code: """
      DenseNet, implemented in Gluon.
      Original paper: 'Densely Connected Convolutional Networks,' https://arxiv.org/abs/1608.06993.
  """
  
  __all__ = ['DenseNet', 'densenet121', 'densenet161', 'densenet169', 'densenet201']
  
  import os
  from mxnet import cpu
  from mxnet.gluon import nn, HybridBlock
  from .common import pre_conv1x1_block, pre_conv3x3_block
  from .preresnet import PreResInitBlock, PreResActivation
  
  
  class DenseUnit(HybridBlock):
      """
      DenseNet unit.
  
      Parameters:
      ----------
      in_channels : int
          Number of input channels.
      out_channels : int
          Number of output channels.
      bn_use_global_stats : bool
          Whether global moving statistics is used instead of local batch-norm for BatchNorm layers.
      dropout_rate : bool
          Parameter of Dropout layer. Faction of the input units to drop.
      """
      def __init__(self,
                   in_channels,
                   out_channels,
                   bn_use_global_stats,
                   dropout_rate,
                   **kwargs):
          super(DenseUnit, self).__init__(**kwargs)
          self.use_dropout = (dropout_rate != 0.0)
          bn_size = 4
          inc_channels = out_channels - in_channels
          mid_channels = inc_channels * bn_size
  
          with self.name_scope():
              self.conv1 = pre_conv1x1_block(
                  in_channels=in_channels,
                  out_channels=mid_channels,
                  bn_use_global_stats=bn_use_global_stats)
              self.conv2 = pre_conv3x3_block(
                  in_channels=mid_channels,
                  out_channels=inc_channels,
                  bn_use_global_stats=bn_use_global_stats)
              if self.use_dropout:
                  self.dropout = nn.Dropout(rate=dropout_rate)
  
      def hybrid_forward(self, F, x):
          identity = x
          x = self.conv1(x)
          x = self.conv2(x)
          if self.use_dropout:
              x = self.dropout(x)
          x = F.concat(identity, x, dim=1)
          return x
  
  
  class TransitionBlock(HybridBlock):
      """
      DenseNet's auxiliary block, which can be treated as the initial part of the DenseNet unit, triggered only in the
      first unit of each stage.
  
      Parameters:
      ----------
      in_channels : int
          Number of input channels.
      out_channels : int
          Number of output channels.
      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,
                   bn_use_global_stats,
                   **kwargs):
          super(TransitionBlock, self).__init__(**kwargs)
          with self.name_scope():
              self.conv = pre_conv1x1_block(
                  in_channels=in_channels,
                  out_channels=out_channels,
                  bn_use_global_stats=bn_use_global_stats)
              self.pool = nn.AvgPool2D(
                  pool_size=2,
                  strides=2,
                  padding=0)
  
      def hybrid_forward(self, F, x):
          x = self.conv(x)
          x = self.pool(x)
          return x
  
  
  class DenseNet(HybridBlock):
      """
      DenseNet model from 'Densely Connected Convolutional Networks,' https://arxiv.org/abs/1608.06993.
  
      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.
      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.
      dropout_rate : float, default 0.0
          Parameter of Dropout layer. Faction of the input units to drop.
      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,
                   bn_use_global_stats=False,
                   dropout_rate=0.0,
                   in_channels=3,
                   in_size=(224, 224),
                   classes=1000,
                   **kwargs):
          super(DenseNet, self).__init__(**kwargs)
          self.in_size = in_size
          self.classes = classes
  
          with self.name_scope():
              self.features = nn.HybridSequential(prefix='')
              self.features.add(PreResInitBlock(
                  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():
                      if i != 0:
                          stage.add(TransitionBlock(
                              in_channels=in_channels,
                              out_channels=(in_channels // 2),
                              bn_use_global_stats=bn_use_global_stats))
                          in_channels = in_channels // 2
                      for j, out_channels in enumerate(channels_per_stage):
                          stage.add(DenseUnit(
                              in_channels=in_channels,
                              out_channels=out_channels,
                              bn_use_global_stats=bn_use_global_stats,
                              dropout_rate=dropout_rate))
                          in_channels = out_channels
                  self.features.add(stage)
              self.features.add(PreResActivation(
                  in_channels=in_channels,
                  bn_use_global_stats=bn_use_global_stats))
              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_densenet(num_layers,
                   model_name=None,
                   pretrained=False,
                   ctx=cpu(),
                   root=os.path.join('~', '.mxnet', 'models'),
                   **kwargs):
      """
      Create DenseNet model with specific parameters.
  
      Parameters:
      ----------
      num_layers : int
          Number of layers.
      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 num_layers == 121:
          init_block_channels = 64
          growth_rate = 32
          layers = [6, 12, 24, 16]
      elif num_layers == 161:
          init_block_channels = 96
          growth_rate = 48
          layers = [6, 12, 36, 24]
      elif num_layers == 169:
          init_block_channels = 64
          growth_rate = 32
          layers = [6, 12, 32, 32]
      elif num_layers == 201:
          init_block_channels = 64
          growth_rate = 32
          layers = [6, 12, 48, 32]
      else:
          raise ValueError("Unsupported DenseNet version with number of layers {}".format(num_layers))
  
      from functools import reduce
      channels = reduce(lambda xi, yi:
                        xi + [reduce(lambda xj, yj:
                                     xj + [xj[-1] + yj],
                                     [growth_rate] * yi,
                                     [xi[-1][-1] // 2])[1:]],
                        layers,
                        [[init_block_channels * 2]])[1:]
  
      net = DenseNet(
          channels=channels,
          init_block_channels=init_block_channels,
          **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 densenet121(**kwargs):
      """
      DenseNet-121 model from 'Densely Connected Convolutional Networks,' https://arxiv.org/abs/1608.06993.
  
      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_densenet(num_layers=121, model_name="densenet121", **kwargs)
  
  
  def densenet161(**kwargs):
      """
      DenseNet-161 model from 'Densely Connected Convolutional Networks,' https://arxiv.org/abs/1608.06993.
  
      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_densenet(num_layers=161, model_name="densenet161", **kwargs)
  
  
  def densenet169(**kwargs):
      """
      DenseNet-169 model from 'Densely Connected Convolutional Networks,' https://arxiv.org/abs/1608.06993.
  
      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_densenet(num_layers=169, model_name="densenet169", **kwargs)
  
  
  def densenet201(**kwargs):
      """
      DenseNet-201 model from 'Densely Connected Convolutional Networks,' https://arxiv.org/abs/1608.06993.
  
      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_densenet(num_layers=201, model_name="densenet201", **kwargs)
  
  
  def _test():
      import numpy as np
      import mxnet as mx
  
      pretrained = False
  
      models = [
          densenet121,
          densenet161,
          densenet169,
          densenet201,
      ]
  
      for model in models:
  
          net = model(pretrained=pretrained)
  
          ctx = mx.cpu()
          if not pretrained:
              net.initialize(ctx=ctx)
  
          # net.hybridize()
          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 != densenet121 or weight_count == 7978856)
          assert (model != densenet161 or weight_count == 28681000)
          assert (model != densenet169 or weight_count == 14149480)
          assert (model != densenet201 or weight_count == 20013928)
  
          x = mx.nd.zeros((1, 3, 224, 224), ctx=ctx)
          y = net(x)
          assert (y.shape == (1, 1000))
  
  
  if __name__ == "__main__":
      _test()
  
apis: numpy.prod