text: ### dropout has been removed in this code. original code had dropout#####
  import torch
  import torch.nn as nn
  import torch.nn.init as init
  import torch.nn.functional as F
  from torch.autograd import Variable
  
  import sys, os
  import numpy as np
  import random
  sys.path.append(os.path.dirname(os.path.abspath(__file__)))
  from mixup import to_one_hot, mixup_process, get_lambda
  import time
  
  act = torch.nn.ReLU()
  
  
  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)
      elif classname.find('BatchNorm') != -1:
          init.constant(m.weight, 1)
          init.constant(m.bias, 0)
  
  class wide_basic(nn.Module):
      def __init__(self, in_planes, planes, stride=1):
          super(wide_basic, self).__init__()
          self.bn1 = nn.BatchNorm2d(in_planes)
          self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, padding=1, bias=True)
          self.bn2 = nn.BatchNorm2d(planes)
          self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=True)
  
          self.shortcut = nn.Sequential()
          if stride != 1 or in_planes != planes:
              self.shortcut = nn.Sequential(
                  nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride, bias=True),
              )
  
      def forward(self, x):
          out = self.conv1(act(self.bn1(x)))
          out = self.conv2(act(self.bn2(out)))
          out += self.shortcut(x)
  
          return out
  
  class Wide_ResNet(nn.Module):
      
      def __init__(self, depth, widen_factor, num_classes, stride = 1):
          super(Wide_ResNet, self).__init__()
          self.num_classes = num_classes
          self.in_planes = 16
  
          assert ((depth-4)%6 ==0), 'Wide-resnet_v2 depth should be 6n+4'
          n = int((depth-4)/6)
          k = widen_factor
  
          print('| Wide-Resnet %dx%d' %(depth, k))
          nStages = [16, 16*k, 32*k, 64*k]
  
          self.conv1 = conv3x3(3,nStages[0], stride = stride)
          self.layer1 = self._wide_layer(wide_basic, nStages[1], n, stride=1)
          self.layer2 = self._wide_layer(wide_basic, nStages[2], n, stride=2)
          self.layer3 = self._wide_layer(wide_basic, nStages[3], n, stride=2)
          self.bn1 = nn.BatchNorm2d(nStages[3], momentum=0.9)
          self.linear = nn.Linear(nStages[3], num_classes)
  
      def _wide_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
  
          return nn.Sequential(*layers)
      
  
      def forward(self, x, target= None, mixup=False, mixup_hidden=False, args = None, grad=None, noise=None, adv_mask1=0, adv_mask2=0, profile=None):
          if mixup_hidden:
              layer_mix = random.randint(0,2)
          elif mixup:
              layer_mix = 0
          else:
              layer_mix = None   
          
          out = x
                  
          if target is not None :
              target_reweighted = to_one_hot(target,self.num_classes)
          
          end =time.time()
          if layer_mix == 0: 
              out, target_reweighted = mixup_process(out, target_reweighted, args=args, grad=grad, noise=noise, adv_mask1=adv_mask1, adv_mask2=adv_mask2)
              profile['gc'].append(time.time()-end)
              if len(profile['data'])%10 == 0: print('gc  : ', np.mean(profile['gc']))
  
          out = self.conv1(out)
          out = self.layer1(out)
          
          if layer_mix == 1:
              out, target_reweighted = mixup_process(out, target_reweighted, args=args, hidden=True)
  
          out = self.layer2(out)
  
          if layer_mix == 2:
              out, target_reweighted = mixup_process(out, target_reweighted, args=args, hidden=True)
          out = self.layer3(out)
          
          if layer_mix == 3:
              out, target_reweighted = mixup_process(out, target_reweighted, args=args, hidden=True)
  
          out = act(self.bn1(out))
          out = F.avg_pool2d(out, 8)
          out = out.view(out.size(0), -1)
          out = self.linear(out)
          
          if target is not None:
              return out, target_reweighted
          else: 
              return out
          
          
  def wrn28_10(num_classes=10, dropout = False, stride = 1):
      #print ('this')
      model = Wide_ResNet(depth=28, widen_factor=10, num_classes=num_classes, stride = stride)
      return model
  
  def wrn28_2(num_classes=10, dropout = False, stride = 1):
      #print ('this')
      model = Wide_ResNet(depth =28, widen_factor =2, num_classes = num_classes, stride = stride)
      return model
  
  
  
  if __name__ == '__main__':
      net=Wide_ResNet(28, 10, 0.3, 10)
      y = net(Variable(torch.randn(1,3,32,32)))
  
      print(y.size())
  