[Fix] Fix lint (#1598)

* [Fix] Fix lint

* [Fix] Fix lint

* Update mmengine/dist/utils.py

Co-authored-by: Zaida Zhou <[email protected]>

---------

Co-authored-by: Zaida Zhou <[email protected]>

.github/workflows/lint.yml

@@ -11,10 +11,10 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v2
-      - name: Set up Python 3.7
+      - name: Set up Python 3.10.15
         uses: actions/setup-python@v2
         with:
-          python-version: 3.7
+          python-version: '3.10.15'
       - name: Install pre-commit hook
         run: |
           pip install pre-commit

.github/workflows/pr_stage_test.yml

@@ -1,5 +1,9 @@
 name: pr_stage_test
 
+env:
+  ACTIONS_ALLOW_USE_UNSECURE_NODE_VERSION: true
+
+
 on:
   pull_request:
     paths-ignore:

.pre-commit-config-zh-cn.yaml

@@ -1,7 +1,11 @@
 exclude: ^tests/data/
 repos:
-  - repo: https://gitee.com/openmmlab/mirrors-flake8
-    rev: 5.0.4
+  - repo: https://github.com/pre-commit/pre-commit
+    rev: v4.0.0
+    hooks:
+      - id: validate_manifest
+  - repo: https://github.com/PyCQA/flake8
+    rev: 7.1.1
     hooks:
       - id: flake8
   - repo: https://gitee.com/openmmlab/mirrors-isort
@@ -13,7 +17,7 @@ repos:
     hooks:
       - id: yapf
   - repo: https://gitee.com/openmmlab/mirrors-pre-commit-hooks
-    rev: v4.3.0
+    rev: v5.0.0
     hooks:
       - id: trailing-whitespace
       - id: check-yaml
@@ -55,11 +59,12 @@ repos:
         args: ["mmengine", "tests"]
       - id: remove-improper-eol-in-cn-docs
   - repo: https://gitee.com/openmmlab/mirrors-mypy
-    rev: v0.812
+    rev: v1.2.0
     hooks:
       - id: mypy
         exclude: |-
           (?x)(
               ^examples
               | ^docs
           )
+        additional_dependencies: ["types-setuptools", "types-requests", "types-PyYAML"]

.pre-commit-config.yaml

@@ -1,7 +1,11 @@
 exclude: ^tests/data/
 repos:
+  - repo: https://github.com/pre-commit/pre-commit
+    rev: v4.0.0
+    hooks:
+      - id: validate_manifest
   - repo: https://github.com/PyCQA/flake8
-    rev: 5.0.4
+    rev: 7.1.1
     hooks:
       - id: flake8
   - repo: https://github.com/PyCQA/isort
@@ -13,7 +17,7 @@ repos:
     hooks:
       - id: yapf
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.3.0
+    rev: v5.0.0
     hooks:
       - id: trailing-whitespace
       - id: check-yaml
@@ -34,12 +38,8 @@ repos:
           - mdformat-openmmlab
           - mdformat_frontmatter
           - linkify-it-py
-  - repo: https://github.com/codespell-project/codespell
-    rev: v2.2.1
-    hooks:
-      - id: codespell
   - repo: https://github.com/myint/docformatter
-    rev: v1.3.1
+    rev: 06907d0
     hooks:
       - id: docformatter
         args: ["--in-place", "--wrap-descriptions", "79"]
@@ -55,11 +55,12 @@ repos:
         args: ["mmengine", "tests"]
       - id: remove-improper-eol-in-cn-docs
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v0.812
+    rev: v1.2.0
     hooks:
       - id: mypy
         exclude: |-
           (?x)(
               ^examples
               | ^docs
           )
+        additional_dependencies: ["types-setuptools", "types-requests", "types-PyYAML"]

mmengine/_strategy/base.py

@@ -499,7 +499,7 @@ def build_optim_wrapper(
                             '"type" and "constructor" are not in '
                             f'optimizer, but got {name}={optim}')
                     optim_wrappers[name] = optim
-                return OptimWrapperDict(**optim_wrappers)
+                return OptimWrapperDict(**optim_wrappers)  # type: ignore
         else:
             raise TypeError('optimizer wrapper should be an OptimWrapper '
                             f'object or dict, but got {optim_wrapper}')

