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Is it necessary for custom transforms to format to tensors (pytorch) in the transform itself? .set_transform(transform) removes the previous useful set.format("torch").
From the example in the docs it seems like one needs to format to tensor themselves in the transform.
Could I use map without caching instead?
Is it possible to somehow reuse TensorFormatter that already provides the formatting to torch?
Another option would be to wrap the dataset in a torch dataset class?
What would be the suggested way of doing transforms?
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Is it necessary for custom transforms to format to tensors (pytorch) in the transform itself?
.set_transform(transform)
removes the previous usefulset.format("torch")
.From the example in the docs it seems like one needs to format to tensor themselves in the transform.
Could I use map without caching instead?
Is it possible to somehow reuse
TensorFormatter
that already provides the formatting to torch?Another option would be to wrap the dataset in a torch dataset class?
What would be the suggested way of doing transforms?
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