[Enhance] Upgrade isort pre-commit hooks. (#687)

* Use new version flake8 and isort hooks

* Fix missing copyright

.pre-commit-config.yaml

@@ -1,15 +1,11 @@
 exclude: ^tests/data/
 repos:
-  - repo: https://gitlab.com/pycqa/flake8.git
-    rev: 3.8.3
+  - repo: https://github.com/PyCQA/flake8
+    rev: 4.0.1
     hooks:
       - id: flake8
-  - repo: https://github.com/asottile/seed-isort-config
-    rev: v2.2.0
-    hooks:
-      - id: seed-isort-config
-  - repo: https://github.com/timothycrosley/isort
-    rev: 4.3.21
+  - repo: https://github.com/PyCQA/isort
+    rev: 5.10.1
     hooks:
       - id: isort
   - repo: https://github.com/pre-commit/mirrors-yapf

mmcls/apis/train.py

@@ -115,7 +115,7 @@ def train_model(model,
         else:
             model = MMDataParallel(model, device_ids=cfg.gpu_ids)
             if not model.device_ids:
-                from mmcv import digit_version, __version__
+                from mmcv import __version__, digit_version
                 assert digit_version(__version__) >= (1, 4, 4), \
                     'To train with CPU, please confirm your mmcv version ' \
                     'is not lower than v1.4.4'

mmcls/core/optimizers/__init__.py

@@ -1,3 +1,4 @@
+# Copyright (c) OpenMMLab. All rights reserved.
 from .lamb import Lamb
 
 __all__ = [

mmcls/core/visualization/__init__.py

@@ -1,3 +1,4 @@
+# Copyright (c) OpenMMLab. All rights reserved.
 from .image import (BaseFigureContextManager, ImshowInfosContextManager,
                     color_val_matplotlib, imshow_infos)
 

mmcls/core/visualization/image.py

@@ -1,3 +1,4 @@
+# Copyright (c) OpenMMLab. All rights reserved.
 import matplotlib.pyplot as plt
 import mmcv
 import numpy as np

mmcls/datasets/builder.py

@@ -25,8 +25,8 @@ SAMPLERS = Registry('sampler')
 
 
 def build_dataset(cfg, default_args=None):
-    from .dataset_wrappers import (ConcatDataset, RepeatDataset,
-                                   ClassBalancedDataset, KFoldDataset)
+    from .dataset_wrappers import (ClassBalancedDataset, ConcatDataset,
+                                   KFoldDataset, RepeatDataset)
     if isinstance(cfg, (list, tuple)):
         dataset = ConcatDataset([build_dataset(c, default_args) for c in cfg])
     elif cfg['type'] == 'RepeatDataset':

mmcls/models/backbones/conformer.py

@@ -1,3 +1,4 @@
+# Copyright (c) OpenMMLab. All rights reserved.
 from typing import Sequence
 
 import torch

mmcls/models/backbones/repvgg.py

@@ -1,3 +1,4 @@
+# Copyright (c) OpenMMLab. All rights reserved.
 import torch
 import torch.nn.functional as F
 import torch.utils.checkpoint as cp

mmcls/models/utils/augment/utils.py

@@ -1,3 +1,4 @@
+# Copyright (c) OpenMMLab. All rights reserved.
 import torch.nn.functional as F
 
 

setup.cfg

@@ -12,9 +12,8 @@ split_before_expression_after_opening_paren = true
 [isort]
 line_length = 79
 multi_line_output = 0
-known_standard_library = pkg_resources,setuptools
+extra_standard_library = pkg_resources,setuptools
 known_first_party = mmcls
-known_third_party = PIL,cv2,matplotlib,mmcv,mmdet,modelindex,numpy,onnxruntime,packaging,pytest,pytorch_sphinx_theme,requests,rich,sphinx,tensorflow,torch,torchvision,ts
 no_lines_before = STDLIB,LOCALFOLDER
 default_section = THIRDPARTY
 

tests/data/retinanet.py

@@ -1,3 +1,4 @@
+# Copyright (c) OpenMMLab. All rights reserved.
 # small RetinaNet
 num_classes = 3
 

tests/test_data/test_builder.py

@@ -1,3 +1,4 @@
+# Copyright (c) OpenMMLab. All rights reserved.
 import os.path as osp
 from copy import deepcopy
 from unittest.mock import patch

tests/test_data/test_pipelines/test_auto_augment.py

@@ -760,7 +760,7 @@ def test_equalize(nb_rand_test=100):
 
     def _imequalize(img):
         # equalize the image using PIL.ImageOps.equalize
-        from PIL import ImageOps, Image
+        from PIL import Image, ImageOps
         img = Image.fromarray(img)
         equalized_img = np.asarray(ImageOps.equalize(img))
         return equalized_img
@@ -932,8 +932,9 @@ def test_posterize():
 def test_contrast(nb_rand_test=100):
 
