is usually sufficient to increase the performance by 2x. Are there any barriers to incorporating such a change within membrain-seg?
I have been using this in my own membrain-seg wrappers, and it has worked very well. However, making jit work with on-the-fly Fourier cropping and rescaling could require more work than just this simple one-line change.
I noticed that membrain-seg does not currently use jit compilation. Using torch.compile (https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html) can be a simple way to accelerate the model performance for training and inference. Just wrapping
model_new = torch.compile(model)is usually sufficient to increase the performance by 2x. Are there any barriers to incorporating such a change within membrain-seg?
I have been using this in my own membrain-seg wrappers, and it has worked very well. However, making jit work with on-the-fly Fourier cropping and rescaling could require more work than just this simple one-line change.