Bump torchmetrics from 0.11.4 to 1.0.0 in /requirements

[dependabot]

Bumps torchmetrics from 0.11.4 to 1.0.0.

Release notes

Sourced from torchmetrics's releases.

Visualize metrics

We are happy to announce that the first major release of Torchmetrics, version v1.0, is publicly available. We have worked hard on a couple of new features for this milestone release, but for v1.0.0, we have also managed to implement over 100 metrics in torchmetrics.

Plotting

The big new feature of v1.0 is a built-in plotting feature. As the old saying goes: "A picture is worth a thousand words". Within machine learning, this is definitely also true for many things. Metrics are one area that, in some cases, is definitely better showcased in a figure than as a list of floats. The only requirement for getting started with the plotting feature is installing matplotlib. Either install with pip install matplotlib or pip install torchmetrics[visual] (the latter option also installs Scienceplots and uses that as the default plotting style).

The basic interface is the same for any metric. Just call the new .plot method:

metric = AnyMetricYouLike()
for _ in range(num_updates):
    metric.update(preds[i], target[i])
fig, ax = metric.plot()

The plot method by default does not require any arguments and will automatically call metric.compute internally on whatever metric states have been accumulated.

[1.0.0] - 2022-07-04

Added

• Added prefix and postfix arguments to ClasswiseWrapper (#1866)
• Added speech-to-reverberation modulation energy ratio (SRMR) metric (#1792, #1872)
• Added new global arg compute_with_cache to control caching behaviour after compute method (#1754)
• Added ComplexScaleInvariantSignalNoiseRatio for audio package (#1785)
• Added Running wrapper for calculate running statistics (#1752)
• AddedRelativeAverageSpectralError and RootMeanSquaredErrorUsingSlidingWindow to image package (#816)
• Added support for SpecificityAtSensitivity Metric (#1432)
• Added support for plotting of metrics through .plot() method (#1328, #1481, #1480, #1490, #1581, #1585, #1593, #1600, #1605, #1610, #1609, #1621, #1624, #1623, #1638, #1631, #1650, #1639, #1660, #1682, #1786)
• Added support for plotting of audio metrics through .plot() method (#1434)
• Added classes to output from MAP metric (#1419)
• Added Binary group fairness metrics to classification package (#1404)
• Added MinkowskiDistance to regression package (#1362)
• Added pairwise_minkowski_distance to pairwise package (#1362)
• Added new detection metric PanopticQuality (#929, #1527)
• Added PSNRB metric (#1421)
• Added ClassificationTask Enum and use in metrics (#1479)
• Added ignore_index option to exact_match metric (#1540)
• Add parameter top_k to RetrievalMAP (#1501)
• Added support for deterministic evaluation on GPU for metrics that uses torch.cumsum operator (#1499)
• Added support for plotting of aggregation metrics through .plot() method (#1485)
• Added support for python 3.11 (#1612)
• Added support for auto clamping of input for metrics that uses the data_range (#1606)
• Added ModifiedPanopticQuality metric to detection package (#1627)

... (truncated)

Changelog

Sourced from torchmetrics's changelog.

[1.0.0] - 2022-07-04

Added

• Added prefix and postfix arguments to ClasswiseWrapper (#1866)
• Added speech-to-reverberation modulation energy ratio (SRMR) metric (#1792, #1872)
• Added new global arg compute_with_cache to control caching behaviour after compute method (#1754)
• Added ComplexScaleInvariantSignalNoiseRatio for audio package (#1785)
• Added Running wrapper for calculate running statistics (#1752)
• AddedRelativeAverageSpectralError and RootMeanSquaredErrorUsingSlidingWindow to image package (#816)
• Added support for SpecificityAtSensitivity Metric (#1432)
• Added support for plotting of metrics through .plot() method ( #1328, #1481, #1480, #1490, #1581, #1585, #1593, #1600, #1605, #1610, #1609, #1621, #1624, #1623, #1638, #1631, #1650, #1639, #1660, #1682, #1786, )
• Added support for plotting of audio metrics through .plot() method (#1434)
• Added classes to output from MAP metric (#1419)
• Added Binary group fairness metrics to classification package (#1404)
• Added MinkowskiDistance to regression package (#1362)
• Added pairwise_minkowski_distance to pairwise package (#1362)
• Added new detection metric PanopticQuality ( #929, #1527, )
• Added PSNRB metric (#1421)
• Added ClassificationTask Enum and use in metrics (#1479)
• Added ignore_index option to exact_match metric (#1540)
• Add parameter top_k to RetrievalMAP (#1501)
• Added support for deterministic evaluation on GPU for metrics that uses torch.cumsum operator (#1499)
• Added support for plotting of aggregation metrics through .plot() method (#1485)
• Added support for python 3.11 (#1612)

... (truncated)

Commits

• ef1af35 releasing 1.0.0
• c0832d5 docs: fetch external S3 resources (#1880)
• 62b7d97 typing n/m (#1879)
• 1bbda8d build(deps): update scikit-learn requirement from <1.2.3,>=1.1.1 to >=1.1.1,<...
• 4cb215d [pre-commit.ci] pre-commit suggestions (#1877)
• d9f7c35 Implement pre and postfix for Classwise Wrapper (#1866)
• 15cf3a4 build(deps): update lightning-utilities requirement from <0.9.0,>=0.7.0 to >=...
• 8c5fab8 Debug CI plot (#1878)
• 13f85d0 build(deps): update pandas requirement from <=2.0.2,>=1.4.0 to >=1.4.0,<=2.0....
• ba34076 SRMR: update note and change default parameters (#1872)
• Additional commits viewable in compare view

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[adamjstewart]

Lightning-AI/torchmetrics#1252 (comment)