text: """
      MENet, implemented in Keras.
      Original paper: 'Merging and Evolution: Improving Convolutional Neural Networks for Mobile Applications,'
      https://arxiv.org/abs/1803.09127.
  """
  
  __all__ = ['menet', 'menet108_8x1_g3', 'menet128_8x1_g4', 'menet160_8x1_g8', 'menet228_12x1_g3', 'menet256_12x1_g4',
             'menet348_12x1_g3', 'menet352_12x1_g8', 'menet456_24x1_g3']
  
  import os
  from keras import backend as K
  from keras import layers as nn
  from keras.models import Model
  from .common import conv2d, conv1x1, channel_shuffle_lambda, GluonBatchNormalization
  
  
  def depthwise_conv3x3(x,
                        channels,
                        strides,
                        name="depthwise_conv3x3"):
      """
      Depthwise convolution 3x3 layer. This is exactly the same layer as in ShuffleNet.
  
      Parameters:
      ----------
      x : keras.backend tensor/variable/symbol
          Input tensor/variable/symbol.
      channels : int
          Number of input/output channels.
      strides : int or tuple/list of 2 int
          Strides of the convolution.
      name : str, default 'depthwise_conv3x3'
          Block name.
  
      Returns
      -------
      keras.backend tensor/variable/symbol
          Resulted tensor/variable/symbol.
      """
      return conv2d(
          x=x,
          in_channels=channels,
          out_channels=channels,
          kernel_size=3,
          strides=strides,
          padding=1,
          groups=channels,
          use_bias=False,
          name=name)
  
  
  def group_conv1x1(x,
                    in_channels,
                    out_channels,
                    groups,
                    name="group_conv1x1"):
      """
      Group convolution 1x1 layer. This is exactly the same layer as in ShuffleNet.
  
      Parameters:
      ----------
      x : keras.backend tensor/variable/symbol
          Input tensor/variable/symbol.
      in_channels : int
          Number of input channels.
      out_channels : int
          Number of output channels.
      groups : int
          Number of groups.
      name : str, default 'depthwise_conv3x3'
          Block name.
  
      Returns
      -------
      keras.backend tensor/variable/symbol
          Resulted tensor/variable/symbol.
      """
      return conv2d(
          x=x,
          in_channels=in_channels,
          out_channels=out_channels,
          kernel_size=1,
          groups=groups,
          use_bias=False,
          name=name)
  
  
  def conv3x3(x,
              in_channels,
              out_channels,
              strides,
              name="conv3x3"):
      """
      Convolution 3x3 layer.
  
      Parameters:
      ----------
      x : keras.backend tensor/variable/symbol
          Input tensor/variable/symbol.
      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.
      name : str, default 'conv3x3'
          Block name.
  
      Returns
      -------
      keras.backend tensor/variable/symbol
          Resulted tensor/variable/symbol.
      """
      return conv2d(
          x=x,
          in_channels=in_channels,
          out_channels=out_channels,
          kernel_size=3,
          strides=strides,
          padding=1,
          use_bias=False,
          name=name)
  
  
  def me_unit(x,
              in_channels,
              out_channels,
              side_channels,
              groups,
              downsample,
              ignore_group,
              name="me_unit"):
      """
      MENet unit.
  
      Parameters:
      ----------
      x : keras.backend tensor/variable/symbol
          Input tensor/variable/symbol.
      in_channels : int
          Number of input channels.
      out_channels : int
          Number of output channels.
      side_channels : int
          Number of side channels.
      groups : int
          Number of groups in convolution layers.
      downsample : bool
          Whether do downsample.
      ignore_group : bool
          Whether ignore group value in the first convolution layer.
      name : str, default 'me_unit'
          Unit name.
  
