def _create_learning_rate_scheduler(lrs_config, optimizer):
"""Create optimizer learning rate scheduler based on config.
Args:
lrs_config: A learning rate scheduler configparser object.
optimizer: An associated optimizer
Returns:
A learning rate scheduler.
Raises:
ValueError: when using an unsupported input data type.
"""
lrs_type = lrs_config['type']
total_step = int(lrs_config['total_step'])
lr_scheduler = None
if lrs_type == 'one_cycle':
lr_max = float(lrs_config['lr_max'])
moms = eval(lrs_config['moms'])
div_factor = float(lrs_config['div_factor'])
pct_start = float(lrs_config['pct_start'])
lr_scheduler = lsf.OneCycle(optimizer, total_step, lr_max, moms,
div_factor, pct_start)
if lr_scheduler is None:
raise ValueError('Learning_rate %s not supported.' % lrs_type)
return lr_scheduler
def _create_learning_rate_scheduler(learning_rate_config, optimizer,
total_step):
"""Create optimizer learning rate scheduler based on config.
Args:
learning_rate_config: A LearningRate proto message.
Returns:
A learning rate.
Raises:
ValueError: when using an unsupported input data type.
"""
lr_scheduler = None
learning_rate_type = learning_rate_config.WhichOneof('learning_rate')
if learning_rate_type == 'multi_phase':
config = learning_rate_config.multi_phase
lr_phases = []
mom_phases = []
for phase_cfg in config.phases:
lr_phases.append((phase_cfg.start, phase_cfg.lambda_func))
mom_phases.append(
(phase_cfg.start, phase_cfg.momentum_lambda_func))
lr_scheduler = lsf.LRSchedulerStep(optimizer, total_step, lr_phases,
mom_phases)
if learning_rate_type == 'one_cycle':
config = learning_rate_config.one_cycle
lr_scheduler = lsf.OneCycle(optimizer, total_step, config.lr_max,
list(config.moms), config.div_factor,
config.pct_start)
if learning_rate_type == 'exponential_decay':
config = learning_rate_config.exponential_decay
lr_scheduler = lsf.ExponentialDecay(optimizer, total_step,
config.initial_learning_rate,
config.decay_length,
config.decay_factor,
config.staircase)
if learning_rate_type == 'manual_stepping':
config = learning_rate_config.manual_stepping
lr_scheduler = lsf.ManualStepping(optimizer, total_step,
list(config.boundaries),
list(config.rates))
if lr_scheduler is None:
raise ValueError('Learning_rate %s not supported.' %
learning_rate_type)
return lr_scheduler