Minor compatibility patch

[1.5.1] - 2024-10-22

Fixed

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

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Key Contributors

@bfolie

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

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.

[1.5.0] - 2024-10-18

Added

• Added segmentation metric HausdorffDistance (#2122)
• Added audio metric DNSMOS (#2525)
• Added shape metric ProcrustesDistance (#2723)
• Added MetricInputTransformer wrapper (#2392)
• Added input_format argument to segmentation metrics (#2572)
• Added multi-output support for MAE metric (#2605)
• Added truncation argument to BERTScore (#2776)

Changed

• Tracker higher is better integration (#2649)
• Updated InfoLM class to dynamically set higher_is_better (#2674)

Deprecated

• Deprecated num_outputs in R2Score (#2705)

Fixed

• Fixed corner case in IoU metric for single empty prediction tensors (#2780)
• Fixed PSNR calculation for integer type input images (#2788)

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Key Contributors

@Astraightrain, @grahamannett, @lgienapp, @matsumotosan, @quancs, @SkafteNicki

New Contributors since 1.4.0

• @kalekundert made their first contribution in #2543
• @lgienapp made their first contribution in #2392
• @sweber1 made their first contribution in #2634
• @gxy-gxy made their first contribution in #2347
• @Astraightrain made their first contribution in #2605
• @ndrwrbgs made their first contribution in #2640
• @grahamannett made their first contribution in #2674
• @petertheprocess made their first contribution in #2721
• @rittik9 made their first contribution in #2726
• @vkinakh made their first contribution in #2698
• @likawind made their first contribution in #2732
• @veera-puthiran-14082 made their first contribution in #2753
• @GPPassos made their first contribution in #2727

Full Changelog: v1.4.0...v1.5.0

Minor patch release

[1.4.3] - 2024-10-10

Fixed

• Fixed for Pearson changes inputs (#2765)
• Fixed bug in PESQ metric where NoUtterancesError prevented calculating on a batch of data (#2753)
• Fixed corner case in MatthewsCorrCoef (#2743)

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Key Contributors

@Borda, @SkafteNicki, @veera-puthiran-14082

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Full Changelog: v1.4.2...v1.4.3

Minor patch release

[1.4.2] - 2022-09-12

Added

• Re-adding Chrf implementation (#2701)

Fixed

• Fixed wrong aggregation in segmentation.MeanIoU (#2698)
• Fixed handling zero division error in binary IoU (Jaccard index) calculation (#2726)
• Corrected the padding related calculation errors in SSIM (#2721)
• Fixed compatibility of audio domain with new scipy (#2733)
• Fixed how prefix/postfix works in MultitaskWrapper (#2722)
• Fixed flakiness in tests related to torch.unique with dim=None (#2650)

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Key Contributors

@Borda, @petertheprocess, @rittik9, @SkafteNicki, @vkinakh

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Full Changelog: v1.4.1...v1.4.2

Minor patch release

[1.4.1] - 2024-08-02

Changed

• Calculate the text color of ConfusionMatrix plot based on luminance (#2590)
• Updated _safe_divide to allow Accuracy to run on the GPU (#2640)
• Improved better error messages for intersection detection metrics for wrong user input (#2577)

Removed

• Dropped Chrf implementation due to licensing issues with the upstream package (#2668)

Fixed

• Fixed bug in MetricCollection when using compute groups and compute is called more than once (#2571)
• Fixed class order of panoptic_quality(..., return_per_class=True) output (#2548)
• Fixed BootstrapWrapper not being reset correctly (#2574)
• Fixed integration between ClasswiseWrapper and MetricCollection with custom _filter_kwargs method (#2575)
• Fixed BertScore calculation: pred target misalignment (#2347)
• Fixed _cumsum helper function in multi-gpu (#2636)
• Fixed bug in MeanAveragePrecision.coco_to_tm (#2588)
• Fixed missed f-strings in exceptions/warnings (#2667)

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Key Contributors

@Borda, @gxy-gxy, @i-aki-y, @ndrwrbgs, @relativityhd, @SkafteNicki

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Full Changelog: v1.4.0...v1.4.1

