I want to train my neural network on sparse tensors that I’ve made by using:
pytorch sparse tensors
But while trying to propagate my sparse tensors into the CNN I get this error:
RuntimeError: Input type (torch.cuda.sparse.FloatTensor) and weight type (torch.cuda.FloatTensor) should be the same
What should I change in my network in order to be able to train on sparse tensors?
I read and learn a lot from your comments here @ptrblck, maybe you could help me here?
We currently don’t have support for convolutions for sparse inputs. Could I kindly ask you to open a feature request on https://github.com/pytorch/pytorch with your intended use case (model, application area, dataset)? Also, please include details on the intended semantics if you want the input to be treated differently if it has a sparse layout vs. a regular strided layout.
the link to the issue I opened on github is here:
link to the issue on github
Thank you very much