[CodeCamp2023-337] New Version of config Adapting ConvNeXt Algorithm

[limafang]

Adapting New Version of config Adapting ConvNeXt Algorithm, based on open-mmlab/OpenMMLabCamp#337

Motivation

MMEngine has introduced a novel configuration mechanism, utilizing which we will perform configuration file adaptation.

Modification

Add 1 config python files in mmpretrain/configs/base/datasets
update 1 config python files in mmpretrain/configs/convnext/
Add 12 config python files in mmpretrain/configs/convnext/

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.

[codecov]

Codecov Report

Patch coverage: 20.00% and project coverage change: -3.01% ⚠️

Comparison is base (f9dcae2) 68.16% compared to head (eff076e) 65.16%.
Report is 69 commits behind head on dev.

❗ Current head eff076e differs from pull request most recent head 92ab21e. Consider uploading reports for the commit 92ab21e to get more accurate results

Additional details and impacted files

@@            Coverage Diff             @@
##              dev    #1760      +/-   ##
==========================================
- Coverage   68.16%   65.16%   -3.01%     
==========================================
  Files         295      362      +67     
  Lines       23372    26181    +2809     
  Branches     3713     4165     +452     
==========================================
+ Hits        15932    17060    +1128     
- Misses       6880     8501    +1621     
- Partials      560      620      +60     

Flag	Coverage Δ
unittests	65.16% <20.00%> (-3.01%)	⬇️

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

Files Changed	Coverage Δ
configs/_base_/datasets/imagenet_bs128_mbv3.py	100.00% <ø> (ø)
configs/_base_/datasets/imagenet_bs32.py	100.00% <ø> (ø)
...onfigs/_base_/datasets/imagenet_bs32_pil_resize.py	100.00% <ø> (ø)
configs/_base_/datasets/imagenet_bs64_swin_224.py	100.00% <ø> (ø)
configs/_base_/datasets/imagenet_bs64_swin_384.py	100.00% <ø> (ø)
mmpretrain/apis/feature_extractor.py	37.50% <0.00%> (ø)
mmpretrain/apis/image_caption.py	30.64% <0.00%> (ø)
mmpretrain/apis/image_retrieval.py	21.42% <0.00%> (ø)
mmpretrain/apis/visual_grounding.py	27.53% <0.00%> (ø)
mmpretrain/apis/visual_question_answering.py	25.67% <0.00%> (ø)
... and 12 more

... and 90 files with indirect coverage changes

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[OpenMMLab-Assistant-004]

Hi @limafang,

We'd like to express our appreciation for your valuable contributions to the mmpretrain. Your efforts have significantly aided in enhancing the project's quality.
It is our pleasure to invite you to join our community thorugh Discord_Special Interest Group (SIG) channel. This is a great place to share your experiences, discuss ideas, and connect with other like-minded people. To become a part of the SIG channel, send a message to the moderator, OpenMMLab, briefly introduce yourself and mention your open-source contributions in the #introductions channel. Our team will gladly facilitate your entry. We eagerly await your presence. Please follow this link to join us: ​https://discord.gg/UjgXkPWNqA.

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Thanks again for your awesome contribution, and we're excited to have you as part of our community!