[Fix] Fix pre-commit-hook for python3.12

.github/workflows/lint.yml

@@ -11,18 +11,18 @@ jobs:
     runs-on: ubuntu-22.04
     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
           pre-commit install
       - name: Linting
         run: pre-commit run --all-files
       - name: Format c/cuda codes with clang-format
-        uses: DoozyX/clang-format-lint-action@v0.11
+        uses: DoozyX/clang-format-lint-action@v0.18
         with:
           source: mmcv/ops/csrc
           extensions: h,c,cpp,hpp,cu,cuh

.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
@@ -53,15 +57,16 @@ repos:
     hooks:
       - id: check-copyright
         args: ["mmcv", "tests", "--excludes", "mmcv/ops"]
-  - repo: https://gitee.com/openmmlab/mirrors-mypy
-    rev: v0.812
+  - repo: https://github.com/pre-commit/mirrors-mypy
+    rev: v1.2.0
     hooks:
       - id: mypy
         exclude: |-
           (?x)(
               ^test
               | ^docs
           )
+        additional_dependencies: ["types-setuptools", "types-requests"]
   # - repo: local
   #   hooks:
   #     - id: clang-format

.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
@@ -39,7 +43,7 @@ repos:
           - mdformat_frontmatter
           - linkify-it-py
   - repo: https://github.com/myint/docformatter
-    rev: v1.3.1
+    rev: 06907d0
     hooks:
       - id: docformatter
         args: ["--in-place", "--wrap-descriptions", "79"]
@@ -54,14 +58,15 @@ repos:
       - id: check-copyright
         args: ["mmcv", "tests", "--excludes", "mmcv/ops"]
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v0.812
+    rev: v1.2.0
     hooks:
       - id: mypy
         exclude: |-
           (?x)(
               ^test
               | ^docs
           )
+        additional_dependencies: ["types-setuptools", "types-requests"]
   # - repo: local
   #   hooks:
   #     - id: clang-format

mmcv/cnn/bricks/drop.py

@@ -13,7 +13,8 @@ def drop_path(x: torch.Tensor,
     residual blocks).
 
     We follow the implementation
-    https://github.com/rwightman/pytorch-image-models/blob/a2727c1bf78ba0d7b5727f5f95e37fb7f8866b1f/timm/models/layers/drop.py  # noqa: E501
+    https://github.com/rwightman/pytorch-image-models/blob/a2727c1bf78ba0d7b5727f5f95e37fb7f8866b1f/timm/models/layers/drop.py
+    # noqa: E501
     """
     if drop_prob == 0. or not training:
         return x

mmcv/cnn/rfsearch/search.py

@@ -71,7 +71,7 @@ def __init__(self,
         self.by_epoch = by_epoch
 
     def init_model(self, model: nn.Module):
-        """init model with search ability.
+        """Init model with search ability.
 
         Args:
             model (nn.Module): pytorch model
@@ -132,7 +132,7 @@ def step(self, model: nn.Module, work_dir: str) -> None:
             )
 
     def estimate_and_expand(self, model: nn.Module) -> None:
-        """estimate and search for RFConvOp.
+        """Estimate and search for RFConvOp.
 
         Args:
             model (nn.Module): pytorch model
@@ -146,7 +146,7 @@ def wrap_model(self,
                    model: nn.Module,
                    search_op: str = 'Conv2d',
                    prefix: str = '') -> None:
-        """wrap model to support searchable conv op.
+        """Wrap model to support searchable conv op.
 
         Args:
             model (nn.Module): pytorch model
@@ -187,7 +187,7 @@ def set_model(self,
                   search_op: str = 'Conv2d',
                   init_rates: Optional[int] = None,
                   prefix: str = '') -> None:
-        """set model based on config.
+        """Set model based on config.
 
         Args:
             model (nn.Module): pytorch model

mmcv/cnn/rfsearch/utils.py

@@ -4,7 +4,7 @@
 
 
 def write_to_json(config: dict, filename: str):
-    """save config to json file.
+    """Save config to json file.
 
     Args:
         config (dict): Config to be saved.
@@ -16,7 +16,7 @@ def write_to_json(config: dict, filename: str):
 
 
 def expand_rates(dilation: tuple, config: dict) -> list:
-    """expand dilation rate according to config.
+    """Expand dilation rate according to config.
 
     Args:
         dilation (int): _description_

mmcv/ops/active_rotated_filter.py

@@ -16,9 +16,9 @@ class ActiveRotatedFilterFunction(Function):
     """Encoding the orientation information and generating orientation-
     sensitive features.
 
