text: #
  # For licensing see accompanying LICENSE file.
  # Copyright (C) 2020 Apple Inc. All Rights Reserved.
  #
  import torch.nn as nn
  
  from args import args
  
  from .builder import Builder
  
  
  # BasicBlock {{{
  class BasicBlock(nn.Module):
      M = 2
      expansion = 1
  
      def __init__(
          self,
          builder,
          inplanes,
          planes,
          stride=1,
          downsample=None,
          base_width=64,
      ):
          super(BasicBlock, self).__init__()
          if base_width / 64 > 1:
              raise ValueError("Base width >64 does not work for BasicBlock")
  
          self.conv1 = builder.conv3x3(inplanes, planes, stride)
          self.bn1 = builder.batchnorm(planes)
          self.relu = nn.ReLU(inplace=True)
          self.conv2 = builder.conv3x3(planes, planes)
          self.bn2 = builder.batchnorm(planes)
          self.downsample = downsample
          self.stride = stride
  
      def forward(self, x):
          residual = x
  
          out = self.conv1(x)
          if self.bn1 is not None:
              out = self.bn1(out)
  
          out = self.relu(out)
  
          out = self.conv2(out)
  
          if self.bn2 is not None:
              out = self.bn2(out)
  
          if self.downsample is not None:
              residual = self.downsample(x)
  
          out += residual
          out = self.relu(out)
  
          return out
  
  
  # BasicBlock }}}
  
  # Bottleneck {{{
  class Bottleneck(nn.Module):
      M = 3
      expansion = 4
  
      def __init__(
          self,
          builder,
          inplanes,
          planes,
          stride=1,
          downsample=None,
          base_width=64,
      ):
          super(Bottleneck, self).__init__()
          width = int(planes * base_width / 64)
          self.conv1 = builder.conv1x1(inplanes, width)
          self.bn1 = builder.batchnorm(width)
          self.conv2 = builder.conv3x3(width, width, stride=stride)
          self.bn2 = builder.batchnorm(width)
          self.conv3 = builder.conv1x1(width, planes * self.expansion)
          self.bn3 = builder.batchnorm(planes * self.expansion)
          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
  
  
  # Bottleneck }}}
  
  # ResNet {{{
  class ResNet(nn.Module):
      def __init__(self, builder, block, layers, num_classes=1000, base_width=64):
          self.inplanes = 64
          super(ResNet, self).__init__()
  
          self.base_width = base_width
          if self.base_width // 64 > 1:
              print(f"==> Using {self.base_width // 64}x wide model")
  
          self.conv1 = builder.conv7x7(3, 64, stride=2)
  
          self.bn1 = builder.batchnorm(64)
          self.relu = nn.ReLU(inplace=True)
          self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
          self.layer1 = self._make_layer(builder, block, 64, layers[0])
          self.layer2 = self._make_layer(builder, block, 128, layers[1], stride=2)
          self.layer3 = self._make_layer(builder, block, 256, layers[2], stride=2)
          self.layer4 = self._make_layer(builder, block, 512, layers[3], stride=2)
          self.avgpool = nn.AdaptiveAvgPool2d(1)
          self.return_feats = False
  
          self.fc = builder.conv1x1(512 * block.expansion, num_classes)
  
      def _make_layer(self, builder, block, planes, blocks, stride=1):
          downsample = None
          if stride != 1 or self.inplanes != planes * block.expansion:
              dconv = builder.conv1x1(
                  self.inplanes, planes * block.expansion, stride=stride
              )
              dbn = builder.batchnorm(planes * block.expansion)
              if dbn is not None:
                  downsample = nn.Sequential(dconv, dbn)
              else:
                  downsample = dconv
  
          layers = []
          layers.append(
              block(
                  builder,
                  self.inplanes,
                  planes,
                  stride,
                  downsample,
                  base_width=self.base_width,
              )
          )
          self.inplanes = planes * block.expansion
          for i in range(1, blocks):
              layers.append(
                  block(
                      builder, self.inplanes, planes, base_width=self.base_width
                  )
              )
  
          return nn.Sequential(*layers)
  
      def forward(self, x):
          x = self.conv1(x)
  
          if self.bn1 is not None:
              x = self.bn1(x)
          x = self.relu(x)
          x = self.maxpool(x)
  
          x = self.layer1(x)
          x = self.layer2(x)
          x = self.layer3(x)
          x = self.layer4(x)
  
          feats = self.avgpool(x)
          x = self.fc(feats)
          x = x.view(x.size(0), -1)
  
          if self.return_feats:
              return x, feats.view(feats.size(0), -1)
          return x
  
  
  # ResNet }}}
  def ResNet18(pretrained=False):
      return ResNet(Builder(), BasicBlock, [2, 2, 2, 2], args.output_size)
  
  
  def ResNet50(pretrained=False):
      return ResNet(Builder(), Bottleneck, [3, 4, 6, 3], args.output_size)
  
  
  def ResNet101(pretrained=False):
      return ResNet(Builder(), Bottleneck, [3, 4, 23, 3], args.output_size)
  
  
  def WideResNet50_2(pretrained=False):
      return ResNet(
          Builder(),
          Bottleneck,
          [3, 4, 6, 3],
          num_classes=args.output_size,
          base_width=64 * 2,
      )
  
  
  def WideResNet101_2(pretrained=False):
      return ResNet(
          Builder(),
          Bottleneck,
          [3, 4, 23, 3],
          num_classes=args.output_size,
          base_width=64 * 2,
      )
  