Bump torchmetrics from 1.1.0 to 1.3.1

[dependabot]

Bumps torchmetrics from 1.1.0 to 1.3.1.

Release notes

Sourced from torchmetrics's releases.

Minor patch release

[1.3.1] - 2024-02-12

Fixed

• Fixed how backprop is handled in LPIPS metric (#2326)
• Fixed MultitaskWrapper not being able to be logged in lightning when using metric collections (#2349)
• Fixed high memory consumption in Perplexity metric (#2346)
• Fixed cached network in FeatureShare not being moved to the correct device (#2348)
• Fix naming of statistics in MeanAveragePrecision with custom max det thresholds (#2367)
• Fixed custom aggregation in retrieval metrics (#2364)
• Fixed initialize aggregation metrics with default floating type (#2366)
• Fixed plotting of confusion matrices (#2358)

Full Changelog: Lightning-AI/torchmetrics@v1.3.0...v1.3.1

Key Contributors

@​Borda, @​fschlatt, @​JonasVerbickas, @​nsmlzl, @​SkafteNicki

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

Minor release patch

Full Changelog: Lightning-AI/torchmetrics@v1.3.0...v1.3.0.post0

New Image metrics & wrappers

TorchMetrics v1.3 is out now! This release introduces seven new metrics in the different subdomains of TorchMetrics, adding some nice features to already established metrics. In this blogpost, we present the new metrics with short code samples.

We are happy to see the continued adoption of TorchMetrics in over 19,000 Github repositories projects, and we are proud to release that we have passed 1,800 GitHub stars.

New metrics

The retrieval domain has received one new metric in this release: RetrievalAUROC. This metric calculates the Area Under the Receiver Operation Curve for document retrieval data. It is similar to the standard AUROC metric from classification but also supports the additional indexes argument that all retrieval metrics support.

from torch import tensor
from torchmetrics.retrieval import RetrievalAUROC
indexes = tensor([0, 0, 0, 1, 1, 1, 1])
preds = tensor([0.2, 0.3, 0.5, 0.1, 0.3, 0.5, 0.2])
target = tensor([False, False, True, False, True, False, True])
r_auroc = RetrievalAUROC()
r_auroc(preds, target, indexes=indexes)
# tensor(0.7500)

The image subdomain is receiving two new metrics in v1.3, which brings the total number image-specific metrics in TorchMetrics to 21! As with other metrics, these two new metrics work by comparing a predicted image tensor to a ground truth image, but they focus on different properties for their metric calculation.

• The first metrics is SpatialCorrelationCoefficient. As the name indicates this metric focuses on how well the spatial structure of the predicted image correlates with the ground truth image.

... (truncated)

Changelog

Sourced from torchmetrics's changelog.

[1.3.1] - 2024-02-12

Fixed

• Fixed how backprop is handled in LPIPS metric (#2326)
• Fixed MultitaskWrapper not being able to be logged in lightning when using metric collections (#2349)
• Fixed high memory consumption in Perplexity metric (#2346)
• Fixed cached network in FeatureShare not being moved to the correct device (#2348)
• Fix naming of statistics in MeanAveragePrecision with custom max det thresholds (#2367)
• Fixed custom aggregation in retrieval metrics (#2364)
• Fixed initialize aggregation metrics with default floating type (#2366)
• Fixed plotting of confusion matrices (#2358)

[1.3.0] - 2024-01-10

Added

• Added more tokenizers for SacreBLEU metric (#2068)
• Added support for logging MultiTaskWrapper directly with lightnings log_dict method (#2213)
• Added FeatureShare wrapper to share submodules containing feature extractors between metrics (#2120)
• Added new metrics to image domain:
  • SpatialDistortionIndex (#2260)
  • Added CriticalSuccessIndex (#2257)
  • Spatial Correlation Coefficient (#2248)
• Added average argument to multiclass versions of PrecisionRecallCurve and ROC (#2084)
• Added confidence scores when extended_summary=True in MeanAveragePrecision (#2212)
• Added RetrievalAUROC metric (#2251)
• Added aggregate argument to retrieval metrics (#2220)
• Added utility functions in segmentation.utils for future segmentation metrics (#2105)

Changed

• Changed minimum supported Pytorch version from 1.8 to 1.10 (#2145)
• Changed x-/y-axis order for PrecisionRecallCurve to be consistent with scikit-learn (#2183)

Deprecated

• Deprecated metric._update_called (#2141)
• Deprecated specicity_at_sensitivity in favour of specificity_at_sensitivity (#2199)

Fixed

• Fixed support for half precision + CPU in metrics requiring topk operator (#2252)
• Fixed warning incorrectly being raised in Running metrics (#2256)
• Fixed integration with custom feature extractor in FID metric (#2277)

---

... (truncated)

Commits

• cdbecce releasing 1.3.1
• 2c3629a Fix backprop in LPIPS (#2326)
• 2ae0c8b Improve confusion matrix plotting (#2358)
• cba48a2 Fix multitask wrapper not being logged in lightning when used together with c...
• 38d6375 Fix naming of statistics in MeanAveragePrecision with custom max det thresh...
• f9f1842 fix ignored custom callable in retrieval metric aggregation (#2364)
• dd4ce92 Initialize aggregation metrics with default floating type (#2366)
• 91902b7 ci: patch run with no tests (#2362)
• 567564c Memory optimization of perplexity metric (#2346)
• 23204d6 docs: Update implement.rst (#2345)
• Additional commits viewable in compare view

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[dependabot]

Looks like torchmetrics is up-to-date now, so this is no longer needed.