text: """
      SE-ResNet, implemented in Keras.
      Original paper: 'Squeeze-and-Excitation Networks,' https://arxiv.org/abs/1709.01507.
  """
  
  __all__ = ['seresnet', 'seresnet18', 'seresnet34', 'seresnet50', 'seresnet50b', 'seresnet101', 'seresnet101b',
             'seresnet152', 'seresnet152b', 'seresnet200', 'seresnet200b']
  
  import os
  from keras import layers as nn
  from keras.models import Model
  from .common import conv1x1_block, se_block, is_channels_first, flatten
  from .resnet import res_block, res_bottleneck_block, res_init_block
  
  
  def seres_unit(x,
                 in_channels,
                 out_channels,
                 strides,
                 bottleneck,
                 conv1_stride,
                 name="seres_unit"):
      """
      SE-ResNet 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.
      strides : int or tuple/list of 2 int
          Strides of the convolution.
      bottleneck : bool
          Whether to use a bottleneck or simple block in units.
      conv1_stride : bool
          Whether to use stride in the first or the second convolution layer of the block.
      name : str, default 'seres_unit'
          Unit name.
  
      Returns
      -------
      keras.backend tensor/variable/symbol
          Resulted tensor/variable/symbol.
      """
      resize_identity = (in_channels != out_channels) or (strides != 1)
      if resize_identity:
          identity = conv1x1_block(
              x=x,
              in_channels=in_channels,
              out_channels=out_channels,
              strides=strides,
              activate=False,
              name=name + "/identity_conv")
      else:
          identity = x
  
      if bottleneck:
          x = res_bottleneck_block(
              x=x,
              in_channels=in_channels,
              out_channels=out_channels,
              strides=strides,
              conv1_stride=conv1_stride,
              name=name + "/body")
      else:
          x = res_block(
              x=x,
              in_channels=in_channels,
              out_channels=out_channels,
              strides=strides,
              name=name + "/body")
  
      x = se_block(
          x=x,
          channels=out_channels,
          name=name + "/se")
  
      x = nn.add([x, identity], name=name + "/add")
  
      x = nn.Activation('relu', name=name + "/activ")(x)
      return x
  
  
  def seresnet(channels,
               init_block_channels,
               bottleneck,
               conv1_stride,
               in_channels=3,
               in_size=(224, 224),
               classes=1000):
      """
      SE-ResNet model from 'Squeeze-and-Excitation Networks,' https://arxiv.org/abs/1709.01507.
  
      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.
      bottleneck : bool
          Whether to use a bottleneck or simple block in units.
      conv1_stride : bool
          Whether to use stride in the first or the second convolution layer in units.
      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 is_channels_first() else (224, 224, in_channels)
      input = nn.Input(shape=input_shape)
  
      x = res_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):
              strides = 2 if (j == 0) and (i != 0) else 1
              x = seres_unit(
                  x=x,
                  in_channels=in_channels,
                  out_channels=out_channels,
                  strides=strides,
                  bottleneck=bottleneck,
                  conv1_stride=conv1_stride,
                  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 = 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_seresnet(blocks,
                   conv1_stride=True,
                   model_name=None,
                   pretrained=False,
                   root=os.path.join('~', '.keras', 'models'),
                   **kwargs):
      """
      Create SE-ResNet model with specific parameters.
  
      Parameters:
      ----------
      blocks : int
          Number of blocks.
      conv1_stride : bool, default True
          Whether to use stride in the first or the second convolution layer in units.
      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.
      """
  
      if blocks == 18:
          layers = [2, 2, 2, 2]
      elif blocks == 34:
          layers = [3, 4, 6, 3]
      elif blocks == 50:
          layers = [3, 4, 6, 3]
      elif blocks == 101:
          layers = [3, 4, 23, 3]
      elif blocks == 152:
          layers = [3, 8, 36, 3]
      elif blocks == 200:
          layers = [3, 24, 36, 3]
      else:
          raise ValueError("Unsupported SE-ResNet with number of blocks: {}".format(blocks))
  
      init_block_channels = 64
  
      if blocks < 50:
          channels_per_layers = [64, 128, 256, 512]
          bottleneck = False
      else:
          channels_per_layers = [256, 512, 1024, 2048]
          bottleneck = True
  
      channels = [[ci] * li for (ci, li) in zip(channels_per_layers, layers)]
  
      net = seresnet(
          channels=channels,
          init_block_channels=init_block_channels,
          bottleneck=bottleneck,
          conv1_stride=conv1_stride,
          **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 download_model
          download_model(
              net=net,
              model_name=model_name,
              local_model_store_dir_path=root)
  
      return net
  
  
  def seresnet18(**kwargs):
      """
      SE-ResNet-18 model from 'Squeeze-and-Excitation Networks,' https://arxiv.org/abs/1709.01507.
  
