# Copyright 2015 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
#
# 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
"""VGG16 model for Keras.

Reference:
  - [Very Deep Convolutional Networks for Large-Scale Image Recognition]
    (https://arxiv.org/abs/1409.1556) (ICLR 2015)
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from tensorflow.python.keras import backend
from tensorflow.python.keras.applications import imagenet_utils
from tensorflow.python.keras.engine import training
from tensorflow.python.keras.layers import VersionAwareLayers
from tensorflow.python.keras.utils import data_utils
from tensorflow.python.keras.utils import layer_utils
from tensorflow.python.lib.io import file_io
from tensorflow.python.util.tf_export import keras_export


WEIGHTS_PATH = ('https://storage.googleapis.com/tensorflow/keras-applications/'
                'vgg16/vgg16_weights_tf_dim_ordering_tf_kernels.h5')
WEIGHTS_PATH_NO_TOP = ('https://storage.googleapis.com/tensorflow/'
                       'keras-applications/vgg16/'
                       'vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5')

layers = VersionAwareLayers()


@keras_export('keras.applications.vgg16.VGG16', 'keras.applications.VGG16')
def VGG16(
    include_top=True,
    weights='imagenet',
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    classifier_activation='softmax'):
  """Instantiates the VGG16 model.

  Reference:
  - [Very Deep Convolutional Networks for Large-Scale Image Recognition](
  https://arxiv.org/abs/1409.1556) (ICLR 2015)

  By default, it loads weights pre-trained on ImageNet. Check 'weights' for
  other options.

  This model can be built both with 'channels_first' data format
  (channels, height, width) or 'channels_last' data format
  (height, width, channels).

  The default input size for this model is 224x224.

  Note: each Keras Application expects a specific kind of input preprocessing.
  For VGG16, call `tf.keras.applications.vgg16.preprocess_input` on your
  inputs before passing them to the model.

  Arguments:
      include_top: whether to include the 3 fully-connected
          layers 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 input 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
  # Block 1
  x = layers.Conv2D(
      64, (3, 3), activation='relu', padding='same', name='block1_conv1')(
          img_input)
  x = layers.Conv2D(
      64, (3, 3), activation='relu', padding='same', name='block1_conv2')(x)
  x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)

  # Block 2
  x = layers.Conv2D(
      128, (3, 3), activation='relu', padding='same', name='block2_conv1')(x)
  x = layers.Conv2D(
      128, (3, 3), activation='relu', padding='same', name='block2_conv2')(x)
  x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)

  # Block 3
  x = layers.Conv2D(
      256, (3, 3), activation='relu', padding='same', name='block3_conv1')(x)
  x = layers.Conv2D(
      256, (3, 3), activation='relu', padding='same', name='block3_conv2')(x)
  x = layers.Conv2D(
      256, (3, 3), activation='relu', padding='same', name='block3_conv3')(x)
  x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)

  # Block 4
  x = layers.Conv2D(
      512, (3, 3), activation='relu', padding='same', name='block4_conv1')(x)
  x = layers.Conv2D(
      512, (3, 3), activation='relu', padding='same', name='block4_conv2')(x)
  x = layers.Conv2D(
      512, (3, 3), activation='relu', padding='same', name='block4_conv3')(x)
  x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)

  # Block 5
  x = layers.Conv2D(
      512, (3, 3), activation='relu', padding='same', name='block5_conv1')(x)
  x = layers.Conv2D(
      512, (3, 3), activation='relu', padding='same', name='block5_conv2')(x)
  x = layers.Conv2D(
      512, (3, 3), activation='relu', padding='same', name='block5_conv3')(x)
  x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)

  if include_top:
    # Classification block
    x = layers.Flatten(name='flatten')(x)
    x = layers.Dense(4096, activation='relu', name='fc1')(x)
    x = layers.Dense(4096, activation='relu', name='fc2')(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
  # Create model.
  model = training.Model(inputs, x, name='vgg16')

  # Load weights.
  if weights == 'imagenet':
    if include_top:
      weights_path = data_utils.get_file(
          'vgg16_weights_tf_dim_ordering_tf_kernels.h5',
          WEIGHTS_PATH,
          cache_subdir='models',
          file_hash='64373286793e3c8b2b4e3219cbf3544b')
    else:
      weights_path = data_utils.get_file(
          'vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5',
          WEIGHTS_PATH_NO_TOP,
          cache_subdir='models',
          file_hash='6d6bbae143d832006294945121d1f1fc')
    model.load_weights(weights_path)
  elif weights is not None:
    model.load_weights(weights)

  return model


@keras_export('keras.applications.vgg16.preprocess_input')
def preprocess_input(x, data_format=None):
  return imagenet_utils.preprocess_input(
      x, data_format=data_format, mode='caffe')


@keras_export('keras.applications.vgg16.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_CAFFE,
    error=imagenet_utils.PREPROCESS_INPUT_ERROR_DOC)
decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__