(data_dir) / "mp3d" / scene_id / f"{scene_id}.glb") + + # Parse position/rotation (may be JSON strings from IPC) + start_position = json.loads(reset_config["start_position"]) \ + if isinstance(reset_config["start_position"], str) else reset_config["start_position"] + start_rotation = json.loads(reset_config["start_rotation"]) \ + if isinstance(reset_config["start_rotation"], str) else reset_config["start_rotation"] + + goal_position = reset_config.get("goal_position") + if isinstance(goal_position, str): + goal_position = json.loads(goal_position) if goal_position != "null" else None + + geodesic_distance = reset_config.get("geodesic_distance") + if isinstance(geodesic_distance, str): + geodesic_distance = float(geodesic_distance) if geodesic_distance != "null" else None + + gt_locations = reset_config.get("gt_locations") + if isinstance(gt_locations, str): + gt_locations = json.loads(gt_locations) if gt_locations != "null" else None + + # Log test split warning + if goal_position is None: + logger.warning( + "Episode %s has no goal_position (test split). " + "Only path_length and steps_taken will be computed.", + reset_config.get("episode_id", "unknown"), + ) + + self._scene_sim = SceneSimulator( + scene_path=scene_path, + start_position=start_position, + start_rotation=start_rotation, + goal_position=goal_position, + gt_locations=gt_locations, + geodesic_distance=geodesic_distance, + success_distance=self._sim_kwargs.get("success_distance", 3.0), + max_steps=self._sim_kwargs.get("max_steps", 500), + gpu_device_id=self._sim_kwargs.get("gpu_device_id", -1), + width=self._sim_kwargs.get("screen_width", 480), + height=self._sim_kwargs.get("screen_height", 480), + hfov=self._sim_kwargs.get("hfov", 90), + sensor_height=self._sim_kwargs.get("sensor_height", 1.25), + forward_step_size=self._sim_kwargs.get("forward_step_size", 0.25), + turn_angle=self._sim_kwargs.get("turn_angle", 15.0), + allow_sliding=self._sim_kwargs.get("allow_sliding", True), + ) + + return self._scene_sim.get_observation() + + def _on_step(self, env, action_text): + """Step the SceneSimulator and return (obs, reward, done, info).""" + obs, done, info = self._scene_sim.step(action_text) + return obs, 0.0, done, info + + def _extract_image(self, obs): + """Extract front RGB from observation (RGBA -> RGB).""" + return obs["color_sensor"][:, :, :3] + + def _extract_info(self, info): + """Filter info to serializable types (int/float/str/bool/None).""" + clean = {} + for k, v in info.items(): + if v is None: + clean[k] = "null" # IPC encodes None as "null" string + elif isinstance(v, (int, float, str, bool)): + clean[k] = v + elif isinstance(v, (list, np.ndarray)): + clean[k] = json.dumps([float(x) for x in v] if hasattr(v, '__iter__') else str(v)) + return clean + + def close(self): + """Close the SceneSimulator.""" + if self._scene_sim is not None: + self._scene_sim.close() + self._scene_sim = None + super().close() + + +if __name__ == "__main__": + VLNCEBridge.main() diff --git a/easi/tasks/vlnce_r2r/prompts.py b/easi/tasks/vlnce_r2r/prompts.py new file mode 100644 index 0000000..6fd7db4 --- /dev/null +++ b/easi/tasks/vlnce_r2r/prompts.py @@ -0,0 +1,166 @@ +"""VLN-CE R2R prompt builder. + +Adapted from LHPR-VLN's EmbodiedBench format for single-goal navigation. +Single front camera, 4 actions (move_forward, turn_left, turn_right, stop). +""" +from __future__ import annotations + +import base64 +import json +from pathlib import Path + +from easi.core.episode import Action +from easi.core.memory import AgentMemory +from easi.utils.json_repair import fix_json +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + +SYSTEM_PROMPT = """You are a robot navigating in a 3D indoor environment. You observe the environment through a front-facing camera and must follow natural language instructions to navigate to a goal location. + +## Available Actions +- move_forward: Move forward by 0.25 meters +- turn_left: Turn left by 15 degrees +- turn_right: Turn right by 15 degrees +- stop: Stop and end navigation (use ONLY when you believe you have reached the destination described in the instruction) + +## Strategy +1. Carefully read the navigation instruction +2. Observe your surroundings in the image +3. Follow the instruction step by step, matching landmarks and directions mentioned +4. Use move_forward to advance and turn_left/turn_right to change direction +5. Use stop ONLY when you are confident you have reached the described destination + +## Response Format +Output a JSON object with exactly these 4 fields: +{ + "visual_state_description": "Describe what you see in the current image", + "reasoning_and_reflection": "Reason about where you are relative to the instruction", + "language_plan": "Describe your next navigation plan in natural language", + "executable_plan": [{"action": ""}] +} + +You may include multiple actions in executable_plan to plan ahead. Actions are executed sequentially.""" + + +def _encode_image_base64(image_path: str) -> str | None: + """Encode image file to base64 data URL.""" + p = Path(image_path) + if not p.exists(): + return None + data = p.read_bytes() + b64 = base64.b64encode(data).decode("utf-8") + return f"data:image/png;base64,{b64}" + + +def _validate_action(name: str, action_space: list[str]) -> str | None: + """Validate and normalize action name.""" + if name in action_space: + return name + lower = name.lower().strip() + for a in action_space: + if a.lower() == lower: + return a + return None + + +class VLNCEPromptBuilder: + """Prompt builder for VLN-CE R2R benchmark.""" + + def __init__( + self, + use_feedback: bool = True, + use_geo_distance: bool = True, + action_history_len: int = 20, + chat_history: bool = False, + message_window_len: int = 5, + ): + self.use_feedback = use_feedback + self.use_geo_distance = use_geo_distance + self.action_history_len = action_history_len + self.chat_history = chat_history + self.message_window_len = message_window_len + self._actions = [] + + def set_action_space(self, actions: list[str]) -> None: + self._actions = list(actions) + + def build_messages(self, memory: AgentMemory) -> list[dict]: + if self._actions != memory.action_space: + self.set_action_space(memory.action_space) + + messages = [{"role": "system", "content": SYSTEM_PROMPT}] + + # Build user message with image + instruction + feedback + content = [] + + # Current observation image + obs = memory.last_observation + if obs and obs.rgb_path: + img_url = _encode_image_base64(obs.rgb_path) + if img_url: + content.append({"type": "image_url", "image_url": {"url": img_url}}) + + # Build text prompt + text_parts = [] + + # Instruction + text_parts.append(f"## Navigation Instruction\n{memory.instruction}") + + # Environmental feedback + if self.use_feedback and obs and obs.info: + feedback = obs.info.get("feedback", "") + if feedback: + text_parts.append(f"\n## Environmental Feedback\n{feedback}") + + # Geodesic distance + if self.use_geo_distance and obs and obs.info: + geo = obs.info.get("geo_distance") + if geo is not None and geo != "null": + text_parts.append(f"Geodesic distance to goal: {float(geo):.1f}m") + + # Action history + if memory.action_history and self.action_history_len > 0: + history = memory.action_history[-self.action_history_len:] + history_lines = [] + for i, (action, step_result) in enumerate(history): + fb = "" + if self.use_feedback and step_result and step_result.info: + fb = f", feedback: {step_result.info.get('feedback', '')}" + history_lines.append(f"Step {i}: {action.action_name}{fb}") + if history_lines: + text_parts.append(f"\n## Action History (last {len(history_lines)} steps)\n" + "\n".join(history_lines)) + + content.append({"type": "text", "text": "\n".join(text_parts)}) + messages.append({"role": "user", "content": content}) + + return messages + + def parse_response(self, llm_response: str, memory: AgentMemory) -> list[Action]: + """Parse LLM JSON response into validated Actions.""" + llm_response = fix_json(llm_response) + + try: + data = json.loads(llm_response) + except json.JSONDecodeError as e: + logger.warning("Failed to parse LLM response as JSON: %s", e) + return [] + + plan = data.get("executable_plan", []) + if not isinstance(plan, list) or not plan: + logger.warning("No executable_plan in LLM response") + return [] + + actions = [] + for entry in plan: + if not isinstance(entry, dict): + continue + action_name = entry.get("action", entry.get("action_name", "")) + validated = _validate_action(action_name, memory.action_space) + if validated: + actions.append(Action(action_name=validated)) + else: + logger.warning("Skipping invalid action: '%s'", action_name) + break + + return actions diff --git a/easi/tasks/vlnce_r2r/task.py b/easi/tasks/vlnce_r2r/task.py new file mode 100644 index 0000000..66042d9 --- /dev/null +++ b/easi/tasks/vlnce_r2r/task.py @@ -0,0 +1,159 @@ +"""VLN-CE R2R task for EASI. + +Adapts VLN-CE Room-to-Room benchmark to EASI's task interface. +Episodes loaded from HuggingFace dataset (oscarqjh/VLN-CE-R2R_easi). + +Metrics (aligned with original VLN-CE): +- Per-episode: success, oracle_success, spl, navigation_error, ndtw, sdtw, + path_length, steps_taken (null for test split except path_length/steps_taken) +- Aggregate: Average of non-null values per metric +""" +from __future__ import annotations + +import json +from pathlib import Path + +from easi.core.base_task import BaseTask +from easi.core.episode import EpisodeRecord, StepResult +from easi.tasks.vlnce_r2r.actions import get_action_space +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + + +class VLNCETask(BaseTask): + + def _build_action_space(self) -> list[str]: + return get_action_space() + + def get_task_yaml_path(self) -> Path: + return Path(__file__).parent / "_base.yaml" + + def get_bridge_script_path(self) -> Path: + return Path(__file__).parent / "bridge.py" + + def get_instruction(self, episode: dict) -> str: + return episode.get("instruction", self.name) + + def format_reset_config(self, episode: dict) -> dict: + """Map HF episode dict to bridge reset config.""" + gt_locations = episode.get("gt_locations") + if isinstance(gt_locations, list): + gt_locations = json.dumps(gt_locations) + + return { + "episode_id": str(episode.get("episode_id", episode.get("id", "unknown"))), + "scene_id": episode["scene_id"], + "instruction": episode.get("instruction", ""), + "start_position": json.dumps(episode["start_position"]), + "start_rotation": json.dumps(episode["start_rotation"]), + "goal_position": json.dumps(episode["goal_position"]) if episode.get("goal_position") else "null", + "geodesic_distance": str(episode["geodesic_distance"]) if episode.get("geodesic_distance") is not None else "null", + "gt_locations": gt_locations if gt_locations else "null", + "data_dir": episode.get("_data_dir", ""), + } + + def evaluate_episode( + self, episode: dict, trajectory: list[StepResult] + ) -> dict[str, float]: + """Extract per-episode metrics from bridge info dict.""" + if not trajectory: + return self._empty_metrics() + + last = trajectory[-1] + info = last.info + + return { + "success": self._parse_nullable(info.get("success")), + "oracle_success": self._parse_nullable(info.get("oracle_success")), + "spl": self._parse_nullable(info.get("spl")), + "navigation_error": self._parse_nullable(info.get("navigation_error")), + "ndtw": self._parse_nullable(info.get("ndtw")), + "sdtw": self._parse_nullable(info.get("sdtw")), + "path_length": float(info.get("path_length", 0.0)), + "steps_taken": float(len(trajectory)), + } + + def aggregate_results(self, records: list[EpisodeRecord]) -> dict: + """Average non-null values per metric across all episodes.""" + if not records: + return {} + + metric_keys = [ + "success", "oracle_success", "spl", "navigation_error", + "ndtw", "sdtw", "path_length", "steps_taken", + ] + + result = {"num_episodes": len(records)} + null_counts = {} + + for key in metric_keys: + values = [] + nulls = 0 + for r in records: + val = r.episode_results.get(key) + if val is None or val == "null": + nulls += 1 + else: + values.append(float(val)) + if values: + result[key] = round(sum(values) / len(values), 4) + else: + result[key] = None + if nulls > 0: + null_counts[key] = nulls + + if null_counts: + logger.info( + "Null metric counts: %s", + ", ".join(f"{k}={v}" for k, v in null_counts.items()), + ) + result["_null_counts"] = null_counts + + # Aliases for consistency with VLN-CE conventions + result["SR"] = result.get("success") + result["SPL"] = result.get("spl") + result["NE"] = result.get("navigation_error") + result["Oracle_SR"] = result.get("oracle_success") + result["NDTW"] = result.get("ndtw") + result["SDTW"] = result.get("sdtw") + + return result + + @staticmethod + def _parse_nullable(value): + """Parse a value that may be None or 'null' string from IPC.""" + if value is None or value == "null": + return None + return float(value) + + def _empty_metrics(self) -> dict[str, float]: + return { + "success": None, + "oracle_success": None, + "spl": None, + "navigation_error": None, + "ndtw": None, + "sdtw": None, + "path_length": 0.0, + "steps_taken": 0.0, + } + + def _get_builtin_episodes(self) -> list[dict]: + """Minimal episode for testing without dataset download.""" + return [ + { + "id": 0, + "episode_id": "test_0", + "scene_id": "17DRP5sb8fy", + "instruction": "Walk down the hallway and turn right into the bedroom.", + "start_position": [3.1678, 0.0711, -0.2367], + "start_rotation": [0, 0.707, 0, 0.707], + "goal_position": [4.5, 0.0711, 1.2], + "goal_radius": 3.0, + "geodesic_distance": 5.2, + "reference_path": "[]", + "gt_locations": None, + "gt_actions": None, + }, + ] diff --git a/easi/tasks/vlnce_r2r/vendor/__init__.py b/easi/tasks/vlnce_r2r/vendor/__init__.py new file mode 100644 index 0000000..8f09d91 --- /dev/null +++ b/easi/tasks/vlnce_r2r/vendor/__init__.py @@ -0,0 +1 @@ +"""Vendored VLN-CE R2R code.""" diff --git a/easi/tasks/vlnce_r2r/vendor/dtw.py b/easi/tasks/vlnce_r2r/vendor/dtw.py new file mode 100644 index 0000000..4efc260 --- /dev/null +++ b/easi/tasks/vlnce_r2r/vendor/dtw.py @@ -0,0 +1,44 @@ +"""NDTW and SDTW metric computation. + +Vendored from VLN-CE habitat_extensions/measures.py. +Uses fastdtw for dynamic time warping distance. +""" +from __future__ import annotations + +import numpy as np + + +def _euclidean_distance(a, b): + return np.linalg.norm(np.array(b) - np.array(a)) + + +def compute_ndtw(agent_positions, gt_locations, success_distance=3.0): + """Compute Normalized Dynamic Time Warping distance. + + Args: + agent_positions: List of [x, y, z] agent positions at each step. + gt_locations: List of [x, y, z] ground truth reference path positions. + success_distance: Success threshold (default 3.0m for VLN-CE R2R). + + Returns: + NDTW score in [0, 1]. Higher is better. + """ + from fastdtw import fastdtw + + dtw_distance = fastdtw( + agent_positions, gt_locations, dist=_euclidean_distance + )[0] + return np.exp(-dtw_distance / (len(gt_locations) * success_distance)) + + +def compute_sdtw(ndtw, success): + """Compute Success weighted by NDTW. + + Args: + ndtw: NDTW score. + success: 1.0 if episode succeeded, 0.0 otherwise. + + Returns: + SDTW score (0.0 if not successful). + """ + return ndtw * success diff --git a/easi/tasks/vlnce_r2r/vendor/scene_config.py b/easi/tasks/vlnce_r2r/vendor/scene_config.py new file mode 100644 index 0000000..63870c5 --- /dev/null +++ b/easi/tasks/vlnce_r2r/vendor/scene_config.py @@ -0,0 +1,50 @@ +"""Habitat-Sim 0.1.7 configuration for VLN-CE R2R. + +Single front RGB camera, 15-degree turns, 0.25m forward step. +Adapted from LHPR-VLN's scene_config.py for habitat-sim 0.1.7 API. +""" +from __future__ import annotations + +import habitat_sim + + +def make_cfg( + scene_path: str, + gpu_device_id: int = -1, + width: int = 480, + height: int = 480, + hfov: int = 90, + sensor_height: float = 1.25, + forward_step_size: float = 0.25, + turn_angle: float = 15.0, + allow_sliding: bool = True, +) -> habitat_sim.Configuration: + """Build habitat-sim 0.1.7 Configuration for VLN-CE R2R.""" + sim_cfg = habitat_sim.SimulatorConfiguration() + sim_cfg.gpu_device_id = gpu_device_id + sim_cfg.scene_id = scene_path + sim_cfg.allow_sliding = allow_sliding + + # Single front-facing RGB sensor + color_sensor = habitat_sim.SensorSpec() + color_sensor.uuid = "color_sensor" + color_sensor.sensor_type = habitat_sim.SensorType.COLOR + color_sensor.resolution = [height, width] + color_sensor.position = [0.0, sensor_height, 0.0] + color_sensor.parameters["hfov"] = str(hfov) + + agent_cfg = habitat_sim.AgentConfiguration() + agent_cfg.sensor_specifications = [color_sensor] + agent_cfg.action_space = { + "move_forward": habitat_sim.ActionSpec( + "move_forward", habitat_sim.ActuationSpec(amount=forward_step_size) + ), + "turn_left": habitat_sim.ActionSpec( + "turn_left", habitat_sim.ActuationSpec(amount=turn_angle) + ), + "turn_right": habitat_sim.ActionSpec( + "turn_right", habitat_sim.ActuationSpec(amount=turn_angle) + ), + } + + return habitat_sim.Configuration(sim_cfg, [agent_cfg]) diff --git a/easi/tasks/vlnce_r2r/vendor/scene_simulator.py b/easi/tasks/vlnce_r2r/vendor/scene_simulator.py new file mode 100644 index 0000000..592992a --- /dev/null +++ b/easi/tasks/vlnce_r2r/vendor/scene_simulator.py @@ -0,0 +1,235 @@ +"""Habitat-Sim 0.1.7 scene simulator for VLN-CE R2R. + +Wraps habitat_sim.Simulator for single-goal navigation with path tracking. +Creates one simulator per episode (Habitat-Sim binds one instance to one scene). + +Adapted from LHPR-VLN's SceneSimulator, simplified for single-goal VLN-CE: +- No subtask tracking (single goal) +- Specific start position/rotation (not random) +- 15° turns, single front RGB camera +- Path tracking for DTW metrics +- Null-safe for test split (no goal_position) +""" +from __future__ import annotations + +import numpy as np + +import habitat_sim + +from easi.tasks.vlnce_r2r.vendor.scene_config import make_cfg +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + + +class SceneSimulator: + """Single-goal navigation simulator for VLN-CE R2R.""" + + def __init__( + self, + *, + scene_path: str, + start_position: list[float], + start_rotation: list[float], + goal_position: list[float] | None = None, + gt_locations: list[list[float]] | None = None, + geodesic_distance: float | None = None, + success_distance: float = 3.0, + max_steps: int = 500, + gpu_device_id: int = -1, + width: int = 480, + height: int = 480, + hfov: int = 90, + sensor_height: float = 1.25, + forward_step_size: float = 0.25, + turn_angle: float = 15.0, + allow_sliding: bool = True, + ): + self._goal_position = np.array(goal_position) if goal_position else None + self._gt_locations = gt_locations + self._geodesic_distance = geodesic_distance + self._success_distance = success_distance + self._max_steps = max_steps + + # Create habitat-sim instance + cfg = make_cfg( + scene_path=scene_path, + gpu_device_id=gpu_device_id, + width=width, + height=height, + hfov=hfov, + sensor_height=sensor_height, + forward_step_size=forward_step_size, + turn_angle=turn_angle, + allow_sliding=allow_sliding, + ) + self._sim = habitat_sim.Simulator(cfg) + + # Set agent to start position/rotation + agent = self._sim.get_agent(0) + state = agent.get_state() + state.position = np.array(start_position, dtype=np.float32) + state.rotation = self._list_to_quaternion(start_rotation) + agent.set_state(state) + + # Tracking state + self._step_count = 0 + self._done = False + self._stopped = False + self._agent_positions = [list(state.position)] + self._path_length = 0.0 + self._min_geodesic_to_goal = float("inf") + + # Compute initial geodesic distance + if self._goal_position is not None: + geo = self._compute_geodesic(state.position) + if geo is not None: + self._min_geodesic_to_goal = geo + + def _list_to_quaternion(self, rot): + """Convert [x, y, z, w] list to habitat quaternion.""" + import quaternion + if len(rot) == 4: + return np.quaternion(rot[3], rot[0], rot[1], rot[2]) + return np.quaternion(1, 0, 0, 0) + + def get_observation(self): + """Get current observation from the simulator.""" + return self._sim.get_sensor_observations() + + def step(self, action: str) -> tuple[dict, bool, dict]: + """Execute an action and return (obs, done, info). + + Args: + action: One of "move_forward", "turn_left", "turn_right", "stop". + + Returns: + obs: Sensor observations dict with "color_sensor" key. + done: Whether episode is over. + info: Step info dict with metrics and feedback. + """ + if self._done: + return self.get_observation(), True, self._build_info() + + if action == "stop": + self._stopped = True + self._done = True + obs = self.get_observation() + return obs, True, self._build_info() + + # Execute movement action + if action in ("move_forward", "turn_left", "turn_right"): + obs = self._sim.step(action) + else: + logger.warning("Unknown action '%s', treating as no-op", action) + obs = self.get_observation() + + self._step_count += 1 + + # Update tracking + pos = self._sim.get_agent(0).get_state().position + prev_pos = np.array(self._agent_positions[-1]) + self._path_length += float(np.linalg.norm(pos - prev_pos)) + self._agent_positions.append(list(pos)) + + # Update min geodesic (for oracle success) + if self._goal_position is not None: + geo = self._compute_geodesic(pos) + if geo is not None and geo < self._min_geodesic_to_goal: + self._min_geodesic_to_goal = geo + + # Check max steps + if self._step_count >= self._max_steps: + self._done = True + + return obs, self._done, self._build_info() + + def _compute_geodesic(self, position) -> float | None: + """Compute geodesic distance from position to goal.""" + if self._goal_position is None: + return None + try: + return self._sim.geodesic_distance(position, self._goal_position) + except Exception: + # Fallback to Euclidean if pathfinder fails + return float(np.linalg.norm(position - self._goal_position)) + + def _build_info(self) -> dict: + """Build step info dict with metrics and feedback.""" + pos = self._sim.get_agent(0).get_state().position + + info = { + "step": self._step_count, + "path_length": self._path_length, + "agent_position": list(pos), + } + + if self._goal_position is not None: + geo = self._compute_geodesic(pos) + ne = geo if geo is not None else float(np.linalg.norm(pos - self._goal_position)) + info["navigation_error"] = ne + info["geo_distance"] = ne # For agent feedback + + success = 1.0 if ne <= self._success_distance and self._stopped else 0.0 + oracle_success = 1.0 if self._min_geodesic_to_goal <= self._success_distance else 0.0 + + info["success"] = success + info["oracle_success"] = oracle_success + + # SPL + if self._geodesic_distance is not None and self._geodesic_distance > 0: + info["spl"] = success * ( + self._geodesic_distance / max(self._path_length, self._geodesic_distance) + ) + else: + info["spl"] = 0.0 + + # Feedback string for agent + info["feedback"] = f"Distance to goal: {ne:.1f}m" + else: + # Test split: no goal + info["navigation_error"] = None + info["success"] = None + info["oracle_success"] = None + info["spl"] = None + info["feedback"] = "Navigating (no distance feedback available)" + + # Compute NDTW/SDTW on episode end + if self._done: + info.update(self._compute_dtw_metrics()) + + return info + + def _compute_dtw_metrics(self) -> dict: + """Compute NDTW and SDTW at episode end.""" + if self._gt_locations is None or len(self._gt_locations) == 0: + return {"ndtw": None, "sdtw": None} + + try: + from easi.tasks.vlnce_r2r.vendor.dtw import compute_ndtw, compute_sdtw + + ndtw = compute_ndtw( + self._agent_positions, self._gt_locations, self._success_distance + ) + # Compute success inline to avoid circular call to _build_info + success = 0.0 + if self._goal_position is not None and self._stopped: + pos = self._sim.get_agent(0).get_state().position + geo = self._compute_geodesic(pos) + ne = geo if geo is not None else float(np.linalg.norm(pos - self._goal_position)) + if ne <= self._success_distance: + success = 1.0 + sdtw = compute_sdtw(ndtw, success) + return {"ndtw": float(ndtw), "sdtw": float(sdtw)} + except ImportError: + logger.warning("fastdtw not installed — NDTW/SDTW unavailable") + return {"ndtw": None, "sdtw": None} + except Exception as e: + logger.warning("DTW computation failed: %s", e) + return {"ndtw": None, "sdtw": None} + + def close(self): + """Close the habitat-sim instance.""" + if self._sim is not None: + self._sim.close() + self._sim = None diff --git a/easi/tasks/vlnce_r2r/vlnce_r2r_test.yaml b/easi/tasks/vlnce_r2r/vlnce_r2r_test.yaml new file mode 100644 index 0000000..f8494b6 --- /dev/null +++ b/easi/tasks/vlnce_r2r/vlnce_r2r_test.yaml @@ -0,0 +1,6 @@ +extends: _base.yaml +name: vlnce_r2r_test +display_name: "VLN-CE R2R Test" +description: "VLN-CE R2R test split (no ground truth)" +dataset: + split: "test" diff --git a/easi/tasks/vlnce_r2r/vlnce_r2r_val_seen.yaml b/easi/tasks/vlnce_r2r/vlnce_r2r_val_seen.yaml new file mode 100644 index 0000000..b782ab9 --- /dev/null +++ b/easi/tasks/vlnce_r2r/vlnce_r2r_val_seen.yaml @@ -0,0 +1,6 @@ +extends: _base.yaml +name: vlnce_r2r_val_seen +display_name: "VLN-CE R2R Val Seen" +description: "VLN-CE R2R validation split (seen environments)" +dataset: + split: "val_seen" diff --git a/easi/tasks/vlnce_r2r/vlnce_r2r_val_unseen.yaml b/easi/tasks/vlnce_r2r/vlnce_r2r_val_unseen.yaml new file mode 100644 index 0000000..c03f15a --- /dev/null +++ b/easi/tasks/vlnce_r2r/vlnce_r2r_val_unseen.yaml @@ -0,0 +1,6 @@ +extends: _base.yaml +name: vlnce_r2r_val_unseen +display_name: "VLN-CE R2R Val Unseen" +description: "VLN-CE R2R validation split (unseen environments)" +dataset: + split: "val_unseen" From 5da4f0d3be62cad9bdfcf0c8b1467668d32b9f5a Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Sun, 8 Mar 2026 12:15:35 +0800 Subject: [PATCH 159/239] fix: resolve VLN-CE R2R integration issues found during testing - Pin numpy<1.24 for habitat-sim 0.1.7 (np.float removed in 1.24) - Set gpu_device_id=0 (GLX crashes with -1 under xvfb) - Override _load_episodes_from_hf to bypass HF split auto-detection (val_seen/val_unseen were merged into single 'validation' split) --- .gitignore | 4 ++- .../habitat_sim/v0_1_7/requirements.txt | 2 +- easi/tasks/vlnce_r2r/_base.yaml | 2 +- easi/tasks/vlnce_r2r/task.py | 30 +++++++++++++++++++ 4 files changed, 35 insertions(+), 3 deletions(-) diff --git a/.gitignore b/.gitignore index 3822a37..7e7e712 100644 --- a/.gitignore +++ b/.gitignore @@ -9,4 +9,6 @@ __pycache__/ /BEHAVIOR-1K .worktrees/ /docs/plans -yolo11n.pt \ No newline at end of file +yolo11n.pt +.tmp* +/scripts diff --git a/easi/simulators/habitat_sim/v0_1_7/requirements.txt b/easi/simulators/habitat_sim/v0_1_7/requirements.txt index 119d235..1bb3290 100644 --- a/easi/simulators/habitat_sim/v0_1_7/requirements.txt +++ b/easi/simulators/habitat_sim/v0_1_7/requirements.txt @@ -1,2 +1,2 @@ -numpy>=1.17,<1.25 +numpy>=1.17,<1.24 Pillow>=7.0,<10.0 diff --git a/easi/tasks/vlnce_r2r/_base.yaml b/easi/tasks/vlnce_r2r/_base.yaml index 298fc92..5a6774f 100644 --- a/easi/tasks/vlnce_r2r/_base.yaml +++ b/easi/tasks/vlnce_r2r/_base.yaml @@ -18,7 +18,7 @@ simulator_configs: screen_width: 480 hfov: 90 sensor_height: 1.25 - gpu_device_id: -1 + gpu_device_id: 0 success_distance: 3.0 forward_step_size: 0.25 turn_angle: 15 diff --git a/easi/tasks/vlnce_r2r/task.py b/easi/tasks/vlnce_r2r/task.py index 66042d9..fb570a9 100644 --- a/easi/tasks/vlnce_r2r/task.py +++ b/easi/tasks/vlnce_r2r/task.py @@ -11,6 +11,7 @@ from __future__ import annotations import json +import tempfile from pathlib import Path from easi.core.base_task import BaseTask @@ -26,6 +27,35 @@ class VLNCETask(BaseTask): def _build_action_space(self) -> list[str]: return get_action_space() + def _load_episodes_from_hf(self, dataset_config: dict) -> list[dict]: + """Load episodes from HF dataset with custom split names. + + HF auto-detection merges val_seen.jsonl and val_unseen.jsonl into + a single 'validation' split. We bypass this by loading the specific + JSONL file directly using data_files parameter. + """ + from datasets import load_dataset + from easi.core.base_task import hf_row_to_episode + + data_dir = self.download_dataset() + split_name = dataset_config.get("split") + data_file = str(data_dir / "data" / f"{split_name}.jsonl") + + logger.info("Loading episodes from %s (split=%s)", data_file, split_name) + + hf_cache = Path(tempfile.gettempdir()) / "easi_hf_cache" + ds = load_dataset( + "json", data_files=data_file, split="train", + cache_dir=str(hf_cache), + ) + episodes = [hf_row_to_episode(row) for row in ds] + + for ep in episodes: + ep["_data_dir"] = str(data_dir) + + logger.info("Loaded %d episodes (split=%s)", len(episodes), split_name) + return episodes + def get_task_yaml_path(self) -> Path: return Path(__file__).parent / "_base.yaml" From abb36e08a973054ec97c739d9f853dda14af80a8 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Sun, 8 Mar 2026 12:35:08 +0800 Subject: [PATCH 160/239] fix: use correct AgentMemory attribute names in VLN-CE prompt builder MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - memory.last_observation → memory.current_observation - memory.instruction → memory.task_description --- easi/tasks/vlnce_r2r/prompts.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/easi/tasks/vlnce_r2r/prompts.py b/easi/tasks/vlnce_r2r/prompts.py index 6fd7db4..7e76283 100644 --- a/easi/tasks/vlnce_r2r/prompts.py +++ b/easi/tasks/vlnce_r2r/prompts.py @@ -95,7 +95,7 @@ def build_messages(self, memory: AgentMemory) -> list[dict]: content = [] # Current observation image - obs = memory.last_observation + obs = memory.current_observation if obs and obs.rgb_path: img_url = _encode_image_base64(obs.rgb_path) if img_url: @@ -105,7 +105,7 @@ def build_messages(self, memory: AgentMemory) -> list[dict]: text_parts = [] # Instruction - text_parts.append(f"## Navigation Instruction\n{memory.instruction}") + text_parts.append(f"## Navigation Instruction\n{memory.task_description}") # Environmental feedback if self.use_feedback and obs and obs.info: From cffec62ada79f2667625d3039755b6fa98901c3c Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Sun, 8 Mar 2026 13:11:55 +0800 Subject: [PATCH 161/239] fix: correct AgentMemory API usage in VLN-CE prompt builder MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - obs.info → obs.metadata (Observation uses metadata, not info) - Unpack action_history as (action_name, feedback) tuples, not objects --- easi/tasks/vlnce_r2r/prompts.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/easi/tasks/vlnce_r2r/prompts.py b/easi/tasks/vlnce_r2r/prompts.py index 7e76283..27b64c3 100644 --- a/easi/tasks/vlnce_r2r/prompts.py +++ b/easi/tasks/vlnce_r2r/prompts.py @@ -108,14 +108,14 @@ def build_messages(self, memory: AgentMemory) -> list[dict]: text_parts.append(f"## Navigation Instruction\n{memory.task_description}") # Environmental feedback - if self.use_feedback and obs and obs.info: - feedback = obs.info.get("feedback", "") + if self.use_feedback and obs and obs.metadata: + feedback = obs.metadata.get("feedback", "") if feedback: text_parts.append(f"\n## Environmental Feedback\n{feedback}") # Geodesic distance - if self.use_geo_distance and obs and obs.info: - geo = obs.info.get("geo_distance") + if self.use_geo_distance and obs and obs.metadata: + geo = obs.metadata.get("geo_distance") if geo is not None and geo != "null": text_parts.append(f"Geodesic distance to goal: {float(geo):.1f}m") @@ -123,11 +123,11 @@ def build_messages(self, memory: AgentMemory) -> list[dict]: if memory.action_history and self.action_history_len > 0: history = memory.action_history[-self.action_history_len:] history_lines = [] - for i, (action, step_result) in enumerate(history): + for i, (action_name, feedback) in enumerate(history): fb = "" - if self.use_feedback and step_result and step_result.info: - fb = f", feedback: {step_result.info.get('feedback', '')}" - history_lines.append(f"Step {i}: {action.action_name}{fb}") + if self.use_feedback and feedback: + fb = f", feedback: {feedback}" + history_lines.append(f"Step {i}: {action_name}{fb}") if history_lines: text_parts.append(f"\n## Action History (last {len(history_lines)} steps)\n" + "\n".join(history_lines)) From 14e1bbe90d7031043f38bd072ff43dd68345ce3f Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Sun, 8 Mar 2026 17:03:16 +0800 Subject: [PATCH 162/239] feat: add VLN-CE RxR task integration MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Independent benchmark from R2R with 6 actions (add look_up/look_down), 30° turns/tilts, 0.88m camera height, 640×480 resolution, 79° HFOV. Dataset: oscarqjh/VLN-CE-RxR_easi (guide role, 3 languages × 2 splits). --- easi/tasks/vlnce_rxr/__init__.py | 2 + easi/tasks/vlnce_rxr/_base.yaml | 42 ++++ easi/tasks/vlnce_rxr/actions.py | 21 ++ easi/tasks/vlnce_rxr/bridge.py | 128 ++++++++++ easi/tasks/vlnce_rxr/prompts.py | 163 ++++++++++++ easi/tasks/vlnce_rxr/task.py | 191 ++++++++++++++ easi/tasks/vlnce_rxr/vendor/__init__.py | 2 + easi/tasks/vlnce_rxr/vendor/dtw.py | 45 ++++ easi/tasks/vlnce_rxr/vendor/scene_config.py | 58 +++++ .../tasks/vlnce_rxr/vendor/scene_simulator.py | 236 ++++++++++++++++++ .../vlnce_rxr/vlnce_rxr_val_seen_en.yaml | 6 + .../vlnce_rxr/vlnce_rxr_val_seen_hi.yaml | 6 + .../vlnce_rxr/vlnce_rxr_val_seen_te.yaml | 6 + .../vlnce_rxr/vlnce_rxr_val_unseen_en.yaml | 6 + .../vlnce_rxr/vlnce_rxr_val_unseen_hi.yaml | 6 + .../vlnce_rxr/vlnce_rxr_val_unseen_te.yaml | 6 + 16 files changed, 924 insertions(+) create mode 100644 easi/tasks/vlnce_rxr/__init__.py create mode 100644 easi/tasks/vlnce_rxr/_base.yaml create mode 100644 easi/tasks/vlnce_rxr/actions.py create mode 100644 easi/tasks/vlnce_rxr/bridge.py create mode 100644 easi/tasks/vlnce_rxr/prompts.py create mode 100644 easi/tasks/vlnce_rxr/task.py create mode 100644 easi/tasks/vlnce_rxr/vendor/__init__.py create mode 100644 easi/tasks/vlnce_rxr/vendor/dtw.py create mode 100644 easi/tasks/vlnce_rxr/vendor/scene_config.py create mode 100644 easi/tasks/vlnce_rxr/vendor/scene_simulator.py create mode 100644 easi/tasks/vlnce_rxr/vlnce_rxr_val_seen_en.yaml create mode 100644 easi/tasks/vlnce_rxr/vlnce_rxr_val_seen_hi.yaml create mode 100644 easi/tasks/vlnce_rxr/vlnce_rxr_val_seen_te.yaml create mode 100644 easi/tasks/vlnce_rxr/vlnce_rxr_val_unseen_en.yaml create mode 100644 easi/tasks/vlnce_rxr/vlnce_rxr_val_unseen_hi.yaml create mode 100644 easi/tasks/vlnce_rxr/vlnce_rxr_val_unseen_te.yaml diff --git a/easi/tasks/vlnce_rxr/__init__.py b/easi/tasks/vlnce_rxr/__init__.py new file mode 100644 index 0000000..7729087 --- /dev/null +++ b/easi/tasks/vlnce_rxr/__init__.py @@ -0,0 +1,2 @@ +# easi/tasks/vlnce_rxr/__init__.py +"""VLN-CE RxR task for EASI.""" diff --git a/easi/tasks/vlnce_rxr/_base.yaml b/easi/tasks/vlnce_rxr/_base.yaml new file mode 100644 index 0000000..6c225ca --- /dev/null +++ b/easi/tasks/vlnce_rxr/_base.yaml @@ -0,0 +1,42 @@ +# easi/tasks/vlnce_rxr/_base.yaml +display_name: "VLN-CE RxR" +description: "Vision-Language Navigation in Continuous Environments (Room-across-Room)" +simulator: "habitat_sim:v0_1_7" +task_class: "easi.tasks.vlnce_rxr.task.VLNCERxRTask" +max_steps: 500 + +dataset: + source: huggingface + repo_id: "oscarqjh/VLN-CE-RxR_easi" + subset: null + hf_data_dir: "data" + zip_files: + - "mp3d_scenes.zip" + +simulator_configs: + render_platform: auto + screen_height: 480 + screen_width: 640 + hfov: 79 + sensor_height: 0.88 + gpu_device_id: 0 + success_distance: 3.0 + forward_step_size: 0.25 + turn_angle: 30 + tilt_angle: 30 + allow_sliding: false + additional_deps: + - "fastdtw>=0.3.4" + +agent: + prompt_builder: "easi.tasks.vlnce_rxr.prompts.VLNCERxRPromptBuilder" + prompt_builder_kwargs: + use_feedback: true + use_geo_distance: true + action_history_len: 20 + chat_history: false + message_window_len: 5 + generation_kwargs: + temperature: 0 + max_tokens: 4096 + top_p: 0.95 diff --git a/easi/tasks/vlnce_rxr/actions.py b/easi/tasks/vlnce_rxr/actions.py new file mode 100644 index 0000000..a3a2e97 --- /dev/null +++ b/easi/tasks/vlnce_rxr/actions.py @@ -0,0 +1,21 @@ +# easi/tasks/vlnce_rxr/actions.py +"""VLN-CE RxR action space. + +6 discrete actions: 0.25m forward, 30° turns, 30° tilts. +""" + +DISCRETE_ACTIONS = [ + "move_forward", + "turn_left", + "turn_right", + "look_up", + "look_down", + "stop", +] + +ACTION_NAME_TO_ID = {name: i for i, name in enumerate(DISCRETE_ACTIONS)} + + +def get_action_space() -> list[str]: + """Return the VLN-CE RxR discrete action space.""" + return list(DISCRETE_ACTIONS) diff --git a/easi/tasks/vlnce_rxr/bridge.py b/easi/tasks/vlnce_rxr/bridge.py new file mode 100644 index 0000000..296f62f --- /dev/null +++ b/easi/tasks/vlnce_rxr/bridge.py @@ -0,0 +1,128 @@ +# easi/tasks/vlnce_rxr/bridge.py +"""VLN-CE RxR bridge — wraps vendored SceneSimulator via BaseBridge. + +This script runs inside the easi_habitat_sim_v0_1_7 conda env (Python 3.8). +Creates a NEW SceneSimulator per episode (Habitat-Sim ties one instance to one scene). + +Usage: + python bridge.py --workspace /tmp/easi_xxx [--data-dir /path/to/data] [--simulator-kwargs '{}'] +""" +from __future__ import annotations + +import json +import sys +from pathlib import Path + +import numpy as np + +_repo_root = Path(__file__).resolve().parents[3] +if str(_repo_root) not in sys.path: + sys.path.insert(0, str(_repo_root)) + +from easi.simulators.base_bridge import BaseBridge +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + + +class VLNCERxRBridge(BaseBridge): + """BaseBridge wrapper for VLN-CE RxR SceneSimulator.""" + + _scene_sim = None + + def _create_env(self, reset_config, simulator_kwargs): + """Store simulator_kwargs; real sim created in _on_reset.""" + self._sim_kwargs = simulator_kwargs + return object() # placeholder + + def _on_reset(self, env, reset_config): + """Create a new SceneSimulator for this episode's scene.""" + from easi.tasks.vlnce_rxr.vendor.scene_simulator import SceneSimulator + + # Close previous simulator if any + if self._scene_sim is not None: + self._scene_sim.close() + self._scene_sim = None + + data_dir = reset_config.get("data_dir") or self._sim_kwargs.get("data_dir", "") + scene_id = reset_config["scene_id"] + scene_path = str(Path(data_dir) / "mp3d" / scene_id / f"{scene_id}.glb") + + # Parse position/rotation (may be JSON strings from IPC) + start_position = json.loads(reset_config["start_position"]) \ + if isinstance(reset_config["start_position"], str) else reset_config["start_position"] + start_rotation = json.loads(reset_config["start_rotation"]) \ + if isinstance(reset_config["start_rotation"], str) else reset_config["start_rotation"] + + goal_position = reset_config.get("goal_position") + if isinstance(goal_position, str): + goal_position = json.loads(goal_position) if goal_position != "null" else None + + geodesic_distance = reset_config.get("geodesic_distance") + if isinstance(geodesic_distance, str): + geodesic_distance = float(geodesic_distance) if geodesic_distance != "null" else None + + gt_locations = reset_config.get("gt_locations") + if isinstance(gt_locations, str): + gt_locations = json.loads(gt_locations) if gt_locations != "null" else None + + if goal_position is None: + logger.warning( + "Episode %s has no goal_position. " + "Only path_length and steps_taken will be computed.", + reset_config.get("episode_id", "unknown"), + ) + + self._scene_sim = SceneSimulator( + scene_path=scene_path, + start_position=start_position, + start_rotation=start_rotation, + goal_position=goal_position, + gt_locations=gt_locations, + geodesic_distance=geodesic_distance, + success_distance=self._sim_kwargs.get("success_distance", 3.0), + max_steps=self._sim_kwargs.get("max_steps", 500), + gpu_device_id=self._sim_kwargs.get("gpu_device_id", 0), + width=self._sim_kwargs.get("screen_width", 640), + height=self._sim_kwargs.get("screen_height", 480), + hfov=self._sim_kwargs.get("hfov", 79), + sensor_height=self._sim_kwargs.get("sensor_height", 0.88), + forward_step_size=self._sim_kwargs.get("forward_step_size", 0.25), + turn_angle=self._sim_kwargs.get("turn_angle", 30.0), + tilt_angle=self._sim_kwargs.get("tilt_angle", 30.0), + allow_sliding=self._sim_kwargs.get("allow_sliding", False), + ) + + return self._scene_sim.get_observation() + + def _on_step(self, env, action_text): + """Step the SceneSimulator and return (obs, reward, done, info).""" + obs, done, info = self._scene_sim.step(action_text) + return obs, 0.0, done, info + + def _extract_image(self, obs): + """Extract front RGB from observation (RGBA -> RGB).""" + return obs["color_sensor"][:, :, :3] + + def _extract_info(self, info): + """Filter info to serializable types (int/float/str/bool/None).""" + clean = {} + for k, v in info.items(): + if v is None: + clean[k] = "null" + elif isinstance(v, (int, float, str, bool)): + clean[k] = v + elif isinstance(v, (list, np.ndarray)): + clean[k] = json.dumps([float(x) for x in v] if hasattr(v, '__iter__') else str(v)) + return clean + + def close(self): + """Close the SceneSimulator.""" + if self._scene_sim is not None: + self._scene_sim.close() + self._scene_sim = None + super().close() + + +if __name__ == "__main__": + VLNCERxRBridge.main() diff --git a/easi/tasks/vlnce_rxr/prompts.py b/easi/tasks/vlnce_rxr/prompts.py new file mode 100644 index 0000000..9b8ed9b --- /dev/null +++ b/easi/tasks/vlnce_rxr/prompts.py @@ -0,0 +1,163 @@ +# easi/tasks/vlnce_rxr/prompts.py +"""VLN-CE RxR prompt builder. + +Single front camera, 6 actions (move_forward, turn_left, turn_right, +look_up, look_down, stop). 30° turns and tilts. +""" +from __future__ import annotations + +import base64 +import json +from pathlib import Path + +from easi.core.episode import Action +from easi.core.memory import AgentMemory +from easi.utils.json_repair import fix_json +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + +SYSTEM_PROMPT = """You are a robot navigating in a 3D indoor environment. You observe the environment through a front-facing camera and must follow natural language instructions to navigate to a goal location. + +## Available Actions +- move_forward: Move forward by 0.25 meters +- turn_left: Turn left by 30 degrees +- turn_right: Turn right by 30 degrees +- look_up: Tilt camera up by 30 degrees +- look_down: Tilt camera down by 30 degrees +- stop: Stop and end navigation (use ONLY when you believe you have reached the destination described in the instruction) + +## Strategy +1. Carefully read the navigation instruction +2. Observe your surroundings in the image +3. Follow the instruction step by step, matching landmarks and directions mentioned +4. Use move_forward to advance and turn_left/turn_right to change direction +5. Use look_up/look_down to observe objects above or below your current view +6. Use stop ONLY when you are confident you have reached the described destination + +## Response Format +Output a JSON object with exactly these 4 fields: +{ + "visual_state_description": "Describe what you see in the current image", + "reasoning_and_reflection": "Reason about where you are relative to the instruction", + "language_plan": "Describe your next navigation plan in natural language", + "executable_plan": [{"action": ""}] +} + +You may include multiple actions in executable_plan to plan ahead. Actions are executed sequentially.""" + + +def _encode_image_base64(image_path: str) -> str | None: + """Encode image file to base64 data URL.""" + p = Path(image_path) + if not p.exists(): + return None + data = p.read_bytes() + b64 = base64.b64encode(data).decode("utf-8") + return f"data:image/png;base64,{b64}" + + +def _validate_action(name: str, action_space: list[str]) -> str | None: + """Validate and normalize action name.""" + if name in action_space: + return name + lower = name.lower().strip() + for a in action_space: + if a.lower() == lower: + return a + return None + + +class VLNCERxRPromptBuilder: + """Prompt builder for VLN-CE RxR benchmark.""" + + def __init__( + self, + use_feedback: bool = True, + use_geo_distance: bool = True, + action_history_len: int = 20, + chat_history: bool = False, + message_window_len: int = 5, + ): + self.use_feedback = use_feedback + self.use_geo_distance = use_geo_distance + self.action_history_len = action_history_len + self.chat_history = chat_history + self.message_window_len = message_window_len + self._actions = [] + + def set_action_space(self, actions: list[str]) -> None: + self._actions = list(actions) + + def build_messages(self, memory: AgentMemory) -> list[dict]: + if self._actions != memory.action_space: + self.set_action_space(memory.action_space) + + messages = [{"role": "system", "content": SYSTEM_PROMPT}] + + content = [] + + obs = memory.current_observation + if obs and obs.rgb_path: + img_url = _encode_image_base64(obs.rgb_path) + if img_url: + content.append({"type": "image_url", "image_url": {"url": img_url}}) + + text_parts = [] + + text_parts.append(f"## Navigation Instruction\n{memory.task_description}") + + if self.use_feedback and obs and obs.metadata: + feedback = obs.metadata.get("feedback", "") + if feedback: + text_parts.append(f"\n## Environmental Feedback\n{feedback}") + + if self.use_geo_distance and obs and obs.metadata: + geo = obs.metadata.get("geo_distance") + if geo is not None and geo != "null": + text_parts.append(f"Geodesic distance to goal: {float(geo):.1f}m") + + if memory.action_history and self.action_history_len > 0: + history = memory.action_history[-self.action_history_len:] + history_lines = [] + for i, (action_name, feedback) in enumerate(history): + fb = "" + if self.use_feedback and feedback: + fb = f", feedback: {feedback}" + history_lines.append(f"Step {i}: {action_name}{fb}") + if history_lines: + text_parts.append(f"\n## Action History (last {len(history_lines)} steps)\n" + "\n".join(history_lines)) + + content.append({"type": "text", "text": "\n".join(text_parts)}) + messages.append({"role": "user", "content": content}) + + return messages + + def parse_response(self, llm_response: str, memory: AgentMemory) -> list[Action]: + """Parse LLM JSON response into validated Actions.""" + llm_response = fix_json(llm_response) + + try: + data = json.loads(llm_response) + except json.JSONDecodeError as e: + logger.warning("Failed to parse LLM response as JSON: %s", e) + return [] + + plan = data.get("executable_plan", []) + if not isinstance(plan, list) or not plan: + logger.warning("No executable_plan in LLM response") + return [] + + actions = [] + for entry in plan: + if not isinstance(entry, dict): + continue + action_name = entry.get("action", entry.get("action_name", "")) + validated = _validate_action(action_name, memory.action_space) + if validated: + actions.append(Action(action_name=validated)) + else: + logger.warning("Skipping invalid action: '%s'", action_name) + break + + return actions diff --git a/easi/tasks/vlnce_rxr/task.py b/easi/tasks/vlnce_rxr/task.py new file mode 100644 index 0000000..f9f762e --- /dev/null +++ b/easi/tasks/vlnce_rxr/task.py @@ -0,0 +1,191 @@ +# easi/tasks/vlnce_rxr/task.py +"""VLN-CE RxR task for EASI. + +Adapts VLN-CE Room-across-Room benchmark to EASI's task interface. +Episodes loaded from HuggingFace dataset (oscarqjh/VLN-CE-RxR_easi). + +Metrics (aligned with original VLN-CE): +- Per-episode: success, oracle_success, spl, navigation_error, ndtw, sdtw, + path_length, steps_taken +- Aggregate: Average of non-null values per metric +""" +from __future__ import annotations + +import json +import tempfile +from pathlib import Path + +from easi.core.base_task import BaseTask +from easi.core.episode import EpisodeRecord, StepResult +from easi.tasks.vlnce_rxr.actions import get_action_space +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + + +class VLNCERxRTask(BaseTask): + + def _build_action_space(self) -> list[str]: + return get_action_space() + + def _load_episodes_from_hf(self, dataset_config: dict) -> list[dict]: + """Load episodes from HF dataset with language-split JSONL files. + + RxR dataset uses per-language files (e.g., val_seen_en.jsonl). + The split name in YAML already encodes split+language (e.g., "val_seen_en"). + """ + from datasets import load_dataset + from easi.core.base_task import hf_row_to_episode + + data_dir = self.download_dataset() + split_name = dataset_config.get("split") + data_file = str(data_dir / "data" / f"{split_name}.jsonl") + + logger.info("Loading episodes from %s (split=%s)", data_file, split_name) + + hf_cache = Path(tempfile.gettempdir()) / "easi_hf_cache" + ds = load_dataset( + "json", data_files=data_file, split="train", + cache_dir=str(hf_cache), + ) + episodes = [hf_row_to_episode(row) for row in ds] + + for ep in episodes: + ep["_data_dir"] = str(data_dir) + + logger.info("Loaded %d episodes (split=%s)", len(episodes), split_name) + return episodes + + def get_task_yaml_path(self) -> Path: + return Path(__file__).parent / "_base.yaml" + + def get_bridge_script_path(self) -> Path: + return Path(__file__).parent / "bridge.py" + + def get_instruction(self, episode: dict) -> str: + return episode.get("instruction", self.name) + + def format_reset_config(self, episode: dict) -> dict: + """Map HF episode dict to bridge reset config.""" + gt_locations = episode.get("gt_locations") + if isinstance(gt_locations, list): + gt_locations = json.dumps(gt_locations) + + return { + "episode_id": str(episode.get("episode_id", episode.get("id", "unknown"))), + "scene_id": episode["scene_id"], + "instruction": episode.get("instruction", ""), + "start_position": json.dumps(episode["start_position"]), + "start_rotation": json.dumps(episode["start_rotation"]), + "goal_position": json.dumps(episode["goal_position"]) if episode.get("goal_position") else "null", + "geodesic_distance": str(episode["geodesic_distance"]) if episode.get("geodesic_distance") is not None else "null", + "gt_locations": gt_locations if gt_locations else "null", + "data_dir": episode.get("_data_dir", ""), + } + + def evaluate_episode( + self, episode: dict, trajectory: list[StepResult] + ) -> dict[str, float]: + """Extract per-episode metrics from bridge info dict.""" + if not trajectory: + return self._empty_metrics() + + last = trajectory[-1] + info = last.info + + return { + "success": self._parse_nullable(info.get("success")), + "oracle_success": self._parse_nullable(info.get("oracle_success")), + "spl": self._parse_nullable(info.get("spl")), + "navigation_error": self._parse_nullable(info.get("navigation_error")), + "ndtw": self._parse_nullable(info.get("ndtw")), + "sdtw": self._parse_nullable(info.get("sdtw")), + "path_length": float(info.get("path_length", 0.0)), + "steps_taken": float(len(trajectory)), + } + + def aggregate_results(self, records: list[EpisodeRecord]) -> dict: + """Average non-null values per metric across all episodes.""" + if not records: + return {} + + metric_keys = [ + "success", "oracle_success", "spl", "navigation_error", + "ndtw", "sdtw", "path_length", "steps_taken", + ] + + result = {"num_episodes": len(records)} + null_counts = {} + + for key in metric_keys: + values = [] + nulls = 0 + for r in records: + val = r.episode_results.get(key) + if val is None or val == "null": + nulls += 1 + else: + values.append(float(val)) + if values: + result[key] = round(sum(values) / len(values), 4) + else: + result[key] = None + if nulls > 0: + null_counts[key] = nulls + + if null_counts: + logger.info( + "Null metric counts: %s", + ", ".join(f"{k}={v}" for k, v in null_counts.items()), + ) + result["_null_counts"] = null_counts + + # Aliases for consistency with VLN-CE conventions + result["SR"] = result.get("success") + result["SPL"] = result.get("spl") + result["NE"] = result.get("navigation_error") + result["Oracle_SR"] = result.get("oracle_success") + result["NDTW"] = result.get("ndtw") + result["SDTW"] = result.get("sdtw") + + return result + + @staticmethod + def _parse_nullable(value): + """Parse a value that may be None or 'null' string from IPC.""" + if value is None or value == "null": + return None + return float(value) + + def _empty_metrics(self) -> dict[str, float]: + return { + "success": None, + "oracle_success": None, + "spl": None, + "navigation_error": None, + "ndtw": None, + "sdtw": None, + "path_length": 0.0, + "steps_taken": 0.0, + } + + def _get_builtin_episodes(self) -> list[dict]: + """Minimal episode for testing without dataset download.""" + return [ + { + "id": 0, + "episode_id": "test_0", + "scene_id": "2n8kARJN3HM", + "instruction": "Turn to your left and walk forward towards the bathtub.", + "language": "en-IN", + "role": "guide", + "start_position": [6.686, -3.369, 9.991], + "start_rotation": [0, 0.999, 0, -0.018], + "goal_position": [10.142, -2.874, 16.334], + "goal_radius": 3.0, + "geodesic_distance": 8.02, + "reference_path": "[]", + "gt_locations": None, + "gt_actions": None, + }, + ] diff --git a/easi/tasks/vlnce_rxr/vendor/__init__.py b/easi/tasks/vlnce_rxr/vendor/__init__.py new file mode 100644 index 0000000..8b7bdb2 --- /dev/null +++ b/easi/tasks/vlnce_rxr/vendor/__init__.py @@ -0,0 +1,2 @@ +# easi/tasks/vlnce_rxr/vendor/__init__.py +"""Vendored VLN-CE RxR code.""" diff --git a/easi/tasks/vlnce_rxr/vendor/dtw.py b/easi/tasks/vlnce_rxr/vendor/dtw.py new file mode 100644 index 0000000..e739d1e --- /dev/null +++ b/easi/tasks/vlnce_rxr/vendor/dtw.py @@ -0,0 +1,45 @@ +# easi/tasks/vlnce_rxr/vendor/dtw.py +"""NDTW and SDTW metric computation. + +Vendored from VLN-CE habitat_extensions/measures.py. +Uses fastdtw for dynamic time warping distance. +""" +from __future__ import annotations + +import numpy as np + + +def _euclidean_distance(a, b): + return np.linalg.norm(np.array(b) - np.array(a)) + + +def compute_ndtw(agent_positions, gt_locations, success_distance=3.0): + """Compute Normalized Dynamic Time Warping distance. + + Args: + agent_positions: List of [x, y, z] agent positions at each step. + gt_locations: List of [x, y, z] ground truth reference path positions. + success_distance: Success threshold (default 3.0m for VLN-CE). + + Returns: + NDTW score in [0, 1]. Higher is better. + """ + from fastdtw import fastdtw + + dtw_distance = fastdtw( + agent_positions, gt_locations, dist=_euclidean_distance + )[0] + return np.exp(-dtw_distance / (len(gt_locations) * success_distance)) + + +def compute_sdtw(ndtw, success): + """Compute Success weighted by NDTW. + + Args: + ndtw: NDTW score. + success: 1.0 if episode succeeded, 0.0 otherwise. + + Returns: + SDTW score (0.0 if not successful). + """ + return ndtw * success diff --git a/easi/tasks/vlnce_rxr/vendor/scene_config.py b/easi/tasks/vlnce_rxr/vendor/scene_config.py new file mode 100644 index 0000000..f0fe7d4 --- /dev/null +++ b/easi/tasks/vlnce_rxr/vendor/scene_config.py @@ -0,0 +1,58 @@ +# easi/tasks/vlnce_rxr/vendor/scene_config.py +"""Habitat-Sim 0.1.7 configuration for VLN-CE RxR. + +Single front RGB camera, 30-degree turns/tilts, 0.25m forward step. +6 actions: move_forward, turn_left, turn_right, look_up, look_down. +""" +from __future__ import annotations + +import habitat_sim + + +def make_cfg( + scene_path: str, + gpu_device_id: int = -1, + width: int = 640, + height: int = 480, + hfov: int = 79, + sensor_height: float = 0.88, + forward_step_size: float = 0.25, + turn_angle: float = 30.0, + tilt_angle: float = 30.0, + allow_sliding: bool = False, +) -> habitat_sim.Configuration: + """Build habitat-sim 0.1.7 Configuration for VLN-CE RxR.""" + sim_cfg = habitat_sim.SimulatorConfiguration() + sim_cfg.gpu_device_id = gpu_device_id + sim_cfg.scene_id = scene_path + sim_cfg.allow_sliding = allow_sliding + + # Single front-facing RGB sensor + color_sensor = habitat_sim.SensorSpec() + color_sensor.uuid = "color_sensor" + color_sensor.sensor_type = habitat_sim.SensorType.COLOR + color_sensor.resolution = [height, width] + color_sensor.position = [0.0, sensor_height, 0.0] + color_sensor.parameters["hfov"] = str(hfov) + + agent_cfg = habitat_sim.AgentConfiguration() + agent_cfg.sensor_specifications = [color_sensor] + agent_cfg.action_space = { + "move_forward": habitat_sim.ActionSpec( + "move_forward", habitat_sim.ActuationSpec(amount=forward_step_size) + ), + "turn_left": habitat_sim.ActionSpec( + "turn_left", habitat_sim.ActuationSpec(amount=turn_angle) + ), + "turn_right": habitat_sim.ActionSpec( + "turn_right", habitat_sim.ActuationSpec(amount=turn_angle) + ), + "look_up": habitat_sim.ActionSpec( + "look_up", habitat_sim.ActuationSpec(amount=tilt_angle) + ), + "look_down": habitat_sim.ActionSpec( + "look_down", habitat_sim.ActuationSpec(amount=tilt_angle) + ), + } + + return habitat_sim.Configuration(sim_cfg, [agent_cfg]) diff --git a/easi/tasks/vlnce_rxr/vendor/scene_simulator.py b/easi/tasks/vlnce_rxr/vendor/scene_simulator.py new file mode 100644 index 0000000..6e0c194 --- /dev/null +++ b/easi/tasks/vlnce_rxr/vendor/scene_simulator.py @@ -0,0 +1,236 @@ +# easi/tasks/vlnce_rxr/vendor/scene_simulator.py +"""Habitat-Sim 0.1.7 scene simulator for VLN-CE RxR. + +Wraps habitat_sim.Simulator for single-goal navigation with path tracking. +Creates one simulator per episode (Habitat-Sim binds one instance to one scene). + +RxR differences from R2R: +- 6 actions: move_forward, turn_left, turn_right, look_up, look_down, stop +- 30° turns and tilts (vs 15° turns in R2R) +- 0.88m sensor height (vs 1.25m) +- 640×480 resolution (vs 480×480) +- 79° HFOV (vs 90°) +- No sliding (vs sliding allowed) +""" +from __future__ import annotations + +import numpy as np + +import habitat_sim + +from easi.tasks.vlnce_rxr.vendor.scene_config import make_cfg +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + + +class SceneSimulator: + """Single-goal navigation simulator for VLN-CE RxR.""" + + def __init__( + self, + *, + scene_path: str, + start_position: list[float], + start_rotation: list[float], + goal_position: list[float] | None = None, + gt_locations: list[list[float]] | None = None, + geodesic_distance: float | None = None, + success_distance: float = 3.0, + max_steps: int = 500, + gpu_device_id: int = -1, + width: int = 640, + height: int = 480, + hfov: int = 79, + sensor_height: float = 0.88, + forward_step_size: float = 0.25, + turn_angle: float = 30.0, + tilt_angle: float = 30.0, + allow_sliding: bool = False, + ): + self._goal_position = np.array(goal_position) if goal_position else None + self._gt_locations = gt_locations + self._geodesic_distance = geodesic_distance + self._success_distance = success_distance + self._max_steps = max_steps + + # Create habitat-sim instance + cfg = make_cfg( + scene_path=scene_path, + gpu_device_id=gpu_device_id, + width=width, + height=height, + hfov=hfov, + sensor_height=sensor_height, + forward_step_size=forward_step_size, + turn_angle=turn_angle, + tilt_angle=tilt_angle, + allow_sliding=allow_sliding, + ) + self._sim = habitat_sim.Simulator(cfg) + + # Set agent to start position/rotation + agent = self._sim.get_agent(0) + state = agent.get_state() + state.position = np.array(start_position, dtype=np.float32) + state.rotation = self._list_to_quaternion(start_rotation) + agent.set_state(state) + + # Tracking state + self._step_count = 0 + self._done = False + self._stopped = False + self._agent_positions = [list(state.position)] + self._path_length = 0.0 + self._min_geodesic_to_goal = float("inf") + + # Compute initial geodesic distance + if self._goal_position is not None: + geo = self._compute_geodesic(state.position) + if geo is not None: + self._min_geodesic_to_goal = geo + + def _list_to_quaternion(self, rot): + """Convert [x, y, z, w] list to habitat quaternion.""" + import quaternion + if len(rot) == 4: + return np.quaternion(rot[3], rot[0], rot[1], rot[2]) + return np.quaternion(1, 0, 0, 0) + + def get_observation(self): + """Get current observation from the simulator.""" + return self._sim.get_sensor_observations() + + def step(self, action: str) -> tuple[dict, bool, dict]: + """Execute an action and return (obs, done, info). + + Args: + action: One of "move_forward", "turn_left", "turn_right", + "look_up", "look_down", "stop". + + Returns: + obs: Sensor observations dict with "color_sensor" key. + done: Whether episode is over. + info: Step info dict with metrics and feedback. + """ + if self._done: + return self.get_observation(), True, self._build_info() + + if action == "stop": + self._stopped = True + self._done = True + obs = self.get_observation() + return obs, True, self._build_info() + + # Execute movement action (includes look_up/look_down) + valid_actions = ("move_forward", "turn_left", "turn_right", "look_up", "look_down") + if action in valid_actions: + obs = self._sim.step(action) + else: + logger.warning("Unknown action '%s', treating as no-op", action) + obs = self.get_observation() + + self._step_count += 1 + + # Update tracking + pos = self._sim.get_agent(0).get_state().position + prev_pos = np.array(self._agent_positions[-1]) + self._path_length += float(np.linalg.norm(pos - prev_pos)) + self._agent_positions.append(list(pos)) + + # Update min geodesic (for oracle success) + if self._goal_position is not None: + geo = self._compute_geodesic(pos) + if geo is not None and geo < self._min_geodesic_to_goal: + self._min_geodesic_to_goal = geo + + # Check max steps + if self._step_count >= self._max_steps: + self._done = True + + return obs, self._done, self._build_info() + + def _compute_geodesic(self, position) -> float | None: + """Compute geodesic distance from position to goal.""" + if self._goal_position is None: + return None + try: + return self._sim.geodesic_distance(position, self._goal_position) + except Exception: + return float(np.linalg.norm(position - self._goal_position)) + + def _build_info(self) -> dict: + """Build step info dict with metrics and feedback.""" + pos = self._sim.get_agent(0).get_state().position + + info = { + "step": self._step_count, + "path_length": self._path_length, + "agent_position": list(pos), + } + + if self._goal_position is not None: + geo = self._compute_geodesic(pos) + ne = geo if geo is not None else float(np.linalg.norm(pos - self._goal_position)) + info["navigation_error"] = ne + info["geo_distance"] = ne + + success = 1.0 if ne <= self._success_distance and self._stopped else 0.0 + oracle_success = 1.0 if self._min_geodesic_to_goal <= self._success_distance else 0.0 + + info["success"] = success + info["oracle_success"] = oracle_success + + if self._geodesic_distance is not None and self._geodesic_distance > 0: + info["spl"] = success * ( + self._geodesic_distance / max(self._path_length, self._geodesic_distance) + ) + else: + info["spl"] = 0.0 + + info["feedback"] = f"Distance to goal: {ne:.1f}m" + else: + info["navigation_error"] = None + info["success"] = None + info["oracle_success"] = None + info["spl"] = None + info["feedback"] = "Navigating (no distance feedback available)" + + if self._done: + info.update(self._compute_dtw_metrics()) + + return info + + def _compute_dtw_metrics(self) -> dict: + """Compute NDTW and SDTW at episode end.""" + if self._gt_locations is None or len(self._gt_locations) == 0: + return {"ndtw": None, "sdtw": None} + + try: + from easi.tasks.vlnce_rxr.vendor.dtw import compute_ndtw, compute_sdtw + + ndtw = compute_ndtw( + self._agent_positions, self._gt_locations, self._success_distance + ) + # Compute success inline to avoid circular call to _build_info + success = 0.0 + if self._goal_position is not None and self._stopped: + pos = self._sim.get_agent(0).get_state().position + geo = self._compute_geodesic(pos) + ne = geo if geo is not None else float(np.linalg.norm(pos - self._goal_position)) + if ne <= self._success_distance: + success = 1.0 + sdtw = compute_sdtw(ndtw, success) + return {"ndtw": float(ndtw), "sdtw": float(sdtw)} + except ImportError: + logger.warning("fastdtw not installed — NDTW/SDTW unavailable") + return {"ndtw": None, "sdtw": None} + except Exception as e: + logger.warning("DTW computation failed: %s", e) + return {"ndtw": None, "sdtw": None} + + def close(self): + """Close the habitat-sim instance.""" + if self._sim is not None: + self._sim.close() + self._sim = None diff --git a/easi/tasks/vlnce_rxr/vlnce_rxr_val_seen_en.yaml b/easi/tasks/vlnce_rxr/vlnce_rxr_val_seen_en.yaml new file mode 100644 index 0000000..5ad1800 --- /dev/null +++ b/easi/tasks/vlnce_rxr/vlnce_rxr_val_seen_en.yaml @@ -0,0 +1,6 @@ +extends: _base.yaml +name: vlnce_rxr_val_seen_en +display_name: "VLN-CE RxR Val Seen (English)" +description: "VLN-CE RxR validation split (seen environments, English)" +dataset: + split: "val_seen_en" diff --git a/easi/tasks/vlnce_rxr/vlnce_rxr_val_seen_hi.yaml b/easi/tasks/vlnce_rxr/vlnce_rxr_val_seen_hi.yaml new file mode 100644 index 0000000..88ec5f0 --- /dev/null +++ b/easi/tasks/vlnce_rxr/vlnce_rxr_val_seen_hi.yaml @@ -0,0 +1,6 @@ +extends: _base.yaml +name: vlnce_rxr_val_seen_hi +display_name: "VLN-CE RxR Val Seen (Hindi)" +description: "VLN-CE RxR validation split (seen environments, Hindi)" +dataset: + split: "val_seen_hi" diff --git a/easi/tasks/vlnce_rxr/vlnce_rxr_val_seen_te.yaml b/easi/tasks/vlnce_rxr/vlnce_rxr_val_seen_te.yaml new file mode 100644 index 0000000..ce23d22 --- /dev/null +++ b/easi/tasks/vlnce_rxr/vlnce_rxr_val_seen_te.yaml @@ -0,0 +1,6 @@ +extends: _base.yaml +name: vlnce_rxr_val_seen_te +display_name: "VLN-CE RxR Val Seen (Telugu)" +description: "VLN-CE RxR validation split (seen environments, Telugu)" +dataset: + split: "val_seen_te" diff --git a/easi/tasks/vlnce_rxr/vlnce_rxr_val_unseen_en.yaml b/easi/tasks/vlnce_rxr/vlnce_rxr_val_unseen_en.yaml new file mode 100644 index 0000000..e470cf3 --- /dev/null +++ b/easi/tasks/vlnce_rxr/vlnce_rxr_val_unseen_en.yaml @@ -0,0 +1,6 @@ +extends: _base.yaml +name: vlnce_rxr_val_unseen_en +display_name: "VLN-CE RxR Val Unseen (English)" +description: "VLN-CE RxR validation split (unseen environments, English)" +dataset: + split: "val_unseen_en" diff --git a/easi/tasks/vlnce_rxr/vlnce_rxr_val_unseen_hi.yaml b/easi/tasks/vlnce_rxr/vlnce_rxr_val_unseen_hi.yaml new file mode 100644 index 0000000..a60830e --- /dev/null +++ b/easi/tasks/vlnce_rxr/vlnce_rxr_val_unseen_hi.yaml @@ -0,0 +1,6 @@ +extends: _base.yaml +name: vlnce_rxr_val_unseen_hi +display_name: "VLN-CE RxR Val Unseen (Hindi)" +description: "VLN-CE RxR validation split (unseen environments, Hindi)" +dataset: + split: "val_unseen_hi" diff --git a/easi/tasks/vlnce_rxr/vlnce_rxr_val_unseen_te.yaml b/easi/tasks/vlnce_rxr/vlnce_rxr_val_unseen_te.yaml new file mode 100644 index 0000000..d13adff --- /dev/null +++ b/easi/tasks/vlnce_rxr/vlnce_rxr_val_unseen_te.yaml @@ -0,0 +1,6 @@ +extends: _base.yaml +name: vlnce_rxr_val_unseen_te +display_name: "VLN-CE RxR Val Unseen (Telugu)" +description: "VLN-CE RxR validation split (unseen environments, Telugu)" +dataset: + split: "val_unseen_te" From 3310eecce268405da45954013e36d117701913b0 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Sun, 8 Mar 2026 21:13:06 +0800 Subject: [PATCH 163/239] docs: add EASI standard prompt format reference Defines the standardized prompt format for benchmarks that don't provide their own (VLN-CE, ManipulaTHOR, Rearrangement, future tasks). Covers system prompt structure, 4-field JSON response format, action and chat history handling, image conventions, and YAML configuration. --- docs/easi-prompt-format-reference.md | 528 +++++++++++++++++++++++++++ 1 file changed, 528 insertions(+) create mode 100644 docs/easi-prompt-format-reference.md diff --git a/docs/easi-prompt-format-reference.md b/docs/easi-prompt-format-reference.md new file mode 100644 index 0000000..2af819b --- /dev/null +++ b/docs/easi-prompt-format-reference.md @@ -0,0 +1,528 @@ +# EASI Standard Prompt Format Reference + +This document defines the standard prompt format for EASI benchmarks that do not provide their own prompt format. EmbodiedBench benchmarks (EB-Alfred, EB-Navigation, EB-Habitat, EB-Manipulation) retain their original published formats for reproducibility. + +## Scope + +**Applies to:** New benchmarks and benchmarks without a published prompt format (VLN-CE R2R, VLN-CE RxR, ManipulaTHOR, AI2-THOR Rearrangement, HAZARD text-plan variants, all future tasks). + +**Does not apply to:** EmbodiedBench benchmarks (retain original format), HAZARD multiple-choice format (fundamentally different paradigm). + +--- + +## System Prompt Structure + +The system prompt uses markdown sections in this fixed order. Required sections must always be present. Optional sections are included only when the benchmark needs them. + +``` +## Role and Environment [REQUIRED] +## Observation Description [OPTIONAL] +## Available Actions [REQUIRED] +## Strategy [OPTIONAL] +## Guidelines [REQUIRED] +## Response Format [REQUIRED] +``` + +### Role and Environment (Required) + +One paragraph establishing who the agent is and what environment it operates in. Keep it concise — 2-3 sentences. + +``` +You are a robot navigating in a 3D indoor environment. You observe the +environment through a front-facing camera and must follow natural language +instructions to navigate to a goal location. +``` + +### Observation Description (Optional) + +Describes what each piece of environment feedback means. Include this section only when the benchmark provides dynamic feedback (geodesic distances, object states, GPS coordinates, etc.) that the LLM needs context to interpret. + +``` +## Observation Description +- **Distance to goal**: Geodesic (shortest walkable path) distance in meters + to the goal location. Decreases as you get closer. +- **Held object**: Name of the object currently being held, or "none". +``` + +Do NOT describe the image observation here — the LLM can see the image directly. Only describe non-visual feedback that appears as text. + +### Available Actions (Required) + +List all actions the agent can take, with a brief description of what each does. Include any validity constraints. + +``` +## Available Actions +- move_forward: Move forward by 0.25 meters +- turn_left: Turn left by 30 degrees +- turn_right: Turn right by 30 degrees +- look_up: Tilt camera up by 30 degrees +- look_down: Tilt camera down by 30 degrees +- stop: Stop and end navigation (use ONLY when you believe you have reached + the destination) +``` + +For benchmarks with parameterized actions, include the parameter format: + +``` +- find : Navigate to the named receptacle +- pick_up : Pick up the named object (must be nearby, hands empty) +``` + +### Strategy (Optional) + +Benchmark-specific tactical advice. Include this when the task has non-obvious strategies that improve performance. Keep it actionable. + +``` +## Strategy +1. Follow the instruction step by step, matching landmarks mentioned +2. Use move_forward to advance and turn_left/turn_right to change direction +3. Use stop ONLY when confident you have reached the described destination +``` + +### Guidelines (Required) + +Universal rules that apply regardless of benchmark. Always include these core guidelines, adding benchmark-specific ones as needed: + +``` +## Guidelines +1. Always output at least one action in executable_plan. +2. Only use actions from the Available Actions list. +3. If previous actions failed, reason about why and try a different approach. +4. Do not repeatedly execute the same action sequence. +5. Keep your plan efficient and concise. +``` + +### Response Format (Required) + +Always use the standard 4-field JSON format: + +``` +## Response Format +Output a JSON object with exactly these 4 fields: +{ + "visual_state_description": "Describe what you see in the current image", + "reasoning_and_reflection": "Reason about your situation, reflect on + history and feedback", + "language_plan": "Describe your next plan in natural language", + "executable_plan": [{"action": ""}] +} + +You may include multiple actions in executable_plan. Actions execute +sequentially. +``` + +--- + +## Response Format Specification + +### JSON Schema + +All EASI prompt builders (within scope) must use this response format: + +```json +{ + "visual_state_description": "string", + "reasoning_and_reflection": "string", + "language_plan": "string", + "executable_plan": [ + {"action": "action_name"}, + {"action": "action_name"} + ] +} +``` + +### Field Definitions + +| Field | Type | Purpose | +|-------|------|---------| +| `visual_state_description` | string | Describe what the agent observes in the current image | +| `reasoning_and_reflection` | string | Reason about current state, reflect on history and feedback, explain why previous actions may have failed | +| `language_plan` | string | Natural language description of the planned actions | +| `executable_plan` | array | Ordered list of actions to execute | + +### Action Entry Format + +Each action in `executable_plan` is an object with an `action` field: + +```json +{"action": "move_forward"} +``` + +Do NOT use `action_id` — numeric IDs are an internal concept. The LLM should always reference actions by name. + +For parameterized actions, include a `params` field: + +```json +{"action": "find", "params": {"target": "Cabinet_2"}} +``` + +### Parsing Rules + +1. Apply `fix_json()` before parsing (handles common LLM JSON errors) +2. Accept both `{"action": "name"}` and `{"action_name": "name"}` +3. Validate each action name against `memory.action_space` +4. On first invalid action, stop parsing (don't skip — the plan is ordered) +5. On complete parse failure, return empty action list (agent will re-prompt) + +--- + +## Action History + +Action history provides the LLM with context about what happened in previous steps. It is a text section embedded in the user message. + +### Format + +``` +## Action History (last N steps) +Step 0: move_forward -> Distance to goal: 8.2m +Step 1: turn_left -> Distance to goal: 8.2m +Step 2: move_forward -> Distance to goal: 7.9m +``` + +Each entry: `Step {i}: {action_name} -> {feedback}` + +If feedback is disabled (`use_feedback: false`), omit the feedback portion: + +``` +Step 0: move_forward +Step 1: turn_left +Step 2: move_forward +``` + +### Configuration + +| YAML Parameter | Type | Default | Description | +|----------------|------|---------|-------------| +| `action_history_len` | int | 20 | Maximum entries to include. 0 = disabled. | +| `use_feedback` | bool | true | Include environment feedback in each entry. | + +### Data Source + +Action history comes from `memory.action_history`, which returns `list[tuple[str, str]]` — pairs of `(action_name, feedback_string)`. + +### Truncation + +When history exceeds `action_history_len`, keep only the most recent entries: + +```python +history = memory.action_history[-self.action_history_len:] +``` + +--- + +## Chat History + +Chat history provides the LLM with its own previous responses, enabling it to maintain reasoning continuity across steps. It is a text section embedded in the user message, parallel to action history. + +### Format + +``` +## Chat History (last N responses) +[Step 0 Response] +{"visual_state_description": "I see a hallway...", "reasoning_and_reflection": "I need to...", "language_plan": "Move forward...", "executable_plan": [{"action": "move_forward"}]} + +[Step 1 Response] +{"visual_state_description": "I see a door...", "reasoning_and_reflection": "The door matches...", "language_plan": "Turn right...", "executable_plan": [{"action": "turn_right"}]} +``` + +Each entry is the full LLM JSON response from that step, preceded by a `[Step N Response]` header. + +### Configuration + +| YAML Parameter | Type | Default | Description | +|----------------|------|---------|-------------| +| `chat_history` | bool | false | Enable chat history section. | +| `message_window_len` | int | 5 | Maximum responses to include. | + +### Data Source + +Chat history comes from `memory.steps`, which contains `StepRecord` objects with `llm_response` fields: + +```python +if self.chat_history: + responses = [ + s.llm_response for s in memory.steps + if s.llm_response is not None + ][-self.message_window_len:] +``` + +### Interaction with Action History + +When both are enabled, action history and chat history appear as separate sections in the user message. Action history provides a compact summary; chat history provides the full reasoning. They are complementary, not redundant. + +Recommended default: `action_history_len: 20, chat_history: false`. Enable chat history only for benchmarks where maintaining reasoning continuity significantly improves performance. + +--- + +## Image Handling + +### Encoding + +All images are encoded as base64 data URLs: + +```python +data:image/png;base64,{base64_encoded_data} +``` + +### Position in Message + +Images appear BEFORE text in the user message content array: + +```python +content = [] +# Images first +content.append({"type": "image_url", "image_url": {"url": img_url}}) +# Text after +content.append({"type": "text", "text": prompt_text}) +``` + +### Multiple Images + +When a benchmark provides multiple images (e.g., RGB + depth, current + goal), label them: + +```python +# In the text portion: +"(Image 1: Current view, Image 2: Goal state)" +``` + +### Image Sources + +| Source | Field | When to Use | +|--------|-------|-------------| +| Primary RGB | `observation.rgb_path` | Always (every benchmark has this) | +| Depth | `observation.metadata["depth_path"]` | When depth sensing is relevant | +| Goal/reference | `observation.metadata["goal_rgb_path"]` | For rearrangement/comparison tasks | +| Multi-view | `observation.metadata["{view}_rgb_path"]` | For panoramic or multi-camera setups | + +--- + +## Environment Feedback + +Environment feedback is benchmark-specific dynamic information from the simulator, delivered via `observation.metadata`. The standard defines where feedback appears and how to toggle it, not what it contains. + +### Where Feedback Appears + +1. **In action history entries**: `"Step N: action -> {feedback_text}"` +2. **As a dedicated section** (optional): For rich contextual feedback that applies to the current state, not just the last action. + +``` +## Environment Feedback +Distance to goal: 5.3m +``` + +### Toggle + +Controlled by `use_feedback: true/false` in YAML config. When false, omit feedback from action history entries and omit the Environment Feedback section. + +### Common Feedback Patterns + +| Pattern | Example | Used By | +|---------|---------|---------| +| Distance to goal | `"Distance to goal: 5.3m"` | VLN-CE R2R/RxR | +| Action success/failure | `"success"` / `"fail: object not reachable"` | EB-Alfred, HAZARD | +| Object states | `"Holding: Apple_1"` | Rearrangement, ManipulaTHOR | +| Spatial info | `"Position: (3.2, 0.1, -1.5), Rotation: 90°"` | Rearrangement, ManipulaTHOR | + +--- + +## YAML Configuration Standard + +Every prompt builder should accept these common kwargs. Benchmark-specific kwargs can be added below them. + +```yaml +agent: + prompt_builder: "easi.tasks..prompts." + prompt_builder_kwargs: + # Standard kwargs (all prompt builders should support these) + use_feedback: true # Include environment feedback + action_history_len: 20 # Max action history entries (0 = disabled) + chat_history: false # Include previous model responses + message_window_len: 5 # Max chat history entries (when chat_history: true) + + # Benchmark-specific kwargs (examples) + # use_geo_distance: true # VLN-CE: show geodesic distance + # n_shot: 3 # Few-shot examples count + # use_depth: false # Depth image toggle + generation_kwargs: + temperature: 0 + max_tokens: 4096 + top_p: 0.95 +``` + +--- + +## User Message Assembly Order + +The user message is assembled in this fixed order: + +``` +[Image(s)] <- base64 encoded, before text +[Text content, assembled as:] + ## Task <- instruction / task description + ## Observation Description <- only if defined (from system prompt context) + ## Environment Feedback <- current-step feedback (if use_feedback) + ## Action History (last N steps) <- if action_history_len > 0 and has history + ## Chat History (last N responses) <- if chat_history and has history + [Response format reminder] <- brief reminder of JSON format +``` + +On the first turn, action history and chat history are empty and omitted. + +--- + +## Message Structure + +Always exactly 2 messages: + +```python +messages = [ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": [image_parts..., text_part]}, +] +``` + +The system message contains the static prompt (role, actions, strategy, guidelines, response format). The user message contains the dynamic per-step content (images, task instruction, feedback, history). + +This is simpler than multi-turn conversation and works consistently across all LLM backends. + +--- + +## Complete Example: Navigation Benchmark + +### System Prompt + +``` +## Role and Environment +You are a robot navigating in a 3D indoor environment. You observe the +environment through a front-facing camera and must follow natural language +instructions to navigate to a goal location. + +## Observation Description +- **Distance to goal**: Geodesic distance in meters to the goal. Decreases + as you approach the destination. + +## Available Actions +- move_forward: Move forward by 0.25 meters +- turn_left: Turn left by 15 degrees +- turn_right: Turn right by 15 degrees +- stop: Stop and end navigation (use ONLY when you believe you have reached + the destination described in the instruction) + +## Strategy +1. Carefully read the navigation instruction +2. Observe your surroundings in the image +3. Follow the instruction step by step, matching landmarks and directions +4. Use stop ONLY when confident you have reached the described destination + +## Guidelines +1. Always output at least one action in executable_plan. +2. Only use actions from the Available Actions list. +3. If previous actions failed, reason about why and try a different approach. +4. Do not repeatedly execute the same action sequence. +5. Keep your plan efficient and concise. + +## Response Format +Output a JSON object with exactly these 4 fields: +{ + "visual_state_description": "Describe what you see in the current image", + "reasoning_and_reflection": "Reason about your situation and history", + "language_plan": "Describe your next plan in natural language", + "executable_plan": [{"action": ""}] +} + +You may include multiple actions in executable_plan. Actions execute +sequentially. +``` + +### User Message (Step 5) + +``` +[Image: base64 encoded current view] + +## Task +Walk down the hallway and turn right into the bedroom. + +## Environment Feedback +Distance to goal: 5.3m + +## Action History (last 5 steps) +Step 0: move_forward -> Distance to goal: 8.2m +Step 1: move_forward -> Distance to goal: 7.9m +Step 2: move_forward -> Distance to goal: 7.6m +Step 3: turn_right -> Distance to goal: 7.6m +Step 4: move_forward -> Distance to goal: 7.3m + +Respond with the JSON format specified above. +``` + +--- + +## Complete Example: Object Manipulation Benchmark + +### System Prompt + +``` +## Role and Environment +You are a robotic arm in an indoor environment. You can pick up, place, and +manipulate objects on a table using discrete actions. + +## Observation Description +- **Held object**: The object currently in the gripper, or "none". +- **Nearby objects**: Objects within interaction range and their positions. + +## Available Actions +- move_to : Move the arm to the named object +- pick_up : Grasp the named object (must be nearby, gripper empty) +- place_on : Place held object on the named receptacle +- open : Open a closed receptacle +- close : Close an open receptacle +- done: Signal task completion + +## Strategy +1. Locate the target object before attempting to pick it up +2. Ensure your gripper is empty before picking up a new object +3. Navigate to the destination before placing an object + +## Guidelines +1. Always output at least one action in executable_plan. +2. Only use actions from the Available Actions list. +3. If previous actions failed, reason about why and try a different approach. +4. Do not repeatedly execute the same action sequence. +5. Keep your plan efficient and concise. + +## Response Format +Output a JSON object with exactly these 4 fields: +{ + "visual_state_description": "Describe what you see in the current image", + "reasoning_and_reflection": "Reason about your situation and history", + "language_plan": "Describe your next plan in natural language", + "executable_plan": [{"action": ""}] +} +``` + +### User Message (Step 3, with chat history enabled) + +``` +[Image: base64 encoded current view] + +## Task +Pick up the apple and place it in the bowl. + +## Environment Feedback +Held object: none +Nearby objects: Apple_1 (0.3m), Bowl_2 (1.2m) + +## Action History (last 3 steps) +Step 0: move_to Apple_1 -> success +Step 1: pick_up Apple_1 -> fail: object not reachable +Step 2: move_to Apple_1 -> success + +## Chat History (last 2 responses) +[Step 1 Response] +{"visual_state_description": "I see a red apple on the counter...", "reasoning_and_reflection": "I moved to the apple successfully...", "language_plan": "Pick up the apple", "executable_plan": [{"action": "pick_up Apple_1"}]} + +[Step 2 Response] +{"visual_state_description": "The apple is still on the counter...", "reasoning_and_reflection": "Pickup failed, I need to get closer...", "language_plan": "Move closer then pick up", "executable_plan": [{"action": "move_to Apple_1"}]} + +Respond with the JSON format specified above. +``` From bc6021c40641aa493a66628c22a4c7784019c424 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Sun, 8 Mar 2026 21:58:36 +0800 Subject: [PATCH 164/239] fix: align all in-scope prompt builders with EASI standard format Updates VLN-CE R2R, VLN-CE RxR, ManipulaTHOR, and AI2-THOR Rearrangement prompt builders to follow the EASI Standard Prompt Format Reference (docs/easi-prompt-format-reference.md): - Standard section headers (Role and Environment, Observation Description, Available Actions, Strategy, Guidelines, Response Format) - Consistent user message sections (Task, Environment Feedback, Action History, Chat History, response format reminder) - Standard action history format: "Step N: action -> feedback" - ManipulaTHOR: restructured from 1-message to 2-message format, removed action_id from response format (kept in parser for compat) - Rearrangement: standardized JSON fields (observation -> visual_state_description, reasoning -> reasoning_and_reflection, plan -> executable_plan), converted multi-turn chat history to text section, added action history - All builders now support action_history_len and chat_history kwargs --- .../ai2thor_rearrangement_2023/_base.yaml | 1 + .../ai2thor_rearrangement_2023/prompts.py | 284 +++++++++------- easi/tasks/manipulathor/_base.yaml | 2 + easi/tasks/manipulathor/prompts.py | 320 ++++++++++-------- easi/tasks/vlnce_r2r/prompts.py | 89 +++-- easi/tasks/vlnce_rxr/prompts.py | 83 ++++- tests/test_manipulathor_task.py | 21 +- 7 files changed, 472 insertions(+), 328 deletions(-) diff --git a/easi/tasks/ai2thor_rearrangement_2023/_base.yaml b/easi/tasks/ai2thor_rearrangement_2023/_base.yaml index ef15fda..468e62b 100644 --- a/easi/tasks/ai2thor_rearrangement_2023/_base.yaml +++ b/easi/tasks/ai2thor_rearrangement_2023/_base.yaml @@ -30,6 +30,7 @@ agent: prompt_builder: "easi.tasks.ai2thor_rearrangement_2023.prompts.AI2THORRearrangement2023PromptBuilder" prompt_builder_kwargs: use_feedback: true + action_history_len: 20 chat_history: true message_window_len: 10 # Sensor toggles (must match simulator_configs) diff --git a/easi/tasks/ai2thor_rearrangement_2023/prompts.py b/easi/tasks/ai2thor_rearrangement_2023/prompts.py index f5e9f62..aa7d18c 100644 --- a/easi/tasks/ai2thor_rearrangement_2023/prompts.py +++ b/easi/tasks/ai2thor_rearrangement_2023/prompts.py @@ -1,5 +1,7 @@ """Prompt builder for AI2-THOR Rearrangement task. +Follows the EASI Standard Prompt Format Reference (docs/easi-prompt-format-reference.md). + Constructs multi-modal prompts with observation images, GPS data, action history, and structured JSON output schema. @@ -19,55 +21,6 @@ logger = get_logger(__name__) -# --- System prompt template (observations section is injected dynamically) --- - -SYSTEM_PROMPT = """\ -You are an embodied AI agent performing an object rearrangement task in a 3D indoor environment (AI2-THOR). - -## Goal -{instruction} - -**Important:** You are evaluated on the final state of ALL objects in the scene. If you accidentally bump into or displace objects that are already in their correct positions, it counts against you. Navigate carefully. - -## Observations -At each step you receive the following sensor inputs: -{observation_description} - -## Available Actions -{action_list} - -## Action Types -- **Navigation**: move_ahead (0.25m step), move_left/right/back, rotate_left/right (90°), look_up/look_down (30°), stand/crouch -- **Pickup**: pickup_ — picks up the nearest visible object of that type (must have empty hands) -- **Drop**: drop_held_object_with_snap — drops held object, snapping to goal position if close enough -- **Open/Close**: open_by_type_ — toggles openness of nearest visible cabinet/drawer/fridge/etc. -- **Done**: Signal that all objects are in their correct positions - -## Strategy -{strategy} - -## Rules -- You can hold only ONE object at a time -- drop_held_object_with_snap works best when the goal location is visible -- If pickup fails, try moving closer or adjusting your view angle -- Grid movement: 0.25m steps, 90° rotations -- **Do NOT disturb objects already in their correct positions** — avoid bumping into furniture or pushing items while navigating. Take careful, minimal paths. -""" - -OUTPUT_SCHEMA = """\ -Respond in this exact JSON format: -```json -{{ - "observation": "describe what you see in the observation images", - "reasoning": "what to do next and why", - "plan": [ - {{"action_name": ""}}, - ... - ] -}} -``` -Plan 1-5 actions. Use exact action names from the list above.""" - class AI2THORRearrangement2023PromptBuilder: """Prompt builder for the rearrangement task. @@ -80,6 +33,7 @@ class AI2THORRearrangement2023PromptBuilder: def __init__( self, use_feedback: bool = True, + action_history_len: int = 20, chat_history: bool = True, message_window_len: int = 10, use_rgb: bool = True, @@ -89,6 +43,7 @@ def __init__( **kwargs, ): self.use_feedback = use_feedback + self.action_history_len = action_history_len self.chat_history = chat_history self.message_window_len = message_window_len self.use_rgb = use_rgb @@ -100,13 +55,78 @@ def __init__( def set_action_space(self, actions: list[str]): self._action_name_set = set(actions) - parts = [f"{i}: {a}" for i, a in enumerate(actions)] - self._action_list_str = ", ".join(parts) + parts = [f"- {a}" for a in actions] + self._action_list_str = "\n".join(parts) + + # --- System prompt --- - # --- System prompt helpers --- + def _build_system_prompt(self) -> str: + """Build the full system prompt following EASI standard sections.""" + sections = [] + + # Role and Environment + sections.append( + "## Role and Environment\n" + "You are an embodied AI agent performing an object rearrangement " + "task in a 3D indoor environment (AI2-THOR). You must rearrange " + "objects to match a goal state. You are evaluated on ALL objects " + "in the scene — avoid accidentally displacing objects already in " + "their correct positions." + ) + + # Observation Description + obs_desc = self._build_observation_description() + if obs_desc: + sections.append(f"## Observation Description\n{obs_desc}") + + # Available Actions + sections.append( + f"## Available Actions\n{self._action_list_str}\n\n" + "**Action Types:**\n" + "- **Navigation**: move_ahead (0.25m step), move_left/right/back, " + "rotate_left/right (90 deg), look_up/look_down (30 deg), stand/crouch\n" + "- **Pickup**: pickup_ — picks up the nearest visible " + "object of that type (must have empty hands)\n" + "- **Drop**: drop_held_object_with_snap — drops held object, " + "snapping to goal position if close enough\n" + "- **Open/Close**: open_by_type_ — toggles openness of " + "nearest visible cabinet/drawer/fridge/etc.