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speeded up ocl::distanceToCenters
This commit is contained in:
Ilya Lavrenov
parent
370235c07b
commit
56d943388d
@ -91,11 +91,7 @@ ocl::distanceToCenters
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----------------------
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For each samples in ``source``, find its closest neighour in ``centers``.
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.. ocv:function:: void ocl::distanceToCenters(oclMat &dists, oclMat &labels, const oclMat &src, const oclMat ¢ers, int distType = NORM_L2SQR, const oclMat &indices = oclMat())
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:param dists: The output distances calculated from each sample to the best matched center.
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:param labels: The output index of best matched center for each row of sample.
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.. ocv:function:: void ocl::distanceToCenters(const oclMat &src, const oclMat ¢ers, Mat &dists, Mat &labels, int distType = NORM_L2SQR)
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:param src: Floating-point matrix of input samples. One row per sample.
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@ -103,10 +99,8 @@ For each samples in ``source``, find its closest neighour in ``centers``.
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:param distType: Distance metric to calculate distances. Supports ``NORM_L1`` and ``NORM_L2SQR``.
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:param indices: Optional source indices. If not empty:
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:param dists: The output distances calculated from each sample to the best matched center.
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* only the indexed source samples will be processed
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* outputs, i.e., ``dists`` and ``labels``, have the same size of indices
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* outputs are in the same order of indices instead of the order of src
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:param labels: The output index of best matched center for each row of sample.
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The method is a utility function which maybe used for multiple clustering algorithms such as K-means.
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@ -878,7 +878,7 @@ namespace cv
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// supports NORM_L1 and NORM_L2 distType
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// if indices is provided, only the indexed rows will be calculated and their results are in the same
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// order of indices
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CV_EXPORTS void distanceToCenters(oclMat &dists, oclMat &labels, const oclMat &src, const oclMat ¢ers, int distType = NORM_L2SQR, const oclMat &indices = oclMat());
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CV_EXPORTS void distanceToCenters(const oclMat &src, const oclMat ¢ers, Mat &dists, Mat &labels, int distType = NORM_L2SQR);
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//!Does k-means procedure on GPU
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// supports CV_32FC1/CV_32FC2/CV_32FC4 data type
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@ -872,58 +872,57 @@ PERF_TEST_P(columnSumFixture, columnSum, OCL_TYPICAL_MAT_SIZES)
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//////////////////////////////distanceToCenters////////////////////////////////////////////////
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CV_ENUM(DistType, NORM_L1, NORM_L2SQR);
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CV_ENUM(DistType, NORM_L1, NORM_L2SQR)
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typedef tuple<Size, DistType> distanceToCentersParameters;
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typedef TestBaseWithParam<distanceToCentersParameters> distanceToCentersFixture;
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static void distanceToCentersPerfTest(Mat& src, Mat& centers, Mat& dists, Mat& labels, int distType)
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{
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Mat batch_dists;
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cv::batchDistance(src,centers,batch_dists, CV_32FC1, noArray(), distType);
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cv::batchDistance(src, centers, batch_dists, CV_32FC1, noArray(), distType);
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std::vector<float> dists_v;
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std::vector<int> labels_v;
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for(int i = 0; i<batch_dists.rows; i++)
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for (int i = 0; i < batch_dists.rows; i++)
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{
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Mat r = batch_dists.row(i);
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double mVal;
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Point mLoc;
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minMaxLoc(r, &mVal, NULL, &mLoc, NULL);
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dists_v.push_back((float)mVal);
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dists_v.push_back(static_cast<float>(mVal));
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labels_v.push_back(mLoc.x);
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}
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Mat temp_dists(dists_v);
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Mat temp_labels(labels_v);
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temp_dists.reshape(1,1).copyTo(dists);
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temp_labels.reshape(1,1).copyTo(labels);
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Mat(dists_v).copyTo(dists);
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Mat(labels_v).copyTo(labels);
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}
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PERF_TEST_P(distanceToCentersFixture, distanceToCenters, ::testing::Combine(::testing::Values(cv::Size(256,256), cv::Size(512,512)), DistType::all()) )
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{
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Size size = get<0>(GetParam());
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int distType = get<1>(GetParam());
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Mat src(size, CV_32FC1);
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Mat centers(size, CV_32FC1);
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Mat dists(cv::Size(src.rows,1), CV_32FC1);
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Mat labels(cv::Size(src.rows,1), CV_32SC1);
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Mat src(size, CV_32FC1), centers(size, CV_32FC1);
