reduce_lr_on_plateau_lr_scheduler.py

# 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.lr_scheduler import LearningRateScheduler


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 = 100.0
        self.count = 0

    def step(self, val_loss: float):
        if val_loss is not None:
            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