Main feature differences between pytorch-meta-dataset and original meta-dataset?

[patricks-lab]

As I am currently using your great library extensively - I was wondering, what are the main feature differences between your dataloader and the one provided by the original meta-dataset authors (https://github.com/google-research/meta-dataset)?

I was wondering because I just recently saw that in the original meta-dataset library, the end of this notebook (https://github.com/google-research/meta-dataset/blob/main/Intro_to_Metadataset.ipynb) describes how to integrate their dataloader with PyTorch, such as an epsiodic dataloader that supports fixed ways, support-shot and query-shot. They also have a batch dataloader that samples batches from the datasets in a "non-episodic manner".

These features seem quite similar to your PyTorch meta-dataset wrapper, so I was wondering what was the main motivation of creating your PyTorch wrapper library. One thing I did notice is that they state that "If we want to use fixed num_ways... We advise using single dataset for using this feature". I assume your dataset supports this unlike the original meta-dataset library, since I have been using fixed ways with multiple data sources with no issue. Are there other major feature differences between your library and the original meta-dataset library?

Thanks again for providing a great tool!
Patrick