Pytorch-metric-learning

Improvements

- [Allow scaling up the memory and batch size when using TripletMarginMiner](https://github.com/KevinMusgrave/pytorch-metric-learning/issues/688)
- Pull request: https://github.com/KevinMusgrave/pytorch-metric-learning/pull/689

Thanks mkmenta !

This is identical to v2.4.0, but includes the LICENSE file which was missing from v2.4.0.

Features

- Added [DynamicSoftMarginLoss](https://kevinmusgrave.github.io/pytorch-metric-learning/losses/#dynamicsoftmarginloss). See PR 659. Thanks domenicoMuscill0!
- Added [RankedListLoss](https://kevinmusgrave.github.io/pytorch-metric-learning/losses/#rankedlistloss). See PR 659. Thanks domenicoMuscill0!

Bug fixes
- Fixed issue where PNPLoss would return NaN when a batch sample had no corresponding positive. See PR 660. Thanks Puzer and interestingzhuo!

Tests
- Fixed the test for HistogramLoss to work with PyTorch 2.1. Thanks GaetanLepage!

Features

- Added [HistogramLoss](https://kevinmusgrave.github.io/pytorch-metric-learning/losses/#histogramloss). See pull request 651. Thanks domenicoMuscill0!

Features

- Added [ManifoldLoss](https://kevinmusgrave.github.io/pytorch-metric-learning/losses/#manifoldloss). See pull request 635. Thanks domenicoMuscill0!
- Added [P2SGradLoss](https://kevinmusgrave.github.io/pytorch-metric-learning/losses/#p2sgradloss). See pull request 635. Thanks domenicoMuscill0!
- Added the `symmetric` flag to [SelfSupervisedLoss](https://kevinmusgrave.github.io/pytorch-metric-learning/losses/#selfsupervisedloss). If `True`, then the embeddings in both `embeddings` and `ref_emb` are used as anchors. If `False`, then only the embeddings in `embeddings` are used as anchors. The previous behavior was equivalent to `symmetric=False`. Now the default is `symmetric=True`, because this is usually what is done in self supervised papers (e.g. SimCLR).

Bug Fixes

- Fixed bug where `set_stats` was not being called in `TripletMarginMiner` (628)
- Made `HierarchicalSampler` extend `torch.utils.data.Sampler` instead of `torch.utils.data.BatchSampler` (613)
- Made samplers documentation clearer (615). Thanks rheum !