content: import math
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  import torch.utils.model_zoo as model_zoo
  from modeling.sync_batchnorm.batchnorm import SynchronizedBatchNorm2d
  
  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 SeparableConv2d(nn.Module):
      def __init__(self, inplanes, planes, kernel_size=3, stride=1, dilation=1, bias=False, BatchNorm=None):
          super(SeparableConv2d, self).__init__()
  
          self.conv1 = nn.Conv2d(inplanes, inplanes, kernel_size, stride, 0, dilation,
                                 groups=inplanes, bias=bias)
          self.bn = BatchNorm(inplanes)
          self.pointwise = nn.Conv2d(inplanes, planes, 1, 1, 0, 1, 1, bias=bias)
  
      def forward(self, x):
          x = fixed_padding(x, self.conv1.kernel_size[0], dilation=self.conv1.dilation[0])
          x = self.conv1(x)
          x = self.bn(x)
          x = self.pointwise(x)
          return x
  
  
  class Block(nn.Module):
      def __init__(self, inplanes, planes, reps, stride=1, dilation=1, BatchNorm=None,
                   start_with_relu=True, grow_first=True, is_last=False):
          super(Block, self).__init__()
  
          if planes != inplanes or stride != 1:
              self.skip = nn.Conv2d(inplanes, planes, 1, stride=stride, bias=False)
              self.skipbn = BatchNorm(planes)
          else:
              self.skip = None
  
          self.relu = nn.ReLU(inplace=True)
          rep = []
  
          filters = inplanes
          if grow_first:
              rep.append(self.relu)
              rep.append(SeparableConv2d(inplanes, planes, 3, 1, dilation, BatchNorm=BatchNorm))
              rep.append(BatchNorm(planes))
              filters = planes
  
          for i in range(reps - 1):
              rep.append(self.relu)
              rep.append(SeparableConv2d(filters, filters, 3, 1, dilation, BatchNorm=BatchNorm))
              rep.append(BatchNorm(filters))
  
          if not grow_first:
              rep.append(self.relu)
              rep.append(SeparableConv2d(inplanes, planes, 3, 1, dilation, BatchNorm=BatchNorm))
              rep.append(BatchNorm(planes))
  
          if stride != 1:
              rep.append(self.relu)
              rep.append(SeparableConv2d(planes, planes, 3, 2, BatchNorm=BatchNorm))
              rep.append(BatchNorm(planes))
  
          if stride == 1 and is_last:
              rep.append(self.relu)
              rep.append(SeparableConv2d(planes, planes, 3, 1, BatchNorm=BatchNorm))
              rep.append(BatchNorm(planes))
  
          if not start_with_relu:
              rep = rep[1:]
  
          self.rep = nn.Sequential(*rep)
  
      def forward(self, inp):
          x = self.rep(inp)
  
          if self.skip is not None:
              skip = self.skip(inp)
              skip = self.skipbn(skip)
          else:
              skip = inp
  
          x = x + skip
  
          return x
  
  
  class AlignedXception(nn.Module):
      """
      Modified Alighed Xception
      """
      def __init__(self, output_stride, BatchNorm,
                   pretrained=True):
          super(AlignedXception, self).__init__()
  
          if output_stride == 16:
              entry_block3_stride = 2
              middle_block_dilation = 1
              exit_block_dilations = (1, 2)
          elif output_stride == 8:
              entry_block3_stride = 1
              middle_block_dilation = 2
              exit_block_dilations = (2, 4)
          else:
              raise NotImplementedError
  
  
          # Entry flow
          self.conv1 = nn.Conv2d(3, 32, 3, stride=2, padding=1, bias=False)
          self.bn1 = BatchNorm(32)
          self.relu = nn.ReLU(inplace=True)
  
          self.conv2 = nn.Conv2d(32, 64, 3, stride=1, padding=1, bias=False)
          self.bn2 = BatchNorm(64)
  
          self.block1 = Block(64, 128, reps=2, stride=2, BatchNorm=BatchNorm, start_with_relu=False)
          self.block2 = Block(128, 256, reps=2, stride=2, BatchNorm=BatchNorm, start_with_relu=False,
                              grow_first=True)
          self.block3 = Block(256, 728, reps=2, stride=entry_block3_stride, BatchNorm=BatchNorm,
                              start_with_relu=True, grow_first=True, is_last=True)
  
