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 V1 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
  
  from pruning_layers import masked_conv2d, masked_fully_connected
  import pdb
  
  FLAGS = tf.app.flags.FLAGS
  
  def custom_concat_func_for_expand(states_to_concat, original_filter_numbers, axis=3):
    
    assert len(states_to_concat) == len(original_filter_numbers)
  
    if FLAGS.history_filters_expand_ratios:
      pivot_positions = [0] * len(original_filter_numbers)
      new_pivot_positions = []
      new_states_to_concat = []
      
      for prev_ratio in FLAGS.history_filters_expand_ratios:
        splits = []
        for state, original_filter_number, pivot_position in zip(states_to_concat, original_filter_numbers, pivot_positions):
          prev_num_conv_filter = int(original_filter_number * prev_ratio)
          splits.append(state[:, :, :, pivot_position:prev_num_conv_filter])
          new_pivot_positions.append(prev_num_conv_filter)
  
        temp = tf.concat(values=splits, axis=axis)
        new_states_to_concat.append(temp)
        pivot_positions = new_pivot_positions
        new_pivot_positions = []
  
      return tf.concat(new_states_to_concat, axis)
    else:
      return tf.concat(states_to_concat, axis)
  
  # Inception-Resnet-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 = masked_conv2d(net, int(32 * FLAGS.filters_expand_ratio), 1, scope='Conv2d_1x1', task_id=FLAGS.task_id)
          with tf.variable_scope('Branch_1'):
              tower_conv1_0 = masked_conv2d(net, int(32 * FLAGS.filters_expand_ratio), 1, scope='Conv2d_0a_1x1', task_id=FLAGS.task_id)
              tower_conv1_1 = masked_conv2d(tower_conv1_0, int(32 * FLAGS.filters_expand_ratio), 3, scope='Conv2d_0b_3x3', task_id=FLAGS.task_id)
          with tf.variable_scope('Branch_2'):
              tower_conv2_0 = masked_conv2d(net, int(32 * FLAGS.filters_expand_ratio), 1, scope='Conv2d_0a_1x1', task_id=FLAGS.task_id)
              tower_conv2_1 = masked_conv2d(tower_conv2_0, int(32 * FLAGS.filters_expand_ratio), 3, scope='Conv2d_0b_3x3', task_id=FLAGS.task_id)
              tower_conv2_2 = masked_conv2d(tower_conv2_1, int(32 * FLAGS.filters_expand_ratio), 3, scope='Conv2d_0c_3x3', task_id=FLAGS.task_id)
          
          states_to_concat = [tower_conv, tower_conv1_1, tower_conv2_2]
  
          mixed = custom_concat_func_for_expand(states_to_concat, [32, 32, 32], 3)
          up = masked_conv2d(mixed, net.get_shape()[3], 1, normalizer_fn=None,
                           activation_fn=None, scope='Conv2d_1x1', task_id=FLAGS.task_id)
          
          net += scale * up
          if activation_fn:
              net = activation_fn(net)
      return net
  
  # Inception-Resnet-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 = masked_conv2d(net, int(128 * FLAGS.filters_expand_ratio), 1, scope='Conv2d_1x1', task_id=FLAGS.task_id)
          with tf.variable_scope('Branch_1'):
              tower_conv1_0 = masked_conv2d(net, int(128 * FLAGS.filters_expand_ratio), 1, scope='Conv2d_0a_1x1', task_id=FLAGS.task_id)
              tower_conv1_1 = masked_conv2d(tower_conv1_0, int(128 * FLAGS.filters_expand_ratio), [1, 7],
                                          scope='Conv2d_0b_1x7', task_id=FLAGS.task_id)
              tower_conv1_2 = masked_conv2d(tower_conv1_1, int(128 * FLAGS.filters_expand_ratio), [7, 1],
                                          scope='Conv2d_0c_7x1', task_id=FLAGS.task_id)
          mixed = custom_concat_func_for_expand([tower_conv, tower_conv1_2], [128, 128], 3)
          up = masked_conv2d(mixed, net.get_shape()[3], 1, normalizer_fn=None,
                           activation_fn=None, scope='Conv2d_1x1', task_id=FLAGS.task_id)
          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 = masked_conv2d(net, int(192 * FLAGS.filters_expand_ratio), 1, scope='Conv2d_1x1', task_id=FLAGS.task_id)
          with tf.variable_scope('Branch_1'):
              tower_conv1_0 = masked_conv2d(net, int(192 * FLAGS.filters_expand_ratio), 1, scope='Conv2d_0a_1x1', task_id=FLAGS.task_id)
              tower_conv1_1 = masked_conv2d(tower_conv1_0, int(192 * FLAGS.filters_expand_ratio), [1, 3],
                                          scope='Conv2d_0b_1x3', task_id=FLAGS.task_id)
              tower_conv1_2 = masked_conv2d(tower_conv1_1, int(192 * FLAGS.filters_expand_ratio), [3, 1],
                                          scope='Conv2d_0c_3x1', task_id=FLAGS.task_id)
          mixed = custom_concat_func_for_expand([tower_conv, tower_conv1_2], [192, 192], 3)
          up = masked_conv2d(mixed, net.get_shape()[3], 1, normalizer_fn=None,
                           activation_fn=None, scope='Conv2d_1x1', task_id=FLAGS.task_id)
          net += scale * up
          if activation_fn:
              net = activation_fn(net)
      return net
    