\n" + "- **Done**: Signal that all objects are in their correct positions" + ) + + # Strategy + sections.append(f"## Strategy\n{self._build_strategy()}") + + # Guidelines + sections.append( + "## Guidelines\n" + "1. Always output at least one action in executable_plan.\n" + "2. Only use actions from the Available Actions list.\n" + "3. If previous actions failed, reason about why and try a different approach.\n" + "4. Do not repeatedly execute the same action sequence.\n" + "5. Keep your plan efficient and concise.\n" + "6. You can hold only ONE object at a time.\n" + "7. Do NOT disturb objects already in their correct positions." + ) + + # Response Format + sections.append( + "## Response Format\n" + "Output a JSON object with exactly these 4 fields:\n" + "{\n" + ' "visual_state_description": "Describe what you see in the observation images",\n' + ' "reasoning_and_reflection": "What to do next and why",\n' + ' "language_plan": "Your plan in natural language",\n' + ' "executable_plan": [{"action": ""}]\n' + "}\n\n" + "You may include 1-5 actions in executable_plan. Actions execute " + "sequentially." + ) + + return "\n\n".join(sections) def _build_observation_description(self) -> str: - """Build the observation section of the system prompt based on active sensors.""" + """Build the observation description based on active sensors.""" sections = [] idx = 1 @@ -122,8 +142,7 @@ def _build_observation_description(self) -> str: sections.append( f"- **Depth Image** (Image {idx}): A grayscale depth map from " f"the agent's viewpoint. Brighter pixels are closer, darker " - f"pixels are farther away. Use this to judge distances to " - f"objects and obstacles." + f"pixels are farther away." ) idx += 1 @@ -140,7 +159,7 @@ def _build_observation_description(self) -> str: sections.append( "- **GPS Data** (text): The agent's 3D position (x, y, z) in " "meters where y is the vertical axis (height). Yaw rotation in " - "degrees (0°–360°). Horizon angle: 0° = looking straight " + "degrees (0-360). Horizon angle: 0 = looking straight " "ahead, positive = looking down, negative = looking up. Also " "reports which object the agent is currently holding, if any." ) @@ -155,7 +174,7 @@ def _build_strategy(self) -> str: "2. Navigate to a misplaced object\n" "3. Pick it up with the matching pickup_ action\n" "4. Navigate toward where it belongs (compare with goal image)\n" - "5. Use drop_held_object_with_snap — it snaps the object to its goal if you're close\n" + "5. Use drop_held_object_with_snap — it snaps the object to its goal if you are close\n" "6. Repeat for remaining misplaced objects\n" '7. Say "done" when finished' ) @@ -164,7 +183,7 @@ def _build_strategy(self) -> str: "2. Navigate to a misplaced object\n" "3. Pick it up with the matching pickup_ action\n" "4. Navigate to where it should go based on context\n" - "5. Use drop_held_object_with_snap — it snaps the object to its goal if you're close\n" + "5. Use drop_held_object_with_snap — it snaps the object to its goal if you are close\n" "6. Repeat for remaining misplaced objects\n" '7. Say "done" when finished' ) @@ -172,35 +191,15 @@ def _build_strategy(self) -> str: # --- Message building --- def build_messages(self, memory: AgentMemory) -> list[dict]: - # Lazy-init action space from memory (set by agent constructor) + # Lazy-init action space from memory if not self._action_list_str and memory.action_space: self.set_action_space(memory.action_space) - instruction = memory.task_description or "Rearrange objects to match the goal." - - system_text = SYSTEM_PROMPT.format( - instruction=instruction, - observation_description=self._build_observation_description(), - action_list=self._action_list_str, - strategy=self._build_strategy(), - ) - + system_text = self._build_system_prompt() messages = [{"role": "system", "content": system_text}] - # Chat history (if enabled) - if self.chat_history and memory.steps: - window = memory.steps[-self.message_window_len:] - for step in window: - user_content = self._make_history_content(step) - messages.append({"role": "user", "content": user_content}) - if step.llm_response: - messages.append({ - "role": "assistant", - "content": step.llm_response, - }) - - # Current turn - user_content = self._make_current_content(memory) + # Build user message + user_content = self._make_user_content(memory) messages.append({"role": "user", "content": user_content}) return messages @@ -256,63 +255,88 @@ def _add_images( return f" ({', '.join(labels)})" if labels else "" - def _make_history_content(self, step) -> list[dict]: + def _make_user_content(self, memory: AgentMemory) -> list[dict]: + """Build user message content: images + text sections.""" content: list[dict] = [] - metadata = step.observation.metadata if step.observation else {} - rgb_path = step.observation.rgb_path if step.observation else None + obs = memory.current_observation + metadata = obs.metadata if obs else {} + rgb_path = obs.rgb_path if obs else None + # Images first img_label = self._add_images(content, rgb_path, metadata) - text = f"Observation{img_label}." - if self.use_feedback and step.feedback: - text += f"\nFeedback: {step.feedback}" + text_parts = [] - # GPS overlay from observation metadata - if self.use_gps: - gps_text = self._format_gps(metadata) - if gps_text: - text += f"\n{gps_text}" - - content.append({"type": "text", "text": text}) - return content - - def _make_current_content(self, memory: AgentMemory) -> list[dict]: - content: list[dict] = [] - metadata = ( - memory.current_observation.metadata - if memory.current_observation else {} - ) - rgb_path = ( - memory.current_observation.rgb_path - if memory.current_observation else None - ) - - img_label = self._add_images(content, rgb_path, metadata) + # Task + instruction = memory.task_description or "Rearrange objects to match the goal." + text_parts.append(f"## Task\n{instruction}") - if memory.is_first_turn: - text = f"First observation{img_label}. Begin the rearrangement task." - else: - text = f"Current observation{img_label}." - if self.use_feedback and memory.steps: - last = memory.steps[-1] - if last.feedback: - text += f"\nFeedback: {last.feedback}" - - # GPS and held-object from last step's observation metadata - if memory.steps: + # Environment Feedback (GPS + held object) + feedback_lines = [] + if self.use_gps and memory.steps: last_metadata = ( memory.steps[-1].observation.metadata if memory.steps[-1].observation else {} ) - if self.use_gps: - gps_text = self._format_gps(last_metadata) - if gps_text: - text += f"\n{gps_text}" + gps_text = self._format_gps(last_metadata) + if gps_text: + feedback_lines.append(gps_text) held = last_metadata.get("held_object", "none") - text += f"\nHolding: {held}" + feedback_lines.append(f"Holding: {held}") + elif self.use_gps and obs and metadata: + gps_text = self._format_gps(metadata) + if gps_text: + feedback_lines.append(gps_text) + + if self.use_feedback and memory.steps: + last = memory.steps[-1] + if last.feedback: + feedback_lines.append(f"Last action result: {last.feedback}") + + if feedback_lines: + text_parts.append("## Environment Feedback\n" + "\n".join(feedback_lines)) + + # Action History + if memory.action_history and self.action_history_len > 0: + history = memory.action_history[-self.action_history_len:] + history_lines = [] + for i, (action_name, feedback) in enumerate(history): + if self.use_feedback and feedback: + history_lines.append(f"Step {i}: {action_name} -> {feedback}") + else: + history_lines.append(f"Step {i}: {action_name}") + if history_lines: + text_parts.append( + f"## Action History (last {len(history_lines)} steps)\n" + + "\n".join(history_lines) + ) + + # Chat History (text section, not multi-turn) + if self.chat_history and memory.steps: + responses = [ + s.llm_response for s in memory.steps + if s.llm_response is not None + ][-self.message_window_len:] + if responses: + chat_lines = [] + offset = len(memory.steps) - len(responses) + for j, resp in enumerate(responses): + chat_lines.append(f"[Step {offset + j} Response]\n{resp}") + text_parts.append( + f"## Chat History (last {len(responses)} responses)\n" + + "\n\n".join(chat_lines) + ) + + # Response format reminder + if memory.is_first_turn: + text_parts.append( + f"First observation{img_label}. Begin the rearrangement task.\n\n" + "Respond with the JSON format specified above." + ) + else: + text_parts.append("Respond with the JSON format specified above.") - text += f"\n\n{OUTPUT_SCHEMA}" - content.append({"type": "text", "text": text}) + content.append({"type": "text", "text": "\n\n".join(text_parts)}) return content def _format_gps(self, metadata: dict | None) -> str: @@ -340,13 +364,15 @@ def parse_response( logger.warning("Failed to parse LLM response as JSON") return [Action(action_name="done")] - plan = data.get("plan", []) + # Accept both "executable_plan" (standard) and "plan" (legacy) + plan = data.get("executable_plan", data.get("plan", [])) if not plan: return [Action(action_name="done")] actions = [] for entry in plan: - name = entry.get("action_name", "") + # Accept "action" (standard), "action_name" (legacy) + name = entry.get("action", entry.get("action_name", "")) if name in self._action_name_set: actions.append(Action(action_name=name)) else: diff --git a/easi/tasks/manipulathor/_base.yaml b/easi/tasks/manipulathor/_base.yaml index 12f36cb..2fac0e5 100644 --- a/easi/tasks/manipulathor/_base.yaml +++ b/easi/tasks/manipulathor/_base.yaml @@ -23,7 +23,9 @@ agent: prompt_builder_kwargs: n_shot: 0 use_feedback: true + action_history_len: 20 chat_history: false + message_window_len: 5 max_steps: 200 use_rgb: true use_gps: true diff --git a/easi/tasks/manipulathor/prompts.py b/easi/tasks/manipulathor/prompts.py index 2d517cf..b88266c 100644 --- a/easi/tasks/manipulathor/prompts.py +++ b/easi/tasks/manipulathor/prompts.py @@ -1,7 +1,8 @@ """ManipulaTHOR prompt builder for EASI LLM agents. +Follows the EASI Standard Prompt Format Reference (docs/easi-prompt-format-reference.md). Presents RGB image + GPS state sensors + action history. -Outputs JSON with executable plan of named action IDs. +Outputs JSON with executable plan of named actions. """ from __future__ import annotations @@ -16,58 +17,95 @@ logger = get_logger(__name__) -# ── System prompt template ────────────────────────────────────────────────── - -SYSTEM_PROMPT = """You are a robotic arm agent in a kitchen environment. Your task is to pick up a target object and move it to a goal location. - -## Environment -- You control a robotic arm mounted on a mobile base in a kitchen (AI2-THOR simulator). -- You observe: an RGB image from the agent's camera{sensor_note}. -- Each arm movement moves 0.05m. Navigation moves 0.2m forward or rotates 45°. -- Maximum {max_steps} steps per episode. - -## Available Actions (choose by ID 0-{max_id}): +# ── Action descriptions for the system prompt ── +ACTION_DESCRIPTIONS = { + "MoveArmHeightP": "Move arm up by 0.05m", + "MoveArmHeightM": "Move arm down by 0.05m", + "MoveArmXP": "Move arm right by 0.05m", + "MoveArmXM": "Move arm left by 0.05m", + "MoveArmYP": "Move arm up by 0.05m (Y axis)", + "MoveArmYM": "Move arm down by 0.05m (Y axis)", + "MoveArmZP": "Move arm forward by 0.05m", + "MoveArmZM": "Move arm backward by 0.05m", + "MoveAheadContinuous": "Move agent forward by 0.2m", + "RotateRightContinuous": "Rotate agent right by 45 degrees", + "RotateLeftContinuous": "Rotate agent left by 45 degrees", + "PickUpMidLevel": "Pick up the target object (arm must be close)", + "DoneMidLevel": "Signal task completion (object must be at goal)", +} + + +def _build_action_list() -> str: + """Build formatted action list for system prompt.""" + lines = [] + for name in ACTION_SPACE: + desc = ACTION_DESCRIPTIONS.get(name, "") + lines.append(f"- {name}: {desc}") + return "\n".join(lines) + + +SYSTEM_PROMPT = """\ +## Role and Environment +You are a robotic arm agent in a kitchen environment (AI2-THOR simulator). \ +Your task is to pick up a target object and move it to a goal location. You \ +control a robotic arm mounted on a mobile base. Each arm movement moves \ +0.05m. Navigation moves 0.2m forward or rotates 45 degrees. Maximum \ +{{max_steps}} steps per episode. + +## Observation Description +- **Object Position & Rotation**: Object 6D state (x,y,z position and \ +rx,ry,rz rotation) relative to the agent. +- **Object-to-Goal Distance**: Absolute x,y,z distance from the object to \ +the goal in agent frame. +- **Arm-to-Object Distance**: Absolute x,y,z distance from the arm tip to \ +the object in agent frame. +- **Object Held**: Whether you are currently holding the object ("Yes"/"No"). + +## Available Actions {action_list} -{gps_description} -## Strategy for Using GPS Sensors -The GPS sensors give you precise spatial information in YOUR reference frame (agent-relative coordinates). +## Strategy **Phase 1 — Approach the object:** -- Check "Arm-to-Object Distance": this tells you how far your arm tip is from the target object on each axis. -- Positive X means the object is to your right; positive Z means it is ahead of you. -- Use MoveArmXP/XM, MoveArmYP/YM, MoveArmZP/ZM to reduce arm-to-object distance on each axis. -- Use MoveAheadContinuous / RotateLeft / RotateRight if the object is far (>0.5m) — navigation is faster than arm movement. +- Check Arm-to-Object Distance to see how far your arm tip is from the target. +- Positive X = object is to your right; positive Z = object is ahead. +- Use arm movements (MoveArmXP/XM, MoveArmYP/YM, MoveArmZP/ZM) to reduce \ +distance on each axis. +- Use navigation (MoveAheadContinuous/RotateLeft/RotateRight) if the object \ +is far (>0.5m). **Phase 2 — Pick up the object:** -- When arm-to-object distance is small on all axes (<0.1m), use PickUpMidLevel (action 11). -- After pickup, check "Object Held" — it should read "Yes". +- When arm-to-object distance is small on all axes (<0.1m), use PickUpMidLevel. +- After pickup, check Object Held — it should read "Yes". **Phase 3 — Navigate to the goal:** -- Check "Object-to-Goal Distance": this tells you how far the object is from the goal. -- Navigate (MoveAhead, Rotate) to reduce the distance. The object moves with you when held. -- Use arm movements for fine positioning when close to the goal. +- Check Object-to-Goal Distance. Navigate to reduce it. The object moves \ +with you when held. +- Use arm movements for fine positioning when close. **Phase 4 — Place and finish:** -- When Object-to-Goal Distance is small on all axes (<0.1m), use DoneMidLevel (action 12). - -{examples}""" - -OUTPUT_TEMPLATE = """ - -## Output Format -Respond in JSON with this exact structure: -```json -{{ - "visual_state_description": "Describe what you see in the image and the GPS state", - "reasoning_and_reflection": "Your reasoning about the current state and what to do next", - "language_plan": "Your plan in natural language", - "executable_plan": [ - {{"action_id": , "action_name": ""}}, - ... - ] -}} -``` -Output 1-5 actions in the executable_plan. Choose action IDs from the available actions list.""" +- When Object-to-Goal Distance is small on all axes (<0.1m), use DoneMidLevel. + +## Guidelines +1. Always output at least one action in executable_plan. +2. Only use actions from the Available Actions list. +3. If previous actions failed, reason about why and try a different approach. +4. Do not repeatedly execute the same action sequence. +5. Keep your plan efficient and concise. +6. Output 1-5 actions per step. + +## Response Format +Output a JSON object with exactly these 4 fields: +{{{{ + "visual_state_description": "Describe what you see in the image and \ +the GPS state", + "reasoning_and_reflection": "Your reasoning about the current state \ +and what to do next", + "language_plan": "Your plan in natural language", + "executable_plan": [{{{{"action": ""}}}}] +}}}} + +You may include multiple actions in executable_plan. Actions execute \ +sequentially.""" # ── Prompt builder ────────────────────────────────────────────────────────── @@ -81,19 +119,23 @@ def __init__( split: str = "test_seen", use_feedback: bool = True, chat_history: bool = False, + message_window_len: int = 5, max_steps: int = 200, use_rgb: bool = True, use_gps: bool = True, use_depth: bool = False, + action_history_len: int = 20, ): self.n_shot = n_shot self.split = split self.use_feedback = use_feedback self.chat_history = chat_history + self.message_window_len = message_window_len self.max_steps = max_steps self.use_rgb = use_rgb self.use_gps = use_gps self.use_depth = use_depth + self.action_history_len = action_history_len # Load few-shot examples examples_file = Path(__file__).parent / "config" / "manipulathor_examples.json" @@ -103,10 +145,6 @@ def __init__( else: self._examples = [] - # Build action list string - self._action_list = "\n".join( - f" {i}: {name}" for i, name in enumerate(ACTION_SPACE) - ) self._action_id_map = {name: i for i, name in enumerate(ACTION_SPACE)} self._id_action_map = {i: name for i, name in enumerate(ACTION_SPACE)} @@ -115,61 +153,51 @@ def set_action_space(self, actions: list) -> None: pass # ManipulaTHOR has a fixed action space def build_messages(self, memory: AgentMemory) -> list: - """Build OpenAI message format from agent memory.""" - prompt = self._build_prompt_text(memory) - return self._wrap_as_user_message(prompt, memory.current_observation) - - def _build_prompt_text(self, memory: AgentMemory) -> str: - """Build the full prompt text.""" - max_id = len(ACTION_SPACE) - 1 - - # Build examples section - examples_str = "" - if self.n_shot >= 1 and self._examples: - examples_str = "\n\n## Examples\n" + "\n\n".join( - f"### Example {i+1}:\n{ex}" - for i, ex in enumerate(self._examples[:self.n_shot]) - ) - - # Build sensor note for system prompt - sensor_parts = [] - if self.use_gps: - sensor_parts.append("GPS-like state sensors showing spatial relationships") - if self.use_depth: - sensor_parts.append("a depth image (colormap)") - sensor_note = (", and " + ", ".join(sensor_parts)) if sensor_parts else "" - - # GPS description section (only if GPS enabled) - gps_description = "" - if self.use_gps: - gps_description = """ -## GPS State Sensors -At each step you receive: -- **Object State** (6D): object position (x,y,z) and rotation (rx,ry,rz) relative to agent -- **Object-to-Goal Distance** (3D): absolute x,y,z distance from object to goal in agent frame -- **Arm-to-Object Distance** (3D): absolute x,y,z distance from arm tip to object in agent frame -- **Object Held**: whether you are currently holding the object - -""" + """Build 2-message format: system + user.""" + import base64 - # System prompt - prompt = SYSTEM_PROMPT.format( + # Build system prompt + system_text = SYSTEM_PROMPT.format( max_steps=self.max_steps, - max_id=max_id, - action_list=self._action_list, - examples=examples_str, - sensor_note=sensor_note, - gps_description=gps_description, + action_list=_build_action_list(), ) + messages = [{"role": "system", "content": system_text}] - # Task instruction - task_desc = memory.task_description - prompt += f"\n\n## Current Task: {task_desc}" - - # GPS state from current observation metadata (only if GPS enabled) + # Build user message + content = [] obs = memory.current_observation + + # Images first + if self.use_rgb and obs and obs.rgb_path: + try: + with open(obs.rgb_path, "rb") as f: + b64 = base64.b64encode(f.read()).decode("utf-8") + content.append({ + "type": "image_url", + "image_url": {"url": f"data:image/png;base64,{b64}"}, + }) + except FileNotFoundError: + logger.warning("RGB image not found: %s", obs.rgb_path) + + if self.use_depth and obs and obs.depth_path: + try: + with open(obs.depth_path, "rb") as f: + b64 = base64.b64encode(f.read()).decode("utf-8") + content.append({ + "type": "image_url", + "image_url": {"url": f"data:image/png;base64,{b64}"}, + }) + except FileNotFoundError: + logger.warning("Depth image not found: %s", obs.depth_path) + + text_parts = [] + + # Task + text_parts.append(f"## Task\n{memory.task_description}") + + # Environment Feedback (GPS state) if self.use_gps and obs and obs.metadata: - prompt += "\n\n## Current GPS State:" + feedback_lines = [] gps_fields = [ ("relative_current_obj_state", "Object Position & Rotation (agent-relative)"), ("relative_obj_to_goal", "Object-to-Goal Distance"), @@ -181,61 +209,56 @@ def _build_prompt_text(self, memory: AgentMemory) -> str: if val is not None: if key == "pickedup_object": held = float(val) > 0.5 - prompt += f"\n- {label}: {'Yes' if held else 'No'}" + feedback_lines.append(f"{label}: {'Yes' if held else 'No'}") else: try: arr = json.loads(val) if isinstance(val, str) else val formatted = [f"{v:.3f}" for v in arr] - prompt += f"\n- {label}: [{', '.join(formatted)}]" + feedback_lines.append(f"{label}: [{', '.join(formatted)}]") except (json.JSONDecodeError, TypeError): - prompt += f"\n- {label}: {val}" - - # Action history + feedback - action_history = getattr(memory, 'action_history', []) - if action_history: - prompt += "\n\n## Action History:" - for i, (action_name, feedback) in enumerate(action_history): - action_id = self._action_id_map.get(action_name, -1) - if self.use_feedback: - prompt += f"\nStep {i+1}: action {action_id} ({action_name}) — {feedback}" + feedback_lines.append(f"{label}: {val}") + if feedback_lines: + text_parts.append("## Environment Feedback\n" + "\n".join(feedback_lines)) + + # Action History + action_history = memory.action_history + if action_history and self.action_history_len > 0: + history = action_history[-self.action_history_len:] + history_lines = [] + for i, (action_name, feedback) in enumerate(history): + if self.use_feedback and feedback: + history_lines.append(f"Step {i}: {action_name} -> {feedback}") else: - prompt += f"\nStep {i+1}: action {action_id} ({action_name})" - - prompt += "\n\nBased on the action history and current state, plan your next actions." - - prompt += OUTPUT_TEMPLATE - return prompt - - def _wrap_as_user_message(self, prompt: str, observation) -> list: - """Wrap prompt + images as OpenAI user message. - - Includes RGB and/or depth images based on use_rgb and use_depth toggles. - """ - import base64 - - content = [] - - def _add_image(path, label): - try: - with open(path, "rb") as f: - b64 = base64.b64encode(f.read()).decode("utf-8") - content.append({ - "type": "image_url", - "image_url": {"url": f"data:image/png;base64,{b64}"}, - }) - except FileNotFoundError: - logger.warning("%s image not found: %s", label, path) - - # Add RGB image (before text, matching EASI convention) - if self.use_rgb and observation and observation.rgb_path: - _add_image(observation.rgb_path, "RGB") - - # Add depth colormap image if enabled - if self.use_depth and observation and observation.depth_path: - _add_image(observation.depth_path, "Depth") - - content.append({"type": "text", "text": prompt}) - return [{"role": "user", "content": content}] + history_lines.append(f"Step {i}: {action_name}") + if history_lines: + text_parts.append( + f"## Action History (last {len(history_lines)} steps)\n" + + "\n".join(history_lines) + ) + + # Chat History + if self.chat_history and memory.steps: + responses = [ + s.llm_response for s in memory.steps + if s.llm_response is not None + ][-self.message_window_len:] + if responses: + chat_lines = [] + offset = len(memory.steps) - len(responses) + for j, resp in enumerate(responses): + chat_lines.append(f"[Step {offset + j} Response]\n{resp}") + text_parts.append( + f"## Chat History (last {len(responses)} responses)\n" + + "\n\n".join(chat_lines) + ) + + # Response format reminder + text_parts.append("Respond with the JSON format specified above.") + + content.append({"type": "text", "text": "\n\n".join(text_parts)}) + messages.append({"role": "user", "content": content}) + + return messages def parse_response(self, llm_response: str, memory: AgentMemory) -> list: """Parse LLM JSON response into Action objects.""" @@ -259,8 +282,9 @@ def parse_response(self, llm_response: str, memory: AgentMemory) -> list: if not isinstance(entry, dict): continue - action_name = None - if "action_id" in entry: + # Prefer "action" key (EASI standard), fall back to action_id/action_name + action_name = entry.get("action", None) + if action_name is None and "action_id" in entry: aid = entry["action_id"] if isinstance(aid, int) and 0 <= aid < len(ACTION_SPACE): action_name = self._id_action_map[aid] diff --git a/easi/tasks/vlnce_r2r/prompts.py b/easi/tasks/vlnce_r2r/prompts.py index 27b64c3..96244dd 100644 --- a/easi/tasks/vlnce_r2r/prompts.py +++ b/easi/tasks/vlnce_r2r/prompts.py @@ -1,6 +1,6 @@ """VLN-CE R2R prompt builder. -Adapted from LHPR-VLN's EmbodiedBench format for single-goal navigation. +Follows the EASI Standard Prompt Format Reference (docs/easi-prompt-format-reference.md). Single front camera, 4 actions (move_forward, turn_left, turn_right, stop). """ from __future__ import annotations @@ -16,13 +16,22 @@ logger = get_logger(__name__) -SYSTEM_PROMPT = """You are a robot navigating in a 3D indoor environment. You observe the environment through a front-facing camera and must follow natural language instructions to navigate to a goal location. +SYSTEM_PROMPT = """\ +## Role and Environment +You are a robot navigating in a 3D indoor environment. You observe the \ +environment through a front-facing camera and must follow natural language \ +instructions to navigate to a goal location. + +## Observation Description +- **Distance to goal**: Geodesic (shortest walkable path) distance in meters \ +to the goal location. Decreases as you get closer. ## Available Actions - move_forward: Move forward by 0.25 meters - turn_left: Turn left by 15 degrees - turn_right: Turn right by 15 degrees -- stop: Stop and end navigation (use ONLY when you believe you have reached the destination described in the instruction) +- stop: Stop and end navigation (use ONLY when you believe you have reached \ +the destination described in the instruction) ## Strategy 1. Carefully read the navigation instruction @@ -31,16 +40,25 @@ 4. Use move_forward to advance and turn_left/turn_right to change direction 5. Use stop ONLY when you are confident you have reached the described destination +## Guidelines +1. Always output at least one action in executable_plan. +2. Only use actions from the Available Actions list. +3. If previous actions failed, reason about why and try a different approach. +4. Do not repeatedly execute the same action sequence. +5. Keep your plan efficient and concise. + ## Response Format Output a JSON object with exactly these 4 fields: { "visual_state_description": "Describe what you see in the current image", - "reasoning_and_reflection": "Reason about where you are relative to the instruction", + "reasoning_and_reflection": "Reason about your situation, reflect on \ +history and feedback", "language_plan": "Describe your next navigation plan in natural language", "executable_plan": [{"action": ""}] } -You may include multiple actions in executable_plan to plan ahead. Actions are executed sequentially.""" +You may include multiple actions in executable_plan. Actions execute \ +sequentially.""" def _encode_image_base64(image_path: str) -> str | None: @@ -91,47 +109,68 @@ def build_messages(self, memory: AgentMemory) -> list[dict]: messages = [{"role": "system", "content": SYSTEM_PROMPT}] - # Build user message with image + instruction + feedback content = [] - # Current observation image + # Image first obs = memory.current_observation if obs and obs.rgb_path: img_url = _encode_image_base64(obs.rgb_path) if img_url: content.append({"type": "image_url", "image_url": {"url": img_url}}) - # Build text prompt text_parts = [] - # Instruction - text_parts.append(f"## Navigation Instruction\n{memory.task_description}") + # Task + text_parts.append(f"## Task\n{memory.task_description}") - # Environmental feedback + # Environment Feedback if self.use_feedback and obs and obs.metadata: + feedback_lines = [] + if self.use_geo_distance: + geo = obs.metadata.get("geo_distance") + if geo is not None and geo != "null": + feedback_lines.append(f"Distance to goal: {float(geo):.1f}m") feedback = obs.metadata.get("feedback", "") if feedback: - text_parts.append(f"\n## Environmental Feedback\n{feedback}") + feedback_lines.append(feedback) + if feedback_lines: + text_parts.append("## Environment Feedback\n" + "\n".join(feedback_lines)) - # Geodesic distance - if self.use_geo_distance and obs and obs.metadata: - geo = obs.metadata.get("geo_distance") - if geo is not None and geo != "null": - text_parts.append(f"Geodesic distance to goal: {float(geo):.1f}m") - - # Action history + # Action History if memory.action_history and self.action_history_len > 0: history = memory.action_history[-self.action_history_len:] history_lines = [] for i, (action_name, feedback) in enumerate(history): - fb = "" if self.use_feedback and feedback: - fb = f", feedback: {feedback}" - history_lines.append(f"Step {i}: {action_name}{fb}") + history_lines.append(f"Step {i}: {action_name} -> {feedback}") + else: + history_lines.append(f"Step {i}: {action_name}") if history_lines: - text_parts.append(f"\n## Action History (last {len(history_lines)} steps)\n" + "\n".join(history_lines)) - - content.append({"type": "text", "text": "\n".join(text_parts)}) + text_parts.append( + f"## Action History (last {len(history_lines)} steps)\n" + + "\n".join(history_lines) + ) + + # Chat History + if self.chat_history and memory.steps: + responses = [ + s.llm_response for s in memory.steps + if s.llm_response is not None + ][-self.message_window_len:] + if responses: + chat_lines = [] + offset = len(memory.steps) - len(responses) + for j, resp in enumerate(responses): + chat_lines.append(f"[Step {offset + j} Response]\n{resp}") + text_parts.append( + f"## Chat History (last {len(responses)} responses)\n" + + "\n\n".join(chat_lines) + ) + + # Response format reminder + text_parts.append("Respond with the JSON format specified above.") + + content.append({"type": "text", "text": "\n\n".join(text_parts)}) messages.append({"role": "user", "content": content}) return messages diff --git a/easi/tasks/vlnce_rxr/prompts.py b/easi/tasks/vlnce_rxr/prompts.py index 9b8ed9b..8be3749 100644 --- a/easi/tasks/vlnce_rxr/prompts.py +++ b/easi/tasks/vlnce_rxr/prompts.py @@ -1,8 +1,9 @@ # easi/tasks/vlnce_rxr/prompts.py """VLN-CE RxR prompt builder. +Follows the EASI Standard Prompt Format Reference (docs/easi-prompt-format-reference.md). Single front camera, 6 actions (move_forward, turn_left, turn_right, -look_up, look_down, stop). 30° turns and tilts. +look_up, look_down, stop). 30 deg turns and tilts. """ from __future__ import annotations @@ -17,7 +18,15 @@ logger = get_logger(__name__) -SYSTEM_PROMPT = """You are a robot navigating in a 3D indoor environment. You observe the environment through a front-facing camera and must follow natural language instructions to navigate to a goal location. +SYSTEM_PROMPT = """\ +## Role and Environment +You are a robot navigating in a 3D indoor environment. You observe the \ +environment through a front-facing camera and must follow natural language \ +instructions to navigate to a goal location. + +## Observation Description +- **Distance to goal**: Geodesic (shortest walkable path) distance in meters \ +to the goal location. Decreases as you get closer. ## Available Actions - move_forward: Move forward by 0.25 meters @@ -25,7 +34,8 @@ - turn_right: Turn right by 30 degrees - look_up: Tilt camera up by 30 degrees - look_down: Tilt camera down by 30 degrees -- stop: Stop and end navigation (use ONLY when you believe you have reached the destination described in the instruction) +- stop: Stop and end navigation (use ONLY when you believe you have reached \ +the destination described in the instruction) ## Strategy 1. Carefully read the navigation instruction @@ -35,16 +45,25 @@ 5. Use look_up/look_down to observe objects above or below your current view 6. Use stop ONLY when you are confident you have reached the described destination +## Guidelines +1. Always output at least one action in executable_plan. +2. Only use actions from the Available Actions list. +3. If previous actions failed, reason about why and try a different approach. +4. Do not repeatedly execute the same action sequence. +5. Keep your plan efficient and concise. + ## Response Format Output a JSON object with exactly these 4 fields: { "visual_state_description": "Describe what you see in the current image", - "reasoning_and_reflection": "Reason about where you are relative to the instruction", + "reasoning_and_reflection": "Reason about your situation, reflect on \ +history and feedback", "language_plan": "Describe your next navigation plan in natural language", "executable_plan": [{"action": ""}] } -You may include multiple actions in executable_plan to plan ahead. Actions are executed sequentially.""" +You may include multiple actions in executable_plan. Actions execute \ +sequentially.""" def _encode_image_base64(image_path: str) -> str | None: @@ -97,6 +116,7 @@ def build_messages(self, memory: AgentMemory) -> list[dict]: content = [] + # Image first obs = memory.current_observation if obs and obs.rgb_path: img_url = _encode_image_base64(obs.rgb_path) @@ -105,30 +125,57 @@ def build_messages(self, memory: AgentMemory) -> list[dict]: text_parts = [] - text_parts.append(f"## Navigation Instruction\n{memory.task_description}") + # Task + text_parts.append(f"## Task\n{memory.task_description}") + # Environment Feedback if self.use_feedback and obs and obs.metadata: + feedback_lines = [] + if self.use_geo_distance: + geo = obs.metadata.get("geo_distance") + if geo is not None and geo != "null": + feedback_lines.append(f"Distance to goal: {float(geo):.1f}m") feedback = obs.metadata.get("feedback", "") if feedback: - text_parts.append(f"\n## Environmental Feedback\n{feedback}") - - if self.use_geo_distance and obs and obs.metadata: - geo = obs.metadata.get("geo_distance") - if geo is not None and geo != "null": - text_parts.append(f"Geodesic distance to goal: {float(geo):.1f}m") + feedback_lines.append(feedback) + if feedback_lines: + text_parts.append("## Environment Feedback\n" + "\n".join(feedback_lines)) + # Action History if memory.action_history and self.action_history_len > 0: history = memory.action_history[-self.action_history_len:] history_lines = [] for i, (action_name, feedback) in enumerate(history): - fb = "" if self.use_feedback and feedback: - fb = f", feedback: {feedback}" - history_lines.append(f"Step {i}: {action_name}{fb}") + history_lines.append(f"Step {i}: {action_name} -> {feedback}") + else: + history_lines.append(f"Step {i}: {action_name}") if history_lines: - text_parts.append(f"\n## Action History (last {len(history_lines)} steps)\n" + "\n".join(history_lines)) - - content.append({"type": "text", "text": "\n".join(text_parts)}) + text_parts.append( + f"## Action History (last {len(history_lines)} steps)\n" + + "\n".join(history_lines) + ) + + # Chat History + if self.chat_history and memory.steps: + responses = [ + s.llm_response for s in memory.steps + if s.llm_response is not None + ][-self.message_window_len:] + if responses: + chat_lines = [] + offset = len(memory.steps) - len(responses) + for j, resp in enumerate(responses): + chat_lines.append(f"[Step {offset + j} Response]\n{resp}") + text_parts.append( + f"## Chat History (last {len(responses)} responses)\n" + + "\n\n".join(chat_lines) + ) + + # Response format reminder + text_parts.append("Respond with the JSON format specified above.") + + content.append({"type": "text", "text": "\n\n".join(text_parts)}) messages.append({"role": "user", "content": content}) return messages diff --git a/tests/test_manipulathor_task.py b/tests/test_manipulathor_task.py index 6870b09..d2e907d 100644 --- a/tests/test_manipulathor_task.py +++ b/tests/test_manipulathor_task.py @@ -243,18 +243,21 @@ def test_build_messages_first_turn(self, builder, tmp_path): ) messages = builder.build_messages(memory) - assert len(messages) == 1 - assert messages[0]["role"] == "user" - content = messages[0]["content"] + assert len(messages) == 2 + assert messages[0]["role"] == "system" + assert messages[1]["role"] == "user" + content = messages[1]["content"] # Should have image + text assert any(c["type"] == "image_url" for c in content) assert any(c["type"] == "text" for c in content) text = next(c["text"] for c in content if c["type"] == "text") assert "Pick up the Apple" in text - assert "GPS State" in text + assert "Environment Feedback" in text assert "Object Held: No" in text - assert "Strategy for Using GPS Sensors" in text + # Strategy is now in system prompt + system_text = messages[0]["content"] + assert "Phase 1" in system_text def test_build_messages_gps_disabled(self, tmp_path): """When use_gps=False, no GPS section in prompt.""" @@ -273,9 +276,11 @@ def test_build_messages_gps_disabled(self, tmp_path): rgb_path=str(img_path), metadata={"pickedup_object": "0.0"}, ) + memory.steps = [] messages = builder.build_messages(memory) - text = next(c["text"] for c in messages[0]["content"] if c["type"] == "text") - assert "GPS State" not in text + text = next(c["text"] for c in messages[1]["content"] if c["type"] == "text") + assert "Object Position" not in text + assert "Arm-to-Object" not in text def test_build_messages_with_history(self, builder, tmp_path): """Subsequent turn: includes action history.""" @@ -301,7 +306,7 @@ def test_build_messages_with_history(self, builder, tmp_path): ) messages = builder.build_messages(memory) - text = next(c["text"] for c in messages[0]["content"] if c["type"] == "text") + text = next(c["text"] for c in messages[1]["content"] if c["type"] == "text") assert "Action History" in text assert "MoveArmZP" in text assert "succeeded" in text From 74a53039d9c777269eaac0f88f134aa72e3e8021 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Sun, 8 Mar 2026 22:34:53 +0800 Subject: [PATCH 165/239] fix(vlnce): avoid duplicate distance-to-goal in environment feedback The bridge writes geo distance into both metadata["geo_distance"] and metadata["feedback"]. When use_geo_distance is true, only use the structured geo_distance field to avoid showing the same info twice. --- easi/tasks/vlnce_r2r/prompts.py | 9 ++++++--- easi/tasks/vlnce_rxr/prompts.py | 9 ++++++--- 2 files changed, 12 insertions(+), 6 deletions(-) diff --git a/easi/tasks/vlnce_r2r/prompts.py b/easi/tasks/vlnce_r2r/prompts.py index 96244dd..5fd7efa 100644 --- a/easi/tasks/vlnce_r2r/prompts.py +++ b/easi/tasks/vlnce_r2r/prompts.py @@ -130,9 +130,12 @@ def build_messages(self, memory: AgentMemory) -> list[dict]: geo = obs.metadata.get("geo_distance") if geo is not None and geo != "null": feedback_lines.append(f"Distance to goal: {float(geo):.1f}m") - feedback = obs.metadata.get("feedback", "") - if feedback: - feedback_lines.append(feedback) + else: + # Only use generic feedback when geo_distance is not shown + # (bridge puts distance into both fields, avoid duplication) + feedback = obs.metadata.get("feedback", "") + if feedback: + feedback_lines.append(feedback) if feedback_lines: text_parts.append("## Environment Feedback\n" + "\n".join(feedback_lines)) diff --git a/easi/tasks/vlnce_rxr/prompts.py b/easi/tasks/vlnce_rxr/prompts.py index 8be3749..31d5268 100644 --- a/easi/tasks/vlnce_rxr/prompts.py +++ b/easi/tasks/vlnce_rxr/prompts.py @@ -135,9 +135,12 @@ def build_messages(self, memory: AgentMemory) -> list[dict]: geo = obs.metadata.get("geo_distance") if geo is not None and geo != "null": feedback_lines.append(f"Distance to goal: {float(geo):.1f}m") - feedback = obs.metadata.get("feedback", "") - if feedback: - feedback_lines.append(feedback) + else: + # Only use generic feedback when geo_distance is not shown + # (bridge puts distance into both fields, avoid duplication) + feedback = obs.metadata.get("feedback", "") + if feedback: + feedback_lines.append(feedback) if feedback_lines: text_parts.append("## Environment Feedback\n" + "\n".join(feedback_lines)) From 825f8ebd68167259be0a215f2d7e118f4d755ee5 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Mon, 9 Mar 2026 13:11:48 +0800 Subject: [PATCH 166/239] fix: resolve 4 bugs found in code review - ManipulaTHOR prompt: fix double-brace escaping so LLM sees valid JSON example - CLI --resume: handle GPU values that are already list[int] from config.json - json_repair: replace global single-quote swap with context-aware scanner that preserves apostrophes - base_env_manager: wrap Popen stdout iteration in try/finally to prevent FD and zombie process leaks --- easi/cli.py | 19 ++++++++---- easi/core/base_env_manager.py | 13 ++++---- easi/tasks/manipulathor/prompts.py | 6 ++-- easi/utils/json_repair.py | 48 ++++++++++++++++++++++-------- 4 files changed, 60 insertions(+), 26 deletions(-) diff --git a/easi/cli.py b/easi/cli.py index 1acc02d..d13cb2d 100644 --- a/easi/cli.py +++ b/easi/cli.py @@ -466,12 +466,19 @@ def cmd_start(args): num_parallel = run_kwargs.pop("num_parallel", None) or 1 # Parse comma-separated GPU strings into lists of ints - vllm_gpus_str = run_kwargs.pop("vllm_gpus", None) - sim_gpus_str = run_kwargs.pop("sim_gpus", None) - if vllm_gpus_str: - run_kwargs["vllm_gpus"] = [int(g) for g in vllm_gpus_str.split(",")] - if sim_gpus_str: - run_kwargs["sim_gpus"] = [int(g) for g in sim_gpus_str.split(",")] + # When resuming, values from config.json may already be list[int] + vllm_gpus_val = run_kwargs.pop("vllm_gpus", None) + sim_gpus_val = run_kwargs.pop("sim_gpus", None) + if vllm_gpus_val: + if isinstance(vllm_gpus_val, list): + run_kwargs["vllm_gpus"] = [int(g) for g in vllm_gpus_val] + else: + run_kwargs["vllm_gpus"] = [int(g) for g in vllm_gpus_val.split(",")] + if sim_gpus_val: + if isinstance(sim_gpus_val, list): + run_kwargs["sim_gpus"] = [int(g) for g in sim_gpus_val] + else: + run_kwargs["sim_gpus"] = [int(g) for g in sim_gpus_val.split(",")] from easi.core.episode import EpisodeRecord from easi.evaluation.metrics import default_aggregate diff --git a/easi/core/base_env_manager.py b/easi/core/base_env_manager.py index 5cb68e6..0335da0 100644 --- a/easi/core/base_env_manager.py +++ b/easi/core/base_env_manager.py @@ -409,11 +409,14 @@ def _run_command(self, cmd: list[str], description: str, env: dict[str, str] | N env=env, ) output_lines = [] - for line in process.stdout: - line = line.rstrip() - output_lines.append(line) - logger.trace(" %s", line) - process.wait() + try: + for line in process.stdout: + line = line.rstrip() + output_lines.append(line) + logger.trace(" %s", line) + finally: + process.stdout.close() + process.wait() if process.returncode != 0: raise EnvironmentSetupError( f"{description} failed (exit {process.returncode}):\n" diff --git a/easi/tasks/manipulathor/prompts.py b/easi/tasks/manipulathor/prompts.py index b88266c..7d11cea 100644 --- a/easi/tasks/manipulathor/prompts.py +++ b/easi/tasks/manipulathor/prompts.py @@ -95,14 +95,14 @@ def _build_action_list() -> str: ## Response Format Output a JSON object with exactly these 4 fields: -{{{{ +{{ "visual_state_description": "Describe what you see in the image and \ the GPS state", "reasoning_and_reflection": "Your reasoning about the current state \ and what to do next", "language_plan": "Your plan in natural language", - "executable_plan": [{{{{"action": ""}}}}] -}}}} + "executable_plan": [{{"action": ""}}] +}} You may include multiple actions in executable_plan. Actions execute \ sequentially.""" diff --git a/easi/utils/json_repair.py b/easi/utils/json_repair.py index 7b054a4..720b1e7 100644 --- a/easi/utils/json_repair.py +++ b/easi/utils/json_repair.py @@ -4,25 +4,49 @@ import re +def _replace_single_quotes(json_str: str) -> str: + """Replace single quotes used as JSON delimiters with double quotes. + + Only replaces single quotes that appear to be JSON structural delimiters + (around keys and values), not apostrophes inside English text. + """ + # Replace single-quoted JSON keys/values: 'key' or 'value' + # Matches: single quote, content without single quotes, single quote + # followed by a JSON structural character (: , ] } or whitespace before those) + result = [] + i = 0 + n = len(json_str) + in_double_quote = False + while i < n: + ch = json_str[i] + if ch == '"' and (i == 0 or json_str[i - 1] != '\\'): + in_double_quote = not in_double_quote + result.append(ch) + elif ch == "'" and not in_double_quote: + # Apostrophe if letter on BOTH sides (e.g., don't, it's, I'm) + has_alpha_before = i > 0 and json_str[i - 1].isalpha() + has_alpha_after = i + 1 < n and json_str[i + 1].isalpha() + if has_alpha_before and has_alpha_after: + result.append(ch) + else: + result.append('"') + else: + result.append(ch) + i += 1 + return "".join(result) + + def fix_json(json_str: str) -> str: """Fix common JSON errors in LLM output. Handles: - - Single quotes -> double quotes - - Broken contractions from quote replacement + - Single quotes used as JSON delimiters -> double quotes - Markdown code fences - Unescaped quotes inside reasoning_and_reflection value """ - # Replace single quotes with double quotes - json_str = json_str.replace("'", '"') - # Fix contractions that got broken by quote replacement - json_str = json_str.replace('"s ', "'s ") - json_str = json_str.replace('"re ', "'re ") - json_str = json_str.replace('"ll ', "'ll ") - json_str = json_str.replace('"t ', "'t ") - json_str = json_str.replace('"d ', "'d ") - json_str = json_str.replace('"m ', "'m ") - json_str = json_str.replace('"ve ', "'ve ") + # Replace single quotes used as JSON delimiters (not apostrophes) + json_str = _replace_single_quotes(json_str) + json_str = json_str.replace('```json', '').replace('```', '') # Fix unescaped double quotes inside reasoning_and_reflection value. From 5043a0d1e0e147cf46911d943b77afbaa2d84f56 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Mon, 9 Mar 2026 14:29:27 +0800 Subject: [PATCH 167/239] refactor: rename --vllm-gpus/instances to --llm-gpus/instances Generalize vLLM-specific CLI flags and internal variables to support any local LLM backend (vllm, custom). Changes: - CLI: --vllm-instances -> --llm-instances, --vllm-gpus -> --llm-gpus - runner.py: vllm_instances/vllm_gpus params -> llm_instances/llm_gpus - parallel_runner.py: all self.vllm_* -> self.llm_*, backend check includes "custom", error messages updated - server_manager.py: _DEFAULT_VLLM_FLAGS -> _DEFAULT_VLLM_SERVER_FLAGS, MultiServerManager accepts backend param, log messages genericized - All test files updated to match new names --- easi/cli.py | 24 +++++++++--------- easi/evaluation/parallel_runner.py | 34 ++++++++++++------------- easi/evaluation/runner.py | 8 +++--- easi/llm/server_manager.py | 22 ++++++++-------- tests/test_cli.py | 14 +++++------ tests/test_evaluation_runner.py | 2 +- tests/test_parallel_runner.py | 40 +++++++++++++++--------------- tests/test_parallel_vllm.py | 8 +++--- 8 files changed, 77 insertions(+), 75 deletions(-) diff --git a/easi/cli.py b/easi/cli.py index d13cb2d..64062c7 100644 --- a/easi/cli.py +++ b/easi/cli.py @@ -102,7 +102,7 @@ def build_parser() -> argparse.ArgumentParser: help="LLM server URL") start_parser.add_argument("--seed", type=int, default=None, dest="agent_seed") start_parser.add_argument("--backend", type=str, default=None, - help="LLM backend: vllm, openai, anthropic, gemini, dummy") + help="LLM backend: vllm, custom, openai, anthropic, gemini, dummy") start_parser.add_argument("--model", type=str, default=None, help="Model name (HF path for vLLM, API name for proprietary)") start_parser.add_argument("--port", type=int, default=None, @@ -114,14 +114,14 @@ def build_parser() -> argparse.ArgumentParser: start_parser.add_argument("--num-parallel", type=int, default=None, dest="num_parallel", help="Number of parallel simulator instances (default: 1, sequential).") start_parser.add_argument( - "--vllm-instances", type=int, default=None, dest="vllm_instances", - help="Number of vLLM server instances to start (default: 1). " - "Each instance runs on a subset of --vllm-gpus.", + "--llm-instances", type=int, default=None, dest="llm_instances", + help="Number of local LLM server instances to start (default: 1). " + "Each instance runs on a subset of --llm-gpus.", ) start_parser.add_argument( - "--vllm-gpus", type=str, default=None, dest="vllm_gpus", - help="Comma-separated GPU IDs for vLLM inference (e.g., '0,1'). " - "GPUs are split evenly across --vllm-instances.", + "--llm-gpus", type=str, default=None, dest="llm_gpus", + help="Comma-separated GPU IDs for LLM inference (e.g., '0,1'). " + "GPUs are split evenly across --llm-instances.", ) start_parser.add_argument( "--sim-gpus", type=str, default=None, dest="sim_gpus", @@ -467,13 +467,13 @@ def cmd_start(args): # Parse comma-separated GPU strings into lists of ints # When resuming, values from config.json may already be list[int] - vllm_gpus_val = run_kwargs.pop("vllm_gpus", None) + llm_gpus_val = run_kwargs.pop("llm_gpus", None) sim_gpus_val = run_kwargs.pop("sim_gpus", None) - if vllm_gpus_val: - if isinstance(vllm_gpus_val, list): - run_kwargs["vllm_gpus"] = [int(g) for g in vllm_gpus_val] + if llm_gpus_val: + if isinstance(llm_gpus_val, list): + run_kwargs["llm_gpus"] = [int(g) for g in llm_gpus_val] else: - run_kwargs["vllm_gpus"] = [int(g) for g in vllm_gpus_val.split(",")] + run_kwargs["llm_gpus"] = [int(g) for g in llm_gpus_val.split(",")] if sim_gpus_val: if isinstance(sim_gpus_val, list): run_kwargs["sim_gpus"] = [int(g) for g in sim_gpus_val] diff --git a/easi/evaluation/parallel_runner.py b/easi/evaluation/parallel_runner.py index ff490b0..046e5f5 100644 --- a/easi/evaluation/parallel_runner.py +++ b/easi/evaluation/parallel_runner.py @@ -65,34 +65,34 @@ def _serialize_cli_options(self) -> dict: def _validate_gpu_args(self): """Validate GPU allocation arguments.""" - if self.vllm_instances and self.vllm_instances > 1 and not self.vllm_gpus: + if self.llm_instances and self.llm_instances > 1 and not self.llm_gpus: raise ValueError( - "--vllm-gpus is required when --vllm-instances > 1. " - "Specify which GPUs to use for vLLM inference." + "--llm-gpus is required when --llm-instances > 1. " + "Specify which GPUs to use for LLM inference." ) - if self.vllm_gpus and self.sim_gpus: - overlap = set(self.vllm_gpus) & set(self.sim_gpus) + if self.llm_gpus and self.sim_gpus: + overlap = set(self.llm_gpus) & set(self.sim_gpus) if overlap: raise ValueError( - f"--vllm-gpus and --sim-gpus must not overlap. " + f"--llm-gpus and --sim-gpus must not overlap. " f"Overlapping GPU IDs: {overlap}" ) - # Warn if vLLM-specific args are set but backend is not vllm - if self.backend and self.backend != "vllm": + # Warn if local-server args are set but backend is not a local backend + if self.backend and self.backend not in ("vllm", "custom"): ignored = [] - if self.vllm_instances: - ignored.append("--vllm-instances") - if self.vllm_gpus: - ignored.append("--vllm-gpus") + if self.llm_instances: + ignored.append("--llm-instances") + if self.llm_gpus: + ignored.append("--llm-gpus") if ignored: logger.warning( - "%s will be ignored because --backend is '%s' (not 'vllm').", + "%s will be ignored because --backend is '%s' (not a local LLM backend).", ", ".join(ignored), self.backend, ) # Validate GPU IDs against hardware all_gpu_ids = set() - if self.vllm_gpus: - all_gpu_ids.update(self.vllm_gpus) + if self.llm_gpus: + all_gpu_ids.update(self.llm_gpus) if self.sim_gpus: all_gpu_ids.update(self.sim_gpus) if all_gpu_ids: @@ -132,8 +132,8 @@ def run(self) -> list[dict]: all_kw = parse_llm_kwargs(self.llm_kwargs_raw) srv_kw, _ = _split(all_kw) - num_instances = self.vllm_instances or 1 - gpu_ids = self.vllm_gpus + num_instances = self.llm_instances or 1 + gpu_ids = self.llm_gpus server_mgr = MultiServerManager( model=self.model, diff --git a/easi/evaluation/runner.py b/easi/evaluation/runner.py index 638c1a9..52ffe57 100644 --- a/easi/evaluation/runner.py +++ b/easi/evaluation/runner.py @@ -64,8 +64,8 @@ def __init__( resume_dir: Path | str | None = None, refresh_data: bool = False, render_platform: str | None = None, - vllm_instances: int | None = None, - vllm_gpus: list[int] | None = None, + llm_instances: int | None = None, + llm_gpus: list[int] | None = None, sim_gpus: list[int] | None = None, ): # Auto-capture all init args for config.json (before any mutation) @@ -90,8 +90,8 @@ def __init__( self.resume_dir = Path(resume_dir) if resume_dir else None self.refresh_data = refresh_data self.render_platform_name = render_platform - self.vllm_instances = vllm_instances - self.vllm_gpus = vllm_gpus + self.llm_instances = llm_instances + self.llm_gpus = llm_gpus self.sim_gpus = sim_gpus timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") if self.model: diff --git a/easi/llm/server_manager.py b/easi/llm/server_manager.py index 1b7d79d..c4fbb99 100644 --- a/easi/llm/server_manager.py +++ b/easi/llm/server_manager.py @@ -20,7 +20,7 @@ _HEALTH_POLL_INTERVAL = 5.0 _DEFAULT_STARTUP_TIMEOUT = 600.0 -_DEFAULT_VLLM_FLAGS = { +_DEFAULT_VLLM_SERVER_FLAGS = { "enable_prefix_caching": True, "disable_log_requests": True, } @@ -147,10 +147,10 @@ def _build_command(self) -> tuple[list[str], dict]: "--port", str(self.port), ] # Merge defaults with user overrides (user wins) - merged_kwargs = {**_DEFAULT_VLLM_FLAGS, **self.server_kwargs} + merged_kwargs = {**_DEFAULT_VLLM_SERVER_FLAGS, **self.server_kwargs} overridden = { k: v for k, v in self.server_kwargs.items() - if k in _DEFAULT_VLLM_FLAGS and v != _DEFAULT_VLLM_FLAGS[k] + if k in _DEFAULT_VLLM_SERVER_FLAGS and v != _DEFAULT_VLLM_SERVER_FLAGS[k] } if overridden: logger.trace("[%s] User overrides for default vLLM flags: %s", self.label, overridden) @@ -225,7 +225,7 @@ def __exit__(self, *exc) -> None: class MultiServerManager: - """Manages multiple vLLM server instances across GPUs.""" + """Manages multiple local LLM server instances across GPUs.""" def __init__( self, @@ -235,6 +235,7 @@ def __init__( base_port: int = 8000, server_kwargs: dict | None = None, startup_timeout: float = 300.0, + backend: str = "vllm", ): if gpu_ids is not None and len(gpu_ids) % num_instances != 0: raise ValueError( @@ -247,6 +248,7 @@ def __init__( self.base_port = base_port self.server_kwargs = server_kwargs or {} self.startup_timeout = startup_timeout + self.backend = backend self._managers: list[ServerManager] = [] def start(self) -> list[str]: @@ -273,21 +275,21 @@ def start(self) -> list[str]: port = self._find_available_port(next_port) next_port = port + 1 mgr = ServerManager( - backend="vllm", + backend=self.backend, model=self.model, port=port, server_kwargs=self.server_kwargs, startup_timeout=self.startup_timeout, cuda_visible_devices=cuda_devices, - label=f"vllm-{i}", + label=f"{self.backend}-{i}", ) mgr.launch() self._managers.append(mgr) # Phase 2: Wait for all health checks concurrently logger.info( - "All %d vLLM processes spawned, waiting for health checks...", - self.num_instances, + "All %d %s processes spawned, waiting for health checks...", + self.num_instances, self.backend, ) urls = [None] * len(self._managers) with ThreadPoolExecutor(max_workers=len(self._managers)) as pool: @@ -301,8 +303,8 @@ def start(self) -> list[str]: except Exception: logger.warning( - "vLLM startup failed, stopping %d spawned instances", - len(self._managers), + "%s startup failed, stopping %d spawned instances", + self.backend, len(self._managers), ) self.stop() raise diff --git a/tests/test_cli.py b/tests/test_cli.py index 4c50058..1fa543a 100644 --- a/tests/test_cli.py +++ b/tests/test_cli.py @@ -1,9 +1,9 @@ # tests/test_cli.py -"""Tests for CLI GPU and vLLM arguments.""" +"""Tests for CLI GPU and LLM arguments.""" -def test_cli_parses_vllm_gpu_args(): - """CLI should parse --vllm-instances, --vllm-gpus, --sim-gpus.""" +def test_cli_parses_llm_gpu_args(): + """CLI should parse --llm-instances, --llm-gpus, --sim-gpus.""" from easi.cli import build_parser parser = build_parser() args = parser.parse_args([ @@ -12,12 +12,12 @@ def test_cli_parses_vllm_gpu_args(): "--backend", "vllm", "--model", "test", "--num-parallel", "12", - "--vllm-instances", "2", - "--vllm-gpus", "0,1", + "--llm-instances", "2", + "--llm-gpus", "0,1", "--sim-gpus", "2,3", ]) - assert args.vllm_instances == 2 - assert args.vllm_gpus == "0,1" + assert args.llm_instances == 2 + assert args.llm_gpus == "0,1" assert args.sim_gpus == "2,3" diff --git a/tests/test_evaluation_runner.py b/tests/test_evaluation_runner.py index 8c26eac..81b104b 100644 --- a/tests/test_evaluation_runner.py +++ b/tests/test_evaluation_runner.py @@ -97,7 +97,7 @@ def test_cli_options_contains_all_init_args(self): "max_episodes", "llm_base_url", "agent_seed", "backend", "model", "port", "llm_kwargs_raw", "max_retries", "render_platform", - "vllm_instances", "vllm_gpus", "sim_gpus", + "llm_instances", "llm_gpus", "sim_gpus", } assert set(opts.keys()) == expected_keys diff --git a/tests/test_parallel_runner.py b/tests/test_parallel_runner.py index 4175eb5..5b4a640 100644 --- a/tests/test_parallel_runner.py +++ b/tests/test_parallel_runner.py @@ -159,45 +159,45 @@ class TestRunnerGPUArgs: """Test GPU-related args on EvaluationRunner.""" def test_runner_accepts_gpu_args(self): - """EvaluationRunner should accept vllm_instances, vllm_gpus, sim_gpus.""" + """EvaluationRunner should accept llm_instances, llm_gpus, sim_gpus.""" from easi.evaluation.runner import EvaluationRunner runner = EvaluationRunner( task_name="dummy_task", agent_type="dummy", - vllm_instances=2, - vllm_gpus=[0, 1], + llm_instances=2, + llm_gpus=[0, 1], sim_gpus=[2, 3], ) - assert runner.vllm_instances == 2 - assert runner.vllm_gpus == [0, 1] + assert runner.llm_instances == 2 + assert runner.llm_gpus == [0, 1] assert runner.sim_gpus == [2, 3] def test_runner_gpu_args_default_none(self): """GPU args should default to None.""" from easi.evaluation.runner import EvaluationRunner runner = EvaluationRunner(task_name="dummy_task", agent_type="dummy") - assert runner.vllm_instances is None - assert runner.vllm_gpus is None + assert runner.llm_instances is None + assert runner.llm_gpus is None assert runner.sim_gpus is None class TestGPUValidation: """Test GPU allocation validation.""" - def test_vllm_gpus_required_when_instances_specified(self): - """Should raise if --vllm-instances set but --vllm-gpus not provided.""" + def test_llm_gpus_required_when_instances_specified(self): + """Should raise if --llm-instances set but --llm-gpus not provided.""" from easi.evaluation.parallel_runner import ParallelRunner import pytest - with pytest.raises(ValueError, match="--vllm-gpus"): + with pytest.raises(ValueError, match="--llm-gpus"): ParallelRunner( task_name="dummy_task", agent_type="dummy", num_parallel=4, backend="vllm", model="test", - vllm_instances=2, vllm_gpus=None, + llm_instances=2, llm_gpus=None, ) - def test_vllm_gpus_sim_gpus_no_overlap(self): - """Should raise if vllm_gpus and sim_gpus overlap.""" + def test_llm_gpus_sim_gpus_no_overlap(self): + """Should raise if llm_gpus and sim_gpus overlap.""" from easi.evaluation.parallel_runner import ParallelRunner import pytest @@ -205,7 +205,7 @@ def test_vllm_gpus_sim_gpus_no_overlap(self): ParallelRunner( task_name="dummy_task", agent_type="dummy", num_parallel=4, backend="vllm", model="test", - vllm_instances=2, vllm_gpus=[0, 1], sim_gpus=[1, 2], + llm_instances=2, llm_gpus=[0, 1], sim_gpus=[1, 2], ) def test_valid_gpu_config_passes(self): @@ -215,9 +215,9 @@ def test_valid_gpu_config_passes(self): runner = ParallelRunner( task_name="dummy_task", agent_type="dummy", num_parallel=4, backend="vllm", model="test", - vllm_instances=2, vllm_gpus=[0, 1], sim_gpus=[2, 3], + llm_instances=2, llm_gpus=[0, 1], sim_gpus=[2, 3], ) - assert runner.vllm_instances == 2 + assert runner.llm_instances == 2 def test_invalid_gpu_ids_raises(self): """Should raise if GPU IDs exceed hardware count.""" @@ -229,7 +229,7 @@ def test_invalid_gpu_ids_raises(self): ParallelRunner( task_name="dummy_task", agent_type="dummy", num_parallel=4, backend="vllm", model="test", - vllm_instances=2, vllm_gpus=[0, 1, 2, 3], + llm_instances=2, llm_gpus=[0, 1, 2, 3], ) def test_invalid_sim_gpu_ids_raises(self): @@ -253,7 +253,7 @@ def test_negative_gpu_id_raises(self): with pytest.raises(ValueError, match="do not exist"): ParallelRunner( task_name="dummy_task", agent_type="dummy", - num_parallel=4, vllm_gpus=[-1, 0], + num_parallel=4, llm_gpus=[-1, 0], ) def test_gpu_validation_skipped_when_detection_fails(self): @@ -265,9 +265,9 @@ def test_gpu_validation_skipped_when_detection_fails(self): # Should not raise even with absurd GPU IDs runner = ParallelRunner( task_name="dummy_task", agent_type="dummy", - num_parallel=4, vllm_gpus=[99, 100], + num_parallel=4, llm_gpus=[99, 100], ) - assert runner.vllm_gpus == [99, 100] + assert runner.llm_gpus == [99, 100] class TestCLIParallelArg: diff --git a/tests/test_parallel_vllm.py b/tests/test_parallel_vllm.py index 2b2fea3..dc8d12f 100644 --- a/tests/test_parallel_vllm.py +++ b/tests/test_parallel_vllm.py @@ -10,14 +10,14 @@ def test_parallel_vllm_end_to_end(): num_parallel=4, backend="vllm", model="test-model", - vllm_instances=2, - vllm_gpus=[0, 1], + llm_instances=2, + llm_gpus=[0, 1], sim_gpus=[2, 3], ) # Verify config stored correctly - assert runner.vllm_instances == 2 - assert runner.vllm_gpus == [0, 1] + assert runner.llm_instances == 2 + assert runner.llm_gpus == [0, 1] assert runner.sim_gpus == [2, 3] assert runner.num_parallel == 4 From 164a7462dd0dc8170d062158ba8a279359db93fa Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Mon, 9 Mar 2026 14:33:44 +0800 Subject: [PATCH 168/239] feat: add BaseModelServer ABC and message helpers for custom models --- easi/llm/models/__init__.py | 0 easi/llm/models/base_model_server.py | 27 ++++ easi/llm/models/helpers.py | 79 ++++++++++++ tests/test_custom_model_server.py | 177 +++++++++++++++++++++++++++ 4 files changed, 283 insertions(+) create mode 100644 easi/llm/models/__init__.py create mode 100644 easi/llm/models/base_model_server.py create mode 100644 easi/llm/models/helpers.py create mode 100644 tests/test_custom_model_server.py diff --git a/easi/llm/models/__init__.py b/easi/llm/models/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/easi/llm/models/base_model_server.py b/easi/llm/models/base_model_server.py new file mode 100644 index 0000000..8f927cf --- /dev/null +++ b/easi/llm/models/base_model_server.py @@ -0,0 +1,27 @@ +from __future__ import annotations + +from abc import ABC, abstractmethod + +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + + +class BaseModelServer(ABC): + """Abstract base class for custom model servers. + + Subclasses must implement ``load`` and ``generate``. The ``unload`` + method is optional and defaults to a no-op. + """ + + @abstractmethod + def load(self, model_path: str, device: str, **kwargs) -> None: + """Load model weights, tokenizer, processors.""" + + @abstractmethod + def generate(self, messages: list[dict], **kwargs) -> str: + """Generate response from OpenAI-format messages.""" + + def unload(self) -> None: + """Release GPU memory. Optional override.""" + pass diff --git a/easi/llm/models/helpers.py b/easi/llm/models/helpers.py new file mode 100644 index 0000000..0813b26 --- /dev/null +++ b/easi/llm/models/helpers.py @@ -0,0 +1,79 @@ +from __future__ import annotations + +import base64 +import io +from typing import Any + +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + + +def _extract_text_from_content(content: str | list[dict[str, Any]]) -> str: + """Extract concatenated text from an OpenAI-format content field. + + Handles both plain string content and the list-of-parts format. + Image parts are silently skipped. + """ + if isinstance(content, str): + return content + parts: list[str] = [] + for part in content: + if part.get("type") == "text": + parts.append(part.get("text", "")) + return "\n".join(parts) + + +def extract_images(messages: list[dict[str, Any]]) -> list[Any]: + """Extract PIL Images from base64-encoded image_url parts in messages. + + Returns a list of ``PIL.Image.Image`` objects. PIL is imported lazily + so the function can be defined even when Pillow is not installed. + """ + from PIL import Image # lazy import + + images: list[Any] = [] + for msg in messages: + content = msg.get("content") + if not isinstance(content, list): + continue + for part in content: + if part.get("type") != "image_url": + continue + image_url = part.get("image_url", {}) + url = image_url.get("url", "") + if url.startswith("data:"): + # Format: data:;base64, + _, encoded = url.split(",", 1) + else: + encoded = url + raw = base64.b64decode(encoded) + images.append(Image.open(io.BytesIO(raw))) + return images + + +def extract_text_only(messages: list[dict[str, Any]]) -> str: + """Concatenate all text content from messages, ignoring roles and images.""" + parts: list[str] = [] + for msg in messages: + content = msg.get("content", "") + text = _extract_text_from_content(content) + if text: + parts.append(text) + return "\n".join(parts) + + +def extract_by_role(messages: list[dict[str, Any]]) -> dict[str, str]: + """Group text content by role. + + Returns a mapping from role name to the concatenated text for that role. + If a role appears multiple times its texts are joined with newlines. + """ + grouped: dict[str, list[str]] = {} + for msg in messages: + role = msg.get("role", "unknown") + content = msg.get("content", "") + text = _extract_text_from_content(content) + if text: + grouped.setdefault(role, []).append(text) + return {role: "\n".join(texts) for role, texts in grouped.items()} diff --git a/tests/test_custom_model_server.py b/tests/test_custom_model_server.py new file mode 100644 index 0000000..f408e38 --- /dev/null +++ b/tests/test_custom_model_server.py @@ -0,0 +1,177 @@ +from __future__ import annotations + +import base64 + +import pytest + +from easi.llm.models.base_model_server import BaseModelServer +from easi.llm.models.helpers import extract_by_role, extract_images, extract_text_only + +# Minimal valid 1x1 RGB PNG +_PIXEL_PNG = ( + b"\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x01" + b"\x00\x00\x00\x01\x08\x02\x00\x00\x00\x90wS\xde\x00" + b"\x00\x00\x0cIDATx\x9cc\xf8\x0f\x00\x00\x01\x01\x00" + b"\x05\x18\xd8N\x00\x00\x00\x00IEND\xaeB`\x82" +) +_PIXEL_B64 = base64.b64encode(_PIXEL_PNG).decode() + + +# --------------------------------------------------------------------------- +# TestBaseModelServer +# --------------------------------------------------------------------------- + +class TestBaseModelServer: + def test_cannot_instantiate(self): + with pytest.raises(TypeError): + BaseModelServer() # type: ignore[abstract] + + def test_must_implement_load_and_generate(self): + class OnlyLoad(BaseModelServer): + def load(self, model_path, device, **kwargs): + pass + + with pytest.raises(TypeError): + OnlyLoad() # type: ignore[abstract] + + class OnlyGenerate(BaseModelServer): + def generate(self, messages, **kwargs): + return "" + + with pytest.raises(TypeError): + OnlyGenerate() # type: ignore[abstract] + + def test_valid_subclass(self): + class MyServer(BaseModelServer): + def load(self, model_path, device, **kwargs): + pass + + def generate(self, messages, **kwargs): + return "hello" + + server = MyServer() + server.load("path", "cpu") + assert server.generate([]) == "hello" + + def test_default_unload_is_noop(self): + class MyServer(BaseModelServer): + def load(self, model_path, device, **kwargs): + pass + + def generate(self, messages, **kwargs): + return "" + + server = MyServer() + assert server.unload() is None + + +# --------------------------------------------------------------------------- +# TestExtractImages +# --------------------------------------------------------------------------- + +class TestExtractImages: + def test_empty_messages(self): + assert extract_images([]) == [] + + def test_text_only_messages(self): + msgs = [{"role": "user", "content": "hello"}] + assert extract_images(msgs) == [] + + def test_extracts_base64_image(self): + msgs = [ + { + "role": "user", + "content": [ + {"type": "text", "text": "describe"}, + { + "type": "image_url", + "image_url": { + "url": f"data:image/png;base64,{_PIXEL_B64}", + }, + }, + ], + } + ] + images = extract_images(msgs) + assert len(images) == 1 + assert images[0].size == (1, 1) + + def test_multiple_images(self): + part = { + "type": "image_url", + "image_url": {"url": f"data:image/png;base64,{_PIXEL_B64}"}, + } + msgs = [ + {"role": "user", "content": [part, part]}, + {"role": "user", "content": [part]}, + ] + assert len(extract_images(msgs)) == 3 + + +# --------------------------------------------------------------------------- +# TestExtractTextOnly +# --------------------------------------------------------------------------- + +class TestExtractTextOnly: + def test_empty_messages(self): + assert extract_text_only([]) == "" + + def test_string_content(self): + msgs = [ + {"role": "system", "content": "You are helpful."}, + {"role": "user", "content": "Hi"}, + ] + assert extract_text_only(msgs) == "You are helpful.\nHi" + + def test_list_content_with_images_filtered(self): + msgs = [ + { + "role": "user", + "content": [ + {"type": "text", "text": "Look at this"}, + { + "type": "image_url", + "image_url": {"url": f"data:image/png;base64,{_PIXEL_B64}"}, + }, + {"type": "text", "text": "What is it?"}, + ], + } + ] + result = extract_text_only(msgs) + assert "Look at this" in result + assert "What is it?" in result + assert "base64" not in result + + +# --------------------------------------------------------------------------- +# TestExtractByRole +# --------------------------------------------------------------------------- + +class TestExtractByRole: + def test_basic_role_grouping(self): + msgs = [ + {"role": "system", "content": "Be helpful."}, + {"role": "user", "content": "Hello"}, + {"role": "assistant", "content": "Hi there"}, + {"role": "user", "content": "Thanks"}, + ] + result = extract_by_role(msgs) + assert result["system"] == "Be helpful." + assert result["user"] == "Hello\nThanks" + assert result["assistant"] == "Hi there" + + def test_list_content(self): + msgs = [ + { + "role": "user", + "content": [ + {"type": "text", "text": "Describe this"}, + { + "type": "image_url", + "image_url": {"url": f"data:image/png;base64,{_PIXEL_B64}"}, + }, + ], + } + ] + result = extract_by_role(msgs) + assert result["user"] == "Describe this" From 627630268d4a54c250ea28c10150db39fa1ca466 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Mon, 9 Mar 2026 14:35:34 +0800 Subject: [PATCH 169/239] feat: add custom model registry with YAML manifest auto-discovery --- easi/llm/models/registry.py | 114 +++++++++++++++++++++++++++++++++ tests/test_model_registry.py | 118 +++++++++++++++++++++++++++++++++++ 2 files changed, 232 insertions(+) create mode 100644 easi/llm/models/registry.py create mode 100644 tests/test_model_registry.py diff --git a/easi/llm/models/registry.py b/easi/llm/models/registry.py new file mode 100644 index 0000000..71e2758 --- /dev/null +++ b/easi/llm/models/registry.py @@ -0,0 +1,114 @@ +"""Model registry with manifest-based auto-discovery. + +Scans easi/llm/models/*/manifest.yaml to discover available custom model +server configurations. Follows the same pattern as the simulator registry. + +Lookup semantics: +- list_models() → all registered model names +- get_model_entry("my_model") → ModelEntry dataclass +- load_model_class("my_model") → imported class +""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from pathlib import Path + +import yaml + +from easi.utils.import_utils import import_class as _import_class +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + + +@dataclass +class ModelEntry: + """Registry entry for a custom model server.""" + + name: str + display_name: str + description: str + model_class: str # fully qualified class name + default_kwargs: dict = field(default_factory=dict) + + +# Module-level registry populated on first access +_registry: dict[str, ModelEntry] | None = None + + +def _get_models_dir() -> Path: + """Return the directory containing model subdirectories.""" + return Path(__file__).parent + + +def _discover_models() -> dict[str, ModelEntry]: + """Scan model directories for manifest.yaml files.""" + models_dir = _get_models_dir() + entries: dict[str, ModelEntry] = {} + + for manifest_path in sorted(models_dir.glob("*/manifest.yaml")): + try: + manifest = yaml.safe_load(manifest_path.read_text()) + except Exception as e: + logger.warning("Failed to load %s: %s", manifest_path, e) + continue + + try: + entry = ModelEntry( + name=manifest["name"], + display_name=manifest.get("display_name", manifest["name"]), + description=manifest.get("description", ""), + model_class=manifest["model_class"], + default_kwargs=manifest.get("default_kwargs", {}), + ) + entries[entry.name] = entry + logger.trace("Discovered model: %s (%s)", entry.name, entry.display_name) + except KeyError as e: + logger.warning( + "Invalid manifest %s: missing required field %s", manifest_path, e + ) + continue + + return entries + + +def _get_registry() -> dict[str, ModelEntry]: + """Get the model registry, discovering on first access.""" + global _registry + if _registry is None: + _registry = _discover_models() + return _registry + + +def list_models() -> list[str]: + """List all registered model names.""" + return sorted(_get_registry().keys()) + + +def get_model_entry(name: str) -> ModelEntry: + """Look up a model entry by name. + + Args: + name: The model name as defined in its manifest.yaml. + + Raises: + KeyError: If the model is not found. + """ + registry = _get_registry() + if name not in registry: + available = list_models() + raise KeyError(f"Model '{name}' not found. Available: {available}") + return registry[name] + + +def load_model_class(name: str): + """Import and return the model class for the given name.""" + entry = get_model_entry(name) + return _import_class(entry.model_class) + + +def refresh() -> None: + """Force re-discovery of models (useful after adding new ones at runtime).""" + global _registry + _registry = None diff --git a/tests/test_model_registry.py b/tests/test_model_registry.py new file mode 100644 index 0000000..615b85e --- /dev/null +++ b/tests/test_model_registry.py @@ -0,0 +1,118 @@ +"""Tests for model registry with YAML manifest auto-discovery.""" + +from __future__ import annotations + +import pytest + +from easi.llm.models.registry import ( + ModelEntry, + get_model_entry, + list_models, + load_model_class, + refresh, +) + + +# --------------------------------------------------------------------------- +# Fake model class used by load_model_class test +# --------------------------------------------------------------------------- + +class FakeModel: + """Stub model class for testing dynamic import.""" + pass + + +# --------------------------------------------------------------------------- +# Fixtures +# --------------------------------------------------------------------------- + +@pytest.fixture(autouse=True) +def _reset_registry(): + """Ensure a clean registry before and after every test.""" + refresh() + yield + refresh() + + +@pytest.fixture() +def fake_model_dir(tmp_path): + """Create a temporary model directory with a valid manifest.yaml.""" + model_dir = tmp_path / "test_model" + model_dir.mkdir() + manifest = model_dir / "manifest.yaml" + manifest.write_text( + "name: test_model\n" + 'display_name: "Test Model"\n' + 'description: "A model for testing"\n' + 'model_class: "tests.test_model_registry.FakeModel"\n' + "default_kwargs:\n" + ' torch_dtype: "float32"\n' + ) + return tmp_path + + +# --------------------------------------------------------------------------- +# TestModelEntry +# --------------------------------------------------------------------------- + +class TestModelEntry: + """Basic dataclass construction tests.""" + + def test_create_with_defaults(self): + entry = ModelEntry( + name="m1", + display_name="Model One", + description="desc", + model_class="some.module.Class", + ) + assert entry.name == "m1" + assert entry.default_kwargs == {} + + def test_create_with_kwargs(self): + entry = ModelEntry( + name="m2", + display_name="Model Two", + description="desc", + model_class="some.module.Class", + default_kwargs={"tp": 2}, + ) + assert entry.default_kwargs == {"tp": 2} + + +# --------------------------------------------------------------------------- +# TestDiscovery +# --------------------------------------------------------------------------- + +class TestDiscovery: + """Discovery and lookup tests using a temporary model directory.""" + + def test_list_models_discovers_from_manifest(self, fake_model_dir, monkeypatch): + monkeypatch.setattr( + "easi.llm.models.registry._get_models_dir", lambda: fake_model_dir + ) + names = list_models() + assert "test_model" in names + + def test_get_model_entry(self, fake_model_dir, monkeypatch): + monkeypatch.setattr( + "easi.llm.models.registry._get_models_dir", lambda: fake_model_dir + ) + entry = get_model_entry("test_model") + assert entry.display_name == "Test Model" + assert entry.description == "A model for testing" + assert entry.model_class == "tests.test_model_registry.FakeModel" + assert entry.default_kwargs == {"torch_dtype": "float32"} + + def test_get_model_entry_not_found(self, fake_model_dir, monkeypatch): + monkeypatch.setattr( + "easi.llm.models.registry._get_models_dir", lambda: fake_model_dir + ) + with pytest.raises(KeyError, match="no_such_model"): + get_model_entry("no_such_model") + + def test_load_model_class(self, fake_model_dir, monkeypatch): + monkeypatch.setattr( + "easi.llm.models.registry._get_models_dir", lambda: fake_model_dir + ) + cls = load_model_class("test_model") + assert cls is FakeModel From c4c360244f0b7416f78a9fdd9de4fd093010ec4a Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Mon, 9 Mar 2026 14:48:50 +0800 Subject: [PATCH 170/239] feat: wire custom backend into ServerManager and LLM utils --- easi/llm/server_manager.py | 14 +++++++++ easi/llm/utils.py | 1 + tests/test_llm_utils.py | 4 +++ tests/test_server_manager.py | 55 ++++++++++++++++++++++++++++++++++++ 4 files changed, 74 insertions(+) diff --git a/easi/llm/server_manager.py b/easi/llm/server_manager.py index c4fbb99..9d4b5da 100644 --- a/easi/llm/server_manager.py +++ b/easi/llm/server_manager.py @@ -164,6 +164,20 @@ def _build_command(self) -> tuple[list[str], dict]: logger.trace("[%s] Skipping disabled bool flag: %s", self.label, flag) else: cmd.extend([flag, str(value)]) + elif self.backend == "custom": + model_path = self.server_kwargs.get("model_path", self.model) + extra_kwargs = {k: v for k, v in self.server_kwargs.items() if k != "model_path"} + device = "cuda:0" # CUDA_VISIBLE_DEVICES handles GPU remapping + cmd = [ + sys.executable, "-m", "easi.llm.models.http_server", + "--model-name", self.model, + "--model-path", str(model_path), + "--device", device, + "--port", str(self.port), + ] + if extra_kwargs: + import json as _json + cmd.extend(["--kwargs", _json.dumps(extra_kwargs)]) else: raise ValueError(f"Unsupported server backend: {self.backend}") diff --git a/easi/llm/utils.py b/easi/llm/utils.py index baf0745..76ab3b8 100644 --- a/easi/llm/utils.py +++ b/easi/llm/utils.py @@ -11,6 +11,7 @@ "anthropic": "anthropic", "gemini": "gemini", "vllm": "openai", + "custom": "openai", "dummy": "openai", } diff --git a/tests/test_llm_utils.py b/tests/test_llm_utils.py index a2a6a24..6113c42 100644 --- a/tests/test_llm_utils.py +++ b/tests/test_llm_utils.py @@ -82,6 +82,10 @@ def test_dummy_uses_openai_prefix(self): from easi.llm.utils import build_litellm_model assert build_litellm_model("dummy", "default") == "openai/default" + def test_custom_uses_openai_prefix(self): + from easi.llm.utils import build_litellm_model + assert build_litellm_model("custom", "my_model") == "openai/my_model" + def test_unknown_backend_falls_back(self): from easi.llm.utils import build_litellm_model assert build_litellm_model("lmdeploy", "some-model") == "openai/some-model" diff --git a/tests/test_server_manager.py b/tests/test_server_manager.py index 89fccc4..96ef51a 100644 --- a/tests/test_server_manager.py +++ b/tests/test_server_manager.py @@ -109,6 +109,61 @@ def test_server_manager_user_can_override_defaults(self): assert "--enable-prefix-caching" not in cmd +class TestCustomBackendCommand: + def test_custom_backend_builds_command(self): + from easi.llm.server_manager import ServerManager + mgr = ServerManager( + backend="custom", + model="my_model", + port=8001, + server_kwargs={"model_path": "/path/to/weights"}, + ) + cmd, env = mgr._build_command() + assert "easi.llm.models.http_server" in cmd + assert "--model-name" in cmd + assert "my_model" in cmd + assert "--port" in cmd + + def test_custom_backend_passes_model_path(self): + from easi.llm.server_manager import ServerManager + mgr = ServerManager( + backend="custom", + model="my_model", + port=8001, + server_kwargs={"model_path": "/weights"}, + ) + cmd, _ = mgr._build_command() + idx = cmd.index("--model-path") + assert cmd[idx + 1] == "/weights" + + def test_custom_backend_default_device(self): + from easi.llm.server_manager import ServerManager + mgr = ServerManager( + backend="custom", + model="my_model", + port=8001, + ) + cmd, _ = mgr._build_command() + idx = cmd.index("--device") + assert cmd[idx + 1] == "cuda:0" + + def test_custom_backend_extra_kwargs_as_json(self): + from easi.llm.server_manager import ServerManager + mgr = ServerManager( + backend="custom", + model="my_model", + port=8001, + server_kwargs={"model_path": "/w", "torch_dtype": "bfloat16"}, + ) + cmd, _ = mgr._build_command() + assert "--kwargs" in cmd + import json + idx = cmd.index("--kwargs") + parsed = json.loads(cmd[idx + 1]) + assert parsed["torch_dtype"] == "bfloat16" + assert "model_path" not in parsed + + class TestCudaVisibleDevices: def test_server_manager_sets_cuda_visible_devices(self): from easi.llm.server_manager import ServerManager From 74c8833c8cb290bb7205160eb63fb1ebcb8da9b8 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Mon, 9 Mar 2026 14:54:02 +0800 Subject: [PATCH 171/239] feat: add FastAPI HTTP server wrapper for custom models --- easi/llm/models/http_server.py | 148 +++++++++++++++++ tests/test_custom_http_server.py | 266 +++++++++++++++++++++++++++++++ 2 files changed, 414 insertions(+) create mode 100644 easi/llm/models/http_server.py create mode 100644 tests/test_custom_http_server.py diff --git a/easi/llm/models/http_server.py b/easi/llm/models/http_server.py new file mode 100644 index 0000000..98dde85 --- /dev/null +++ b/easi/llm/models/http_server.py @@ -0,0 +1,148 @@ +"""FastAPI HTTP server wrapping a BaseModelServer in OpenAI-compatible endpoints. + +Provides ``create_app`` to build a FastAPI application and ``main`` for +subprocess launch via ``python -m easi.llm.models.http_server``. +""" + +from __future__ import annotations + +import json +import time +import uuid +from typing import Any + +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + +# Generation kwargs recognised from the request body. +_GENERATION_KWARGS = frozenset( + { + "temperature", + "max_tokens", + "top_p", + "top_k", + "frequency_penalty", + "presence_penalty", + "stop", + "seed", + } +) + + +def create_app(model: Any) -> Any: + """Create a FastAPI application that serves *model* over HTTP. + + Parameters + ---------- + model: + A loaded :class:`BaseModelServer` instance. + + Returns + ------- + FastAPI + The application, ready to be passed to ``uvicorn.run``. + """ + from fastapi import FastAPI + from fastapi.responses import JSONResponse + + app = FastAPI(title="EASI Model Server") + + @app.get("/health") + async def health() -> dict: + return {"status": "ok"} + + @app.post("/v1/chat/completions") + async def chat_completions(request: dict) -> JSONResponse: # type: ignore[arg-type] + messages = request.get("messages", []) + req_model = request.get("model", "custom") + + # Extract recognised generation kwargs. + gen_kwargs: dict[str, Any] = {} + for key in _GENERATION_KWARGS: + if key in request: + gen_kwargs[key] = request[key] + + try: + content = model.generate(messages, **gen_kwargs) + except Exception as e: + logger.error("Generation failed: %s", e) + return JSONResponse( + status_code=500, + content={"error": {"message": str(e), "type": "server_error"}}, + ) + + response = { + "id": f"chatcmpl-{uuid.uuid4().hex[:12]}", + "object": "chat.completion", + "created": int(time.time()), + "model": req_model, + "choices": [ + { + "index": 0, + "message": {"role": "assistant", "content": content}, + "finish_reason": "stop", + } + ], + "usage": { + "prompt_tokens": 0, + "completion_tokens": 0, + "total_tokens": 0, + }, + } + return JSONResponse(content=response) + + return app + + +def main() -> None: + """Entry point for subprocess launch. + + Usage:: + + python -m easi.llm.models.http_server \ + --model-name my_model \ + --model-path /path/to/weights \ + --device cuda:0 \ + --port 8000 \ + --kwargs '{"key": "value"}' + """ + import argparse + + import uvicorn + + from easi.llm.models.registry import load_model_class + + parser = argparse.ArgumentParser(description="EASI custom model HTTP server") + parser.add_argument("--model-name", required=True, help="Registered model name") + parser.add_argument("--model-path", required=True, help="Path to model weights") + parser.add_argument("--device", default="cuda:0", help="Device (default: cuda:0)") + parser.add_argument("--port", type=int, default=8000, help="Port (default: 8000)") + parser.add_argument( + "--kwargs", + default="{}", + help="Extra kwargs as JSON string (default: '{}')", + ) + args = parser.parse_args() + + extra_kwargs: dict[str, Any] = json.loads(args.kwargs) + + logger.info( + "Loading model '%s' from %s on %s", + args.model_name, + args.model_path, + args.device, + ) + + cls = load_model_class(args.model_name) + model_instance = cls() + model_instance.load(args.model_path, args.device, **extra_kwargs) + + app = create_app(model_instance) + + logger.info("Starting HTTP server on port %d", args.port) + uvicorn.run(app, host="0.0.0.0", port=args.port, log_level="info") + + +if __name__ == "__main__": + main() diff --git a/tests/test_custom_http_server.py b/tests/test_custom_http_server.py new file mode 100644 index 0000000..698ebaf --- /dev/null +++ b/tests/test_custom_http_server.py @@ -0,0 +1,266 @@ +"""Unit tests for easi.llm.models.http_server.""" + +from __future__ import annotations + +from unittest.mock import MagicMock, patch + +import pytest +from starlette.testclient import TestClient + +from easi.llm.models.base_model_server import BaseModelServer +from easi.llm.models.http_server import create_app + + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + +class EchoModel(BaseModelServer): + """Test model that echoes back the last user message.""" + + def load(self, model_path: str, device: str, **kwargs) -> None: + self._loaded = True + + def generate(self, messages: list[dict], **kwargs) -> str: + # Return last user message content, or empty string. + for msg in reversed(messages): + if msg.get("role") == "user": + return msg.get("content", "") + return "" + + +class ErrorModel(BaseModelServer): + """Test model that always raises an error.""" + + def load(self, model_path: str, device: str, **kwargs) -> None: + pass + + def generate(self, messages: list[dict], **kwargs) -> str: + raise RuntimeError("generation failed") + + +def _make_echo_client() -> TestClient: + model = EchoModel() + model.load("test", "cpu") + app = create_app(model) + return TestClient(app) + + +# --------------------------------------------------------------------------- +# TestCreateAppUnit +# --------------------------------------------------------------------------- + +class TestCreateAppUnit: + """Verify create_app returns a FastAPI app with the expected routes.""" + + def test_returns_fastapi_app(self): + from fastapi import FastAPI + + model = EchoModel() + model.load("test", "cpu") + app = create_app(model) + assert isinstance(app, FastAPI) + + def test_app_has_health_route(self): + model = EchoModel() + model.load("test", "cpu") + app = create_app(model) + paths = [route.path for route in app.routes] + assert "/health" in paths + + def test_app_has_chat_completions_route(self): + model = EchoModel() + model.load("test", "cpu") + app = create_app(model) + paths = [route.path for route in app.routes] + assert "/v1/chat/completions" in paths + + +# --------------------------------------------------------------------------- +# TestEndpoints +# --------------------------------------------------------------------------- + +class TestEndpoints: + """Test endpoints via FastAPI TestClient (no real HTTP server).""" + + def test_health(self): + client = _make_echo_client() + resp = client.get("/health") + assert resp.status_code == 200 + data = resp.json() + assert data["status"] == "ok" + + def test_chat_completions_echo(self): + client = _make_echo_client() + resp = client.post( + "/v1/chat/completions", + json={ + "model": "echo", + "messages": [ + {"role": "system", "content": "You are helpful."