text: import torch.nn as nn
  import spconv
  from functools import partial
  
  
  def post_act_block(in_channels, out_channels, kernel_size, indice_key=None, stride=1, padding=0,
                     conv_type='subm', norm_fn=None):
      # subm和spconv不同在spconv有stride，padding
      if conv_type == 'subm':
          conv = spconv.SubMConv3d(in_channels, out_channels, kernel_size, bias=False, indice_key=indice_key)
      elif conv_type == 'spconv':
          conv = spconv.SparseConv3d(in_channels, out_channels, kernel_size, stride=stride, padding=padding,
                                     bias=False, indice_key=indice_key)
      elif conv_type == 'inverseconv':
          conv = spconv.SparseInverseConv3d(in_channels, out_channels, kernel_size, indice_key=indice_key, bias=False)
      else:
          raise NotImplementedError
  
      m = spconv.SparseSequential(
          conv,
          norm_fn(out_channels),
          nn.ReLU(),
      )
  
      return m
  
  
  class VoxelBackBone8x(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(
              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(),
          )
          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
  