text: '''
  resnet for cifar in pytorch
  Reference:
  [1] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In CVPR, 2016.
  [2] K. He, X. Zhang, S. Ren, and J. Sun. Identity mappings in deep residual networks. In ECCV, 2016.
  '''
  
  import torch
  import torch.nn as nn
  import math
  
  
  def conv3x3(in_planes, out_planes, stride=1):
      " 3x3 convolution with padding "
      return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
  
  
  class BasicBlock(nn.Module):
      expansion=1
  
      def __init__(self, inplanes, planes, stride=1, downsample=None):
          super(BasicBlock, self).__init__()
          self.conv1 = conv3x3(inplanes, planes, stride)
          self.bn1 = nn.BatchNorm2d(planes)
          self.relu = nn.ReLU(inplace=True)
          self.conv2 = conv3x3(planes, planes)
          self.bn2 = nn.BatchNorm2d(planes)
          self.downsample = downsample
          self.stride = stride
  
      def forward(self, x):
          residual = x
  
          out = self.conv1(x)
          out = self.bn1(out)
          out = self.relu(out)
  
          out = self.conv2(out)
          out = self.bn2(out)
  
          if self.downsample is not None:
              residual = self.downsample(x)
  
          out += residual
          out = self.relu(out)
  
          return out
  
  
  class ResNet_Cifar(nn.Module):
  
      def __init__(self, block, layers, input_resolution,  num_classes=100):
          super(ResNet_Cifar, self).__init__()
          self.inplanes = 16
          self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1, bias=False)
          self.bn1 = nn.BatchNorm2d(16)
          self.relu = nn.ReLU(inplace=True)
          self.layer1 = self._make_layer(block, 16, layers[0])
          self.layer2 = self._make_layer(block, 32, layers[1], stride=2)
          self.layer3 = self._make_layer(block, 64, layers[2], stride=2)
          # self.avgpool = nn.AvgPool2d(int(input_resolution/4), stride=1)
          self.avgpool= nn.AdaptiveAvgPool2d(output_size=(1, 1))
          self.fc = nn.Linear(64 * block.expansion, num_classes)
  
          for m in self.modules():
              if isinstance(m, nn.Conv2d):
                  n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                  m.weight.data.normal_(0, math.sqrt(2. / n))
              elif isinstance(m, nn.BatchNorm2d):
                  m.weight.data.fill_(0.5)
                  m.bias.data.zero_()
  
      def _make_layer(self, block, planes, blocks, stride=1):
          downsample = None
          if stride != 1 or self.inplanes != planes * block.expansion:
              downsample = nn.Sequential(
                  nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
                  nn.BatchNorm2d(planes * block.expansion)
              )
  
          layers = []
          layers.append(block(self.inplanes, planes, stride, downsample))
          self.inplanes = planes * block.expansion
          for _ in range(1, blocks):
              layers.append(block(self.inplanes, planes))
  
          return nn.Sequential(*layers)
  
      def forward(self, x):
          x = self.conv1(x)
          x = self.bn1(x)
          x = self.relu(x)
  
          x = self.layer1(x)
          x = self.layer2(x)
          x = self.layer3(x)
  
          x = self.avgpool(x)
          x = x.view(x.size(0), -1)
          x = self.fc(x)
  
          return x
  
  
  def resnet56_cifar(**kwargs):
      model = ResNet_Cifar(BasicBlock, [9, 9, 9], **kwargs)
      return model
  
  
  def resnet110_cifar(**kwargs):
      model = ResNet_Cifar(BasicBlock, [18, 18, 18], **kwargs)
      return model
  
  
  
  if __name__ == '__main__':
      net = resnet56_cifar()
      y = net(torch.randn(1, 3, 64, 64))
      print(net)
      print(y.size())