mmengine/_strategy/colossalai.py

@@ -361,7 +361,7 @@ def resume(
         map_location: Union[str, Callable] = 'default',
         callback: Optional[Callable] = None,
     ) -> dict:
-        """override this method since colossalai resume optimizer from filename
+        """Override this method since colossalai resume optimizer from filename
         directly."""
         self.logger.info(f'Resume checkpoint from {filename}')
 

mmengine/_strategy/distributed.py

@@ -53,7 +53,7 @@ def _setup_distributed(  # type: ignore
             init_dist(launcher, backend, **kwargs)
 
     def convert_model(self, model: nn.Module) -> nn.Module:
-        """convert all ``BatchNorm`` layers in the model to ``SyncBatchNorm``
+        """Convert all ``BatchNorm`` layers in the model to ``SyncBatchNorm``
         (SyncBN) or ``mmcv.ops.sync_bn.SyncBatchNorm`` (MMSyncBN) layers.
 
         Args:

mmengine/config/config.py

@@ -393,7 +393,7 @@ class Config:
 
     def __init__(
         self,
-        cfg_dict: dict = None,
+        cfg_dict: Optional[dict] = None,
         cfg_text: Optional[str] = None,
         filename: Optional[Union[str, Path]] = None,
         env_variables: Optional[dict] = None,
@@ -1227,7 +1227,8 @@ def is_base_line(c):
                 if base_code is not None:
                     base_code = ast.Expression(  # type: ignore
                         body=base_code.value)  # type: ignore
-                    base_files = eval(compile(base_code, '', mode='eval'))
+                    base_files = eval(compile(base_code, '',
+                                              mode='eval'))  # type: ignore
                 else:
                     base_files = []
         elif file_format in ('.yml', '.yaml', '.json'):
@@ -1288,7 +1289,7 @@ def _get_cfg_path(cfg_path: str,
     def _merge_a_into_b(a: dict,
                         b: dict,
                         allow_list_keys: bool = False) -> dict:
-        """merge dict ``a`` into dict ``b`` (non-inplace).
+        """Merge dict ``a`` into dict ``b`` (non-inplace).
 
         Values in ``a`` will overwrite ``b``. ``b`` is copied first to avoid
         in-place modifications.
@@ -1358,22 +1359,22 @@ def auto_argparser(description=None):
 
     @property
     def filename(self) -> str:
-        """get file name of config."""
+        """Get file name of config."""
         return self._filename
 
     @property
     def text(self) -> str:
-        """get config text."""
+        """Get config text."""
         return self._text
 
     @property
     def env_variables(self) -> dict:
-        """get used environment variables."""
+        """Get used environment variables."""
         return self._env_variables
 
     @property
     def pretty_text(self) -> str:
-        """get formatted python config text."""
+        """Get formatted python config text."""
 
         indent = 4
 
@@ -1727,17 +1728,17 @@ def to_dict(self, keep_imported: bool = False):
 
 
 class DictAction(Action):
-    """
-    argparse action to split an argument into KEY=VALUE form
-    on the first = and append to a dictionary. List options can
-    be passed as comma separated values, i.e 'KEY=V1,V2,V3', or with explicit
-    brackets, i.e. 'KEY=[V1,V2,V3]'. It also support nested brackets to build
-    list/tuple values. e.g. 'KEY=[(V1,V2),(V3,V4)]'
+    """Argparse action to split an argument into KEY=VALUE form on the first =
+    and append to a dictionary.
+
+    List options can be passed as comma separated values, i.e 'KEY=V1,V2,V3',
+    or with explicit brackets, i.e. 'KEY=[V1,V2,V3]'. It also support nested
+    brackets to build list/tuple values. e.g. 'KEY=[(V1,V2),(V3,V4)]'
     """
 
     @staticmethod
     def _parse_int_float_bool(val: str) -> Union[int, float, bool, Any]:
-        """parse int/float/bool value in the string."""
+        """Parse int/float/bool value in the string."""
         try:
             return int(val)
         except ValueError:
@@ -1822,7 +1823,7 @@ def __call__(self,
                  parser: ArgumentParser,
                  namespace: Namespace,
                  values: Union[str, Sequence[Any], None],
-                 option_string: str = None):
+                 option_string: str = None):  # type: ignore
         """Parse Variables in string and add them into argparser.
 
