Refactor: typing

[Borda]

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What does this PR do?

Fixes # (issue).

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[codecov]

Codecov Report

Merging #330 (9e1df81) into master (4f7928e) will decrease coverage by 0.04%.
The diff coverage is 93.51%.

@@            Coverage Diff             @@
##           master     #330      +/-   ##
==========================================
- Coverage   96.59%   96.54%   -0.05%     
==========================================
  Files         111      111              
  Lines        3552     3559       +7     
==========================================
+ Hits         3431     3436       +5     
- Misses        121      123       +2     

Flag	Coverage Δ
Linux	75.86% <79.62%> (+0.04%)	⬆️
Windows	75.86% <79.62%> (+0.04%)	⬆️
cpu	96.48% <93.51%> (-0.05%)	⬇️
gpu	96.45% <93.51%> (-0.05%)	⬇️
macOS	96.48% <93.51%> (-0.05%)	⬇️
pytest	96.54% <93.51%> (-0.05%)	⬇️
python3.6	95.61% <87.03%> (-0.05%)	⬇️
python3.8	96.48% <93.51%> (-0.05%)	⬇️
python3.9	96.37% <93.51%> (-0.05%)	⬇️
torch1.3.1	95.61% <87.03%> (-0.05%)	⬇️
torch1.4.0	95.70% <87.03%> (-0.05%)	⬇️
torch1.9.0	96.37% <93.51%> (-0.05%)	⬇️

Flags with carried forward coverage won't be shown. Click here to find out more.

Impacted Files	Coverage Δ
...ics/functional/classification/average_precision.py	100.00% <ø> (ø)
torchmetrics/functional/classification/iou.py	100.00% <ø> (ø)
...etrics/functional/regression/explained_variance.py	100.00% <ø> (ø)
torchmetrics/functional/regression/pearson.py	96.00% <ø> (ø)
torchmetrics/regression/r2score.py	93.54% <ø> (ø)
torchmetrics/functional/classification/dice.py	92.59% <80.00%> (ø)
torchmetrics/functional/classification/auroc.py	85.50% <81.81%> (-1.06%)	⬇️
...unctional/classification/precision_recall_curve.py	90.66% <92.00%> (-0.77%)	⬇️
torchmetrics/functional/classification/roc.py	92.68% <93.10%> (-2.06%)	⬇️
torchmetrics/functional/classification/accuracy.py	94.28% <100.00%> (ø)
... and 13 more

---

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[pep8speaks]

Hello @Borda! Thanks for updating this PR.

There are currently no PEP 8 issues detected in this Pull Request. Cheers! 🍻

Comment last updated at 2021-06-30 07:27:56 UTC

[Borda]

@SkafteNicki @justusschock any idea why the differentiability tests are failing? Cannot reproduce it locally 😕

[SkafteNicki]

Few commets, else LGTM :]

[SkafteNicki]

This does not seem correct to me. Even when setting multioutput='raw_values it is still a single tensor that is returned

[Borda]

we return the Tensor now or am I missing anything?