code: import torch
  import torch.nn as nn
  import spconv
  from functools import partial
  from .spconv_backbone import post_act_block
  from ...utils import common_utils
  
  
  class SparseBasicBlock(spconv.SparseModule):
      expansion = 1
  
      def __init__(self, inplanes, planes, stride=1, downsample=None, indice_key=None, norm_fn=None):
          super(SparseBasicBlock, self).__init__()
          self.conv1 = spconv.SubMConv3d(
              inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=False, indice_key=indice_key
          )
          self.bn1 = norm_fn(planes)
          self.relu = nn.ReLU()
          self.conv2 = spconv.SubMConv3d(
              planes, planes, kernel_size=3, stride=1, padding=1, bias=False, indice_key=indice_key
          )
          self.bn2 = norm_fn(planes)
          self.downsample = downsample
          self.stride = stride
  
      def forward(self, x):
          identity = x.features
  
          assert x.features.dim() == 2, 'x.features.dim()=%d' % x.features.dim()
  
          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
          out.features = self.relu(out.features)
  
          return out
  
  
  class UNetV2(nn.Module):
      """
      Sparse Convolution based UNet for point-wise feature learning.
      Reference Paper: https://arxiv.org/abs/1907.03670 (Shaoshuai Shi, et. al)
      From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network
      """
      def __init__(self, model_cfg, input_channels, grid_size, voxel_size, point_cloud_range, **kwargs):
          super().__init__()
          self.model_cfg = model_cfg
          self.sparse_shape = grid_size[::-1] + [1, 0, 0]
          self.voxel_size = voxel_size
          self.point_cloud_range = point_cloud_range
  
          norm_fn = partial(nn.BatchNorm1d, eps=1e-3, momentum=0.01)
  
          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(
              block(16, 16, 3, norm_fn=norm_fn, padding=1, indice_key='subm1'),
          )
  
          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'),
              block(32, 32, 3, norm_fn=norm_fn, padding=1, indice_key='subm2'),
              block(32, 32, 3, norm_fn=norm_fn, padding=1, indice_key='subm2'),
          )
  
          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'),
              block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm3'),
              block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm3'),
          )
  
          self.conv4 = spconv.SparseSequential(
              # [400, 352, 11] <- [200, 176, 5]
              block(64, 64, 3, norm_fn=norm_fn, stride=2, padding=(0, 1, 1), indice_key='spconv4', conv_type='spconv'),
              block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm4'),
              block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm4'),
          )
  
          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(64, 128, (3, 1, 1), stride=(2, 1, 1), padding=last_pad,
                                  bias=False, indice_key='spconv_down2'),
              norm_fn(128),
              nn.ReLU(),
          )
  
          # decoder
          # [400, 352, 11] <- [200, 176, 5]
          self.conv_up_t4 = SparseBasicBlock(64, 64, indice_key='subm4', norm_fn=norm_fn)
          self.conv_up_m4 = block(128, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm4')
          self.inv_conv4 = block(64, 64, 3, norm_fn=norm_fn, indice_key='spconv4', conv_type='inverseconv')
  
          # [800, 704, 21] <- [400, 352, 11]
          self.conv_up_t3 = SparseBasicBlock(64, 64, indice_key='subm3', norm_fn=norm_fn)
          self.conv_up_m3 = block(128, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm3')
          self.inv_conv3 = block(64, 32, 3, norm_fn=norm_fn, indice_key='spconv3', conv_type='inverseconv')
  
          # [1600, 1408, 41] <- [800, 704, 21]
          self.conv_up_t2 = SparseBasicBlock(32, 32, indice_key='subm2', norm_fn=norm_fn)
          self.conv_up_m2 = block(64, 32, 3, norm_fn=norm_fn, indice_key='subm2')
          self.inv_conv2 = block(32, 16, 3, norm_fn=norm_fn, indice_key='spconv2', conv_type='inverseconv')
  
          # [1600, 1408, 41] <- [1600, 1408, 41]
          self.conv_up_t1 = SparseBasicBlock(16, 16, indice_key='subm1', norm_fn=norm_fn)
          self.conv_up_m1 = block(32, 16, 3, norm_fn=norm_fn, indice_key='subm1')
  
          self.conv5 = spconv.SparseSequential(
              block(16, 16, 3, norm_fn=norm_fn, padding=1, indice_key='subm1')
          )
          self.num_point_features = 16
  
      def UR_block_forward(self, x_lateral, x_bottom, conv_t, conv_m, conv_inv):
          x_trans = conv_t(x_lateral)
          x = x_trans
          x.features = torch.cat((x_bottom.features, x_trans.features), dim=1)
          x_m = conv_m(x)
          x = self.channel_reduction(x, x_m.features.shape[1])
          x.features = x_m.features + x.features
          x = conv_inv(x)
          return x
  
      @staticmethod
      def channel_reduction(x, out_channels):
          """
          Args:
              x: x.features (N, C1)
              out_channels: C2
  
          Returns:
  
          """
          features = x.features
          n, in_channels = features.shape
          assert (in_channels % out_channels == 0) and (in_channels >= out_channels)
  
          x.features = features.view(n, out_channels, -1).sum(dim=2)
          return x
  
      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
                  point_features: (N, C)
          """
          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)
  
          # for segmentation head
          # [400, 352, 11] <- [200, 176, 5]
          x_up4 = self.UR_block_forward(x_conv4, x_conv4, self.conv_up_t4, self.conv_up_m4, self.inv_conv4)
          # [800, 704, 21] <- [400, 352, 11]
          x_up3 = self.UR_block_forward(x_conv3, x_up4, self.conv_up_t3, self.conv_up_m3, self.inv_conv3)
          # [1600, 1408, 41] <- [800, 704, 21]
          x_up2 = self.UR_block_forward(x_conv2, x_up3, self.conv_up_t2, self.conv_up_m2, self.inv_conv2)
          # [1600, 1408, 41] <- [1600, 1408, 41]
          x_up1 = self.UR_block_forward(x_conv1, x_up2, self.conv_up_t1, self.conv_up_m1, self.conv5)
  
          batch_dict['point_features'] = x_up1.features
          point_coords = common_utils.get_voxel_centers(
              x_up1.indices[:, 1:], downsample_times=1, voxel_size=self.voxel_size,
              point_cloud_range=self.point_cloud_range
          )
          batch_dict['point_coords'] = torch.cat((x_up1.indices[:, 0:1].float(), point_coords), dim=1)
          batch_dict['encoded_spconv_tensor'] = out
          batch_dict['encoded_spconv_tensor_stride'] = 8
          return batch_dict
  
apis: torch.nn.ReLU torch.cat