如何在torchvision.transforms中找到归一化均值和标准差的最佳值

import torchvision.transforms as transforms
import torchvision.datasets as datasets

dataset = datasets.CIFAR10(root='./data', train=True, download=True, transform=None)

transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
])

dataloader = torch.utils.data.DataLoader(dataset, batch_size=64, shuffle=True)

mean = 0.0
std = 0.0
total_samples = 0

for images, _ in dataloader:
    batch_samples = images.size(0)
    images = images.view(batch_samples, images.size(1), -1)
    mean += images.mean(2).sum(0)
    std += images.std(2).sum(0)
    total_samples += batch_samples

mean /= total_samples
std /= total_samples

print("Mean:", mean)
print("Std:", std)