Anything like transformer for TensorDataset?

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Up to now I’ve figure out two approaches to use a custom dataset(with the standard DataLodar).
The first is to use ImageFolder, somehow similar to creating a filelist and get access to data. Transformations such as randomcrop and scaling is convenient with that scheme,but I guess it is I/O-consuming.

dataset = trochvision.datasets.ImageFolder(root=‘main_dir’,transformer=torchvision.transform.Compose([…])

So the second scheme is to read in a bunch of data into the memory at a time into tensors:

dataset = torch.utils.data.TensorDataset(data_tensor,label_tensor)

Anything like the transformer in the first approach for TensorDataset？

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No, we don’t support that at the moment but it would make sense to add that. We’re going to be rewriting some transforms soon and we’ll do that then. Thanks for a suggestion!

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Is transform available for TensorDataset now？

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this looks like something you can integrate into.

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@apaszke any updates on adding transforms for TensorDataset?

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