OpenCL 图像卷积 1
▶ 书上的代码改进而成，从文件读入一张 256 阶灰度图，按照给定的卷积窗口计算卷积，并输出到文件中。
● 代码，使用 9 格的均值窗口，居然硬读写 .bmp 文件，算是了解一下该文件的具体格式，留作纪念吧。
// convolution.cl
__kernel void convolution01(__read_only image2d_t inputImage, __write_only image2d_t outputImage,
int imageRow, int imageCol, __constant float* filter, int filterWidth, sampler_t sampler)
{
const int row = get_global_id(), col = get_global_id(); // 注意工作项的顺序，可以和图像读取不一样
const int halfWidth = filterWidth / ;
float4 sum = { 0.0f, 0.0f, 0.0f, 0.0f }, pixel; // 输出数据类型是四元浮点数
int i, j, filterIdx; // 卷积窗口单独用一个下标遍历
for (filterIdx = , i = -halfWidth; i <= halfWidth; i++)
{
for (j = -halfWidth; j <= halfWidth; j++)
{
pixel = read_imagef(inputImage, sampler, (int2)(col + j, row + i)); // 读取目标坐标，注意列在前行在后
sum.x += pixel.x * filter[filterIdx++]; // 采用了单通道，只有第一分量有效
}
}
if (row < imageRow && col < imageCol) // 将落在有效范围内的计算数据输出
write_imagef(outputImage, (int2)(col, row), sum);
return;
}
// main.cpp
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include <cl.h> #pragma warning(disable : 4996) char *sourceText = "D:/Code/OpenCL/OpenCLProjectTemp/OpenCLProjectTemp/convolution.cl";
const char *inputFile = "R:/input.bmp";
const char *outputFile = "R:/output.bmp"; bool floatEq(const float a, const float b)// 相等返回 1
{
return (b == ) ? fabs(a) < 0.001 : fabs(a / b - ) < 0.001;
} int readText(const char* kernelPath, char **pcode)// 读取文本文件放入 pcode，返回字符串长度
{
FILE *fp;
int size;
//printf("<readText> File: %s\n", kernelPath);
fopen_s(&fp, kernelPath, "rb");
if (!fp)
{
printf("<readText> Open file failed\n");
getchar();
exit(-);
}
if (fseek(fp, , SEEK_END) != )
{
printf("<readText> Seek end of file failed\n");
getchar();
exit(-);
}
if ((size = ftell(fp)) < )
{
printf("<readText> Get file position failed\n");
getchar();
exit(-);
}
rewind(fp);
if ((*pcode = (char *)malloc(size + )) == NULL)
{
printf("<readText> Allocate space failed\n");
getchar();
exit(-);
}
fread(*pcode, , size, fp);
(*pcode)[size] = '\0';
fclose(fp);
return size + ;
} void storeImage(float *imageOut, const char *filename, const char *refFilename)// 输出图片
{
FILE *ifp, *ofp;
unsigned char *metaData, temp;
int offset, i, j, row, col, mod; if (fopen_s(&ifp, refFilename, "rb") != )// 从 参考图片（输入文件）中读取需要的行列数
{
printf(filename);
exit(-);
}
fseek(ifp, , SEEK_SET);
fread(&offset, , , ifp);
fseek(ifp, , SEEK_SET);
fread(&col, , , ifp);
fread(&row, , , ifp);
fseek(ifp, , SEEK_SET);
if ((metaData = (unsigned char *)malloc(offset)) == NULL)
{
printf("<storeImage> Allocate space failed\n");
getchar();
exit(-);
}
fread(metaData, , offset, ifp); // 从输入文件中读取元信息
//printf("Output image %s\n", filename);
if (fopen_s(&ofp, filename, "wb") != )
{
printf("<storeImage> Open output file failed\n");
getchar();
exit(-);
}
if (fwrite(metaData, , offset, ofp) != offset) // 将元信息原封不动的放入输出文件中
{
printf("<storeImage> Write output metaData failed\n");
getchar();
exit(-);
}
for (i = row - , mod = (col % == ? : - col % ); i >= ; i--)// .bmp 行是颠倒的，倒着填充
{
for (j = ; j < col; j++)
{
temp = (unsigned char)imageOut[i * col + j];
fwrite(&temp, sizeof(unsigned char), , ofp);
}
