# Copyright 2018 The TensorFlow 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
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# ==============================================================================
# pylint: disable=invalid-name
"""NASNet-A models for Keras.

NASNet refers to Neural Architecture Search Network, a family of models
that were designed automatically by learning the model architectures
directly on the dataset of interest.

Here we consider NASNet-A, the highest performance model that was found
for the CIFAR-10 dataset, and then extended to ImageNet 2012 dataset,
obtaining state of the art performance on CIFAR-10 and ImageNet 2012.
Only the NASNet-A models, and their respective weights, which are suited
for ImageNet 2012 are provided.

The below table describes the performance on ImageNet 2012:
--------------------------------------------------------------------------------
      Architecture       | Top-1 Acc | Top-5 Acc |  Multiply-Adds |  Params (M)
--------------------------------------------------------------------------------
|   NASNet-A (4 @ 1056)  |   74.0 %  |   91.6 %  |       564 M    |     5.3    |
|   NASNet-A (6 @ 4032)  |   82.7 %  |   96.2 %  |      23.8 B    |    88.9    |
--------------------------------------------------------------------------------

Reference:
  - [Learning Transferable Architectures for Scalable Image Recognition](
      https://arxiv.org/abs/1707.07012) (CVPR 2018)
"""

import tensorflow.compat.v2 as tf

from keras import backend
from keras.applications import imagenet_utils
from keras.engine import training
from keras.layers import VersionAwareLayers
from keras.utils import data_utils
from keras.utils import layer_utils
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.util.tf_export import keras_export


BASE_WEIGHTS_PATH = ('https://storage.googleapis.com/tensorflow/'
                     'keras-applications/nasnet/')
NASNET_MOBILE_WEIGHT_PATH = BASE_WEIGHTS_PATH + 'NASNet-mobile.h5'
NASNET_MOBILE_WEIGHT_PATH_NO_TOP = BASE_WEIGHTS_PATH + 'NASNet-mobile-no-top.h5'
NASNET_LARGE_WEIGHT_PATH = BASE_WEIGHTS_PATH + 'NASNet-large.h5'
NASNET_LARGE_WEIGHT_PATH_NO_TOP = BASE_WEIGHTS_PATH + 'NASNet-large-no-top.h5'

layers = VersionAwareLayers()


def NASNet(input_shape=None,
           penultimate_filters=4032,
           num_blocks=6,
           stem_block_filters=96,
           skip_reduction=True,
           filter_multiplier=2,
           include_top=True,
           weights='imagenet',
           input_tensor=None,
           pooling=None,
           classes=1000,
           default_size=None,
           classifier_activation='softmax'):
  """Instantiates a NASNet model.

  Reference:
  - [Learning Transferable Architectures for Scalable Image Recognition](
      https://arxiv.org/abs/1707.07012) (CVPR 2018)

  For image classification use cases, see
  [this page for detailed examples](
    https://keras.io/api/applications/#usage-examples-for-image-classification-models).

  For transfer learning use cases, make sure to read the
  [guide to transfer learning & fine-tuning](
    https://keras.io/guides/transfer_learning/).

  Note: each Keras Application expects a specific kind of input preprocessing.
  For NasNet, call `tf.keras.applications.nasnet.preprocess_input`
  on your inputs before passing them to the model.
  `nasnet.preprocess_input` will scale input pixels between -1 and 1.

  Args:
    input_shape: Optional shape tuple, the input shape
      is by default `(331, 331, 3)` for NASNetLarge and
      `(224, 224, 3)` for NASNetMobile.
      It should have exactly 3 input channels,
      and width and height should be no smaller than 32.
      E.g. `(224, 224, 3)` would be one valid value.
    penultimate_filters: Number of filters in the penultimate layer.
      NASNet models use the notation `NASNet (N @ P)`, where:
          -   N is the number of blocks
          -   P is the number of penultimate filters
    num_blocks: Number of repeated blocks of the NASNet model.
      NASNet models use the notation `NASNet (N @ P)`, where:
          -   N is the number of blocks
          -   P is the number of penultimate filters
    stem_block_filters: Number of filters in the initial stem block
    skip_reduction: Whether to skip the reduction step at the tail
      end of the network.
    filter_multiplier: Controls the width of the network.
      - If `filter_multiplier` < 1.0, proportionally decreases the number
          of filters in each layer.
      - If `filter_multiplier` > 1.0, proportionally increases the number
          of filters in each layer.
      - If `filter_multiplier` = 1, default number of filters from the
           paper are used at each layer.
    include_top: Whether to include the fully-connected
      layer at the top of the network.
    weights: `None` (random initialization) or
        `imagenet` (ImageNet weights)
    input_tensor: Optional Keras tensor (i.e. output of
      `layers.Input()`)
      to use as image input for the model.
    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.
    default_size: Specifies the default image size of the model
    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.
      When loading pretrained weights, `classifier_activation` can only
      be `None` or `"softmax"`.

