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 the definition of the Inception Resnet V2 architecture.
  As described in http://arxiv.org/abs/1602.07261.
    Inception-v4, Inception-ResNet and the Impact of Residual Connections
      on Learning
    Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi
  """
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import tensorflow as tf
  import tensorflow.contrib.slim as slim
  
  # Inception-Renset-A
  def block35(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None):
      """Builds the 35x35 resnet block."""
      with tf.variable_scope(scope, 'Block35', [net], reuse=reuse):
          with tf.variable_scope('Branch_0'):
              tower_conv = slim.conv2d(net, 32, 1, scope='Conv2d_1x1')
          with tf.variable_scope('Branch_1'):
              tower_conv1_0 = slim.conv2d(net, 32, 1, scope='Conv2d_0a_1x1')
              tower_conv1_1 = slim.conv2d(tower_conv1_0, 32, 3, scope='Conv2d_0b_3x3')
          with tf.variable_scope('Branch_2'):
              tower_conv2_0 = slim.conv2d(net, 32, 1, scope='Conv2d_0a_1x1')
              tower_conv2_1 = slim.conv2d(tower_conv2_0, 48, 3, scope='Conv2d_0b_3x3')
              tower_conv2_2 = slim.conv2d(tower_conv2_1, 64, 3, scope='Conv2d_0c_3x3')
          mixed = tf.concat([tower_conv, tower_conv1_1, tower_conv2_2], 3)
          up = slim.conv2d(mixed, net.get_shape()[3], 1, normalizer_fn=None,
                           activation_fn=None, scope='Conv2d_1x1')
          net += scale * up
          if activation_fn:
              net = activation_fn(net)
      return net
  
  # Inception-Renset-B
  def block17(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None):
      """Builds the 17x17 resnet block."""
      with tf.variable_scope(scope, 'Block17', [net], reuse=reuse):
          with tf.variable_scope('Branch_0'):
              tower_conv = slim.conv2d(net, 192, 1, scope='Conv2d_1x1')
          with tf.variable_scope('Branch_1'):
              tower_conv1_0 = slim.conv2d(net, 128, 1, scope='Conv2d_0a_1x1')
              tower_conv1_1 = slim.conv2d(tower_conv1_0, 160, [1, 7],
                                          scope='Conv2d_0b_1x7')
              tower_conv1_2 = slim.conv2d(tower_conv1_1, 192, [7, 1],
                                          scope='Conv2d_0c_7x1')
          mixed = tf.concat([tower_conv, tower_conv1_2], 3)
          up = slim.conv2d(mixed, net.get_shape()[3], 1, normalizer_fn=None,
                           activation_fn=None, scope='Conv2d_1x1')
          net += scale * up
          if activation_fn:
              net = activation_fn(net)
      return net
  
  
  # Inception-Resnet-C
  def block8(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None):
      """Builds the 8x8 resnet block."""
      with tf.variable_scope(scope, 'Block8', [net], reuse=reuse):
          with tf.variable_scope('Branch_0'):
              tower_conv = slim.conv2d(net, 192, 1, scope='Conv2d_1x1')
          with tf.variable_scope('Branch_1'):
              tower_conv1_0 = slim.conv2d(net, 192, 1, scope='Conv2d_0a_1x1')
              tower_conv1_1 = slim.conv2d(tower_conv1_0, 224, [1, 3],
                                          scope='Conv2d_0b_1x3')
              tower_conv1_2 = slim.conv2d(tower_conv1_1, 256, [3, 1],
                                          scope='Conv2d_0c_3x1')
          mixed = tf.concat([tower_conv, tower_conv1_2], 3)
          up = slim.conv2d(mixed, net.get_shape()[3], 1, normalizer_fn=None,
                           activation_fn=None, scope='Conv2d_1x1')
          net += scale * up
          if activation_fn:
              net = activation_fn(net)
      return net
    
