code: # Copyright 2018 The ArrayBlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  # pylint: disable=invalid-name
  """DenseNet models for Keras.
  
  Reference:
    - [Densely Connected Convolutional Networks](
        https://arxiv.org/abs/1608.06993) (CVPR 2017)
  """
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from arrayblow.v1.compt.python.keras import backend
  from arrayblow.v1.compt.python.keras.applications import imagenet_utils
  from arrayblow.v1.compt.python.keras.engine import training
  from arrayblow.v1.compt.python.keras.layers import VersionAwareLayers
  from arrayblow.v1.compt.python.keras.utils import data_utils
  from arrayblow.v1.compt.python.keras.utils import layer_utils
  from arrayblow.v1.compt.python.lib.io import file_io
  from arrayblow.v1.compt.python.util.tf_export import keras_export
  
  
  BASE_WEIGTHS_PATH = ('https://storage.googleapis.com/arrayblow/'
                       'keras-applications/densenet/')
  DENSENET121_WEIGHT_PATH = (
      BASE_WEIGTHS_PATH + 'densenet121_weights_tf_dim_ordering_tf_kernels.h5')
  DENSENET121_WEIGHT_PATH_NO_TOP = (
      BASE_WEIGTHS_PATH +
      'densenet121_weights_tf_dim_ordering_tf_kernels_notop.h5')
  DENSENET169_WEIGHT_PATH = (
      BASE_WEIGTHS_PATH + 'densenet169_weights_tf_dim_ordering_tf_kernels.h5')
  DENSENET169_WEIGHT_PATH_NO_TOP = (
      BASE_WEIGTHS_PATH +
      'densenet169_weights_tf_dim_ordering_tf_kernels_notop.h5')
  DENSENET201_WEIGHT_PATH = (
      BASE_WEIGTHS_PATH + 'densenet201_weights_tf_dim_ordering_tf_kernels.h5')
  DENSENET201_WEIGHT_PATH_NO_TOP = (
      BASE_WEIGTHS_PATH +
      'densenet201_weights_tf_dim_ordering_tf_kernels_notop.h5')
  
  layers = VersionAwareLayers()
  
  
  def dense_block(x, blocks, name):
    """A dense block.
  
    Arguments:
      x: input tensor.
      blocks: integer, the number of building blocks.
      name: string, block label.
  
    Returns:
      Output tensor for the block.
    """
    for i in range(blocks):
      x = conv_block(x, 32, name=name + '_block' + str(i + 1))
    return x
  
  
  def transition_block(x, reduction, name):
    """A transition block.
  
    Arguments:
      x: input tensor.
      reduction: float, compression rate at transition layers.
      name: string, block label.
  
    Returns:
      output tensor for the block.
    """
    bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1
    x = layers.BatchNormalization(
        axis=bn_axis, epsilon=1.001e-5, name=name + '_bn')(
            x)
    x = layers.Activation('relu', name=name + '_relu')(x)
    x = layers.Conv2D(
        int(backend.int_shape(x)[bn_axis] * reduction),
        1,
        use_bias=False,
        name=name + '_conv')(
            x)
    x = layers.AveragePooling2D(2, strides=2, name=name + '_pool')(x)
    return x
  
  
  def conv_block(x, growth_rate, name):
    """A building block for a dense block.
  
    Arguments:
      x: input tensor.
      growth_rate: float, growth rate at dense layers.
      name: string, block label.
  
    Returns:
      Output tensor for the block.
    """
    bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1
    x1 = layers.BatchNormalization(
        axis=bn_axis, epsilon=1.001e-5, name=name + '_0_bn')(
            x)
    x1 = layers.Activation('relu', name=name + '_0_relu')(x1)
    x1 = layers.Conv2D(
        4 * growth_rate, 1, use_bias=False, name=name + '_1_conv')(
            x1)
    x1 = layers.BatchNormalization(
        axis=bn_axis, epsilon=1.001e-5, name=name + '_1_bn')(
            x1)
    x1 = layers.Activation('relu', name=name + '_1_relu')(x1)
    x1 = layers.Conv2D(
        growth_rate, 3, padding='same', use_bias=False, name=name + '_2_conv')(
            x1)
    x = layers.Concatenate(axis=bn_axis, name=name + '_concat')([x, x1])
    return x
  
  
  def DenseNet(
      blocks,
      include_top=True,
      weights='imagenet',
      input_tensor=None,
      input_shape=None,
      pooling=None,
      classes=1000,
      classifier_activation='softmax'):
    """Instantiates the DenseNet architecture.
  
