text: from torch import nn
  # from torchvision.models.utils import load_state_dict_from_url
  
  def _make_divisible(v, divisor, min_value=None):
      if min_value is None:
          min_value = divisor
      new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
      if new_v < 0.9 * v:
          new_v += divisor
      return new_v
  
  class ConvBNReLU(nn.Sequential):
      def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, groups=1):
          padding = (kernel_size - 1) // 2
          super(ConvBNReLU, self).__init__(
              nn.Conv2d(in_planes, out_planes, kernel_size, stride, padding, groups=groups, bias=False),
              nn.BatchNorm2d(out_planes),
              nn.ReLU6(inplace=True)
          )
          self.out_channels = out_planes
  
  class InvertedResidual(nn.Module):
      def __init__(self, inp, oup, stride, expand_ratio):
          super(InvertedResidual, self).__init__()
          self.stride = stride
          assert stride in [1, 2]
  
          hidden_dim = int(round(inp * expand_ratio))
          self.use_res_connect = self.stride == 1 and inp == oup
  
          layers = []
          if expand_ratio != 1:
              layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1))
          layers.extend([
              ConvBNReLU(hidden_dim, hidden_dim, stride=stride, groups=hidden_dim),
              nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
              nn.BatchNorm2d(oup),
          ])
          self.conv = nn.Sequential(*layers)
  
          self.out_channels = oup
  
      def forward(self, x):
          if self.use_res_connect:
              return x + self.conv(x)
          else:
              return self.conv(x)
  
  class MobileNetV2(nn.Module):
      def __init__(self, num_classes=1000, width_mult=1.0, inverted_residual_setting=None, round_nearest=8):
          super(MobileNetV2, self).__init__()
          block = InvertedResidual
          input_channel = 32
          last_channel = 1280
  
          if inverted_residual_setting is None:
              inverted_residual_setting = [
                  [1, 16, 1, 1],
                  [6, 24, 2, 2],
                  [6, 32, 3, 2],
                  [6, 64, 4, 2],
                  [6, 96, 3, 1],
                  [6, 160, 3, 2],
                  [6, 320, 1, 1],
              ]
  
          if len(inverted_residual_setting) == 0 or len(inverted_residual_setting[0]) != 4:
              raise ValueError("inverted_residual_setting should be non-empty "
                               "or a 4-element list, got {}".format(inverted_residual_setting))
  
          input_channel = _make_divisible(input_channel * width_mult, round_nearest)
          self.last_channel = _make_divisible(last_channel * max(1.0, width_mult), round_nearest)
          features = [ConvBNReLU(3, input_channel, stride=2)]
          for t, c, n, s in inverted_residual_setting:
              output_channel = _make_divisible(c * width_mult, round_nearest)
              for i in range(n):
                  stride = s if i == 0 else 1
                  features.append(block(input_channel, output_channel, stride, expand_ratio=t))
                  input_channel = output_channel
          features.append(ConvBNReLU(input_channel, self.last_channel, kernel_size=1))
          self.features = nn.Sequential(*features)
  
          self.classifier = nn.Sequential(
              nn.Dropout(0.2),
              nn.Linear(self.last_channel, num_classes),
          )
  
          for m in self.modules():
              if isinstance(m, nn.Conv2d):
                  nn.init.kaiming_normal_(m.weight, mode='fan_out')
                  if m.bias is not None:
                      nn.init.zeros_(m.bias)
              elif isinstance(m, nn.BatchNorm2d):
                  nn.init.ones_(m.weight)
                  nn.init.zeros_(m.bias)
              elif isinstance(m, nn.Linear):
                  nn.init.normal_(m.weight, 0, 0.01)
                  nn.init.zeros_(m.bias)
  
      def forward(self, x):
          x = self.features(x)
          x = x.mean([2, 3])
          x = self.classifier(x)
          return x
  
  def mobilenet_v2(pretrained=False, progress=True, **kwargs):
      model = MobileNetV2(**kwargs)
      # if pretrained:
      #     state_dict = load_state_dict_from_url('https://download.pytorch.org/models/mobilenet_v2-b0353104.pth', model_dir="./model_data",
      #                                           progress=progress)
      #     model.load_state_dict(state_dict)
      del model.classifier
      return model
  
  if __name__ == "__main__":
      net = mobilenet_v2()
      for i, layer in enumerate(net.features):
          print(i, layer)