I am in the process of porting a few PyTorch CPU generative models to GPU, and converting the import dataset from MNIST float to one hot. I am wondering what issues I am going to run up against in terms of best practices for porting such as different loss functions for integer valued data etc.
Also, I noticed that PyTorch doesn’t seem to natively support one hot encoding and sparse inputs yet, so I think I might run into trouble with GPU memory (I have 6GB but this dataset is huge due to one hot encoding). From my research it looks like the scatter_() function or the embedding module is suggested for this.
Any thoughts on the best way to proceed?
Thank you in advance!