torchmetrics 1.6.0

More metrics

on Python PyPI

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