[Fix] Fix spconv block (#2531)

mmdet3d/models/layers/sparse_block.py

 # Copyright (c) OpenMMLab. All rights reserved.
-from typing import Tuple, Union
+from typing import Optional, Tuple, Union
 
 from mmcv.cnn import build_conv_layer, build_norm_layer
 from mmdet.models.backbones.resnet import BasicBlock, Bottleneck
@@ -35,6 +35,7 @@ class SparseBottleneck(Bottleneck, SparseModule):
         stride (int or Tuple[int]): Stride of the first block. Defaults to 1.
         downsample (Module, optional): Down sample module for block.
             Defaults to None.
+        indice_key (str): Indice key for spconv. Default to None.
         conv_cfg (:obj:`ConfigDict` or dict, optional): Config dict for
             convolution layer. Defaults to None.
         norm_cfg (:obj:`ConfigDict` or dict, optional): Config dict for
@@ -48,10 +49,16 @@ class SparseBottleneck(Bottleneck, SparseModule):
                  planes: int,
                  stride: Union[int, Tuple[int]] = 1,
                  downsample: nn.Module = None,
+                 indice_key=None,
                  conv_cfg: OptConfigType = None,
                  norm_cfg: OptConfigType = None) -> None:
 
         SparseModule.__init__(self)
+        if conv_cfg is None:
+            conv_cfg = dict(type='SubMConv3d')
+        conv_cfg.setdefault('indice_key', indice_key)
+        if norm_cfg is None:
+            norm_cfg = dict(type='BN1d')
         Bottleneck.__init__(
             self,
             inplanes,
@@ -76,7 +83,7 @@ class SparseBottleneck(Bottleneck, SparseModule):
         out = replace_feature(out, self.bn3(out.features))
 
         if self.downsample is not None:
-            identity = self.downsample(x)
+            identity = self.downsample(x).features
 
         out = replace_feature(out, out.features + identity)
         out = replace_feature(out, self.relu(out.features))
@@ -95,6 +102,7 @@ class SparseBasicBlock(BasicBlock, SparseModule):
         stride (int or Tuple[int]): Stride of the first block. Defaults to 1.
         downsample (Module, optional): Down sample module for block.
             Defaults to None.
+        indice_key (str): Indice key for spconv. Default to None.
         conv_cfg (:obj:`ConfigDict` or dict, optional): Config dict for
             convolution layer. Defaults to None.
         norm_cfg (:obj:`ConfigDict` or dict, optional): Config dict for
@@ -108,9 +116,15 @@ class SparseBasicBlock(BasicBlock, SparseModule):
                  planes: int,
                  stride: Union[int, Tuple[int]] = 1,
                  downsample: nn.Module = None,
+                 indice_key: Optional[str] = None,
                  conv_cfg: OptConfigType = None,
                  norm_cfg: OptConfigType = None) -> None:
         SparseModule.__init__(self)
+        if conv_cfg is None:
+            conv_cfg = dict(type='SubMConv3d')
+        conv_cfg.setdefault('indice_key', indice_key)
+        if norm_cfg is None:
+            norm_cfg = dict(type='BN1d')
         BasicBlock.__init__(
             self,
             inplanes,
@@ -132,7 +146,7 @@ class SparseBasicBlock(BasicBlock, SparseModule):
         out = replace_feature(out, self.norm2(out.features))
 
         if self.downsample is not None:
-            identity = self.downsample(x)
+            identity = self.downsample(x).features
 
         out = replace_feature(out, out.features + identity)
         out = replace_feature(out, self.relu(out.features))
@@ -140,17 +154,16 @@ class SparseBasicBlock(BasicBlock, SparseModule):
         return out
 
 
-def make_sparse_convmodule(
-    in_channels: int,
-    out_channels: int,
-    kernel_size: Union[int, Tuple[int]],
-    indice_key: str,
-    stride: Union[int, Tuple[int]] = 1,
-    padding: Union[int, Tuple[int]] = 0,
-    conv_type: str = 'SubMConv3d',
-    norm_cfg: OptConfigType = None,
-    order: Tuple[str] = ('conv', 'norm', 'act')
-) -> SparseSequential:
+def make_sparse_convmodule(in_channels: int,
+                           out_channels: int,
+                           kernel_size: Union[int, Tuple[int]],
+                           indice_key: Optional[str] = None,
+                           stride: Union[int, Tuple[int]] = 1,
+                           padding: Union[int, Tuple[int]] = 0,
+                           conv_type: str = 'SubMConv3d',
+                           norm_cfg: OptConfigType = None,
+                           order: Tuple[str] = ('conv', 'norm', 'act'),
+                           **kwargs) -> SparseSequential:
     """Make sparse convolution module.
 
     Args:
@@ -175,6 +188,8 @@ def make_sparse_convmodule(
     assert set(order) | {'conv', 'norm', 'act'} == {'conv', 'norm', 'act'}
 
     conv_cfg = dict(type=conv_type, indice_key=indice_key)
+    if norm_cfg is None:
+        norm_cfg = dict(type='BN1d')
 
     layers = list()
     for layer in order: