text: import torch
  import torch.nn as nn
  import torch.nn.functional as F
  import copy
  from torch.autograd import Variable
  import sys
  import torch.nn.init as init
  import numpy as np
  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_Original, [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
      
      def get_similarity_matrix(self, x): #Computes batchwise correlation matrix for the feature activations of imagebatch x
          x = x.view(x.shape[0],-1)
          x_s = (x-x.mean(dim=1, keepdim=True))/x.std(dim=1,keepdim=True)
          sim = (x_s@x_s.t())/x.shape[1]
          return sim
      
      def forward(self, x):
          (x, S)=x
          out = self.conv1(x)
          s1_x = self.get_similarity_matrix(out)
          out = F.relu(self.bn1(out))
          out = self.conv2(out)
          s2_x = self.get_similarity_matrix(out)
          out = F.relu(self.bn2(out))
          if(len(self.shortcut)>0):
              out2 = self.shortcut[0](x)
              sSkip = self.get_similarity_matrix(out2)
              out2 = self.shortcut[1](out2)
              out+=out2
              out = F.relu(out) 
              return (out, S+[s1_x, s2_x, sSkip])
          else:
              out += self.shortcut(x)
              out = F.relu(out) 
              return (out, S+[s1_x, s2_x])
  
  class Bottleneck_Original(nn.Module):
      expansion = 4
  
      def __init__(self, in_planes, planes, stride=1):
          super(Bottleneck_Original, 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 get_similarity_matrix(self, x): #Compute cosine similarity matrix over image batch
          x = x.view(x.shape[0],-1)
          x_s = (x-x.mean(dim=1, keepdim=True))/x.std(dim=1,keepdim=True)
          sim = (x_s@x_s.t())/x.shape[1]
          return sim
      
      def forward(self, x):
          #(x, S)=x
          out = self.conv1(x)
          #s1_x = self.get_similarity_matrix(out)
          out = F.relu(self.bn1(out))
          out = self.conv2(out)
          #s2_x = self.get_similarity_matrix(out)
          out = F.relu(self.bn2(out))
          out = self.conv3(out)
          #s3_x = self.get_similarity_matrix(out)
          out = self.bn3(out)
  #         if(len(self.shortcut)>0):
  #             out2 = self.shortcut[0](x)
  #             sSkip = self.get_similarity_matrix(out2)
  #             out2 = self.shortcut[1](out2)
  #             out+=out2
  #             out = F.relu(out) 
  #             return (out, S+[s1_x, s2_x, s3_x, sSkip])
  #         else:
  #             out += self.shortcut(x)
  #             out = F.relu(out) 
  #             return (out, S+[s1_x, s2_x, s3_x])  
          return out
  class ResNet_Original(nn.Module):
      def __init__(self, depth, num_classes):
          super(ResNet_Original, 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 get_similarity_matrix(self, x):
          y = x
          y = y.view(y.shape[0],-1)
          x_s = (y-y.mean(dim=1, keepdim=True))/y.std(dim=1,keepdim=True)
          sim = (x_s@x_s.t())/y.shape[1]
          return sim
      
      def forward(self, x, compute_similarity=False):
          S = []
          out = self.conv1(x)
          S.append(self.get_similarity_matrix(out))
          out = F.relu(self.bn1(out))
          if compute_similarity:
              out_s1 = out
              out = F.relu(self.layer1[0].bn1(self.layer1[0].conv1(out)))
              out = F.relu(self.layer1[0].bn2(self.layer1[0].conv2(out)))
              out = self.layer1[0].bn3(self.layer1[0].conv3(out))
              out_s1 = self.layer1[0].shortcut[0](out_s1)
              #sSkip1 = self.get_similarity_matrix(out_s1)
              out_s1 = self.layer1[0].shortcut[1](out_s1)
              out+=out_s1
              out = F.relu(out)
                           
                           
              out = self.layer1[1](out)
              out_1 = out
              out = F.relu(self.layer1[2].bn1(self.layer1[2].conv1(out)))
              out = F.relu(self.layer1[2].bn2(self.layer1[2].conv2(out)))
              out = self.layer1[2].conv3(out)
              s10_x = self.get_similarity_matrix(out)
              #print(s10_x)
              out = self.layer1[2].bn3(out)
              out+=self.layer1[2].shortcut(out_1)
              out = F.relu(out)
              
              out_s2 = out
              out = F.relu(self.layer2[0].bn1(self.layer2[0].conv1(out)))
              out = F.relu(self.layer2[0].bn2(self.layer2[0].conv2(out)))
              out = self.layer2[0].bn3(self.layer2[0].conv3(out))
              out_s2 = self.layer2[0].shortcut[0](out_s2)
              #sSkip2 = self.get_similarity_matrix(out_s2)
              out_s2 = self.layer2[0].shortcut[1](out_s2)
              out+=out_s2
              out = F.relu(out)           
                           