  def reduction_a(net, k, l, m, n):
      with tf.variable_scope('Branch_0'):
          tower_conv = masked_conv2d(net, int(n * FLAGS.filters_expand_ratio), 3, stride=2, padding='VALID',
                                   scope='Conv2d_1a_3x3', task_id=FLAGS.task_id)
      with tf.variable_scope('Branch_1'):
          tower_conv1_0 = masked_conv2d(net, int(k * FLAGS.filters_expand_ratio), 1, scope='Conv2d_0a_1x1', task_id=FLAGS.task_id)
          tower_conv1_1 = masked_conv2d(tower_conv1_0, int(l * FLAGS.filters_expand_ratio), 3,
                                      scope='Conv2d_0b_3x3', task_id=FLAGS.task_id)
          tower_conv1_2 = masked_conv2d(tower_conv1_1, int(m * FLAGS.filters_expand_ratio), 3,
                                      stride=2, padding='VALID',
                                      scope='Conv2d_1a_3x3', task_id=FLAGS.task_id)
      with tf.variable_scope('Branch_2'):
          tower_pool = slim.max_pool2d(net, 3, stride=2, padding='VALID',
                                       scope='MaxPool_1a_3x3')
      net = custom_concat_func_for_expand([tower_conv, tower_conv1_2, tower_pool], [n, m, 256], 3)
      return net
  
  def reduction_b(net):
      with tf.variable_scope('Branch_0'):
          tower_conv = masked_conv2d(net, int(256 * FLAGS.filters_expand_ratio), 1, scope='Conv2d_0a_1x1', task_id=FLAGS.task_id)
          tower_conv_1 = masked_conv2d(tower_conv, int(384 * FLAGS.filters_expand_ratio), 3, stride=2,
                                     padding='VALID', scope='Conv2d_1a_3x3', task_id=FLAGS.task_id)
      with tf.variable_scope('Branch_1'):
          tower_conv1 = masked_conv2d(net, int(256 * FLAGS.filters_expand_ratio), 1, scope='Conv2d_0a_1x1', task_id=FLAGS.task_id)
          tower_conv1_1 = masked_conv2d(tower_conv1, int(256 * FLAGS.filters_expand_ratio), 3, stride=2,
                                      padding='VALID', scope='Conv2d_1a_3x3', task_id=FLAGS.task_id)
      with tf.variable_scope('Branch_2'):
          tower_conv2 = masked_conv2d(net, int(256 * FLAGS.filters_expand_ratio), 1, scope='Conv2d_0a_1x1', task_id=FLAGS.task_id)
          tower_conv2_1 = masked_conv2d(tower_conv2, int(256 * FLAGS.filters_expand_ratio), 3,
                                      scope='Conv2d_0b_3x3', task_id=FLAGS.task_id)
          tower_conv2_2 = masked_conv2d(tower_conv2_1, int(256 * FLAGS.filters_expand_ratio), 3, stride=2,
                                      padding='VALID', scope='Conv2d_1a_3x3', task_id=FLAGS.task_id)
      with tf.variable_scope('Branch_3'):
          tower_pool = slim.max_pool2d(net, 3, stride=2, padding='VALID',
                                       scope='MaxPool_1a_3x3')
      
      net = custom_concat_func_for_expand([tower_conv_1, tower_conv1_1,
                          tower_conv2_2, tower_pool], [384, 256, 256, 896],3)
      