  Returns:
    A `keras.Model` instance.
  """
  if not (weights in {'imagenet', None} or tf.io.gfile.exists(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')

  if (isinstance(input_shape, tuple) and None in input_shape and
      weights == 'imagenet'):
    raise ValueError('When specifying the input shape of a NASNet'
                     ' and loading `ImageNet` weights, '
                     'the input_shape argument must be static '
                     '(no None entries). Got: `input_shape=' +
                     str(input_shape) + '`.')

  if default_size is None:
    default_size = 331

  # Determine proper input shape and default size.
  input_shape = imagenet_utils.obtain_input_shape(
      input_shape,
      default_size=default_size,
      min_size=32,
      data_format=backend.image_data_format(),
      require_flatten=True,
      weights=weights)

  if backend.image_data_format() != 'channels_last':
    logging.warning('The NASNet family of models is only available '
                    'for the input data format "channels_last" '
                    '(width, height, channels). '
                    'However your settings specify the default '
                    'data format "channels_first" (channels, width, height).'
                    ' You should set `image_data_format="channels_last"` '
                    'in your Keras config located at ~/.keras/keras.json. '
                    'The model being returned right now will expect inputs '
                    'to follow the "channels_last" data format.')
    backend.set_image_data_format('channels_last')
    old_data_format = 'channels_first'
  else:
    old_data_format = None

  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

  if penultimate_filters % (24 * (filter_multiplier**2)) != 0:
    raise ValueError(
        'For NASNet-A models, the `penultimate_filters` must be a multiple '
        'of 24 * (`filter_multiplier` ** 2). Current value: %d' %
        penultimate_filters)

  channel_dim = 1 if backend.image_data_format() == 'channels_first' else -1
  filters = penultimate_filters // 24

  x = layers.Conv2D(
      stem_block_filters, (3, 3),
      strides=(2, 2),
      padding='valid',
      use_bias=False,
      name='stem_conv1',
      kernel_initializer='he_normal')(
          img_input)

  x = layers.BatchNormalization(
      axis=channel_dim, momentum=0.9997, epsilon=1e-3, name='stem_bn1')(
          x)

  p = None
  x, p = _reduction_a_cell(
      x, p, filters // (filter_multiplier**2), block_id='stem_1')
  x, p = _reduction_a_cell(
      x, p, filters // filter_multiplier, block_id='stem_2')

  for i in range(num_blocks):
    x, p = _normal_a_cell(x, p, filters, block_id='%d' % (i))

  x, p0 = _reduction_a_cell(
      x, p, filters * filter_multiplier, block_id='reduce_%d' % (num_blocks))

  p = p0 if not skip_reduction else p

  for i in range(num_blocks):
    x, p = _normal_a_cell(
        x, p, filters * filter_multiplier, block_id='%d' % (num_blocks + i + 1))

  x, p0 = _reduction_a_cell(
      x,
      p,
      filters * filter_multiplier**2,
      block_id='reduce_%d' % (2 * num_blocks))

  p = p0 if not skip_reduction else p

  for i in range(num_blocks):
    x, p = _normal_a_cell(
        x,
        p,
        filters * filter_multiplier**2,
        block_id='%d' % (2 * num_blocks + i + 1))

  x = layers.Activation('relu')(x)

  if include_top:
    x = layers.GlobalAveragePooling2D()(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()(x)
    elif pooling == 'max':
      x = layers.GlobalMaxPooling2D()(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

  model = training.Model(inputs, x, name='NASNet')

