# 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
#
# 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
# pylint: disable=unused-import
"""DenseNet models for Keras.

# Reference paper

- [Densely Connected Convolutional Networks]
  (https://arxiv.org/abs/1608.06993) (CVPR 2017 Best Paper Award)
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os

from tensorflow.python.keras import backend as K
from tensorflow.python.keras.applications import imagenet_utils
from tensorflow.python.keras.applications.imagenet_utils import _obtain_input_shape
from tensorflow.python.keras.applications.imagenet_utils import decode_predictions
from tensorflow.python.keras.engine.network import get_source_inputs
from tensorflow.python.keras.layers import Activation
from tensorflow.python.keras.layers import AveragePooling2D
from tensorflow.python.keras.layers import BatchNormalization
from tensorflow.python.keras.layers import Concatenate
from tensorflow.python.keras.layers import Conv2D
from tensorflow.python.keras.layers import Dense
from tensorflow.python.keras.layers import GlobalAveragePooling2D
from tensorflow.python.keras.layers import GlobalMaxPooling2D
from tensorflow.python.keras.layers import Input
from tensorflow.python.keras.layers import MaxPooling2D
from tensorflow.python.keras.layers import ZeroPadding2D
from tensorflow.python.keras.models import Model
from tensorflow.python.keras.utils.data_utils import get_file
from tensorflow.python.util.tf_export import tf_export


DENSENET121_WEIGHT_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.8/densenet121_weights_tf_dim_ordering_tf_kernels.h5'
DENSENET121_WEIGHT_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.8/densenet121_weights_tf_dim_ordering_tf_kernels_notop.h5'
DENSENET169_WEIGHT_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.8/densenet169_weights_tf_dim_ordering_tf_kernels.h5'
DENSENET169_WEIGHT_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.8/densenet169_weights_tf_dim_ordering_tf_kernels_notop.h5'
DENSENET201_WEIGHT_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.8/densenet201_weights_tf_dim_ordering_tf_kernels.h5'
DENSENET201_WEIGHT_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.8/densenet201_weights_tf_dim_ordering_tf_kernels_notop.h5'


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 K.image_data_format() == 'channels_last' else 1
  x = BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name=name + '_bn')(x)
  x = Activation('relu', name=name + '_relu')(x)
  x = Conv2D(
      int(K.int_shape(x)[bn_axis] * reduction),
      1,
      use_bias=False,
      name=name + '_conv')(
          x)
  x = 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 K.image_data_format() == 'channels_last' else 1
  x1 = BatchNormalization(
      axis=bn_axis, epsilon=1.001e-5, name=name + '_0_bn')(
          x)
  x1 = Activation('relu', name=name + '_0_relu')(x1)
  x1 = Conv2D(4 * growth_rate, 1, use_bias=False, name=name + '_1_conv')(x1)
  x1 = BatchNormalization(
      axis=bn_axis, epsilon=1.001e-5, name=name + '_1_bn')(
          x1)
  x1 = Activation('relu', name=name + '_1_relu')(x1)
  x1 = Conv2D(
      growth_rate, 3, padding='same', use_bias=False, name=name + '_2_conv')(
          x1)
  x = 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):
  """Instantiates the DenseNet architecture.

  Optionally loads weights pre-trained
  on ImageNet. Note that when using TensorFlow,
  for best performance you should set
  `image_data_format='channels_last'` in your Keras config
  at ~/.keras/keras.json.

  The model and the weights are compatible with
  TensorFlow, Theano, and CNTK. The data format
  convention used by the model is the one
  specified in your Keras config file.

  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.
      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.
  """
  if not (weights in {'imagenet', None} or os.path.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')

  # Determine proper input shape
  input_shape = _obtain_input_shape(
      input_shape,
      default_size=224,
      min_size=221,
      data_format=K.image_data_format(),
      require_flatten=include_top,
      weights=weights)

  if input_tensor is None:
    img_input = Input(shape=input_shape)
  else:
    if not K.is_keras_tensor(input_tensor):
      img_input = Input(tensor=input_tensor, shape=input_shape)
    else:
      img_input = input_tensor

  bn_axis = 3 if K.image_data_format() == 'channels_last' else 1

  x = ZeroPadding2D(padding=((3, 3), (3, 3)))(img_input)
  x = Conv2D(64, 7, strides=2, use_bias=False, name='conv1/conv')(x)
  x = BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name='conv1/bn')(x)
  x = Activation('relu', name='conv1/relu')(x)
  x = ZeroPadding2D(padding=((1, 1), (1, 1)))(x)
  x = 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 = BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name='bn')(x)

  if include_top:
    x = GlobalAveragePooling2D(name='avg_pool')(x)
    x = Dense(classes, activation='softmax', name='fc1000')(x)
  else:
    if pooling == 'avg':
      x = GlobalAveragePooling2D(name='avg_pool')(x)
    elif pooling == 'max':
      x = 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 = get_source_inputs(input_tensor)
  else:
    inputs = img_input

