[CI]: Upgrade pre-commit-hook in the dev-1.x branch (#1893)

* update pre-commit-hook config

* fix typos in new pre-commit-hook

.pre-commit-config.yaml

 repos:
   - repo: https://github.com/PyCQA/flake8
-    rev: 3.8.3
+    rev: 5.0.4
     hooks:
       - id: flake8
   - repo: https://github.com/PyCQA/isort
@@ -8,11 +8,11 @@ repos:
     hooks:
       - id: isort
   - repo: https://github.com/pre-commit/mirrors-yapf
-    rev: v0.30.0
+    rev: v0.32.0
     hooks:
       - id: yapf
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v3.1.0
+    rev: v4.3.0
     hooks:
       - id: trailing-whitespace
       - id: check-yaml
@@ -25,7 +25,7 @@ repos:
       - id: mixed-line-ending
         args: ["--fix=lf"]
   - repo: https://github.com/codespell-project/codespell
-    rev: v2.1.0
+    rev: v2.2.1
     hooks:
       - id: codespell
   - repo: https://github.com/executablebooks/mdformat

docs/en/advanced_guides/customize_runtime.md

@@ -234,7 +234,7 @@ Actually, both [`IterBasedTrainLoop`](https://github.com/open-mmlab/mmengine/blo
 
 ```python
 # Before 365001th iteration, we do evaluation every 5000 iterations.
-# After 365000th iteration, we do evaluation every 368750 iteraions,
+# After 365000th iteration, we do evaluation every 368750 iterations,
 # which means that we do evaluation at the end of training.
 
 interval = 5000

docs/en/user_guides/config.md

@@ -411,7 +411,7 @@ param_scheduler = [
 
 ### Hook config
 
-Users can attach hooks to training, validation, and testing loops to insert some oprations during running. There are two different hook fields, one is `default_hooks` and the other is `custom_hooks`.
+Users can attach hooks to training, validation, and testing loops to insert some operations during running. There are two different hook fields, one is `default_hooks` and the other is `custom_hooks`.
 
 `default_hooks` is a dict of hook configs. `default_hooks` are the hooks must required at runtime. They have default priority which should not be modified. If not set, runner will use the default values. To disable a default hook, users can set its config to `None`.
 

mmdet3d/datasets/transforms/dbsampler.py

@@ -98,7 +98,8 @@ class DataBaseSampler(object):
             for more details. Defaults to dict(backend='disk').
     """
 
-    def __init__(self,
+    def __init__(
+        self,
         info_path: str,
         data_root: str,
         rate: float,
@@ -110,7 +111,8 @@ class DataBaseSampler(object):
             coord_type='LIDAR',
             load_dim=4,
             use_dim=[0, 1, 2, 3]),
-                 file_client_args: dict = dict(backend='disk')) -> None:
+        file_client_args: dict = dict(backend='disk')
+    ) -> None:
         super().__init__()
         self.data_root = data_root
         self.info_path = info_path
@@ -304,9 +306,7 @@ class DataBaseSampler(object):
 
         return ret
 
-    def sample_class_v2(self,
-                        name: str,
-                        num: int,
+    def sample_class_v2(self, name: str, num: int,
                         gt_bboxes: np.ndarray) -> List[dict]:
         """Sampling specific categories of bounding boxes.
 

mmdet3d/datasets/transforms/formating.py

@@ -72,7 +72,8 @@ class Pack3DDetInputs(BaseTransform):
                             'sample_idx', 'pcd_scale_factor', 'pcd_rotation',
                             'pcd_rotation_angle', 'lidar_path',
                             'transformation_3d_flow', 'trans_mat',
-                            'affine_aug')) -> None:
+                            'affine_aug')
+    ) -> None:
         self.keys = keys
         self.meta_keys = meta_keys
 

mmdet3d/datasets/transforms/loading.py

@@ -25,11 +25,9 @@ class LoadMultiViewImageFromFiles(BaseTransform):
             Defaults to 'unchanged'.
     """
 
-    def __init__(
-        self,
+    def __init__(self,
                  to_float32: bool = False,
-        color_type: str = 'unchanged'
-    ) -> None:
+                 color_type: str = 'unchanged') -> None:
         self.to_float32 = to_float32
         self.color_type = color_type
 
@@ -154,16 +152,14 @@ class LoadPointsFromMultiSweeps(BaseTransform):
             Defaults to False.
     """
 
-    def __init__(
-        self,
+    def __init__(self,
                  sweeps_num: int = 10,
                  load_dim: int = 5,
                  use_dim: List[int] = [0, 1, 2, 4],
                  file_client_args: dict = dict(backend='disk'),
                  pad_empty_sweeps: bool = False,
                  remove_close: bool = False,
-        test_mode: bool = False
-    ) -> None:
+                 test_mode: bool = False) -> None:
         self.load_dim = load_dim
         self.sweeps_num = sweeps_num
         self.use_dim = use_dim
@@ -195,11 +191,9 @@ class LoadPointsFromMultiSweeps(BaseTransform):
                 points = np.fromfile(pts_filename, dtype=np.float32)
