Hi,
There is something with PyTorch data augmentation that I would like to understand. I used the following code to create a training data loader:
rgb_mean = (0.4914, 0.4822, 0.4465)
rgb_std = (0.2023, 0.1994, 0.2010)

transform_train = transforms.Compose([
    transforms.RandomCrop(32, padding=4),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize(rgb_mean, rgb_std),
])

kwargs = {'num_workers': 2, 'pin_memory': True} if args.cuda else {}
train_loader = torch.utils.data.DataLoader(datasets.CIFAR10(root=data_root, train=True, transform=transform_train,
    transforms.Normalize(rgb_mean, rgb_std),
]), download=False), batch_size=128, **kwargs)

print('len(train_loader)=',len(train_loader))

I guess that data augmentation was used with two transformations: random crop and random horizontal flip. Thus, I would expect the obtained total number of training samples to be 3 times the size of the training set of Cifar-10, i.e. 3*50000 = 150000. However, the output of the above code is:
len(train_loader) = 391
which means there are approximately 391*128 ~= 50000 samples. Where are the augmented data?
Thank you in advance for your help!