code: # 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
  #
  # 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.
  # ==============================================================================
  
  """Functions to build DetectionModel training optimizers."""
  
  import tensorflow as tf
  from object_detection.utils import learning_schedules
  
  slim = tf.contrib.slim
  
  
  def build(optimizer_config, global_summaries):
    """Create optimizer based on config.
  
    Args:
      optimizer_config: A Optimizer proto message.
      global_summaries: A set to attach learning rate summary to.
  
    Returns:
      An optimizer.
  
    Raises:
      ValueError: when using an unsupported input data type.
    """
    optimizer_type = optimizer_config.WhichOneof('optimizer')
    optimizer = None
  
    if optimizer_type == 'rms_prop_optimizer':
      config = optimizer_config.rms_prop_optimizer
      optimizer = tf.train.RMSPropOptimizer(
          _create_learning_rate(config.learning_rate, global_summaries),
          decay=config.decay,
          momentum=config.momentum_optimizer_value,
          epsilon=config.epsilon)
  
    if optimizer_type == 'momentum_optimizer':
      config = optimizer_config.momentum_optimizer
      optimizer = tf.train.MomentumOptimizer(
          _create_learning_rate(config.learning_rate, global_summaries),
          momentum=config.momentum_optimizer_value)
  
    if optimizer_type == 'adam_optimizer':
      config = optimizer_config.adam_optimizer
      optimizer = tf.train.AdamOptimizer(
          _create_learning_rate(config.learning_rate, global_summaries))
  
    if optimizer is None:
      raise ValueError('Optimizer %s not supported.' % optimizer_type)
  
    if optimizer_config.use_moving_average:
      optimizer = tf.contrib.opt.MovingAverageOptimizer(
          optimizer, average_decay=optimizer_config.moving_average_decay)
  
    return optimizer
  
  
  def _create_learning_rate(learning_rate_config, global_summaries):
    """Create optimizer learning rate based on config.
  
    Args:
      learning_rate_config: A LearningRate proto message.
      global_summaries: A set to attach learning rate summary to.
  
    Returns:
      A learning rate.
  
    Raises:
      ValueError: when using an unsupported input data type.
    """
    learning_rate = None
    learning_rate_type = learning_rate_config.WhichOneof('learning_rate')
    if learning_rate_type == 'constant_learning_rate':
      config = learning_rate_config.constant_learning_rate
      learning_rate = config.learning_rate
  
    if learning_rate_type == 'exponential_decay_learning_rate':
      config = learning_rate_config.exponential_decay_learning_rate
      learning_rate = tf.train.exponential_decay(
          config.initial_learning_rate,
          slim.get_or_create_global_step(),
          config.decay_steps,
          config.decay_factor,
          staircase=config.staircase)
  
    if learning_rate_type == 'manual_step_learning_rate':
      config = learning_rate_config.manual_step_learning_rate
      if not config.schedule:
        raise ValueError('Empty learning rate schedule.')
      learning_rate_step_boundaries = [x.step for x in config.schedule]
      learning_rate_sequence = [config.initial_learning_rate]
      learning_rate_sequence += [x.learning_rate for x in config.schedule]
      learning_rate = learning_schedules.manual_stepping(
          slim.get_or_create_global_step(), learning_rate_step_boundaries,
          learning_rate_sequence)
  
    if learning_rate is None:
      raise ValueError('Learning_rate %s not supported.' % learning_rate_type)
  
    global_summaries.add(tf.summary.scalar('Learning Rate', learning_rate))
    return learning_rate
  
apis: tensorflow.contrib.opt.MovingAverageOptimizer tensorflow.summary.scalar