Add typehint for code under `mmdet3d/models/backbones` (#2464)

* [Docs] Update link of registry tutorial (#2442)

Registry docs are now under Advanced Tutorials.

* add typehint to the backbones of model.

* Update typehint with OptMultiConfig and Tensor to torch.Tensor

* Update mmdet3d/models/backbones/dgcnn.py
Co-authored-by: Xiang Xu <xuxiang0103@gmail.com>

* Update mmdet3d/models/backbones/dgcnn.py
Co-authored-by: Xiang Xu <xuxiang0103@gmail.com>

* Update mmdet3d/models/backbones/dgcnn.py
Co-authored-by: Xiang Xu <xuxiang0103@gmail.com>

* Update mmdet3d/models/backbones/dgcnn.py
Co-authored-by: Xiang Xu <xuxiang0103@gmail.com>

* Update mmdet3d/models/backbones/dgcnn.py
Co-authored-by: Xiang Xu <xuxiang0103@gmail.com>

* Update mmdet3d/models/backbones/dgcnn.py
Co-authored-by: Xiang Xu <xuxiang0103@gmail.com>

* Update mmdet3d/models/backbones/dgcnn.py
Co-authored-by: Xiang Xu <xuxiang0103@gmail.com>

* Update mmdet3d/models/backbones/dla.py
Co-authored-by: Xiang Xu <xuxiang0103@gmail.com>

* Update mmdet3d/models/backbones/dla.py
Co-authored-by: Xiang Xu <xuxiang0103@gmail.com>

* Update mmdet3d/models/backbones/dgcnn.py
Co-authored-by: Xiang Xu <xuxiang0103@gmail.com>

* Update typehint.

* add typehint to function mono_cam_box2vis and remove assert keyword.

* Update typehint.

* Fixed some problems.

---------
Co-authored-by: pd-michaelstanley <88335018+pd-michaelstanley@users.noreply.github.com>
Co-authored-by: Xiang Xu <xuxiang0103@gmail.com>

mmdet3d/models/backbones/base_pointnet.py

 # Copyright (c) OpenMMLab. All rights reserved.
 import warnings
 from abc import ABCMeta
+from typing import Optional, Tuple
 
 from mmengine.model import BaseModule
+from torch import Tensor
+
+from mmdet3d.utils import OptMultiConfig
 
 
 class BasePointNet(BaseModule, metaclass=ABCMeta):
     """Base class for PointNet."""
 
-    def __init__(self, init_cfg=None, pretrained=None):
+    def __init__(self,
+                 init_cfg: OptMultiConfig = None,
+                 pretrained: Optional[str] = None):
         super(BasePointNet, self).__init__(init_cfg)
         assert not (init_cfg and pretrained), \
             'init_cfg and pretrained cannot be setting at the same time'
@@ -18,7 +24,7 @@ class BasePointNet(BaseModule, metaclass=ABCMeta):
             self.init_cfg = dict(type='Pretrained', checkpoint=pretrained)
 
     @staticmethod
-    def _split_point_feats(points):
+    def _split_point_feats(points: Tensor) -> Tuple[Tensor, Optional[Tensor]]:
         """Split coordinates and features of input points.
 
         Args:

mmdet3d/models/backbones/cylinder3d.py

@@ -13,6 +13,7 @@ from mmcv.cnn import build_activation_layer, build_norm_layer
 from mmcv.ops import (SparseConv3d, SparseConvTensor, SparseInverseConv3d,
                       SubMConv3d)
 from mmengine.model import BaseModule
+from torch import Tensor
 
 from mmdet3d.registry import MODELS
 from mmdet3d.utils import ConfigType
@@ -456,7 +457,7 @@ class Asymm3DSpconv(BaseModule):
             indice_key='ddcm',
             norm_cfg=norm_cfg)
 
-    def forward(self, voxel_features: torch.Tensor, coors: torch.Tensor,
+    def forward(self, voxel_features: Tensor, coors: Tensor,
                 batch_size: int) -> SparseConvTensor:
         """Forward pass."""
         coors = coors.int()

mmdet3d/models/backbones/dgcnn.py

 # Copyright (c) OpenMMLab. All rights reserved.
+from typing import Sequence, Union
+
 from mmengine.model import BaseModule
+from torch import Tensor
 from torch import nn as nn
 
 from mmdet3d.models.layers import DGCNNFAModule, DGCNNGFModule
 from mmdet3d.registry import MODELS
+from mmdet3d.utils import ConfigType, OptMultiConfig
 
