text: import torch.nn as nn
  import math
  
  
  def conv_bn(inp, oup, stride):
      return nn.Sequential(
          nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
          nn.BatchNorm2d(oup),
          nn.ReLU6(inplace=True)
      )
  
  
  def conv_1x1_bn(inp, oup):
      return nn.Sequential(
          nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
          nn.BatchNorm2d(oup),
          nn.ReLU6(inplace=True)
      )
  
  
  def make_divisible(x, divisible_by=8):
      import numpy as np
      return int(np.ceil(x * 1. / divisible_by) * divisible_by)
  
  
  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(inp * expand_ratio)
          self.use_res_connect = self.stride == 1 and inp == oup
  
          if expand_ratio == 1:
              self.conv = nn.Sequential(
                  # dw
                  nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False),
                  nn.BatchNorm2d(hidden_dim),
                  nn.ReLU6(inplace=True),
                  # pw-linear
                  nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
                  nn.BatchNorm2d(oup),
              )
          else:
              self.conv = nn.Sequential(
                  # pw
                  nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False),
                  nn.BatchNorm2d(hidden_dim),
                  nn.ReLU6(inplace=True),
                  # dw
                  nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False),
                  nn.BatchNorm2d(hidden_dim),
                  nn.ReLU6(inplace=True),
                  # pw-linear
                  nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
                  nn.BatchNorm2d(oup),
              )
  
      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, n_class=1000, input_size=224, width_mult=1.):
          super(MobileNetV2, self).__init__()
          block = InvertedResidual
          input_channel = 32
          last_channel = 1280
          interverted_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
          assert input_size % 32 == 0
          # input_channel = make_divisible(input_channel * width_mult)  # first channel is always 32!
          self.last_channel = make_divisible(last_channel * width_mult) if width_mult > 1.0 else last_channel
          self.features = [conv_bn(3, input_channel, 2)]
          # building inverted residual blocks
          for t, c, n, s in interverted_residual_setting:
              output_channel = make_divisible(c * width_mult) if t > 1 else c
              for i in range(n):
                  if i == 0:
                      self.features.append(block(input_channel, output_channel, s, expand_ratio=t))
                  else:
                      self.features.append(block(input_channel, output_channel, 1, expand_ratio=t))
                  input_channel = output_channel
          # building last several layers
          self.features.append(conv_1x1_bn(input_channel, self.last_channel))
          # make it nn.Sequential
          self.features = nn.Sequential(*self.features)
  
          # building classifier
          self.classifier = nn.Linear(self.last_channel, n_class)
  
          self._initialize_weights()
  
      def forward(self, x):
          x = self.features(x)
          x = x.mean(3).mean(2)
          x = self.classifier(x)
          return x
  
      def _initialize_weights(self):
          for m in self.modules():
              if isinstance(m, nn.Conv2d):
                  n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                  m.weight.data.normal_(0, math.sqrt(2. / n))
                  if m.bias is not None:
                      m.bias.data.zero_()
              elif isinstance(m, nn.BatchNorm2d):
                  m.weight.data.fill_(1)
                  m.bias.data.zero_()
              elif isinstance(m, nn.Linear):
                  n = m.weight.size(1)
                  m.weight.data.normal_(0, 0.01)
                  m.bias.data.zero_()
  
  
  def mobilenet_v2(pretrained=True):
      model = MobileNetV2(width_mult=1)
  
      if pretrained:
          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
          state_dict = load_state_dict_from_url(
              'https://www.dropbox.com/s/47tyzpofuuyyv1b/mobilenetv2_1.0-f2a8633.pth.tar?dl=1', progress=True)
          model.load_state_dict(state_dict)
      return model
  
  
  if __name__ == '__main__':
      net = mobilenet_v2(True)
  
  
  
  
  
  