for (j = ; j < mod; fwrite(&temp, sizeof(unsigned char), , ofp), j++);// 列数非 4 的倍数时补上 junk padding } fclose(ifp);
fclose(ofp);
free(metaData);
return;
} float *readImage(const char *filename, int *outputRow, int *outputCol)// 从文件读取图片
{
unsigned char temp;
int i, j, row, col, offset, mod;
float *outputImage;
FILE *fp; if (fopen_s(&fp, filename, "rb") != )
{
printf("<readImage> Open file failed\n");
getchar();
exit(-);
}
fseek(fp, , SEEK_SET); // 第 10 字节的位置
fread(&offset, , , fp); // 元信息大小
fseek(fp, , SEEK_SET); // 第 18 字节位置
fread(&col, , , fp); // 读取列数和行数
fread(&row, , , fp);
printf("<readImage> Input image %s, col = %d, row = %d\n", filename, col, row); if ((outputImage = (float*)malloc(sizeof(float) * col * row)) == NULL)
{
printf("<readImage> Allocate space failed\n");
getchar();
exit(-);
}
fseek(fp, offset, SEEK_SET);// 元信息结束的地方，开始读图像数据
fflush(NULL);
for (i = row - , mod = (col % == ? : - col % ); i >= ; i--)// .bmp 行是颠倒的，顺着读文件，倒着填充，mod 为列的 junk pading 厚度
{
for (j = ; j < col; j++)
{
fread(&temp, sizeof(unsigned char), , fp);
outputImage[i * col + j] = (float)temp;
}
for (j = ; j < mod; fread(&temp, sizeof(unsigned char), , fp), j++);// 读取 junk padding，不传入数据中(.bmp 文件中有这几列，但是不显示)
}
fclose(fp); *outputRow = row;
*outputCol = col;
return outputImage;
} int main()
{
int imageRow, imageCol, dataSize, row, col, i, j, correct;
float *inputImage, *outputImage, sum; inputImage = readImage(inputFile, &imageRow, &imageCol);// 从文件读取图像数据和行列信息
dataSize = imageRow * imageCol * sizeof(float);
outputImage = (float*)malloc(dataSize); const int filterWidth = , filterSize = filterWidth * filterWidth, halfFilterWidth = filterWidth / ;
float filter[] =
{ , , , , , , ,
, , , , , , ,
, ,.f / ,.f / ,.f / , , ,
, ,.f / ,.f / ,.f / , , ,
, ,.f / ,.f / ,.f / , , ,
, , , , , , ,
, , , , , ,
}; cl_int status;
cl_uint nPlatform;
clGetPlatformIDs(, NULL, &nPlatform);
cl_platform_id *listPlatform = (cl_platform_id*)malloc(nPlatform * sizeof(cl_platform_id));
clGetPlatformIDs(nPlatform, listPlatform, NULL);
cl_uint nDevice = ;
clGetDeviceIDs(listPlatform[], CL_DEVICE_TYPE_ALL, , NULL, &nDevice);
cl_device_id *listDevice = (cl_device_id*)malloc(nDevice * sizeof(cl_device_id));
clGetDeviceIDs(listPlatform[], CL_DEVICE_TYPE_ALL, nDevice, listDevice, NULL);
cl_context context = clCreateContext(NULL, nDevice, listDevice, NULL, NULL, &status);
cl_command_queue queue = clCreateCommandQueue(context, listDevice[], , &status); cl_image_format format; // 图像格式描述符
format.image_channel_order = CL_R; // 单通道
format.image_channel_data_type = CL_FLOAT; // 浮点类型（读进来还是 uchar，但是计算需要浮点） cl_mem d_inputImage, d_outputImage, d_filter;
if (true)// 旧 OpenCL 函数 clCreateImage2D
{
d_inputImage = clCreateImage2D(context, , &format, imageCol, imageRow, , NULL, &status);
d_outputImage = clCreateImage2D(context, , &format, imageCol, imageRow, , NULL, &status);
}
if (false)// 新 OpenCL 使用描述符图像描述符 cl_image_desc 和函数 clCreateImage
{
cl_image_desc desc;
desc.image_type = CL_MEM_OBJECT_IMAGE2D;
desc.image_width = imageCol;
desc.image_height = imageRow;
desc.image_depth = ;
