content: import torch
  import torch.nn.functional as F
  import torch.nn as nn
  import math
  from modeling.sync_batchnorm.batchnorm import SynchronizedBatchNorm2d
  import torch.utils.model_zoo as model_zoo
  
  def conv_bn(inp, oup, stride, BatchNorm):
      return nn.Sequential(
          nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
          BatchNorm(oup),
          nn.ReLU6(inplace=True)
      )
  
  
  def fixed_padding(inputs, kernel_size, dilation):
      kernel_size_effective = kernel_size + (kernel_size - 1) * (dilation - 1)
      pad_total = kernel_size_effective - 1
      pad_beg = pad_total // 2
      pad_end = pad_total - pad_beg
      padded_inputs = F.pad(inputs, (pad_beg, pad_end, pad_beg, pad_end))
      return padded_inputs
  
  
  class InvertedResidual(nn.Module):
      def __init__(self, inp, oup, stride, dilation, expand_ratio, BatchNorm):
          super(InvertedResidual, self).__init__()
          self.stride = stride
          assert stride in [1, 2]
  
          hidden_dim = round(inp * expand_ratio)
          self.use_res_connect = self.stride == 1 and inp == oup
          self.kernel_size = 3
          self.dilation = dilation
  
          if expand_ratio == 1:
              self.conv = nn.Sequential(
                  # dw
                  nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 0, dilation, groups=hidden_dim, bias=False),
                  BatchNorm(hidden_dim),
                  nn.ReLU6(inplace=True),
                  # pw-linear
                  nn.Conv2d(hidden_dim, oup, 1, 1, 0, 1, 1, bias=False),
                  BatchNorm(oup),
              )
          else:
              self.conv = nn.Sequential(
                  # pw
                  nn.Conv2d(inp, hidden_dim, 1, 1, 0, 1, bias=False),
                  BatchNorm(hidden_dim),
                  nn.ReLU6(inplace=True),
                  # dw
                  nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 0, dilation, groups=hidden_dim, bias=False),
                  BatchNorm(hidden_dim),
                  nn.ReLU6(inplace=True),
                  # pw-linear
                  nn.Conv2d(hidden_dim, oup, 1, 1, 0, 1, bias=False),
                  BatchNorm(oup),
              )
  
      def forward(self, x):
          x_pad = fixed_padding(x, self.kernel_size, dilation=self.dilation)
          if self.use_res_connect:
              x = x + self.conv(x_pad)
          else:
              x = self.conv(x_pad)
          return x
  
  
  class MobileNetV2(nn.Module):
      def __init__(self, output_stride=8, BatchNorm=None, width_mult=1., pretrained=True):
          super(MobileNetV2, self).__init__()
          block = InvertedResidual
          input_channel = 32
          current_stride = 1
          rate = 1
          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
          input_channel = int(input_channel * width_mult)
          self.features = [conv_bn(3, input_channel, 2, BatchNorm)]
          current_stride *= 2
          # building inverted residual blocks
          for t, c, n, s in interverted_residual_setting:
              if current_stride == output_stride:
                  stride = 1
                  dilation = rate
                  rate *= s
              else:
                  stride = s
                  dilation = 1
                  current_stride *= s
              output_channel = int(c * width_mult)
              for i in range(n):
                  if i == 0:
                      self.features.append(block(input_channel, output_channel, stride, dilation, t, BatchNorm))
                  else:
                      self.features.append(block(input_channel, output_channel, 1, dilation, t, BatchNorm))
                  input_channel = output_channel
          self.features = nn.Sequential(*self.features)
          self._initialize_weights()
  
          if pretrained:
              self._load_pretrained_model()
  
          self.low_level_features = self.features[0:4]
          self.high_level_features = self.features[4:]
  
      def forward(self, x):
          low_level_feat = self.low_level_features(x)
          x = self.high_level_features(low_level_feat)
          return x, low_level_feat
  
      def _load_pretrained_model(self):
          pretrain_dict = model_zoo.load_url('http://jeff95.me/models/mobilenet_v2-6a65762b.pth')
          model_dict = {}
          state_dict = self.state_dict()
          for k, v in pretrain_dict.items():
              if k in state_dict:
                  model_dict[k] = v
          state_dict.update(model_dict)
          self.load_state_dict(state_dict)
  
      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))
                  torch.nn.init.kaiming_normal_(m.weight)
              elif isinstance(m, SynchronizedBatchNorm2d):
                  m.weight.data.fill_(1)
                  m.bias.data.zero_()
              elif isinstance(m, nn.BatchNorm2d):
                  m.weight.data.fill_(1)
                  m.bias.data.zero_()
  
  if __name__ == "__main__":
      input = torch.rand(1, 3, 512, 512)
      model = MobileNetV2(output_stride=16, BatchNorm=nn.BatchNorm2d)
      output, low_level_feat = model(input)
      print(output.size())
      print(low_level_feat.size())
  