models/cifar/allconv_cnsn.py [126:145]:
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    self.cn_modules = []
    for m in self.modules():
      if isinstance(m, nn.Conv2d):
        n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
        m.weight.data.normal_(0, math.sqrt(2. / n))  # He initialization
      elif isinstance(m, nn.BatchNorm2d):
        m.weight.data.fill_(1)
        m.bias.data.zero_()
      elif isinstance(m, nn.Linear):
        m.bias.data.zero_()
      elif isinstance(m, CrossNorm):
          self.cn_modules.append(m)

    if 'cn' in cnsn_type:
        self.cn_num = len(self.cn_modules)
        assert self.cn_num > 0
        print('cn_num: {}'.format(self.cn_num))
        self.active_num = active_num
        assert self.active_num > 0
        print('active_num: {}'.format(self.active_num))
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models/cifar/densenet_cnsn.py [184:203]:
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    self.cn_modules = []
    for m in self.modules():
      if isinstance(m, nn.Conv2d):
        n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
        m.weight.data.normal_(0, math.sqrt(2. / n))
      elif isinstance(m, nn.BatchNorm2d):
        m.weight.data.fill_(1)
        m.bias.data.zero_()
      elif isinstance(m, nn.Linear):
        m.bias.data.zero_()
      elif isinstance(m, CrossNorm):
          self.cn_modules.append(m)

    if 'cn' in cnsn_type:
        self.cn_num = len(self.cn_modules)
        assert self.cn_num > 0
        print('cn_num: {}'.format(self.cn_num))
        self.active_num = active_num
        assert self.active_num > 0
        print('active_num: {}'.format(self.active_num))
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