desc.image_array_size = ;
desc.image_row_pitch = ;
desc.image_slice_pitch = ;
desc.num_mip_levels = ;
desc.num_samples = ;
desc.buffer = NULL; d_inputImage = clCreateImage(context, CL_MEM_READ_ONLY, &format, &desc, NULL, &status);
d_outputImage = clCreateImage(context, CL_MEM_WRITE_ONLY, &format, &desc, NULL, &status);
}
d_filter = clCreateBuffer(context, , filterSize * sizeof(float), NULL, &status); size_t origin[] = { , , }, region[] = { imageCol, imageRow, }; // 拷贝图像数据用的原点和尺寸，注意尺寸是先数列再数行
clEnqueueWriteImage(queue, d_inputImage, CL_TRUE, origin, region, , , inputImage, , NULL, NULL);
clEnqueueWriteBuffer(queue, d_filter, CL_FALSE, , filterSize * sizeof(float), filter, , NULL, NULL); cl_sampler sampler = clCreateSampler(context, CL_FALSE, CL_ADDRESS_CLAMP_TO_EDGE, CL_FILTER_NEAREST, &status);// 采样器 char *code;
size_t length = readText(sourceText, &code);
cl_program program = clCreateProgramWithSource(context, , (const char **)&code, &length, NULL);
clBuildProgram(program, , listDevice, NULL, NULL, NULL);
cl_kernel kernel = clCreateKernel(program, "convolution01", &status); clSetKernelArg(kernel, , sizeof(cl_mem), &d_inputImage);
clSetKernelArg(kernel, , sizeof(cl_mem), &d_outputImage);
clSetKernelArg(kernel, , sizeof(int), &imageRow);
clSetKernelArg(kernel, , sizeof(int), &imageCol);
clSetKernelArg(kernel, , sizeof(cl_mem), &d_filter);
clSetKernelArg(kernel, , sizeof(int), &filterWidth);
clSetKernelArg(kernel, , sizeof(cl_sampler), &sampler); size_t globalSize[] = { imageRow, imageCol };// localSize = { 1, 1}; 可以用 NULL 代替
status = clEnqueueNDRangeKernel(queue, kernel, , NULL, globalSize, NULL, , NULL, NULL);
clEnqueueReadImage(queue, d_outputImage, CL_TRUE, origin, region, , , outputImage, , NULL, NULL); storeImage(outputImage, outputFile, inputFile);// 将输出图像写入文件中 for (row = , correct = ; row < imageRow && correct; row++)// 检查计算结果
{
for (col = ; col < imageCol && correct; col++)
{
sum = ;
for (i = -halfFilterWidth; i <= halfFilterWidth; i++)
{
for (j = -halfFilterWidth; j <= halfFilterWidth; j++)
{
if (row + i >= && row + i < imageRow && col + j >= && col + j < imageCol)
sum += inputImage[(row + i) * imageCol + col + j] * filter[(i + halfFilterWidth) * filterWidth + j + halfFilterWidth];
}
}
if (row >= halfFilterWidth && row < imageRow - halfFilterWidth && col >= halfFilterWidth && col < imageCol - halfFilterWidth &&
!floatEq(outputImage[row * imageCol + col], sum))
{
printf("Error at [%d,%d], output:%f, ref:%f\n", row, col, outputImage[row*imageCol + col], sum);
correct = ;
}
}
}
if (correct)
printf("Result correct.\n"); free(listPlatform);
free(listDevice);
free(inputImage);
free(outputImage);
free(code);
clReleaseContext(context);
clReleaseCommandQueue(queue);
clReleaseProgram(program);
clReleaseKernel(kernel);
clReleaseMemObject(d_inputImage);
clReleaseMemObject(d_outputImage);
clReleaseMemObject(d_filter);
clReleaseSampler(sampler);
getchar();
return ;
}
● 输出结果，给了一张 400 × 400 的图片参与，另外，计算一张 5040 × 7000 的图片需要 23 ms。
<readImage> Input image R:/input.bmp, width = , height =
Output image R:/output.bmp
Result correct.
