Minor patch release

[0.9.2] - 2022-06-29

Fixed

• Fixed mAP calculation for areas with 0 predictions (#1080)
• Fixed bug where avg precision state and auroc state was not merge when using MetricCollections (#1086)
• Skip box conversion if no boxes are present in MeanAveragePrecision (#1097)
• Fixed inconsistency in docs and code when setting average="none" in AvaragePrecision metric (#1116)

Contributors

@23pointsNorth, @kouyk, @SkafteNicki

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Minor PL compatibility patch

[0.9.1] - 2022-06-08

Added

• Added specific RuntimeError when metric object is on the wrong device (#1056)
• Added an option to specify own n-gram weights for BLEUScore and SacreBLEUScore instead of using uniform weights only. (#1075)

Fixed

• Fixed aggregation metrics when input only contains zero (#1070)
• Fixed TypeError when providing superclass arguments as kwargs (#1069)
• Fixed bug related to state reference in metric collection when using compute groups (#1076)

Contributors

@jlcsilva, @SkafteNicki, @stancld

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Faster forward

Highligths

TorchMetrics v0.9 is now out, and it brings significant changes to how the forward method works. This blog post goes over these improvements and how they affect both users of TorchMetrics and users that implement custom metrics. TorchMetrics v0.9 also includes several new metrics and bug fixes.

Blog: TorchMetrics v0.9 — Faster forward

The Story of the Forward Method

Since the beginning of TorchMetrics, Forward has served the dual purpose of calculating the metric on the current batch and accumulating in a global state. Internally, this was achieved by calling update twice: one for each purpose, which meant repeating the same computation. However, for many metrics, calling update twice is unnecessary to achieve both the local batch statistics and accumulating globally because the global statistics are simple reductions of the local batch states.

In v0.9, we have finally implemented a logic that can take advantage of this and will only call update once before making a simple reduction. As you can see in the figure below, this can lead to a single call of forward being 2x faster in v0.9 compared to v0.8 of the same metric.

With the improvements to forward, many metrics have become significantly faster (up to 2x)
It should be noted that this change mainly benefits metrics (for example, confusionmatrix) where calling update is expensive.

We went through all existing metrics in TorchMetrics and enabled this feature for all appropriate metrics, which was almost 95% of all metrics. We want to stress that if you are using metrics from TorchMetrics, nothing has changed to the API, and no code changes are necessary.

[0.9.0] - 2022-05-31

Added

• Added RetrievalPrecisionRecallCurve and RetrievalRecallAtFixedPrecision to retrieval package (#951)
• Added class property full_state_update that determines forward should call update once or twice (#984,#1033)
• Added support for nested metric collections (#1003)
• Added Dice to classification package (#1021)
• Added support to segmentation type segm as IOU for mean average precision (#822)

Changed

• Renamed reduction argument to average in Jaccard score and added additional options (#874)

Removed

• Removed deprecated compute_on_step argument (#962, #967, #979 ,#990, #991, #993, #1005, #1004, #1007)

Fixed

• Fixed non-empty state dict for a few metrics (#1012)
• Fixed bug when comparing states while finding compute groups (#1022)
• Fixed torch.double support in stat score metrics (#1023)
• Fixed FID calculation for non-equal size real and fake input (#1028)
• Fixed case where KLDivergence could output Nan (#1030)
• Fixed deterministic for PyTorch<1.8 (#1035)
• Fixed default value for mdmc_average in Accuracy (#1036)
• Fixed missing copy of property when using compute groups in MetricCollection (#1052)

Contributors

@Borda, @burglarhobbit, @charlielito, @gianscarpe, @MrShevan, @phaseolud, @razmikmelikbekyan, @SkafteNicki, @tanmoyio, @vumichien

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Minor patch release

[0.8.2] - 2022-05-06

Fixed

• Fixed multi-device aggregation in PearsonCorrCoef (#998)
• Fixed MAP metric when using a custom list of thresholds (#995)
• Fixed compatibility between compute groups in MetricCollection and prefix/postfix arg (#1007)
• Fixed compatibility with future Pytorch 1.12 in safe_matmul (#1011, #1014)

Contributors

@ben-davidson-6, @Borda, @SkafteNicki, @tanmoyio

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Minor patch release

[0.8.1] - 2022-04-27

Changed

• Reimplemented the signal_distortion_ratio metric, which removed the absolute requirement of fast-bss-eval (#964)

Fixed

• Fixed "Sort currently does not support bool dtype on CUDA" error in MAP for empty preds (#983)
• Fixed BinnedPrecisionRecallCurve when thresholds argument is not provided (#968)
• Fixed CalibrationError to work on logit input (#985)

Contributors

@DuYicong515, @krshrimali, @quancs, @SkafteNicki

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Faster collection and more metrics!

