python 实现cosine annealing strategy

import math
import matplotlib.pyplot as plt
import torch.optim as optim
from torchvision.models import resnet18

lr_rate = 0.0001
model = resnet18(num_classes=10)

# T_max = 1000

epoch_total = 25
epoch_iter = 609
warm_up = 800

lambda1 = lambda epoch: (epoch / warm_up) if epoch < warm_up else 0.5 * (math.cos((epoch - warm_up)/(epoch_total*epoch_iter - warm_up) * math.pi) + 1)
optimizer = optim.SGD(model.parameters(), lr=lr_rate, momentum=0.9, nesterov=True)
scheduler = optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda1)

index = 0
x = []
y = []
for epoch in range(epoch_total):
    for batch in range(609):
        x.appe