I am using the following code to do data augmentation of MNIST:
train_loader = torch.utils.data.DataLoader( datasets.MNIST('../data', train=True, download=True, transform=transforms.Compose([ transforms.RandomHorizontalFlip(), transforms.RandomResizedCrop(28), transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)), ])), batch_size=args.batch_size, shuffle=True, **kwargs)
I have a question about the line
transforms.Normalize((0.1307,), (0.3081,)), 0.1307 and 0.3801 are mean and standard deviation of the original MNIST dataset. They should have been changed after those augmentation. So should I use the new mean and deviation to do normalization? Another question: should I do the same augmentation on test set? If not, training with augmentation and test would be from different distribution, right?