Upgrade the version of isort

.pre-commit-config.yaml

@@ -4,12 +4,8 @@ repos:
     rev: 3.8.3
     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

mmcv/cnn/bricks/transformer.py

@@ -19,7 +19,8 @@
 
 # Avoid BC-breaking of importing MultiScaleDeformableAttention from this file
 try:
-    from mmcv.ops.multi_scale_deform_attn import MultiScaleDeformableAttention  # noqa F401
+    from mmcv.ops.multi_scale_deform_attn import \
+        MultiScaleDeformableAttention  # noqa F401
     warnings.warn(
         ImportWarning(
             '``MultiScaleDeformableAttention`` has been moved to '

mmcv/fileio/handlers/yaml_handler.py

@@ -2,7 +2,8 @@
 import yaml
 
 try:
-    from yaml import CLoader as Loader, CDumper as Dumper
+    from yaml import CDumper as Dumper
+    from yaml import CLoader as Loader
 except ImportError:
     from yaml import Loader, Dumper
 

mmcv/onnx/symbolic.py

@@ -409,8 +409,8 @@ def cummin(g, input, dim):
 
 @parse_args('v', 'v', 'is')
 def roll(g, input, shifts, dims):
-    from torch.onnx.symbolic_opset9 import squeeze
     from packaging import version
+    from torch.onnx.symbolic_opset9 import squeeze
     input_shape = g.op('Shape', input)
 
     need_flatten = len(dims) == 0

mmcv/ops/nms.py

@@ -48,6 +48,7 @@ def symbolic(g, bboxes, scores, iou_threshold, offset, score_threshold,
                 offset_i=int(offset))
         else:
             from torch.onnx.symbolic_opset9 import select, squeeze, unsqueeze
+
             from ..onnx.onnx_utils.symbolic_helper import _size_helper
 
             boxes = unsqueeze(g, bboxes, 0)

mmcv/ops/roi_align.py

@@ -30,9 +30,10 @@ def symbolic(g, input, rois, output_size, spatial_scale, sampling_ratio,
                 mode_s=pool_mode,
                 aligned_i=aligned)
         else:
-            from torch.onnx.symbolic_opset9 import sub, squeeze
-            from torch.onnx.symbolic_helper import _slice_helper
             from torch.onnx import TensorProtoDataType
+            from torch.onnx.symbolic_helper import _slice_helper
+            from torch.onnx.symbolic_opset9 import squeeze, sub
+
             # batch_indices = rois[:, 0].long()
             batch_indices = _slice_helper(
                 g, rois, axes=[1], starts=[0], ends=[1])

mmcv/utils/__init__.py

@@ -37,16 +37,20 @@
     ]
 else:
     from .env import collect_env
+    from .hub import load_url
     from .logging import get_logger, print_log
     from .parrots_jit import jit, skip_no_elena
-    from .parrots_wrapper import (
-        TORCH_VERSION, BuildExtension, CppExtension, CUDAExtension, DataLoader,
-        PoolDataLoader, SyncBatchNorm, _AdaptiveAvgPoolNd, _AdaptiveMaxPoolNd,
-        _AvgPoolNd, _BatchNorm, _ConvNd, _ConvTransposeMixin, _InstanceNorm,
-        _MaxPoolNd, get_build_config, is_rocm_pytorch, _get_cuda_home)
+    # yapf: disable
+    from .parrots_wrapper import (TORCH_VERSION, BuildExtension, CppExtension,
+                                  CUDAExtension, DataLoader, PoolDataLoader,
+                                  SyncBatchNorm, _AdaptiveAvgPoolNd,
+                                  _AdaptiveMaxPoolNd, _AvgPoolNd, _BatchNorm,
+                                  _ConvNd, _ConvTransposeMixin, _get_cuda_home,
+                                  _InstanceNorm, _MaxPoolNd, get_build_config,
+                                  is_rocm_pytorch)
+    # yapf: enable
     from .registry import Registry, build_from_cfg
     from .trace import is_jit_tracing
-    from .hub import load_url
     __all__ = [
         'Config', 'ConfigDict', 'DictAction', 'collect_env', 'get_logger',
         'print_log', 'is_str', 'iter_cast', 'list_cast', 'tuple_cast',

mmcv/utils/hub.py

@@ -10,12 +10,13 @@
         '1.7.0'):
     # Modified from https://github.com/pytorch/pytorch/blob/master/torch/hub.py
     import os
-    import torch
-    import warnings
-    from urllib.parse import urlparse
     import sys
+    import warnings
     import zipfile
-    from torch.hub import download_url_to_file, _get_torch_home, HASH_REGEX
+    from urllib.parse import urlparse
+
+    import torch
+    from torch.hub import HASH_REGEX, _get_torch_home, download_url_to_file
 
