text: # Copyright 2016 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.
  # ==============================================================================
  """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
  from tensorflow.contrib import slim as contrib_slim
  
  slim = 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], 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,
            reuse=None,
            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.
      reuse: whether or not the network and its variables should be reused. To be
        able to reuse 'scope' must be given.
      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], reuse=reuse) 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,
             reuse=None,
             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.
      reuse: whether or not the network and its variables should be reused. To be
        able to reuse 'scope' must be given.
      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], reuse=reuse) 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,
             reuse=None,
             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.
      reuse: whether or not the network and its variables should be reused. To be
        able to reuse 'scope' must be given.
      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], reuse=reuse) 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
  