feat(deployment): centerpoint deployment integration by vividf · Pull Request #181 · tier4/AWML

vividf · 2026-02-02T10:24:31Z

Summary

Integrates CenterPoint into the unified deployment framework, enabling deployment and evaluation of ONNX and TensorRT models.

Note, this PR include changes in #180

Changes

Integrated CenterPoint with deployment framework:
- Moved deployment code from projects/CenterPoint to deployment/projects/centerpoint
- Implemented component-based export pipeline for ONNX and TensorRT
- Added runtime inference support with PyTorch, ONNX Runtime, and TensorRT backends
Deployment capabilities:
- Export CenterPoint models to ONNX format
- Export CenterPoint models to TensorRT engines
- Component-based architecture (voxel encoder, backbone+head) for flexible deployment
Evaluation capabilities:
- Evaluate ONNX models using ONNX Runtime
- Evaluate TensorRT engines
- Integrated metrics evaluation with deployment pipeline
Updated CLI: Replaced old deploy.py script with new unified CLI (deployment.cli.main)
Added Docker support: Created Dockerfile for deployment environment with TensorRT dependencies
Updated documentation: Added deployment and evaluation instructions in README

Migration Notes

Old deployment script (projects/CenterPoint/scripts/deploy.py) is removed
Use new CLI: python -m deployment.cli.main centerpoint <deploy_config> <model_config>
ONNX model variants are now registered via deployment.projects.centerpoint.onnx_models

How to run

python -m deployment.cli.main centerpoint   deployment/projects/centerpoint/config/deploy_config.py   projects/CenterPoint/configs/t4dataset/Centerpoint/second_secfpn_4xb16_121m_j6gen2_base_amp_t4metric_v2.py   --rot-y-axis-reference

Exported ONNX (Same)

Voxel Encoder

Backbone Head

KSeangTan

Done the first round of reviewing, please consider to use dataclass and pydantic for configs, and do type checking there.

Therefore, we can remove all the type checking in the code

deployment/projects/centerpoint/config/deploy_config.py

KSeangTan · 2026-03-02T03:14:40Z

deployment/projects/centerpoint/config/deploy_config.py

 verification = dict(
    enabled=False,
-    tolerance=1e-1,
+    tolerance=1,


Explain what is tolerance here, and why updating from 0.1 to 1

The value was originally set for calibration classification and later copied to CenterPoint, but it does not work correctly for CenterPoint.

INFO:deployment.core.evaluation.verification_mixin: tensorrt (cuda:0) latency: 205.08 ms INFO:deployment.core.evaluation.verification_mixin: output[heatmap]: shape=(1, 5, 510, 510), max_diff=0.070197, mean_diff=0.007674 INFO:deployment.core.evaluation.verification_mixin: output[reg]: shape=(1, 2, 510, 510), max_diff=0.007944, mean_diff=0.001120 INFO:deployment.core.evaluation.verification_mixin: output[height]: shape=(1, 1, 510, 510), max_diff=0.025401, mean_diff=0.002122 INFO:deployment.core.evaluation.verification_mixin: output[dim]: shape=(1, 3, 510, 510), max_diff=0.031920, mean_diff=0.001143 INFO:deployment.core.evaluation.verification_mixin: output[rot]: shape=(1, 2, 510, 510), max_diff=0.075215, mean_diff=0.004582 INFO:deployment.core.evaluation.verification_mixin: output[vel]: shape=(1, 2, 510, 510), max_diff=0.221999, mean_diff=0.004940 INFO:deployment.core.evaluation.verification_mixin: Overall Max difference: 0.221999 INFO:deployment.core.evaluation.verification_mixin: Overall Mean difference: 0.004347 WARNING:deployment.core.evaluation.verification_mixin: tensorrt (cuda:0) verification FAILED ✗ (max diff: 0.221999 > tolerance: 0.100000) INFO:deployment.core.evaluation.verification_mixin:

Do you know any reason why it fail? Since it seems like a verification, it's always better to check the reason rather than update the tolerance

It doesn't necessarily indicate a failure.
When converting from PyTorch to TensorRT, some numerical differences are expected due to different kernels, precision handling, and TensorRT optimizations.

The verification is mainly used as a safeguard to detect major issues (e.g., incorrect conversion settings) rather than to enforce exact numerical equivalence.

Since 1e-1 is when we set for resnet18 for calibration classification, it is different in the cases.