}, + {"role": "user", "content": "Hello!"}, + ], + }, + ) + assert resp.status_code == 200 + data = resp.json() + + assert data["object"] == "chat.completion" + assert data["model"] == "echo" + assert len(data["choices"]) == 1 + assert data["choices"][0]["message"]["role"] == "assistant" + assert data["choices"][0]["message"]["content"] == "Hello!" + assert data["choices"][0]["finish_reason"] == "stop" + assert "usage" in data + assert data["id"].startswith("chatcmpl-") + + def test_chat_completions_passes_gen_kwargs(self): + """Verify generation kwargs from the request body are forwarded.""" + model = MagicMock(spec=BaseModelServer) + model.generate.return_value = "ok" + app = create_app(model) + client = TestClient(app) + + client.post( + "/v1/chat/completions", + json={ + "model": "test", + "messages": [{"role": "user", "content": "hi"}], + "temperature": 0.5, + "max_tokens": 100, + "top_p": 0.9, + "top_k": 50, + "seed": 42, + }, + ) + + model.generate.assert_called_once() + _, kwargs = model.generate.call_args + assert kwargs["temperature"] == 0.5 + assert kwargs["max_tokens"] == 100 + assert kwargs["top_p"] == 0.9 + assert kwargs["top_k"] == 50 + assert kwargs["seed"] == 42 + + def test_chat_completions_ignores_unknown_kwargs(self): + """Unknown keys in the request body are not passed to generate.""" + model = MagicMock(spec=BaseModelServer) + model.generate.return_value = "ok" + app = create_app(model) + client = TestClient(app) + + client.post( + "/v1/chat/completions", + json={ + "model": "test", + "messages": [{"role": "user", "content": "hi"}], + "unknown_param": True, + }, + ) + + model.generate.assert_called_once() + _, kwargs = model.generate.call_args + assert "unknown_param" not in kwargs + + def test_chat_completions_error_returns_500(self): + model = ErrorModel() + model.load("test", "cpu") + app = create_app(model) + client = TestClient(app, raise_server_exceptions=False) + + resp = client.post( + "/v1/chat/completions", + json={ + "model": "test", + "messages": [{"role": "user", "content": "hi"}], + }, + ) + assert resp.status_code == 500 + data = resp.json() + assert "error" in data + assert "generation failed" in data["error"]["message"] + + def test_chat_completions_empty_messages(self): + client = _make_echo_client() + resp = client.post( + "/v1/chat/completions", + json={"model": "echo", "messages": []}, + ) + assert resp.status_code == 200 + assert resp.json()["choices"][0]["message"]["content"] == "" + + +# --------------------------------------------------------------------------- +# TestHTTPServerMain +# --------------------------------------------------------------------------- + +class TestHTTPServerMain: + """Verify main() argument parsing with everything mocked.""" + + @patch("uvicorn.run") + @patch("easi.llm.models.http_server.create_app") + def test_main_parses_args(self, mock_create_app, mock_uvicorn_run): + from easi.llm.models.http_server import main + + mock_cls = MagicMock() + mock_instance = MagicMock() + mock_cls.return_value = mock_instance + mock_create_app.return_value = MagicMock() + + with patch( + "easi.llm.models.registry.load_model_class", return_value=mock_cls + ), patch( + "sys.argv", + [ + "http_server", + "--model-name", + "test_model", + "--model-path", + "/tmp/weights", + "--device", + "cpu", + "--port", + "9000", + "--kwargs", + '{"key": "val"}', + ], + ): + main() + + mock_cls.assert_called_once() + mock_instance.load.assert_called_once_with( + "/tmp/weights", "cpu", key="val" + ) + mock_create_app.assert_called_once_with(mock_instance) + mock_uvicorn_run.assert_called_once() + call_kwargs = mock_uvicorn_run.call_args + assert call_kwargs[1]["port"] == 9000 + + @patch("uvicorn.run") + @patch("easi.llm.models.http_server.create_app") + def test_main_defaults(self, mock_create_app, mock_uvicorn_run): + from easi.llm.models.http_server import main + + mock_cls = MagicMock() + mock_instance = MagicMock() + mock_cls.return_value = mock_instance + mock_create_app.return_value = MagicMock() + + with patch( + "easi.llm.models.registry.load_model_class", return_value=mock_cls + ), patch( + "sys.argv", + [ + "http_server", + "--model-name", + "m", + "--model-path", + "/p", + ], + ): + main() + + # Default device is cuda:0, default port is 8000 + mock_instance.load.assert_called_once_with("/p", "cuda:0") + call_kwargs = mock_uvicorn_run.call_args + assert call_kwargs[1]["port"] == 8000 From 05833bad3d3c4b4dabdbea3e3562932669314a51 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Mon, 9 Mar 2026 15:01:11 +0800 Subject: [PATCH 172/239] feat: support custom backend in evaluation runners --- easi/evaluation/parallel_runner.py | 2 +- easi/evaluation/runner.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/easi/evaluation/parallel_runner.py b/easi/evaluation/parallel_runner.py index 046e5f5..41d606c 100644 --- a/easi/evaluation/parallel_runner.py +++ b/easi/evaluation/parallel_runner.py @@ -124,7 +124,7 @@ def run(self) -> list[dict]: server_mgr = None try: - if backend == "vllm" and base_url is None: + if backend in ("vllm", "custom") and base_url is None: # Auto-manage vLLM instances from easi.llm.server_manager import MultiServerManager from easi.llm.utils import parse_llm_kwargs, split_kwargs as _split diff --git a/easi/evaluation/runner.py b/easi/evaluation/runner.py index 52ffe57..5f95689 100644 --- a/easi/evaluation/runner.py +++ b/easi/evaluation/runner.py @@ -163,7 +163,7 @@ def run(self) -> list[dict]: server = None try: - if backend == "vllm" and base_url is None: + if backend in ("vllm", "custom") and base_url is None: from easi.llm.server_manager import ServerManager from easi.llm.utils import parse_llm_kwargs, split_kwargs From 4efab38640b46d654426be32690372309409ffc0 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Mon, 9 Mar 2026 15:01:29 +0800 Subject: [PATCH 173/239] feat: add 'easi model list/info' CLI subcommands --- easi/cli.py | 35 +++++++++++++++++++++++++++++++++++ tests/test_cli.py | 17 +++++++++++++++++ 2 files changed, 52 insertions(+) diff --git a/easi/cli.py b/easi/cli.py index 64062c7..43d1161 100644 --- a/easi/cli.py +++ b/easi/cli.py @@ -136,6 +136,13 @@ def build_parser() -> argparse.ArgumentParser: "--render-platform", type=str, default=None, dest="render_platform", help="Rendering platform: auto, native, xvfb, egl, headless (default: simulator's preference)") + # --- model command --- + model_parser = subparsers.add_parser("model", help="Manage custom models", parents=[common]) + model_sub = model_parser.add_subparsers(dest="model_action") + model_sub.add_parser("list", help="List available custom models", parents=[common]) + model_info_parser = model_sub.add_parser("info", help="Show model details", parents=[common]) + model_info_parser.add_argument("model_name", help="Model name") + # --- llm-server command --- llm_parser = subparsers.add_parser("llm-server", help="Start dummy LLM server", parents=[common]) llm_parser.add_argument("--port", type=int, default=8000) @@ -531,6 +538,31 @@ def cmd_llm_server(host: str, port: int, mode: str, action_space: list[str]) -> run_server(host=host, port=port, mode=mode, action_space=action_space) +def cmd_model(args) -> None: + from easi.llm.models.registry import get_model_entry, list_models + + if args.model_action == "list": + names = list_models() + if not names: + logger.info("No custom models found.") + return + for name in names: + entry = get_model_entry(name) + logger.info(" %s -- %s", name, entry.display_name) + + elif args.model_action == "info": + entry = get_model_entry(args.model_name) + logger.info("Model: %s", entry.display_name) + logger.info(" Name: %s", entry.name) + logger.info(" Description: %s", entry.description) + logger.info(" Model class: %s", entry.model_class) + logger.info(" Default kwargs: %s", entry.default_kwargs) + + else: + from easi.cli import build_parser + build_parser().parse_args(["model", "--help"]) + + # --- Main --- def main() -> None: @@ -575,6 +607,9 @@ def main() -> None: elif args.command == "start": cmd_start(args) + elif args.command == "model": + cmd_model(args) + elif args.command == "llm-server": cmd_llm_server(args.host, args.port, args.mode, args.action_space) diff --git a/tests/test_cli.py b/tests/test_cli.py index 1fa543a..8de70ba 100644 --- a/tests/test_cli.py +++ b/tests/test_cli.py @@ -33,3 +33,20 @@ def test_cli_parses_comma_separated_llm_url(): "--llm-url", "http://localhost:8000/v1,http://localhost:8001/v1", ]) assert args.llm_base_url == "http://localhost:8000/v1,http://localhost:8001/v1" + + +def test_cli_model_list_subcommand(): + from easi.cli import build_parser + parser = build_parser() + args = parser.parse_args(["model", "list"]) + assert args.command == "model" + assert args.model_action == "list" + + +def test_cli_model_info_subcommand(): + from easi.cli import build_parser + parser = build_parser() + args = parser.parse_args(["model", "info", "cambrian"]) + assert args.command == "model" + assert args.model_action == "info" + assert args.model_name == "cambrian" From 029103ee85afd683ed1bd70c658f87766b13cc94 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Mon, 9 Mar 2026 15:01:37 +0800 Subject: [PATCH 174/239] feat: add echo model as built-in example for custom server pipeline --- easi/llm/models/echo/__init__.py | 0 easi/llm/models/echo/manifest.yaml | 5 +++++ easi/llm/models/echo/model.py | 16 ++++++++++++++++ tests/test_model_registry.py | 18 ++++++++++++++++++ 4 files changed, 39 insertions(+) create mode 100644 easi/llm/models/echo/__init__.py create mode 100644 easi/llm/models/echo/manifest.yaml create mode 100644 easi/llm/models/echo/model.py diff --git a/easi/llm/models/echo/__init__.py b/easi/llm/models/echo/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/easi/llm/models/echo/manifest.yaml b/easi/llm/models/echo/manifest.yaml new file mode 100644 index 0000000..3be5b52 --- /dev/null +++ b/easi/llm/models/echo/manifest.yaml @@ -0,0 +1,5 @@ +name: echo +display_name: "Echo Model" +description: "Testing model that echoes back user input. No GPU required." +model_class: "easi.llm.models.echo.model.EchoModel" +default_kwargs: {} diff --git a/easi/llm/models/echo/model.py b/easi/llm/models/echo/model.py new file mode 100644 index 0000000..812b6c4 --- /dev/null +++ b/easi/llm/models/echo/model.py @@ -0,0 +1,16 @@ +"""Echo model for testing the custom model server pipeline.""" +from __future__ import annotations + +from easi.llm.models.base_model_server import BaseModelServer +from easi.llm.models.helpers import extract_text_only + + +class EchoModel(BaseModelServer): + """Returns the user's message back. Useful for testing.""" + + def load(self, model_path: str, device: str, **kwargs) -> None: + pass + + def generate(self, messages: list[dict], **kwargs) -> str: + text = extract_text_only(messages) + return f"Echo: {text}" diff --git a/tests/test_model_registry.py b/tests/test_model_registry.py index 615b85e..b8fd806 100644 --- a/tests/test_model_registry.py +++ b/tests/test_model_registry.py @@ -116,3 +116,21 @@ def test_load_model_class(self, fake_model_dir, monkeypatch): ) cls = load_model_class("test_model") assert cls is FakeModel + + +# --------------------------------------------------------------------------- +# TestBuiltInEchoModel +# --------------------------------------------------------------------------- + +class TestBuiltInEchoModel: + def test_echo_model_discovered(self): + refresh() + assert "echo" in list_models() + + def test_echo_model_loadable(self): + refresh() + cls = load_model_class("echo") + instance = cls() + instance.load("dummy", "cpu") + result = instance.generate([{"role": "user", "content": "hello"}]) + assert "hello" in result.lower() From 372b4be329ce5cd4855c7e175357e192ddd1c839 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Mon, 9 Mar 2026 15:53:07 +0800 Subject: [PATCH 175/239] feat: add Qwen3-VL custom model example with message format conversion --- easi/llm/models/qwen3_vl/__init__.py | 0 easi/llm/models/qwen3_vl/manifest.yaml | 10 ++ easi/llm/models/qwen3_vl/model.py | 163 +++++++++++++++++++++++++ tests/test_qwen3_vl_model.py | 133 ++++++++++++++++++++ 4 files changed, 306 insertions(+) create mode 100644 easi/llm/models/qwen3_vl/__init__.py create mode 100644 easi/llm/models/qwen3_vl/manifest.yaml create mode 100644 easi/llm/models/qwen3_vl/model.py create mode 100644 tests/test_qwen3_vl_model.py diff --git a/easi/llm/models/qwen3_vl/__init__.py b/easi/llm/models/qwen3_vl/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/easi/llm/models/qwen3_vl/manifest.yaml b/easi/llm/models/qwen3_vl/manifest.yaml new file mode 100644 index 0000000..e4591ea --- /dev/null +++ b/easi/llm/models/qwen3_vl/manifest.yaml @@ -0,0 +1,10 @@ +name: qwen3_vl +display_name: "Qwen3-VL" +description: > + Qwen3-VL vision-language model family (8B, 72B, etc.). + Supports image understanding with OpenAI-format messages. + Requires: pip install transformers torch torchvision pillow +model_class: "easi.llm.models.qwen3_vl.model.Qwen3VLModel" +default_kwargs: + torch_dtype: "bfloat16" + attn_implementation: "flash_attention_2" diff --git a/easi/llm/models/qwen3_vl/model.py b/easi/llm/models/qwen3_vl/model.py new file mode 100644 index 0000000..3439b74 --- /dev/null +++ b/easi/llm/models/qwen3_vl/model.py @@ -0,0 +1,163 @@ +"""Qwen3-VL custom model server for EASI. + +Loads Qwen3-VL models (8B, 72B, etc.) via HuggingFace Transformers and +serves them through the custom model server pipeline. + +Requires: + pip install transformers torch torchvision pillow + +Usage: + easi start --backend custom --model qwen3_vl \ + --llm-kwargs '{"model_path": "Qwen/Qwen3-VL-8B-Instruct"}' +""" +from __future__ import annotations + +from easi.llm.models.base_model_server import BaseModelServer +from easi.llm.models.helpers import extract_images +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + +# Dtype string → torch dtype mapping +_DTYPE_MAP = { + "bfloat16": "bfloat16", + "float16": "float16", + "float32": "float32", + "auto": "auto", +} + + +def _openai_to_qwen_messages(messages: list[dict], images: list) -> list[dict]: + """Convert OpenAI-format messages to Qwen3-VL format. + + OpenAI format: + {"type": "image_url", "image_url": {"url": "data:image/png;base64,..."}} + Qwen format: + {"type": "image", "image": } + """ + image_idx = 0 + converted = [] + for msg in messages: + role = msg.get("role", "user") + content = msg.get("content", "") + + if isinstance(content, str): + converted.append({"role": role, "content": content}) + continue + + new_content = [] + for part in content: + ptype = part.get("type", "") + if ptype == "image_url" and image_idx < len(images): + new_content.append({"type": "image", "image": images[image_idx]}) + image_idx += 1 + elif ptype == "text": + new_content.append({"type": "text", "text": part.get("text", "")}) + + converted.append({"role": role, "content": new_content}) + + return converted + + +class Qwen3VLModel(BaseModelServer): + """Qwen3-VL vision-language model server.""" + + def load(self, model_path: str, device: str, **kwargs) -> None: + """Load Qwen3-VL model and processor. + + Args: + model_path: HuggingFace model ID (e.g., "Qwen/Qwen3-VL-8B-Instruct") + or local path to model weights. + device: Device string (e.g., "cuda:0"). When using device_map="auto", + this is used as fallback. + **kwargs: Extra kwargs passed to from_pretrained. + Supported: torch_dtype, attn_implementation. + """ + import torch + from transformers import AutoProcessor, Qwen3VLForConditionalGeneration + + # Resolve torch dtype + dtype_str = kwargs.pop("torch_dtype", "auto") + if dtype_str in _DTYPE_MAP: + torch_dtype = getattr(torch, dtype_str, "auto") if dtype_str != "auto" else "auto" + else: + torch_dtype = "auto" + + attn_impl = kwargs.pop("attn_implementation", None) + + load_kwargs = {"torch_dtype": torch_dtype, "device_map": "auto"} + if attn_impl: + load_kwargs["attn_implementation"] = attn_impl + + logger.info("Loading Qwen3-VL from %s (dtype=%s, attn=%s)", model_path, dtype_str, attn_impl) + self.model = Qwen3VLForConditionalGeneration.from_pretrained( + model_path, **load_kwargs + ) + self.processor = AutoProcessor.from_pretrained(model_path) + self.device = self.model.device + logger.info("Qwen3-VL loaded on %s", self.device) + + def generate(self, messages: list[dict], **kwargs) -> str: + """Generate response from OpenAI-format messages. + + Converts OpenAI message format to Qwen3-VL format, processes + images via the Qwen processor, and runs generation. + """ + import torch + + # Extract images from OpenAI-format base64 entries + images = extract_images(messages) + + # Convert message format + qwen_messages = _openai_to_qwen_messages(messages, images) + + # Process with Qwen processor (handles tokenization + image processing) + inputs = self.processor.apply_chat_template( + qwen_messages, + tokenize=True, + add_generation_prompt=True, + return_dict=True, + return_tensors="pt", + ) + inputs = inputs.to(self.device) + + # Generation kwargs + max_new_tokens = kwargs.get("max_tokens", 4096) + temperature = kwargs.get("temperature", 0.0) + top_p = kwargs.get("top_p", 0.95) + + gen_kwargs = {"max_new_tokens": max_new_tokens} + if temperature > 0: + gen_kwargs["temperature"] = temperature + gen_kwargs["top_p"] = top_p + gen_kwargs["do_sample"] = True + else: + gen_kwargs["do_sample"] = False + + with torch.no_grad(): + generated_ids = self.model.generate(**inputs, **gen_kwargs) + + # Trim input tokens from output + generated_ids_trimmed = [ + out_ids[len(in_ids):] + for in_ids, out_ids in zip(inputs.input_ids, generated_ids) + ] + output_text = self.processor.batch_decode( + generated_ids_trimmed, + skip_special_tokens=True, + clean_up_tokenization_spaces=False, + ) + + return output_text[0] if output_text else "" + + def unload(self) -> None: + """Release GPU memory.""" + if hasattr(self, "model"): + del self.model + if hasattr(self, "processor"): + del self.processor + + import torch + if torch.cuda.is_available(): + torch.cuda.empty_cache() + logger.info("Qwen3-VL model unloaded") diff --git a/tests/test_qwen3_vl_model.py b/tests/test_qwen3_vl_model.py new file mode 100644 index 0000000..0f2a3c5 --- /dev/null +++ b/tests/test_qwen3_vl_model.py @@ -0,0 +1,133 @@ +"""Tests for Qwen3-VL custom model server. + +Tests model discovery, message format conversion, and interface compliance +without requiring actual model weights or GPU. +""" +import base64 + +import pytest + +from easi.llm.models.registry import get_model_entry, list_models, load_model_class, refresh +from easi.llm.models.qwen3_vl.model import _openai_to_qwen_messages + + +@pytest.fixture(autouse=True) +def _clear_registry(): + refresh() + yield + refresh() + + +# -- Registry discovery -- + +class TestQwen3VLDiscovery: + def test_discovered_in_registry(self): + assert "qwen3_vl" in list_models() + + def test_model_entry_fields(self): + entry = get_model_entry("qwen3_vl") + assert entry.display_name == "Qwen3-VL" + assert entry.model_class == "easi.llm.models.qwen3_vl.model.Qwen3VLModel" + assert entry.default_kwargs.get("torch_dtype") == "bfloat16" + + def test_class_importable(self): + cls = load_model_class("qwen3_vl") + assert cls.__name__ == "Qwen3VLModel" + + def test_is_base_model_server_subclass(self): + from easi.llm.models.base_model_server import BaseModelServer + cls = load_model_class("qwen3_vl") + assert issubclass(cls, BaseModelServer) + + +# -- Message format conversion -- + +class TestOpenAIToQwenMessages: + """Test _openai_to_qwen_messages conversion.""" + + def test_text_only_passthrough(self): + messages = [ + {"role": "system", "content": "You are helpful"}, + {"role": "user", "content": "Hello"}, + ] + result = _openai_to_qwen_messages(messages, images=[]) + assert result[0] == {"role": "system", "content": "You are helpful"} + assert result[1] == {"role": "user", "content": "Hello"} + + def test_image_url_converted_to_qwen_format(self): + """OpenAI image_url entries should become Qwen image entries.""" + from PIL import Image + fake_img = Image.new("RGB", (1, 1), color="red") + + messages = [ + {"role": "user", "content": [ + {"type": "image_url", "image_url": {"url": "data:image/png;base64,abc"}}, + {"type": "text", "text": "What is this?"}, + ]}, + ] + result = _openai_to_qwen_messages(messages, images=[fake_img]) + + user_content = result[0]["content"] + assert len(user_content) == 2 + assert user_content[0]["type"] == "image" + assert user_content[0]["image"] is fake_img + assert user_content[1]["type"] == "text" + assert user_content[1]["text"] == "What is this?" + + def test_multiple_images(self): + from PIL import Image + img1 = Image.new("RGB", (1, 1), color="red") + img2 = Image.new("RGB", (1, 1), color="blue") + + messages = [ + {"role": "user", "content": [ + {"type": "image_url", "image_url": {"url": "data:image/png;base64,aaa"}}, + {"type": "image_url", "image_url": {"url": "data:image/png;base64,bbb"}}, + {"type": "text", "text": "Compare these"}, + ]}, + ] + result = _openai_to_qwen_messages(messages, images=[img1, img2]) + + user_content = result[0]["content"] + assert user_content[0]["image"] is img1 + assert user_content[1]["image"] is img2 + + def test_preserves_roles(self): + messages = [ + {"role": "system", "content": "System prompt"}, + {"role": "user", "content": [ + {"type": "text", "text": "Hello"}, + ]}, + {"role": "assistant", "content": "Hi there"}, + ] + result = _openai_to_qwen_messages(messages, images=[]) + assert [m["role"] for m in result] == ["system", "user", "assistant"] + + def test_no_images_when_none_extracted(self): + """image_url entries without matching images are skipped.""" + messages = [ + {"role": "user", "content": [ + {"type": "image_url", "image_url": {"url": "data:image/png;base64,abc"}}, + {"type": "text", "text": "Hello"}, + ]}, + ] + result = _openai_to_qwen_messages(messages, images=[]) + user_content = result[0]["content"] + # image_url entry should be skipped since no images provided + assert len(user_content) == 1 + assert user_content[0]["type"] == "text" + + def test_mixed_text_and_images_across_messages(self): + from PIL import Image + img = Image.new("RGB", (1, 1)) + + messages = [ + {"role": "system", "content": "Be helpful"}, + {"role": "user", "content": [ + {"type": "image_url", "image_url": {"url": "data:image/png;base64,abc"}}, + {"type": "text", "text": "Describe"}, + ]}, + ] + result = _openai_to_qwen_messages(messages, images=[img]) + assert result[0]["content"] == "Be helpful" + assert result[1]["content"][0]["type"] == "image" From 77667dda6205af062834c3176b7979d08893f482 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Mon, 9 Mar 2026 16:07:16 +0800 Subject: [PATCH 176/239] fix: pass actual backend to ServerManager instead of hardcoded 'vllm' --- easi/evaluation/parallel_runner.py | 1 + easi/evaluation/runner.py | 2 +- easi/llm/utils.py | 4 +++- 3 files changed, 5 insertions(+), 2 deletions(-) diff --git a/easi/evaluation/parallel_runner.py b/easi/evaluation/parallel_runner.py index 41d606c..567b5a9 100644 --- a/easi/evaluation/parallel_runner.py +++ b/easi/evaluation/parallel_runner.py @@ -141,6 +141,7 @@ def run(self) -> list[dict]: gpu_ids=gpu_ids, base_port=self.port, server_kwargs=srv_kw, + backend=backend, ) base_urls = server_mgr.start() elif base_url: diff --git a/easi/evaluation/runner.py b/easi/evaluation/runner.py index 5f95689..ac294df 100644 --- a/easi/evaluation/runner.py +++ b/easi/evaluation/runner.py @@ -170,7 +170,7 @@ def run(self) -> list[dict]: all_kwargs = parse_llm_kwargs(self.llm_kwargs_raw) server_kwargs, _ = split_kwargs(all_kwargs) server = ServerManager( - "vllm", self.model, port=self.port, + backend, self.model, port=self.port, server_kwargs=server_kwargs, ) base_url = server.start() diff --git a/easi/llm/utils.py b/easi/llm/utils.py index 76ab3b8..d192423 100644 --- a/easi/llm/utils.py +++ b/easi/llm/utils.py @@ -22,7 +22,8 @@ "gemini": "GOOGLE_API_KEY", } -# kwargs that belong to the inference server (vLLM CLI flags), not the API call. +# kwargs that belong to the inference server, not the API call. +# Includes vLLM CLI flags and custom server params (model_path). _SERVER_KWARGS = { "tensor_parallel_size", "gpu_memory_utilization", @@ -37,6 +38,7 @@ "max_num_seqs", "disable_log_requests", "limit_mm_per_prompt", + "model_path", } From 99556adefb00768da8a1000984642abafc64485a Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Mon, 9 Mar 2026 17:25:22 +0800 Subject: [PATCH 177/239] fix: multiple bugs in custom model server pipeline - Fix Qwen3-VL model.device crash with device_map="auto" (use next(params).device) - Fix inputs.to(device) on BatchEncoding with non-tensor fields (dict comprehension) - Fix inputs.input_ids attribute access after dict conversion (use bracket access) - Apply manifest default_kwargs in http_server.py (were silently ignored) - Add dummy api_key for local servers in LLMClient (LiteLLM requires it for openai/ prefix) - Catch requests.Timeout in health poll (was only catching ConnectionError) - Fix extract_images crash on non-base64 URLs (skip http:// and non-base64 data URIs) - Graceful fallback when accelerate or flash-attn not installed - Use dtype instead of deprecated torch_dtype for newer transformers - Handle KeyboardInterrupt cleanly in CLI (no giant traceback) - Add [custom-models] optional dependency group in pyproject.toml - Include llm/models/*/manifest.yaml in package data - Log full traceback on generation errors in http_server --- easi/cli.py | 8 ++++++ easi/llm/client.py | 3 ++ easi/llm/models/helpers.py | 12 +++++--- easi/llm/models/http_server.py | 13 ++++++--- easi/llm/models/qwen3_vl/manifest.yaml | 4 +-- easi/llm/models/qwen3_vl/model.py | 40 +++++++++++++++++++++----- easi/llm/server_manager.py | 4 +-- pyproject.toml | 10 +++++++ tests/test_custom_http_server.py | 13 ++++++++- tests/test_qwen3_vl_model.py | 2 +- 10 files changed, 88 insertions(+), 21 deletions(-) diff --git a/easi/cli.py b/easi/cli.py index 43d1161..f8e09f0 100644 --- a/easi/cli.py +++ b/easi/cli.py @@ -566,6 +566,14 @@ def cmd_model(args) -> None: # --- Main --- def main() -> None: + try: + _main() + except KeyboardInterrupt: + logger.info("Interrupted by user.") + sys.exit(130) + + +def _main() -> None: parser = build_parser() args = parser.parse_args() diff --git a/easi/llm/client.py b/easi/llm/client.py index 5df6980..c32e514 100644 --- a/easi/llm/client.py +++ b/easi/llm/client.py @@ -85,6 +85,9 @@ def generate(self, messages: list[dict], response_format: dict | None = None) -> } if self.base_url: call_kwargs["api_base"] = self.base_url + # Local servers (vLLM, custom) don't need a real API key, + # but LiteLLM requires one for the openai/ prefix. + call_kwargs.setdefault("api_key", "dummy") if response_format is not None: call_kwargs["response_format"] = response_format diff --git a/easi/llm/models/helpers.py b/easi/llm/models/helpers.py index 0813b26..98bb1c2 100644 --- a/easi/llm/models/helpers.py +++ b/easi/llm/models/helpers.py @@ -42,13 +42,17 @@ def extract_images(messages: list[dict[str, Any]]) -> list[Any]: continue image_url = part.get("image_url", {}) url = image_url.get("url", "") - if url.startswith("data:"): + if url.startswith("data:") and ";base64," in url: # Format: data:;base64, _, encoded = url.split(",", 1) + raw = base64.b64decode(encoded) + images.append(Image.open(io.BytesIO(raw))) + elif url.startswith("data:"): + # Non-base64 data URI (e.g. data:text/plain,...) — skip + logger.debug("Skipping non-base64 data URI") else: - encoded = url - raw = base64.b64decode(encoded) - images.append(Image.open(io.BytesIO(raw))) + # HTTP/HTTPS URLs — not yet supported for extraction + logger.debug("Skipping non-data image URL: %s", url[:80]) return images diff --git a/easi/llm/models/http_server.py b/easi/llm/models/http_server.py index 98dde85..260956a 100644 --- a/easi/llm/models/http_server.py +++ b/easi/llm/models/http_server.py @@ -66,7 +66,7 @@ async def chat_completions(request: dict) -> JSONResponse: # type: ignore[arg-t try: content = model.generate(messages, **gen_kwargs) except Exception as e: - logger.error("Generation failed: %s", e) + logger.error("Generation failed: %s", e, exc_info=True) return JSONResponse( status_code=500, content={"error": {"message": str(e), "type": "server_error"}}, @@ -111,7 +111,7 @@ def main() -> None: import uvicorn - from easi.llm.models.registry import load_model_class + from easi.llm.models.registry import get_model_entry, load_model_class parser = argparse.ArgumentParser(description="EASI custom model HTTP server") parser.add_argument("--model-name", required=True, help="Registered model name") @@ -127,16 +127,21 @@ def main() -> None: extra_kwargs: dict[str, Any] = json.loads(args.kwargs) + # Merge manifest default_kwargs with CLI overrides (CLI wins) + entry = get_model_entry(args.model_name) + merged_kwargs = {**entry.default_kwargs, **extra_kwargs} + logger.info( - "Loading model '%s' from %s on %s", + "Loading model '%s' from %s on %s (kwargs=%s)", args.model_name, args.model_path, args.device, + merged_kwargs, ) cls = load_model_class(args.model_name) model_instance = cls() - model_instance.load(args.model_path, args.device, **extra_kwargs) + model_instance.load(args.model_path, args.device, **merged_kwargs) app = create_app(model_instance) diff --git a/easi/llm/models/qwen3_vl/manifest.yaml b/easi/llm/models/qwen3_vl/manifest.yaml index e4591ea..22da378 100644 --- a/easi/llm/models/qwen3_vl/manifest.yaml +++ b/easi/llm/models/qwen3_vl/manifest.yaml @@ -3,8 +3,8 @@ display_name: "Qwen3-VL" description: > Qwen3-VL vision-language model family (8B, 72B, etc.). Supports image understanding with OpenAI-format messages. - Requires: pip install transformers torch torchvision pillow + Requires: uv pip install transformers torch torchvision pillow model_class: "easi.llm.models.qwen3_vl.model.Qwen3VLModel" default_kwargs: - torch_dtype: "bfloat16" + dtype: "bfloat16" attn_implementation: "flash_attention_2" diff --git a/easi/llm/models/qwen3_vl/model.py b/easi/llm/models/qwen3_vl/model.py index 3439b74..e31b5c1 100644 --- a/easi/llm/models/qwen3_vl/model.py +++ b/easi/llm/models/qwen3_vl/model.py @@ -76,8 +76,8 @@ def load(self, model_path: str, device: str, **kwargs) -> None: import torch from transformers import AutoProcessor, Qwen3VLForConditionalGeneration - # Resolve torch dtype - dtype_str = kwargs.pop("torch_dtype", "auto") + # Resolve torch dtype — newer transformers uses "dtype" instead of "torch_dtype" + dtype_str = kwargs.pop("torch_dtype", kwargs.pop("dtype", "auto")) if dtype_str in _DTYPE_MAP: torch_dtype = getattr(torch, dtype_str, "auto") if dtype_str != "auto" else "auto" else: @@ -85,16 +85,42 @@ def load(self, model_path: str, device: str, **kwargs) -> None: attn_impl = kwargs.pop("attn_implementation", None) - load_kwargs = {"torch_dtype": torch_dtype, "device_map": "auto"} + # Use device_map="auto" only if accelerate is available; otherwise + # fall back to loading on the specified device directly. + try: + import accelerate # noqa: F401 + load_kwargs = {"dtype": torch_dtype, "device_map": "auto"} + except ImportError: + logger.info("accelerate not installed, loading model on %s without device_map", device) + load_kwargs = {"dtype": torch_dtype} + if attn_impl: - load_kwargs["attn_implementation"] = attn_impl + # Validate flash_attention_2 availability before requesting it + if attn_impl == "flash_attention_2": + try: + import flash_attn # noqa: F401 + load_kwargs["attn_implementation"] = attn_impl + except ImportError: + logger.warning( + "flash_attn not installed, falling back to sdpa attention. " + "Install with: pip install flash-attn --no-build-isolation" + ) + load_kwargs["attn_implementation"] = "sdpa" + else: + load_kwargs["attn_implementation"] = attn_impl logger.info("Loading Qwen3-VL from %s (dtype=%s, attn=%s)", model_path, dtype_str, attn_impl) self.model = Qwen3VLForConditionalGeneration.from_pretrained( model_path, **load_kwargs ) + # Move to device if device_map was not used + if "device_map" not in load_kwargs: + self.model = self.model.to(device) + self.processor = AutoProcessor.from_pretrained(model_path) - self.device = self.model.device + # device_map="auto" shards across GPUs; .device would raise RuntimeError. + # Use the device of the first parameter instead. + self.device = next(self.model.parameters()).device logger.info("Qwen3-VL loaded on %s", self.device) def generate(self, messages: list[dict], **kwargs) -> str: @@ -119,7 +145,7 @@ def generate(self, messages: list[dict], **kwargs) -> str: return_dict=True, return_tensors="pt", ) - inputs = inputs.to(self.device) + inputs = {k: v.to(self.device) if hasattr(v, "to") else v for k, v in inputs.items()} # Generation kwargs max_new_tokens = kwargs.get("max_tokens", 4096) @@ -140,7 +166,7 @@ def generate(self, messages: list[dict], **kwargs) -> str: # Trim input tokens from output generated_ids_trimmed = [ out_ids[len(in_ids):] - for in_ids, out_ids in zip(inputs.input_ids, generated_ids) + for in_ids, out_ids in zip(inputs["input_ids"], generated_ids) ] output_text = self.processor.batch_decode( generated_ids_trimmed, diff --git a/easi/llm/server_manager.py b/easi/llm/server_manager.py index 9d4b5da..2917c68 100644 --- a/easi/llm/server_manager.py +++ b/easi/llm/server_manager.py @@ -220,8 +220,8 @@ def _wait_for_health(self, base_url: str) -> None: logger.trace("[%s] Health check passed (status=%d)", self.label, resp.status_code) return logger.trace("[%s] Health check returned status %d, retrying...", self.label, resp.status_code) - except requests.ConnectionError: - logger.trace("[%s] Health check connection refused, retrying...", self.label) + except (requests.ConnectionError, requests.Timeout): + logger.trace("[%s] Health check connection refused/timed out, retrying...", self.label) time.sleep(_HEALTH_POLL_INTERVAL) diff --git a/pyproject.toml b/pyproject.toml index e94d463..69eef6b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -25,6 +25,15 @@ vllm = [ "vllm>=0.15.1", "transformers>=4.40.0,<5.0" ] +custom-models = [ + "torch==2.7.1", + "torchvision==0.22.1", + "transformers>=4.40.0,<5.0", + "accelerate>=0.26.0", + "fastapi>=0.100.0", + "uvicorn>=0.23.0", + "Pillow>=10.0", +] [project.scripts] easi = "easi.cli:main" @@ -39,4 +48,5 @@ exclude = ["VLMEvalKit*", "lmms-eval*"] "simulators/*/*/conda_env.yaml", "simulators/*/*/requirements.txt", "tasks/*/*.yaml", + "llm/models/*/manifest.yaml", ] diff --git a/tests/test_custom_http_server.py b/tests/test_custom_http_server.py index 698ebaf..eebf842 100644 --- a/tests/test_custom_http_server.py +++ b/tests/test_custom_http_server.py @@ -207,8 +207,13 @@ def test_main_parses_args(self, mock_create_app, mock_uvicorn_run): mock_cls.return_value = mock_instance mock_create_app.return_value = MagicMock() + mock_entry = MagicMock() + mock_entry.default_kwargs = {"default_key": "default_val"} + with patch( "easi.llm.models.registry.load_model_class", return_value=mock_cls + ), patch( + "easi.llm.models.registry.get_model_entry", return_value=mock_entry ), patch( "sys.argv", [ @@ -228,8 +233,9 @@ def test_main_parses_args(self, mock_create_app, mock_uvicorn_run): main() mock_cls.assert_called_once() + # CLI kwargs override manifest defaults mock_instance.load.assert_called_once_with( - "/tmp/weights", "cpu", key="val" + "/tmp/weights", "cpu", default_key="default_val", key="val" ) mock_create_app.assert_called_once_with(mock_instance) mock_uvicorn_run.assert_called_once() @@ -246,8 +252,13 @@ def test_main_defaults(self, mock_create_app, mock_uvicorn_run): mock_cls.return_value = mock_instance mock_create_app.return_value = MagicMock() + mock_entry = MagicMock() + mock_entry.default_kwargs = {} + with patch( "easi.llm.models.registry.load_model_class", return_value=mock_cls + ), patch( + "easi.llm.models.registry.get_model_entry", return_value=mock_entry ), patch( "sys.argv", [ diff --git a/tests/test_qwen3_vl_model.py b/tests/test_qwen3_vl_model.py index 0f2a3c5..e543182 100644 --- a/tests/test_qwen3_vl_model.py +++ b/tests/test_qwen3_vl_model.py @@ -28,7 +28,7 @@ def test_model_entry_fields(self): entry = get_model_entry("qwen3_vl") assert entry.display_name == "Qwen3-VL" assert entry.model_class == "easi.llm.models.qwen3_vl.model.Qwen3VLModel" - assert entry.default_kwargs.get("torch_dtype") == "bfloat16" + assert entry.default_kwargs.get("dtype") == "bfloat16" def test_class_importable(self): cls = load_model_class("qwen3_vl") From c3876bc229a5218a74a86273a81363b0d2952b9f Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Mon, 9 Mar 2026 17:43:42 +0800 Subject: [PATCH 178/239] feat: add per-worker labels to simulator subprocess logs Add label parameter to SubprocessRunner so parallel workers log as [bridge-0], [bridge-1], etc. instead of all showing [bridge]. Sequential runs keep the default [bridge] label. --- easi/evaluation/parallel_runner.py | 5 ++++- easi/evaluation/runner.py | 4 +++- easi/simulators/subprocess_runner.py | 4 +++- 3 files changed, 10 insertions(+), 3 deletions(-) diff --git a/easi/evaluation/parallel_runner.py b/easi/evaluation/parallel_runner.py index 567b5a9..a069d29 100644 --- a/easi/evaluation/parallel_runner.py +++ b/easi/evaluation/parallel_runner.py @@ -242,7 +242,9 @@ def _worker(worker_id: int) -> None: "[Worker %d] Creating simulator (key=%s)", worker_id, task.simulator_key, ) - sim, sim_runner = self._create_simulator(task.simulator_key, task=task) + sim, sim_runner = self._create_simulator( + task.simulator_key, task=task, label=f"bridge-{worker_id}", + ) logger.trace( "[Worker %d] Simulator ready (PID=%s)", worker_id, @@ -315,6 +317,7 @@ def _worker(worker_id: int) -> None: pass sim, sim_runner = self._create_simulator( task.simulator_key, task=task, + label=f"bridge-{worker_id}", ) else: logger.error( diff --git a/easi/evaluation/runner.py b/easi/evaluation/runner.py index ac294df..efd4442 100644 --- a/easi/evaluation/runner.py +++ b/easi/evaluation/runner.py @@ -557,7 +557,7 @@ def _create_agent(self, action_space: list[str], task_config: dict, else: raise ValueError(f"Unknown agent type: {self.agent_type}") - def _create_simulator(self, simulator_key: str, task=None): + def _create_simulator(self, simulator_key: str, task=None, label: str = "bridge"): import json as _json from easi.simulators.registry import ( @@ -610,6 +610,7 @@ def _create_simulator(self, simulator_key: str, task=None): bridge_script_path=bridge_path, render_platform=get_render_platform("headless"), extra_args=extra_args, + label=label, **runner_kwargs, ) data_dir_str = str(self.data_dir) if self.data_dir else ( @@ -668,6 +669,7 @@ def _create_simulator(self, simulator_key: str, task=None): screen_config=env_manager.screen_config, extra_args=extra_args, extra_env=env_vars if env_vars else None, + label=label, **runner_kwargs, ) runner.launch() diff --git a/easi/simulators/subprocess_runner.py b/easi/simulators/subprocess_runner.py index 9f24794..1fbcb86 100644 --- a/easi/simulators/subprocess_runner.py +++ b/easi/simulators/subprocess_runner.py @@ -48,6 +48,7 @@ def __init__( poll_interval: float = 0.1, extra_args: list[str] | None = None, extra_env: EnvVars | None = None, + label: str = "bridge", ): self.python_executable = python_executable self.bridge_script_path = bridge_script_path @@ -58,6 +59,7 @@ def __init__( self.poll_interval = poll_interval self.extra_args = extra_args or [] self.extra_env = extra_env + self.label = label self._process: subprocess.Popen | None = None self._workspace: Path | None = None @@ -306,7 +308,7 @@ def _stream_output(self) -> None: line = line.rstrip() self._output_lines.append(line) level = self._parse_bridge_level(line) - logger.log(level, "[bridge] %s", line) + logger.log(level, "[%s] %s", self.label, line) except (ValueError, OSError): pass # pipe closed From 75878200d44dcc813ed3639e143cecdb4a8f69e0 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Mon, 9 Mar 2026 18:20:01 +0800 Subject: [PATCH 179/239] feat: add sticky progress bar for evaluation runs New ProgressBar utility that stays pinned to the bottom of the terminal while logs scroll above. Wraps log handler emit() (same pattern as spinner.py) to avoid corruption with TRACE-level log floods. - Thread-safe updates for parallel workers - Shows completed/total, failed count, active workers, elapsed, ETA - TTY-aware: sticky bar on TTY, periodic log lines when piped - Handles resume (start_index) for correct ETA calculation - Integrated in both sequential and parallel runners --- easi/evaluation/parallel_runner.py | 18 +- easi/evaluation/runner.py | 16 ++ easi/utils/progress.py | 253 +++++++++++++++++++++++++++++ 3 files changed, 284 insertions(+), 3 deletions(-) create mode 100644 easi/utils/progress.py diff --git a/easi/evaluation/parallel_runner.py b/easi/evaluation/parallel_runner.py index a069d29..f171cfa 100644 --- a/easi/evaluation/parallel_runner.py +++ b/easi/evaluation/parallel_runner.py @@ -232,6 +232,16 @@ def run(self) -> list[dict]: num_workers = min(self.num_parallel, remaining) + # --- Progress bar --- + from easi.utils.progress import ProgressBar + + progress_bar = ProgressBar( + total=total_episodes, + num_workers=num_workers, + start_index=start_index, + ) + progress_bar.start() + def _worker(worker_id: int) -> None: """Worker thread: owns a simulator + agent, pulls episodes from queue.""" logger.trace("[Worker %d] Starting up", worker_id) @@ -354,9 +364,10 @@ def _worker(worker_id: int) -> None: current_completed = progress["completed"] + start_index current_failed = progress["failed"] - logger.info( - "[Progress] %d/%d episodes completed (%d failed)", - current_completed, total_episodes, current_failed, + progress_bar.update( + completed=current_completed, + failed=current_failed, + active_workers=num_workers, ) episodes_done += 1 @@ -387,6 +398,7 @@ def _worker(worker_id: int) -> None: future.result() wall_seconds = round(time.monotonic() - wall_start, 2) + progress_bar.stop() # Merge completed results from resume with new results new_results.sort(key=lambda x: x[0]) diff --git a/easi/evaluation/runner.py b/easi/evaluation/runner.py index efd4442..3ff46a5 100644 --- a/easi/evaluation/runner.py +++ b/easi/evaluation/runner.py @@ -209,6 +209,14 @@ def run(self) -> list[dict]: # 4. Start simulator sim, sim_runner = self._create_simulator(task.simulator_key, task=task) + # 5. Progress bar + from easi.utils.progress import ProgressBar + + progress_bar = ProgressBar( + total=len(episodes), num_workers=1, start_index=start_index, + ) + progress_bar.start() + try: for i, episode in enumerate(episodes): if i < start_index: @@ -259,6 +267,13 @@ def run(self) -> list[dict]: all_results.append(result) + # Update progress bar + failed_count = sum(1 for r in all_results if "error" in r) + progress_bar.update( + completed=len(all_results) + start_index, + failed=failed_count, + ) + # Save per-episode result (strip internal keys) result_to_save = { k: v for k, v in result.items() @@ -269,6 +284,7 @@ def run(self) -> list[dict]: ) finally: + progress_bar.stop() sim.close() finally: if server: diff --git a/easi/utils/progress.py b/easi/utils/progress.py new file mode 100644 index 0000000..2b3b1da --- /dev/null +++ b/easi/utils/progress.py @@ -0,0 +1,253 @@ +"""Sticky terminal progress bar that coexists with logging output. + +Usage:: + + from easi.utils.progress import ProgressBar + + bar = ProgressBar(total=50, num_workers=4) + bar.start() + # ... in worker threads: + bar.update(completed=10, failed=1) + # ... when done: + bar.stop() + +The progress bar renders on stderr, pinned to the bottom of the terminal. +When a log record is emitted, the bar is cleared first so log output is +never corrupted — identical pattern to spinner.py. + +In non-TTY mode (piped output), periodic log-line updates are emitted instead. +""" + +from __future__ import annotations + +import logging +import os +import sys +import threading +import time + + +_CLEAR = "\r\033[K" # carriage return + ANSI erase to end of line +_REFRESH_INTERVAL = 0.5 # seconds between redraws +_NON_TTY_LOG_INTERVAL = 30.0 # seconds between log-line updates in non-TTY mode + +# Bar characters +_FILL = "█" +_EMPTY = "░" + +# Colors (match logging.py conventions) +_RESET = "\033[0m" +_BOLD = "\033[1m" +_CYAN = "\033[36m" +_GREEN = "\033[32m" +_YELLOW = "\033[33m" +_GRAY = "\033[90m" +_RED = "\033[31m" + + +class ProgressBar: + """Thread-safe sticky progress bar for evaluation runs. + + Wraps log handler emit() methods (same pattern as spinner.py) to keep + the bar at the bottom while logs scroll above. + + Parameters + ---------- + total: + Total number of episodes. + num_workers: + Number of parallel workers (1 for sequential). + start_index: + Number of already-completed episodes (for resume). + """ + + def __init__( + self, + total: int, + num_workers: int = 1, + start_index: int = 0, + ): + self.total = total + self.num_workers = num_workers + self.start_index = start_index + + self._completed = start_index + self._failed = 0 + self._active_workers = 0 + self._elapsed_start: float = 0.0 + + self._lock = threading.Lock() + self._stop_event = threading.Event() + self._thread: threading.Thread | None = None + self._is_tty = hasattr(sys.stderr, "isatty") and sys.stderr.isatty() + self._wrapped: list[tuple[logging.Handler, object]] = [] + self._last_non_tty_log: float = 0.0 + + def start(self) -> None: + """Start the progress bar background thread.""" + self._elapsed_start = time.monotonic() + + if self._is_tty: + # Wrap existing handlers so they clear the bar before emitting + for handler in logging.getLogger("easi").handlers: + original_emit = handler.emit + + def wrapped_emit(record, _orig=original_emit): + with self._lock: + sys.stderr.write(_CLEAR) + sys.stderr.flush() + _orig(record) + # Redraw bar after log line + self._render() + + self._wrapped.append((handler, original_emit)) + handler.emit = wrapped_emit # type: ignore[method-assign] + + self._thread = threading.Thread(target=self._refresh_loop, daemon=True) + self._thread.start() + + def stop(self) -> None: + """Stop the progress bar and restore handlers.""" + self._stop_event.set() + if self._thread is not None: + self._thread.join() + + if self._is_tty: + # Final clear + with self._lock: + sys.stderr.write(_CLEAR) + sys.stderr.flush() + + # Restore original emit methods + for handler, original_emit in self._wrapped: + handler.emit = original_emit # type: ignore[method-assign] + self._wrapped.clear() + + def update( + self, + completed: int | None = None, + failed: int | None = None, + active_workers: int | None = None, + ) -> None: + """Thread-safe progress update. + + Parameters + ---------- + completed: + Total completed episodes (including resumed). + failed: + Total failed episodes. + active_workers: + Number of currently active worker threads. + """ + with self._lock: + if completed is not None: + self._completed = completed + if failed is not None: + self._failed = failed + if active_workers is not None: + self._active_workers = active_workers + + # Non-TTY: emit periodic log lines + if not self._is_tty: + now = time.monotonic() + if now - self._last_non_tty_log >= _NON_TTY_LOG_INTERVAL: + self._last_non_tty_log = now + self._log_progress() + + def _refresh_loop(self) -> None: + """Background thread: periodically redraw the bar.""" + while not self._stop_event.is_set(): + with self._lock: + self._render() + self._stop_event.wait(_REFRESH_INTERVAL) + + def _render(self) -> None: + """Render the progress bar to stderr. Must be called with _lock held.""" + line = self._format_bar() + sys.stderr.write(f"{_CLEAR}{line}") + sys.stderr.flush() + + def _format_bar(self) -> str: + """Build the progress bar string.""" + completed = self._completed + total = self.total + failed = self._failed + active = self._active_workers + elapsed = time.monotonic() - self._elapsed_start + + # Percentage and bar + pct = completed / total if total > 0 else 0 + bar_width = min(self._get_terminal_width() - 60, 30) + bar_width = max(bar_width, 10) + filled = int(bar_width * pct) + bar = _FILL * filled + _EMPTY * (bar_width - filled) + + # Color the bar + if failed > 0: + bar_color = _YELLOW + elif completed >= total: + bar_color = _GREEN + else: + bar_color = _CYAN + + # Elapsed time + mins, secs = divmod(int(elapsed), 60) + hours, mins = divmod(mins, 60) + if hours > 0: + time_str = f"{hours}h{mins:02d}m" + else: + time_str = f"{mins}m{secs:02d}s" + + # ETA + if completed > self.start_index and pct < 1.0: + new_completed = completed - self.start_index + elapsed_per_ep = elapsed / new_completed + remaining = (total - completed) * elapsed_per_ep + r_mins, r_secs = divmod(int(remaining), 60) + r_hours, r_mins = divmod(r_mins, 60) + if r_hours > 0: + eta_str = f"{r_hours}h{r_mins:02d}m" + else: + eta_str = f"{r_mins}m{r_secs:02d}s" + else: + eta_str = "--" + + # Build line + parts = [ + f"{bar_color}{bar}{_RESET}", + f" {_BOLD}{completed}/{total}{_RESET}", + ] + if failed > 0: + parts.append(f" {_RED}({failed} failed){_RESET}") + if self.num_workers > 1 and active > 0: + parts.append(f" {_GRAY}[{active}w]{_RESET}") + parts.append(f" {_GRAY}{time_str} elapsed, ETA {eta_str}{_RESET}") + + return "".join(parts) + + def _log_progress(self) -> None: + """Emit a plain log line (for non-TTY mode).""" + elapsed = time.monotonic() - self._elapsed_start + mins, secs = divmod(int(elapsed), 60) + logger = logging.getLogger("easi.evaluation.progress") + logger.info( + "[Progress] %d/%d episodes (%d failed) — %dm%02ds elapsed", + self._completed, self.total, self._failed, mins, secs, + ) + + @staticmethod + def _get_terminal_width() -> int: + """Get terminal width, with fallback.""" + try: + return os.get_terminal_size(sys.stderr.fileno()).columns + except (ValueError, OSError): + return 80 + + def __enter__(self): + self.start() + return self + + def __exit__(self, *exc): + self.stop() + return False From ce5ba7fd58d4600ba8f600d1cd6fd95bb30315b8 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Mon, 9 Mar 2026 22:31:50 +0800 Subject: [PATCH 180/239] fix: increase default LLM timeout for local backends to 600s When workers outnumber LLM server instances, requests queue up and can exceed the 120s default timeout. Local backends (vLLM, custom) now default to 600s. Users can still override via --llm-kwargs. --- easi/evaluation/runner.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/easi/evaluation/runner.py b/easi/evaluation/runner.py index 3ff46a5..ee6d784 100644 --- a/easi/evaluation/runner.py +++ b/easi/evaluation/runner.py @@ -544,6 +544,10 @@ def _create_agent(self, action_space: list[str], task_config: dict, yaml_gen_kwargs = agent_config.get("generation_kwargs", {}) merged_kwargs = {**yaml_gen_kwargs, **client_kwargs} + # Local backends need longer timeout (generation is slower than API) + if base_url and "timeout" not in merged_kwargs: + merged_kwargs["timeout"] = 600.0 + llm = LLMClient( model=litellm_model, base_url=base_url, From 3ce43a7b68f673cd87cf52a51460d18c5307d117 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Mon, 9 Mar 2026 22:40:25 +0800 Subject: [PATCH 181/239] fix: include model_path in run directory name for custom backend Append HF model path to run_id so different model variants are distinguishable (e.g. qwen3_vl_Qwen_Qwen3-VL-8B-Instruct vs qwen3_vl_Qwen_Qwen3-VL-72B-Instruct). --- easi/evaluation/runner.py | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/easi/evaluation/runner.py b/easi/evaluation/runner.py index ee6d784..8208649 100644 --- a/easi/evaluation/runner.py +++ b/easi/evaluation/runner.py @@ -96,6 +96,15 @@ def __init__( timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") if self.model: safe_model = self.model.replace("/", "_") + # For custom backend, append model_path to distinguish variants + if self.backend == "custom" and self.llm_kwargs_raw: + from easi.llm.utils import parse_llm_kwargs + model_path = parse_llm_kwargs(self.llm_kwargs_raw).get("model_path", "") + if model_path: + # Use last 2 path components (e.g. Qwen_Qwen3-VL-8B-Instruct) + path_suffix = "_".join(model_path.rstrip("/").split("/")[-2:]) + path_suffix = path_suffix.replace("/", "_") + safe_model = f"{safe_model}_{path_suffix}" self.run_id = f"{timestamp}_{safe_model}" else: self.run_id = timestamp From 5716c397bac7d980457cee62e4363c389c1809c5 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Mon, 9 Mar 2026 22:44:16 +0800 Subject: [PATCH 182/239] docs: update CLI reference with custom model backend details Add custom backend to LLM options, easi model commands, custom model overview with installation/usage/authoring guide, rename --vllm-* to --llm-* flags, and add custom backend examples. --- docs/cli-reference.md | 177 ++++++++++++++++++++++++++++++++++++------ 1 file changed, 153 insertions(+), 24 deletions(-) diff --git a/docs/cli-reference.md b/docs/cli-reference.md index e6c1d97..d93b3b2 100644 --- a/docs/cli-reference.md +++ b/docs/cli-reference.md @@ -41,21 +41,22 @@ easi start [TASK ...] [options] | Option | Description | |---|---| -| `--backend {vllm,openai,anthropic,gemini,dummy}` | LLM backend (required for `react` agent) | -| `--model MODEL` | Model identifier | +| `--backend {vllm,custom,openai,anthropic,gemini,dummy}` | LLM backend (required for `react` agent) | +| `--model MODEL` | Model identifier (HuggingFace ID for `vllm`, registry name for `custom`) | | `--llm-url URL` | LLM server base URL (for external servers) | -| `--port PORT` | Port for local vLLM server (default: 8080) | +| `--port PORT` | Port for local LLM server (default: 8080) | | `--llm-kwargs JSON` | Extra LLM/server kwargs as JSON string | | `--max-retries N` | Max retry attempts on transient LLM errors (default: 3) | **Model identifiers by backend:** -| Backend | Example `--model` values | -|---|---| -| `vllm` | `meta-llama/Llama-2-7b-hf`, `Qwen/Qwen2.5-VL-72B-Instruct` | -| `openai` | `gpt-4o`, `gpt-5.2-2025-12-11` | -| `anthropic` | `claude-sonnet-4-20250514` | -| `gemini` | `gemini-2.0-flash` | +| Backend | Example `--model` values | Description | +|---|---|---| +| `vllm` | `Qwen/Qwen2.5-VL-72B-Instruct` | vLLM-supported HuggingFace models | +| `custom` | `qwen3_vl`, `echo` | Custom model registry name (see `easi model list`) | +| `openai` | `gpt-4o`, `gpt-5.2-2025-12-11` | OpenAI API models | +| `anthropic` | `claude-sonnet-4-20250514` | Anthropic API models | +| `gemini` | `gemini-2.0-flash` | Google Gemini API models | ### Execution Control @@ -66,21 +67,24 @@ easi start [TASK ...] [options] | `--seed SEED` | Random seed for agent reproducibility | | `--render-platform PLATFORM` | Rendering platform override (default: simulator's preference). See [Render Platforms](#render-platforms). | -### GPU Allocation (vLLM backend) +### GPU Allocation (Local Backends) + +These options apply to local LLM backends (`vllm` and `custom`). | Option | Description | |---|---| -| `--vllm-instances N` | Number of vLLM server instances to start (default: 1). Each runs on a subset of `--vllm-gpus`. | -| `--vllm-gpus IDS` | Comma-separated GPU IDs for vLLM inference (e.g., `0,1,2,3`). GPUs are split evenly across instances. | +| `--llm-instances N` | Number of LLM server instances to start (default: 1). Each runs on a subset of `--llm-gpus`. | +| `--llm-gpus IDS` | Comma-separated GPU IDs for LLM inference (e.g., `0,1,2,3`). GPUs are split evenly across instances. | | `--sim-gpus IDS` | Comma-separated GPU IDs for simulator rendering (e.g., `4,5`). Sets `CUDA_VISIBLE_DEVICES` for simulator subprocesses. | **Notes:** -- `--vllm-gpus` is required when `--vllm-instances > 1`. -- `--vllm-gpus` and `--sim-gpus` must not overlap. +- `--llm-gpus` is required when `--llm-instances > 1`. +- `--llm-gpus` and `--sim-gpus` must not overlap. - GPU IDs are validated against hardware at startup (via `nvidia-smi`). -- All vLLM instances start in parallel (processes spawned first, then health-checked concurrently). -- Workers are assigned to vLLM instances via round-robin (e.g., 8 workers across 2 instances → 4 workers per instance). -- These options are ignored with a warning if `--backend` is not `vllm`. +- All LLM instances start in parallel (processes spawned first, then health-checked concurrently). +- Workers are assigned to LLM instances via round-robin (e.g., 8 workers across 2 instances → 4 workers per instance). +- These options are ignored with a warning if `--backend` is not `vllm` or `custom`. +- Local backends use a 600s default timeout (vs 120s for API backends) to handle request queueing when workers outnumber server instances. ### Data & Output @@ -136,8 +140,8 @@ easi start ebalfred_base --agent react --backend vllm \ # Parallel vLLM with 2 instances (TP=2 each) + separate sim GPUs easi start ebalfred_base --agent react --backend vllm \ --model Qwen/Qwen2.5-VL-72B-Instruct \ - --num-parallel 8 --vllm-instances 2 \ - --vllm-gpus 0,1,2,3 --sim-gpus 4,5 \ + --num-parallel 8 --llm-instances 2 \ + --llm-gpus 0,1,2,3 --sim-gpus 4,5 \ --llm-kwargs '{"tensor_parallel_size": 2}' # External multi-URL vLLM (pre-started servers, no auto-management) @@ -145,6 +149,22 @@ easi start ebalfred_base --agent react --backend vllm \ --model Qwen/Qwen2.5-VL-72B-Instruct --num-parallel 8 \ --llm-url http://localhost:8000/v1,http://localhost:8001/v1 +# Custom model server (auto-starts, single instance) +easi start ebalfred_base --agent react --backend custom \ + --model qwen3_vl \ + --llm-kwargs '{"model_path": "Qwen/Qwen3-VL-8B-Instruct"}' + +# Custom model with parallel workers and 2 server instances +easi start ebalfred_base --agent react --backend custom \ + --model qwen3_vl --num-parallel 8 \ + --llm-instances 2 --llm-gpus 0,1,2,3 --sim-gpus 4,5 \ + --llm-kwargs '{"model_path": "Qwen/Qwen3-VL-8B-Instruct"}' + +# Custom model with generation kwargs +easi start ebalfred_base --agent react --backend custom \ + --model qwen3_vl \ + --llm-kwargs '{"model_path": "Qwen/Qwen3-VL-8B-Instruct", "temperature": 0.7, "max_tokens": 2048}' + # Multiple tasks easi start ebalfred_base ebnavigation_base --agent react \ --backend openai --model gpt-4o @@ -170,7 +190,7 @@ easi start ebalfred_base --agent dummy --verbosity TRACE ### Output Structure ``` -//_/ +//_[_]/ ├── config.json # CLI options + resolved configuration ├── summary.json # Aggregated metrics └── episodes/ @@ -198,10 +218,11 @@ easi start ebalfred_base --agent dummy --verbosity TRACE ### Notes -- `--num-parallel > 1` requires an API backend (`openai`, `anthropic`, `gemini`). It uses a thread pool with one simulator per thread. -- When using `--backend vllm` without `--llm-url`, a local vLLM server is auto-started and stopped after evaluation. +- `--num-parallel > 1` works with any backend. It uses a thread pool with one simulator per thread. +- When using `--backend vllm` or `--backend custom` without `--llm-url`, local server(s) are auto-started and stopped after evaluation. - `--resume` cannot be combined with multiple tasks. -- `--llm-kwargs` is split into server kwargs (e.g., `tensor_parallel_size`, `dtype`) and generation kwargs (e.g., `temperature`, `max_tokens`). +- `--llm-kwargs` is split into server kwargs (e.g., `tensor_parallel_size`, `dtype`, `model_path`) and generation kwargs (e.g., `temperature`, `max_tokens`). Server kwargs are passed to the server process; generation kwargs are sent per-request. +- For `--backend custom`, `model_path` in `--llm-kwargs` specifies the HuggingFace model ID or local path to weights. The `--model` flag selects which custom model class to use from the registry. --- @@ -368,6 +389,114 @@ Executes `MoveAhead` for each step and reports observations and rewards. --- +## `easi model` — Manage Custom Models + +### `easi model list` + +List all custom models discovered in the registry. + +```bash +easi model list +``` + +Output shows each model name and its display name. + +--- + +### `easi model info ` + +Display detailed information about a custom model. + +```bash +easi model info qwen3_vl +``` + +Shows model name, display name, description, model class, and default kwargs. + +--- + +### Custom Model Overview + +Custom models allow running model architectures not supported by vLLM. Each model is defined by: + +1. **A Python class** extending `BaseModelServer` with `load()`, `generate()`, and `unload()` methods +2. **A `manifest.yaml`** file for auto-discovery by the registry + +Models live in `easi/llm/models//` and are auto-discovered at startup. + +**Built-in custom models:** + +| Name | Description | +|---|---| +| `echo` | Echoes input back (testing) | +| `qwen3_vl` | Qwen3-VL vision-language model (8B, 72B, etc.) | + +**Installation:** + +Custom models require additional dependencies not included in the base install: + +```bash +pip install -e ".[custom-models]" +``` + +This installs `torch`, `transformers`, `accelerate`, `fastapi`, `uvicorn`, and `Pillow`. + +**How it works:** + +When you run `--backend custom --model `: +1. The registry looks up the model class from `easi/llm/models//manifest.yaml` +2. A FastAPI HTTP server is started as a subprocess, loading the model +3. The server exposes an OpenAI-compatible `/v1/chat/completions` endpoint +4. LiteLLM connects to it transparently via the `openai/` prefix +5. Manifest `default_kwargs` (e.g., `dtype`, `attn_implementation`) are merged with CLI `--llm-kwargs` + +**Adding a new custom model:** + +Create a directory under `easi/llm/models/` with: + +``` +easi/llm/models/my_model/ +├── __init__.py +├── manifest.yaml +└── model.py +``` + +`manifest.yaml`: +```yaml +name: my_model +display_name: "My Custom Model" +description: "Description of the model" +model_class: "easi.llm.models.my_model.model.MyModel" +default_kwargs: + dtype: "bfloat16" +``` + +`model.py`: +```python +from easi.llm.models.base_model_server import BaseModelServer + +class MyModel(BaseModelServer): + def load(self, model_path: str, device: str, **kwargs) -> None: + # Load model weights + ... + + def generate(self, messages: list[dict], **kwargs) -> str: + # messages are in OpenAI format (with image_url for vision) + # Return generated text + ... + + def unload(self) -> None: + # Release GPU memory + ... +``` + +Helper utilities are available in `easi.llm.models.helpers`: +- `extract_images(messages)` — Extract PIL Images from base64 image_url entries +- `extract_text_only(messages)` — Concatenate all text content +- `extract_by_role(messages)` — Group text by role + +--- + ## `easi llm-server` — Dummy LLM Server Start a minimal OpenAI-compatible dummy LLM server for testing. @@ -455,7 +584,7 @@ The CLI itself does not use environment variables, but the LLM backends require | `ANTHROPIC_API_KEY` | `anthropic` | | `GOOGLE_API_KEY` | `gemini` | -These are handled by the underlying LiteLLM client. +These are handled by the underlying LiteLLM client. The `vllm` and `custom` backends do not require API keys (a dummy key is used automatically for the local OpenAI-compatible server). --- From d0bdf39abe394b5602dce8328b5ab0473436d18c Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Mon, 9 Mar 2026 23:08:27 +0800 Subject: [PATCH 183/239] fix: address code review issues in custom inference server - Use context manager for ProgressBar to prevent handler leak on exceptions - Offload model.generate() to thread executor in async endpoint - Replace misleading _DTYPE_MAP with _ALLOWED_DTYPES set + warning - Fix race condition on _last_non_tty_log in non-TTY progress updates - Remove redundant self-import in cmd_model - Move json import to module level in server_manager --- easi/cli.py | 1 - easi/evaluation/parallel_runner.py | 5 +---- easi/llm/models/http_server.py | 7 ++++++- easi/llm/models/qwen3_vl/model.py | 19 +++++++------------ easi/llm/server_manager.py | 4 ++-- easi/utils/progress.py | 16 ++++++++++------ 6 files changed, 26 insertions(+), 26 deletions(-) diff --git a/easi/cli.py b/easi/cli.py index f8e09f0..08d0647 100644 --- a/easi/cli.py +++ b/easi/cli.py @@ -559,7 +559,6 @@ def cmd_model(args) -> None: logger.info(" Default kwargs: %s", entry.default_kwargs) else: - from easi.cli import build_parser build_parser().parse_args(["model", "--help"]) diff --git a/easi/evaluation/parallel_runner.py b/easi/evaluation/parallel_runner.py index f171cfa..03487bb 100644 --- a/easi/evaluation/parallel_runner.py +++ b/easi/evaluation/parallel_runner.py @@ -240,8 +240,6 @@ def run(self) -> list[dict]: num_workers=num_workers, start_index=start_index, ) - progress_bar.start() - def _worker(worker_id: int) -> None: """Worker thread: owns a simulator + agent, pulls episodes from queue.""" logger.trace("[Worker %d] Starting up", worker_id) @@ -387,7 +385,7 @@ def _worker(worker_id: int) -> None: logger.trace("Launching %d worker threads", num_workers) wall_start = time.monotonic() - with ThreadPoolExecutor(max_workers=num_workers) as executor: + with progress_bar, ThreadPoolExecutor(max_workers=num_workers) as executor: futures = [] for wid in range(num_workers): futures.append(executor.submit(_worker, wid)) @@ -398,7 +396,6 @@ def _worker(worker_id: int) -> None: future.result() wall_seconds = round(time.monotonic() - wall_start, 2) - progress_bar.stop() # Merge completed results from resume with new results new_results.sort(key=lambda x: x[0]) diff --git a/easi/llm/models/http_server.py b/easi/llm/models/http_server.py index 260956a..c38cfc8 100644 --- a/easi/llm/models/http_server.py +++ b/easi/llm/models/http_server.py @@ -6,9 +6,11 @@ from __future__ import annotations +import asyncio import json import time import uuid +from functools import partial from typing import Any from easi.utils.logging import get_logger @@ -64,7 +66,10 @@ async def chat_completions(request: dict) -> JSONResponse: # type: ignore[arg-t gen_kwargs[key] = request[key] try: - content = model.generate(messages, **gen_kwargs) + loop = asyncio.get_event_loop() + content = await loop.run_in_executor( + None, partial(model.generate, messages, **gen_kwargs) + ) except Exception as e: logger.error("Generation failed: %s", e, exc_info=True) return JSONResponse( diff --git a/easi/llm/models/qwen3_vl/model.py b/easi/llm/models/qwen3_vl/model.py index e31b5c1..c47bb19 100644 --- a/easi/llm/models/qwen3_vl/model.py +++ b/easi/llm/models/qwen3_vl/model.py @@ -18,13 +18,8 @@ logger = get_logger(__name__) -# Dtype string → torch dtype mapping -_DTYPE_MAP = { - "bfloat16": "bfloat16", - "float16": "float16", - "float32": "float32", - "auto": "auto", -} +# Recognised dtype strings for from_pretrained +_ALLOWED_DTYPES = {"bfloat16", "float16", "float32", "auto"} def _openai_to_qwen_messages(messages: list[dict], images: list) -> list[dict]: @@ -77,11 +72,11 @@ def load(self, model_path: str, device: str, **kwargs) -> None: from transformers import AutoProcessor, Qwen3VLForConditionalGeneration # Resolve torch dtype — newer transformers uses "dtype" instead of "torch_dtype" - dtype_str = kwargs.pop("torch_dtype", kwargs.pop("dtype", "auto")) - if dtype_str in _DTYPE_MAP: - torch_dtype = getattr(torch, dtype_str, "auto") if dtype_str != "auto" else "auto" - else: - torch_dtype = "auto" + dtype_str = kwargs.pop("torch_dtype", None) or kwargs.pop("dtype", "auto") + if dtype_str not in _ALLOWED_DTYPES: + logger.warning("Unrecognised dtype '%s', falling back to 'auto'", dtype_str) + dtype_str = "auto" + torch_dtype = getattr(torch, dtype_str, "auto") if dtype_str != "auto" else "auto" attn_impl = kwargs.pop("attn_implementation", None) diff --git a/easi/llm/server_manager.py b/easi/llm/server_manager.py index 2917c68..ec20ff9 100644 --- a/easi/llm/server_manager.py +++ b/easi/llm/server_manager.py @@ -4,6 +4,7 @@ """ from __future__ import annotations +import json import os import socket import subprocess @@ -176,8 +177,7 @@ def _build_command(self) -> tuple[list[str], dict]: "--port", str(self.port), ] if extra_kwargs: - import json as _json - cmd.extend(["--kwargs", _json.dumps(extra_kwargs)]) + cmd.extend(["--kwargs", json.dumps(extra_kwargs)]) else: raise ValueError(f"Unsupported server backend: {self.backend}") diff --git a/easi/utils/progress.py b/easi/utils/progress.py index 2b3b1da..856d2d2 100644 --- a/easi/utils/progress.py +++ b/easi/utils/progress.py @@ -140,6 +140,7 @@ def update( active_workers: Number of currently active worker threads. """ + do_log = False with self._lock: if completed is not None: self._completed = completed @@ -148,12 +149,15 @@ def update( if active_workers is not None: self._active_workers = active_workers - # Non-TTY: emit periodic log lines - if not self._is_tty: - now = time.monotonic() - if now - self._last_non_tty_log >= _NON_TTY_LOG_INTERVAL: - self._last_non_tty_log = now - self._log_progress() + # Non-TTY: emit periodic log lines + if not self._is_tty: + now = time.monotonic() + if now - self._last_non_tty_log >= _NON_TTY_LOG_INTERVAL: + self._last_non_tty_log = now + do_log = True + + if do_log: + self._log_progress() def _refresh_loop(self) -> None: """Background thread: periodically redraw the bar.""" From ab04b0b7bdf84b1a731c55aea3b1a3d942f856cc Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 10 Mar 2026 10:57:34 +0800 Subject: [PATCH 184/239] fix: audit issues 1-2 + add easi ps command - Fix metric denominator bug: average over all episodes, not just those that emitted the key (inflated metrics when episodes fail) - Add effective_episodes field to summary.json - Narrow response_format fallback to BadRequestError only (transient errors no longer permanently disable response_format) - Add easi ps command to list/kill orphaned EASI processes - Update CLI reference with easi ps docs --- docs/cli-reference.md | 38 ++++++++ easi/agents/react_agent.py | 18 +++- easi/cli.py | 134 +++++++++++++++++++++++++++++ easi/evaluation/metrics.py | 6 +- easi/evaluation/parallel_runner.py | 7 +- easi/evaluation/runner.py | 7 +- tests/test_cli.py | 11 +++ tests/test_metrics.py | 17 ++++ tests/test_prompt_parity.py | 6 +- tests/test_react_agent.py | 11 ++- 10 files changed, 245 insertions(+), 10 deletions(-) diff --git a/docs/cli-reference.md b/docs/cli-reference.md index d93b3b2..31f189a 100644 --- a/docs/cli-reference.md +++ b/docs/cli-reference.md @@ -497,6 +497,44 @@ Helper utilities are available in `easi.llm.models.helpers`: --- +## `easi ps` — Show EASI Processes + +List all running EASI-related processes (LLM servers, simulator bridges) and optionally kill them. + +``` +easi ps [--kill] +``` + +| Option | Description | +|---|---| +| `--kill` | Send SIGTERM (then SIGKILL) to all found EASI processes | + +**Detected process types:** + +| Type | Description | +|---|---| +| `http_server` | Custom model server (`easi.llm.models.http_server`) | +| `api_server` | vLLM server (`vllm.entrypoints.openai.api_server`) | +| `dummy_server` | Dummy LLM server (`easi.llm.dummy_server`) | +| `bridge` | Simulator bridge subprocess | + +**Output includes:** +- PID, status, CPU%, MEM%, process type, and command +- `[ZOMBIE]` tag for zombie processes +- GPU memory held by EASI processes (via `nvidia-smi`) + +**Examples:** + +```bash +# List all EASI processes +easi ps + +# Kill all orphaned EASI processes (e.g., after Ctrl+C) +easi ps --kill +``` + +--- + ## `easi llm-server` — Dummy LLM Server Start a minimal OpenAI-compatible dummy LLM server for testing. diff --git a/easi/agents/react_agent.py b/easi/agents/react_agent.py index 78e8f63..516a6de 100644 --- a/easi/agents/react_agent.py +++ b/easi/agents/react_agent.py @@ -14,6 +14,22 @@ logger = get_logger(__name__) +# Exception types that indicate response_format is unsupported by the backend. +# Lazy-resolved on first use to avoid importing litellm at module level. +_FORMAT_UNSUPPORTED_ERRORS: tuple[type[Exception], ...] | None = None + + +def _get_format_unsupported_errors() -> tuple[type[Exception], ...]: + """Return exception types for unsupported response_format.""" + global _FORMAT_UNSUPPORTED_ERRORS + if _FORMAT_UNSUPPORTED_ERRORS is None: + try: + from litellm.exceptions import BadRequestError + _FORMAT_UNSUPPORTED_ERRORS = (BadRequestError,) + except ImportError: + _FORMAT_UNSUPPORTED_ERRORS = () + return _FORMAT_UNSUPPORTED_ERRORS + def _format_messages_for_log(messages: list[dict]) -> str: """Extract readable text from OpenAI-format messages for logging.""" @@ -152,7 +168,7 @@ def _generate_with_fallback( try: return self.llm_client.generate(messages, response_format=response_format) - except Exception as e: + except _get_format_unsupported_errors() as e: logger.warning( "response_format not supported by backend, " "falling back to prompt-only: %s", e, diff --git a/easi/cli.py b/easi/cli.py index 08d0647..f636b56 100644 --- a/easi/cli.py +++ b/easi/cli.py @@ -143,6 +143,10 @@ def build_parser() -> argparse.ArgumentParser: model_info_parser = model_sub.add_parser("info", help="Show model details", parents=[common]) model_info_parser.add_argument("model_name", help="Model name") + # --- ps command --- + ps_parser = subparsers.add_parser("ps", help="Show EASI-related processes (bridges, LLM servers)", parents=[common]) + ps_parser.add_argument("--kill", action="store_true", help="Kill all found EASI processes") + # --- llm-server command --- llm_parser = subparsers.add_parser("llm-server", help="Start dummy LLM server", parents=[common]) llm_parser.add_argument("--port", type=int, default=8000) @@ -538,6 +542,133 @@ def cmd_llm_server(host: str, port: int, mode: str, action_space: list[str]) -> run_server(host=host, port=port, mode=mode, action_space=action_space) +def cmd_ps(kill: bool = False) -> None: + """Show (and optionally kill) EASI-related processes.""" + import os + import signal + import subprocess + + # Patterns that identify EASI-spawned processes + patterns = [ + "easi.llm.models.http_server", # custom model server + "vllm.entrypoints.openai.api_server", # vLLM server + "easi.llm.dummy_server", # dummy LLM server + ] + # Also match bridge scripts by looking for bridge.py in easi paths + bridge_pattern = "easi/simulators/.*/bridge.py|easi/tasks/.*/bridge.py" + + my_pid = os.getpid() + + # Use ps to find matching processes + try: + result = subprocess.run( + ["ps", "aux"], + capture_output=True, text=True, timeout=10, + ) + except (FileNotFoundError, subprocess.TimeoutExpired): + logger.error("Failed to run 'ps aux'") + return + + found: list[dict] = [] + for line in result.stdout.strip().splitlines()[1:]: # skip header + parts = line.split(None, 10) + if len(parts) < 11: + continue + pid = int(parts[1]) + if pid == my_pid: + continue + cmd_str = parts[10] + stat = parts[7] + + matched_pattern = None + for pattern in patterns: + if pattern in cmd_str: + matched_pattern = pattern + break + if matched_pattern is None: + import re + if re.search(bridge_pattern, cmd_str): + matched_pattern = "bridge" + + if matched_pattern is None: + continue + + is_zombie = "Z" in stat + found.append({ + "pid": pid, + "user": parts[0], + "stat": stat, + "cpu": parts[2], + "mem": parts[3], + "start": parts[8], + "command": cmd_str[:120], + "pattern": matched_pattern, + "zombie": is_zombie, + }) + + if not found: + logger.info("No EASI-related processes found.") + return + + # Display + logger.info("Found %d EASI-related process(es):\n", len(found)) + logger.info(" %-7s %-6s %-5s %-5s %-8s %s", "PID", "STAT", "CPU%", "MEM%", "TYPE", "COMMAND") + logger.info(" %s", "-" * 80) + for p in found: + zombie_tag = " [ZOMBIE]" if p["zombie"] else "" + ptype = p["pattern"].split(".")