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
  
  __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.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):
          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, out.size()[3])
          out = out.view(out.size(0), -1)
          out = self.fc(out)
          return out
  
  
  def resnet20():
      return ResNet(BasicBlock, [3, 3, 3])
  
  
  def resnet32():
      return ResNet(BasicBlock, [5, 5, 5])
  
  
  def resnet44():
      return ResNet(BasicBlock, [7, 7, 7])
  
  
  def resnet56():
      return ResNet(BasicBlock, [9, 9, 9])
  
  
  def resnet110():
      return ResNet(BasicBlock, [18, 18, 18])
  
  
  def resnet1202():
      return ResNet(BasicBlock, [200, 200, 200])
  
  
  def test(net):
      import numpy as np
      total_params = 0
  
      for x in filter(lambda p: p.requires_grad, net.parameters()):
          total_params += np.prod(x.data.numpy().shape)
      print("Total number of params", total_params)
      print("Total layers", len(list(filter(lambda p: p.requires_grad and len(p.data.size())>1, net.parameters()))))
  
  
  if __name__ == "__main__":
      for net_name in __all__:
          if net_name.startswith('resnet'):
              print(net_name)
              test(globals()[net_name]())
              print()