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The cornucopia package provides a generic framework for preprocessing, augmentation, and domain randomization; along with an abundance of specific layers, mostly targeted at (medical) imaging. cornucopia is written using a PyTorch backend, and therefore runs on the CPU or GPU.

Cornucopia is intended to be used on the GPU for on-line augmentation. A quick benchmark of affine and elastic augmentation shows that while cornucopia is slower than TorchIO on the CPU (~ 3s vs 1s), it is greatly accelerated on the GPU (~ 50ms).

Since gradients are not expected to backpropagate through its layers, it can theoretically be used within any dataloader pipeline, independent of the downstream learning framework (pytorch, tensorflow, jax, ...).

Other augmentation packages

There are other great, and much more mature, augmentation packages out-there (although few run on the GPU). Here's a non-exhaustive list:

• MONAI
• TorchIO
• Albumentations (2D only)
• Volumentations (3D extension of Albumentations)

Contributions

If you find this project useful and wish to contribute, please reach out!