Adding quality check action (#165)

kevinaer · web-flow · commit f4cb5937b5a2 · 2021-04-09T13:58:32.000-04:00
diff --git a/.github/workflows/quality-check.yaml b/.github/workflows/quality-check.yaml
@@ -0,0 +1,11 @@
+name: Quality Checks
+on: [push]
+jobs:
+  quality-check:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+      - name: "⚙️ Install dependencies"
+        run: pip3 install .[dev]
+      - name: "🧹 Running quality checks"
+        run: make quality
diff --git a/README.md b/README.md
@@ -16,6 +16,9 @@ limitations under the License.
 
 # ![icon for SparseMl](https://raw.githubusercontent.com/neuralmagic/sparseml/main/docs/source/icon-sparseml.png) SparseML
 
+
+[![quality-check Actions Status](https://github.com/neuralmagic/sparseml/actions/workflows/quality-check.yaml/badge.svg)](https://github.com/neuralmagic/sparseml/actions)
+
 ### Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models
 
 <p>
diff --git a/integrations/transformers/README.md b/integrations/transformers/README.md
@@ -1,14 +1,17 @@
 <!--
 Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
 You may obtain a copy of the License at
+
    http://www.apache.org/licenses/LICENSE-2.0
+
 Unless required by applicable law or agreed to in writing,
 software distributed under the License is distributed on an "AS IS" BASIS,
 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
-lim itations under the License.
+limitations under the License.
 -->
 # Transformers-SparseML Integration
 This folder contains an example on how to use sparseml with transformers. 
diff --git a/integrations/transformers/trainer_qa.py b/integrations/transformers/trainer_qa.py
@@ -12,6 +12,12 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+
+# neuralmagic: no copyright
+# isort: skip_file
+# flake8: noqa
+# fmt: off
+
 """
 A subclass of `Trainer` specific to Question-Answering tasks
 """
diff --git a/integrations/transformers/utils_qa.py b/integrations/transformers/utils_qa.py
@@ -12,6 +12,12 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+
+# neuralmagic: no copyright
+# isort: skip_file
+# flake8: noqa
+# fmt: off
+
 """
 Post-processing utilities for question answering.
 """
@@ -421,4 +427,4 @@ def postprocess_qa_predictions_with_beam_search(
             with open(null_odds_file, "w") as writer:
                 writer.write(json.dumps(scores_diff_json, indent=4) + "\n")
 
-    return all_predictions, scores_diff_json
+    return all_predictions, scores_diff_json
diff --git a/integrations/ultralytics/deepsparse/benchmark.py b/integrations/ultralytics/deepsparse/benchmark.py
@@ -102,8 +102,10 @@
 
 #########
 Full list of SparseZoo stubs for benchmarking
-* Baseline dense YOLOv3 - "zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/base-none"
-* Pruned YOLOv3 (87% sparse) - "zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/pruned-aggressive-97"
+* Baseline dense YOLOv3 -
+    "zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/base-none"
+* Pruned YOLOv3 (87% sparse) -
+    "zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/pruned-aggressive-97"
 * Pruned-Quantized YOLOv3 (83% sparse, CPU must support VNNI) -
  "zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/pruned_quant-aggressive-94"
 """
@@ -116,14 +118,13 @@
 from pathlib import Path
 from typing import Any, Iterable, List, Optional, Tuple, Union
 
-import cv2
 import numpy
 import onnx
 import onnxruntime
 import torch
 from tqdm.auto import tqdm
 
-from deepsparse import compile_model, cpu
+from deepsparse import compile_model
 from deepsparse.benchmark import BenchmarkResults
 from deepsparse_utils import (
     YoloPostprocessor,
@@ -172,10 +173,10 @@ def parse_args():
         type=Optional[str],
         default=None,
         help=(
-            "Optional filepath to image examples to run the benchmark on. Can be path to "
-            "directory, single image jpg file, or a glob path. All files should be "
-            "in jpg format. If not provided, sample COCO images will be downloaded from "
-            "the SparseZoo"
+            "Optional filepath to image examples to run the benchmark on. Can be path "
+            "to directory, single image jpg file, or a glob path. All files should be "
+            "in jpg format. If not provided, sample COCO images will be downloaded "
+            "from the SparseZoo"
         ),
     )
 