          # Middle flow
          self.block4  = Block(728, 728, reps=3, stride=1, dilation=middle_block_dilation,
                               BatchNorm=BatchNorm, start_with_relu=True, grow_first=True)
          self.block5  = Block(728, 728, reps=3, stride=1, dilation=middle_block_dilation,
                               BatchNorm=BatchNorm, start_with_relu=True, grow_first=True)
          self.block6  = Block(728, 728, reps=3, stride=1, dilation=middle_block_dilation,
                               BatchNorm=BatchNorm, start_with_relu=True, grow_first=True)
          self.block7  = Block(728, 728, reps=3, stride=1, dilation=middle_block_dilation,
                               BatchNorm=BatchNorm, start_with_relu=True, grow_first=True)
          self.block8  = Block(728, 728, reps=3, stride=1, dilation=middle_block_dilation,
                               BatchNorm=BatchNorm, start_with_relu=True, grow_first=True)
          self.block9  = Block(728, 728, reps=3, stride=1, dilation=middle_block_dilation,
                               BatchNorm=BatchNorm, start_with_relu=True, grow_first=True)
          self.block10 = Block(728, 728, reps=3, stride=1, dilation=middle_block_dilation,
                               BatchNorm=BatchNorm, start_with_relu=True, grow_first=True)
          self.block11 = Block(728, 728, reps=3, stride=1, dilation=middle_block_dilation,
                               BatchNorm=BatchNorm, start_with_relu=True, grow_first=True)
          self.block12 = Block(728, 728, reps=3, stride=1, dilation=middle_block_dilation,
                               BatchNorm=BatchNorm, start_with_relu=True, grow_first=True)
          self.block13 = Block(728, 728, reps=3, stride=1, dilation=middle_block_dilation,
                               BatchNorm=BatchNorm, start_with_relu=True, grow_first=True)
          self.block14 = Block(728, 728, reps=3, stride=1, dilation=middle_block_dilation,
                               BatchNorm=BatchNorm, start_with_relu=True, grow_first=True)
          self.block15 = Block(728, 728, reps=3, stride=1, dilation=middle_block_dilation,
                               BatchNorm=BatchNorm, start_with_relu=True, grow_first=True)
          self.block16 = Block(728, 728, reps=3, stride=1, dilation=middle_block_dilation,
                               BatchNorm=BatchNorm, start_with_relu=True, grow_first=True)
          self.block17 = Block(728, 728, reps=3, stride=1, dilation=middle_block_dilation,
                               BatchNorm=BatchNorm, start_with_relu=True, grow_first=True)
          self.block18 = Block(728, 728, reps=3, stride=1, dilation=middle_block_dilation,
                               BatchNorm=BatchNorm, start_with_relu=True, grow_first=True)
          self.block19 = Block(728, 728, reps=3, stride=1, dilation=middle_block_dilation,
                               BatchNorm=BatchNorm, start_with_relu=True, grow_first=True)
  
          # Exit flow
          self.block20 = Block(728, 1024, reps=2, stride=1, dilation=exit_block_dilations[0],
                               BatchNorm=BatchNorm, start_with_relu=True, grow_first=False, is_last=True)
  
          self.conv3 = SeparableConv2d(1024, 1536, 3, stride=1, dilation=exit_block_dilations[1], BatchNorm=BatchNorm)
          self.bn3 = BatchNorm(1536)
  
          self.conv4 = SeparableConv2d(1536, 1536, 3, stride=1, dilation=exit_block_dilations[1], BatchNorm=BatchNorm)
          self.bn4 = BatchNorm(1536)
  
          self.conv5 = SeparableConv2d(1536, 2048, 3, stride=1, dilation=exit_block_dilations[1], BatchNorm=BatchNorm)
          self.bn5 = BatchNorm(2048)
  
          # Init weights
          self._init_weight()
  
          # Load pretrained model
          if pretrained:
              self._load_pretrained_model()
  
      def forward(self, x):
          # Entry flow
          x = self.conv1(x)
          x = self.bn1(x)
          x = self.relu(x)
  
          x = self.conv2(x)
          x = self.bn2(x)
          x = self.relu(x)
  
          x = self.block1(x)
          # add relu here
          x = self.relu(x)
          low_level_feat = x
          x = self.block2(x)
          x = self.block3(x)
  
          # Middle flow
          x = self.block4(x)
          x = self.block5(x)
          x = self.block6(x)
          x = self.block7(x)
          x = self.block8(x)
          x = self.block9(x)
          x = self.block10(x)
          x = self.block11(x)
          x = self.block12(x)
          x = self.block13(x)
          x = self.block14(x)
          x = self.block15(x)
          x = self.block16(x)
          x = self.block17(x)
          x = self.block18(x)
          x = self.block19(x)
  
          # Exit flow
          x = self.block20(x)
          x = self.relu(x)
          x = self.conv3(x)
          x = self.bn3(x)
          x = self.relu(x)
  
          x = self.conv4(x)
          x = self.bn4(x)
          x = self.relu(x)
  
          x = self.conv5(x)
          x = self.bn5(x)
          x = self.relu(x)
  
          return x, low_level_feat
  
      def _init_weight(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))
              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_()
  
  
      def _load_pretrained_model(self):
          pretrain_dict = model_zoo.load_url('http://data.lip6.fr/cadene/pretrainedmodels/xception-b5690688.pth')
          model_dict = {}
          state_dict = self.state_dict()
  
          for k, v in pretrain_dict.items():
              if k in state_dict:
                  if 'pointwise' in k:
                      v = v.unsqueeze(-1).unsqueeze(-1)
                  if k.startswith('block11'):
                      model_dict[k] = v
                      model_dict[k.replace('block11', 'block12')] = v
                      model_dict[k.replace('block11', 'block13')] = v
                      model_dict[k.replace('block11', 'block14')] = v
                      model_dict[k.replace('block11', 'block15')] = v
                      model_dict[k.replace('block11', 'block16')] = v
                      model_dict[k.replace('block11', 'block17')] = v
                      model_dict[k.replace('block11', 'block18')] = v
                      model_dict[k.replace('block11', 'block19')] = v
                  elif k.startswith('block12'):
                      model_dict[k.replace('block12', 'block20')] = v
                  elif k.startswith('bn3'):
                      model_dict[k] = v
                      model_dict[k.replace('bn3', 'bn4')] = v
                  elif k.startswith('conv4'):
                      model_dict[k.replace('conv4', 'conv5')] = v
                  elif k.startswith('bn4'):
                      model_dict[k.replace('bn4', 'bn5')] = v
                  else:
                      model_dict[k] = v
          state_dict.update(model_dict)
          self.load_state_dict(state_dict)
  
  
  
  if __name__ == "__main__":
      import torch
      model = AlignedXception(BatchNorm=nn.BatchNorm2d, pretrained=True, output_stride=16)
      input = torch.rand(1, 3, 512, 512)
      output, low_level_feat = model(input)
      print(output.size())
      print(low_level_feat.size())
  