conv2.h
5.02 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
#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