Pytorch lr_scheduler 各个函数的用法及可视化

最新推荐文章于 2025-02-21 16:49:24 发布

seeney

最新推荐文章于 2025-02-21 16:49:24 发布

阅读量3.6k

点赞数 2

分类专栏：深度学习文章标签： pytorch lr_scheduler 学习率 warmup 学习率策略可视化

本文链接：https://blog.youkuaiyun.com/Mr_try/article/details/114450673

版权

深度学习专栏收录该内容

1 篇文章

订阅专栏

Pytorch lr_scheduler 各个函数的用法及可视化

- - - - 参考博客
      - 说几个我之前不这么了解的吧，也不怎么用的

参考博客

说几个我之前不这么了解的吧，也不怎么用的

CyclicLR

在这里插入图片描述

这个函数没用过，其实估计效果应该不错，回头试试，这个mode有三种模式。“triangular2"是一个周期就减半，即缩放是0.5；“exp_range”是可以自定义这个每个周期后的缩放比例，通过设置"gamma"参数实现。
在这里插入图片描述

import torch
import matplotlib.pyplot as plt
lr = 0.001
epochs = 10
iters = 4
cyc_epoch = 1
# triangular, triangular2, exp_range
# 三角的形式，0.0001代表最小的学习率， 0.001代表最大的学习率， cyc_epoch*iters代表一个升降周期
scheduler_cyc0 = torch.optim.lr_scheduler.CyclicLR(torch.optim.SGD([torch.ones(1)], lr), 0.0001, 0.001, cyc_epoch*iters, mode='triangular')
scheduler_cyc1 = torch.optim.lr_scheduler.CyclicLR(torch.optim.SGD([torch.ones(1)], lr), 0.0001, 0.001, cyc_epoch*iters, mode='triangular2')
scheduler_cyc2 = torch.optim.lr_scheduler.CyclicLR(torch.optim.SGD([torch.ones(1)], lr), 0.0001, 0.001, cyc_epoch*iters, mode='exp_range', gamma=0.8)                         
triangular  = []
triangular2 = []
exp_range  = []
for epoch in range(epochs):
    for i in range(iters):
        # print (scheduler_cyc0.get_lr()) #[0.000325]
        triangular += (scheduler_cyc0.get_lr()) # list之间， + 和 append有区别
        triangular2 += scheduler_cyc1.get_lr()
        exp_range  += scheduler_cyc2.get_lr()
        scheduler_cyc0.step()
        scheduler_cyc1.step()
        scheduler_cyc2.step()
print (triangular)
print (triangular2)
print (exp_range)
x = list(range(len(triangular)))
plt.figure(figsize=(12,7))
plt.plot(x, triangular, "r")
plt.plot(x, triangular2, "g--")
plt.plot(x, exp_range, "b-.")
# plt.plot(triangular2)
plt.legend(['triangular','triangular2','exp_range'], fontsize=20)
plt.xlabel('iters', size=15)
plt.ylabel('lr', size=15)
# plt.savefig("CyclicLR.png")
plt.show()

这边讲个小细节，以前注意过。

a ,b = [1], [2]
a.append(b) # [1, [2]]
a += [b] # [1, 2]

CosineAnnealingWarmRestarts && CosineAnnealingWarmRestarts

lr = 0.001
epochs = 100
iters = 32
# T_0代表第一个余弦退火的周期，T_mult代表周期的成长因子，例如：T_0=5, T_mult=5. [5, 5+25, 5+25+50]
scheduler_cosW = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(torch.optim.SGD([torch.ones(1)], lr), T_0=500, T_mult=5)
# lr从initial_lr = max_lr/div_factor在pct_start*total_steps(=epochs*steps_per_epoch)步数里
# 采用cos(可选还有线性）升到max_lr,
# 然后用cos(可选还有线性）退火降至min_lr = initial_lr/final_div_factor
scheduler_onecyc = torch.optim.lr_scheduler.OneCycleLR(torch.optim.SGD([torch.ones(1)], lr), max_lr=lr, steps_per_epoch=iters, epochs=epochs, anneal_strategy='cos')
scheduler_onecyc_linear = torch.optim.lr_scheduler.OneCycleLR(torch.optim.SGD([torch.ones(1)], lr), max_lr=lr, steps_per_epoch=iters, epochs=epochs, anneal_strategy='linear')

