# 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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# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
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#
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# SOFTWARE.
import torch
from typing import Optional
from torch.optim import Optimizer
from .lr_scheduler import LearningRateScheduler
[docs]class WarmupLRScheduler(LearningRateScheduler):
"""
Warmup learning rate until `total_steps`
Args:
optimizer (Optimizer): wrapped optimizer.
"""
def __init__(
self,
optimizer: Optimizer,
init_lr: float,
peak_lr: float,
warmup_steps: int,
) -> None:
super(WarmupLRScheduler, self).__init__(optimizer, init_lr)
self.init_lr = init_lr
if warmup_steps != 0:
warmup_rate = peak_lr - init_lr
self.warmup_rate = warmup_rate / warmup_steps
else:
self.warmup_rate = 0
self.update_steps = 1
self.lr = init_lr
self.warmup_steps = warmup_steps
[docs] def step(self, val_loss: Optional[torch.FloatTensor] = None):
if self.update_steps < self.warmup_steps:
lr = self.init_lr + self.warmup_rate * self.update_steps
self.set_lr(self.optimizer, lr)
self.lr = lr
self.update_steps += 1
return self.lr