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"""Functions to build DetectionModel training optimizers.""" |
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import tensorflow as tf |
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from object_detection.utils import learning_schedules |
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def build(optimizer_config): |
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"""Create optimizer based on config. |
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Args: |
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optimizer_config: A Optimizer proto message. |
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Returns: |
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An optimizer and a list of variables for summary. |
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Raises: |
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ValueError: when using an unsupported input data type. |
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""" |
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optimizer_type = optimizer_config.WhichOneof('optimizer') |
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optimizer = None |
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summary_vars = [] |
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if optimizer_type == 'rms_prop_optimizer': |
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config = optimizer_config.rms_prop_optimizer |
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learning_rate = _create_learning_rate(config.learning_rate) |
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summary_vars.append(learning_rate) |
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optimizer = tf.train.RMSPropOptimizer( |
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learning_rate, |
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decay=config.decay, |
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momentum=config.momentum_optimizer_value, |
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epsilon=config.epsilon) |
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if optimizer_type == 'momentum_optimizer': |
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config = optimizer_config.momentum_optimizer |
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learning_rate = _create_learning_rate(config.learning_rate) |
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summary_vars.append(learning_rate) |
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optimizer = tf.train.MomentumOptimizer( |
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learning_rate, |
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momentum=config.momentum_optimizer_value) |
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if optimizer_type == 'adam_optimizer': |
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config = optimizer_config.adam_optimizer |
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learning_rate = _create_learning_rate(config.learning_rate) |
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summary_vars.append(learning_rate) |
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optimizer = tf.train.AdamOptimizer(learning_rate) |
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if optimizer is None: |
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raise ValueError('Optimizer %s not supported.' % optimizer_type) |
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if optimizer_config.use_moving_average: |
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optimizer = tf.contrib.opt.MovingAverageOptimizer( |
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optimizer, average_decay=optimizer_config.moving_average_decay) |
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return optimizer, summary_vars |
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def _create_learning_rate(learning_rate_config): |
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"""Create optimizer learning rate based on config. |
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Args: |
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learning_rate_config: A LearningRate proto message. |
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Returns: |
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A learning rate. |
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Raises: |
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ValueError: when using an unsupported input data type. |
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""" |
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learning_rate = None |
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learning_rate_type = learning_rate_config.WhichOneof('learning_rate') |
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if learning_rate_type == 'constant_learning_rate': |
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config = learning_rate_config.constant_learning_rate |
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learning_rate = tf.constant(config.learning_rate, dtype=tf.float32, |
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name='learning_rate') |
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if learning_rate_type == 'exponential_decay_learning_rate': |
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config = learning_rate_config.exponential_decay_learning_rate |
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learning_rate = learning_schedules.exponential_decay_with_burnin( |
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tf.train.get_or_create_global_step(), |
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config.initial_learning_rate, |
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config.decay_steps, |
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config.decay_factor, |
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burnin_learning_rate=config.burnin_learning_rate, |
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burnin_steps=config.burnin_steps, |
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min_learning_rate=config.min_learning_rate, |
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staircase=config.staircase) |
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if learning_rate_type == 'manual_step_learning_rate': |
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config = learning_rate_config.manual_step_learning_rate |
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if not config.schedule: |
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raise ValueError('Empty learning rate schedule.') |
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learning_rate_step_boundaries = [x.step for x in config.schedule] |
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learning_rate_sequence = [config.initial_learning_rate] |
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learning_rate_sequence += [x.learning_rate for x in config.schedule] |
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learning_rate = learning_schedules.manual_stepping( |
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tf.train.get_or_create_global_step(), learning_rate_step_boundaries, |
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learning_rate_sequence, config.warmup) |
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if learning_rate_type == 'cosine_decay_learning_rate': |
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config = learning_rate_config.cosine_decay_learning_rate |
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learning_rate = learning_schedules.cosine_decay_with_warmup( |
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tf.train.get_or_create_global_step(), |
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config.learning_rate_base, |
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config.total_steps, |
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config.warmup_learning_rate, |
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config.warmup_steps, |
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config.hold_base_rate_steps) |
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if learning_rate is None: |
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raise ValueError('Learning_rate %s not supported.' % learning_rate_type) |
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return learning_rate |
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