[Enhance] Imporve efficiency of precision, recall, f1_score and support.

[mzr1996]

Motivation

Please describe the motivation of this PR and the goal you want to achieve through this PR.

Modification

Test on data shape: (50000, 1000), which is the validation set size of ImageNet.

metric	original speed (ms)	new speed (ms)
accuracy	76.7	17.9
precision / recall / f1_score	3770	201
support	531	15.5

BC-breaking

The return value of accuracy is changed to float | list[float] | list[list[float]], which is unified with other metrics. And according to the docstring of accuracy, it should be float, but the original return value is wrong.

Checklist

Before PR:

• Pre-commit or other linting tools are used to fix the potential lint issues.
• Bug fixes are fully covered by unit tests, the case that causes the bug should be added in the unit tests.
• The modification is covered by complete unit tests. If not, please add more unit test to ensure the correctness.
• The documentation has been modified accordingly, like docstring or example tutorials.

After PR:

• If the modification has potential influence on downstream or other related projects, this PR should be tested with those projects, like MMDet or MMSeg.
• CLA has been signed and all committers have signed the CLA in this PR.

[codecov]

Codecov Report

Merging #595 (ff1716e) into master (188aa6e) will increase coverage by 0.02%.
The diff coverage is 89.47%.

@@            Coverage Diff             @@
##           master     #595      +/-   ##
==========================================
+ Coverage   80.07%   80.10%   +0.02%     
==========================================
  Files         111      111              
  Lines        6450     6458       +8     
  Branches     1108     1106       -2     
==========================================
+ Hits         5165     5173       +8     
+ Misses       1149     1148       -1     
- Partials      136      137       +1     

Flag	Coverage Δ
unittests	80.10% <89.47%> (+0.02%)	⬆️

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Impacted Files	Coverage Δ
mmcls/core/evaluation/eval_metrics.py	82.35% <86.20%> (+1.58%)	⬆️
mmcls/datasets/base_dataset.py	93.18% <100.00%> (ø)
mmcls/models/losses/accuracy.py	91.78% <100.00%> (+0.11%)	⬆️

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[0x4f5da2]

LGTM