Refactoring and Support New Methods and Documents (V0.2.7)

[Lupin1998]

Code refactoring:

1. Refactor openmixup.core (instead of openmixup.hooks) and openmixup.models.augments (contains mixup augmentation methods which are originally implemented in openmixup.models.utils). After code refactoring, the macro design of OpenMixup is similar to most projects of MMLab.
2. Support deployment of ONNX and TorchScript in openmixup.core.export and tools/deployment. We refactored the abstract class BaseModel (implemented in openmixup/models/classifiers/base_model.py) to support forward_inference (for custom inference and visualization). We also refactored openmixup.models.heads and openmixup.models.losses to support forward_inference. You can deploy the classification models in OpenMixup according to deployment tutorials.
3. Support testing API methods in openmixup/apis/test.py for evaluation and deployment of classification models.
4. Refactor openmixup.core.optimizers to separate optimizers and builders and support the latest Adan optimizer.

Supporting new features:

1. Support detailed usage instructions in README of config files for image classification methods in configs/classification, e.g., mixups on ImageNet. We will update READMEs of other methods in configs/selfsup and configs/semisup in a few weeks.
2. Refine the origianzation of README files according to README-Template.
3. Support the new mixup augmentation method (AlignMix) and provide the relevant config files in various datasets.

Updating documents:

1. Update documents of mixup benchmarks on ImageNet in Model_Zoo_sup.md. Update config files for supported mixup methods.
2. Update formats (figures, introductions and content tables) of awesome lists in Awesome Mixups and Awesome MIM and provide the latest methods.

[Lupin1998]

OpenMixup V0.2.7 is now released! We will fix reported bugs and update documents of V0.2.7 if new issues are raised.