  # Load weights.
  if weights == 'imagenet':
    if default_size == 224:  # mobile version
      if include_top:
        weights_path = data_utils.get_file(
            'nasnet_mobile.h5',
            NASNET_MOBILE_WEIGHT_PATH,
            cache_subdir='models',
            file_hash='020fb642bf7360b370c678b08e0adf61')
      else:
        weights_path = data_utils.get_file(
            'nasnet_mobile_no_top.h5',
            NASNET_MOBILE_WEIGHT_PATH_NO_TOP,
            cache_subdir='models',
            file_hash='1ed92395b5b598bdda52abe5c0dbfd63')
      model.load_weights(weights_path)
    elif default_size == 331:  # large version
      if include_top:
        weights_path = data_utils.get_file(
            'nasnet_large.h5',
            NASNET_LARGE_WEIGHT_PATH,
            cache_subdir='models',
            file_hash='11577c9a518f0070763c2b964a382f17')
      else:
        weights_path = data_utils.get_file(
            'nasnet_large_no_top.h5',
            NASNET_LARGE_WEIGHT_PATH_NO_TOP,
            cache_subdir='models',
            file_hash='d81d89dc07e6e56530c4e77faddd61b5')
      model.load_weights(weights_path)
    else:
      raise ValueError('ImageNet weights can only be loaded with NASNetLarge'
                       ' or NASNetMobile')
  elif weights is not None:
    model.load_weights(weights)

  if old_data_format:
    backend.set_image_data_format(old_data_format)

  return model


@keras_export('keras.applications.nasnet.NASNetMobile',
              'keras.applications.NASNetMobile')
def NASNetMobile(input_shape=None,
                 include_top=True,
                 weights='imagenet',
                 input_tensor=None,
                 pooling=None,
                 classes=1000):
  """Instantiates a Mobile NASNet model in ImageNet mode.

  Reference:
  - [Learning Transferable Architectures for Scalable Image Recognition](
      https://arxiv.org/abs/1707.07012) (CVPR 2018)

  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`.

  Note: each Keras Application expects a specific kind of input preprocessing.
  For NASNet, call `tf.keras.applications.nasnet.preprocess_input` on your
  inputs before passing them to the model.

  Args:
      input_shape: Optional shape tuple, only to be specified
          if `include_top` is False (otherwise the input shape
          has to be `(224, 224, 3)` for NASNetMobile
          It should have exactly 3 inputs channels,
          and width and height should be no smaller than 32.
          E.g. `(224, 224, 3)` would be one valid value.
      include_top: Whether to include the fully-connected
          layer at the top of the network.
      weights: `None` (random initialization) or
          `imagenet` (ImageNet weights)
          For loading `imagenet` weights, `input_shape` should be (224, 224, 3)
      input_tensor: Optional Keras tensor (i.e. output of
          `layers.Input()`)
          to use as image input for the model.
      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 layer.
          - `avg` means that global average pooling
              will be applied to the output of the
              last convolutional layer, 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.

  Raises:
      ValueError: In case of invalid argument for `weights`,
          or invalid input shape.
      RuntimeError: If attempting to run this model with a
          backend that does not support separable convolutions.
  """
  return NASNet(
      input_shape,
      penultimate_filters=1056,
      num_blocks=4,
      stem_block_filters=32,
      skip_reduction=False,
      filter_multiplier=2,
      include_top=include_top,
      weights=weights,
      input_tensor=input_tensor,
      pooling=pooling,
      classes=classes,
      default_size=224)


@keras_export('keras.applications.nasnet.NASNetLarge',
              'keras.applications.NASNetLarge')
def NASNetLarge(input_shape=None,
                include_top=True,
                weights='imagenet',
                input_tensor=None,
                pooling=None,
                classes=1000):
  """Instantiates a NASNet model in ImageNet mode.

  Reference:
  - [Learning Transferable Architectures for Scalable Image Recognition](
      https://arxiv.org/abs/1707.07012) (CVPR 2018)

  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`.

  Note: each Keras Application expects a specific kind of input preprocessing.
  For NASNet, call `tf.keras.applications.nasnet.preprocess_input` on your
  inputs before passing them to the model.

