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
  import torch.nn as nn
  import torch.nn.functional as F
  
  
  class BasicBlock(nn.Module):
      expansion = 1
  
      def __init__(self, in_planes, planes, stride=1):
          super(BasicBlock, self).__init__()
          self.conv1 = nn.Conv2d(
              in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
          self.bn1 = nn.BatchNorm2d(planes)
          self.conv2 = nn.Conv2d(planes, planes, kernel_size=3,
                                 stride=1, padding=1, bias=False)
          self.bn2 = nn.BatchNorm2d(planes)
  
          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)
              )
  
      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):
          super(Bottleneck, self).__init__()
          self.conv1 = nn.Conv2d(in_planes, 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, self.expansion *
                                 planes, kernel_size=1, bias=False)
          self.bn3 = nn.BatchNorm2d(self.expansion*planes)
  
          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)
              )
  
      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_classes=10, width_multiplier=1.0, num_make_layers=4):
          super(ResNet, self).__init__()
          self.num_make_layers = num_make_layers
          self.in_planes = 64
          self.width_multiplier = width_multiplier
          self.conv1 = nn.Conv2d(3, 64, kernel_size=3,
                                 stride=1, padding=1, bias=False)
          self.bn1 = nn.BatchNorm2d(64)
  
          if num_make_layers == 1:
              self.layer1 = self._make_layer(block, int(64 * self.width_multiplier), num_blocks[0], stride=1)
              self.linear1 = nn.Linear(int(4096 * self.width_multiplier)*block.expansion, num_classes)
  
          if num_make_layers == 2:
              self.layer1 = self._make_layer(block, int(64 * self.width_multiplier), num_blocks[0], stride=1)
              self.layer2 = self._make_layer(block, int(128 * self.width_multiplier), num_blocks[1], stride=2)
              self.linear2 = nn.Linear(int(2048 * self.width_multiplier)*block.expansion, num_classes)
  
          if num_make_layers == 3:
              self.layer1 = self._make_layer(block, int(64 * self.width_multiplier), num_blocks[0], stride=1)
              self.layer2 = self._make_layer(block, int(128 * self.width_multiplier), num_blocks[1], stride=2)
              self.layer3 = self._make_layer(block, int(256 * self.width_multiplier), num_blocks[2], stride=2)
              self.linear3 = nn.Linear(int(1024 * self.width_multiplier)*block.expansion, num_classes)        
  
          if num_make_layers == 4:
              self.layer1 = self._make_layer(block, int(64 * self.width_multiplier), num_blocks[0], stride=1)
              self.layer2 = self._make_layer(block, int(128 * self.width_multiplier), num_blocks[1], stride=2)
              self.layer3 = self._make_layer(block, int(256 * self.width_multiplier), num_blocks[2], stride=2)
              self.layer4 = self._make_layer(block, int(512 * self.width_multiplier), num_blocks[3], stride=2)
              self.linear4 = nn.Linear(int(512 * self.width_multiplier)*block.expansion, num_classes)
  
      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.in_planes = planes * block.expansion
          return nn.Sequential(*layers)
  
      def forward(self, x):
          out = F.relu(self.bn1(self.conv1(x)))
          if self.num_make_layers > 0:
              out = self.layer1(out)
  
          if self.num_make_layers > 1:
              out = self.layer2(out)
  
          if self.num_make_layers > 2:
              out = self.layer3(out)
  
          if self.num_make_layers > 3:
              out = self.layer4(out)
  
          out = F.avg_pool2d(out, 4)
          out = out.view(out.size(0), -1)
          if self.num_make_layers == 1:
              out = self.linear1(out)
          if self.num_make_layers == 2:
              out = self.linear2(out)
          if self.num_make_layers == 3:
              out = self.linear3(out)
          if self.num_make_layers == 4:
              out = self.linear4(out)
          return out
  
  def ResNet6(num_classes,width_multiplier):
      return ResNet(BasicBlock, [2, 2, 2, 2], num_classes=num_classes, width_multiplier=width_multiplier, num_make_layers=1)
  
  def ResNet8(num_classes,width_multiplier):
      return ResNet(BasicBlock, [3, 2, 2, 2], num_classes=num_classes, width_multiplier=width_multiplier, num_make_layers=1)
  
  def ResNet10(num_classes,width_multiplier):
      return ResNet(BasicBlock, [2, 2, 2, 2], num_classes=num_classes, width_multiplier=width_multiplier, num_make_layers=2)
  
  def ResNet12(num_classes,width_multiplier):
      return ResNet(BasicBlock, [2, 3, 2, 2], num_classes=num_classes, width_multiplier=width_multiplier, num_make_layers=2)
  
  def ResNet14(num_classes,width_multiplier):
      return ResNet(BasicBlock, [2, 2, 2, 2], num_classes=num_classes, width_multiplier=width_multiplier, num_make_layers=3)
  
  def ResNet16(num_classes,width_multiplier):
      return ResNet(BasicBlock, [2, 2, 3, 2], num_classes=num_classes, width_multiplier=width_multiplier, num_make_layers=3)
  
  def ResNet18(num_classes, width_multiplier):
      return ResNet(BasicBlock, [2, 2, 2, 2], num_classes=num_classes, width_multiplier=width_multiplier)
  
  #def ResNet18(num_classes):
  #    return ResNet(BasicBlock, [2, 2, 2, 2], num_classes=num_classes)
  
  #def ResNet34(num_classes):
  #    return ResNet(BasicBlock, [3, 4, 6, 3], num_classes=num_classes)
  
  #def ResNet50(num_classes):
  #    return ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes)
  
  
  #def ResNet101(num_classes):
  #    return ResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes)
  
  #def ResNet152(num_classes):
  #    return ResNet(Bottleneck, [3, 8, 36, 3], num_classes=num_classes)
  
  def ResNetFlex(num_classes, width_multiplier, depth):
      return ResNet(BasicBlock, [depth, depth, depth, depth], num_classes=num_classes, width_multiplier=width_multiplier)
  
  def ResNetFlex34(num_classes, width_multiplier):
      return ResNet(BasicBlock, [3, 4, 6, 3], num_classes=num_classes, width_multiplier=width_multiplier)
  
  def ResNetFlex50(num_classes, width_multiplier):
      return ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes, width_multiplier=width_multiplier)
  
  def ResNetFlex101(num_classes, width_multiplier):
      return ResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes, width_multiplier=width_multiplier)
  
  def ResNetFlex152(num_classes, width_multiplier):
      return ResNet(Bottleneck, [3, 8, 36, 3], num_classes=num_classes, width_multiplier=width_multiplier)
  
  def test():
      net = ResNet18()
      y = net(torch.randn(1, 3, 32, 32))
      print(y.size())
  
  # test()
  