 
 @MODELS.register_module()
@@ -30,14 +34,15 @@ class DGCNNBackbone(BaseModule):
     """
 
     def __init__(self,
-                 in_channels,
-                 num_samples=(20, 20, 20),
-                 knn_modes=('D-KNN', 'F-KNN', 'F-KNN'),
-                 radius=(None, None, None),
-                 gf_channels=((64, 64), (64, 64), (64, )),
-                 fa_channels=(1024, ),
-                 act_cfg=dict(type='ReLU'),
-                 init_cfg=None):
+                 in_channels: int,
+                 num_samples: Sequence[int] = (20, 20, 20),
+                 knn_modes: Sequence[str] = ('D-KNN', 'F-KNN', 'F-KNN'),
+                 radius: Sequence[Union[float, None]] = (None, None, None),
+                 gf_channels: Sequence[Sequence[int]] = ((64, 64), (64, 64),
+                                                         (64, )),
+                 fa_channels: Sequence[int] = (1024, ),
+                 act_cfg: ConfigType = dict(type='ReLU'),
+                 init_cfg: OptMultiConfig = None):
         super().__init__(init_cfg=init_cfg)
         self.num_gf = len(gf_channels)
 
@@ -71,7 +76,7 @@ class DGCNNBackbone(BaseModule):
         self.FA_module = DGCNNFAModule(
             mlp_channels=cur_fa_mlps, act_cfg=act_cfg)
 
-    def forward(self, points):
+    def forward(self, points: Tensor) -> dict:
         """Forward pass.
 
         Args:

mmdet3d/models/backbones/dla.py

 # Copyright (c) OpenMMLab. All rights reserved.
 import warnings
+from typing import List, Optional, Sequence, Tuple
 
 import torch
 from mmcv.cnn import build_conv_layer, build_norm_layer
 from mmengine.model import BaseModule
-from torch import nn
+from torch import Tensor, nn
 
 from mmdet3d.registry import MODELS
+from mmdet3d.utils import ConfigType, OptConfigType, OptMultiConfig
 
 
-def dla_build_norm_layer(cfg, num_features):
+def dla_build_norm_layer(cfg: ConfigType,
+                         num_features: int) -> Tuple[str, nn.Module]:
     """Build normalization layer specially designed for DLANet.
 
     Args:
@@ -53,13 +56,13 @@ class BasicBlock(BaseModule):
     """
 
     def __init__(self,
-                 in_channels,
-                 out_channels,
-                 norm_cfg,
-                 conv_cfg,
-                 stride=1,
-                 dilation=1,
-                 init_cfg=None):
+                 in_channels: int,
+                 out_channels: int,
+                 norm_cfg: ConfigType,
+                 conv_cfg: ConfigType,
+                 stride: int = 1,
+                 dilation: int = 1,
+                 init_cfg: OptMultiConfig = None):
         super(BasicBlock, self).__init__(init_cfg)
         self.conv1 = build_conv_layer(
             conv_cfg,
@@ -84,7 +87,7 @@ class BasicBlock(BaseModule):
         self.norm2 = dla_build_norm_layer(norm_cfg, out_channels)[1]
         self.stride = stride
 
-    def forward(self, x, identity=None):
+    def forward(self, x: Tensor, identity: Optional[Tensor] = None) -> Tensor:
         """Forward function."""
 
         if identity is None:
@@ -117,13 +120,13 @@ class Root(BaseModule):
     """
 
     def __init__(self,
-                 in_channels,
-                 out_channels,
-                 norm_cfg,
-                 conv_cfg,
-                 kernel_size,
-                 add_identity,
-                 init_cfg=None):
+                 in_channels: int,
+                 out_channels: int,
+                 norm_cfg: ConfigType,
+                 conv_cfg: ConfigType,
+                 kernel_size: int,
+                 add_identity: bool,
+                 init_cfg: OptMultiConfig = None):
         super(Root, self).__init__(init_cfg)
         self.conv = build_conv_layer(
             conv_cfg,
@@ -137,7 +140,7 @@ class Root(BaseModule):
         self.relu = nn.ReLU(inplace=True)
         self.add_identity = add_identity
 
-    def forward(self, feat_list):
+    def forward(self, feat_list: List[Tensor]) -> Tensor:
         """Forward function.
 
