[Enhancement] Get scores from inference api

[okotaku]

Motivation

Get pred_scores from inference api.

Modification

from mmcls.apis import inference_model, init_model
from mmcls.utils import register_all_modules

config_path = './configs/resnet/resnet50_8xb32_in1k.py'
checkpoint_path = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth' # can be a local path
img_path = 'demo/demo.JPEG'   # you can specify your own picture path

# register all modules and set mmcls as the default scope.
register_all_modules()
# build the model from a config file and a checkpoint file
model = init_model(config_path, checkpoint_path, device="cpu")  # device can be 'cuda:0'
# test a single image
result = inference_model(model, img_path)

result is a dictionary containing pred_label, pred_score, pred_scores and pred_class, the result is as follows:

{"pred_label":65,"pred_score":0.6649366617202759,"pred_class":"sea snake", "pred_scores": [..., 0.6649366617202759, ...]}

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: 91.54% // Increases project coverage by +91.52% 🎉

Coverage data is based on head (fbd78b2) compared to base (b8b31e9).
Patch coverage: 87.46% of modified lines in pull request are covered.

Additional details and impacted files

@@             Coverage Diff              @@
##           dev-1.x    #1070       +/-   ##
============================================
+ Coverage     0.02%   91.54%   +91.52%     
============================================
  Files          121      127        +6     
  Lines         8217     9450     +1233     
  Branches      1368     1485      +117     
============================================
+ Hits             2     8651     +8649     
+ Misses        8215      619     -7596     
- Partials         0      180      +180     

Flag	Coverage Δ
unittests	91.54% <87.46%> (+91.52%)	⬆️

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

Impacted Files	Coverage Δ
mmcls/apis/inference.py	0.00% <0.00%> (ø)
mmcls/datasets/multi_label.py	100.00% <ø> (+100.00%)	⬆️
mmcls/models/backbones/mvit.py	92.46% <ø> (ø)
mmcls/models/backbones/repvgg.py	96.84% <ø> (+96.84%)	⬆️
mmcls/models/backbones/res2net.py	95.50% <ø> (+95.50%)	⬆️
mmcls/models/backbones/resnet.py	100.00% <ø> (+100.00%)	⬆️
mmcls/models/backbones/shufflenet_v1.py	97.91% <ø> (+97.91%)	⬆️
mmcls/models/backbones/shufflenet_v2.py	96.63% <ø> (+96.63%)	⬆️
mmcls/models/backbones/swin_transformer.py	85.58% <ø> (+85.58%)	⬆️
mmcls/models/backbones/swin_transformer_v2.py	89.47% <ø> (ø)
... and 140 more

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[mzr1996]

Look good.