@@ -201,8 +202,8 @@ def parse_args():
         default=None,
         help=(
             "The number of physical cores to run the benchmark on, "
-            "defaults to None where it uses all physical cores available on the system. "
-            "For DeepSparse benchmarks, this value is the number of cores per socket"
+            "defaults to None where it uses all physical cores available on the system."
+            " For DeepSparse benchmarks, this value is the number of cores per socket"
         ),
     )
     parser.add_argument(
@@ -266,7 +267,7 @@ def parse_args():
 def _parse_device(device: Union[str, int]) -> Union[str, int]:
     try:
         return int(device)
-    except:
+    except Exception:
         return device
 
 
diff --git a/integrations/ultralytics/deepsparse/client.py b/integrations/ultralytics/deepsparse/client.py
@@ -44,8 +44,8 @@ def detect(
         """
         :param images: list of or singular file paths or numpy arrays of images to
             run the detection model on. Number of images should equal model batch size
-        :return: list of post-processed object detection results for each image including
-            class label, likelihood, and bounding box coordinates
+        :return: list of post-processed object detection results for each image
+            including class label, likelihood, and bounding box coordinates
         """
         if not isinstance(images, List):
             images = [images]
diff --git a/integrations/ultralytics/deepsparse/deepsparse_utils.py b/integrations/ultralytics/deepsparse/deepsparse_utils.py
@@ -37,7 +37,7 @@
 
 __all__ = [
     "load_image",
-    "pre_nms_postprocess",
+    "YoloPostprocessor",
     "postprocess_nms",
 ]
 
@@ -98,7 +98,8 @@ def pre_nms_postprocess(self, outputs: List[numpy.ndarray]) -> torch.Tensor:
             # decode xywh box values
             pred[..., 0:2] = (pred[..., 0:2] * 2.0 - 0.5 + grid) * stride
             pred[..., 2:4] = (pred[..., 2:4] * 2) ** 2 * anchor_grid
-            # flatten anchor and grid dimensions -> (bs, num_predictions, num_classes + 5)
+            # flatten anchor and grid dimensions ->
+            #       (bs, num_predictions, num_classes + 5)
             processed_outputs.append(pred.view(pred.size(0), -1, pred.size(-1)))
         return torch.cat(processed_outputs, 1)
 
diff --git a/scripts/keras_classification.py b/scripts/keras_classification.py
@@ -265,7 +265,8 @@
      --dataset imagenette \
      --dataset-path <DATASET_DIR> \
      --train-batch-size 64 \
-     --recipe-path zoo:cv/classification/resnet_v1-50/keras/keras.applications/imagenette/pruned-moderate?recipe_type=original
+     --recipe-path zoo:cv/classification/resnet_v1-50/keras/\
+        keras.applications/imagenette/pruned-moderate?recipe_type=original
 ############
 Example command for loading a ResNet50 model from a checkpoint, then
 evaluate it on the validation of the Imagenette dataset.
diff --git a/src/sparseml/keras/datasets/dataset.py b/src/sparseml/keras/datasets/dataset.py
@@ -79,7 +79,9 @@ def build(
     @abstractmethod
     def creator(self) -> tensorflow.data.Dataset:
         """
-        Implemented by sub classes to create a tensorflow.data dataset for the given impl.
+        Implemented by sub classes to create a tensorflow.data dataset for the given
+        impl.
+
         :return: a created tensorflow.data dataset
         """
         raise NotImplementedError()
diff --git a/src/sparseml/keras/models/classification/__init__.py b/src/sparseml/keras/models/classification/__init__.py
@@ -12,4 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+# flake8: noqa
+
 from .resnet import *
diff --git a/src/sparseml/keras/models/classification/resnet.py b/src/sparseml/keras/models/classification/resnet.py
@@ -249,18 +249,6 @@ def create(
                     beta_initializer=beta_initializer,
                     gamma_initializer=gamma_initializer,
                 )
-            else:
-                out = _basic_block(
-                    name=block_name,
-                    x_tens=out,
-                    training=training,
-                    out_channels=self.out_channels,
-                    stride=stride,
-                    kernel_initializer=kernel_initializer,
-                    bias_initializer=bias_initializer,
-                    beta_initializer=beta_initializer,
-                    gamma_initializer=gamma_initializer,
-                )
 
             stride = 1
 
diff --git a/src/sparseml/keras/models/registry.py b/src/sparseml/keras/models/registry.py
@@ -16,7 +16,7 @@
 Code related to the Keras model registry for easily creating models.
 """
 