-    The details are described in the paper `Align Deep Features for Oriented
-    Object Detection  <https://arxiv.org/abs/2008.09397>_`.
-    """
+    The details are described in the paper
+    `Align Deep Features for Oriented Object Detection  <https://arxiv.org/abs/2008.09397>_`.
+    """  # noqa: E501
 
     @staticmethod
     def forward(ctx, input: torch.Tensor,

mmcv/ops/cc_attention.py

@@ -51,7 +51,7 @@ def __init__(self, in_channels: int) -> None:
         self.in_channels = in_channels
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
-        """forward function of Criss-Cross Attention.
+        """Forward function of Criss-Cross Attention.
 
         Args:
             x (torch.Tensor): Input feature with the shape of

mmcv/ops/chamfer_distance.py

@@ -16,7 +16,8 @@ class ChamferDistanceFunction(Function):
     """This is an implementation of the 2D Chamfer Distance.
 
     It has been used in the paper `Oriented RepPoints for Aerial Object
-    Detection (CVPR 2022) <https://arxiv.org/abs/2105.11111>_`.
+    Detection (CVPR 2022)
+    <https://arxiv.org/abs/2105.11111>_`.
     """
 
     @staticmethod

mmcv/ops/correlation.py

@@ -116,7 +116,7 @@ def _output_size(ctx, input1):
 
 
 class Correlation(nn.Module):
-    r"""Correlation operator
+    r"""Correlation operator.
 
     This correlation operator works for optical flow correlation computation.
 

mmcv/ops/deform_conv.py

@@ -449,7 +449,9 @@ def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
 
     @MODELS.register_module('DCN', force=True)
     class DeformConv2dPack_MLU(DeformConv2d):
-        """This class is the DCN implementation of the MLU device. The MLU
+        """This class is the DCN implementation of the MLU device.
+
+        The MLU
         backend support of the operator has been implemented in torchvision.
         The mmcv registration mechanism is used for multiplexing here. The
         torchvision implementation of DCN is called.

mmcv/ops/iou3d.py

@@ -42,7 +42,7 @@ def boxes_iou3d(boxes_a: Tensor, boxes_b: Tensor) -> Tensor:
     Returns:
         torch.Tensor: 3D IoU result with shape (M, N).
     """
-    assert boxes_a.shape[1] == boxes_b.shape[1] == 7,\
+    assert boxes_a.shape[1] == boxes_b.shape[1] == 7, \
         'Input boxes shape should be (N, 7)'
 
     boxes_a_height_max = (boxes_a[:, 2] + boxes_a[:, 5] / 2).view(-1, 1)

mmcv/ops/rotated_feature_align.py

@@ -17,9 +17,9 @@ class RotatedFeatureAlignFunction(Function):
     correspond to the refined rotate anchors and reconstruct the feature maps
     in pixel-wise manner to achieve feature alignment.
 
-    The details are described in the paper
-    `R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating
-    Object <https://arxiv.org/abs/1908.05612>`_.
+    The details are described in the paper `R3Det: Refined Single-Stage
+    Detector with Feature Refinement for Rotating Object
+    <https://arxiv.org/abs/1908.05612>`_.
     """
 
     @staticmethod

mmcv/ops/scatter_points.py

@@ -20,7 +20,7 @@ def forward(ctx: Any,
                 feats: torch.Tensor,
                 coors: torch.Tensor,
                 reduce_type: str = 'max') -> Tuple[torch.Tensor, torch.Tensor]:
-        """convert kitti points(N, >=3) to voxels.
+        """Convert kitti points(N, >=3) to voxels.
 
         Args:
             feats (torch.Tensor): [N, C]. Points features to be reduced

mmcv/ops/sparse_modules.py

@@ -47,16 +47,15 @@ def _mean_update(vals: Union[int, List], m_vals: Union[int, List],
 
 
 class SparseModule(nn.Module):
-    """place holder, All module subclass from this will take sptensor in
+    """Place holder, All module subclass from this will take sptensor in
     SparseSequential."""
     pass
 
 
 class SparseSequential(SparseModule):
-    r"""A sequential container.
-    Modules will be added to it in the order they are passed in the
-    constructor.
-    Alternatively, an ordered dict of modules can also be passed in.
+    r"""A sequential container. Modules will be added to it in the order they
+    are passed in the constructor. Alternatively, an ordered dict of modules
+    can also be passed in.
 
