feat: Kornia GPU augmentation backend for detection training

[Borda]

What does this PR do?

• Add augmentation_backend field to TrainConfig (cpu/auto/gpu); cpu is the default
• New src/rfdetr/datasets/kornia_transforms.py: registry of 8 transform factories, build_kornia_pipeline, build_normalize, collate_boxes/unpack_boxes box utilities
• Wire gpu_postprocess flag through coco.py and yolo.py so CPU Albumentations augmentation and normalize are skipped when GPU path is active
• Add _setup_kornia_pipeline + on_after_batch_transfer to RFDETRDataModule; segmentation models skip GPU aug (phase 2) with a one-time warning
• Add kornia>=0.7,<1 optional dep group in pyproject.toml
• 12 new tests across test_module_data.py and test_kornia_transforms.py

Closes #862

Type of Change

• New feature (non-breaking change that adds functionality)

Testing

• I have tested this change locally
• I have added/updated tests for this change

Additional Context

[Copilot]

Pull request overview

Adds an opt-in GPU-side augmentation path for detection training by introducing an augmentation_backend switch and routing normalization/augmentation to run after the batch is transferred to device (via RFDETRDataModule.on_after_batch_transfer), while keeping the existing CPU Albumentations pipeline as the default.

Changes:

• Add TrainConfig.augmentation_backend ("cpu" | "auto" | "gpu") and a new optional dependency group kornia.
• Thread a gpu_postprocess flag through COCO/YOLO dataset builders so CPU Albumentations + Normalize can be skipped when GPU postprocessing is active.
• Add DataModule logic + tests for backend resolution and the on_after_batch_transfer hook.

Reviewed changes

Copilot reviewed 9 out of 9 changed files in this pull request and generated 8 comments.

Show a summary per file

File	Description
src/rfdetr/training/module_data.py	Adds Kornia pipeline setup and on_after_batch_transfer GPU postprocessing hook.
src/rfdetr/datasets/coco.py	Adds gpu_postprocess option to training transforms and wires backend flag through dataset builders.
src/rfdetr/datasets/yolo.py	Wires backend flag through Roboflow-from-YOLO builder.
src/rfdetr/config.py	Introduces augmentation_backend on TrainConfig.
src/rfdetr/datasets/aug_config.py	Documents Kornia GPU backend and Phase 1 limitations.
pyproject.toml	Adds optional dependency group kornia.
tests/training/test_module_data.py	Adds tests for backend resolution and on_after_batch_transfer.
tests/training/conftest.py	Adds autouse fixture to restore RFDETRDataModule.trainer property after tests.
CHANGELOG.md	Documents the new augmentation_backend feature.

Comments suppressed due to low confidence (1)

src/rfdetr/datasets/coco.py:356

• The make_coco_transforms() docstring and Args list no longer match behavior now that gpu_postprocess can skip Albumentations and Normalize() for the train split. Please document the new gpu_postprocess parameter and clarify that normalization is deferred to the DataModule GPU path when it’s enabled.

    """Build the standard COCO transform pipeline for a given dataset split.

    Returns a composed transform that resizes images to the target ``resolution``
    (with optional multi-scale jitter), applies Albumentations-based augmentations
    during training, and normalises pixel values with ImageNet statistics.

    For the ``"train"`` split the pipeline uses a two-branch ``OneOf`` between a
    direct resize and a resize → random-crop → resize sequence (built via
    :func:`_build_train_resize_config`), followed by the augmentation stack and
    normalisation.  For ``"val"``, ``"test"``, and ``"val_speed"`` only resize and
    normalisation are applied — no augmentation.

[codecov]

Codecov Report

❌ Patch coverage is 72.92576% with 62 lines in your changes missing coverage. Please review.
✅ Project coverage is 77%. Comparing base (fb3b771) to head (2aa18f5).
⚠️ Report is 14 commits behind head on develop.

❌ Your patch check has failed because the patch coverage (73%) is below the target coverage (95%). You can increase the patch coverage or adjust the target coverage.
❌ Your project check has failed because the head coverage (77%) is below the target coverage (95%). You can increase the head coverage or adjust the target coverage.

Additional details and impacted files

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##           develop   #874    +/-   ##
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  Coverage       77%    77%            
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  Files           97     98     +1     
  Lines         7593   7817   +224     
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+ Hits          5856   6029   +173     
- Misses        1737   1788    +51     

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[Copilot]

Pull request overview

Copilot reviewed 13 out of 13 changed files in this pull request and generated 6 comments.