问题描述:

I recently start to learn opencl and how to create kernel for OpenCV as well.

I am still working with the basics.

I need to implement an opencl kernel for calculate the covariance matrix.

This function don't have any opencl kernel already implemented by opencv's fundation.

Technically what I want to process is that :

cv::RNG tutu(std::time(nullptr)); // This is only for check the improvement of the method over the executions

int sz = tutu.uniform(1,20);

cv::Mat_<float> a1(1,sz);

cv::Mat_<float> a2(a1.size());

cv::Mat_<float> c2;

for(std::size_t i=0;i<sz;i++)

{

a1(i) = tutu.uniform(0,300);

a2(i) = tutu.uniform(300,600);

}

cv::Mat_<float> f;

cv::vconcat(a1,a2,f);

// process the Covariance Matrix :

cv::gemm(one,f,-0.5f,f,1.f,c2,0);

cv::gemm(c2.t(),c2,1.f,cv::noArray(),0.f,c2);

I found OpenCV's fundation have implemented an OpenCL kernel for both gemm and the function transpose.

So I tried to derivate an implementation from an exemple directly from OpenCV's source.

I wrote this :

ocl.h :

void CovarMatrix( cv::Mat_<float>& src,cv::Mat_<float>& covar);

ocl.cpp :

 #include <memory>

#include <fstream>

#include <sstream>

#include <iterator>

#include <opencv2/core.hpp>

#include <opencv2/core/ocl.hpp>

namespace test

{

namespace ocl

{

namespace

{

std::unique_ptr<cv::ocl::ProgramSource> cov_src;

void init_cov()

{

std::ifstream stream("../mahalanobis/covarianceMatrix.cl");

std::ostringstream sstream;

sstream << stream.rdbuf();

cv::String norm_file_content = sstream.str();

stream.close();

cov_src.reset(new cv::ocl::ProgramSource(norm_file_content));

}

}

static bool ocl_gemm( cv::Mat_<float>& matA, cv::Mat_<float>& matB, cv::Mat_<float>& CV_OUT matD)

{

cv::Mat_<float> tmp;

cv::Mat_<float> tmp2;

cv::Size sizeA = matA.size(), sizeB = matB.size();

cv::Size sizeD(sizeB.width, sizeA.height);

const cv::ocl::Device & dev = cv::ocl::Device::getDefault();

int max_wg_size = (int)dev.maxWorkGroupSize();

int block_size = (max_wg_size / 32 < 32) ? (max_wg_size / 16 < 16) ? (max_wg_size / 8 < 8) ? 1 : 8 : 16 : 32;

// matD.create(sizeD);

// tmp2.create(matD.t().size());

tmp.create(sizeD);

tmp2.create(tmp.t().size());

matD.create(sizeD.width,sizeD.width);

cv::UMat A = matA.getUMat(cv::ACCESS_READ,cv::USAGE_ALLOCATE_DEVICE_MEMORY);

cv::UMat B = matB.getUMat(cv::ACCESS_READ,cv::USAGE_ALLOCATE_DEVICE_MEMORY);

// cv::UMat D = matD.getUMat(cv::ACCESS_WRITE,cv::USAGE_ALLOCATE_DEVICE_MEMORY);

cv::UMat D = tmp.getUMat(cv::ACCESS_WRITE,cv::USAGE_ALLOCATE_DEVICE_MEMORY);

// cv::UMat E(sizeD.width,sizeD.height,CV_32FC1,cv::Scalar::all(0.),cv::USAGE_ALLOCATE_DEVICE_MEMORY);

cv::UMat E = tmp2.getUMat(cv::ACCESS_WRITE,cv::USAGE_ALLOCATE_DEVICE_MEMORY);

cv::UMat F = matD.getUMat(cv::ACCESS_WRITE,cv::USAGE_ALLOCATE_DEVICE_MEMORY);

matB.copyTo(D);

int vectorWidths[] = { 4, 4, 2, 2, 1, 4, 1, -1 };

int kercn = cv::ocl::checkOptimalVectorWidth(vectorWidths, B, D);

cv::String opts = cv::format(

"-I /home/administrateur/lib_dir/opencv_dir/opencv_304/opencv/modules/core/src/opencl/ -D T=float -D T1=float -D WT=%s -D cn=1 -D kercn=%d -D LOCAL_SIZE=%d %s -D HAVE_C -D TILE_DIM=32 -D BLOCK_ROWS=8 -D rowsPerWI=1 ",

cv::ocl::typeToStr(CV_32FC(kercn)),

kercn, block_size,

(sizeA.width % block_size !=0) ? "-D NO_MULT" : "");

cv::ocl::Kernel k("covarianceMatrix", *cov_src, opts);

k.args(cv::ocl::KernelArg::ReadOnlyNoSize(A),

cv::ocl::KernelArg::ReadOnlyNoSize(B, 1, kercn),

cv::ocl::KernelArg::ReadWrite(D, 1, kercn),

sizeA.width,

cv::ocl::KernelArg::ReadWrite(E,kercn,1),

cv::ocl::KernelArg::ReadWrite(F,kercn,kercn)

