text: # code adapted from https://github.com/jfzhang95/pytorch-deeplab-xception/
  import math
  from collections import OrderedDict
  import torch.nn as nn
  import torch.nn.functional as F
  from encoding.nn import SyncBatchNorm, GlobalAvgPool2d
  from ..model_store import get_model_file
  
  __all__ = ['Xception65', 'Xception71', 'xception65']
  
  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, norm_layer=None):
          super(SeparableConv2d, self).__init__()
  
          self.conv1 = nn.Conv2d(inplanes, inplanes, kernel_size, stride, 0, dilation,
                                 groups=inplanes, bias=bias)
          self.bn = norm_layer(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, norm_layer=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 = norm_layer(planes)
          else:
              self.skip = None
          self.relu = nn.ReLU(inplace=True)
          rep = []
          filters = inplanes
          if grow_first:
              if start_with_relu:
                  rep.append(self.relu)
              rep.append(SeparableConv2d(inplanes, planes, 3, 1, dilation, norm_layer=norm_layer))
              rep.append(norm_layer(planes))
              filters = planes
          for i in range(reps - 1):
              if grow_first or start_with_relu:
                  rep.append(self.relu)
              rep.append(SeparableConv2d(filters, filters, 3, 1, dilation, norm_layer=norm_layer))
              rep.append(norm_layer(filters))
          if not grow_first:
              rep.append(self.relu)
              rep.append(SeparableConv2d(inplanes, planes, 3, 1, dilation, norm_layer=norm_layer))
              rep.append(norm_layer(planes))
          if stride != 1:
              rep.append(self.relu)
              rep.append(SeparableConv2d(planes, planes, 3, 2, norm_layer=norm_layer))
              rep.append(norm_layer(planes))
          elif is_last:
              rep.append(self.relu)
              rep.append(SeparableConv2d(planes, planes, 3, 1, dilation, norm_layer=norm_layer))
              rep.append(norm_layer(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 Xception65(nn.Module):
      """Modified Aligned Xception
      """
      def __init__(self, output_stride=32, norm_layer=nn.BatchNorm2d):
          super(Xception65, self).__init__()
  
          if output_stride == 32:
              entry_block3_stride = 2
              middle_block_dilation = 1
              exit_block20_stride = 2
              exit_block_dilations = (1, 1)
          elif output_stride == 16:
              entry_block3_stride = 2
              middle_block_dilation = 1
              exit_block20_stride = 1
              exit_block_dilations = (1, 2)
          elif output_stride == 8:
              entry_block3_stride = 1
              middle_block_dilation = 2
              exit_block20_stride = 1
              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 = norm_layer(32)
          self.relu = nn.ReLU(inplace=True)
  
          self.conv2 = nn.Conv2d(32, 64, 3, stride=1, padding=1, bias=False)
          self.bn2 = norm_layer(64)
  
          self.block1 = Block(64, 128, reps=2, stride=2, norm_layer=norm_layer, start_with_relu=False)
          self.block2 = Block(128, 256, reps=2, stride=2, norm_layer=norm_layer, start_with_relu=False,
                              grow_first=True)
          #print('self.block2', self.block2)
          self.block3 = Block(256, 728, reps=2, stride=entry_block3_stride, norm_layer=norm_layer,
                              start_with_relu=True, grow_first=True, is_last=True)
  
          # Middle flow
          midflowblocks = []
          for i in range(4, 20):
              midflowblocks.append(('block%d'%i, Block(728, 728, reps=3, stride=1,
                                                       dilation=middle_block_dilation,
                                                       norm_layer=norm_layer, start_with_relu=True,
                                                       grow_first=True)))
          self.midflow = nn.Sequential(OrderedDict(midflowblocks))
  
          # Exit flow
          self.block20 = Block(728, 1024, reps=2, stride=exit_block20_stride, dilation=exit_block_dilations[0],
                               norm_layer=norm_layer, start_with_relu=True, grow_first=False, is_last=True)
  
          self.conv3 = SeparableConv2d(1024, 1536, 3, stride=1, dilation=exit_block_dilations[1], norm_layer=norm_layer)
          self.bn3 = norm_layer(1536)
  
          self.conv4 = SeparableConv2d(1536, 1536, 3, stride=1, dilation=exit_block_dilations[1], norm_layer=norm_layer)
          self.bn4 = norm_layer(1536)
  
          self.conv5 = SeparableConv2d(1536, 2048, 3, stride=1, dilation=exit_block_dilations[1], norm_layer=norm_layer)
          self.bn5 = norm_layer(2048)
  
          self.avgpool = GlobalAvgPool2d()
          self.fc = nn.Linear(2048, 1000)
  
