This is the PyTorch implementation of Diagonalwise Refactorization.
Diagonalwise Refactorization is an efficient implementation for depthwise convolutions. The key idea of Diagonalwise Refactorization is to rearrange the weight vectors of a depthwise convolution into a large diagonal weight matrixi, so as to convert the depthwise convolution into one single standard convolution, which is well supported by the cuDNN library that is highly-optimized for GPU computations.
In PyTorch, Diagonalwise Refactorization is implemented in Python and can be further accelerated using C++.