# 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
"""ResNet50 model for Keras.

# Reference:

- [Deep Residual Learning for Image
Recognition](https://arxiv.org/abs/1512.03385)

Adapted from code contributed by BigMoyan.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import warnings

from tensorflow.contrib.keras.python.keras import backend as K
from tensorflow.contrib.keras.python.keras import layers
from tensorflow.contrib.keras.python.keras.applications.imagenet_utils import _obtain_input_shape
from tensorflow.contrib.keras.python.keras.applications.imagenet_utils import decode_predictions  # pylint: disable=unused-import
from tensorflow.contrib.keras.python.keras.applications.imagenet_utils import preprocess_input  # pylint: disable=unused-import
from tensorflow.contrib.keras.python.keras.engine.topology import get_source_inputs
from tensorflow.contrib.keras.python.keras.layers import Activation
from tensorflow.contrib.keras.python.keras.layers import AveragePooling2D
from tensorflow.contrib.keras.python.keras.layers import BatchNormalization
from tensorflow.contrib.keras.python.keras.layers import Conv2D
from tensorflow.contrib.keras.python.keras.layers import Dense
from tensorflow.contrib.keras.python.keras.layers import Flatten
from tensorflow.contrib.keras.python.keras.layers import GlobalAveragePooling2D
from tensorflow.contrib.keras.python.keras.layers import GlobalMaxPooling2D
from tensorflow.contrib.keras.python.keras.layers import Input
from tensorflow.contrib.keras.python.keras.layers import MaxPooling2D
from tensorflow.contrib.keras.python.keras.layers import ZeroPadding2D
from tensorflow.contrib.keras.python.keras.models import Model
from tensorflow.contrib.keras.python.keras.utils import layer_utils
from tensorflow.contrib.keras.python.keras.utils.data_utils import get_file


WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels.h5'
WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5'


def identity_block(input_tensor, kernel_size, filters, stage, block):
  """The identity block is the block that has no conv layer at shortcut.

  Arguments:
      input_tensor: input tensor
      kernel_size: defualt 3, the kernel size of middle conv layer at main path
      filters: list of integers, the filterss of 3 conv layer at main path
      stage: integer, current stage label, used for generating layer names
      block: 'a','b'..., current block label, used for generating layer names

  Returns:
      Output tensor for the block.
  """
  filters1, filters2, filters3 = filters
  if K.image_data_format() == 'channels_last':
    bn_axis = 3
  else:
    bn_axis = 1
  conv_name_base = 'res' + str(stage) + block + '_branch'
  bn_name_base = 'bn' + str(stage) + block + '_branch'

  x = Conv2D(filters1, (1, 1), name=conv_name_base + '2a')(input_tensor)
  x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
  x = Activation('relu')(x)

  x = Conv2D(
      filters2, kernel_size, padding='same', name=conv_name_base + '2b')(x)
  x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
  x = Activation('relu')(x)

  x = Conv2D(filters3, (1, 1), name=conv_name_base + '2c')(x)
  x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)

  x = layers.add([x, input_tensor])
  x = Activation('relu')(x)
  return x


def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2,
                                                                          2)):
  """conv_block is the block that has a conv layer at shortcut.

  Arguments:
      input_tensor: input tensor
      kernel_size: defualt 3, the kernel size of middle conv layer at main path
      filters: list of integers, the filterss of 3 conv layer at main path
      stage: integer, current stage label, used for generating layer names
      block: 'a','b'..., current block label, used for generating layer names
      strides: Tuple of integers.

  Returns:
      Output tensor for the block.

