text: import torch
  import torch.nn as nn
  
  
  from ssd.modeling import registry
  from ssd.utils.model_zoo import load_state_dict_from_url
  
  model_urls = {
      'mobilenet_v2': 'https://download.pytorch.org/models/mobilenet_v2-b0353104.pth',
  }
  
  
  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)
          )
  
  
  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:
              # pw
              layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1))
          layers.extend([
              # dw
              ConvBNReLU(hidden_dim, hidden_dim, stride=stride, groups=hidden_dim),
              # pw-linear
              nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
              nn.BatchNorm2d(oup),
          ])
          self.conv = nn.Sequential(*layers)
  
      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, width_mult=1.0, inverted_residual_setting=None):
          super(MobileNetV2, self).__init__()
          block = InvertedResidual
          input_channel = 32
          last_channel = 1280
  
          if inverted_residual_setting is None:
              inverted_residual_setting = [
                  # t, c, n, s
                  [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],
              ]
  
          # only check the first element, assuming user knows t,c,n,s are required
          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))
  
          # building first layer
          input_channel = int(input_channel * width_mult)
          self.last_channel = int(last_channel * max(1.0, width_mult))
          features = [ConvBNReLU(3, input_channel, stride=2)]
          # building inverted residual blocks
          for t, c, n, s in inverted_residual_setting:
              output_channel = int(c * width_mult)
              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
          # building last several layers
          features.append(ConvBNReLU(input_channel, self.last_channel, kernel_size=1))
          # make it nn.Sequential
          self.features = nn.Sequential(*features)
          self.extras = nn.ModuleList([
              InvertedResidual(1280, 512, 2, 0.2),
              InvertedResidual(512, 256, 2, 0.25),
              InvertedResidual(256, 256, 2, 0.5),
              InvertedResidual(256, 64, 2, 0.25)
          ])
  
          self.reset_parameters()
  
      def reset_parameters(self):
          # weight initialization
          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):
          features = []
          for i in range(14):
              x = self.features[i](x)
          features.append(x)
  
          for i in range(14, len(self.features)):
              x = self.features[i](x)
          features.append(x)
  
          for i in range(len(self.extras)):
              x = self.extras[i](x)
              features.append(x)
  
          return tuple(features)
  
  
  @registry.BACKBONES.register('mobilenet_v2')
  def mobilenet_v2(cfg, pretrained=True):
      model = MobileNetV2()
      if pretrained:
          model.load_state_dict(load_state_dict_from_url(model_urls['mobilenet_v2']), strict=False)
      return model
  