     # Hub used to support automatically extracts from zipfile manually
     # compressed by users. The legacy zip format expects only one file from

mmcv/utils/parrots_wrapper.py

@@ -83,8 +83,8 @@ def _get_norm():
         from parrots.nn.modules.batchnorm import _BatchNorm, _InstanceNorm
         SyncBatchNorm_ = torch.nn.SyncBatchNorm2d
     else:
-        from torch.nn.modules.instancenorm import _InstanceNorm
         from torch.nn.modules.batchnorm import _BatchNorm
+        from torch.nn.modules.instancenorm import _InstanceNorm
         SyncBatchNorm_ = torch.nn.SyncBatchNorm
     return _BatchNorm, _InstanceNorm, SyncBatchNorm_
 

mmcv/utils/testing.py

@@ -111,8 +111,9 @@ def assert_is_norm_layer(module) -> bool:
     Returns:
         bool: Whether the module is a norm layer.
     """
-    from .parrots_wrapper import _BatchNorm, _InstanceNorm
     from torch.nn import GroupNorm, LayerNorm
+
+    from .parrots_wrapper import _BatchNorm, _InstanceNorm
     norm_layer_candidates = (_BatchNorm, _InstanceNorm, GroupNorm, LayerNorm)
     return isinstance(module, norm_layer_candidates)
 

setup.cfg

@@ -12,7 +12,7 @@ split_before_expression_after_opening_paren = true
 [isort]
 line_length = 79
 multi_line_output = 0
-known_standard_library = pkg_resources,setuptools,logging,os,warnings,abc
+extra_standard_library = pkg_resources,setuptools,logging,os,warnings,abc
 known_first_party = mmcv
 known_third_party = addict,cv2,numpy,onnx,onnxruntime,packaging,pytest,pytorch_sphinx_theme,scipy,sphinx,tensorrt,torch,torchvision,yaml,yapf
 no_lines_before = STDLIB,LOCALFOLDER

setup.py

@@ -184,6 +184,7 @@ def get_extensions():
     if EXT_TYPE == 'parrots':
         ext_name = 'mmcv._ext'
         from parrots.utils.build_extension import Extension
+
         # new parrots op impl do not use MMCV_USE_PARROTS
         # define_macros = [('MMCV_USE_PARROTS', None)]
         define_macros = []
@@ -314,8 +315,8 @@ def get_extensions():
 
     if EXT_TYPE == 'pytorch' and os.getenv('MMCV_WITH_ORT', '0') != '0':
         ext_name = 'mmcv._ext_ort'
-        from torch.utils.cpp_extension import library_paths, include_paths
         import onnxruntime
+        from torch.utils.cpp_extension import include_paths, library_paths
         library_dirs = []
         libraries = []
         include_dirs = []

tests/test_image/test_photometric.py

@@ -112,7 +112,7 @@ def test_imequalize(self, 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
@@ -141,8 +141,8 @@ def test_adjust_brightness(self, 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)
@@ -169,8 +169,9 @@ def _adjust_brightness(img, factor):
     def test_adjust_contrast(self, 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')
@@ -204,8 +205,9 @@ def _adjust_contrast(img, factor):
     def test_auto_contrast(self, nb_rand_test=100):
 
         def _auto_contrast(img, cutoff=0):
-            from PIL.ImageOps import autocontrast
             from PIL import Image
+            from PIL.ImageOps import autocontrast
+
             # Image.fromarray defaultly supports RGB, not BGR.
             # convert from BGR to RGB
             img = Image.fromarray(img[..., ::-1], mode='RGB')
@@ -250,8 +252,8 @@ def test_adjust_sharpness(self, 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_ops/test_border_align.py

@@ -51,7 +51,7 @@ def _test_border_align_allclose(device, dtype, pool_size):
     if not torch.cuda.is_available() and device == 'cuda':
         pytest.skip('test requires GPU')
     try:
-        from mmcv.ops import border_align, BorderAlign
+        from mmcv.ops import BorderAlign, border_align
     except ModuleNotFoundError:
         pytest.skip('BorderAlign op is not successfully compiled')
 

tests/test_runner/test_basemodule.py

@@ -88,9 +88,11 @@ def __init__(self,
 def test_initilization_info_logger():
     # 'override' has higher priority
 
+    import os
+
     import torch.nn as nn
+
     from mmcv.utils.logging import get_logger
-    import os
 
     class OverloadInitConv(nn.Conv2d, BaseModule):
 

tests/test_runner/test_checkpoint.py

@@ -197,7 +197,6 @@ def __init__(self):
 
 def test_load_checkpoint():
     import os
-
     import re
     import tempfile
 
@@ -230,7 +229,6 @@ def __init__(self):
 
 def test_load_checkpoint_metadata():
     import os
-
     import tempfile
 
     from mmcv.runner import load_checkpoint, save_checkpoint
@@ -301,7 +299,6 @@ def _load_from_state_dict(self, state_dict, prefix, local_metadata,
 
 def test_load_classes_name():
     import os
-
     import tempfile
 
     from mmcv.runner import load_checkpoint, save_checkpoint
@@ -335,7 +332,6 @@ def test_load_classes_name():
 
 def test_checkpoint_loader():
     import os
-
     import tempfile
 
     from mmcv.runner import CheckpointLoader, _load_checkpoint, save_checkpoint

tests/test_utils/test_misc.py

@@ -132,7 +132,6 @@ def func_c():
 def test_import_modules_from_strings():
     # multiple imports
     import os.path as osp_
-
     import sys as sys_
     osp, sys = mmcv.import_modules_from_strings(['os.path', 'sys'])
     assert osp == osp_