Feature proposal: TorchIO hub - A system to store and fetch transform objects for reproducibility

🚀 Feature

Introducing a public TorchIO hub where researchers can save the transform object used to randomly sample and augment their data during training with one line of code - transform = tio.from_hub("cool_recent_paper")

Motivation

DL researchers and practitioners hoping to reproduce other people’s work can easily fetch model weights and architectural definitions (e.g Torch Hub or MONAI Bundle), training parameters (e.g AutoModel from HuggingFace) and preprocessing strategies (e.g AutoFeatureExtractor from HuggingFace) however, one thing which is still an obstacle in reproducing someone’s setup in a few lines of code is data augmentation. Libraries like Albumentations and TorchIO provide a variety of common data augmentation strategies - but they lack the Hub features of HF or Pytorch to easily store and fetch strategies.

Pitch

Not sure how you would implement this. As an MVP you could have a separate repo where users submit model transforms as code and a big dictionary lookup between some chosen string and their transforms.

[fepegar]

You mean something like this?

@classmethod
def from_hub(cls, *args, **kwargs):
    return torch.hub.load(*args, **kwargs)

[tomvars]

I really like this API! You could maybe create a new repo like fepegar/torchiohub:main and have a single hubconf.py file as the access point to different preprocessing functions. In the repo users could append their transform functions to a large transforms.py file and the hubconf.py would have lines such as from transforms import ronneberger_unet_2015_transform