text: """
      ResNeXt for ImageNet-1K, implemented in Gluon.
      Original paper: 'Aggregated Residual Transformations for Deep Neural Networks,' http://arxiv.org/abs/1611.05431.
  """
  
  __all__ = ['ResNeXt', 'resnext14_16x4d', 'resnext14_32x2d', 'resnext14_32x4d', 'resnext26_16x4d', 'resnext26_32x2d',
             'resnext26_32x4d', 'resnext38_32x4d', 'resnext50_32x4d', 'resnext101_32x4d', 'resnext101_64x4d',
             'ResNeXtBottleneck', 'ResNeXtUnit']
  
  import os
  import math
  from mxnet import cpu
  from mxnet.gluon import nn, HybridBlock
  from .common import conv1x1_block, conv3x3_block
  from .resnet import ResInitBlock
  
  
  class ResNeXtBottleneck(HybridBlock):
      """
      ResNeXt bottleneck block for residual path in ResNeXt unit.
  
      Parameters:
      ----------
      in_channels : int
          Number of input channels.
      out_channels : int
          Number of output channels.
      strides : int or tuple/list of 2 int
          Strides of the convolution.
      cardinality: int
          Number of groups.
      bottleneck_width: int
          Width of bottleneck block.
      bn_use_global_stats : bool
          Whether global moving statistics is used instead of local batch-norm for BatchNorm layers.
      bottleneck_factor : int, default 4
          Bottleneck factor.
      """
      def __init__(self,
                   in_channels,
                   out_channels,
                   strides,
                   cardinality,
                   bottleneck_width,
                   bn_use_global_stats,
                   bottleneck_factor=4,
                   **kwargs):
          super(ResNeXtBottleneck, self).__init__(**kwargs)
          mid_channels = out_channels // bottleneck_factor
          D = int(math.floor(mid_channels * (bottleneck_width / 64.0)))
          group_width = cardinality * D
  
          with self.name_scope():
              self.conv1 = conv1x1_block(
                  in_channels=in_channels,
                  out_channels=group_width,
                  bn_use_global_stats=bn_use_global_stats)
              self.conv2 = conv3x3_block(
                  in_channels=group_width,
                  out_channels=group_width,
                  strides=strides,
                  groups=cardinality,
                  bn_use_global_stats=bn_use_global_stats)
              self.conv3 = conv1x1_block(
                  in_channels=group_width,
                  out_channels=out_channels,
                  bn_use_global_stats=bn_use_global_stats,
                  activation=None)
  
      def hybrid_forward(self, F, x):
          x = self.conv1(x)
          x = self.conv2(x)
          x = self.conv3(x)
          return x
  
  
  class ResNeXtUnit(HybridBlock):
      """
      ResNeXt unit with residual connection.
  
      Parameters:
      ----------
      in_channels : int
          Number of input channels.
      out_channels : int
          Number of output channels.
      strides : int or tuple/list of 2 int
          Strides of the convolution.
      cardinality: int
          Number of groups.
      bottleneck_width: int
          Width of bottleneck block.
      bn_use_global_stats : bool
          Whether global moving statistics is used instead of local batch-norm for BatchNorm layers.
      """
      def __init__(self,
                   in_channels,
                   out_channels,
                   strides,
                   cardinality,
                   bottleneck_width,
                   bn_use_global_stats,
                   **kwargs):
          super(ResNeXtUnit, self).__init__(**kwargs)
          self.resize_identity = (in_channels != out_channels) or (strides != 1)
  
          with self.name_scope():
              self.body = ResNeXtBottleneck(
                  in_channels=in_channels,
                  out_channels=out_channels,
                  strides=strides,
                  cardinality=cardinality,
                  bottleneck_width=bottleneck_width,
                  bn_use_global_stats=bn_use_global_stats)
              if self.resize_identity:
                  self.identity_conv = conv1x1_block(
                      in_channels=in_channels,
                      out_channels=out_channels,
                      strides=strides,
                      bn_use_global_stats=bn_use_global_stats,
                      activation=None)
              self.activ = nn.Activation("relu")
  
      def hybrid_forward(self, F, x):
          if self.resize_identity:
              identity = self.identity_conv(x)
          else:
              identity = x
          x = self.body(x)
          x = x + identity
          x = self.activ(x)
          return x
  
  
  class ResNeXt(HybridBlock):
      """
      ResNeXt model from 'Aggregated Residual Transformations for Deep Neural Networks,' http://arxiv.org/abs/1611.05431.
  
