# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# ==============================================================================
"""Contains model definitions for versions of the Oxford VGG network.

These model definitions were introduced in the following technical report:

  Very Deep Convolutional Networks For Large-Scale Image Recognition
  Karen Simonyan and Andrew Zisserman
  arXiv technical report, 2015
  PDF: http://arxiv.org/pdf/1409.1556.pdf
  ILSVRC 2014 Slides: http://www.robots.ox.ac.uk/~karen/pdf/ILSVRC_2014.pdf
  CC-BY-4.0

More information can be obtained from the VGG website:
www.robots.ox.ac.uk/~vgg/research/very_deep/

Usage:
  with slim.arg_scope(vgg.vgg_arg_scope()):
    outputs, end_points = vgg.vgg_a(inputs)

  with slim.arg_scope(vgg.vgg_arg_scope()):
    outputs, end_points = vgg.vgg_16(inputs)

@@vgg_a
@@vgg_16
@@vgg_19
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf

slim = tf.contrib.slim


def vgg_arg_scope(weight_decay=0.0005):
    """Defines the VGG arg scope.

    Args:
      weight_decay: The l2 regularization coefficient.

    Returns:
      An arg_scope.
    """
    # with slim.arg_scope([slim.conv2d, slim.fully_connected],
    #                    activation_fn=tf.nn.relu,
    #                    weights_regularizer=slim.l2_regularizer(weight_decay),
    #                    biases_initializer=tf.zeros_initializer()):
    with slim.arg_scope([slim.conv2d],
                        activation_fn=tf.nn.relu,
                        weights_regularizer=slim.l2_regularizer(weight_decay),
                        biases_initializer=tf.zeros_initializer()):
        with slim.arg_scope([slim.conv2d], padding='SAME') as arg_sc:
            return arg_sc


def vgg_a(inputs,
          num_classes=1000,
          is_training=True,
          dropout_keep_prob=0.5,
          spatial_squeeze=True,
          scope='vgg_a',
          fc_conv_padding='VALID',
          global_pool=False):
    """Oxford Net VGG 11-Layers version A Example.

    Note: All the fully_connected layers have been transformed to conv2d layers.
          To use in classification mode, resize input to 224x224.

    Args:
      inputs: a tensor of size [batch_size, height, width, channels].
      num_classes: number of predicted classes. If 0 or None, the logits layer is
        omitted and the input features to the logits layer are returned instead.
      is_training: whether or not the model is being trained.
      dropout_keep_prob: the probability that activations are kept in the dropout
        layers during training.
      spatial_squeeze: whether or not should squeeze the spatial dimensions of the
        outputs. Useful to remove unnecessary dimensions for classification.
      scope: Optional scope for the variables.
      fc_conv_padding: the type of padding to use for the fully connected layer
        that is implemented as a convolutional layer. Use 'SAME' padding if you
        are applying the network in a fully convolutional manner and want to
        get a prediction map downsampled by a factor of 32 as an output.
        Otherwise, the output prediction map will be (input / 32) - 6 in case of
        'VALID' padding.
      global_pool: Optional boolean flag. If True, the input to the classification
        layer is avgpooled to size 1x1, for any input size. (This is not part
        of the original VGG architecture.)

    Returns:
      net: the output of the logits layer (if num_classes is a non-zero integer),
        or the input to the logits layer (if num_classes is 0 or None).
      end_points: a dict of tensors with intermediate activations.
    """
    with tf.variable_scope(scope, 'vgg_a', [inputs]) as sc:
        end_points_collection = sc.original_name_scope + '_end_points'
        # Collect outputs for conv2d, fully_connected and max_pool2d.
        with slim.arg_scope([slim.conv2d, slim.max_pool2d],
                            outputs_collections=end_points_collection):
            net = slim.repeat(inputs, 1, slim.conv2d, 64, [3, 3], scope='conv1')
            net = slim.max_pool2d(net, [2, 2], scope='pool1')
            net = slim.repeat(net, 1, slim.conv2d, 128, [3, 3], scope='conv2')
            net = slim.max_pool2d(net, [2, 2], scope='pool2')
            net = slim.repeat(net, 2, slim.conv2d, 256, [3, 3], scope='conv3')
            net = slim.max_pool2d(net, [2, 2], scope='pool3')
            net = slim.repeat(net, 2, slim.conv2d, 512, [3, 3], scope='conv4')
            net = slim.max_pool2d(net, [2, 2], scope='pool4')
            net = slim.repeat(net, 2, slim.conv2d, 512, [3, 3], scope='conv5')
            net = slim.max_pool2d(net, [2, 2], scope='pool5')