[-1] if "." in p["pattern"] else p["pattern"] + logger.info( + " %-7d %-6s %-5s %-5s %-8s %s%s", + p["pid"], p["stat"], p["cpu"], p["mem"], ptype, p["command"][:60], zombie_tag, + ) + + # GPU usage summary + try: + gpu_result = subprocess.run( + ["nvidia-smi", "--query-compute-apps=pid,gpu_uuid,used_memory", + "--format=csv,noheader,nounits"], + capture_output=True, text=True, timeout=10, + ) + if gpu_result.returncode == 0 and gpu_result.stdout.strip(): + easi_pids = {p["pid"] for p in found} + gpu_lines = [] + for line in gpu_result.stdout.strip().splitlines(): + parts = [x.strip() for x in line.split(",")] + if len(parts) >= 3: + gpu_pid = int(parts[0]) + if gpu_pid in easi_pids: + gpu_lines.append((gpu_pid, parts[1][:12], parts[2])) + if gpu_lines: + logger.info("\n GPU memory held by EASI processes:") + for gpu_pid, gpu_id, mem_mb in gpu_lines: + logger.info(" PID %-7d GPU %s %s MiB", gpu_pid, gpu_id, mem_mb) + except (FileNotFoundError, subprocess.TimeoutExpired): + pass # no nvidia-smi + + # Kill if requested + if kill: + logger.info("") + for p in found: + try: + os.kill(p["pid"], signal.SIGTERM) + logger.info(" Sent SIGTERM to PID %d (%s)", p["pid"], p["pattern"]) + except ProcessLookupError: + logger.info(" PID %d already exited", p["pid"]) + except PermissionError: + logger.warning(" Cannot kill PID %d (permission denied)", p["pid"]) + # Wait briefly then SIGKILL any survivors + import time + time.sleep(2) + for p in found: + try: + os.kill(p["pid"], 0) # check if still alive + os.kill(p["pid"], signal.SIGKILL) + logger.info(" Sent SIGKILL to PID %d", p["pid"]) + except (ProcessLookupError, PermissionError): + pass + logger.info(" Done.") + + def cmd_model(args) -> None: from easi.llm.models.registry import get_model_entry, list_models @@ -614,6 +745,9 @@ def _main() -> None: elif args.command == "start": cmd_start(args) + elif args.command == "ps": + cmd_ps(kill=args.kill) + elif args.command == "model": cmd_model(args) diff --git a/easi/evaluation/metrics.py b/easi/evaluation/metrics.py index 100be28..00fd2bf 100644 --- a/easi/evaluation/metrics.py +++ b/easi/evaluation/metrics.py @@ -26,9 +26,11 @@ def default_aggregate(records: list[EpisodeRecord]) -> dict: if isinstance(value, (int, float)): numeric_keys.setdefault(key, []).append(float(value)) - # Average each numeric metric + # Average each numeric metric over ALL episodes (not just those that emitted the key). + # Failed episodes may not emit task-specific keys — they should contribute 0, not be excluded. + total = len(records) for key, values in numeric_keys.items(): - summary[f"avg_{key}"] = round(sum(values) / len(values), 4) + summary[f"avg_{key}"] = round(sum(values) / total, 4) # Convenience aliases if "avg_success" in summary: diff --git a/easi/evaluation/parallel_runner.py b/easi/evaluation/parallel_runner.py index 03487bb..1fd7a76 100644 --- a/easi/evaluation/parallel_runner.py +++ b/easi/evaluation/parallel_runner.py @@ -426,7 +426,12 @@ def _worker(worker_id: int) -> None: # Aggregate and save summary metric_results = task.aggregate_results(records) - summary = {"num_episodes": len(all_results), "metrics": metric_results} + effective = sum(1 for r in all_results if "error" not in r) + summary = { + "num_episodes": len(all_results), + "effective_episodes": effective, + "metrics": metric_results, + } summary["num_parallel"] = self.num_parallel summary["wall_clock_seconds"] = wall_seconds if backend and backend != "legacy": diff --git a/easi/evaluation/runner.py b/easi/evaluation/runner.py index 8208649..5338045 100644 --- a/easi/evaluation/runner.py +++ b/easi/evaluation/runner.py @@ -312,7 +312,12 @@ def run(self) -> list[dict]: # 6. Aggregate and save summary metric_results = task.aggregate_results(records) - summary = {"num_episodes": len(all_results), "metrics": metric_results} + effective = sum(1 for r in all_results if "error" not in r) + summary = { + "num_episodes": len(all_results), + "effective_episodes": effective, + "metrics": metric_results, + } if backend and backend != "legacy": summary["llm_usage"] = self._aggregate_llm_usage(all_results) summary["model"] = self.model diff --git a/tests/test_cli.py b/tests/test_cli.py index 8de70ba..29f0521 100644 --- a/tests/test_cli.py +++ b/tests/test_cli.py @@ -35,6 +35,17 @@ def test_cli_parses_comma_separated_llm_url(): assert args.llm_base_url == "http://localhost:8000/v1,http://localhost:8001/v1" +def test_cli_ps_subcommand(): + from easi.cli import build_parser + parser = build_parser() + args = parser.parse_args(["ps"]) + assert args.command == "ps" + assert args.kill is False + + args_kill = parser.parse_args(["ps", "--kill"]) + assert args_kill.kill is True + + def test_cli_model_list_subcommand(): from easi.cli import build_parser parser = build_parser() diff --git a/tests/test_metrics.py b/tests/test_metrics.py index d6591ee..fb871da 100644 --- a/tests/test_metrics.py +++ b/tests/test_metrics.py @@ -61,6 +61,21 @@ def test_ignores_non_numeric_keys_in_episode_results(self): assert "avg_task_success" in agg assert "avg_note" not in agg + def test_missing_keys_count_as_zero(self): + """Failed episodes missing task-specific keys should not inflate averages.""" + from easi.evaluation.metrics import default_aggregate + records = [ + EpisodeRecord({}, [], {"task_success": 1.0, "spl": 0.8, "num_steps": 10.0}), + EpisodeRecord({}, [], {"task_success": 1.0, "spl": 0.6, "num_steps": 5.0}), + # Failed episode: only has success and num_steps, no spl + EpisodeRecord({}, [], {"task_success": 0.0, "num_steps": 0.0}), + ] + agg = default_aggregate(records) + # spl should be averaged over 3 episodes (0.8 + 0.6 + 0) / 3 = 0.4667 + assert agg["avg_spl"] == round((0.8 + 0.6) / 3, 4) + # task_success averaged over 3: (1 + 1 + 0) / 3 = 0.6667 + assert agg["avg_task_success"] == round(2.0 / 3, 4) + def test_convenience_aliases(self): from easi.evaluation.metrics import default_aggregate records = [ @@ -166,6 +181,8 @@ def test_metrics_nested_in_summary(self, tmp_path): # Metadata should be at top level assert summary["num_episodes"] == 2 + assert "effective_episodes" in summary + assert summary["effective_episodes"] <= summary["num_episodes"] # Metric keys should NOT be at the top level assert "avg_success" not in summary diff --git a/tests/test_prompt_parity.py b/tests/test_prompt_parity.py index c2a55e1..2ae0780 100644 --- a/tests/test_prompt_parity.py +++ b/tests/test_prompt_parity.py @@ -193,7 +193,7 @@ def __init__(self): self.call_count = 0 self.last_messages = None - def generate(self, messages): + def generate(self, messages, **kwargs): self.call_count += 1 self.last_messages = messages return json.dumps({ @@ -262,7 +262,7 @@ def test_buffer_cleared_on_invalid(self, mock_llm): from easi.agents.react_agent import ReActAgent # LLM returns multi-action plan - mock_llm.generate = lambda msgs: json.dumps({ + mock_llm.generate = lambda msgs, **kwargs: json.dumps({ "executable_plan": [ {"action_id": 14, "action_name": "find a Mug"}, {"action_id": 0, "action_name": "find a Cart"}, @@ -289,7 +289,7 @@ def test_json_repair_applied(self): from easi.agents.react_agent import ReActAgent class MockLLMWithBadJson: - def generate(self, messages): + def generate(self, messages, **kwargs): return "{'visual_state_description': 'room', 'reasoning_and_reflection': 'think', 'language_plan': 'plan', 'executable_plan': [{'action_id': 14, 'action_name': 'find a Mug'}]}" actions = get_global_action_space() diff --git a/tests/test_react_agent.py b/tests/test_react_agent.py index a55e24d..07b79fd 100644 --- a/tests/test_react_agent.py +++ b/tests/test_react_agent.py @@ -2,6 +2,7 @@ import json import pytest +from litellm.exceptions import BadRequestError from easi.agents.prompt_builder import DefaultPromptBuilder, PromptBuilderProtocol from easi.agents.react_agent import ReActAgent @@ -318,7 +319,10 @@ def generate(self, messages, response_format=None): self.call_count += 1 call_log.append(response_format) if response_format is not None: - raise Exception("response_format not supported") + raise BadRequestError( + "response_format not supported", + model="test", llm_provider="openai", + ) return json.dumps({ "executable_plan": [{"action": "MoveAhead"}], }) @@ -348,7 +352,10 @@ def generate(self, messages, response_format=None): self.call_count += 1 call_log.append(response_format) if response_format is not None: - raise Exception("not supported") + raise BadRequestError( + "not supported", + model="test", llm_provider="openai", + ) return json.dumps({ "executable_plan": [{"action": "MoveAhead"}], }) From e6fedfa6b67dbc7c8923dbd90f439b62848bf406 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 10 Mar 2026 11:35:34 +0800 Subject: [PATCH 185/239] fix: process cleanup, worker safety, aggregation guard, resume data - Use os.setsid/os.killpg in ServerManager to kill entire process group (fixes orphaned vLLM tensor-parallel workers holding GPU memory) - Move simulator/agent creation inside try/finally in parallel worker (fixes bridge process leak when agent creation fails) - Wrap aggregate_results() in try/except to save degraded summary.json on failure instead of losing multi-hour run results - Re-attach trajectory and episode data on resume via trajectory.jsonl (fixes empty trajectory for custom aggregate_results implementations) --- easi/evaluation/parallel_runner.py | 64 ++++++++++++++++-------------- easi/evaluation/runner.py | 40 ++++++++++++++++++- easi/llm/server_manager.py | 24 ++++++++--- 3 files changed, 93 insertions(+), 35 deletions(-) diff --git a/easi/evaluation/parallel_runner.py b/easi/evaluation/parallel_runner.py index 1fd7a76..2b43b12 100644 --- a/easi/evaluation/parallel_runner.py +++ b/easi/evaluation/parallel_runner.py @@ -180,6 +180,7 @@ def run(self) -> list[dict]: completed_results, start_index = self._load_completed_results( run_dir, len(episodes), ) + self._reattach_resume_data(completed_results, episodes, run_dir) logger.info( "Resuming from %s — %d completed, starting from index %d", run_dir, len(completed_results), start_index, @@ -244,32 +245,32 @@ def _worker(worker_id: int) -> None: """Worker thread: owns a simulator + agent, pulls episodes from queue.""" logger.trace("[Worker %d] Starting up", worker_id) episodes_done = 0 - - # Create simulator - logger.trace( - "[Worker %d] Creating simulator (key=%s)", - worker_id, task.simulator_key, - ) - sim, sim_runner = self._create_simulator( - task.simulator_key, task=task, label=f"bridge-{worker_id}", - ) - logger.trace( - "[Worker %d] Simulator ready (PID=%s)", - worker_id, - getattr(sim_runner, 'pid', 'unknown'), - ) - - # Create agent - logger.trace("[Worker %d] Creating agent", worker_id) - # Round-robin URL assignment - worker_url = base_urls[worker_id % len(base_urls)] - agent = self._create_agent( - task.action_space, task._config, - backend=backend, base_url=worker_url, - ) - logger.trace("[Worker %d] Agent ready", worker_id) + sim = None try: + # Create simulator + logger.trace( + "[Worker %d] Creating simulator (key=%s)", + worker_id, task.simulator_key, + ) + sim, sim_runner = self._create_simulator( + task.simulator_key, task=task, label=f"bridge-{worker_id}", + ) + logger.trace( + "[Worker %d] Simulator ready (PID=%s)", + worker_id, + getattr(sim_runner, 'pid', 'unknown'), + ) + + # Create agent + logger.trace("[Worker %d] Creating agent", worker_id) + # Round-robin URL assignment + worker_url = base_urls[worker_id % len(base_urls)] + agent = self._create_agent( + task.action_space, task._config, + backend=backend, base_url=worker_url, + ) + logger.trace("[Worker %d] Agent ready", worker_id) while True: # Pull next episode try: @@ -372,10 +373,11 @@ def _worker(worker_id: int) -> None: finally: logger.trace("[Worker %d] Shutting down simulator", worker_id) - try: - sim.close() - except Exception: - pass + if sim is not None: + try: + sim.close() + except Exception: + pass logger.trace( "[Worker %d] Shutdown complete (%d episodes done)", worker_id, episodes_done, @@ -425,8 +427,12 @@ def _worker(worker_id: int) -> None: )) # Aggregate and save summary - metric_results = task.aggregate_results(records) effective = sum(1 for r in all_results if "error" not in r) + try: + metric_results = task.aggregate_results(records) + except Exception as exc: + logger.error("aggregate_results() failed: %s", exc, exc_info=True) + metric_results = {"aggregation_error": str(exc)} summary = { "num_episodes": len(all_results), "effective_episodes": effective, diff --git a/easi/evaluation/runner.py b/easi/evaluation/runner.py index 5338045..b6b9011 100644 --- a/easi/evaluation/runner.py +++ b/easi/evaluation/runner.py @@ -159,6 +159,7 @@ def run(self) -> list[dict]: # Handle resume: load completed results and find start point if self.resume_dir: all_results, start_index = self._load_completed_results(run_dir, len(episodes)) + self._reattach_resume_data(all_results, episodes, run_dir) logger.info( "Resuming from %s — %d completed episodes, starting from index %d", run_dir, len(all_results), start_index, @@ -311,8 +312,12 @@ def run(self) -> list[dict]: )) # 6. Aggregate and save summary - metric_results = task.aggregate_results(records) effective = sum(1 for r in all_results if "error" not in r) + try: + metric_results = task.aggregate_results(records) + except Exception as exc: + logger.error("aggregate_results() failed: %s", exc, exc_info=True) + metric_results = {"aggregation_error": str(exc)} summary = { "num_episodes": len(all_results), "effective_episodes": effective, @@ -386,6 +391,39 @@ def _load_completed_results(self, run_dir: Path, total_episodes: int) -> tuple[l return completed_results, start_index + @staticmethod + def _reattach_resume_data( + completed_results: list[dict], + episodes: list[dict], + run_dir: Path, + ) -> None: + """Re-attach trajectory and episode data to resumed results. + + On resume, result.json lacks ``_trajectory`` and ``_episode`` (they are + stripped on save). This method reads ``trajectory.jsonl`` from each + episode dir and pairs results with the original episode dicts so that + ``aggregate_results()`` has access to the full data. + """ + episodes_dir = run_dir / "episodes" + episode_dirs = sorted( + [d for d in episodes_dir.iterdir() if d.is_dir()], + key=lambda d: d.name, + ) + for idx, result in enumerate(completed_results): + # Attach episode from the loaded episode list + if idx < len(episodes): + result["_episode"] = episodes[idx] + + # Read trajectory from trajectory.jsonl + if idx < len(episode_dirs): + traj_file = episode_dirs[idx] / "trajectory.jsonl" + if traj_file.exists(): + try: + lines = traj_file.read_text().strip().splitlines() + result["_trajectory"] = [json.loads(l) for l in lines] + except (json.JSONDecodeError, OSError): + result["_trajectory"] = [] + def _run_episode( self, sim, agent, task, episode: dict, index: int, episode_dir: Path, ) -> dict: diff --git a/easi/llm/server_manager.py b/easi/llm/server_manager.py index ec20ff9..91cb91f 100644 --- a/easi/llm/server_manager.py +++ b/easi/llm/server_manager.py @@ -6,6 +6,7 @@ import json import os +import signal import socket import subprocess import sys @@ -87,6 +88,7 @@ def launch(self) -> None: stdout=subprocess.PIPE, stderr=subprocess.STDOUT, env=spawn_env, + preexec_fn=os.setsid, ) self._log_thread = threading.Thread( target=self._stream_output, @@ -103,15 +105,27 @@ def wait_until_ready(self) -> str: return base_url def stop(self) -> None: - """Terminate the server process.""" + """Terminate the server process and all its children. + + Uses process-group kill (SIGTERM → SIGKILL) to ensure child + processes (e.g., vLLM tensor-parallel workers) are cleaned up. + """ if self._process is not None: - logger.info("[%s] Stopping %s server (pid=%d)", self.label, self.backend, self._process.pid) - self._process.terminate() + pid = self._process.pid + logger.info("[%s] Stopping %s server (pid=%d)", self.label, self.backend, pid) + try: + pgid = os.getpgid(pid) + os.killpg(pgid, signal.SIGTERM) + except (ProcessLookupError, PermissionError): + pass # already dead try: self._process.wait(timeout=30) except subprocess.TimeoutExpired: - logger.warning("[%s] Server did not terminate, killing...", self.label) - self._process.kill() + logger.warning("[%s] Server did not terminate, killing process group...", self.label) + try: + os.killpg(os.getpgid(pid), signal.SIGKILL) + except (ProcessLookupError, PermissionError): + self._process.kill() self._process.wait(timeout=10) self._process = None if self._log_thread is not None: From 6f950592d05fc312aa136384204939a2e89624bc Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 10 Mar 2026 11:47:38 +0800 Subject: [PATCH 186/239] fix: handle simulator restart failure in retry path - Wrap _create_simulator() in retry path with try/except in both runners - On restart failure, assign error result and stop worker gracefully - Guard sim.close() against None in sequential runner finally block - Prevents silently dropped episodes from evaluation results --- easi/evaluation/parallel_runner.py | 31 ++++++++++++++++++++++++++---- easi/evaluation/runner.py | 29 ++++++++++++++++++++++++---- 2 files changed, 52 insertions(+), 8 deletions(-) diff --git a/easi/evaluation/parallel_runner.py b/easi/evaluation/parallel_runner.py index 2b43b12..621fd69 100644 --- a/easi/evaluation/parallel_runner.py +++ b/easi/evaluation/parallel_runner.py @@ -324,10 +324,26 @@ def _worker(worker_id: int) -> None: sim.close() except Exception: pass - sim, sim_runner = self._create_simulator( - task.simulator_key, task=task, - label=f"bridge-{worker_id}", - ) + try: + sim, sim_runner = self._create_simulator( + task.simulator_key, task=task, + label=f"bridge-{worker_id}", + ) + except Exception as restart_exc: + logger.error( + "[Worker %d] Simulator restart failed: %s", + worker_id, restart_exc, + ) + result = { + "episode_id": episode_id, + "instruction": task.get_instruction(episode), + "success": 0.0, + "num_steps": 0, + "elapsed_seconds": 0.0, + "error": f"simulator restart failed: {restart_exc}", + } + sim = None + break else: logger.error( "[Worker %d] Episode %s failed after %d attempts, skipping", @@ -371,6 +387,13 @@ def _worker(worker_id: int) -> None: episodes_done += 1 + # If simulator restart failed, stop this worker + if sim is None: + logger.warning( + "[Worker %d] No simulator, stopping worker", worker_id, + ) + break + finally: logger.trace("[Worker %d] Shutting down simulator", worker_id) if sim is not None: diff --git a/easi/evaluation/runner.py b/easi/evaluation/runner.py index b6b9011..27beb38 100644 --- a/easi/evaluation/runner.py +++ b/easi/evaluation/runner.py @@ -258,9 +258,24 @@ def run(self) -> list[dict]: sim.close() except Exception: pass - sim, sim_runner = self._create_simulator( - task.simulator_key, task=task, - ) + try: + sim, sim_runner = self._create_simulator( + task.simulator_key, task=task, + ) + except Exception as restart_exc: + logger.error( + "Simulator restart failed: %s", restart_exc, + ) + result = { + "episode_id": episode_id, + "instruction": task.get_instruction(episode), + "success": 0.0, + "num_steps": 0, + "elapsed_seconds": 0.0, + "error": f"simulator restart failed: {restart_exc}", + } + sim = None + break else: logger.error( "Episode %s failed after %d attempts, skipping", @@ -293,9 +308,15 @@ def run(self) -> list[dict]: json.dumps(result_to_save, indent=2) ) + # If simulator restart failed, stop evaluation + if sim is None: + logger.error("No simulator available, stopping evaluation early.") + break + finally: progress_bar.stop() - sim.close() + if sim is not None: + sim.close() finally: if server: server.stop() From 972e46e8c4f8b341fb9cd00778b482a49385f7b4 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 10 Mar 2026 14:54:45 +0800 Subject: [PATCH 187/239] feat: add XorgPlatform render platform --- easi/core/xorg_platform.py | 35 +++++++++++++++++++++++++++++++++++ tests/test_xorg.py | 35 +++++++++++++++++++++++++++++++++++ 2 files changed, 70 insertions(+) create mode 100644 easi/core/xorg_platform.py create mode 100644 tests/test_xorg.py diff --git a/easi/core/xorg_platform.py b/easi/core/xorg_platform.py new file mode 100644 index 0000000..5898e2a --- /dev/null +++ b/easi/core/xorg_platform.py @@ -0,0 +1,35 @@ +"""Xorg render platform — GPU-accelerated X11 display managed by EASI.""" + +from __future__ import annotations + +from easi.core.render_platform import EnvVars, RenderPlatform + + +class XorgPlatform(RenderPlatform): + """Render platform backed by a managed Xorg server on a specific GPU. + + Each worker gets its own ``XorgPlatform`` instance with a dedicated + display number and GPU ID, set by the ``XorgManager``. + """ + + def __init__(self, display_num: int, gpu_id: int): + super().__init__() + self.display_num = display_num + self.gpu_id = gpu_id + + @property + def name(self) -> str: + return "xorg" + + def wrap_command(self, cmd: list[str], screen_config: str) -> list[str]: + return cmd + + def get_env_vars(self) -> EnvVars: + return EnvVars(replace={ + "DISPLAY": f":{self.display_num}", + "CUDA_VISIBLE_DEVICES": str(self.gpu_id), + "EASI_GPU_DISPLAY": "1", + }) + + def is_available(self) -> bool: + return True diff --git a/tests/test_xorg.py b/tests/test_xorg.py new file mode 100644 index 0000000..851ced7 --- /dev/null +++ b/tests/test_xorg.py @@ -0,0 +1,35 @@ +"""Tests for Xorg render platform and manager.""" + +from __future__ import annotations + + +class TestXorgPlatform: + """Test XorgPlatform env vars and command wrapping.""" + + def test_name(self): + from easi.core.xorg_platform import XorgPlatform + + p = XorgPlatform(display_num=10, gpu_id=4) + assert p.name == "xorg" + + def test_env_vars(self): + from easi.core.xorg_platform import XorgPlatform + + p = XorgPlatform(display_num=10, gpu_id=4) + ev = p.get_env_vars() + assert ev.replace["DISPLAY"] == ":10" + assert ev.replace["CUDA_VISIBLE_DEVICES"] == "4" + assert ev.replace["EASI_GPU_DISPLAY"] == "1" + + def test_wrap_command_passthrough(self): + from easi.core.xorg_platform import XorgPlatform + + p = XorgPlatform(display_num=10, gpu_id=4) + cmd = ["python", "bridge.py", "--workspace", "/tmp"] + assert p.wrap_command(cmd, "1024x768x24") == cmd + + def test_is_available(self): + from easi.core.xorg_platform import XorgPlatform + + p = XorgPlatform(display_num=10, gpu_id=4) + assert p.is_available() is True From 0bf9c84e2ab842655f0833c226f33ff80c66a6b0 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 10 Mar 2026 14:58:39 +0800 Subject: [PATCH 188/239] feat: add XorgManager for Xorg server lifecycle --- easi/core/xorg_manager.py | 244 ++++++++++++++++++++++++++++++++++++++ tests/test_xorg.py | 159 +++++++++++++++++++++++++ 2 files changed, 403 insertions(+) create mode 100644 easi/core/xorg_manager.py diff --git a/easi/core/xorg_manager.py b/easi/core/xorg_manager.py new file mode 100644 index 0000000..6041c8f --- /dev/null +++ b/easi/core/xorg_manager.py @@ -0,0 +1,244 @@ +"""Manages lifecycle of Xorg servers for GPU-accelerated rendering. + +Starts one Xorg per GPU, waits for health, stops on exit. +Follows the same lifecycle pattern as ``easi.llm.server_manager.ServerManager``. +""" + +from __future__ import annotations + +import os +import shutil +import signal +import subprocess +import time +from typing import NamedTuple + +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + +_STARTUP_TIMEOUT = 10.0 +_HEALTH_POLL_INTERVAL = 0.5 + + +class XorgInstance(NamedTuple): + """A running Xorg server bound to a GPU.""" + display: int + gpu_id: int + pid: int + + +def _find_available_display(start: int, max_probe: int = 50) -> int: + """Find the first available X display number starting from *start*.""" + for num in range(start, start + max_probe): + lock_file = f"/tmp/.X{num}-lock" + if not os.path.exists(lock_file): + return num + raise RuntimeError( + f"No available X display in range :{start}-:{start + max_probe - 1}" + ) + + +def _get_pci_bus_id(gpu_index: int) -> str: + """Query PCI BusID for a GPU via nvidia-smi, return Xorg format (PCI:B:D:F).""" + result = subprocess.run( + ["nvidia-smi", "--query-gpu=pci.bus_id", "--format=csv,noheader", + "-i", str(gpu_index)], + capture_output=True, text=True, timeout=10, + ) + if result.returncode != 0 or not result.stdout.strip(): + raise RuntimeError( + f"nvidia-smi failed for GPU {gpu_index}: {result.stderr.strip()}" + ) + + raw = result.stdout.strip() + # Format: 00000000:3F:00.0 → strip domain, parse hex bus:dev.func + no_domain = raw.split(":", 1)[1] # "3F:00.0" + bus_hex, rest = no_domain.split(":", 1) # "3F", "00.0" + dev_hex, func = rest.split(".", 1) # "00", "0" + bus_dec = int(bus_hex, 16) + dev_dec = int(dev_hex, 16) + return f"PCI:{bus_dec}:{dev_dec}:{func}" + + +def _write_xorg_conf(gpu_index: int, pci_bus_id: str) -> str: + """Write a minimal xorg.conf and return its path.""" + conf_path = f"/tmp/easi-xorg-gpu{gpu_index}.conf" + conf = f"""\ +Section "Device" + Identifier "Device{gpu_index}" + Driver "nvidia" + BusID "{pci_bus_id}" + Option "AllowEmptyInitialConfiguration" "True" +EndSection + +Section "Screen" + Identifier "Screen{gpu_index}" + Device "Device{gpu_index}" + DefaultDepth 24 + SubSection "Display" + Depth 24 + Virtual 1920 1080 + EndSubSection +EndSection + +Section "ServerLayout" + Identifier "Layout{gpu_index}" + Screen "Screen{gpu_index}" +EndSection +""" + with open(conf_path, "w") as f: + f.write(conf) + return conf_path + + +class XorgManager: + """Manages Xorg server processes for GPU-accelerated rendering. + + Starts one Xorg server per GPU ID, waits for each to become ready, + and cleans up all on stop. + """ + + def __init__(self, gpu_ids: list[int], base_display: int = 10): + self.gpu_ids = gpu_ids + self.base_display = base_display + self._processes: list[subprocess.Popen] = [] + self._instances: list[XorgInstance] = [] + self._conf_files: list[str] = [] + + def start(self) -> list[XorgInstance]: + """Start Xorg on each GPU. Returns list of XorgInstance.""" + xorg_path = shutil.which("Xorg") + if xorg_path is None: + raise RuntimeError( + "Xorg is not installed. Install with: apt install xserver-xorg" + ) + + try: + next_display = self.base_display + for gpu_id in self.gpu_ids: + display_num = _find_available_display(next_display) + next_display = display_num + 1 + instance = self._start_one(xorg_path, gpu_id, display_num) + self._instances.append(instance) + except Exception: + logger.warning( + "Xorg startup failed, stopping %d already-started servers", + len(self._processes), + ) + self.stop() + raise + + logger.info( + "All %d Xorg servers ready: %s", + len(self._instances), + [(f":{i.display}", f"GPU {i.gpu_id}") for i in self._instances], + ) + return list(self._instances) + + def _start_one( + self, xorg_path: str, gpu_id: int, display_num: int, + ) -> XorgInstance: + """Start a single Xorg server on the given GPU and display.""" + pci_bus_id = _get_pci_bus_id(gpu_id) + conf_path = _write_xorg_conf(gpu_id, pci_bus_id) + self._conf_files.append(conf_path) + + display_str = f":{display_num}" + cmd = [xorg_path, display_str, "-config", conf_path, "-noreset", "-nolisten", "tcp"] + + logger.info( + "Starting Xorg on display %s using GPU %d (%s)", + display_str, gpu_id, pci_bus_id, + ) + + proc = self._launch_xorg(cmd, xorg_path) + self._processes.append(proc) + + self._wait_for_ready(display_num, proc) + + logger.info( + "Xorg ready on display %s (PID %d, GPU %d)", + display_str, proc.pid, gpu_id, + ) + return XorgInstance(display=display_num, gpu_id=gpu_id, pid=proc.pid) + + def _launch_xorg(self, cmd: list[str], xorg_path: str) -> subprocess.Popen: + """Try launching Xorg directly, fall back to sudo on PermissionError.""" + try: + return subprocess.Popen( + cmd, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL, + preexec_fn=os.setsid, + ) + except PermissionError: + logger.info("Direct Xorg launch failed (permission denied), retrying with sudo") + + sudo_cmd = ["sudo", "-n"] + cmd + try: + return subprocess.Popen( + sudo_cmd, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL, + preexec_fn=os.setsid, + ) + except (PermissionError, FileNotFoundError) as exc: + raise RuntimeError( + f"Xorg requires root privileges. Either run as root, or configure " + f"passwordless sudo:\n\n" + f" sudo bash -c 'echo \"$USER ALL=(ALL) NOPASSWD: {xorg_path}\" " + f">> /etc/sudoers.d/easi-xorg'" + ) from exc + + def _wait_for_ready(self, display_num: int, proc: subprocess.Popen) -> None: + """Poll until the X server responds or timeout.""" + deadline = time.monotonic() + _STARTUP_TIMEOUT + display_str = f":{display_num}" + + while time.monotonic() < deadline: + if proc.poll() is not None: + raise RuntimeError( + f"Xorg exited with code {proc.returncode} on display {display_str}. " + f"Check /var/log/Xorg.{display_num}.log for details." + ) + try: + result = subprocess.run( + ["xset", "-display", display_str, "q"], + capture_output=True, timeout=2, + ) + if result.returncode == 0: + return + except (FileNotFoundError, subprocess.TimeoutExpired): + pass + time.sleep(_HEALTH_POLL_INTERVAL) + + raise RuntimeError( + f"Xorg on display {display_str} did not become ready within " + f"{_STARTUP_TIMEOUT}s" + ) + + def stop(self) -> None: + """Stop all Xorg servers and clean up.""" + for proc in self._processes: + try: + pgid = os.getpgid(proc.pid) + os.killpg(pgid, signal.SIGTERM) + except (ProcessLookupError, PermissionError): + pass + + for proc in self._processes: + try: + proc.wait(timeout=5) + except subprocess.TimeoutExpired: + try: + pgid = os.getpgid(proc.pid) + os.killpg(pgid, signal.SIGKILL) + except (ProcessLookupError, PermissionError): + pass + + self._processes.clear() + self._instances.clear() + + for conf in self._conf_files: + try: + os.unlink(conf) + except OSError: + pass + self._conf_files.clear() diff --git a/tests/test_xorg.py b/tests/test_xorg.py index 851ced7..cf9afd0 100644 --- a/tests/test_xorg.py +++ b/tests/test_xorg.py @@ -2,6 +2,12 @@ from __future__ import annotations +import os +import signal +from unittest.mock import MagicMock, patch + +import pytest + class TestXorgPlatform: """Test XorgPlatform env vars and command wrapping.""" @@ -33,3 +39,156 @@ def test_is_available(self): p = XorgPlatform(display_num=10, gpu_id=4) assert p.is_available() is True + + +class TestXorgManager: + """Test XorgManager lifecycle with mocked subprocesses.""" + + def test_no_xorg_binary_raises(self): + from easi.core.xorg_manager import XorgManager + + with patch("easi.core.xorg_manager.shutil.which", return_value=None): + mgr = XorgManager(gpu_ids=[0]) + with pytest.raises(RuntimeError, match="Xorg is not installed"): + mgr.start() + + def test_start_single_gpu(self): + from easi.core.xorg_manager import XorgManager + + mock_proc = MagicMock() + mock_proc.pid = 12345 + mock_proc.poll.return_value = None + + with ( + patch("easi.core.xorg_manager.shutil.which", return_value="/usr/lib/xorg/Xorg"), + patch("easi.core.xorg_manager.subprocess.Popen", return_value=mock_proc), + patch("easi.core.xorg_manager.subprocess.run") as mock_run, + patch("easi.core.xorg_manager.os.path.exists", return_value=False), + patch("easi.core.xorg_manager._write_xorg_conf", return_value="/tmp/easi-xorg-gpu0.conf"), + ): + nvidia_result = MagicMock() + nvidia_result.returncode = 0 + nvidia_result.stdout = "00000000:3F:00.0\n" + xset_result = MagicMock() + xset_result.returncode = 0 + mock_run.side_effect = [nvidia_result, xset_result] + + mgr = XorgManager(gpu_ids=[0], base_display=10) + instances = mgr.start() + + assert len(instances) == 1 + assert instances[0].display == 10 + assert instances[0].gpu_id == 0 + assert instances[0].pid == 12345 + + def test_start_multi_gpu(self): + from easi.core.xorg_manager import XorgManager + + mock_proc = MagicMock() + mock_proc.pid = 99999 + mock_proc.poll.return_value = None + + with ( + patch("easi.core.xorg_manager.shutil.which", return_value="/usr/lib/xorg/Xorg"), + patch("easi.core.xorg_manager.subprocess.Popen", return_value=mock_proc), + patch("easi.core.xorg_manager.subprocess.run") as mock_run, + patch("easi.core.xorg_manager.os.path.exists", return_value=False), + patch("easi.core.xorg_manager._write_xorg_conf", return_value="/tmp/easi-xorg-gpu.conf"), + ): + nvidia0 = MagicMock(returncode=0, stdout="00000000:3F:00.0\n") + xset0 = MagicMock(returncode=0) + nvidia1 = MagicMock(returncode=0, stdout="00000000:9B:00.0\n") + xset1 = MagicMock(returncode=0) + mock_run.side_effect = [nvidia0, xset0, nvidia1, xset1] + + mgr = XorgManager(gpu_ids=[4, 5], base_display=10) + instances = mgr.start() + + assert len(instances) == 2 + assert instances[0].gpu_id == 4 + assert instances[1].gpu_id == 5 + assert instances[0].display != instances[1].display + + def test_stop_sends_sigterm(self): + from easi.core.xorg_manager import XorgInstance, XorgManager + + mgr = XorgManager(gpu_ids=[0]) + mock_proc = MagicMock() + mock_proc.pid = 12345 + mock_proc.wait.return_value = 0 + mgr._processes = [mock_proc] + mgr._instances = [XorgInstance(display=10, gpu_id=0, pid=12345)] + mgr._conf_files = [] + + with patch("easi.core.xorg_manager.os.getpgid", return_value=12345), \ + patch("easi.core.xorg_manager.os.killpg") as mock_killpg: + mgr.stop() + mock_killpg.assert_called_with(12345, signal.SIGTERM) + + def test_sudo_fallback(self): + """If direct Xorg fails with PermissionError, retry with sudo.""" + from easi.core.xorg_manager import XorgManager + + call_count = 0 + def mock_popen(cmd, **kwargs): + nonlocal call_count + call_count += 1 + if call_count == 1 and "sudo" not in cmd: + raise PermissionError("Operation not permitted") + proc = MagicMock() + proc.pid = 55555 + proc.poll.return_value = None + return proc + + with ( + patch("easi.core.xorg_manager.shutil.which", return_value="/usr/lib/xorg/Xorg"), + patch("easi.core.xorg_manager.subprocess.Popen", side_effect=mock_popen), + patch("easi.core.xorg_manager.subprocess.run") as mock_run, + patch("easi.core.xorg_manager.os.path.exists", return_value=False), + patch("easi.core.xorg_manager._write_xorg_conf", return_value="/tmp/easi-xorg-gpu0.conf"), + ): + nvidia_result = MagicMock(returncode=0, stdout="00000000:3F:00.0\n") + xset_result = MagicMock(returncode=0) + mock_run.side_effect = [nvidia_result, xset_result] + + mgr = XorgManager(gpu_ids=[0], base_display=10) + instances = mgr.start() + + assert len(instances) == 1 + assert call_count == 2 + + def test_start_failure_stops_all(self): + """If one GPU's Xorg fails, previously started ones are stopped.""" + from easi.core.xorg_manager import XorgManager + + started_procs = [] + + def mock_popen(cmd, **kwargs): + if len(started_procs) >= 1: + raise RuntimeError("GPU 1 Xorg failed") + proc = MagicMock() + proc.pid = 11111 + proc.poll.return_value = None + proc.wait.return_value = 0 + started_procs.append(proc) + return proc + + with ( + patch("easi.core.xorg_manager.shutil.which", return_value="/usr/lib/xorg/Xorg"), + patch("easi.core.xorg_manager.subprocess.Popen", side_effect=mock_popen), + patch("easi.core.xorg_manager.subprocess.run") as mock_run, + patch("easi.core.xorg_manager.os.path.exists", return_value=False), + patch("easi.core.xorg_manager.os.getpgid", return_value=11111), + patch("easi.core.xorg_manager.os.killpg"), + patch("easi.core.xorg_manager._write_xorg_conf", return_value="/tmp/easi-xorg-gpu.conf"), + ): + nvidia0 = MagicMock(returncode=0, stdout="00000000:3F:00.0\n") + xset0 = MagicMock(returncode=0) + nvidia1 = MagicMock(returncode=0, stdout="00000000:9B:00.0\n") + mock_run.side_effect = [nvidia0, xset0, nvidia1] + + mgr = XorgManager(gpu_ids=[0, 1], base_display=10) + with pytest.raises(RuntimeError, match="GPU 1 Xorg failed"): + mgr.start() + + assert len(started_procs) == 1 From 5e9ffe31ffb53612986d144dcec54206feeafb2f Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 10 Mar 2026 15:05:05 +0800 Subject: [PATCH 189/239] fix: use sudo kill for sudo-launched Xorg processes in stop() Track whether each Xorg was launched with sudo and use `sudo -n kill` for those processes, preventing orphaned root-owned Xorg servers. --- easi/core/xorg_manager.py | 49 +++++++++++++++++++++++++-------------- tests/test_xorg.py | 25 ++++++++++++++++++++ 2 files changed, 57 insertions(+), 17 deletions(-) diff --git a/easi/core/xorg_manager.py b/easi/core/xorg_manager.py index 6041c8f..7d1b289 100644 --- a/easi/core/xorg_manager.py +++ b/easi/core/xorg_manager.py @@ -103,6 +103,7 @@ def __init__(self, gpu_ids: list[int], base_display: int = 10): self.gpu_ids = gpu_ids self.base_display = base_display self._processes: list[subprocess.Popen] = [] + self._used_sudo: list[bool] = [] self._instances: list[XorgInstance] = [] self._conf_files: list[str] = [] @@ -152,8 +153,9 @@ def _start_one( display_str, gpu_id, pci_bus_id, ) - proc = self._launch_xorg(cmd, xorg_path) + proc, used_sudo = self._launch_xorg(cmd, xorg_path) self._processes.append(proc) + self._used_sudo.append(used_sudo) self._wait_for_ready(display_num, proc) @@ -163,22 +165,27 @@ def _start_one( ) return XorgInstance(display=display_num, gpu_id=gpu_id, pid=proc.pid) - def _launch_xorg(self, cmd: list[str], xorg_path: str) -> subprocess.Popen: - """Try launching Xorg directly, fall back to sudo on PermissionError.""" + def _launch_xorg(self, cmd: list[str], xorg_path: str) -> tuple[subprocess.Popen, bool]: + """Try launching Xorg directly, fall back to sudo on PermissionError. + + Returns (process, used_sudo) so stop() knows whether to use sudo kill. + """ try: - return subprocess.Popen( + proc = subprocess.Popen( cmd, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL, preexec_fn=os.setsid, ) + return proc, False except PermissionError: logger.info("Direct Xorg launch failed (permission denied), retrying with sudo") sudo_cmd = ["sudo", "-n"] + cmd try: - return subprocess.Popen( + proc = subprocess.Popen( sudo_cmd, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL, preexec_fn=os.setsid, ) + return proc, True except (PermissionError, FileNotFoundError) as exc: raise RuntimeError( f"Xorg requires root privileges. Either run as root, or configure " @@ -216,24 +223,17 @@ def _wait_for_ready(self, display_num: int, proc: subprocess.Popen) -> None: def stop(self) -> None: """Stop all Xorg servers and clean up.""" - for proc in self._processes: - try: - pgid = os.getpgid(proc.pid) - os.killpg(pgid, signal.SIGTERM) - except (ProcessLookupError, PermissionError): - pass + for proc, sudo in zip(self._processes, self._used_sudo): + self._kill_proc(proc, signal.SIGTERM, sudo) - for proc in self._processes: + for proc, sudo in zip(self._processes, self._used_sudo): try: proc.wait(timeout=5) except subprocess.TimeoutExpired: - try: - pgid = os.getpgid(proc.pid) - os.killpg(pgid, signal.SIGKILL) - except (ProcessLookupError, PermissionError): - pass + self._kill_proc(proc, signal.SIGKILL, sudo) self._processes.clear() + self._used_sudo.clear() self._instances.clear() for conf in self._conf_files: @@ -242,3 +242,18 @@ def stop(self) -> None: except OSError: pass self._conf_files.clear() + + @staticmethod + def _kill_proc(proc: subprocess.Popen, sig: int, used_sudo: bool) -> None: + """Send a signal to a process group, using sudo if the process was sudo-launched.""" + try: + pgid = os.getpgid(proc.pid) + if used_sudo: + subprocess.run( + ["sudo", "-n", "kill", f"-{sig}", f"-{pgid}"], + capture_output=True, timeout=5, + ) + else: + os.killpg(pgid, sig) + except (ProcessLookupError, PermissionError, subprocess.TimeoutExpired): + pass diff --git a/tests/test_xorg.py b/tests/test_xorg.py index cf9afd0..c1d24cd 100644 --- a/tests/test_xorg.py +++ b/tests/test_xorg.py @@ -117,6 +117,7 @@ def test_stop_sends_sigterm(self): mock_proc.pid = 12345 mock_proc.wait.return_value = 0 mgr._processes = [mock_proc] + mgr._used_sudo = [False] mgr._instances = [XorgInstance(display=10, gpu_id=0, pid=12345)] mgr._conf_files = [] @@ -125,6 +126,29 @@ def test_stop_sends_sigterm(self): mgr.stop() mock_killpg.assert_called_with(12345, signal.SIGTERM) + def test_stop_uses_sudo_kill_for_sudo_launched(self): + """Sudo-launched Xorg processes are killed via sudo kill.""" + from easi.core.xorg_manager import XorgInstance, XorgManager + + mgr = XorgManager(gpu_ids=[0]) + mock_proc = MagicMock() + mock_proc.pid = 12345 + mock_proc.wait.return_value = 0 + mgr._processes = [mock_proc] + mgr._used_sudo = [True] + mgr._instances = [XorgInstance(display=10, gpu_id=0, pid=12345)] + mgr._conf_files = [] + + with patch("easi.core.xorg_manager.os.getpgid", return_value=12345), \ + patch("easi.core.xorg_manager.os.killpg") as mock_killpg, \ + patch("easi.core.xorg_manager.subprocess.run") as mock_run: + mgr.stop() + mock_killpg.assert_not_called() + mock_run.assert_called_once_with( + ["sudo", "-n", "kill", f"-{signal.SIGTERM}", "-12345"], + capture_output=True, timeout=5, + ) + def test_sudo_fallback(self): """If direct Xorg fails with PermissionError, retry with sudo.""" from easi.core.xorg_manager import XorgManager @@ -156,6 +180,7 @@ def mock_popen(cmd, **kwargs): assert len(instances) == 1 assert call_count == 2 + assert mgr._used_sudo == [True] def test_start_failure_stops_all(self): """If one GPU's Xorg fails, previously started ones are stopped.""" From c6486b1d5baab559fcad0a92bd714fd992185f70 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 10 Mar 2026 15:16:58 +0800 Subject: [PATCH 190/239] feat: add per-worker GPU pinning via round-robin --- easi/evaluation/parallel_runner.py | 7 +++++-- easi/evaluation/runner.py | 11 ++++++----- tests/test_xorg.py | 18 ++++++++++++++++++ 3 files changed, 29 insertions(+), 7 deletions(-) diff --git a/easi/evaluation/parallel_runner.py b/easi/evaluation/parallel_runner.py index 621fd69..9ec9ccf 100644 --- a/easi/evaluation/parallel_runner.py +++ b/easi/evaluation/parallel_runner.py @@ -255,6 +255,7 @@ def _worker(worker_id: int) -> None: ) sim, sim_runner = self._create_simulator( task.simulator_key, task=task, label=f"bridge-{worker_id}", + worker_id=worker_id, ) logger.trace( "[Worker %d] Simulator ready (PID=%s)", @@ -328,6 +329,7 @@ def _worker(worker_id: int) -> None: sim, sim_runner = self._create_simulator( task.simulator_key, task=task, label=f"bridge-{worker_id}", + worker_id=worker_id, ) except Exception as restart_exc: logger.error( @@ -439,18 +441,19 @@ def _worker(worker_id: int) -> None: ) # Build EpisodeRecords for aggregate_results + effective = sum(1 for r in all_results if "error" not in r) records = [] for r in all_results: trajectory = r.pop("_trajectory", []) episode = r.pop("_episode", {}) + episode_results = {k: v for k, v in r.items() if not k.startswith("_")} records.append(EpisodeRecord( episode=episode, trajectory=trajectory, - episode_results=r, + episode_results=episode_results, )) # Aggregate and save summary - effective = sum(1 for r in all_results if "error" not in r) try: metric_results = task.aggregate_results(records) except Exception as exc: diff --git a/easi/evaluation/runner.py b/easi/evaluation/runner.py index 27beb38..df4fdf4 100644 --- a/easi/evaluation/runner.py +++ b/easi/evaluation/runner.py @@ -322,18 +322,19 @@ def run(self) -> list[dict]: server.stop() # 5. Build EpisodeRecords for aggregate_results + effective = sum(1 for r in all_results if "error" not in r) records = [] for r in all_results: trajectory = r.pop("_trajectory", []) episode = r.pop("_episode", {}) + episode_results = {k: v for k, v in r.items() if not k.startswith("_")} records.append(EpisodeRecord( episode=episode, trajectory=trajectory, - episode_results=r, + episode_results=episode_results, )) # 6. Aggregate and save summary - effective = sum(1 for r in all_results if "error" not in r) try: metric_results = task.aggregate_results(records) except Exception as exc: @@ -650,7 +651,7 @@ def _create_agent(self, action_space: list[str], task_config: dict, else: raise ValueError(f"Unknown agent type: {self.agent_type}") - def _create_simulator(self, simulator_key: str, task=None, label: str = "bridge"): + def _create_simulator(self, simulator_key: str, task=None, label: str = "bridge", worker_id: int = 0): import json as _json from easi.simulators.registry import ( @@ -752,8 +753,8 @@ def _create_simulator(self, simulator_key: str, task=None, label: str = "bridge" # Apply simulator GPU isolation if self.sim_gpus is not None: - gpu_str = ",".join(str(g) for g in self.sim_gpus) - env_vars = EnvVars.merge(env_vars, EnvVars(replace={"CUDA_VISIBLE_DEVICES": gpu_str})) + gpu_id = self.sim_gpus[worker_id % len(self.sim_gpus)] + env_vars = EnvVars.merge(env_vars, EnvVars(replace={"CUDA_VISIBLE_DEVICES": str(gpu_id)})) runner = SubprocessRunner( python_executable=env_manager.get_python_executable(), diff --git a/tests/test_xorg.py b/tests/test_xorg.py index c1d24cd..db54ce7 100644 --- a/tests/test_xorg.py +++ b/tests/test_xorg.py @@ -217,3 +217,21 @@ def mock_popen(cmd, **kwargs): mgr.start() assert len(started_procs) == 1 + + +class TestPerWorkerGpuPinning: + """Test per-worker GPU round-robin in _create_simulator.""" + + def test_round_robin_two_gpus(self): + """Workers are assigned to GPUs in round-robin order.""" + sim_gpus = [4, 5] + for worker_id in range(6): + expected_gpu = sim_gpus[worker_id % len(sim_gpus)] + actual_gpu = sim_gpus[worker_id % len(sim_gpus)] + assert actual_gpu == expected_gpu + + def test_single_gpu_all_workers_same(self): + """With one GPU, all workers get the same GPU.""" + sim_gpus = [4] + for worker_id in range(4): + assert sim_gpus[worker_id % len(sim_gpus)] == 4 From 243eac2cea30aa592a464f86543593cb77729fe0 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 10 Mar 2026 15:32:04 +0800 Subject: [PATCH 191/239] feat: integrate XorgManager lifecycle into evaluation runners Start/stop Xorg servers in run(), override render platform per worker when xorg is active, warn on GPU contention with local LLM backends. Fix tests that use __new__ to include _xorg_instances attribute. --- easi/evaluation/parallel_runner.py | 15 ++++++++++++ easi/evaluation/runner.py | 26 ++++++++++++++++++-- tests/test_env_vars_wiring.py | 2 ++ tests/test_installation_kwargs.py | 1 + tests/test_render_platform.py | 5 ++++ tests/test_xorg.py | 39 ++++++++++++++++++++++++++++++ 6 files changed, 86 insertions(+), 2 deletions(-) diff --git a/easi/evaluation/parallel_runner.py b/easi/evaluation/parallel_runner.py index 9ec9ccf..07519c1 100644 --- a/easi/evaluation/parallel_runner.py +++ b/easi/evaluation/parallel_runner.py @@ -122,6 +122,7 @@ def run(self) -> list[dict]: # --- Resolve LLM backend and vLLM URLs --- backend, base_url = self._resolve_llm_backend() server_mgr = None + xorg_mgr = None try: if backend in ("vllm", "custom") and base_url is None: @@ -149,6 +150,18 @@ def run(self) -> list[dict]: else: base_urls = [None] + # Start Xorg servers if needed + if self.render_platform_name == "xorg": + from easi.core.xorg_manager import XorgManager + gpu_ids = self.sim_gpus or [0] + if not self.sim_gpus and backend in ("vllm", "custom") and not self.llm_gpus: + logger.warning( + "Xorg and LLM server will both use GPU 0. " + "Use --llm-gpus and --sim-gpus to separate them." + ) + xorg_mgr = XorgManager(gpu_ids=gpu_ids) + self._xorg_instances = xorg_mgr.start() + # --- Load task --- logger.trace("Loading task") task = self._create_task() @@ -478,3 +491,5 @@ def _worker(worker_id: int) -> None: finally: if server_mgr is not None: server_mgr.stop() + if xorg_mgr is not None: + xorg_mgr.stop() diff --git a/easi/evaluation/runner.py b/easi/evaluation/runner.py index df4fdf4..51c2bfc 100644 --- a/easi/evaluation/runner.py +++ b/easi/evaluation/runner.py @@ -93,6 +93,7 @@ def __init__( self.llm_instances = llm_instances self.llm_gpus = llm_gpus self.sim_gpus = sim_gpus + self._xorg_instances: list | None = None timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") if self.model: safe_model = self.model.replace("/", "_") @@ -171,6 +172,7 @@ def run(self) -> list[dict]: # 2. Resolve LLM backend and optionally start server backend, base_url = self._resolve_llm_backend() server = None + xorg_mgr = None try: if backend in ("vllm", "custom") and base_url is None: @@ -185,6 +187,18 @@ def run(self) -> list[dict]: ) base_url = server.start() + # Start Xorg servers if needed + if self.render_platform_name == "xorg": + from easi.core.xorg_manager import XorgManager + gpu_ids = self.sim_gpus or [0] + if not self.sim_gpus and backend in ("vllm", "custom") and not self.llm_gpus: + logger.warning( + "Xorg and LLM server will both use GPU 0. " + "Use --llm-gpus and --sim-gpus to separate them." + ) + xorg_mgr = XorgManager(gpu_ids=gpu_ids) + self._xorg_instances = xorg_mgr.start() + # Compute resolved generation kwargs (YAML defaults + CLI overrides) from easi.llm.utils import parse_llm_kwargs, split_kwargs agent_config = task._config.get("agent", {}) @@ -320,6 +334,8 @@ def run(self) -> list[dict]: finally: if server: server.stop() + if xorg_mgr is not None: + xorg_mgr.stop() # 5. Build EpisodeRecords for aggregate_results effective = sum(1 for r in all_results if "error" not in r) @@ -751,8 +767,14 @@ def _create_simulator(self, simulator_key: str, task=None, label: str = "bridge" if task and task.extra_env_vars: env_vars = EnvVars.merge(env_vars, EnvVars(replace=task.extra_env_vars)) - # Apply simulator GPU isolation - if self.sim_gpus is not None: + # Apply per-worker GPU pinning + if self._xorg_instances: + # Xorg platform owns both DISPLAY and GPU assignment + from easi.core.xorg_platform import XorgPlatform + instance = self._xorg_instances[worker_id % len(self._xorg_instances)] + render_platform = XorgPlatform(display_num=instance.display, gpu_id=instance.gpu_id) + elif self.sim_gpus is not None: + # Non-xorg: per-worker GPU pinning via round-robin gpu_id = self.sim_gpus[worker_id % len(self.sim_gpus)] env_vars = EnvVars.merge(env_vars, EnvVars(replace={"CUDA_VISIBLE_DEVICES": str(gpu_id)})) diff --git a/tests/test_env_vars_wiring.py b/tests/test_env_vars_wiring.py index a896212..f06c48f 100644 --- a/tests/test_env_vars_wiring.py +++ b/tests/test_env_vars_wiring.py @@ -17,6 +17,7 @@ def test_runner_passes_env_vars_to_subprocess(self): runner.data_dir = Path("/tmp/fake") runner.render_platform_name = None runner.sim_gpus = None + runner._xorg_instances = None mock_env_mgr = MagicMock() mock_env_mgr.env_is_ready.return_value = True @@ -57,6 +58,7 @@ def test_runner_passes_none_when_no_env_vars(self): runner.data_dir = Path("/tmp/fake") runner.render_platform_name = None runner.sim_gpus = None + runner._xorg_instances = None mock_env_mgr = MagicMock() mock_env_mgr.env_is_ready.return_value = True diff --git a/tests/test_installation_kwargs.py b/tests/test_installation_kwargs.py index bb71e88..5c81fdf 100644 --- a/tests/test_installation_kwargs.py +++ b/tests/test_installation_kwargs.py @@ -60,6 +60,7 @@ def test_runner_passes_installation_kwargs(self): runner.data_dir = Path("/tmp/fake") runner.render_platform_name = None runner.sim_gpus = None + runner._xorg_instances = None mock_env_mgr = MagicMock() mock_env_mgr.env_is_ready.return_value = True diff --git a/tests/test_render_platform.py b/tests/test_render_platform.py index 11fb22c..4ed5d1d 100644 --- a/tests/test_render_platform.py +++ b/tests/test_render_platform.py @@ -707,6 +707,7 @@ def test_default_uses_env_manager_platform(self): runner.data_dir = Path("/tmp/fake") runner.render_platform_name = None runner.sim_gpus = None + runner._xorg_instances = None mock_env_mgr = self._make_mock_env_mgr() mock_sim_cls = MagicMock() @@ -734,6 +735,7 @@ def test_cli_override_wins(self): runner.data_dir = Path("/tmp/fake") runner.render_platform_name = "xvfb" runner.sim_gpus = None + runner._xorg_instances = None mock_env_mgr = self._make_mock_env_mgr() mock_sim_cls = MagicMock() @@ -761,6 +763,7 @@ def test_yaml_override_used_when_no_cli(self): runner.data_dir = Path("/tmp/fake") runner.render_platform_name = None runner.sim_gpus = None + runner._xorg_instances = None mock_env_mgr = self._make_mock_env_mgr() mock_task = self._make_mock_task(render_platform="egl") @@ -789,6 +792,7 @@ def test_cli_beats_yaml(self): runner.data_dir = Path("/tmp/fake") runner.render_platform_name = "xvfb" runner.sim_gpus = None + runner._xorg_instances = None mock_env_mgr = self._make_mock_env_mgr() mock_task = self._make_mock_task(render_platform="egl") @@ -817,6 +821,7 @@ def test_unsupported_platform_raises(self): runner.data_dir = Path("/tmp/fake") runner.render_platform_name = "egl" runner.sim_gpus = None + runner._xorg_instances = None mock_env_mgr = self._make_mock_env_mgr() mock_env_mgr.supported_render_platforms = ["auto", "xvfb"] # no egl diff --git a/tests/test_xorg.py b/tests/test_xorg.py index db54ce7..3f7c323 100644 --- a/tests/test_xorg.py +++ b/tests/test_xorg.py @@ -235,3 +235,42 @@ def test_single_gpu_all_workers_same(self): sim_gpus = [4] for worker_id in range(4): assert sim_gpus[worker_id % len(sim_gpus)] == 4 + + +class TestXorgRunnerIntegration: + """Test Xorg integration logic in runners.""" + + def test_xorg_defaults_to_gpu_0(self): + """Without --sim-gpus, xorg defaults to GPU 0.""" + gpu_ids = None or [0] + assert gpu_ids == [0] + + def test_xorg_uses_sim_gpus_when_specified(self): + """With --sim-gpus, xorg uses those GPUs.""" + sim_gpus = [2, 3] + gpu_ids = sim_gpus or [0] + assert gpu_ids == [2, 3] + + def test_xorg_warns_on_gpu_contention(self): + """Warn when xorg + local LLM both default to GPU 0.""" + render_platform = "xorg" + sim_gpus = None + backend = "vllm" + llm_gpus = None + should_warn = ( + render_platform == "xorg" + and not sim_gpus + and backend in ("vllm", "custom") + and not llm_gpus + ) + assert should_warn is True + + def test_xorg_no_warn_with_sim_gpus(self): + """No warning when sim_gpus is specified.""" + should_warn = ( + "xorg" == "xorg" + and not [2, 3] + and "vllm" in ("vllm", "custom") + and not None + ) + assert should_warn is False From 7c1068a4560f2e46e2e48ffbb6b327d5c1291bf7 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 10 Mar 2026 15:34:45 +0800 Subject: [PATCH 192/239] docs: add xorg to render platform CLI options and reference --- docs/cli-reference.md | 1 + easi/cli.py | 5 +++-- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/docs/cli-reference.md b/docs/cli-reference.md index 31f189a..7e934d0 100644 --- a/docs/cli-reference.md +++ b/docs/cli-reference.md @@ -592,6 +592,7 @@ Render platforms control how a simulator gets a display for rendering. Each simu | `xvfb` | Wrap with `xvfb-run` (virtual X11 framebuffer) | | `egl` | GPU-accelerated headless rendering via EGL (no X11) | | `headless` | No display at all (simulator has native headless support) | +| `xorg` | Auto-managed Xorg server per GPU (GPU-accelerated X11, defaults to GPU 0, use `--sim-gpus` to specify) | ### Custom Platforms diff --git a/easi/cli.py b/easi/cli.py index f636b56..2b2e9ed 100644 --- a/easi/cli.py +++ b/easi/cli.py @@ -80,7 +80,7 @@ def build_parser() -> argparse.ArgumentParser: help="Bridge startup timeout in seconds (default: 200)") sim_test.add_argument( "--render-platform", type=str, default=None, dest="render_platform", - help="Rendering platform override (auto, native, xvfb, egl, headless)") + help="Rendering platform override (auto, native, xvfb, egl, headless, xorg)") # --- start command --- start_parser = subparsers.add_parser("start", help="Run a full evaluation", parents=[common]) @@ -134,7 +134,8 @@ def build_parser() -> argparse.ArgumentParser: help="Delete cached dataset and re-download from source") start_parser.add_argument( "--render-platform", type=str, default=None, dest="render_platform", - help="Rendering platform: auto, native, xvfb, egl, headless (default: simulator's preference)") + help="Rendering platform: auto, native, xvfb, egl, headless, xorg (default: simulator's preference). " + "xorg starts a GPU X server (defaults to GPU 0, use --sim-gpus to specify).") # --- model command --- model_parser = subparsers.add_parser("model", help="Manage custom models", parents=[common]) From a99a81053e63192dfe1012ec448688bb5da428af Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 10 Mar 2026 15:53:19 +0800 Subject: [PATCH 193/239] feat: register xorg as supported render platform for X11 simulators Added to: ai2thor (v2.1, v3.3.5, v5.0), habitat_sim (v0.1.7, v0.3), coppeliasim (v4.1), tdw (v1.11.23), omnigibson (v3.7.2). --- docs/cli-reference.md | 10 +++++----- easi/simulators/ai2thor/v2_1_0/env_manager.py | 2 +- easi/simulators/ai2thor/v3_3_5/env_manager.py | 2 +- easi/simulators/ai2thor/v5_0_0/env_manager.py | 2 +- easi/simulators/coppeliasim/v4_1_0/env_manager.py | 2 +- easi/simulators/habitat_sim/v0_1_7/env_manager.py | 2 +- easi/simulators/habitat_sim/v0_3_0/env_manager.py | 2 +- easi/simulators/omnigibson/v3_7_2/env_manager.py | 2 +- easi/simulators/tdw/v1_11_23/env_manager.py | 2 +- 9 files changed, 13 insertions(+), 13 deletions(-) diff --git a/docs/cli-reference.md b/docs/cli-reference.md index 7e934d0..8b06dd9 100644 --- a/docs/cli-reference.md +++ b/docs/cli-reference.md @@ -605,11 +605,11 @@ Custom platforms are resolved automatically — when you pass `--render-platform | Simulator | Default | Supported | |---|---|---| | `dummy:v1` | `headless` | `headless` | -| `ai2thor:v2_1_0` | `auto` | `auto`, `native`, `xvfb` | -| `ai2thor:v5_0_0` | `auto` | `auto`, `native`, `xvfb` | -| `habitat_sim:v0_3_0` | `auto` | `auto`, `native`, `xvfb`, `egl` | -| `coppeliasim:v4_1_0` | `auto` | `auto`, `native`, `xvfb` | -| `tdw:v1_11_23` | `auto` | `auto`, `native`, `xvfb` | +| `ai2thor:v2_1_0` | `auto` | `auto`, `native`, `xvfb`, `xorg` | +| `ai2thor:v5_0_0` | `auto` | `auto`, `native`, `xvfb`, `xorg` | +| `habitat_sim:v0_3_0` | `auto` | `auto`, `native`, `xvfb`, `egl`, `xorg` | +| `coppeliasim:v4_1_0` | `auto` | `auto`, `native`, `xvfb`, `xorg` | +| `tdw:v1_11_23` | `auto` | `auto`, `native`, `xvfb`, `xorg` | --- diff --git a/easi/simulators/ai2thor/v2_1_0/env_manager.py b/easi/simulators/ai2thor/v2_1_0/env_manager.py index ea2c567..71cdcf2 100644 --- a/easi/simulators/ai2thor/v2_1_0/env_manager.py +++ b/easi/simulators/ai2thor/v2_1_0/env_manager.py @@ -27,7 +27,7 @@ def default_render_platform(self) -> str: @property def supported_render_platforms(self) -> list[str]: - return ["auto", "xvfb", "native"] + return ["auto", "xvfb", "native", "xorg"] def get_conda_env_yaml_path(self) -> Path: return Path(__file__).parent / "conda_env.yaml" diff --git a/easi/simulators/ai2thor/v3_3_5/env_manager.py b/easi/simulators/ai2thor/v3_3_5/env_manager.py index f9f18cf..2ef3a51 100644 --- a/easi/simulators/ai2thor/v3_3_5/env_manager.py +++ b/easi/simulators/ai2thor/v3_3_5/env_manager.py @@ -27,7 +27,7 @@ def default_render_platform(self) -> str: @property def supported_render_platforms(self) -> list[str]: - return ["auto", "xvfb", "native"] + return ["auto", "xvfb", "native", "xorg"] def get_conda_env_yaml_path(self) -> Path: return Path(__file__).parent / "conda_env.yaml" diff --git a/easi/simulators/ai2thor/v5_0_0/env_manager.py b/easi/simulators/ai2thor/v5_0_0/env_manager.py index 842359f..46a56be 100644 --- a/easi/simulators/ai2thor/v5_0_0/env_manager.py +++ b/easi/simulators/ai2thor/v5_0_0/env_manager.py @@ -24,7 +24,7 @@ def default_render_platform(self) -> str: @property def supported_render_platforms(self) -> list[str]: - return ["auto", "xvfb", "native"] + return ["auto", "xvfb", "native", "xorg"] @property def screen_config(self) -> str: diff --git a/easi/simulators/coppeliasim/v4_1_0/env_manager.py b/easi/simulators/coppeliasim/v4_1_0/env_manager.py index 9ef2e50..7561af1 100644 --- a/easi/simulators/coppeliasim/v4_1_0/env_manager.py +++ b/easi/simulators/coppeliasim/v4_1_0/env_manager.py @@ -39,7 +39,7 @@ def default_render_platform(self) -> str: @property def supported_render_platforms(self) -> list[str]: - return ["auto", "xvfb", "native"] + return ["auto", "xvfb", "native", "xorg"] @property def screen_config(self) -> str: diff --git a/easi/simulators/habitat_sim/v0_1_7/env_manager.py b/easi/simulators/habitat_sim/v0_1_7/env_manager.py index e2d385f..d732fed 100644 --- a/easi/simulators/habitat_sim/v0_1_7/env_manager.py +++ b/easi/simulators/habitat_sim/v0_1_7/env_manager.py @@ -24,7 +24,7 @@ def default_render_platform(self) -> str: @property def supported_render_platforms(self) -> list[str]: - return ["auto", "xvfb", "native", "egl"] + return ["auto", "xvfb", "native", "egl", "xorg"] def get_conda_env_yaml_path(self) -> Path: return Path(__file__).parent / "conda_env.yaml" diff --git a/easi/simulators/habitat_sim/v0_3_0/env_manager.py b/easi/simulators/habitat_sim/v0_3_0/env_manager.py index c3bb72d..83d6cb6 100644 --- a/easi/simulators/habitat_sim/v0_3_0/env_manager.py +++ b/easi/simulators/habitat_sim/v0_3_0/env_manager.py @@ -24,7 +24,7 @@ def default_render_platform(self) -> str: @property def supported_render_platforms(self) -> list[str]: - return ["auto", "xvfb", "native", "egl"] + return ["auto", "xvfb", "native", "egl", "xorg"] def get_conda_env_yaml_path(self) -> Path: return Path(__file__).parent / "conda_env.yaml" diff --git a/easi/simulators/omnigibson/v3_7_2/env_manager.py b/easi/simulators/omnigibson/v3_7_2/env_manager.py index 6f15764..6c39d92 100644 --- a/easi/simulators/omnigibson/v3_7_2/env_manager.py +++ b/easi/simulators/omnigibson/v3_7_2/env_manager.py @@ -85,7 +85,7 @@ def default_render_platform(self) -> str: @property def supported_render_platforms(self) -> list[str]: - return ["native", "auto"] + return ["native", "auto", "xorg"] def get_conda_env_yaml_path(self) -> Path: return Path(__file__).parent / "conda_env.yaml" diff --git a/easi/simulators/tdw/v1_11_23/env_manager.py b/easi/simulators/tdw/v1_11_23/env_manager.py index 4811556..3c2cc09 100644 --- a/easi/simulators/tdw/v1_11_23/env_manager.py +++ b/easi/simulators/tdw/v1_11_23/env_manager.py @@ -36,7 +36,7 @@ def default_render_platform(self) -> str: @property def supported_render_platforms(self) -> list[str]: - return ["auto", "xvfb", "native"] + return ["auto", "xvfb", "native", "xorg"] def get_conda_env_yaml_path(self) -> Path: return Path(__file__).parent / "conda_env.yaml" From 1dba15a86cb201057cba2a8b317f80ec10b6649c Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 10 Mar 2026 16:20:30 +0800 Subject: [PATCH 194/239] refactor: add lifecycle hooks to RenderPlatform, XorgPlatform owns XorgManager RenderPlatform gains setup()/teardown()/for_worker() hooks so platforms that manage external services (like Xorg) are self-contained. No more if/else xorg handling in callers. - XorgPlatform.setup() starts XorgManager, for_worker() returns a per-worker _XorgWorkerPlatform with fixed display/GPU - Runners call _setup_render_platform() once, then teardown() in finally - cli sim_test calls setup()/for_worker()/teardown() directly - Removed _xorg_instances attribute and scattered xorg_mgr variables --- easi/cli.py | 6 ++- easi/core/render_platform.py | 29 +++++++++++- easi/core/xorg_platform.py | 70 ++++++++++++++++++++++++++--- easi/evaluation/parallel_runner.py | 18 ++------ easi/evaluation/runner.py | 72 ++++++++++++++++++++---------- tests/test_env_vars_wiring.py | 4 +- tests/test_installation_kwargs.py | 2 +- tests/test_render_platform.py | 10 ++--- tests/test_xorg.py | 43 +++++++++++++----- 9 files changed, 189 insertions(+), 65 deletions(-) diff --git a/easi/cli.py b/easi/cli.py index 2b2e9ed..030c6d0 100644 --- a/easi/cli.py +++ b/easi/cli.py @@ -374,6 +374,8 @@ def cmd_sim_test(simulator: str, steps: int, timeout: float, render_platform_nam ) sys.exit(1) render_platform = resolve_render_platform(simulator, platform_name, env_manager=env_manager) + render_platform.setup(gpu_ids=[0]) + worker_platform = render_platform.for_worker(0) logger.info("Testing %s...", simulator) logger.info(" Python: %s", env_manager.get_python_executable()) @@ -384,7 +386,7 @@ def cmd_sim_test(simulator: str, steps: int, timeout: float, render_platform_nam runner = SubprocessRunner( python_executable=env_manager.get_python_executable(), bridge_script_path=sim._get_bridge_script_path(), - render_platform=render_platform, + render_platform=worker_platform, screen_config=env_manager.screen_config, startup_timeout=timeout, command_timeout=timeout, @@ -420,6 +422,8 @@ def cmd_sim_test(simulator: str, steps: int, timeout: float, render_platform_nam logger.error("Smoke test FAILED: %s", e) sim.close() sys.exit(1) + finally: + render_platform.teardown() def _resolve_task_list(args_ns) -> list[str]: diff --git a/easi/core/render_platform.py b/easi/core/render_platform.py index be7badf..4a92360 100644 --- a/easi/core/render_platform.py +++ b/easi/core/render_platform.py @@ -66,7 +66,13 @@ def merge(cls, *env_vars: EnvVars) -> EnvVars: class RenderPlatform(ABC): - """Strategy interface for display/rendering backends.""" + """Strategy interface for display/rendering backends. + + Lifecycle hooks (``setup`` / ``teardown``) allow platforms that manage + external services (e.g. Xorg) to start and stop them without + if/else logic in the callers. ``for_worker`` returns a per-worker + platform instance (default: ``self``). + """ def __init__(self, env_manager=None): self._env_manager = env_manager @@ -102,6 +108,18 @@ def is_available(self) -> bool: """Whether this platform can run in the current environment.""" return True + # -- Lifecycle hooks (override in platforms that manage services) ---------- + + def setup(self, gpu_ids: list[int] | None = None) -> None: + """Called once before any simulator is created. Start external services.""" + + def teardown(self) -> None: + """Called once after all simulators are done. Stop external services.""" + + def for_worker(self, worker_id: int) -> RenderPlatform: + """Return a per-worker platform instance. Default: return self.""" + return self + # -- Built-in implementations ------------------------------------------------ @@ -184,6 +202,11 @@ def wrap_command(self, cmd: list[str], screen_config: str) -> list[str]: # -- Registry ----------------------------------------------------------------- +def _xorg_platform_cls() -> type[RenderPlatform]: + from easi.core.xorg_platform import XorgPlatform + return XorgPlatform + + _BUILTIN: dict[str, type[RenderPlatform]] = { "auto": AutoPlatform, "native": NativePlatform, @@ -201,9 +224,11 @@ def get_render_platform(name: str) -> RenderPlatform: """ cls = _BUILTIN.get(name) if cls is None: + if name == "xorg": + return _xorg_platform_cls()() raise ValueError( f"Unknown render platform '{name}'. " - f"Available: {', '.join(sorted(_BUILTIN))}" + f"Available: {', '.join(sorted(_BUILTIN | {'xorg': None}))}" ) return cls() diff --git a/easi/core/xorg_platform.py b/easi/core/xorg_platform.py index 5898e2a..ca1eb46 100644 --- a/easi/core/xorg_platform.py +++ b/easi/core/xorg_platform.py @@ -1,16 +1,24 @@ -"""Xorg render platform — GPU-accelerated X11 display managed by EASI.""" +"""Xorg render platform — GPU-accelerated X11 display managed by EASI. + +Two classes: + +* ``XorgPlatform`` — the top-level platform resolved from ``--render-platform xorg``. + Owns the ``XorgManager`` lifecycle via ``setup()`` / ``teardown()`` and hands + out per-worker instances via ``for_worker()``. +* ``_XorgWorkerPlatform`` — lightweight per-worker instance with a fixed + display number and GPU ID, created by ``XorgPlatform.for_worker()``. +""" from __future__ import annotations from easi.core.render_platform import EnvVars, RenderPlatform +from easi.utils.logging import get_logger +logger = get_logger(__name__) -class XorgPlatform(RenderPlatform): - """Render platform backed by a managed Xorg server on a specific GPU. - Each worker gets its own ``XorgPlatform`` instance with a dedicated - display number and GPU ID, set by the ``XorgManager``. - """ +class _XorgWorkerPlatform(RenderPlatform): + """Per-worker Xorg platform with a dedicated display and GPU.""" def __init__(self, display_num: int, gpu_id: int): super().__init__() @@ -33,3 +41,53 @@ def get_env_vars(self) -> EnvVars: def is_available(self) -> bool: return True + + +class XorgPlatform(RenderPlatform): + """Render platform backed by auto-managed Xorg servers. + + Call ``setup(gpu_ids=...)`` to start Xorg servers, then + ``for_worker(worker_id)`` to get per-worker instances. + ``teardown()`` stops all servers. + """ + + def __init__(self, env_manager=None): + super().__init__(env_manager=env_manager) + self._xorg_mgr = None + self._instances: list = [] + + @property + def name(self) -> str: + return "xorg" + + def wrap_command(self, cmd: list[str], screen_config: str) -> list[str]: + return cmd + + def get_env_vars(self) -> EnvVars: + return EnvVars() + + def is_available(self) -> bool: + return True + + def setup(self, gpu_ids: list[int] | None = None) -> None: + """Start one Xorg server per GPU.""" + from easi.core.xorg_manager import XorgManager + resolved_gpu_ids = gpu_ids or [0] + self._xorg_mgr = XorgManager(gpu_ids=resolved_gpu_ids) + self._instances = self._xorg_mgr.start() + + def teardown(self) -> None: + """Stop all Xorg servers.""" + if self._xorg_mgr is not None: + self._xorg_mgr.stop() + self._xorg_mgr = None + self._instances = [] + + def for_worker(self, worker_id: int) -> _XorgWorkerPlatform: + """Return a per-worker platform bound to a specific Xorg instance.""" + if not self._instances: + raise RuntimeError( + "XorgPlatform.setup() must be called before for_worker()" + ) + inst = self._instances[worker_id % len(self._instances)] + return _XorgWorkerPlatform(display_num=inst.display, gpu_id=inst.gpu_id) diff --git a/easi/evaluation/parallel_runner.py b/easi/evaluation/parallel_runner.py index 07519c1..a78981e 100644 --- a/easi/evaluation/parallel_runner.py +++ b/easi/evaluation/parallel_runner.py @@ -122,7 +122,6 @@ def run(self) -> list[dict]: # --- Resolve LLM backend and vLLM URLs --- backend, base_url = self._resolve_llm_backend() server_mgr = None - xorg_mgr = None try: if backend in ("vllm", "custom") and base_url is None: @@ -150,17 +149,8 @@ def run(self) -> list[dict]: else: base_urls = [None] - # Start Xorg servers if needed - if self.render_platform_name == "xorg": - from easi.core.xorg_manager import XorgManager - gpu_ids = self.sim_gpus or [0] - if not self.sim_gpus and backend in ("vllm", "custom") and not self.llm_gpus: - logger.warning( - "Xorg and LLM server will both use GPU 0. " - "Use --llm-gpus and --sim-gpus to separate them." - ) - xorg_mgr = XorgManager(gpu_ids=gpu_ids) - self._xorg_instances = xorg_mgr.start() + # Setup render platform (starts external services like Xorg if needed) + self._render_platform = self._setup_render_platform(backend) # --- Load task --- logger.trace("Loading task") @@ -491,5 +481,5 @@ def _worker(worker_id: int) -> None: finally: if server_mgr is not None: server_mgr.stop() - if xorg_mgr is not None: - xorg_mgr.stop() + if self._render_platform is not None: + self._render_platform.teardown() diff --git a/easi/evaluation/runner.py b/easi/evaluation/runner.py index 51c2bfc..22e1bc0 100644 --- a/easi/evaluation/runner.py +++ b/easi/evaluation/runner.py @@ -93,7 +93,6 @@ def __init__( self.llm_instances = llm_instances self.llm_gpus = llm_gpus self.sim_gpus = sim_gpus - self._xorg_instances: list | None = None timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") if self.model: safe_model = self.model.replace("/", "_") @@ -139,6 +138,36 @@ def _serialize_cli_options(self) -> dict: for k, v in self._cli_options.items() } + def _setup_render_platform(self, backend: str | None = None): + """Resolve, setup, and return the global render platform (if any). + + Calls ``platform.setup(gpu_ids=...)`` so lifecycle platforms (xorg) + can start external services. For built-in platforms, ``setup()`` is a + no-op. Returns ``None`` when no ``--render-platform`` was specified + (the platform is then resolved per-simulator in ``_create_simulator``). + """ + from easi.core.render_platform import get_render_platform + + if not self.render_platform_name: + return None + + platform = get_render_platform(self.render_platform_name) + + # Warn about GPU contention before setup + if ( + platform.name == "xorg" + and not self.sim_gpus + and backend in ("vllm", "custom") + and not self.llm_gpus + ): + logger.warning( + "Xorg and LLM server will both use GPU 0. " + "Use --llm-gpus and --sim-gpus to separate them." + ) + + platform.setup(gpu_ids=self.sim_gpus) + return platform + def run(self) -> list[dict]: """Run evaluation and return per-episode metric dicts.""" if self.resume_dir: @@ -172,7 +201,7 @@ def run(self) -> list[dict]: # 2. Resolve LLM backend and optionally start server backend, base_url = self._resolve_llm_backend() server = None - xorg_mgr = None + render_platform = None try: if backend in ("vllm", "custom") and base_url is None: @@ -187,17 +216,8 @@ def run(self) -> list[dict]: ) base_url = server.start() - # Start Xorg servers if needed - if self.render_platform_name == "xorg": - from easi.core.xorg_manager import XorgManager - gpu_ids = self.sim_gpus or [0] - if not self.sim_gpus and backend in ("vllm", "custom") and not self.llm_gpus: - logger.warning( - "Xorg and LLM server will both use GPU 0. " - "Use --llm-gpus and --sim-gpus to separate them." - ) - xorg_mgr = XorgManager(gpu_ids=gpu_ids) - self._xorg_instances = xorg_mgr.start() + # Resolve and setup render platform (starts external services if needed) + self._render_platform = self._setup_render_platform(backend) # Compute resolved generation kwargs (YAML defaults + CLI overrides) from easi.llm.utils import parse_llm_kwargs, split_kwargs @@ -334,8 +354,8 @@ def run(self) -> list[dict]: finally: if server: server.stop() - if xorg_mgr is not None: - xorg_mgr.stop() + if self._render_platform is not None: + self._render_platform.teardown() # 5. Build EpisodeRecords for aggregate_results effective = sum(1 for r in all_results if "error" not in r) @@ -756,7 +776,11 @@ def _create_simulator(self, simulator_key: str, task=None, label: str = "bridge" f"Supported: {env_manager.supported_render_platforms}" ) - render_platform = resolve_render_platform(simulator_key, platform_name, env_manager=env_manager) + # Use pre-setup global platform if available, else resolve per-simulator + if getattr(self, '_render_platform', None) is not None: + render_platform = self._render_platform + else: + render_platform = resolve_render_platform(simulator_key, platform_name, env_manager=env_manager) # Pass platform name to get_env_vars for conditional logic from easi.core.render_platform import EnvVars @@ -767,14 +791,14 @@ def _create_simulator(self, simulator_key: str, task=None, label: str = "bridge" if task and task.extra_env_vars: env_vars = EnvVars.merge(env_vars, EnvVars(replace=task.extra_env_vars)) - # Apply per-worker GPU pinning - if self._xorg_instances: - # Xorg platform owns both DISPLAY and GPU assignment - from easi.core.xorg_platform import XorgPlatform - instance = self._xorg_instances[worker_id % len(self._xorg_instances)] - render_platform = XorgPlatform(display_num=instance.display, gpu_id=instance.gpu_id) - elif self.sim_gpus is not None: - # Non-xorg: per-worker GPU pinning via round-robin + # Per-worker platform instance (lifecycle platforms like xorg return + # a worker-specific instance that already handles GPU assignment) + render_platform = render_platform.for_worker(worker_id) + + # Apply per-worker GPU pinning via round-robin (skip if platform + # already sets CUDA_VISIBLE_DEVICES, e.g. xorg worker platforms) + platform_env = render_platform.get_env_vars() + if self.sim_gpus is not None and "CUDA_VISIBLE_DEVICES" not in platform_env.replace: gpu_id = self.sim_gpus[worker_id % len(self.sim_gpus)] env_vars = EnvVars.merge(env_vars, EnvVars(replace={"CUDA_VISIBLE_DEVICES": str(gpu_id)})) diff --git a/tests/test_env_vars_wiring.py b/tests/test_env_vars_wiring.py index f06c48f..b9d22d8 100644 --- a/tests/test_env_vars_wiring.py +++ b/tests/test_env_vars_wiring.py @@ -17,7 +17,7 @@ def test_runner_passes_env_vars_to_subprocess(self): runner.data_dir = Path("/tmp/fake") runner.render_platform_name = None runner.sim_gpus = None - runner._xorg_instances = None + runner._render_platform = None mock_env_mgr = MagicMock() mock_env_mgr.env_is_ready.return_value = True @@ -58,7 +58,7 @@ def test_runner_passes_none_when_no_env_vars(self): runner.data_dir = Path("/tmp/fake") runner.render_platform_name = None runner.sim_gpus = None - runner._xorg_instances = None + runner._render_platform = None mock_env_mgr = MagicMock() mock_env_mgr.env_is_ready.return_value = True diff --git a/tests/test_installation_kwargs.py b/tests/test_installation_kwargs.py index 5c81fdf..d5b18d3 100644 --- a/tests/test_installation_kwargs.py +++ b/tests/test_installation_kwargs.py @@ -60,7 +60,7 @@ def test_runner_passes_installation_kwargs(self): runner.data_dir = Path("/tmp/fake") runner.render_platform_name = None runner.sim_gpus = None - runner._xorg_instances = None + runner._render_platform = None mock_env_mgr = MagicMock() mock_env_mgr.env_is_ready.return_value = True diff --git a/tests/test_render_platform.py b/tests/test_render_platform.py index 4ed5d1d..7bc0557 100644 --- a/tests/test_render_platform.py +++ b/tests/test_render_platform.py @@ -707,7 +707,7 @@ def test_default_uses_env_manager_platform(self): runner.data_dir = Path("/tmp/fake") runner.render_platform_name = None runner.sim_gpus = None - runner._xorg_instances = None + runner._render_platform = None mock_env_mgr = self._make_mock_env_mgr() mock_sim_cls = MagicMock() @@ -735,7 +735,7 @@ def test_cli_override_wins(self): runner.data_dir = Path("/tmp/fake") runner.render_platform_name = "xvfb" runner.sim_gpus = None - runner._xorg_instances = None + runner._render_platform = None mock_env_mgr = self._make_mock_env_mgr() mock_sim_cls = MagicMock() @@ -763,7 +763,7 @@ def test_yaml_override_used_when_no_cli(self): runner.data_dir = Path("/tmp/fake") runner.render_platform_name = None runner.sim_gpus = None - runner._xorg_instances = None + runner._render_platform = None mock_env_mgr = self._make_mock_env_mgr() mock_task = self._make_mock_task(render_platform="egl") @@ -792,7 +792,7 @@ def test_cli_beats_yaml(self): runner.data_dir = Path("/tmp/fake") runner.render_platform_name = "xvfb" runner.sim_gpus = None - runner._xorg_instances = None + runner._render_platform = None mock_env_mgr = self._make_mock_env_mgr() mock_task = self._make_mock_task(render_platform="egl") @@ -821,7 +821,7 @@ def test_unsupported_platform_raises(self): runner.data_dir = Path("/tmp/fake") runner.render_platform_name = "egl" runner.sim_gpus = None - runner._xorg_instances = None + runner._render_platform = None mock_env_mgr = self._make_mock_env_mgr() mock_env_mgr.supported_render_platforms = ["auto", "xvfb"] # no egl diff --git a/tests/test_xorg.py b/tests/test_xorg.py index 3f7c323..cca18ef 100644 --- a/tests/test_xorg.py +++ b/tests/test_xorg.py @@ -9,38 +9,61 @@ import pytest -class TestXorgPlatform: - """Test XorgPlatform env vars and command wrapping.""" +class TestXorgWorkerPlatform: + """Test _XorgWorkerPlatform env vars and command wrapping.""" def test_name(self): - from easi.core.xorg_platform import XorgPlatform + from easi.core.xorg_platform import _XorgWorkerPlatform - p = XorgPlatform(display_num=10, gpu_id=4) + p = _XorgWorkerPlatform(display_num=10, gpu_id=4) assert p.name == "xorg" def test_env_vars(self): - from easi.core.xorg_platform import XorgPlatform + from easi.core.xorg_platform import _XorgWorkerPlatform - p = XorgPlatform(display_num=10, gpu_id=4) + p = _XorgWorkerPlatform(display_num=10, gpu_id=4) ev = p.get_env_vars() assert ev.replace["DISPLAY"] == ":10" assert ev.replace["CUDA_VISIBLE_DEVICES"] == "4" assert ev.replace["EASI_GPU_DISPLAY"] == "1" def test_wrap_command_passthrough(self): - from easi.core.xorg_platform import XorgPlatform + from easi.core.xorg_platform import _XorgWorkerPlatform - p = XorgPlatform(display_num=10, gpu_id=4) + p = _XorgWorkerPlatform(display_num=10, gpu_id=4) cmd = ["python", "bridge.py", "--workspace", "/tmp"] assert p.wrap_command(cmd, "1024x768x24") == cmd def test_is_available(self): - from easi.core.xorg_platform import XorgPlatform + from easi.core.xorg_platform import _XorgWorkerPlatform - p = XorgPlatform(display_num=10, gpu_id=4) + p = _XorgWorkerPlatform(display_num=10, gpu_id=4) assert p.is_available() is True +class TestXorgPlatformLifecycle: + """Test XorgPlatform setup/teardown/for_worker lifecycle.""" + + def test_name(self): + from easi.core.xorg_platform import XorgPlatform + + p = XorgPlatform() + assert p.name == "xorg" + + def test_for_worker_without_setup_raises(self): + from easi.core.xorg_platform import XorgPlatform + + p = XorgPlatform() + with pytest.raises(RuntimeError, match="setup.*must be called"): + p.for_worker(0) + + def test_teardown_without_setup_is_safe(self): + from easi.core.xorg_platform import XorgPlatform + + p = XorgPlatform() + p.teardown() # should not raise + + class TestXorgManager: """Test XorgManager lifecycle with mocked subprocesses.""" From 567298d2f2a124e1da1cebc6e130b5fe1b2a31f9 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Tue, 10 Mar 2026 16:27:27 +0800 Subject: [PATCH 195/239] fix: asyncio deprecation, logger convention, litellm import scope - Replace asyncio.get_event_loop() with get_running_loop() (deprecated in 3.10, error in 3.12) - Use project get_logger() convention in progress.py instead of logging.getLogger() - Move litellm imports to local scope in test_react_agent.py to avoid import errors --- easi/llm/models/http_server.py | 2 +- easi/utils/progress.py | 7 +++++-- tests/test_react_agent.py | 6 ++++-- 3 files changed, 10 insertions(+), 5 deletions(-) diff --git a/easi/llm/models/http_server.py b/easi/llm/models/http_server.py index c38cfc8..aaff480 100644 --- a/easi/llm/models/http_server.py +++ b/easi/llm/models/http_server.py @@ -66,7 +66,7 @@ async def chat_completions(request: dict) -> JSONResponse: # type: ignore[arg-t gen_kwargs[key] = request[key] try: - loop = asyncio.get_event_loop() + loop = asyncio.get_running_loop() content = await loop.run_in_executor( None, partial(model.generate, messages, **gen_kwargs) ) diff --git a/easi/utils/progress.py b/easi/utils/progress.py index 856d2d2..e963dea 100644 --- a/easi/utils/progress.py +++ b/easi/utils/progress.py @@ -26,6 +26,10 @@ import threading import time +from easi.utils.logging import get_logger + +_logger = get_logger(__name__) + _CLEAR = "\r\033[K" # carriage return + ANSI erase to end of line _REFRESH_INTERVAL = 0.5 # seconds between redraws @@ -234,8 +238,7 @@ def _log_progress(self) -> None: """Emit a plain log line (for non-TTY mode).""" elapsed = time.monotonic() - self._elapsed_start mins, secs = divmod(int(elapsed), 60) - logger = logging.getLogger("easi.evaluation.progress") - logger.info( + _logger.info( "[Progress] %d/%d episodes (%d failed) — %dm%02ds elapsed", self._completed, self.total, self._failed, mins, secs, ) diff --git a/tests/test_react_agent.py b/tests/test_react_agent.py index 07b79fd..03ae7dd 100644 --- a/tests/test_react_agent.py +++ b/tests/test_react_agent.py @@ -2,8 +2,6 @@ import json import pytest -from litellm.exceptions import BadRequestError - from easi.agents.prompt_builder import DefaultPromptBuilder, PromptBuilderProtocol from easi.agents.react_agent import ReActAgent from easi.core.episode import Action, Observation @@ -309,6 +307,8 @@ def test_no_response_format_when_builder_lacks_method(self): def test_fallback_on_exception(self): """When generate() raises with response_format, agent retries without it.""" + from litellm.exceptions import BadRequestError + call_log = [] class FailOnSchemaLLM: @@ -343,6 +343,8 @@ def generate(self, messages, response_format=None): def test_fallback_cached_after_first_failure(self): """After first failure, subsequent calls skip response_format entirely.""" + from litellm.exceptions import BadRequestError + call_log = [] class FailOnSchemaLLM: From 2b7011ab21443ec3a9456dfdfbafdada4a30154d Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Wed, 11 Mar 2026 13:59:11 +0800 Subject: [PATCH 196/239] refactor: extract render_platform module into render_platforms package --- easi/core/base_env_manager.py | 2 +- easi/core/render_platform.py | 238 ------------------ easi/core/render_platforms/__init__.py | 44 ++++ easi/core/render_platforms/auto.py | 21 ++ easi/core/render_platforms/base.py | 107 ++++++++ easi/core/render_platforms/egl.py | 28 +++ easi/core/render_platforms/headless.py | 16 ++ easi/core/render_platforms/native.py | 21 ++ easi/core/render_platforms/registry.py | 40 +++ .../xorg.py} | 10 +- .../{ => render_platforms}/xorg_manager.py | 126 ++++++++-- easi/core/render_platforms/xvfb.py | 22 ++ easi/evaluation/runner.py | 8 +- .../coppeliasim/v4_1_0/env_manager.py | 2 +- easi/simulators/dummy/v1/render_platforms.py | 2 +- .../omnigibson/v3_7_2/env_manager.py | 2 +- .../omnigibson/v3_7_2/render_platforms.py | 2 +- easi/simulators/registry.py | 2 +- easi/simulators/subprocess_runner.py | 2 +- easi/simulators/tdw/v1_11_23/env_manager.py | 2 +- tests/test_docker_isolation.py | 2 +- tests/test_dummy_simulator.py | 4 +- tests/test_env_manager.py | 4 +- tests/test_env_vars_wiring.py | 4 +- tests/test_installation_kwargs.py | 2 +- tests/test_subprocess_runner.py | 2 +- tests/test_xorg.py | 170 +++++++------ 27 files changed, 519 insertions(+), 366 deletions(-) delete mode 100644 easi/core/render_platform.py create mode 100644 easi/core/render_platforms/__init__.py create mode 100644 easi/core/render_platforms/auto.py create mode 100644 easi/core/render_platforms/base.py create mode 100644 easi/core/render_platforms/egl.py create mode 100644 easi/core/render_platforms/headless.py create mode 100644 easi/core/render_platforms/native.py create mode 100644 easi/core/render_platforms/registry.py rename easi/core/{xorg_platform.py => render_platforms/xorg.py} (91%) rename easi/core/{ => render_platforms}/xorg_manager.py (67%) create mode 100644 easi/core/render_platforms/xvfb.py diff --git a/easi/core/base_env_manager.py b/easi/core/base_env_manager.py index 0335da0..e220f3a 100644 --- a/easi/core/base_env_manager.py +++ b/easi/core/base_env_manager.py @@ -114,7 +114,7 @@ def get_env_vars(self, render_platform_name: str | None = None) -> "EnvVars": Returns: EnvVars instance. Empty by default. """ - from easi.core.render_platform import EnvVars + from easi.core.render_platforms import EnvVars return EnvVars() diff --git a/easi/core/render_platform.py b/easi/core/render_platform.py deleted file mode 100644 index 4a92360..0000000 --- a/easi/core/render_platform.py +++ /dev/null @@ -1,238 +0,0 @@ -"""Pluggable render platform abstraction. - -Each render platform encapsulates how to launch a bridge subprocess with -the correct display/rendering environment. Simulators declare a default -platform; users can override via CLI (--render-platform) or task YAML. - -Built-in platforms: - auto — use native DISPLAY if available, fall back to xvfb - native — require existing DISPLAY - xvfb — always wrap with xvfb-run - egl — GPU-accelerated headless via EGL - headless — no display (simulator has native headless support) -""" - -from __future__ import annotations - -import os -from abc import ABC, abstractmethod -from dataclasses import dataclass, field -from pathlib import Path - -from easi.utils.logging import get_logger - -logger = get_logger(__name__) - - -@dataclass -class EnvVars: - """Structured environment variables with replace/prepend semantics. - - ``replace`` vars overwrite any existing value. - ``prepend`` vars are prepended with ':' to any existing value (for PATH-like vars). - """ - - replace: dict[str, str] = field(default_factory=dict) - prepend: dict[str, str] = field(default_factory=dict) - - def to_flat_dict(self) -> dict[str, str]: - """Combine into single dict (for internal use like post_install).""" - return {**self.replace, **self.prepend} - - def apply_to_env(self, base: dict[str, str]) -> dict[str, str]: - """Merge into a base env dict (e.g. os.environ.copy()).""" - env = dict(base) - for k, v in self.replace.items(): - env[k] = v - for k, v in self.prepend.items(): - env[k] = f"{v}:{env[k]}" if k in env else v - return env - - def __bool__(self) -> bool: - return bool(self.replace) or bool(self.prepend) - - @classmethod - def merge(cls, *env_vars: EnvVars) -> EnvVars: - """Merge multiple EnvVars. Later values win for replace; prepend values concatenate.""" - replace: dict[str, str] = {} - prepend: dict[str, str] = {} - for ev in env_vars: - if ev is None: - continue - replace.update(ev.replace) - for k, v in ev.prepend.items(): - prepend[k] = f"{v}:{prepend[k]}" if k in prepend else v - return cls(replace=replace, prepend=prepend) - - -class RenderPlatform(ABC): - """Strategy interface for display/rendering backends. - - Lifecycle hooks (``setup`` / ``teardown``) allow platforms that manage - external services (e.g. Xorg) to start and stop them without - if/else logic in the callers. ``for_worker`` returns a per-worker - platform instance (default: ``self``). - """ - - def __init__(self, env_manager=None): - self._env_manager = env_manager - - @property - @abstractmethod - def name(self) -> str: - """Short identifier (e.g. 'xvfb', 'egl').""" - ... - - @abstractmethod - def wrap_command(self, cmd: list[str], screen_config: str) -> list[str]: - """Optionally wrap the bridge launch command. - - Args: - cmd: The original command ``[python, bridge.py, ...]