code: def replace_convtranspose2d(convtrans, mask):
      """
      We need anothor replace function for
      convtranspose2d, because the layout of
      the weight is different from traditional
      conv layers. The layout of the weight is [N_in, N_out, ksize_1, ksize_2]
      Parameters
      ----------
      convtrans : torch.nn.ConvTranspose2d
          The conv2d module to be replaced
      mask : ModuleMasks
          The masks of this module
      Returns
      -------
      torch.nn.ConvTranspose2d
          The new conv2d module
      """
      assert isinstance(mask, ModuleMasks)
      assert isinstance(convtrans, torch.nn.ConvTranspose2d)
      if mask.input_mask is None:
          in_channels = convtrans.in_channels
      else:
          in_channels_index = mask.input_mask.mask_index[1]
          in_channels = in_channels_index.size(0)
      if mask.output_mask is None:
          out_channels = convtrans.out_channels
      else:
          out_channels_index = mask.output_mask.mask_index[1]
          out_channels = out_channels_index.size(0)
      groups = convtrans.groups
      # check if can remove the whole group of filters
      if (
          convtrans.in_channels
          == convtrans.out_channels
          == convtrans.groups
      ):
          # remove groups for depthwise layers
          # this needs the group dependency to be fixed before the speedup
          assert in_channels == out_channels
          groups = in_channels
      _logger.debug(
          "Replace convtranspose2d %s with in_channels:%d out_channels:%d",
          mask.module_name,
          in_channels,
          out_channels,
      )
      new_convtrans = torch.nn.ConvTranspose2d(
          in_channels=in_channels,
          out_channels=out_channels,
          kernel_size=convtrans.kernel_size,
          stride=convtrans.stride,
          padding=convtrans.padding,
          dilation=convtrans.dilation,
          groups=groups,
          bias=convtrans.bias is not None,
          padding_mode=convtrans.padding_mode,
      )
      new_convtrans.to(convtrans.weight.device)
      tmp_weight_data = None
      if mask.input_mask is not None:
          # in convtranspose2d we need to select the input channel first
          tmp_weight_data = torch.index_select(
              convtrans.weight.data, 0, in_channels_index
          )
      else:
          tmp_weight_data = convtrans.weight.data
      # we need to handle the output channel group by group like the conv layer
      out_step = int(convtrans.out_channels / convtrans.groups)
      out_channel_group = int(out_channels / groups)
      new_in_per_group = int(in_channels / groups)
  
      if mask.output_mask is not None and not (
          in_channels == out_channels == groups
      ):
          for groupid in range(convtrans.groups):
              start = groupid * out_step
              end = (groupid + 1) * out_step
              current_output_index = list(
                  filter(
                      lambda x: start <= x and x < end,
                      out_channels_index.tolist(),
                  )
              )
              # we need to shift the index into the group-wise
              current_output_index = [
                  x - start for x in current_output_index
              ]
              if not current_output_index:
                  # No kept channel in the current group
                  raise Exception(
                      " Donnot support removing the whole group filter except in the depth-wise conv temporarily"
                  )
              assert (
                  len(current_output_index) == out_channel_group
              ), "Output channel of each group should be the same after pruning"
              current_output_index = torch.tensor(
                  current_output_index
              ).to(
                  tmp_weight_data.device
              )  # pylint: disable=not-callable
              new_start = groupid * new_in_per_group
              new_end = (groupid + 1) * new_in_per_group
              new_convtrans.weight.data[
                  new_start:new_end
              ] = torch.index_select(
                  tmp_weight_data[new_start:new_end],
                  1,
                  current_output_index,
              )
      else:
          new_convtrans.weight.data.copy_(tmp_weight_data)
      if convtrans.bias is not None:
          if mask.output_mask is not None:
              new_convtrans.bias.data[:] = torch.index_select(
                  convtrans.bias.data, 0, out_channels_index
              )
          else:
              new_convtrans.bias.data.copy_(convtrans.bias.data)
      return new_convtrans
  
file_path: auptimizer/src/aup/compression/torch/speedup/compress_modules.py
imports: import logging
  import torch
  from .infer_shape import ModuleMasks
label_desc: Use image node_id 5 for calling a global function with example usage: replace_convtranspose2d(convtrans, mask) and returns: new_convtrans