     To make it easier to understand, given is a small example::
 
@@ -189,14 +188,14 @@ def fused(self):
 
 
 class ToDense(SparseModule):
-    """convert SparseConvTensor to NCHW dense tensor."""
+    """Convert SparseConvTensor to NCHW dense tensor."""
 
     def forward(self, x: SparseConvTensor):
         return x.dense()
 
 
 class RemoveGrid(SparseModule):
-    """remove pre-allocated grid buffer."""
+    """Remove pre-allocated grid buffer."""
 
     def forward(self, x: SparseConvTensor):
         x.grid = None

mmcv/ops/sparse_structure.py

@@ -6,7 +6,7 @@
 
 def scatter_nd(indices: torch.Tensor, updates: torch.Tensor,
                shape: torch.Tensor) -> torch.Tensor:
-    """pytorch edition of tensorflow scatter_nd.
+    """Pytorch edition of tensorflow scatter_nd.
 
     this function don't contain except handle code. so use this carefully when
     indice repeats, don't support repeat add which is supported in tensorflow.

mmcv/ops/tin_shift.py

@@ -55,10 +55,12 @@ class TINShift(nn.Module):
     Temporal Interlace shift is a differentiable temporal-wise frame shifting
     which is proposed in "Temporal Interlacing Network"
 
-    Please refer to `Temporal Interlacing Network
-    <https://arxiv.org/abs/2001.06499>`_ for more details.
+    Please refer to
+    `Temporal Interlacing Network <https://arxiv.org/abs/2001.06499>`_
+     for more details.
 
-    Code is modified from https://github.com/mit-han-lab/temporal-shift-module
+    Code is modified from
+    https://github.com/mit-han-lab/temporal-shift-module
     """
 
     def forward(self, input, shift):

mmcv/ops/upfirdn2d.py

@@ -19,7 +19,7 @@
 
 
 def _parse_scaling(scaling):
-    """parse scaling into list [x, y]"""
+    """Parse scaling into list [x, y]"""
     if isinstance(scaling, int):
         scaling = [scaling, scaling]
     assert isinstance(scaling, (list, tuple))
@@ -30,7 +30,7 @@ def _parse_scaling(scaling):
 
 
 def _parse_padding(padding):
-    """parse padding into list [padx0, padx1, pady0, pady1]"""
+    """Parse padding into list [padx0, padx1, pady0, pady1]"""
     if isinstance(padding, int):
         padding = [padding, padding]
     assert isinstance(padding, (list, tuple))
@@ -43,7 +43,7 @@ def _parse_padding(padding):
 
 
 def _get_filter_size(filter):
-    """get width and height of filter kernel."""
+    """Get width and height of filter kernel."""
     if filter is None:
         return 1, 1
     assert isinstance(filter, torch.Tensor) and filter.ndim in [1, 2]

mmcv/transforms/formatting.py

@@ -110,6 +110,7 @@ def __init__(self, keys: dict) -> None:
     def transform(self, results: dict) -> dict:
         """Transform function to convert image in results to
         :obj:`torch.Tensor` and transpose the channel order.
+
         Args:
             results (dict): Result dict contains the image data to convert.
         Returns:

mmcv/transforms/processing.py

@@ -1457,7 +1457,7 @@ class RandomResize(BaseTransform):
     def __init__(
         self,
         scale: Union[Tuple[int, int], Sequence[Tuple[int, int]]],
-        ratio_range: Tuple[float, float] = None,
+        ratio_range: Optional[Tuple[float, float]] = None,
         resize_type: str = 'Resize',
         **resize_kwargs,
     ) -> None:

mmcv/transforms/wrappers.py

@@ -177,7 +177,7 @@ class KeyMapper(BaseTransform):
     """
 
     def __init__(self,
-                 transforms: Union[Transform, List[Transform]] = None,
+                 transforms: Union[Transform, List[Transform], None] = None,
                  mapping: Optional[Dict] = None,
                  remapping: Optional[Dict] = None,
                  auto_remap: Optional[bool] = None,

mmcv/video/optflow.py

@@ -149,8 +149,8 @@ def dequantize_flow(dx: np.ndarray,
     dx, dy = (dequantize(d, -max_val, max_val, 255) for d in [dx, dy])
 
     if denorm:
-        dx *= dx.shape[1]
-        dy *= dx.shape[0]
+        dx *= dx.shape[1]  # type: ignore
+        dy *= dx.shape[0]  # type: ignore
     flow = np.dstack((dx, dy))
     return flow
 

mmcv/visualization/image.py

@@ -91,7 +91,7 @@ def imshow_bboxes(img: Union[str, np.ndarray],
 def imshow_det_bboxes(img: Union[str, np.ndarray],
                       bboxes: np.ndarray,
                       labels: np.ndarray,
-                      class_names: List[str] = None,
+                      class_names: Optional[List[str]] = None,
                       score_thr: float = 0,
                       bbox_color: ColorType = 'green',
                       text_color: ColorType = 'green',