         return points
 
-    def _remove_close(
-        self,
+    def _remove_close(self,
                       points: Union[np.ndarray, BasePoints],
-        radius: float = 1.0
-    ) -> Union[np.ndarray, BasePoints]:
+                      radius: float = 1.0) -> Union[np.ndarray, BasePoints]:
         """Removes point too close within a certain radius from origin.
 
         Args:

mmdet3d/datasets/transforms/test_time_aug.py

@@ -46,7 +46,7 @@ class MultiScaleFlipAug3D(BaseTransform):
         self.img_scale = img_scale if isinstance(img_scale,
                                                  list) else [img_scale]
         self.pts_scale_ratio = pts_scale_ratio \
-            if isinstance(pts_scale_ratio, list) else[float(pts_scale_ratio)]
+            if isinstance(pts_scale_ratio, list) else [float(pts_scale_ratio)]
 
         assert mmengine.is_list_of(self.img_scale, tuple)
         assert mmengine.is_list_of(self.pts_scale_ratio, float)

mmdet3d/datasets/transforms/transforms_3d.py

@@ -1221,10 +1221,8 @@ class IndoorPatchPointSample(BaseTransform):
         return points
 
     def _patch_points_sampling(
-        self,
-        points: BasePoints,
-        sem_mask: np.ndarray
-    ) -> Tuple[BasePoints, np.ndarray]:
+            self, points: BasePoints,
+            sem_mask: np.ndarray) -> Tuple[BasePoints, np.ndarray]:
         """Patch points sampling.
 
         First sample a valid patch.
@@ -1470,9 +1468,7 @@ class VoxelBasedPointSampler(BaseTransform):
             self.prev_voxel_generator = None
             self.prev_voxel_num = 0
 
-    def _sample_points(self,
-                       points: np.ndarray,
-                       sampler: VoxelGenerator,
+    def _sample_points(self, points: np.ndarray, sampler: VoxelGenerator,
                        point_dim: int) -> np.ndarray:
         """Sample points for each points subset.
 

mmdet3d/models/builder.py

@@ -92,7 +92,7 @@ def build_segmentor(cfg, train_cfg=None, test_cfg=None):
 
 
 def build_model(cfg, train_cfg=None, test_cfg=None):
-    """A function warpper for building 3D detector or segmentor according to
+    """A function wrapper for building 3D detector or segmentor according to
     cfg.
 
     Should be deprecated in the future.

mmdet3d/models/detectors/dynamic_voxelnet.py

@@ -10,7 +10,8 @@ from .voxelnet import VoxelNet
 
 @MODELS.register_module()
 class DynamicVoxelNet(VoxelNet):
-    r"""VoxelNet using `dynamic voxelization <https://arxiv.org/abs/1910.06528>`_.
+    r"""VoxelNet using `dynamic voxelization
+    <https://arxiv.org/abs/1910.06528>`_.
     """
 
     def __init__(self,

mmdet3d/models/layers/transformer.py

@@ -6,7 +6,7 @@ from torch import nn as nn
 
 @MODELS.register_module()
 class GroupFree3DMHA(MultiheadAttention):
-    """A warpper for torch.nn.MultiheadAttention for GroupFree3D.
+    """A wrapper for torch.nn.MultiheadAttention for GroupFree3D.
 
     This module implements MultiheadAttention with identity connection,
     and positional encoding used in DETR is also passed as input.

mmdet3d/models/middle_encoders/sparse_encoder.py

@@ -278,7 +278,7 @@ class SparseEncoderSASSD(SparseEncoder):
         Returns:
             dict: Backbone features.
             tuple[torch.Tensor]: Mean feature value of the points,
-                Classificaion result of the points,
+                Classification result of the points,
                 Regression offsets of the points.
         """
         coors = coors.int()
@@ -409,7 +409,7 @@ class SparseEncoderSASSD(SparseEncoder):
 
         Args:
             points (torch.Tensor): Mean feature value of the points.
-            point_cls (torch.Tensor): Classificaion result of the points.
+            point_cls (torch.Tensor): Classification result of the points.
             point_reg (torch.Tensor): Regression offsets of the points.
             gt_bboxes (list[:obj:`BaseInstance3DBoxes`]): Ground truth
                 boxes for each sample.

setup.cfg

@@ -13,4 +13,4 @@ no_lines_before = STDLIB,LOCALFOLDER
 default_section = THIRDPARTY
 
 [codespell]
-ignore-words-list = ans,refridgerator,crate,hist,formating,dout,wan,nd,fo,avod,AVOD
+ignore-words-list = ans,refridgerator,crate,hist,formating,dout,wan,nd,fo,avod,AVOD,warmup

tests/test_structures/test_bbox/test_box3d.py

@@ -566,7 +566,7 @@ def test_lidar_boxes3d():
 def test_boxes_conversion():
     """Test the conversion of boxes between different modes.
 
-    ComandLine:
+    CommandLine:
         xdoctest tests/test_box3d.py::test_boxes_conversion zero
     """
     lidar_boxes = LiDARInstance3DBoxes(
@@ -1121,7 +1121,7 @@ def test_camera_boxes3d():
 def test_boxes3d_overlaps():
     """Test the iou calculation of boxes in different modes.
 
-    ComandLine:
+    CommandLine:
         xdoctest tests/test_box3d.py::test_boxes3d_overlaps zero
     """
     if not torch.cuda.is_available():