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
  import math
  
  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(BasicBlock,self).__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, kernel_size=3, padding=1, bias=False),
              nn.BatchNorm2d(out_channels)
          )
  
          #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, kernel_size=1, stride=stride, bias=False),
                  nn.BatchNorm2d(out_channels)
              )
          
      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(BottleNeck,self).__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=7):
          super(ResNet,self).__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.feature = nn.Linear(512*1*1, 512)
          self.feture_relu = nn.PReLU()
  
          self.fc = nn.Linear(512 * block.expansion, num_classes)
  
          self._initialize_weights()
  
      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 _initialize_weights(self):
          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))
                  if m.bias is not None:
                      m.bias.data.zero_()
              elif isinstance(m, nn.BatchNorm2d):
                  m.weight.data.fill_(1)
                  m.bias.data.zero_()
              elif isinstance(m, nn.Linear):
                  m.weight.data.normal_(0, 0.01)
                  m.bias.data.zero_()
  
      def forward(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)
  
          feature = output.view(-1, 512*1*1)
          feature = self.feature(feature)
          feature = self.feture_relu(feature)
  
          output = output.view(output.size(0),-1)
          output = self.fc(output)
  
          return output,feature
  
  def resnet18(num_classes):
      """ return a ResNet 18 object
      """
      return ResNet(BasicBlock, [2, 2, 2, 2],num_classes=num_classes)
  
  def resnet34(num_classes):
      """ return a ResNet 34 object
      """
      return ResNet(BasicBlock, [3, 4, 6, 3],num_classes=num_classes)
  
  def resnet50(num_classes):
      """ return a ResNet 50 object
      """
      return ResNet(BottleNeck, [3, 4, 6, 3],num_classes=num_classes)
  
  def resnet101(num_classes):
      """ return a ResNet 101 object
      """
      return ResNet(BottleNeck, [3, 4, 23, 3],num_classes=num_classes)
  
  def resnet152(num_classes):
      """ return a ResNet 152 object
      """
      return ResNet(BottleNeck, [3, 8, 36, 3],num_classes=num_classes)
  
  
  
  