New Export API (#1318)

* Designing export API

* Export WIP

* ONNX NMS

* Export WIP

* Refactor test and move benchmark API to functino

* Export WIP

* Make the top_k a constant and not variable since TRT export does not work with dynamic top_k

* Refactor test and move benchmark API to functino

* Added option to change the output format

* Refactor test and move benchmark API to functino

* Added option to change the output format

* Refactor test and move benchmark API to functino

* Fixing export to make it TRT friendly

* Fixing export to make it TRT friendly

* Fixing export to make it TRT friendly

* Fixing export to make it TRT friendly

* Remove unused classes

* Remove unused classes

* Remove unused classes

* Remove unused classes

* Fixing export to FP16

* Fixing export to FP16

* Improve output of the benchmark result

* Improve device handling when exporting NMS

* Improve device handling when exporting NMS

* Fix nms format conversion modules export

* Revert unit test

* Improve model device handling

* Adding docs

* Adding docs

* Adding docs

* Adding docs

* Address TODO's after code review

* Added check whether model is already quantized

* Install pytorch quantization package

* Added printin of user-friendly description on how to use the exported model

* Update docs

* Update docs

* Uninstall SG

* Added onnx_graphsurgeon

* Added onnx_graphsurgeon

* Put extra index url at the top

* Put extra index url before the package that requires it

* Fix --index-url to --extra-index-url

* get_requirements to handle --extra-index-url correctly

* Made method draw_box_title public

* Fix tests

* Fix missing HasPredict for BaseClassifier model

* Make quantization parameters overridable

Makefile

@@ -14,3 +14,7 @@ recipe_accuracy_tests:
 	python src/super_gradients/train_from_recipe.py --config-name=cityscapes_regseg48          experiment_name=shortened_cityscapes_regseg48_iou_test   epochs=10 training_hyperparams.average_best_models=False multi_gpu=DDP num_gpus=4
 	python src/super_gradients/examples/convert_recipe_example/convert_recipe_example.py --config-name=cifar10_conversion_params experiment_name=shortened_cifar10_resnet_accuracy_test
 	coverage run --source=super_gradients -m unittest tests/deci_core_recipe_test_suite_runner.py
+
+
+examples_to_docs:
+	jupyter nbconvert --to markdown --output-dir="documentation/source/" --execute src/super_gradients/examples/model_export/models_export.ipynb