      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_seresnet(blocks=18, model_name="seresnet18", **kwargs)
  
  
  def seresnet34(**kwargs):
      """
      SE-ResNet-34 model from 'Squeeze-and-Excitation Networks,' https://arxiv.org/abs/1709.01507.
  
      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_seresnet(blocks=34, model_name="seresnet34", **kwargs)
  
  
  def seresnet50(**kwargs):
      """
      SE-ResNet-50 model from 'Squeeze-and-Excitation Networks,' https://arxiv.org/abs/1709.01507.
  
      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_seresnet(blocks=50, model_name="seresnet50", **kwargs)
  
  
  def seresnet50b(**kwargs):
      """
      SE-ResNet-50 model with stride at the second convolution in bottleneck block from 'Squeeze-and-Excitation
      Networks,' https://arxiv.org/abs/1709.01507.
  
      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_seresnet(blocks=50, conv1_stride=False, model_name="seresnet50b", **kwargs)
  
  
  def seresnet101(**kwargs):
      """
      SE-ResNet-101 model from 'Squeeze-and-Excitation Networks,' https://arxiv.org/abs/1709.01507.
  
      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_seresnet(blocks=101, model_name="seresnet101", **kwargs)
  
  
  def seresnet101b(**kwargs):
      """
      SE-ResNet-101 model with stride at the second convolution in bottleneck block from 'Squeeze-and-Excitation
      Networks,' https://arxiv.org/abs/1709.01507.
  
      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_seresnet(blocks=101, conv1_stride=False, model_name="seresnet101b", **kwargs)
  
  
  def seresnet152(**kwargs):
      """
      SE-ResNet-152 model from 'Squeeze-and-Excitation Networks,' https://arxiv.org/abs/1709.01507.
  
      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_seresnet(blocks=152, model_name="seresnet152", **kwargs)
  
  
  def seresnet152b(**kwargs):
      """
      SE-ResNet-152 model with stride at the second convolution in bottleneck block from 'Squeeze-and-Excitation
      Networks,' https://arxiv.org/abs/1709.01507.
  
      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_seresnet(blocks=152, conv1_stride=False, model_name="seresnet152b", **kwargs)
  
  
  def seresnet200(**kwargs):
      """
      SE-ResNet-200 model from 'Squeeze-and-Excitation Networks,' https://arxiv.org/abs/1709.01507.
      It's an experimental model.
  
      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_seresnet(blocks=200, model_name="seresnet200", **kwargs)
  
  
  def seresnet200b(**kwargs):
      """
      SE-ResNet-200 model with stride at the second convolution in bottleneck block from 'Squeeze-and-Excitation
      Networks,' https://arxiv.org/abs/1709.01507. It's an experimental model.
  
      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_seresnet(blocks=200, conv1_stride=False, model_name="seresnet200b", **kwargs)
  
  
  def _test():
      import numpy as np
      import keras
  
      pretrained = False
  
      models = [
          seresnet18,
          seresnet34,
          seresnet50,
          seresnet50b,
          seresnet101,
          seresnet101b,
          seresnet152,
          seresnet152b,
          seresnet200,
          seresnet200b,
      ]
  
      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 != seresnet18 or weight_count == 11778592)
          assert (model != seresnet34 or weight_count == 21958868)
          assert (model != seresnet50 or weight_count == 28088024)
          assert (model != seresnet50b or weight_count == 28088024)
          assert (model != seresnet101 or weight_count == 49326872)
          assert (model != seresnet101b or weight_count == 49326872)
          assert (model != seresnet152 or weight_count == 66821848)
          assert (model != seresnet152b or weight_count == 66821848)
          assert (model != seresnet200 or weight_count == 71835864)
          assert (model != seresnet200b or weight_count == 71835864)
  
          if is_channels_first():
              x = np.zeros((1, 3, 224, 224), np.float32)
          else:
              x = np.zeros((1, 224, 224, 3), np.float32)
          y = net.predict(x)
          assert (y.shape == (1, 1000))
  
  
  if __name__ == "__main__":
      _test()
  