Minor dependency correction

Full Changelog: v1.4.0...v1.4.0.post0

Metrics for segmentation

In Torchmetrics v1.4, we are happy to introduce a new domain of metrics to the library: segmentation metrics. Segmentation metrics are used to evaluate how well segmentation algorithms are performing, e.g., algorithms that take in an image and pixel-by-pixel decide what kind of object it is. These kind of algorithms are necessary in applications such as self driven cars. Segmentations are closely related to classification metrics, but for now, in Torchmetrics, expect the input to be formatted differently; see the documentation for more info. For now, MeanIoU and GeneralizedDiceScore have been added to the subpackage, with many more to follow in upcoming releases of Torchmetrics. We are happy to receive any feedback on metrics to add in the future or the user interface for the new segmentation metrics.

Torchmetrics v1.3 adds new metrics to the classification and image subpackage and has multiple bug fixes and other quality-of-life improvements. We refer to the changelog for the complete list of changes.

[1.4.0] - 2024-05-03

Added

• Added SensitivityAtSpecificity metric to classification subpackage (#2217)
• Added QualityWithNoReference metric to image subpackage (#2288)
• Added a new segmentation metric:
  • MeanIoU (#1236)
  • GeneralizedDiceScore (#1090)
• Added support for calculating segmentation quality and recognition quality in PanopticQuality metric (#2381)
• Added pretty-errors for improving error prints (#2431)
• Added support for torch.float weighted networks for FID and KID calculations (#2483)
• Added zero_division argument to selected classification metrics (#2198)

Changed

• Made __getattr__ and __setattr__ of ClasswiseWrapper more general (#2424)

Fixed

• Fix getitem for metric collection when prefix/postfix is set (#2430)
• Fixed axis names with Precision-Recall curve (#2462)
• Fixed list synchronization with partly empty lists (#2468)
• Fixed memory leak in metrics using list states (#2492)
• Fixed bug in computation of ERGAS metric (#2498)
• Fixed BootStrapper wrapper not working with kwargs provided argument (#2503)
• Fixed warnings being suppressed in MeanAveragePrecision when requested (#2501)
• Fixed corner-case in binary_average_precision when only negative samples are provided (#2507)

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Key Contributors

@baskrahmer, @Borda, @ChristophReich1996, @daniel-code, @furkan-celik, @i-aki-y, @jlcsilva, @NielsRogge, @oguz-hanoglu, @SkafteNicki, @ywchan2005

New Contributors since 1.3.0

• @eamonn-zh made their first contribution in #2345
• @nsmlzl made their first contribution in #2346
• @fschlatt made their first contribution in #2364
• @JonasVerbickas made their first contribution in #2358
• @AtomicVar made their first contribution in #2391
• @JDongian made their first contribution in #2400
• @daniel-code made their first contribution in #2390
• @baskrahmer made their first contribution in #2457
• @ChristophReich1996 made their first contribution in #2381
• @lukazso made their first contribution in #2491
• @S-aiueo32 made their first contribution in #2499
• @dominicgkerr made their first contribution in #2493
• @Shoumik-Gandre made their first contribution in #2482
• @randombenj made their first contribution in #2511
• @NielsRogge made their first contribution in #1236
• @i-aki-y made their first contribution in #2198

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Full Changelog: v1.3.0...v1.4.0

Minor patch release

[1.3.2] - 2024-03-18

Fixed

• Fixed negative variance estimates in certain image metrics (#2378)
• Fixed dtype being changed by deepspeed for certain regression metrics (#2379)
• Fixed plotting of metric collection when prefix/postfix is set (#2429)
• Fixed bug when top_k>1 and average="macro" for classification metrics (#2423)
• Fixed case where label prediction tensors in classification metrics were not validated correctly (#2427)
• Fixed how auc scores are calculated in PrecisionRecallCurve.plot methods (#2437)

Full Changelog: v1.3.1...v1.3.2

Key Contributors

@Borda, @SkafteNicki

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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: v1.3.0...v1.3.1

Key Contributors

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

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Minor release patch

Full Changelog: v1.3.0...v1.3.0.post0