[WIP] Upgrade Augmentations Pipeline in OTX

[kprokofi]

Summary

resolves #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