text: import math
  
  import torch
  import torch.nn as nn
  import torch.utils.model_zoo as model_zoo
  
  from .utils import load_weights_from_file, adapt_network_input_channels
  
  __all__ = ['mobilenet_v2']
  
  
  imagenet_model_urls = {
      'mobilenet_v2': 'https://download.pytorch.org/models/mobilenet_v2-b0353104.pth',
  }
  
  cifar_model_files = {
      'mobilenet_v2': './data/checkpoints/mobilenet_v2_cifar10.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)
  
  
  class MobileNetV2(nn.Module):
      def __init__(self, num_classes=1000, width_mult=1.0, inverted_residual_setting=None,
                   round_nearest=8, num_channels=3, input_size=(224, 224)):
          """
          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
          """
          super(MobileNetV2, self).__init__()
          block = InvertedResidual
          input_channel = 32
          feature_channels = 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],
              ]
  
          self.feature_size = input_size
          if min(input_size) // 32 < 3:
              self.is_low_resolution_model = True
              first_layer_stride = 1
              inverted_residual_setting[1][3] = 1
          else:
              self.is_low_resolution_model = False
              first_layer_stride = 2
  
          # 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 = _make_divisible(input_channel * width_mult, round_nearest)
          self.feature_channels = _make_divisible(feature_channels * max(1.0, width_mult), round_nearest)
          features = [ConvBNReLU(num_channels, input_channel, stride=first_layer_stride)]
          # building inverted residual blocks
          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
  
              # keep output feature shape information for the user
              self.feature_size = tuple(in_size // s for in_size in self.feature_size)
  
          # building last several layers
          features.append(ConvBNReLU(input_channel, self.feature_channels, kernel_size=1))
          # make it nn.Sequential
          self.features = nn.Sequential(*features)
  
          # building classifier
          self.classifier = nn.Sequential(
              nn.Dropout(0.2),
              nn.Linear(self.feature_channels, num_classes),
          )
  
          # 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):
          x = self.features(x)
          x = x.mean([2, 3])
          x = self.classifier(x)
          return x
  
  
  def load_pretrained_model(model, arch, average_pretrained_input_weights=True, **kwargs):
  
      if model.is_low_resolution_model:
          pretrained_weights = load_weights_from_file(cifar_model_files[arch])
      else:
          pretrained_weights = model_zoo.load_url(imagenet_model_urls[arch])
  
      adapt_network_input_channels(pretrained_weights, 'features.0.0.weight',
                                   model.features[0][0].in_channels,
                                   average_pretrained_input_weights)
      model.load_state_dict(pretrained_weights)
  
      return model
  
  
  def mobilenet_v2(pretrained=False, **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/Cifar10
      """
      model = MobileNetV2(**kwargs)
      if pretrained:
          model = load_pretrained_model(model, 'mobilenet_v2', **kwargs)
      return model
  