Bump torchmetrics from 0.11.0 to 1.2.1

[dependabot]

Bumps torchmetrics from 0.11.0 to 1.2.1.

Release notes

Sourced from torchmetrics's releases.

Lazy imports

[1.2.1] - 2023-11-30

Added

• Added error if NoTrainInceptionV3 is being initialized without torch-fidelity not being installed (#2143)
• Added support for Pytorch v2.1 (#2142)

Changed

• Change default state of SpectralAngleMapper and UniversalImageQualityIndex to be tensors (#2089)
• Use arange and repeat for deterministic bincount (#2184)

Removed

• Removed unused lpips third-party package as dependency of LearnedPerceptualImagePatchSimilarity metric (#2230)

Fixed

• Fixed numerical stability bug in LearnedPerceptualImagePatchSimilarity metric (#2144)
• Fixed numerical stability issue in UniversalImageQualityIndex metric (#2222)
• Fixed incompatibility for MeanAveragePrecision with pycocotools backend when too little max_detection_thresholds are provided (#2219)
• Fixed support for half precision in Perplexity metric (#2235)
• Fixed device and dtype for LearnedPerceptualImagePatchSimilarity functional metric (#2234)
• Fixed bug in Metric._reduce_states(...) when using dist_sync_fn="cat" (#2226)
• Fixed bug in CosineSimilarity where 2d is expected but 1d input was given (#2241)
• Fixed bug in MetricCollection when using compute groups and compute is called more than once (#2211)

Full Changelog: Lightning-AI/torchmetrics@v1.2.0...v1.2.1

Key Contributors

@​Borda, @​jankng, @​kyle-dorman, @​SkafteNicki, @​tanguymagne

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

Clustering metrics

Torchmetrics v1.2 is out now! The latest release includes 11 new metrics within a new subdomain: Clustering. In this blog post, we briefly explain what clustering is, why it’s a useful measure and newly added metrics that can be used with code samples.

Clustering - what is it?

Clustering is an unsupervised learning technique. The term unsupervised here refers to the fact that we do not have ground truth targets as we do in classification. The primary goal of clustering is to discover hidden patterns or structures within data without prior knowledge about the meaning or importance of particular features. Thus, clustering is a form of data exploration compared to supervised learning, where the goal is “just” to predict if a data point belongs to one class.

The key goal of clustering algorithms is to split data into clusters/sets where data points from the same cluster are more similar to each other than any other points from the remaining clusters. Some of the most common and widely used clustering algorithms are K-Means, Hierarchical clustering, and Gaussian Mixture Models (GMM).

An objective quality evaluation/measure is required regardless of the clustering algorithm or internal optimization criterion used. In general, we can divide all clustering metrics into two categories: extrinsic metrics and intrinsic metrics.

Extrinsic metrics

... (truncated)

Changelog

Sourced from torchmetrics's changelog.

[1.2.1] - 2023-11-30

Added

• Added error if NoTrainInceptionV3 is being initialized without torch-fidelity not being installed (#2143)
• Added support for Pytorch v2.1 (#2142)

Changed

• Change default state of SpectralAngleMapper and UniversalImageQualityIndex to be tensors (#2089)
• Use arange and repeat for deterministic bincount (#2184)

Removed

• Removed unused lpips third-party package as dependency of LearnedPerceptualImagePatchSimilarity metric (#2230)

Fixed

• Fixed numerical stability bug in LearnedPerceptualImagePatchSimilarity metric (#2144)
• Fixed numerical stability issue in UniversalImageQualityIndex metric (#2222)
• Fixed incompatibility for MeanAveragePrecision with pycocotools backend when too little max_detection_thresholds are provided (#2219)
• Fixed support for half precision in Perplexity metric (#2235)
• Fixed device and dtype for LearnedPerceptualImagePatchSimilarity functional metric (#2234)
• Fixed bug in Metric._reduce_states(...) when using dist_sync_fn="cat" (#2226)
• Fixed bug in CosineSimilarity where 2d is expected but 1d input was given (#2241)
• Fixed bug in MetricCollection when using compute groups and compute is called more than once (#2211)

[1.2.0] - 2023-09-22

Added

• Added metric to cluster package:
  • MutualInformationScore (#2008)
  • RandScore (#2025)
  • NormalizedMutualInfoScore (#2029)
  • AdjustedRandScore (#2032)
  • CalinskiHarabaszScore (#2036)
  • DunnIndex (#2049)
  • HomogeneityScore (#2053)
  • CompletenessScore (#2053)
  • VMeasureScore (#2053)
  • FowlkesMallowsIndex (#2066)
  • AdjustedMutualInfoScore (#2058)
  • DaviesBouldinScore (#2071)
• Added backend argument to MeanAveragePrecision (#2034)

[1.1.2] - 2023-09-11

Fixed

... (truncated)

Commits

• 3514d71 releasing 1.2.1
• 62510ca Add error on wrong shape in CosineSimilarity metric (#2241)
• cce3072 Fix MetricCollection with repeated compute calls (#2211)
• 9acafd1 Raise error in MeanAveragePrecision if too little/many detection are provid...
• d85afc7 Docs and tests for how to save and load metrics (#2237)
• 9010e44 build(deps): update lightning-utilities requirement from <0.10.0,>=0.8.0 to >...
• d104412 Lazy import in image/multimodal domains (#2215)
• 66ac67f build(deps): update scienceplots requirement from <=2.1.0,>=2.0.0 to >=2.0.0,...
• 0f7d31a build(deps): bump pytest-rerunfailures from 12.0 to 13.0 in /requirements (#2...
• 48e2a48 build(deps): bump mypy from 1.7.0 to 1.7.1 in /requirements (#2244)
• Additional commits viewable in compare view

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