text: # Copyright 2017 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
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  # 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 warnings
  
  import tensorflow as tf
  
  slim = tf.contrib.slim
  
  
  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(
          axis=3, values=[tower_conv, tower_conv1_1, tower_conv2_2])
      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 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(axis=3, values=[tower_conv, tower_conv1_2])
      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 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(axis=3, values=[tower_conv, tower_conv1_2])
      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 inception_resnet_v2_base(inputs,
                               final_endpoint='Conv2d_7b_1x1',
                               output_stride=16,
                               align_feature_maps=False,
                               scope=None):
    """Inception model from  http://arxiv.org/abs/1602.07261.
  
    Constructs an Inception Resnet v2 network from inputs to the given final
    endpoint. This method can construct the network up to the final inception
    block Conv2d_7b_1x1.
  
    Args:
      inputs: a tensor of size [batch_size, height, width, channels].
      final_endpoint: specifies the endpoint to construct the network up to. It
        can be one of ['Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3',
        'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3', 'MaxPool_5a_3x3',
        'Mixed_5b', 'Mixed_6a', 'PreAuxLogits', 'Mixed_7a', 'Conv2d_7b_1x1']
      output_stride: A scalar that specifies the requested ratio of input to
        output spatial resolution. Only supports 8 and 16.
      align_feature_maps: When true, changes all the VALID paddings in the network
        to SAME padding so that the feature maps are aligned.
      scope: Optional variable_scope.
  
    Returns:
      tensor_out: output tensor corresponding to the final_endpoint.
      end_points: a set of activations for external use, for example summaries or
                  losses.
  
    Raises:
      ValueError: if final_endpoint is not set to one of the predefined values,
        or if the output_stride is not 8 or 16, or if the output_stride is 8 and
        we request an end point after 'PreAuxLogits'.
    """
    if output_stride != 8 and output_stride != 16:
      raise ValueError('output_stride must be 8 or 16.')
  
    padding = 'SAME' if align_feature_maps else 'VALID'
  
    end_points = {}
  
    def add_and_check_final(name, net):
      """
      TODO: write this
      """
      end_points[name] = net
      return name == final_endpoint
  
    with tf.variable_scope(scope, 'InceptionResnetV2', [inputs]):
      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=padding,
                          scope='Conv2d_1a_3x3')
        if add_and_check_final('Conv2d_1a_3x3', net):
          return net, end_points
  
        # 147 x 147 x 32
        net = slim.conv2d(net, 32, 3, padding=padding,
                          scope='Conv2d_2a_3x3')
        if add_and_check_final('Conv2d_2a_3x3', net):
          return net, end_points
        # 147 x 147 x 64
        net = slim.conv2d(net, 64, 3, scope='Conv2d_2b_3x3')
        if add_and_check_final('Conv2d_2b_3x3', net):
          return net, end_points
        # 73 x 73 x 64
        net = slim.max_pool2d(net, 3, stride=2, padding=padding,
                              scope='MaxPool_3a_3x3')
        if add_and_check_final('MaxPool_3a_3x3', net):
          return net, end_points
        # 73 x 73 x 80
        net = slim.conv2d(net, 80, 1, padding=padding,
                          scope='Conv2d_3b_1x1')
        if add_and_check_final('Conv2d_3b_1x1', net):
          return net, end_points
        # 71 x 71 x 192
        net = slim.conv2d(net, 192, 3, padding=padding,
                          scope='Conv2d_4a_3x3')
        if add_and_check_final('Conv2d_4a_3x3', net):
          return net, end_points
        # 35 x 35 x 192
        net = slim.max_pool2d(net, 3, stride=2, padding=padding,
                              scope='MaxPool_5a_3x3')
        if add_and_check_final('MaxPool_5a_3x3', net):
          return net, end_points
  
        # 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)
  
        if add_and_check_final('Mixed_5b', net):
          return net, end_points
        # TODO(alemi): Register intermediate endpoints
        net = slim.repeat(net, 10, block35, scale=0.17)
  
        # 17 x 17 x 1088 if output_stride == 8,
        # 33 x 33 x 1088 if output_stride == 16
        use_atrous = output_stride == 8
  
        with tf.variable_scope('Mixed_6a'):
          with tf.variable_scope('Branch_0'):
            tower_conv = slim.conv2d(net, 384, 3, stride=1 if use_atrous else 2,
                                     padding=padding,
                                     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=1 if use_atrous else 2,
                                        padding=padding,
                                        scope='Conv2d_1a_3x3')
          with tf.variable_scope('Branch_2'):
            tower_pool = slim.max_pool2d(net, 3, stride=1 if use_atrous else 2,
                                         padding=padding,
                                         scope='MaxPool_1a_3x3')
          net = tf.concat([tower_conv, tower_conv1_2, tower_pool], 3)
  
        if add_and_check_final('Mixed_6a', net):
          return net, end_points
  
        # TODO(alemi): register intermediate endpoints
        with slim.arg_scope([slim.conv2d], rate=2 if use_atrous else 1):
          net = slim.repeat(net, 20, block17, scale=0.10)
        if add_and_check_final('PreAuxLogits', net):
          return net, end_points
  
        if output_stride == 8:
          # TODO(gpapan): Properly support output_stride for the rest of the net.
          raise ValueError('output_stride==8 is only supported up to the '
                           'PreAuxlogits end_point for now.')
  
