text: import torch
  import torch.nn as nn
  import torch.nn.functional as F
  import torch.nn.utils.weight_norm as weightNorm
  
  from torch.autograd import Variable
  import sys
  
  def conv3x3(in_planes, out_planes, stride=1):
      return weightNorm(nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=True))
  
  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
  coord = torch.zeros([1, 2, 64, 64])
  for i in range(64):
      for j in range(64):
          coord[0, 0, i, j] = i / 63.
          coord[0, 1, i, j] = j / 63.
          coord = coord.to(device)        
  
  class TReLU(nn.Module):
      def __init__(self):
          super(TReLU, self).__init__()
          self.alpha = nn.Parameter(torch.FloatTensor(1), requires_grad=True)
          self.alpha.data.fill_(0)
          
      def forward(self, x):
          x = F.relu(x - self.alpha) + self.alpha
          return x
  
  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.conv2 = conv3x3(planes, planes)
  
          self.shortcut = nn.Sequential()
          if stride != 1 or in_planes != self.expansion * planes:
              self.shortcut = nn.Sequential(
                  weightNorm(nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=True)),
              )
          self.relu_1 = TReLU()
          self.relu_2 = TReLU()
  
      def forward(self, x):
          out = self.relu_1(self.conv1(x))
          out = self.conv2(out)
          out += self.shortcut(x)
          out = self.relu_2(out)
  
          return out
  
  class Bottleneck(nn.Module):
      expansion = 4
  
      def __init__(self, in_planes, planes, stride=1):
          super(Bottleneck, self).__init__()
          self.conv1 = weightNorm(nn.Conv2d(in_planes, planes, kernel_size=1, bias=True))
          self.conv2 = weightNorm(nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=True))
          self.conv3 = weightNorm(nn.Conv2d(planes, self.expansion*planes, kernel_size=1, bias=True))
          self.relu_1 = TReLU()
          self.relu_2 = TReLU()
          self.relu_3 = TReLU()
  
          self.shortcut = nn.Sequential()
          if stride != 1 or in_planes != self.expansion*planes:
              self.shortcut = nn.Sequential(
                  weightNorm(nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=True)),
              )
  
      def forward(self, x):
          out = self.relu_1(self.conv1(x))
          out = self.relu_2(self.conv2(out))
          out = self.conv3(out)
          out += self.shortcut(x)
          out = self.relu_3(out)
  
          return out
  
  class ResNet_wobn(nn.Module):
      def __init__(self, num_inputs, depth, num_outputs):
          super(ResNet_wobn, self).__init__()
          self.in_planes = 64
  
          block, num_blocks = cfg(depth)
          self.conv0 = conv3x3(num_inputs, 32, 2) # 64        
          self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=2) # 32
          self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2) # 16
          self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
          self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=1)
          self.conv4 = weightNorm(nn.Conv2d(512, 1, 1, 1, 0))
          self.relu_1 = TReLU()
          self.conv1 = weightNorm(nn.Conv2d(65 + 2, 64, 1, 1, 0))        
          self.conv2 = weightNorm(nn.Conv2d(64, 64, 1, 1, 0))
          self.conv3 = weightNorm(nn.Conv2d(64, 32, 1, 1, 0))
          self.relu_2 = TReLU()
          self.relu_3 = TReLU()
          self.relu_4 = TReLU()
  
      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 a2img(self, x):
          tmp = coord.expand(x.shape[0], 2, 64, 64)
          x = x.repeat(64, 64, 1, 1).permute(2, 3, 0, 1)
          x = self.relu_2(self.conv1(torch.cat([x, tmp], 1)))
          x = self.relu_3(self.conv2(x))
          x = self.relu_4(self.conv3(x))
          return x
          
      def forward(self, input):
          x, a = input
          a = self.a2img(a)
          x = self.relu_1(self.conv0(x))
          x = torch.cat([x, a], 1)        
          x = self.layer1(x)
          x = self.layer2(x)
          x = self.layer3(x)
          x = self.layer4(x)
          x = self.conv4(x)
          return x.view(x.size(0), 64)
  