Merge load / resize / cache to optimize data loading efficiency for classification

[goodsong81]

Summary

[Changes]

• Implement base LoadResizeDataFromOTXDataset to merge & wrap
  • LoadImageFromOTXDataset
  • (Optional) LoadAnnotationFromOTXDataset
  • (Optional) Resize
• Enable in-memory cache after resize for memory efficiency
• Implement ResizeTo to run Resize only if the expected size is different from current size
• Apply to classification (other tasks will be handled in upcoming PRs)
• Apply to configurable input size

[Results]

Branch	Size	Cache	Data time	Iter time	E2E time
develop	224x224	off	0.0997	0.1670	15.94
	224x224	35M/2G	0.0304	0.0970	13.29
	64x64	off	0.0897	0.1349	13.21
	64x64	35M/2G	0.0164	0.0624	10.61
feat/load-resize-cache	224x224	off	0.1044	0.1745	13.07
	224x224	18M/2G	0.0304	0.0983	11.06
	64x64	off	0.0881	0.1348	12.28
	64x64	9M/2G	0.0176	0.0636	10.03

How to test

[Example CMD]

otx train src/otx/algorithms/classification/configs/efficientnet_b0_cls_incr/template.yaml --train-data-roots ./data/CUB_200_2011_64/train --val-data-roots ./data/CUB_200_2011_64/val --workspace outputs/load-resize-cls//effb0_CUB_200_2011_64_64x64_cache2g params --learning_parameters.input_size 64x64 --algo_backend.mem_cache_size 2000000000

Checklist

• I have added unit tests to cover my changes.​
• I have added integration tests to cover my changes.​
• I have added e2e tests for validation.
• I have added the description of my changes into CHANGELOG in my target branch (e.g., CHANGELOG in develop).​
• I have updated the documentation in my target branch accordingly (e.g., documentation in develop).
• I have linked related issues.

License

• I submit my code changes under the same Apache License that covers the project.
  Feel free to contact the maintainers if that's a concern.
• I have updated the license header for each file (see an example below).

# Copyright (C) 2023 Intel Corporation
# SPDX-License-Identifier: Apache-2.0

[eunwoosh]

Thanks for your work! LGTM but I have some questions. please take a look.