Btw, this is the verification result in tensorrt fp16 right? If that's the case, it makes sense

Anyway, 5e-1 can be a better value

Running onnx (cuda:0) reference... 2026-03-10 15:20:07.511273431 [V:onnxruntime:, execution_steps.cc:103 Execute] stream 0 activate notification with index 0 2026-03-10 15:20:07.567219724 [V:onnxruntime:, execution_steps.cc:47 Execute] stream 0 wait on Notification with id: 0 INFO:deployment.core.evaluation.verification_mixin: onnx (cuda:0) latency: 1423.80 ms INFO:deployment.core.evaluation.verification_mixin: Running tensorrt (cuda:0) test... INFO:deployment.core.evaluation.verification_mixin: tensorrt (cuda:0) latency: 1141.26 ms INFO:deployment.core.evaluation.verification_mixin: output[heatmap]: shape=(1, 5, 510, 510), max_diff=0.464849, mean_diff=0.056135 INFO:deployment.core.evaluation.verification_mixin: output[reg]: shape=(1, 2, 510, 510), max_diff=0.056639, mean_diff=0.006198 INFO:deployment.core.evaluation.verification_mixin: output[height]: shape=(1, 1, 510, 510), max_diff=0.227012, mean_diff=0.065522 INFO:deployment.core.evaluation.verification_mixin: output[dim]: shape=(1, 3, 510, 510), max_diff=0.336713, mean_diff=0.028087 INFO:deployment.core.evaluation.verification_mixin: output[rot]: shape=(1, 2, 510, 510), max_diff=0.515039, mean_diff=0.023962 INFO:deployment.core.evaluation.verification_mixin: output[vel]: shape=(1, 2, 510, 510), max_diff=0.932002, mean_diff=0.034206 INFO:deployment.core.evaluation.verification_mixin: Overall Max difference: 0.932002 INFO:deployment.core.evaluation.verification_mixin: Overall Mean difference: 0.037279 WARNING:deployment.core.evaluation.verification_mixin: tensorrt (cuda:0) verification FAILED ✗ (max diff: 0.932002 > tolerance: 0.500000)

On a different computer, it can have different values.
I will leave 1 for now

Did you set any random seed to set this validation since the randomness (for example, shuffling pointclouds) significantly affects the results. Otherwise, i believe the difference between computer is too huge

KSeangTan · 2026-03-02T03:15:09Z

deployment/projects/centerpoint/export/component_extractor.py

+    @property
+    def _components_cfg(self) -> Dict[str, Any]:
+        """Get unified components configuration."""
+        if "components" not in self.config.deploy_cfg:


we can use assert here

Thanks, this is fixed in the big refactor b3b8355

deployment/projects/centerpoint/export/component_extractor.py

KSeangTan · 2026-03-02T03:16:32Z

deployment/projects/centerpoint/export/component_extractor.py

+    def _onnx_config(self) -> Dict[str, Any]:
+        """Get shared ONNX export settings."""
+        onnx_config_raw = self.config.onnx_config
+        if onnx_config_raw is None:


Same, consider to use assert

KSeangTan · 2026-03-02T03:41:45Z

deployment/projects/centerpoint/onnx_models/__init__.py

+    use ONNX model variants.
+    """
+    # Import triggers @MODELS.register_module() registrations
+    from deployment.projects.centerpoint.onnx_models import centerpoint_head_onnx as _head  # noqa: F401


Please remove register_models and simply move these to the top statements, and call them in __all__ individually

Thanks, fixed in b3b8355

deployment/projects/centerpoint/pipelines/centerpoint_pipeline.py

deployment/projects/centerpoint/pipelines/onnx.py

KSeangTan · 2026-03-02T04:03:10Z

deployment/projects/centerpoint/pipelines/onnx.py

+        try:
+            outputs = self._components_cfg["backbone_head"]["io"]["outputs"]
+        except KeyError as exc:
+            raise KeyError("Missing required config path: components_cfg['backbone_head']['io']['outputs']") from exc


Do we need these? I believe we should just use dataclass instead of dict, and thus we can remove these checking

Thanks, fixed in the big refactor b3b8355#diff-f5b73e91a7e42d76185a9036cfa8599384848052498bbd125f3eef66a08ada77

KSeangTan · 2026-03-11T03:55:52Z

Some of the modules, for example, dataloader should be able to be reused for the same detection3d tasks right?

KSeangTan · 2026-03-11T03:57:21Z

deployment/projects/centerpoint/entrypoint.py

+    model_cfg = Config.fromfile(args.model_cfg)
+    config = BaseDeploymentConfig(deploy_cfg)
+
+    _validate_required_components(config.components_cfg)


move _validate_required_components to BaseDeploymentConfig

KSeangTan · 2026-03-11T03:59:37Z

deployment/projects/centerpoint/entrypoint.py

+
+    context = CenterPointExportContext(rot_y_axis_reference=bool(getattr(args, "rot_y_axis_reference", False)))
+    runner.run(context=context)
+    return 0


Do we need to return status code here?

