build(deps): bump torchmetrics from 0.9.2 to 1.0.1

[dependabot]

Bumps torchmetrics from 0.9.2 to 1.0.1.

Release notes

Sourced from torchmetrics's releases.

Weekly patch release

[1.0.1] - 2022-07-13

Fixed

• Fixes corner case when using MetricCollection together with aggregation metrics (#1896)
• Fixed the use of max_fpr in AUROC metric when only one class is present (#1895)
• Fixed bug related to empty predictions for IntersectionOverUnion metric (#1892)
• Fixed bug related to MeanMetric and broadcasting of weights when Nans are present (#1898)
• Fixed bug related to expected input format of pycoco in MeanAveragePrecision (#1913)

Contributors

@​fansuregrin, @​SkafteNicki

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

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)

... (truncated)

Changelog

Sourced from torchmetrics's changelog.

[1.0.1] - 2022-07-13

Fixed

• Fixes corner case when using MetricCollection together with aggregation metrics (#1896)
• Fixed the use of max_fpr in AUROC metric when only one class is present (#1895)
• Fixed bug related to empty predictions for IntersectionOverUnion metric (#1892)
• Fixed bug related to MeanMetric and broadcasting of weights when Nans are present (#1898)
• Fixed bug related to expected input format of pycoco in MeanAveragePrecision (#1913)

[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)

... (truncated)

Commits

• 035e433 releasing 1.0.1
• f9b0c72 Fix expected box format by pycoco (#1913)
• 0b7d6c7 Bugfix for using metric collection and aggregation metric (#1896)
• a07544a pyupgrade: 3.8+ (#1912)
• 39361f3 Fix meanmetric broadcasting for Nan values (#1898)
• efcc32e build(deps): bump pytest-rerunfailures from 11.1.2 to 12.0 in /requirements (...
• 1f4cb01 build(deps): update matplotlib requirement from <=3.7.1,>=3.2.0 to >=3.2.0,<=...
• e7f15d6 Fix Auroc metric when max_fpr is set and a class is missing (#1895)
• 629e2eb Fix computing iou when there have empty predicted boxes (#1892)
• 2b499ff Hotfix for CI docs (#1894)
• Additional commits viewable in compare view

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[dependabot]

Superseded by #46.