Super-gradients

What's new?
- Eval recipe- perform validation by recipe name.
- Supported strings class for convenient autocomplete in IDE's.
- Registry: metrics, dataloaders, models, losses (so they can be passed as strings when using train_from_config).
- Return model, results from train
- Load backbone fix https://github.com/Deci-AI/super-gradients/pull/408/files
- PPLiteSeg training recipes.
- AWS env check removal
- New resolvers support: "+" "if" for yaml recipes (i.e Hydra resolvers).
- Support for custom STDC.
- ShelfNet "classes_num" -> "num_classes" bug fix.
- Improve cross-platform compatibility when parsing a readme description.
- Added the ability to download and import external code for models from ADK.

- DatasetInterface class removal- refactored as torch.DataLoader objects configured by src/training/recipes, using super_gradients.dataloaders.get() ([see new updated tutorials and snippets)](https://github.com/Deci-AI/super-gradients#readme).

- Trainer.build_model() removal- models initialisation refactored with super_gradients.models.get() ([see updated tutorials and notebooks).](https://github.com/Deci-AI/super-gradients#readme)

- Coded DDP launch (no need for python -m torch.distributed.launch ...), see new snippets [here](https://github.com/Deci-AI/super-gradients#using-ddp) .

- Updated notebooks, tutorials and code snippets in [readme.md](https://github.com/Deci-AI/super-gradients#readme).

- Extract recipes training hyper_params config with super_gradients.training_hyperparams.get() ([see updated tutorials and notebooks](https://github.com/Deci-AI/super-gradients#readme)).

- Simplfied resume- now passed through train_params in SgTrainer.train() (see updated snippets in [readme.md](https://github.com/Deci-AI/super-gradients#readme)).

- Removal of "loss_loggging_items_names" from train_params in Trainer.train().

- Trainer.__init__ old, unnecessary args removed.

- Add support for getting models from Deci's platform using super_gradients.models.get(), more info regarding Deci's platform in [readme.md](https://github.com/Deci-AI/super-gradients#readme).

This GitHub Release was done automatically by CircleCI

This GitHub Release was done automatically by CircleCI

This GitHub Release was done automatically by CircleCI

- YoloX architectures.
- SSDLite Mobilenet V2 COCO recipe
- QAT support with Nvidia's pytorch-quantization (optional dependency).
- COCO mAP calculation support in DDP (torch metric object, supports "crowd" labels).
- Pre_prediction_callback- support for input and targets manipulation right before forward pass + multiscaling pre_prediction_callbacks that work out of the box in DDP (classification and Object detection).
- Training stage switch callback to support multi-stage training.