models/trunks/spconv_backbone.py [119:169]:
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                                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
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models/trunks/spconv_backbone.py [218:267]:
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                                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
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