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Mat dists(src.rows, 1, CV_32FC1), labels(src.rows, 1, CV_32SC1);
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declare.in(src, centers, WARMUP_RNG).out(dists, labels);
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if (RUN_OCL_IMPL)
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{
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ocl::oclMat ocl_src(src);
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ocl::oclMat ocl_centers(centers);
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ocl::oclMat ocl_dists(dists);
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ocl::oclMat ocl_labels(labels);
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ocl::oclMat ocl_src(src), ocl_centers(centers);
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OCL_TEST_CYCLE() ocl::distanceToCenters(ocl_dists,ocl_labels,ocl_src, ocl_centers, distType);
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ocl_dists.download(dists);
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ocl_labels.download(labels);
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OCL_TEST_CYCLE() ocl::distanceToCenters(ocl_src, ocl_centers, dists, labels, distType);
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SANITY_CHECK(dists, 1e-6, ERROR_RELATIVE);
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SANITY_CHECK(labels);
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}
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else if (RUN_PLAIN_IMPL)
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{
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TEST_CYCLE() distanceToCentersPerfTest(src,centers,dists,labels,distType);
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TEST_CYCLE() distanceToCentersPerfTest(src, centers, dists, labels, distType);
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SANITY_CHECK(dists, 1e-6, ERROR_RELATIVE);
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SANITY_CHECK(labels);
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}
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@ -160,63 +160,66 @@ static void generateCentersPP(const Mat& _data, Mat& _out_centers,
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}
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}
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void cv::ocl::distanceToCenters(oclMat &dists, oclMat &labels, const oclMat &src, const oclMat ¢ers, int distType, const oclMat &indices)
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void cv::ocl::distanceToCenters(const oclMat &src, const oclMat ¢ers, Mat &dists, Mat &labels, int distType)
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{
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CV_Assert(src.cols*src.oclchannels() == centers.cols*centers.oclchannels());
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CV_Assert(src.cols * src.channels() == centers.cols * centers.channels());
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CV_Assert(src.depth() == CV_32F && centers.depth() == CV_32F);
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bool is_label_row_major = false;
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ensureSizeIsEnough(1, src.rows, CV_32FC1, dists);
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if(labels.empty() || (!labels.empty() && labels.rows == src.rows && labels.cols == 1))
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{
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ensureSizeIsEnough(src.rows, 1, CV_32SC1, labels);
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is_label_row_major = true;
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}
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CV_Assert(distType == NORM_L1 || distType == NORM_L2SQR);
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dists.create(src.rows, 1, CV_32FC1);
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labels.create(src.rows, 1, CV_32SC1);
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std::stringstream build_opt_ss;
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build_opt_ss
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<< (distType == NORM_L1 ? "-D L1_DIST" : "-D L2SQR_DIST")
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<< (indices.empty() ? "" : " -D USE_INDEX");
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build_opt_ss << (distType == NORM_L1 ? "-D L1_DIST" : "-D L2SQR_DIST");
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String build_opt = build_opt_ss.str();
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int src_step = src.step / src.elemSize1();
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int centers_step = centers.step / centers.elemSize1();
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int feature_width = centers.cols * centers.oclchannels();
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int src_offset = src.offset / src.elemSize1();
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int centers_offset = centers.offset / centers.elemSize1();
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const int src_step = (int)(src.oclchannels() * src.step / src.elemSize());
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const int centers_step = (int)(centers.oclchannels() * centers.step / centers.elemSize());
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const int colsNumb = centers.cols*centers.oclchannels();
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const int label_step = is_label_row_major ? (int)(labels.step / labels.elemSize()) : 1;
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String kernelname = "distanceToCenters";
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const int number_of_input = indices.empty() ? src.rows : indices.size().area();
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const int src_offset = (int)src.offset/src.elemSize();
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const int centers_offset = (int)centers.offset/centers.elemSize();
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size_t globalThreads[3] = {number_of_input, 1, 1};
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int all_dist_count = src.rows * centers.rows;
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oclMat all_dist(1, all_dist_count, CV_32FC1);
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vector<pair<size_t, const void *> > args;
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args.push_back(make_pair(sizeof(cl_mem), (void *)&src.data));
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args.push_back(make_pair(sizeof(cl_mem), (void *)¢ers.data));
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if(!indices.empty())
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{
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args.push_back(make_pair(sizeof(cl_mem), (void *)&indices.data));
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}
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args.push_back(make_pair(sizeof(cl_mem), (void *)&labels.data));