          # Init weights
          self._init_weight()
  
      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)
          #c1 = x
          x = self.block2(x)
          #c2 = x
          x = self.block3(x)
  
          # Middle flow
          x = self.midflow(x)
          #c3 = 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)
   
          x = self.avgpool(x)
          x = x.view(x.size(0), -1)
          x = self.fc(x)
          return x
  
      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, SyncBatchNorm):
                  m.weight.data.fill_(1)
                  m.bias.data.zero_()
              elif isinstance(m, nn.BatchNorm2d):
                  m.weight.data.fill_(1)
                  m.bias.data.zero_()
  
  class Xception71(nn.Module):
      """Modified Aligned Xception
      """
      def __init__(self, output_stride=32, norm_layer=nn.BatchNorm2d):
          super(Xception71, self).__init__()
  
          if output_stride == 32:
              entry_block3_stride = 2
              middle_block_dilation = 1
              exit_block20_stride = 2
              exit_block_dilations = (1, 1)
          elif output_stride == 16:
              entry_block3_stride = 2
              middle_block_dilation = 1
              exit_block20_stride = 1
              exit_block_dilations = (1, 2)
          elif output_stride == 8:
              entry_block3_stride = 1
              middle_block_dilation = 2
              exit_block20_stride = 1
              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 = norm_layer(32)
          self.relu = nn.ReLU(inplace=True)
  
          self.conv2 = nn.Conv2d(32, 64, 3, stride=1, padding=1, bias=False)
          self.bn2 = norm_layer(64)
  
          self.block1 = Block(64, 128, reps=2, stride=2, norm_layer=norm_layer, start_with_relu=False)
          block2 = []
          block2.append(Block(128, 256, reps=2, stride=1, norm_layer=norm_layer, start_with_relu=False,
                              grow_first=True))
          block2.append(Block(256, 256, reps=2, stride=2, norm_layer=norm_layer, start_with_relu=False,
                              grow_first=True))
          block2.append(Block(256, 728, reps=2, stride=1, norm_layer=norm_layer, start_with_relu=False,
                              grow_first=True))
          self.block2 = nn.Sequential(*block2)
          self.block3 = Block(728, 728, reps=2, stride=entry_block3_stride, norm_layer=norm_layer,
                              start_with_relu=True, grow_first=True, is_last=True)
  
          # Middle flow
          midflowblocks = []
          for i in range(4, 20):
              midflowblocks.append(('block%d'%i, Block(728, 728, reps=3, stride=1,
                                                       dilation=middle_block_dilation,
                                                       norm_layer=norm_layer, start_with_relu=True,
                                                       grow_first=True)))
          self.midflow = nn.Sequential(OrderedDict(midflowblocks))
  
          # Exit flow
          self.block20 = Block(728, 1024, reps=2, stride=exit_block20_stride, dilation=exit_block_dilations[0],
                               norm_layer=norm_layer, start_with_relu=True, grow_first=False, is_last=True)
  
          self.conv3 = SeparableConv2d(1024, 1536, 3, stride=1, dilation=exit_block_dilations[1], norm_layer=norm_layer)
          self.bn3 = norm_layer(1536)
  
          self.conv4 = SeparableConv2d(1536, 1536, 3, stride=1, dilation=exit_block_dilations[1], norm_layer=norm_layer)
          self.bn4 = norm_layer(1536)
  
          self.conv5 = SeparableConv2d(1536, 2048, 3, stride=1, dilation=exit_block_dilations[1], norm_layer=norm_layer)
          self.bn5 = norm_layer(2048)
  
          self.avgpool = GlobalAvgPool2d()
          self.fc = nn.Linear(2048, 1000)
  
          # Init weights
          self._init_weight()
  
      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.midflow(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)
  
          x = self.avgpool(x)
          x = x.view(x.size(0), -1)
          x = self.fc(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, SyncBatchNorm):
                  m.weight.data.fill_(1)
                  m.bias.data.zero_()
              elif isinstance(m, nn.BatchNorm2d):
                  m.weight.data.fill_(1)
                  m.bias.data.zero_()
  
  
  def xception65(pretrained=False, **kwargs):
      """Constructs a ResNet-18 model.
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = Xception65(**kwargs)
      if pretrained:
          model.load_state_dict(torch.load(get_model_file('xception65', root=root)))
      return model
  