这段代码实现了一个带有预热阶段的余弦退火学习率调度器。其目的是在训练过程中动态调整学习率，开始时通过预热线性增加学习率，然后在余弦曲线的基础上逐渐减少学习率。调度器的设计可以帮助模型在训练初期稳定学习，并随着训练的进行逐步减少学习率，避免训练后期的过拟合问题。
from lrgb.cosine_scheduler import cosine_with_warmup_scheduler
import mathimport torch.optim as optim
from torch.optim import Optimizerdef cosine_with_warmup_scheduler(optimizer: Optimizer,num_warmup_epochs: int, max_epoch: int):scheduler = get_cosine_schedule_with_warmup(optimizer=optimizer,num_warmup_steps=num_warmup_epochs,num_training_steps=max_epoch)return schedulerdef get_cosine_schedule_with_warmup(optimizer: Optimizer, num_warmup_steps: int, num_training_steps: int,num_cycles: float = 0.5, last_epoch: int = -1):"""Implementation by Huggingface:https://github.com/huggingface/transformers/blob/v4.16.2/src/transformers/optimization.pyCreate a schedule with a learning rate that decreases following the valuesof the cosine function between the initial lr set in the optimizer to 0,after a warmup period during which it increases linearly between 0 and theinitial lr set in the optimizer.Args:optimizer ([`~torch.optim.Optimizer`]):The optimizer for which to schedule the learning rate.num_warmup_steps (`int`):The number of steps for the warmup phase.num_training_steps (`int`):The total number of training steps.num_cycles (`float`, *optional*, defaults to 0.5):The number of waves in the cosine schedule (the defaults is to justdecrease from the max value to 0 following a half-cosine).last_epoch (`int`, *optional*, defaults to -1):The index of the last epoch when resuming training.Return:`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule."""
