code: '''
  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.nn as nn
  import torch.nn.functional as F
  import torch.nn.init as init
  
  from layers.layers import MaskedConv
  
  __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, MaskedConv):
      init.xavier_normal_(m.weight)
  
  
  _AFFINE = True
  
  
  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):
      super(BasicBlock, self).__init__()
      self.conv1 = MaskedConv(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
      self.bn1 = nn.BatchNorm2d(planes, affine=_AFFINE)
      self.conv2 = MaskedConv(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
      self.bn2 = nn.BatchNorm2d(planes, affine=_AFFINE)
  
      self.downsample = None
      self.bn3 = None
      if stride != 1 or in_planes != planes:
        self.downsample = nn.Sequential(
          MaskedConv(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False))
        self.bn3 = nn.BatchNorm2d(self.expansion * planes, affine=_AFFINE)
  
    def forward(self, x):
      # x: batch_size * in_c * h * w
      residual = x
      out = F.relu(self.bn1(self.conv1(x)))
      out = self.bn2(self.conv2(out))
      if self.downsample is not None:
        residual = self.bn3(self.downsample(x))
      out += residual
      out = F.relu(out)
      return out
  
  
  class ResNet(nn.Module):
    def __init__(self, block, num_blocks, num_classes=10):
      super(ResNet, self).__init__()
      _outputs = [32, 64, 128]
      self.in_planes = _outputs[0]
  
      self.conv1 = MaskedConv(3, _outputs[0], kernel_size=3, stride=1, padding=1, bias=False)
      self.bn = nn.BatchNorm2d(_outputs[0], affine=_AFFINE)
      self.layer1 = self._make_layer(block, _outputs[0], num_blocks[0], stride=1)
      self.layer2 = self._make_layer(block, _outputs[1], num_blocks[1], stride=2)
      self.layer3 = self._make_layer(block, _outputs[2], num_blocks[2], stride=2)
      self.linear = nn.Linear(_outputs[2], 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.bn(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.linear(out)
      return out
  
  
  def resnet20(num_classes):
    return ResNet(BasicBlock, [3, 3, 3], num_classes=num_classes)
  
  
  def resnet32(num_classes):
    return ResNet(BasicBlock, [5, 5, 5], num_classes=num_classes)
  
  
  def resnet44(num_classes):
    return ResNet(BasicBlock, [7, 7, 7], num_classes=num_classes)
  
  
  def resnet56(num_classes):
    return ResNet(BasicBlock, [9, 9, 9], num_classes=num_classes)
  
  
  def resnet110(num_classes):
    return ResNet(BasicBlock, [18, 18, 18], num_classes=num_classes)
  
  
  def resnet1202(num_classes):
    return ResNet(BasicBlock, [200, 200, 200], num_clases=num_classes)
  
apis: torch.nn.BatchNorm2d torch.nn.Linear torch.nn.init.xavier_normal_ torch.nn.functional.relu torch.nn.Sequential