text: import sys
  
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  
  def conv3x3(in_planes, out_planes, stride=1):
      return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=True)
  
  def conv_init(m):
      classname = m.__class__.__name__
      if classname.find('Conv') != -1:
          init.xavier_uniform(m.weight, gain=np.sqrt(2))
          init.constant(m.bias, 0)
  
  def cfg(depth):
      depth_lst = [18, 34, 50, 101, 152]
      assert (depth in depth_lst), "Error : Resnet depth should be either 18, 34, 50, 101, 152"
      cf_dict = {
          '18': (BasicBlock, [2,2,2,2]),
          '34': (BasicBlock, [3,4,6,3]),
          '50': (Bottleneck, [3,4,6,3]),
          '101':(Bottleneck, [3,4,23,3]),
          '152':(Bottleneck, [3,8,36,3]),
      }
  
      return cf_dict[str(depth)]
  
  class BasicBlock(nn.Module):
      expansion = 1
  
      def __init__(self, in_planes, planes, stride=1):
          super(BasicBlock, self).__init__()
          self.conv1 = conv3x3(in_planes, planes, stride)
          self.bn1 = nn.BatchNorm2d(planes)
          self.conv2 = conv3x3(planes, planes)
          self.bn2 = nn.BatchNorm2d(planes)
  
          self.shortcut = nn.Sequential()
          if stride != 1 or in_planes != self.expansion * planes:
              self.shortcut = nn.Sequential(
                  nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=True),
                  nn.BatchNorm2d(self.expansion*planes)
              )
  
      def forward(self, x):
          out = F.relu(self.bn1(self.conv1(x)))
          out = self.bn2(self.conv2(out))
          out += self.shortcut(x)
          out = F.relu(out)
  
          return out
  
  class Bottleneck(nn.Module):
      expansion = 4
  
      def __init__(self, in_planes, planes, stride=1):
          super(Bottleneck, self).__init__()
          self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=True)
          self.bn1 = nn.BatchNorm2d(planes)
          self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=True)
          self.bn2 = nn.BatchNorm2d(planes)
          self.conv3 = nn.Conv2d(planes, self.expansion*planes, kernel_size=1, bias=True)
          self.bn3 = nn.BatchNorm2d(self.expansion*planes)
  
          self.shortcut = nn.Sequential()
          if stride != 1 or in_planes != self.expansion*planes:
              self.shortcut = nn.Sequential(
                  nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=True),
                  nn.BatchNorm2d(self.expansion*planes)
              )
  
      def forward(self, x):
          out = F.relu(self.bn1(self.conv1(x)))
          out = F.relu(self.bn2(self.conv2(out)))
          out = self.bn3(self.conv3(out))
          out += self.shortcut(x)
          out = F.relu(out)
  
          return out
  
  class ResNet(nn.Module):
      def __init__(self, depth, num_classes):
          super(ResNet, self).__init__()
          self.in_planes = 16
  
          block, num_blocks = cfg(depth)
  
          self.conv1 = conv3x3(3,16)
          self.bn1 = nn.BatchNorm2d(16)
          self.layer1 = self._make_layer(block, 16, num_blocks[0], stride=1)
          self.layer2 = self._make_layer(block, 32, num_blocks[1], stride=2)
          self.layer3 = self._make_layer(block, 64, num_blocks[2], stride=2)
          self.linear = nn.Linear(64*block.expansion, num_classes)
  
      def _make_layer(self, block, planes, num_blocks, stride):
          strides = [stride] + [1]*(num_blocks-1)
          layers = []
  
          for stride in strides:
              layers.append(block(self.in_planes, planes, stride))
              self.in_planes = planes * block.expansion
  
          return nn.Sequential(*layers)
  
      def forward(self, x):
          out = F.relu(self.bn1(self.conv1(x)))
          out = self.layer1(out)
          out = self.layer2(out)
          out = self.layer3(out)
          out = F.avg_pool2d(out, 8)
          out = out.view(out.size(0), -1)
          out = self.linear(out)
  
          return out
  
  if __name__ == '__main__':
      net=ResNet(50, 10)
      y = net(torch.randn(1,3,32,32))
      print(y.size())
  