     def _adjust_contrast(img, factor):
-        from PIL.ImageEnhance import Contrast
         from PIL import Image
+        from PIL.ImageEnhance import Contrast
+
         # Image.fromarray defaultly supports RGB, not BGR.
         # convert from BGR to RGB
         img = Image.fromarray(img[..., ::-1], mode='RGB')
@@ -1066,8 +1067,8 @@ def test_brightness(nb_rand_test=100):
     def _adjust_brightness(img, factor):
         # adjust the brightness of image using
         # PIL.ImageEnhance.Brightness
-        from PIL.ImageEnhance import Brightness
         from PIL import Image
+        from PIL.ImageEnhance import Brightness
         img = Image.fromarray(img)
         brightened_img = Brightness(img).enhance(factor)
         return np.asarray(brightened_img)
@@ -1128,8 +1129,8 @@ def test_sharpness(nb_rand_test=100):
     def _adjust_sharpness(img, factor):
         # adjust the sharpness of image using
         # PIL.ImageEnhance.Sharpness
-        from PIL.ImageEnhance import Sharpness
         from PIL import Image
+        from PIL.ImageEnhance import Sharpness
         img = Image.fromarray(img)
         sharpened_img = Sharpness(img).enhance(factor)
         return np.asarray(sharpened_img)

tests/test_models/test_backbones/test_efficientnet.py

@@ -1,3 +1,4 @@
+# Copyright (c) OpenMMLab. All rights reserved.
 import pytest
 import torch
 from torch.nn.modules import GroupNorm

tests/test_models/test_backbones/test_repvgg.py

@@ -1,3 +1,4 @@
+# Copyright (c) OpenMMLab. All rights reserved.
 import os
 import tempfile
 

tests/test_models/test_utils/test_se.py

@@ -1,3 +1,4 @@
+# Copyright (c) OpenMMLab. All rights reserved.
 import pytest
 import torch
 from torch.nn.modules import GroupNorm

tests/test_runtime/test_hooks.py

@@ -1,3 +1,4 @@
+# Copyright (c) OpenMMLab. All rights reserved.
 import logging
 import shutil
 import tempfile

tests/test_runtime/test_num_class_hook.py

@@ -1,3 +1,4 @@
+# Copyright (c) OpenMMLab. All rights reserved.
 import logging
 import tempfile
 from unittest.mock import MagicMock

tests/test_runtime/test_optimizer.py

@@ -1,3 +1,4 @@
+# Copyright (c) OpenMMLab. All rights reserved.
 import functools
 from collections import OrderedDict
 from copy import deepcopy

tests/test_runtime/test_preciseBN_hook.py

@@ -1,3 +1,4 @@
+# Copyright (c) OpenMMLab. All rights reserved.
 import numpy as np
 import pytest
 import torch

tools/deployment/onnx2tensorrt.py

@@ -52,8 +52,8 @@ def onnx2tensorrt(onnx_file,
     print(f'Successfully created TensorRT engine: {trt_file}')
 
     if verify:
-        import torch
         import onnxruntime as ort
+        import torch
 
         input_img = torch.randn(*input_shape)
         input_img_cpu = input_img.detach().cpu().numpy()

tools/deployment/pytorch2onnx.py

@@ -108,9 +108,9 @@ def pytorch2onnx(model,
     model.forward = origin_forward
 
     if do_simplify:
-        from mmcv import digit_version
-        import onnxsim
         import onnx
+        import onnxsim
+        from mmcv import digit_version
 
         min_required_version = '0.3.0'
         assert digit_version(mmcv.__version__) >= digit_version(

tools/visualizations/vis_cam.py

@@ -17,10 +17,10 @@ from mmcls.apis import init_model
 from mmcls.datasets.pipelines import Compose
 
 try:
-    from pytorch_grad_cam import (EigenCAM, GradCAM, GradCAMPlusPlus, XGradCAM,
-                                  EigenGradCAM, LayerCAM)
-    from pytorch_grad_cam.activations_and_gradients import (
-        ActivationsAndGradients)
+    from pytorch_grad_cam import (EigenCAM, EigenGradCAM, GradCAM,
+                                  GradCAMPlusPlus, LayerCAM, XGradCAM)
+    from pytorch_grad_cam.activations_and_gradients import \
+        ActivationsAndGradients
     from pytorch_grad_cam.utils.image import show_cam_on_image
 except ImportError:
     raise ImportError('Please run `pip install "grad-cam>=1.3.6"` to install '
@@ -336,8 +336,8 @@ def main():
     if args.target_category:
         grad_cam_v = pkg_resources.get_distribution('grad_cam').version
         if digit_version(grad_cam_v) >= digit_version('1.3.7'):
-            from pytorch_grad_cam.utils.model_targets import (
-                ClassifierOutputTarget)
+            from pytorch_grad_cam.utils.model_targets import \
+                ClassifierOutputTarget
             targets = [ClassifierOutputTarget(c) for c in args.target_category]
         else:
             targets = args.target_category