在这里插入图片描述
从图中可以看出，OneCycleLR才是我们口中常说的warmup吧，而且anneal_strategy这个参数居然没用，都是cos退火。我觉得以后可以直接用OneCycleLR。当然也有一些其他scheduler带上warmup的，自己改下即可。

Warmup 改写的https://github.com/xiaoyufenfei/Efficient-Segmentation-Networks/blob/master/utils/scheduler/lr_scheduler.py

import math
from torch.optim.lr_scheduler import MultiStepLR, _LRScheduler

class WarmupMultiStepLR(MultiStepLR):
    r"""
    # max_iter = epochs * steps_per_epoch
    Args:
        optimizer (Optimizer): Wrapped optimizer.
        max_iter (int): The total number of steps.
        milestones (list) – List of iter indices. Must be increasing.
        gamma (float): Multiplicative factor of learning rate decay. Default: 0.1.
        pct_start (float): The percentage of the cycle (in number of steps) spent
                    increasing the learning rate.
                    Default: 0.3
        warmup_factor (float):         
        last_epoch (int): The index of last epoch. Default: -1.
    """
    def __init__(self, optimizer, max_iter, milestones, gamma=0.1, pct_start=0.3, warmup_factor=1.0 / 2,
                  last_epoch=-1):
        self.warmup_factor = warmup_factor
        self.warmup_iters = int(pct_start * max_iter)
        super().__init__(optimizer, milestones, gamma, last_epoch)

    def get_lr(self):
        if self.last_epoch <= self.warmup_iters:
            alpha = self.last_epoch / self.warmup_iters
            warmup_factor = self.warmup_factor * (1 - alpha) + alpha
            return [lr * warmup_factor for lr in self.base_lrs]
        else:
            lr = super().get_lr()
        return lr

class WarmupCosineLR(_LRScheduler):
    def __init__(self, optimizer, max_iter, pct_start=0.3, warmup_factor=1.0 / 3, 
                 eta_min=0, last_epoch=-1):
        self.warmup_factor = warmup_factor
        self.warmup_iters = int(pct_start * max_iter)
        self.max_iter, self.eta_min = max_iter, eta_min
        super().__init__(optimizer)

    def get_lr(self):
        if self.last_epoch <= self.warmup_iters:
            alpha = self.last_epoch / self.warmup_iters
            warmup_factor = self.warmup_factor * (1 - alpha) + alpha
            return [lr * warmup_factor for lr in self.base_lrs]
        else:
            # print ("after warmup")
            return [self.eta_min + (base_lr - self.eta_min) *
                    (1 + math.cos(
                        math.pi * (self.last_epoch - self.warmup_iters) / (self.max_iter - self.warmup_iters))) / 2
                    for base_lr in self.base_lrs]

class WarmupPolyLR(_LRScheduler):
    def __init__(self, optimizer, T_max, pct_start=0.3, warmup_factor=1.0 / 4, 
                 eta_min=0, power=0.9):
        self.warmup_factor = warmup_factor
        self.warmup_iters = int(pct_start * T_max)
        self.power = power
        self.T_max, self.eta_min = T_max, eta_min
        super().__init__(optimizer)

    def get_lr(self):
        if self.last_epoch <= self.warmup_iters:
            alpha = self.last_epoch / self.warmup_iters
            warmup_factor = self.warmup_factor * (1 - alpha) + alpha
            return [lr * warmup_factor for lr in self.base_lrs]
        else:
            return [self.eta_min + (base_lr - self.eta_min) *
                    math.pow(1 - (self.last_epoch - self.warmup_iters) / (self.T_max - self.warmup_iters),
                             self.power) for base_lr in self.base_lrs]

if __name__ == '__main__':

    import matplotlib.pyplot as plt
    import torch
    import sys
    # sys.setrecursionlimit(12000)
    max_iter = 10000
    lr=5e-4
    optimizer = torch.optim.SGD([torch.ones(1)], lr)

    scheduler_WP = WarmupPolyLR(optimizer, T_max=max_iter)
    scheduler_WS = WarmupCosineLR(optimizer, max_iter)
    scheduler_WM = WarmupMultiStepLR(optimizer, max_iter, [5000, 7000, 9000])

    lrs_wp = []
    lrs_ws = []
    lrs_wm = []

    for cur_iter in range(max_iter):

