Hi! Thanks for sharing your work :)

I am trying to perform some tests using paws on a multi-label problem. Major changes I've have already implemented: (1) my dataset custom class and its corresponding "Trans" class as the "TransImageNet"; (2) implemented custom _make_data_transforms functions and _make_multicrop_data_transforms.

Now I'm working on adapting the ClassStratifiedSampler, and therefore also labels_matrix. I'm having trouble fully understanding how these two work together: is labels_matrix simply concatenating one-hot labels from the sampler (ClassStratifiedSampler) and smoothing it? Also, do you think it make sense to adapt ClassStratifiedSampler to a multi-label dataset, or should I just use a regular sampler (then I could do as mentioned in #22 (comment))?

Thanks in advance for any tip!