stim/math/filters/conv2.h

#ifndef STIM_CUDA_CONV2_H
#define STIM_CUDA_CONV2_H
//#define __CUDACC__
#ifdef __CUDACC__
#include <stim/cuda/cudatools.h>
#include <stim/cuda/sharedmem.cuh>
#endif
namespace stim {
#ifdef __CUDACC__
	//Kernel function that performs the 2D convolution.
	template<typename T, typename K = T>
	__global__ void kernel_conv2(T* out, T* in, K* kernel, size_t sx, size_t sy, size_t kx, size_t ky) {
		extern __shared__ T s[];								//declare a shared memory array
		size_t xi = blockIdx.x * blockDim.x + threadIdx.x;	//threads correspond to indices into the output image
		size_t yi = blockIdx.y * blockDim.y + threadIdx.y;
		size_t tid = threadIdx.y * blockDim.x + threadIdx.x;
		size_t nt = blockDim.x * blockDim.y;
		size_t cx = blockIdx.x * blockDim.x;					//find the upper left corner of the input region
		size_t cy = blockIdx.y * blockDim.y;
		size_t X = sx - kx + 1;								//calculate the size of the output image
		size_t Y = sy - ky + 1;
		if (cx >= X || cy >= Y) return;						//return if the entire block is outside the image
		size_t smx = min(blockDim.x + kx - 1, sx - cx);			//size of the shared copy of the input image
		size_t smy = min(blockDim.y + ky - 1, sy - cy);			//	min function is used to deal with boundary blocks
		stim::cuda::threadedMemcpy2D<T>(s, smx, smy, in, cx, cy, sx, sy, tid, nt);	//copy the input region to shared memory
		__syncthreads();
		if (xi >= X || yi >= Y) return;						//returns if the thread is outside of the output image
		
		//loop through the kernel
		size_t kxi, kyi;
		K v = 0;
		for (kyi = 0; kyi < ky; kyi++) {
			for (kxi = 0; kxi < kx; kxi++) {
				v += s[(threadIdx.y + kyi) * smx + threadIdx.x + kxi] * kernel[kyi * kx + kxi];
				//v += in[(yi + kyi) * sx + xi + kxi] * kernel[kyi * kx + kxi];
			}
		}
		out[yi * X + xi] = (T)v;								//write the result to global memory
	}
	//Performs a convolution of a 2D image using the GPU. All pointers are assumed to be to memory on the current device.
	//@param out is a pointer to the output image
	//@param in is a pointer to the input image
	//@param sx is the size of the input image along X
	//@param sy is the size of the input image along Y
	//@param kx is the size of the kernel along X
	//@param ky is the size of the kernel along Y
	template<typename T, typename K = T>
	void gpu_conv2(T* out, T* in, K* kernel, size_t sx, size_t sy, size_t kx, size_t ky) {
		cudaDeviceProp p;
		HANDLE_ERROR(cudaGetDeviceProperties(&p, 0));
		size_t tmax = p.maxThreadsPerBlock;
		dim3 nt(sqrt(tmax), sqrt(tmax));					//calculate the block dimensions
		size_t X = sx - kx + 1;								//calculate the size of the output image
		size_t Y = sy - ky + 1;
		dim3 nb(X / nt.x + 1, Y / nt.y + 1);							//calculate the grid dimensions
		size_t sm = (nt.x + kx - 1) * (nt.y + ky - 1) * sizeof(T);		//shared memory bytes required to store block data
		if (sm > p.sharedMemPerBlock) {
			std::cout << "Error in stim::gpu_conv2() - insufficient shared memory for this kernel." << std::endl;
			exit(1);
		}
		kernel_conv2 <<<nb, nt, sm>>> (out, in, kernel, sx, sy, kx, ky);	//launch the kernel
	}
#endif
	//Performs a convolution of a 2D image. Only valid pixels based on the kernel are returned.
	//	As a result, the output image will be smaller than the input image by (kx-1, ky-1)
	//@param out is a pointer to the output image
	//@param in is a pointer to the input image
	//@param sx is the size of the input image along X
	//@param sy is the size of the input image along Y
	//@param kx is the size of the kernel along X
	//@param ky is the size of the kernel along Y
	template<typename T, typename K = T>
	void cpu_conv2(T* out, T* in, K* kernel, size_t sx, size_t sy, size_t kx, size_t ky) {
		size_t X = sx - kx + 1;					//x size of the output image
		size_t Y = sy - ky + 1;					//y size of the output image
#ifdef __CUDACC__
		//allocate memory and copy everything to the GPU
		T* gpu_in;
		HANDLE_ERROR(cudaMalloc(&gpu_in, sx * sy * sizeof(T)));
		HANDLE_ERROR(cudaMemcpy(gpu_in, in, sx * sy * sizeof(T), cudaMemcpyHostToDevice));
		K* gpu_kernel;
		HANDLE_ERROR(cudaMalloc(&gpu_kernel, kx * ky * sizeof(K)));
		HANDLE_ERROR(cudaMemcpy(gpu_kernel, kernel, kx * ky * sizeof(K), cudaMemcpyHostToDevice));
		T* gpu_out;
		HANDLE_ERROR(cudaMalloc(&gpu_out, X * Y * sizeof(T)));
		gpu_conv2(gpu_out, gpu_in, gpu_kernel, sx, sy, kx, ky);								//execute the GPU kernel
		HANDLE_ERROR(cudaMemcpy(out, gpu_out, X * Y * sizeof(T), cudaMemcpyDeviceToHost));	//copy the result to the host
		HANDLE_ERROR(cudaFree(gpu_in));
		HANDLE_ERROR(cudaFree(gpu_kernel));
		HANDLE_ERROR(cudaFree(gpu_out));
#else
		K v;												//register stores the integral of the current pixel value
		size_t yi, xi, kyi, kxi, yi_kyi_sx;
		for (yi = 0; yi < Y; yi++) {					//for each pixel in the output image
			for (xi = 0; xi < X; xi++) {
				v = 0;
				for (kyi = 0; kyi < ky; kyi++) {		//for each pixel in the kernel
					yi_kyi_sx = (yi + kyi) * sx;
					for (kxi = 0; kxi < kx; kxi++) {
						v += in[yi_kyi_sx + xi + kxi] * kernel[kyi * kx + kxi];
					}
				}
				out[yi * X + xi] = v;						//save the result to the output array
			}
		}
		
#endif
	}
}
#endif