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pytorch-image-models/timm/scheduler/scheduler_factory.py

108 lines
3.6 KiB

""" Scheduler Factory
Hacked together by / Copyright 2021 Ross Wightman
"""
from .cosine_lr import CosineLRScheduler
from .multistep_lr import MultiStepLRScheduler
from .plateau_lr import PlateauLRScheduler
from .poly_lr import PolyLRScheduler
from .step_lr import StepLRScheduler
from .tanh_lr import TanhLRScheduler
def create_scheduler(args, optimizer):
num_epochs = args.epochs
if getattr(args, 'lr_noise', None) is not None:
lr_noise = getattr(args, 'lr_noise')
if isinstance(lr_noise, (list, tuple)):
noise_range = [n * num_epochs for n in lr_noise]
if len(noise_range) == 1:
noise_range = noise_range[0]
else:
noise_range = lr_noise * num_epochs
else:
noise_range = None
noise_args = dict(
noise_range_t=noise_range,
noise_pct=getattr(args, 'lr_noise_pct', 0.67),
noise_std=getattr(args, 'lr_noise_std', 1.),
noise_seed=getattr(args, 'seed', 42),
)
cycle_args = dict(
cycle_mul=getattr(args, 'lr_cycle_mul', 1.),
cycle_decay=getattr(args, 'lr_cycle_decay', 0.1),
cycle_limit=getattr(args, 'lr_cycle_limit', 1),
)
lr_scheduler = None
if args.sched == 'cosine':
lr_scheduler = CosineLRScheduler(
optimizer,
t_initial=num_epochs,
lr_min=args.min_lr,
warmup_lr_init=args.warmup_lr,
warmup_t=args.warmup_epochs,
k_decay=getattr(args, 'lr_k_decay', 1.0),
**cycle_args,
**noise_args,
)
num_epochs = lr_scheduler.get_cycle_length() + args.cooldown_epochs
elif args.sched == 'tanh':
lr_scheduler = TanhLRScheduler(
optimizer,
t_initial=num_epochs,
lr_min=args.min_lr,
warmup_lr_init=args.warmup_lr,
warmup_t=args.warmup_epochs,
t_in_epochs=True,
**cycle_args,
**noise_args,
)
num_epochs = lr_scheduler.get_cycle_length() + args.cooldown_epochs
elif args.sched == 'step':
lr_scheduler = StepLRScheduler(
optimizer,
decay_t=args.decay_epochs,
decay_rate=args.decay_rate,
warmup_lr_init=args.warmup_lr,
warmup_t=args.warmup_epochs,
**noise_args,
)
elif args.sched == 'multistep':
lr_scheduler = MultiStepLRScheduler(
optimizer,
decay_t=args.decay_epochs,
decay_rate=args.decay_rate,
warmup_lr_init=args.warmup_lr,
warmup_t=args.warmup_epochs,
**noise_args,
)
elif args.sched == 'plateau':
mode = 'min' if 'loss' in getattr(args, 'eval_metric', '') else 'max'
lr_scheduler = PlateauLRScheduler(
optimizer,
decay_rate=args.decay_rate,
patience_t=args.patience_epochs,
lr_min=args.min_lr,
mode=mode,
warmup_lr_init=args.warmup_lr,
warmup_t=args.warmup_epochs,
cooldown_t=0,
**noise_args,
)
elif args.sched == 'poly':
lr_scheduler = PolyLRScheduler(
optimizer,
power=args.decay_rate, # overloading 'decay_rate' as polynomial power
t_initial=num_epochs,
lr_min=args.min_lr,
warmup_lr_init=args.warmup_lr,
warmup_t=args.warmup_epochs,
k_decay=getattr(args, 'lr_k_decay', 1.0),
**cycle_args,
**noise_args,
)
num_epochs = lr_scheduler.get_cycle_length() + args.cooldown_epochs
return lr_scheduler, num_epochs