  def inference(images, keep_probability, phase_train=True, 
                bottleneck_layer_size=128, weight_decay=0.0, reuse=None):
      batch_norm_params = {
          # Decay for the moving averages.
          'decay': 0.995,
          # epsilon to prevent 0s in variance.
          'epsilon': 0.001,
          # force in-place updates of mean and variance estimates
          'updates_collections': None,
          # Moving averages ends up in the trainable variables collection
          'variables_collections': [ tf.GraphKeys.TRAINABLE_VARIABLES ],
  }
      with slim.arg_scope([slim.conv2d, slim.fully_connected],
                          weights_initializer=tf.truncated_normal_initializer(stddev=0.1),
                          weights_regularizer=slim.l2_regularizer(weight_decay),
                          normalizer_fn=slim.batch_norm,
                          normalizer_params=batch_norm_params):
          return inception_resnet_v2(images, is_training=phase_train,
                dropout_keep_prob=keep_probability, bottleneck_layer_size=bottleneck_layer_size, reuse=reuse)
  
  
  def inception_resnet_v2(inputs, is_training=True,
                          dropout_keep_prob=0.8,
                          bottleneck_layer_size=128,
                          reuse=None,
                          scope='InceptionResnetV2'):
      """Creates the Inception Resnet V2 model.
      Args:
        inputs: a 4-D tensor of size [batch_size, height, width, 3].
        num_classes: number of predicted classes.
        is_training: whether is training or not.
        dropout_keep_prob: float, the fraction to keep before final layer.
        reuse: whether or not the network and its variables should be reused. To be
          able to reuse 'scope' must be given.
        scope: Optional variable_scope.
      Returns:
        logits: the logits outputs of the model.
        end_points: the set of end_points from the inception model.
      """
      end_points = {}
    
      with tf.variable_scope(scope, 'InceptionResnetV2', [inputs], reuse=reuse):
          with slim.arg_scope([slim.batch_norm, slim.dropout],
                              is_training=is_training):
              with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
                                  stride=1, padding='SAME'):
        
                  # 149 x 149 x 32
                  net = slim.conv2d(inputs, 32, 3, stride=2, padding='VALID',
                                    scope='Conv2d_1a_3x3')
                  end_points['Conv2d_1a_3x3'] = net
                  # 147 x 147 x 32
                  net = slim.conv2d(net, 32, 3, padding='VALID',
                                    scope='Conv2d_2a_3x3')
                  end_points['Conv2d_2a_3x3'] = net
                  # 147 x 147 x 64
                  net = slim.conv2d(net, 64, 3, scope='Conv2d_2b_3x3')
                  end_points['Conv2d_2b_3x3'] = net
                  # 73 x 73 x 64
                  net = slim.max_pool2d(net, 3, stride=2, padding='VALID',
                                        scope='MaxPool_3a_3x3')
                  end_points['MaxPool_3a_3x3'] = net
                  # 73 x 73 x 80
                  net = slim.conv2d(net, 80, 1, padding='VALID',
                                    scope='Conv2d_3b_1x1')
                  end_points['Conv2d_3b_1x1'] = net
                  # 71 x 71 x 192
                  net = slim.conv2d(net, 192, 3, padding='VALID',
                                    scope='Conv2d_4a_3x3')
                  end_points['Conv2d_4a_3x3'] = net
                  # 35 x 35 x 192
                  net = slim.max_pool2d(net, 3, stride=2, padding='VALID',
                                        scope='MaxPool_5a_3x3')
                  end_points['MaxPool_5a_3x3'] = net
          
                  # 35 x 35 x 320
                  with tf.variable_scope('Mixed_5b'):
                      with tf.variable_scope('Branch_0'):
                          tower_conv = slim.conv2d(net, 96, 1, scope='Conv2d_1x1')
                      with tf.variable_scope('Branch_1'):
                          tower_conv1_0 = slim.conv2d(net, 48, 1, scope='Conv2d_0a_1x1')
                          tower_conv1_1 = slim.conv2d(tower_conv1_0, 64, 5,
                                                      scope='Conv2d_0b_5x5')
                      with tf.variable_scope('Branch_2'):
                          tower_conv2_0 = slim.conv2d(net, 64, 1, scope='Conv2d_0a_1x1')
                          tower_conv2_1 = slim.conv2d(tower_conv2_0, 96, 3,
                                                      scope='Conv2d_0b_3x3')
                          tower_conv2_2 = slim.conv2d(tower_conv2_1, 96, 3,
                                                      scope='Conv2d_0c_3x3')
                      with tf.variable_scope('Branch_3'):
                          tower_pool = slim.avg_pool2d(net, 3, stride=1, padding='SAME',
                                                       scope='AvgPool_0a_3x3')
                          tower_pool_1 = slim.conv2d(tower_pool, 64, 1,
                                                     scope='Conv2d_0b_1x1')
                      net = tf.concat([tower_conv, tower_conv1_1,
                                          tower_conv2_2, tower_pool_1], 3)
          
