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69 lines
2.3 KiB
69 lines
2.3 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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factor=0.1,
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patience=10,
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verbose=False,
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threshold=1e-4,
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cooldown_epochs=0,
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warmup_updates=0,
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warmup_lr_init=0,
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lr_min=0,
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):
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super().__init__(optimizer, 'lr', initialize=False)
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self.lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
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self.optimizer.optimizer,
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patience=patience,
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factor=factor,
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verbose=verbose,
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threshold=threshold,
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cooldown=cooldown_epochs,
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min_lr=lr_min
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)
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self.warmup_updates = warmup_updates
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self.warmup_lr_init = warmup_lr_init
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if self.warmup_updates:
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self.warmup_active = warmup_updates > 0 # this state updates with num_updates
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self.warmup_steps = [(v - warmup_lr_init) / self.warmup_updates 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, val_loss=None):
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"""Update the learning rate at the end of the given epoch."""
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if val_loss is not None and not self.warmup_active:
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self.lr_scheduler.step(val_loss, epoch)
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else:
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self.lr_scheduler.last_epoch = epoch
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def get_update_values(self, num_updates: int):
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if num_updates < self.warmup_updates:
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lrs = [self.warmup_lr_init + num_updates * s for s in self.warmup_steps]
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else:
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self.warmup_active = False # warmup cancelled by first update past warmup_update count
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lrs = None # no change on update after warmup stage
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return lrs
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