[Enhancement] upgrade isort in pre-commit config

.pre-commit-config.yaml

@@ -3,12 +3,8 @@ repos:
     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

mmdeploy/apis/ncnn/__init__.py

@@ -4,6 +4,6 @@
 __all__ = ['is_available', 'is_plugin_available']
 
 if is_available():
-    from mmdeploy.backend.ncnn.onnx2ncnn import (onnx2ncnn,
-                                                 get_output_model_file)
+    from mmdeploy.backend.ncnn.onnx2ncnn import (get_output_model_file,
+                                                 onnx2ncnn)
     __all__ += ['onnx2ncnn', 'get_output_model_file']

mmdeploy/apis/openvino/__init__.py

@@ -4,8 +4,8 @@
 __all__ = ['is_available']
 
 if is_available():
-    from mmdeploy.backend.openvino.onnx2openvino \
-        import onnx2openvino, get_output_model_file
+    from mmdeploy.backend.openvino.onnx2openvino import (get_output_model_file,
+                                                         onnx2openvino)
     from .utils import get_input_info_from_cfg
     __all__ += [
         'onnx2openvino', 'get_output_model_file', 'get_input_info_from_cfg'

mmdeploy/backend/openvino/__init__.py

@@ -12,6 +12,6 @@ def is_available() -> bool:
 
 
 if is_available():
-    from .wrapper import OpenVINOWrapper
     from .onnx2openvino import get_output_model_file
+    from .wrapper import OpenVINOWrapper
     __all__ = ['OpenVINOWrapper', 'get_output_model_file']

mmdeploy/codebase/mmcls/deploy/classification.py

@@ -230,6 +230,7 @@ def evaluate_outputs(model_cfg: mmcv.Config,
                 Defaults to `None` and the results will only print on stdout.
         """
         import warnings
+
         from mmcv.utils import get_logger
         logger = get_logger('test', log_file=log_file, log_level=logging.INFO)
 

mmdeploy/codebase/mmdet/deploy/object_detection.py

@@ -109,8 +109,8 @@ def create_input(self,
         Returns:
             tuple: (data, img), meta information for the input image and input.
         """
-        from mmdet.datasets.pipelines import Compose
         from mmcv.parallel import collate, scatter
+        from mmdet.datasets.pipelines import Compose
         if not isinstance(imgs, (list, tuple)):
             imgs = [imgs]
         dynamic_flag = is_dynamic_shape(self.deploy_cfg)

mmdeploy/codebase/mmdet/deploy/object_detection_model.py

@@ -399,6 +399,7 @@ def __init__(self, backend: Backend, backend_files: Sequence[str],
         super().__init__(backend, backend_files, device, class_names,
                          deploy_cfg, **kwargs)
         from mmdet.models.builder import build_head, build_roi_extractor
+
         from ..models.roi_heads.bbox_head import bbox_head__get_bboxes
 
         self.bbox_roi_extractor = build_roi_extractor(

mmdeploy/codebase/mmdet/models/dense_heads/yolox_head.py

@@ -139,8 +139,8 @@ def yolox_head__get_bboxes__ncnn(ctx,
         output__ncnn (Tensor): outputs, shape is [N, num_det, 6].
     """
     from mmdeploy.codebase.mmdet.core.ops import ncnn_detection_output_forward
-    from mmdeploy.utils.config_utils import is_dynamic_shape
     from mmdeploy.utils import get_root_logger
+    from mmdeploy.utils.config_utils import is_dynamic_shape
     dynamic_flag = is_dynamic_shape(ctx.cfg)
     if dynamic_flag:
         logger = get_root_logger()

mmdeploy/codebase/mmedit/deploy/mmediting.py

@@ -55,6 +55,7 @@ def build_dataset(dataset_cfg: Union[str, mmcv.Config], *args,
             Dataset: A PyTorch dataset.
         """
         from mmedit.datasets import build_dataset as build_dataset_mmedit
+
         from mmdeploy.utils import load_config
         dataset_cfg = load_config(dataset_cfg)[0]
         data = dataset_cfg.data

