text: import logging
  
  from mmcv.runner import load_checkpoint
  import torch
  import torch.nn as nn
  from mmcv.cnn import (constant_init, kaiming_init, normal_init)
  from ..registry import BACKBONES
  
  
  model_urls = {
      'mobilenet_v2': 'https://download.pytorch.org/models/mobilenet_v2-b0353104.pth',
  }
  
  
  def _make_divisible(v, divisor, min_value=None):
      """
      This function is taken from the original tf repo.
      It ensures that all layers have a channel number that is divisible by 8
      It can be seen here:
      https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
      :param v:
      :param divisor:
      :param min_value:
      :return:
      """
      if min_value is None:
          min_value = divisor
      new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
      # Make sure that round down does not go down by more than 10%.
      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)
          )
  
  
  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)
  
  
  @BACKBONES.register_module
  class MobilenetV2(nn.Module):
      def __init__(self,
                   width_mult=1.0,
                   inverted_residual_setting=None,
                   round_nearest=8,
                   block=None):
          """
          MobileNet V2 main class
  
          Args:
              num_classes (int): Number of classes
              width_mult (float): Width multiplier - adjusts number of channels in each layer by this amount
              inverted_residual_setting: Network structure
              round_nearest (int): Round the number of channels in each layer to be a multiple of this number
              Set to 1 to turn off rounding
              block: Module specifying inverted residual building block for mobilenet
  
          """
          super(MobilenetV2, self).__init__()
  
          if block is None:
              block = InvertedResidual
          input_channel = 32
          last_channel = 1280
  
          if inverted_residual_setting is None:
              inverted_residual_setting = [
                  # t, c, n, s, output
                  [1, 16, 1, 1, False],
                  [6, 24, 2, 2, True],
                  [6, 32, 3, 2, True],
                  [6, 64, 4, 2, False],
                  [6, 96, 3, 1, True],
                  [6, 160, 3, 2, False],
                  [6, 320, 1, 1, False],
              ]
  
          # 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]) != 5:
              raise ValueError("inverted_residual_setting should be non-empty "
                               "or a 5-element list, got {}".format(inverted_residual_setting))
  
          # building first layer
          input_channel = _make_divisible(input_channel * width_mult, round_nearest)
          self.last_channel = _make_divisible(last_channel * max(1.0, width_mult), round_nearest)
          self.output = []
          features = [ConvBNReLU(3, input_channel, stride=2)]
          # building inverted residual blocks
          for t, c, n, s, o 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
                  if o and i == n-1:
                      self.output.append(True)
                  else:
                      self.output.append(False)
              # building last several layers
          features.append(ConvBNReLU(input_channel, self.last_channel, kernel_size=1))
          self.output.append(True)
          # make it nn.Sequential
          self.features = nn.Sequential(*features)
  
  
  
  
      def init_weights(self, pretrained):
          # weight initialization
          if isinstance(pretrained, str):
              logger = logging.getLogger()
              load_checkpoint(self, pretrained, strict=False, logger=logger)
          elif pretrained is None:
              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_impl(self, x):
          # This exists since TorchScript doesn't support inheritance, so the superclass method
          # (this one) needs to have a name other than `forward` that can be accessed in a subclass
          out = []
          for layer, output in zip(self.features, self.output):
              x = layer(x)
              if output:
                  out.append(x)
          return out
  
      def forward(self, x):
          return self._forward_impl(x)