text: '''Resnet for cifar dataset.
  Ported form
  https://github.com/facebook/fb.resnet.torch
  and
  https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
  (c) YANG, Wei
  '''
  import torch.nn as nn
  import math
  
  
  __all__ = ['resnet']
  
  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 Bottleneck(nn.Module):
      expansion = 4
  
      def __init__(self, inplanes, planes, stride=1, downsample=None):
          super(Bottleneck, self).__init__()
          self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
          self.bn1 = nn.BatchNorm2d(planes)
          self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                                 padding=1, bias=False)
          self.bn2 = nn.BatchNorm2d(planes)
          self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
          self.bn3 = nn.BatchNorm2d(planes * 4)
          self.relu = nn.ReLU(inplace=True)
          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)
          out = self.relu(out)
  
          out = self.conv3(out)
          out = self.bn3(out)
  
          if self.downsample is not None:
              residual = self.downsample(x)
  
          out += residual
          out = self.relu(out)
  
          return out
  
  
  class ResNet(nn.Module):
  
      def __init__(self, depth, num_classes=1000, block_name='BasicBlock', **kwargs):
          super(ResNet, self).__init__()
          # Model type specifies number of layers for CIFAR-10 model
          if block_name.lower() == 'basicblock':
              assert (depth - 2) % 6 == 0, 'When use basicblock, depth should be 6n+2, e.g. 20, 32, 44, 56, 110, 1202'
              n = (depth - 2) // 6
              self.block = BasicBlock
          elif block_name.lower() == 'bottleneck':
              assert (depth - 2) % 9 == 0, 'When use bottleneck, depth should be 9n+2, e.g. 20, 29, 47, 56, 110, 1199'
              n = (depth - 2) // 9
              self.block = Bottleneck
          else:
              raise ValueError('block_name shoule be Basicblock or Bottleneck')
  
          self.num_classes = num_classes
  
          self.inplanes = 16
          self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1,
                                 bias=False)
          self.bn1 = nn.BatchNorm2d(16)
          self.relu = nn.ReLU(inplace=True)
          self.layer1 = self._make_layer(self.block, 16, n)
          self.layer2 = self._make_layer(self.block, 32, n, stride=2)
          self.layer3 = self._make_layer(self.block, 64, n, stride=2)
          self.avgpool = nn.AvgPool2d(8)
          self.fc = nn.Linear(64 * self.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_(1)
                  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 i 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)    # 32x32
  
          x = self.layer1(x)  # 32x32
          x = self.layer2(x)  # 16x16
          x = self.layer3(x)  # 8x8
  
          x = self.avgpool(x)
  
          image_embedding = x.view(x.size(0), -1)
  
          classifier_scores = self.fc(image_embedding)
  
          return classifier_scores
  
  def resnet20(**kwargs):
      model = ResNet(depth=20, **kwargs)
      return model
  
  def resnet32(**kwargs):
      model = ResNet(depth=32, **kwargs)
      return model
  
  def resnet56(**kwargs):
      model = ResNet(depth=56, block_name="bottleneck", **kwargs)
      return model
  
  def resnet110(**kwargs):
      model = ResNet(depth=110, block_name="bottleneck", **kwargs)
      return model