      Parameters:
      ----------
      channels : list of list of int
          Number of output channels for each unit.
      init_block_channels : int
          Number of output channels for the initial unit.
      cardinality: int
          Number of groups.
      bottleneck_width: int
          Width of bottleneck block.
      bn_use_global_stats : bool, default False
          Whether global moving statistics is used instead of local batch-norm for BatchNorm layers.
          Useful for fine-tuning.
      in_channels : int, default 3
          Number of input channels.
      in_size : tuple of two ints, default (224, 224)
          Spatial size of the expected input image.
      classes : int, default 1000
          Number of classification classes.
      """
      def __init__(self,
                   channels,
                   init_block_channels,
                   cardinality,
                   bottleneck_width,
                   bn_use_global_stats=False,
                   in_channels=3,
                   in_size=(224, 224),
                   classes=1000,
                   **kwargs):
          super(ResNeXt, self).__init__(**kwargs)
          self.in_size = in_size
          self.classes = classes
  
          with self.name_scope():
              self.features = nn.HybridSequential(prefix="")
              self.features.add(ResInitBlock(
                  in_channels=in_channels,
                  out_channels=init_block_channels,
                  bn_use_global_stats=bn_use_global_stats))
              in_channels = init_block_channels
              for i, channels_per_stage in enumerate(channels):
                  stage = nn.HybridSequential(prefix="stage{}_".format(i + 1))
                  with stage.name_scope():
                      for j, out_channels in enumerate(channels_per_stage):
                          strides = 2 if (j == 0) and (i != 0) else 1
                          stage.add(ResNeXtUnit(
                              in_channels=in_channels,
                              out_channels=out_channels,
                              strides=strides,
                              cardinality=cardinality,
                              bottleneck_width=bottleneck_width,
                              bn_use_global_stats=bn_use_global_stats))
                          in_channels = out_channels
                  self.features.add(stage)
              self.features.add(nn.AvgPool2D(
                  pool_size=7,
                  strides=1))
  
              self.output = nn.HybridSequential(prefix="")
              self.output.add(nn.Flatten())
              self.output.add(nn.Dense(
                  units=classes,
                  in_units=in_channels))
  
      def hybrid_forward(self, F, x):
          x = self.features(x)
          x = self.output(x)
          return x
  
  
  def get_resnext(blocks,
                  cardinality,
                  bottleneck_width,
                  model_name=None,
                  pretrained=False,
                  ctx=cpu(),
                  root=os.path.join("~", ".mxnet", "models"),
                  **kwargs):
      """
      Create ResNeXt model with specific parameters.
  
      Parameters:
      ----------
      blocks : int
          Number of blocks.
      cardinality: int
          Number of groups.
      bottleneck_width: int
          Width of bottleneck block.
      model_name : str or None, default None
          Model name for loading pretrained model.
      pretrained : bool, default False
          Whether to load the pretrained weights for model.
      ctx : Context, default CPU
          The context in which to load the pretrained weights.
      root : str, default '~/.mxnet/models'
          Location for keeping the model parameters.
      """
  
      if blocks == 14:
          layers = [1, 1, 1, 1]
      elif blocks == 26:
          layers = [2, 2, 2, 2]
      elif blocks == 38:
          layers = [3, 3, 3, 3]
      elif blocks == 50:
          layers = [3, 4, 6, 3]
      elif blocks == 101:
          layers = [3, 4, 23, 3]
      else:
          raise ValueError("Unsupported ResNeXt with number of blocks: {}".format(blocks))
  
      assert (sum(layers) * 3 + 2 == blocks)
  
      init_block_channels = 64
      channels_per_layers = [256, 512, 1024, 2048]
  
      channels = [[ci] * li for (ci, li) in zip(channels_per_layers, layers)]
  
      net = ResNeXt(
          channels=channels,
          init_block_channels=init_block_channels,
          cardinality=cardinality,
          bottleneck_width=bottleneck_width,
          **kwargs)
  
      if pretrained:
          if (model_name is None) or (not model_name):
              raise ValueError("Parameter `model_name` should be properly initialized for loading pretrained model.")
          from .model_store import get_model_file
          net.load_parameters(
              filename=get_model_file(
                  model_name=model_name,
                  local_model_store_dir_path=root),
              ctx=ctx)
  
      return net
  
  
  def resnext14_16x4d(**kwargs):
      """
      ResNeXt-14 (16x4d) model from 'Aggregated Residual Transformations for Deep Neural Networks,'
      http://arxiv.org/abs/1611.05431.
  