      Returns
      -------
      keras.backend tensor/variable/symbol
          Resulted tensor/variable/symbol.
      """
      mid_channels = out_channels // 4
  
      if downsample:
          out_channels -= in_channels
  
      identity = x
  
      # pointwise group convolution 1
      x = group_conv1x1(
          x=x,
          in_channels=in_channels,
          out_channels=mid_channels,
          groups=(1 if ignore_group else groups),
          name=name + "/compress_conv1")
      x = GluonBatchNormalization(name=name + "/compress_bn1")(x)
      x = nn.Activation("relu", name=name + "/compress_activ")(x)
  
      x = channel_shuffle_lambda(
          channels=mid_channels,
          groups=groups,
          name=name + "/c_shuffle")(x)
  
      # merging
      y = conv1x1(
          out_channels=side_channels,
          name=name + "/s_merge_conv")(x)
      y = GluonBatchNormalization(name=name + "/s_merge_bn")(y)
      y = nn.Activation("relu", name=name + "/s_merge_activ")(y)
  
      # depthwise convolution (bottleneck)
      x = depthwise_conv3x3(
          x=x,
          channels=mid_channels,
          strides=(2 if downsample else 1),
          name=name + "/dw_conv2")
      x = GluonBatchNormalization(name=name + "/dw_bn2")(x)
  
      # evolution
      y = conv3x3(
          x=y,
          in_channels=side_channels,
          out_channels=side_channels,
          strides=(2 if downsample else 1),
          name=name + "/s_conv")
      y = GluonBatchNormalization(name=name + "/s_conv_bn")(y)
      y = nn.Activation("relu", name=name + "/s_conv_activ")(y)
  
      y = conv1x1(
          out_channels=mid_channels,
          name=name + "/s_evolve_conv")(y)
      y = GluonBatchNormalization(name=name + "/s_evolve_bn")(y)
      y = nn.Activation('sigmoid', name=name + "/s_evolve_activ")(y)
  
      x = nn.multiply([x, y], name=name + "/mul")
  
      # pointwise group convolution 2
      x = group_conv1x1(
          x=x,
          in_channels=mid_channels,
          out_channels=out_channels,
          groups=groups,
          name=name + "/expand_conv3")
      x = GluonBatchNormalization(name=name + "/expand_bn3")(x)
  
      x._keras_shape = tuple([d if d != 0 else None for d in x.shape])
  
      if downsample:
          identity = nn.AvgPool2D(
              pool_size=3,
              strides=2,
              padding="same",
              name=name + "/avgpool")(identity)
  
          channel_axis = 1 if K.image_data_format() == 'channels_first' else -1
          x = nn.concatenate([x, identity], axis=channel_axis, name=name + "/concat")
      else:
          x = nn.add([x, identity], name=name + "/add")
  
      x = nn.Activation("relu", name=name + "/final_activ")(x)
      return x
  
  
  def me_init_block(x,
                    in_channels,
                    out_channels,
                    name="me_init_block"):
      """
      MENet specific initial block.
  
      Parameters:
      ----------
      x : keras.backend tensor/variable/symbol
          Input tensor/variable/symbol.
      in_channels : int
          Number of input channels.
      out_channels : int
          Number of output channels.
      name : str, default 'shuffle_init_block'
          Block name.
  
      Returns
      -------
      keras.backend tensor/variable/symbol
          Resulted tensor/variable/symbol.
      """
      x = conv2d(
          x=x,
          in_channels=in_channels,
          out_channels=out_channels,
          kernel_size=3,
          strides=2,
          padding=1,
          use_bias=False,
          name=name + "/conv")
      x = GluonBatchNormalization(name=name + "/bn")(x)
      x = nn.Activation("relu", name=name + "/activ")(x)
      x = nn.MaxPool2D(
          pool_size=3,
          strides=2,
          padding="same",
          name=name + "/pool")(x)
      return x
  
  
  def menet(channels,
            init_block_channels,
            side_channels,
            groups,
            in_channels=3,
            in_size=(224, 224),
            classes=1000):
      """
      ShuffleNet model from 'ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices,'
      https://arxiv.org/abs/1707.01083.
  
      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.
      side_channels : int
          Number of side channels in a ME-unit.
      groups : int
          Number of groups in convolution layers.
      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.
      """
      input_shape = (in_channels, 224, 224) if K.image_data_format() == 'channels_first' else (224, 224, in_channels)
      input = nn.Input(shape=input_shape)
  
      x = me_init_block(
          x=input,
          in_channels=in_channels,
          out_channels=init_block_channels,
          name="features/init_block")
      in_channels = init_block_channels
      for i, channels_per_stage in enumerate(channels):
          for j, out_channels in enumerate(channels_per_stage):
              downsample = (j == 0)
              ignore_group = (i == 0) and (j == 0)
              x = me_unit(
                  x=x,
                  in_channels=in_channels,
                  out_channels=out_channels,
                  side_channels=side_channels,
                  groups=groups,
                  downsample=downsample,
                  ignore_group=ignore_group,
                  name="features/stage{}/unit{}".format(i + 1, j + 1))
              in_channels = out_channels
      x = nn.AvgPool2D(
          pool_size=7,
          strides=1,
          name="features/final_pool")(x)
  
