new yapf lint

mmengine/_strategy/deepspeed.py

@@ -310,10 +310,10 @@ def __init__(
             self.config.setdefault('gradient_accumulation_steps', 1)
         self.config['steps_per_print'] = steps_per_print
         self._inputs_to_half = inputs_to_half
-        assert (exclude_frozen_parameters is None or
-                digit_version(deepspeed.__version__) >= digit_version('0.13.2')
-                ), ('DeepSpeed >= 0.13.2 is required to enable '
-                    'exclude_frozen_parameters')
+        assert (exclude_frozen_parameters is None or digit_version(
+            deepspeed.__version__) >= digit_version('0.13.2')), (
+                'DeepSpeed >= 0.13.2 is required to enable '
+                'exclude_frozen_parameters')
         self.exclude_frozen_parameters = exclude_frozen_parameters
 
         register_deepspeed_optimizers()

mmengine/config/config.py

@@ -48,9 +48,10 @@
 def _lazy2string(cfg_dict, dict_type=None):
     if isinstance(cfg_dict, dict):
         dict_type = dict_type or type(cfg_dict)
-        return dict_type(
-            {k: _lazy2string(v, dict_type)
-             for k, v in dict.items(cfg_dict)})
+        return dict_type({
+            k: _lazy2string(v, dict_type)
+            for k, v in dict.items(cfg_dict)
+        })
     elif isinstance(cfg_dict, (tuple, list)):
         return type(cfg_dict)(_lazy2string(v, dict_type) for v in cfg_dict)
     elif isinstance(cfg_dict, (LazyAttr, LazyObject)):
@@ -273,13 +274,15 @@ def __reduce_ex__(self, proto):
         # called by CPython interpreter during pickling. See more details in
         # https://github.com/python/cpython/blob/8d61a71f9c81619e34d4a30b625922ebc83c561b/Objects/typeobject.c#L6196  # noqa: E501
         if digit_version(platform.python_version()) < digit_version('3.8'):
-            return (self.__class__, ({k: v
-                                      for k, v in super().items()}, ), None,
-                    None, None)
+            return (self.__class__, ({
+                k: v
+                for k, v in super().items()
+            }, ), None, None, None)
         else:
-            return (self.__class__, ({k: v
-                                      for k, v in super().items()}, ), None,
-                    None, None, None)
+            return (self.__class__, ({
+                k: v
+                for k, v in super().items()
+            }, ), None, None, None, None)
 
     def __eq__(self, other):
         if isinstance(other, ConfigDict):

mmengine/dataset/utils.py

@@ -158,7 +158,8 @@ def default_collate(data_batch: Sequence) -> Any:
                 return [default_collate(samples) for samples in transposed]
     elif isinstance(data_item, Mapping):
         return data_item_type({
-            key: default_collate([d[key] for d in data_batch])
+            key:
+            default_collate([d[key] for d in data_batch])
             for key in data_item
         })
     else:

mmengine/fileio/backends/local_backend.py

@@ -156,8 +156,8 @@ def isfile(self, filepath: Union[str, Path]) -> bool:
         """
         return osp.isfile(filepath)
 
-    def join_path(self, filepath: Union[str, Path],
-                  *filepaths: Union[str, Path]) -> str:
+    def join_path(self, filepath: Union[str, Path], *filepaths:
+                  Union[str, Path]) -> str:
         r"""Concatenate all file paths.
 
         Join one or more filepath components intelligently. The return value

mmengine/fileio/file_client.py

@@ -385,8 +385,8 @@ def isfile(self, filepath: Union[str, Path]) -> bool:
         """
         return self.client.isfile(filepath)
 
-    def join_path(self, filepath: Union[str, Path],
-                  *filepaths: Union[str, Path]) -> str:
+    def join_path(self, filepath: Union[str, Path], *filepaths:
+                  Union[str, Path]) -> str:
         r"""Concatenate all file paths.
 
         Join one or more filepath components intelligently. The return value

mmengine/hooks/checkpoint_hook.py

@@ -196,10 +196,10 @@ def __init__(self,
         self.save_best = save_best
 
         # rule logic
-        assert (isinstance(rule, str) or is_list_of(rule, str)
-                or (rule is None)), (
-                    '"rule" should be a str or list of str or None, '
-                    f'but got {type(rule)}')
+        assert (isinstance(rule, str) or is_list_of(rule, str) or
+                (rule
+                 is None)), ('"rule" should be a str or list of str or None, '
+                             f'but got {type(rule)}')
         if isinstance(rule, list):
             # check the length of rule list
             assert len(rule) in [

mmengine/model/test_time_aug.py

@@ -124,9 +124,10 @@ def test_step(self, data):
         data_list: Union[List[dict], List[list]]
         if isinstance(data, dict):
             num_augs = len(data[next(iter(data))])
-            data_list = [{key: value[idx]
-                          for key, value in data.items()}
-                         for idx in range(num_augs)]
+            data_list = [{
+                key: value[idx]
+                for key, value in data.items()
+            } for idx in range(num_augs)]
         elif isinstance(data, (tuple, list)):
             num_augs = len(data[0])
             data_list = [[_data[idx] for _data in data]

mmengine/utils/dl_utils/torch_ops.py

@@ -4,9 +4,9 @@
 from ..version_utils import digit_version
 from .parrots_wrapper import TORCH_VERSION
 
