Releases · Lightning-AI/torchmetrics

12 Nov 19:29

Borda

v1.6.0

58147e0

More metrics Latest

Latest

The latest release of TorchMetrics introduces several significant enhancements and new features that will greatly benefit users across various domains. This update includes the addition of new metrics and methods that enhance the library's functionality and usability.

One of the key additions is the NISQA audio metric, which provides advanced capabilities for evaluating audio quality. In the classification domain, the new LogAUC and NegativePredictiveValue metrics offer improved tools for assessing model performance, particularly in imbalanced datasets. For regression tasks, the NormalizedRootMeanSquaredError metric has been introduced, providing a normalized measure of prediction accuracy that is less sensitive to outliers.

In the field of image segmentation, the new Dice metric enhances the evaluation of segmentation models by providing a robust measure of overlap between predicted and ground truth masks. Additionally, the merge_state method has been added to the Metric class, allowing for more efficient state management and aggregation across multiple devices or processes.

Furthermore, this release includes support for the propagation of the autograd graph in Distributed Data-Parallel (DDP) settings, enabling more efficient and scalable training of models across multiple GPUs. These enhancements collectively make TorchMetrics a more powerful and versatile tool for machine learning practitioners, enabling more accurate and efficient model evaluation across a wide range of applications.

[1.6.0] - 2024-11-12

Added

Added audio metric NISQA (#2792)
Added classification metric LogAUC (#2377)
Added classification metric NegativePredictiveValue (#2433)
Added regression metric NormalizedRootMeanSquaredError (#2442)
Added segmentation metric Dice (#2725)
Added method merge_state to Metric (#2786)
Added support for propagation of the autograd graph in DDP setting (#2754)

Changed

Changed naming and input order arguments in KLDivergence (#2800)

Deprecated

Deprecated Dice from classification metrics (#2725)

Removed

Changed minimum supported Pytorch version to 2.0 (#2671)
Dropped support for Python 3.8 (#2827)
Removed num_outputs in R2Score (#2800)

Fixed

Fixed segmentation Dice + GeneralizedDice for 2d index tensors (#2832)
Fixed mixed results of rouge_score with accumulate='best' (#2830)

Key Contributors

@Borda, @cw-tan, @philgzl, @rittik9, @SkafteNicki

New Contributors since `1.5.0`

@bfolie made their first contribution in #2793
@StalkerShurik made their first contribution in #2811
@philgzl made their first contribution in #2792
@cw-tan made their first contribution in #2754

Full Changelog: v1.5.0...v1.6.0

Contributors

bfolie, Borda, and 5 other contributors

Assets 4

08 Nov 10:35

Borda

v1.5.2

6ddc05a

Minor patch release

[1.5.2] - 2024-11-07

Changed

Re-adding numpy 2+ support (#2804)

Fixed

Fixed iou scores in detection for either empty predictions/targets leading to wrong scores (#2805)
Fixed MetricCollection compatibility with torch.jit.script (#2813)
Fixed assert in PIT (#2811)
Patched np.Inf for numpy 2.0+ (#2826)

Key Contributors

@adamjstewart, @Borda, @SkafteNicki, @StalkerShurik, @yurithefury

If we forgot someone due to not matching commit email with GitHub account, let us know :]

Full Changelog: v1.5.1...v1.5.2

Contributors

Borda, yurithefury, and 3 other contributors

Assets 4

23 Oct 07:05

Borda

v1.5.1

0a64b3f

Minor compatibility patch

[1.5.1] - 2024-10-22

Fixed

Changing _modules dict type in Pytorch 2.5 preventing to fail collections metrics (#2793)

Key Contributors

@bfolie

If we forgot someone due to not matching commit email with GitHub account, let us know :]

Full Changelog: v1.5.0...v1.5.1

Contributors

bfolie

Assets 4

18 Oct 19:35

Borda

v1.5.0

a8e8337

Shape metric

Shape metrics are quantitative methods used to assess and compare the geometric properties of objects, often in datasets that represent shapes. One such metric is the Procrustes Disparity, which measures the sum of the squared differences between two datasets after applying a Procrustes transformation. This transformation involves scaling, rotating, and translating the datasets to achieve optimal alignment. The Procrustes Disparity is particularly useful when comparing datasets that are similar in structure but not perfectly aligned, allowing for more meaningful comparison by minimizing differences due to orientation or size.