Machine-Learning-Collection/ML/Pytorch/Basics/pytorch_std_mean.py

import torch
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
import torchvision.datasets as datasets
from tqdm import tqdm

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
train_set = datasets.CIFAR10(root="ds/", transform=transforms.ToTensor(), download=True)
train_loader = DataLoader(dataset=train_set, batch_size=64, shuffle=True)

def get_mean_std(loader):
    # var[X] = E[X**2] - E[X]**2
    channels_sum, channels_sqrd_sum, num_batches = 0, 0, 0

    for data, _ in tqdm(loader):
        channels_sum += torch.mean(data, dim=[0, 2, 3])
        channels_sqrd_sum += torch.mean(data ** 2, dim=[0, 2, 3])
        num_batches += 1

    mean = channels_sum / num_batches
    std = (channels_sqrd_sum / num_batches - mean ** 2) ** 0.5

    return mean, std


mean, std = get_mean_std(train_loader)
print(mean)
print(std)