         Args:

mmengine/dist/utils.py

@@ -563,7 +563,7 @@ def cast_data_device(
         Tensor or list or dict: ``data`` was casted to ``device``.
     """
     if out is not None:
-        if type(data) != type(out):
+        if type(data) is not type(out):
             raise TypeError(
                 'out should be the same type with data, but got data is '
                 f'{type(data)} and out is {type(data)}')

mmengine/evaluator/metric.py

@@ -175,11 +175,11 @@ def __init__(self,
         self.out_file_path = out_file_path
 
     def process(self, data_batch: Any, predictions: Sequence[dict]) -> None:
-        """transfer tensors in predictions to CPU."""
+        """Transfer tensors in predictions to CPU."""
         self.results.extend(_to_cpu(predictions))
 
     def compute_metrics(self, results: list) -> dict:
-        """dump the prediction results to a pickle file."""
+        """Dump the prediction results to a pickle file."""
         dump(results, self.out_file_path)
         print_log(
             f'Results has been saved to {self.out_file_path}.',
@@ -188,7 +188,7 @@ def compute_metrics(self, results: list) -> dict:
 
 
 def _to_cpu(data: Any) -> Any:
-    """transfer all tensors and BaseDataElement to cpu."""
+    """Transfer all tensors and BaseDataElement to cpu."""
     if isinstance(data, (Tensor, BaseDataElement)):
         return data.to('cpu')
     elif isinstance(data, list):

mmengine/hooks/profiler_hook.py

@@ -233,7 +233,7 @@ def after_train_epoch(self, runner):
             self._export_chrome_trace(runner)
 
     def after_train_iter(self, runner, batch_idx, data_batch, outputs):
-        """profiler will call `step` method if it is not closed."""
+        """Profiler will call `step` method if it is not closed."""
         if not self._closed:
             self.profiler.step()
         if runner.iter == self.profile_times - 1 and not self.by_epoch:

mmengine/logging/history_buffer.py

@@ -58,7 +58,7 @@ def _set_default_statistics(self) -> None:
         self._statistics_methods.setdefault('mean', HistoryBuffer.mean)
 
     def update(self, log_val: Union[int, float], count: int = 1) -> None:
-        """update the log history.
+        """Update the log history.
 
         If the length of the buffer exceeds ``self._max_length``, the oldest
         element will be removed from the buffer.

mmengine/model/base_model/data_preprocessor.py

@@ -253,17 +253,17 @@ def forward(self, data: dict, training: bool = False) -> Union[dict, list]:
             dict or list: Data in the same format as the model input.
         """
         data = self.cast_data(data)  # type: ignore
-        _batch_inputs = data['inputs']
+        _batch_inputs = data['inputs']  # type: ignore
         # Process data with `pseudo_collate`.
         if is_seq_of(_batch_inputs, torch.Tensor):
             batch_inputs = []
             for _batch_input in _batch_inputs:
                 # channel transform
                 if self._channel_conversion:
-                    _batch_input = _batch_input[[2, 1, 0], ...]
+                    _batch_input = _batch_input[[2, 1, 0], ...]  # type: ignore
                 # Convert to float after channel conversion to ensure
                 # efficiency
-                _batch_input = _batch_input.float()
+                _batch_input = _batch_input.float()  # type: ignore
                 # Normalization.
                 if self._enable_normalize:
                     if self.mean.shape[0] == 3:
@@ -302,7 +302,7 @@ def forward(self, data: dict, training: bool = False) -> Union[dict, list]:
         else:
             raise TypeError('Output of `cast_data` should be a dict of '
                             'list/tuple with inputs and data_samples, '
-                            f'but got {type(data)}: {data}')
-        data['inputs'] = batch_inputs
-        data.setdefault('data_samples', None)
-        return data
+                            f'but got {type(data)}: {data}')  # type: ignore
+        data['inputs'] = batch_inputs  # type: ignore
+        data.setdefault('data_samples', None)  # type: ignore
+        return data  # type: ignore

mmengine/model/weight_init.py

@@ -119,7 +119,7 @@ def caffe2_xavier_init(module, bias=0):
 
 
 def bias_init_with_prob(prior_prob):
-    """initialize conv/fc bias value according to a given probability value."""
+    """Initialize conv/fc bias value according to a given probability value."""
     bias_init = float(-np.log((1 - prior_prob) / prior_prob))
     return bias_init
 
