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ML/Pytorch/Basics/pytorch_std_mean.py

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+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)