How to use different data augmentation for Subsets in PyTorch

Question

How to use different data augmentation (transforms) for different Subsets in PyTorch?

For instance:

train, test = torch.utils.data.random_split(dataset, [80000, 2000])

train and test will have the same transforms as dataset. How to use custom transforms for these subsets?

Answer 1

My current solution is not very elegant, but works:

from copy import copy

train_dataset, test_dataset = random_split(full_dataset, [train_size, test_size])
train_dataset.dataset = copy(full_dataset)

test_dataset.dataset.transform = transforms.Compose([
    transforms.Resize(img_resolution),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406],
                         std=[0.229, 0.224, 0.225])
])

train_dataset.dataset.transform = transforms.Compose([
    transforms.RandomResizedCrop(img_resolution[0]),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406],
                         std=[0.229, 0.224, 0.225])
])

Basically, I'm defining a new dataset (which is a copy of the original dataset) for one of the splits, and then I define a custom transform for each split.

Note: train_dataset.dataset.transform works since I'm using an ImageFolder dataset, which uses the .tranform attribute to perform the transforms.

If anybody knows a better solution, please share with us!