classification-main/block/lr_get.py

import math
import torch


def adam(regularization, r_value, param, lr, betas):
    if regularization == 'L2':
        optimizer = torch.optim.Adam(param, lr=lr, betas=betas, weight_decay=r_value)
    else:
        optimizer = torch.optim.Adam(param, lr=lr, betas=betas)
    return optimizer


class lr_adjust:
    def __init__(self, args, step_epoch, epoch_finished):
        print(f"初始化 lr_end_epoch: {args.lr_end_epoch}, step_epoch: {step_epoch}")
        self.lr_start = args.lr_start  # 初始学习率
        self.lr_end = args.lr_end_ratio * args.lr_start  # 最终学习率
        self.lr_end_epoch = args.lr_end_epoch  # 最终学习率达到的轮数
        self.step_all = self.lr_end_epoch * step_epoch  # 总调整步数
        if self.step_all == 0:
            raise ValueError("计算总调整步数时出错： step_all 不能为0， 请检查 lr_end_epoch 和 step_epoch 的值。")
        self.step_finished = epoch_finished * step_epoch  # 已调整步数
        self.warmup_step = max(5, int(args.warmup_ratio * self.step_all))  # 预热训练步数
        print(f"总调整步数 step_all: {self.step_all}")  # 这里将显示 step_all 的值

    def __call__(self, optimizer):
        self.step_finished += 1
        step_now = self.step_finished
        if self.step_all == 0:
            raise ValueError("调用时出错：step_all 不能为0。")
        decay = step_now / self.step_all
        lr = self.lr_end + (self.lr_start - self.lr_end) * math.cos(math.pi / 2 * decay)
        if step_now <= self.warmup_step:
            lr = lr * (0.1 + 0.9 * step_now / self.warmup_step)
        lr = max(lr, 0.000001)
        for i in range(len(optimizer.param_groups)):
            optimizer.param_groups[i]['lr'] = lr
        return optimizer


# 示例参数，假设的一些值
class Args:
    def __init__(self):
        self.lr_start = 0.001
        self.lr_end_ratio = 0.1
        self.lr_end_epoch = 10
        self.warmup_ratio = 0.1

if __name__ == "__main__":
    # 伪造一些输入参数和初始状态
    args = Args()
    step_epoch = 100  # 假设每个epoch有100步
    epoch_finished = 0  # 假设从第0 epoch开始

    # 初始化调整器
    lr_adjuster = lr_adjust(args, step_epoch, epoch_finished)

    # 创建一个假的优化器
    params = [torch.randn(10, 10, requires_grad=True)]
    optimizer = adam('L2', 0.01, params, args.lr_start, (0.9, 0.999))

    # 调用lr_adjuster来调整学习率
    optimizer = lr_adjuster(optimizer)
    print(f"调整后的学习率: {optimizer.param_groups[0]['lr']}")