text: """mobilenetv2 in pytorch
  
  
  
  [1] Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen
  
      MobileNetV2: Inverted Residuals and Linear Bottlenecks
      https://arxiv.org/abs/1801.04381
  """
  
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  
  class LinearBottleNeck(nn.Module):
  
      def __init__(self, in_channels, out_channels, stride, t=6, class_num=100):
          super().__init__()
  
          self.residual = nn.Sequential(
              nn.Conv2d(in_channels, in_channels * t, 1),
              nn.BatchNorm2d(in_channels * t),
              nn.ReLU6(inplace=True),
  
              nn.Conv2d(in_channels * t, in_channels * t, 3, stride=stride, padding=1, groups=in_channels * t),
              nn.BatchNorm2d(in_channels * t),
              nn.ReLU6(inplace=True),
  
              nn.Conv2d(in_channels * t, out_channels, 1),
              nn.BatchNorm2d(out_channels)
          )
  
          self.stride = stride
          self.in_channels = in_channels
          self.out_channels = out_channels
      
      def forward(self, x):
  
          residual = self.residual(x)
  
          if self.stride == 1 and self.in_channels == self.out_channels:
              residual += x
          
          return residual
  
  class MobileNetV2(nn.Module):
  
      def __init__(self, class_num=100):
          super().__init__()
  
          self.pre = nn.Sequential(
              nn.Conv2d(3, 32, 1, padding=1),
              nn.BatchNorm2d(32),
              nn.ReLU6(inplace=True)
          )
  
          self.stage1 = LinearBottleNeck(32, 16, 1, 1)
          self.stage2 = self._make_stage(2, 16, 24, 2, 6)
          self.stage3 = self._make_stage(3, 24, 32, 2, 6)
          self.stage4 = self._make_stage(4, 32, 64, 2, 6)
          self.stage5 = self._make_stage(3, 64, 96, 1, 6)
          self.stage6 = self._make_stage(3, 96, 160, 1, 6)
          self.stage7 = LinearBottleNeck(160, 320, 1, 6)
  
          self.conv1 = nn.Sequential(
              nn.Conv2d(320, 1280, 1),
              nn.BatchNorm2d(1280),
              nn.ReLU6(inplace=True)
          )
  
          self.conv2 = nn.Conv2d(1280, class_num, 1)
              
      def forward(self, x):
          x = self.pre(x)
          x = self.stage1(x)
          x = self.stage2(x)
          x = self.stage3(x)
          x = self.stage4(x)
          x = self.stage5(x)
          x = self.stage6(x)
          x = self.stage7(x)
          x = self.conv1(x)
          x = F.adaptive_avg_pool2d(x, 1)
          x = self.conv2(x)
          x = x.view(x.size(0), -1)
  
          return x
      
      def _make_stage(self, repeat, in_channels, out_channels, stride, t):
  
          layers = []
          layers.append(LinearBottleNeck(in_channels, out_channels, stride, t))
          
          while repeat - 1:
              layers.append(LinearBottleNeck(out_channels, out_channels, 1, t))
              repeat -= 1
          
          return nn.Sequential(*layers)
  
  def mobilenetv2():
      return MobileNetV2()