● 代码，使用局部内存优化
// convolution.cl
__kernel void convolution02(__global float* inputImage, __global float* outputImage, int imageRow, int imageCol,
__constant float* filter, int filterWidth, __local float* localMem, int localMemRow, int localMemCol)
{
const int groupCol = get_group_id() * get_local_size(), groupRow = get_group_id() * get_local_size();
const int localCol = get_local_id(), localRow = get_local_id();
const int globalCol = groupCol + localCol, globalRow = groupRow + localRow;
const int halfWidth = filterWidth / ;
int i, j, curRow, curCol, filterIndex;
float sum; // 将源图数据读入局部内存
for (i = localRow; i < localMemRow; i += get_local_size())
{
curRow = groupRow + i;
for (j = localCol; j < localMemCol; j += get_local_size())
{
curCol = groupCol + j;
if (curRow < imageRow && curCol < imageCol)
localMem[i * localMemCol + j] = inputImage[curRow * imageCol + curCol];
}
}
barrier(CLK_LOCAL_MEM_FENCE); // 计算卷积
if (globalRow < imageRow - filterWidth + && globalCol < imageCol - filterWidth + )// 选取位于有效范围内的工作组
{
sum = 0.0f, filterIndex = ;
for (i = localRow; i < localRow + filterWidth; i++)
{
for (j = localCol; j < localCol + filterWidth; j++)
sum += localMem[i * localMemCol + j] * filter[filterIndex++];
}
// 循环展开
/*
for (i = localRow; i < localRow + filterWidth; i++)
{
int offset = i * localMemCol + localCol;
sum += localMem[offset++] * filter[filterIndex++];// 行数等于 filterWidth
sum += localMem[offset++] * filter[filterIndex++];
sum += localMem[offset++] * filter[filterIndex++];
sum += localMem[offset++] * filter[filterIndex++];
sum += localMem[offset++] * filter[filterIndex++];
sum += localMem[offset++] * filter[filterIndex++];
sum += localMem[offset++] * filter[filterIndex++];
}
*/
// 循环完全展开
/*
int offset = localRow*localMemCol + localCol;
sum += localMem[offset + 0] * filter[filterIndex++];
sum += localMem[offset + 1] * filter[filterIndex++];
sum += localMem[offset + 2] * filter[filterIndex++];
sum += localMem[offset + 3] * filter[filterIndex++];
sum += localMem[offset + 4] * filter[filterIndex++];
sum += localMem[offset + 5] * filter[filterIndex++];
sum += localMem[offset + 6] * filter[filterIndex++];
offset += localMemCol;
sum += localMem[offset + 0] * filter[filterIndex++];
sum += localMem[offset + 1] * filter[filterIndex++];
sum += localMem[offset + 2] * filter[filterIndex++];
sum += localMem[offset + 3] * filter[filterIndex++];
sum += localMem[offset + 4] * filter[filterIndex++];
sum += localMem[offset + 5] * filter[filterIndex++];
sum += localMem[offset + 6] * filter[filterIndex++];
offset += localMemCol;
sum += localMem[offset + 0] * filter[filterIndex++];
sum += localMem[offset + 1] * filter[filterIndex++];
sum += localMem[offset + 2] * filter[filterIndex++];
sum += localMem[offset + 3] * filter[filterIndex++];
sum += localMem[offset + 4] * filter[filterIndex++];
sum += localMem[offset + 5] * filter[filterIndex++];
sum += localMem[offset + 6] * filter[filterIndex++];
offset += localMemCol;
sum += localMem[offset + 0] * filter[filterIndex++];
sum += localMem[offset + 1] * filter[filterIndex++];
sum += localMem[offset + 2] * filter[filterIndex++];
sum += localMem[offset + 3] * filter[filterIndex++];
sum += localMem[offset + 4] * filter[filterIndex++];
sum += localMem[offset + 5] * filter[filterIndex++];