@@ -662,12 +662,12 @@ def trunc_normal_(tensor: Tensor,
                   std: float = 1.,
                   a: float = -2.,
                   b: float = 2.) -> Tensor:
-    r"""Fills the input Tensor with values drawn from a truncated
-    normal distribution. The values are effectively drawn from the
-    normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
-    with values outside :math:`[a, b]` redrawn until they are within
-    the bounds. The method used for generating the random values works
-    best when :math:`a \leq \text{mean} \leq b`.
+    r"""Fills the input Tensor with values drawn from a truncated normal
+    distribution. The values are effectively drawn from the normal distribution
+    :math:`\mathcal{N}(\text{mean}, \text{std}^2)` with values outside
+    :math:`[a, b]` redrawn until they are within the bounds. The method used
+    for generating the random values works best when :math:`a \leq \text{mean}
+    \leq b`.
 
     Modified from
     https://github.com/pytorch/pytorch/blob/master/torch/nn/init.py

mmengine/model/wrappers/fully_sharded_distributed.py

@@ -127,7 +127,8 @@ def __init__(
         auto_wrap_policy: Union[str, Callable, None] = None,
         backward_prefetch: Union[str, BackwardPrefetch, None] = None,
         mixed_precision: Union[dict, MixedPrecision, None] = None,
-        param_init_fn: Union[str, Callable[[nn.Module], None]] = None,
+        param_init_fn: Union[str, Callable[
+            [nn.Module], None]] = None,  # type: ignore # noqa: E501
         use_orig_params: bool = True,
         **kwargs,
     ):
@@ -362,7 +363,7 @@ def optim_state_dict(
             optim: torch.optim.Optimizer,
             group: Optional[dist.ProcessGroup] = None,
         ) -> Dict[str, Any]:
-            """copied from pytorch 2.0.1 which has fixed some bugs."""
+            """Copied from pytorch 2.0.1 which has fixed some bugs."""
             state_dict_settings = FullyShardedDataParallel.get_state_dict_type(
                 model)
             return FullyShardedDataParallel._optim_state_dict_impl(
@@ -384,7 +385,7 @@ def set_state_dict_type(
             state_dict_config: Optional[StateDictConfig] = None,
             optim_state_dict_config: Optional[OptimStateDictConfig] = None,
         ) -> StateDictSettings:
-            """copied from pytorch 2.0.1 which has fixed some bugs."""
+            """Copied from pytorch 2.0.1 which has fixed some bugs."""
             import torch.distributed.fsdp._traversal_utils as traversal_utils
             _state_dict_type_to_config = {
                 StateDictType.FULL_STATE_DICT: FullStateDictConfig,

mmengine/optim/optimizer/apex_optimizer_wrapper.py

@@ -123,8 +123,7 @@ def backward(self, loss: torch.Tensor, **kwargs) -> None:
         self._inner_count += 1
 
     def state_dict(self) -> dict:
-        """Get the state dictionary of :attr:`optimizer` and
-        :attr:`apex_amp`.
+        """Get the state dictionary of :attr:`optimizer` and :attr:`apex_amp`.
 
         Based on the state dictionary of the optimizer, the returned state
         dictionary will add a key named "apex_amp".

mmengine/optim/optimizer/default_constructor.py

@@ -131,7 +131,7 @@ def __init__(self,
         self._validate_cfg()
 
     def _validate_cfg(self) -> None:
-        """verify the correctness of the config."""
+        """Verify the correctness of the config."""
         if not isinstance(self.paramwise_cfg, dict):
             raise TypeError('paramwise_cfg should be None or a dict, '
                             f'but got {type(self.paramwise_cfg)}')
@@ -155,7 +155,7 @@ def _validate_cfg(self) -> None:
                 raise ValueError('base_wd should not be None')
 
     def _is_in(self, param_group: dict, param_group_list: list) -> bool:
-        """check whether the `param_group` is in the`param_group_list`"""
+        """Check whether the `param_group` is in the`param_group_list`"""
         assert is_list_of(param_group_list, dict)
         param = set(param_group['params'])
         param_set = set()