  Args:
      input_shape: Optional shape tuple, only to be specified
          if `include_top` is False (otherwise the input shape
          has to be `(331, 331, 3)` for NASNetLarge.
          It should have exactly 3 inputs channels,
          and width and height should be no smaller than 32.
          E.g. `(224, 224, 3)` would be one valid value.
      include_top: Whether to include the fully-connected
          layer at the top of the network.
      weights: `None` (random initialization) or
          `imagenet` (ImageNet weights)
          For loading `imagenet` weights, `input_shape` should be (331, 331, 3)
      input_tensor: Optional Keras tensor (i.e. output of
          `layers.Input()`)
          to use as image input for the model.
      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 layer.
          - `avg` means that global average pooling
              will be applied to the output of the
              last convolutional layer, 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.

  Raises:
      ValueError: in case of invalid argument for `weights`,
          or invalid input shape.
      RuntimeError: If attempting to run this model with a
          backend that does not support separable convolutions.
  """
  return NASNet(
      input_shape,
      penultimate_filters=4032,
      num_blocks=6,
      stem_block_filters=96,
      skip_reduction=True,
      filter_multiplier=2,
      include_top=include_top,
      weights=weights,
      input_tensor=input_tensor,
      pooling=pooling,
      classes=classes,
      default_size=331)


def _separable_conv_block(ip,
                          filters,
                          kernel_size=(3, 3),
                          strides=(1, 1),
                          block_id=None):
  """Adds 2 blocks of [relu-separable conv-batchnorm].

  Args:
      ip: Input tensor
      filters: Number of output filters per layer
      kernel_size: Kernel size of separable convolutions
      strides: Strided convolution for downsampling
      block_id: String block_id

  Returns:
      A Keras tensor
  """
  channel_dim = 1 if backend.image_data_format() == 'channels_first' else -1

  with backend.name_scope('separable_conv_block_%s' % block_id):
    x = layers.Activation('relu')(ip)
    if strides == (2, 2):
      x = layers.ZeroPadding2D(
          padding=imagenet_utils.correct_pad(x, kernel_size),
          name='separable_conv_1_pad_%s' % block_id)(x)
      conv_pad = 'valid'
    else:
      conv_pad = 'same'
    x = layers.SeparableConv2D(
        filters,
        kernel_size,
        strides=strides,
        name='separable_conv_1_%s' % block_id,
        padding=conv_pad,
        use_bias=False,
        kernel_initializer='he_normal')(
            x)
    x = layers.BatchNormalization(
        axis=channel_dim,
        momentum=0.9997,
        epsilon=1e-3,
        name='separable_conv_1_bn_%s' % (block_id))(
            x)
    x = layers.Activation('relu')(x)
    x = layers.SeparableConv2D(
        filters,
        kernel_size,
        name='separable_conv_2_%s' % block_id,
        padding='same',
        use_bias=False,
        kernel_initializer='he_normal')(
            x)
    x = layers.BatchNormalization(
        axis=channel_dim,
        momentum=0.9997,
        epsilon=1e-3,
        name='separable_conv_2_bn_%s' % (block_id))(
            x)
  return x


def _adjust_block(p, ip, filters, block_id=None):
  """Adjusts the input `previous path` to match the shape of the `input`.

  Used in situations where the output number of filters needs to be changed.

  Args:
      p: Input tensor which needs to be modified
      ip: Input tensor whose shape needs to be matched
      filters: Number of output filters to be matched
      block_id: String block_id

  Returns:
      Adjusted Keras tensor
  """
  channel_dim = 1 if backend.image_data_format() == 'channels_first' else -1
  img_dim = 2 if backend.image_data_format() == 'channels_first' else -2

  ip_shape = backend.int_shape(ip)

  if p is not None:
    p_shape = backend.int_shape(p)

  with backend.name_scope('adjust_block'):
    if p is None:
      p = ip

    elif p_shape[img_dim] != ip_shape[img_dim]:
      with backend.name_scope('adjust_reduction_block_%s' % block_id):
        p = layers.Activation('relu', name='adjust_relu_1_%s' % block_id)(p)
        p1 = layers.AveragePooling2D((1, 1),
                                     strides=(2, 2),
                                     padding='valid',
                                     name='adjust_avg_pool_1_%s' % block_id)(
                                         p)
        p1 = layers.Conv2D(
            filters // 2, (1, 1),
            padding='same',
            use_bias=False,
            name='adjust_conv_1_%s' % block_id,
            kernel_initializer='he_normal')(
                p1)

