add VoxelResBackbone8x 3D backbone

pcdet/models/backbones_3d/__init__.py

-from .spconv_backbone import VoxelBackBone8x
+from .spconv_backbone import VoxelBackBone8x, VoxelResBackBone8x
 from .spconv_unet import UNetV2
 
 __all__ = {
     'VoxelBackBone8x': VoxelBackBone8x,
+    'VoxelResBackBone8x': VoxelResBackBone8x,
     'UNetV2': UNetV2
 }

pcdet/models/backbones_3d/spconv_backbone.py

@@ -25,6 +25,45 @@ def post_act_block(in_channels, out_channels, kernel_size, indice_key=None, stri
     return m
 
 
+class SparseBasicBlock(spconv.SparseModule):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1, norm_fn=None, downsample=None, indice_key=None):
+        super(SparseBasicBlock, self).__init__()
+
+        assert norm_fn is not None
+        bias = norm_fn is not None
+        self.conv1 = spconv.SubMConv3d(
+            inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=bias, indice_key=indice_key
+        )
+        self.bn1 = norm_fn(planes)
+        self.relu = nn.ReLU()
+        self.conv2 = spconv.SubMConv3d(
+            planes, planes, kernel_size=3, stride=stride, padding=1, bias=bias, indice_key=indice_key
+        )
+        self.bn2 = norm_fn(planes)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        identity = x
+
+        out = self.conv1(x)
+        out.features = self.bn1(out.features)
+        out.features = self.relu(out.features)
+
+        out = self.conv2(out)
+        out.features = self.bn2(out.features)
+
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        out.features += identity.features
+        out.features = self.relu(out.features)
+
+        return out
+
+
 class VoxelBackBone8x(nn.Module):
     def __init__(self, model_cfg, input_channels, grid_size, **kwargs):
         super().__init__()
@@ -121,3 +160,101 @@ class VoxelBackBone8x(nn.Module):
         })
 
         return batch_dict
+
+
+class VoxelResBackBone8x(nn.Module):
+    def __init__(self, model_cfg, input_channels, grid_size, **kwargs):
+        super().__init__()
+        self.model_cfg = model_cfg
+        norm_fn = partial(nn.BatchNorm1d, eps=1e-3, momentum=0.01)
+
+        self.sparse_shape = grid_size[::-1] + [1, 0, 0]
+
+        self.conv_input = spconv.SparseSequential(
+            spconv.SubMConv3d(input_channels, 16, 3, padding=1, bias=False, indice_key='subm1'),
+            norm_fn(16),
+            nn.ReLU(),
+        )
+        block = post_act_block
+
+        self.conv1 = spconv.SparseSequential(
+            SparseBasicBlock(16, 16, norm_fn=norm_fn, indice_key='res1'),
+            SparseBasicBlock(16, 16, norm_fn=norm_fn, indice_key='res1'),
+        )
+
+        self.conv2 = spconv.SparseSequential(
+            # [1600, 1408, 41] <- [800, 704, 21]
+            block(16, 32, 3, norm_fn=norm_fn, stride=2, padding=1, indice_key='spconv2', conv_type='spconv'),
+            SparseBasicBlock(32, 32, norm_fn=norm_fn, indice_key='res2'),
+            SparseBasicBlock(32, 32, norm_fn=norm_fn, indice_key='res2'),
+        )
+
+        self.conv3 = spconv.SparseSequential(
+            # [800, 704, 21] <- [400, 352, 11]
+            block(32, 64, 3, norm_fn=norm_fn, stride=2, padding=1, indice_key='spconv3', conv_type='spconv'),
+            SparseBasicBlock(64, 64, norm_fn=norm_fn, indice_key='res3'),
+            SparseBasicBlock(64, 64, norm_fn=norm_fn, indice_key='res3'),
+        )
+
+        self.conv4 = spconv.SparseSequential(
+            # [400, 352, 11] <- [200, 176, 5]
+            block(64, 128, 3, norm_fn=norm_fn, stride=2, padding=(0, 1, 1), indice_key='spconv4', conv_type='spconv'),
+            SparseBasicBlock(128, 128, norm_fn=norm_fn, indice_key='res3'),
+            SparseBasicBlock(128, 128, norm_fn=norm_fn, indice_key='res3'),
+        )
+
+        last_pad = 0
+        last_pad = self.model_cfg.get('last_pad', last_pad)
+        self.conv_out = spconv.SparseSequential(
+            # [200, 150, 5] -> [200, 150, 2]
+            spconv.SparseConv3d(128, 128, (3, 1, 1), stride=(2, 1, 1), padding=last_pad,
+                                bias=False, indice_key='spconv_down2'),
+            norm_fn(128),
+            nn.ReLU(),
+        )
+        self.num_point_features = 128
+
+    def forward(self, batch_dict):
+        """
+        Args:
+            batch_dict:
+                batch_size: int
+                vfe_features: (num_voxels, C)
+                voxel_coords: (num_voxels, 4), [batch_idx, z_idx, y_idx, x_idx]
+        Returns:
+            batch_dict:
+                encoded_spconv_tensor: sparse tensor
+        """
+        voxel_features, voxel_coords = batch_dict['voxel_features'], batch_dict['voxel_coords']
+        batch_size = batch_dict['batch_size']
+        input_sp_tensor = spconv.SparseConvTensor(
+            features=voxel_features,
+            indices=voxel_coords.int(),
+            spatial_shape=self.sparse_shape,
+            batch_size=batch_size
+        )
+        x = self.conv_input(input_sp_tensor)
+
+        x_conv1 = self.conv1(x)
+        x_conv2 = self.conv2(x_conv1)
+        x_conv3 = self.conv3(x_conv2)
+        x_conv4 = self.conv4(x_conv3)
+
+        # for detection head
+        # [200, 176, 5] -> [200, 176, 2]
+        out = self.conv_out(x_conv4)
+
+        batch_dict.update({
+            'encoded_spconv_tensor': out,
+            'encoded_spconv_tensor_stride': 8
+        })
+        batch_dict.update({
+            'multi_scale_3d_features': {
+                'x_conv1': x_conv1,
+                'x_conv2': x_conv2,
+                'x_conv3': x_conv3,
+                'x_conv4': x_conv4,
+            }
+        })
+
+        return batch_dict