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Merge pull request #1759 from ilya-lavrenov:ocl_distanceToCenters

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This commit is contained in:
Roman Donchenko
2013-11-08 12:39:13 +04:00
committed by OpenCV Buildbot
parent 0be2752366 56d943388d
commit 800d53f76b
6 changed files with 141 additions and 182 deletions
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94
modules/ocl/src/kmeans.cpp
View File

@@ -160,63 +160,66 @@ static void generateCentersPP(const Mat& _data, Mat& _out_centers,
}
}
void cv::ocl::distanceToCenters(oclMat &dists, oclMat &labels, const oclMat &src, const oclMat &centers, int distType, const oclMat &indices)
void cv::ocl::distanceToCenters(const oclMat &src, const oclMat &centers, Mat &dists, Mat &labels, int distType)
{
CV_Assert(src.cols*src.oclchannels() == centers.cols*centers.oclchannels());
CV_Assert(src.cols * src.channels() == centers.cols * centers.channels());
CV_Assert(src.depth() == CV_32F && centers.depth() == CV_32F);
bool is_label_row_major = false;
ensureSizeIsEnough(1, src.rows, CV_32FC1, dists);
if(labels.empty() || (!labels.empty() && labels.rows == src.rows && labels.cols == 1))
{
ensureSizeIsEnough(src.rows, 1, CV_32SC1, labels);
is_label_row_major = true;
}
CV_Assert(distType == NORM_L1 || distType == NORM_L2SQR);
dists.create(src.rows, 1, CV_32FC1);
labels.create(src.rows, 1, CV_32SC1);
std::stringstream build_opt_ss;
build_opt_ss
<< (distType == NORM_L1 ? "-D L1_DIST" : "-D L2SQR_DIST")
<< (indices.empty() ? "" : " -D USE_INDEX");
build_opt_ss << (distType == NORM_L1 ? "-D L1_DIST" : "-D L2SQR_DIST");
String build_opt = build_opt_ss.str();
int src_step = src.step / src.elemSize1();
int centers_step = centers.step / centers.elemSize1();
int feature_width = centers.cols * centers.oclchannels();
int src_offset = src.offset / src.elemSize1();
int centers_offset = centers.offset / centers.elemSize1();
const int src_step = (int)(src.oclchannels() * src.step / src.elemSize());
const int centers_step = (int)(centers.oclchannels() * centers.step / centers.elemSize());
const int colsNumb = centers.cols*centers.oclchannels();
const int label_step = is_label_row_major ? (int)(labels.step / labels.elemSize()) : 1;
String kernelname = "distanceToCenters";
const int number_of_input = indices.empty() ? src.rows : indices.size().area();
const int src_offset = (int)src.offset/src.elemSize();
const int centers_offset = (int)centers.offset/centers.elemSize();
size_t globalThreads[3] = {number_of_input, 1, 1};
int all_dist_count = src.rows * centers.rows;
oclMat all_dist(1, all_dist_count, CV_32FC1);
vector<pair<size_t, const void *> > args;
args.push_back(make_pair(sizeof(cl_mem), (void *)&src.data));
args.push_back(make_pair(sizeof(cl_mem), (void *)&centers.data));
if(!indices.empty())
{
args.push_back(make_pair(sizeof(cl_mem), (void *)&indices.data));
}
args.push_back(make_pair(sizeof(cl_mem), (void *)&labels.data));
args.push_back(make_pair(sizeof(cl_mem), (void *)&dists.data));
args.push_back(make_pair(sizeof(cl_int), (void *)&colsNumb));
args.push_back(make_pair(sizeof(cl_mem), (void *)&all_dist.data));
args.push_back(make_pair(sizeof(cl_int), (void *)&feature_width));
args.push_back(make_pair(sizeof(cl_int), (void *)&src_step));
args.push_back(make_pair(sizeof(cl_int), (void *)&centers_step));
args.push_back(make_pair(sizeof(cl_int), (void *)&label_step));
args.push_back(make_pair(sizeof(cl_int), (void *)&number_of_input));
args.push_back(make_pair(sizeof(cl_int), (void *)&src.rows));
args.push_back(make_pair(sizeof(cl_int), (void *)&centers.rows));
args.push_back(make_pair(sizeof(cl_int), (void *)&src_offset));
args.push_back(make_pair(sizeof(cl_int), (void *)&centers_offset));
size_t globalThreads[3] = { all_dist_count, 1, 1 };
openCLExecuteKernel(Context::getContext(), &kmeans_kernel,
kernelname, globalThreads, NULL, args, -1, -1, build_opt.c_str());
"distanceToCenters", globalThreads, NULL, args, -1, -1, build_opt_ss.str().c_str());
Mat all_dist_cpu;
all_dist.download(all_dist_cpu);
for (int i = 0; i < src.rows; ++i)
{
Point p;
double minVal;
Rect roi(i * centers.rows, 0, centers.rows, 1);
Mat hdr(all_dist_cpu, roi);
cv::minMaxLoc(hdr, &minVal, NULL, &p);
dists.at<float>(i, 0) = static_cast<float>(minVal);
labels.at<int>(i, 0) = p.x;
}
}
///////////////////////////////////k - means /////////////////////////////////////////////////////////
double cv::ocl::kmeans(const oclMat &_src, int K, oclMat &_bestLabels,
TermCriteria criteria, int attempts, int flags, oclMat &_centers)
{
@@ -429,28 +432,19 @@ double cv::ocl::kmeans(const oclMat &_src, int K, oclMat &_bestLabels,
break;
// assign labels
oclMat _dists(1, N, CV_64F);
_bestLabels.upload(_labels);
Mat dists(1, N, CV_64F);
_centers.upload(centers);
distanceToCenters(_src, _centers, dists, _labels);
_bestLabels.upload(_labels);
distanceToCenters(_dists, _bestLabels, _src, _centers);
Mat dists;
_dists.download(dists);
_bestLabels.download(_labels);
float* dist = dists.ptr<float>(0);
compactness = 0;
for( i = 0; i < N; i++ )
{
compactness += (double)dist[i];
}
compactness += (double)dist[i];
}
if( compactness < best_compactness )
{
best_compactness = compactness;
}
}
return best_compactness;