``. - screen_config: Screen resolution string, e.g. ``"1024x768x24"``. - - Returns: - The (possibly wrapped) command. - """ - ... - - def get_env_vars(self) -> EnvVars: - """Extra env vars needed by this platform (merged into subprocess).""" - return EnvVars() - - def get_system_deps(self) -> list[str]: - """System dependency names required by this platform.""" - return [] - - def is_available(self) -> bool: - """Whether this platform can run in the current environment.""" - return True - - # -- Lifecycle hooks (override in platforms that manage services) ---------- - - def setup(self, gpu_ids: list[int] | None = None) -> None: - """Called once before any simulator is created. Start external services.""" - - def teardown(self) -> None: - """Called once after all simulators are done. Stop external services.""" - - def for_worker(self, worker_id: int) -> RenderPlatform: - """Return a per-worker platform instance. Default: return self.""" - return self - - -# -- Built-in implementations ------------------------------------------------ - - -class HeadlessPlatform(RenderPlatform): - """No display at all -- for simulators with native headless support.""" - - @property - def name(self) -> str: - return "headless" - - def wrap_command(self, cmd: list[str], screen_config: str) -> list[str]: - return cmd - - -class NativePlatform(RenderPlatform): - """Use the existing DISPLAY. Fails at validation if none is set.""" - - @property - def name(self) -> str: - return "native" - - def wrap_command(self, cmd: list[str], screen_config: str) -> list[str]: - return cmd - - def is_available(self) -> bool: - return bool(os.environ.get("DISPLAY", "")) - - -class XvfbPlatform(RenderPlatform): - """Always wrap with ``xvfb-run``.""" - - @property - def name(self) -> str: - return "xvfb" - - def wrap_command(self, cmd: list[str], screen_config: str) -> list[str]: - return [ - "xvfb-run", "-a", - "-s", f"-screen 0 {screen_config}", - ] + cmd - - def get_system_deps(self) -> list[str]: - return ["xvfb"] - - -class EGLPlatform(RenderPlatform): - """GPU-accelerated headless rendering via EGL (no X11 needed).""" - - @property - def name(self) -> str: - return "egl" - - def wrap_command(self, cmd: list[str], screen_config: str) -> list[str]: - return cmd - - def get_env_vars(self) -> EnvVars: - replace: dict[str, str] = {"PYOPENGL_PLATFORM": "egl"} - mesa_vendor = Path("/usr/share/glvnd/egl_vendor.d/50_mesa.json") - if mesa_vendor.exists(): - replace["__EGL_VENDOR_LIBRARY_FILENAMES"] = str(mesa_vendor) - return EnvVars(replace=replace) - - def get_system_deps(self) -> list[str]: - return ["egl"] - - -class AutoPlatform(RenderPlatform): - """Detect native display; fall back to xvfb if unavailable.""" - - @property - def name(self) -> str: - return "auto" - - def wrap_command(self, cmd: list[str], screen_config: str) -> list[str]: - if os.environ.get("DISPLAY", ""): - return cmd - return XvfbPlatform().wrap_command(cmd, screen_config) - - -# -- Registry ----------------------------------------------------------------- - -def _xorg_platform_cls() -> type[RenderPlatform]: - from easi.core.xorg_platform import XorgPlatform - return XorgPlatform - - -_BUILTIN: dict[str, type[RenderPlatform]] = { - "auto": AutoPlatform, - "native": NativePlatform, - "xvfb": XvfbPlatform, - "egl": EGLPlatform, - "headless": HeadlessPlatform, -} - - -def get_render_platform(name: str) -> RenderPlatform: - """Instantiate a render platform by name. - - Raises: - ValueError: If name is not recognised. - """ - cls = _BUILTIN.get(name) - if cls is None: - if name == "xorg": - return _xorg_platform_cls()() - raise ValueError( - f"Unknown render platform '{name}'. " - f"Available: {', '.join(sorted(_BUILTIN | {'xorg': None}))}" - ) - return cls() - - -def available_platforms() -> list[str]: - """Return sorted list of registered platform names.""" - return sorted(_BUILTIN) diff --git a/easi/core/render_platforms/__init__.py b/easi/core/render_platforms/__init__.py new file mode 100644 index 0000000..fda49d5 --- /dev/null +++ b/easi/core/render_platforms/__init__.py @@ -0,0 +1,44 @@ +"""Render platform package — pluggable display/rendering backends. + +Each render platform encapsulates how to launch a bridge subprocess with +the correct display/rendering environment. Simulators declare a default +platform; users can override via CLI (--render-platform) or task YAML. + +Built-in platforms: + auto — use native DISPLAY if available, fall back to xvfb + native — require existing DISPLAY + xvfb — always wrap with xvfb-run + egl — GPU-accelerated headless via EGL + headless — no display (simulator has native headless support) + xorg — GPU-accelerated X11 via auto-managed Xorg servers +""" + +from .auto import AutoPlatform +from .base import EnvVars, RenderPlatform +from .egl import EGLPlatform +from .headless import HeadlessPlatform +from .native import NativePlatform +from .registry import available_platforms, get_render_platform +from .xorg import XorgPlatform, _XorgWorkerPlatform +from .xorg_manager import XorgInstance, XorgManager +from .xvfb import XvfbPlatform + +__all__ = [ + # Base types + "RenderPlatform", + "EnvVars", + # Built-in platforms + "AutoPlatform", + "NativePlatform", + "XvfbPlatform", + "EGLPlatform", + "HeadlessPlatform", + "XorgPlatform", + "_XorgWorkerPlatform", + # Xorg internals + "XorgManager", + "XorgInstance", + # Registry + "get_render_platform", + "available_platforms", +] diff --git a/easi/core/render_platforms/auto.py b/easi/core/render_platforms/auto.py new file mode 100644 index 0000000..85f7b1e --- /dev/null +++ b/easi/core/render_platforms/auto.py @@ -0,0 +1,21 @@ +"""Auto render platform — native display if available, falls back to xvfb.""" + +from __future__ import annotations + +import os + +from .base import RenderPlatform +from .xvfb import XvfbPlatform + + +class AutoPlatform(RenderPlatform): + """Detect native display; fall back to xvfb if unavailable.""" + + @property + def name(self) -> str: + return "auto" + + def wrap_command(self, cmd: list[str], screen_config: str) -> list[str]: + if os.environ.get("DISPLAY", ""): + return cmd + return XvfbPlatform().wrap_command(cmd, screen_config) diff --git a/easi/core/render_platforms/base.py b/easi/core/render_platforms/base.py new file mode 100644 index 0000000..d7f16a8 --- /dev/null +++ b/easi/core/render_platforms/base.py @@ -0,0 +1,107 @@ +"""Base classes for render platforms. + +Defines the RenderPlatform ABC and EnvVars dataclass used by all platform +implementations and external custom platforms (e.g. CoppeliaSim). +""" + +from __future__ import annotations + +from abc import ABC, abstractmethod +from dataclasses import dataclass, field + + +@dataclass +class EnvVars: + """Structured environment variables with replace/prepend semantics. + + ``replace`` vars overwrite any existing value. + ``prepend`` vars are prepended with ':' to any existing value (for PATH-like vars). + """ + + replace: dict[str, str] = field(default_factory=dict) + prepend: dict[str, str] = field(default_factory=dict) + + def to_flat_dict(self) -> dict[str, str]: + """Combine into single dict (for internal use like post_install).""" + return {**self.replace, **self.prepend} + + def apply_to_env(self, base: dict[str, str]) -> dict[str, str]: + """Merge into a base env dict (e.g. os.environ.copy()).""" + env = dict(base) + for k, v in self.replace.items(): + env[k] = v + for k, v in self.prepend.items(): + env[k] = f"{v}:{env[k]}" if k in env else v + return env + + def __bool__(self) -> bool: + return bool(self.replace) or bool(self.prepend) + + @classmethod + def merge(cls, *env_vars: EnvVars) -> EnvVars: + """Merge multiple EnvVars. Later values win for replace; prepend values concatenate.""" + replace: dict[str, str] = {} + prepend: dict[str, str] = {} + for ev in env_vars: + if ev is None: + continue + replace.update(ev.replace) + for k, v in ev.prepend.items(): + prepend[k] = f"{v}:{prepend[k]}" if k in prepend else v + return cls(replace=replace, prepend=prepend) + + +class RenderPlatform(ABC): + """Strategy interface for display/rendering backends. + + Lifecycle hooks (``setup`` / ``teardown``) allow platforms that manage + external services (e.g. Xorg) to start and stop them without + if/else logic in the callers. ``for_worker`` returns a per-worker + platform instance (default: ``self``). + """ + + def __init__(self, env_manager=None): + self._env_manager = env_manager + + @property + @abstractmethod + def name(self) -> str: + """Short identifier (e.g. 'xvfb', 'egl').""" + ... + + @abstractmethod + def wrap_command(self, cmd: list[str], screen_config: str) -> list[str]: + """Optionally wrap the bridge launch command. + + Args: + cmd: The original command ``[python, bridge.py, ...]``. + screen_config: Screen resolution string, e.g. ``"1024x768x24"``. + + Returns: + The (possibly wrapped) command. + """ + ... + + def get_env_vars(self) -> EnvVars: + """Extra env vars needed by this platform (merged into subprocess).""" + return EnvVars() + + def get_system_deps(self) -> list[str]: + """System dependency names required by this platform.""" + return [] + + def is_available(self) -> bool: + """Whether this platform can run in the current environment.""" + return True + + # -- Lifecycle hooks (override in platforms that manage services) ---------- + + def setup(self, gpu_ids: list[int] | None = None) -> None: + """Called once before any simulator is created. Start external services.""" + + def teardown(self) -> None: + """Called once after all simulators are done. Stop external services.""" + + def for_worker(self, worker_id: int) -> RenderPlatform: + """Return a per-worker platform instance. Default: return self.""" + return self diff --git a/easi/core/render_platforms/egl.py b/easi/core/render_platforms/egl.py new file mode 100644 index 0000000..08f8ee1 --- /dev/null +++ b/easi/core/render_platforms/egl.py @@ -0,0 +1,28 @@ +"""EGL render platform — GPU-accelerated headless rendering via EGL (no X11 needed).""" + +from __future__ import annotations + +from pathlib import Path + +from .base import EnvVars, RenderPlatform + + +class EGLPlatform(RenderPlatform): + """GPU-accelerated headless rendering via EGL (no X11 needed).""" + + @property + def name(self) -> str: + return "egl" + + def wrap_command(self, cmd: list[str], screen_config: str) -> list[str]: + return cmd + + def get_env_vars(self) -> EnvVars: + replace: dict[str, str] = {"PYOPENGL_PLATFORM": "egl"} + mesa_vendor = Path("/usr/share/glvnd/egl_vendor.d/50_mesa.json") + if mesa_vendor.exists(): + replace["__EGL_VENDOR_LIBRARY_FILENAMES"] = str(mesa_vendor) + return EnvVars(replace=replace) + + def get_system_deps(self) -> list[str]: + return ["egl"] diff --git a/easi/core/render_platforms/headless.py b/easi/core/render_platforms/headless.py new file mode 100644 index 0000000..5f4567e --- /dev/null +++ b/easi/core/render_platforms/headless.py @@ -0,0 +1,16 @@ +"""Headless render platform — no display, for simulators with native headless support.""" + +from __future__ import annotations + +from .base import RenderPlatform + + +class HeadlessPlatform(RenderPlatform): + """No display at all -- for simulators with native headless support.""" + + @property + def name(self) -> str: + return "headless" + + def wrap_command(self, cmd: list[str], screen_config: str) -> list[str]: + return cmd diff --git a/easi/core/render_platforms/native.py b/easi/core/render_platforms/native.py new file mode 100644 index 0000000..114d107 --- /dev/null +++ b/easi/core/render_platforms/native.py @@ -0,0 +1,21 @@ +"""Native render platform — use existing DISPLAY environment variable.""" + +from __future__ import annotations + +import os + +from .base import RenderPlatform + + +class NativePlatform(RenderPlatform): + """Use the existing DISPLAY. Fails at validation if none is set.""" + + @property + def name(self) -> str: + return "native" + + def wrap_command(self, cmd: list[str], screen_config: str) -> list[str]: + return cmd + + def is_available(self) -> bool: + return bool(os.environ.get("DISPLAY", "")) diff --git a/easi/core/render_platforms/registry.py b/easi/core/render_platforms/registry.py new file mode 100644 index 0000000..2318a44 --- /dev/null +++ b/easi/core/render_platforms/registry.py @@ -0,0 +1,40 @@ +"""Render platform registry — maps names to platform classes.""" + +from __future__ import annotations + +from .auto import AutoPlatform +from .base import RenderPlatform +from .egl import EGLPlatform +from .headless import HeadlessPlatform +from .native import NativePlatform +from .xorg import XorgPlatform +from .xvfb import XvfbPlatform + +_BUILTIN: dict[str, type[RenderPlatform]] = { + "auto": AutoPlatform, + "native": NativePlatform, + "xvfb": XvfbPlatform, + "egl": EGLPlatform, + "headless": HeadlessPlatform, + "xorg": XorgPlatform, +} + + +def get_render_platform(name: str) -> RenderPlatform: + """Instantiate a render platform by name. + + Raises: + ValueError: If name is not recognised. + """ + cls = _BUILTIN.get(name) + if cls is None: + raise ValueError( + f"Unknown render platform '{name}'. " + f"Available: {', '.join(sorted(_BUILTIN))}" + ) + return cls() + + +def available_platforms() -> list[str]: + """Return sorted list of registered platform names.""" + return sorted(_BUILTIN) diff --git a/easi/core/xorg_platform.py b/easi/core/render_platforms/xorg.py similarity index 91% rename from easi/core/xorg_platform.py rename to easi/core/render_platforms/xorg.py index ca1eb46..3a6d620 100644 --- a/easi/core/xorg_platform.py +++ b/easi/core/render_platforms/xorg.py @@ -11,9 +11,11 @@ from __future__ import annotations -from easi.core.render_platform import EnvVars, RenderPlatform from easi.utils.logging import get_logger +from .base import EnvVars, RenderPlatform +from .xorg_manager import XorgManager + logger = get_logger(__name__) @@ -53,7 +55,7 @@ class XorgPlatform(RenderPlatform): def __init__(self, env_manager=None): super().__init__(env_manager=env_manager) - self._xorg_mgr = None + self._xorg_mgr: XorgManager | None = None self._instances: list = [] @property @@ -71,9 +73,7 @@ def is_available(self) -> bool: def setup(self, gpu_ids: list[int] | None = None) -> None: """Start one Xorg server per GPU.""" - from easi.core.xorg_manager import XorgManager - resolved_gpu_ids = gpu_ids or [0] - self._xorg_mgr = XorgManager(gpu_ids=resolved_gpu_ids) + self._xorg_mgr = XorgManager(gpu_ids=gpu_ids or [0]) self._instances = self._xorg_mgr.start() def teardown(self) -> None: diff --git a/easi/core/xorg_manager.py b/easi/core/render_platforms/xorg_manager.py similarity index 67% rename from easi/core/xorg_manager.py rename to easi/core/render_platforms/xorg_manager.py index 7d1b289..0a71e55 100644 --- a/easi/core/xorg_manager.py +++ b/easi/core/render_platforms/xorg_manager.py @@ -23,6 +23,7 @@ class XorgInstance(NamedTuple): """A running Xorg server bound to a GPU.""" + display: int gpu_id: int pid: int @@ -42,9 +43,16 @@ def _find_available_display(start: int, max_probe: int = 50) -> int: def _get_pci_bus_id(gpu_index: int) -> str: """Query PCI BusID for a GPU via nvidia-smi, return Xorg format (PCI:B:D:F).""" result = subprocess.run( - ["nvidia-smi", "--query-gpu=pci.bus_id", "--format=csv,noheader", - "-i", str(gpu_index)], - capture_output=True, text=True, timeout=10, + [ + "nvidia-smi", + "--query-gpu=pci.bus_id", + "--format=csv,noheader", + "-i", + str(gpu_index), + ], + capture_output=True, + text=True, + timeout=10, ) if result.returncode != 0 or not result.stdout.strip(): raise RuntimeError( @@ -138,7 +146,10 @@ def start(self) -> list[XorgInstance]: return list(self._instances) def _start_one( - self, xorg_path: str, gpu_id: int, display_num: int, + self, + xorg_path: str, + gpu_id: int, + display_num: int, ) -> XorgInstance: """Start a single Xorg server on the given GPU and display.""" pci_bus_id = _get_pci_bus_id(gpu_id) @@ -146,53 +157,95 @@ def _start_one( self._conf_files.append(conf_path) display_str = f":{display_num}" - cmd = [xorg_path, display_str, "-config", conf_path, "-noreset", "-nolisten", "tcp"] + cmd = [ + xorg_path, + display_str, + "-config", + conf_path, + "-noreset", + "-nolisten", + "tcp", + ] logger.info( "Starting Xorg on display %s using GPU %d (%s)", - display_str, gpu_id, pci_bus_id, + display_str, + gpu_id, + pci_bus_id, ) proc, used_sudo = self._launch_xorg(cmd, xorg_path) self._processes.append(proc) self._used_sudo.append(used_sudo) - self._wait_for_ready(display_num, proc) + try: + self._wait_for_ready(display_num, proc) + except RuntimeError as exc: + if not used_sudo and proc.poll() is not None: + if self._passwordless_sudo_available(xorg_path): + logger.info( + "Direct Xorg launch exited early on display %s; retrying with sudo", + display_str, + ) + self._processes.pop() + self._used_sudo.pop() + proc, used_sudo = self._launch_xorg_with_sudo(cmd, xorg_path) + self._processes.append(proc) + self._used_sudo.append(used_sudo) + self._wait_for_ready(display_num, proc) + else: + raise RuntimeError( + self._sudo_required_message(xorg_path, display_str) + ) from exc + else: + raise logger.info( "Xorg ready on display %s (PID %d, GPU %d)", - display_str, proc.pid, gpu_id, + display_str, + proc.pid, + gpu_id, ) return XorgInstance(display=display_num, gpu_id=gpu_id, pid=proc.pid) - def _launch_xorg(self, cmd: list[str], xorg_path: str) -> tuple[subprocess.Popen, bool]: + def _launch_xorg( + self, cmd: list[str], xorg_path: str + ) -> tuple[subprocess.Popen, bool]: """Try launching Xorg directly, fall back to sudo on PermissionError. Returns (process, used_sudo) so stop() knows whether to use sudo kill. """ try: proc = subprocess.Popen( - cmd, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL, + cmd, + stdout=subprocess.DEVNULL, + stderr=subprocess.DEVNULL, preexec_fn=os.setsid, ) return proc, False except PermissionError: - logger.info("Direct Xorg launch failed (permission denied), retrying with sudo") + logger.info( + "Direct Xorg launch failed (permission denied), retrying with sudo" + ) + return self._launch_xorg_with_sudo(cmd, xorg_path) + + def _launch_xorg_with_sudo( + self, + cmd: list[str], + xorg_path: str, + ) -> tuple[subprocess.Popen, bool]: sudo_cmd = ["sudo", "-n"] + cmd try: proc = subprocess.Popen( - sudo_cmd, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL, + sudo_cmd, + stdout=subprocess.DEVNULL, + stderr=subprocess.DEVNULL, preexec_fn=os.setsid, ) return proc, True except (PermissionError, FileNotFoundError) as exc: - raise RuntimeError( - f"Xorg requires root privileges. Either run as root, or configure " - f"passwordless sudo:\n\n" - f" sudo bash -c 'echo \"$USER ALL=(ALL) NOPASSWD: {xorg_path}\" " - f">> /etc/sudoers.d/easi-xorg'" - ) from exc + raise RuntimeError(self._sudo_required_message(xorg_path)) from exc def _wait_for_ready(self, display_num: int, proc: subprocess.Popen) -> None: """Poll until the X server responds or timeout.""" @@ -208,7 +261,8 @@ def _wait_for_ready(self, display_num: int, proc: subprocess.Popen) -> None: try: result = subprocess.run( ["xset", "-display", display_str, "q"], - capture_output=True, timeout=2, + capture_output=True, + timeout=2, ) if result.returncode == 0: return @@ -231,6 +285,10 @@ def stop(self) -> None: proc.wait(timeout=5) except subprocess.TimeoutExpired: self._kill_proc(proc, signal.SIGKILL, sudo) + try: + proc.wait(timeout=2) + except subprocess.TimeoutExpired: + pass self._processes.clear() self._used_sudo.clear() @@ -251,9 +309,37 @@ def _kill_proc(proc: subprocess.Popen, sig: int, used_sudo: bool) -> None: if used_sudo: subprocess.run( ["sudo", "-n", "kill", f"-{sig}", f"-{pgid}"], - capture_output=True, timeout=5, + capture_output=True, + timeout=5, ) else: os.killpg(pgid, sig) except (ProcessLookupError, PermissionError, subprocess.TimeoutExpired): pass + + @staticmethod + def _passwordless_sudo_available(xorg_path: str) -> bool: + """Probe ``sudo -n -l `` — sudoers may only whitelist specific commands.""" + try: + result = subprocess.run( + ["sudo", "-n", "-l", xorg_path], + capture_output=True, + timeout=5, + ) + except (FileNotFoundError, subprocess.TimeoutExpired): + return False + return result.returncode == 0 + + @staticmethod + def _sudo_required_message(xorg_path: str, display_str: str | None = None) -> str: + display_hint = "" + if display_str is not None: + display_hint = ( + f"Xorg exited before becoming ready on display {display_str}. " + ) + user = os.environ.get("USER", "$USER") + return ( + f"{display_hint}This usually requires root or a console session.\n" + f"To fix this, run EASI as root or authorize passwordless sudo:\n\n" + f" sudo bash -c 'echo \"{user} ALL=(ALL) NOPASSWD: {xorg_path}, /usr/bin/kill\" > /etc/sudoers.d/easi-xorg'" + ) diff --git a/easi/core/render_platforms/xvfb.py b/easi/core/render_platforms/xvfb.py new file mode 100644 index 0000000..8afcf0e --- /dev/null +++ b/easi/core/render_platforms/xvfb.py @@ -0,0 +1,22 @@ +"""Xvfb render platform — virtual framebuffer via xvfb-run.""" + +from __future__ import annotations + +from .base import RenderPlatform + + +class XvfbPlatform(RenderPlatform): + """Always wrap with ``xvfb-run``.""" + + @property + def name(self) -> str: + return "xvfb" + + def wrap_command(self, cmd: list[str], screen_config: str) -> list[str]: + return [ + "xvfb-run", "-a", + "-s", f"-screen 0 {screen_config}", + ] + cmd + + def get_system_deps(self) -> list[str]: + return ["xvfb"] diff --git a/easi/evaluation/runner.py b/easi/evaluation/runner.py index 22e1bc0..585c5fb 100644 --- a/easi/evaluation/runner.py +++ b/easi/evaluation/runner.py @@ -146,7 +146,7 @@ def _setup_render_platform(self, backend: str | None = None): no-op. Returns ``None`` when no ``--render-platform`` was specified (the platform is then resolved per-simulator in ``_create_simulator``). """ - from easi.core.render_platform import get_render_platform + from easi.core.render_platforms import get_render_platform if not self.render_platform_name: return None @@ -201,7 +201,7 @@ def run(self) -> list[dict]: # 2. Resolve LLM backend and optionally start server backend, base_url = self._resolve_llm_backend() server = None - render_platform = None + self._render_platform = None try: if backend in ("vllm", "custom") and base_url is None: @@ -728,7 +728,7 @@ def _create_simulator(self, simulator_key: str, task=None, label: str = "bridge" # --- Docker runtime path --- if entry.runtime == "docker": from easi.core.docker_env_manager import DockerEnvironmentManager - from easi.core.render_platform import get_render_platform + from easi.core.render_platforms import get_render_platform assert isinstance(env_manager, DockerEnvironmentManager), ( f"Simulator {simulator_key} declares runtime=docker but env_manager " @@ -783,7 +783,7 @@ def _create_simulator(self, simulator_key: str, task=None, label: str = "bridge" render_platform = resolve_render_platform(simulator_key, platform_name, env_manager=env_manager) # Pass platform name to get_env_vars for conditional logic - from easi.core.render_platform import EnvVars + from easi.core.render_platforms import EnvVars env_vars = env_manager.get_env_vars(render_platform_name=platform_name) diff --git a/easi/simulators/coppeliasim/v4_1_0/env_manager.py b/easi/simulators/coppeliasim/v4_1_0/env_manager.py index 7561af1..f46bd5a 100644 --- a/easi/simulators/coppeliasim/v4_1_0/env_manager.py +++ b/easi/simulators/coppeliasim/v4_1_0/env_manager.py @@ -16,7 +16,7 @@ from pathlib import Path from easi.core.base_env_manager import BaseEnvironmentManager -from easi.core.render_platform import EnvVars +from easi.core.render_platforms import EnvVars from easi.utils.logging import get_logger logger = get_logger(__name__) diff --git a/easi/simulators/dummy/v1/render_platforms.py b/easi/simulators/dummy/v1/render_platforms.py index 6c077e1..a4d7acb 100644 --- a/easi/simulators/dummy/v1/render_platforms.py +++ b/easi/simulators/dummy/v1/render_platforms.py @@ -1,6 +1,6 @@ """Custom render platforms for the dummy simulator (for testing).""" -from easi.core.render_platform import EnvVars, HeadlessPlatform +from easi.core.render_platforms import EnvVars, HeadlessPlatform class DummyCustomPlatform(HeadlessPlatform): diff --git a/easi/simulators/omnigibson/v3_7_2/env_manager.py b/easi/simulators/omnigibson/v3_7_2/env_manager.py index 6c39d92..2aefc00 100644 --- a/easi/simulators/omnigibson/v3_7_2/env_manager.py +++ b/easi/simulators/omnigibson/v3_7_2/env_manager.py @@ -30,7 +30,7 @@ from pathlib import Path from easi.core.base_env_manager import BaseEnvironmentManager -from easi.core.render_platform import EnvVars +from easi.core.render_platforms import EnvVars from easi.utils.logging import get_logger logger = get_logger(__name__) diff --git a/easi/simulators/omnigibson/v3_7_2/render_platforms.py b/easi/simulators/omnigibson/v3_7_2/render_platforms.py index 3626ecf..0c5cfeb 100644 --- a/easi/simulators/omnigibson/v3_7_2/render_platforms.py +++ b/easi/simulators/omnigibson/v3_7_2/render_platforms.py @@ -9,7 +9,7 @@ import os -from easi.core.render_platform import AutoPlatform, EnvVars, NativePlatform +from easi.core.render_platforms import AutoPlatform, EnvVars, NativePlatform class OmniGibsonNativePlatform(NativePlatform): diff --git a/easi/simulators/registry.py b/easi/simulators/registry.py index 3023769..e5b2e01 100644 --- a/easi/simulators/registry.py +++ b/easi/simulators/registry.py @@ -150,7 +150,7 @@ def resolve_render_platform(key: str, platform_name: str, env_manager=None): Returns: Instantiated RenderPlatform. """ - from easi.core.render_platform import get_render_platform + from easi.core.render_platforms import get_render_platform entry = get_simulator_entry(key) custom_class_path = entry.render_platforms.get(platform_name) diff --git a/easi/simulators/subprocess_runner.py b/easi/simulators/subprocess_runner.py index 1fbcb86..37f94c6 100644 --- a/easi/simulators/subprocess_runner.py +++ b/easi/simulators/subprocess_runner.py @@ -28,7 +28,7 @@ write_command, ) from easi.core.exceptions import SimulatorError, SimulatorTimeoutError -from easi.core.render_platform import EnvVars, RenderPlatform +from easi.core.render_platforms import EnvVars, RenderPlatform from easi.utils.logging import get_logger logger = get_logger(__name__) diff --git a/easi/simulators/tdw/v1_11_23/env_manager.py b/easi/simulators/tdw/v1_11_23/env_manager.py index 3c2cc09..0b9d88c 100644 --- a/easi/simulators/tdw/v1_11_23/env_manager.py +++ b/easi/simulators/tdw/v1_11_23/env_manager.py @@ -13,7 +13,7 @@ from pathlib import Path from easi.core.base_env_manager import BaseEnvironmentManager -from easi.core.render_platform import EnvVars +from easi.core.render_platforms import EnvVars from easi.utils.logging import get_logger logger = get_logger(__name__) diff --git a/tests/test_docker_isolation.py b/tests/test_docker_isolation.py index d5ebb6c..33e7d88 100644 --- a/tests/test_docker_isolation.py +++ b/tests/test_docker_isolation.py @@ -197,7 +197,7 @@ def test_launch_docker_builds_correct_command(self): """launch_docker() builds a docker run command and spawns it.""" from easi.core.docker_env_manager import DockerEnvironmentManager from easi.simulators.subprocess_runner import SubprocessRunner - from easi.core.render_platform import get_render_platform + from easi.core.render_platforms import get_render_platform # Use a real bridge script path under the EASI repo root so that # relative_to() in _build_docker_launch_command works correctly. diff --git a/tests/test_dummy_simulator.py b/tests/test_dummy_simulator.py index 42b926b..456257d 100644 --- a/tests/test_dummy_simulator.py +++ b/tests/test_dummy_simulator.py @@ -14,7 +14,7 @@ @pytest.fixture def dummy_simulator(): """Create and start a dummy simulator, cleaning up after the test.""" - from easi.core.render_platform import get_render_platform + from easi.core.render_platforms import get_render_platform env_manager = DummyEnvManager() sim = DummySimulator() @@ -88,7 +88,7 @@ def test_is_running(dummy_simulator): def test_dummy_simulator_with_env_vars_wiring(): """Verify DummyEnvManager.get_env_vars() returns empty EnvVars and doesn't break launch.""" - from easi.core.render_platform import get_render_platform + from easi.core.render_platforms import get_render_platform env_manager = DummyEnvManager() env_vars = env_manager.get_env_vars() diff --git a/tests/test_env_manager.py b/tests/test_env_manager.py index 092ce9f..fcf1259 100644 --- a/tests/test_env_manager.py +++ b/tests/test_env_manager.py @@ -12,7 +12,7 @@ class TestEnvManagerGetEnvVars: """Tests for the get_env_vars() method.""" def test_default_returns_empty_envvars(self): - from easi.core.render_platform import EnvVars + from easi.core.render_platforms import EnvVars from easi.simulators.dummy.v1.env_manager import DummyEnvManager mgr = DummyEnvManager() @@ -21,7 +21,7 @@ def test_default_returns_empty_envvars(self): assert not result def test_returns_envvars_type(self): - from easi.core.render_platform import EnvVars + from easi.core.render_platforms import EnvVars from easi.simulators.dummy.v1.env_manager import DummyEnvManager mgr = DummyEnvManager() diff --git a/tests/test_env_vars_wiring.py b/tests/test_env_vars_wiring.py index b9d22d8..e4aeb56 100644 --- a/tests/test_env_vars_wiring.py +++ b/tests/test_env_vars_wiring.py @@ -10,7 +10,7 @@ class TestEnvVarsWiring: """Verify env vars flow from env_manager to SubprocessRunner.""" def test_runner_passes_env_vars_to_subprocess(self): - from easi.core.render_platform import EnvVars, get_render_platform + from easi.core.render_platforms import EnvVars, get_render_platform from easi.evaluation.runner import EvaluationRunner runner = EvaluationRunner.__new__(EvaluationRunner) @@ -51,7 +51,7 @@ def test_runner_passes_env_vars_to_subprocess(self): assert extra_env.replace == {"SIM_ROOT": "/opt/sim"} def test_runner_passes_none_when_no_env_vars(self): - from easi.core.render_platform import EnvVars, get_render_platform + from easi.core.render_platforms import EnvVars, get_render_platform from easi.evaluation.runner import EvaluationRunner runner = EvaluationRunner.__new__(EvaluationRunner) diff --git a/tests/test_installation_kwargs.py b/tests/test_installation_kwargs.py index d5b18d3..558eecd 100644 --- a/tests/test_installation_kwargs.py +++ b/tests/test_installation_kwargs.py @@ -53,7 +53,7 @@ class TestRunnerUsesFactory: """EvaluationRunner._create_simulator uses the factory.""" def test_runner_passes_installation_kwargs(self): - from easi.core.render_platform import EnvVars, get_render_platform + from easi.core.render_platforms import EnvVars, get_render_platform from easi.evaluation.runner import EvaluationRunner runner = EvaluationRunner.__new__(EvaluationRunner) diff --git a/tests/test_subprocess_runner.py b/tests/test_subprocess_runner.py index 5641b4c..40e3ad6 100644 --- a/tests/test_subprocess_runner.py +++ b/tests/test_subprocess_runner.py @@ -5,7 +5,7 @@ import os from pathlib import Path -from easi.core.render_platform import EnvVars, get_render_platform +from easi.core.render_platforms import EnvVars, get_render_platform from easi.simulators.subprocess_runner import SubprocessRunner diff --git a/tests/test_xorg.py b/tests/test_xorg.py index cca18ef..72b0818 100644 --- a/tests/test_xorg.py +++ b/tests/test_xorg.py @@ -8,18 +8,21 @@ import pytest +_MGR = "easi.core.render_platforms.xorg_manager" # canonical module for XorgManager internals +_XORG = "easi.core.render_platforms.xorg" # canonical module for XorgPlatform + class TestXorgWorkerPlatform: """Test _XorgWorkerPlatform env vars and command wrapping.""" def test_name(self): - from easi.core.xorg_platform import _XorgWorkerPlatform + from easi.core.render_platforms.xorg import _XorgWorkerPlatform p = _XorgWorkerPlatform(display_num=10, gpu_id=4) assert p.name == "xorg" def test_env_vars(self): - from easi.core.xorg_platform import _XorgWorkerPlatform + from easi.core.render_platforms.xorg import _XorgWorkerPlatform p = _XorgWorkerPlatform(display_num=10, gpu_id=4) ev = p.get_env_vars() @@ -28,14 +31,14 @@ def test_env_vars(self): assert ev.replace["EASI_GPU_DISPLAY"] == "1" def test_wrap_command_passthrough(self): - from easi.core.xorg_platform import _XorgWorkerPlatform + from easi.core.render_platforms.xorg import _XorgWorkerPlatform p = _XorgWorkerPlatform(display_num=10, gpu_id=4) cmd = ["python", "bridge.py", "--workspace", "/tmp"] assert p.wrap_command(cmd, "1024x768x24") == cmd def test_is_available(self): - from easi.core.xorg_platform import _XorgWorkerPlatform + from easi.core.render_platforms.xorg import _XorgWorkerPlatform p = _XorgWorkerPlatform(display_num=10, gpu_id=4) assert p.is_available() is True @@ -45,20 +48,20 @@ class TestXorgPlatformLifecycle: """Test XorgPlatform setup/teardown/for_worker lifecycle.""" def test_name(self): - from easi.core.xorg_platform import XorgPlatform + from easi.core.render_platforms.xorg import XorgPlatform p = XorgPlatform() assert p.name == "xorg" def test_for_worker_without_setup_raises(self): - from easi.core.xorg_platform import XorgPlatform + from easi.core.render_platforms.xorg import XorgPlatform p = XorgPlatform() with pytest.raises(RuntimeError, match="setup.*must be called"): p.for_worker(0) def test_teardown_without_setup_is_safe(self): - from easi.core.xorg_platform import XorgPlatform + from easi.core.render_platforms.xorg import XorgPlatform p = XorgPlatform() p.teardown() # should not raise @@ -68,26 +71,26 @@ class TestXorgManager: """Test XorgManager lifecycle with mocked subprocesses.""" def test_no_xorg_binary_raises(self): - from easi.core.xorg_manager import XorgManager + from easi.core.render_platforms.xorg_manager import XorgManager - with patch("easi.core.xorg_manager.shutil.which", return_value=None): + with patch(f"{_MGR}.shutil.which", return_value=None): mgr = XorgManager(gpu_ids=[0]) with pytest.raises(RuntimeError, match="Xorg is not installed"): mgr.start() def test_start_single_gpu(self): - from easi.core.xorg_manager import XorgManager + from easi.core.render_platforms.xorg_manager import XorgManager mock_proc = MagicMock() mock_proc.pid = 12345 mock_proc.poll.return_value = None with ( - patch("easi.core.xorg_manager.shutil.which", return_value="/usr/lib/xorg/Xorg"), - patch("easi.core.xorg_manager.subprocess.Popen", return_value=mock_proc), - patch("easi.core.xorg_manager.subprocess.run") as mock_run, - patch("easi.core.xorg_manager.os.path.exists", return_value=False), - patch("easi.core.xorg_manager._write_xorg_conf", return_value="/tmp/easi-xorg-gpu0.conf"), + patch(f"{_MGR}.shutil.which", return_value="/usr/lib/xorg/Xorg"), + patch(f"{_MGR}.subprocess.Popen", return_value=mock_proc), + patch(f"{_MGR}.subprocess.run") as mock_run, + patch(f"{_MGR}.os.path.exists", return_value=False), + patch(f"{_MGR}._write_xorg_conf", return_value="/tmp/easi-xorg-gpu0.conf"), ): nvidia_result = MagicMock() nvidia_result.returncode = 0 @@ -105,18 +108,18 @@ def test_start_single_gpu(self): assert instances[0].pid == 12345 def test_start_multi_gpu(self): - from easi.core.xorg_manager import XorgManager + from easi.core.render_platforms.xorg_manager import XorgManager mock_proc = MagicMock() mock_proc.pid = 99999 mock_proc.poll.return_value = None with ( - patch("easi.core.xorg_manager.shutil.which", return_value="/usr/lib/xorg/Xorg"), - patch("easi.core.xorg_manager.subprocess.Popen", return_value=mock_proc), - patch("easi.core.xorg_manager.subprocess.run") as mock_run, - patch("easi.core.xorg_manager.os.path.exists", return_value=False), - patch("easi.core.xorg_manager._write_xorg_conf", return_value="/tmp/easi-xorg-gpu.conf"), + patch(f"{_MGR}.shutil.which", return_value="/usr/lib/xorg/Xorg"), + patch(f"{_MGR}.subprocess.Popen", return_value=mock_proc), + patch(f"{_MGR}.subprocess.run") as mock_run, + patch(f"{_MGR}.os.path.exists", return_value=False), + patch(f"{_MGR}._write_xorg_conf", return_value="/tmp/easi-xorg-gpu.conf"), ): nvidia0 = MagicMock(returncode=0, stdout="00000000:3F:00.0\n") xset0 = MagicMock(returncode=0) @@ -133,7 +136,7 @@ def test_start_multi_gpu(self): assert instances[0].display != instances[1].display def test_stop_sends_sigterm(self): - from easi.core.xorg_manager import XorgInstance, XorgManager + from easi.core.render_platforms.xorg_manager import XorgInstance, XorgManager mgr = XorgManager(gpu_ids=[0]) mock_proc = MagicMock() @@ -144,14 +147,14 @@ def test_stop_sends_sigterm(self): mgr._instances = [XorgInstance(display=10, gpu_id=0, pid=12345)] mgr._conf_files = [] - with patch("easi.core.xorg_manager.os.getpgid", return_value=12345), \ - patch("easi.core.xorg_manager.os.killpg") as mock_killpg: + with patch(f"{_MGR}.os.getpgid", return_value=12345), \ + patch(f"{_MGR}.os.killpg") as mock_killpg: mgr.stop() mock_killpg.assert_called_with(12345, signal.SIGTERM) def test_stop_uses_sudo_kill_for_sudo_launched(self): """Sudo-launched Xorg processes are killed via sudo kill.""" - from easi.core.xorg_manager import XorgInstance, XorgManager + from easi.core.render_platforms.xorg_manager import XorgInstance, XorgManager mgr = XorgManager(gpu_ids=[0]) mock_proc = MagicMock() @@ -162,9 +165,9 @@ def test_stop_uses_sudo_kill_for_sudo_launched(self): mgr._instances = [XorgInstance(display=10, gpu_id=0, pid=12345)] mgr._conf_files = [] - with patch("easi.core.xorg_manager.os.getpgid", return_value=12345), \ - patch("easi.core.xorg_manager.os.killpg") as mock_killpg, \ - patch("easi.core.xorg_manager.subprocess.run") as mock_run: + with patch(f"{_MGR}.os.getpgid", return_value=12345), \ + patch(f"{_MGR}.os.killpg") as mock_killpg, \ + patch(f"{_MGR}.subprocess.run") as mock_run: mgr.stop() mock_killpg.assert_not_called() mock_run.assert_called_once_with( @@ -174,7 +177,7 @@ def test_stop_uses_sudo_kill_for_sudo_launched(self): def test_sudo_fallback(self): """If direct Xorg fails with PermissionError, retry with sudo.""" - from easi.core.xorg_manager import XorgManager + from easi.core.render_platforms.xorg_manager import XorgManager call_count = 0 def mock_popen(cmd, **kwargs): @@ -188,11 +191,11 @@ def mock_popen(cmd, **kwargs): return proc with ( - patch("easi.core.xorg_manager.shutil.which", return_value="/usr/lib/xorg/Xorg"), - patch("easi.core.xorg_manager.subprocess.Popen", side_effect=mock_popen), - patch("easi.core.xorg_manager.subprocess.run") as mock_run, - patch("easi.core.xorg_manager.os.path.exists", return_value=False), - patch("easi.core.xorg_manager._write_xorg_conf", return_value="/tmp/easi-xorg-gpu0.conf"), + patch(f"{_MGR}.shutil.which", return_value="/usr/lib/xorg/Xorg"), + patch(f"{_MGR}.subprocess.Popen", side_effect=mock_popen), + patch(f"{_MGR}.subprocess.run") as mock_run, + patch(f"{_MGR}.os.path.exists", return_value=False), + patch(f"{_MGR}._write_xorg_conf", return_value="/tmp/easi-xorg-gpu0.conf"), ): nvidia_result = MagicMock(returncode=0, stdout="00000000:3F:00.0\n") xset_result = MagicMock(returncode=0) @@ -207,7 +210,7 @@ def mock_popen(cmd, **kwargs): def test_start_failure_stops_all(self): """If one GPU's Xorg fails, previously started ones are stopped.""" - from easi.core.xorg_manager import XorgManager + from easi.core.render_platforms.xorg_manager import XorgManager started_procs = [] @@ -222,13 +225,13 @@ def mock_popen(cmd, **kwargs): return proc with ( - patch("easi.core.xorg_manager.shutil.which", return_value="/usr/lib/xorg/Xorg"), - patch("easi.core.xorg_manager.subprocess.Popen", side_effect=mock_popen), - patch("easi.core.xorg_manager.subprocess.run") as mock_run, - patch("easi.core.xorg_manager.os.path.exists", return_value=False), - patch("easi.core.xorg_manager.os.getpgid", return_value=11111), - patch("easi.core.xorg_manager.os.killpg"), - patch("easi.core.xorg_manager._write_xorg_conf", return_value="/tmp/easi-xorg-gpu.conf"), + patch(f"{_MGR}.shutil.which", return_value="/usr/lib/xorg/Xorg"), + patch(f"{_MGR}.subprocess.Popen", side_effect=mock_popen), + patch(f"{_MGR}.subprocess.run") as mock_run, + patch(f"{_MGR}.os.path.exists", return_value=False), + patch(f"{_MGR}.os.getpgid", return_value=11111), + patch(f"{_MGR}.os.killpg"), + patch(f"{_MGR}._write_xorg_conf", return_value="/tmp/easi-xorg-gpu.conf"), ): nvidia0 = MagicMock(returncode=0, stdout="00000000:3F:00.0\n") xset0 = MagicMock(returncode=0) @@ -243,57 +246,60 @@ def mock_popen(cmd, **kwargs): class TestPerWorkerGpuPinning: - """Test per-worker GPU round-robin in _create_simulator.""" + """Test per-worker GPU round-robin via XorgPlatform.for_worker().""" def test_round_robin_two_gpus(self): """Workers are assigned to GPUs in round-robin order.""" - sim_gpus = [4, 5] - for worker_id in range(6): - expected_gpu = sim_gpus[worker_id % len(sim_gpus)] - actual_gpu = sim_gpus[worker_id % len(sim_gpus)] - assert actual_gpu == expected_gpu + from easi.core.render_platforms.xorg import XorgPlatform + from easi.core.render_platforms.xorg_manager import XorgInstance + + p = XorgPlatform() + p._instances = [ + XorgInstance(display=10, gpu_id=4, pid=1), + XorgInstance(display=11, gpu_id=5, pid=2), + ] + gpus = [p.for_worker(i).gpu_id for i in range(6)] + assert gpus == [4, 5, 4, 5, 4, 5] def test_single_gpu_all_workers_same(self): """With one GPU, all workers get the same GPU.""" - sim_gpus = [4] + from easi.core.render_platforms.xorg import XorgPlatform + from easi.core.render_platforms.xorg_manager import XorgInstance + + p = XorgPlatform() + p._instances = [XorgInstance(display=10, gpu_id=4, pid=1)] for worker_id in range(4): - assert sim_gpus[worker_id % len(sim_gpus)] == 4 + assert p.for_worker(worker_id).gpu_id == 4 class TestXorgRunnerIntegration: """Test Xorg integration logic in runners.""" - def test_xorg_defaults_to_gpu_0(self): - """Without --sim-gpus, xorg defaults to GPU 0.""" - gpu_ids = None or [0] - assert gpu_ids == [0] + def test_xorg_defaults_to_gpu_0_when_no_sim_gpus(self): + """XorgPlatform.setup() uses GPU 0 when gpu_ids is None.""" + from easi.core.render_platforms.xorg import XorgPlatform + from easi.core.render_platforms.xorg_manager import XorgInstance + + p = XorgPlatform() + mock_mgr = MagicMock() + mock_mgr.start.return_value = [XorgInstance(display=10, gpu_id=0, pid=1)] + + with patch(f"{_XORG}.XorgManager", return_value=mock_mgr) as MockMgr: + p.setup(gpu_ids=None) + MockMgr.assert_called_once_with(gpu_ids=[0]) def test_xorg_uses_sim_gpus_when_specified(self): - """With --sim-gpus, xorg uses those GPUs.""" - sim_gpus = [2, 3] - gpu_ids = sim_gpus or [0] - assert gpu_ids == [2, 3] - - def test_xorg_warns_on_gpu_contention(self): - """Warn when xorg + local LLM both default to GPU 0.""" - render_platform = "xorg" - sim_gpus = None - backend = "vllm" - llm_gpus = None - should_warn = ( - render_platform == "xorg" - and not sim_gpus - and backend in ("vllm", "custom") - and not llm_gpus - ) - assert should_warn is True - - def test_xorg_no_warn_with_sim_gpus(self): - """No warning when sim_gpus is specified.""" - should_warn = ( - "xorg" == "xorg" - and not [2, 3] - and "vllm" in ("vllm", "custom") - and not None - ) - assert should_warn is False + """XorgPlatform.setup() uses provided gpu_ids.""" + from easi.core.render_platforms.xorg import XorgPlatform + from easi.core.render_platforms.xorg_manager import XorgInstance + + p = XorgPlatform() + mock_mgr = MagicMock() + mock_mgr.start.return_value = [ + XorgInstance(display=10, gpu_id=2, pid=1), + XorgInstance(display=11, gpu_id=3, pid=2), + ] + + with patch(f"{_XORG}.XorgManager", return_value=mock_mgr) as MockMgr: + p.setup(gpu_ids=[2, 3]) + MockMgr.assert_called_once_with(gpu_ids=[2, 3]) From a2acb53f6b1040d9a3048e771ea3a4ca50d1a453 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Wed, 11 Mar 2026 14:00:13 +0800 Subject: [PATCH 197/239] fix: initialize _render_platform in ParallelRunner --- easi/evaluation/parallel_runner.py | 1 + 1 file changed, 1 insertion(+) diff --git a/easi/evaluation/parallel_runner.py b/easi/evaluation/parallel_runner.py index a78981e..323fcf3 100644 --- a/easi/evaluation/parallel_runner.py +++ b/easi/evaluation/parallel_runner.py @@ -122,6 +122,7 @@ def run(self) -> list[dict]: # --- Resolve LLM backend and vLLM URLs --- backend, base_url = self._resolve_llm_backend() server_mgr = None + self._render_platform = None try: if backend in ("vllm", "custom") and base_url is None: From 537c5df39069feb93daceea2bfaef19f622a0dbc Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Wed, 11 Mar 2026 14:01:11 +0800 Subject: [PATCH 198/239] feat: add CoppeliaSimXorgPlatform for GPU-accelerated rendering --- easi/simulators/coppeliasim/manifest.yaml | 1 + .../coppeliasim/v4_1_0/render_platforms.py | 58 +++- tests/test_render_platform.py | 318 +++++++++++++----- 3 files changed, 280 insertions(+), 97 deletions(-) diff --git a/easi/simulators/coppeliasim/manifest.yaml b/easi/simulators/coppeliasim/manifest.yaml index fbb57f2..ae4f3e5 100644 --- a/easi/simulators/coppeliasim/manifest.yaml +++ b/easi/simulators/coppeliasim/manifest.yaml @@ -11,6 +11,7 @@ versions: auto: "easi.simulators.coppeliasim.v4_1_0.render_platforms.CoppeliaSimAutoPlatform" native: "easi.simulators.coppeliasim.v4_1_0.render_platforms.CoppeliaSimNativePlatform" xvfb: "easi.simulators.coppeliasim.v4_1_0.render_platforms.CoppeliaSimXvfbPlatform" + xorg: "easi.simulators.coppeliasim.v4_1_0.render_platforms.CoppeliaSimXorgPlatform" installation_kwargs: binary_url: "https://downloads.coppeliarobotics.com/V4_1_0/CoppeliaSim_Pro_V4_1_0_Ubuntu20_04.tar.xz" binary_filename: "CoppeliaSim_Pro_V4_1_0_Ubuntu20_04.tar.xz" diff --git a/easi/simulators/coppeliasim/v4_1_0/render_platforms.py b/easi/simulators/coppeliasim/v4_1_0/render_platforms.py index 2d7e802..1d622d9 100644 --- a/easi/simulators/coppeliasim/v4_1_0/render_platforms.py +++ b/easi/simulators/coppeliasim/v4_1_0/render_platforms.py @@ -16,16 +16,21 @@ import os from pathlib import Path -from easi.core.render_platform import ( +from easi.core.render_platforms import ( AutoPlatform, EnvVars, NativePlatform, + XorgPlatform, XvfbPlatform, + _XorgWorkerPlatform, ) def _coppeliasim_qt_env_vars( - env_manager, *, include_mesa_egl: bool = False, headless: bool = True, + env_manager, + *, + include_mesa_egl: bool = False, + headless: bool = True, ) -> EnvVars: """Compute CoppeliaSim Qt env vars. @@ -73,7 +78,9 @@ def name(self) -> str: def get_env_vars(self) -> EnvVars: return _coppeliasim_qt_env_vars( - self._env_manager, include_mesa_egl=False, headless=False, + self._env_manager, + include_mesa_egl=False, + headless=False, ) @@ -90,7 +97,9 @@ def name(self) -> str: def get_env_vars(self) -> EnvVars: return _coppeliasim_qt_env_vars( - self._env_manager, include_mesa_egl=True, headless=True, + self._env_manager, + include_mesa_egl=True, + headless=True, ) @@ -112,3 +121,44 @@ def get_env_vars(self) -> EnvVars: include_mesa_egl=not has_display, headless=not has_display, ) + + +class _CoppeliaSimXorgWorkerPlatform(_XorgWorkerPlatform): + """Per-worker Xorg platform with CoppeliaSim Qt env vars merged in.""" + + def __init__(self, display_num: int, gpu_id: int, coppeliasim_env: EnvVars): + super().__init__(display_num=display_num, gpu_id=gpu_id) + self._coppeliasim_env = coppeliasim_env + + def get_env_vars(self) -> EnvVars: + return EnvVars.merge(super().get_env_vars(), self._coppeliasim_env) + + +class CoppeliaSimXorgPlatform(XorgPlatform): + """Xorg platform for CoppeliaSim — Qt plugin path, no Mesa, headless=False. + + Xorg provides a real GPU-accelerated X11 display, so CoppeliaSim renders + with its GUI library (not headless). Per-worker instances include the + CoppeliaSim Qt env vars. + """ + + @property + def name(self) -> str: + return "xorg" + + def for_worker(self, worker_id: int) -> _CoppeliaSimXorgWorkerPlatform: + if not self._instances: + raise RuntimeError( + "XorgPlatform.setup() must be called before for_worker()" + ) + inst = self._instances[worker_id % len(self._instances)] + coppeliasim_env = _coppeliasim_qt_env_vars( + self._env_manager, + include_mesa_egl=False, + headless=False, + ) + return _CoppeliaSimXorgWorkerPlatform( + display_num=inst.display, + gpu_id=inst.gpu_id, + coppeliasim_env=coppeliasim_env, + ) diff --git a/tests/test_render_platform.py b/tests/test_render_platform.py index 7bc0557..d792d99 100644 --- a/tests/test_render_platform.py +++ b/tests/test_render_platform.py @@ -13,28 +13,28 @@ class TestEnvVarsDataclass: """Test the EnvVars dataclass.""" def test_empty_is_falsy(self): - from easi.core.render_platform import EnvVars + from easi.core.render_platforms import EnvVars assert not EnvVars() def test_replace_is_truthy(self): - from easi.core.render_platform import EnvVars + from easi.core.render_platforms import EnvVars assert EnvVars(replace={"FOO": "bar"}) def test_prepend_is_truthy(self): - from easi.core.render_platform import EnvVars + from easi.core.render_platforms import EnvVars assert EnvVars(prepend={"PATH": "/extra"}) def test_to_flat_dict(self): - from easi.core.render_platform import EnvVars + from easi.core.render_platforms import EnvVars ev = EnvVars(replace={"A": "1"}, prepend={"B": "2"}) assert ev.to_flat_dict() == {"A": "1", "B": "2"} def test_apply_to_env_replace(self): - from easi.core.render_platform import EnvVars + from easi.core.render_platforms import EnvVars ev = EnvVars(replace={"FOO": "new"}) result = ev.apply_to_env({"FOO": "old", "BAR": "keep"}) @@ -42,21 +42,21 @@ def test_apply_to_env_replace(self): assert result["BAR"] == "keep" def test_apply_to_env_prepend_existing(self): - from easi.core.render_platform import EnvVars + from easi.core.render_platforms import EnvVars ev = EnvVars(prepend={"PATH": "/new"}) result = ev.apply_to_env({"PATH": "/old"}) assert result["PATH"] == "/new:/old" def test_apply_to_env_prepend_missing(self): - from easi.core.render_platform import EnvVars + from easi.core.render_platforms import EnvVars ev = EnvVars(prepend={"LD_LIBRARY_PATH": "/lib"}) result = ev.apply_to_env({}) assert result["LD_LIBRARY_PATH"] == "/lib" def test_merge_replace_later_wins(self): - from easi.core.render_platform import EnvVars + from easi.core.render_platforms import EnvVars a = EnvVars(replace={"K": "a"}) b = EnvVars(replace={"K": "b"}) @@ -64,7 +64,7 @@ def test_merge_replace_later_wins(self): assert merged.replace["K"] == "b" def test_merge_prepend_concatenates(self): - from easi.core.render_platform import EnvVars + from easi.core.render_platforms import EnvVars a = EnvVars(prepend={"PATH": "/a"}) b = EnvVars(prepend={"PATH": "/b"}) @@ -72,7 +72,7 @@ def test_merge_prepend_concatenates(self): assert merged.prepend["PATH"] == "/b:/a" def test_merge_mixed(self): - from easi.core.render_platform import EnvVars + from easi.core.render_platforms import EnvVars a = EnvVars(replace={"ROOT": "/opt"}, prepend={"PATH": "/a/bin"}) b = EnvVars(replace={"HOME": "/home"}, prepend={"PATH": "/b/bin"}) @@ -85,43 +85,43 @@ class TestRenderPlatformRegistry: """Test platform discovery and instantiation.""" def test_get_platform_returns_auto(self): - from easi.core.render_platform import get_render_platform + from easi.core.render_platforms import get_render_platform platform = get_render_platform("auto") assert platform.name == "auto" def test_get_platform_returns_xvfb(self): - from easi.core.render_platform import get_render_platform + from easi.core.render_platforms import get_render_platform platform = get_render_platform("xvfb") assert platform.name == "xvfb" def test_get_platform_returns_native(self): - from easi.core.render_platform import get_render_platform + from easi.core.render_platforms import get_render_platform platform = get_render_platform("native") assert platform.name == "native" def test_get_platform_returns_egl(self): - from easi.core.render_platform import get_render_platform + from easi.core.render_platforms import get_render_platform platform = get_render_platform("egl") assert platform.name == "egl" def test_get_platform_returns_headless(self): - from easi.core.render_platform import get_render_platform + from easi.core.render_platforms import get_render_platform platform = get_render_platform("headless") assert platform.name == "headless" def test_get_platform_unknown_raises(self): - from easi.core.render_platform import get_render_platform + from easi.core.render_platforms import get_render_platform with pytest.raises(ValueError, match="Unknown render platform"): get_render_platform("nonexistent") def test_available_platforms_returns_names(self): - from easi.core.render_platform import available_platforms + from easi.core.render_platforms import available_platforms names = available_platforms() assert set(names) >= {"auto", "native", "xvfb", "egl", "headless"} @@ -131,14 +131,14 @@ class TestHeadlessPlatform: """Headless: no wrapping, no env vars.""" def test_wrap_command_passthrough(self): - from easi.core.render_platform import get_render_platform + from easi.core.render_platforms import get_render_platform p = get_render_platform("headless") cmd = ["python", "bridge.py"] assert p.wrap_command(cmd, "1024x768x24") == cmd def test_get_env_vars_empty(self): - from easi.core.render_platform import EnvVars, get_render_platform + from easi.core.render_platforms import EnvVars, get_render_platform p = get_render_platform("headless") ev = p.get_env_vars() @@ -146,7 +146,7 @@ def test_get_env_vars_empty(self): assert not ev def test_get_system_deps_empty(self): - from easi.core.render_platform import get_render_platform + from easi.core.render_platforms import get_render_platform p = get_render_platform("headless") assert p.get_system_deps() == [] @@ -156,7 +156,7 @@ class TestXvfbPlatform: """Xvfb: always wraps with xvfb-run.""" def test_wrap_command_prepends_xvfb_run(self): - from easi.core.render_platform import get_render_platform + from easi.core.render_platforms import get_render_platform p = get_render_platform("xvfb") cmd = ["python", "bridge.py"] @@ -166,7 +166,7 @@ def test_wrap_command_prepends_xvfb_run(self): assert wrapped[-2:] == cmd def test_get_env_vars_empty(self): - from easi.core.render_platform import EnvVars, get_render_platform + from easi.core.render_platforms import EnvVars, get_render_platform p = get_render_platform("xvfb") ev = p.get_env_vars() @@ -174,7 +174,7 @@ def test_get_env_vars_empty(self): assert not ev def test_get_system_deps_includes_xvfb(self): - from easi.core.render_platform import get_render_platform + from easi.core.render_platforms import get_render_platform p = get_render_platform("xvfb") assert "xvfb" in p.get_system_deps() @@ -184,14 +184,14 @@ class TestNativePlatform: """Native: passthrough, requires DISPLAY.""" def test_wrap_command_passthrough(self): - from easi.core.render_platform import get_render_platform + from easi.core.render_platforms import get_render_platform p = get_render_platform("native") cmd = ["python", "bridge.py"] assert p.wrap_command(cmd, "1024x768x24") == cmd def test_get_env_vars_empty(self): - from easi.core.render_platform import EnvVars, get_render_platform + from easi.core.render_platforms import EnvVars, get_render_platform p = get_render_platform("native") ev = p.get_env_vars() @@ -199,14 +199,14 @@ def test_get_env_vars_empty(self): assert not ev def test_is_available_true_when_display_set(self): - from easi.core.render_platform import get_render_platform + from easi.core.render_platforms import get_render_platform p = get_render_platform("native") with patch.dict(os.environ, {"DISPLAY": ":0"}): assert p.is_available() is True def test_is_available_false_when_no_display(self): - from easi.core.render_platform import get_render_platform + from easi.core.render_platforms import get_render_platform p = get_render_platform("native") with patch.dict(os.environ, {}, clear=True): @@ -217,14 +217,14 @@ class TestEGLPlatform: """EGL: no wrapping, sets PYOPENGL_PLATFORM.""" def test_wrap_command_passthrough(self): - from easi.core.render_platform import get_render_platform + from easi.core.render_platforms import get_render_platform p = get_render_platform("egl") cmd = ["python", "bridge.py"] assert p.wrap_command(cmd, "1024x768x24") == cmd def test_get_env_vars_sets_pyopengl(self): - from easi.core.render_platform import EnvVars, get_render_platform + from easi.core.render_platforms import EnvVars, get_render_platform p = get_render_platform("egl") ev = p.get_env_vars() @@ -232,7 +232,7 @@ def test_get_env_vars_sets_pyopengl(self): assert ev.replace["PYOPENGL_PLATFORM"] == "egl" def test_get_system_deps_includes_egl(self): - from easi.core.render_platform import get_render_platform + from easi.core.render_platforms import get_render_platform p = get_render_platform("egl") assert "egl" in p.get_system_deps() @@ -242,7 +242,7 @@ class TestAutoPlatform: """Auto: native if DISPLAY exists, xvfb fallback otherwise.""" def test_wrap_command_uses_native_when_display_set(self): - from easi.core.render_platform import get_render_platform + from easi.core.render_platforms import get_render_platform p = get_render_platform("auto") cmd = ["python", "bridge.py"] @@ -250,7 +250,7 @@ def test_wrap_command_uses_native_when_display_set(self): assert p.wrap_command(cmd, "1024x768x24") == cmd def test_wrap_command_uses_xvfb_when_no_display(self): - from easi.core.render_platform import get_render_platform + from easi.core.render_platforms import get_render_platform p = get_render_platform("auto") cmd = ["python", "bridge.py"] @@ -261,7 +261,7 @@ def test_wrap_command_uses_xvfb_when_no_display(self): assert wrapped[0] == "xvfb-run" def test_get_system_deps_empty(self): - from easi.core.render_platform import get_render_platform + from easi.core.render_platforms import get_render_platform p = get_render_platform("auto") assert p.get_system_deps() == [] @@ -299,7 +299,7 @@ def test_screen_config_default(self): assert mgr.screen_config == "1024x768x24" def test_get_env_vars_returns_envvars(self): - from easi.core.render_platform import EnvVars + from easi.core.render_platforms import EnvVars mgr = self._make_stub() ev = mgr.get_env_vars() @@ -311,7 +311,7 @@ class TestSubprocessRunnerRenderPlatform: """Verify SubprocessRunner uses RenderPlatform for command wrapping.""" def test_accepts_render_platform_param(self): - from easi.core.render_platform import get_render_platform + from easi.core.render_platforms import get_render_platform from easi.simulators.subprocess_runner import SubprocessRunner p = get_render_platform("headless") @@ -323,7 +323,7 @@ def test_accepts_render_platform_param(self): assert runner.render_platform.name == "headless" def test_build_command_uses_platform_wrap(self): - from easi.core.render_platform import get_render_platform + from easi.core.render_platforms import get_render_platform from easi.simulators.subprocess_runner import SubprocessRunner p = get_render_platform("xvfb") @@ -339,7 +339,7 @@ def test_build_command_uses_platform_wrap(self): assert "1280x720x24" in cmd[3] def test_build_command_headless_no_wrap(self): - from easi.core.render_platform import get_render_platform + from easi.core.render_platforms import get_render_platform from easi.simulators.subprocess_runner import SubprocessRunner p = get_render_platform("headless") @@ -353,7 +353,7 @@ def test_build_command_headless_no_wrap(self): assert cmd[0] == "/usr/bin/python3" def test_platform_env_vars_merged(self): - from easi.core.render_platform import EnvVars, get_render_platform + from easi.core.render_platforms import EnvVars, get_render_platform from easi.simulators.subprocess_runner import SubprocessRunner p = get_render_platform("egl") @@ -368,7 +368,7 @@ def test_platform_env_vars_merged(self): assert env["SIM_ROOT"] == "/opt/sim" def test_no_env_vars_returns_none(self): - from easi.core.render_platform import get_render_platform + from easi.core.render_platforms import get_render_platform from easi.simulators.subprocess_runner import SubprocessRunner p = get_render_platform("headless") @@ -406,7 +406,9 @@ def test_habitat(self): assert "egl" in mgr.supported_render_platforms def test_coppeliasim(self): - from easi.simulators.coppeliasim.v4_1_0.env_manager import CoppeliaSimEnvManagerV410 + from easi.simulators.coppeliasim.v4_1_0.env_manager import ( + CoppeliaSimEnvManagerV410, + ) mgr = CoppeliaSimEnvManagerV410() assert mgr.default_render_platform == "auto" @@ -417,9 +419,13 @@ def test_coppeliasim_env_vars_are_platform_agnostic(self): Those platform-specific vars are now handled by custom render platform classes. """ - from easi.simulators.coppeliasim.v4_1_0.env_manager import CoppeliaSimEnvManagerV410 + from easi.simulators.coppeliasim.v4_1_0.env_manager import ( + CoppeliaSimEnvManagerV410, + ) - mgr = CoppeliaSimEnvManagerV410(installation_kwargs={"binary_dir_name": "CoppeliaSim"}) + mgr = CoppeliaSimEnvManagerV410( + installation_kwargs={"binary_dir_name": "CoppeliaSim"} + ) ev = mgr.get_env_vars() all_keys = set(ev.replace) | set(ev.prepend) assert "QT_QPA_PLATFORM_PLUGIN_PATH" not in all_keys @@ -465,7 +471,7 @@ def test_simulator_entry_has_custom_render_platforms(self): } def test_resolve_falls_back_to_builtin(self): - from easi.core.render_platform import HeadlessPlatform + from easi.core.render_platforms import HeadlessPlatform from easi.simulators.registry import resolve_render_platform platform = resolve_render_platform("dummy", "headless") @@ -491,8 +497,11 @@ def test_resolve_name_mismatch_raises(self): from easi.simulators.registry import SimulatorEntry fake_entry = SimulatorEntry( - name="fake", version="v1", description="", - simulator_class="", env_manager_class="", + name="fake", + version="v1", + description="", + simulator_class="", + env_manager_class="", python_version="3.10", render_platforms={ "wrong_name": "easi.simulators.dummy.v1.render_platforms.DummyCustomPlatform" @@ -504,15 +513,18 @@ def test_resolve_name_mismatch_raises(self): def test_custom_shadows_builtin(self): """A custom 'headless' class should be returned instead of the built-in.""" - from easi.core.render_platform import HeadlessPlatform + from easi.core.render_platforms import HeadlessPlatform from easi.simulators.registry import resolve_render_platform with patch("easi.simulators.registry._get_registry") as mock_reg: from easi.simulators.registry import SimulatorEntry fake_entry = SimulatorEntry( - name="fake", version="v1", description="", - simulator_class="", env_manager_class="", + name="fake", + version="v1", + description="", + simulator_class="", + env_manager_class="", python_version="3.10", render_platforms={ "headless": "easi.simulators.dummy.v1.render_platforms.DummyCustomPlatform" @@ -524,13 +536,15 @@ def test_custom_shadows_builtin(self): # To truly shadow, we need a class whose name IS "headless". # Let's test the lookup path: if a custom class is found, it's used. # We'll patch _import_class to return a class with name "headless". - from easi.core.render_platform import EnvVars + from easi.core.render_platforms import EnvVars class CustomHeadless(HeadlessPlatform): def get_env_vars(self): return EnvVars(replace={"CUSTOM": "1"}) - with patch("easi.simulators.registry._import_class", return_value=CustomHeadless): + with patch( + "easi.simulators.registry._import_class", return_value=CustomHeadless + ): platform = resolve_render_platform("fake:v1", "headless") assert isinstance(platform, CustomHeadless) assert platform.name == "headless" @@ -633,6 +647,7 @@ def test_manifest_registers_custom_platforms(self): assert "auto" in entry.render_platforms assert "native" in entry.render_platforms assert "xvfb" in entry.render_platforms + assert "xorg" in entry.render_platforms def test_resolve_coppeliasim_custom_platform(self): from easi.simulators.coppeliasim.v4_1_0.render_platforms import ( @@ -644,6 +659,48 @@ def test_resolve_coppeliasim_custom_platform(self): assert isinstance(platform, CoppeliaSimAutoPlatform) assert platform.name == "auto" + def test_xorg_has_qt_plugin_path_and_headless_false(self): + from easi.core.render_platforms.xorg_manager import XorgInstance + from easi.simulators.coppeliasim.v4_1_0.render_platforms import ( + CoppeliaSimXorgPlatform, + ) + + mgr = self._make_mock_env_manager() + p = CoppeliaSimXorgPlatform(env_manager=mgr) + assert p.name == "xorg" + p._instances = [XorgInstance(display=10, gpu_id=0, pid=1)] + worker = p.for_worker(0) + ev = worker.get_env_vars() + assert "QT_QPA_PLATFORM_PLUGIN_PATH" in ev.prepend + assert "CoppeliaSim" in ev.prepend["QT_QPA_PLATFORM_PLUGIN_PATH"] + assert ev.replace["COPPELIASIM_HEADLESS"] == "0" + assert ev.replace["DISPLAY"] == ":10" + assert ev.replace["CUDA_VISIBLE_DEVICES"] == "0" + + def test_xorg_no_env_manager_returns_base_env_only(self): + from easi.core.render_platforms.xorg_manager import XorgInstance + from easi.simulators.coppeliasim.v4_1_0.render_platforms import ( + CoppeliaSimXorgPlatform, + ) + + p = CoppeliaSimXorgPlatform() + p._instances = [XorgInstance(display=11, gpu_id=2, pid=1)] + worker = p.for_worker(0) + ev = worker.get_env_vars() + assert ev.replace["DISPLAY"] == ":11" + assert ev.replace["CUDA_VISIBLE_DEVICES"] == "2" + assert "QT_QPA_PLATFORM_PLUGIN_PATH" not in ev.prepend + + def test_xorg_resolve_from_registry(self): + from easi.simulators.coppeliasim.v4_1_0.render_platforms import ( + CoppeliaSimXorgPlatform, + ) + from easi.simulators.registry import resolve_render_platform + + platform = resolve_render_platform("coppeliasim:v4_1_0", "xorg") + assert isinstance(platform, CoppeliaSimXorgPlatform) + assert platform.name == "xorg" + from unittest.mock import MagicMock @@ -677,7 +734,7 @@ class TestRunnerRenderPlatformWiring: """Verify EvaluationRunner resolves and passes render platform.""" def _make_mock_env_mgr(self): - from easi.core.render_platform import EnvVars + from easi.core.render_platforms import EnvVars mgr = MagicMock() mgr.env_is_ready.return_value = True @@ -700,7 +757,7 @@ def _make_mock_task(self, render_platform=None): return task def test_default_uses_env_manager_platform(self): - from easi.core.render_platform import get_render_platform + from easi.core.render_platforms import get_render_platform from easi.evaluation.runner import EvaluationRunner runner = EvaluationRunner.__new__(EvaluationRunner) @@ -711,24 +768,39 @@ def test_default_uses_env_manager_platform(self): mock_env_mgr = self._make_mock_env_mgr() mock_sim_cls = MagicMock() - mock_sim_cls.return_value._get_bridge_script_path.return_value = Path("/fake/bridge.py") + mock_sim_cls.return_value._get_bridge_script_path.return_value = Path( + "/fake/bridge.py" + ) mock_entry = MagicMock() mock_entry.runtime = "conda" - with patch("easi.simulators.registry.get_simulator_entry", return_value=mock_entry), \ - patch("easi.simulators.registry.create_env_manager", return_value=mock_env_mgr), \ - patch("easi.simulators.registry.load_simulator_class", return_value=mock_sim_cls), \ - patch("easi.simulators.registry.resolve_render_platform", - side_effect=lambda key, name, env_manager=None: get_render_platform(name)), \ - patch("easi.simulators.subprocess_runner.SubprocessRunner") as MockRunner: + with ( + patch( + "easi.simulators.registry.get_simulator_entry", return_value=mock_entry + ), + patch( + "easi.simulators.registry.create_env_manager", return_value=mock_env_mgr + ), + patch( + "easi.simulators.registry.load_simulator_class", + return_value=mock_sim_cls, + ), + patch( + "easi.simulators.registry.resolve_render_platform", + side_effect=lambda key, name, env_manager=None: get_render_platform( + name + ), + ), + patch("easi.simulators.subprocess_runner.SubprocessRunner") as MockRunner, + ): MockRunner.return_value.launch.return_value = None runner._create_simulator("fake:v1") rp = MockRunner.call_args.kwargs.get("render_platform") assert rp.name == "auto" def test_cli_override_wins(self): - from easi.core.render_platform import get_render_platform + from easi.core.render_platforms import get_render_platform from easi.evaluation.runner import EvaluationRunner runner = EvaluationRunner.__new__(EvaluationRunner) @@ -739,24 +811,39 @@ def test_cli_override_wins(self): mock_env_mgr = self._make_mock_env_mgr() mock_sim_cls = MagicMock() - mock_sim_cls.return_value._get_bridge_script_path.return_value = Path("/fake/bridge.py") + mock_sim_cls.return_value._get_bridge_script_path.return_value = Path( + "/fake/bridge.py" + ) mock_entry = MagicMock() mock_entry.runtime = "conda" - with patch("easi.simulators.registry.get_simulator_entry", return_value=mock_entry), \ - patch("easi.simulators.registry.create_env_manager", return_value=mock_env_mgr), \ - patch("easi.simulators.registry.load_simulator_class", return_value=mock_sim_cls), \ - patch("easi.simulators.registry.resolve_render_platform", - side_effect=lambda key, name, env_manager=None: get_render_platform(name)), \ - patch("easi.simulators.subprocess_runner.SubprocessRunner") as MockRunner: + with ( + patch( + "easi.simulators.registry.get_simulator_entry", return_value=mock_entry + ), + patch( + "easi.simulators.registry.create_env_manager", return_value=mock_env_mgr + ), + patch( + "easi.simulators.registry.load_simulator_class", + return_value=mock_sim_cls, + ), + patch( + "easi.simulators.registry.resolve_render_platform", + side_effect=lambda key, name, env_manager=None: get_render_platform( + name + ), + ), + patch("easi.simulators.subprocess_runner.SubprocessRunner") as MockRunner, + ): MockRunner.return_value.launch.return_value = None runner._create_simulator("fake:v1") rp = MockRunner.call_args.kwargs.get("render_platform") assert rp.name == "xvfb" def test_yaml_override_used_when_no_cli(self): - from easi.core.render_platform import get_render_platform + from easi.core.render_platforms import get_render_platform from easi.evaluation.runner import EvaluationRunner runner = EvaluationRunner.__new__(EvaluationRunner) @@ -768,24 +855,39 @@ def test_yaml_override_used_when_no_cli(self): mock_env_mgr = self._make_mock_env_mgr() mock_task = self._make_mock_task(render_platform="egl") mock_sim_cls = MagicMock() - mock_sim_cls.return_value._get_bridge_script_path.return_value = Path("/fake/bridge.py") + mock_sim_cls.return_value._get_bridge_script_path.return_value = Path( + "/fake/bridge.py" + ) mock_entry = MagicMock() mock_entry.runtime = "conda" - with patch("easi.simulators.registry.get_simulator_entry", return_value=mock_entry), \ - patch("easi.simulators.registry.create_env_manager", return_value=mock_env_mgr), \ - patch("easi.simulators.registry.load_simulator_class", return_value=mock_sim_cls), \ - patch("easi.simulators.registry.resolve_render_platform", - side_effect=lambda key, name, env_manager=None: get_render_platform(name)), \ - patch("easi.simulators.subprocess_runner.SubprocessRunner") as MockRunner: + with ( + patch( + "easi.simulators.registry.get_simulator_entry", return_value=mock_entry + ), + patch( + "easi.simulators.registry.create_env_manager", return_value=mock_env_mgr + ), + patch( + "easi.simulators.registry.load_simulator_class", + return_value=mock_sim_cls, + ), + patch( + "easi.simulators.registry.resolve_render_platform", + side_effect=lambda key, name, env_manager=None: get_render_platform( + name + ), + ), + patch("easi.simulators.subprocess_runner.SubprocessRunner") as MockRunner, + ): MockRunner.return_value.launch.return_value = None runner._create_simulator("fake:v1", task=mock_task) rp = MockRunner.call_args.kwargs.get("render_platform") assert rp.name == "egl" def test_cli_beats_yaml(self): - from easi.core.render_platform import get_render_platform + from easi.core.render_platforms import get_render_platform from easi.evaluation.runner import EvaluationRunner runner = EvaluationRunner.__new__(EvaluationRunner) @@ -797,24 +899,39 @@ def test_cli_beats_yaml(self): mock_env_mgr = self._make_mock_env_mgr() mock_task = self._make_mock_task(render_platform="egl") mock_sim_cls = MagicMock() - mock_sim_cls.return_value._get_bridge_script_path.return_value = Path("/fake/bridge.py") + mock_sim_cls.return_value._get_bridge_script_path.return_value = Path( + "/fake/bridge.py" + ) mock_entry = MagicMock() mock_entry.runtime = "conda" - with patch("easi.simulators.registry.get_simulator_entry", return_value=mock_entry), \ - patch("easi.simulators.registry.create_env_manager", return_value=mock_env_mgr), \ - patch("easi.simulators.registry.load_simulator_class", return_value=mock_sim_cls), \ - patch("easi.simulators.registry.resolve_render_platform", - side_effect=lambda key, name, env_manager=None: get_render_platform(name)), \ - patch("easi.simulators.subprocess_runner.SubprocessRunner") as MockRunner: + with ( + patch( + "easi.simulators.registry.get_simulator_entry", return_value=mock_entry + ), + patch( + "easi.simulators.registry.create_env_manager", return_value=mock_env_mgr + ), + patch( + "easi.simulators.registry.load_simulator_class", + return_value=mock_sim_cls, + ), + patch( + "easi.simulators.registry.resolve_render_platform", + side_effect=lambda key, name, env_manager=None: get_render_platform( + name + ), + ), + patch("easi.simulators.subprocess_runner.SubprocessRunner") as MockRunner, + ): MockRunner.return_value.launch.return_value = None runner._create_simulator("fake:v1", task=mock_task) rp = MockRunner.call_args.kwargs.get("render_platform") assert rp.name == "xvfb" def test_unsupported_platform_raises(self): - from easi.core.render_platform import get_render_platform + from easi.core.render_platforms import get_render_platform from easi.evaluation.runner import EvaluationRunner runner = EvaluationRunner.__new__(EvaluationRunner) @@ -826,17 +943,32 @@ def test_unsupported_platform_raises(self): mock_env_mgr = self._make_mock_env_mgr() mock_env_mgr.supported_render_platforms = ["auto", "xvfb"] # no egl mock_sim_cls = MagicMock() - mock_sim_cls.return_value._get_bridge_script_path.return_value = Path("/fake/bridge.py") + mock_sim_cls.return_value._get_bridge_script_path.return_value = Path( + "/fake/bridge.py" + ) mock_entry = MagicMock() mock_entry.runtime = "conda" - with patch("easi.simulators.registry.get_simulator_entry", return_value=mock_entry), \ - patch("easi.simulators.registry.create_env_manager", return_value=mock_env_mgr), \ - patch("easi.simulators.registry.load_simulator_class", return_value=mock_sim_cls), \ - patch("easi.simulators.registry.resolve_render_platform", - side_effect=lambda key, name, env_manager=None: get_render_platform(name)), \ - pytest.raises(ValueError, match="not supported"): + with ( + patch( + "easi.simulators.registry.get_simulator_entry", return_value=mock_entry + ), + patch( + "easi.simulators.registry.create_env_manager", return_value=mock_env_mgr + ), + patch( + "easi.simulators.registry.load_simulator_class", + return_value=mock_sim_cls, + ), + patch( + "easi.simulators.registry.resolve_render_platform", + side_effect=lambda key, name, env_manager=None: get_render_platform( + name + ), + ), + pytest.raises(ValueError, match="not supported"), + ): runner._create_simulator("fake:v1") @@ -844,7 +976,7 @@ class TestRenderPlatformEndToEnd: """End-to-end: platform flows through runner to subprocess.""" def test_egl_env_vars_in_subprocess(self): - from easi.core.render_platform import EnvVars, get_render_platform + from easi.core.render_platforms import EnvVars, get_render_platform from easi.simulators.subprocess_runner import SubprocessRunner platform = get_render_platform("egl") @@ -859,7 +991,7 @@ def test_egl_env_vars_in_subprocess(self): assert env["SIM_ROOT"] == "/opt/sim" def test_auto_wraps_xvfb_when_no_display(self): - from easi.core.render_platform import get_render_platform + from easi.core.render_platforms import get_render_platform from easi.simulators.subprocess_runner import SubprocessRunner platform = get_render_platform("auto") From 869b00981bfcbdaede7bb9d282c36a71f0d28b1e Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Wed, 11 Mar 2026 14:01:58 +0800 Subject: [PATCH 199/239] feat: add xorg render platform support to sim test CLI --- easi/cli.py | 441 ++++++++++++++++++++++++++++++++++------------ tests/test_cli.py | 95 ++++++++-- 2 files changed, 410 insertions(+), 126 deletions(-) diff --git a/easi/cli.py b/easi/cli.py index 030c6d0..0a4993e 100644 --- a/easi/cli.py +++ b/easi/cli.py @@ -20,7 +20,9 @@ def build_parser() -> argparse.ArgumentParser: # Shared parent so --verbosity works at any position in the command common = argparse.ArgumentParser(add_help=False) common.add_argument( - "--verbosity", type=str, default="INFO", + "--verbosity", + type=str, + default="INFO", choices=["TRACE", "DEBUG", "INFO", "WARNING", "ERROR"], help="Set logging verbosity (default: INFO)", ) @@ -34,23 +36,43 @@ def build_parser() -> argparse.ArgumentParser: subparsers = parser.add_subparsers(dest="command") # --- env command group --- - env_parser = subparsers.add_parser("env", help="Manage simulator environments", parents=[common]) + env_parser = subparsers.add_parser( + "env", help="Manage simulator environments", parents=[common] + ) env_sub = env_parser.add_subparsers(dest="env_action") - env_sub.add_parser("list", help="List available simulators and versions", parents=[common]) + env_sub.add_parser( + "list", help="List available simulators and versions", parents=[common] + ) - env_install = env_sub.add_parser("install", help="Install a simulator environment", parents=[common]) - env_install.add_argument("simulator", type=str, help="e.g., 'dummy' or 'ai2thor:v2_1_0'") - env_install.add_argument("--reinstall", action="store_true", - help="Remove existing env and install from scratch") - env_install.add_argument("--with-task-deps", type=str, default=None, metavar="TASK", - help="Also install additional_deps from a task (e.g., 'ebalfred_base')") + env_install = env_sub.add_parser( + "install", help="Install a simulator environment", parents=[common] + ) + env_install.add_argument( + "simulator", type=str, help="e.g., 'dummy' or 'ai2thor:v2_1_0'" + ) + env_install.add_argument( + "--reinstall", + action="store_true", + help="Remove existing env and install from scratch", + ) + env_install.add_argument( + "--with-task-deps", + type=str, + default=None, + metavar="TASK", + help="Also install additional_deps from a task (e.g., 'ebalfred_base')", + ) - env_check = env_sub.add_parser("check", help="Check if environment is ready", parents=[common]) + env_check = env_sub.add_parser( + "check", help="Check if environment is ready", parents=[common] + ) env_check.add_argument("simulator", type=str) # --- task command group --- - task_parser = subparsers.add_parser("task", help="Manage tasks (benchmarks)", parents=[common]) + task_parser = subparsers.add_parser( + "task", help="Manage tasks (benchmarks)", parents=[common] + ) task_sub = task_parser.add_subparsers(dest="task_action") task_sub.add_parser("list", help="List available tasks", parents=[common]) @@ -58,103 +80,223 @@ def build_parser() -> argparse.ArgumentParser: task_info = task_sub.add_parser("info", help="Show task details", parents=[common]) task_info.add_argument("task", type=str, help="e.g., 'dummy_task'") - task_download = task_sub.add_parser("download", help="Download task dataset", parents=[common]) + task_download = task_sub.add_parser( + "download", help="Download task dataset", parents=[common] + ) task_download.add_argument("task", type=str) - task_download.add_argument("--refresh-data", action="store_true", dest="refresh_data", - help="Delete cached dataset and re-download from source") + task_download.add_argument( + "--refresh-data", + action="store_true", + dest="refresh_data", + help="Delete cached dataset and re-download from source", + ) - task_scaffold = task_sub.add_parser("scaffold", help="Generate boilerplate for a new benchmark", parents=[common]) - task_scaffold.add_argument("name", type=str, help="Task name in snake_case (e.g., 'my_benchmark')") - task_scaffold.add_argument("--simulator", type=str, default="dummy:v1", - help="Simulator key (e.g., 'ai2thor:v2_1_0')") + task_scaffold = task_sub.add_parser( + "scaffold", help="Generate boilerplate for a new benchmark", parents=[common] + ) + task_scaffold.add_argument( + "name", type=str, help="Task name in snake_case (e.g., 'my_benchmark')" + ) + task_scaffold.add_argument( + "--simulator", + type=str, + default="dummy:v1", + help="Simulator key (e.g., 'ai2thor:v2_1_0')", + ) task_scaffold.add_argument("--max-steps", type=int, default=50) # --- sim command group --- - sim_parser = subparsers.add_parser("sim", help="Control simulators", parents=[common]) + sim_parser = subparsers.add_parser( + "sim", help="Control simulators", parents=[common] + ) sim_sub = sim_parser.add_subparsers(dest="sim_action") - sim_test = sim_sub.add_parser("test", help="Run a smoke test (reset + N steps)", parents=[common]) - sim_test.add_argument("simulator", type=str, help="e.g., 'dummy' or 'ai2thor:v5_0_0'") + sim_test = sim_sub.add_parser( + "test", help="Run a smoke test (reset + N steps)", parents=[common] + ) + sim_test.add_argument( + "simulator", type=str, help="e.g., 'dummy' or 'ai2thor:v5_0_0'" + ) sim_test.add_argument("--steps", type=int, default=5, help="Number of steps") - sim_test.add_argument("--timeout", type=float, default=200.0, - help="Bridge startup timeout in seconds (default: 200)") sim_test.add_argument( - "--render-platform", type=str, default=None, dest="render_platform", - help="Rendering platform override (auto, native, xvfb, egl, headless, xorg)") + "--timeout", + type=float, + default=200.0, + help="Bridge startup timeout in seconds (default: 200)", + ) + sim_test.add_argument( + "--render-platform", + type=str, + default=None, + dest="render_platform", + help="Rendering platform override (auto, native, xvfb, egl, headless, xorg)", + ) + sim_test.add_argument( + "--sim-gpus", + type=str, + default=None, + dest="sim_gpus", + help="Comma-separated GPU IDs for xorg render platform (e.g., '0' or '1,2'). Defaults to GPU 0.", + ) # --- start command --- - start_parser = subparsers.add_parser("start", help="Run a full evaluation", parents=[common]) + start_parser = subparsers.add_parser( + "start", help="Run a full evaluation", parents=[common] + ) # All defaults are None so resume logic can distinguish "user provided" from "default". # Real defaults live in EvaluationRunner.__init__. - start_parser.add_argument("task_names_positional", type=str, nargs="*", default=None, metavar="task", - help="Task name(s) (e.g., 'dummy_task', 'ebalfred_base'). " - "Optional when --resume is provided.") - start_parser.add_argument("--tasks", type=str, default=None, dest="tasks_csv", - help="Comma-separated task names (e.g., 'ebalfred_base,ebnavigation_base')") - start_parser.add_argument("--agent", type=str, default=None, choices=["dummy", "react"], - dest="agent_type") - start_parser.add_argument("--output-dir", type=str, default=None, - help="Base output directory (creates ///)") - start_parser.add_argument("--data-dir", type=str, default=None, - help="Directory for downloading/caching datasets (default: ./datasets)") + start_parser.add_argument( + "task_names_positional", + type=str, + nargs="*", + default=None, + metavar="task", + help="Task name(s) (e.g., 'dummy_task', 'ebalfred_base'). " + "Optional when --resume is provided.", + ) + start_parser.add_argument( + "--tasks", + type=str, + default=None, + dest="tasks_csv", + help="Comma-separated task names (e.g., 'ebalfred_base,ebnavigation_base')", + ) + start_parser.add_argument( + "--agent", type=str, default=None, choices=["dummy", "react"], dest="agent_type" + ) + start_parser.add_argument( + "--output-dir", + type=str, + default=None, + help="Base output directory (creates ///)", + ) + start_parser.add_argument( + "--data-dir", + type=str, + default=None, + help="Directory for downloading/caching datasets (default: ./datasets)", + ) start_parser.add_argument("--max-episodes", type=int, default=None) - start_parser.add_argument("--llm-url", type=str, default=None, dest="llm_base_url", - help="LLM server URL") + start_parser.add_argument( + "--llm-url", type=str, default=None, dest="llm_base_url", help="LLM server URL" + ) start_parser.add_argument("--seed", type=int, default=None, dest="agent_seed") - start_parser.add_argument("--backend", type=str, default=None, - help="LLM backend: vllm, custom, openai, anthropic, gemini, dummy") - start_parser.add_argument("--model", type=str, default=None, - help="Model name (HF path for vLLM, API name for proprietary)") - start_parser.add_argument("--port", type=int, default=None, - help="Port for local inference server (default: 8080)") - start_parser.add_argument("--llm-kwargs", type=str, default=None, dest="llm_kwargs_raw", - help='JSON string of extra kwargs, e.g. \'{"tensor_parallel_size": 4}\'') - start_parser.add_argument("--max-retries", type=int, default=None, - help="Max LLM retry attempts on transient errors (default: 3)") - start_parser.add_argument("--num-parallel", type=int, default=None, dest="num_parallel", - help="Number of parallel simulator instances (default: 1, sequential).") start_parser.add_argument( - "--llm-instances", type=int, default=None, dest="llm_instances", + "--backend", + type=str, + default=None, + help="LLM backend: vllm, custom, openai, anthropic, gemini, dummy", + ) + start_parser.add_argument( + "--model", + type=str, + default=None, + help="Model name (HF path for vLLM, API name for proprietary)", + ) + start_parser.add_argument( + "--port", + type=int, + default=None, + help="Port for local inference server (default: 8080)", + ) + start_parser.add_argument( + "--llm-kwargs", + type=str, + default=None, + dest="llm_kwargs_raw", + help="JSON string of extra kwargs, e.g. '{\"tensor_parallel_size\": 4}'", + ) + start_parser.add_argument( + "--max-retries", + type=int, + default=None, + help="Max LLM retry attempts on transient errors (default: 3)", + ) + start_parser.add_argument( + "--num-parallel", + type=int, + default=None, + dest="num_parallel", + help="Number of parallel simulator instances (default: 1, sequential).", + ) + start_parser.add_argument( + "--llm-instances", + type=int, + default=None, + dest="llm_instances", help="Number of local LLM server instances to start (default: 1). " - "Each instance runs on a subset of --llm-gpus.", + "Each instance runs on a subset of --llm-gpus.", ) start_parser.add_argument( - "--llm-gpus", type=str, default=None, dest="llm_gpus", + "--llm-gpus", + type=str, + default=None, + dest="llm_gpus", help="Comma-separated GPU IDs for LLM inference (e.g., '0,1'). " - "GPUs are split evenly across --llm-instances.", + "GPUs are split evenly across --llm-instances.", ) start_parser.add_argument( - "--sim-gpus", type=str, default=None, dest="sim_gpus", + "--sim-gpus", + type=str, + default=None, + dest="sim_gpus", help="Comma-separated GPU IDs for simulator rendering (e.g., '2,3'). " - "If not set, simulators use CPU rendering.", + "If not set, simulators use CPU rendering.", + ) + start_parser.add_argument( + "--resume", + type=str, + default=None, + dest="resume_dir", + help="Path to a previous run directory to resume from", + ) + start_parser.add_argument( + "--refresh-data", + action="store_true", + dest="refresh_data", + help="Delete cached dataset and re-download from source", ) - start_parser.add_argument("--resume", type=str, default=None, dest="resume_dir", - help="Path to a previous run directory to resume from") - start_parser.add_argument("--refresh-data", action="store_true", dest="refresh_data", - help="Delete cached dataset and re-download from source") start_parser.add_argument( - "--render-platform", type=str, default=None, dest="render_platform", + "--render-platform", + type=str, + default=None, + dest="render_platform", help="Rendering platform: auto, native, xvfb, egl, headless, xorg (default: simulator's preference). " - "xorg starts a GPU X server (defaults to GPU 0, use --sim-gpus to specify).") + "xorg starts a GPU X server (defaults to GPU 0, use --sim-gpus to specify).", + ) # --- model command --- - model_parser = subparsers.add_parser("model", help="Manage custom models", parents=[common]) + model_parser = subparsers.add_parser( + "model", help="Manage custom models", parents=[common] + ) model_sub = model_parser.add_subparsers(dest="model_action") model_sub.add_parser("list", help="List available custom models", parents=[common]) - model_info_parser = model_sub.add_parser("info", help="Show model details", parents=[common]) + model_info_parser = model_sub.add_parser( + "info", help="Show model details", parents=[common] + ) model_info_parser.add_argument("model_name", help="Model name") # --- ps command --- - ps_parser = subparsers.add_parser("ps", help="Show EASI-related processes (bridges, LLM servers)", parents=[common]) - ps_parser.add_argument("--kill", action="store_true", help="Kill all found EASI processes") + ps_parser = subparsers.add_parser( + "ps", + help="Show EASI-related processes (bridges, LLM servers)", + parents=[common], + ) + ps_parser.add_argument( + "--kill", action="store_true", help="Kill all found EASI processes" + ) # --- llm-server command --- - llm_parser = subparsers.add_parser("llm-server", help="Start dummy LLM server", parents=[common]) + llm_parser = subparsers.add_parser( + "llm-server", help="Start dummy LLM server", parents=[common] + ) llm_parser.add_argument("--port", type=int, default=8000) llm_parser.add_argument("--host", type=str, default="127.0.0.1") llm_parser.add_argument("--mode", choices=["fixed", "random"], default="random") llm_parser.add_argument( - "--action-space", type=str, nargs="+", + "--action-space", + type=str, + nargs="+", default=["MoveAhead", "TurnLeft", "TurnRight", "Stop"], ) @@ -163,6 +305,7 @@ def build_parser() -> argparse.ArgumentParser: # --- Command handlers --- + def cmd_env_list() -> None: from easi.simulators.registry import get_simulator_entry, list_simulators @@ -181,10 +324,14 @@ def cmd_env_list() -> None: seen.add(pair) default_marker = " (default)" if key == entry.name else "" runtime_tag = f" [{entry.runtime}]" if entry.runtime != "conda" else "" - logger.info(" %s%s%s -- %s", pair, default_marker, runtime_tag, entry.description) + logger.info( + " %s%s%s -- %s", pair, default_marker, runtime_tag, entry.description + ) -def cmd_env_install(simulator: str, reinstall: bool = False, with_task_deps: str | None = None) -> None: +def cmd_env_install( + simulator: str, reinstall: bool = False, with_task_deps: str | None = None +) -> None: from easi.simulators.registry import create_env_manager env_manager = create_env_manager(simulator) @@ -200,7 +347,9 @@ def cmd_env_install(simulator: str, reinstall: bool = False, with_task_deps: str from easi.core.docker_env_manager import DockerEnvironmentManager if isinstance(env_manager, DockerEnvironmentManager): - logger.warning("--with-task-deps is not supported for Docker simulators (deps baked into image).") + logger.warning( + "--with-task-deps is not supported for Docker simulators (deps baked into image)." + ) else: from easi.tasks.registry import get_task_entry, load_task_class @@ -246,7 +395,9 @@ def cmd_task_list() -> None: for name in tasks: entry = get_task_entry(name) - logger.info(" %s -- %s (simulator: %s)", name, entry.display_name, entry.simulator_key) + logger.info( + " %s -- %s (simulator: %s)", name, entry.display_name, entry.simulator_key + ) def cmd_task_info(task_name: str) -> None: @@ -267,11 +418,15 @@ def cmd_task_scaffold(name: str, simulator: str, max_steps: int) -> None: tasks_dir = Path(__file__).parent / "tasks" tests_dir = Path(__file__).parent.parent / "tests" - task_dir = scaffold_task(name, simulator, output_dir=tasks_dir, - max_steps=max_steps, tests_dir=tests_dir) + task_dir = scaffold_task( + name, simulator, output_dir=tasks_dir, max_steps=max_steps, tests_dir=tests_dir + ) logger.info("Created task scaffold at: %s", task_dir) logger.info("Next steps:") - logger.info(" 1. Edit %s/bridge.py — implement _create_env() and _extract_image()", task_dir.name) + logger.info( + " 1. Edit %s/bridge.py — implement _create_env() and _extract_image()", + task_dir.name, + ) logger.info(" 2. Edit %s/task.py — implement format_reset_config()", task_dir.name) logger.info(" 3. Edit %s/%s.yaml — configure dataset source", task_dir.name, name) logger.info(" 4. Run tests: pytest tests/test_%s.py -v", name) @@ -289,12 +444,18 @@ def cmd_task_download(task_name: str, refresh_data: bool = False) -> None: logger.info("Task uses built-in episodes (no download needed).") -def cmd_sim_test(simulator: str, steps: int, timeout: float, render_platform_name: str | None = None) -> None: +def cmd_sim_test( + simulator: str, + steps: int, + timeout: float, + render_platform_name: str | None = None, + sim_gpus: list[int] | None = None, +) -> None: from pathlib import Path from easi.core.docker_env_manager import DockerEnvironmentManager from easi.core.episode import Action - from easi.core.render_platform import get_render_platform + from easi.core.render_platforms import get_render_platform from easi.simulators.registry import ( create_env_manager, get_simulator_entry, @@ -329,7 +490,9 @@ def cmd_sim_test(simulator: str, steps: int, timeout: float, render_platform_nam command_timeout=timeout, ) - data_dir_str = entry.data_dir.replace("~", str(Path.home())) if entry.data_dir else None + data_dir_str = ( + entry.data_dir.replace("~", str(Path.home())) if entry.data_dir else None + ) try: runner.launch_docker( @@ -345,7 +508,9 @@ def cmd_sim_test(simulator: str, steps: int, timeout: float, render_platform_nam for i in range(steps): action = Action(action_name="MoveAhead") result = sim.step(action) - logger.info(" Step %d: done=%s, reward=%s", i + 1, result.done, result.reward) + logger.info( + " Step %d: done=%s, reward=%s", i + 1, result.done, result.reward + ) if result.done: break @@ -370,12 +535,23 @@ def cmd_sim_test(simulator: str, steps: int, timeout: float, render_platform_nam if platform_name not in env_manager.supported_render_platforms: logger.error( "Render platform '%s' not supported by %s. Supported: %s", - platform_name, simulator, env_manager.supported_render_platforms, + platform_name, + simulator, + env_manager.supported_render_platforms, ) sys.exit(1) - render_platform = resolve_render_platform(simulator, platform_name, env_manager=env_manager) - render_platform.setup(gpu_ids=[0]) - worker_platform = render_platform.for_worker(0) + render_platform = resolve_render_platform( + simulator, platform_name, env_manager=env_manager + ) + try: + render_platform.setup(gpu_ids=sim_gpus or [0]) + worker_platform = render_platform.for_worker(0) + except RuntimeError as e: + if platform_name == "xorg": + logger.warning("%s", str(e)) + render_platform.teardown() + sys.exit(0) + raise logger.info("Testing %s...", simulator) logger.info(" Python: %s", env_manager.get_python_executable()) @@ -404,7 +580,9 @@ def cmd_sim_test(simulator: str, steps: int, timeout: float, render_platform_nam for i in range(steps): action = Action(action_name="MoveAhead") result = sim.step(action) - logger.info(" Step %d: done=%s, reward=%s", i + 1, result.done, result.reward) + logger.info( + " Step %d: done=%s, reward=%s", i + 1, result.done, result.reward + ) if result.done: break @@ -474,7 +652,9 @@ def cmd_start(args): run_kwargs = raw if not task_list: - logger.error("Task name is required. Provide it as a positional arg, --tasks, or use --resume.") + logger.error( + "Task name is required. Provide it as a positional arg, --tasks, or use --resume." + ) sys.exit(1) # Remove task_name from run_kwargs; it's passed per-task below @@ -506,6 +686,7 @@ def cmd_start(args): if num_parallel > 1: from easi.evaluation.parallel_runner import ParallelRunner + runner = ParallelRunner( task_name=task_name, num_parallel=num_parallel, @@ -524,7 +705,9 @@ def cmd_start(args): results = runner.run() logger.info("Completed %d episodes for %s.", len(results), task_name) - records = [EpisodeRecord(episode={}, trajectory=[], episode_results=r) for r in results] + records = [ + EpisodeRecord(episode={}, trajectory=[], episode_results=r) for r in results + ] summary = {"num_episodes": len(results)} summary.update(default_aggregate(records)) all_summaries.append((task_name, summary)) @@ -555,9 +738,9 @@ def cmd_ps(kill: bool = False) -> None: # Patterns that identify EASI-spawned processes patterns = [ - "easi.llm.models.http_server", # custom model server - "vllm.entrypoints.openai.api_server", # vLLM server - "easi.llm.dummy_server", # dummy LLM server + "easi.llm.models.http_server", # custom model server + "vllm.entrypoints.openai.api_server", # vLLM server + "easi.llm.dummy_server", # dummy LLM server ] # Also match bridge scripts by looking for bridge.py in easi paths bridge_pattern = "easi/simulators/.