Functionality
A differentiable bilateral filter CUDA kernel for PyTorch. It takes a tensor of shape (N,C,H,W) and applies a bilateral filter to each channel in parallel. The algorithm is a brute force bilateral filter using a 5x5 window and zero padding. The sigma parameters for distance and intensity can be modified.
History
This was created in June 2018 with PyTorch 0.4 for research on adversarial examples, so it may not work with newer versions of PyTorch.
Gradients were derived by hand. I will try to find and upload the equations. The gradients with respect to the input match finite differences. The gradients with respect to sigma do not always match finite differences; I double checked the math and implementation and wasn't able to figure out why.
Notes
Coming soon: Setup instructions and example code, including verification of the backward pass with a gradient checker and sample input-output images.
Warning: For CUDA reasons, do not use the function with too large of a batch size or channel count. If batch_size*channels > 65535, the function will throw an error.