107
modules/ocl/src/opencl/kmeans_kernel.cl
View File

@@ -44,81 +44,64 @@
//
//M*/
#ifdef L1_DIST
# define DISTANCE(A, B) fabs((A) - (B))
#elif defined L2SQR_DIST
# define DISTANCE(A, B) ((A) - (B)) * ((A) - (B))
#else
# define DISTANCE(A, B) ((A) - (B)) * ((A) - (B))
#endif
inline float dist(__global const float * center, __global const float * src, int feature_cols)
static float distance_(__global const float * center, __global const float * src, int feature_length)
{
float res = 0;
float4 tmp4;
int i;
for(i = 0; i < feature_cols / 4; i += 4, center += 4, src += 4)
{
tmp4 = vload4(0, center) - vload4(0, src);
float4 v0, v1, v2;
int i = 0;
#ifdef L1_DIST
tmp4 = fabs(tmp4);
#else
tmp4 *= tmp4;
float4 sum = (float4)(0.0f);
#endif
for ( ; i <= feature_length - 4; i += 4)
{
v0 = vload4(0, center + i);
v1 = vload4(0, src + i);
v2 = v1 - v0;
#ifdef L1_DIST
v0 = fabs(v2);
sum += v0;
#else
res += dot(v2, v2);
#endif
res += tmp4.x + tmp4.y + tmp4.z + tmp4.w;
}
for(; i < feature_cols; ++i, ++center, ++src)
#ifdef L1_DIST
res = sum.x + sum.y + sum.z + sum.w;
#endif
for ( ; i < feature_length; ++i)
{
res += DISTANCE(*src, *center);
float t0 = src[i];
float t1 = center[i];
#ifdef L1_DIST
res += fabs(t0 - t1);
#else
float t2 = t0 - t1;
res += t2 * t2;
#endif
}
return res;
}
// to be distinguished with distanceToCenters in kmeans_kernel.cl
__kernel void distanceToCenters(
__global const float *src,
__global const float *centers,
#ifdef USE_INDEX
__global const int *indices,
#endif
__global int *labels,
__global float *dists,
int feature_cols,
int src_step,
int centers_step,
int label_step,
int input_size,
int K,
int offset_src,
int offset_centers
)
__kernel void distanceToCenters(__global const float * src, __global const float * centers,
__global float * dists, int feature_length,
int src_step, int centers_step,
int features_count, int centers_count,
int src_offset, int centers_offset)
{
int gid = get_global_id(0);
float euDist, minval;
int minCentroid;
if(gid >= input_size)
if (gid < (features_count * centers_count))
{
return;
int feature_index = gid / centers_count;
int center_index = gid % centers_count;
int center_idx = mad24(center_index, centers_step, centers_offset);
int src_idx = mad24(feature_index, src_step, src_offset);
dists[gid] = distance_(centers + center_idx, src + src_idx, feature_length);
}
src += offset_src;
centers += offset_centers;
#ifdef USE_INDEX
src += indices[gid] * src_step;
#else
src += gid * src_step;
#endif
minval = dist(centers, src, feature_cols);
minCentroid = 0;
for(int i = 1 ; i < K; i++)
{
euDist = dist(centers + i * centers_step, src, feature_cols);
if(euDist < minval)
{
minval = euDist;
minCentroid = i;
}
}
labels[gid * label_step] = minCentroid;
dists[gid] = minval;
}