-_torch_version_meshgrid_indexing = (
-    'parrots' not in TORCH_VERSION
-    and digit_version(TORCH_VERSION) >= digit_version('1.10.0a0'))
+_torch_version_meshgrid_indexing = ('parrots' not in TORCH_VERSION
+                                    and digit_version(TORCH_VERSION)
+                                    >= digit_version('1.10.0a0'))
 
 
 def torch_meshgrid(*tensors):

mmengine/visualization/visualizer.py

@@ -754,8 +754,9 @@ def draw_bboxes(
         assert bboxes.shape[-1] == 4, (
             f'The shape of `bboxes` should be (N, 4), but got {bboxes.shape}')
 
-        assert (bboxes[:, 0] <= bboxes[:, 2]).all() and (bboxes[:, 1] <=
-                                                         bboxes[:, 3]).all()
+        assert (bboxes[:, 0] <= bboxes[:, 2]).all() and (bboxes[:, 1]
+                                                         <= bboxes[:,
+                                                                   3]).all()
         if not self._is_posion_valid(bboxes.reshape((-1, 2, 2))):
             warnings.warn(
                 'Warning: The bbox is out of bounds,'

tests/test_analysis/test_jit_analysis.py

@@ -634,9 +634,10 @@ def dummy_ops_handle(inputs: List[Any],
 
         dummy_flops = {}
         for name, counts in model.flops.items():
-            dummy_flops[name] = Counter(
-                {op: flop
-                 for op, flop in counts.items() if op != self.lin_op})
+            dummy_flops[name] = Counter({
+                op: flop
+                for op, flop in counts.items() if op != self.lin_op
+            })
         dummy_flops[''][dummy_name] = 2 * dummy_out
         dummy_flops['fc'][dummy_name] = dummy_out
         dummy_flops['submod'][dummy_name] = dummy_out

tests/test_dataset/test_base_dataset.py

@@ -733,13 +733,13 @@ def test_length(self):
     def test_getitem(self):
         assert (
             self.cat_datasets[0]['imgs'] == self.dataset_a[0]['imgs']).all()
-        assert (self.cat_datasets[0]['imgs'] !=
-                self.dataset_b[0]['imgs']).all()
+        assert (self.cat_datasets[0]['imgs']
+                != self.dataset_b[0]['imgs']).all()
 
         assert (
             self.cat_datasets[-1]['imgs'] == self.dataset_b[-1]['imgs']).all()
-        assert (self.cat_datasets[-1]['imgs'] !=
-                self.dataset_a[-1]['imgs']).all()
+        assert (self.cat_datasets[-1]['imgs']
+                != self.dataset_a[-1]['imgs']).all()
 
     def test_get_data_info(self):
         assert self.cat_datasets.get_data_info(

tests/test_optim/test_optimizer/test_optimizer_wrapper.py

@@ -455,8 +455,8 @@ def test_init(self):
         not torch.cuda.is_available(),
         reason='`torch.cuda.amp` is only available when pytorch-gpu installed')
     def test_step(self, dtype):
-        if dtype is not None and (digit_version(TORCH_VERSION) <
-                                  digit_version('1.10.0')):
+        if dtype is not None and (digit_version(TORCH_VERSION)
+                                  < digit_version('1.10.0')):
             raise unittest.SkipTest('Require PyTorch version >= 1.10.0 to '
                                     'support `dtype` argument in autocast')
         if dtype == 'bfloat16' and not bf16_supported():
@@ -478,8 +478,8 @@ def test_step(self, dtype):
         not torch.cuda.is_available(),
         reason='`torch.cuda.amp` is only available when pytorch-gpu installed')
     def test_backward(self, dtype):
-        if dtype is not None and (digit_version(TORCH_VERSION) <
-                                  digit_version('1.10.0')):
+        if dtype is not None and (digit_version(TORCH_VERSION)
+                                  < digit_version('1.10.0')):
             raise unittest.SkipTest('Require PyTorch version >= 1.10.0 to '
                                     'support `dtype` argument in autocast')
         if dtype == 'bfloat16' and not bf16_supported():
@@ -539,8 +539,8 @@ def test_load_state_dict(self):
         not torch.cuda.is_available(),
         reason='`torch.cuda.amp` is only available when pytorch-gpu installed')
     def test_optim_context(self, dtype, target_dtype):
-        if dtype is not None and (digit_version(TORCH_VERSION) <
-                                  digit_version('1.10.0')):
+        if dtype is not None and (digit_version(TORCH_VERSION)
+                                  < digit_version('1.10.0')):
             raise unittest.SkipTest('Require PyTorch version >= 1.10.0 to '
                                     'support `dtype` argument in autocast')
         if dtype == 'bfloat16' and not bf16_supported():