text: """ResNet in PyTorch.
  
  For Pre-activation ResNet, see 'preact_resnet.py'.
  
  Reference:
  [1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
      Deep Residual Learning for Image Recognition. arXiv:1512.03385
  """
  import torch.nn as nn
  import torch.nn.functional as F
  
  from .. import utils
  
  
  class BasicBlock(nn.Module):
      expansion = 1
  
      def __init__(self, in_planes, planes, stride=1, linear_bias=True, bn_affine=True):
          super(BasicBlock, self).__init__()
  
          self.linear_bias = linear_bias
          self.bn_affine = bn_affine
  
          self.conv1 = nn.Conv2d(
              in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False
          )
          self.bn1 = nn.BatchNorm2d(planes, affine=self.bn_affine)
          self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
          self.bn2 = nn.BatchNorm2d(planes, affine=self.bn_affine)
  
          self.shortcut = nn.Sequential()
          if stride != 1 or in_planes != self.expansion * planes:
              self.shortcut = nn.Sequential(
                  nn.Conv2d(
                      in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False
                  ),
                  nn.BatchNorm2d(self.expansion * planes, affine=self.bn_affine),
              )
  
      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 Bottleneck(nn.Module):
      expansion = 4
  
      def __init__(self, in_planes, planes, stride=1, linear_bias=True, bn_affine=True):
          super(Bottleneck, self).__init__()
  
          self.linear_bias = linear_bias
          self.bn_affine = bn_affine
  
          self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
          self.bn1 = nn.BatchNorm2d(planes, affine=self.bn_affine)
          self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
          self.bn2 = nn.BatchNorm2d(planes, affine=self.bn_affine)
          self.conv3 = nn.Conv2d(planes, self.expansion * planes, kernel_size=1, bias=False)
          self.bn3 = nn.BatchNorm2d(self.expansion * planes, affine=self.bn_affine)
  
          self.shortcut = nn.Sequential()
          if stride != 1 or in_planes != self.expansion * planes:
              self.shortcut = nn.Sequential(
                  nn.Conv2d(
                      in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False
                  ),
                  nn.BatchNorm2d(self.expansion * planes, affine=self.bn_affine),
              )
  
      def forward(self, x):
          out = F.relu(self.bn1(self.conv1(x)))
          out = F.relu(self.bn2(self.conv2(out)))
          out = self.bn3(self.conv3(out))
          out += self.shortcut(x)
          out = F.relu(out)
          return out
  
  
  class ResNet(nn.Module):
      def __init__(self, block, num_blocks, num_channels=3, num_classes=10, linear_bias=True, bn_affine=True):
          super(ResNet, self).__init__()
          self.in_planes = 64
  
          self.linear_bias = linear_bias
          self.bn_affine = bn_affine
  
          self.conv1 = nn.Conv2d(num_channels, 64, kernel_size=3, stride=1, padding=1, bias=False)
          self.bn1 = nn.BatchNorm2d(64, affine=self.bn_affine)
          self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
          self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
          self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
          self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
          self.linear = nn.Linear(512 * block.expansion, num_classes, bias=self.linear_bias)
  
      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.linear_bias, self.bn_affine))
              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 = self.layer4(out)
          out = F.avg_pool2d(out, 4)
          out = out.view(out.size(0), -1)
          out = self.linear(out)
          return out
  
  
  @utils.register_model(dataset='cifar10', name='resnet18')
  @utils.register_model(dataset='cifar100', name='resnet18')
  class ResNet18(ResNet):
      def __init__(self, num_channels=3, num_classes=10, linear_bias=True, bn_affine=True, **kwargs):
          super(ResNet18, self).__init__(
              BasicBlock, [2, 2, 2, 2], num_channels=num_channels, num_classes=num_classes,
              linear_bias=linear_bias, bn_affine=bn_affine
          )
  
  
  @utils.register_model(dataset='cifar10', name='resnet34')
  @utils.register_model(dataset='cifar100', name='resnet34')
  class ResNet34(ResNet):
      def __init__(self, num_channels=3, num_classes=10, linear_bias=True, bn_affine=True, **kwargs):
          super(ResNet34, self).__init__(
              BasicBlock, [3, 4, 6, 3], num_channels=num_channels, num_classes=num_classes,
              linear_bias=linear_bias, bn_affine=bn_affine
          )
  
          
  @utils.register_model(dataset='cifar10', name='resnet50')
  @utils.register_model(dataset='cifar100', name='resnet50')
  class ResNet50(ResNet):
      def __init__(self, num_channels=3, num_classes=10, linear_bias=True, bn_affine=True, **kwargs):
          super(ResNet50, self).__init__(
              Bottleneck, [3, 4, 23, 3], num_channels=num_channels, num_classes=num_classes,
              linear_bias=linear_bias, bn_affine=bn_affine
          )
  
  
  @utils.register_model(dataset='cifar10', name='resnet101')
  @utils.register_model(dataset='cifar100', name='resnet101')
  class ResNet101(ResNet):
      def __init__(self, num_channels=3, num_classes=10, linear_bias=True, bn_affine=True, **kwargs):
          super(ResNet101, self).__init__(
              Bottleneck, [3, 8, 36, 3], num_channels=num_channels, num_classes=num_classes,
              linear_bias=linear_bias, bn_affine=bn_affine
          )