text: """resnet in pytorch
  
  
  
  [1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun.
  
      Deep Residual Learning for Image Recognition
      https://arxiv.org/abs/1512.03385v1
  """
  
  import torch
  import torch.nn as nn
  
  class BasicBlock(nn.Module):
      """Basic Block for resnet 18 and resnet 34
  
      """
  
      #BasicBlock and BottleNeck block
      #have different output size
      #we use class attribute expansion
      #to distinct
      expansion = 1
  
      def __init__(self, in_channels, out_channels, stride=1):
          super().__init__()
  
          #residual function
          self.residual_function = nn.Sequential(
              nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False),
              nn.BatchNorm2d(out_channels),
              nn.ReLU(inplace=True),
              nn.Conv2d(out_channels, out_channels * BasicBlock.expansion, kernel_size=3, padding=1, bias=False),
              nn.BatchNorm2d(out_channels * BasicBlock.expansion)
          )
  
          #shortcut
          self.shortcut = nn.Sequential()
  
          #the shortcut output dimension is not the same with residual function
          #use 1*1 convolution to match the dimension
          if stride != 1 or in_channels != BasicBlock.expansion * out_channels:
              self.shortcut = nn.Sequential(
                  nn.Conv2d(in_channels, out_channels * BasicBlock.expansion, kernel_size=1, stride=stride, bias=False),
                  nn.BatchNorm2d(out_channels * BasicBlock.expansion)
              )
  
      def forward(self, x):
          return nn.ReLU(inplace=True)(self.residual_function(x) + self.shortcut(x))
  
  class BottleNeck(nn.Module):
      """Residual block for resnet over 50 layers
  
      """
      expansion = 4
      def __init__(self, in_channels, out_channels, stride=1):
          super().__init__()
          self.residual_function = nn.Sequential(
              nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False),
              nn.BatchNorm2d(out_channels),
              nn.ReLU(inplace=True),
              nn.Conv2d(out_channels, out_channels, stride=stride, kernel_size=3, padding=1, bias=False),
              nn.BatchNorm2d(out_channels),
              nn.ReLU(inplace=True),
              nn.Conv2d(out_channels, out_channels * BottleNeck.expansion, kernel_size=1, bias=False),
              nn.BatchNorm2d(out_channels * BottleNeck.expansion),
          )
  
          self.shortcut = nn.Sequential()
  
          if stride != 1 or in_channels != out_channels * BottleNeck.expansion:
              self.shortcut = nn.Sequential(
                  nn.Conv2d(in_channels, out_channels * BottleNeck.expansion, stride=stride, kernel_size=1, bias=False),
                  nn.BatchNorm2d(out_channels * BottleNeck.expansion)
              )
  
      def forward(self, x):
          return nn.ReLU(inplace=True)(self.residual_function(x) + self.shortcut(x))
  
  class ResNet(nn.Module):
  
      def __init__(self, block, num_block, num_classes=100):
          super().__init__()
  
          self.in_channels = 64
  
          self.conv1 = nn.Sequential(
              nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False),
              nn.BatchNorm2d(64),
              nn.ReLU(inplace=True))
          #we use a different inputsize than the original paper
          #so conv2_x's stride is 1
          self.conv2_x = self._make_layer(block, 64, num_block[0], 1)
          self.conv3_x = self._make_layer(block, 128, num_block[1], 2)
          self.conv4_x = self._make_layer(block, 256, num_block[2], 2)
          self.conv5_x = self._make_layer(block, 512, num_block[3], 2)
          self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
          self.fc = nn.Linear(512 * block.expansion, num_classes)
  
      def _make_layer(self, block, out_channels, num_blocks, stride):
          """make resnet layers(by layer i didnt mean this 'layer' was the
          same as a neuron netowork layer, ex. conv layer), one layer may
          contain more than one residual block
  
          Args:
              block: block type, basic block or bottle neck block
              out_channels: output depth channel number of this layer
              num_blocks: how many blocks per layer
              stride: the stride of the first block of this layer
  
          Return:
              return a resnet layer
          """
  
          # we have num_block blocks per layer, the first block
          # could be 1 or 2, other blocks would always be 1
          strides = [stride] + [1] * (num_blocks - 1)
          layers = []
          for stride in strides:
              layers.append(block(self.in_channels, out_channels, stride))
              self.in_channels = out_channels * block.expansion
  
          return nn.Sequential(*layers)
  
      def extract(self, x):
          output = self.conv1(x)
          output = self.conv2_x(output)
          output = self.conv3_x(output)
          output = self.conv4_x(output)
          output = self.conv5_x(output)
          output = self.avg_pool(output)
          output = output.view(output.size(0), -1)
          return output
  
      def forward(self, x):
          output = self.extract(x)
          output = self.fc(output)
  
          return output
  
  def resnet18(num_class=100):
      """ return a ResNet 18 object
      """
      return ResNet(BasicBlock, [2, 2, 2, 2], num_class)
  
  def resnet34(num_class=100):
      """ return a ResNet 34 object
      """
      return ResNet(BasicBlock, [3, 4, 6, 3], num_class)
  
  def resnet50(num_class=100):
      """ return a ResNet 50 object
      """
      return ResNet(BottleNeck, [3, 4, 6, 3], num_class)
  
  def resnet101(num_class=100):
      """ return a ResNet 101 object
      """
      return ResNet(BottleNeck, [3, 4, 23, 3], num_class)
  
  def resnet152(num_class=100):
      """ return a ResNet 152 object
      """
      return ResNet(BottleNeck, [3, 8, 36, 3], num_class)
  
  
  
  