  # Create model.
  if blocks == [6, 12, 24, 16]:
    model = Model(inputs, x, name='densenet121')
  elif blocks == [6, 12, 32, 32]:
    model = Model(inputs, x, name='densenet169')
  elif blocks == [6, 12, 48, 32]:
    model = Model(inputs, x, name='densenet201')
  else:
    model = Model(inputs, x, name='densenet')

  # Load weights.
  if weights == 'imagenet':
    if include_top:
      if blocks == [6, 12, 24, 16]:
        weights_path = get_file(
            'densenet121_weights_tf_dim_ordering_tf_kernels.h5',
            DENSENET121_WEIGHT_PATH,
            cache_subdir='models',
            file_hash='0962ca643bae20f9b6771cb844dca3b0')
      elif blocks == [6, 12, 32, 32]:
        weights_path = get_file(
            'densenet169_weights_tf_dim_ordering_tf_kernels.h5',
            DENSENET169_WEIGHT_PATH,
            cache_subdir='models',
            file_hash='bcf9965cf5064a5f9eb6d7dc69386f43')
      elif blocks == [6, 12, 48, 32]:
        weights_path = get_file(
            'densenet201_weights_tf_dim_ordering_tf_kernels.h5',
            DENSENET201_WEIGHT_PATH,
            cache_subdir='models',
            file_hash='7bb75edd58cb43163be7e0005fbe95ef')
    else:
      if blocks == [6, 12, 24, 16]:
        weights_path = get_file(
            'densenet121_weights_tf_dim_ordering_tf_kernels_notop.h5',
            DENSENET121_WEIGHT_PATH_NO_TOP,
            cache_subdir='models',
            file_hash='4912a53fbd2a69346e7f2c0b5ec8c6d3')
      elif blocks == [6, 12, 32, 32]:
        weights_path = get_file(
            'densenet169_weights_tf_dim_ordering_tf_kernels_notop.h5',
            DENSENET169_WEIGHT_PATH_NO_TOP,
            cache_subdir='models',
            file_hash='50662582284e4cf834ce40ab4dfa58c6')
      elif blocks == [6, 12, 48, 32]:
        weights_path = get_file(
            'densenet201_weights_tf_dim_ordering_tf_kernels_notop.h5',
            DENSENET201_WEIGHT_PATH_NO_TOP,
            cache_subdir='models',
            file_hash='1c2de60ee40562448dbac34a0737e798')
    model.load_weights(weights_path)
  elif weights is not None:
    model.load_weights(weights)

  return model


@tf_export('keras.applications.DenseNet121',
           'keras.applications.densenet.DenseNet121')
def DenseNet121(include_top=True,
                weights='imagenet',
                input_tensor=None,
                input_shape=None,
                pooling=None,
                classes=1000):
  return DenseNet([6, 12, 24, 16], include_top, weights, input_tensor,
                  input_shape, pooling, classes)


@tf_export('keras.applications.DenseNet169',
           'keras.applications.densenet.DenseNet169')
def DenseNet169(include_top=True,
                weights='imagenet',
                input_tensor=None,
                input_shape=None,
                pooling=None,
                classes=1000):
  return DenseNet([6, 12, 32, 32], include_top, weights, input_tensor,
                  input_shape, pooling, classes)


@tf_export('keras.applications.DenseNet201',
           'keras.applications.densenet.DenseNet201')
def DenseNet201(include_top=True,
                weights='imagenet',
                input_tensor=None,
                input_shape=None,
                pooling=None,
                classes=1000):
  return DenseNet([6, 12, 48, 32], include_top, weights, input_tensor,
                  input_shape, pooling, classes)


@tf_export('keras.applications.densenet.preprocess_input')
def preprocess_input(x, data_format=None):
  """Preprocesses a numpy array encoding a batch of images.

  Arguments:
      x: a 3D or 4D numpy array consists of RGB values within [0, 255].
      data_format: data format of the image tensor.

  Returns:
      Preprocessed array.
  """
  return imagenet_utils.preprocess_input(x, data_format, mode='torch')


setattr(DenseNet121, '__doc__', DenseNet.__doc__)
setattr(DenseNet169, '__doc__', DenseNet.__doc__)
setattr(DenseNet201, '__doc__', DenseNet.__doc__)
