text: import torch.nn as nn
  import math
  import torch.utils.model_zoo as model_zoo
  
  
  __all__ = ['FBResNet',
             #'fbresnet18', 'fbresnet34', 'fbresnet50', 'fbresnet101',
             'fbresnet152']
  
  pretrained_settings = {
      'fbresnet152': {
          'imagenet': {
              'url': 'http://data.lip6.fr/cadene/pretrainedmodels/fbresnet152-2e20f6b4.pth',
              'input_space': 'RGB',
              'input_size': [3, 224, 224],
              'input_range': [0, 1],
              'mean': [0.485, 0.456, 0.406],
              'std': [0.229, 0.224, 0.225],
              'num_classes': 1000
          }
      }
  }
  
  
  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=True)
  
  
  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=True)
          self.bn1 = nn.BatchNorm2d(planes)
          self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                                 padding=1, bias=True)
          self.bn2 = nn.BatchNorm2d(planes)
          self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=True)
          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 FBResNet(nn.Module):
  
      def __init__(self, block, layers, num_classes=1000):
          self.inplanes = 64
          # Special attributs
          self.input_space = None
          self.input_size = (299, 299, 3)
          self.mean = None
          self.std = None
          super(FBResNet, self).__init__()
          # Modules
          self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                                  bias=True)
          self.bn1 = nn.BatchNorm2d(64)
          self.relu = nn.ReLU(inplace=True)
          self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
          self.layer1 = self._make_layer(block, 64, layers[0])
          self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
          self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
          self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
          self.avgpool = nn.AvgPool2d(7)
          self.last_linear = nn.Linear(512 * 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=True),
                  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 features(self, input):
          x = self.conv1(input)
          self.conv1_input = x.clone()
          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)
          return x
  
      def logits(self, features):
          x = self.avgpool(features)
          x = x.view(x.size(0), -1)
          x = self.last_linear(x)
          return x
  
      def forward(self, input):
          x = self.features(input)
          x = self.logits(x)
          return x
  
  
  def fbresnet18(num_classes=1000):
      """Constructs a ResNet-18 model.
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = FBResNet(BasicBlock, [2, 2, 2, 2], num_classes=num_classes)
      return model
  
  
  def fbresnet34(num_classes=1000):
      """Constructs a ResNet-34 model.
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = FBResNet(BasicBlock, [3, 4, 6, 3], num_classes=num_classes)
      return model
  
  
  def fbresnet50(num_classes=1000):
      """Constructs a ResNet-50 model.
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = FBResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes)
      return model
  
  
  def fbresnet101(num_classes=1000):
      """Constructs a ResNet-101 model.
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = FBResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes)
      return model
  
  
  def fbresnet152(num_classes=1000, pretrained='imagenet'):
      """Constructs a ResNet-152 model.
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = FBResNet(Bottleneck, [3, 8, 36, 3], num_classes=num_classes)
      if pretrained is not None:
          settings = pretrained_settings['fbresnet152'][pretrained]
          assert num_classes == settings['num_classes'], \
              "num_classes should be {}, but is {}".format(settings['num_classes'], num_classes)
          model.load_state_dict(model_zoo.load_url(settings['url']))
          model.input_space = settings['input_space']
          model.input_size = settings['input_size']
          model.input_range = settings['input_range']
          model.mean = settings['mean']
          model.std = settings['std']
      return model
  
  
  