text: from __future__ import division
  
  import torch.nn as nn
  import torch.nn.functional as F
  from torch.nn import init
  
  
  __all__ = ['resnext']
  
  class ResNeXtBottleneck(nn.Module):
      """
      RexNeXt bottleneck type C (https://github.com/facebookresearch/ResNeXt/blob/master/models/resnext.lua)
      """
      def __init__(self, in_channels, out_channels, stride, cardinality, widen_factor):
          """ Constructor
          Args:
              in_channels: input channel dimensionality
              out_channels: output channel dimensionality
              stride: conv stride. Replaces pooling layer.
              cardinality: num of convolution groups.
              widen_factor: factor to reduce the input dimensionality before convolution.
          """
          super(ResNeXtBottleneck, self).__init__()
          D = cardinality * out_channels // widen_factor
          self.conv_reduce = nn.Conv2d(in_channels, D, kernel_size=1, stride=1, padding=0, bias=False)
          self.bn_reduce = nn.BatchNorm2d(D)
          self.conv_conv = nn.Conv2d(D, D, kernel_size=3, stride=stride, padding=1, groups=cardinality, bias=False)
          self.bn = nn.BatchNorm2d(D)
          self.conv_expand = nn.Conv2d(D, out_channels, kernel_size=1, stride=1, padding=0, bias=False)
          self.bn_expand = nn.BatchNorm2d(out_channels)
  
          self.shortcut = nn.Sequential()
          if in_channels != out_channels:
              self.shortcut.add_module('shortcut_conv', nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, padding=0, bias=False))
              self.shortcut.add_module('shortcut_bn', nn.BatchNorm2d(out_channels))
  
      def forward(self, x):
          bottleneck = self.conv_reduce.forward(x)
          bottleneck = F.relu(self.bn_reduce.forward(bottleneck), inplace=True)
          bottleneck = self.conv_conv.forward(bottleneck)
          bottleneck = F.relu(self.bn.forward(bottleneck), inplace=True)
          bottleneck = self.conv_expand.forward(bottleneck)
          bottleneck = self.bn_expand.forward(bottleneck)
          residual = self.shortcut.forward(x)
          return F.relu(residual + bottleneck, inplace=True)
  
  
  class CifarResNeXt(nn.Module):
      """
      ResNext optimized for the Cifar dataset, as specified in
      https://arxiv.org/pdf/1611.05431.pdf
      """
      def __init__(self, cardinality, depth, num_classes, widen_factor=4, dropRate=0):
          """ Constructor
          Args:
              cardinality: number of convolution groups.
              depth: number of layers.
              num_classes: number of classes
              widen_factor: factor to adjust the channel dimensionality
          """
          super(CifarResNeXt, self).__init__()
          self.cardinality = cardinality
          self.depth = depth
          self.block_depth = (self.depth - 2) // 9
          self.widen_factor = widen_factor
          self.num_classes = num_classes
          self.output_size = 64
          self.stages = [64, 64 * self.widen_factor, 128 * self.widen_factor, 256 * self.widen_factor]
  
          self.conv_1_3x3 = nn.Conv2d(3, 64, 3, 1, 1, bias=False)
          self.bn_1 = nn.BatchNorm2d(64)
          self.stage_1 = self.block('stage_1', self.stages[0], self.stages[1], 1)
          self.stage_2 = self.block('stage_2', self.stages[1], self.stages[2], 2)
          self.stage_3 = self.block('stage_3', self.stages[2], self.stages[3], 2)
          self.classifier = nn.Linear(1024, num_classes)
          init.kaiming_normal(self.classifier.weight)
  
          for key in self.state_dict():
              if key.split('.')[-1] == 'weight':
                  if 'conv' in key:
                      init.kaiming_normal(self.state_dict()[key], mode='fan_out')
                  if 'bn' in key:
                      self.state_dict()[key][...] = 1
              elif key.split('.')[-1] == 'bias':
                  self.state_dict()[key][...] = 0
  
      def block(self, name, in_channels, out_channels, pool_stride=2):
          """ Stack n bottleneck modules where n is inferred from the depth of the network.
          Args:
              name: string name of the current block.
              in_channels: number of input channels
              out_channels: number of output channels
              pool_stride: factor to reduce the spatial dimensionality in the first bottleneck of the block.
          Returns: a Module consisting of n sequential bottlenecks.
          """
          block = nn.Sequential()
          for bottleneck in range(self.block_depth):
              name_ = '%s_bottleneck_%d' % (name, bottleneck)
              if bottleneck == 0:
                  block.add_module(name_, ResNeXtBottleneck(in_channels, out_channels, pool_stride, self.cardinality,
                                                            self.widen_factor))
              else:
                  block.add_module(name_,
                                   ResNeXtBottleneck(out_channels, out_channels, 1, self.cardinality, self.widen_factor))
          return block
  
      def forward(self, x):
          x = self.conv_1_3x3.forward(x)
          x = F.relu(self.bn_1.forward(x), inplace=True)
          x = self.stage_1.forward(x)
          x = self.stage_2.forward(x)
          x = self.stage_3.forward(x)
          x = F.avg_pool2d(x, 8, 1)
          x = x.view(-1, 1024)
          return self.classifier(x)
  
  def resnext(**kwargs):
      """Constructs a ResNeXt.
      """
      model = CifarResNeXt(**kwargs)
      return model