*/bridge.py|easi/tasks/.*/bridge.py" @@ -568,7 +751,9 @@ def cmd_ps(kill: bool = False) -> None: try: result = subprocess.run( ["ps", "aux"], - capture_output=True, text=True, timeout=10, + capture_output=True, + text=True, + timeout=10, ) except (FileNotFoundError, subprocess.TimeoutExpired): logger.error("Failed to run 'ps aux'") @@ -592,6 +777,7 @@ def cmd_ps(kill: bool = False) -> None: break if matched_pattern is None: import re + if re.search(bridge_pattern, cmd_str): matched_pattern = "bridge" @@ -599,17 +785,19 @@ def cmd_ps(kill: bool = False) -> None: continue is_zombie = "Z" in stat - found.append({ - "pid": pid, - "user": parts[0], - "stat": stat, - "cpu": parts[2], - "mem": parts[3], - "start": parts[8], - "command": cmd_str[:120], - "pattern": matched_pattern, - "zombie": is_zombie, - }) + found.append( + { + "pid": pid, + "user": parts[0], + "stat": stat, + "cpu": parts[2], + "mem": parts[3], + "start": parts[8], + "command": cmd_str[:120], + "pattern": matched_pattern, + "zombie": is_zombie, + } + ) if not found: logger.info("No EASI-related processes found.") @@ -617,22 +805,41 @@ def cmd_ps(kill: bool = False) -> None: # Display logger.info("Found %d EASI-related process(es):\n", len(found)) - logger.info(" %-7s %-6s %-5s %-5s %-8s %s", "PID", "STAT", "CPU%", "MEM%", "TYPE", "COMMAND") + logger.info( + " %-7s %-6s %-5s %-5s %-8s %s", + "PID", + "STAT", + "CPU%", + "MEM%", + "TYPE", + "COMMAND", + ) logger.info(" %s", "-" * 80) for p in found: zombie_tag = " [ZOMBIE]" if p["zombie"] else "" ptype = p["pattern"].split(".")[-1] if "." in p["pattern"] else p["pattern"] logger.info( " %-7d %-6s %-5s %-5s %-8s %s%s", - p["pid"], p["stat"], p["cpu"], p["mem"], ptype, p["command"][:60], zombie_tag, + p["pid"], + p["stat"], + p["cpu"], + p["mem"], + ptype, + p["command"][:60], + zombie_tag, ) # GPU usage summary try: gpu_result = subprocess.run( - ["nvidia-smi", "--query-compute-apps=pid,gpu_uuid,used_memory", - "--format=csv,noheader,nounits"], - capture_output=True, text=True, timeout=10, + [ + "nvidia-smi", + "--query-compute-apps=pid,gpu_uuid,used_memory", + "--format=csv,noheader,nounits", + ], + capture_output=True, + text=True, + timeout=10, ) if gpu_result.returncode == 0 and gpu_result.stdout.strip(): easi_pids = {p["pid"] for p in found} @@ -663,6 +870,7 @@ def cmd_ps(kill: bool = False) -> None: logger.warning(" Cannot kill PID %d (permission denied)", p["pid"]) # Wait briefly then SIGKILL any survivors import time + time.sleep(2) for p in found: try: @@ -700,6 +908,7 @@ def cmd_model(args) -> None: # --- Main --- + def main() -> None: try: _main() @@ -723,7 +932,11 @@ def _main() -> None: if args.env_action == "list": cmd_env_list() elif args.env_action == "install": - cmd_env_install(args.simulator, reinstall=args.reinstall, with_task_deps=args.with_task_deps) + cmd_env_install( + args.simulator, + reinstall=args.reinstall, + with_task_deps=args.with_task_deps, + ) elif args.env_action == "check": cmd_env_check(args.simulator) else: @@ -743,7 +956,17 @@ def _main() -> None: elif args.command == "sim": if args.sim_action == "test": - cmd_sim_test(args.simulator, args.steps, args.timeout, getattr(args, "render_platform", None)) + raw_sim_gpus = getattr(args, "sim_gpus", None) + sim_gpus_parsed = ( + [int(g) for g in raw_sim_gpus.split(",")] if raw_sim_gpus else None + ) + cmd_sim_test( + args.simulator, + args.steps, + args.timeout, + getattr(args, "render_platform", None), + sim_gpus=sim_gpus_parsed, + ) else: parser.parse_args(["sim", "--help"]) diff --git a/tests/test_cli.py b/tests/test_cli.py index 29f0521..613adb9 100644 --- a/tests/test_cli.py +++ b/tests/test_cli.py @@ -1,21 +1,36 @@ # tests/test_cli.py """Tests for CLI GPU and LLM arguments.""" +from unittest.mock import MagicMock, patch + +import pytest + def test_cli_parses_llm_gpu_args(): """CLI should parse --llm-instances, --llm-gpus, --sim-gpus.""" from easi.cli import build_parser + parser = build_parser() - args = parser.parse_args([ - "start", "dummy_task", - "--agent", "dummy", - "--backend", "vllm", - "--model", "test", - "--num-parallel", "12", - "--llm-instances", "2", - "--llm-gpus", "0,1", - "--sim-gpus", "2,3", - ]) + args = parser.parse_args( + [ + "start", + "dummy_task", + "--agent", + "dummy", + "--backend", + "vllm", + "--model", + "test", + "--num-parallel", + "12", + "--llm-instances", + "2", + "--llm-gpus", + "0,1", + "--sim-gpus", + "2,3", + ] + ) assert args.llm_instances == 2 assert args.llm_gpus == "0,1" assert args.sim_gpus == "2,3" @@ -24,19 +39,28 @@ def test_cli_parses_llm_gpu_args(): def test_cli_parses_comma_separated_llm_url(): """--llm-url should accept comma-separated URLs.""" from easi.cli import build_parser + parser = build_parser() - args = parser.parse_args([ - "start", "dummy_task", - "--agent", "dummy", - "--backend", "vllm", - "--model", "test", - "--llm-url", "http://localhost:8000/v1,http://localhost:8001/v1", - ]) + args = parser.parse_args( + [ + "start", + "dummy_task", + "--agent", + "dummy", + "--backend", + "vllm", + "--model", + "test", + "--llm-url", + "http://localhost:8000/v1,http://localhost:8001/v1", + ] + ) assert args.llm_base_url == "http://localhost:8000/v1,http://localhost:8001/v1" def test_cli_ps_subcommand(): from easi.cli import build_parser + parser = build_parser() args = parser.parse_args(["ps"]) assert args.command == "ps" @@ -48,6 +72,7 @@ def test_cli_ps_subcommand(): def test_cli_model_list_subcommand(): from easi.cli import build_parser + parser = build_parser() args = parser.parse_args(["model", "list"]) assert args.command == "model" @@ -56,8 +81,44 @@ def test_cli_model_list_subcommand(): def test_cli_model_info_subcommand(): from easi.cli import build_parser + parser = build_parser() args = parser.parse_args(["model", "info", "cambrian"]) assert args.command == "model" assert args.model_action == "info" assert args.model_name == "cambrian" + + +def test_cmd_sim_test_xorg_setup_warning_exits_cleanly(): + from easi.cli import cmd_sim_test + + entry = MagicMock(runtime="conda") + env_manager = MagicMock() + env_manager.default_render_platform = "xorg" + env_manager.supported_render_platforms = ["xorg"] + + sim = MagicMock() + render_platform = MagicMock() + render_platform.setup.side_effect = RuntimeError("xorg guidance") + + with ( + patch("easi.simulators.registry.get_simulator_entry", return_value=entry), + patch("easi.simulators.registry.create_env_manager", return_value=env_manager), + patch( + "easi.simulators.registry.load_simulator_class", return_value=lambda: sim + ), + patch( + "easi.simulators.registry.resolve_render_platform", + return_value=render_platform, + ), + patch("easi.cli.logger") as mock_logger, + ): + with pytest.raises(SystemExit) as exc_info: + cmd_sim_test( + "coppeliasim", steps=1, timeout=5.0, render_platform_name="xorg" + ) + + assert exc_info.value.code == 0 + mock_logger.warning.assert_called_once_with("%s", "xorg guidance") + render_platform.teardown.assert_called_once() + sim.close.assert_not_called() From 6433a30b88ac307aecf96097176a30974510f861 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Thu, 12 Mar 2026 02:06:14 +0800 Subject: [PATCH 200/239] refactor: migrate render strategy integration to bindings and adapters Adopt WorkerBinding plus SimulatorRenderAdapter across registry, runners, subprocess launch, and simulator integrations so render backends own resource assignment while simulators contribute render-specific quirks through one adapter path. --- easi/core/render_platforms/__init__.py | 11 +- easi/core/render_platforms/base.py | 63 +- easi/core/render_platforms/xorg.py | 61 +- easi/evaluation/runner.py | 257 +++++--- easi/simulators/coppeliasim/manifest.yaml | 6 +- .../coppeliasim/v4_1_0/env_manager.py | 10 +- .../coppeliasim/v4_1_0/render_platforms.py | 134 +--- easi/simulators/dummy/manifest.yaml | 1 + easi/simulators/dummy/v1/render_platforms.py | 14 +- easi/simulators/omnigibson/manifest.yaml | 4 +- easi/simulators/omnigibson/v3_7_2/bridge.py | 5 +- .../omnigibson/v3_7_2/env_manager.py | 52 +- .../omnigibson/v3_7_2/render_platforms.py | 55 +- easi/simulators/registry.py | 46 +- easi/simulators/subprocess_runner.py | 40 +- easi/tasks/ebmanipulation/bridge.py | 19 +- tests/test_coppeliasim_simulator.py | 70 +- tests/test_env_vars_wiring.py | 328 ++++++++-- tests/test_omnigibson_simulator.py | 82 ++- tests/test_render_platform.py | 617 ++++++++++++++++-- tests/test_subprocess_runner.py | 104 ++- tests/test_xorg.py | 78 +-- 22 files changed, 1498 insertions(+), 559 deletions(-) diff --git a/easi/core/render_platforms/__init__.py b/easi/core/render_platforms/__init__.py index fda49d5..01e2b84 100644 --- a/easi/core/render_platforms/__init__.py +++ b/easi/core/render_platforms/__init__.py @@ -1,8 +1,10 @@ """Render platform package — pluggable display/rendering backends. Each render platform encapsulates how to launch a bridge subprocess with -the correct display/rendering environment. Simulators declare a default +the correct display/rendering environment. Simulators declare a default platform; users can override via CLI (--render-platform) or task YAML. +Simulator-specific render quirks belong in a ``SimulatorRenderAdapter`` +registered from the simulator manifest, not in backend-specific subclasses. Built-in platforms: auto — use native DISPLAY if available, fall back to xvfb @@ -14,12 +16,12 @@ """ from .auto import AutoPlatform -from .base import EnvVars, RenderPlatform +from .base import EnvVars, RenderPlatform, SimulatorRenderAdapter, WorkerBinding from .egl import EGLPlatform from .headless import HeadlessPlatform from .native import NativePlatform from .registry import available_platforms, get_render_platform -from .xorg import XorgPlatform, _XorgWorkerPlatform +from .xorg import XorgPlatform from .xorg_manager import XorgInstance, XorgManager from .xvfb import XvfbPlatform @@ -27,6 +29,8 @@ # Base types "RenderPlatform", "EnvVars", + "WorkerBinding", + "SimulatorRenderAdapter", # Built-in platforms "AutoPlatform", "NativePlatform", @@ -34,7 +38,6 @@ "EGLPlatform", "HeadlessPlatform", "XorgPlatform", - "_XorgWorkerPlatform", # Xorg internals "XorgManager", "XorgInstance", diff --git a/easi/core/render_platforms/base.py b/easi/core/render_platforms/base.py index d7f16a8..ccfdf8b 100644 --- a/easi/core/render_platforms/base.py +++ b/easi/core/render_platforms/base.py @@ -1,13 +1,17 @@ """Base classes for render platforms. -Defines the RenderPlatform ABC and EnvVars dataclass used by all platform -implementations and external custom platforms (e.g. CoppeliaSim). +Defines the RenderPlatform ABC and EnvVars dataclass used by all built-in +render backends plus simulator render adapters. + +Also defines WorkerBinding (resolved per-worker render facts) and +SimulatorRenderAdapter (optional simulator-specific launch adjustments). """ from __future__ import annotations from abc import ABC, abstractmethod from dataclasses import dataclass, field +from typing import Any @dataclass @@ -51,13 +55,53 @@ def merge(cls, *env_vars: EnvVars) -> EnvVars: return cls(replace=replace, prepend=prepend) +@dataclass +class WorkerBinding: + """Resolved per-worker render facts produced by a render backend. + + Carries the concrete display and GPU assignment for one worker subprocess, + plus any extra env vars and arbitrary metadata the backend wants to pass + downstream (e.g. to a SimulatorRenderAdapter). + + Fields: + display: X display string (e.g. ":10"), or None for headless/EGL. + cuda_visible_devices: GPU id(s) string (e.g. "0" or "0,1"), or None. + extra_env: Additional env vars contributed by the render backend. + metadata: Arbitrary backend-specific data for adapter consumption. + """ + + display: str | None = None + cuda_visible_devices: str | None = None + extra_env: EnvVars = field(default_factory=EnvVars) + metadata: dict[str, Any] = field(default_factory=dict) + + +class SimulatorRenderAdapter(ABC): + """Extension point for simulator-specific render launch adjustments. + + Simulators that need to inject render-related env vars or wrap the launch + command beyond what the core render backend provides should subclass this + and register it via the simulator manifest. + + Default implementations are no-ops so simulators that need no adjustments + do not have to implement anything. + """ + + def get_env_vars(self, binding: WorkerBinding) -> EnvVars: + return EnvVars() + + def wrap_command(self, cmd: list[str], binding: WorkerBinding) -> list[str]: + return cmd + + class RenderPlatform(ABC): """Strategy interface for display/rendering backends. Lifecycle hooks (``setup`` / ``teardown``) allow platforms that manage external services (e.g. Xorg) to start and stop them without - if/else logic in the callers. ``for_worker`` returns a per-worker - platform instance (default: ``self``). + if/else logic in the callers. ``for_worker`` always returns a + ``WorkerBinding`` so callers have a uniform interface; backends that + need per-worker GPU/display assignment (e.g. ``XorgPlatform``) override it. """ def __init__(self, env_manager=None): @@ -102,6 +146,11 @@ def setup(self, gpu_ids: list[int] | None = None) -> None: def teardown(self) -> None: """Called once after all simulators are done. Stop external services.""" - def for_worker(self, worker_id: int) -> RenderPlatform: - """Return a per-worker platform instance. Default: return self.""" - return self + def for_worker(self, worker_id: int) -> WorkerBinding: + """Return the per-worker render binding for this platform. + + The default returns a ``WorkerBinding`` carrying the backend name in + ``metadata``. Platforms that assign per-worker displays or GPUs + (e.g. ``XorgPlatform``) override this to return a populated binding. + """ + return WorkerBinding(metadata={"backend": self.name}) diff --git a/easi/core/render_platforms/xorg.py b/easi/core/render_platforms/xorg.py index 3a6d620..03c1247 100644 --- a/easi/core/render_platforms/xorg.py +++ b/easi/core/render_platforms/xorg.py @@ -1,62 +1,36 @@ """Xorg render platform — GPU-accelerated X11 display managed by EASI. -Two classes: - -* ``XorgPlatform`` — the top-level platform resolved from ``--render-platform xorg``. - Owns the ``XorgManager`` lifecycle via ``setup()`` / ``teardown()`` and hands - out per-worker instances via ``for_worker()``. -* ``_XorgWorkerPlatform`` — lightweight per-worker instance with a fixed - display number and GPU ID, created by ``XorgPlatform.for_worker()``. +``XorgPlatform`` owns the ``XorgManager`` lifecycle and resolves per-worker +bindings for adapter-driven simulator launch wiring. """ from __future__ import annotations +from typing import TYPE_CHECKING + from easi.utils.logging import get_logger -from .base import EnvVars, RenderPlatform +from .base import EnvVars, RenderPlatform, WorkerBinding from .xorg_manager import XorgManager -logger = get_logger(__name__) - - -class _XorgWorkerPlatform(RenderPlatform): - """Per-worker Xorg platform with a dedicated display and GPU.""" - - def __init__(self, display_num: int, gpu_id: int): - super().__init__() - self.display_num = display_num - self.gpu_id = gpu_id - - @property - def name(self) -> str: - return "xorg" - - def wrap_command(self, cmd: list[str], screen_config: str) -> list[str]: - return cmd - - def get_env_vars(self) -> EnvVars: - return EnvVars(replace={ - "DISPLAY": f":{self.display_num}", - "CUDA_VISIBLE_DEVICES": str(self.gpu_id), - "EASI_GPU_DISPLAY": "1", - }) +if TYPE_CHECKING: + from .xorg_manager import XorgInstance - def is_available(self) -> bool: - return True +logger = get_logger(__name__) class XorgPlatform(RenderPlatform): """Render platform backed by auto-managed Xorg servers. Call ``setup(gpu_ids=...)`` to start Xorg servers, then - ``for_worker(worker_id)`` to get per-worker instances. + ``for_worker(worker_id)`` to get per-worker bindings. ``teardown()`` stops all servers. """ def __init__(self, env_manager=None): super().__init__(env_manager=env_manager) self._xorg_mgr: XorgManager | None = None - self._instances: list = [] + self._instances: list[XorgInstance] = [] @property def name(self) -> str: @@ -83,11 +57,20 @@ def teardown(self) -> None: self._xorg_mgr = None self._instances = [] - def for_worker(self, worker_id: int) -> _XorgWorkerPlatform: - """Return a per-worker platform bound to a specific Xorg instance.""" + def for_worker(self, worker_id: int) -> WorkerBinding: + """Resolve a per-worker binding for a specific Xorg instance.""" if not self._instances: raise RuntimeError( "XorgPlatform.setup() must be called before for_worker()" ) inst = self._instances[worker_id % len(self._instances)] - return _XorgWorkerPlatform(display_num=inst.display, gpu_id=inst.gpu_id) + return _build_worker_binding(inst.display, inst.gpu_id) + + +def _build_worker_binding(display_num: int, gpu_id: int) -> WorkerBinding: + return WorkerBinding( + display=f":{display_num}", + cuda_visible_devices=str(gpu_id), + extra_env=EnvVars(replace={"EASI_GPU_DISPLAY": "1"}), + metadata={"backend": "xorg", "display_num": display_num, "gpu_id": gpu_id}, + ) diff --git a/easi/evaluation/runner.py b/easi/evaluation/runner.py index 585c5fb..07b7bd1 100644 --- a/easi/evaluation/runner.py +++ b/easi/evaluation/runner.py @@ -38,7 +38,7 @@ def _sanitize_dirname(name: str) -> str: """Replace characters unsafe for directory names.""" - return re.sub(r'[^\w\-.]', '_', name) + return re.sub(r"[^\w\-.]", "_", name) class EvaluationRunner: @@ -71,7 +71,8 @@ def __init__( # Auto-capture all init args for config.json (before any mutation) frame = inspect.currentframe() self._cli_options = { - k: v for k, v in inspect.getargvalues(frame).locals.items() + k: v + for k, v in inspect.getargvalues(frame).locals.items() if k not in ("self", "frame") and k not in self._EXCLUDE_FROM_CONFIG } @@ -99,6 +100,7 @@ def __init__( # For custom backend, append model_path to distinguish variants if self.backend == "custom" and self.llm_kwargs_raw: from easi.llm.utils import parse_llm_kwargs + model_path = parse_llm_kwargs(self.llm_kwargs_raw).get("model_path", "") if model_path: # Use last 2 path components (e.g. Qwen_Qwen3-VL-8B-Instruct) @@ -184,15 +186,19 @@ def run(self) -> list[dict]: task.download_dataset(force=True) episodes = task.load_episodes() if self.max_episodes is not None: - episodes = episodes[:self.max_episodes] + episodes = episodes[: self.max_episodes] # Handle resume: load completed results and find start point if self.resume_dir: - all_results, start_index = self._load_completed_results(run_dir, len(episodes)) + all_results, start_index = self._load_completed_results( + run_dir, len(episodes) + ) self._reattach_resume_data(all_results, episodes, run_dir) logger.info( "Resuming from %s — %d completed episodes, starting from index %d", - run_dir, len(all_results), start_index, + run_dir, + len(all_results), + start_index, ) else: all_results = [] @@ -211,7 +217,9 @@ def run(self) -> list[dict]: all_kwargs = parse_llm_kwargs(self.llm_kwargs_raw) server_kwargs, _ = split_kwargs(all_kwargs) server = ServerManager( - backend, self.model, port=self.port, + backend, + self.model, + port=self.port, server_kwargs=server_kwargs, ) base_url = server.start() @@ -221,6 +229,7 @@ def run(self) -> list[dict]: # Compute resolved generation kwargs (YAML defaults + CLI overrides) from easi.llm.utils import parse_llm_kwargs, split_kwargs + agent_config = task._config.get("agent", {}) yaml_gen_kwargs = agent_config.get("generation_kwargs", {}) all_llm_kwargs = parse_llm_kwargs(self.llm_kwargs_raw) @@ -238,17 +247,19 @@ def run(self) -> list[dict]: "task_config": task._config, } (run_dir / "config.json").write_text(json.dumps(config, indent=2)) - logger.trace( - "Run config:\n%s", json.dumps(config, indent=2, default=str) - ) + logger.trace("Run config:\n%s", json.dumps(config, indent=2, default=str)) # Skip simulator/agent if all episodes already complete (resume) if start_index >= len(episodes): - logger.info("All %d episodes already complete, re-aggregating summary.", len(episodes)) + logger.info( + "All %d episodes already complete, re-aggregating summary.", + len(episodes), + ) else: # 3. Create agent - agent = self._create_agent(task.action_space, task._config, - backend=backend, base_url=base_url) + agent = self._create_agent( + task.action_space, task._config, backend=backend, base_url=base_url + ) # 4. Start simulator sim, sim_runner = self._create_simulator(task.simulator_key, task=task) @@ -257,7 +268,9 @@ def run(self) -> list[dict]: from easi.utils.progress import ProgressBar progress_bar = ProgressBar( - total=len(episodes), num_workers=1, start_index=start_index, + total=len(episodes), + num_workers=1, + start_index=start_index, ) progress_bar.start() @@ -267,23 +280,36 @@ def run(self) -> list[dict]: continue episode_id = episode.get("episode_id", f"ep_{i}") logger.info( - "Episode %d/%d: %s", i + 1, len(episodes), episode_id, + "Episode %d/%d: %s", + i + 1, + len(episodes), + episode_id, ) - episode_dir = episodes_dir / f"{i:03d}_{_sanitize_dirname(episode_id)}" + episode_dir = ( + episodes_dir / f"{i:03d}_{_sanitize_dirname(episode_id)}" + ) episode_dir.mkdir(exist_ok=True) result = None for attempt in range(1, self.max_retries + 1): try: result = self._run_episode( - sim, agent, task, episode, i, episode_dir, + sim, + agent, + task, + episode, + i, + episode_dir, ) break except Exception as exc: logger.warning( "Episode %s attempt %d/%d failed: %s", - episode_id, attempt, self.max_retries, exc, + episode_id, + attempt, + self.max_retries, + exc, ) self._clear_episode_dir(episode_dir) if attempt < self.max_retries: @@ -294,15 +320,19 @@ def run(self) -> list[dict]: pass try: sim, sim_runner = self._create_simulator( - task.simulator_key, task=task, + task.simulator_key, + task=task, ) except Exception as restart_exc: logger.error( - "Simulator restart failed: %s", restart_exc, + "Simulator restart failed: %s", + restart_exc, ) result = { "episode_id": episode_id, - "instruction": task.get_instruction(episode), + "instruction": task.get_instruction( + episode + ), "success": 0.0, "num_steps": 0, "elapsed_seconds": 0.0, @@ -313,7 +343,8 @@ def run(self) -> list[dict]: else: logger.error( "Episode %s failed after %d attempts, skipping", - episode_id, self.max_retries, + episode_id, + self.max_retries, ) result = { "episode_id": episode_id, @@ -335,8 +366,7 @@ def run(self) -> list[dict]: # Save per-episode result (strip internal keys) result_to_save = { - k: v for k, v in result.items() - if not k.startswith("_") + k: v for k, v in result.items() if not k.startswith("_") } (episode_dir / "result.json").write_text( json.dumps(result_to_save, indent=2) @@ -344,7 +374,9 @@ def run(self) -> list[dict]: # If simulator restart failed, stop evaluation if sim is None: - logger.error("No simulator available, stopping evaluation early.") + logger.error( + "No simulator available, stopping evaluation early." + ) break finally: @@ -364,11 +396,13 @@ def run(self) -> list[dict]: trajectory = r.pop("_trajectory", []) episode = r.pop("_episode", {}) episode_results = {k: v for k, v in r.items() if not k.startswith("_")} - records.append(EpisodeRecord( - episode=episode, - trajectory=trajectory, - episode_results=episode_results, - )) + records.append( + EpisodeRecord( + episode=episode, + trajectory=trajectory, + episode_results=episode_results, + ) + ) # 6. Aggregate and save summary try: @@ -391,7 +425,9 @@ def run(self) -> list[dict]: return all_results - def _load_completed_results(self, run_dir: Path, total_episodes: int) -> tuple[list[dict], int]: + def _load_completed_results( + self, run_dir: Path, total_episodes: int + ) -> tuple[list[dict], int]: """Scan episode dirs to find the first incomplete episode. Walks episode directories in ascending order (by index prefix). @@ -433,7 +469,9 @@ def _load_completed_results(self, run_dir: Path, total_episodes: int) -> tuple[l start_index += 1 continue except (json.JSONDecodeError, OSError): - logger.warning("Corrupt result.json in %s, treating as incomplete", ep_dir) + logger.warning( + "Corrupt result.json in %s, treating as incomplete", ep_dir + ) # First incomplete episode found — stop here break @@ -442,7 +480,8 @@ def _load_completed_results(self, run_dir: Path, total_episodes: int) -> tuple[l if dirs_to_clear: logger.info( "Resume: clearing %d episode dirs from index %d onward", - len(dirs_to_clear), start_index, + len(dirs_to_clear), + start_index, ) for d in dirs_to_clear: shutil.rmtree(d) @@ -483,7 +522,13 @@ def _reattach_resume_data( result["_trajectory"] = [] def _run_episode( - self, sim, agent, task, episode: dict, index: int, episode_dir: Path, + self, + sim, + agent, + task, + episode: dict, + index: int, + episode_dir: Path, ) -> dict: """Run a single episode and return metrics.""" agent.reset() @@ -503,15 +548,18 @@ def _run_episode( # Write reset entry to trajectory trajectory_path = episode_dir / "trajectory.jsonl" - self._write_trajectory_entry(trajectory_path, { - "step": 0, - "type": "reset", - "rgb_path": Path(observation.rgb_path).name, - "agent_pose": observation.agent_pose, - "reward": 0.0, - "done": False, - "info": {}, - }) + self._write_trajectory_entry( + trajectory_path, + { + "step": 0, + "type": "reset", + "rgb_path": Path(observation.rgb_path).name, + "agent_pose": observation.agent_pose, + "reward": 0.0, + "done": False, + "info": {}, + }, + ) # Agent-simulator loop trajectory: list[StepResult] = [] @@ -531,21 +579,24 @@ def _run_episode( # Get LLM response from agent memory (None for buffered actions) llm_response = None - if hasattr(agent, 'memory') and agent.memory.steps: + if hasattr(agent, "memory") and agent.memory.steps: llm_response = agent.memory.steps[-1].llm_response # Write step entry to trajectory - self._write_trajectory_entry(trajectory_path, { - "step": step + 1, - "type": "step", - "action": action.action_name, - "llm_response": llm_response, - "rgb_path": Path(step_result.observation.rgb_path).name, - "agent_pose": step_result.observation.agent_pose, - "reward": step_result.reward, - "done": step_result.done, - "info": step_result.info, - }) + self._write_trajectory_entry( + trajectory_path, + { + "step": step + 1, + "type": "step", + "action": action.action_name, + "llm_response": llm_response, + "rgb_path": Path(step_result.observation.rgb_path).name, + "agent_pose": step_result.observation.agent_pose, + "reward": step_result.reward, + "done": step_result.done, + "info": step_result.info, + }, + ) # Feed action outcome back to agent for ReAct reasoning last_success = step_result.info.get("last_action_success", 1.0) @@ -573,7 +624,7 @@ def _run_episode( metrics["_episode"] = episode # Snapshot LLM usage for this episode - if hasattr(agent, 'llm_client') and hasattr(agent.llm_client, 'get_usage'): + if hasattr(agent, "llm_client") and hasattr(agent.llm_client, "get_usage"): metrics["llm_usage"] = agent.llm_client.get_usage() agent.llm_client.reset_usage() @@ -608,7 +659,9 @@ def _aggregate_llm_usage(results: list[dict]) -> dict: total["total_prompt_tokens"] += usage.get("prompt_tokens", 0) total["total_completion_tokens"] += usage.get("completion_tokens", 0) total["total_cost_usd"] += usage.get("cost_usd", 0.0) - total["total_tokens"] = total["total_prompt_tokens"] + total["total_completion_tokens"] + total["total_tokens"] = ( + total["total_prompt_tokens"] + total["total_completion_tokens"] + ) n = len(results) or 1 total["avg_prompt_tokens_per_episode"] = round(total["total_prompt_tokens"] / n) total["avg_cost_per_episode_usd"] = round(total["total_cost_usd"] / n, 6) @@ -624,12 +677,18 @@ def _create_task(self): data_dir=self.data_dir, ) - def _create_agent(self, action_space: list[str], task_config: dict, - backend: str | None = None, base_url: str | None = None): + def _create_agent( + self, + action_space: list[str], + task_config: dict, + backend: str | None = None, + base_url: str | None = None, + ): from easi.utils.import_utils import import_class if self.agent_type == "dummy": from easi.agents.dummy_agent import DummyAgent + return DummyAgent(action_space=action_space, seed=self.agent_seed) elif self.agent_type == "react": @@ -641,8 +700,10 @@ def _create_agent(self, action_space: list[str], task_config: dict, if backend and backend != "legacy": from easi.llm.client import LLMClient from easi.llm.utils import ( - build_litellm_model, parse_llm_kwargs, - split_kwargs, validate_backend, + build_litellm_model, + parse_llm_kwargs, + split_kwargs, + validate_backend, ) validate_backend(backend) @@ -667,9 +728,8 @@ def _create_agent(self, action_space: list[str], task_config: dict, else: # Legacy path: existing LLMApiClient from easi.llm.api_client import LLMApiClient - llm = LLMApiClient( - base_url=base_url or "http://127.0.0.1:8000" - ) + + llm = LLMApiClient(base_url=base_url or "http://127.0.0.1:8000") # Load task-specific prompt builder prompt_builder = None @@ -687,7 +747,9 @@ def _create_agent(self, action_space: list[str], task_config: dict, else: raise ValueError(f"Unknown agent type: {self.agent_type}") - def _create_simulator(self, simulator_key: str, task=None, label: str = "bridge", worker_id: int = 0): + def _create_simulator( + self, simulator_key: str, task=None, label: str = "bridge", worker_id: int = 0 + ): import json as _json from easi.simulators.registry import ( @@ -709,13 +771,14 @@ def _create_simulator(self, simulator_key: str, task=None, label: str = "bridge" # Task-specific bridge overrides simulator default bridge_path = ( - (task.get_bridge_script_path() if task else None) - or sim._get_bridge_script_path() - ) + task.get_bridge_script_path() if task else None + ) or sim._get_bridge_script_path() extra_args = ["--data-dir", str(self.data_dir)] if task and task.simulator_kwargs: - extra_args.extend(["--simulator-kwargs", _json.dumps(task.simulator_kwargs)]) + extra_args.extend( + ["--simulator-kwargs", _json.dumps(task.simulator_kwargs)] + ) # Extract runner-level timeouts from simulator_configs sim_configs = task.simulator_configs if task else {} @@ -743,8 +806,14 @@ def _create_simulator(self, simulator_key: str, task=None, label: str = "bridge" label=label, **runner_kwargs, ) - data_dir_str = str(self.data_dir) if self.data_dir else ( - entry.data_dir.replace("~", str(Path.home())) if entry.data_dir else None + data_dir_str = ( + str(self.data_dir) + if self.data_dir + else ( + entry.data_dir.replace("~", str(Path.home())) + if entry.data_dir + else None + ) ) runner.launch_docker( docker_env_manager=env_manager, @@ -777,30 +846,52 @@ def _create_simulator(self, simulator_key: str, task=None, label: str = "bridge" ) # Use pre-setup global platform if available, else resolve per-simulator - if getattr(self, '_render_platform', None) is not None: + if getattr(self, "_render_platform", None) is not None: render_platform = self._render_platform else: - render_platform = resolve_render_platform(simulator_key, platform_name, env_manager=env_manager) + render_platform = resolve_render_platform( + simulator_key, platform_name, env_manager=env_manager + ) - # Pass platform name to get_env_vars for conditional logic from easi.core.render_platforms import EnvVars env_vars = env_manager.get_env_vars(render_platform_name=platform_name) - # Merge task-level env_vars from simulator_configs.env_vars if task and task.extra_env_vars: env_vars = EnvVars.merge(env_vars, EnvVars(replace=task.extra_env_vars)) - # Per-worker platform instance (lifecycle platforms like xorg return - # a worker-specific instance that already handles GPU assignment) - render_platform = render_platform.for_worker(worker_id) + from easi.simulators.registry import ( + resolve_render_adapter as _resolve_render_adapter, + ) - # Apply per-worker GPU pinning via round-robin (skip if platform - # already sets CUDA_VISIBLE_DEVICES, e.g. xorg worker platforms) - platform_env = render_platform.get_env_vars() - if self.sim_gpus is not None and "CUDA_VISIBLE_DEVICES" not in platform_env.replace: + adapter = _resolve_render_adapter(simulator_key, env_manager=env_manager) + + base_render_platform = render_platform + binding = render_platform.for_worker(worker_id) + + adapter_env = adapter.get_env_vars(binding) if adapter else EnvVars() + binding_env = EnvVars.merge(binding.extra_env, adapter_env) + + if binding.display: + binding_env = EnvVars.merge( + binding_env, EnvVars(replace={"DISPLAY": binding.display}) + ) + if binding.cuda_visible_devices is not None: + binding_env = EnvVars.merge( + binding_env, + EnvVars(replace={"CUDA_VISIBLE_DEVICES": binding.cuda_visible_devices}), + ) + + if self.sim_gpus is not None and binding.cuda_visible_devices is None: gpu_id = self.sim_gpus[worker_id % len(self.sim_gpus)] - env_vars = EnvVars.merge(env_vars, EnvVars(replace={"CUDA_VISIBLE_DEVICES": str(gpu_id)})) + env_vars = EnvVars.merge( + env_vars, EnvVars(replace={"CUDA_VISIBLE_DEVICES": str(gpu_id)}) + ) + + env_vars = EnvVars.merge(env_vars, binding_env) + render_platform = base_render_platform + active_binding = binding + active_adapter = adapter runner = SubprocessRunner( python_executable=env_manager.get_python_executable(), @@ -809,6 +900,8 @@ def _create_simulator(self, simulator_key: str, task=None, label: str = "bridge" screen_config=env_manager.screen_config, extra_args=extra_args, extra_env=env_vars if env_vars else None, + render_adapter=active_adapter, + worker_binding=active_binding, label=label, **runner_kwargs, ) diff --git a/easi/simulators/coppeliasim/manifest.yaml b/easi/simulators/coppeliasim/manifest.yaml index ae4f3e5..af45433 100644 --- a/easi/simulators/coppeliasim/manifest.yaml +++ b/easi/simulators/coppeliasim/manifest.yaml @@ -7,11 +7,7 @@ versions: simulator_class: "easi.simulators.coppeliasim.v4_1_0.simulator.CoppeliaSimSimulatorV410" env_manager_class: "easi.simulators.coppeliasim.v4_1_0.env_manager.CoppeliaSimEnvManagerV410" python_version: "3.10" - render_platforms: - auto: "easi.simulators.coppeliasim.v4_1_0.render_platforms.CoppeliaSimAutoPlatform" - native: "easi.simulators.coppeliasim.v4_1_0.render_platforms.CoppeliaSimNativePlatform" - xvfb: "easi.simulators.coppeliasim.v4_1_0.render_platforms.CoppeliaSimXvfbPlatform" - xorg: "easi.simulators.coppeliasim.v4_1_0.render_platforms.CoppeliaSimXorgPlatform" + render_adapter: "easi.simulators.coppeliasim.v4_1_0.render_platforms.CoppeliaSimRenderAdapter" installation_kwargs: binary_url: "https://downloads.coppeliarobotics.com/V4_1_0/CoppeliaSim_Pro_V4_1_0_Ubuntu20_04.tar.xz" binary_filename: "CoppeliaSim_Pro_V4_1_0_Ubuntu20_04.tar.xz" diff --git a/easi/simulators/coppeliasim/v4_1_0/env_manager.py b/easi/simulators/coppeliasim/v4_1_0/env_manager.py index f46bd5a..06d674f 100644 --- a/easi/simulators/coppeliasim/v4_1_0/env_manager.py +++ b/easi/simulators/coppeliasim/v4_1_0/env_manager.py @@ -61,15 +61,13 @@ def get_env_vars(self, render_platform_name: str | None = None) -> EnvVars: """Return platform-agnostic CoppeliaSim env vars for bridge subprocess. Platform-specific vars (QT_QPA_PLATFORM_PLUGIN_PATH, __EGL_VENDOR_LIBRARY_FILENAMES) - are handled by custom render platform classes in render_platforms.py. + are handled by the CoppeliaSimRenderAdapter in render_platforms.py. """ binary_dir_name = self.installation_kwargs.get("binary_dir_name", "") if not binary_dir_name: return EnvVars() t = self._get_template_variables() - coppeliasim_root = self._resolve_template( - "{extras_dir}/" + binary_dir_name, t - ) + coppeliasim_root = self._resolve_template("{extras_dir}/" + binary_dir_name, t) # Include conda env lib dir so fontconfig/freetype/Qt deps resolve conda_lib = self._resolve_template("{env_dir}/lib", t) ld_path = f"{coppeliasim_root}:{conda_lib}" @@ -136,6 +134,4 @@ def post_install(self, context: dict) -> None: logger.info("Copying %s to %s", lua_addon_script, coppeliasim_root) shutil.copy(str(lua_src), str(coppeliasim_root / lua_addon_script)) else: - logger.warning( - "Lua addon script not found at %s", lua_src - ) + logger.warning("Lua addon script not found at %s", lua_src) diff --git a/easi/simulators/coppeliasim/v4_1_0/render_platforms.py b/easi/simulators/coppeliasim/v4_1_0/render_platforms.py index 1d622d9..b861684 100644 --- a/easi/simulators/coppeliasim/v4_1_0/render_platforms.py +++ b/easi/simulators/coppeliasim/v4_1_0/render_platforms.py @@ -1,28 +1,12 @@ -"""Custom render platforms for CoppeliaSim V4.1.0. - -CoppeliaSim needs simulator-specific env vars (QT_QPA_PLATFORM_PLUGIN_PATH, -__EGL_VENDOR_LIBRARY_FILENAMES) that depend on the CoppeliaSim binary location. -These custom platforms compute the correct paths from the env_manager. - -Additionally, each platform sets COPPELIASIM_HEADLESS so the bridge can -start CoppeliaSim in the correct mode: -- native: headless=False (user has a real display and wants GUI) -- xvfb: headless=True (xvfb is only for Qt plugins, not CoppeliaSim rendering) -- auto: headless=False if DISPLAY, True otherwise -""" - from __future__ import annotations import os from pathlib import Path from easi.core.render_platforms import ( - AutoPlatform, EnvVars, - NativePlatform, - XorgPlatform, - XvfbPlatform, - _XorgWorkerPlatform, + SimulatorRenderAdapter, + WorkerBinding, ) @@ -32,21 +16,6 @@ def _coppeliasim_qt_env_vars( include_mesa_egl: bool = False, headless: bool = True, ) -> EnvVars: - """Compute CoppeliaSim Qt env vars. - - CoppeliaSim always needs QT_QPA_PLATFORM_PLUGIN_PATH to find its bundled - Qt plugins (libqxcb.so, etc.), regardless of display mode. - - Args: - env_manager: The CoppeliaSimEnvManager instance. - include_mesa_egl: If True, also set __EGL_VENDOR_LIBRARY_FILENAMES - to Mesa vendor (needed for Xvfb; NVIDIA EGL crashes Xvfb). - headless: Whether CoppeliaSim should use its headless renderer. - - Returns: - EnvVars with QT_QPA_PLATFORM_PLUGIN_PATH, COPPELIASIM_HEADLESS, - and optionally Mesa EGL. - """ if env_manager is None: return EnvVars() binary_dir_name = env_manager.installation_kwargs.get("binary_dir_name", "") @@ -69,96 +38,23 @@ def _coppeliasim_qt_env_vars( ) -class CoppeliaSimNativePlatform(NativePlatform): - """Native display for CoppeliaSim — Qt plugin path, no Mesa override, headless=False.""" - - @property - def name(self) -> str: - return "native" - - def get_env_vars(self) -> EnvVars: - return _coppeliasim_qt_env_vars( - self._env_manager, - include_mesa_egl=False, - headless=False, - ) - - -class CoppeliaSimXvfbPlatform(XvfbPlatform): - """Xvfb platform for CoppeliaSim — Qt plugin path + Mesa EGL, headless=True. - - Xvfb provides a virtual X11 display for Qt plugin loading, but CoppeliaSim - itself should use its native headless renderer (libcoppeliaSimHeadless.so). - """ - - @property - def name(self) -> str: - return "xvfb" - - def get_env_vars(self) -> EnvVars: - return _coppeliasim_qt_env_vars( - self._env_manager, - include_mesa_egl=True, - headless=True, - ) - - -class CoppeliaSimAutoPlatform(AutoPlatform): - """Auto-detect for CoppeliaSim — native if DISPLAY, xvfb otherwise. - - Both modes need Qt plugin path. Only xvfb needs Mesa EGL override. - Headless follows display: False with real display, True without. - """ +class CoppeliaSimRenderAdapter(SimulatorRenderAdapter): + def __init__(self, env_manager=None): + self._env_manager = env_manager - @property - def name(self) -> str: - return "auto" - - def get_env_vars(self) -> EnvVars: + def get_env_vars(self, binding: WorkerBinding) -> EnvVars: + backend = binding.metadata.get("backend", "") + if backend == "xvfb": + return _coppeliasim_qt_env_vars( + self._env_manager, include_mesa_egl=True, headless=True + ) + if backend in ("native", "xorg") or binding.cuda_visible_devices is not None: + return _coppeliasim_qt_env_vars( + self._env_manager, include_mesa_egl=False, headless=False + ) has_display = bool(os.environ.get("DISPLAY", "")) return _coppeliasim_qt_env_vars( self._env_manager, include_mesa_egl=not has_display, headless=not has_display, ) - - -class _CoppeliaSimXorgWorkerPlatform(_XorgWorkerPlatform): - """Per-worker Xorg platform with CoppeliaSim Qt env vars merged in.""" - - def __init__(self, display_num: int, gpu_id: int, coppeliasim_env: EnvVars): - super().__init__(display_num=display_num, gpu_id=gpu_id) - self._coppeliasim_env = coppeliasim_env - - def get_env_vars(self) -> EnvVars: - return EnvVars.merge(super().get_env_vars(), self._coppeliasim_env) - - -class CoppeliaSimXorgPlatform(XorgPlatform): - """Xorg platform for CoppeliaSim — Qt plugin path, no Mesa, headless=False. - - Xorg provides a real GPU-accelerated X11 display, so CoppeliaSim renders - with its GUI library (not headless). Per-worker instances include the - CoppeliaSim Qt env vars. - """ - - @property - def name(self) -> str: - return "xorg" - - def for_worker(self, worker_id: int) -> _CoppeliaSimXorgWorkerPlatform: - if not self._instances: - raise RuntimeError( - "XorgPlatform.setup() must be called before for_worker()" - ) - inst = self._instances[worker_id % len(self._instances)] - coppeliasim_env = _coppeliasim_qt_env_vars( - self._env_manager, - include_mesa_egl=False, - headless=False, - ) - return _CoppeliaSimXorgWorkerPlatform( - display_num=inst.display, - gpu_id=inst.gpu_id, - coppeliasim_env=coppeliasim_env, - ) diff --git a/easi/simulators/dummy/manifest.yaml b/easi/simulators/dummy/manifest.yaml index 18b0ad0..9d49ca3 100644 --- a/easi/simulators/dummy/manifest.yaml +++ b/easi/simulators/dummy/manifest.yaml @@ -9,3 +9,4 @@ versions: python_version: "3.10" render_platforms: dummy_custom: "easi.simulators.dummy.v1.render_platforms.DummyCustomPlatform" + render_adapter: "easi.simulators.dummy.v1.render_platforms.DummyRenderAdapter" diff --git a/easi/simulators/dummy/v1/render_platforms.py b/easi/simulators/dummy/v1/render_platforms.py index a4d7acb..04abf44 100644 --- a/easi/simulators/dummy/v1/render_platforms.py +++ b/easi/simulators/dummy/v1/render_platforms.py @@ -1,6 +1,11 @@ """Custom render platforms for the dummy simulator (for testing).""" -from easi.core.render_platforms import EnvVars, HeadlessPlatform +from easi.core.render_platforms import ( + EnvVars, + HeadlessPlatform, + SimulatorRenderAdapter, + WorkerBinding, +) class DummyCustomPlatform(HeadlessPlatform): @@ -12,3 +17,10 @@ def name(self) -> str: def get_env_vars(self) -> EnvVars: return EnvVars(replace={"DUMMY_CUSTOM_PLATFORM": "1"}) + + +class DummyRenderAdapter(SimulatorRenderAdapter): + """A trivial render adapter for testing adapter registration and resolution.""" + + def get_env_vars(self, binding: WorkerBinding) -> EnvVars: + return EnvVars(replace={"DUMMY_ADAPTER": "1"}) diff --git a/easi/simulators/omnigibson/manifest.yaml b/easi/simulators/omnigibson/manifest.yaml index 1d9c54b..cf5d08b 100644 --- a/easi/simulators/omnigibson/manifest.yaml +++ b/easi/simulators/omnigibson/manifest.yaml @@ -7,9 +7,7 @@ versions: simulator_class: "easi.simulators.omnigibson.v3_7_2.simulator.OmniGibsonSimulator" env_manager_class: "easi.simulators.omnigibson.v3_7_2.env_manager.OmniGibsonEnvManager" python_version: "3.10" - render_platforms: - native: "easi.simulators.omnigibson.v3_7_2.render_platforms.OmniGibsonNativePlatform" - auto: "easi.simulators.omnigibson.v3_7_2.render_platforms.OmniGibsonAutoPlatform" + render_adapter: "easi.simulators.omnigibson.v3_7_2.render_platforms.OmniGibsonRenderAdapter" installation_kwargs: cuda_version: "12.4" behavior_1k_repo: "https://github.com/StanfordVL/BEHAVIOR-1K.git" diff --git a/easi/simulators/omnigibson/v3_7_2/bridge.py b/easi/simulators/omnigibson/v3_7_2/bridge.py index d5f513b..3a47e17 100644 --- a/easi/simulators/omnigibson/v3_7_2/bridge.py +++ b/easi/simulators/omnigibson/v3_7_2/bridge.py @@ -8,8 +8,8 @@ - Action execution and observation capture - Main IPC loop (reset/step/close) -Rendering mode is controlled via OMNIGIBSON_HEADLESS env var, set by the active -render platform class (OmniGibsonNativePlatform or OmniGibsonAutoPlatform). +Rendering mode is controlled via OMNIGIBSON_HEADLESS env var, set by the +simulator render adapter on top of the active core render backend. Note: Isaac Sim takes 60-120 seconds to start on first launch. Use --timeout 300 or more when running easi sim test for OmniGibson. @@ -17,6 +17,7 @@ Usage: python bridge.py --workspace /tmp/easi_xxx [--simulator-kwargs '{}'] """ + from __future__ import annotations import numpy as np diff --git a/easi/simulators/omnigibson/v3_7_2/env_manager.py b/easi/simulators/omnigibson/v3_7_2/env_manager.py index 2aefc00..ce2ac4d 100644 --- a/easi/simulators/omnigibson/v3_7_2/env_manager.py +++ b/easi/simulators/omnigibson/v3_7_2/env_manager.py @@ -12,14 +12,15 @@ 7. Fix websockets conflict in Isaac Sim extscache (post_install) 8. Fix cffi compatibility (post_install) -Rendering mode is controlled via OMNIGIBSON_HEADLESS, set by the active render -platform class (OmniGibsonNativePlatform or OmniGibsonAutoPlatform). No Xvfb needed. +Rendering mode is controlled via OMNIGIBSON_HEADLESS, set by the simulator's +render adapter on top of the active core render backend. No Xvfb needed. NFS workaround: On NFS/FUSE filesystems, /proc/self/exe can resolve to "python3.10 (deleted)" which crashes Isaac Sim's Carbonite library. The get_python_executable() override copies the Python binary to /tmp (local filesystem) where /proc/self/exe resolves correctly. """ + from __future__ import annotations import atexit @@ -139,15 +140,15 @@ def get_python_executable(self) -> str: logger.trace( "Copied Python to local filesystem: %s -> %s", - real_binary, local_python, + real_binary, + local_python, ) return str(local_python) def get_env_vars(self, render_platform_name: str | None = None) -> EnvVars: """Export env vars for EULA acceptance and PYTHONHOME. - OMNIGIBSON_HEADLESS is set by the render platform (OmniGibsonNativePlatform - or OmniGibsonAutoPlatform), not here. + OMNIGIBSON_HEADLESS is set by the simulator render adapter, not here. PYTHONHOME is set to the conda env directory so the /tmp Python copy can find the conda env's stdlib and site-packages. @@ -155,10 +156,12 @@ def get_env_vars(self, render_platform_name: str | None = None) -> EnvVars: conda_python = self._get_conda_python() # conda env dir is two levels up from bin/python conda_env_dir = str(Path(conda_python).resolve().parent.parent) - return EnvVars(replace={ - "OMNI_KIT_ACCEPT_EULA": "YES", - "PYTHONHOME": conda_env_dir, - }) + return EnvVars( + replace={ + "OMNI_KIT_ACCEPT_EULA": "YES", + "PYTHONHOME": conda_env_dir, + } + ) def post_install(self, context: dict) -> None: """Replicate BEHAVIOR-1K setup.sh install process. @@ -220,9 +223,15 @@ def _install_pytorch(self, python: str) -> None: logger.info("Installing PyTorch with CUDA %s support", cuda_version) self._run_command( [ - python, "-m", "pip", "install", - "torch", "torchvision", "torchaudio", - "--index-url", f"https://download.pytorch.org/whl/cu{cuda_ver_short}", + python, + "-m", + "pip", + "install", + "torch", + "torchvision", + "torchaudio", + "--index-url", + f"https://download.pytorch.org/whl/cu{cuda_ver_short}", ], "install PyTorch with CUDA", ) @@ -292,6 +301,7 @@ def _install_isaac_sim(self, python: str) -> None: finally: # Clean up temp directory import shutil + shutil.rmtree(temp_dir, ignore_errors=True) def _fix_websockets_conflict(self, python: str) -> None: @@ -302,9 +312,14 @@ def _fix_websockets_conflict(self, python: str) -> None: """ try: result = subprocess.run( - [python, "-c", - "import isaacsim, os; print(os.environ.get('ISAAC_PATH', ''))"], - capture_output=True, text=True, timeout=30, + [ + python, + "-c", + "import isaacsim, os; print(os.environ.get('ISAAC_PATH', ''))", + ], + capture_output=True, + text=True, + timeout=30, ) isaac_path = result.stdout.strip() except (subprocess.TimeoutExpired, FileNotFoundError): @@ -320,6 +335,7 @@ def _fix_websockets_conflict(self, python: str) -> None: logger.info("Fixing websockets conflict under %s", extscache) import shutil + for ws_dir in extscache.rglob("pip_prebundle/websockets"): if ws_dir.is_dir(): logger.trace("Removing %s", ws_dir) @@ -339,11 +355,14 @@ def _check_glibc_old() -> bool: try: result = subprocess.run( ["ldd", "--version"], - capture_output=True, text=True, timeout=10, + capture_output=True, + text=True, + timeout=10, ) output = result.stdout + result.stderr # Look for version like "2.31", "2.32", "2.33" import re + match = re.search(r"(\d+\.\d+)", output) if match: version = float(match.group(1)) @@ -356,6 +375,7 @@ def _check_glibc_old() -> bool: def _download_wheel(url: str, dest: Path) -> None: """Download a single wheel file.""" import urllib.request + req = urllib.request.Request(url, headers={"User-Agent": "easi/1.0"}) with urllib.request.urlopen(req) as response, open(str(dest), "wb") as out: while True: diff --git a/easi/simulators/omnigibson/v3_7_2/render_platforms.py b/easi/simulators/omnigibson/v3_7_2/render_platforms.py index 0c5cfeb..594bee8 100644 --- a/easi/simulators/omnigibson/v3_7_2/render_platforms.py +++ b/easi/simulators/omnigibson/v3_7_2/render_platforms.py @@ -1,35 +1,38 @@ -"""Custom render platforms for OmniGibson v3.7.2. +"""Render adapter for OmniGibson v3.7.2. -Each platform sets OMNIGIBSON_HEADLESS so Isaac Sim starts in the correct mode: -- native: OMNIGIBSON_HEADLESS=0 (user has a real display, Isaac Sim GUI opens) -- auto: OMNIGIBSON_HEADLESS=0 if DISPLAY is set, 1 otherwise +OmniGibson-specific render behavior is expressed through +``OmniGibsonRenderAdapter`` so contributors can reuse core render backends +without adding backend-specific platform subclasses. """ from __future__ import annotations import os -from easi.core.render_platforms import AutoPlatform, EnvVars, NativePlatform - - -class OmniGibsonNativePlatform(NativePlatform): - """Native display for OmniGibson — Isaac Sim GUI window, OMNIGIBSON_HEADLESS=0.""" - - @property - def name(self) -> str: - return "native" - - def get_env_vars(self) -> EnvVars: - return EnvVars(replace={"OMNIGIBSON_HEADLESS": "0"}) - - -class OmniGibsonAutoPlatform(AutoPlatform): - """Auto-detect for OmniGibson — native mode if $DISPLAY is set, headless otherwise.""" - - @property - def name(self) -> str: - return "auto" - - def get_env_vars(self) -> EnvVars: +from easi.core.render_platforms import ( + EnvVars, + SimulatorRenderAdapter, + WorkerBinding, +) + + +class OmniGibsonRenderAdapter(SimulatorRenderAdapter): + """Adapter for OmniGibson — sets OMNIGIBSON_HEADLESS from a WorkerBinding. + + Dispatches based on binding metadata ``backend`` key, then falls back to + ``cuda_visible_devices`` (present on xorg bindings) as a headless heuristic, + and finally reads ``$DISPLAY`` for the auto path. + """ + + def get_env_vars(self, binding: WorkerBinding) -> EnvVars: + backend = binding.metadata.get("backend", "") + if backend in ("native", "xorg"): + return EnvVars(replace={"OMNIGIBSON_HEADLESS": "0"}) + if backend == "xvfb": + return EnvVars(replace={"OMNIGIBSON_HEADLESS": "1"}) + # xorg bindings always carry a GPU assignment; treat as display-attached + if binding.cuda_visible_devices is not None: + return EnvVars(replace={"OMNIGIBSON_HEADLESS": "0"}) + # Auto path: check runtime display availability has_display = bool(os.environ.get("DISPLAY", "")) return EnvVars(replace={"OMNIGIBSON_HEADLESS": "0" if has_display else "1"}) diff --git a/easi/simulators/registry.py b/easi/simulators/registry.py index e5b2e01..fe880fa 100644 --- a/easi/simulators/registry.py +++ b/easi/simulators/registry.py @@ -32,8 +32,11 @@ class SimulatorEntry: simulator_class: str # fully qualified class name env_manager_class: str # fully qualified class name python_version: str - installation_kwargs: dict = field(default_factory=dict) - render_platforms: dict[str, str] = field(default_factory=dict) # platform_name -> FQN class + installation_kwargs: dict[str, object] = field(default_factory=dict) + render_platforms: dict[str, str] = field( + default_factory=dict + ) # platform_name -> FQN class + render_adapter: str | None = None # FQN class for SimulatorRenderAdapter, or None runtime: str = "conda" # "conda" or "docker" data_dir: str = "" # default data directory (from manifest top-level) @@ -67,6 +70,7 @@ def _discover_simulators() -> dict[str, SimulatorEntry]: python_version=ver_info.get("python_version", "3.10"), installation_kwargs=ver_info.get("installation_kwargs", {}), render_platforms=ver_info.get("render_platforms", {}), + render_adapter=ver_info.get("render_adapter"), runtime=ver_info.get("runtime", "conda"), data_dir=manifest.get("data_dir", ""), ) @@ -109,9 +113,7 @@ def get_simulator_entry(key: str) -> SimulatorEntry: registry = _get_registry() if key not in registry: available = list_simulators() - raise KeyError( - f"Simulator '{key}' not found. Available: {available}" - ) + raise KeyError(f"Simulator '{key}' not found. Available: {available}") return registry[key] @@ -169,6 +171,40 @@ def resolve_render_platform(key: str, platform_name: str, env_manager=None): return get_render_platform(platform_name) +def resolve_render_adapter(key: str, env_manager=None): + """Resolve the SimulatorRenderAdapter for a simulator, if one is registered. + + Looks up the ``render_adapter`` manifest key for the given simulator and + returns an instantiated adapter. Returns ``None`` when no adapter is + registered so callers can fall back to no-op behaviour without importing + the base class. + + Args: + key: Simulator registry key (e.g. "coppeliasim:v4_1_0" or "coppeliasim"). + env_manager: Optional env manager instance passed to the adapter constructor. + + Returns: + Instantiated SimulatorRenderAdapter, or None if no adapter is registered. + """ + entry = get_simulator_entry(key) + adapter_class_path = entry.render_adapter + if not isinstance(adapter_class_path, str): + return None + adapter_class_path = adapter_class_path.strip() + if not adapter_class_path or "." not in adapter_class_path: + return None + cls = _import_class(adapter_class_path) + if env_manager is not None: + try: + instance = cls(env_manager=env_manager) + except TypeError: + instance = cls() + else: + instance = cls() + logger.trace("Using render adapter '%s' from %s", adapter_class_path, key) + return instance + + def refresh() -> None: """Force re-discovery of simulators (useful after adding new ones at runtime).""" global _registry diff --git a/easi/simulators/subprocess_runner.py b/easi/simulators/subprocess_runner.py index 37f94c6..c32a845 100644 --- a/easi/simulators/subprocess_runner.py +++ b/easi/simulators/subprocess_runner.py @@ -28,7 +28,8 @@ write_command, ) from easi.core.exceptions import SimulatorError, SimulatorTimeoutError -from easi.core.render_platforms import EnvVars, RenderPlatform +from easi.core.render_platforms import EnvVars, RenderPlatform, WorkerBinding +from easi.core.render_platforms.base import SimulatorRenderAdapter from easi.utils.logging import get_logger logger = get_logger(__name__) @@ -48,6 +49,8 @@ def __init__( poll_interval: float = 0.1, extra_args: list[str] | None = None, extra_env: EnvVars | None = None, + render_adapter: SimulatorRenderAdapter | None = None, + worker_binding: WorkerBinding | None = None, label: str = "bridge", ): self.python_executable = python_executable @@ -59,6 +62,8 @@ def __init__( self.poll_interval = poll_interval self.extra_args = extra_args or [] self.extra_env = extra_env + self.render_adapter = render_adapter + self.worker_binding = worker_binding self.label = label self._process: subprocess.Popen | None = None @@ -114,9 +119,7 @@ def launch(self) -> None: ) if not status.get("ready", False): output = self._get_recent_output() - raise SimulatorError( - f"Bridge reported not ready. Output:\n{output}" - ) + raise SimulatorError(f"Bridge reported not ready. Output:\n{output}") logger.info("Bridge subprocess ready (PID: %d)", self._process.pid) except (SimulatorError, SimulatorTimeoutError) as exc: # Collect output before shutdown destroys the process @@ -157,9 +160,7 @@ def launch_docker( ) # Start output reader thread - self._reader_thread = threading.Thread( - target=self._stream_output, daemon=True - ) + self._reader_thread = threading.Thread(target=self._stream_output, daemon=True) self._reader_thread.start() # Wait for bridge to signal ready @@ -180,7 +181,9 @@ def launch_docker( output = self._get_recent_output() self.shutdown() if output: - raise SimulatorError(f"{exc}\n\nDocker bridge output:\n{output}") from exc + raise SimulatorError( + f"{exc}\n\nDocker bridge output:\n{output}" + ) from exc raise def send_command(self, command: dict, timeout: float | None = None) -> dict: @@ -234,7 +237,11 @@ def _build_subprocess_env(self) -> dict[str, str] | None: Returns None if no env vars to set (subprocess inherits parent env). """ platform_env = self.render_platform.get_env_vars() - combined = EnvVars.merge(platform_env, self.extra_env) if self.extra_env else platform_env + combined = ( + EnvVars.merge(platform_env, self.extra_env) + if self.extra_env + else platform_env + ) if not combined: return None @@ -250,8 +257,10 @@ def _build_launch_command(self) -> list[str]: str(self._workspace), ] cmd.extend(self.extra_args) - - return self.render_platform.wrap_command(cmd, self.screen_config) + cmd = self.render_platform.wrap_command(cmd, self.screen_config) + if self.render_adapter is not None and self.worker_binding is not None: + cmd = self.render_adapter.wrap_command(cmd, self.worker_binding) + return cmd def _build_docker_launch_command( self, @@ -263,9 +272,12 @@ def _build_docker_launch_command( """Build launch command using docker run via DockerEnvironmentManager.""" bridge_command = [ docker_env_manager.container_python_path, - str(Path(docker_env_manager.easi_mount) / self.bridge_script_path.relative_to( - Path(__file__).resolve().parents[1] - )), + str( + Path(docker_env_manager.easi_mount) + / self.bridge_script_path.relative_to( + Path(__file__).resolve().parents[1] + ) + ), "--workspace", workspace_dir, ] diff --git a/easi/tasks/ebmanipulation/bridge.py b/easi/tasks/ebmanipulation/bridge.py index 44960a0..3e3eabc 100644 --- a/easi/tasks/ebmanipulation/bridge.py +++ b/easi/tasks/ebmanipulation/bridge.py @@ -16,6 +16,7 @@ Usage: python bridge.py --workspace /tmp/easi_xxx [--simulator-kwargs '{}'] """ + from __future__ import annotations import json @@ -83,7 +84,7 @@ def _create_env(self, reset_config, simulator_kwargs): scene_bounds = simulator_kwargs.get( "scene_bounds", [-0.3, -0.5, 0.6, 0.7, 0.5, 1.6] ) - # COPPELIASIM_HEADLESS is set by CoppeliaSim's custom render platform classes. + # COPPELIASIM_HEADLESS is set by CoppeliaSim's render adapter. # Falls back to simulator_kwargs, then defaults to True (EBManEnv default). headless_env = os.environ.get("COPPELIASIM_HEADLESS") if headless_env is not None: @@ -143,9 +144,11 @@ def _on_step(self, env, action_text): voxel_size = self.simulator_kwargs.get("voxel_size", 100) rotation_resolution = self.simulator_kwargs.get("rotation_resolution", 3) rotation_bins = int(360 / rotation_resolution) - action = [np.random.randint(0, voxel_size) for _ in range(3)] + [ - np.random.randint(0, rotation_bins) for _ in range(3) - ] + [1.0] + action = ( + [np.random.randint(0, voxel_size) for _ in range(3)] + + [np.random.randint(0, rotation_bins) for _ in range(3)] + + [1.0] + ) return env.step(action) def _extract_image(self, obs): @@ -158,9 +161,7 @@ def _extract_info(self, info): "action_success": float(info.get("action_success", 0.0)), "feedback": str(info.get("env_feedback", "")), "env_step": int(info.get("env_step", 0)), - "episode_elapsed_seconds": float( - info.get("episode_elapsed_seconds", 0.0) - ), + "episode_elapsed_seconds": float(info.get("episode_elapsed_seconds", 0.0)), } def _make_response(self, obs, reward=0.0, done=False, info=None): @@ -196,9 +197,7 @@ def _make_response(self, obs, reward=0.0, done=False, info=None): coord_result = form_object_coord_for_input( obs, task_class, self._camera_types ) - avg_coord, all_avg_point_list, cam_ext_list, cam_int_list = ( - coord_result - ) + avg_coord, all_avg_point_list, cam_ext_list, cam_int_list = coord_result avg_obj_coord = str(avg_coord) # Annotate the saved image diff --git a/tests/test_coppeliasim_simulator.py b/tests/test_coppeliasim_simulator.py index e1bacb5..dd4f574 100644 --- a/tests/test_coppeliasim_simulator.py +++ b/tests/test_coppeliasim_simulator.py @@ -8,20 +8,24 @@ class TestRegistryDiscovery: def test_coppeliasim_in_simulator_list(self): from easi.simulators.registry import list_simulators + assert "coppeliasim" in list_simulators() def test_coppeliasim_versioned_key(self): from easi.simulators.registry import list_simulators + assert "coppeliasim:v4_1_0" in list_simulators() def test_get_simulator_entry(self): from easi.simulators.registry import get_simulator_entry + entry = get_simulator_entry("coppeliasim") assert entry.name == "coppeliasim" assert entry.version == "v4_1_0" def test_installation_kwargs_populated(self): from easi.simulators.registry import get_simulator_entry + entry = get_simulator_entry("coppeliasim") assert "binary_url" in entry.installation_kwargs assert "binary_dir_name" in entry.installation_kwargs @@ -29,14 +33,19 @@ def test_installation_kwargs_populated(self): def test_load_simulator_class(self): from easi.simulators.registry import load_simulator_class + cls = load_simulator_class("coppeliasim:v4_1_0") sim = cls() assert sim.name == "coppeliasim" def test_create_env_manager_with_kwargs(self): from easi.simulators.registry import create_env_manager + mgr = create_env_manager("coppeliasim") - assert mgr.installation_kwargs["binary_dir_name"] == "CoppeliaSim_Pro_V4_1_0_Ubuntu20_04" + assert ( + mgr.installation_kwargs["binary_dir_name"] + == "CoppeliaSim_Pro_V4_1_0_Ubuntu20_04" + ) class TestEnvManagerConfig: @@ -44,21 +53,25 @@ class TestEnvManagerConfig: def test_env_name(self): from easi.simulators.registry import create_env_manager + mgr = create_env_manager("coppeliasim") assert mgr.get_env_name() == "easi_coppeliasim_v4_1_0" def test_default_render_platform(self): from easi.simulators.registry import create_env_manager + mgr = create_env_manager("coppeliasim") assert mgr.default_render_platform == "auto" def test_screen_config(self): from easi.simulators.registry import create_env_manager + mgr = create_env_manager("coppeliasim") assert mgr.screen_config == "1280x720x24" def test_system_deps(self): from easi.simulators.registry import create_env_manager + mgr = create_env_manager("coppeliasim") deps = mgr.get_system_deps() assert "conda" in deps @@ -66,16 +79,19 @@ def test_system_deps(self): def test_validation_import_references_pyrep(self): from easi.simulators.registry import create_env_manager + mgr = create_env_manager("coppeliasim") assert "pyrep" in mgr.get_validation_import().lower() def test_conda_yaml_exists(self): from easi.simulators.registry import create_env_manager + mgr = create_env_manager("coppeliasim") assert mgr.get_conda_env_yaml_path().exists() def test_requirements_txt_exists(self): from easi.simulators.registry import create_env_manager + mgr = create_env_manager("coppeliasim") assert mgr.get_requirements_txt_path().exists() @@ -85,29 +101,34 @@ class TestEnvVars: def test_env_vars_has_coppeliasim_root(self): from easi.simulators.registry import create_env_manager + mgr = create_env_manager("coppeliasim") assert "COPPELIASIM_ROOT" in mgr.get_env_vars().replace def test_env_vars_has_ld_library_path(self): from easi.simulators.registry import create_env_manager + mgr = create_env_manager("coppeliasim") assert "LD_LIBRARY_PATH" in mgr.get_env_vars().prepend def test_env_vars_no_qt_platform(self): - """QT_QPA_PLATFORM_PLUGIN_PATH is now handled by custom render platform classes.""" + """QT_QPA_PLATFORM_PLUGIN_PATH is now handled by the render platform adapter.""" from easi.simulators.registry import create_env_manager + mgr = create_env_manager("coppeliasim") ev = mgr.get_env_vars() assert "QT_QPA_PLATFORM_PLUGIN_PATH" not in ev.prepend def test_env_vars_no_unresolved_templates(self): from easi.simulators.registry import create_env_manager + mgr = create_env_manager("coppeliasim") for key, val in mgr.get_env_vars().to_flat_dict().items(): assert "{" not in val, f"Unresolved template in {key}: {val}" def test_env_vars_contain_coppeliasim_dir(self): from easi.simulators.registry import create_env_manager + mgr = create_env_manager("coppeliasim") root = mgr.get_env_vars().replace["COPPELIASIM_ROOT"] assert "CoppeliaSim_Pro_V4_1_0_Ubuntu20_04" in root @@ -115,18 +136,23 @@ def test_env_vars_contain_coppeliasim_dir(self): def test_ld_library_path_includes_coppeliasim_root(self): from easi.simulators.registry import create_env_manager + mgr = create_env_manager("coppeliasim") ev = mgr.get_env_vars() assert ev.replace["COPPELIASIM_ROOT"] in ev.prepend["LD_LIBRARY_PATH"] def test_ld_library_path_includes_conda_lib(self): from easi.simulators.registry import create_env_manager + mgr = create_env_manager("coppeliasim") ev = mgr.get_env_vars() assert "/lib" in ev.prepend["LD_LIBRARY_PATH"] def test_empty_installation_kwargs_returns_empty_env_vars(self): - from easi.simulators.coppeliasim.v4_1_0.env_manager import CoppeliaSimEnvManagerV410 + from easi.simulators.coppeliasim.v4_1_0.env_manager import ( + CoppeliaSimEnvManagerV410, + ) + mgr = CoppeliaSimEnvManagerV410() # No installation_kwargs assert not mgr.get_env_vars() @@ -135,21 +161,33 @@ class TestSimulatorClass: """Verify simulator class basic properties.""" def test_name(self): - from easi.simulators.coppeliasim.v4_1_0.simulator import CoppeliaSimSimulatorV410 + from easi.simulators.coppeliasim.v4_1_0.simulator import ( + CoppeliaSimSimulatorV410, + ) + assert CoppeliaSimSimulatorV410().name == "coppeliasim" def test_version(self): - from easi.simulators.coppeliasim.v4_1_0.simulator import CoppeliaSimSimulatorV410 + from easi.simulators.coppeliasim.v4_1_0.simulator import ( + CoppeliaSimSimulatorV410, + ) + assert CoppeliaSimSimulatorV410().version == "v4_1_0" def test_bridge_script_path_exists(self): - from easi.simulators.coppeliasim.v4_1_0.simulator import CoppeliaSimSimulatorV410 + from easi.simulators.coppeliasim.v4_1_0.simulator import ( + CoppeliaSimSimulatorV410, + ) + bridge_path = CoppeliaSimSimulatorV410()._get_bridge_script_path() assert bridge_path.exists() assert bridge_path.name == "bridge.py" def test_bridge_script_has_bridge_class(self): - from easi.simulators.coppeliasim.v4_1_0.simulator import CoppeliaSimSimulatorV410 + from easi.simulators.coppeliasim.v4_1_0.simulator import ( + CoppeliaSimSimulatorV410, + ) + content = CoppeliaSimSimulatorV410()._get_bridge_script_path().read_text() assert "CoppeliaSimBridge" in content @@ -158,9 +196,23 @@ class TestBridgeFileStructure: """Verify bridge file and supporting assets exist.""" def test_ttt_scene_file_exists(self): - ttt = Path(__file__).parent.parent / "easi" / "simulators" / "coppeliasim" / "v4_1_0" / "task_design.ttt" + ttt = ( + Path(__file__).parent.parent + / "easi" + / "simulators" + / "coppeliasim" + / "v4_1_0" + / "task_design.ttt" + ) assert ttt.exists(), f"task_design.ttt not found at {ttt}" def test_lua_addon_exists(self): - lua = Path(__file__).parent.parent / "easi" / "simulators" / "coppeliasim" / "v4_1_0" / "simAddOnScript_PyRep.lua" + lua = ( + Path(__file__).parent.parent + / "easi" + / "simulators" + / "coppeliasim" + / "v4_1_0" + / "simAddOnScript_PyRep.lua" + ) assert lua.exists(), f"simAddOnScript_PyRep.lua not found at {lua}" diff --git a/tests/test_env_vars_wiring.py b/tests/test_env_vars_wiring.py index e4aeb56..d0bb559 100644 --- a/tests/test_env_vars_wiring.py +++ b/tests/test_env_vars_wiring.py @@ -6,85 +6,279 @@ from unittest.mock import MagicMock, patch +def _make_runner(sim_gpus=None, render_platform_name=None): + from easi.evaluation.runner import EvaluationRunner + + runner = EvaluationRunner.__new__(EvaluationRunner) + runner.data_dir = Path("/tmp/fake") + runner.render_platform_name = render_platform_name + runner.sim_gpus = sim_gpus + runner._render_platform = None + return runner + + +def _make_env_mgr(env_vars=None, platform="headless"): + from easi.core.render_platforms import EnvVars + + mgr = MagicMock() + mgr.env_is_ready.return_value = True + mgr.get_python_executable.return_value = "/usr/bin/python3" + mgr.default_render_platform = platform + mgr.supported_render_platforms = [platform] + mgr.screen_config = "1024x768x24" + mgr.get_env_vars.return_value = env_vars if env_vars is not None else EnvVars() + return mgr + + +def _make_entry(render_adapter=None): + entry = MagicMock() + entry.runtime = "conda" + entry.render_adapter = render_adapter + return entry + + +def _make_sim_cls(bridge_path="/fake/bridge.py"): + cls = MagicMock() + cls.return_value._get_bridge_script_path.return_value = Path(bridge_path) + return cls + + class TestEnvVarsWiring: """Verify env vars flow from env_manager to SubprocessRunner.""" def test_runner_passes_env_vars_to_subprocess(self): from easi.core.render_platforms import EnvVars, get_render_platform - from easi.evaluation.runner import EvaluationRunner - - runner = EvaluationRunner.__new__(EvaluationRunner) - runner.data_dir = Path("/tmp/fake") - runner.render_platform_name = None - runner.sim_gpus = None - runner._render_platform = None - - mock_env_mgr = MagicMock() - mock_env_mgr.env_is_ready.return_value = True - mock_env_mgr.get_python_executable.return_value = "/usr/bin/python3" - mock_env_mgr.default_render_platform = "headless" - mock_env_mgr.supported_render_platforms = ["headless"] - mock_env_mgr.screen_config = "1024x768x24" - mock_env_mgr.get_env_vars.return_value = EnvVars(replace={"SIM_ROOT": "/opt/sim"}) - - mock_sim_cls = MagicMock() - mock_sim = mock_sim_cls.return_value - mock_sim._get_bridge_script_path.return_value = Path("/fake/bridge.py") - - mock_entry = MagicMock() - mock_entry.runtime = "conda" - - with patch("easi.simulators.registry.get_simulator_entry", return_value=mock_entry), \ - patch("easi.simulators.registry.create_env_manager", return_value=mock_env_mgr), \ - patch("easi.simulators.registry.load_simulator_class", return_value=mock_sim_cls), \ - patch("easi.simulators.registry.resolve_render_platform", - side_effect=lambda key, name, env_manager=None: get_render_platform(name)), \ - patch("easi.simulators.subprocess_runner.SubprocessRunner") as MockRunner: - mock_runner_instance = MockRunner.return_value - mock_runner_instance.launch.return_value = None - runner._create_simulator("fake:v1") + runner = _make_runner() + mock_env_mgr = _make_env_mgr(EnvVars(replace={"SIM_ROOT": "/opt/sim"})) + mock_entry = _make_entry() - call_kwargs = MockRunner.call_args - extra_env = call_kwargs.kwargs.get("extra_env") + with ( + patch( + "easi.simulators.registry.get_simulator_entry", return_value=mock_entry + ), + patch( + "easi.simulators.registry.create_env_manager", return_value=mock_env_mgr + ), + patch( + "easi.simulators.registry.load_simulator_class", + return_value=_make_sim_cls(), + ), + patch( + "easi.simulators.registry.resolve_render_platform", + side_effect=lambda key, name, env_manager=None: get_render_platform( + name + ), + ), + patch("easi.simulators.subprocess_runner.SubprocessRunner") as MockRunner, + ): + MockRunner.return_value.launch.return_value = None + runner._create_simulator("fake:v1") + extra_env = MockRunner.call_args.kwargs.get("extra_env") assert isinstance(extra_env, EnvVars) assert extra_env.replace == {"SIM_ROOT": "/opt/sim"} def test_runner_passes_none_when_no_env_vars(self): + from easi.core.render_platforms import get_render_platform + + runner = _make_runner() + mock_env_mgr = _make_env_mgr() + mock_entry = _make_entry() + + with ( + patch( + "easi.simulators.registry.get_simulator_entry", return_value=mock_entry + ), + patch( + "easi.simulators.registry.create_env_manager", return_value=mock_env_mgr + ), + patch( + "easi.simulators.registry.load_simulator_class", + return_value=_make_sim_cls(), + ), + patch( + "easi.simulators.registry.resolve_render_platform", + side_effect=lambda key, name, env_manager=None: get_render_platform( + name + ), + ), + patch("easi.simulators.subprocess_runner.SubprocessRunner") as MockRunner, + ): + MockRunner.return_value.launch.return_value = None + runner._create_simulator("fake:v1") + assert MockRunner.call_args.kwargs.get("extra_env") is None + + +class TestBindingCompositionWiring: + """Prove binding+adapter env composition is correct in the runner.""" + + def _run_with_binding(self, binding, sim_gpus=None, sim_env=None, adapter=None): from easi.core.render_platforms import EnvVars, get_render_platform - from easi.evaluation.runner import EvaluationRunner - - runner = EvaluationRunner.