text: '''
  Properly implemented ResNet-s for CIFAR10 as described in paper [1].
  The implementation and structure of this file is hugely influenced by [2]
  which is implemented for ImageNet and doesn't have option A for identity.
  Moreover, most of the implementations on the web is copy-paste from
  torchvision's resnet and has wrong number of params.
  Proper ResNet-s for CIFAR10 (for fair comparision and etc.) has following
  number of layers and parameters:
  name      | layers | params
  ResNet20  |    20  | 0.27M
  ResNet32  |    32  | 0.46M
  ResNet44  |    44  | 0.66M
  ResNet56  |    56  | 0.85M
  ResNet110 |   110  |  1.7M
  ResNet1202|  1202  | 19.4m
  which this implementation indeed has.
  Reference:
  [1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
      Deep Residual Learning for Image Recognition. arXiv:1512.03385
  [2] https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
  If you use this implementation in you work, please don't forget to mention the
  author, Yerlan Idelbayev.
  '''
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  import torch.nn.init as init
  
  from torch.autograd import Variable
  from advertorch.utils import NormalizeByChannelMeanStd
  
  __all__ = ['ResNet', 'resnet20', 'resnet32', 'resnet44', 'resnet56', 'resnet110', 'resnet1202']
  
  
  def _weights_init(m):
      classname = m.__class__.__name__
      #print(classname)
      if isinstance(m, nn.Linear) or isinstance(m, nn.Conv2d):
          init.kaiming_normal_(m.weight)
  
  class LambdaLayer(nn.Module):
      def __init__(self, lambd):
          super(LambdaLayer, self).__init__()
          self.lambd = lambd
  
      def forward(self, x):
          return self.lambd(x)
  
  
  class BasicBlock(nn.Module):
      expansion = 1
  
      def __init__(self, in_planes, planes, stride=1, option='A'):
          super(BasicBlock, self).__init__()
          self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
          self.bn1 = nn.BatchNorm2d(planes)
          self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
          self.bn2 = nn.BatchNorm2d(planes)
  
          self.shortcut = nn.Sequential()
          if stride != 1 or in_planes != planes:
              if option == 'A':
                  """
                  For CIFAR10 ResNet paper uses option A.
                  """
                  self.shortcut = LambdaLayer(lambda x:
                                              F.pad(x[:, :, ::2, ::2], (0, 0, 0, 0, planes//4, planes//4), "constant", 0))
              elif option == 'B':
                  self.shortcut = nn.Sequential(
                       nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False),
                       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 ResNet(nn.Module):
      def __init__(self, block, num_blocks, num_classes=10):
          super(ResNet, self).__init__()
          self.in_planes = 16
  
          self.normalize = NormalizeByChannelMeanStd(
              mean=[0.4914, 0.4822, 0.4465], std=[0.2470, 0.2435, 0.2616])
  
          self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1, bias=False)
          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.fc = nn.Linear(64, num_classes)
  
          self.apply(_weights_init)
  
      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):
          #x = self.normalize(x)
          out = F.relu(self.bn1(self.conv1(x)))
          #print(out)
          out = self.layer1(out)
          #print(out)
          out = self.layer2(out)
          #print(out)
          out = self.layer3(out)
          #print(out)
          out = F.avg_pool2d(out, out.size()[3])
          #print(out)
          out = out.view(out.size(0), -1)
          #print(out)
          out = self.fc(out)
          return out
  
  
  def resnet20(number_class=10):
      return ResNet(BasicBlock, [3, 3, 3], num_classes=number_class)
  
  
  def resnet32(number_class=10):
      return ResNet(BasicBlock, [5, 5, 5], num_classes=number_class)
  
  
  def resnet44(number_class=10):
      return ResNet(BasicBlock, [7, 7, 7], num_classes=number_class)
  
  
  def resnet56(number_class=10):
      return ResNet(BasicBlock, [9, 9, 9], num_classes=number_class)
  
  
  def resnet110(number_class=10):
      return ResNet(BasicBlock, [18, 18, 18], num_classes=number_class)
  
  
  def resnet1202(number_class=10):
      return ResNet(BasicBlock, [200, 200, 200], num_classes=number_class)
  
  
  if __name__ == '__main__':
      import torch.nn as nn
      import torch.nn.utils.prune as prune
      def prune_model_custom(model, mask_dict):
          for name,m in model.named_modules():
              if isinstance(m, nn.Conv2d):
                  print('pruning layer with custom mask:', name)
                  try:
                      prune.CustomFromMask.apply(m, 'weight', mask=mask_dict[name+'.weight_mask'])
                  except:
                      pass
      model=resnet20(10)
      model.eval()
      model.load_state_dict(torch.load('init.pt'))
      mask = torch.load('21-mask.pt')
      prune_model_custom(model, mask)
      print(model(torch.ones(1,3,28,28)))