         Args:
@@ -181,19 +184,19 @@ class Tree(BaseModule):
     """
 
     def __init__(self,
-                 levels,
-                 block,
-                 in_channels,
-                 out_channels,
-                 norm_cfg,
-                 conv_cfg,
-                 stride=1,
-                 level_root=False,
-                 root_dim=None,
-                 root_kernel_size=1,
-                 dilation=1,
-                 add_identity=False,
-                 init_cfg=None):
+                 levels: int,
+                 block: nn.Module,
+                 in_channels: int,
+                 out_channels: int,
+                 norm_cfg: ConfigType,
+                 conv_cfg: ConfigType,
+                 stride: int = 1,
+                 level_root: bool = False,
+                 root_dim: Optional[int] = None,
+                 root_kernel_size: int = 1,
+                 dilation: int = 1,
+                 add_identity: bool = False,
+                 init_cfg: OptMultiConfig = None):
         super(Tree, self).__init__(init_cfg)
         if root_dim is None:
             root_dim = 2 * out_channels
@@ -258,7 +261,10 @@ class Tree(BaseModule):
                     bias=False),
                 dla_build_norm_layer(norm_cfg, out_channels)[1])
 
-    def forward(self, x, identity=None, children=None):
+    def forward(self,
+                x: Tensor,
+                identity: Optional[Tensor] = None,
+                children: Optional[List[Tensor]] = None) -> Tensor:
         children = [] if children is None else children
         bottom = self.downsample(x) if self.downsample else x
         identity = self.project(bottom) if self.project else bottom
@@ -302,16 +308,17 @@ class DLANet(BaseModule):
     }
 
     def __init__(self,
-                 depth,
-                 in_channels=3,
-                 out_indices=(0, 1, 2, 3, 4, 5),
-                 frozen_stages=-1,
-                 norm_cfg=None,
-                 conv_cfg=None,
-                 layer_with_level_root=(False, True, True, True),
-                 with_identity_root=False,
-                 pretrained=None,
-                 init_cfg=None):
+                 depth: int,
+                 in_channels: int = 3,
+                 out_indices: Sequence[int] = (0, 1, 2, 3, 4, 5),
+                 frozen_stages: int = -1,
+                 norm_cfg: OptConfigType = None,
+                 conv_cfg: OptConfigType = None,
+                 layer_with_level_root: Sequence[bool] = (False, True, True,
+                                                          True),
+                 with_identity_root: bool = False,
+                 pretrained: Optional[str] = None,
+                 init_cfg: OptMultiConfig = None):
         super(DLANet, self).__init__(init_cfg)
         if depth not in self.arch_settings:
             raise KeyError(f'invalida depth {depth} for DLA')
@@ -380,13 +387,13 @@ class DLANet(BaseModule):
         self._freeze_stages()
 
     def _make_conv_level(self,
-                         in_channels,
-                         out_channels,
-                         num_convs,
-                         norm_cfg,
-                         conv_cfg,
-                         stride=1,
-                         dilation=1):
+                         in_channels: int,
+                         out_channels: int,
+                         num_convs: int,
+                         norm_cfg: ConfigType,
+                         conv_cfg: ConfigType,
+                         stride: int = 1,
+                         dilation: int = 1) -> nn.Sequential:
         """Conv modules.
 
         Args:
@@ -418,7 +425,7 @@ class DLANet(BaseModule):
             in_channels = out_channels
         return nn.Sequential(*modules)
 
-    def _freeze_stages(self):
+    def _freeze_stages(self) -> None:
         if self.frozen_stages >= 0:
             self.base_layer.eval()
             for param in self.base_layer.parameters():
@@ -436,7 +443,7 @@ class DLANet(BaseModule):
             for param in m.parameters():
                 param.requires_grad = False
 
-    def forward(self, x):
+    def forward(self, x: Tensor) -> Tuple[Tensor, ...]:
         outs = []
         x = self.base_layer(x)
         for i in range(self.num_levels):

mmdet3d/models/backbones/multi_backbone.py

 # Copyright (c) OpenMMLab. All rights reserved.
 import copy
 import warnings
+from typing import Dict, List, Optional, Sequence, Tuple, Union
 
 import torch
 from mmcv.cnn import ConvModule
 from mmengine.model import BaseModule
-from torch import nn as nn
+from torch import Tensor, nn
 
 from mmdet3d.registry import MODELS
+from mmdet3d.utils import ConfigType, OptMultiConfig
 