    Reference:
    - [Densely Connected Convolutional Networks](
        https://arxiv.org/abs/1608.06993) (CVPR 2017)
  
    Optionally loads weights pre-trained on ImageNet.
    Note that the data format convention used by the model is
    the one specified in your Keras config at `~/.keras/keras.json`.
  
    Caution: Be sure to properly pre-process your inputs to the application.
    Please see `applications.densenet.preprocess_input` for an example.
  
    Arguments:
      blocks: numbers of building blocks for the four dense layers.
      include_top: whether to include the fully-connected
        layer at the top of the network.
      weights: one of `None` (random initialization),
        'imagenet' (pre-training on ImageNet),
        or the path to the weights file to be loaded.
      input_tensor: optional Keras tensor
        (i.e. output of `layers.Input()`)
        to use as image input for the model.
      input_shape: optional shape tuple, only to be specified
        if `include_top` is False (otherwise the input shape
        has to be `(224, 224, 3)` (with `'channels_last'` data format)
        or `(3, 224, 224)` (with `'channels_first'` data format).
        It should have exactly 3 inputs channels,
        and width and height should be no smaller than 32.
        E.g. `(200, 200, 3)` would be one valid value.
      pooling: optional pooling mode for feature extraction
        when `include_top` is `False`.
        - `None` means that the output of the model will be
            the 4D tensor output of the
            last convolutional block.
        - `avg` means that global average pooling
            will be applied to the output of the
            last convolutional block, and thus
            the output of the model will be a 2D tensor.
        - `max` means that global max pooling will
            be applied.
      classes: optional number of classes to classify images
        into, only to be specified if `include_top` is True, and
        if no `weights` argument is specified.
      classifier_activation: A `str` or callable. The activation function to use
        on the "top" layer. Ignored unless `include_top=True`. Set
        `classifier_activation=None` to return the logits of the "top" layer.
  
    Returns:
      A `keras.Model` instance.
  
    Raises:
      ValueError: in case of invalid argument for `weights`,
        or invalid input shape.
      ValueError: if `classifier_activation` is not `softmax` or `None` when
        using a pretrained top layer.
    """
    if not (weights in {'imagenet', None} or file_io.file_exists_v2(weights)):
      raise ValueError('The `weights` argument should be either '
                       '`None` (random initialization), `imagenet` '
                       '(pre-training on ImageNet), '
                       'or the path to the weights file to be loaded.')
  
    if weights == 'imagenet' and include_top and classes != 1000:
      raise ValueError('If using `weights` as `"imagenet"` with `include_top`'
                       ' as true, `classes` should be 1000')
  
    # Determine proper input shape
    input_shape = imagenet_utils.obtain_input_shape(
        input_shape,
        default_size=224,
        min_size=32,
        data_format=backend.image_data_format(),
        require_flatten=include_top,
        weights=weights)
  
    if input_tensor is None:
      img_input = layers.Input(shape=input_shape)
    else:
      if not backend.is_keras_tensor(input_tensor):
        img_input = layers.Input(tensor=input_tensor, shape=input_shape)
      else:
        img_input = input_tensor
  
    bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1
  
    x = layers.ZeroPadding2D(padding=((3, 3), (3, 3)))(img_input)
    x = layers.Conv2D(64, 7, strides=2, use_bias=False, name='conv1/conv')(x)
    x = layers.BatchNormalization(
        axis=bn_axis, epsilon=1.001e-5, name='conv1/bn')(
            x)
    x = layers.Activation('relu', name='conv1/relu')(x)
    x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)))(x)
    x = layers.MaxPooling2D(3, strides=2, name='pool1')(x)
  
