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Improving the transforms

See original GitHub issue

The most recent implementation of transforms asks for an OrderedDict of callables or mne.Raw/Epochs methods that are called successively on the internal Raw/Epochs objects in a ConcatDataset ( I wanted to discuss possible improvements to this design:

  1. The current design requires the callables or methods to act in-place, i.e., they must directly modify the Raw/Epochs object. However, apart from .resample and .filter, most mne methods do not seem to act in-place. As for callables, they can be made to modify the objects in-place, but this would mean all the transforms would have to be mne object-aware. I don’t have a specific solution in mind, but maybe we could discuss this here.
  2. There is currently no way to apply a transform on-the-fly, i.e., every time a window is about to be returned. This is central to preprocessing steps in a lazy loading scenario however. The design we had started to implement during the sprint used Transform objects that were saved in the dataset object, and called inside __getitem__. This is how torch-vision does it for instance.

What are your thoughts @gemeinl @sbbrandt ?

Issue Analytics

  • State:closed
  • Created 4 years ago
  • Comments:11 (6 by maintainers)

github_iconTop GitHub Comments

robintiborcommented, Mar 2, 2020

I will have a try with that right now actually since it is giving unexpected results with dict. we can still see which interface we prefer after.

robintiborcommented, Mar 2, 2020

oki, now implemented in #94

Read more comments on GitHub >

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