[Feature] Support NoCap dataset based on BLIP.

[wangbo-zhao]

Support NoCap dataset based on BLIP.

Motivation

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Modification

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BC-breaking (Optional)

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Use cases (Optional)

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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

Patch coverage: 13.91% and project coverage change: -1.05 ⚠️

Comparison is base (f9dcae2) 68.16% compared to head (89c1dfe) 67.12%.

❗ Current head 89c1dfe differs from pull request most recent head 234659b. Consider uploading reports for the commit 234659b to get more accurate results

Additional details and impacted files

@@            Coverage Diff             @@
##              dev    #1582      +/-   ##
==========================================
- Coverage   68.16%   67.12%   -1.05%     
==========================================
  Files         295      303       +8     
  Lines       23372    23854     +482     
  Branches     3713     3777      +64     
==========================================
+ Hits        15932    16012      +80     
- Misses       6880     7279     +399     
- Partials      560      563       +3     

Flag	Coverage Δ
unittests	67.12% <13.91%> (-1.05%)	⬇️

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

Impacted Files	Coverage Δ
mmpretrain/datasets/__init__.py	70.27% <0.00%> (-4.02%)	⬇️
mmpretrain/datasets/gqa_dataset.py	0.00% <0.00%> (ø)
mmpretrain/datasets/nocaps.py	0.00% <0.00%> (ø)
mmpretrain/datasets/scienceqa.py	0.00% <ø> (ø)
mmpretrain/models/multimodal/__init__.py	40.00% <0.00%> (-4.45%)	⬇️
...retrain/models/multimodal/chinese_clip/__init__.py	0.00% <0.00%> (ø)
mmpretrain/models/multimodal/chinese_clip/bert.py	0.00% <0.00%> (ø)
...ain/models/multimodal/chinese_clip/chinese_clip.py	0.00% <0.00%> (ø)
mmpretrain/models/multimodal/chinese_clip/utils.py	0.00% <0.00%> (ø)
mmpretrain/models/utils/__init__.py	83.33% <0.00%> (ø)
... and 11 more

... and 1 file with indirect coverage changes

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[wangbo-zhao]

I run this and it achieves expected performance.