                  end_points['Mixed_5b'] = net
                  net = slim.repeat(net, 10, block35, scale=0.17)
          
                  # 17 x 17 x 1024
                  with tf.variable_scope('Mixed_6a'):
                      with tf.variable_scope('Branch_0'):
                          tower_conv = slim.conv2d(net, 384, 3, stride=2, padding='VALID',
                                                   scope='Conv2d_1a_3x3')
                      with tf.variable_scope('Branch_1'):
                          tower_conv1_0 = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1')
                          tower_conv1_1 = slim.conv2d(tower_conv1_0, 256, 3,
                                                      scope='Conv2d_0b_3x3')
                          tower_conv1_2 = slim.conv2d(tower_conv1_1, 384, 3,
                                                      stride=2, padding='VALID',
                                                      scope='Conv2d_1a_3x3')
                      with tf.variable_scope('Branch_2'):
                          tower_pool = slim.max_pool2d(net, 3, stride=2, padding='VALID',
                                                       scope='MaxPool_1a_3x3')
                      net = tf.concat([tower_conv, tower_conv1_2, tower_pool], 3)
          
                  end_points['Mixed_6a'] = net
                  net = slim.repeat(net, 20, block17, scale=0.10)
          
                  with tf.variable_scope('Mixed_7a'):
                      with tf.variable_scope('Branch_0'):
                          tower_conv = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1')
                          tower_conv_1 = slim.conv2d(tower_conv, 384, 3, stride=2,
                                                     padding='VALID', scope='Conv2d_1a_3x3')
                      with tf.variable_scope('Branch_1'):
                          tower_conv1 = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1')
                          tower_conv1_1 = slim.conv2d(tower_conv1, 288, 3, stride=2,
                                                      padding='VALID', scope='Conv2d_1a_3x3')
                      with tf.variable_scope('Branch_2'):
                          tower_conv2 = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1')
                          tower_conv2_1 = slim.conv2d(tower_conv2, 288, 3,
                                                      scope='Conv2d_0b_3x3')
                          tower_conv2_2 = slim.conv2d(tower_conv2_1, 320, 3, stride=2,
                                                      padding='VALID', scope='Conv2d_1a_3x3')
                      with tf.variable_scope('Branch_3'):
                          tower_pool = slim.max_pool2d(net, 3, stride=2, padding='VALID',
                                                       scope='MaxPool_1a_3x3')
                      net = tf.concat([tower_conv_1, tower_conv1_1,
                                          tower_conv2_2, tower_pool], 3)
          
                  end_points['Mixed_7a'] = net
          
                  net = slim.repeat(net, 9, block8, scale=0.20)
                  net = block8(net, activation_fn=None)
          
                  net = slim.conv2d(net, 1536, 1, scope='Conv2d_7b_1x1')
                  end_points['Conv2d_7b_1x1'] = net
          
                  with tf.variable_scope('Logits'):
                      end_points['PrePool'] = net
                      #pylint: disable=no-member
                      net = slim.avg_pool2d(net, net.get_shape()[1:3], padding='VALID',
                                            scope='AvgPool_1a_8x8')
                      net = slim.flatten(net)
            
                      net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
                                         scope='Dropout')
            
                      end_points['PreLogitsFlatten'] = net
                  
                  net = slim.fully_connected(net, bottleneck_layer_size, activation_fn=None, 
                          scope='Bottleneck', reuse=False)
    
      return net, end_points
  