We are excited to announce that TorchMetrics v0.8 is now available. The release includes several new metrics in the classification and image domains and some performance improvements for those working with metrics collections.

Metric collections just got faster

Common wisdom dictates that you should never evaluate the performance of your models using only a single metric but instead a collection of metrics. For example, it is common to simultaneously evaluate the accuracy, precision, recall, and f1 score in classification. In TorchMetrics, we have for a long time provided the MetricCollection object for chaining such metrics together for an easy interface to calculate them all at once. However, in many cases, such a collection of metrics shares some of the underlying computations that have been repeated for every metric in the collection. In Torchmetrics v0.8 we have introduced the concept of compute_groups to MetricCollection that will, as default, be auto-detected and group metrics that share some of the same computations.

Thus, if you are using MetricCollections in your code, upgrading to TorchMetrics v0.8 should automatically make your code run faster without any code changes.

Many exciting new metrics

TorchMetrics v0.8 includes several new metrics within the classification and image domain, both for the functional and modular API. We refer to the documentation for the full description of all metrics if you want to learn more about them.

• SpectralAngleMapper or SAM was added to the image package. This metric can calculate the spectral similarity between given reference spectra and estimated spectra.
• CoverageError was added to the classification package. This metric can be used when you are working with multi-label data. The metric works similar to the sklearn counterpart and computes how far you need to go through ranked scores such that all true labels are covered.
• LabelRankingAveragePrecision and LabelRankingLoss were added to the classification package. Both metrics are used in multi-label ranking problems, where the goal is to give a better rank to the labels associated with each sample. Each metric gives a measure of how well your model is doing this.
• ErrorRelativeGlobalDimensionlessSynthesis or ERGAS was added to the image package. This metric can be used to calculate the accuracy of Pan sharpened images considering the normalized average error of each band of the resulting image.
• UniversalImageQualityIndex was added to the image package. This metric can assess the difference between two images, which considers three different factors when computed: loss of correlation, luminance distortion, and contrast distortion.
• ClasswiseWrapper was added to the wrapper package. This wrapper can be used in combinations with metrics that return multiple values (such as classification metrics with the average=None argument). The wrapper will unwrap the result into a dict with a label for each value.

[0.8.0] - 2022-04-14

Added

• Added WeightedMeanAbsolutePercentageError to regression package (#948)
• Added new classification metrics:
  • CoverageError (#787)
  • LabelRankingAveragePrecision and LabelRankingLoss (#787)
• Added new image metric:
  • SpectralAngleMapper (#885)
  • ErrorRelativeGlobalDimensionlessSynthesis (#894)
  • UniversalImageQualityIndex (#824)
  • SpectralDistortionIndex (#873)
• Added support for MetricCollection in MetricTracker (#718)
• Added support for 3D image and uniform kernel in StructuralSimilarityIndexMeasure (#818)
• Added smart update of MetricCollection (#709)
• Added ClasswiseWrapper for better logging of classification metrics with multiple output values (#832)
• Added **kwargs argument for passing additional arguments to base class (#833)
• Added negative ignore_index for the Accuracy metric (#362)
• Added adaptive_k for the RetrievalPrecision metric (#910)
• Added reset_real_features argument image quality assessment metrics (#722)
• Added new keyword argument compute_on_cpu to all metrics (#867)

Changed

• Made num_classes in jaccard_index a required argument (#853, #914)
• Added normalizer, tokenizer to ROUGE metric (#838)
• Improved shape checking of permutation_invariant_training (#864)
• Allowed reduction None (#891)
• MetricTracker.best_metric will now give a warning when computing on metric that do not have a best (#913)

Deprecated

• Deprecated argument compute_on_step (#792)
• Deprecated passing in dist_sync_on_step, process_group, dist_sync_fn direct argument (#833)

