# MIT License
#
# Copyright (c) 2021 Soohwan Kim
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
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# furnished to do so, subject to the following conditions:
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# SOFTWARE.
from torch.optim import Optimizer
from torch.optim.lr_scheduler import ReduceLROnPlateau
from typing import Optional
from .lr_scheduler import LearningRateScheduler
from .reduce_lr_on_plateau_lr_scheduler import ReduceLROnPlateauScheduler
from .warmup_lr_scheduler import WarmupLRScheduler
[docs]class WarmupReduceLROnPlateauScheduler(LearningRateScheduler, ReduceLROnPlateau):
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,
),
]
[docs] def load_state_dict(self, state_dict):
pass
[docs] def state_dict(self):
return {}
def _decide_stage(self):
if self.update_steps < self.warmup_steps:
return 0, self.update_steps
else:
return 1, None
[docs] 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()