sum += localMem[offset + 6] * filter[filterIndex++];
offset += localMemCol;
sum += localMem[offset + 0] * filter[filterIndex++];
sum += localMem[offset + 1] * filter[filterIndex++];
sum += localMem[offset + 2] * filter[filterIndex++];
sum += localMem[offset + 3] * filter[filterIndex++];
sum += localMem[offset + 4] * filter[filterIndex++];
sum += localMem[offset + 5] * filter[filterIndex++];
sum += localMem[offset + 6] * filter[filterIndex++];
offset += localMemCol;
sum += localMem[offset + 0] * filter[filterIndex++];
sum += localMem[offset + 1] * filter[filterIndex++];
sum += localMem[offset + 2] * filter[filterIndex++];
sum += localMem[offset + 3] * filter[filterIndex++];
sum += localMem[offset + 4] * filter[filterIndex++];
sum += localMem[offset + 5] * filter[filterIndex++];
sum += localMem[offset + 6] * filter[filterIndex++];
offset += localMemCol;
sum += localMem[offset + 0] * filter[filterIndex++];
sum += localMem[offset + 1] * filter[filterIndex++];
sum += localMem[offset + 2] * filter[filterIndex++];
sum += localMem[offset + 3] * filter[filterIndex++];
sum += localMem[offset + 4] * filter[filterIndex++];
sum += localMem[offset + 5] * filter[filterIndex++];
sum += localMem[offset + 6] * filter[filterIndex++];
*/
// 数据输出
outputImage[(globalRow + halfWidth) * imageCol + (globalCol + halfWidth)] = sum;
}
return;
} __kernel void convolution03(__global float4* inputImage, __global float* outputImage, int imageRow, int imageCol,
__constant float* filter, int filterWidth, __local float* localMem, int localMemRow, int localMemCol)
{
const int groupCol4 = get_group_id() * get_local_size() / , groupRow4 = get_group_id() * get_local_size();
const int localId = get_local_id() * get_local_size() + get_local_id();
int localCol = (localId % (localMemCol / )), localRow = (localId / (localMemCol / ));
int globalCol = groupCol4 + localCol, globalRow = groupRow4 + localRow;
const int halfWidth = filterWidth / ; __local float4 *localImage4 = (__local float4*)&localMem[localRow*localMemCol + localCol * ];// 局部内存数据 if (globalRow < imageRow && globalCol < imageCol / && localRow < localMemRow)
localImage4[] = inputImage[globalRow*imageCol / + globalCol];
barrier(CLK_LOCAL_MEM_FENCE); // 重设 坐标以输出
localCol = get_local_id();
localRow = get_local_id();
globalCol = get_group_id()*get_local_size() + localCol;
globalRow = get_group_id()*get_local_size() + localRow; // 计算卷积
int i, j, filterIndex;
float sum;
if (globalRow < imageRow - filterWidth + && globalCol < imageCol - filterWidth + )
{
sum = 0.0f, filterIndex = ;
for (i = localRow; i < localRow + filterWidth; i++)
{
for (int j = localCol; j < localCol + filterWidth; j++)
sum += localMem[i * localMemCol + j] * filter[filterIndex++];
}
// 循环展开同 convolution02
// 输出数据
outputImage[(globalRow + halfWidth) * imageCol + (globalCol + halfWidth)] = ;//sum;
}
return;
}
// main.cpp
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include <cl.h> #pragma warning(disable : 4996) #define NON_OPTIMIZED // 不使用优化，使用函数 convolution02
//#define READ_ALIGNED // 使用内存对齐优化，使用函数 convolution02
//#define READ4 // 局部内存使用 float4 读取优化，使用函数 convolution03，有点问题
#define WGX 16 // 工作组尺寸
#define WGY 16 char *sourceText = "D:/Code/OpenCL/OpenCLProjectTemp/OpenCLProjectTemp/convolution.cl";
const char *inputFile = "R:/input.bmp";
