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build(deps): update torchmetrics requirement from <1.5,>=1.0 to >=1.0,<1.7 #368

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@dependabot dependabot bot commented on behalf of github Dec 1, 2024

Updates the requirements on torchmetrics to permit the latest version.

Release notes

Sourced from torchmetrics's releases.

More metrics

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

... (truncated)

Changelog

Sourced from torchmetrics's changelog.

[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)

[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)

[1.5.1] - 2024-10-22

Fixed

... (truncated)

Commits
  • 58147e0 releasing 1.6.0
  • d73e6c1 New metric: LogAUC (#2377)
  • 8f6936d Fix segmentation Dice + GeneralizedDice for 2d index tensors (#2832)
  • e2543c8 build(deps): update tqdm requirement from <4.67.0 to <4.68.0 in /requirements...
  • 47bd4b8 build(deps): update pygithub requirement from <2.5.0,>2.0.0 to >2.0.0,<2.6.0 ...
  • bb1af09 build(deps): update regex requirement from <=2024.9.11,>=2021.9.24 to >=2021....
  • 0d009de build(deps): bump pytest-cov from 5.0.0 to 6.0.0 in /requirements (#2825)
  • 7147275 Fix mixed results of rouge_score with accumulate='best' (#2830)
  • ea29c89 bump: drop support for python 3.8 (#2827)
  • bf030e0 docs: update chlog after 1.5.2
  • Additional commits viewable in compare view

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Updates the requirements on [torchmetrics](https://github.com/Lightning-AI/torchmetrics) to permit the latest version.
- [Release notes](https://github.com/Lightning-AI/torchmetrics/releases)
- [Changelog](https://github.com/Lightning-AI/torchmetrics/blob/master/CHANGELOG.md)
- [Commits](Lightning-AI/torchmetrics@v1.0.0...v1.6.0)

---
updated-dependencies:
- dependency-name: torchmetrics
  dependency-type: direct:production
...

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codecov bot commented Dec 3, 2024

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 69%. Comparing base (901c2e1) to head (03afc94).

Additional details and impacted files
@@         Coverage Diff         @@
##           main   #368   +/-   ##
===================================
  Coverage    69%    69%           
===================================
  Files         2      2           
  Lines       441    441           
===================================
  Hits        306    306           
  Misses      135    135           

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