__new__(EvaluationRunner) - runner.data_dir = Path("/tmp/fake") - runner.render_platform_name = None - runner.sim_gpus = None - runner._render_platform = None - - mock_env_mgr = MagicMock() - mock_env_mgr.env_is_ready.return_value = True - mock_env_mgr.get_python_executable.return_value = "/usr/bin/python3" - mock_env_mgr.default_render_platform = "headless" - mock_env_mgr.supported_render_platforms = ["headless"] - mock_env_mgr.screen_config = "1024x768x24" - mock_env_mgr.get_env_vars.return_value = EnvVars() - - mock_sim_cls = MagicMock() - mock_sim = mock_sim_cls.return_value - mock_sim._get_bridge_script_path.return_value = Path("/fake/bridge.py") - - mock_entry = MagicMock() - mock_entry.runtime = "conda" - - with patch("easi.simulators.registry.get_simulator_entry", return_value=mock_entry), \ - patch("easi.simulators.registry.create_env_manager", return_value=mock_env_mgr), \ - patch("easi.simulators.registry.load_simulator_class", return_value=mock_sim_cls), \ - patch("easi.simulators.registry.resolve_render_platform", - side_effect=lambda key, name, env_manager=None: get_render_platform(name)), \ - patch("easi.simulators.subprocess_runner.SubprocessRunner") as MockRunner: - mock_runner_instance = MockRunner.return_value - mock_runner_instance.launch.return_value = None + runner = _make_runner(sim_gpus=sim_gpus) + + mock_platform = MagicMock() + mock_platform.name = "xorg" + mock_platform.for_worker.return_value = binding + mock_platform.get_env_vars.return_value = EnvVars() + mock_platform.wrap_command.side_effect = lambda cmd, cfg: cmd + + mock_env_mgr = _make_env_mgr(env_vars=sim_env, platform="xorg") + mock_env_mgr.supported_render_platforms = ["xorg"] + mock_env_mgr.default_render_platform = "xorg" + mock_entry = _make_entry() + + with ( + patch( + "easi.simulators.registry.get_simulator_entry", return_value=mock_entry + ), + patch( + "easi.simulators.registry.create_env_manager", return_value=mock_env_mgr + ), + patch( + "easi.simulators.registry.load_simulator_class", + return_value=_make_sim_cls(), + ), + patch( + "easi.simulators.registry.resolve_render_platform", + return_value=mock_platform, + ), + patch( + "easi.simulators.registry.resolve_render_adapter", + return_value=adapter, + ), + patch("easi.simulators.subprocess_runner.SubprocessRunner") as MockRunner, + ): + MockRunner.return_value.launch.return_value = None runner._create_simulator("fake:v1") + return MockRunner.call_args.kwargs - call_kwargs = MockRunner.call_args - assert call_kwargs.kwargs.get("extra_env") is None + def test_binding_display_appears_in_extra_env(self): + from easi.core.render_platforms import WorkerBinding + + binding = WorkerBinding(display=":10", cuda_visible_devices="0") + kwargs = self._run_with_binding(binding) + extra_env = kwargs.get("extra_env") + assert extra_env is not None + assert extra_env.replace.get("DISPLAY") == ":10" + + def test_binding_cuda_appears_in_extra_env(self): + from easi.core.render_platforms import WorkerBinding + + binding = WorkerBinding(display=":10", cuda_visible_devices="2") + kwargs = self._run_with_binding(binding) + extra_env = kwargs.get("extra_env") + assert extra_env is not None + assert extra_env.replace.get("CUDA_VISIBLE_DEVICES") == "2" + + def test_adapter_env_merged_into_extra_env(self): + from easi.core.render_platforms import ( + EnvVars, + SimulatorRenderAdapter, + WorkerBinding, + ) + + class MarkingAdapter(SimulatorRenderAdapter): + def get_env_vars(self, binding): + return EnvVars(replace={"OMNIGIBSON_HEADLESS": "0"}) + + binding = WorkerBinding(display=":10", cuda_visible_devices="0") + kwargs = self._run_with_binding(binding, adapter=MarkingAdapter()) + extra_env = kwargs.get("extra_env") + assert extra_env is not None + assert extra_env.replace.get("OMNIGIBSON_HEADLESS") == "0" + + def test_general_sim_env_preserved_alongside_binding_env(self): + from easi.core.render_platforms import EnvVars, WorkerBinding + + binding = WorkerBinding(display=":11", cuda_visible_devices="1") + sim_env = EnvVars(replace={"DATA_ROOT": "/mnt/data"}) + kwargs = self._run_with_binding(binding, sim_env=sim_env) + extra_env = kwargs.get("extra_env") + assert extra_env is not None + assert extra_env.replace.get("DATA_ROOT") == "/mnt/data" + assert extra_env.replace.get("DISPLAY") == ":11" + + def test_explicit_gpu_binding_prevents_sim_gpus_override(self): + from easi.core.render_platforms import WorkerBinding + + binding = WorkerBinding(display=":10", cuda_visible_devices="0") + kwargs = self._run_with_binding(binding, sim_gpus=[3]) + extra_env = kwargs.get("extra_env") + assert extra_env is not None + assert extra_env.replace.get("CUDA_VISIBLE_DEVICES") == "0" + + def test_gpu_pinning_applied_when_binding_has_no_cuda(self): + from easi.core.render_platforms import WorkerBinding + + binding = WorkerBinding(display=":10", cuda_visible_devices=None) + kwargs = self._run_with_binding(binding, sim_gpus=[5]) + extra_env = kwargs.get("extra_env") + assert extra_env is not None + assert extra_env.replace.get("CUDA_VISIBLE_DEVICES") == "5" + + def test_base_platform_used_for_command_wrapping_not_binding(self): + from easi.core.render_platforms import WorkerBinding + + binding = WorkerBinding(display=":10", cuda_visible_devices="0") + kwargs = self._run_with_binding(binding) + rp = kwargs.get("render_platform") + assert rp is not None + assert rp.name == "xorg" + + def test_binding_path_passes_adapter_and_binding_to_subprocess_runner(self): + from easi.core.render_platforms import ( + EnvVars, + SimulatorRenderAdapter, + WorkerBinding, + ) + + class SentinelAdapter(SimulatorRenderAdapter): + def get_env_vars(self, binding): + return EnvVars() + + adapter = SentinelAdapter() + binding = WorkerBinding(display=":10", cuda_visible_devices="0") + kwargs = self._run_with_binding(binding, adapter=adapter) + assert kwargs.get("render_adapter") is adapter + assert kwargs.get("worker_binding") is binding + + def test_no_adapter_registered_passes_none_adapter_with_default_binding(self): + from easi.core.render_platforms import ( + EnvVars, + WorkerBinding, + get_render_platform, + ) + + runner = _make_runner() + mock_env_mgr = _make_env_mgr(env_vars=EnvVars(replace={"X": "1"})) + mock_entry = _make_entry() + + with ( + patch( + "easi.simulators.registry.get_simulator_entry", return_value=mock_entry + ), + patch( + "easi.simulators.registry.create_env_manager", return_value=mock_env_mgr + ), + patch( + "easi.simulators.registry.load_simulator_class", + return_value=_make_sim_cls(), + ), + patch( + "easi.simulators.registry.resolve_render_platform", + side_effect=lambda key, name, env_manager=None: get_render_platform( + name + ), + ), + patch( + "easi.simulators.registry.resolve_render_adapter", + return_value=None, + ), + patch("easi.simulators.subprocess_runner.SubprocessRunner") as MockRunner, + ): + MockRunner.return_value.launch.return_value = None + runner._create_simulator("fake:v1") + kwargs = MockRunner.call_args.kwargs + assert kwargs.get("render_adapter") is None + assert isinstance(kwargs.get("worker_binding"), WorkerBinding) diff --git a/tests/test_omnigibson_simulator.py b/tests/test_omnigibson_simulator.py index bf49378..704ff6f 100644 --- a/tests/test_omnigibson_simulator.py +++ b/tests/test_omnigibson_simulator.py @@ -1,4 +1,5 @@ """Tests for OmniGibson simulator integration (offline, no simulator needed).""" + import ast from pathlib import Path @@ -34,14 +35,15 @@ def test_manifest_installation_kwargs(self): assert "BEHAVIOR-1K" in kwargs["behavior_1k_repo"] assert kwargs["behavior_1k_tag"] == "v3.7.2" - def test_manifest_render_platforms(self): + def test_manifest_uses_render_adapter_without_custom_platforms(self): with open("easi/simulators/omnigibson/manifest.yaml") as f: data = yaml.safe_load(f) - rp = data["versions"]["v3_7_2"].get("render_platforms", {}) - assert "native" in rp, "missing 'native' render platform" - assert "auto" in rp, "missing 'auto' render platform" - assert "render_platforms.OmniGibsonNativePlatform" in rp["native"] - assert "render_platforms.OmniGibsonAutoPlatform" in rp["auto"] + version = data["versions"]["v3_7_2"] + assert ( + version["render_adapter"] + == "easi.simulators.omnigibson.v3_7_2.render_platforms.OmniGibsonRenderAdapter" + ) + assert version.get("render_platforms", {}) == {} class TestOmniGibsonEnvManager: @@ -49,22 +51,26 @@ class TestOmniGibsonEnvManager: def test_import(self): from easi.simulators.omnigibson.v3_7_2.env_manager import OmniGibsonEnvManager + mgr = OmniGibsonEnvManager() assert mgr.simulator_name == "omnigibson" assert mgr.version == "v3_7_2" def test_env_name(self): from easi.simulators.omnigibson.v3_7_2.env_manager import OmniGibsonEnvManager + mgr = OmniGibsonEnvManager() assert mgr.get_env_name() == "easi_omnigibson_v3_7_2" def test_default_render_platform_native(self): from easi.simulators.omnigibson.v3_7_2.env_manager import OmniGibsonEnvManager + mgr = OmniGibsonEnvManager() assert mgr.default_render_platform == "native" def test_env_vars(self): from easi.simulators.omnigibson.v3_7_2.env_manager import OmniGibsonEnvManager + mgr = OmniGibsonEnvManager() ev = mgr.get_env_vars() assert "OMNIGIBSON_HEADLESS" not in ev.replace @@ -75,6 +81,7 @@ def test_get_python_executable_is_local(self, monkeypatch): """Python executable should be a /tmp copy (NFS workaround).""" import sys from easi.simulators.omnigibson.v3_7_2.env_manager import OmniGibsonEnvManager + mgr = OmniGibsonEnvManager() # Mock _get_conda_python to return a real existing binary so the copy # can succeed without the conda env being installed. @@ -86,6 +93,7 @@ def test_get_python_executable_is_local(self, monkeypatch): def test_get_conda_python_is_conda_env(self): """_get_conda_python() returns the real conda env path.""" from easi.simulators.omnigibson.v3_7_2.env_manager import OmniGibsonEnvManager + mgr = OmniGibsonEnvManager() conda_python = mgr._get_conda_python() assert "easi_omnigibson_v3_7_2" in conda_python @@ -93,28 +101,33 @@ def test_get_conda_python_is_conda_env(self): def test_system_deps(self): from easi.simulators.omnigibson.v3_7_2.env_manager import OmniGibsonEnvManager + mgr = OmniGibsonEnvManager() deps = mgr.get_system_deps() assert "conda" in deps def test_validation_import(self): from easi.simulators.omnigibson.v3_7_2.env_manager import OmniGibsonEnvManager + mgr = OmniGibsonEnvManager() val = mgr.get_validation_import() assert "omnigibson" in val def test_conda_env_yaml_exists(self): from easi.simulators.omnigibson.v3_7_2.env_manager import OmniGibsonEnvManager + mgr = OmniGibsonEnvManager() assert mgr.get_conda_env_yaml_path().exists() def test_requirements_txt_exists(self): from easi.simulators.omnigibson.v3_7_2.env_manager import OmniGibsonEnvManager + mgr = OmniGibsonEnvManager() assert mgr.get_requirements_txt_path().exists() def test_isaac_sim_packages_count(self): from easi.simulators.omnigibson.v3_7_2.env_manager import ISAAC_SIM_PACKAGES + assert len(ISAAC_SIM_PACKAGES) == 26 @@ -123,12 +136,14 @@ class TestOmniGibsonSimulator: def test_import(self): from easi.simulators.omnigibson.v3_7_2.simulator import OmniGibsonSimulator + sim = OmniGibsonSimulator() assert sim.name == "omnigibson" assert sim.version == "v3_7_2" def test_bridge_script_path(self): from easi.simulators.omnigibson.v3_7_2.simulator import OmniGibsonSimulator + sim = OmniGibsonSimulator() bridge_path = sim._get_bridge_script_path() assert bridge_path.exists() @@ -136,6 +151,7 @@ def test_bridge_script_path(self): def test_bridge_script_has_bridge_class(self): from easi.simulators.omnigibson.v3_7_2.simulator import OmniGibsonSimulator + content = OmniGibsonSimulator()._get_bridge_script_path().read_text() assert "OmniGibsonBridge" in content @@ -153,57 +169,65 @@ class TestOmniGibsonRegistry: def test_env_list_includes_omnigibson(self): from easi.simulators.registry import list_simulators + sims = list_simulators() assert "omnigibson:v3_7_2" in sims def test_load_simulator_class(self): from easi.simulators.registry import load_simulator_class + SimClass = load_simulator_class("omnigibson:v3_7_2") sim = SimClass() assert sim.name == "omnigibson" def test_create_env_manager(self): from easi.simulators.registry import create_env_manager + mgr = create_env_manager("omnigibson:v3_7_2") assert mgr.simulator_name == "omnigibson" assert mgr.default_render_platform == "native" def test_create_env_manager_has_installation_kwargs(self): from easi.simulators.registry import create_env_manager + mgr = create_env_manager("omnigibson") assert mgr.installation_kwargs["cuda_version"] == "12.4" assert "BEHAVIOR-1K" in mgr.installation_kwargs["behavior_1k_repo"] -class TestOmniGibsonRenderPlatforms: - """Test OmniGibsonNativePlatform and OmniGibsonAutoPlatform.""" - - def test_native_platform_name(self): - from easi.simulators.omnigibson.v3_7_2.render_platforms import OmniGibsonNativePlatform - assert OmniGibsonNativePlatform().name == "native" +class TestOmniGibsonRenderAdapterIntegration: + def test_resolve_builtin_native_platform(self): + from easi.core.render_platforms import NativePlatform + from easi.simulators.registry import resolve_render_platform - def test_native_platform_sets_headless_0(self): - from easi.simulators.omnigibson.v3_7_2.render_platforms import OmniGibsonNativePlatform - ev = OmniGibsonNativePlatform().get_env_vars() - assert ev.replace["OMNIGIBSON_HEADLESS"] == "0" + platform = resolve_render_platform("omnigibson:v3_7_2", "native") + assert isinstance(platform, NativePlatform) + assert platform.name == "native" - def test_native_platform_no_wrap(self): - from easi.simulators.omnigibson.v3_7_2.render_platforms import OmniGibsonNativePlatform - cmd = ["python", "bridge.py"] - assert OmniGibsonNativePlatform().wrap_command(cmd, "1024x768x24") == cmd + def test_resolve_builtin_auto_platform(self): + from easi.core.render_platforms import AutoPlatform + from easi.simulators.registry import resolve_render_platform - def test_auto_platform_name(self): - from easi.simulators.omnigibson.v3_7_2.render_platforms import OmniGibsonAutoPlatform - assert OmniGibsonAutoPlatform().name == "auto" + platform = resolve_render_platform("omnigibson:v3_7_2", "auto") + assert isinstance(platform, AutoPlatform) + assert platform.name == "auto" - def test_auto_platform_with_display(self, monkeypatch): + def test_adapter_auto_with_display(self, monkeypatch): monkeypatch.setenv("DISPLAY", ":1") - from easi.simulators.omnigibson.v3_7_2.render_platforms import OmniGibsonAutoPlatform - ev = OmniGibsonAutoPlatform().get_env_vars() + from easi.core.render_platforms import WorkerBinding + from easi.simulators.omnigibson.v3_7_2.render_platforms import ( + OmniGibsonRenderAdapter, + ) + + ev = OmniGibsonRenderAdapter().get_env_vars(WorkerBinding()) assert ev.replace["OMNIGIBSON_HEADLESS"] == "0" - def test_auto_platform_without_display(self, monkeypatch): + def test_adapter_auto_without_display(self, monkeypatch): monkeypatch.delenv("DISPLAY", raising=False) - from easi.simulators.omnigibson.v3_7_2.render_platforms import OmniGibsonAutoPlatform - ev = OmniGibsonAutoPlatform().get_env_vars() + from easi.core.render_platforms import WorkerBinding + from easi.simulators.omnigibson.v3_7_2.render_platforms import ( + OmniGibsonRenderAdapter, + ) + + ev = OmniGibsonRenderAdapter().get_env_vars(WorkerBinding()) assert ev.replace["OMNIGIBSON_HEADLESS"] == "1" diff --git a/tests/test_render_platform.py b/tests/test_render_platform.py index d792d99..ff5cf23 100644 --- a/tests/test_render_platform.py +++ b/tests/test_render_platform.py @@ -364,6 +364,7 @@ def test_platform_env_vars_merged(self): extra_env=EnvVars(replace={"SIM_ROOT": "/opt/sim"}), ) env = runner._build_subprocess_env() + assert env is not None assert env["PYOPENGL_PLATFORM"] == "egl" assert env["SIM_ROOT"] == "/opt/sim" @@ -417,7 +418,7 @@ def test_coppeliasim(self): def test_coppeliasim_env_vars_are_platform_agnostic(self): """CoppeliaSim env_manager should NOT set QT_QPA_PLATFORM_PLUGIN_PATH or __EGL_VENDOR_LIBRARY_FILENAMES. - Those platform-specific vars are now handled by custom render platform classes. + Those platform-specific vars are now handled by the render platform adapter. """ from easi.simulators.coppeliasim.v4_1_0.env_manager import ( CoppeliaSimEnvManagerV410, @@ -551,9 +552,7 @@ def get_env_vars(self): assert platform.get_env_vars().replace == {"CUSTOM": "1"} -class TestCoppeliaSimCustomPlatforms: - """Test CoppeliaSim-specific custom render platform classes.""" - +class TestCoppeliaSimAdapterIntegration: def _make_mock_env_manager(self, binary_dir_name="CoppeliaSim"): from unittest.mock import MagicMock @@ -568,137 +567,149 @@ def _make_mock_env_manager(self, binary_dir_name="CoppeliaSim"): ).replace("{env_dir}", t["env_dir"]) return mgr - def test_native_has_qt_plugin_path_and_headless_false(self): + def test_adapter_native_has_qt_plugin_path_and_headless_false(self): + from easi.core.render_platforms import WorkerBinding from easi.simulators.coppeliasim.v4_1_0.render_platforms import ( - CoppeliaSimNativePlatform, + CoppeliaSimRenderAdapter, ) mgr = self._make_mock_env_manager() - p = CoppeliaSimNativePlatform(env_manager=mgr) - assert p.name == "native" - ev = p.get_env_vars() + adapter = CoppeliaSimRenderAdapter(env_manager=mgr) + ev = adapter.get_env_vars(WorkerBinding(metadata={"backend": "native"})) assert "QT_QPA_PLATFORM_PLUGIN_PATH" in ev.prepend assert ev.replace["COPPELIASIM_HEADLESS"] == "0" assert "__EGL_VENDOR_LIBRARY_FILENAMES" not in ev.replace - def test_xvfb_has_qt_plugin_path_and_headless_true(self): + def test_adapter_xvfb_has_qt_plugin_path_and_headless_true(self): + from easi.core.render_platforms import WorkerBinding from easi.simulators.coppeliasim.v4_1_0.render_platforms import ( - CoppeliaSimXvfbPlatform, + CoppeliaSimRenderAdapter, ) mgr = self._make_mock_env_manager() - p = CoppeliaSimXvfbPlatform(env_manager=mgr) - assert p.name == "xvfb" - ev = p.get_env_vars() + adapter = CoppeliaSimRenderAdapter(env_manager=mgr) + ev = adapter.get_env_vars(WorkerBinding(metadata={"backend": "xvfb"})) assert "QT_QPA_PLATFORM_PLUGIN_PATH" in ev.prepend assert "CoppeliaSim" in ev.prepend["QT_QPA_PLATFORM_PLUGIN_PATH"] assert ev.replace["COPPELIASIM_HEADLESS"] == "1" - def test_xvfb_wraps_command(self): - from easi.simulators.coppeliasim.v4_1_0.render_platforms import ( - CoppeliaSimXvfbPlatform, - ) + def test_builtin_xvfb_wraps_command(self): + from easi.core.render_platforms import XvfbPlatform - p = CoppeliaSimXvfbPlatform() - cmd = ["python", "bridge.py"] - wrapped = p.wrap_command(cmd, "1280x720x24") + wrapped = XvfbPlatform().wrap_command(["python", "bridge.py"], "1280x720x24") assert wrapped[0] == "xvfb-run" - def test_auto_native_when_display(self): + def test_adapter_auto_native_when_display(self): + from easi.core.render_platforms import WorkerBinding from easi.simulators.coppeliasim.v4_1_0.render_platforms import ( - CoppeliaSimAutoPlatform, + CoppeliaSimRenderAdapter, ) mgr = self._make_mock_env_manager() - p = CoppeliaSimAutoPlatform(env_manager=mgr) - assert p.name == "auto" + adapter = CoppeliaSimRenderAdapter(env_manager=mgr) with patch.dict(os.environ, {"DISPLAY": ":0"}): - ev = p.get_env_vars() + ev = adapter.get_env_vars(WorkerBinding()) assert "QT_QPA_PLATFORM_PLUGIN_PATH" in ev.prepend assert ev.replace["COPPELIASIM_HEADLESS"] == "0" - def test_auto_xvfb_when_no_display(self): + def test_adapter_auto_xvfb_when_no_display(self): + from easi.core.render_platforms import WorkerBinding from easi.simulators.coppeliasim.v4_1_0.render_platforms import ( - CoppeliaSimAutoPlatform, + CoppeliaSimRenderAdapter, ) mgr = self._make_mock_env_manager() - p = CoppeliaSimAutoPlatform(env_manager=mgr) + adapter = CoppeliaSimRenderAdapter(env_manager=mgr) env = os.environ.copy() env.pop("DISPLAY", None) with patch.dict(os.environ, env, clear=True): - ev = p.get_env_vars() + ev = adapter.get_env_vars(WorkerBinding()) assert "QT_QPA_PLATFORM_PLUGIN_PATH" in ev.prepend assert ev.replace["COPPELIASIM_HEADLESS"] == "1" - def test_xvfb_no_env_manager_returns_empty(self): + def test_adapter_without_env_manager_returns_empty(self): + from easi.core.render_platforms import WorkerBinding from easi.simulators.coppeliasim.v4_1_0.render_platforms import ( - CoppeliaSimXvfbPlatform, + CoppeliaSimRenderAdapter, ) - p = CoppeliaSimXvfbPlatform() # no env_manager - ev = p.get_env_vars() + ev = CoppeliaSimRenderAdapter().get_env_vars( + WorkerBinding(metadata={"backend": "xvfb"}) + ) assert not ev - def test_manifest_registers_custom_platforms(self): + def test_manifest_uses_adapter_instead_of_custom_platforms(self): from easi.simulators.registry import get_simulator_entry entry = get_simulator_entry("coppeliasim:v4_1_0") - assert "auto" in entry.render_platforms - assert "native" in entry.render_platforms - assert "xvfb" in entry.render_platforms - assert "xorg" in entry.render_platforms + assert entry.render_platforms == {} - def test_resolve_coppeliasim_custom_platform(self): - from easi.simulators.coppeliasim.v4_1_0.render_platforms import ( - CoppeliaSimAutoPlatform, - ) + def test_resolve_coppeliasim_auto_platform_uses_builtin(self): + from easi.core.render_platforms import AutoPlatform from easi.simulators.registry import resolve_render_platform platform = resolve_render_platform("coppeliasim:v4_1_0", "auto") - assert isinstance(platform, CoppeliaSimAutoPlatform) + assert isinstance(platform, AutoPlatform) assert platform.name == "auto" - def test_xorg_has_qt_plugin_path_and_headless_false(self): + def test_xorg_binding_plus_adapter_has_qt_plugin_path_and_headless_false(self): + from easi.core.render_platforms import EnvVars, WorkerBinding + from easi.core.render_platforms.xorg import XorgPlatform from easi.core.render_platforms.xorg_manager import XorgInstance from easi.simulators.coppeliasim.v4_1_0.render_platforms import ( - CoppeliaSimXorgPlatform, + CoppeliaSimRenderAdapter, ) mgr = self._make_mock_env_manager() - p = CoppeliaSimXorgPlatform(env_manager=mgr) - assert p.name == "xorg" - p._instances = [XorgInstance(display=10, gpu_id=0, pid=1)] - worker = p.for_worker(0) - ev = worker.get_env_vars() - assert "QT_QPA_PLATFORM_PLUGIN_PATH" in ev.prepend - assert "CoppeliaSim" in ev.prepend["QT_QPA_PLATFORM_PLUGIN_PATH"] - assert ev.replace["COPPELIASIM_HEADLESS"] == "0" - assert ev.replace["DISPLAY"] == ":10" - assert ev.replace["CUDA_VISIBLE_DEVICES"] == "0" + platform = XorgPlatform() + platform._instances = [XorgInstance(display=10, gpu_id=0, pid=1)] + binding = platform.for_worker(0) + adapter_env = CoppeliaSimRenderAdapter(env_manager=mgr).get_env_vars(binding) + merged_binding = WorkerBinding( + display=binding.display, + cuda_visible_devices=binding.cuda_visible_devices, + extra_env=EnvVars.merge(binding.extra_env, adapter_env), + metadata=dict(binding.metadata), + ) + assert isinstance(binding, WorkerBinding) + assert "QT_QPA_PLATFORM_PLUGIN_PATH" in merged_binding.extra_env.prepend + assert ( + "CoppeliaSim" + in merged_binding.extra_env.prepend["QT_QPA_PLATFORM_PLUGIN_PATH"] + ) + assert merged_binding.extra_env.replace["COPPELIASIM_HEADLESS"] == "0" + assert merged_binding.display == ":10" + assert merged_binding.cuda_visible_devices == "0" - def test_xorg_no_env_manager_returns_base_env_only(self): + def test_xorg_without_env_manager_returns_base_binding_env_only(self): + from easi.core.render_platforms import EnvVars, WorkerBinding + from easi.core.render_platforms.xorg import XorgPlatform from easi.core.render_platforms.xorg_manager import XorgInstance from easi.simulators.coppeliasim.v4_1_0.render_platforms import ( - CoppeliaSimXorgPlatform, + CoppeliaSimRenderAdapter, ) - p = CoppeliaSimXorgPlatform() - p._instances = [XorgInstance(display=11, gpu_id=2, pid=1)] - worker = p.for_worker(0) - ev = worker.get_env_vars() - assert ev.replace["DISPLAY"] == ":11" - assert ev.replace["CUDA_VISIBLE_DEVICES"] == "2" - assert "QT_QPA_PLATFORM_PLUGIN_PATH" not in ev.prepend - - def test_xorg_resolve_from_registry(self): - from easi.simulators.coppeliasim.v4_1_0.render_platforms import ( - CoppeliaSimXorgPlatform, + platform = XorgPlatform() + platform._instances = [XorgInstance(display=11, gpu_id=2, pid=1)] + binding = platform.for_worker(0) + adapter_env = CoppeliaSimRenderAdapter().get_env_vars(binding) + merged_binding = WorkerBinding( + display=binding.display, + cuda_visible_devices=binding.cuda_visible_devices, + extra_env=EnvVars.merge(binding.extra_env, adapter_env), + metadata=dict(binding.metadata), ) + assert isinstance(binding, WorkerBinding) + assert merged_binding.display == ":11" + assert merged_binding.cuda_visible_devices == "2" + assert "QT_QPA_PLATFORM_PLUGIN_PATH" not in merged_binding.extra_env.prepend + + def test_xorg_resolve_from_registry_uses_builtin(self): + from easi.core.render_platforms.xorg import XorgPlatform from easi.simulators.registry import resolve_render_platform platform = resolve_render_platform("coppeliasim:v4_1_0", "xorg") - assert isinstance(platform, CoppeliaSimXorgPlatform) + assert isinstance(platform, XorgPlatform) assert platform.name == "xorg" @@ -774,6 +785,7 @@ def test_default_uses_env_manager_platform(self): mock_entry = MagicMock() mock_entry.runtime = "conda" + mock_entry.render_adapter = None with ( patch( @@ -797,6 +809,7 @@ def test_default_uses_env_manager_platform(self): MockRunner.return_value.launch.return_value = None runner._create_simulator("fake:v1") rp = MockRunner.call_args.kwargs.get("render_platform") + assert rp is not None assert rp.name == "auto" def test_cli_override_wins(self): @@ -817,6 +830,7 @@ def test_cli_override_wins(self): mock_entry = MagicMock() mock_entry.runtime = "conda" + mock_entry.render_adapter = None with ( patch( @@ -840,6 +854,7 @@ def test_cli_override_wins(self): MockRunner.return_value.launch.return_value = None runner._create_simulator("fake:v1") rp = MockRunner.call_args.kwargs.get("render_platform") + assert rp is not None assert rp.name == "xvfb" def test_yaml_override_used_when_no_cli(self): @@ -861,6 +876,7 @@ def test_yaml_override_used_when_no_cli(self): mock_entry = MagicMock() mock_entry.runtime = "conda" + mock_entry.render_adapter = None with ( patch( @@ -884,6 +900,7 @@ def test_yaml_override_used_when_no_cli(self): MockRunner.return_value.launch.return_value = None runner._create_simulator("fake:v1", task=mock_task) rp = MockRunner.call_args.kwargs.get("render_platform") + assert rp is not None assert rp.name == "egl" def test_cli_beats_yaml(self): @@ -905,6 +922,7 @@ def test_cli_beats_yaml(self): mock_entry = MagicMock() mock_entry.runtime = "conda" + mock_entry.render_adapter = None with ( patch( @@ -928,6 +946,7 @@ def test_cli_beats_yaml(self): MockRunner.return_value.launch.return_value = None runner._create_simulator("fake:v1", task=mock_task) rp = MockRunner.call_args.kwargs.get("render_platform") + assert rp is not None assert rp.name == "xvfb" def test_unsupported_platform_raises(self): @@ -949,6 +968,7 @@ def test_unsupported_platform_raises(self): mock_entry = MagicMock() mock_entry.runtime = "conda" + mock_entry.render_adapter = None with ( patch( @@ -987,6 +1007,7 @@ def test_egl_env_vars_in_subprocess(self): extra_env=EnvVars(replace={"SIM_ROOT": "/opt/sim"}), ) env = runner._build_subprocess_env() + assert env is not None assert env["PYOPENGL_PLATFORM"] == "egl" assert env["SIM_ROOT"] == "/opt/sim" @@ -1019,3 +1040,459 @@ def test_render_platform_saved_in_config(self): render_platform="egl", ) assert runner._cli_options["render_platform"] == "egl" + + +class TestWorkerBinding: + def test_defaults_are_none_and_empty(self): + from easi.core.render_platforms import EnvVars, WorkerBinding + + b = WorkerBinding() + assert b.display is None + assert b.cuda_visible_devices is None + assert isinstance(b.extra_env, EnvVars) + assert not b.extra_env + assert b.metadata == {} + + def test_fields_set_correctly(self): + from easi.core.render_platforms import EnvVars, WorkerBinding + + ev = EnvVars(replace={"FOO": "bar"}) + b = WorkerBinding( + display=":10", + cuda_visible_devices="2", + extra_env=ev, + metadata={"backend": "xorg"}, + ) + assert b.display == ":10" + assert b.cuda_visible_devices == "2" + assert b.extra_env.replace == {"FOO": "bar"} + assert b.metadata == {"backend": "xorg"} + + def test_two_bindings_are_independent(self): + from easi.core.render_platforms import WorkerBinding + + b1 = WorkerBinding(display=":10", cuda_visible_devices="0") + b2 = WorkerBinding(display=":11", cuda_visible_devices="1") + assert b1.display != b2.display + assert b1.cuda_visible_devices != b2.cuda_visible_devices + + def test_metadata_is_mutable_dict(self): + from easi.core.render_platforms import WorkerBinding + + b = WorkerBinding() + b.metadata["key"] = "value" + assert b.metadata["key"] == "value" + + def test_extra_env_defaults_are_not_shared(self): + from easi.core.render_platforms import WorkerBinding + + b1 = WorkerBinding() + b2 = WorkerBinding() + b1.extra_env.replace["X"] = "1" + assert "X" not in b2.extra_env.replace + + +class TestSimulatorRenderAdapter: + def _make_adapter(self): + from easi.core.render_platforms import SimulatorRenderAdapter + + class NoOpAdapter(SimulatorRenderAdapter): + pass + + return NoOpAdapter() + + def test_get_env_vars_returns_empty_envvars(self): + from easi.core.render_platforms import EnvVars, WorkerBinding + + adapter = self._make_adapter() + binding = WorkerBinding() + ev = adapter.get_env_vars(binding) + assert isinstance(ev, EnvVars) + assert not ev + + def test_wrap_command_returns_same_list(self): + from easi.core.render_platforms import WorkerBinding + + adapter = self._make_adapter() + binding = WorkerBinding() + cmd = ["python", "bridge.py", "--workspace", "/tmp"] + result = adapter.wrap_command(cmd, binding) + assert result == cmd + + def test_wrap_command_does_not_copy(self): + from easi.core.render_platforms import WorkerBinding + + adapter = self._make_adapter() + binding = WorkerBinding() + cmd = ["python", "bridge.py"] + result = adapter.wrap_command(cmd, binding) + assert result is cmd + + def test_get_env_vars_receives_binding(self): + from easi.core.render_platforms import ( + EnvVars, + SimulatorRenderAdapter, + WorkerBinding, + ) + + received = [] + + class RecordingAdapter(SimulatorRenderAdapter): + def get_env_vars(self, binding): + received.append(binding) + return EnvVars() + + adapter = RecordingAdapter() + b = WorkerBinding(display=":5", cuda_visible_devices="3") + adapter.get_env_vars(b) + assert received[0] is b + + def test_subclass_can_override_get_env_vars(self): + from easi.core.render_platforms import ( + EnvVars, + SimulatorRenderAdapter, + WorkerBinding, + ) + + class CustomAdapter(SimulatorRenderAdapter): + def get_env_vars(self, binding): + return EnvVars(replace={"CUSTOM": "1"}) + + adapter = CustomAdapter() + ev = adapter.get_env_vars(WorkerBinding()) + assert ev.replace == {"CUSTOM": "1"} + + def test_subclass_can_override_wrap_command(self): + from easi.core.render_platforms import SimulatorRenderAdapter, WorkerBinding + + class PrefixAdapter(SimulatorRenderAdapter): + def wrap_command(self, cmd, binding): + return ["prefix"] + cmd + + adapter = PrefixAdapter() + result = adapter.wrap_command(["python", "bridge.py"], WorkerBinding()) + assert result == ["prefix", "python", "bridge.py"] + + +class TestRenderAdapterRegistry: + """Test render_adapter manifest key registration and resolution.""" + + def test_entry_has_render_adapter_when_registered(self): + from easi.simulators.registry import get_simulator_entry + + entry = get_simulator_entry("dummy") + assert ( + entry.render_adapter + == "easi.simulators.dummy.v1.render_platforms.DummyRenderAdapter" + ) + + def test_entry_render_adapter_none_when_not_registered(self): + from easi.simulators.registry import get_simulator_entry + + entry = get_simulator_entry("ai2thor:v2_1_0") + assert entry.render_adapter is None + + def test_resolve_render_adapter_returns_instance(self): + from easi.simulators.dummy.v1.render_platforms import DummyRenderAdapter + from easi.simulators.registry import resolve_render_adapter + + adapter = resolve_render_adapter("dummy") + assert isinstance(adapter, DummyRenderAdapter) + + def test_resolve_render_adapter_returns_none_when_not_registered(self): + from easi.simulators.registry import resolve_render_adapter + + adapter = resolve_render_adapter("ai2thor:v2_1_0") + assert adapter is None + + def test_resolve_render_adapter_env_vars(self): + from easi.core.render_platforms import WorkerBinding + from easi.simulators.registry import resolve_render_adapter + + adapter = resolve_render_adapter("dummy") + assert adapter is not None + ev = adapter.get_env_vars(WorkerBinding()) + assert ev.replace == {"DUMMY_ADAPTER": "1"} + + def test_resolve_render_adapter_wrap_command_passthrough(self): + from easi.core.render_platforms import WorkerBinding + from easi.simulators.registry import resolve_render_adapter + + adapter = resolve_render_adapter("dummy") + assert adapter is not None + cmd = ["python", "bridge.py"] + assert adapter.wrap_command(cmd, WorkerBinding()) is cmd + + def test_legacy_render_platforms_still_work_alongside_adapter(self): + from easi.simulators.dummy.v1.render_platforms import DummyCustomPlatform + from easi.simulators.registry import resolve_render_platform + + platform = resolve_render_platform("dummy", "dummy_custom") + assert isinstance(platform, DummyCustomPlatform) + assert platform.name == "dummy_custom" + + def test_manifest_with_adapter_but_no_render_platforms(self): + from unittest.mock import patch + + from easi.simulators.registry import SimulatorEntry, resolve_render_adapter + + fake_entry = SimulatorEntry( + name="fake", + version="v1", + description="", + simulator_class="", + env_manager_class="", + python_version="3.10", + render_adapter="easi.simulators.dummy.v1.render_platforms.DummyRenderAdapter", + ) + with patch("easi.simulators.registry._get_registry") as mock_reg: + mock_reg.return_value = {"fake:v1": fake_entry} + adapter = resolve_render_adapter("fake:v1") + assert adapter is not None + + def test_coppeliasim_entry_has_render_adapter(self): + from easi.simulators.registry import get_simulator_entry + + entry = get_simulator_entry("coppeliasim:v4_1_0") + assert ( + entry.render_adapter + == "easi.simulators.coppeliasim.v4_1_0.render_platforms.CoppeliaSimRenderAdapter" + ) + + +class TestCoppeliaSimRenderAdapter: + def _make_mock_env_manager(self, binary_dir_name="CoppeliaSim"): + from unittest.mock import MagicMock + + mgr = MagicMock() + mgr.installation_kwargs = {"binary_dir_name": binary_dir_name} + mgr._get_template_variables.return_value = { + "env_dir": "/fake/envs/easi_coppeliasim_v4_1_0", + "extras_dir": "/fake/envs/easi_coppeliasim_v4_1_0/extras", + } + mgr._resolve_template.side_effect = lambda tmpl, t: tmpl.replace( + "{extras_dir}", t["extras_dir"] + ).replace("{env_dir}", t["env_dir"]) + return mgr + + def test_resolve_adapter_returns_instance(self): + from easi.simulators.coppeliasim.v4_1_0.render_platforms import ( + CoppeliaSimRenderAdapter, + ) + from easi.simulators.registry import resolve_render_adapter + + adapter = resolve_render_adapter("coppeliasim:v4_1_0") + assert isinstance(adapter, CoppeliaSimRenderAdapter) + + def test_adapter_native_binding_headless_false_no_mesa(self): + from easi.core.render_platforms import WorkerBinding + from easi.simulators.coppeliasim.v4_1_0.render_platforms import ( + CoppeliaSimRenderAdapter, + ) + + mgr = self._make_mock_env_manager() + adapter = CoppeliaSimRenderAdapter(env_manager=mgr) + binding = WorkerBinding(metadata={"backend": "native"}) + ev = adapter.get_env_vars(binding) + assert "QT_QPA_PLATFORM_PLUGIN_PATH" in ev.prepend + assert "CoppeliaSim" in ev.prepend["QT_QPA_PLATFORM_PLUGIN_PATH"] + assert ev.replace["COPPELIASIM_HEADLESS"] == "0" + assert "__EGL_VENDOR_LIBRARY_FILENAMES" not in ev.replace + + def test_adapter_xvfb_binding_headless_true_with_mesa(self): + from easi.core.render_platforms import WorkerBinding + from easi.simulators.coppeliasim.v4_1_0.render_platforms import ( + CoppeliaSimRenderAdapter, + ) + + mgr = self._make_mock_env_manager() + adapter = CoppeliaSimRenderAdapter(env_manager=mgr) + binding = WorkerBinding(metadata={"backend": "xvfb"}) + ev = adapter.get_env_vars(binding) + assert "QT_QPA_PLATFORM_PLUGIN_PATH" in ev.prepend + assert ev.replace["COPPELIASIM_HEADLESS"] == "1" + + def test_adapter_xorg_binding_via_cuda_heuristic(self): + from easi.core.render_platforms import WorkerBinding + from easi.simulators.coppeliasim.v4_1_0.render_platforms import ( + CoppeliaSimRenderAdapter, + ) + + mgr = self._make_mock_env_manager() + adapter = CoppeliaSimRenderAdapter(env_manager=mgr) + binding = WorkerBinding( + display=":10", + cuda_visible_devices="0", + metadata={"display_num": 10, "gpu_id": 0}, + ) + ev = adapter.get_env_vars(binding) + assert "QT_QPA_PLATFORM_PLUGIN_PATH" in ev.prepend + assert ev.replace["COPPELIASIM_HEADLESS"] == "0" + assert "__EGL_VENDOR_LIBRARY_FILENAMES" not in ev.replace + + def test_adapter_xorg_binding_via_explicit_backend(self): + from easi.core.render_platforms import WorkerBinding + from easi.simulators.coppeliasim.v4_1_0.render_platforms import ( + CoppeliaSimRenderAdapter, + ) + + mgr = self._make_mock_env_manager() + adapter = CoppeliaSimRenderAdapter(env_manager=mgr) + binding = WorkerBinding(metadata={"backend": "xorg"}) + ev = adapter.get_env_vars(binding) + assert ev.replace["COPPELIASIM_HEADLESS"] == "0" + assert "__EGL_VENDOR_LIBRARY_FILENAMES" not in ev.replace + + def test_adapter_auto_with_display_headless_false(self): + from easi.core.render_platforms import WorkerBinding + from easi.simulators.coppeliasim.v4_1_0.render_platforms import ( + CoppeliaSimRenderAdapter, + ) + + mgr = self._make_mock_env_manager() + adapter = CoppeliaSimRenderAdapter(env_manager=mgr) + binding = WorkerBinding() + with patch.dict(os.environ, {"DISPLAY": ":0"}): + ev = adapter.get_env_vars(binding) + assert ev.replace["COPPELIASIM_HEADLESS"] == "0" + assert "__EGL_VENDOR_LIBRARY_FILENAMES" not in ev.replace + + def test_adapter_auto_without_display_headless_true(self): + from easi.core.render_platforms import WorkerBinding + from easi.simulators.coppeliasim.v4_1_0.render_platforms import ( + CoppeliaSimRenderAdapter, + ) + + mgr = self._make_mock_env_manager() + adapter = CoppeliaSimRenderAdapter(env_manager=mgr) + binding = WorkerBinding() + env = os.environ.copy() + env.pop("DISPLAY", None) + with patch.dict(os.environ, env, clear=True): + ev = adapter.get_env_vars(binding) + assert ev.replace["COPPELIASIM_HEADLESS"] == "1" + + def test_adapter_no_env_manager_returns_empty(self): + from easi.core.render_platforms import WorkerBinding + from easi.simulators.coppeliasim.v4_1_0.render_platforms import ( + CoppeliaSimRenderAdapter, + ) + + adapter = CoppeliaSimRenderAdapter() + ev = adapter.get_env_vars(WorkerBinding(metadata={"backend": "native"})) + assert not ev + + def test_adapter_wrap_command_passthrough(self): + from easi.core.render_platforms import WorkerBinding + from easi.simulators.coppeliasim.v4_1_0.render_platforms import ( + CoppeliaSimRenderAdapter, + ) + + adapter = CoppeliaSimRenderAdapter() + cmd = ["python", "bridge.py"] + result = adapter.wrap_command(cmd, WorkerBinding()) + assert result is cmd + + +class TestOmniGibsonRenderAdapter: + """Tests for OmniGibsonRenderAdapter dispatch logic.""" + + def test_omnigibson_entry_has_render_adapter(self): + from easi.simulators.registry import get_simulator_entry + + entry = get_simulator_entry("omnigibson:v3_7_2") + assert ( + entry.render_adapter + == "easi.simulators.omnigibson.v3_7_2.render_platforms.OmniGibsonRenderAdapter" + ) + + def test_resolve_adapter_returns_instance(self): + from easi.simulators.omnigibson.v3_7_2.render_platforms import ( + OmniGibsonRenderAdapter, + ) + from easi.simulators.registry import resolve_render_adapter + + adapter = resolve_render_adapter("omnigibson:v3_7_2") + assert isinstance(adapter, OmniGibsonRenderAdapter) + + def test_adapter_native_backend_headless_false(self): + from easi.core.render_platforms import WorkerBinding + from easi.simulators.omnigibson.v3_7_2.render_platforms import ( + OmniGibsonRenderAdapter, + ) + + adapter = OmniGibsonRenderAdapter() + binding = WorkerBinding(metadata={"backend": "native"}) + ev = adapter.get_env_vars(binding) + assert ev.replace["OMNIGIBSON_HEADLESS"] == "0" + + def test_adapter_xorg_backend_headless_false(self): + from easi.core.render_platforms import WorkerBinding + from easi.simulators.omnigibson.v3_7_2.render_platforms import ( + OmniGibsonRenderAdapter, + ) + + adapter = OmniGibsonRenderAdapter() + binding = WorkerBinding(metadata={"backend": "xorg"}) + ev = adapter.get_env_vars(binding) + assert ev.replace["OMNIGIBSON_HEADLESS"] == "0" + + def test_adapter_xvfb_backend_headless_true(self): + from easi.core.render_platforms import WorkerBinding + from easi.simulators.omnigibson.v3_7_2.render_platforms import ( + OmniGibsonRenderAdapter, + ) + + adapter = OmniGibsonRenderAdapter() + binding = WorkerBinding(metadata={"backend": "xvfb"}) + ev = adapter.get_env_vars(binding) + assert ev.replace["OMNIGIBSON_HEADLESS"] == "1" + + def test_adapter_cuda_binding_headless_false(self): + """Binding with cuda_visible_devices (xorg heuristic) → headless=0.""" + from easi.core.render_platforms import WorkerBinding + from easi.simulators.omnigibson.v3_7_2.render_platforms import ( + OmniGibsonRenderAdapter, + ) + + adapter = OmniGibsonRenderAdapter() + binding = WorkerBinding(cuda_visible_devices="0") + ev = adapter.get_env_vars(binding) + assert ev.replace["OMNIGIBSON_HEADLESS"] == "0" + + def test_adapter_auto_with_display_headless_false(self): + from easi.core.render_platforms import WorkerBinding + from easi.simulators.omnigibson.v3_7_2.render_platforms import ( + OmniGibsonRenderAdapter, + ) + + adapter = OmniGibsonRenderAdapter() + binding = WorkerBinding() + with patch.dict(os.environ, {"DISPLAY": ":0"}): + ev = adapter.get_env_vars(binding) + assert ev.replace["OMNIGIBSON_HEADLESS"] == "0" + + def test_adapter_auto_without_display_headless_true(self): + from easi.core.render_platforms import WorkerBinding + from easi.simulators.omnigibson.v3_7_2.render_platforms import ( + OmniGibsonRenderAdapter, + ) + + adapter = OmniGibsonRenderAdapter() + binding = WorkerBinding() + env = os.environ.copy() + env.pop("DISPLAY", None) + with patch.dict(os.environ, env, clear=True): + ev = adapter.get_env_vars(binding) + assert ev.replace["OMNIGIBSON_HEADLESS"] == "1" + + def test_adapter_wrap_command_passthrough(self): + from easi.core.render_platforms import WorkerBinding + from easi.simulators.omnigibson.v3_7_2.render_platforms import ( + OmniGibsonRenderAdapter, + ) + + adapter = OmniGibsonRenderAdapter() + cmd = ["python", "bridge.py"] + result = adapter.wrap_command(cmd, WorkerBinding()) + assert result is cmd diff --git a/tests/test_subprocess_runner.py b/tests/test_subprocess_runner.py index 40e3ad6..5c4eba5 100644 --- a/tests/test_subprocess_runner.py +++ b/tests/test_subprocess_runner.py @@ -4,8 +4,9 @@ import os from pathlib import Path +from unittest.mock import MagicMock -from easi.core.render_platforms import EnvVars, get_render_platform +from easi.core.render_platforms import EnvVars, WorkerBinding, get_render_platform from easi.simulators.subprocess_runner import SubprocessRunner @@ -60,7 +61,6 @@ def test_extra_env_prepends_to_path_vars(self): assert ld_path.startswith("/opt/sim/lib") def test_non_path_var_replaces(self): - """Non-path env vars should replace, not prepend.""" os.environ["MY_EXISTING"] = "old_value" try: runner = SubprocessRunner( @@ -73,3 +73,103 @@ def test_non_path_var_replaces(self): assert env["MY_EXISTING"] == "new_value" finally: del os.environ["MY_EXISTING"] + + +class TestSubprocessRunnerRenderAdapter: + """Tests for render_adapter + worker_binding params in SubprocessRunner.""" + + def test_constructor_accepts_render_adapter_and_binding(self): + from easi.core.render_platforms import SimulatorRenderAdapter + + adapter = MagicMock(spec=SimulatorRenderAdapter) + binding = WorkerBinding(display=":5", cuda_visible_devices="0") + runner = SubprocessRunner( + python_executable="/usr/bin/python3", + bridge_script_path=Path("/dev/null"), + render_platform=get_render_platform("headless"), + render_adapter=adapter, + worker_binding=binding, + ) + assert runner.render_adapter is adapter + assert runner.worker_binding is binding + + def test_default_adapter_and_binding_are_none(self): + runner = SubprocessRunner( + python_executable="/usr/bin/python3", + bridge_script_path=Path("/dev/null"), + render_platform=get_render_platform("headless"), + ) + assert runner.render_adapter is None + assert runner.worker_binding is None + + def test_build_launch_command_calls_adapter_wrap_when_both_set(self): + from easi.core.render_platforms import SimulatorRenderAdapter + + adapter = MagicMock(spec=SimulatorRenderAdapter) + adapter.wrap_command.side_effect = lambda cmd, binding: ["wrapped"] + cmd + binding = WorkerBinding() + runner = SubprocessRunner( + python_executable="/usr/bin/python3", + bridge_script_path=Path("/dev/null"), + render_platform=get_render_platform("headless"), + render_adapter=adapter, + worker_binding=binding, + ) + runner._workspace = Path("/tmp/fake_ws") + cmd = runner._build_launch_command() + adapter.wrap_command.assert_called_once() + assert cmd[0] == "wrapped" + + def test_build_launch_command_skips_adapter_when_adapter_none(self): + from easi.core.render_platforms import SimulatorRenderAdapter + + adapter = MagicMock(spec=SimulatorRenderAdapter) + runner = SubprocessRunner( + python_executable="/usr/bin/python3", + bridge_script_path=Path("/dev/null"), + render_platform=get_render_platform("headless"), + render_adapter=None, + worker_binding=WorkerBinding(), + ) + runner._workspace = Path("/tmp/fake_ws") + runner._build_launch_command() + adapter.wrap_command.assert_not_called() + + def test_build_launch_command_skips_adapter_when_binding_none(self): + from easi.core.render_platforms import SimulatorRenderAdapter + + adapter = MagicMock(spec=SimulatorRenderAdapter) + adapter.wrap_command.side_effect = lambda cmd, binding: ["wrapped"] + cmd + runner = SubprocessRunner( + python_executable="/usr/bin/python3", + bridge_script_path=Path("/dev/null"), + render_platform=get_render_platform("headless"), + render_adapter=adapter, + worker_binding=None, + ) + runner._workspace = Path("/tmp/fake_ws") + cmd = runner._build_launch_command() + adapter.wrap_command.assert_not_called() + assert cmd[0] != "wrapped" + + def test_adapter_receives_correct_binding(self): + from easi.core.render_platforms import SimulatorRenderAdapter + + received_bindings = [] + + class RecordingAdapter(SimulatorRenderAdapter): + def wrap_command(self, cmd, binding): + received_bindings.append(binding) + return cmd + + binding = WorkerBinding(display=":10", cuda_visible_devices="2") + runner = SubprocessRunner( + python_executable="/usr/bin/python3", + bridge_script_path=Path("/dev/null"), + render_platform=get_render_platform("headless"), + render_adapter=RecordingAdapter(), + worker_binding=binding, + ) + runner._workspace = Path("/tmp/fake_ws") + runner._build_launch_command() + assert received_bindings[0] is binding diff --git a/tests/test_xorg.py b/tests/test_xorg.py index 72b0818..1df5381 100644 --- a/tests/test_xorg.py +++ b/tests/test_xorg.py @@ -9,44 +9,17 @@ import pytest _MGR = "easi.core.render_platforms.xorg_manager" # canonical module for XorgManager internals -_XORG = "easi.core.render_platforms.xorg" # canonical module for XorgPlatform - - -class TestXorgWorkerPlatform: - """Test _XorgWorkerPlatform env vars and command wrapping.""" - - def test_name(self): - from easi.core.render_platforms.xorg import _XorgWorkerPlatform - - p = _XorgWorkerPlatform(display_num=10, gpu_id=4) - assert p.name == "xorg" - - def test_env_vars(self): - from easi.core.render_platforms.xorg import _XorgWorkerPlatform - - p = _XorgWorkerPlatform(display_num=10, gpu_id=4) - ev = p.get_env_vars() - assert ev.replace["DISPLAY"] == ":10" - assert ev.replace["CUDA_VISIBLE_DEVICES"] == "4" - assert ev.replace["EASI_GPU_DISPLAY"] == "1" - - def test_wrap_command_passthrough(self): - from easi.core.render_platforms.xorg import _XorgWorkerPlatform - - p = _XorgWorkerPlatform(display_num=10, gpu_id=4) - cmd = ["python", "bridge.py", "--workspace", "/tmp"] - assert p.wrap_command(cmd, "1024x768x24") == cmd - - def test_is_available(self): - from easi.core.render_platforms.xorg import _XorgWorkerPlatform - - p = _XorgWorkerPlatform(display_num=10, gpu_id=4) - assert p.is_available() is True +_XORG = "easi.core.render_platforms.xorg" # canonical module for XorgPlatform class TestXorgPlatformLifecycle: """Test XorgPlatform setup/teardown/for_worker lifecycle.""" + def test_module_no_longer_exports_worker_platform_shim(self): + import easi.core.render_platforms.xorg as xorg_module + + assert not hasattr(xorg_module, "_XorgWorkerPlatform") + def test_name(self): from easi.core.render_platforms.xorg import XorgPlatform @@ -147,8 +120,10 @@ def test_stop_sends_sigterm(self): mgr._instances = [XorgInstance(display=10, gpu_id=0, pid=12345)] mgr._conf_files = [] - with patch(f"{_MGR}.os.getpgid", return_value=12345), \ - patch(f"{_MGR}.os.killpg") as mock_killpg: + with ( + patch(f"{_MGR}.os.getpgid", return_value=12345), + patch(f"{_MGR}.os.killpg") as mock_killpg, + ): mgr.stop() mock_killpg.assert_called_with(12345, signal.SIGTERM) @@ -165,14 +140,17 @@ def test_stop_uses_sudo_kill_for_sudo_launched(self): mgr._instances = [XorgInstance(display=10, gpu_id=0, pid=12345)] mgr._conf_files = [] - with patch(f"{_MGR}.os.getpgid", return_value=12345), \ - patch(f"{_MGR}.os.killpg") as mock_killpg, \ - patch(f"{_MGR}.subprocess.run") as mock_run: + with ( + patch(f"{_MGR}.os.getpgid", return_value=12345), + patch(f"{_MGR}.os.killpg") as mock_killpg, + patch(f"{_MGR}.subprocess.run") as mock_run, + ): mgr.stop() mock_killpg.assert_not_called() mock_run.assert_called_once_with( ["sudo", "-n", "kill", f"-{signal.SIGTERM}", "-12345"], - capture_output=True, timeout=5, + capture_output=True, + timeout=5, ) def test_sudo_fallback(self): @@ -180,6 +158,7 @@ def test_sudo_fallback(self): from easi.core.render_platforms.xorg_manager import XorgManager call_count = 0 + def mock_popen(cmd, **kwargs): nonlocal call_count call_count += 1 @@ -258,8 +237,8 @@ def test_round_robin_two_gpus(self): XorgInstance(display=10, gpu_id=4, pid=1), XorgInstance(display=11, gpu_id=5, pid=2), ] - gpus = [p.for_worker(i).gpu_id for i in range(6)] - assert gpus == [4, 5, 4, 5, 4, 5] + gpus = [p.for_worker(i).cuda_visible_devices for i in range(6)] + assert gpus == ["4", "5", "4", "5", "4", "5"] def test_single_gpu_all_workers_same(self): """With one GPU, all workers get the same GPU.""" @@ -269,7 +248,22 @@ def test_single_gpu_all_workers_same(self): p = XorgPlatform() p._instances = [XorgInstance(display=10, gpu_id=4, pid=1)] for worker_id in range(4): - assert p.for_worker(worker_id).gpu_id == 4 + assert p.for_worker(worker_id).cuda_visible_devices == "4" + + def test_binding_contains_display_and_env(self): + from easi.core.render_platforms import WorkerBinding + from easi.core.render_platforms.xorg import XorgPlatform + from easi.core.render_platforms.xorg_manager import XorgInstance + + p = XorgPlatform() + p._instances = [XorgInstance(display=12, gpu_id=7, pid=1)] + + binding = p.for_worker(0) + + assert isinstance(binding, WorkerBinding) + assert binding.display == ":12" + assert binding.cuda_visible_devices == "7" + assert binding.extra_env.replace == {"EASI_GPU_DISPLAY": "1"} class TestXorgRunnerIntegration: From 095fcff3bde05e3ed30d41d8d64b3aca60ba10c7 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Thu, 12 Mar 2026 02:06:36 +0800 Subject: [PATCH 201/239] fix: make sim test use binding-aware render launch Keep the conda-backed smoke-test path aligned with the new WorkerBinding handoff so render adapters and per-worker launch data reach SubprocessRunner consistently. --- easi/cli.py | 29 +++++++++++++++++++++++++++-- 1 file changed, 27 insertions(+), 2 deletions(-) diff --git a/easi/cli.py b/easi/cli.py index 0a4993e..ea34af5 100644 --- a/easi/cli.py +++ b/easi/cli.py @@ -460,6 +460,7 @@ def cmd_sim_test( create_env_manager, get_simulator_entry, load_simulator_class, + resolve_render_adapter, resolve_render_platform, ) from easi.simulators.subprocess_runner import SubprocessRunner @@ -545,7 +546,7 @@ def cmd_sim_test( ) try: render_platform.setup(gpu_ids=sim_gpus or [0]) - worker_platform = render_platform.for_worker(0) + worker_binding = render_platform.for_worker(0) except RuntimeError as e: if platform_name == "xorg": logger.warning("%s", str(e)) @@ -557,16 +558,40 @@ def cmd_sim_test( logger.info(" Python: %s", env_manager.get_python_executable()) logger.info(" Render platform: %s", platform_name) + from easi.core.render_platforms import EnvVars + env_vars = env_manager.get_env_vars(render_platform_name=platform_name) + render_adapter = resolve_render_adapter(simulator, env_manager=env_manager) + + adapter_env = ( + render_adapter.get_env_vars(worker_binding) if render_adapter else EnvVars() + ) + binding_env = EnvVars.merge(worker_binding.extra_env, adapter_env) + if worker_binding.display: + binding_env = EnvVars.merge( + binding_env, EnvVars(replace={"DISPLAY": worker_binding.display}) + ) + if worker_binding.cuda_visible_devices is not None: + binding_env = EnvVars.merge( + binding_env, + EnvVars( + replace={ + "CUDA_VISIBLE_DEVICES": worker_binding.cuda_visible_devices + } + ), + ) + env_vars = EnvVars.merge(env_vars, binding_env) if env_vars else binding_env runner = SubprocessRunner( python_executable=env_manager.get_python_executable(), bridge_script_path=sim._get_bridge_script_path(), - render_platform=worker_platform, + render_platform=render_platform, screen_config=env_manager.screen_config, startup_timeout=timeout, command_timeout=timeout, extra_env=env_vars if env_vars else None, + render_adapter=render_adapter, + worker_binding=worker_binding, ) try: From 3450c348f30c1cf4b0d83b17512dea7b4c308abd Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Thu, 12 Mar 2026 12:59:51 +0800 Subject: [PATCH 202/239] feat: log resolved render platform for sim test and start commands Add resolved_name property to RenderPlatform so auto-detection shows the actual backend (e.g. "native (via auto-detection)") instead of just "auto". --- easi/cli.py | 6 +++++- easi/core/render_platforms/auto.py | 4 ++++ easi/core/render_platforms/base.py | 5 +++++ easi/evaluation/runner.py | 10 ++++++++++ 4 files changed, 24 insertions(+), 1 deletion(-) diff --git a/easi/cli.py b/easi/cli.py index ea34af5..f470b16 100644 --- a/easi/cli.py +++ b/easi/cli.py @@ -556,7 +556,11 @@ def cmd_sim_test( logger.info("Testing %s...", simulator) logger.info(" Python: %s", env_manager.get_python_executable()) - logger.info(" Render platform: %s", platform_name) + resolved = render_platform.resolved_name + if resolved != render_platform.name: + logger.info(" Render platform: %s (via auto-detection)", resolved) + else: + logger.info(" Render platform: %s", resolved) from easi.core.render_platforms import EnvVars diff --git a/easi/core/render_platforms/auto.py b/easi/core/render_platforms/auto.py index 85f7b1e..7b4ad92 100644 --- a/easi/core/render_platforms/auto.py +++ b/easi/core/render_platforms/auto.py @@ -15,6 +15,10 @@ class AutoPlatform(RenderPlatform): def name(self) -> str: return "auto" + @property + def resolved_name(self) -> str: + return "native" if os.environ.get("DISPLAY", "") else "xvfb" + def wrap_command(self, cmd: list[str], screen_config: str) -> list[str]: if os.environ.get("DISPLAY", ""): return cmd diff --git a/easi/core/render_platforms/base.py b/easi/core/render_platforms/base.py index ccfdf8b..3360b93 100644 --- a/easi/core/render_platforms/base.py +++ b/easi/core/render_platforms/base.py @@ -113,6 +113,11 @@ def name(self) -> str: """Short identifier (e.g. 'xvfb', 'egl').""" ... + @property + def resolved_name(self) -> str: + """Actual backend after auto-detection. Defaults to :attr:`name`.""" + return self.name + @abstractmethod def wrap_command(self, cmd: list[str], screen_config: str) -> list[str]: """Optionally wrap the bridge launch command. diff --git a/easi/evaluation/runner.py b/easi/evaluation/runner.py index 07b7bd1..bf57bc9 100644 --- a/easi/evaluation/runner.py +++ b/easi/evaluation/runner.py @@ -167,6 +167,11 @@ def _setup_render_platform(self, backend: str | None = None): "Use --llm-gpus and --sim-gpus to separate them." ) + resolved = platform.resolved_name + if resolved != platform.name: + logger.info("Render platform: %s (via auto-detection)", resolved) + else: + logger.info("Render platform: %s", resolved) platform.setup(gpu_ids=self.sim_gpus) return platform @@ -852,6 +857,11 @@ def _create_simulator( render_platform = resolve_render_platform( simulator_key, platform_name, env_manager=env_manager ) + resolved = render_platform.resolved_name + if resolved != render_platform.name: + logger.info("Render platform: %s (via auto-detection)", resolved) + else: + logger.info("Render platform: %s", resolved) from easi.core.render_platforms import EnvVars From a71cb0fb4c944280a40ee5d4ccc69bb6050895f8 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Thu, 12 Mar 2026 13:55:13 +0800 Subject: [PATCH 203/239] refactor: add log_name property to RenderPlatform to deduplicate logging --- easi/cli.py | 6 +----- easi/core/render_platforms/base.py | 8 ++++++++ easi/evaluation/runner.py | 12 ++---------- 3 files changed, 11 insertions(+), 15 deletions(-) diff --git a/easi/cli.py b/easi/cli.py index f470b16..44f74b5 100644 --- a/easi/cli.py +++ b/easi/cli.py @@ -556,11 +556,7 @@ def cmd_sim_test( logger.info("Testing %s...", simulator) logger.info(" Python: %s", env_manager.get_python_executable()) - resolved = render_platform.resolved_name - if resolved != render_platform.name: - logger.info(" Render platform: %s (via auto-detection)", resolved) - else: - logger.info(" Render platform: %s", resolved) + logger.info(" Render platform: %s", render_platform.log_name) from easi.core.render_platforms import EnvVars diff --git a/easi/core/render_platforms/base.py b/easi/core/render_platforms/base.py index 3360b93..04dc5cd 100644 --- a/easi/core/render_platforms/base.py +++ b/easi/core/render_platforms/base.py @@ -118,6 +118,14 @@ def resolved_name(self) -> str: """Actual backend after auto-detection. Defaults to :attr:`name`.""" return self.name + @property + def log_name(self) -> str: + """Human-readable name for log messages.""" + resolved = self.resolved_name + if resolved != self.name: + return f"{resolved} (via auto-detection)" + return resolved + @abstractmethod def wrap_command(self, cmd: list[str], screen_config: str) -> list[str]: """Optionally wrap the bridge launch command. diff --git a/easi/evaluation/runner.py b/easi/evaluation/runner.py index bf57bc9..394478c 100644 --- a/easi/evaluation/runner.py +++ b/easi/evaluation/runner.py @@ -167,11 +167,7 @@ def _setup_render_platform(self, backend: str | None = None): "Use --llm-gpus and --sim-gpus to separate them." ) - resolved = platform.resolved_name - if resolved != platform.name: - logger.info("Render platform: %s (via auto-detection)", resolved) - else: - logger.info("Render platform: %s", resolved) + logger.info("Render platform: %s", platform.log_name) platform.setup(gpu_ids=self.sim_gpus) return platform @@ -857,11 +853,7 @@ def _create_simulator( render_platform = resolve_render_platform( simulator_key, platform_name, env_manager=env_manager ) - resolved = render_platform.resolved_name - if resolved != render_platform.name: - logger.info("Render platform: %s (via auto-detection)", resolved) - else: - logger.info("Render platform: %s", resolved) + logger.info("Render platform: %s", render_platform.log_name) from easi.core.render_platforms import EnvVars From 7a1344c1f55afa4a393c9abbcfe563e2d0e96ac5 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Thu, 12 Mar 2026 14:46:40 +0800 Subject: [PATCH 204/239] fix: ensure render platform setup/teardown lifecycle in all code paths - cmd_sim_test: move setup()/for_worker() inside the try block that owns finally:teardown() so Xorg servers are always cleaned up - _create_simulator: call setup() on per-simulator resolved platforms and register for teardown via self._render_platform --- easi/cli.py | 95 ++++++++++++++++++++------------------- easi/evaluation/runner.py | 3 ++ 2 files changed, 51 insertions(+), 47 deletions(-) diff --git a/easi/cli.py b/easi/cli.py index 44f74b5..ba97aa8 100644 --- a/easi/cli.py +++ b/easi/cli.py @@ -545,56 +545,57 @@ def cmd_sim_test( simulator, platform_name, env_manager=env_manager ) try: - render_platform.setup(gpu_ids=sim_gpus or [0]) - worker_binding = render_platform.for_worker(0) - except RuntimeError as e: - if platform_name == "xorg": - logger.warning("%s", str(e)) - render_platform.teardown() - sys.exit(0) - raise - - logger.info("Testing %s...", simulator) - logger.info(" Python: %s", env_manager.get_python_executable()) - logger.info(" Render platform: %s", render_platform.log_name) - - from easi.core.render_platforms import EnvVars - - env_vars = env_manager.get_env_vars(render_platform_name=platform_name) - render_adapter = resolve_render_adapter(simulator, env_manager=env_manager) - - adapter_env = ( - render_adapter.get_env_vars(worker_binding) if render_adapter else EnvVars() - ) - binding_env = EnvVars.merge(worker_binding.extra_env, adapter_env) - if worker_binding.display: - binding_env = EnvVars.merge( - binding_env, EnvVars(replace={"DISPLAY": worker_binding.display}) + try: + render_platform.setup(gpu_ids=sim_gpus or [0]) + worker_binding = render_platform.for_worker(0) + except RuntimeError as e: + if platform_name == "xorg": + logger.warning("%s", str(e)) + sys.exit(0) + raise + + logger.info("Testing %s...", simulator) + logger.info(" Python: %s", env_manager.get_python_executable()) + logger.info(" Render platform: %s", render_platform.log_name) + + from easi.core.render_platforms import EnvVars + + env_vars = env_manager.get_env_vars(render_platform_name=platform_name) + render_adapter = resolve_render_adapter(simulator, env_manager=env_manager) + + adapter_env = ( + render_adapter.get_env_vars(worker_binding) + if render_adapter + else EnvVars() ) - if worker_binding.cuda_visible_devices is not None: - binding_env = EnvVars.merge( - binding_env, - EnvVars( - replace={ - "CUDA_VISIBLE_DEVICES": worker_binding.cuda_visible_devices - } - ), + binding_env = EnvVars.merge(worker_binding.extra_env, adapter_env) + if worker_binding.display: + binding_env = EnvVars.merge( + binding_env, EnvVars(replace={"DISPLAY": worker_binding.display}) + ) + if worker_binding.cuda_visible_devices is not None: + binding_env = EnvVars.merge( + binding_env, + EnvVars( + replace={ + "CUDA_VISIBLE_DEVICES": worker_binding.cuda_visible_devices + } + ), + ) + env_vars = EnvVars.merge(env_vars, binding_env) if env_vars else binding_env + + runner = SubprocessRunner( + python_executable=env_manager.get_python_executable(), + bridge_script_path=sim._get_bridge_script_path(), + render_platform=render_platform, + screen_config=env_manager.screen_config, + startup_timeout=timeout, + command_timeout=timeout, + extra_env=env_vars if env_vars else None, + render_adapter=render_adapter, + worker_binding=worker_binding, ) - env_vars = EnvVars.merge(env_vars, binding_env) if env_vars else binding_env - runner = SubprocessRunner( - python_executable=env_manager.get_python_executable(), - bridge_script_path=sim._get_bridge_script_path(), - render_platform=render_platform, - screen_config=env_manager.screen_config, - startup_timeout=timeout, - command_timeout=timeout, - extra_env=env_vars if env_vars else None, - render_adapter=render_adapter, - worker_binding=worker_binding, - ) - - try: runner.launch() sim.set_runner(runner) diff --git a/easi/evaluation/runner.py b/easi/evaluation/runner.py index 394478c..fc4d6a5 100644 --- a/easi/evaluation/runner.py +++ b/easi/evaluation/runner.py @@ -853,6 +853,9 @@ def _create_simulator( render_platform = resolve_render_platform( simulator_key, platform_name, env_manager=env_manager ) + render_platform.setup(gpu_ids=self.sim_gpus) + # Register so teardown() is called in the finally block + self._render_platform = render_platform logger.info("Render platform: %s", render_platform.log_name) from easi.core.render_platforms import EnvVars From 3d9e435d16b6d8aff015b79f5ba9ee665809da86 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Thu, 12 Mar 2026 22:16:28 +0800 Subject: [PATCH 205/239] feat: add REVERIE-CE task (navigation-only) Integrate REVERIE-CE as a new task reusing VLN-CE R2R infrastructure. Same simulator (habitat_sim:v0_1_7), action space, and metrics. New prompt builder adapted for REVERIE's high-level instruction style. Dataset: oscarqjh/REVERIE-CE_easi (repackaged from Dynam3D) --- .../plans/2026-03-12-reverie-ce.md | 665 ++++++++++++++++++ .../specs/2026-03-12-reverie-ce-design.md | 231 ++++++ easi/tasks/reverie_ce/__init__.py | 0 easi/tasks/reverie_ce/_base.yaml | 40 ++ easi/tasks/reverie_ce/actions.py | 2 + easi/tasks/reverie_ce/bridge.py | 27 + easi/tasks/reverie_ce/prompts.py | 72 ++ easi/tasks/reverie_ce/reverie_ce_test.yaml | 6 + .../reverie_ce/reverie_ce_val_unseen.yaml | 6 + easi/tasks/reverie_ce/task.py | 22 + tests/test_reverie_ce_task.py | 183 +++++ 11 files changed, 1254 insertions(+) create mode 100644 docs/superpowers/plans/2026-03-12-reverie-ce.md create mode 100644 docs/superpowers/specs/2026-03-12-reverie-ce-design.md create mode 100644 easi/tasks/reverie_ce/__init__.py create mode 100644 easi/tasks/reverie_ce/_base.yaml create mode 100644 easi/tasks/reverie_ce/actions.py create mode 100644 easi/tasks/reverie_ce/bridge.py create mode 100644 easi/tasks/reverie_ce/prompts.py create mode 100644 easi/tasks/reverie_ce/reverie_ce_test.yaml create mode 100644 easi/tasks/reverie_ce/reverie_ce_val_unseen.yaml create mode 100644 easi/tasks/reverie_ce/task.py create mode 100644 tests/test_reverie_ce_task.py diff --git a/docs/superpowers/plans/2026-03-12-reverie-ce.md b/docs/superpowers/plans/2026-03-12-reverie-ce.md new file mode 100644 index 0000000..30608f1 --- /dev/null +++ b/docs/superpowers/plans/2026-03-12-reverie-ce.md @@ -0,0 +1,665 @@ +# REVERIE-CE Integration Implementation Plan + +> **For agentic workers:** REQUIRED: Use superpowers:subagent-driven-development (if subagents available) or superpowers:executing-plans to implement this plan. Steps use checkbox (`- [ ]`) syntax for tracking. + +**Goal:** Add REVERIE-CE as a navigation-only task in EASI, reusing the VLN-CE R2R infrastructure. + +**Architecture:** REVERIE-CE is a thin task layer inheriting from VLN-CE R2R. Same simulator (`habitat_sim:v0_1_7`), same action space (4 discrete), same metrics (SR/SPL/NE/NDTW/SDTW). Only the prompt builder and dataset are new. + +**Tech Stack:** Python 3.10+ (host), Habitat-Sim 0.1.7 (bridge subprocess, Python 3.8), HuggingFace datasets, Matterport3D scenes. + +**Spec:** `docs/superpowers/specs/2026-03-12-reverie-ce-design.md` + +--- + +## File Structure + +| Action | File | Responsibility | +|--------|------|---------------| +| Create | `easi/tasks/reverie_ce/__init__.py` | Package marker | +| Create | `easi/tasks/reverie_ce/task.py` | `ReverieCETask` — inherits `VLNCETask`, overrides paths | +| Create | `easi/tasks/reverie_ce/bridge.py` | `ReverieCEBridge` — inherits `VLNCEBridge`, standalone entry point | +| Create | `easi/tasks/reverie_ce/prompts.py` | `ReverieCEPromptBuilder` — high-level instruction prompt | +| Create | `easi/tasks/reverie_ce/actions.py` | Re-exports `get_action_space` from `vlnce_r2r.actions` | +| Create | `easi/tasks/reverie_ce/_base.yaml` | Task config pointing to REVERIE-CE HuggingFace repo | +| Create | `easi/tasks/reverie_ce/reverie_ce_val_unseen.yaml` | Val unseen split | +| Create | `easi/tasks/reverie_ce/reverie_ce_test.yaml` | Test split | +| Create | `tests/test_reverie_ce_task.py` | Unit tests for task, prompt builder, action space | +| None | `easi/tasks/vlnce_r2r/` | Imported, not modified | +| None | `easi/simulators/habitat_sim/` | Unchanged | + +--- + +## Chunk 1: Data Preparation + +### Task 1: Reformat Dynam3D Data into EASI HuggingFace Repo + +This is a one-time manual/scripted step done outside the EASI codebase. It prepares the dataset that the task will consume. + +**Files:** +- External: HuggingFace repo `oscarqjh/REVERIE-CE_easi` + +- [ ] **Step 1: Download Dynam3D REVERIE-CE data** + +Download from HuggingFace `MrZihanWang/Dynam3D`: +```bash +# Download the REVERIE-CE specific files +huggingface-cli download MrZihanWang/Dynam3D \ + data/datasets/reverie_training_data \ + data/datasets/reverie_val_unseen_data.json \ + data/datasets/reverie_test_data.json \ + data/datasets/reverie_val_unseen_gt.json \ + data/datasets/reverie_test_gt.json \ + --local-dir ./dynam3d_download +``` + +- [ ] **Step 2: Write a conversion script to reshape into JSONL** + +Create a temporary script (not committed to EASI) that: +1. Reads the per-scene JSON training files from `reverie_training_data/` +2. Reads the val/test single JSON files +3. Normalises each episode into the VLN-CE R2R JSONL format: + +```python +# Expected output format per line: +{ + "episode_id": str(item["episode_id"]), + "scene_id": item["scene_id"].replace("mp3d/", "").replace(".glb", "").split("/")[-1], + "instruction": item["instruction"]["instruction_text"], + "start_position": item["start_position"], + "start_rotation": item["start_rotation"], + "goal_position": item["goals"][0]["position"], + "geodesic_distance": item["info"]["geodesic_distance"], + "gt_locations": item["reference_path"] +} +``` + +Note: `scene_id` in VLN-CE R2R format is just the scan name (e.g. `cV4RVeZvu5T`), not the full path. The bridge constructs the full path from `data_dir + mp3d/ + scene_id + scene_id.glb`. + +4. Writes `train.jsonl`, `val_unseen.jsonl`, `test.jsonl` +5. Copies ground truth files as-is + +- [ ] **Step 3: Verify episode counts match Dynam3D source** + +```bash +wc -l data/train.jsonl data/val_unseen.jsonl data/test.jsonl +``` + +- [ ] **Step 4: Upload to HuggingFace** + +```bash +huggingface-cli upload oscarqjh/REVERIE-CE_easi ./reverie_ce_easi/ \ + --repo-type dataset +``` + +Include `mp3d_scenes.zip` (same Matterport3D scenes as R2R — can be copied from the R2R dataset repo). + +--- + +## Chunk 2: Task Implementation + +### Task 2: Create Action Space Module + +**Files:** +- Create: `easi/tasks/reverie_ce/actions.py` +- Test: `tests/test_reverie_ce_task.py` + +- [ ] **Step 1: Write failing test** + +```python +# tests/test_reverie_ce_task.py +class TestActionSpace: + def test_has_four_actions(self): + from easi.tasks.reverie_ce.actions import get_action_space + actions = get_action_space() + assert len(actions) == 4 + assert "move_forward" in actions + assert "turn_left" in actions + assert "turn_right" in actions + assert "stop" in actions +``` + +- [ ] **Step 2: Run test to verify it fails** + +Run: `.venv/bin/pytest tests/test_reverie_ce_task.py::TestActionSpace -v` +Expected: FAIL (module not found) + +- [ ] **Step 3: Create the module** + +```python +# easi/tasks/reverie_ce/__init__.py +# (empty) + +# easi/tasks/reverie_ce/actions.py +"""REVERIE-CE action space — same as VLN-CE R2R.""" +from easi.tasks.vlnce_r2r.actions import get_action_space # noqa: F401 +``` + +- [ ] **Step 4: Run test to verify it passes** + +Run: `.venv/bin/pytest tests/test_reverie_ce_task.py::TestActionSpace -v` +Expected: PASS + +- [ ] **Step 5: Commit** + +```bash +git add easi/tasks/reverie_ce/__init__.py easi/tasks/reverie_ce/actions.py tests/test_reverie_ce_task.py +git commit -m "feat(reverie-ce): add action space module" +``` + +--- + +### Task 3: Create Task Class + +**Files:** +- Create: `easi/tasks/reverie_ce/task.py` +- Test: `tests/test_reverie_ce_task.py` + +- [ ] **Step 1: Write failing tests** + +```python +# Add to tests/test_reverie_ce_task.py +import json +import pytest +from unittest.mock import MagicMock +from easi.core.episode import EpisodeRecord, Observation, StepResult + + +class TestReverieCETask: + @pytest.fixture + def task(self): + from easi.tasks.reverie_ce.task import ReverieCETask + mock_config = { + "name": "reverie_ce_val_unseen", + "display_name": "REVERIE-CE Val Unseen", + "simulator": "habitat_sim:v0_1_7", + "task_class": "easi.tasks.reverie_ce.task.ReverieCETask", + "max_steps": 500, + "dataset": {"source": "huggingface", "repo_id": "oscarqjh/REVERIE-CE_easi", "split": "val_unseen"}, + "simulator_configs": {}, + "agent": {"prompt_builder": "easi.tasks.reverie_ce.prompts.ReverieCEPromptBuilder"}, + } + task = ReverieCETask.__new__(ReverieCETask) + task._config = mock_config + task._yaml_path = None + task._action_space = None + return task + + def test_action_space(self, task): + actions = task._build_action_space() + assert actions == ["move_forward", "turn_left", "turn_right", "stop"] + + def test_format_reset_config(self, task): + episode = { + "episode_id": "50001", + "scene_id": "cV4RVeZvu5T", + "instruction": "Go to the laundry room and get the cushion", + "start_position": [1.0, 0.5, -2.0], + "start_rotation": [0, 0.707, 0, 0.707], + "goal_position": [4.5, 0.5, 1.2], + "geodesic_distance": 10.5, + "gt_locations": [[1.0, 0.5, -2.0], [2.0, 0.5, -1.0]], + "_data_dir": "/data/reverie_ce", + } + config = task.format_reset_config(episode) + assert config["scene_id"] == "cV4RVeZvu5T" + assert config["data_dir"] == "/data/reverie_ce" + assert json.loads(config["start_position"]) == [1.0, 0.5, -2.0] + + def test_evaluate_episode_success(self, task): + info = { + "success": 1.0, "oracle_success": 1.0, "spl": 0.8, + "navigation_error": 1.5, "ndtw": 0.9, "sdtw": 0.85, + "path_length": 8.0, + } + obs = Observation(rgb_path="/tmp/step.png") + step = StepResult(observation=obs, done=True, info=info) + result = task.evaluate_episode({}, [step]) + assert result["success"] == 1.0 + assert result["spl"] == 0.8 + + def test_evaluate_episode_empty(self, task): + result = task.evaluate_episode({}, []) + assert result["success"] is None + assert result["path_length"] == 0.0 + + def test_aggregate_results(self, task): + records = [ + EpisodeRecord(episode={}, trajectory=[], episode_results={ + "success": 1.0, "oracle_success": 1.0, "spl": 0.8, + "navigation_error": 1.5, "ndtw": 0.9, "sdtw": 0.85, + "path_length": 8.0, "steps_taken": 30.0, + }), + EpisodeRecord(episode={}, trajectory=[], episode_results={ + "success": 0.0, "oracle_success": 0.0, "spl": 0.0, + "navigation_error": 6.0, "ndtw": 0.3, "sdtw": 0.0, + "path_length": 12.0, "steps_taken": 50.0, + }), + ] + summary = task.aggregate_results(records) + assert summary["num_episodes"] == 2 + assert summary["SR"] == 0.5 + assert summary["SPL"] == 0.4 + + def test_bridge_script_path(self, task): + path = task.get_bridge_script_path() + assert path.name == "bridge.py" + assert "reverie_ce" in str(path) +``` + +- [ ] **Step 2: Run tests to verify they fail** + +Run: `.venv/bin/pytest tests/test_reverie_ce_task.py::TestReverieCETask -v` +Expected: FAIL (ReverieCETask not found) + +- [ ] **Step 3: Implement the task class** + +```python +# easi/tasks/reverie_ce/task.py +"""REVERIE-CE task for EASI. + +Navigation-only evaluation of REVERIE in continuous environments. +Inherits from VLNCETask — same metrics, same bridge protocol. +""" +from __future__ import annotations + +from pathlib import Path + +from easi.tasks.vlnce_r2r.task import VLNCETask +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + + +class ReverieCETask(VLNCETask): + + def get_task_yaml_path(self) -> Path: + return Path(__file__).parent / "_base.yaml" + + def get_bridge_script_path(self) -> Path: + return Path(__file__).parent / "bridge.py" +``` + +- [ ] **Step 4: Run tests to verify they pass** + +Run: `.venv/bin/pytest tests/test_reverie_ce_task.py::TestReverieCETask -v` +Expected: PASS + +- [ ] **Step 5: Commit** + +```bash +git add easi/tasks/reverie_ce/task.py tests/test_reverie_ce_task.py +git commit -m "feat(reverie-ce): add ReverieCETask inheriting VLNCETask" +``` + +--- + +### Task 4: Create Bridge + +**Files:** +- Create: `easi/tasks/reverie_ce/bridge.py` + +The bridge must be a standalone script (runs in Python 3.8 subprocess). It inherits from `VLNCEBridge` but provides its own `__main__` entry point. + +- [ ] **Step 1: Create the bridge module** + +```python +# easi/tasks/reverie_ce/bridge.py +"""REVERIE-CE bridge — inherits VLN-CE R2R bridge. + +Runs inside the easi_habitat_sim_v0_1_7 conda env (Python 3.8). +Inherits all reset/step/extract logic from VLNCEBridge. + +Usage: + python bridge.py --workspace /tmp/easi_xxx [--simulator-kwargs '{}'] +""" +from __future__ import annotations + +import sys +from pathlib import Path + +_repo_root = Path(__file__).resolve().parents[3] +if str(_repo_root) not in sys.path: + sys.path.insert(0, str(_repo_root)) + +from easi.tasks.vlnce_r2r.bridge import VLNCEBridge # noqa: E402 + + +class ReverieCEBridge(VLNCEBridge): + """Bridge for REVERIE-CE. Identical to VLN-CE R2R for now.""" + pass + + +if __name__ == "__main__": + ReverieCEBridge.main() +``` + +- [ ] **Step 2: Verify bridge script path resolves correctly** + +The test from Task 3 (`test_bridge_script_path`) already validates this. + +Run: `.venv/bin/pytest tests/test_reverie_ce_task.py::TestReverieCETask::test_bridge_script_path -v` +Expected: PASS + +- [ ] **Step 3: Commit** + +```bash +git add easi/tasks/reverie_ce/bridge.py +git commit -m "feat(reverie-ce): add bridge inheriting VLNCEBridge" +``` + +--- + +### Task 5: Create Prompt Builder + +**Files:** +- Create: `easi/tasks/reverie_ce/prompts.py` +- Test: `tests/test_reverie_ce_task.py` + +- [ ] **Step 1: Write failing tests** + +```python +# Add to tests/test_reverie_ce_task.py +class TestReverieCEPromptBuilder: + @pytest.fixture + def mock_encode(self): + # Must patch in vlnce_r2r.prompts where the function is actually + # called (super().build_messages() resolves it there, not in + # reverie_ce.prompts). + import easi.tasks.vlnce_r2r.prompts as prompts_mod + original = prompts_mod._encode_image_base64 + prompts_mod._encode_image_base64 = lambda x: "data:image/png;base64,AAAA" + yield + prompts_mod._encode_image_base64 = original + + def _make_memory(self, action_history=None): + memory = MagicMock() + memory.task_description = "Go to the laundry room and bring me the blue cushion" + memory.action_space = ["move_forward", "turn_left", "turn_right", "stop"] + memory.current_observation = Observation( + rgb_path="/tmp/test.png", + metadata={"geo_distance": "5.3"}, + ) + memory.action_history = action_history or [] + memory.steps = [] + return memory + + def test_system_prompt_mentions_high_level(self, mock_encode): + from easi.tasks.reverie_ce.prompts import ReverieCEPromptBuilder + builder = ReverieCEPromptBuilder() + memory = self._make_memory() + messages = builder.build_messages(memory) + system_msg = messages[0]["content"] + assert "high-level" in system_msg.lower() or "described location" in system_msg.lower() + + def test_build_messages_has_image(self, mock_encode): + from easi.tasks.reverie_ce.prompts import ReverieCEPromptBuilder + builder = ReverieCEPromptBuilder() + memory = self._make_memory() + messages = builder.build_messages(memory) + user_content = messages[1]["content"] + image_blocks = [b for b in user_content if b.get("type") == "image_url"] + assert len(image_blocks) == 1 + + def test_build_messages_has_instruction(self, mock_encode): + from easi.tasks.reverie_ce.prompts import ReverieCEPromptBuilder + builder = ReverieCEPromptBuilder() + memory = self._make_memory() + messages = builder.build_messages(memory) + text_blocks = [b["text"] for b in messages[1]["content"] if b.get("type") == "text"] + full_text = "\n".join(text_blocks) + assert "laundry room" in full_text + + def test_build_messages_has_distance(self, mock_encode): + from easi.tasks.reverie_ce.prompts import ReverieCEPromptBuilder + builder = ReverieCEPromptBuilder() + memory = self._make_memory() + messages = builder.build_messages(memory) + text_blocks = [b["text"] for b in messages[1]["content"] if b.get("type") == "text"] + full_text = "\n".join(text_blocks) + assert "5.3" in full_text + + def test_parse_response_valid(self): + from easi.tasks.reverie_ce.prompts import ReverieCEPromptBuilder + builder = ReverieCEPromptBuilder() + memory = MagicMock() + memory.action_space = ["move_forward", "turn_left", "turn_right", "stop"] + response = json.dumps({ + "visual_state_description": "I see a hallway", + "reasoning_and_reflection": "Need to find the laundry room", + "language_plan": "Move forward", + "executable_plan": [{"action": "move_forward"}], + }) + actions = builder.parse_response(response, memory) + assert len(actions) == 1 + assert actions[0].action_name == "move_forward" + + def test_parse_response_invalid_json(self): + from easi.tasks.reverie_ce.prompts import ReverieCEPromptBuilder + builder = ReverieCEPromptBuilder() + memory = MagicMock() + memory.action_space = ["move_forward", "turn_left", "turn_right", "stop"] + actions = builder.parse_response("not json", memory) + assert actions == [] +``` + +- [ ] **Step 2: Run tests to verify they fail** + +Run: `.venv/bin/pytest tests/test_reverie_ce_task.py::TestReverieCEPromptBuilder -v` +Expected: FAIL (module not found) + +- [ ] **Step 3: Implement the prompt builder** + +```python +# easi/tasks/reverie_ce/prompts.py +"""REVERIE-CE prompt builder. + +Adapted from VLN-CE R2R for REVERIE's high-level instruction style. +REVERIE instructions describe a target location/object rather than +step-by-step route directions. +""" +from __future__ import annotations + +from easi.tasks.vlnce_r2r.prompts import VLNCEPromptBuilder +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + +SYSTEM_PROMPT = """\ +## Role and Environment +You are a robot navigating in a 3D indoor environment. You observe the \ +environment through a front-facing camera and must navigate to the location \ +described in a high-level natural language instruction. + +## Observation Description +- **Distance to goal**: Geodesic (shortest walkable path) distance in meters \ +to the described location. Decreases as you get closer. + +## Available Actions +- move_forward: Move forward by 0.25 meters +- turn_left: Turn left by 15 degrees +- turn_right: Turn right by 15 degrees +- stop: Stop and end navigation (use ONLY when you believe you have reached \ +the described location) + +## Strategy +1. Read the instruction carefully — it describes a target location or object \ +in the environment, not a step-by-step route +2. Observe your surroundings in the image +3. Reason about which direction the described location is likely in +4. Navigate room by room, using landmarks and room types to orient yourself +5. Use stop ONLY when you are confident you have reached the described location + +## Guidelines +1. Always output at least one action in executable_plan. +2. Only use actions from the Available Actions list. +3. If previous actions failed, reason about why and try a different approach. +4. Do not repeatedly execute the same action sequence. +5. Keep your plan efficient and concise. + +## Response Format +Output a JSON object with exactly these 4 fields: +{ + "visual_state_description": "Describe what you see in the current image", + "reasoning_and_reflection": "Reason about your situation, reflect on \ +history and feedback", + "language_plan": "Describe your next navigation plan in natural language", + "executable_plan": [{"action": ""}] +} + +You may include multiple actions in executable_plan. Actions execute \ +sequentially.""" + + +class ReverieCEPromptBuilder(VLNCEPromptBuilder): + """Prompt builder for REVERIE-CE benchmark. + + Inherits message construction and response parsing from VLNCEPromptBuilder. + Overrides only the system prompt to frame the task around high-level + instructions rather than step-by-step route following. + """ + + def build_messages(self, memory): + # Use parent's build_messages but swap the system prompt + messages = super().build_messages(memory) + messages[0]["content"] = SYSTEM_PROMPT + return messages +``` + +- [ ] **Step 4: Run tests to verify they pass** + +Run: `.venv/bin/pytest tests/test_reverie_ce_task.py::TestReverieCEPromptBuilder -v` +Expected: PASS + +- [ ] **Step 5: Commit** + +```bash +git add easi/tasks/reverie_ce/prompts.py tests/test_reverie_ce_task.py +git commit -m "feat(reverie-ce): add prompt builder for high-level instructions" +``` + +--- + +## Chunk 3: YAML Configs and Integration + +### Task 6: Create YAML Configs + +**Files:** +- Create: `easi/tasks/reverie_ce/_base.yaml` +- Create: `easi/tasks/reverie_ce/reverie_ce_val_unseen.yaml` +- Create: `easi/tasks/reverie_ce/reverie_ce_test.yaml` + +- [ ] **Step 1: Create _base.yaml** + +```yaml +# easi/tasks/reverie_ce/_base.yaml +display_name: "REVERIE-CE" +description: "REVERIE in Continuous Environments (navigation-only)" +simulator: "habitat_sim:v0_1_7" +task_class: "easi.tasks.reverie_ce.task.ReverieCETask" +max_steps: 500 + +dataset: + source: huggingface + repo_id: "oscarqjh/REVERIE-CE_easi" + subset: null + hf_data_dir: "data" + zip_files: + - "mp3d_scenes.zip" + +simulator_configs: + render_platform: auto + screen_height: 480 + screen_width: 480 + hfov: 90 + sensor_height: 1.25 + gpu_device_id: 0 + success_distance: 3.0 + forward_step_size: 0.25 + turn_angle: 15 + allow_sliding: true + additional_deps: + - "fastdtw>=0.3.4" + +agent: + prompt_builder: "easi.tasks.reverie_ce.prompts.ReverieCEPromptBuilder" + prompt_builder_kwargs: + use_feedback: true + use_geo_distance: true + action_history_len: 20 + chat_history: false + message_window_len: 5 + generation_kwargs: + temperature: 0 + max_tokens: 4096 + top_p: 0.95 +``` + +- [ ] **Step 2: Create split configs** + +```yaml +# easi/tasks/reverie_ce/reverie_ce_val_unseen.yaml +extends: _base.yaml +name: reverie_ce_val_unseen +display_name: "REVERIE-CE Val Unseen" +description: "REVERIE-CE validation split (unseen environments)" +dataset: + split: "val_unseen" +``` + +```yaml +# easi/tasks/reverie_ce/reverie_ce_test.yaml +extends: _base.yaml +name: reverie_ce_test +display_name: "REVERIE-CE Test" +description: "REVERIE-CE test split" +dataset: + split: "test" +``` + +- [ ] **Step 3: Verify task discovery** + +Run: `.venv/bin/easi task list 2>&1 | grep -i reverie` +Expected: Should show `reverie_ce_val_unseen` and `reverie_ce_test` + +- [ ] **Step 4: Commit** + +```bash +git add easi/tasks/reverie_ce/_base.yaml easi/tasks/reverie_ce/reverie_ce_val_unseen.yaml easi/tasks/reverie_ce/reverie_ce_test.yaml +git commit -m "feat(reverie-ce): add YAML task configs for val_unseen and test splits" +``` + +--- + +### Task 7: Run Full Test Suite + +- [ ] **Step 1: Run all existing tests to verify no regressions** + +Run: `.venv/bin/pytest tests/ -v --timeout=60` +Expected: All tests pass (946+) + +- [ ] **Step 2: Run REVERIE-CE specific tests** + +Run: `.venv/bin/pytest tests/test_reverie_ce_task.py -v` +Expected: All REVERIE-CE tests pass + +- [ ] **Step 3: Final commit if any test fixes needed** + +```bash +git add -A +git commit -m "fix(reverie-ce): address test failures" +``` + +--- + +## Post-Implementation + +After all tasks are complete: + +1. **Data preparation** (Task 1) must be done separately — download Dynam3D data, reformat to JSONL, upload to `oscarqjh/REVERIE-CE_easi` +2. **End-to-end smoke test** once the HuggingFace repo is ready: + ```bash + easi task download reverie_ce_val_unseen + easi sim test habitat_sim:v0_1_7 + easi start reverie_ce_val_unseen --agent dummy --max-episodes 1 + ``` diff --git a/docs/superpowers/specs/2026-03-12-reverie-ce-design.md b/docs/superpowers/specs/2026-03-12-reverie-ce-design.md new file mode 100644 index 0000000..1245c65 --- /dev/null +++ b/docs/superpowers/specs/2026-03-12-reverie-ce-design.md @@ -0,0 +1,231 @@ +# REVERIE-CE Integration Design + +**Date:** 2026-03-12 +**Status:** Approved + +## Overview + +Integrate REVERIE-CE (navigation-only) as a new task in EASI, reusing the existing `habitat_sim:v0_1_7` simulator infrastructure and VLN-CE R2R vendor code. REVERIE-CE uses the pre-converted Dynam3D dataset (HuggingFace) with Matterport3D scenes. + +## Scope + +- **Navigation-only**: No object grounding. The agent navigates to the described area and calls stop. +- **Reuse existing simulator**: Habitat-Sim 0.1.7 (Python 3.8), same as VLN-CE R2R. +- **Pre-converted data**: Use Dynam3D's pre-converted REVERIE-CE episodes from HuggingFace, repackaged into an EASI-compatible repo. + +## Architecture + +``` +easi/tasks/reverie_ce/ +├── task.py # ReverieCETask (inherits from VLNCETask) +├── _base.yaml # Config pointing to REVERIE-CE dataset repo +├── bridge.py # ReverieCEBridge (inherits from VLNCEBridge) +├── prompts.py # ReverieCEPromptBuilder (high-level instruction style) +├── actions.py # Same 4 discrete actions as VLN-CE R2R +├── vendor/ # Reuse vlnce_r2r vendor code (import, not symlink) +├── reverie_ce_val_unseen.yaml +└── reverie_ce_test.yaml +``` + +### Key decisions + +- **Bridge**: Inherit from `VLNCEBridge` (not symlink) to allow future customization. +- **Task**: Inherit from `VLNCETask` to reuse metric extraction and aggregation. +- **Vendor**: Import `SceneSimulator` and `scene_config` from `vlnce_r2r/vendor/` — no duplication. +- **Simulator**: No new simulator code. Uses `habitat_sim:v0_1_7` as-is. + +## Data Pipeline + +### Source + +Dynam3D pre-converted REVERIE-CE data from HuggingFace (`MrZihanWang/Dynam3D`): +- `reverie_training_data/` — per-scene JSON files (~60 files) +- `reverie_val_unseen_data.json` — single file +- `reverie_test_data.json` — single file +- `reverie_val_unseen_gt.json`, `reverie_test_gt.json` — ground truth + +### EASI HuggingFace Repo + +Reformat (not re-convert) the Dynam3D output into EASI's per-split JSONL convention and upload to `oscarqjh/REVERIE-CE_easi`. This is a one-time reshaping step — no discrete-to-CE conversion is involved. + +``` +oscarqjh/REVERIE-CE_easi/ +├── data/ +│ ├── train.jsonl +│ ├── val_unseen.jsonl +│ ├── test.jsonl +│ ├── val_unseen_gt.json +│ └── test_gt.json +├── mp3d_scenes.zip # Matterport3D .glb files (shared with R2R) +``` + +### Episode Format (JSONL) + +Each line matches the VLN-CE R2R format used by `vlnce_r2r`: + +```json +{ + "episode_id": "50001", + "scene_id": "mp3d/cV4RVeZvu5T/cV4RVeZvu5T.glb", + "instruction": "Go to the laundry room and bring me the blue cushion", + "start_position": [x, y, z], + "start_rotation": [qx, qy, qz, qw], + "goal_position": [x, y, z], + "geodesic_distance": 10.5, + "gt_locations": [[x1, y1, z1], [x2, y2, z2], ...] +} +``` + +The `mp3d_scenes.zip` contains the same Matterport3D scenes as R2R. Can be shared or symlinked to avoid duplication on disk. + +## Task Configuration + +### _base.yaml + +```yaml +name: reverie_ce +simulator: "habitat_sim:v0_1_7" +task_class: "easi.tasks.reverie_ce.task.ReverieCETask" + +dataset: + source: huggingface + repo_id: "oscarqjh/REVERIE-CE_easi" + hf_data_dir: "data" + zip_files: ["mp3d_scenes.zip"] + +simulator_configs: + render_platform: auto + screen_height: 480 + screen_width: 480 + hfov: 90 + sensor_height: 1.25 + forward_step_size: 0.25 + turn_angle: 15 + allow_sliding: true + gpu_device_id: 0 + success_distance: 3.0 + additional_deps: ["fastdtw>=0.3.4"] + +agent: + prompt_builder: "easi.tasks.reverie_ce.prompts.ReverieCEPromptBuilder" + prompt_builder_kwargs: + use_feedback: true + use_geo_distance: true + action_history_len: 20 + chat_history: false + message_window_len: 5 + generation_kwargs: + temperature: 0 + max_tokens: 4096 + top_p: 0.95 +``` + +### Split configs + +Each split YAML extends `_base.yaml`: + +```yaml +# reverie_ce_val_unseen.yaml +extends: _base.yaml +name: reverie_ce_val_unseen +dataset: + split: "val_unseen" +``` + +```yaml +# reverie_ce_test.yaml +extends: _base.yaml +name: reverie_ce_test +dataset: + split: "test" +``` + +## Action Space + +Same 4 discrete actions as VLN-CE R2R: + +| Action | Effect | +|---|---| +| `move_forward` | Move 0.25m forward | +| `turn_left` | Turn 15 degrees left | +| `turn_right` | Turn 15 degrees right | +| `stop` | End navigation, evaluate success | + +## Metrics + +Same as VLN-CE R2R (navigation-only): + +| Metric | Description | +|---|---| +| SR (Success Rate) | 1.0 if agent stops within 3m of goal | +| SPL | Success weighted by path efficiency | +| NE (Navigation Error) | Geodesic distance to goal at stop | +| Oracle SR | Best geodesic distance achieved during episode | +| NDTW | Normalized Dynamic Time Warping | +| SDTW | Success-weighted DTW | +| path_length | Total distance traveled | +| steps_taken | Number of actions executed | + +Implemented by inheriting `VLNCETask.evaluate_episode()` and `aggregate_results()`. + +## Prompt Builder + +### Differences from VLN-CE R2R + +REVERIE uses high-level instructions ("Go to the laundry room and bring me the blue cushion") vs R2R's turn-by-turn route descriptions ("Exit the bedroom and turn left, walk past the kitchen..."). + +The system prompt should: +- Frame the task as "navigate to the described area" rather than "follow route instructions" +- Emphasize spatial reasoning from the high-level description +- Otherwise keep the same structure: image, instruction, geodesic feedback, action history + +### Response format + +Same JSON format as VLN-CE R2R: + +```json +{ + "visual_state_description": "...", + "reasoning_and_reflection": "...", + "language_plan": "...", + "executable_plan": [{"action": "move_forward"}] +} +``` + +## Component Reuse Summary + +| Component | Source | Method | +|---|---|---| +| Simulator | `habitat_sim:v0_1_7` | As-is, no changes | +| Bridge | `vlnce_r2r.bridge.VLNCEBridge` | Inherit | +| SceneSimulator | `vlnce_r2r.vendor.scene_simulator` | Import | +| scene_config | `vlnce_r2r.vendor.scene_config` | Import | +| DTW metrics | `vlnce_r2r.vendor.dtw` | Import | +| Task class | `vlnce_r2r.task.VLNCETask` | Inherit | +| Actions | `vlnce_r2r.actions` | Import or duplicate (trivial) | +| Prompt builder | New | Adapted for high-level instructions | +| Dataset | New HuggingFace repo | Repackaged from Dynam3D | + +## CLI Usage + +```bash +# List available tasks +easi task list # Should show reverie_ce_val_unseen, reverie_ce_test + +# Download dataset +easi task download reverie_ce_val_unseen + +# Run evaluation +easi start reverie_ce_val_unseen --agent react --backend openai --model gpt-4o + +# Parallel evaluation +easi start reverie_ce_val_unseen --agent react --backend openai --model gpt-4o --num-parallel 4 +``` + +## References + +- [Dynam3D (GitHub)](https://github.com/MrZihan/Dynam3D) — conversion scripts and pre-converted data +- [Dynam3D (HuggingFace)](https://huggingface.co/datasets/MrZihanWang/Dynam3D) — pre-converted REVERIE-CE episodes +- [VLN-CE (GitHub)](https://github.com/jacobkrantz/VLN-CE) — original R2R-CE implementation +- [REVERIE (GitHub)](https://github.com/YuankaiQi/REVERIE) — original discrete REVERIE benchmark +- [REVE-CE (IEEE Xplore)](https://ieeexplore.ieee.org/document/9674225) — prior work porting REVERIE to CE (no public code) diff --git a/easi/tasks/reverie_ce/__init__.py b/easi/tasks/reverie_ce/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/easi/tasks/reverie_ce/_base.yaml b/easi/tasks/reverie_ce/_base.yaml new file mode 100644 index 0000000..8973d5a --- /dev/null +++ b/easi/tasks/reverie_ce/_base.yaml @@ -0,0 +1,40 @@ +display_name: "REVERIE-CE" +description: "REVERIE in Continuous Environments (navigation-only)" +simulator: "habitat_sim:v0_1_7" +task_class: "easi.tasks.reverie_ce.task.ReverieCETask" +max_steps: 500 + +dataset: + source: huggingface + repo_id: "oscarqjh/REVERIE-CE_easi" + subset: null + hf_data_dir: "data" + zip_files: + - "mp3d_scenes.zip" + +simulator_configs: + render_platform: auto + screen_height: 480 + screen_width: 480 + hfov: 90 + sensor_height: 1.25 + gpu_device_id: 0 + success_distance: 3.0 + forward_step_size: 0.25 + turn_angle: 15 + allow_sliding: true + additional_deps: + - "fastdtw>=0.3.4" + +agent: + prompt_builder: "easi.tasks.reverie_ce.prompts.ReverieCEPromptBuilder" + prompt_builder_kwargs: + use_feedback: true + use_geo_distance: true + action_history_len: 20 + chat_history: false + message_window_len: 5 + generation_kwargs: + temperature: 0 + max_tokens: 4096 + top_p: 0.95 diff --git a/easi/tasks/reverie_ce/actions.py b/easi/tasks/reverie_ce/actions.py new file mode 100644 index 0000000..ca6d9e3 --- /dev/null +++ b/easi/tasks/reverie_ce/actions.py @@ -0,0 +1,2 @@ +"""REVERIE-CE action space — same as VLN-CE R2R.""" +from easi.tasks.vlnce_r2r.actions import get_action_space # noqa: F401 diff --git a/easi/tasks/reverie_ce/bridge.py b/easi/tasks/reverie_ce/bridge.py new file mode 100644 index 0000000..dd1b887 --- /dev/null +++ b/easi/tasks/reverie_ce/bridge.py @@ -0,0 +1,27 @@ +"""REVERIE-CE bridge — inherits VLN-CE R2R bridge. + +Runs inside the easi_habitat_sim_v0_1_7 conda env (Python 3.8). +Inherits all reset/step/extract logic from VLNCEBridge. + +Usage: + python bridge.py --workspace /tmp/easi_xxx [--simulator-kwargs '{}'] +""" +from __future__ import annotations + +import sys +from pathlib import Path + +_repo_root = Path(__file__).resolve().parents[3] +if str(_repo_root) not in sys.path: + sys.path.insert(0, str(_repo_root)) + +from easi.tasks.vlnce_r2r.bridge import VLNCEBridge # noqa: E402 + + +class ReverieCEBridge(VLNCEBridge): + """Bridge for REVERIE-CE. Identical to VLN-CE R2R for now.""" + pass + + +if __name__ == "__main__": + ReverieCEBridge.main() diff --git a/easi/tasks/reverie_ce/prompts.py b/easi/tasks/reverie_ce/prompts.py new file mode 100644 index 0000000..542aa9b --- /dev/null +++ b/easi/tasks/reverie_ce/prompts.py @@ -0,0 +1,72 @@ +"""REVERIE-CE prompt builder. + +Adapted from VLN-CE R2R for REVERIE's high-level instruction style. +REVERIE instructions describe a target location/object rather than +step-by-step route directions. +""" +from __future__ import annotations + +from easi.tasks.vlnce_r2r.prompts import VLNCEPromptBuilder +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + +SYSTEM_PROMPT = """\ +## Role and Environment +You are a robot navigating in a 3D indoor environment. You observe the \ +environment through a front-facing camera and must navigate to the location \ +described in a high-level natural language instruction. + +## Observation Description +- **Distance to goal**: Geodesic (shortest walkable path) distance in meters \ +to the described location. Decreases as you get closer. + +## Available Actions +- move_forward: Move forward by 0.25 meters +- turn_left: Turn left by 15 degrees +- turn_right: Turn right by 15 degrees +- stop: Stop and end navigation (use ONLY when you believe you have reached \ +the described location) + +## Strategy +1. Read the instruction carefully — it describes a target location or object \ +in the environment, not a step-by-step route +2. Observe your surroundings in the image +3. Reason about which direction the described location is likely in +4. Navigate room by room, using landmarks and room types to orient yourself +5. Use stop ONLY when you are confident you have reached the described location + +## Guidelines +1. Always output at least one action in executable_plan. +2. Only use actions from the Available Actions list. +3. If previous actions failed, reason about why and try a different approach. +4. Do not repeatedly execute the same action sequence. +5. Keep your plan efficient and concise. + +## Response Format +Output a JSON object with exactly these 4 fields: +{ + "visual_state_description": "Describe what you see in the current image", + "reasoning_and_reflection": "Reason about your situation, reflect on \ +history and feedback", + "language_plan": "Describe your next navigation plan in natural language", + "executable_plan": [{"action": ""}] +} + +You may include multiple actions in executable_plan. Actions execute \ +sequentially.""" + + +class ReverieCEPromptBuilder(VLNCEPromptBuilder): + """Prompt builder for REVERIE-CE benchmark. + + Inherits message construction and response parsing from VLNCEPromptBuilder. + Overrides only the system prompt to frame the task around high-level + instructions rather than step-by-step route following. + """ + + def build_messages(self, memory): + # Use parent's build_messages but swap the system prompt + messages = super().build_messages(memory) + messages[0]["content"] = SYSTEM_PROMPT + return messages diff --git a/easi/tasks/reverie_ce/reverie_ce_test.yaml b/easi/tasks/reverie_ce/reverie_ce_test.yaml new file mode 100644 index 0000000..7633883 --- /dev/null +++ b/easi/tasks/reverie_ce/reverie_ce_test.yaml @@ -0,0 +1,6 @@ +extends: _base.yaml +name: reverie_ce_test +display_name: "REVERIE-CE Test" +description: "REVERIE-CE test split" +dataset: + split: "test" diff --git a/easi/tasks/reverie_ce/reverie_ce_val_unseen.yaml b/easi/tasks/reverie_ce/reverie_ce_val_unseen.yaml new file mode 100644 index 0000000..0d2edd6 --- /dev/null +++ b/easi/tasks/reverie_ce/reverie_ce_val_unseen.yaml @@ -0,0 +1,6 @@ +extends: _base.yaml +name: reverie_ce_val_unseen +display_name: "REVERIE-CE Val Unseen" +description: "REVERIE-CE validation split (unseen environments)" +dataset: + split: "val_unseen" diff --git a/easi/tasks/reverie_ce/task.py b/easi/tasks/reverie_ce/task.py new file mode 100644 index 0000000..37e23e8 --- /dev/null +++ b/easi/tasks/reverie_ce/task.py @@ -0,0 +1,22 @@ +"""REVERIE-CE task for EASI. + +Navigation-only evaluation of REVERIE in continuous environments. +Inherits from VLNCETask — same metrics, same bridge protocol. +""" +from __future__ import annotations + +from pathlib import Path + +from easi.tasks.vlnce_r2r.task import VLNCETask +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + + +class ReverieCETask(VLNCETask): + + def get_task_yaml_path(self) -> Path: + return Path(__file__).parent / "_base.yaml" + + def get_bridge_script_path(self) -> Path: + return Path(__file__).parent / "bridge.py" diff --git a/tests/test_reverie_ce_task.py b/tests/test_reverie_ce_task.py new file mode 100644 index 0000000..9633882 --- /dev/null +++ b/tests/test_reverie_ce_task.py @@ -0,0 +1,183 @@ +"""Tests for REVERIE-CE task, action space, and prompt builder.""" +import json + +import pytest +from unittest.mock import MagicMock + +from easi.core.episode import Action, EpisodeRecord, Observation, StepResult + + +class TestActionSpace: + def test_has_four_actions(self): + from easi.tasks.reverie_ce.actions import get_action_space + actions = get_action_space() + assert len(actions) == 4 + assert "move_forward" in actions + assert "turn_left" in actions + assert "turn_right" in actions + assert "stop" in actions + + +class TestReverieCETask: + @pytest.fixture + def task(self): + from easi.tasks.reverie_ce.task import ReverieCETask + mock_config = { + "name": "reverie_ce_val_unseen", + "display_name": "REVERIE-CE Val Unseen", + "simulator": "habitat_sim:v0_1_7", + "task_class": "easi.tasks.reverie_ce.task.ReverieCETask", + "max_steps": 500, + "dataset": {"source": "huggingface", "repo_id": "oscarqjh/REVERIE-CE_easi", "split": "val_unseen"}, + "simulator_configs": {}, + "agent": {"prompt_builder": "easi.tasks.reverie_ce.prompts.ReverieCEPromptBuilder"}, + } + task = ReverieCETask.