Removed

• Removed support for versions of Lightning lower than v1.5 (#788)
• Removed deprecated functions, and warnings in Text (#773)
  • WER and functional.wer
• Removed deprecated functions and warnings in Image (#796)
  • SSIM and functional.ssim
  • PSNR and functional.psnr
• Removed deprecated functions, and warnings in classification and regression (#806)
  • FBeta and functional.fbeta
  • F1 and functional.f1
  • Hinge and functional.hinge
  • IoU and functional.iou
  • MatthewsCorrcoef
  • PearsonCorrcoef
  • SpearmanCorrcoef
• Removed deprecated functions, and warnings in detection and pairwise (#804)
  • MAP and functional.pairwise.manhatten
• Removed deprecated functions, and warnings in Audio (#805)
  • PESQ and functional.audio.pesq
  • PIT and functional.audio.pit
  • SDR and functional.audio.sdr and functional.audio.si_sdr
  • SNR and functional.audio.snr and functional.audio.si_snr
  • STOI and functional.audio.stoi

Fixed

• Fixed device mismatch for MAP metric in specific cases (#950)
• Improved testing speed (#820)
• Fixed compatibility of ClasswiseWrapper with the prefix argument of MetricCollection (#843)
• Fixed BestScore on GPU (#912)
• Fixed Lsum computation for ROUGEScore (#944)

Contributors

@ankitaS11, @ashutoshml, @Borda, @hookSSi, @justusschock, @lucadiliello, @quancs, @rusty1s, @SkafteNicki, @stancld, @vumichien, @weningerleon, @yassersouri

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Minor patch release

[0.7.3] - 2022-03-22

Fixed

• Fixed unsafe log operation in TweedieDeviace for power=1 (#847)
• Fixed bug in MAP metric related to either no ground truth or no predictions (#884)
• Fixed ConfusionMatrix, AUROC and AveragePrecision on GPU when running in deterministic mode (#900)
• Fixed NaN or Inf results returned by signal_distortion_ratio (#899)
• Fixed memory leak when using update method with tensor where requires_grad=True (#902)

Contributors

@mtailanian, @quancs, @SkafteNicki

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JOSS paper

[0.7.2] - 2022-02-10

Fixed

• Minor patches in JOSS paper.

Improve mAP performance

[0.7.1] - 2022-02-03

Changed

• Used torch.bucketize in calibration error when torch>1.8 for faster computations (#769)
• Improve mAP performance (#742)

Fixed

• Fixed check for available modules (#772)
• Fixed Matthews correlation coefficient when the denominator is 0 (#781)

Contributors

@Borda, @ramonemiliani93, @SkafteNicki, @twsl

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New NLP metrics and improved API

We are excited to announce that TorchMetrics v0.7 is now publicly available. This release is pretty significant. It includes several new metrics (mainly for NLP), naming and import changes, general improvements to the API, and some other great features. TorchMetrics thus now has over 60+ metrics, and the package is more user-friendly than ever.

NLP metrics - Text package

Text package is a part of TorchMetrics as of v0.5. With the growing capability of language generation models, there is also a real need to have reliable evaluation metrics. With several added metrics and unified API, TorchMetrics makes the usage of various metrics even easier! TorchMetrics v0.7 newly includes a couple of machine translation metrics such as chrF, chrF++, Translation Edit Rate, or Extended Edit Distance. Furthermore, it also supports other metrics - Match Error Rate, Word Information Lost, Word Information Preserved, and SQuAD evaluation metrics. Last but not least, we also made possible the evaluation of the ROUGE score using multiple references.

Argument unification

Importantly, all text metrics assume preds, target input order with these explicit keyword arguments. If different naming was used before v0.7, it is deprecated and completely removed in v0.8.

Import and naming changes

TorchMetrics v0.7 brings more extensive and minor changes to how metrics should be imported. The import changes directly impact v0.7, meaning that you will most likely need to change the import statement for some specific metrics. All naming changes follow our standard deprecation process, meaning that in v0.7, any metric that is renamed will still work but raise an error asking to use the new metric name. From v0.8, the old metric names will no longer be available.

