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pytorch-image-models/scheduler/plateau_lr.py

69 lines
2.3 KiB

import torch
from .scheduler import Scheduler
class PlateauLRScheduler(Scheduler):
"""Decay the LR by a factor every time the validation loss plateaus."""
def __init__(self,
optimizer,
factor=0.1,
patience=10,
verbose=False,
threshold=1e-4,
cooldown_epochs=0,
warmup_updates=0,
warmup_lr_init=0,
lr_min=0,
):
super().__init__(optimizer, 'lr', initialize=False)
self.lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
self.optimizer.optimizer,
patience=patience,
factor=factor,
verbose=verbose,
threshold=threshold,
cooldown=cooldown_epochs,
min_lr=lr_min
)
self.warmup_updates = warmup_updates
self.warmup_lr_init = warmup_lr_init
if self.warmup_updates:
self.warmup_active = warmup_updates > 0 # this state updates with num_updates
self.warmup_steps = [(v - warmup_lr_init) / self.warmup_updates for v in self.base_values]
super().update_groups(self.warmup_lr_init)
else:
self.warmup_steps = [1 for _ in self.base_values]
def state_dict(self):
return {
'best': self.lr_scheduler.best,
'last_epoch': self.lr_scheduler.last_epoch,
}
def load_state_dict(self, state_dict):
self.lr_scheduler.best = state_dict['best']
if 'last_epoch' in state_dict:
self.lr_scheduler.last_epoch = state_dict['last_epoch']
# override the base class step fn completely
def step(self, epoch, val_loss=None):
"""Update the learning rate at the end of the given epoch."""
if val_loss is not None and not self.warmup_active:
self.lr_scheduler.step(val_loss, epoch)
else:
self.lr_scheduler.last_epoch = epoch
def get_update_values(self, num_updates: int):
if num_updates < self.warmup_updates:
lrs = [self.warmup_lr_init + num_updates * s for s in self.warmup_steps]
else:
self.warmup_active = False # warmup cancelled by first update past warmup_update count
lrs = None # no change on update after warmup stage
return lrs