const char *outputFile = "R:/output.bmp"; unsigned int roundUp(unsigned int value, unsigned int base)// 将 value 向上取整到 multiple 的整数倍
{
unsigned int remainder = value % base;
return remainder == ? value : (value + base - remainder);
} int readText(const char* kernelPath, char **pcode)
{
FILE *fp;
int size;
//printf("<readText> File: %s\n", kernelPath);
fopen_s(&fp, kernelPath, "rb");
if (!fp)
{
printf("<readText> Open file failed\n");
getchar();
exit(-);
}
if (fseek(fp, , SEEK_END) != )
{
printf("<readText> Seek end of file failed\n");
getchar();
exit(-);
}
if ((size = ftell(fp)) < )
{
printf("<readText> Get file position failed\n");
getchar();
exit(-);
}
rewind(fp);
if ((*pcode = (char *)malloc(size + )) == NULL)
{
printf("<readText> Allocate space failed\n");
getchar();
exit(-);
}
fread(*pcode, , size, fp);
(*pcode)[size] = '\0';
fclose(fp);
return size + ;
} void storeImage(float *imageOut, const char *filename, const char *refFilename)
{
FILE *ifp, *ofp;
unsigned char *metaData, temp;
int offset, i, j, row, col, mod; if (fopen_s(&ifp, refFilename, "rb") != )
{
printf(filename);
exit(-);
}
fseek(ifp, , SEEK_SET);
fread(&offset, , , ifp);
fseek(ifp, , SEEK_SET);
fread(&col, , , ifp);
fread(&row, , , ifp);
fseek(ifp, , SEEK_SET);
if ((metaData = (unsigned char *)malloc(offset)) == NULL)
{
printf("<storeImage> Allocate space failed\n");
getchar();
exit(-);
}
fread(metaData, , offset, ifp); if (fopen_s(&ofp, filename, "wb") != )
{
printf("<storeImage> Open output file failed\n");
getchar();
exit(-);
}
if (fwrite(metaData, , offset, ofp) != offset)
{
printf("<storeImage> Write output metaData failed\n");
getchar();
exit(-);
}
for (i = row - , mod = (col % == ? : - col % ); i >= ; i--)
{
for (j = ; j < col; j++)
{
temp = (unsigned char)imageOut[i * col + j];
fwrite(&temp, sizeof(unsigned char), , ofp);
}
for (j = ; j < mod; fwrite(&temp, sizeof(unsigned char), , ofp), j++); } fclose(ifp);
fclose(ofp);
free(metaData);
return;
} float *readImage(const char *filename, int *outputRow, int *outputCol)
{
unsigned char temp;
int i, j, row, col, offset, mod;
float *outputImage;
FILE *fp; if (fopen_s(&fp, filename, "rb") != )
{
printf("<readImage> Open file failed\n");
getchar();
exit(-);
}
fseek(fp, , SEEK_SET);
fread(&offset, , , fp);
fseek(fp, , SEEK_SET);
fread(&col, , , fp);
fread(&row, , , fp);
printf("<readImage> Input image %s, col = %d, row = %d\n", filename, col, row); if ((outputImage = (float*)malloc(sizeof(float) * col * row)) == NULL)
{
printf("<readImage> Allocate space failed\n");
getchar();
exit(-);
}
fseek(fp, offset, SEEK_SET);
fflush(NULL);
for (i = row - , mod = (col % == ? : - col % ); i >= ; i--)
{
for (j = ; j < col; j++)
{
fread(&temp, sizeof(unsigned char), , fp);
outputImage[i * col + j] = (float)temp;
}
for (j = ; j < mod; fread(&temp, sizeof(unsigned char), , fp), j++);
}
fclose(fp); *outputRow = row;
*outputCol = col;
return outputImage;
} int main()
{
int imageRow, imageCol, dataSize, deviceRow, deviceCol, deviceDataSize;
float *inputImage, *outputImage;
inputImage = readImage(inputFile, &imageRow, &imageCol);
dataSize = imageRow * imageCol * sizeof(float);
outputImage = (float*)malloc(dataSize); // 调整列数
#ifdef NON_OPTIMIZED // 不调整
deviceCol = imageCol;
#else // 增加道工作组尺寸的整数倍
deviceCol = roundUp(imageCol, WGX);
