text: import logging
  
  import torch.nn as nn
  from mmcv.runner import load_checkpoint
  
  from ..registry import BACKBONES
  
  
  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):
      """
      MobileNetV2 is taken from pytorch hub.
      https://github.com/pytorch/vision/blob/master/torchvision/models/mobilenet.py
      """
  
      def __init__(self,
                   out_indices=(1, 2, 4, 6),
                   frozen_stages=-1,
                   width_mult=1.0,
                   inverted_residual_setting=None,
                   round_nearest=8):
          """
          MobileNet V2 main class
          Args:
              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
          """
          super(MobileNetV2, self).__init__()
          block = InvertedResidual
          input_channel = 32
  
          if inverted_residual_setting is None:
              inverted_residual_setting = [
                  # t, c, n, s
                  [1, 16, 1, 1],  # 0
                  [6, 24, 2, 2],  # 1
                  [6, 32, 3, 2],  # 2
                  [6, 64, 4, 2],  # 3
                  [6, 96, 3, 1],  # 4
                  [6, 160, 3, 2],  # 5
                  [6, 320, 1, 1],  # 6
              ]
  
          # 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))
  
          self.frozen_stages = frozen_stages
          self.out_indices = out_indices
          assert max(out_indices) < len(inverted_residual_setting)
          # building first layer
          input_channel = _make_divisible(input_channel * width_mult,
                                          round_nearest)
          self.conv1 = ConvBNReLU(3, input_channel, stride=2)
          # building inverted residual blocks
          self.stages = []
          for si, (t, c, n, s) in enumerate(inverted_residual_setting):
              output_channel = _make_divisible(c * width_mult, round_nearest)
              stage = []
              for i in range(n):
                  stride = s if i == 0 else 1
                  stage.append(
                      block(
                          input_channel, output_channel, stride, expand_ratio=t))
                  input_channel = output_channel
              stage_name = 'stage{}'.format(si + 1)
              self.add_module(stage_name, nn.Sequential(*stage))
              self.stages.append(stage_name)
  
          self._freeze_stages()
  
      def _freeze_stages(self):
          if self.frozen_stages >= 0:
              self.conv1.eval()
              for param in self.conv1.parameters():
                  param.requires_grad = False
  
          for i in range(1, self.frozen_stages + 1):
              m = getattr(self, 'stage{}'.format(i))
              m.eval()
              for param in m.parameters():
                  param.requires_grad = False
  
      def init_weights(self, pretrained=None):
          if isinstance(pretrained, str):
              logger = logging.getLogger()
              load_checkpoint(self, pretrained, strict=False, logger=logger)
          elif pretrained is None:
              # 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)
          else:
              raise TypeError('pretrained must be a str or None')
  
      def forward(self, x):
          x = self.conv1(x)
          outs = []
          for i, stage_name in enumerate(self.stages):
              stage = getattr(self, stage_name)
              x = stage(x)
              if i in self.out_indices:
                  outs.append(x)
          return tuple(outs)
  