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Replaced most of the usages of parallel_for with that of parallel_for_.

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This should allow many algorithms to take advantage of more parallelization
technologies.
This commit is contained in:
Roman Donchenko
2013-05-30 18:44:33 +04:00
parent 37091b086c
commit 29b13ec1de
24 changed files with 232 additions and 352 deletions
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modules/ml/src/gbt.cpp
+17 -37
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@@ -900,7 +900,7 @@ float CvGBTrees::predict_serial( const CvMat* _sample, const CvMat* _missing,
}
class Tree_predictor
class Tree_predictor : public cv::ParallelLoopBody
{
private:
pCvSeq* weak;
@@ -910,9 +910,7 @@ private:
const CvMat* missing;
const float shrinkage;
#ifdef HAVE_TBB
static tbb::spin_mutex SumMutex;
#endif
static cv::Mutex SumMutex;
public:
@@ -931,14 +929,11 @@ public:
Tree_predictor& operator=( const Tree_predictor& )
{ return *this; }
virtual void operator()(const cv::BlockedRange& range) const
virtual void operator()(const cv::Range& range) const
{
#ifdef HAVE_TBB
tbb::spin_mutex::scoped_lock lock;
#endif
CvSeqReader reader;
int begin = range.begin();
int end = range.end();
int begin = range.start;
int end = range.end;
int weak_count = end - begin;
CvDTree* tree;
@@ -956,13 +951,11 @@ public:
tmp_sum += shrinkage*(float)(tree->predict(sample, missing)->value);
}
}
#ifdef HAVE_TBB
lock.acquire(SumMutex);
sum[i] += tmp_sum;
lock.release();
#else
sum[i] += tmp_sum;
#endif
{
cv::AutoLock lock(SumMutex);
sum[i] += tmp_sum;
}
}
} // Tree_predictor::operator()
@@ -970,11 +963,7 @@ public:
}; // class Tree_predictor
#ifdef HAVE_TBB
tbb::spin_mutex Tree_predictor::SumMutex;
#endif
cv::Mutex Tree_predictor::SumMutex;
float CvGBTrees::predict( const CvMat* _sample, const CvMat* _missing,
@@ -992,12 +981,7 @@ float CvGBTrees::predict( const CvMat* _sample, const CvMat* _missing,
Tree_predictor predictor = Tree_predictor(weak_seq, class_count,
params.shrinkage, _sample, _missing, sum);
//#ifdef HAVE_TBB
// tbb::parallel_for(cv::BlockedRange(begin, end), predictor,
// tbb::auto_partitioner());
//#else
cv::parallel_for(cv::BlockedRange(begin, end), predictor);
//#endif
cv::parallel_for_(cv::Range(begin, end), predictor);
for (int i=0; i<class_count; ++i)
sum[i] = sum[i] /** params.shrinkage*/ + base_value;
@@ -1228,7 +1212,7 @@ void CvGBTrees::read( CvFileStorage* fs, CvFileNode* node )
//===========================================================================
class Sample_predictor
class Sample_predictor : public cv::ParallelLoopBody
{
private:
const CvGBTrees* gbt;
@@ -1258,10 +1242,10 @@ public:
{}
virtual void operator()(const cv::BlockedRange& range) const
virtual void operator()(const cv::Range& range) const
{
int begin = range.begin();
int end = range.end();
int begin = range.start;
int end = range.end;
CvMat x;
CvMat miss;
@@ -1317,11 +1301,7 @@ CvGBTrees::calc_error( CvMLData* _data, int type, std::vector<float> *resp )
Sample_predictor predictor = Sample_predictor(this, pred_resp, _data->get_values(),
_data->get_missing(), _sample_idx);
//#ifdef HAVE_TBB
// tbb::parallel_for(cv::BlockedRange(0,n), predictor, tbb::auto_partitioner());
//#else
cv::parallel_for(cv::BlockedRange(0,n), predictor);
//#endif
cv::parallel_for_(cv::Range(0,n), predictor);
int* sidx = _sample_idx ? _sample_idx->data.i : 0;
int r_step = CV_IS_MAT_CONT(response->type) ?