#endif
deviceRow = imageRow; // 行数不变
deviceDataSize = sizeof(float) * deviceRow * deviceCol; const int filterWidth = , filterSize = filterWidth * filterWidth, halfFilterWidth = filterWidth / ;
float filter[] =
{
, , , , , , ,
, , , , , , ,
, ,.f / ,.f / ,.f / , , ,
, ,.f / ,.f / ,.f / , , ,
, ,.f / ,.f / ,.f / , , ,
, , , , , , ,
, , , , , ,
}; cl_int status;
cl_uint nPlatform;
clGetPlatformIDs(, NULL, &nPlatform);
cl_platform_id *listPlatform = (cl_platform_id*)malloc(nPlatform * sizeof(cl_platform_id));
clGetPlatformIDs(nPlatform, listPlatform, NULL);
cl_uint nDevice = ;
clGetDeviceIDs(listPlatform[], CL_DEVICE_TYPE_ALL, , NULL, &nDevice);
cl_device_id *listDevice = (cl_device_id*)malloc(nDevice * sizeof(cl_device_id));
clGetDeviceIDs(listPlatform[], CL_DEVICE_TYPE_ALL, nDevice, listDevice, NULL);
cl_context context = clCreateContext(NULL, nDevice, listDevice, NULL, NULL, &status);
cl_command_queue queue = clCreateCommandQueue(context, listDevice[], , &status); // 使用普通的缓冲区，而不用 image
cl_mem d_inputImage = clCreateBuffer(context, CL_MEM_READ_ONLY, deviceDataSize, NULL, NULL);
cl_mem d_outputImage = clCreateBuffer(context, CL_MEM_WRITE_ONLY, deviceDataSize, NULL, NULL);
cl_mem d_filter = clCreateBuffer(context, CL_MEM_READ_ONLY, sizeof(float) * filterSize, NULL, NULL); // 缓冲区写入
#if defined NON_OPTIMIZED // 直接写入
clEnqueueWriteBuffer(queue, d_inputImage, CL_TRUE, , deviceDataSize, inputImage, , NULL, NULL);
#else // 对齐写入
size_t d_origin[] = { ,, }, h_origin[] = { ,, }, region[] = { sizeof(float) * deviceCol, deviceRow, };
clEnqueueWriteBufferRect(queue, d_inputImage, CL_TRUE, d_origin, h_origin, region, sizeof(float) * deviceCol, , sizeof(float) * imageCol, , inputImage, , NULL, NULL);
#endif
clEnqueueWriteBuffer(queue, d_filter, CL_TRUE, , sizeof(float) * filterSize, filter, , NULL, NULL); char *code;
size_t length = readText(sourceText, &code);
cl_program program = clCreateProgramWithSource(context, , (const char **)&code, &length, &status);
status = clBuildProgram(program, , listDevice, NULL, NULL, NULL); // 创建不同的内核
#if defined NON_OPTIMIZED || defined READ_ALIGNED
cl_kernel kernel = clCreateKernel(program, "convolution02", NULL);
#else
cl_kernel kernel = clCreateKernel(program, "convolution03", NULL);
#endif size_t globalSize[] = { roundUp(imageCol - filterWidth + , WGX), roundUp(imageRow - filterWidth + , WGY) }, localSize[] = { WGX, WGY }; // 局部内存大小
int localRow, localCol;
localRow = localSize[] + filterWidth - ; // 把一个工作组的大小垫起光环元素的宽度
#if defined NON_OPTIMIZED || defined READ_ALIGNED
localCol = localSize[] + filterWidth - ;
#else
localCol = roundUp(localSize[] + filterWidth - , ); // 垫起之外还要对齐到 4 的倍数上
#endif
clSetKernelArg(kernel, , sizeof(cl_mem), &d_inputImage);
clSetKernelArg(kernel, , sizeof(cl_mem), &d_outputImage);
clSetKernelArg(kernel, , sizeof(int), &deviceRow);
clSetKernelArg(kernel, , sizeof(int), &deviceCol);
clSetKernelArg(kernel, , sizeof(cl_mem), &d_filter);
clSetKernelArg(kernel, , sizeof(int), &filterWidth);
clSetKernelArg(kernel, , sizeof(float) * localCol * localRow, NULL);
clSetKernelArg(kernel, , sizeof(int), &localRow);
clSetKernelArg(kernel, , sizeof(int), &localCol); status = clEnqueueNDRangeKernel(queue, kernel, , NULL, globalSize, localSize, , NULL, NULL); // 结果写回
#if defined NON_OPTIMIZED
