update metric mAP@k for image retrieval

[zzc98]

Motivation

Support mAP@k in image retrieval

Use cases

1. Use in the code

>>> import torch
>>> import numpy as np
>>> from mmcls.evaluation import RetrievalAveragePrecision

# using index format inputs
>>> pred = [ torch.Tensor([idx for idx in range(100)]) ] * 3
>>> target = [[0, 3, 6, 8, 35], [1, 2, 54, 105], [2, 42, 205]]
>>> RetrievalAveragePrecision.calculate(pred, target, 10, True, True)
29.246031746031747

# using tensor format inputs
>>> pred = np.array([np.linspace(0.95, 0.05, 10)] * 2)
>>> target = torch.Tensor([[1, 0, 1, 0, 0, 1, 0, 0, 1, 1]] * 2)
>>> RetrievalAveragePrecision.calculate(pred, target, 10)
62.222222222222214

2. use in configs

val_evaluator = dict(type='RetrievalAveragePrecision', topk=100)
test_evaluator = val_evaluator

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

Base: 0.02% // Head: 86.82% // Increases project coverage by +86.80% 🎉

Coverage data is based on head (d2c51c6) compared to base (b8b31e9).
Patch has no changes to coverable lines.

❗ Current head d2c51c6 differs from pull request most recent head e6d49dc. Consider uploading reports for the commit e6d49dc to get more accurate results

Additional details and impacted files

@@             Coverage Diff              @@
##           dev-1.x     #980       +/-   ##
============================================
+ Coverage     0.02%   86.82%   +86.80%     
============================================
  Files          121      169       +48     
  Lines         8217    14016     +5799     
  Branches      1368     2227      +859     
============================================
+ Hits             2    12170    +12168     
+ Misses        8215     1462     -6753     
- Partials         0      384      +384     

Flag	Coverage Δ
unittests	86.82% <ø> (+86.80%)	⬆️

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

Impacted Files	Coverage Δ
mmcls/datasets/transforms/compose.py
mmcls/models/utils/norm.py	80.00% <0.00%> (ø)
mmcls/evaluation/metrics/retrieval.py	86.61% <0.00%> (ø)
mmcls/models/backbones/xcit.py	82.78% <0.00%> (ø)
mmcls/datasets/multi_task.py	74.46% <0.00%> (ø)
mmcls/models/classifiers/hugging_face.py	25.64% <0.00%> (ø)
mmcls/utils/analyze.py	100.00% <0.00%> (ø)
mmcls/models/backbones/mvit.py	92.46% <0.00%> (ø)
mmcls/models/retrievers/__init__.py	100.00% <0.00%> (ø)
mmcls/evaluation/metrics/multi_task.py	94.59% <0.00%> (ø)
... and 160 more

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[Ezra-Yu]

LGTM.