 
 @MODELS.register_module()
@@ -27,16 +29,17 @@ class MultiBackbone(BaseModule):
     """
 
     def __init__(self,
-                 num_streams,
-                 backbones,
-                 aggregation_mlp_channels=None,
-                 conv_cfg=dict(type='Conv1d'),
-                 norm_cfg=dict(type='BN1d', eps=1e-5, momentum=0.01),
-                 act_cfg=dict(type='ReLU'),
-                 suffixes=('net0', 'net1'),
-                 init_cfg=None,
-                 pretrained=None,
-                 **kwargs):
+                 num_streams: int,
+                 backbones: Union[List[dict], Dict],
+                 aggregation_mlp_channels: Optional[Sequence[int]] = None,
+                 conv_cfg: ConfigType = dict(type='Conv1d'),
+                 norm_cfg: ConfigType = dict(
+                     type='BN1d', eps=1e-5, momentum=0.01),
+                 act_cfg: ConfigType = dict(type='ReLU'),
+                 suffixes: Tuple[str] = ('net0', 'net1'),
+                 init_cfg: OptMultiConfig = None,
+                 pretrained: Optional[str] = None,
+                 **kwargs) -> None:
         super().__init__(init_cfg=init_cfg)
         assert isinstance(backbones, dict) or isinstance(backbones, list)
         if isinstance(backbones, dict):
@@ -89,7 +92,7 @@ class MultiBackbone(BaseModule):
                           'please use "init_cfg" instead')
             self.init_cfg = dict(type='Pretrained', checkpoint=pretrained)
 
-    def forward(self, points):
+    def forward(self, points: Tensor) -> dict:
         """Forward pass.
 
         Args:

mmdet3d/models/backbones/nostem_regnet.py

 # Copyright (c) OpenMMLab. All rights reserved.
+from typing import Tuple
+
+import torch.nn as nn
 from mmdet.models.backbones import RegNet
+from torch import Tensor
 
 from mmdet3d.registry import MODELS
+from mmdet3d.utils import OptMultiConfig
 
 
 @MODELS.register_module()
@@ -59,15 +64,19 @@ class NoStemRegNet(RegNet):
         (1, 1008, 1, 1)
     """
 
-    def __init__(self, arch, init_cfg=None, **kwargs):
+    def __init__(self,
+                 arch: dict,
+                 init_cfg: OptMultiConfig = None,
+                 **kwargs) -> None:
         super(NoStemRegNet, self).__init__(arch, init_cfg=init_cfg, **kwargs)
 
-    def _make_stem_layer(self, in_channels, base_channels):
+    def _make_stem_layer(self, in_channels: int,
+                         base_channels: int) -> nn.Module:
         """Override the original function that do not initialize a stem layer
         since 3D detector's voxel encoder works like a stem layer."""
         return
 
-    def forward(self, x):
+    def forward(self, x: Tensor) -> Tuple[Tensor, ...]:
         """Forward function of backbone.
 
         Args:

mmdet3d/models/backbones/pointnet2_sa_msg.py

@@ -3,7 +3,7 @@ from typing import Tuple
 
 import torch
 from mmcv.cnn import ConvModule
-from torch import nn as nn
+from torch import Tensor, nn
 
 from mmdet3d.models.layers.pointnet_modules import build_sa_module
 from mmdet3d.registry import MODELS
@@ -141,7 +141,7 @@ class PointNet2SAMSG(BasePointNet):
                         bias=True))
                 sa_in_channel = cur_aggregation_channel
 
-    def forward(self, points: torch.Tensor):
+    def forward(self, points: Tensor):
         """Forward pass.
 
