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

111 lines
3.5 KiB

""" Plateau Scheduler
Adapts PyTorch plateau scheduler and allows application of noise, warmup.
Hacked together by / Copyright 2020 Ross Wightman
"""
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,
decay_rate=0.1,
patience_t=10,
verbose=True,
threshold=1e-4,
cooldown_t=0,
warmup_t=0,
warmup_lr_init=0,
lr_min=0,
mode='max',
noise_range_t=None,
noise_type='normal',
noise_pct=0.67,
noise_std=1.0,
noise_seed=None,
initialize=True,
):
super().__init__(
optimizer,
'lr',
noise_range_t=noise_range_t,
noise_type=noise_type,
noise_pct=noise_pct,
noise_std=noise_std,
noise_seed=noise_seed,
initialize=initialize,
)
self.lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
self.optimizer,
patience=patience_t,
factor=decay_rate,
verbose=verbose,
threshold=threshold,
cooldown=cooldown_t,
mode=mode,
min_lr=lr_min
)
self.warmup_t = warmup_t
self.warmup_lr_init = warmup_lr_init
if self.warmup_t:
self.warmup_steps = [(v - warmup_lr_init) / self.warmup_t for v in self.base_values]
super().update_groups(self.warmup_lr_init)
else:
self.warmup_steps = [1 for _ in self.base_values]
self.restore_lr = None
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, metric=None):
if epoch <= self.warmup_t:
lrs = [self.warmup_lr_init + epoch * s for s in self.warmup_steps]
super().update_groups(lrs)
else:
if self.restore_lr is not None:
# restore actual LR from before our last noise perturbation before stepping base
for i, param_group in enumerate(self.optimizer.param_groups):
param_group['lr'] = self.restore_lr[i]
self.restore_lr = None
self.lr_scheduler.step(metric, epoch) # step the base scheduler
if self._is_apply_noise(epoch):
self._apply_noise(epoch)
def step_update(self, num_updates: int, metric: float = None):
return None
def _apply_noise(self, epoch):
noise = self._calculate_noise(epoch)
# apply the noise on top of previous LR, cache the old value so we can restore for normal
# stepping of base scheduler
restore_lr = []
for i, param_group in enumerate(self.optimizer.param_groups):
old_lr = float(param_group['lr'])
restore_lr.append(old_lr)
new_lr = old_lr + old_lr * noise
param_group['lr'] = new_lr
self.restore_lr = restore_lr
def _get_lr(self, t: int) -> float:
assert False, 'should not be called as step is overridden'