docstr: Fix the mask conflict before the mask inference for the layers that
  has group dependencies. This function should be called before the
  mask inference of the 'speedup' module.
docstr_tok: ["Fix","the","mask","conflict","before","the","mask","inference","for","the","layers","that","has","group","dependencies",".","This","function","should","be","called","before","the","mask","inference","of","the","'speedup","'","module","."]
code: def fix_mask(self):
      """
      Fix the mask conflict before the mask inference for the layers that
      has group dependencies. This function should be called before the
      mask inference of the 'speedup' module.
      """
      group_depen = GroupDependency(
          self.model, self.dummy_input, self.traced
      )
      depens = group_depen.dependency
      _logger.info(depens)
      for layername in depens:
          group = depens[layername]
          if layername not in self.masks:
              # this layer not pruned
              continue
          w_mask = self.masks[layername]["weight"]
          shape = w_mask.size()
          count = np.prod(shape[1:])
          all_ones = (
              (w_mask.flatten(1).sum(-1) == count)
              .nonzero()
              .squeeze(1)
              .tolist()
          )
          all_zeros = (
              (w_mask.flatten(1).sum(-1) == 0)
              .nonzero()
              .squeeze(1)
              .tolist()
          )
          if len(all_ones) + len(all_zeros) < w_mask.size(0):
              # In fine-grained pruning, skip this layer
              _logger.info(
                  "Layers %s using fine-grained pruning", layername
              )
              continue
          assert shape[0] % group == 0
          # Find the number of masked filter for each group (mini_masked).
          # Because we have to keep the pruned filter can still
          # be divided into the same number of groups, so we only can
          # prune mini_masked filters for each group.
          step = shape[0] / group
          group_masked = []
          for i in range(group):
              _start = step * i
              _end = step * (i + 1)
              _tmp_list = list(
                  filter(lambda x: _start <= x and x < _end, all_zeros)
              )
              group_masked.append(_tmp_list)
          mini_masked = min([len(x) for x in group_masked])
          for gm in group_masked:
              for i in range(mini_masked, len(gm)):
                  # To keep the output channel number still being divisible to
                  # groups, we set the masks of following filters to be zero.
                  pos = gm[i]
                  self.masks[layername]["weight"][pos] = torch.ones(
                      shape[1:]
                  )
                  if (
                      "bias" in self.masks[layername]
                      and self.masks[layername]["bias"] is not None
                  ):
                      self.masks[layername]["bias"][pos] = 1
      return self.masks
  
imports: import os
  import logging
  import torch
  import numpy
  from .shape_dependency import ChannelDependency, GroupDependency, CatPaddingDependency, InputChannelDependency
  from .utils import get_module_by_name
label_desc: Use image node_id 2 for calling the GroupMaskConflict obj's underlying member method code with example usage: obj.fix_mask() and returns: self