KSeangTan · 2026-03-11T04:01:08Z

deployment/projects/centerpoint/pipelines/tensorrt.py

+    def _release_gpu_resources(self) -> None:
+        """Release TensorRT resources (engines and contexts) and CUDA events."""
+        # Destroy CUDA events
+        if hasattr(self, "_backbone_start_event"):


Use for-loop to achieve this

KSeangTan · 2026-03-11T04:03:34Z

deployment/projects/centerpoint/pipelines/tensorrt.py

+        }
+
+        for component_name, engine_path in engine_files.items():
+            if not osp.exists(engine_path):


This error validation should be done in resolve_artifact_path

Signed-off-by: vividf <yihsiang.fang@tier4.jp>

…erpoint Signed-off-by: vividf <yihsiang.fang@tier4.jp>

Signed-off-by: vividf <yihsiang.fang@tier4.jp>

vividf changed the title ~~Feat/centerpoint deployment integration~~ feat(deployment): centerpoint deployment integration Feb 2, 2026

vividf mentioned this pull request Feb 2, 2026

feat(centerpoint): integrate CenterPoint into unified deployment pipeline #161

Closed

vividf requested review from KSeangTan and yamsam February 2, 2026 16:33

vividf self-assigned this Feb 2, 2026

vividf marked this pull request as ready for review February 3, 2026 04:31

vividf force-pushed the feat/centerpoint_deployment_integration branch 2 times, most recently from bfb778f to 441d06e Compare February 16, 2026 06:08

KSeangTan requested changes Mar 2, 2026

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vividf force-pushed the feat/centerpoint_deployment_integration branch from caa92a6 to 93e5558 Compare March 5, 2026 17:24

vividf changed the base branch from feat/new_deployment_and_evaluation_pipeline to main March 5, 2026 17:27

vividf changed the base branch from main to feat/new_deployment_and_evaluation_pipeline March 5, 2026 17:27

vividf force-pushed the feat/centerpoint_deployment_integration branch 3 times, most recently from de7020e to 6470ac5 Compare March 10, 2026 14:40

KSeangTan reviewed Mar 11, 2026

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vividf requested a review from KSeangTan March 11, 2026 04:01

KSeangTan requested changes Mar 11, 2026

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KSeangTan reviewed Mar 11, 2026

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vividf force-pushed the feat/new_deployment_and_evaluation_pipeline branch from 5256306 to 2b28f60 Compare March 11, 2026 04:27

vividf added 7 commits March 11, 2026 13:28

feat: refactor config

f8f557d

Signed-off-by: vividf <yihsiang.fang@tier4.jp>

chore: fix property

ec97293

Signed-off-by: vividf <yihsiang.fang@tier4.jp>

chore: clean code

e0a9448

Signed-off-by: vividf <yihsiang.fang@tier4.jp>

chore: temp remove centerpoint files

b73d2da

Signed-off-by: vividf <yihsiang.fang@tier4.jp>

chore: clean code

ce8dc0d

Signed-off-by: vividf <yihsiang.fang@tier4.jp>

feat: integrate centerpoint to deployment framework

cb55c2a

Signed-off-by: vividf <yihsiang.fang@tier4.jp>

chore: centerpoint - clean or {}

592e42a

Signed-off-by: vividf <yihsiang.fang@tier4.jp>

vividf and others added 20 commits March 11, 2026 13:28

chore: centerpoint-clean code

9e3c362

Signed-off-by: vividf <yihsiang.fang@tier4.jp>

chore: temp remove centerpoint files

7dfd76f

Signed-off-by: vividf <yihsiang.fang@tier4.jp>

chore: add files back

691a992

Signed-off-by: vividf <yihsiang.fang@tier4.jp>

ci(pre-commit): autofix

3b1b173

chore: clean code

043a2ee

Signed-off-by: vividf <yihsiang.fang@tier4.jp>

chore: update threshold for centerpoint

c474027

Signed-off-by: vividf <yihsiang.fang@tier4.jp>

chore: clean up code

11a1702

Signed-off-by: vividf <yihsiang.fang@tier4.jp>

chore: refactor base config - centerpoint

caa8f5d

Signed-off-by: vividf <yihsiang.fang@tier4.jp>

chore: clean up code: device spec, remove unused fucntion .etc - cent…

fb8a111

…erpoint Signed-off-by: vividf <yihsiang.fang@tier4.jp>

chore: fix Any

b1ceb04

Signed-off-by: vividf <yihsiang.fang@tier4.jp>

chore: add docstring

175f075

Signed-off-by: vividf <yihsiang.fang@tier4.jp>

chore: refactor export compenent - centerpoint

ace2afb

Signed-off-by: vividf <yihsiang.fang@tier4.jp>

chore: fix more Device spec - centerpoint

2261126

Signed-off-by: vividf <yihsiang.fang@tier4.jp>

chore: fix

bd91470

Signed-off-by: vividf <yihsiang.fang@tier4.jp>

chore: add more docstring

a387b49

Signed-off-by: vividf <yihsiang.fang@tier4.jp>

chore: change file name

e9b987b

Signed-off-by: vividf <yihsiang.fang@tier4.jp>

chore: remove redundant check

ec0bafc

Signed-off-by: vividf <yihsiang.fang@tier4.jp>

chore: orangize directory

d6c4dc6

Signed-off-by: vividf <yihsiang.fang@tier4.jp>

chore: rename sample file

54b0818

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chore: remove init

a6b9840

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vividf force-pushed the feat/centerpoint_deployment_integration branch from 1ca0e1c to a6b9840 Compare March 11, 2026 04:28

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Summary

Changes

Migration Notes

How to run

Exported ONNX (Same)

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KSeangTan commented Mar 11, 2026

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