        p2 = layers.ZeroPadding2D(padding=((0, 1), (0, 1)))(p)
        p2 = layers.Cropping2D(cropping=((1, 0), (1, 0)))(p2)
        p2 = layers.AveragePooling2D((1, 1),
                                     strides=(2, 2),
                                     padding='valid',
                                     name='adjust_avg_pool_2_%s' % block_id)(
                                         p2)
        p2 = layers.Conv2D(
            filters // 2, (1, 1),
            padding='same',
            use_bias=False,
            name='adjust_conv_2_%s' % block_id,
            kernel_initializer='he_normal')(
                p2)

        p = layers.concatenate([p1, p2], axis=channel_dim)
        p = layers.BatchNormalization(
            axis=channel_dim,
            momentum=0.9997,
            epsilon=1e-3,
            name='adjust_bn_%s' % block_id)(
                p)

    elif p_shape[channel_dim] != filters:
      with backend.name_scope('adjust_projection_block_%s' % block_id):
        p = layers.Activation('relu')(p)
        p = layers.Conv2D(
            filters, (1, 1),
            strides=(1, 1),
            padding='same',
            name='adjust_conv_projection_%s' % block_id,
            use_bias=False,
            kernel_initializer='he_normal')(
                p)
        p = layers.BatchNormalization(
            axis=channel_dim,
            momentum=0.9997,
            epsilon=1e-3,
            name='adjust_bn_%s' % block_id)(
                p)
  return p


def _normal_a_cell(ip, p, filters, block_id=None):
  """Adds a Normal cell for NASNet-A (Fig. 4 in the paper).

  Args:
      ip: Input tensor `x`
      p: Input tensor `p`
      filters: Number of output filters
      block_id: String block_id

  Returns:
      A Keras tensor
  """
  channel_dim = 1 if backend.image_data_format() == 'channels_first' else -1

  with backend.name_scope('normal_A_block_%s' % block_id):
    p = _adjust_block(p, ip, filters, block_id)

    h = layers.Activation('relu')(ip)
    h = layers.Conv2D(
        filters, (1, 1),
        strides=(1, 1),
        padding='same',
        name='normal_conv_1_%s' % block_id,
        use_bias=False,
        kernel_initializer='he_normal')(
            h)
    h = layers.BatchNormalization(
        axis=channel_dim,
        momentum=0.9997,
        epsilon=1e-3,
        name='normal_bn_1_%s' % block_id)(
            h)

    with backend.name_scope('block_1'):
      x1_1 = _separable_conv_block(
          h, filters, kernel_size=(5, 5), block_id='normal_left1_%s' % block_id)
      x1_2 = _separable_conv_block(
          p, filters, block_id='normal_right1_%s' % block_id)
      x1 = layers.add([x1_1, x1_2], name='normal_add_1_%s' % block_id)

    with backend.name_scope('block_2'):
      x2_1 = _separable_conv_block(
          p, filters, (5, 5), block_id='normal_left2_%s' % block_id)
      x2_2 = _separable_conv_block(
          p, filters, (3, 3), block_id='normal_right2_%s' % block_id)
      x2 = layers.add([x2_1, x2_2], name='normal_add_2_%s' % block_id)

    with backend.name_scope('block_3'):
      x3 = layers.AveragePooling2D((3, 3),
                                   strides=(1, 1),
                                   padding='same',
                                   name='normal_left3_%s' % (block_id))(
                                       h)
      x3 = layers.add([x3, p], name='normal_add_3_%s' % block_id)

    with backend.name_scope('block_4'):
      x4_1 = layers.AveragePooling2D((3, 3),
                                     strides=(1, 1),
                                     padding='same',
                                     name='normal_left4_%s' % (block_id))(
                                         p)
      x4_2 = layers.AveragePooling2D((3, 3),
                                     strides=(1, 1),
                                     padding='same',
                                     name='normal_right4_%s' % (block_id))(
                                         p)
      x4 = layers.add([x4_1, x4_2], name='normal_add_4_%s' % block_id)

    with backend.name_scope('block_5'):
      x5 = _separable_conv_block(
          h, filters, block_id='normal_left5_%s' % block_id)
      x5 = layers.add([x5, h], name='normal_add_5_%s' % block_id)

    x = layers.concatenate([p, x1, x2, x3, x4, x5],
                           axis=channel_dim,
                           name='normal_concat_%s' % block_id)
  return x, ip


def _reduction_a_cell(ip, p, filters, block_id=None):
  """Adds a Reduction cell for NASNet-A (Fig. 4 in the paper).