clEnqueueReadBuffer(queue, d_outputImage, CL_TRUE, , deviceDataSize, outputImage, , NULL, NULL);
#else // 最边上一圈 filterWidth / 2 的部分不写回
d_origin[] = * sizeof(float), d_origin[] = , d_origin[] = ;
h_origin[] = * sizeof(float), h_origin[] = , h_origin[] = ;
region[] = (imageCol - filterWidth + ) * sizeof(float), region[] = (imageRow - filterWidth + ), region[] = ;
clEnqueueReadBufferRect(queue, d_outputImage, CL_TRUE, d_origin, h_origin, region, sizeof(float) * deviceCol, , sizeof(float) * imageCol, , outputImage, , NULL, NULL);
#endif
storeImage(outputImage, outputFile, inputFile); // 去掉了检查结果的部分
free(listPlatform);
free(listDevice);
free(inputImage);
free(outputImage);
free(code);
clReleaseContext(context);
clReleaseCommandQueue(queue);
clReleaseProgram(program);
clReleaseKernel(kernel);
clReleaseMemObject(d_inputImage);
clReleaseMemObject(d_outputImage);
clReleaseMemObject(d_filter);
printf("Finshed.\n");
getchar();
return ;
}
● 输出结果，与上面的简单方法相同
● 用到的函数和定义
//cl.h
// 采样器越界处理方案
#define CL_ADDRESS_NONE 0x1130
#define CL_ADDRESS_CLAMP_TO_EDGE 0x1131
#define CL_ADDRESS_CLAMP 0x1132
#define CL_ADDRESS_REPEAT 0x1133
#define CL_ADDRESS_MIRRORED_REPEAT 0x1134 // 插值方案
#define CL_FILTER_NEAREST 0x1140
#define CL_FILTER_LINEAR 0x1141 // 用到的采样器和描述符的定义
typedef struct _cl_sampler* cl_sampler;
typedef struct _cl_image_format
{
cl_channel_order image_channel_order;
cl_channel_type image_channel_data_type;
} cl_image_format; typedef struct _cl_image_desc
{
cl_mem_object_type image_type;
size_t image_width;
size_t image_height;
size_t image_depth;
size_t image_array_size;
size_t image_row_pitch;
size_t image_slice_pitch;
cl_uint num_mip_levels;
cl_uint num_samples;
cl_mem buffer;
} cl_image_desc; extern CL_API_ENTRY CL_EXT_PREFIX__VERSION_1_1_DEPRECATED cl_mem CL_API_CALL clCreateImage2D(// OpenCL1.2 中废弃的 image 创建函数
cl_context, // 上下文
cl_mem_flags, // 特殊标志
const cl_image_format *,// image 描述符
size_t, // 宽度
size_t, // 高度
size_t, // 行跨步
void *, // 自动传入主机数据
cl_int * // 返回结果状态的指针
) CL_EXT_SUFFIX__VERSION_1_1_DEPRECATED; extern CL_API_ENTRY cl_mem CL_API_CALL clCreateImage(// OpenCL1.2 中开始使用的 image 创建函数
cl_context, // 上下文
cl_mem_flags, // 特殊标志
const cl_image_format *,// image 格式描述符
const cl_image_desc *, // image 描述符
void *, // 主机数据
cl_int * // 返回结果状态的指针
) CL_API_SUFFIX__VERSION_1_2; extern CL_API_ENTRY cl_sampler CL_API_CALL clCreateSampler(// 初始化采样器
cl_context, // 上下文
cl_bool, // 是否使用归一化坐标
cl_addressing_mode, // 越界处理方案
cl_filter_mode, // 差值方案
cl_int * // 返回结果状态的指针
) CL_API_SUFFIX__VERSION_1_0; extern CL_API_ENTRY cl_int CL_API_CALL clEnqueueWriteBufferRect(// 矩形缓冲区写入
cl_command_queue, // 命令队列
cl_mem, // 目标缓冲区
cl_bool, // 阻塞标记
const size_t *, // 缓冲区写入起点
const size_t *, // 源数据写入起点
const size_t *, // 写入范围，是一个三维数组，分别为：一行数据量（Byte），行数，层数
size_t, // 缓冲区行间跨度
size_t, // 缓冲区层间跨度
size_t, // 源数据行间跨度
size_t, // 源数据层间跨度
const void *, // 源数据指针
cl_uint, // 时间列表长度
const cl_event *, // 时间列表
cl_event * // 本事件标记
) CL_API_SUFFIX__VERSION_1_1; extern CL_API_ENTRY cl_int CL_API_CALL clEnqueueReadBufferRect(// 矩形缓冲区读出，参数定义同上
cl_command_queue,
cl_mem,
cl_bool,
const size_t *,
const size_t *,
const size_t *,
size_t,
size_t,
size_t,
size_t,
void *,
cl_uint,
const cl_event *,
cl_event *
) CL_API_SUFFIX__VERSION_1_1;
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