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args.push_back(make_pair(sizeof(cl_mem), (void *)&dists.data));
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args.push_back(make_pair(sizeof(cl_int), (void *)&colsNumb));
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args.push_back(make_pair(sizeof(cl_mem), (void *)&all_dist.data));
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args.push_back(make_pair(sizeof(cl_int), (void *)&feature_width));
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args.push_back(make_pair(sizeof(cl_int), (void *)&src_step));
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args.push_back(make_pair(sizeof(cl_int), (void *)¢ers_step));
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args.push_back(make_pair(sizeof(cl_int), (void *)&label_step));
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args.push_back(make_pair(sizeof(cl_int), (void *)&number_of_input));
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args.push_back(make_pair(sizeof(cl_int), (void *)&src.rows));
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args.push_back(make_pair(sizeof(cl_int), (void *)¢ers.rows));
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args.push_back(make_pair(sizeof(cl_int), (void *)&src_offset));
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args.push_back(make_pair(sizeof(cl_int), (void *)¢ers_offset));
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size_t globalThreads[3] = { all_dist_count, 1, 1 };
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openCLExecuteKernel(Context::getContext(), &kmeans_kernel,
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kernelname, globalThreads, NULL, args, -1, -1, build_opt.c_str());
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"distanceToCenters", globalThreads, NULL, args, -1, -1, build_opt_ss.str().c_str());
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Mat all_dist_cpu;
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all_dist.download(all_dist_cpu);
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for (int i = 0; i < src.rows; ++i)
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{
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Point p;
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double minVal;
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Rect roi(i * centers.rows, 0, centers.rows, 1);
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Mat hdr(all_dist_cpu, roi);
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cv::minMaxLoc(hdr, &minVal, NULL, &p);
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dists.at<float>(i, 0) = static_cast<float>(minVal);
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labels.at<int>(i, 0) = p.x;
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}
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}
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///////////////////////////////////k - means /////////////////////////////////////////////////////////
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double cv::ocl::kmeans(const oclMat &_src, int K, oclMat &_bestLabels,
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TermCriteria criteria, int attempts, int flags, oclMat &_centers)
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{
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@ -429,28 +432,19 @@ double cv::ocl::kmeans(const oclMat &_src, int K, oclMat &_bestLabels,
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break;
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// assign labels
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oclMat _dists(1, N, CV_64F);
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_bestLabels.upload(_labels);
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Mat dists(1, N, CV_64F);
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_centers.upload(centers);
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distanceToCenters(_src, _centers, dists, _labels);
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_bestLabels.upload(_labels);
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distanceToCenters(_dists, _bestLabels, _src, _centers);
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Mat dists;
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_dists.download(dists);
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_bestLabels.download(_labels);
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float* dist = dists.ptr<float>(0);
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compactness = 0;
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for( i = 0; i < N; i++ )
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{
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compactness += (double)dist[i];
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}
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compactness += (double)dist[i];
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}
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if( compactness < best_compactness )
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{
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best_compactness = compactness;
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}
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}
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return best_compactness;
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@ -44,81 +44,64 @@
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//
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//M*/
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#ifdef L1_DIST
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# define DISTANCE(A, B) fabs((A) - (B))
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#elif defined L2SQR_DIST
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# define DISTANCE(A, B) ((A) - (B)) * ((A) - (B))
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#else
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# define DISTANCE(A, B) ((A) - (B)) * ((A) - (B))
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#endif
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inline float dist(__global const float * center, __global const float * src, int feature_cols)
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static float distance_(__global const float * center, __global const float * src, int feature_length)
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{
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float res = 0;
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float4 tmp4;
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int i;
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for(i = 0; i < feature_cols / 4; i += 4, center += 4, src += 4)
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{
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tmp4 = vload4(0, center) - vload4(0, src);
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float4 v0, v1, v2;
