v0.9.98

AccuracyCalculator breaking change (issue #290)

The k parameter in AccuracyCalculator has a new behavior. The allowed values are:

• None. This means k will be set to the total number of reference embeddings.
• An integer greater than 0. This means k will be set to the input integer.
• "max_bin_count". This means k will be set to max(bincount(reference_labels)) - self_count where self_count == 1 if the query and reference embeddings come from the same source.

The old behavior is described here.

If your dataset is large, you might find the k-nn search is now very slow. This is because the new default behavior is to set k to len(reference_embeddings). To avoid this, you can set k to a number, like k = 1000 or try k = "max_bin_count" to get behavior similar (though not identical) to the old default.

Apologies for the drastic change. I'm hoping to have things stable and following semantic versioning when v1.0 arrives.

Bug fixes

• lmu.convert_to_triplets has been fixed (#291)
• Losses and miners should now be compatible with autocast (#293)

New features / improvements

• The loss used in Supervised Contrastive Learning. Documentation: SupConLoss. By @fjsj (#281, #288)
• Vectorized convert_to_triplets (#279)