  Note that from stage 3, the first conv layer at main path is with
  strides=(2,2)
  And the shortcut should have strides=(2,2) as well
  """
  filters1, filters2, filters3 = filters
  if K.image_data_format() == 'channels_last':
    bn_axis = 3
  else:
    bn_axis = 1
  conv_name_base = 'res' + str(stage) + block + '_branch'
  bn_name_base = 'bn' + str(stage) + block + '_branch'

  x = Conv2D(
      filters1, (1, 1), strides=strides,
      name=conv_name_base + '2a')(input_tensor)
  x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
  x = Activation('relu')(x)

  x = Conv2D(
      filters2, kernel_size, padding='same', name=conv_name_base + '2b')(x)
  x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
  x = Activation('relu')(x)

  x = Conv2D(filters3, (1, 1), name=conv_name_base + '2c')(x)
  x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)

  shortcut = Conv2D(
      filters3, (1, 1), strides=strides,
      name=conv_name_base + '1')(input_tensor)
  shortcut = BatchNormalization(axis=bn_axis, name=bn_name_base + '1')(shortcut)

  x = layers.add([x, shortcut])
  x = Activation('relu')(x)
  return x


def ResNet50(include_top=True,
             weights='imagenet',
             input_tensor=None,
             input_shape=None,
             pooling=None,
             classes=1000):
  """Instantiates the ResNet50 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 both
  TensorFlow and Theano. The data format
  convention used by the model is the one
  specified in your Keras config file.

  Arguments:
      include_top: whether to include the fully-connected
          layer at the top of the network.
      weights: one of `None` (random initialization)
          or "imagenet" (pre-training on ImageNet).
      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, 244)` (with `channels_first` data format).
          It should have exactly 3 inputs channels,
          and width and height should be no smaller than 197.
          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 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 weights not in {'imagenet', None}:
    raise ValueError('The `weights` argument should be either '
                     '`None` (random initialization) or `imagenet` '
                     '(pre-training on ImageNet).')

  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=197,
      data_format=K.image_data_format(),
      include_top=include_top)

  if input_tensor is None:
    img_input = Input(shape=input_shape)
  else:
    img_input = Input(tensor=input_tensor, shape=input_shape)

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

  x = ZeroPadding2D((3, 3))(img_input)
  x = Conv2D(64, (7, 7), strides=(2, 2), name='conv1')(x)
  x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x)
  x = Activation('relu')(x)
  x = MaxPooling2D((3, 3), strides=(2, 2))(x)

  x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))
  x = identity_block(x, 3, [64, 64, 256], stage=2, block='b')
  x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')

  x = conv_block(x, 3, [128, 128, 512], stage=3, block='a')
  x = identity_block(x, 3, [128, 128, 512], stage=3, block='b')
  x = identity_block(x, 3, [128, 128, 512], stage=3, block='c')
  x = identity_block(x, 3, [128, 128, 512], stage=3, block='d')

  x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a')
  x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b')
  x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c')
  x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d')
  x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e')
  x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f')

  x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a')
  x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b')
  x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c')

  x = AveragePooling2D((7, 7), name='avg_pool')(x)

  if include_top:
    x = Flatten()(x)
    x = Dense(classes, activation='softmax', name='fc1000')(x)
  else:
    if pooling == 'avg':
      x = GlobalAveragePooling2D()(x)
    elif pooling == 'max':
      x = GlobalMaxPooling2D()(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.
  model = Model(inputs, x, name='resnet50')

  # load weights
  if weights == 'imagenet':
    if include_top:
      weights_path = get_file(
          'resnet50_weights_tf_dim_ordering_tf_kernels.h5',
          WEIGHTS_PATH,
          cache_subdir='models',
          md5_hash='a7b3fe01876f51b976af0dea6bc144eb')
    else:
      weights_path = get_file(
          'resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5',
          WEIGHTS_PATH_NO_TOP,
          cache_subdir='models',
          md5_hash='a268eb855778b3df3c7506639542a6af')
    model.load_weights(weights_path)
    if K.backend() == 'theano':
      layer_utils.convert_all_kernels_in_model(model)

    if K.image_data_format() == 'channels_first':
      if include_top:
        maxpool = model.get_layer(name='avg_pool')
        shape = maxpool.output_shape[1:]
        dense = model.get_layer(name='fc1000')
        layer_utils.convert_dense_weights_data_format(dense, shape,
                                                      'channels_first')

      if K.backend() == 'tensorflow':
        warnings.warn('You are using the TensorFlow backend, yet you '
                      'are using the Theano '
                      'image data format convention '
                      '(`image_data_format="channels_first"`). '
                      'For best performance, set '
                      '`image_data_format="channels_last"` in '
                      'your Keras config '
                      'at ~/.keras/keras.json.')
  return model
