code: import torch.nn as nn
  import torch
  from torch.autograd import Variable
  import math
  import torch.utils.model_zoo as model_zoo
  from commons.siam_mask.models.features import Features
  
  __all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
             'resnet152']
  
  
  model_urls = {
      'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
      'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
      'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
      'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
      'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
  }
  
  
  def conv3x3(in_planes, out_planes, stride=1):
      "3x3 convolution with padding"
      return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                       padding=1, bias=False)
  
  
  class BasicBlock(nn.Module):
      expansion = 1
  
      def __init__(self, inplanes, planes, stride=1, downsample=None):
          super(BasicBlock, self).__init__()
          self.conv1 = conv3x3(inplanes, planes, stride)
          self.bn1 = nn.BatchNorm2d(planes)
          self.relu = nn.ReLU(inplace=True)
          self.conv2 = conv3x3(planes, planes)
          self.bn2 = nn.BatchNorm2d(planes)
          self.downsample = downsample
          self.stride = stride
  
      def forward(self, x):
          residual = x
  
          out = self.conv1(x)
          out = self.bn1(out)
          out = self.relu(out)
  
          out = self.conv2(out)
          out = self.bn2(out)
  
          if self.downsample is not None:
              residual = self.downsample(x)
  
          out += residual
          out = self.relu(out)
  
          return out
  
  
  class Bottleneck(Features):
      expansion = 4
  
      def __init__(self, inplanes, planes, stride=1, downsample=None, dilation=1):
          super(Bottleneck, self).__init__()
          self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
          self.bn1 = nn.BatchNorm2d(planes)
          # padding = (2 - stride) + (dilation // 2 - 1)
          padding = 2 - stride
          assert stride==1 or dilation==1, "stride and dilation must have one equals to zero at least"
          if dilation > 1:
              padding = dilation
          self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                                  padding=padding, bias=False, dilation=dilation)
          self.bn2 = nn.BatchNorm2d(planes)
          self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
          self.bn3 = nn.BatchNorm2d(planes * 4)
          self.relu = nn.ReLU(inplace=True)
          self.downsample = downsample
          self.stride = stride
  
      def forward(self, x):
          residual = x
  
          out = self.conv1(x)
          out = self.bn1(out)
          out = self.relu(out)
  
          out = self.conv2(out)
          out = self.bn2(out)
          out = self.relu(out)
  
          out = self.conv3(out)
          out = self.bn3(out)
  
          if self.downsample is not None:
              residual = self.downsample(x)
  
          if out.size() != residual.size():
              print(out.size(), residual.size())
          out += residual
  
          out = self.relu(out)
  
          return out
  
  
  class ResNet(nn.Module):
  
      def __init__(self, block, layers, layer4=False, layer3=False):
          self.inplanes = 64
          super(ResNet, self).__init__()
          self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=0, # 3
                                 bias=False)
          self.bn1 = nn.BatchNorm2d(64)
          self.relu = nn.ReLU(inplace=True)
          self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
          self.layer1 = self._make_layer(block, 64, layers[0])
          self.layer2 = self._make_layer(block, 128, layers[1], stride=2) # 31x31, 15x15
  
          self.feature_size = 128 * block.expansion
  
          if layer3:
              self.layer3 = self._make_layer(block, 256, layers[2], stride=1, dilation=2) # 15x15, 7x7
              self.feature_size = (256 + 128) * block.expansion
          else:
              self.layer3 = lambda x:x # identity
  
          if layer4:
              self.layer4 = self._make_layer(block, 512, layers[3], stride=1, dilation=4) # 7x7, 3x3
              self.feature_size = 512 * block.expansion
          else:
              self.layer4 = lambda x:x  # identity
  
          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, nn.BatchNorm2d):
                  m.weight.data.fill_(1)
                  m.bias.data.zero_()
  
      def _make_layer(self, block, planes, blocks, stride=1, dilation=1):
          downsample = None
          dd = dilation
          if stride != 1 or self.inplanes != planes * block.expansion:
              if stride == 1 and dilation == 1:
                  downsample = nn.Sequential(
                      nn.Conv2d(self.inplanes, planes * block.expansion,
                                kernel_size=1, stride=stride, bias=False),
                      nn.BatchNorm2d(planes * block.expansion),
                  )
              else:
                  if dilation > 1:
                      dd = dilation // 2
                      padding = dd
                  else:
                      dd = 1
                      padding = 0
                  downsample = nn.Sequential(
                      nn.Conv2d(self.inplanes, planes * block.expansion,
                                kernel_size=3, stride=stride, bias=False,
                                padding=padding, dilation=dd),
                      nn.BatchNorm2d(planes * block.expansion),
                  )
  
          layers = []
          # layers.append(block(self.inplanes, planes, stride, downsample, dilation=dilation))
          layers.append(block(self.inplanes, planes, stride, downsample, dilation=dd))
          self.inplanes = planes * block.expansion
          for i in range(1, blocks):
              layers.append(block(self.inplanes, planes, dilation=dilation))
  
          return nn.Sequential(*layers)
  
      def forward(self, x):
          x = self.conv1(x)
          x = self.bn1(x)
          p0 = self.relu(x)
          x = self.maxpool(p0)
  
          p1 = self.layer1(x)
          p2 = self.layer2(p1)
          p3 = self.layer3(p2)
  
          return p0, p1, p2, p3
  
  
  def resnet18(pretrained=False, **kwargs):
      """Constructs a ResNet-18 model.
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
      if pretrained:
          model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
      return model
  
  
  def resnet34(pretrained=False, **kwargs):
      """Constructs a ResNet-34 model.
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
      if pretrained:
          model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))
      return model
  
  
  def resnet50(pretrained=False, **kwargs):
      """Constructs a ResNet-50 model.
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
      if pretrained:
          model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
      return model
  
  
  def resnet101(pretrained=False, **kwargs):
      """Constructs a ResNet-101 model.
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
      if pretrained:
          model.load_state_dict(model_zoo.load_url(model_urls['resnet101']))
      return model
  
  
  def resnet152(pretrained=False, **kwargs):
      """Constructs a ResNet-152 model.
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
      if pretrained:
          model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))
      return model
  
  
  if __name__ == '__main__':
      net = resnet50()
      print(net)
      net = net.cuda()
  
      var = torch.FloatTensor(1,3,127,127).cuda()
      var = Variable(var)
  
      net(var)
      print('*************')
      var = torch.FloatTensor(1,3,255,255).cuda()
      var = Variable(var)
  
      net(var)
  
  
apis: torch.nn.MaxPool2d torch.nn.BatchNorm2d torch.FloatTensor torch.autograd.Variable torch.nn.Conv2d torch.nn.Sequential torch.utils.model_zoo.load_url torch.nn.ReLU