I have a dataset consisting of gray-scale images of 16-bit pixel values. PyTorch requires the input to be a float before feeding into the model. I want to know which floating-point precision should I use to not lose any information from the images. So far, I have been using inputs.float() which casts the inputs to torch.float32. Further, does it have any effect on the training time using a higher precision float like torch.double().