i’m using torchvision.datasets.ImageFolder (which takes transform as input) to read my data, then i split it to train and test sets using torch.utils.data.Subset. until now i applied the same transforms to all images, doesn’t matter whether they’re train or test, but now i want to change it.
is it possible to do so without writing a custom dataset? i don’t want to write a new ImageFolder like class just for that.
this happens because the images folder contains all the data and then i split it randomly after loading.
btw, is it also not smart to do so because of statistics consistency (and should be using the same train/test split every time)?