);

std::size_t globalsize[2] = { static_cast<std::size_t>(sizeD.width / kercn), static_cast<std::size_t>(sizeD.height)};

std::size_t localsize[2] = { static_cast<std::size_t>(block_size), static_cast<std::size_t>(block_size)};

return k.run(2, globalsize, block_size!=1 ? localsize : nullptr, false);

}

void CovarMatrix( cv::Mat_<float>& src,cv::Mat_<float>& covar)

{

if(!covar.empty())

covar.release();

cv::Mat_<float> o = cv::Mat_<float>::ones(src.rows,src.rows);

if(!cov_src)

init_cov();

ocl_gemm(o,src,covar);

}

covarianceMatrix.cl :

#include "gemm.cl"

#include "transpose.cl"

__kernel void covarianceMatrix

(

__global const uchar * A_ptr, int A_step, int A_offset,

__global const uchar * B_ptr, int B_step, int B_offset,

__global uchar * D_ptr, int D_step, int D_offset, int D_rows, int D_cols,

int n,

__global uchar * E_ptr, int E_step, int E_offset, int E_rows, int E_cols,

__global uchar * F_ptr, int F_step, int F_offset, int F_rows, int F_cols

)

{

// cv::gemm(src2,src1,-0.5,src1,1.f,src2);

// cv::gemm(src2.t(),src2,1.f,cv::noArray(),0.f,dest);

gemm(A_ptr,A_step,A_offset,

B_ptr,B_step,B_offset,

D_ptr,D_step,D_offset,D_rows,D_cols,

n,-0.5f,1.f);

transpose(D_ptr,D_step,D_offset,D_rows,D_cols*sizeof(float),

E_ptr,E_step,E_offset);

gemm(E_ptr,E_step,E_offset,

D_ptr,D_step,D_offset,

F_ptr,F_step,F_offset,F_rows,F_cols,

n,1.f,0.f);

}

If the size of the matrix is fewer than 6 is work perfectly :).

Otherwise ... not really.

It can be check with this code :

cv::RNG tutu(std::time(nullptr));

int sz = tutu.uniform(1,20);

cv::Mat_<float> a1(1,sz);

cv::Mat_<float> a2(a1.size());

for(std::size_t i=0;i<sz;i++)

{

a1(i) = tutu.uniform(0,300);

a2(i) = tutu.uniform(300,600);

}

cv::Mat_<float> f;

cv::vconcat(a1,a2,f);

cv::Mat_<float> c1;

cv::Mat_<float> c2;

cv::Mat_<float> mean;

// reference

cv::calcCovarMatrix(f,c1,mean,cv::COVAR_ROWS | cv::COVAR_NORMAL,CV_32F);

// check

test::ocl::CovarMatrix(f,c2);

std::size_t cnt(0.f);

for(auto it = c1.begin(),it2 = c2.begin();it != c1.end();it++,it2++)

if(*it == *it2)

cnt++;

std::cout<<"check "<<cnt<<" "<<c1.total()<<std::endl;

I am still new in OpenCL and I am interrested to know what I did wrong.

Does someone already implemented an OpenCL kernel for process the covariance matrix with OpenCV ?

Thank in advance for any help.

网友答案:

I resign myself to write this code :

void ocl_CovarMatrix(cv::Mat_<float>& src,cv::Mat_<float>& covar)
{

    cv::UMat usrc = src.getUMat(cv::ACCESS_READ,cv::USAGE_ALLOCATE_DEVICE_MEMORY);
    cv::UMat ones = cv::UMat::ones(usrc.rows,usrc.rows,usrc.type());
    cv::UMat utmp;

    double beta = 1.;
    double alpha = -1. / static_cast<double>(usrc.rows);

    cv::gemm(ones,usrc,alpha,usrc,beta,utmp);
    cv::gemm(utmp.t(),utmp,beta,cv::noArray(),0.,utmp);

    utmp.copyTo(covar);

    ones.release();
    utmp.release();
    usrc.release();
}

I suspect the GPU memory is updated everytime a function is call, that make that code slower rather than if it has been written in one Kernel. But it work efficiently.

I am still interested by another solution if maybe someone have an idea.

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