      x = nn.Flatten()(x)
      x = nn.Dense(
          units=classes,
          input_dim=in_channels,
          name="output")(x)
  
      model = Model(inputs=input, outputs=x)
      model.in_size = in_size
      model.classes = classes
      return model
  
  
  def get_menet(first_stage_channels,
                side_channels,
                groups,
                model_name=None,
                pretrained=False,
                root=os.path.join('~', '.keras', 'models'),
                **kwargs):
      """
      Create MENet model with specific parameters.
  
      Parameters:
      ----------
      first_stage_channels : int
          Number of output channels at the first stage.
      side_channels : int
          Number of side channels in a ME-unit.
      groups : int
          Number of groups in convolution 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.
      root : str, default '~/.keras/models'
          Location for keeping the model parameters.
      """
  
      layers = [4, 8, 4]
  
      if first_stage_channels == 108:
          init_block_channels = 12
          channels_per_layers = [108, 216, 432]
      elif first_stage_channels == 128:
          init_block_channels = 12
          channels_per_layers = [128, 256, 512]
      elif first_stage_channels == 160:
          init_block_channels = 16
          channels_per_layers = [160, 320, 640]
      elif first_stage_channels == 228:
          init_block_channels = 24
          channels_per_layers = [228, 456, 912]
      elif first_stage_channels == 256:
          init_block_channels = 24
          channels_per_layers = [256, 512, 1024]
      elif first_stage_channels == 348:
          init_block_channels = 24
          channels_per_layers = [348, 696, 1392]
      elif first_stage_channels == 352:
          init_block_channels = 24
          channels_per_layers = [352, 704, 1408]
      elif first_stage_channels == 456:
          init_block_channels = 48
          channels_per_layers = [456, 912, 1824]
      else:
          raise ValueError("The {} of `first_stage_channels` is not supported".format(first_stage_channels))
  
      channels = [[ci] * li for (ci, li) in zip(channels_per_layers, layers)]
  
      net = menet(
          channels=channels,
          init_block_channels=init_block_channels,
          side_channels=side_channels,
          groups=groups,
          **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_weights(
              filepath=get_model_file(
                  model_name=model_name,
                  local_model_store_dir_path=root))
  
      return net
  
  
  def menet108_8x1_g3(**kwargs):
      """
      108-MENet-8x1 (g=3) model from 'Merging and Evolution: Improving Convolutional Neural Networks for Mobile
      Applications,' https://arxiv.org/abs/1803.09127.
  
      Parameters:
      ----------
      pretrained : bool, default False
          Whether to load the pretrained weights for model.
      root : str, default '~/.keras/models'
          Location for keeping the model parameters.
      """
      return get_menet(first_stage_channels=108, side_channels=8, groups=3, model_name="menet108_8x1_g3", **kwargs)
  
  
  def menet128_8x1_g4(**kwargs):
      """
      128-MENet-8x1 (g=4) model from 'Merging and Evolution: Improving Convolutional Neural Networks for Mobile
      Applications,' https://arxiv.org/abs/1803.09127.
  
      Parameters:
      ----------
      pretrained : bool, default False
          Whether to load the pretrained weights for model.
      root : str, default '~/.keras/models'
          Location for keeping the model parameters.
      """
      return get_menet(first_stage_channels=128, side_channels=8, groups=4, model_name="menet128_8x1_g4", **kwargs)
  
  
  def menet160_8x1_g8(**kwargs):
      """
      160-MENet-8x1 (g=8) model from 'Merging and Evolution: Improving Convolutional Neural Networks for Mobile
      Applications,' https://arxiv.org/abs/1803.09127.
  