        # lr = optimizer.param_groups[0]['lr']
        # lrs_wp.append(scheduler_WP.get_lr()[0])

        lrs_wp += scheduler_WP.get_lr()
        lrs_ws += scheduler_WS.get_lr()
        lrs_wm += scheduler_WM.get_lr()

        optimizer.step()
        scheduler_WP.step()
        scheduler_WS.step()
        scheduler_WM.step()

    x = list(range(len(lrs_wm)))
    plt.figure(figsize=(12,7))
    plt.plot(x, lrs_wp, "r",
            x, lrs_ws, "g--",
            x, lrs_wm, "b-.")
    # plt.plot(triangular2)
    plt.legend(['WarmupPolyLR','WarmupCosineLR','WarmupMultiStepLR'], fontsize=20)
    plt.xlabel('iters', size=15)
    plt.ylabel('lr', size=15)
    # plt.savefig("CyclicLR.png")
    plt.show()

在这里插入图片描述

other

class MyLRScheduler(object):
    '''
    CLass that defines cyclic learning rate that decays the learning rate linearly till the end of cycle and then restarts
    at the maximum value.
    '''
    def __init__(self, initial=0.1, cycle_len=5, ep_cycle=50, ep_max=100):
        super(MyLRScheduler, self).__init__()

        self.min_lr = initial# minimum learning rate
        self.m = cycle_len
        self.ep_cycle = ep_cycle
        self.ep_max = ep_max
        self.poly_start = initial
        self.step = initial/ self.ep_cycle
        print('Using Cyclic LR Scheduler with warm restarts and poly step '
              + str(self.step))

    def get_lr(self, epoch):
        if epoch==0:
            current_lr = self.min_lr
        elif 0< epoch and epoch <= self.ep_cycle:
            counter = (epoch-1) % self.m
            current_lr = round((self.min_lr * self.m) - (counter * self.min_lr), 5)
        else:

            current_lr = round(self.poly_start - (epoch-self.ep_cycle )*self.step, 8)

            # current_lr = round(self.poly_start * (1 - (epoch-self.ep_cycle) / (self.ep_max-self.ep_cycle)) ** 0.9, 8)

        return current_lr


class WarmupPoly(object):
    '''
    CLass that defines cyclic learning rate that decays the learning rate linearly till the end of cycle and then restarts
    at the maximum value.
    '''
    def __init__(self, init_lr, total_ep, warmup_ratio=0.05, poly_pow = 0.98):
        super(WarmupPoly, self).__init__()
        self.init_lr = init_lr
        self.total_ep = total_ep
        self.warmup_ep = int(warmup_ratio*total_ep)
        print("warup unitl " + str(self.warmup_ep))
        self.poly_pow = poly_pow

    def get_lr(self, epoch):
        #
        if epoch < self.warmup_ep:
            curr_lr =  self.init_lr*pow((((epoch+1) / self.warmup_ep)), self.poly_pow)

        else:
            curr_lr = self.init_lr*pow((1 - ((epoch- self.warmup_ep)  / (self.total_ep-self.warmup_ep))), self.poly_pow)

        return curr_lr
        
if __name__ == '__main__':
    import matplotlib.pyplot as plt
    max_epochs = 300
    lrSched = MyLRScheduler(initial=0.0001, cycle_len=10, ep_cycle=150, ep_max=300)
    lrSched1 = WarmupPoly(1e-3, max_epochs , poly_pow=0.95)

    x = []
    y = []
    y1 = []
    for i in range(max_epochs):
        x.append(i)
        y.append(lrSched.get_lr(i))
        y1.append(lrSched1.get_lr(i))
    print (y[0], y[-1]) # 0.0001 6.7e-07
    print (y1[0], y1[-1]) # 7.633317097623927e-05 4.654760957913004e-06
    plt.figure(figsize=(12,7))
    plt.plot(x, y, "r",
            x, y1, "b-.")
    plt.legend(['cyclic','WarmupPoly',], fontsize=20)
    plt.xlabel('iters', size=15)
    plt.ylabel('lr', size=15)
    plt.savefig("MyLRScheduler.png")
    plt.show()