        # 8 x 8 x 2080
        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=padding,
                                       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=padding,
                                        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=padding,
                                        scope='Conv2d_1a_3x3')
          with tf.variable_scope('Branch_3'):
            tower_pool = slim.max_pool2d(net, 3, stride=2,
                                         padding=padding,
                                         scope='MaxPool_1a_3x3')
          net = tf.concat(
              [tower_conv_1, tower_conv1_1, tower_conv2_2, tower_pool], 3)
  
        if add_and_check_final('Mixed_7a', net):
          return net, end_points
  
        # TODO(alemi): register intermediate endpoints
        net = slim.repeat(net, 9, block8, scale=0.20)
        net = block8(net, activation_fn=None)
  
        # 8 x 8 x 1536
        net = slim.conv2d(net, 1536, 1, scope='Conv2d_7b_1x1')
        if add_and_check_final('Conv2d_7b_1x1', net):
          return net, end_points
  
      raise ValueError('final_endpoint (%s) not recognized' % final_endpoint)
  
  
  def inception_resnet_v2(inputs, nb_classes=1001, is_training=True,
                          dropout_keep_prob=0.8,
                          reuse=None,
                          scope='InceptionResnetV2',
                          create_aux_logits=True,
                          num_classes=None):
    """Creates the Inception Resnet V2 model.
  
    Args:
      inputs: a 4-D tensor of size [batch_size, height, width, 3].
      nb_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.
      create_aux_logits: Whether to include the auxilliary logits.
      num_classes: depricated alias for nb_classes
  
    Returns:
      logits: the logits outputs of the model.
      end_points: the set of end_points from the inception model.
    """
    if num_classes is not None:
      warnings.warn("`num_classes` is deprecated. Switch to `nb_classes`."
                    " `num_classes` may be removed on or after 2019-04-23.")
      nb_classes = num_classes
      del num_classes
    end_points = {}
  
    with tf.variable_scope(scope, 'InceptionResnetV2', [inputs, nb_classes],
                           reuse=reuse) as var_scope:
      with slim.arg_scope([slim.batch_norm, slim.dropout],
                          is_training=is_training):
  
        net, end_points = inception_resnet_v2_base(inputs, scope=var_scope)
  
        if create_aux_logits:
          with tf.variable_scope('AuxLogits'):
            aux = end_points['PreAuxLogits']
            aux = slim.avg_pool2d(aux, 5, stride=3, padding='VALID',
                                  scope='Conv2d_1a_3x3')
            aux = slim.conv2d(aux, 128, 1, scope='Conv2d_1b_1x1')
            aux = slim.conv2d(aux, 768, aux.get_shape()[1:3],
                              padding='VALID', scope='Conv2d_2a_5x5')
            aux = slim.flatten(aux)
            aux = slim.fully_connected(aux, nb_classes, activation_fn=None,
                                       scope='Logits')
            end_points['AuxLogits'] = aux
  
        with tf.variable_scope('Logits'):
          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
          logits = slim.fully_connected(net, nb_classes, activation_fn=None,
                                        scope='Logits')
          end_points['Logits'] = logits
          end_points['Predictions'] = tf.nn.softmax(logits, name='Predictions')
  
      return logits, end_points
  
  
  inception_resnet_v2.default_image_size = 299
  
  
  def inception_resnet_v2_arg_scope(weight_decay=0.00004,
                                    batch_norm_decay=0.9997,
                                    batch_norm_epsilon=0.001):
    """Returns the scope with the default parameters for inception_resnet_v2.
  
    Args:
      weight_decay: the weight decay for weights variables.
      batch_norm_decay: decay for the moving average of batch_norm momentums.
      batch_norm_epsilon: small float added to variance to avoid dividing by zero.
  
    Returns:
      a arg_scope with the parameters needed for inception_resnet_v2.
    """
    # Set weight_decay for weights in conv2d and fully_connected layers.
    with slim.arg_scope([slim.conv2d, slim.fully_connected],
                        weights_regularizer=slim.l2_regularizer(weight_decay),
                        biases_regularizer=slim.l2_regularizer(weight_decay)):
  
      batch_norm_params = {
          'decay': batch_norm_decay,
          'epsilon': batch_norm_epsilon,
      }
      # Set activation_fn and parameters for batch_norm.
      with slim.arg_scope([slim.conv2d], activation_fn=tf.nn.relu,
                          normalizer_fn=slim.batch_norm,
                          normalizer_params=batch_norm_params) as scope:
        return scope
  