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61 lines
1.9 KiB
61 lines
1.9 KiB
import torch
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from .scheduler import Scheduler
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class PlateauLRScheduler(Scheduler):
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"""Decay the LR by a factor every time the validation loss plateaus."""
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def __init__(self,
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optimizer,
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decay_rate=0.1,
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patience_t=10,
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verbose=True,
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threshold=1e-4,
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cooldown_t=0,
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warmup_t=0,
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warmup_lr_init=0,
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lr_min=0,
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mode='min',
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initialize=True,
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):
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super().__init__(optimizer, 'lr', initialize=initialize)
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self.lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
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self.optimizer,
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patience=patience_t,
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factor=decay_rate,
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verbose=verbose,
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threshold=threshold,
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cooldown=cooldown_t,
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mode=mode,
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min_lr=lr_min
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)
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self.warmup_t = warmup_t
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self.warmup_lr_init = warmup_lr_init
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if self.warmup_t:
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self.warmup_steps = [(v - warmup_lr_init) / self.warmup_t for v in self.base_values]
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super().update_groups(self.warmup_lr_init)
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else:
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self.warmup_steps = [1 for _ in self.base_values]
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def state_dict(self):
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return {
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'best': self.lr_scheduler.best,
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'last_epoch': self.lr_scheduler.last_epoch,
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}
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def load_state_dict(self, state_dict):
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self.lr_scheduler.best = state_dict['best']
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if 'last_epoch' in state_dict:
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self.lr_scheduler.last_epoch = state_dict['last_epoch']
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# override the base class step fn completely
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def step(self, epoch, metric=None):
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if epoch <= self.warmup_t:
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lrs = [self.warmup_lr_init + epoch * s for s in self.warmup_steps]
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super().update_groups(lrs)
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else:
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self.lr_scheduler.step(metric, epoch)
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