[0.7.0] - 2022-01-17

Added

• Added NLP metrics:
  • MatchErrorRate (#619)
  • WordInfoLost and WordInfoPreserved (#630)
  • SQuAD (#623)
  • CHRFScore (#641)
  • TranslationEditRate (#646)
  • ExtendedEditDistance (#668)
• Added MultiScaleSSIM into image metrics (#679)
• Added Signal to Distortion Ratio (SDR) to audio package (#565)
• Added MinMaxMetric to wrappers (#556)
• Added ignore_index to retrieval metrics (#676)
• Added support for multi references in ROUGEScore (#680)
• Added a default VSCode devcontainer configuration (#621)

Changed

• Scalar metrics will now consistently have additional dimensions squeezed (#622)
• Metrics having third party dependencies removed from global import (#463)
• Untokenized for BLEUScore input stay consistent with all the other text metrics (#640)
• Arguments reordered for TER, BLEUScore, SacreBLEUScore, CHRFScore now the expected input order is predictions first and target second (#696)
• Changed dtype of metric state from torch.float to torch.long in ConfusionMatrix to accommodate larger values (#715)
• Unify preds, target input argument's naming across all text metrics (#723, #727)
  • bert, bleu, chrf, sacre_bleu, wip, wil, cer, ter, wer, mer, rouge, squad

Deprecated

• Renamed IoU -> Jaccard Index (#662)
• Renamed text WER metric: (#714)
  • functional.wer -> functional.word_error_rate
  • WER -> WordErrorRate
• Renamed correlation coefficient classes: (#710)
  • MatthewsCorrcoef -> MatthewsCorrCoef
  • PearsonCorrcoef -> PearsonCorrCoef
  • SpearmanCorrcoef -> SpearmanCorrCoef
• Renamed audio STOI metric: (#753, #758)
  • audio.STOI to audio.ShortTimeObjectiveIntelligibility
  • functional.audio.stoi to functional.audio.short_time_objective_intelligibility
• Renamed audio PESQ metrics: (#751)
  • functional.audio.pesq -> functional.audio.perceptual_evaluation_speech_quality
  • audio.PESQ -> audio.PerceptualEvaluationSpeechQuality
• Renamed audio SDR metrics: (#711)
  • functional.sdr -> functional.signal_distortion_ratio
  • functional.si_sdr -> functional.scale_invariant_signal_distortion_ratio
  • SDR -> SignalDistortionRatio
  • SI_SDR -> ScaleInvariantSignalDistortionRatio
• Renamed audio SNR metrics: (#712)
  • functional.snr -> functional.signal_distortion_ratio
  • functional.si_snr -> functional.scale_invariant_signal_noise_ratio
  • SNR -> SignalNoiseRatio
  • SI_SNR -> ScaleInvariantSignalNoiseRatio
• Renamed F-score metrics: (#731, #740)
  • functional.f1 -> functional.f1_score
  • F1 -> F1Score
  • functional.fbeta -> functional.fbeta_score
  • FBeta -> FBetaScore
• Renamed Hinge metric: (#734)
  • functional.hinge -> functional.hinge_loss
  • Hinge -> HingeLoss
• Renamed image PSNR metrics (#732)
  • functional.psnr -> functional.peak_signal_noise_ratio
  • PSNR -> PeakSignalNoiseRatio
• Renamed image PIT metric: (#737)
  • functional.pit -> functional.permutation_invariant_training
  • PIT -> PermutationInvariantTraining
• Renamed image SSIM metric: (#747)
  • functional.ssim -> functional.scale_invariant_signal_noise_ratio
  • SSIM -> StructuralSimilarityIndexMeasure
• Renamed detection MAP to MeanAveragePrecision metric (#754)
• Renamed Fidelity & LPIPS image metric: (#752)
  • image.FID -> image.FrechetInceptionDistance
  • image.KID -> image.KernelInceptionDistance
  • image.LPIPS -> image.LearnedPerceptualImagePatchSimilarity

Removed

• Removed embedding_similarity metric (#638)
• Removed argument concatenate_texts from wer metric (#638)
• Removed arguments newline_sep and decimal_places from rouge metric (#638)

Fixed

• Fixed MetricCollection kwargs filtering when no kwargs are present in update signature (#707)

Contributors

@ashutoshml, @Borda, @cuent, @Fariborzzz, @getgaurav2, @janhenriklambrechts, @justusschock, @karthikrangasai, @lucadiliello, @mahinlma, @mathemusician, @mona0809, @mrleu, @puhuk, @quancs, @SkafteNicki, @stancld, @twsl

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