            # Use conv2d instead of fully_connected layers.
            net = slim.conv2d(net, 4096, [7, 7], padding=fc_conv_padding, scope='fc6')
            net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
                               scope='dropout6')
            net = slim.conv2d(net, 4096, [1, 1], scope='fc7')
            # Convert end_points_collection into a end_point dict.
            end_points = slim.utils.convert_collection_to_dict(end_points_collection)
            if global_pool:
                net = tf.reduce_mean(net, [1, 2], keep_dims=True, name='global_pool')
                end_points['global_pool'] = net
            if num_classes:
                net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
                                   scope='dropout7')
                net = slim.conv2d(net, num_classes, [1, 1],
                                  activation_fn=None,
                                  normalizer_fn=None,
                                  scope='fc8')
                if spatial_squeeze:
                    net = tf.squeeze(net, [1, 2], name='fc8/squeezed')
                end_points[sc.name + '/fc8'] = net
            return net, end_points


vgg_a.default_image_size = 224


def vgg_16(inputs,
           num_classes=1000,
           is_training=True,
           dropout_keep_prob=0.5,
           spatial_squeeze=True,
           scope='vgg_16',
           fc_conv_padding='VALID',
           global_pool=False):
    """Oxford Net VGG 16-Layers version D Example.

    Note: All the fully_connected layers have been transformed to conv2d layers.
          To use in classification mode, resize input to 224x224.

    Args:
      inputs: a tensor of size [batch_size, height, width, channels].
      num_classes: number of predicted classes. If 0 or None, the logits layer is
        omitted and the input features to the logits layer are returned instead.
      is_training: whether or not the model is being trained.
      dropout_keep_prob: the probability that activations are kept in the dropout
        layers during training.
      spatial_squeeze: whether or not should squeeze the spatial dimensions of the
        outputs. Useful to remove unnecessary dimensions for classification.
      scope: Optional scope for the variables.
      fc_conv_padding: the type of padding to use for the fully connected layer
        that is implemented as a convolutional layer. Use 'SAME' padding if you
        are applying the network in a fully convolutional manner and want to
        get a prediction map downsampled by a factor of 32 as an output.
        Otherwise, the output prediction map will be (input / 32) - 6 in case of
        'VALID' padding.
      global_pool: Optional boolean flag. If True, the input to the classification
        layer is avgpooled to size 1x1, for any input size. (This is not part
        of the original VGG architecture.)

    Returns:
      net: the output of the logits layer (if num_classes is a non-zero integer),
        or the input to the logits layer (if num_classes is 0 or None).
      end_points: a dict of tensors with intermediate activations.
    """
    with tf.variable_scope(scope, 'vgg_16', [inputs]) as sc:
        end_points_collection = sc.original_name_scope + '_end_points'
        # Collect outputs for conv2d, fully_connected and max_pool2d.
        with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d],
                            outputs_collections=end_points_collection):
            net = slim.repeat(inputs, 2, slim.conv2d, 64, [3, 3], scope='conv1')
            net = slim.max_pool2d(net, [2, 2], scope='pool1')
            net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], scope='conv2')
            net = slim.max_pool2d(net, [2, 2], scope='pool2')
            net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3], scope='conv3')
            net = slim.max_pool2d(net, [2, 2], scope='pool3')
            net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv4')
            net = slim.max_pool2d(net, [2, 2], scope='pool4')
            net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv5')
            net = slim.max_pool2d(net, [2, 2], scope='pool5')

