WARPLoss

Implementation of WARP Loss for MultiLabel target in PyTorch. It also supports Binary and MultiClass if you rewrite them as MultiLabel classification.

This implementation has only one for loop over batches as I wanted to make a single model that works with both MultiLabel and binary tasks.

How to use

from warp_loss import WARPLoss


# Set max number of trials to 100
loss_func = WARPLoss(max_num_trials=100)
# Pass logits as inputs and target
loss = loss_func(inputs, target)
loss.backward()

What's next

1. Rewrite it without for loop and make it fully compatible with GPU parallelism seems to be pretty possible for Binary and MultiClass tasks so I would try it sometime.
2. Implement current WARPLoss model on CUDA C++ to efficiently work with for loop over batch size.
3. Implement some kind of masking for operations with positives and negatives in WARPLoss. Maybe it would eliminate the need for custom backward and for loop over batch size.