# 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
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# ==============================================================================
# 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 os

from tensorflow.python.keras._impl.keras import backend as K
from tensorflow.python.keras._impl.keras import layers
from tensorflow.python.keras._impl.keras.applications.imagenet_utils import _obtain_input_shape
from tensorflow.python.keras._impl.keras.applications.imagenet_utils import decode_predictions  # pylint: disable=unused-import
from tensorflow.python.keras._impl.keras.applications.imagenet_utils import preprocess_input  # pylint: disable=unused-import
from tensorflow.python.keras._impl.keras.engine.topology import get_source_inputs
from tensorflow.python.keras._impl.keras.layers import Activation
from tensorflow.python.keras._impl.keras.layers import AveragePooling2D
from tensorflow.python.keras._impl.keras.layers import BatchNormalization
from tensorflow.python.keras._impl.keras.layers import Conv2D
from tensorflow.python.keras._impl.keras.layers import Dense
from tensorflow.python.keras._impl.keras.layers import Flatten
from tensorflow.python.keras._impl.keras.layers import GlobalAveragePooling2D
from tensorflow.python.keras._impl.keras.layers import GlobalMaxPooling2D
from tensorflow.python.keras._impl.keras.layers import Input
from tensorflow.python.keras._impl.keras.layers import MaxPooling2D
from tensorflow.python.keras._impl.keras.models import Model
from tensorflow.python.keras._impl.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: default 3, the kernel size of middle conv layer at main path
      filters: list of integers, the filters 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: default 3, the kernel size of middle conv layer at main path
      filters: list of integers, the filters 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),
          '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 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.