How can a cupy function be created for calculating the mean in 3D using a reduction kernel and applied to cupy's generic filter?

edit

Here is an example of creating a Cupy function for calculating the mean in 3D using a reduction kernel and applying it to Cupy's generic filter:

import cupy as cp

def mean_3d(input_array, size=None, mode='reflect'):
    # Define the reduction kernel
    kernel = cp.RawKernel(r'''
        extern "C" __global__
        void reduce(const float* input, float* output, int size_x, int size_y, int size_z) {
            int ix = blockDim.x * blockIdx.x + threadIdx.x;
            int iy = blockDim.y * blockIdx.y + threadIdx.y;
            int iz = blockDim.z * blockIdx.z + threadIdx.z;
            int index = ix + size_x * (iy + size_y * iz);
            if (ix >= size_x || iy >= size_y || iz >= size_z) return;
            output[0] += input[index];
            output[1] += 1.0;
        }
        ''', 'reduce')

    # Get the size of the input array
    size_x, size_y, size_z = input_array.shape

    # Create output array
    output_array = cp.zeros(input_array.shape, dtype=float)

    # Apply the generic filter
    cp.filters.generic_filter(input_array, lambda x: kernel(x, size_x, size_y, size_z), size=size, mode=mode, output=output_array)

    # Normalize the output array
    output_array /= size ** 3

    return output_array

To use this function, simply pass in a 3D input array, along with an optional filter size and mode:

input_array = cp.random.rand(10, 10, 10)
output_array = mean_3d(input_array, size=3, mode='reflect')

This code calculates the mean of a 3D input array with a filter size of 3 at each point. The mode parameter determines how the edges of the input array are handled during the filter operation. The size parameter determines the size of the neighborhood over which the mean is calculated.