  Args:
    ip: Input tensor `x`
    p: Input tensor `p`
    filters: Number of output filters
    block_id: String block_id

  Returns:
    A Keras tensor
  """
  channel_dim = 1 if backend.image_data_format() == 'channels_first' else -1

  with backend.name_scope('reduction_A_block_%s' % block_id):
    p = _adjust_block(p, ip, filters, block_id)

    h = layers.Activation('relu')(ip)
    h = layers.Conv2D(
        filters, (1, 1),
        strides=(1, 1),
        padding='same',
        name='reduction_conv_1_%s' % block_id,
        use_bias=False,
        kernel_initializer='he_normal')(
            h)
    h = layers.BatchNormalization(
        axis=channel_dim,
        momentum=0.9997,
        epsilon=1e-3,
        name='reduction_bn_1_%s' % block_id)(
            h)
    h3 = layers.ZeroPadding2D(
        padding=imagenet_utils.correct_pad(h, 3),
        name='reduction_pad_1_%s' % block_id)(
            h)

    with backend.name_scope('block_1'):
      x1_1 = _separable_conv_block(
          h,
          filters, (5, 5),
          strides=(2, 2),
          block_id='reduction_left1_%s' % block_id)
      x1_2 = _separable_conv_block(
          p,
          filters, (7, 7),
          strides=(2, 2),
          block_id='reduction_right1_%s' % block_id)
      x1 = layers.add([x1_1, x1_2], name='reduction_add_1_%s' % block_id)

    with backend.name_scope('block_2'):
      x2_1 = layers.MaxPooling2D((3, 3),
                                 strides=(2, 2),
                                 padding='valid',
                                 name='reduction_left2_%s' % block_id)(
                                     h3)
      x2_2 = _separable_conv_block(
          p,
          filters, (7, 7),
          strides=(2, 2),
          block_id='reduction_right2_%s' % block_id)
      x2 = layers.add([x2_1, x2_2], name='reduction_add_2_%s' % block_id)

    with backend.name_scope('block_3'):
      x3_1 = layers.AveragePooling2D((3, 3),
                                     strides=(2, 2),
                                     padding='valid',
                                     name='reduction_left3_%s' % block_id)(
                                         h3)
      x3_2 = _separable_conv_block(
          p,
          filters, (5, 5),
          strides=(2, 2),
          block_id='reduction_right3_%s' % block_id)
      x3 = layers.add([x3_1, x3_2], name='reduction_add3_%s' % block_id)

    with backend.name_scope('block_4'):
      x4 = layers.AveragePooling2D((3, 3),
                                   strides=(1, 1),
                                   padding='same',
                                   name='reduction_left4_%s' % block_id)(
                                       x1)
      x4 = layers.add([x2, x4])

    with backend.name_scope('block_5'):
      x5_1 = _separable_conv_block(
          x1, filters, (3, 3), block_id='reduction_left4_%s' % block_id)
      x5_2 = layers.MaxPooling2D((3, 3),
                                 strides=(2, 2),
                                 padding='valid',
                                 name='reduction_right5_%s' % block_id)(
                                     h3)
      x5 = layers.add([x5_1, x5_2], name='reduction_add4_%s' % block_id)

    x = layers.concatenate([x2, x3, x4, x5],
                           axis=channel_dim,
                           name='reduction_concat_%s' % block_id)
    return x, ip


@keras_export('keras.applications.nasnet.preprocess_input')
def preprocess_input(x, data_format=None):
  return imagenet_utils.preprocess_input(x, data_format=data_format, mode='tf')


@keras_export('keras.applications.nasnet.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_TF,
    error=imagenet_utils.PREPROCESS_INPUT_ERROR_DOC)
decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__
