[Feature] Add Switch Recipe Hook

[Ezra-Yu]

Motivation

Add Switch Recipe Hook

some papers could use this hook:
efficientnetv2
mobileone
Data Augmentation Revisited: Rethinking the Distribution Gap between Clean and Augmented Data

Modification

This PR is migrated from #729 of 0.x branch.

Use cases (Optional)

 base_train_pipeline = [                                                                                                                                                                           
     dict(type='LoadImageFromFile'),                                                                                                                                                               
     dict(type='RandomResizedCrop', scale=224, backend='pillow'),                                                                                                                                  
     dict(type='RandomFlip', prob=0.5, direction='horizontal'),                                                                                                                                    
     dict(type='PackClsInputs')                                                                                                                                                                    
 ]                                                                                                                                                                                                 
                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        
 import copy                                                                                                                                                                                       
 # modify 37 epoch's RandomResizedCrop.scale to 192                                                                                                                                                 
 train_pipeline_37e = copy.deepcopy(base_train_pipeline)                                                                                                                                           
 train_pipeline_37e[1]['scale'] = 192                                                                                                                                                              
                                                                                                                                                                                                                                                                                                                               
 custom_hooks = [                                                                                                                                                                                  
     dict(                                                                                                                                                                                         
         type='SwitchRecipeHook',                                                                                                                                                                 
         schedule=[
             dict(
                 action_epoch=30,
                 train_pipeline=pipeline_after_37e,
                 batch_augments=batch_augments_after_37e,
                 loss=loss_after_37e,),
             dict(
                 action_epoch=60,
                 # Disable batch augmentations after 112e
                 # and keep other settings.
                 batch_augments=None,),   
          ]
     )                                                                                                                                                                                                                                                                                                                 
 ] 

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.

[codecov]

Codecov Report

Base: 0.02% // Head: 89.08% // Increases project coverage by +89.06% 🎉

Coverage data is based on head (54f73a0) compared to base (b8b31e9).
Patch has no changes to coverable lines.

Additional details and impacted files

@@             Coverage Diff              @@
##           dev-1.x    #1101       +/-   ##
============================================
+ Coverage     0.02%   89.08%   +89.06%     
============================================
  Files          121      143       +22     
  Lines         8217    10885     +2668     
  Branches      1368     1731      +363     
============================================
+ Hits             2     9697     +9695     
+ Misses        8215      947     -7268     
- Partials         0      241      +241     

Flag	Coverage Δ
unittests	89.08% <ø> (+89.06%)	⬆️

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/transforms/compose.py
mmcls/models/retrievers/__init__.py	100.00% <0.00%> (ø)
mmcls/models/retrievers/image2image.py	92.38% <0.00%> (ø)
mmcls/models/classifiers/hugging_face.py	25.33% <0.00%> (ø)
mmcls/models/utils/layer_scale.py	86.66% <0.00%> (ø)
mmcls/models/backbones/mvit.py	92.46% <0.00%> (ø)
mmcls/models/backbones/edgenext.py	95.20% <0.00%> (ø)
mmcls/models/classifiers/timm.py	25.67% <0.00%> (ø)
mmcls/models/backbones/deit3.py	94.52% <0.00%> (ø)
... and 133 more

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