# MIT License
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# Copyright (c) 2021 Soohwan Kim
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from omegaconf import DictConfig
from torch.optim import Optimizer
from .lr_scheduler import LearningRateScheduler
[docs]class ReduceLROnPlateauScheduler(LearningRateScheduler):
r"""
Reduce learning rate when a metric has stopped improving. Models often benefit from reducing the learning rate by
a factor of 2-10 once learning stagnates. This scheduler reads a metrics quantity and if no improvement is seen
for a ‘patience’ number of epochs, the learning rate is reduced.
Args:
optimizer (Optimizer): Optimizer.
lr (float): Initial learning rate.
patience (int): Number of epochs with no improvement after which learning rate will be reduced.
factor (float): Factor by which the learning rate will be reduced. new_lr = lr * factor.
"""
def __init__(
self,
optimizer: Optimizer,
lr: float,
patience: int = 1,
factor: float = 0.3,
) -> None:
super(ReduceLROnPlateauScheduler, self).__init__(optimizer, lr)
self.lr = lr
self.patience = patience
self.factor = factor
self.val_loss = float("inf")
self.count = 0
self._old_val_loss = None
[docs] def step(self, val_loss: float):
if self.val_loss < val_loss:
self.count += 1
self.val_loss = val_loss
else:
self.count = 0
self.val_loss = val_loss
if self.patience == self.count:
self.count = 0
self.lr *= self.factor
self.set_lr(self.optimizer, self.lr)
return self.lr