            # Use conv2d instead of fully_connected layers.
            net = slim.conv2d(net, 4096, [7, 7], padding=fc_conv_padding, scope='fc6')
            net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
                               scope='dropout6')
            net = slim.conv2d(net, 4096, [1, 1], scope='fc7')
            # Convert end_points_collection into a end_point dict.
            end_points = slim.utils.convert_collection_to_dict(end_points_collection)
            if global_pool:
                net = tf.reduce_mean(net, [1, 2], keep_dims=True, name='global_pool')
                end_points['global_pool'] = net
            if num_classes:
                net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
                                   scope='dropout7')
                net = slim.conv2d(net, num_classes, [1, 1],
                                  activation_fn=None,
                                  normalizer_fn=None,
                                  scope='fc8')
                if spatial_squeeze:
                    net = tf.squeeze(net, [1, 2], name='fc8/squeezed')
                end_points[sc.name + '/fc8'] = net
            return net, end_points


vgg_16.default_image_size = 224


def vgg_19(inputs,
           num_classes=1000,
           is_training=True,
           dropout_keep_prob=0.5,
           spatial_squeeze=True,
           scope='vgg_19',
           fc_conv_padding='VALID',
           global_pool=False):
    """Oxford Net VGG 19-Layers version E Example.

    Note: All the fully_connected layers have been transformed to conv2d layers.
          To use in classification mode, resize input to 224x224.

    Args:
      inputs: a tensor of size [batch_size, height, width, channels].
      num_classes: number of predicted classes. If 0 or None, the logits layer is
        omitted and the input features to the logits layer are returned instead.
      is_training: whether or not the model is being trained.
      dropout_keep_prob: the probability that activations are kept in the dropout
        layers during training.
      spatial_squeeze: whether or not should squeeze the spatial dimensions of the
        outputs. Useful to remove unnecessary dimensions for classification.
      scope: Optional scope for the variables.
      fc_conv_padding: the type of padding to use for the fully connected layer
        that is implemented as a convolutional layer. Use 'SAME' padding if you
        are applying the network in a fully convolutional manner and want to
        get a prediction map downsampled by a factor of 32 as an output.
        Otherwise, the output prediction map will be (input / 32) - 6 in case of
        'VALID' padding.
      global_pool: Optional boolean flag. If True, the input to the classification
        layer is avgpooled to size 1x1, for any input size. (This is not part
        of the original VGG architecture.)

    Returns:
      net: the output of the logits layer (if num_classes is a non-zero integer),
        or the non-dropped-out input to the logits layer (if num_classes is 0 or
        None).
      end_points: a dict of tensors with intermediate activations.
    """
    with tf.variable_scope(scope, 'vgg_19', [inputs]) as sc:
        end_points_collection = sc.original_name_scope + '_end_points'
        # Collect outputs for conv2d, fully_connected and max_pool2d.
        with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d],
                            outputs_collections=end_points_collection):
            net = slim.repeat(inputs, 2, slim.conv2d, 64, [3, 3], scope='conv1')
            net = slim.max_pool2d(net, [2, 2], scope='pool1')
            net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], scope='conv2')
            net = slim.max_pool2d(net, [2, 2], scope='pool2')
            net = slim.repeat(net, 4, slim.conv2d, 256, [3, 3], scope='conv3')
            net = slim.max_pool2d(net, [2, 2], scope='pool3')
            net = slim.repeat(net, 4, slim.conv2d, 512, [3, 3], scope='conv4')
            net = slim.max_pool2d(net, [2, 2], scope='pool4')
            net = slim.repeat(net, 4, slim.conv2d, 512, [3, 3], scope='conv5')
            net = slim.max_pool2d(net, [2, 2], scope='pool5')

            # Use conv2d instead of fully_connected layers.
            net = slim.conv2d(net, 4096, [7, 7], padding=fc_conv_padding, scope='fc6')
            net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
                               scope='dropout6')
            net = slim.conv2d(net, 4096, [1, 1], scope='fc7')
            # Convert end_points_collection into a end_point dict.
            end_points = slim.utils.convert_collection_to_dict(end_points_collection)
            if global_pool:
                net = tf.reduce_mean(net, [1, 2], keep_dims=True, name='global_pool')
                end_points['global_pool'] = net
            if num_classes:
                net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
                                   scope='dropout7')
                net = slim.conv2d(net, num_classes, [1, 1],
                                  activation_fn=None,
                                  normalizer_fn=None,
                                  scope='fc8')
                if spatial_squeeze:
                    net = tf.squeeze(net, [1, 2], name='fc8/squeezed')
                end_points[sc.name + '/fc8'] = net
            return net, end_points


vgg_19.default_image_size = 224

# Alias
vgg_d = vgg_16
vgg_e = vgg_19
