# 3.0.0rc3

This is the third release candidate for version 3.0.0. It changes defaults and
sampler behavior that are easier to change before the final release. The
[3.0.0rc1 notes](3.0.0rc1.md) describe version 3 as a whole, and the
[migration guide](../migration-2-to-3.md) covers version 2 names.

## Changed defaults

- `DotProductSimilarity()` now computes a plain dot product. Pass
  `normalize_embeddings=True` for the previous behavior. `NPairsLoss` and
  `NormalizedSoftmaxLoss` request normalization explicitly, so their default
  objectives do not change
  ([#670](https://github.com/KevinMusgrave/pytorch-metric-learning/issues/670)).
- `TripletMarginLoss` now defaults to `margin=0.2`, matching
  `TripletMarginMiner` and FaceNet. Pass `margin=0.05` for the previous default
  ([#466](https://github.com/KevinMusgrave/pytorch-metric-learning/issues/466)).

## New features

- `SelfSupervisedLoss` accepts more than two views:
  `loss_func(view_1, view_2, view_3)`. All views of a sample are positives of
  each other, and they reach the wrapped loss in one call. With
  `symmetric=False`, only the first view provides anchors
  ([#580](https://github.com/KevinMusgrave/pytorch-metric-learning/issues/580)).

## Sampler changes

- `MPerClassSampler` now yields whole batches. Pass it as
  `DataLoader(dataset, batch_sampler=sampler)` and remove the `DataLoader`
  `batch_size`. `batch_size` is a required sampler argument, and `num_batches`
  replaces `num_samples`
  ([#612](https://github.com/KevinMusgrave/pytorch-metric-learning/issues/612)).
- `MPerClassSampler` rotates through shuffled passes of classes and of each
  class's examples. Class counts, and example counts within a class, stay within
  one of each other. The default length visits every example at least once, and
  exactly once when equal class sizes divide evenly. See the
  [coverage guarantees](../samplers.md#coverage-guarantees)
  ([#684](https://github.com/KevinMusgrave/pytorch-metric-learning/issues/684)).

## Documentation

- The [macOS OpenMP section](../retrieval.md#macos-openmp-runtimes) now covers
  using Faiss and UMAP in one process. Importing this package loads neither
  Faiss nor UMAP, so the conflict arises only when an application uses Faiss
  with PyTorch
  ([#617](https://github.com/KevinMusgrave/pytorch-metric-learning/issues/617)).

- Recipe pages link to readable source on GitHub for the matching release
  instead of Python files that browsers download.