      Parameters:
      ----------
      pretrained : bool, default False
          Whether to load the pretrained weights for model.
      root : str, default '~/.keras/models'
          Location for keeping the model parameters.
      """
      return get_menet(first_stage_channels=160, side_channels=8, groups=8, model_name="menet160_8x1_g8", **kwargs)
  
  
  def menet228_12x1_g3(**kwargs):
      """
      228-MENet-12x1 (g=3) model from 'Merging and Evolution: Improving Convolutional Neural Networks for Mobile
      Applications,' https://arxiv.org/abs/1803.09127.
  
      Parameters:
      ----------
      pretrained : bool, default False
          Whether to load the pretrained weights for model.
      root : str, default '~/.keras/models'
          Location for keeping the model parameters.
      """
      return get_menet(first_stage_channels=228, side_channels=12, groups=3, model_name="menet228_12x1_g3", **kwargs)
  
  
  def menet256_12x1_g4(**kwargs):
      """
      256-MENet-12x1 (g=4) model from 'Merging and Evolution: Improving Convolutional Neural Networks for Mobile
      Applications,' https://arxiv.org/abs/1803.09127.
  
      Parameters:
      ----------
      pretrained : bool, default False
          Whether to load the pretrained weights for model.
      root : str, default '~/.keras/models'
          Location for keeping the model parameters.
      """
      return get_menet(first_stage_channels=256, side_channels=12, groups=4, model_name="menet256_12x1_g4", **kwargs)
  
  
  def menet348_12x1_g3(**kwargs):
      """
      348-MENet-12x1 (g=3) model from 'Merging and Evolution: Improving Convolutional Neural Networks for Mobile
      Applications,' https://arxiv.org/abs/1803.09127.
  
      Parameters:
      ----------
      pretrained : bool, default False
          Whether to load the pretrained weights for model.
      root : str, default '~/.keras/models'
          Location for keeping the model parameters.
      """
      return get_menet(first_stage_channels=348, side_channels=12, groups=3, model_name="menet348_12x1_g3", **kwargs)
  
  
  def menet352_12x1_g8(**kwargs):
      """
      352-MENet-12x1 (g=8) model from 'Merging and Evolution: Improving Convolutional Neural Networks for Mobile
      Applications,' https://arxiv.org/abs/1803.09127.
  
      Parameters:
      ----------
      pretrained : bool, default False
          Whether to load the pretrained weights for model.
      root : str, default '~/.keras/models'
          Location for keeping the model parameters.
      """
      return get_menet(first_stage_channels=352, side_channels=12, groups=8, model_name="menet352_12x1_g8", **kwargs)
  
  
  def menet456_24x1_g3(**kwargs):
      """
      456-MENet-24x1 (g=3) model from 'Merging and Evolution: Improving Convolutional Neural Networks for Mobile
      Applications,' https://arxiv.org/abs/1803.09127.
  
      Parameters:
      ----------
      pretrained : bool, default False
          Whether to load the pretrained weights for model.
      root : str, default '~/.keras/models'
          Location for keeping the model parameters.
      """
      return get_menet(first_stage_channels=456, side_channels=24, groups=3, model_name="menet456_24x1_g3", **kwargs)
  
  
  def _test():
      import numpy as np
      import keras
  
      pretrained = False
  
      models = [
          menet108_8x1_g3,
          menet128_8x1_g4,
          menet160_8x1_g8,
          menet228_12x1_g3,
          menet256_12x1_g4,
          menet348_12x1_g3,
          menet352_12x1_g8,
          menet456_24x1_g3,
      ]
  
      for model in models:
  
          net = model(pretrained=pretrained)
          # net.summary()
          weight_count = keras.utils.layer_utils.count_params(net.trainable_weights)
          print("m={}, {}".format(model.__name__, weight_count))
          assert (model != menet108_8x1_g3 or weight_count == 654516)
          assert (model != menet128_8x1_g4 or weight_count == 750796)
          assert (model != menet160_8x1_g8 or weight_count == 850120)
          assert (model != menet228_12x1_g3 or weight_count == 1806568)
          assert (model != menet256_12x1_g4 or weight_count == 1888240)
          assert (model != menet348_12x1_g3 or weight_count == 3368128)
          assert (model != menet352_12x1_g8 or weight_count == 2272872)
          assert (model != menet456_24x1_g3 or weight_count == 5304784)
  
          x = np.zeros((1, 3, 224, 224), np.float32)
          y = net.predict(x)
          assert (y.shape == (1, 1000))
  
  
  if __name__ == "__main__":
      _test()
  