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int i = 0;
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#ifdef L1_DIST
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tmp4 = fabs(tmp4);
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#else
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tmp4 *= tmp4;
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float4 sum = (float4)(0.0f);
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#endif
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for ( ; i <= feature_length - 4; i += 4)
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{
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v0 = vload4(0, center + i);
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v1 = vload4(0, src + i);
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v2 = v1 - v0;
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#ifdef L1_DIST
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v0 = fabs(v2);
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sum += v0;
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#else
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res += dot(v2, v2);
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#endif
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res += tmp4.x + tmp4.y + tmp4.z + tmp4.w;
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}
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for(; i < feature_cols; ++i, ++center, ++src)
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#ifdef L1_DIST
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res = sum.x + sum.y + sum.z + sum.w;
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#endif
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for ( ; i < feature_length; ++i)
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{
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res += DISTANCE(*src, *center);
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float t0 = src[i];
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float t1 = center[i];
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#ifdef L1_DIST
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res += fabs(t0 - t1);
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#else
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float t2 = t0 - t1;
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res += t2 * t2;
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#endif
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}
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return res;
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}
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// to be distinguished with distanceToCenters in kmeans_kernel.cl
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__kernel void distanceToCenters(
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__global const float *src,
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__global const float *centers,
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#ifdef USE_INDEX
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__global const int *indices,
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#endif
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__global int *labels,
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__global float *dists,
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int feature_cols,
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int src_step,
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int centers_step,
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int label_step,
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int input_size,
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int K,
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int offset_src,
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int offset_centers
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)
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__kernel void distanceToCenters(__global const float * src, __global const float * centers,
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__global float * dists, int feature_length,
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int src_step, int centers_step,
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int features_count, int centers_count,
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int src_offset, int centers_offset)
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{
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int gid = get_global_id(0);
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float euDist, minval;
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int minCentroid;
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if(gid >= input_size)
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if (gid < (features_count * centers_count))
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{
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return;
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int feature_index = gid / centers_count;
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int center_index = gid % centers_count;
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int center_idx = mad24(center_index, centers_step, centers_offset);
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int src_idx = mad24(feature_index, src_step, src_offset);
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dists[gid] = distance_(centers + center_idx, src + src_idx, feature_length);
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}
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src += offset_src;
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centers += offset_centers;
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#ifdef USE_INDEX
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src += indices[gid] * src_step;
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#else
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src += gid * src_step;
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#endif
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minval = dist(centers, src, feature_cols);
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minCentroid = 0;
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for(int i = 1 ; i < K; i++)
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{
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euDist = dist(centers + i * centers_step, src, feature_cols);
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if(euDist < minval)
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{
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minval = euDist;
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minCentroid = i;
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}
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}
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labels[gid * label_step] = minCentroid;
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dists[gid] = minval;
