[WIP] Upgrade Augmentations Pipeline in OTX

[kprokofi]

Summary

resolves https://github.com/open-edge-platform/training_extensions/issues/5020

• [ ] Provide batch augmentations support per model per task
• [ ] Change augmentation pipeline to hybrid mode (batch augmentation + torchvision.v2). Operate only tensors
• [ ] Remove all custom unnecessary augmentations
• [ ] Provide DataAugmentationFactory to support configurable augmentation both for dataset based and batch based. This solution should work with Geti templates.
• [ ] Provide benchmark results

How to test

Checklist

• [ ] The PR title and description are clear and descriptive
• [ ] I have manually tested the changes
• [ ] All changes are covered by automated tests
• [ ] All related issues are linked to this PR (if applicable)
• [ ] Documentation has been updated (if applicable)

[kprokofi]

First validation results:

Batch based augmentations + tensor only operations can improve iteration time almost 2x. The larger batch the larger improvements

Method	Task	Model	iter_time
Current	classification	efficientnet_b0	0.446
Proposed	classification	efficientnet_b0	0.225
Current	detection	YOLOX_X	0.369
Proposed	detection	YOLOX_X	0.180