mmdeploy/codebase/mmocr/deploy/mmocr.py

@@ -139,6 +139,7 @@ def single_gpu_test(model: torch.nn.Module,
             list: The prediction results.
         """
         import mmocr
+
         # fixed the bug when using `--show-dir` after mocr v0.4.1
         if version.parse(mmocr.__version__) < version.parse('0.4.1'):
             from mmdet.apis import single_gpu_test

mmdeploy/codebase/mmseg/deploy/segmentation.py

@@ -88,8 +88,8 @@ def init_pytorch_model(self,
             nn.Module: An initialized torch model generated by OpenMMLab
                 codebases.
         """
-        from mmseg.apis import init_segmentor
         from mmcv.cnn.utils import revert_sync_batchnorm
+        from mmseg.apis import init_segmentor
         model = init_segmentor(self.model_cfg, model_checkpoint, self.device)
         model = revert_sync_batchnorm(model)
         return model.eval()
@@ -109,8 +109,8 @@ def create_input(self,
         Returns:
             tuple: (data, img), meta information for the input image and input.
         """
-        from mmseg.datasets.pipelines import Compose
         from mmcv.parallel import collate, scatter
+        from mmseg.datasets.pipelines import Compose
         if not isinstance(imgs, (list, tuple)):
             imgs = [imgs]
         cfg = process_model_config(self.model_cfg, imgs, input_shape)

setup.cfg

@@ -12,8 +12,8 @@ split_before_expression_after_opening_paren = true
 [isort]
 line_length = 79
 multi_line_output = 0
-known_standard_library = setuptools
+extra_standard_library = setuptools
 known_first_party = mmdeploy
-known_third_party = mmcv
+known_third_party = h5py,m2r,mmcls,mmcv,mmdeploy_python,mmdet,mmedit,mmocr,mmseg,ncnn,numpy,onnx,onnxruntime,packaging,pyppeteer,pyppl,pytest,pytorch_sphinx_theme,recommonmark,setuptools,sphinx,tensorrt,torch,torchvision
 no_lines_before = STDLIB,LOCALFOLDER
 default_section = THIRDPARTY

setup.py

@@ -38,9 +38,9 @@ def parse_requirements(fname='requirements.txt', with_version=True):
     CommandLine:
         python -c "import setup; print(setup.parse_requirements())"
     """
+    import re
     import sys
     from os.path import exists
-    import re
     require_fpath = fname
 
     def parse_line(line):

tests/test_backend/test_wrapper.py

@@ -46,8 +46,8 @@ def generate_onnx_file():
 
 def onnx2backend(backend, onnx_file):
     if backend == Backend.TENSORRT:
-        from mmdeploy.backend.tensorrt import create_trt_engine,\
-            save_trt_engine
+        from mmdeploy.backend.tensorrt import (create_trt_engine,
+                                               save_trt_engine)
         backend_file = tempfile.NamedTemporaryFile(suffix='.engine').name
         engine = create_trt_engine(
             onnx_file, {
@@ -74,8 +74,7 @@ def onnx2backend(backend, onnx_file):
         subprocess.call([onnx2ncnn_path, onnx_file, param_file, bin_file])
         return param_file, bin_file
     elif backend == Backend.OPENVINO:
-        from mmdeploy.apis.openvino import onnx2openvino,\
-            get_output_model_file
+        from mmdeploy.apis.openvino import get_output_model_file, onnx2openvino
         backend_dir = tempfile.TemporaryDirectory().name
         backend_file = get_output_model_file(onnx_file, backend_dir)
         input_info = {'input': test_img.shape}

tests/test_codebase/test_mmcls/test_classification.py

@@ -95,7 +95,7 @@ def test_get_partition_cfg():
 
 
 def test_build_dataset_and_dataloader():
-    from torch.utils.data import Dataset, DataLoader
+    from torch.utils.data import DataLoader, Dataset
     dataset = task_processor.build_dataset(
         dataset_cfg=model_cfg, dataset_type='test')
     assert isinstance(dataset, Dataset), 'Failed to build dataset'

tests/test_codebase/test_mmdet/test_object_detection.py

@@ -70,14 +70,14 @@ def backend_model():
 