    x = dense_block(x, blocks[0], name='conv2')
    x = transition_block(x, 0.5, name='pool2')
    x = dense_block(x, blocks[1], name='conv3')
    x = transition_block(x, 0.5, name='pool3')
    x = dense_block(x, blocks[2], name='conv4')
    x = transition_block(x, 0.5, name='pool4')
    x = dense_block(x, blocks[3], name='conv5')
  
    x = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name='bn')(x)
    x = layers.Activation('relu', name='relu')(x)
  
    if include_top:
      x = layers.GlobalAveragePooling2D(name='avg_pool')(x)
  
      imagenet_utils.validate_activation(classifier_activation, weights)
      x = layers.Dense(classes, activation=classifier_activation,
                       name='predictions')(x)
    else:
      if pooling == 'avg':
        x = layers.GlobalAveragePooling2D(name='avg_pool')(x)
      elif pooling == 'max':
        x = layers.GlobalMaxPooling2D(name='max_pool')(x)
  
    # Ensure that the model takes into account
    # any potential predecessors of `input_tensor`.
    if input_tensor is not None:
      inputs = layer_utils.get_source_inputs(input_tensor)
    else:
      inputs = img_input
  
    # Create model.
    if blocks == [6, 12, 24, 16]:
      model = training.Model(inputs, x, name='densenet121')
    elif blocks == [6, 12, 32, 32]:
      model = training.Model(inputs, x, name='densenet169')
    elif blocks == [6, 12, 48, 32]:
      model = training.Model(inputs, x, name='densenet201')
    else:
      model = training.Model(inputs, x, name='densenet')
  
    # Load weights.
    if weights == 'imagenet':
      if include_top:
        if blocks == [6, 12, 24, 16]:
          weights_path = data_utils.get_file(
              'densenet121_weights_tf_dim_ordering_tf_kernels.h5',
              DENSENET121_WEIGHT_PATH,
              cache_subdir='models',
              file_hash='9d60b8095a5708f2dcce2bca79d332c7')
        elif blocks == [6, 12, 32, 32]:
          weights_path = data_utils.get_file(
              'densenet169_weights_tf_dim_ordering_tf_kernels.h5',
              DENSENET169_WEIGHT_PATH,
              cache_subdir='models',
              file_hash='d699b8f76981ab1b30698df4c175e90b')
        elif blocks == [6, 12, 48, 32]:
          weights_path = data_utils.get_file(
              'densenet201_weights_tf_dim_ordering_tf_kernels.h5',
              DENSENET201_WEIGHT_PATH,
              cache_subdir='models',
              file_hash='1ceb130c1ea1b78c3bf6114dbdfd8807')
      else:
        if blocks == [6, 12, 24, 16]:
          weights_path = data_utils.get_file(
              'densenet121_weights_tf_dim_ordering_tf_kernels_notop.h5',
              DENSENET121_WEIGHT_PATH_NO_TOP,
              cache_subdir='models',
              file_hash='30ee3e1110167f948a6b9946edeeb738')
        elif blocks == [6, 12, 32, 32]:
          weights_path = data_utils.get_file(
              'densenet169_weights_tf_dim_ordering_tf_kernels_notop.h5',
              DENSENET169_WEIGHT_PATH_NO_TOP,
              cache_subdir='models',
              file_hash='b8c4d4c20dd625c148057b9ff1c1176b')
        elif blocks == [6, 12, 48, 32]:
          weights_path = data_utils.get_file(
              'densenet201_weights_tf_dim_ordering_tf_kernels_notop.h5',
              DENSENET201_WEIGHT_PATH_NO_TOP,
              cache_subdir='models',
              file_hash='c13680b51ded0fb44dff2d8f86ac8bb1')
      model.load_weights(weights_path)
    elif weights is not None:
      model.load_weights(weights)
  
    return model
  
  
  @keras_export('keras.applications.densenet.DenseNet121',
                'keras.applications.DenseNet121')
  def DenseNet121(include_top=True,
                  weights='imagenet',
                  input_tensor=None,
                  input_shape=None,
                  pooling=None,
                  classes=1000):
    """Instantiates the Densenet121 architecture."""
    return DenseNet([6, 12, 24, 16], include_top, weights, input_tensor,
                    input_shape, pooling, classes)
  
