[Feature] Add clip backbone

[Scarecrow0]

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Motivation

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

Modification

Add the CLIP backbone for inference time of Imagenet classification.

BC-breaking (Optional)

Does the modification introduce changes that break the backward compatibility of the downstream repositories?
If so, please describe how it breaks the compatibility and how the downstream projects should modify their code to keep compatibility with this PR.

Use cases (Optional)

If this PR introduces a new feature, it is better to list some use cases here and update the documentation.

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.

[CLAassistant]

All committers have signed the CLA.

[tonysy]

The configs/clip/vit_base_224_dataproc.py and other similar files should be moved to configs/_base_/datasets, please follow the filename convention like imagenet_bs64_clip_224.py

[tonysy]

As inline comments

[mzr1996]

Please rename the model name according to https://mmclassification.readthedocs.io/en/dev-1.x/advanced_guides/convention.html
For example:
clip-vit-base-p16_openai-pre_3rdparty_in1k
clip-vit-base-p16_laion2b-in12k-pre_3rdparty_in1k-384px

[Scarecrow0]

Done! Thanks for the help.

[Scarecrow0]

As inline comments

Changes are applied.

[codecov]

Codecov Report

Base: 0.02% // Head: 88.65% // Increases project coverage by +88.62% 🎉

Coverage data is based on head (148f77a) compared to base (b8b31e9).
Patch has no changes to coverable lines.

❗ Current head 148f77a differs from pull request most recent head 063ef99. Consider uploading reports for the commit 063ef99 to get more accurate results

Additional details and impacted files

@@             Coverage Diff              @@
##           dev-1.x    #1258       +/-   ##
============================================
+ Coverage     0.02%   88.65%   +88.62%     
============================================
  Files          121      152       +31     
  Lines         8217    12127     +3910     
  Branches      1368     1931      +563     
============================================
+ Hits             2    10751    +10749     
+ Misses        8215     1064     -7151     
- Partials         0      312      +312     

Flag	Coverage Δ
unittests	88.65% <ø> (+88.62%)	⬆️

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/backbones/deit3.py	94.52% <0.00%> (ø)
mmcls/models/backbones/revvit.py	64.39% <0.00%> (ø)
mmcls/models/backbones/replknet.py	93.00% <0.00%> (ø)
mmcls/engine/optimizers/adan_t.py	10.60% <0.00%> (ø)
mmcls/models/backbones/edgenext.py	95.20% <0.00%> (ø)
mmcls/models/backbones/mobileone.py	94.47% <0.00%> (ø)
mmcls/engine/hooks/ema_hook.py	81.69% <0.00%> (ø)
mmcls/models/backbones/davit.py	84.94% <0.00%> (ø)
mmcls/engine/hooks/margin_head_hooks.py	92.59% <0.00%> (ø)
... and 143 more

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[Scarecrow0]

Request for further review. @mzr1996

The directory of converted checkpoints for CLIP model: /mnt/petrelfs/share_data/lirongjie/clip_backbone_ckpt

[tonysy]

Please upadte the files according to zerun's comments

[openmmlab-bot]

Dear Scarecrow0 ,
First of all, we want to express our gratitude for your significant PR in the MMCls project. Your contribution is highly appreciated, and we are grateful for your efforts in helping improve this open-source project during your personal time. We believe that many developers will benefit from your PR.
We are looking forward to continuing our collaboration with you. OpenMMLab has established a special contributors' organization called MMSIG, which provides contributors with open-source certificates, a recognition system, and exclusive rewards. You can contact us by adding our WeChat（if you have WeChat): openmmlabwx, or join in our discord：https://discord.gg/raweFPmdzG. We sincerely hope you will join us!
Best regards！ @Scarecrow0