         Args:

mmdet3d/models/backbones/pointnet2_sa_ssg.py

 # Copyright (c) OpenMMLab. All rights reserved.
+from typing import Dict, List, Sequence
+
 import torch
-from torch import nn as nn
+from torch import Tensor, nn
 
 from mmdet3d.models.layers import PointFPModule, build_sa_module
 from mmdet3d.registry import MODELS
+from mmdet3d.utils import ConfigType, OptMultiConfig
 from .base_pointnet import BasePointNet
 
 
@@ -31,20 +34,23 @@ class PointNet2SASSG(BasePointNet):
     """
 
     def __init__(self,
-                 in_channels,
-                 num_points=(2048, 1024, 512, 256),
-                 radius=(0.2, 0.4, 0.8, 1.2),
-                 num_samples=(64, 32, 16, 16),
-                 sa_channels=((64, 64, 128), (128, 128, 256), (128, 128, 256),
+                 in_channels: int,
+                 num_points: Sequence[int] = (2048, 1024, 512, 256),
+                 radius: Sequence[float] = (0.2, 0.4, 0.8, 1.2),
+                 num_samples: Sequence[int] = (64, 32, 16, 16),
+                 sa_channels: Sequence[Sequence[int]] = ((64, 64, 128),
+                                                         (128, 128, 256),
+                                                         (128, 128, 256),
                                                          (128, 128, 256)),
-                 fp_channels=((256, 256), (256, 256)),
-                 norm_cfg=dict(type='BN2d'),
-                 sa_cfg=dict(
+                 fp_channels: Sequence[Sequence[int]] = ((256, 256), (256,
+                                                                      256)),
+                 norm_cfg: ConfigType = dict(type='BN2d'),
+                 sa_cfg: ConfigType = dict(
                      type='PointSAModule',
                      pool_mod='max',
                      use_xyz=True,
                      normalize_xyz=True),
-                 init_cfg=None):
+                 init_cfg: OptMultiConfig = None):
         super().__init__(init_cfg=init_cfg)
         self.num_sa = len(sa_channels)
         self.num_fp = len(fp_channels)
@@ -85,7 +91,7 @@ class PointNet2SASSG(BasePointNet):
                 fp_source_channel = cur_fp_mlps[-1]
                 fp_target_channel = skip_channel_list.pop()
 
-    def forward(self, points):
+    def forward(self, points: Tensor) -> Dict[str, List[Tensor]]:
         """Forward pass.
 
         Args:

mmdet3d/models/backbones/second.py

 # Copyright (c) OpenMMLab. All rights reserved.
 import warnings
+from typing import Optional, Sequence, Tuple
 
 from mmcv.cnn import build_conv_layer, build_norm_layer
 from mmengine.model import BaseModule
+from torch import Tensor
 from torch import nn as nn
 
 from mmdet3d.registry import MODELS
+from mmdet3d.utils import ConfigType, OptMultiConfig
 
 
 @MODELS.register_module()
@@ -22,14 +25,15 @@ class SECOND(BaseModule):
     """
 
     def __init__(self,
-                 in_channels=128,
-                 out_channels=[128, 128, 256],
-                 layer_nums=[3, 5, 5],
-                 layer_strides=[2, 2, 2],
-                 norm_cfg=dict(type='BN', eps=1e-3, momentum=0.01),
-                 conv_cfg=dict(type='Conv2d', bias=False),
-                 init_cfg=None,
-                 pretrained=None):
+                 in_channels: int = 128,
+                 out_channels: Sequence[int] = [128, 128, 256],
+                 layer_nums: Sequence[int] = [3, 5, 5],
+                 layer_strides: Sequence[int] = [2, 2, 2],
+                 norm_cfg: ConfigType = dict(
+                     type='BN', eps=1e-3, momentum=0.01),
+                 conv_cfg: ConfigType = dict(type='Conv2d', bias=False),
+                 init_cfg: OptMultiConfig = None,
+                 pretrained: Optional[str] = None) -> None:
         super(SECOND, self).__init__(init_cfg=init_cfg)
         assert len(layer_strides) == len(layer_nums)
         assert len(out_channels) == len(layer_nums)
@@ -75,7 +79,7 @@ class SECOND(BaseModule):
         else:
             self.init_cfg = dict(type='Kaiming', layer='Conv2d')
 
-    def forward(self, x):
+    def forward(self, x: Tensor) -> Tuple[Tensor, ...]:
         """Forward function.
 
         Args:

mmdet3d/structures/bbox_3d/utils.py

@@ -285,7 +285,6 @@ def mono_cam_box2vis(cam_box):
     from .cam_box3d import CameraInstance3DBoxes
     assert isinstance(cam_box, CameraInstance3DBoxes), \
         'input bbox should be CameraInstance3DBoxes!'
-
     loc = cam_box.gravity_center
     dim = cam_box.dims
     yaw = cam_box.yaw