      Parameters:
      ----------
      pretrained : bool, default False
          Whether to load the pretrained weights for model.
      ctx : Context, default CPU
          The context in which to load the pretrained weights.
      root : str, default '~/.mxnet/models'
          Location for keeping the model parameters.
      """
      return get_resnext(blocks=14, cardinality=16, bottleneck_width=4, model_name="resnext14_16x4d", **kwargs)
  
  
  def resnext14_32x2d(**kwargs):
      """
      ResNeXt-14 (32x2d) model from 'Aggregated Residual Transformations for Deep Neural Networks,'
      http://arxiv.org/abs/1611.05431.
  
      Parameters:
      ----------
      pretrained : bool, default False
          Whether to load the pretrained weights for model.
      ctx : Context, default CPU
          The context in which to load the pretrained weights.
      root : str, default '~/.mxnet/models'
          Location for keeping the model parameters.
      """
      return get_resnext(blocks=14, cardinality=32, bottleneck_width=2, model_name="resnext14_32x2d", **kwargs)
  
  
  def resnext14_32x4d(**kwargs):
      """
      ResNeXt-14 (32x4d) model from 'Aggregated Residual Transformations for Deep Neural Networks,'
      http://arxiv.org/abs/1611.05431.
  
      Parameters:
      ----------
      pretrained : bool, default False
          Whether to load the pretrained weights for model.
      ctx : Context, default CPU
          The context in which to load the pretrained weights.
      root : str, default '~/.mxnet/models'
          Location for keeping the model parameters.
      """
      return get_resnext(blocks=14, cardinality=32, bottleneck_width=4, model_name="resnext14_32x4d", **kwargs)
  
  
  def resnext26_16x4d(**kwargs):
      """
      ResNeXt-26 (16x4d) model from 'Aggregated Residual Transformations for Deep Neural Networks,'
      http://arxiv.org/abs/1611.05431.
  
      Parameters:
      ----------
      pretrained : bool, default False
          Whether to load the pretrained weights for model.
      ctx : Context, default CPU
          The context in which to load the pretrained weights.
      root : str, default '~/.mxnet/models'
          Location for keeping the model parameters.
      """
      return get_resnext(blocks=26, cardinality=16, bottleneck_width=4, model_name="resnext26_16x4d", **kwargs)
  
  
  def resnext26_32x2d(**kwargs):
      """
      ResNeXt-26 (32x2d) model from 'Aggregated Residual Transformations for Deep Neural Networks,'
      http://arxiv.org/abs/1611.05431.
  
      Parameters:
      ----------
      pretrained : bool, default False
          Whether to load the pretrained weights for model.
      ctx : Context, default CPU
          The context in which to load the pretrained weights.
      root : str, default '~/.mxnet/models'
          Location for keeping the model parameters.
      """
      return get_resnext(blocks=26, cardinality=32, bottleneck_width=2, model_name="resnext26_32x2d", **kwargs)
  
  
  def resnext26_32x4d(**kwargs):
      """
      ResNeXt-26 (32x4d) model from 'Aggregated Residual Transformations for Deep Neural Networks,'
      http://arxiv.org/abs/1611.05431.
  
      Parameters:
      ----------
      pretrained : bool, default False
          Whether to load the pretrained weights for model.
      ctx : Context, default CPU
          The context in which to load the pretrained weights.
      root : str, default '~/.mxnet/models'
          Location for keeping the model parameters.
      """
      return get_resnext(blocks=26, cardinality=32, bottleneck_width=4, model_name="resnext26_32x4d", **kwargs)
  
  
  def resnext38_32x4d(**kwargs):
      """
      ResNeXt-38 (32x4d) model from 'Aggregated Residual Transformations for Deep Neural Networks,'
      http://arxiv.org/abs/1611.05431.
  