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}
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@ -61,7 +61,7 @@ PARAM_TEST_CASE(Kmeans, int, int, int)
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int type;
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int K;
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int flags;
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cv::Mat src ;
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Mat src ;
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ocl::oclMat d_src, d_dists;
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Mat labels, centers;
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@ -73,7 +73,7 @@ PARAM_TEST_CASE(Kmeans, int, int, int)
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flags = GET_PARAM(2);
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// MWIDTH=256, MHEIGHT=256. defined in utility.hpp
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cv::Size size = cv::Size(MWIDTH, MHEIGHT);
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Size size = Size(MWIDTH, MHEIGHT);
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src.create(size, type);
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int row_idx = 0;
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const int max_neighbour = MHEIGHT / K - 1;
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@ -159,15 +159,15 @@ INSTANTIATE_TEST_CASE_P(OCL_ML, Kmeans, Combine(
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/////////////////////////////// DistanceToCenters //////////////////////////////////////////
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CV_ENUM(DistType, NORM_L1, NORM_L2SQR);
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CV_ENUM(DistType, NORM_L1, NORM_L2SQR)
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PARAM_TEST_CASE(distanceToCenters, DistType, bool)
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{
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cv::Size size;
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int distType;
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bool useRoi;
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cv::Mat src, centers, src_roi, centers_roi;
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cv::ocl::oclMat ocl_src, ocl_centers, ocl_src_roi, ocl_centers_roi;
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Mat src, centers, src_roi, centers_roi;
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ocl::oclMat ocl_src, ocl_centers, ocl_src_roi, ocl_centers_roi;
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virtual void SetUp()
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{
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@ -177,70 +177,59 @@ PARAM_TEST_CASE(distanceToCenters, DistType, bool)
|
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|
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void random_roi()
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{
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Size roiSize_src = randomSize(10,1000);
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Size roiSize_centers = randomSize(10, 1000);
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roiSize_src.width = roiSize_centers.width;
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Size roiSizeSrc = randomSize(1, MAX_VALUE);
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Size roiSizeCenters = randomSize(1, MAX_VALUE);
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roiSizeSrc.width = roiSizeCenters.width;
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||||
|
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Border srcBorder = randomBorder(0, useRoi ? 500 : 0);
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randomSubMat(src, src_roi, roiSize_src, srcBorder, CV_32FC1, -SHRT_MAX, SHRT_MAX);
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Border srcBorder = randomBorder(0, useRoi ? MAX_VALUE : 0);
|
||||
randomSubMat(src, src_roi, roiSizeSrc, srcBorder, CV_32FC1, -MAX_VALUE, MAX_VALUE);
|
||||
|
||||
Border centersBorder = randomBorder(0, useRoi ? 500 : 0);
|
||||
randomSubMat(centers, centers_roi, roiSize_centers, centersBorder, CV_32FC1, -SHRT_MAX, SHRT_MAX);
|
||||
randomSubMat(centers, centers_roi, roiSizeCenters, centersBorder, CV_32FC1, -MAX_VALUE, MAX_VALUE);
|
||||
|
||||
for(int i = 0; i<centers.rows; i++)
|
||||
centers.at<float>(i, randomInt(0,centers.cols-1)) = (float)randomDouble(SHRT_MAX, INT_MAX);
|
||||
|
||||
generateOclMat(ocl_src, ocl_src_roi, src, roiSize_src, srcBorder);
|
||||
generateOclMat(ocl_centers, ocl_centers_roi, centers, roiSize_centers, centersBorder);
|
||||
for (int i = 0; i < centers.rows; i++)
|
||||
centers.at<float>(i, randomInt(0, centers.cols)) = (float)randomDouble(SHRT_MAX, INT_MAX);
|
||||
|
||||
generateOclMat(ocl_src, ocl_src_roi, src, roiSizeSrc, srcBorder);
|
||||
generateOclMat(ocl_centers, ocl_centers_roi, centers, roiSizeCenters, centersBorder);
|
||||
}
|
||||
|
||||
};
|
||||
|
||||
OCL_TEST_P(distanceToCenters, Accuracy)
|
||||
{
|
||||
for(int j = 0; j< LOOP_TIMES; j++)
|
||||
for (int j = 0; j < LOOP_TIMES; j++)
|
||||
{
|
||||
random_roi();
|
||||
|
||||
cv::ocl::oclMat ocl_dists;
|
||||
cv::ocl::oclMat ocl_labels;
|
||||
|
||||
cv::ocl::distanceToCenters(ocl_dists,ocl_labels,ocl_src_roi, ocl_centers_roi, distType);
|
||||
|
||||
Mat labels, dists;
|
||||
ocl_labels.download(labels);
|
||||
ocl_dists.download(dists);
|
||||
ocl::distanceToCenters(ocl_src_roi, ocl_centers_roi, dists, labels, distType);
|
||||
|
||||
ASSERT_EQ(ocl_dists.cols, ocl_labels.rows);
|
||||
EXPECT_EQ(dists.size(), labels.size());
|
||||
|
||||
Mat batch_dists;
|
||||
|
||||
cv::batchDistance(src_roi, centers_roi, batch_dists, CV_32FC1, noArray(), distType);
|
||||
|
||||
std::vector<double> gold_dists_v;
|
||||
std::vector<float> gold_dists_v;
|
||||
gold_dists_v.reserve(batch_dists.rows);
|
||||
|
||||
for(int i = 0; i<batch_dists.rows; i++)
|
||||
for (int i = 0; i < batch_dists.rows; i++)
|
||||
{
|
||||
Mat r = batch_dists.row(i);
|
||||
double mVal;
|
||||
Point mLoc;
|
||||
minMaxLoc(r, &mVal, NULL, &mLoc, NULL);
|
||||
|
||||
int ocl_label = *(int*)labels.row(i).col(0).data;
|
||||
ASSERT_EQ(mLoc.x, ocl_label);
|
||||
int ocl_label = labels.at<int>(i, 0);
|
||||
EXPECT_EQ(mLoc.x, ocl_label);
|
||||
|
||||
gold_dists_v.push_back(mVal);
|
||||
gold_dists_v.push_back(static_cast<float>(mVal));
|
||||
}
|
||||
Mat gold_dists(gold_dists_v);
|
||||
dists.convertTo(dists, CV_64FC1);
|
||||
double relative_error = cv::norm(gold_dists.t(), dists, NORM_INF|NORM_RELATIVE);
|
||||
|
||||
double relative_error = cv::norm(Mat(gold_dists_v), dists, NORM_INF | NORM_RELATIVE);
|
||||
ASSERT_LE(relative_error, 1e-5);
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
INSTANTIATE_TEST_CASE_P (OCL_ML, distanceToCenters, Combine(DistType::all(), Bool()) );
|
||||
|
||||
INSTANTIATE_TEST_CASE_P (OCL_ML, distanceToCenters, Combine(DistType::all(), Bool()));
|
||||
|
||||
#endif
|
||||
|
||||
Loading…
Reference in New Issue
Block a user