 
 def test_init_backend_model(backend_model):
-    from mmdeploy.codebase.mmdet.deploy.object_detection_model \
-        import End2EndModel
+    from mmdeploy.codebase.mmdet.deploy.object_detection_model import \
+        End2EndModel
     assert isinstance(backend_model, End2EndModel)
 
 
 def test_can_postprocess_masks():
-    from mmdeploy.codebase.mmdet.deploy.object_detection_model \
-        import End2EndModel
+    from mmdeploy.codebase.mmdet.deploy.object_detection_model import \
+        End2EndModel
     num_dets = [0, 1, 5]
     for num_det in num_dets:
         det_bboxes = np.random.randn(num_det, 4)

tests/test_codebase/test_mmdet/test_object_detection_model.py

@@ -418,6 +418,7 @@ class TestGetClassesFromCfg:
                              [data_cfg1, data_cfg2, data_cfg3, data_cfg4])
     def test_get_classes_from_cfg(self, cfg):
         from mmdet.datasets import DATASETS
+
         from mmdeploy.codebase.mmdet.deploy.object_detection_model import \
             get_classes_from_config
 

tests/test_codebase/test_mmedit/test_super_resolution_model.py

@@ -33,8 +33,8 @@ def setup_class(cls):
             }})
         model_cfg = 'tests/test_codebase/test_mmedit/data/model.py'
         model_cfg = load_config(model_cfg)[0]
-        from mmdeploy.codebase.mmedit.deploy.super_resolution_model\
-            import End2EndModel
+        from mmdeploy.codebase.mmedit.deploy.super_resolution_model import \
+            End2EndModel
         cls.end2end_model = End2EndModel(Backend.ONNXRUNTIME, [''], 'cpu',
                                          model_cfg, deploy_cfg)
 

tests/test_codebase/test_mmocr/test_mmocr_models.py

@@ -481,6 +481,7 @@ def get_sar_model_cfg(decoder_type: str):
 def test_sar_model(backend: Backend, decoder_type):
     check_backend(backend)
     import os.path as osp
+
     import onnx
     from mmocr.models.textrecog import SARNet
     sar_cfg = get_sar_model_cfg(decoder_type)

tests/test_codebase/test_mmocr/test_text_detection.py

@@ -94,7 +94,7 @@ def test_get_partition_cfg():
 
 
 def test_build_dataset_and_dataloader():
-    from torch.utils.data import Dataset, DataLoader
+    from torch.utils.data import DataLoader, Dataset
     dataset = task_processor.build_dataset(
         dataset_cfg=model_cfg, dataset_type='test')
     assert isinstance(dataset, Dataset), 'Failed to build dataset'

tests/test_codebase/test_mmocr/test_text_detection_models.py

@@ -39,8 +39,8 @@ def setup_class(cls):
         model_cfg_path = 'tests/test_codebase/test_mmocr/data/dbnet.py'
         model_cfg = load_config(model_cfg_path)[0]
 
-        from mmdeploy.codebase.mmocr.deploy.text_detection_model \
-            import End2EndModel
+        from mmdeploy.codebase.mmocr.deploy.text_detection_model import \
+            End2EndModel
         cls.end2end_model = End2EndModel(
             Backend.ONNXRUNTIME, [''],
             device='cpu',
@@ -96,8 +96,8 @@ def test_build_text_detection_model():
     # simplify backend inference
     with SwitchBackendWrapper(ORTWrapper) as wrapper:
         wrapper.set(model_cfg=model_cfg, deploy_cfg=deploy_cfg)
-        from mmdeploy.codebase.mmocr.deploy.text_detection_model import \
-            build_text_detection_model, End2EndModel
+        from mmdeploy.codebase.mmocr.deploy.text_detection_model import (
+            End2EndModel, build_text_detection_model)
         segmentor = build_text_detection_model([''], model_cfg, deploy_cfg,
                                                'cpu')
         assert isinstance(segmentor, End2EndModel)

tests/test_codebase/test_mmocr/test_text_recognition.py

@@ -95,7 +95,7 @@ def test_get_partition_cfg():
 
 
 def test_build_dataset_and_dataloader():
-    from torch.utils.data import Dataset, DataLoader
+    from torch.utils.data import DataLoader, Dataset
     dataset = task_processor.build_dataset(
         dataset_cfg=model_cfg, dataset_type='test')
     assert isinstance(dataset, Dataset), 'Failed to build dataset'

tests/test_codebase/test_mmocr/test_text_recognition_models.py

@@ -36,8 +36,8 @@ def setup_class(cls):
         model_cfg_path = 'tests/test_codebase/test_mmocr/data/crnn.py'
         model_cfg = load_config(model_cfg_path)[0]
 