      Parameters:
      ----------
      pretrained : bool, default False
          Whether to load the pretrained weights for model.
      ctx : Context, default CPU
          The context in which to load the pretrained weights.
      root : str, default '~/.mxnet/models'
          Location for keeping the model parameters.
      """
      return get_resnext(blocks=38, cardinality=32, bottleneck_width=4, model_name="resnext38_32x4d", **kwargs)
  
  
  def resnext50_32x4d(**kwargs):
      """
      ResNeXt-50 (32x4d) model from 'Aggregated Residual Transformations for Deep Neural Networks,'
      http://arxiv.org/abs/1611.05431.
  
      Parameters:
      ----------
      pretrained : bool, default False
          Whether to load the pretrained weights for model.
      ctx : Context, default CPU
          The context in which to load the pretrained weights.
      root : str, default '~/.mxnet/models'
          Location for keeping the model parameters.
      """
      return get_resnext(blocks=50, cardinality=32, bottleneck_width=4, model_name="resnext50_32x4d", **kwargs)
  
  
  def resnext101_32x4d(**kwargs):
      """
      ResNeXt-101 (32x4d) model from 'Aggregated Residual Transformations for Deep Neural Networks,'
      http://arxiv.org/abs/1611.05431.
  
      Parameters:
      ----------
      pretrained : bool, default False
          Whether to load the pretrained weights for model.
      ctx : Context, default CPU
          The context in which to load the pretrained weights.
      root : str, default '~/.mxnet/models'
          Location for keeping the model parameters.
      """
      return get_resnext(blocks=101, cardinality=32, bottleneck_width=4, model_name="resnext101_32x4d", **kwargs)
  
  
  def resnext101_64x4d(**kwargs):
      """
      ResNeXt-101 (64x4d) model from 'Aggregated Residual Transformations for Deep Neural Networks,'
      http://arxiv.org/abs/1611.05431.
  
      Parameters:
      ----------
      pretrained : bool, default False
          Whether to load the pretrained weights for model.
      ctx : Context, default CPU
          The context in which to load the pretrained weights.
      root : str, default '~/.mxnet/models'
          Location for keeping the model parameters.
      """
      return get_resnext(blocks=101, cardinality=64, bottleneck_width=4, model_name="resnext101_64x4d", **kwargs)
  
  
  def _test():
      import numpy as np
      import mxnet as mx
  
      pretrained = False
  
      models = [
          resnext14_16x4d,
          resnext14_32x2d,
          resnext14_32x4d,
          resnext26_16x4d,
          resnext26_32x2d,
          resnext26_32x4d,
          resnext38_32x4d,
          resnext50_32x4d,
          resnext101_32x4d,
          resnext101_64x4d,
      ]
  
      for model in models:
  
          net = model(pretrained=pretrained)
  
          ctx = mx.cpu()
          if not pretrained:
              net.initialize(ctx=ctx)
  
          net_params = net.collect_params()
          weight_count = 0
          for param in net_params.values():
              if (param.shape is None) or (not param._differentiable):
                  continue
              weight_count += np.prod(param.shape)
          print("m={}, {}".format(model.__name__, weight_count))
          assert (model != resnext14_16x4d or weight_count == 7127336)
          assert (model != resnext14_32x2d or weight_count == 7029416)
          assert (model != resnext14_32x4d or weight_count == 9411880)
          assert (model != resnext26_16x4d or weight_count == 10119976)
          assert (model != resnext26_32x2d or weight_count == 9924136)
          assert (model != resnext26_32x4d or weight_count == 15389480)
          assert (model != resnext38_32x4d or weight_count == 21367080)
          assert (model != resnext50_32x4d or weight_count == 25028904)
          assert (model != resnext101_32x4d or weight_count == 44177704)
          assert (model != resnext101_64x4d or weight_count == 83455272)
  
          x = mx.nd.zeros((1, 3, 224, 224), ctx=ctx)
          y = net(x)
          assert (y.shape == (1, 1000))
  
  
  if __name__ == "__main__":
      _test()
  