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@KevinMusgrave KevinMusgrave released this 04 Mar 00:46
· 590 commits to master since this release
v0.9.97
0492d17

Bug fixes

  • Small fix for NTXentLoss with no negative pairs #272
  • Fixed .detach() bug in NTXentLoss #282
  • Fixed parameter override bug in MatchFinder.get_matching_pairs() #286 by @joaqo

New features and improvements

AccuracyCalculator now uses torch instead of numpy

  • All the calculations (except for NMI and AMI) are done with torch. Calculations will be done on the same device and dtype as the input query tensor.
  • You can still pass numpy arrays into AccuracyCalculator.get_accuracy, but the arrays will be immediately converted to torch tensors.

Faster custom label comparisons in AccuracyCalculator

Numerical stability improvement for DistanceWeightedMiner

See #278 by @z1w

UniformHistogramMiner

This is like DistanceWeightedMiner, except that it works well with high dimension embeddings, and works with any distance metric (not just L2 normalized distance). Documentation

PerAnchorReducer

This converts unreduced pairs to unreduced elements. For example, NTXentLoss returns losses per positive pair. If you used PerAnchorReducer with NTXentLoss, then the losses per pair would first be converted to losses per batch element, before being passed to the inner reducer. See the documentation

BaseTester no longer converts embeddings from torch to numpy

This includes the get_all_embeddings function. If you want get_all_embeddings to return numpy arrays, you can set the return_as_numpy flag to True:

embeddings, labels = tester.get_all_embeddings(dataset, model, return_as_numpy=True)

The embeddings are converted to numpy only for the visualizer and visualizer_hook, if specified.

Reduced usage of .to(device) and .type(dtype)

Tensors are initialized on device and with the necessary dtype, and they are moved to device and cast to dtypes only when necessary. See this code snippet for details.

Simplified DivisorReducer

Replaced "divisor_summands" with "divisor".

Assets 2