PyTorch计算均值和标准差

在pytorch计算均值、标准差对数据进行归一化，只需加载每一个batch之后在计算一个均值即可。

from skimage import io, transform
import torch
from torchvision import transforms, utils

mytransform = transforms.Compose([
    # transforms.ToPILImage(),
    transforms.ToTensor()
])

# dataset = ClassificationDataset("../../data/DatasetA_train/train", "../../data/DatasetA_train/label_list.txt", "../../data/DatasetA_train/train.txt", transform=mytransform)


dataloader = DataLoader($your dataset$, batch_size=20, shuffle=False, num_workers=4)

pop_mean = []
pop_std0 = []
# print(dataset)
for i, (img, label) in enumerate(dataloader):
    # print(img, label)
    print(i, label)
    # shape (batch_size, 3, height, width)
    numpy_image = img.numpy()

    # shape (3,)
    batch_mean = np.mean(numpy_image, axis=(0, 2, 3))
    batch_std0 = np.std(numpy_image, axis=(0, 2, 3))

    pop_mean.append(batch_mean)
    pop_std0.append(batch_std0)

# shape (num_iterations, 3) -> (mean across 0th axis) -> shape (3,)
pop_mean = np.array(pop_mean).mean(axis=0)
pop_std0 = np.array(pop_std0).mean(axis=0)

print(pop_mean, pop_std0)

最后输出三个通道的标准差和均值，因为transforms.ToTensor()会将数据归一化道，所以这里计算的标准差和均值在，计算完之后：

mytransform = transforms.Compose([
    # transforms.ToPILImage(),
    transforms.ToTensor(),
    transforms.Normalize((0.4814507, 0.44941443, 0.3985094), (0.2703836, 0.2638982, 0.27239165))
])