      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([masked_conv2d, masked_fully_connected],
                          weights_initializer=slim.initializers.xavier_initializer(), 
                          weights_regularizer=slim.l2_regularizer(weight_decay),
                          normalizer_fn=slim.batch_norm,
                          normalizer_params=batch_norm_params):
          return inception_resnet_v1(images, is_training=phase_train,
                dropout_keep_prob=keep_probability, bottleneck_layer_size=bottleneck_layer_size, reuse=reuse)
  
  
  def inception_resnet_v1(inputs, is_training=True,
                          dropout_keep_prob=0.8,
                          bottleneck_layer_size=128,
                          reuse=None, 
                          scope='InceptionResnetV1'):
      """Creates the Inception Resnet V1 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, 'InceptionResnetV1', [inputs], reuse=reuse):
          with slim.arg_scope([slim.batch_norm, slim.dropout],
                              is_training=is_training):
              with slim.arg_scope([masked_conv2d, slim.max_pool2d, slim.avg_pool2d],
                                  stride=1, padding='SAME'):
        
                  # 149 x 149 x 32
                  net = masked_conv2d(inputs, int(32 * FLAGS.filters_expand_ratio), 3, stride=2, padding='VALID',
                                    scope='Conv2d_1a_3x3', task_id=FLAGS.task_id)
                  end_points['Conv2d_1a_3x3'] = net
                  # 147 x 147 x 32
                  net = masked_conv2d(net, int(32 * FLAGS.filters_expand_ratio), 3, padding='VALID',
                                    scope='Conv2d_2a_3x3', task_id=FLAGS.task_id)
                  end_points['Conv2d_2a_3x3'] = net
                  # 147 x 147 x 64
                  net = masked_conv2d(net, int(64 * FLAGS.filters_expand_ratio), 3, scope='Conv2d_2b_3x3', task_id=FLAGS.task_id)
                  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 = masked_conv2d(net, int(80 * FLAGS.filters_expand_ratio), 1, padding='VALID',
                                    scope='Conv2d_3b_1x1', task_id=FLAGS.task_id)
                  end_points['Conv2d_3b_1x1'] = net
                  # 71 x 71 x 192
                  net = masked_conv2d(net, int(192 * FLAGS.filters_expand_ratio), 3, padding='VALID',
                                    scope='Conv2d_4a_3x3', task_id=FLAGS.task_id)
                  end_points['Conv2d_4a_3x3'] = net
                  # 35 x 35 x 256
                  net = masked_conv2d(net, int(256 * FLAGS.filters_expand_ratio), 3, stride=2, padding='VALID',
                                    scope='Conv2d_4b_3x3', task_id=FLAGS.task_id)
                  end_points['Conv2d_4b_3x3'] = net
                  
                  # 5 x Inception-resnet-A
                  net = slim.repeat(net, 5, block35, scale=0.17)
                  end_points['Mixed_5a'] = net
          
                  # Reduction-A
                  with tf.variable_scope('Mixed_6a'):
                      net = reduction_a(net, 192, 192, 256, 384)
                  end_points['Mixed_6a'] = net
                  
                  # 10 x Inception-Resnet-B
                  net = slim.repeat(net, 10, block17, scale=0.10)
                  end_points['Mixed_6b'] = net
                  
                  # Reduction-B
                  with tf.variable_scope('Mixed_7a'):
                      net = reduction_b(net)
                  end_points['Mixed_7a'] = net
                  
                  # 5 x Inception-Resnet-C
                  net = slim.repeat(net, 5, block8, scale=0.20)
                  end_points['Mixed_8a'] = net
                  
                  net = block8(net, activation_fn=None)
                  end_points['Mixed_8b'] = 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 = masked_fully_connected(net, bottleneck_layer_size, activation_fn=None, 
                          scope='Bottleneck', reuse=False, task_id=FLAGS.task_id)
                  
      return net, end_points
  