-from typing import Any, Callable, Dict, List, NamedTuple, Tuple, Union
+from typing import Any, Callable, Dict, List, NamedTuple, Union
 
 from merge_args import merge_args
 from sparseml import get_main_logger
@@ -292,8 +292,6 @@ def wrapper(
                 (ex: imagenet, mnist, etc).
                 If not supplied will default to the one preconfigured for the model.
             """
-            attributes = ModelRegistry._ATTRIBUTES[key]
-
             if isinstance(pretrained, str):
                 if pretrained.lower() == "true":
                     pretrained = True
diff --git a/src/sparseml/onnx/utils/graph_editor.py b/src/sparseml/onnx/utils/graph_editor.py
@@ -87,8 +87,8 @@ def get_node_parents(
     ) -> List[Union[NodeProto, TensorProto, None]]:
         """
         :param node: node to get the input objects for
-        :return: input nodes or tensors of this node in order. if an input doesn't exist,
-            None will be returned in its place
+        :return: input nodes or tensors of this node in order. if an input doesn't
+            exist, None will be returned in its place
         """
         inputs = []
         for input_id in node.input:
@@ -184,8 +184,8 @@ def delete_initializers(self, initializers: List[Union[str, TensorProto]]):
 
     def delete_unused_initializers(self):
         """
-        deletes tensors in the initializer list that are not listed as inputs to any node
-        in the current graph state
+        deletes tensors in the initializer list that are not listed as inputs to any
+        node in the current graph state
         """
         self.delete_initializers(
             [
diff --git a/src/sparseml/pytorch/nn/activations.py b/src/sparseml/pytorch/nn/activations.py
@@ -27,7 +27,7 @@
 
 try:
     from torch.nn import SiLU
-except:
+except ImportError:
     SiLU = None
 
 
diff --git a/src/sparseml/pytorch/optim/mask_creator_pruning.py b/src/sparseml/pytorch/optim/mask_creator_pruning.py
@@ -18,7 +18,7 @@
 
 import random
 from abc import ABC, abstractmethod
-from typing import Callable, Iterable, List, Union
+from typing import Iterable, List, Union
 
 import torch
 from torch import Tensor
diff --git a/src/sparseml/pytorch/optim/modifier_pruning.py b/src/sparseml/pytorch/optim/modifier_pruning.py
@@ -42,8 +42,8 @@
 )
 from sparseml.pytorch.utils.logger import PyTorchLogger
 from sparseml.utils import (
-    ALL_TOKEN,
     ALL_PRUNABLE_TOKEN,
+    ALL_TOKEN,
     INTERPOLATION_FUNCS,
     convert_to_bool,
     interpolate,
diff --git a/src/sparseml/pytorch/utils/quantization/quantize_qat_export.py b/src/sparseml/pytorch/utils/quantization/quantize_qat_export.py
@@ -622,8 +622,8 @@ def _skip_input_quantize(model: ModelProto) -> Optional[str]:
     ]
     if not all(node.op_type == "QuantizeLinear" for node in input_children):
         return (
-            "Not modifying ONNX graph inputs - only QuantizeLinear nodes may follow the "
-            "FP32 input tensor in original graph, prior to converting to uint8"
+            "Not modifying ONNX graph inputs - only QuantizeLinear nodes may follow"
+            "the FP32 input tensor in original graph, prior to converting to uint8"
         )
 
     graph = ONNXGraph(model)
diff --git a/tests/sparseml/pytorch/datasets/detection/test_voc.py b/tests/sparseml/pytorch/datasets/detection/test_voc.py
@@ -16,9 +16,9 @@
 
 import pytest
 import torch
+from packaging import version
 from torch.utils.data import Dataset
 
-from packaging import version
 from sparseml.pytorch.datasets import DatasetRegistry, VOCDetectionDataset
 
 
diff --git a/tests/sparseml/pytorch/utils/quantization/test_quantize_qat_export.py b/tests/sparseml/pytorch/utils/quantization/test_quantize_qat_export.py
@@ -94,5 +94,5 @@ def test_skip_onnx_input_quantize_expected_exception():
         [],
     )
     model = onnx.helper.make_model(graph)
-    with pytest.raises(RuntimeError) as err:
+    with pytest.raises(RuntimeError):
         skip_onnx_input_quantize(model)
diff --git a/tests/sparseml/tensorflow_v1/datasets/classification/test_imagenette.py b/tests/sparseml/tensorflow_v1/datasets/classification/test_imagenette.py
@@ -16,8 +16,8 @@
 
 import pytest
 import tensorflow
-
 from packaging import version
+
 from sparseml.tensorflow_v1.datasets import (
     DatasetRegistry,
     ImageFolderDataset,
diff --git a/utils/pytorch2keras.py b/utils/pytorch2keras.py
@@ -42,12 +42,12 @@
     --model-type pruned-moderate \
     --imagenet-dir /hdd/datasets/ILSVRC \
     --save-dir /hdd/src/sparseml/pytorch2keras_models/ \
-    --test-image-file-path /hdd/datasets/ILSVRC/val/n02481823/ILSVRC2012_val_00024600.JPEG
+    --test-image-file-path \
+        /hdd/datasets/ILSVRC/val/n02481823/ILSVRC2012_val_00024600.JPEG
 """
 import argparse
 import os
 import re
-from collections import namedtuple
 from typing import Dict
 
 import numpy as np
