text: from torch import nn
  try:
      from torch.hub import load_state_dict_from_url
  except ImportError:
      from torch.utils.model_zoo import load_url as load_state_dict_from_url
  
  
  model_urls = {
      'mobilenet_v2': 'https://download.pytorch.org/models/mobilenet_v2-b0353104.pth',
  }
  
  CONV = {'2d': nn.Conv2d, '3d': nn.Conv3d}
  BATCH_NORM = {'2d': nn.BatchNorm2d, '3d': nn.BatchNorm3d}
  
  
  class ConvBNReLU(nn.Sequential):
      
  
      def __init__(self, in_planes, out_planes, mode='2d', kernel_size=3, stride=1, groups=1):
          padding = (kernel_size - 1) // 2
          mode = mode.lower()
          super(ConvBNReLU, self).__init__(
              CONV[mode](in_planes, out_planes, kernel_size, stride, padding, groups=groups, bias=False),
              BATCH_NORM[mode](out_planes),
              nn.ReLU6(inplace=True)
          )
  
  
  class InvertedResidual(nn.Module):
      
  
      def __init__(self, inp, oup, stride, expand_ratio, mode='2d'):
          super(InvertedResidual, self).__init__()
          mode = mode.lower()
          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, mode=mode))
          layers.extend([
              # dw
              ConvBNReLU(hidden_dim, hidden_dim, stride=stride, groups=hidden_dim, mode=mode),
              # pw-linear
              CONV[mode](hidden_dim, oup, 1, 1, 0, bias=False),
              BATCH_NORM[mode](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, num_classes=1000, width_mult=1.0, mode='2d'):
          super(MobileNetV2, self).__init__()
          
          # settings
          self.input_range = [0, 1]
          self.mean = [0.5, 0.5, 0.5]
          self.std  = [0.5, 0.5, 0.5]
          mode = mode.lower()
          self.mode = mode
          block = InvertedResidual
          input_channel = 32
          last_channel = 1280
          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],
          ]
  
          # 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, mode=mode)]
          # 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, mode=mode))
                  input_channel = output_channel
          # building last several layers
          features.append(ConvBNReLU(input_channel, self.last_channel, kernel_size=1, mode=mode))
          # make it nn.Sequential
          self.features = nn.Sequential(*features)
  
          # building classifier
          self.classifier = nn.Sequential(
              nn.Dropout(0.2),
              nn.Linear(self.last_channel, num_classes),
          )
  
          # weight initialization
          for m in self.modules():
              if isinstance(m, CONV[mode]):
                  nn.init.kaiming_normal_(m.weight, mode='fan_out')
                  if m.bias is not None:
                      nn.init.zeros_(m.bias)
              elif isinstance(m, BATCH_NORM[mode]):
                  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)
          if self.mode == '3d':
              x = x.mean([2, 3, 4])
          elif self.mode == '2d':
              x = x.mean([2, 3])
          x = self.classifier(x)
          return x
  
  
  def _mobilenetv2(pretrained=None, progress=True, **kwargs):
      """
      Constructs a MobileNetV2 architecture from
      `"MobileNetV2: Inverted Residuals and Linear Bottlenecks" <https://arxiv.org/abs/1801.04381>`_.
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
          progress (bool): If True, displays a progress bar of the download to stderr
      """
      model = MobileNetV2(**kwargs)
      if pretrained:
          state_dict = load_state_dict_from_url(model_urls['mobilenet_v2'],
                                                progress=progress)
          model.load_state_dict(state_dict)
      return model
  
  
  def MobileNet3D(pretrained=None, progress=True, **kwargs):
      model = MobileNetV2(mode='3d', **kwargs)
      if pretrained:
          weights_2d = _mobilenetv2(pretrained=True).state_dict() 
          for w in list(weights_2d.keys()):
              try:
                  model.state_dict()[w].data.copy_(weights_2d[w])
              except RuntimeError:
                  tmp_2d_weights = weights_2d[w].unsqueeze(2).repeat(1,1,model.state_dict()[w].shape[-1],1,1)
                  model.state_dict()[w].data.copy_(tmp_2d_weights / tmp_2d_weights.shape[-1])
      # Change temporal stride
      model.features[2].conv[1][0].stride = (1,2,2)
      model.features[4].conv[1][0].stride = (1,2,2)
      return model
  
  