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Scheduler

Warm-up + CosineAnnealingLR

import mathwarm_up_iter = 10
T_max = 50
lr_max = 0.1
lr_min = 1e-5def WarmupCosineAnnealingLR(cur_iter):if cur_iter < warm_up_iter:return (lr_max - lr_min) * (cur_iter / warm_up_iter) + lr_minelse:return lr_min + 0.5 * (lr_max - lr_min) * (1 + math.cos((cur_iter - warm_up_iter) / (T_max - warm_up_iter) * math.pi))

前 warm_up_iter 步，学习率从 lr_min 线性增加到 lr_max；后 T_max - warm_up_iter 步，学习率按照余弦退火从 lr_max 降到 lr_min。

Warm-up + ExponentialLR

import mathwarm_up_iter = 10
T_max = 50
lr_max = 0.1
lr_min = 1e-5def WarmupExponentialLR(cur_iter):gamma = math.exp(math.log(lr_min / lr_max) / (T_max - warm_up_iter))if cur_iter < warm_up_iter:return (lr_max - lr_min) * (cur_iter / warm_up_iter) + lr_minelse:return lr_max * gamma ** (cur_iter - warm_up_iter)

前 warm_up_iter 步，学习率从 lr_min 线性增加到 lr_max；后 T_max - warm_up_iter 步，学习率按照指数衰减从 lr_max 降到 lr_min。

Warm-up + StepLR

import mathwarm_up_iter = 10
T_max = 50
lr_max = 0.1
lr_min = 1e-5
step_size = 3def WarmupStepLR(cur_iter):gamma = math.exp(math.log(lr_min / lr_max) / ((T_max - warm_up_iter) // step_size))if cur_iter < warm_up_iter:return (lr_max - lr_min) * (cur_iter / warm_up_iter) + lr_minelse:return lr_max * gamma ** ((cur_iter - warm_up_iter) // step_size)

前 warm_up_iter 步，学习率从 lr_min 线性增加到 lr_max；后 T_max - warm_up_iter 步，学习率按照指数衰减从 lr_max 降到 lr_min，每 step_size 步衰减一次。

可视化对比

Pytorch 模板

import math
import torch
from torch import nn, optim
from torch.optim.lr_scheduler import LambdaLRwarm_up_iter = 10
T_max = 50
lr_max = 0.1
lr_min = 1e-5def WarmupCosineAnnealingLR(cur_iter):if cur_iter < warm_up_iter:return (lr_max - lr_min) * (cur_iter / warm_up_iter) + lr_minelse:return lr_min + 0.5 * (lr_max - lr_min) * (1 + math.cos((cur_iter - warm_up_iter) / (T_max - warm_up_iter) * math.pi))model = nn.Linear(1, 1)
optimizer = optim.AdamW(model.parameters(), lr=0.1)
scheduler = LambdaLR(optimizer, lr_lambda=WarmupCosineAnnealingLR)for epoch in range(T_max):train(...)valid(...)scheduler.step()