[Fix] Update pre-commit-config-zh-cn.yaml and add typehints for PointNet2SAMSG (open-mmlab#2396)

Author: chriscarving

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

@@ -1,4 +1,3 @@
-exclude: ^tests/data/
 repos:
   - repo: https://gitee.com/openmmlab/mirrors-flake8
     rev: 5.0.4
@@ -25,6 +24,10 @@ repos:
         args: ["--remove"]
       - id: mixed-line-ending
         args: ["--fix=lf"]
+  - repo: https://gitee.com/openmmlab/mirrors-codespell
+    rev: v2.2.1
+    hooks:
+      - id: codespell
   - repo: https://gitee.com/openmmlab/mirrors-mdformat
     rev: 0.7.9
     hooks:
@@ -34,20 +37,11 @@ repos:
           - mdformat-openmmlab
           - mdformat_frontmatter
           - linkify-it-py
-  - repo: https://gitee.com/openmmlab/mirrors-codespell
-    rev: v2.2.1
-    hooks:
-      - id: codespell
   - repo: https://gitee.com/openmmlab/mirrors-docformatter
     rev: v1.3.1
     hooks:
       - id: docformatter
         args: ["--in-place", "--wrap-descriptions", "79"]
-  - repo: https://gitee.com/openmmlab/mirrors-pyupgrade
-    rev: v3.0.0
-    hooks:
-      - id: pyupgrade
-        args: ["--py36-plus"]
   - repo: https://gitee.com/openmmlab/pre-commit-hooks
     rev: v0.2.0
     hooks:

mmdet3d/models/backbones/pointnet2_sa_msg.py

@@ -1,12 +1,19 @@
 # Copyright (c) OpenMMLab. All rights reserved.
+from typing import Tuple
+
 import torch
 from mmcv.cnn import ConvModule
 from torch import nn as nn
 
 from mmdet3d.models.layers.pointnet_modules import build_sa_module
 from mmdet3d.registry import MODELS
+from mmdet3d.utils import OptConfigType
 from .base_pointnet import BasePointNet
 
+ThreeTupleIntType = Tuple[Tuple[Tuple[int, int, int]]]
+TwoTupleIntType = Tuple[Tuple[int, int, int]]
+TwoTupleStrType = Tuple[Tuple[str]]
+
 
 @MODELS.register_module()
 class PointNet2SAMSG(BasePointNet):
@@ -22,7 +29,7 @@ class PointNet2SAMSG(BasePointNet):
         sa_channels (tuple[tuple[int]]): Out channels of each mlp in SA module.
         aggregation_channels (tuple[int]): Out channels of aggregation
             multi-scale grouping features.
-        fps_mods (tuple[int]): Mod of FPS for each SA module.
+        fps_mods Sequence[Tuple[str]]: Mod of FPS for each SA module.
         fps_sample_range_lists (tuple[tuple[int]]): The number of sampling
             points which each SA module samples.
         dilated_group (tuple[bool]): Whether to use dilated ball query for
@@ -38,26 +45,37 @@ class PointNet2SAMSG(BasePointNet):
     """
 
     def __init__(self,
-                 in_channels,
-                 num_points=(2048, 1024, 512, 256),
-                 radii=((0.2, 0.4, 0.8), (0.4, 0.8, 1.6), (1.6, 3.2, 4.8)),
-                 num_samples=((32, 32, 64), (32, 32, 64), (32, 32, 32)),
-                 sa_channels=(((16, 16, 32), (16, 16, 32), (32, 32, 64)),
-                              ((64, 64, 128), (64, 64, 128), (64, 96, 128)),
-                              ((128, 128, 256), (128, 192, 256), (128, 256,
-                                                                  256))),
-                 aggregation_channels=(64, 128, 256),
-                 fps_mods=(('D-FPS'), ('FS'), ('F-FPS', 'D-FPS')),
-                 fps_sample_range_lists=((-1), (-1), (512, -1)),
-                 dilated_group=(True, True, True),
-                 out_indices=(2, ),
-                 norm_cfg=dict(type='BN2d'),
-                 sa_cfg=dict(
+                 in_channels: int,
+                 num_points: Tuple[int] = (2048, 1024, 512, 256),
+                 radii: Tuple[Tuple[float, float, float]] = (
+                     (0.2, 0.4, 0.8),
+                     (0.4, 0.8, 1.6),
+                     (1.6, 3.2, 4.8),
+                 ),
+                 num_samples: TwoTupleIntType = ((32, 32, 64), (32, 32, 64),
+                                                 (32, 32, 32)),
+                 sa_channels: ThreeTupleIntType = (((16, 16, 32), (16, 16, 32),
+                                                    (32, 32, 64)),
+                                                   ((64, 64, 128),
+                                                    (64, 64, 128), (64, 96,
+                                                                    128)),
+                                                   ((128, 128, 256),
+                                                    (128, 192, 256), (128, 256,
+                                                                      256))),
+                 aggregation_channels: Tuple[int] = (64, 128, 256),
+                 fps_mods: TwoTupleStrType = (('D-FPS'), ('FS'), ('F-FPS',
+                                                                  'D-FPS')),
+                 fps_sample_range_lists: TwoTupleIntType = ((-1), (-1), (512,
+                                                                         -1)),
+                 dilated_group: Tuple[bool] = (True, True, True),
+                 out_indices: Tuple[int] = (2, ),
+                 norm_cfg: dict = dict(type='BN2d'),
+                 sa_cfg: dict = dict(
                      type='PointSAModuleMSG',
                      pool_mod='max',
                      use_xyz=True,
                      normalize_xyz=False),
-                 init_cfg=None):
+                 init_cfg: OptConfigType = None):
         super().__init__(init_cfg=init_cfg)
         self.num_sa = len(sa_channels)
         self.out_indices = out_indices
@@ -123,7 +141,7 @@ def __init__(self,
                         bias=True))
                 sa_in_channel = cur_aggregation_channel
 
-    def forward(self, points):
+    def forward(self, points: torch.Tensor):
         """Forward pass.
 
         Args:

mmdet3d/models/layers/pointnet_modules/builder.py

@@ -4,7 +4,9 @@
 from mmengine.registry import Registry
 from torch import nn as nn
 
-SA_MODULES = Registry('point_sa_module')
+SA_MODULES = Registry(
+    name='point_sa_module',
+    locations=['mmdet3d.models.layers.pointnet_modules'])
 
 
 def build_sa_module(cfg: Union[dict, None], *args, **kwargs) -> nn.Module: