text: from __future__ import division
  """ 
  Creates a ResNeXt Model as defined in:
  Xie, S., Girshick, R., Dollar, P., Tu, Z., & He, K. (2016). 
  Aggregated residual transformations for deep neural networks. 
  arXiv preprint arXiv:1611.05431.
  import from https://github.com/facebookresearch/ResNeXt/blob/master/models/resnext.lua
  """
  import math
  import torch.nn as nn
  import torch.nn.functional as F
  from torch.nn import init
  import torch
  
  __all__ = ['resnext50', 'resnext101', 'resnext152']
  
  class Bottleneck(nn.Module):
      """
      RexNeXt bottleneck type C
      """
      expansion = 4
  
      def __init__(self, inplanes, planes, baseWidth, cardinality, stride=1, downsample=None):
          """ Constructor
          Args:
              inplanes: input channel dimensionality
              planes: output channel dimensionality
              baseWidth: base width.
              cardinality: num of convolution groups.
              stride: conv stride. Replaces pooling layer.
          """
          super(Bottleneck, self).__init__()
  
          D = int(math.floor(planes * (baseWidth / 64)))
          C = cardinality
  
          self.conv1 = nn.Conv2d(inplanes, D*C, kernel_size=1, stride=1, padding=0, bias=False)
          self.bn1 = nn.BatchNorm2d(D*C)
          self.conv2 = nn.Conv2d(D*C, D*C, kernel_size=3, stride=stride, padding=1, groups=C, bias=False)
          self.bn2 = nn.BatchNorm2d(D*C)
          self.conv3 = nn.Conv2d(D*C, planes * 4, kernel_size=1, stride=1, padding=0, bias=False)
          self.bn3 = nn.BatchNorm2d(planes * 4)
          self.relu = nn.ReLU(inplace=True)
  
          self.downsample = downsample
  
      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 ResNeXt(nn.Module):
      """
      ResNext optimized for the ImageNet dataset, as specified in
      https://arxiv.org/pdf/1611.05431.pdf
      """
      def __init__(self, baseWidth, cardinality, layers, num_classes):
          """ Constructor
          Args:
              baseWidth: baseWidth for ResNeXt.
              cardinality: number of convolution groups.
              layers: config of layers, e.g., [3, 4, 6, 3]
              num_classes: number of classes
          """
          super(ResNeXt, self).__init__()
          block = Bottleneck
  
          self.cardinality = cardinality
          self.baseWidth = baseWidth
          self.num_classes = num_classes
          self.inplanes = 64
          self.output_size = 64
  
          self.conv1 = nn.Conv2d(3, 64, 7, 2, 3, bias=False)
          self.bn1 = nn.BatchNorm2d(64)
          self.relu = nn.ReLU(inplace=True)
          self.maxpool1 = 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], 2)
          self.layer3 = self._make_layer(block, 256, layers[2], 2)
          self.layer4 = self._make_layer(block, 512, layers[3], 2)
          self.avgpool = nn.AvgPool2d(7)      
          self.fc = 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):
          """ Stack n bottleneck modules where n is inferred from the depth of the network.
          Args:
              block: block type used to construct ResNext
              planes: number of output channels (need to multiply by block.expansion)
              blocks: number of blocks to be built
              stride: factor to reduce the spatial dimensionality in the first bottleneck of the block.
          Returns: a Module consisting of n sequential bottlenecks.
          """
          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=False),
                  nn.BatchNorm2d(planes * block.expansion),
              )
  
          layers = []
          layers.append(block(self.inplanes, planes, self.baseWidth, self.cardinality, stride, downsample))
          self.inplanes = planes * block.expansion
          for i in range(1, blocks):
              layers.append(block(self.inplanes, planes, self.baseWidth, self.cardinality))
  
          return nn.Sequential(*layers)
  
      def forward(self, x):
          x = self.conv1(x)
          x = self.bn1(x)
          x = self.relu(x)
          x = self.maxpool1(x)
          x = self.layer1(x)
          x = self.layer2(x)
          x = self.layer3(x)
          x = self.layer4(x)
          x = self.avgpool(x)
          x = x.view(x.size(0), -1)
          x = self.fc(x)
  
          return x
  
  
  def resnext50(baseWidth, cardinality):
      """
      Construct ResNeXt-50.
      """
      model = ResNeXt(baseWidth, cardinality, [3, 4, 6, 3], 1000)
      return model
  
  
  def resnext101(baseWidth, cardinality):
      """
      Construct ResNeXt-101.
      """
      model = ResNeXt(baseWidth, cardinality, [3, 4, 23, 3], 1000)
      return model
  
  
  def resnext152(baseWidth, cardinality):
      """
      Construct ResNeXt-152.
      """
      model = ResNeXt(baseWidth, cardinality, [3, 8, 36, 3], 1000)
      return model
  