warmup_reduce_lr_on_plateau_scheduler.py

# MIT License
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# Copyright (c) 2021 Soohwan Kim
#
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from torch.optim import Optimizer
from typing import Optional

from lr_scheduler.lr_scheduler import LearningRateScheduler
from lr_scheduler.reduce_lr_on_plateau_lr_scheduler import ReduceLROnPlateauScheduler
from lr_scheduler.warmup_lr_scheduler import WarmupLRScheduler


class WarmupReduceLROnPlateauScheduler(LearningRateScheduler):
    r"""
    Warmup learning rate until `warmup_steps` and reduce learning rate on plateau after.

    Args:
        optimizer (Optimizer): wrapped optimizer.
        init_lr (float): Initial learning rate.
        peak_lr (float): Maximum learning rate.
        warmup_steps (int): Warmup the learning rate linearly for the first N updates.
        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,
            init_lr: float,
            peak_lr: float,
            warmup_steps: int,
            patience: int = 1,
            factor: float = 0.3,
    ) -> None:
        super(WarmupReduceLROnPlateauScheduler, self).__init__(optimizer, init_lr)
        self.warmup_steps = warmup_steps
        self.update_steps = 0
        self.warmup_rate = (peak_lr - init_lr) / self.warmup_steps \
            if self.warmup_steps != 0 else 0
        self.schedulers = [
            WarmupLRScheduler(
                optimizer=optimizer,
                init_lr=init_lr,
                peak_lr=peak_lr,
                warmup_steps=warmup_steps,
            ),
            ReduceLROnPlateauScheduler(
                optimizer=optimizer,
                lr=peak_lr,
                patience=patience,
                factor=factor,
            ),
        ]

    def _decide_stage(self):
        if self.update_steps < self.warmup_steps:
            return 0, self.update_steps
        else:
            return 1, None

    def step(self, val_loss: Optional[float] = None):
        stage, steps_in_stage = self._decide_stage()

        if stage == 0:
            self.schedulers[0].step()
        elif stage == 1:
            self.schedulers[1].step(val_loss)

        self.update_steps += 1

        return self.get_lr()