              
              out = self.layer2[1:3](out)
              out_2 = out
              out = self.layer2[3].conv1(out)
              s20_x = self.get_similarity_matrix(out)
              out = F.relu(self.layer2[3].bn1(out))
              out = self.layer2[3].conv2(out)
              #s21_x = self.get_similarity_matrix(out)
              out = F.relu(self.layer2[3].bn2(out))
              out = self.layer2[3].bn3(self.layer2[3].conv3(out))
              out += self.layer2[3].shortcut(out_2)
              out = F.relu(out)
  
              out_s3 = out
              out = F.relu(self.layer3[0].bn1(self.layer3[0].conv1(out)))
              out = F.relu(self.layer3[0].bn2(self.layer3[0].conv2(out)))
              out = self.layer3[0].bn3(self.layer3[0].conv3(out))
              out_s3 = self.layer3[0].shortcut[0](out_s3)
              #sSkip3 = self.get_similarity_matrix(out_s3)
              out_s3 = self.layer3[0].shortcut[1](out_s3)
              out+=out_s3
              out = F.relu(out)
                           
              out = self.layer3[1](out)
              out_3 = out
              out = F.relu(self.layer3[2].bn1((self.layer3[2].conv1(out))))
              out = self.layer3[2].conv2(out)
              s30_x = self.get_similarity_matrix(out)
              out = F.relu(self.layer3[2].bn2(out))
              out = self.layer3[2].bn3(self.layer3[2].conv3(out))
              out += self.layer3[2].shortcut(out_3)
              out = F.relu(out)
              
              out = self.layer3[3:5](out)
              out_4 = out
              out = self.layer3[5].conv1(out)
              #s38_x = self.get_similarity_matrix(out)
              out = F.relu(self.layer3[5].bn1(out))
              out = F.relu(self.layer3[5].bn2((self.layer3[5].conv2(out))))
              out = self.layer3[5].conv3(out)
              s40_x = self.get_similarity_matrix(out)
              out = self.layer3[5].bn3(out)
              out += self.layer3[5].shortcut(out_4)
              out = F.relu(out)
              out = F.avg_pool2d(out, 8)
              out = out.view(out.size(0),-1)
              #print(out.shape)
              out = self.linear(out)
              S = [s10_x, s20_x, s30_x, s40_x]
              return (out, S)
  
          else:
              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
          
  
  class ResNet_2Read(nn.Module):
      def __init__(self, depth, num_classes):
          super(ResNet_2Read, 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_noise = nn.Linear(64*block.expansion, num_classes)
          self.linear_clean = 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 get_similarity_matrix(self, x):
          x = x.view(x.shape[0],-1)
          x_s = (x-x.mean(dim=1, keepdim=True))/x.std(dim=1,keepdim=True)
          #print(y.shape[0])
          sim = (x_s@x_s.t())/x.shape[1]
          return sim
      
      def forward(self, x, img_type="noise", compute_similarity=False):
          S = []
          out = self.conv1(x)
          S.append(self.get_similarity_matrix(out))
          out = F.relu(self.bn1(out))
          if(compute_similarity):
          
  
  
              out, S = self.layer1((out, S))
              out, S = self.layer2((out, S))
              out, S = self.layer3((out, S))
              out = F.avg_pool2d(out, 8)
              out = out.view(out.size(0), -1)
              
              if(img_type=="clean"):
                  out = self.linear_clean(out)
              elif(img_type=="noise"):
                  out = self.linear_noise(out)
              return (out, torch.stack(S))
          else:
              out, _ = self.layer1((out, S))
              out, _ = self.layer2((out, S))
              out, _ = self.layer3((out, S))
              out = F.avg_pool2d(out, 8)
              out = out.view(out.size(0), -1)
              if(img_type=="clean"):
                  out = self.linear_clean(out)
              elif(img_type=="noise"):
                  out = self.linear_noise(out)
              return out
  
          
  
          
          
          
  if __name__ == '__main__':
      net=ResNet(50, 10)
      y = net(Variable(torch.randn(1,3,32,32)))
      print(y.size())
  