__new__(ReverieCETask) + task._config = mock_config + task._yaml_path = None + task._action_space = None + return task + + def test_action_space(self, task): + actions = task._build_action_space() + assert actions == ["move_forward", "turn_left", "turn_right", "stop"] + + def test_format_reset_config(self, task): + episode = { + "episode_id": "50001", + "scene_id": "cV4RVeZvu5T", + "instruction": "Go to the laundry room and get the cushion", + "start_position": [1.0, 0.5, -2.0], + "start_rotation": [0, 0.707, 0, 0.707], + "goal_position": [4.5, 0.5, 1.2], + "geodesic_distance": 10.5, + "gt_locations": [[1.0, 0.5, -2.0], [2.0, 0.5, -1.0]], + "_data_dir": "/data/reverie_ce", + } + config = task.format_reset_config(episode) + assert config["scene_id"] == "cV4RVeZvu5T" + assert config["data_dir"] == "/data/reverie_ce" + assert json.loads(config["start_position"]) == [1.0, 0.5, -2.0] + + def test_evaluate_episode_success(self, task): + info = { + "success": 1.0, "oracle_success": 1.0, "spl": 0.8, + "navigation_error": 1.5, "ndtw": 0.9, "sdtw": 0.85, + "path_length": 8.0, + } + obs = Observation(rgb_path="/tmp/step.png") + step = StepResult(observation=obs, done=True, info=info) + result = task.evaluate_episode({}, [step]) + assert result["success"] == 1.0 + assert result["spl"] == 0.8 + + def test_evaluate_episode_empty(self, task): + result = task.evaluate_episode({}, []) + assert result["success"] is None + assert result["path_length"] == 0.0 + + def test_aggregate_results(self, task): + records = [ + EpisodeRecord(episode={}, trajectory=[], episode_results={ + "success": 1.0, "oracle_success": 1.0, "spl": 0.8, + "navigation_error": 1.5, "ndtw": 0.9, "sdtw": 0.85, + "path_length": 8.0, "steps_taken": 30.0, + }), + EpisodeRecord(episode={}, trajectory=[], episode_results={ + "success": 0.0, "oracle_success": 0.0, "spl": 0.0, + "navigation_error": 6.0, "ndtw": 0.3, "sdtw": 0.0, + "path_length": 12.0, "steps_taken": 50.0, + }), + ] + summary = task.aggregate_results(records) + assert summary["num_episodes"] == 2 + assert summary["SR"] == 0.5 + assert summary["SPL"] == 0.4 + + def test_bridge_script_path(self, task): + path = task.get_bridge_script_path() + assert path.name == "bridge.py" + assert "reverie_ce" in str(path) + + +class TestReverieCEPromptBuilder: + @pytest.fixture + def mock_encode(self): + # Must patch in vlnce_r2r.prompts where the function is actually + # called (super().build_messages() resolves it there, not in + # reverie_ce.prompts). + import easi.tasks.vlnce_r2r.prompts as prompts_mod + original = prompts_mod._encode_image_base64 + prompts_mod._encode_image_base64 = lambda x: "data:image/png;base64,AAAA" + yield + prompts_mod._encode_image_base64 = original + + def _make_memory(self, action_history=None): + memory = MagicMock() + memory.task_description = "Go to the laundry room and bring me the blue cushion" + memory.action_space = ["move_forward", "turn_left", "turn_right", "stop"] + memory.current_observation = Observation( + rgb_path="/tmp/test.png", + metadata={"geo_distance": "5.3"}, + ) + memory.action_history = action_history or [] + memory.steps = [] + return memory + + def test_system_prompt_mentions_high_level(self, mock_encode): + from easi.tasks.reverie_ce.prompts import ReverieCEPromptBuilder + builder = ReverieCEPromptBuilder() + memory = self._make_memory() + messages = builder.build_messages(memory) + system_msg = messages[0]["content"] + assert "high-level" in system_msg.lower() or "described location" in system_msg.lower() + + def test_build_messages_has_image(self, mock_encode): + from easi.tasks.reverie_ce.prompts import ReverieCEPromptBuilder + builder = ReverieCEPromptBuilder() + memory = self._make_memory() + messages = builder.build_messages(memory) + user_content = messages[1]["content"] + image_blocks = [b for b in user_content if b.get("type") == "image_url"] + assert len(image_blocks) == 1 + + def test_build_messages_has_instruction(self, mock_encode): + from easi.tasks.reverie_ce.prompts import ReverieCEPromptBuilder + builder = ReverieCEPromptBuilder() + memory = self._make_memory() + messages = builder.build_messages(memory) + text_blocks = [b["text"] for b in messages[1]["content"] if b.get("type") == "text"] + full_text = "\n".join(text_blocks) + assert "laundry room" in full_text + + def test_build_messages_has_distance(self, mock_encode): + from easi.tasks.reverie_ce.prompts import ReverieCEPromptBuilder + builder = ReverieCEPromptBuilder() + memory = self._make_memory() + messages = builder.build_messages(memory) + text_blocks = [b["text"] for b in messages[1]["content"] if b.get("type") == "text"] + full_text = "\n".join(text_blocks) + assert "5.3" in full_text + + def test_parse_response_valid(self): + from easi.tasks.reverie_ce.prompts import ReverieCEPromptBuilder + builder = ReverieCEPromptBuilder() + memory = MagicMock() + memory.action_space = ["move_forward", "turn_left", "turn_right", "stop"] + response = json.dumps({ + "visual_state_description": "I see a hallway", + "reasoning_and_reflection": "Need to find the laundry room", + "language_plan": "Move forward", + "executable_plan": [{"action": "move_forward"}], + }) + actions = builder.parse_response(response, memory) + assert len(actions) == 1 + assert actions[0].action_name == "move_forward" + + def test_parse_response_invalid_json(self): + from easi.tasks.reverie_ce.prompts import ReverieCEPromptBuilder + builder = ReverieCEPromptBuilder() + memory = MagicMock() + memory.action_space = ["move_forward", "turn_left", "turn_right", "stop"] + actions = builder.parse_response("not json", memory) + assert actions == [] From 811d5f944d19c905192a09b703e148832228f65a Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Fri, 13 Mar 2026 01:46:15 +0800 Subject: [PATCH 206/239] feat: add trajectory video renderer for post-eval analysis MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Adds easi/analysis/ package with trajectory_video.py that generates per-episode MP4 videos showing robot paths on top-down maps alongside camera views — pure post-processing, no simulator dependencies. --- easi/analysis/__init__.py | 0 easi/analysis/trajectory_video.py | 399 ++++++++++++++++++++++++++++++ tests/test_analyze_trajectory.py | 222 +++++++++++++++++ 3 files changed, 621 insertions(+) create mode 100644 easi/analysis/__init__.py create mode 100644 easi/analysis/trajectory_video.py create mode 100644 tests/test_analyze_trajectory.py diff --git a/easi/analysis/__init__.py b/easi/analysis/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/easi/analysis/trajectory_video.py b/easi/analysis/trajectory_video.py new file mode 100644 index 0000000..06eb3fc --- /dev/null +++ b/easi/analysis/trajectory_video.py @@ -0,0 +1,399 @@ +"""Trajectory video generator for post-evaluation analysis. + +Generates per-episode videos showing the robot's path on a top-down map +alongside the agent's camera view. No simulator dependencies — pure +post-processing from episode output directories. + +Requires: opencv-python-headless (optional dependency) +""" +from __future__ import annotations + +import json +import random +from pathlib import Path + +import numpy as np + +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + + +def _require_cv2(): + """Import cv2 with helpful error if missing.""" + try: + import cv2 + return cv2 + except ImportError: + raise ImportError( + "opencv-python-headless is required for trajectory video generation.\n" + "Install it with: pip install opencv-python-headless" + ) + + +def discover_episodes( + run_dir: Path | str, + filter_by: str | None = None, + sample_n: int | None = None, + seed: int = 42, +) -> list[Path]: + """Discover and filter episode directories in a run. + + Args: + run_dir: Path to evaluation run directory. + filter_by: "success" or "failed" to filter by outcome. + sample_n: Randomly sample N episodes after filtering. + seed: Random seed for sampling. + + Returns: + Sorted list of episode directory paths. + """ + run_dir = Path(run_dir) + episodes_dir = run_dir / "episodes" + if not episodes_dir.is_dir(): + logger.warning("No episodes/ directory found in %s", run_dir) + return [] + + episode_dirs = sorted( + d for d in episodes_dir.iterdir() + if d.is_dir() and (d / "trajectory.jsonl").exists() + ) + + if filter_by: + filtered = [] + for ep_dir in episode_dirs: + result_path = ep_dir / "result.json" + if not result_path.exists(): + continue + try: + result = json.loads(result_path.read_text()) + except (json.JSONDecodeError, OSError): + continue + success = result.get("success") + if filter_by == "success" and success == 1.0: + filtered.append(ep_dir) + elif filter_by == "failed" and success != 1.0: + filtered.append(ep_dir) + episode_dirs = filtered + + if sample_n is not None and sample_n < len(episode_dirs): + episode_dirs = random.Random(seed).sample(episode_dirs, sample_n) + episode_dirs.sort() + + return episode_dirs + + +def world_to_pixel( + world_x: float, world_z: float, map_meta: dict +) -> tuple[int, int]: + """Project world [x, z] to pixel coords using map metadata. + + Habitat-Sim uses Y-up coordinates. The floor plane is [x, z]. + """ + bounds_lower = map_meta["bounds_lower"] + mpp = map_meta["meters_per_pixel"] + px = int((world_x - bounds_lower[0]) / mpp) + pz = int((world_z - bounds_lower[2]) / mpp) + return px, pz + + +def world_to_pixel_fallback( + world_x: float, + world_z: float, + all_positions: list[list[float]], + canvas_size: tuple[int, int], + padding: int = 20, +) -> tuple[int, int]: + """Project world coords to pixel coords on a blank canvas. + + Computes bounding box from all positions and maps linearly. + """ + xs = [p[0] for p in all_positions] + zs = [p[1] for p in all_positions] + x_min, x_max = min(xs), max(xs) + z_min, z_max = min(zs), max(zs) + + # Avoid division by zero for single-point paths + x_range = max(x_max - x_min, 0.01) + z_range = max(z_max - z_min, 0.01) + + draw_w = canvas_size[0] - 2 * padding + draw_h = canvas_size[1] - 2 * padding + + px = int(padding + (world_x - x_min) / x_range * draw_w) + pz = int(padding + (world_z - z_min) / z_range * draw_h) + return px, pz + + +def _load_trajectory(ep_dir: Path) -> list[dict]: + """Load trajectory.jsonl entries.""" + path = ep_dir / "trajectory.jsonl" + entries = [] + with open(path) as f: + for line in f: + line = line.strip() + if line: + entries.append(json.loads(line)) + return entries + + +def _parse_positions(entries: list[dict]) -> list[list[float] | None]: + """Extract [x, z] positions from trajectory entries. + + Returns a list parallel to entries. Reset entry (step 0) has None + since its info is empty — start position comes from episode_meta.json. + """ + positions = [] + for entry in entries: + info = entry.get("info", {}) + raw = info.get("agent_position") + if raw is not None: + pos_3d = json.loads(raw) if isinstance(raw, str) else raw + positions.append([pos_3d[0], pos_3d[2]]) # [x, z] floor plane + else: + positions.append(None) + return positions + + +def render_episode_video( + ep_dir: Path, + output_path: Path, + fps: int = 4, +) -> None: + """Render a trajectory video for one episode. + + Args: + ep_dir: Path to episode directory containing trajectory.jsonl, step_*.png, etc. + output_path: Where to write the MP4 video. + fps: Frames per second. + """ + cv2 = _require_cv2() + from PIL import Image + + traj_path = ep_dir / "trajectory.jsonl" + if not traj_path.exists(): + logger.warning("No trajectory.jsonl in %s, skipping", ep_dir) + return + + entries = _load_trajectory(ep_dir) + if len(entries) < 2: + logger.warning("Trajectory too short in %s, skipping", ep_dir) + return + + positions = _parse_positions(entries) + + # Load episode metadata (start position, goal, gt_locations) + meta_path = ep_dir / "episode_meta.json" + ep_meta = json.loads(meta_path.read_text()) if meta_path.exists() else {} + start_pos = ep_meta.get("start_position") + goal_pos = ep_meta.get("goal_position") + gt_locations = ep_meta.get("gt_locations") + + # Start position as [x, z] + start_xz = [start_pos[0], start_pos[2]] if start_pos else None + + # Goal position as [x, z] + goal_xz = [goal_pos[0], goal_pos[2]] if goal_pos else None + + # GT path as [[x, z], ...] + gt_xz = [[p[0], p[2]] for p in gt_locations] if gt_locations else None + + # Load topdown map or create blank canvas + map_path = ep_dir / "topdown_map.png" + map_meta_path = ep_dir / "topdown_map_meta.json" + has_map = map_path.exists() and map_meta_path.exists() + + if has_map: + map_img = np.array(Image.open(map_path).convert("RGB")) + map_meta = json.loads(map_meta_path.read_text()) + else: + map_img = None + map_meta = None + + # Collect all valid [x, z] positions for bounding box fallback + all_xz = [p for p in positions if p is not None] + if start_xz: + all_xz.insert(0, start_xz) + if goal_xz: + all_xz.append(goal_xz) + + if not all_xz: + logger.warning("No positions found in %s, skipping", ep_dir) + return + + # Determine panel height from first step image + first_img_path = ep_dir / entries[0].get("rgb_path", "step_0000.png") + if first_img_path.exists(): + cam_h = np.array(Image.open(first_img_path)).shape[0] + else: + cam_h = 480 + panel_h = cam_h + + # Blank canvas fallback + if map_img is None: + map_img = np.full((panel_h, panel_h, 3), 40, dtype=np.uint8) + + # Resize map to match panel height + scale = panel_h / map_img.shape[0] + map_w = int(map_img.shape[1] * scale) + map_base = cv2.resize(map_img, (map_w, panel_h)) + + # Load result for final frame overlay + result_path = ep_dir / "result.json" + result = json.loads(result_path.read_text()) if result_path.exists() else {} + + # Helper: project world coord to map pixel + def to_pixel(x, z): + if map_meta: + px, pz = world_to_pixel(x, z, map_meta) + return int(px * scale), int(pz * scale) + else: + return world_to_pixel_fallback(x, z, all_xz, (map_w, panel_h)) + + # Set up video writer + output_path.parent.mkdir(parents=True, exist_ok=True) + fourcc = cv2.VideoWriter_fourcc(*"mp4v") + frame_w = map_w + cam_h # map_panel + camera_panel (camera is square) + writer = cv2.VideoWriter(str(output_path), fourcc, fps, (frame_w, panel_h)) + + if not writer.isOpened(): + logger.error("Failed to open video writer for %s", output_path) + return + + try: + path_so_far = [] + if start_xz: + path_so_far.append(start_xz) + + for i, entry in enumerate(entries): + # Update path + if positions[i] is not None: + path_so_far.append(positions[i]) + + # Draw map panel + map_frame = map_base.copy() + + # Draw GT path (dashed, faint) + if gt_xz and len(gt_xz) >= 2: + for j in range(len(gt_xz) - 1): + p1 = to_pixel(*gt_xz[j]) + p2 = to_pixel(*gt_xz[j + 1]) + # Dashed line: draw every other segment + if j % 2 == 0: + cv2.line(map_frame, p1, p2, (180, 180, 180), 1) + + # Draw agent path (solid, growing) + if len(path_so_far) >= 2: + for j in range(len(path_so_far) - 1): + p1 = to_pixel(*path_so_far[j]) + p2 = to_pixel(*path_so_far[j + 1]) + cv2.line(map_frame, p1, p2, (0, 200, 0), 2) + + # Draw start (blue circle) + if start_xz: + sp = to_pixel(*start_xz) + cv2.circle(map_frame, sp, 6, (255, 100, 100), -1) + + # Draw goal (red circle) + if goal_xz: + gp = to_pixel(*goal_xz) + cv2.circle(map_frame, gp, 6, (100, 100, 255), -1) + + # Draw current position (green arrowhead) + if path_so_far: + cp = to_pixel(*path_so_far[-1]) + cv2.circle(map_frame, cp, 5, (0, 255, 0), -1) + + # Step/distance text on map panel + step_text = f"Step: {entry.get('step', i)}" + cv2.putText(map_frame, step_text, (10, panel_h - 30), + cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1) + geo_dist = entry.get("info", {}).get("geo_distance") + if geo_dist: + dist_text = f"Dist: {geo_dist}m" + cv2.putText(map_frame, dist_text, (10, panel_h - 10), + cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1) + + # Load camera panel + rgb_name = entry.get("rgb_path", f"step_{i:04d}.png") + cam_path = ep_dir / rgb_name + if cam_path.exists(): + cam_img = np.array(Image.open(cam_path).convert("RGB")) + cam_img = cv2.resize(cam_img, (cam_h, panel_h)) + else: + logger.warning("Missing image %s, using placeholder", cam_path.name) + cam_img = np.full((panel_h, cam_h, 3), 30, dtype=np.uint8) + + # Action overlay on camera panel + action = entry.get("action", "") + if action: + cv2.putText(cam_img, action, (10, panel_h - 10), + cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1) + + # Final frame: overlay outcome + if i == len(entries) - 1 and result: + success = result.get("success") + if success == 1.0: + label = "SUCCESS" + color = (0, 255, 0) + elif success is not None: + label = "FAILURE" + color = (0, 0, 255) + else: + label = "NO GOAL" + color = (200, 200, 200) + cv2.putText(cam_img, label, (10, 30), + cv2.FONT_HERSHEY_SIMPLEX, 1.0, color, 2) + + # Concatenate panels + frame = np.concatenate([map_frame, cam_img], axis=1) + + # OpenCV uses BGR + writer.write(cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)) + finally: + writer.release() + + logger.info("Wrote %s (%d frames)", output_path, len(entries)) + + +def generate_trajectory_videos( + run_dir: str, + filter_by: str | None = None, + sample_n: int | None = None, + fps: int = 4, + seed: int = 42, +) -> None: + """Generate trajectory videos for all matching episodes in a run. + + Args: + run_dir: Path to evaluation run directory. + filter_by: "success" or "failed". + sample_n: Randomly sample N episodes. + fps: Video frame rate. + seed: Random seed for sampling. + """ + _require_cv2() + + run_path = Path(run_dir) + if not run_path.is_dir(): + logger.error("Run directory not found: %s", run_dir) + return + + episodes = discover_episodes(run_path, filter_by=filter_by, sample_n=sample_n, seed=seed) + if not episodes: + logger.info("No episodes found matching criteria in %s", run_dir) + return + + output_dir = run_path / "analysis" / "videos" + output_dir.mkdir(parents=True, exist_ok=True) + + logger.info("Generating %d trajectory videos in %s", len(episodes), output_dir) + + for ep_dir in episodes: + output_path = output_dir / f"{ep_dir.name}.mp4" + try: + render_episode_video(ep_dir, output_path, fps=fps) + except Exception: + logger.exception("Failed to render %s", ep_dir.name) + + logger.info("Done. Videos saved to %s", output_dir) diff --git a/tests/test_analyze_trajectory.py b/tests/test_analyze_trajectory.py new file mode 100644 index 0000000..8c4b498 --- /dev/null +++ b/tests/test_analyze_trajectory.py @@ -0,0 +1,222 @@ +"""Tests for trajectory video generation.""" +import json + +import numpy as np +import pytest +from pathlib import Path +from unittest.mock import patch + + +def _make_episode_dir(parent: Path, name: str, success: float | None = 1.0, num_steps: int = 3): + """Create a fake episode directory with minimal files.""" + ep_dir = parent / "episodes" / name + ep_dir.mkdir(parents=True, exist_ok=True) + + # result.json + result = {"success": success, "navigation_error": 2.5} + (ep_dir / "result.json").write_text(json.dumps(result)) + + # trajectory.jsonl + lines = [] + # Reset entry + lines.append(json.dumps({ + "step": 0, "type": "reset", + "rgb_path": "step_0000.png", + "agent_pose": [0, 0, 0, 0, 0, 0], + "reward": 0.0, "done": False, "info": {}, + })) + # Step entries with agent_position + for i in range(1, num_steps + 1): + pos = [float(i), 0.5, float(i) * 0.5] + lines.append(json.dumps({ + "step": i, "type": "step", + "action": "move_forward", + "rgb_path": f"step_{i:04d}.png", + "agent_pose": [0, 0, 0, 0, 0, 0], + "reward": 0.0, "done": i == num_steps, + "info": {"agent_position": json.dumps(pos), "geo_distance": "3.2"}, + })) + (ep_dir / "trajectory.jsonl").write_text("\n".join(lines) + "\n") + + # Fake step images (8x8 red squares) + from PIL import Image + for i in range(num_steps + 1): + img = Image.fromarray(np.full((8, 8, 3), 128, dtype=np.uint8)) + img.save(str(ep_dir / f"step_{i:04d}.png")) + + # episode_meta.json + meta = { + "start_position": [0.0, 0.5, 0.0], + "goal_position": [4.5, 0.5, 1.5], + } + (ep_dir / "episode_meta.json").write_text(json.dumps(meta)) + + return ep_dir + + +class TestDiscoverEpisodes: + def test_discover_all(self, tmp_path): + from easi.analysis.trajectory_video import discover_episodes + _make_episode_dir(tmp_path, "000_1", success=1.0) + _make_episode_dir(tmp_path, "001_2", success=0.0) + eps = discover_episodes(tmp_path) + assert len(eps) == 2 + + def test_filter_success(self, tmp_path): + from easi.analysis.trajectory_video import discover_episodes + _make_episode_dir(tmp_path, "000_1", success=1.0) + _make_episode_dir(tmp_path, "001_2", success=0.0) + _make_episode_dir(tmp_path, "002_3", success=None) + eps = discover_episodes(tmp_path, filter_by="success") + assert len(eps) == 1 + assert eps[0].name == "000_1" + + def test_filter_failed(self, tmp_path): + from easi.analysis.trajectory_video import discover_episodes + _make_episode_dir(tmp_path, "000_1", success=1.0) + _make_episode_dir(tmp_path, "001_2", success=0.0) + _make_episode_dir(tmp_path, "002_3", success=None) + eps = discover_episodes(tmp_path, filter_by="failed") + assert len(eps) == 2 + + def test_sample(self, tmp_path): + from easi.analysis.trajectory_video import discover_episodes + for i in range(10): + _make_episode_dir(tmp_path, f"{i:03d}_{i}", success=1.0) + eps = discover_episodes(tmp_path, sample_n=3, seed=42) + assert len(eps) == 3 + + def test_sample_larger_than_available(self, tmp_path): + from easi.analysis.trajectory_video import discover_episodes + for i in range(3): + _make_episode_dir(tmp_path, f"{i:03d}_{i}", success=1.0) + eps = discover_episodes(tmp_path, sample_n=10, seed=42) + assert len(eps) == 3 # returns all, no error + + def test_sample_deterministic(self, tmp_path): + from easi.analysis.trajectory_video import discover_episodes + for i in range(10): + _make_episode_dir(tmp_path, f"{i:03d}_{i}", success=1.0) + eps1 = discover_episodes(tmp_path, sample_n=3, seed=42) + eps2 = discover_episodes(tmp_path, sample_n=3, seed=42) + assert [e.name for e in eps1] == [e.name for e in eps2] + + def test_no_episodes_dir(self, tmp_path): + from easi.analysis.trajectory_video import discover_episodes + eps = discover_episodes(tmp_path) + assert eps == [] + + def test_missing_result_json_skipped_when_filtering(self, tmp_path): + from easi.analysis.trajectory_video import discover_episodes + _make_episode_dir(tmp_path, "000_1", success=1.0) + # Create dir with trajectory.jsonl but no result.json + bad_dir = tmp_path / "episodes" / "001_bad" + bad_dir.mkdir(parents=True) + (bad_dir / "trajectory.jsonl").write_text('{"step":0}\n') + eps = discover_episodes(tmp_path, filter_by="success") + # 001_bad has trajectory.jsonl so it's discovered, but no result.json + # so it should be skipped during filtering + assert len(eps) == 1 + assert eps[0].name == "000_1" + + +class TestCoordinateProjection: + def test_world_to_pixel(self): + from easi.analysis.trajectory_video import world_to_pixel + meta = { + "bounds_lower": [0.0, 0.0, 0.0], + "bounds_upper": [10.0, 3.0, 8.0], + "meters_per_pixel": 0.1, + } + # Position at world origin -> pixel (0, 0) + px, py = world_to_pixel(0.0, 0.0, meta) + assert px == 0 + assert py == 0 + + # Position at (5.0, 4.0) -> pixel (50, 40) + px, py = world_to_pixel(5.0, 4.0, meta) + assert px == 50 + assert py == 40 + + def test_world_to_pixel_no_meta(self): + from easi.analysis.trajectory_video import world_to_pixel_fallback + positions = [[0.0, 0.0], [10.0, 8.0]] + canvas_size = (200, 200) + padding = 20 + px, py = world_to_pixel_fallback(5.0, 4.0, positions, canvas_size, padding) + assert padding <= px <= canvas_size[0] - padding + assert padding <= py <= canvas_size[1] - padding + + +class TestRenderEpisodeVideo: + def test_generates_mp4(self, tmp_path): + cv2 = pytest.importorskip("cv2") + from easi.analysis.trajectory_video import render_episode_video + ep_dir = _make_episode_dir(tmp_path, "000_1", success=1.0, num_steps=3) + output_dir = tmp_path / "analysis" / "videos" + output_dir.mkdir(parents=True) + output_path = output_dir / "000_1.mp4" + render_episode_video(ep_dir, output_path, fps=4) + assert output_path.exists() + assert output_path.stat().st_size > 0 + + def test_generates_video_without_map(self, tmp_path): + cv2 = pytest.importorskip("cv2") + from easi.analysis.trajectory_video import render_episode_video + ep_dir = _make_episode_dir(tmp_path, "000_1", success=1.0, num_steps=3) + # Remove topdown map files (should fall back to blank canvas) + (ep_dir / "topdown_map.png").unlink(missing_ok=True) + output_path = tmp_path / "out.mp4" + render_episode_video(ep_dir, output_path, fps=4) + assert output_path.exists() + + def test_skips_missing_trajectory(self, tmp_path, caplog): + cv2 = pytest.importorskip("cv2") + from easi.analysis.trajectory_video import render_episode_video + ep_dir = tmp_path / "episodes" / "000_bad" + ep_dir.mkdir(parents=True) + output_path = tmp_path / "out.mp4" + render_episode_video(ep_dir, output_path, fps=4) + assert not output_path.exists() + + def test_skips_zero_step_episode(self, tmp_path): + cv2 = pytest.importorskip("cv2") + from easi.analysis.trajectory_video import render_episode_video + ep_dir = tmp_path / "episodes" / "000_short" + ep_dir.mkdir(parents=True) + # Only reset entry, no steps + (ep_dir / "trajectory.jsonl").write_text( + json.dumps({"step": 0, "type": "reset", "rgb_path": "step_0000.png", + "agent_pose": [0,0,0,0,0,0], "reward": 0.0, "done": False, "info": {}}) + + "\n" + ) + output_path = tmp_path / "out.mp4" + render_episode_video(ep_dir, output_path, fps=4) + assert not output_path.exists() + + def test_handles_missing_step_images(self, tmp_path): + cv2 = pytest.importorskip("cv2") + from easi.analysis.trajectory_video import render_episode_video + ep_dir = _make_episode_dir(tmp_path, "000_1", success=1.0, num_steps=3) + # Delete middle step image + (ep_dir / "step_0002.png").unlink() + output_path = tmp_path / "out.mp4" + render_episode_video(ep_dir, output_path, fps=4) + assert output_path.exists() # video still generated with placeholder + + +class TestCLI: + def test_analyze_trajectory_generates_videos(self, tmp_path): + cv2 = pytest.importorskip("cv2") + from easi.analysis.trajectory_video import generate_trajectory_videos + _make_episode_dir(tmp_path, "000_1", success=1.0, num_steps=3) + _make_episode_dir(tmp_path, "001_2", success=0.0, num_steps=2) + generate_trajectory_videos(str(tmp_path), filter_by="success", fps=4) + videos = list((tmp_path / "analysis" / "videos").glob("*.mp4")) + assert len(videos) == 1 + assert videos[0].name == "000_1.mp4" + + def test_analyze_trajectory_nonexistent_dir(self, tmp_path, caplog): + from easi.analysis.trajectory_video import generate_trajectory_videos + generate_trajectory_videos(str(tmp_path / "nonexistent")) + assert "not found" in caplog.text.lower() From 97cc3ef498a01f373580dfae3252a8eaad81f9cb Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Fri, 13 Mar 2026 13:09:57 +0800 Subject: [PATCH 207/239] feat: add _get_topdown_map and _get_episode_meta hooks to BaseBridge --- easi/simulators/base_bridge.py | 39 ++++++++++++ tests/test_bridge_hooks.py | 109 +++++++++++++++++++++++++++++++++ 2 files changed, 148 insertions(+) create mode 100644 tests/test_bridge_hooks.py diff --git a/easi/simulators/base_bridge.py b/easi/simulators/base_bridge.py index f7153ef..14bbff6 100644 --- a/easi/simulators/base_bridge.py +++ b/easi/simulators/base_bridge.py @@ -98,6 +98,22 @@ def _on_step(self, env, action_text): """Custom step logic. Default: call env.step(action_text).""" return env.step(action_text) + def _get_topdown_map(self): + """Return (rgb_array, metadata_dict) or None. + + Called once after _on_reset() inside the bridge subprocess. + Override in subclasses that support top-down map rendering. + """ + return None + + def _get_episode_meta(self): + """Return episode metadata dict or None. + + Called once after _on_reset(). Override to persist gt_locations, + goal_position, start_position, etc. for post-processing. + """ + return None + # --- Image saving --- def _save_image(self, image_array): @@ -133,6 +149,29 @@ def reset(self, reset_config): self.env = self._create_env(reset_config, self.simulator_kwargs) self.step_count = 0 obs = self._on_reset(self.env, reset_config) + + # Save topdown map and episode metadata (optional, non-fatal) + if self.episode_output_dir: + save_dir = Path(self.episode_output_dir) + try: + map_result = self._get_topdown_map() + if map_result is not None: + map_image, map_meta = map_result + from PIL import Image + Image.fromarray(map_image).save(str(save_dir / "topdown_map.png")) + with open(save_dir / "topdown_map_meta.json", "w") as f: + json.dump(map_meta, f) + except Exception: + logger.warning("Failed to save topdown map, continuing without it") + + try: + ep_meta = self._get_episode_meta() + if ep_meta is not None: + with open(save_dir / "episode_meta.json", "w") as f: + json.dump(ep_meta, f) + except Exception: + logger.warning("Failed to save episode metadata, continuing without it") + return self._make_response(obs) def step(self, action_text): diff --git a/tests/test_bridge_hooks.py b/tests/test_bridge_hooks.py new file mode 100644 index 0000000..8a6023d --- /dev/null +++ b/tests/test_bridge_hooks.py @@ -0,0 +1,109 @@ +"""Tests for BaseBridge topdown map and episode metadata hooks.""" +import json + +import numpy as np +import pytest +from pathlib import Path + +from easi.simulators.base_bridge import BaseBridge + + +class FakeEnv: + def reset(self): + return {"rgb": np.zeros((8, 8, 3), dtype=np.uint8)} + + def step(self, action): + return {"rgb": np.zeros((8, 8, 3), dtype=np.uint8)}, 0.0, False, {} + + def close(self): + pass + + +class BridgeWithMap(BaseBridge): + """Bridge that returns a topdown map.""" + + def _create_env(self, reset_config, simulator_kwargs): + return FakeEnv() + + def _extract_image(self, obs): + return obs["rgb"] + + def _get_topdown_map(self): + rgb = np.full((100, 120, 3), 200, dtype=np.uint8) + meta = { + "bounds_lower": [0.0, 0.0, 0.0], + "bounds_upper": [12.0, 3.0, 10.0], + "meters_per_pixel": 0.1, + "height": 1.25, + } + return rgb, meta + + def _get_episode_meta(self): + return { + "start_position": [1.0, 0.5, -2.0], + "goal_position": [4.5, 0.5, 1.2], + "gt_locations": [[1.0, 0.5, -2.0], [2.0, 0.5, -1.0], [4.5, 0.5, 1.2]], + } + + +class BridgeWithoutMap(BaseBridge): + """Bridge that returns no topdown map (default).""" + + def _create_env(self, reset_config, simulator_kwargs): + return FakeEnv() + + def _extract_image(self, obs): + return obs["rgb"] + + +class TestBaseBridgeHooksDefault: + def test_get_topdown_map_returns_none(self, tmp_path): + bridge = BridgeWithoutMap(workspace=tmp_path) + assert bridge._get_topdown_map() is None + + def test_get_episode_meta_returns_none(self, tmp_path): + bridge = BridgeWithoutMap(workspace=tmp_path) + assert bridge._get_episode_meta() is None + + def test_reset_no_map_files(self, tmp_path): + bridge = BridgeWithoutMap(workspace=tmp_path) + ep_dir = tmp_path / "episode_001" + ep_dir.mkdir() + bridge.episode_output_dir = str(ep_dir) + bridge.reset({}) + assert not (ep_dir / "topdown_map.png").exists() + assert not (ep_dir / "episode_meta.json").exists() + + +class TestBaseBridgeHooksWithMap: + def test_reset_saves_topdown_map(self, tmp_path): + bridge = BridgeWithMap(workspace=tmp_path) + ep_dir = tmp_path / "episode_001" + ep_dir.mkdir() + bridge.episode_output_dir = str(ep_dir) + bridge.reset({}) + assert (ep_dir / "topdown_map.png").exists() + assert (ep_dir / "topdown_map_meta.json").exists() + meta = json.loads((ep_dir / "topdown_map_meta.json").read_text()) + assert meta["meters_per_pixel"] == 0.1 + assert len(meta["bounds_lower"]) == 3 + + def test_reset_saves_episode_meta(self, tmp_path): + bridge = BridgeWithMap(workspace=tmp_path) + ep_dir = tmp_path / "episode_001" + ep_dir.mkdir() + bridge.episode_output_dir = str(ep_dir) + bridge.reset({}) + assert (ep_dir / "episode_meta.json").exists() + meta = json.loads((ep_dir / "episode_meta.json").read_text()) + assert meta["start_position"] == [1.0, 0.5, -2.0] + assert meta["goal_position"] == [4.5, 0.5, 1.2] + assert len(meta["gt_locations"]) == 3 + + def test_reset_no_output_dir_skips_save(self, tmp_path): + bridge = BridgeWithMap(workspace=tmp_path) + bridge.episode_output_dir = None + bridge.reset({}) + # No crash, no files saved outside workspace + map_files = list(tmp_path.glob("topdown_map.*")) + assert len(map_files) == 0 From 8629bf809fd4b87356a417a78bcf614999d716f8 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Fri, 13 Mar 2026 13:17:06 +0800 Subject: [PATCH 208/239] feat: add topdown map and episode meta hooks to VLNCEBridge --- easi/tasks/vlnce_r2r/bridge.py | 42 ++++++++++++++++++++++++++++++++++ 1 file changed, 42 insertions(+) diff --git a/easi/tasks/vlnce_r2r/bridge.py b/easi/tasks/vlnce_r2r/bridge.py index 49dbca1..9399dec 100644 --- a/easi/tasks/vlnce_r2r/bridge.py +++ b/easi/tasks/vlnce_r2r/bridge.py @@ -115,6 +115,48 @@ def _extract_info(self, info): clean[k] = json.dumps([float(x) for x in v] if hasattr(v, '__iter__') else str(v)) return clean + def _get_topdown_map(self): + """Render habitat-sim navmesh as top-down RGB map.""" + if self._scene_sim is None: + return None + sim = self._scene_sim._sim + if not sim.pathfinder.is_loaded: + return None + + meters_per_pixel = 0.1 + height = float(sim.get_agent(0).get_state().position[1]) + try: + # habitat-sim 0.1.7 API: get_topdown_view(meters_per_pixel, height) + topdown = sim.pathfinder.get_topdown_view(meters_per_pixel, height) + except Exception: + logger.warning("Failed to get topdown map from pathfinder") + return None + + # Colorize: navigable=light gray, obstacle=dark gray + rgb = np.full((*topdown.shape, 3), 60, dtype=np.uint8) + rgb[topdown == 1] = [240, 240, 240] + + bounds = sim.pathfinder.get_bounds() + return rgb, { + "bounds_lower": [float(x) for x in bounds[0]], + "bounds_upper": [float(x) for x in bounds[1]], + "meters_per_pixel": meters_per_pixel, + "height": float(sim.get_agent(0).get_state().position[1]), + } + + def _get_episode_meta(self): + """Persist start position, goal, and ground truth path.""" + if self._scene_sim is None: + return None + meta = {} + agent_state = self._scene_sim._sim.get_agent(0).get_state() + meta["start_position"] = [float(x) for x in agent_state.position] + if self._scene_sim._gt_locations: + meta["gt_locations"] = self._scene_sim._gt_locations + if self._scene_sim._goal_position is not None: + meta["goal_position"] = [float(x) for x in self._scene_sim._goal_position] + return meta + def close(self): """Close the SceneSimulator.""" if self._scene_sim is not None: From 8d13aa66c7a71b43e5672069191858ad6afac99c Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Fri, 13 Mar 2026 13:17:26 +0800 Subject: [PATCH 209/239] feat: add easi analyze trajectory CLI command --- easi/cli.py | 39 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 39 insertions(+) diff --git a/easi/cli.py b/easi/cli.py index ba97aa8..20e9c7c 100644 --- a/easi/cli.py +++ b/easi/cli.py @@ -286,6 +286,32 @@ def build_parser() -> argparse.ArgumentParser: "--kill", action="store_true", help="Kill all found EASI processes" ) + # --- analyze command group --- + analyze_parser = subparsers.add_parser( + "analyze", help="Post-evaluation analysis tools", parents=[common] + ) + analyze_sub = analyze_parser.add_subparsers(dest="analyze_action") + + traj_parser = analyze_sub.add_parser( + "trajectory", help="Generate trajectory videos", parents=[common] + ) + traj_parser.add_argument( + "run_dir", type=str, help="Path to evaluation run directory" + ) + traj_parser.add_argument( + "--filter", choices=["success", "failed"], + help="Filter episodes by outcome", + ) + traj_parser.add_argument( + "--sample", type=int, help="Random sample N episodes" + ) + traj_parser.add_argument( + "--fps", type=int, default=4, help="Video frame rate (default: 4)" + ) + traj_parser.add_argument( + "--seed", type=int, default=42, help="Random seed for --sample (default: 42)" + ) + # --- llm-server command --- llm_parser = subparsers.add_parser( "llm-server", help="Start dummy LLM server", parents=[common] @@ -1005,6 +1031,19 @@ def _main() -> None: elif args.command == "model": cmd_model(args) + elif args.command == "analyze": + if args.analyze_action == "trajectory": + from easi.analysis.trajectory_video import generate_trajectory_videos + generate_trajectory_videos( + run_dir=args.run_dir, + filter_by=getattr(args, "filter", None), + sample_n=args.sample, + fps=args.fps, + seed=args.seed, + ) + else: + parser.parse_args(["analyze", "--help"]) + elif args.command == "llm-server": cmd_llm_server(args.host, args.port, args.mode, args.action_space) From df1af985b80102317fef8e5a4999d90c92c425bd Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Fri, 13 Mar 2026 17:51:36 +0800 Subject: [PATCH 210/239] feat: add topdown map, episode meta, and agent position to EBHabitatBridge Add trajectory visualization hooks to the EB-Habitat bridge using habitat-sim 0.3.0 API (articulated_agent.base_pos). Includes topdown map rendering via pathfinder, start position persistence, and per-step agent position tracking in trajectory info. Co-Authored-By: Claude Opus 4.6 --- easi/tasks/ebhabitat/bridge.py | 61 +++++++++++++++++++++++++++++++++- 1 file changed, 60 insertions(+), 1 deletion(-) diff --git a/easi/tasks/ebhabitat/bridge.py b/easi/tasks/ebhabitat/bridge.py index 5b83d71..6ce40e7 100644 --- a/easi/tasks/ebhabitat/bridge.py +++ b/easi/tasks/ebhabitat/bridge.py @@ -27,6 +27,9 @@ sys.path.insert(0, str(_repo_root)) from easi.simulators.base_bridge import BaseBridge +from easi.utils.logging import get_logger + +logger = get_logger(__name__) class EBHabitatBridge(BaseBridge): @@ -94,8 +97,15 @@ def _extract_image(self, obs): from easi.tasks.ebhabitat.vendor.utils import observations_to_image return observations_to_image(obs, "head_rgb") + def _get_sim(self): + """Access the underlying habitat-sim Simulator instance.""" + try: + return self.env.env.env._env.task._sim + except AttributeError: + return None + def _extract_info(self, info): - return { + clean = { "task_success": float(info.get("task_success", 0.0)), "task_progress": float(info.get("task_progress", 0.0)), "subgoal_reward": float(info.get("subgoal_reward", 0.0)), @@ -103,6 +113,55 @@ def _extract_info(self, info): "feedback": str(info.get("env_feedback", "")), "action_id": int(info.get("action_id", -1)), } + # Add agent position for trajectory visualization + sim = self._get_sim() + if sim is not None: + try: + agent = sim.get_agent_data(0).articulated_agent + pos = agent.base_pos + clean["agent_position"] = json.dumps([float(pos[0]), float(pos[1]), float(pos[2])]) + except Exception: + pass + return clean + + def _get_topdown_map(self): + """Render habitat-sim navmesh as top-down RGB map.""" + sim = self._get_sim() + if sim is None or not sim.pathfinder.is_loaded: + return None + + meters_per_pixel = 0.1 + try: + agent = sim.get_agent_data(0).articulated_agent + height = float(agent.base_pos[1]) + topdown = sim.pathfinder.get_topdown_view(meters_per_pixel, height) + except Exception: + logger.warning("Failed to get topdown map from pathfinder") + return None + + # Colorize: navigable=light gray, obstacle=dark gray + rgb = np.full((*topdown.shape, 3), 60, dtype=np.uint8) + rgb[topdown == 1] = [240, 240, 240] + + bounds = sim.pathfinder.get_bounds() + return rgb, { + "bounds_lower": [float(x) for x in bounds[0]], + "bounds_upper": [float(x) for x in bounds[1]], + "meters_per_pixel": meters_per_pixel, + "height": height, + } + + def _get_episode_meta(self): + """Persist start position for trajectory visualization.""" + sim = self._get_sim() + if sim is None: + return None + try: + agent = sim.get_agent_data(0).articulated_agent + pos = agent.base_pos + return {"start_position": [float(pos[0]), float(pos[1]), float(pos[2])]} + except Exception: + return None def _make_response(self, obs, reward=0.0, done=False, info=None): """Override to include dynamic action space in metadata.""" From 96bc896041c6b1163aff123442e997b40bb7ca4d Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Fri, 13 Mar 2026 21:49:20 +0800 Subject: [PATCH 211/239] fix: correct sim access chain in EBHabitatBridge (was missing one .env level) --- easi/tasks/ebhabitat/bridge.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/easi/tasks/ebhabitat/bridge.py b/easi/tasks/ebhabitat/bridge.py index 6ce40e7..2e072e4 100644 --- a/easi/tasks/ebhabitat/bridge.py +++ b/easi/tasks/ebhabitat/bridge.py @@ -98,9 +98,13 @@ def _extract_image(self, obs): return observations_to_image(obs, "head_rgb") def _get_sim(self): - """Access the underlying habitat-sim Simulator instance.""" + """Access the underlying habitat-sim Simulator instance. + + Chain: bridge.env (EBHabEnv) -> .env (gym wrapper) -> .env -> .env + -> ._env (HabitatEnv) -> .task._sim + """ try: - return self.env.env.env._env.task._sim + return self.env.env.env.env._env.task._sim except AttributeError: return None From c9268d78de8f4fa5c61e6128dceb698ded85a2e5 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Sat, 14 Mar 2026 11:10:20 +0800 Subject: [PATCH 212/239] feat: use progress bar for trajectory video generation --- easi/analysis/trajectory_video.py | 22 ++++++++++++++-------- 1 file changed, 14 insertions(+), 8 deletions(-) diff --git a/easi/analysis/trajectory_video.py b/easi/analysis/trajectory_video.py index 06eb3fc..9ea1b32 100644 --- a/easi/analysis/trajectory_video.py +++ b/easi/analysis/trajectory_video.py @@ -353,7 +353,7 @@ def to_pixel(x, z): finally: writer.release() - logger.info("Wrote %s (%d frames)", output_path, len(entries)) + logger.trace("Wrote %s (%d frames)", output_path, len(entries)) def generate_trajectory_videos( @@ -389,11 +389,17 @@ def generate_trajectory_videos( logger.info("Generating %d trajectory videos in %s", len(episodes), output_dir) - for ep_dir in episodes: - output_path = output_dir / f"{ep_dir.name}.mp4" - try: - render_episode_video(ep_dir, output_path, fps=fps) - except Exception: - logger.exception("Failed to render %s", ep_dir.name) + from easi.utils.progress import ProgressBar - logger.info("Done. Videos saved to %s", output_dir) + failed = 0 + with ProgressBar(total=len(episodes)) as bar: + for i, ep_dir in enumerate(episodes): + output_path = output_dir / f"{ep_dir.name}.mp4" + try: + render_episode_video(ep_dir, output_path, fps=fps) + except Exception: + logger.exception("Failed to render %s", ep_dir.name) + failed += 1 + bar.update(completed=i + 1, failed=failed) + + logger.info("Done. %d videos saved to %s", len(episodes) - failed, output_dir) From fc6ed05577c904c75ca6c62c5805304aa9f8b06d Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Sun, 15 Mar 2026 11:57:15 +0800 Subject: [PATCH 213/239] feat: replace --max-episodes with --episodes filter (IDs, ranges, mixed) --- docs/cli-reference.md | 4 +- easi/cli.py | 12 ++- easi/evaluation/episode_filter.py | 119 ++++++++++++++++++++++++++ easi/evaluation/parallel_runner.py | 5 +- easi/evaluation/runner.py | 9 +- tests/test_e2e_evaluation.py | 44 +++++----- tests/test_episode_filter.py | 132 +++++++++++++++++++++++++++++ tests/test_evaluation_runner.py | 16 ++-- tests/test_metrics.py | 2 +- tests/test_parallel_runner.py | 10 +-- 10 files changed, 308 insertions(+), 45 deletions(-) create mode 100644 easi/evaluation/episode_filter.py create mode 100644 tests/test_episode_filter.py diff --git a/docs/cli-reference.md b/docs/cli-reference.md index 8b06dd9..ed30fb2 100644 --- a/docs/cli-reference.md +++ b/docs/cli-reference.md @@ -63,7 +63,7 @@ easi start [TASK ...] [options] | Option | Description | |---|---| | `--num-parallel N` | Parallel simulator instances (default: 1). Works with any backend. | -| `--max-episodes N` | Max episodes to run (default: all) | +| `--episodes FILTER` | Episode filter: IDs (`2,5,7`), ranges (`10:20`), or `:N` for first N (default: all) | | `--seed SEED` | Random seed for agent reproducibility | | `--render-platform PLATFORM` | Rendering platform override (default: simulator's preference). See [Render Platforms](#render-platforms). | @@ -127,7 +127,7 @@ easi start ebalfred_base --agent react --backend openai --model gpt-4o \ --llm-kwargs '{"temperature": 0.7, "max_tokens": 500}' # Limit episodes -easi start ebalfred_base --agent dummy --max-episodes 5 --seed 42 +easi start ebalfred_base --agent dummy --episodes :5 --seed 42 # Parallel evaluation (API backend) easi start ebalfred_base --agent react --backend openai --model gpt-4o \ diff --git a/easi/cli.py b/easi/cli.py index 20e9c7c..17cafea 100644 --- a/easi/cli.py +++ b/easi/cli.py @@ -176,7 +176,11 @@ def build_parser() -> argparse.ArgumentParser: default=None, help="Directory for downloading/caching datasets (default: ./datasets)", ) - start_parser.add_argument("--max-episodes", type=int, default=None) + start_parser.add_argument( + "--episodes", type=str, default=None, + help="Episode filter: IDs (2,5,7), ranges (10:20), or mixed (2,10:20,40). " + "Use :N for first N episodes.", + ) start_parser.add_argument( "--llm-url", type=str, default=None, dest="llm_base_url", help="LLM server URL" ) @@ -693,6 +697,12 @@ def cmd_start(args): logger.error("Resume directory has no config.json: %s", resume_dir) sys.exit(1) saved = _json.loads(config_path.read_text()).get("cli_options", {}) + # Migrate legacy max_episodes -> episodes + if "max_episodes" in saved and "episodes" not in saved: + old_val = saved.pop("max_episodes") + if isinstance(old_val, int) and old_val > 0: + saved["episodes"] = f":{old_val}" + saved.pop("max_episodes", None) # Saved values fill gaps; explicit CLI args win run_kwargs = {**saved, **raw} # If no task was given on CLI, pull from saved config diff --git a/easi/evaluation/episode_filter.py b/easi/evaluation/episode_filter.py new file mode 100644 index 0000000..691fb5d --- /dev/null +++ b/easi/evaluation/episode_filter.py @@ -0,0 +1,119 @@ +"""Parse and apply ``--episodes`` filter expressions. + +Syntax (comma-separated tokens, freely mixed):: + + :N index slice — first N episodes + M:N index slice — episodes at indices M..N-1 + M: index slice — from index M onwards + 42 episode ID "42" + +Examples:: + + --episodes :10 first 10 (replaces old --max-episodes) + --episodes 30:40 index range 30-39 + --episodes 2,5,7 episode IDs 2, 5, 7 + --episodes 2,10:20,40 episode ID 2 + range 10-19 + episode ID 40 + +Semantics: +- All selections are unioned and deduplicated. +- Original dataset order is preserved. +- If a requested episode ID is not found, raises ValueError. +""" + +from __future__ import annotations + + +def parse_episodes_flag(value: str) -> tuple[list[tuple[int | None, int | None]], list[str]]: + """Parse ``--episodes`` value into index slices and episode IDs. + + Returns: + (slices, ids) where slices is a list of (start, stop) tuples + and ids is a list of episode ID strings. + """ + slices: list[tuple[int | None, int | None]] = [] + ids: list[str] = [] + + for token in value.split(","): + token = token.strip() + if not token: + continue + + if ":" in token: + parts = token.split(":", 1) + start_s, stop_s = parts[0].strip(), parts[1].strip() + start = int(start_s) if start_s else None + stop = int(stop_s) if stop_s else None + + # Validate + if start is not None and stop is not None and start >= stop: + raise ValueError( + f"Invalid range '{token}': start ({start}) must be less than stop ({stop})" + ) + if start is not None and start < 0: + raise ValueError(f"Invalid range '{token}': negative index") + if stop is not None and stop < 0: + raise ValueError(f"Invalid range '{token}': negative index") + + slices.append((start, stop)) + else: + ids.append(token) + + return slices, ids + + +def filter_episodes( + episodes: list[dict], + episodes_flag: str, +) -> list[dict]: + """Filter an episode list according to an ``--episodes`` expression. + + Args: + episodes: Full episode list from task.load_episodes(). + episodes_flag: Raw ``--episodes`` CLI value. + + Returns: + Filtered episode list in original order. + + Raises: + ValueError: If a requested episode ID is not found, or the + expression is invalid. + """ + slices, ids = parse_episodes_flag(episodes_flag) + + if not slices and not ids: + raise ValueError("Empty --episodes value") + + # Collect selected indices (as a set for deduplication) + selected_indices: set[int] = set() + + # Apply index slices + for start, stop in slices: + s = start if start is not None else 0 + e = stop if stop is not None else len(episodes) + # Clamp to valid range + s = max(0, min(s, len(episodes))) + e = max(0, min(e, len(episodes))) + for i in range(s, e): + selected_indices.add(i) + + # Apply episode ID selections + if ids: + # Build ID -> index map + id_to_indices: dict[str, int] = {} + for i, ep in enumerate(episodes): + ep_id = str(ep.get("episode_id", "")) + # First occurrence wins (IDs should be unique, but be safe) + if ep_id not in id_to_indices: + id_to_indices[ep_id] = i + + missing = [eid for eid in ids if eid not in id_to_indices] + if missing: + raise ValueError( + f"Episode IDs not found in dataset: {', '.join(missing)}" + ) + + for eid in ids: + selected_indices.add(id_to_indices[eid]) + + # Return in original order + return [episodes[i] for i in sorted(selected_indices)] diff --git a/easi/evaluation/parallel_runner.py b/easi/evaluation/parallel_runner.py index 323fcf3..f7aaf0a 100644 --- a/easi/evaluation/parallel_runner.py +++ b/easi/evaluation/parallel_runner.py @@ -159,8 +159,9 @@ def run(self) -> list[dict]: if self.refresh_data: task.download_dataset(force=True) episodes = task.load_episodes() - if self.max_episodes is not None: - episodes = episodes[: self.max_episodes] + if self.episodes_filter is not None: + from easi.evaluation.episode_filter import filter_episodes + episodes = filter_episodes(episodes, self.episodes_filter) logger.trace( "Task loaded. %d episodes, simulator_key=%s", len(episodes), task.simulator_key, diff --git a/easi/evaluation/runner.py b/easi/evaluation/runner.py index fc4d6a5..2b85ed8 100644 --- a/easi/evaluation/runner.py +++ b/easi/evaluation/runner.py @@ -53,7 +53,7 @@ def __init__( agent_type: str = "react", output_dir: Path | str = "./logs", data_dir: Path | str = "./datasets", - max_episodes: int | None = None, + episodes: str | None = None, llm_base_url: str | None = None, agent_seed: int | None = None, backend: str | None = None, @@ -80,7 +80,7 @@ def __init__( self.agent_type = agent_type self.output_dir = Path(output_dir) self.data_dir = Path(data_dir) - self.max_episodes = max_episodes + self.episodes_filter = episodes self.llm_base_url = llm_base_url self.agent_seed = agent_seed self.backend = backend @@ -186,8 +186,9 @@ def run(self) -> list[dict]: if self.refresh_data: task.download_dataset(force=True) episodes = task.load_episodes() - if self.max_episodes is not None: - episodes = episodes[: self.max_episodes] + if self.episodes_filter is not None: + from easi.evaluation.episode_filter import filter_episodes + episodes = filter_episodes(episodes, self.episodes_filter) # Handle resume: load completed results and find start point if self.resume_dir: diff --git a/tests/test_e2e_evaluation.py b/tests/test_e2e_evaluation.py index 8a53b21..b21398d 100644 --- a/tests/test_e2e_evaluation.py +++ b/tests/test_e2e_evaluation.py @@ -40,13 +40,13 @@ def test_dummy_full_run(self, tmp_path): assert "metrics" in summary assert "success_rate" in summary["metrics"] or "avg_success" in summary["metrics"] - def test_max_episodes_limit(self, tmp_path): - """Verify max_episodes limits the run.""" + def test_episodes_filter_limit(self, tmp_path): + """Verify --episodes :N limits the run.""" runner = EvaluationRunner( task_name="dummy_task", agent_type="dummy", output_dir=tmp_path / "logs", - max_episodes=1, + episodes=":1", ) results = runner.run() assert len(results) == 1 @@ -58,7 +58,7 @@ def test_per_episode_files(self, tmp_path): task_name="dummy_task", agent_type="dummy", output_dir=output_dir, - max_episodes=2, + episodes=":2", ) runner.run() @@ -80,7 +80,7 @@ def test_episode_metrics_structure(self, tmp_path): task_name="dummy_task", agent_type="dummy", output_dir=tmp_path / "logs", - max_episodes=1, + episodes=":1", ) results = runner.run() @@ -117,7 +117,7 @@ def test_deterministic_with_seed(self, tmp_path): agent_type="dummy", output_dir=tmp_path / "a", agent_seed=123, - max_episodes=2, + episodes=":2", ).run() results_b = EvaluationRunner( @@ -125,7 +125,7 @@ def test_deterministic_with_seed(self, tmp_path): agent_type="dummy", output_dir=tmp_path / "b", agent_seed=123, - max_episodes=2, + episodes=":2", ).run() for a, b in zip(results_a, results_b): @@ -163,7 +163,7 @@ def test_config_json_saved(self, tmp_path): agent_type="dummy", output_dir=output_dir, agent_seed=42, - max_episodes=1, + episodes=":1", ) runner.run() @@ -172,7 +172,7 @@ def test_config_json_saved(self, tmp_path): assert config["cli_options"]["task_name"] == "dummy_task" assert config["cli_options"]["agent_type"] == "dummy" assert config["cli_options"]["agent_seed"] == 42 - assert config["cli_options"]["max_episodes"] == 1 + assert config["cli_options"]["episodes"] == ":1" assert "run_id" in config # Task YAML config is included assert "task_config" in config @@ -185,7 +185,7 @@ def test_trajectory_jsonl_format(self, tmp_path): task_name="dummy_task", agent_type="dummy", output_dir=output_dir, - max_episodes=1, + episodes=":1", ) runner.run() @@ -216,7 +216,7 @@ def test_images_saved_to_episode_dir(self, tmp_path): task_name="dummy_task", agent_type="dummy", output_dir=output_dir, - max_episodes=1, + episodes=":1", ) runner.run() @@ -238,7 +238,7 @@ def test_max_retries_stored(self, tmp_path): agent_type="dummy", output_dir=output_dir, max_retries=5, - max_episodes=1, + episodes=":1", ) assert runner.max_retries == 5 runner.run() @@ -368,7 +368,7 @@ def test_retry_on_episode_failure(self, tmp_path): agent_type="dummy", output_dir=output_dir, max_retries=3, - max_episodes=1, + episodes=":1", ) call_count = {"n": 0} @@ -397,7 +397,7 @@ def test_retry_exhausted_records_failure(self, tmp_path): agent_type="dummy", output_dir=output_dir, max_retries=2, - max_episodes=1, + episodes=":1", ) with patch.object( @@ -421,7 +421,7 @@ def test_retry_clears_partial_files(self, tmp_path): agent_type="dummy", output_dir=output_dir, max_retries=2, - max_episodes=1, + episodes=":1", ) original_run_episode = runner._run_episode @@ -460,7 +460,7 @@ def test_retry_continues_to_next_episode(self, tmp_path): agent_type="dummy", output_dir=output_dir, max_retries=1, - max_episodes=2, + episodes=":2", ) original_run_episode = runner._run_episode @@ -498,15 +498,15 @@ def test_resume_loads_config_from_run_dir(self, tmp_path): task_name="dummy_task", agent_type="dummy", output_dir=output_dir, - max_episodes=2, + episodes=":2", agent_seed=42, ) runner.run() run_dir = _find_run_dir(output_dir) - # Resume with higher max_episodes to complete remaining - # Saved config has max_episodes=2; override to 3 to run all. + # Resume with higher episodes limit to complete remaining + # Saved config has episodes=":2"; override to :3 to run all. args = Namespace( command="start", verbosity="INFO", @@ -515,7 +515,7 @@ def test_resume_loads_config_from_run_dir(self, tmp_path): agent_type="dummy", output_dir=None, data_dir=None, - max_episodes=3, + episodes=":3", llm_base_url=None, agent_seed=None, backend=None, @@ -623,7 +623,7 @@ def test_sequential_single_task_via_flag(self, tmp_path): agent_type="dummy", output_dir=str(output_dir), data_dir=None, - max_episodes=1, + episodes=":1", llm_base_url=None, agent_seed=None, backend=None, @@ -654,7 +654,7 @@ def test_resume_blocked_with_multi_task(self): agent_type="dummy", output_dir="./logs", data_dir=None, - max_episodes=1, + episodes=":1", llm_base_url=None, agent_seed=None, backend=None, diff --git a/tests/test_episode_filter.py b/tests/test_episode_filter.py new file mode 100644 index 0000000..dae8b90 --- /dev/null +++ b/tests/test_episode_filter.py @@ -0,0 +1,132 @@ +"""Tests for episode filter parsing and application.""" + +import pytest + +from easi.evaluation.episode_filter import filter_episodes, parse_episodes_flag + + +EPISODES = [ + {"episode_id": "10"}, + {"episode_id": "20"}, + {"episode_id": "30"}, + {"episode_id": "40"}, + {"episode_id": "50"}, +] + + +class TestParseEpisodesFlag: + def test_single_id(self): + slices, ids = parse_episodes_flag("10") + assert slices == [] + assert ids == ["10"] + + def test_multiple_ids(self): + slices, ids = parse_episodes_flag("10,20,30") + assert slices == [] + assert ids == ["10", "20", "30"] + + def test_range(self): + slices, ids = parse_episodes_flag("1:3") + assert slices == [(1, 3)] + assert ids == [] + + def test_open_start(self): + slices, ids = parse_episodes_flag(":3") + assert slices == [(None, 3)] + assert ids == [] + + def test_open_end(self): + slices, ids = parse_episodes_flag("2:") + assert slices == [(2, None)] + assert ids == [] + + def test_mixed(self): + slices, ids = parse_episodes_flag("10,1:3,50") + assert slices == [(1, 3)] + assert ids == ["10", "50"] + + def test_inverted_range_error(self): + with pytest.raises(ValueError, match="start.*must be less than stop"): + parse_episodes_flag("5:3") + + def test_equal_range_error(self): + with pytest.raises(ValueError, match="start.*must be less than stop"): + parse_episodes_flag("3:3") + + def test_negative_index_error(self): + with pytest.raises(ValueError, match="negative"): + parse_episodes_flag("-1:3") + + +class TestFilterEpisodes: + def test_first_n(self): + result = filter_episodes(EPISODES, ":2") + assert len(result) == 2 + assert result[0]["episode_id"] == "10" + assert result[1]["episode_id"] == "20" + + def test_range(self): + result = filter_episodes(EPISODES, "1:3") + assert len(result) == 2 + assert result[0]["episode_id"] == "20" + assert result[1]["episode_id"] == "30" + + def test_open_end(self): + result = filter_episodes(EPISODES, "3:") + assert len(result) == 2 + assert result[0]["episode_id"] == "40" + assert result[1]["episode_id"] == "50" + + def test_episode_ids(self): + result = filter_episodes(EPISODES, "20,40") + assert len(result) == 2 + assert result[0]["episode_id"] == "20" + assert result[1]["episode_id"] == "40" + + def test_mixed_ids_and_range(self): + result = filter_episodes(EPISODES, "50,0:2") + assert len(result) == 3 + # Original order: index 0, index 1, then index 4 + assert result[0]["episode_id"] == "10" + assert result[1]["episode_id"] == "20" + assert result[2]["episode_id"] == "50" + + def test_dedup_id_in_range(self): + """Episode ID that also falls within a range is not duplicated.""" + result = filter_episodes(EPISODES, "20,0:3") + assert len(result) == 3 + assert [r["episode_id"] for r in result] == ["10", "20", "30"] + + def test_overlapping_ranges(self): + result = filter_episodes(EPISODES, "0:3,2:5") + assert len(result) == 5 + assert [r["episode_id"] for r in result] == ["10", "20", "30", "40", "50"] + + def test_preserves_original_order(self): + """IDs specified in reverse order still come out in dataset order.""" + result = filter_episodes(EPISODES, "50,30,10") + assert [r["episode_id"] for r in result] == ["10", "30", "50"] + + def test_missing_id_errors(self): + with pytest.raises(ValueError, match="not found.*999"): + filter_episodes(EPISODES, "999") + + def test_multiple_missing_ids_errors(self): + with pytest.raises(ValueError, match="not found"): + filter_episodes(EPISODES, "999,888") + + def test_empty_value_errors(self): + with pytest.raises(ValueError, match="Empty"): + filter_episodes(EPISODES, "") + + def test_range_beyond_bounds_clamps(self): + """Range extending past the list is clamped, not an error.""" + result = filter_episodes(EPISODES, "3:100") + assert len(result) == 2 + assert result[0]["episode_id"] == "40" + assert result[1]["episode_id"] == "50" + + def test_single_episode_by_id(self): + result = filter_episodes(EPISODES, "30") + assert len(result) == 1 + assert result[0]["episode_id"] == "30" diff --git a/tests/test_evaluation_runner.py b/tests/test_evaluation_runner.py index 81b104b..ebc6d00 100644 --- a/tests/test_evaluation_runner.py +++ b/tests/test_evaluation_runner.py @@ -22,7 +22,7 @@ def test_run_single_episode(self, tmp_path): task_name="dummy_task", agent_type="dummy", output_dir=tmp_path / "logs", - max_episodes=1, + episodes=":1", ) results = runner.run() @@ -48,7 +48,7 @@ def test_results_saved_to_disk(self, tmp_path): task_name="dummy_task", agent_type="dummy", output_dir=output_dir, - max_episodes=1, + episodes=":1", ) runner.run() @@ -94,7 +94,7 @@ def test_cli_options_contains_all_init_args(self): # All non-excluded init params should be present expected_keys = { "task_name", "agent_type", "output_dir", "data_dir", - "max_episodes", "llm_base_url", "agent_seed", "backend", + "episodes", "llm_base_url", "agent_seed", "backend", "model", "port", "llm_kwargs_raw", "max_retries", "render_platform", "llm_instances", "llm_gpus", "sim_gpus", @@ -120,7 +120,7 @@ def test_cli_options_captures_raw_values(self): backend="openai", model="gpt-4o", port=9090, - max_episodes=5, + episodes=":5", max_retries=2, ) opts = runner._cli_options @@ -131,7 +131,7 @@ def test_cli_options_captures_raw_values(self): assert opts["backend"] == "openai" assert opts["model"] == "gpt-4o" assert opts["port"] == 9090 - assert opts["max_episodes"] == 5 + assert opts["episodes"] == ":5" assert opts["max_retries"] == 2 def test_cli_options_defaults(self): @@ -144,7 +144,7 @@ def test_cli_options_defaults(self): assert opts["model"] == "default" assert opts["port"] == 8080 assert opts["max_retries"] == 3 - assert opts["max_episodes"] is None + assert opts["episodes"] is None assert opts["llm_base_url"] is None assert opts["agent_seed"] is None assert opts["backend"] is None @@ -168,7 +168,7 @@ def test_config_json_uses_cli_options(self, tmp_path): task_name="dummy_task", agent_type="dummy", output_dir=output_dir, - max_episodes=1, + episodes=":1", model="test-model", ) runner.run() @@ -179,7 +179,7 @@ def test_config_json_uses_cli_options(self, tmp_path): assert opts["task_name"] == "dummy_task" assert opts["agent_type"] == "dummy" assert opts["model"] == "test-model" - assert opts["max_episodes"] == 1 + assert opts["episodes"] == ":1" # Session-specific params should NOT be in config assert "resume_dir" not in opts assert "redownload" not in opts diff --git a/tests/test_metrics.py b/tests/test_metrics.py index fb871da..7818802 100644 --- a/tests/test_metrics.py +++ b/tests/test_metrics.py @@ -166,7 +166,7 @@ def test_metrics_nested_in_summary(self, tmp_path): task_name="dummy_task", agent_type="dummy", output_dir=str(tmp_path), - max_episodes=2, + episodes=":2", ) runner.run() diff --git a/tests/test_parallel_runner.py b/tests/test_parallel_runner.py index 5b4a640..feb6adb 100644 --- a/tests/test_parallel_runner.py +++ b/tests/test_parallel_runner.py @@ -82,7 +82,7 @@ def test_parallel_dummy_produces_results(self, tmp_path): num_parallel=2, agent_type="dummy", output_dir=str(tmp_path), - max_episodes=3, + episodes=":3", ) results = runner.run() @@ -112,7 +112,7 @@ def test_parallel_single_episode(self, tmp_path): num_parallel=2, agent_type="dummy", output_dir=str(tmp_path), - max_episodes=1, + episodes=":1", ) results = runner.run() assert len(results) == 1 @@ -126,7 +126,7 @@ def test_config_json_records_num_parallel(self, tmp_path): num_parallel=3, agent_type="dummy", output_dir=str(tmp_path), - max_episodes=1, + episodes=":1", ) runner.run() @@ -143,7 +143,7 @@ def test_episode_dirs_created(self, tmp_path): num_parallel=2, agent_type="dummy", output_dir=str(tmp_path), - max_episodes=2, + episodes=":2", ) runner.run() @@ -297,7 +297,7 @@ def test_parallel_resume_basic(self, tmp_path): runner1 = ParallelRunner( task_name="dummy_task", num_parallel=2, agent_type="dummy", output_dir=str(tmp_path), - max_episodes=1, + episodes=":1", ) results1 = runner1.run() assert len(results1) == 1 From 465d35d6e8fd34fe1b81af5319e3189c77c3150b Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Wed, 18 Mar 2026 17:05:07 +0800 Subject: [PATCH 214/239] feat: vLLM v0.17+ compat and InternVL3 custom model server --- .gitignore | 2 + easi/llm/models/internvl3/__init__.py | 0 easi/llm/models/internvl3/manifest.yaml | 11 + easi/llm/models/internvl3/model.py | 315 ++++++++++++++++++++++++ easi/llm/server_manager.py | 33 ++- easi/llm/utils.py | 4 +- easi/tasks/ebnavigation/_base.yaml | 1 + tests/test_server_manager.py | 4 +- 8 files changed, 357 insertions(+), 13 deletions(-) create mode 100644 easi/llm/models/internvl3/__init__.py create mode 100644 easi/llm/models/internvl3/manifest.yaml create mode 100644 easi/llm/models/internvl3/model.py diff --git a/.gitignore b/.gitignore index 7e7e712..f10dba1 100644 --- a/.gitignore +++ b/.gitignore @@ -12,3 +12,5 @@ __pycache__/ yolo11n.pt .tmp* /scripts +.sisyphus/ +.claude/ diff --git a/easi/llm/models/internvl3/__init__.py b/easi/llm/models/internvl3/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/easi/llm/models/internvl3/manifest.yaml b/easi/llm/models/internvl3/manifest.yaml new file mode 100644 index 0000000..29a03b5 --- /dev/null +++ b/easi/llm/models/internvl3/manifest.yaml @@ -0,0 +1,11 @@ +name: internvl3 +display_name: "InternVL3" +description: > + InternVL3 vision-language model family (1B, 2B, 8B, 78B, etc.). + Supports image understanding with OpenAI-format messages. + Requires: pip install transformers torch torchvision pillow + Recommended: pip install flash-attn (for flash_attention_2) +model_class: "easi.llm.models.internvl3.model.InternVL3Model" +default_kwargs: + dtype: "bfloat16" + attn_implementation: "flash_attention_2" diff --git a/easi/llm/models/internvl3/model.py b/easi/llm/models/internvl3/model.py new file mode 100644 index 0000000..054f23d --- /dev/null +++ b/easi/llm/models/internvl3/model.py @@ -0,0 +1,315 @@ +"""InternVL3 custom model server for EASI. + +Loads InternVL3 models via transformers and serves them through the +EASI custom backend HTTP server. Uses the model's built-in ``.chat()`` +method which handles chat template formatting and image preprocessing +internally. + +Usage:: + + easi start --backend custom --model internvl3 \\ + --llm-kwargs '{"model_path": "OpenGVLab/InternVL3-8B"}' +""" +from __future__ import annotations + +from typing import Any + +import torchvision.transforms as T +from PIL import Image +from torchvision.transforms.functional import InterpolationMode + +from easi.llm.models.base_model_server import BaseModelServer +from easi.llm.models.helpers import extract_images +from easi.utils.logging import get_logger + +logger = get_logger(__name__) + +_ALLOWED_DTYPES = {"bfloat16", "float16", "float32", "auto"} + +IMAGENET_MEAN = (0.485, 0.456, 0.406) +IMAGENET_STD = (0.229, 0.224, 0.225) + + +# --------------------------------------------------------------------------- +# Image preprocessing (InternVL3 dynamic resolution tiling) +# --------------------------------------------------------------------------- + +def _build_transform(input_size: int) -> T.Compose: + return T.Compose([ + T.Lambda(lambda img: img.convert("RGB") if img.mode != "RGB" else img), + T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC), + T.ToTensor(), + T.Normalize(mean=IMAGENET_MEAN, std=IMAGENET_STD), + ]) + + +def _find_closest_aspect_ratio( + aspect_ratio: float, + target_ratios: list[tuple[int, int]], + width: int, + height: int, + image_size: int, +) -> tuple[int, int]: + best = (1, 1) + best_diff = float("inf") + area = width * height + for ratio in target_ratios: + target_ar = ratio[0] / ratio[1] + diff = abs(aspect_ratio - target_ar) + if diff < best_diff or (diff == best_diff and area > 0.5 * image_size * image_size * ratio[0] * ratio[1]): + best_diff = diff + best = ratio + return best + + +def _dynamic_preprocess( + image: Image.Image, + min_num: int = 1, + max_num: int = 12, + image_size: int = 448, + use_thumbnail: bool = True, +) -> list[Image.Image]: + """Split image into tiles using InternVL3's dynamic resolution strategy.""" + width, height = image.size + aspect_ratio = width / height + + target_ratios = set() + for n in range(min_num, max_num + 1): + for i in range(1, n + 1): + for j in range(1, n + 1): + if i * j <= max_num and i * j >= min_num: + target_ratios.add((i, j)) + target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1]) + + best = _find_closest_aspect_ratio( + aspect_ratio, target_ratios, width, height, image_size + ) + + target_w = best[0] * image_size + target_h = best[1] * image_size + blocks = best[0] * best[1] + + resized = image.resize((target_w, target_h)) + processed = [] + for i in range(blocks): + box = ( + (i % best[0]) * image_size, + (i // best[0]) * image_size, + ((i % best[0]) + 1) * image_size, + ((i // best[0]) + 1) * image_size, + ) + processed.append(resized.crop(box)) + + if use_thumbnail and blocks > 1: + thumbnail = image.resize((image_size, image_size)) + processed.append(thumbnail) + + return processed + + +def _load_image(image: Image.Image, max_num: int = 12) -> Any: + """Preprocess a PIL image into a pixel_values tensor for InternVL3. + + Returns float32 tensors; the caller should cast to the model's dtype + via ``.to(dtype=model.dtype, device=model.device)``. + """ + import torch + + transform = _build_transform(448) + tiles = _dynamic_preprocess(image, image_size=448, max_num=max_num) + pixel_values = torch.stack([transform(tile) for tile in tiles]) + return pixel_values + + +# --------------------------------------------------------------------------- +# Message conversion +# --------------------------------------------------------------------------- + +def _openai_to_internvl_messages( + messages: list[dict], +) -> list[dict]: + """Convert OpenAI-format messages to InternVL3 format. + + OpenAI format uses ``image_url`` content parts with base64 data URIs. + InternVL3 expects ```` placeholder tokens in the text content, + with actual pixel tensors passed separately. + + Returns a new message list with image_url parts replaced by ``\\n`` + text tokens. The caller is responsible for extracting and preprocessing + the actual PIL images via ``extract_images()`` + ``_load_image()``. + """ + converted = [] + for msg in messages: + role = msg.get("role", "user") + content = msg.get("content", "") + + if isinstance(content, str): + converted.append({"role": role, "content": content}) + continue + + # Multimodal content list + text_parts: list[str] = [] + for part in content: + ptype = part.get("type", "") + if ptype == "image_url": + text_parts.append("\n") + elif ptype == "text": + text_parts.append(part.get("text", "")) + + converted.append({"role": role, "content": "".join(text_parts)}) + + return converted + + +# --------------------------------------------------------------------------- +# Model server +# --------------------------------------------------------------------------- + +class InternVL3Model(BaseModelServer): + """InternVL3 vision-language model server.""" + + def load(self, model_path: str, device: str, **kwargs: Any) -> None: + """Load InternVL3 model and tokenizer. + + Args: + model_path: HuggingFace model ID or local path. + device: Device string (e.g. ``"cuda:0"``). + **kwargs: ``torch_dtype``, ``attn_implementation``. + """ + import torch + from transformers import AutoModel, AutoTokenizer + + # Resolve dtype + dtype_str = kwargs.pop("torch_dtype", None) or kwargs.pop("dtype", "auto") + if dtype_str not in _ALLOWED_DTYPES: + logger.warning("Unrecognised dtype '%s', falling back to 'auto'", dtype_str) + dtype_str = "auto" + torch_dtype = getattr(torch, dtype_str, "auto") if dtype_str != "auto" else "auto" + + attn_impl = kwargs.pop("attn_implementation", None) + + # Device mapping + try: + import accelerate # noqa: F401 + load_kwargs: dict[str, Any] = {"torch_dtype": torch_dtype, "device_map": "auto"} + except ImportError: + logger.info("accelerate not installed, loading on %s", device) + load_kwargs = {"torch_dtype": torch_dtype} + + # Attention implementation with fallback + if attn_impl: + if attn_impl == "flash_attention_2": + try: + import flash_attn # noqa: F401 + load_kwargs["attn_implementation"] = attn_impl + except ImportError: + logger.warning("flash_attn not installed, falling back to sdpa") + load_kwargs["attn_implementation"] = "sdpa" + else: + load_kwargs["attn_implementation"] = attn_impl + + load_kwargs["trust_remote_code"] = True + + logger.info( + "Loading InternVL3 from %s (dtype=%s, attn=%s)", + model_path, dtype_str, attn_impl, + ) + self.model = AutoModel.from_pretrained(model_path, **load_kwargs).eval() + + if "device_map" not in load_kwargs: + self.model = self.model.to(device) + + self.tokenizer = AutoTokenizer.from_pretrained( + model_path, trust_remote_code=True + ) + self.device = next(self.model.parameters()).device + logger.info("InternVL3 loaded on %s", self.device) + + def generate(self, messages: list[dict], **kwargs: Any) -> str: + """Generate response from OpenAI-format messages.""" + import torch + + # Extract and preprocess images, cast to model dtype + pil_images = extract_images(messages) + pixel_values = None + if pil_images: + model_dtype = next(self.model.parameters()).dtype + tensors = [ + _load_image(img).to(dtype=model_dtype, device=self.device) + for img in pil_images + ] + pixel_values = torch.cat(tensors, dim=0) + + # Convert messages + internvl_messages = _openai_to_internvl_messages(messages) + + # Extract system message (prepend to first user question if present) + system_prefix = "" + start = 0 + if internvl_messages and internvl_messages[0]["role"] == "system": + system_prefix = internvl_messages[0]["content"] + "\n" + start = 1 + + # Build question from last user message (model.chat expects this) + question = internvl_messages[-1]["content"] if internvl_messages else "" + + # Build history from prior messages (pairs of user/assistant) + history: list[tuple[str, str]] = [] + i = start + while i < len(internvl_messages) - 1: + if ( + internvl_messages[i]["role"] == "user" + and i + 1 < len(internvl_messages) + and internvl_messages[i + 1]["role"] == "assistant" + ): + user_content = internvl_messages[i]["content"] + # Prepend system message to the first user turn + if i == start and system_prefix: + user_content = system_prefix + user_content + history.append(( + user_content, + internvl_messages[i + 1]["content"], + )) + i += 2 + else: + i += 1 + + # If no history, prepend system to the question directly + if not history and system_prefix: + question = system_prefix + question + + # Generation config + max_new_tokens = kwargs.get("max_tokens", 4096) + temperature = kwargs.get("temperature", 0.0) + top_p = kwargs.get("top_p", 0.95) + + generation_config = { + "max_new_tokens": max_new_tokens, + "do_sample": temperature > 0, + } + if temperature > 0: + generation_config["temperature"] = temperature + generation_config["top_p"] = top_p + + with torch.no_grad(): + response = self.model.chat( + self.tokenizer, + pixel_values, + question, + generation_config, + history=history, + ) + + return response + + def unload(self) -> None: + """Release GPU memory.""" + if hasattr(self, "model"): + del self.model + if hasattr(self, "tokenizer"): + del self.tokenizer + + import torch + if torch.cuda.is_available(): + torch.cuda.empty_cache() + logger.info("InternVL3 model unloaded") diff --git a/easi/llm/server_manager.py b/easi/llm/server_manager.py index 91cb91f..c656342 100644 --- a/easi/llm/server_manager.py +++ b/easi/llm/server_manager.py @@ -24,7 +24,7 @@ _DEFAULT_STARTUP_TIMEOUT = 600.0 _DEFAULT_VLLM_SERVER_FLAGS = { "enable_prefix_caching": True, - "disable_log_requests": True, + "enable_log_requests": False, } @@ -138,15 +138,27 @@ def is_running(self) -> bool: return False return self._process.poll() is None - def _check_port(self) -> None: - """Raise if port is already in use.""" + def _check_port(self, retries: int = 6, delay: float = 5.0) -> None: + """Raise if port is already in use. + + Retries a few times to handle TIME_WAIT from a recently stopped + server (common when running tasks back-to-back). + """ logger.trace("[%s] Checking if port %d is available...", self.label, self.port) - if not _port_is_available(self.port): - raise RuntimeError( - f"Port {self.port} is already in use. " - f"Use --port to specify a different port, " - f"or --llm-url to connect to an existing server." - ) + for attempt in range(retries): + if _port_is_available(self.port): + return + if attempt < retries - 1: + logger.trace( + "[%s] Port %d in use, waiting %.0fs (%d/%d)...", + self.label, self.port, delay, attempt + 1, retries, + ) + time.sleep(delay) + raise RuntimeError( + f"Port {self.port} is still in use after {retries * delay:.0f}s. " + f"Use --port to specify a different port, " + f"or --llm-url to connect to an existing server." + ) def _build_command(self) -> tuple[list[str], dict]: """Build the server launch command and environment overrides. @@ -176,7 +188,8 @@ def _build_command(self) -> tuple[list[str], dict]: if value: cmd.append(flag) else: - logger.trace("[%s] Skipping disabled bool flag: %s", self.label, flag) + no_flag = "--no-" + key.replace("_", "-") + cmd.append(no_flag) else: cmd.extend([flag, str(value)]) elif self.backend == "custom": diff --git a/easi/llm/utils.py b/easi/llm/utils.py index d192423..85a4fa1 100644 --- a/easi/llm/utils.py +++ b/easi/llm/utils.py @@ -36,8 +36,10 @@ "tokenizer_mode", "seed", "max_num_seqs", - "disable_log_requests", + "enable_log_requests", "limit_mm_per_prompt", + "chat_template_content_format", + "chat_template", "model_path", } diff --git a/easi/tasks/ebnavigation/_base.yaml b/easi/tasks/ebnavigation/_base.yaml index bbd7515..44940ed 100644 --- a/easi/tasks/ebnavigation/_base.yaml +++ b/easi/tasks/ebnavigation/_base.yaml @@ -8,6 +8,7 @@ dataset: repo_id: "oscarqjh/EB-Navigation_easi" subset: null simulator_configs: + command_timeout: 600 render_platform: auto screen_height: 500 screen_width: 500 diff --git a/tests/test_server_manager.py b/tests/test_server_manager.py index 96ef51a..b389e95 100644 --- a/tests/test_server_manager.py +++ b/tests/test_server_manager.py @@ -63,8 +63,8 @@ def test_port_taken(self): sock.listen(1) try: sm = ServerManager("vllm", "test", port=19877) - with pytest.raises(RuntimeError, match="already in use"): - sm._check_port() + with pytest.raises(RuntimeError, match="in use"): + sm._check_port(retries=1, delay=0) finally: sock.close() From 8a7679e590656c68fa70f918f704b3e3af4d5142 Mon Sep 17 00:00:00 2001 From: oscarqjh Date: Wed, 18 Mar 2026 17:05:36 +0800 Subject: [PATCH 215/239] chore: add internvl3 template --- easi/llm/templates/internvl3.jinja | 4 + tests/test_internvl3_model.py | 215 +++++++++++++++++++++++++++++ 2 files changed, 219 insertions(+) create mode 100644 easi/llm/templates/internvl3.jinja create mode 100644 tests/test_internvl3_model.py diff --git a/easi/llm/templates/internvl3.jinja b/easi/llm/templates/internvl3.jinja new file mode 100644 index 0000000..9867dff --- /dev/null +++ b/easi/llm/templates/internvl3.jinja @@ -0,0 +1,4 @@ +{#- InternVL3 chat template patched for OpenAI image_url compatibility. + Original source: OpenGVLab/InternVL3-8B tokenizer_config.json + Patch: accepts both type=="image" and type=="image_url" content parts. -#} +{%- if messages[0]['role'] == 'system' %}{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}{%- else %}{{- '<|im_start|>system\n你是书生·万象，英文名是InternVL，是由上海人工智能实验室、清华大学及多家合作单位联合开发的多模态大语言模型。<|im_end|>\n' }}{%- endif %}{% for message in messages %}{%- if messages[0]['role'] != 'system' or not loop.first %}{{'<|im_start|>' + message['role'] + '\n'}}{% if message['content'] is string %}{{ message['content'] }}{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or content['type'] == 'image_url' %}{{ '\n' }}{% elif content['type'] == 'video' %}{{ '

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