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