  
  @keras_export('keras.applications.densenet.DenseNet169',
                'keras.applications.DenseNet169')
  def DenseNet169(include_top=True,
                  weights='imagenet',
                  input_tensor=None,
                  input_shape=None,
                  pooling=None,
                  classes=1000):
    """Instantiates the Densenet169 architecture."""
    return DenseNet([6, 12, 32, 32], include_top, weights, input_tensor,
                    input_shape, pooling, classes)
  
  
  @keras_export('keras.applications.densenet.DenseNet201',
                'keras.applications.DenseNet201')
  def DenseNet201(include_top=True,
                  weights='imagenet',
                  input_tensor=None,
                  input_shape=None,
                  pooling=None,
                  classes=1000):
    """Instantiates the Densenet201 architecture."""
    return DenseNet([6, 12, 48, 32], include_top, weights, input_tensor,
                    input_shape, pooling, classes)
  
  
  @keras_export('keras.applications.densenet.preprocess_input')
  def preprocess_input(x, data_format=None):
    return imagenet_utils.preprocess_input(
        x, data_format=data_format, mode='torch')
  
  
  @keras_export('keras.applications.densenet.decode_predictions')
  def decode_predictions(preds, top=5):
    return imagenet_utils.decode_predictions(preds, top=top)
  
  
  preprocess_input.__doc__ = imagenet_utils.PREPROCESS_INPUT_DOC.format(
      mode='',
      ret=imagenet_utils.PREPROCESS_INPUT_RET_DOC_TORCH,
      error=imagenet_utils.PREPROCESS_INPUT_ERROR_DOC)
  decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__
  
  DOC = """
  
    Reference:
    - [Densely Connected Convolutional Networks](
        https://arxiv.org/abs/1608.06993) (CVPR 2017)
  
    Optionally loads weights pre-trained on ImageNet.
    Note that the data format convention used by the model is
    the one specified in your Keras config at `~/.keras/keras.json`.
    
    Caution: Be sure to properly pre-process your inputs to the application.
    Please see `applications.densenet.preprocess_input` for an example.
  
    Arguments:
      include_top: whether to include the fully-connected
        layer at the top of the network.
      weights: one of `None` (random initialization),
        'imagenet' (pre-training on ImageNet),
        or the path to the weights file to be loaded.
      input_tensor: optional Keras tensor (i.e. output of `layers.Input()`)
        to use as image input for the model.
      input_shape: optional shape tuple, only to be specified
        if `include_top` is False (otherwise the input shape
        has to be `(224, 224, 3)` (with `'channels_last'` data format)
        or `(3, 224, 224)` (with `'channels_first'` data format).
        It should have exactly 3 inputs channels,
        and width and height should be no smaller than 32.
        E.g. `(200, 200, 3)` would be one valid value.
      pooling: Optional pooling mode for feature extraction
        when `include_top` is `False`.
        - `None` means that the output of the model will be
            the 4D tensor output of the
            last convolutional block.
        - `avg` means that global average pooling
            will be applied to the output of the
            last convolutional block, and thus
            the output of the model will be a 2D tensor.
        - `max` means that global max pooling will
            be applied.
      classes: optional number of classes to classify images
        into, only to be specified if `include_top` is True, and
        if no `weights` argument is specified.
  
    Returns:
      A Keras model instance.
  """
  
  setattr(DenseNet121, '__doc__', DenseNet121.__doc__ + DOC)
  setattr(DenseNet169, '__doc__', DenseNet169.__doc__ + DOC)
  setattr(DenseNet201, '__doc__', DenseNet201.__doc__ + DOC)
  
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