-        from mmdeploy.codebase.mmocr.deploy.text_recognition_model \
-            import End2EndModel
+        from mmdeploy.codebase.mmocr.deploy.text_recognition_model import \
+            End2EndModel
         cls.end2end_model = End2EndModel(
             Backend.ONNXRUNTIME, [''],
             device='cpu',
@@ -94,8 +94,8 @@ def test_build_text_recognition_model():
     # simplify backend inference
     with SwitchBackendWrapper(ORTWrapper) as wrapper:
         wrapper.set(model_cfg=model_cfg, deploy_cfg=deploy_cfg)
-        from mmdeploy.codebase.mmocr.deploy.text_recognition_model import \
-            build_text_recognition_model, End2EndModel
+        from mmdeploy.codebase.mmocr.deploy.text_recognition_model import (
+            End2EndModel, build_text_recognition_model)
         segmentor = build_text_recognition_model([''], model_cfg, deploy_cfg,
                                                  'cpu')
         assert isinstance(segmentor, End2EndModel)

tests/test_codebase/test_mmseg/test_segmentation.py

@@ -95,7 +95,7 @@ def test_get_partition_cfg():
 
 
 def test_build_dataset_and_dataloader():
-    from torch.utils.data import Dataset, DataLoader
+    from torch.utils.data import DataLoader, Dataset
     dataset = task_processor.build_dataset(
         dataset_cfg=model_cfg, dataset_type='test')
     assert isinstance(dataset, Dataset), 'Failed to build dataset'

tests/test_codebase/test_mmseg/test_segmentation_model.py

@@ -38,8 +38,8 @@ def setup_class(cls):
                 'output_names': ['outputs']
             }})
 
-        from mmdeploy.codebase.mmseg.deploy.segmentation_model \
-            import End2EndModel
+        from mmdeploy.codebase.mmseg.deploy.segmentation_model import \
+            End2EndModel
         class_names = ['' for i in range(NUM_CLASS)]
         palette = np.random.randint(0, 255, size=(NUM_CLASS, 3))
         cls.end2end_model = End2EndModel(
@@ -86,8 +86,9 @@ def test_show_result(self):
 @pytest.mark.parametrize('data_type', ['train', 'val', 'test'])
 def test_get_classes_palette_from_config(from_file, data_type):
     from mmseg.datasets import DATASETS
-    from mmdeploy.codebase.mmseg.deploy.segmentation_model \
-        import get_classes_palette_from_config
+
+    from mmdeploy.codebase.mmseg.deploy.segmentation_model import \
+        get_classes_palette_from_config
     dataset_type = 'CityscapesDataset'
     data_cfg = mmcv.Config({
         'data': {
@@ -131,8 +132,8 @@ def test_build_segmentation_model():
     # simplify backend inference
     with SwitchBackendWrapper(ORTWrapper) as wrapper:
         wrapper.set(model_cfg=model_cfg, deploy_cfg=deploy_cfg)
-        from mmdeploy.codebase.mmseg.deploy.segmentation_model import \
-            build_segmentation_model, End2EndModel
+        from mmdeploy.codebase.mmseg.deploy.segmentation_model import (
+            End2EndModel, build_segmentation_model)
         segmentor = build_segmentation_model([''], model_cfg, deploy_cfg,
                                              'cpu')
         assert isinstance(segmentor, End2EndModel)

tests/test_ops/test_ops.py

@@ -619,8 +619,9 @@ def test_gather(backend,
 
     # ncnn mat has implicit batch for mat, the ncnn_output is a mat,
     # so the ncnn_outputs has 2 dimensions, not 1.
-    import onnxruntime
     import importlib
+
+    import onnxruntime
     assert importlib.util.find_spec('onnxruntime') is not None, 'onnxruntime \
          not installed.'