Hi, all. Now I get a gpu variable tensor from my network output, and I want to apply some operations from scipy on it, for example, dilate, erode and gaussian blur. What I want to know is that if I can finish the task as following:
gpu variable tensor -> cpu variable tensor -> cpu numpy array -> cpu numpy array after scipy operation -> cpu variable tensor -> gpu variable tenor. Will the above method work ？ Will above method influence the gradient? If it can’t achieve the goal, can you tell me some other ways to accomplish the request?
if you have a scipy path, you generally have to write an autograd.Function, because
- converting to numpy array has to be done with
x.data.numpy()which detaches the gradient path. - pytorch doesn’t know the gradient of a scipy function
See this page for an example:
http://pytorch.org/tutorials/advanced/numpy_extensions_tutorial.html