The implement of rank-aware contrastive loss

[KonataMyLove]

Thank you for your open-source code.
Here I have a question about the code of rank-aware contrastive loss metioned in the Section3.4 of the paper:

This is the rank-aware contrastive loss formula listed in the paper,

This is the code of rank-aware contrastive loss computation in the qd_detr/model.py:
# softmax
exp_logits = torch.exp(logits)
log_prob = logits - torch.log(exp_logits.sum(1, keepdim=True) + 1e-6)
mean_log_prob_pos = (pos_mask * log_prob * vid_token_mask).sum(1) / (pos_mask.sum(1) + 1e-6)
loss = - mean_log_prob_pos * batch_drop_mask
loss_rank_contrastive = loss_rank_contrastive + loss.mean()

I don't think the code implement matched the formula? Especially the bolded code makes me confused.
If I get it wrong, please inform me. Thank you for your time.

[wjun0830]

For the contrastive equation and the implementation, you can find descriptions in supervised contrastive Learning paper and repository.
For the rank, there is a for loop to implement it.

[KonataMyLove]

Thanks. I rewrote the formula according to the repository and the code, it does match the paper. Problem solved.