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core(test): refactor PCA test

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- CV_L2 -> relative NORM_L2
- eigenEps: 1e-6 ==> 1e-4
- evalEps: 1e-6 ==> 1e-5
- evecEps: 1e-3 ==> 5e-3
- RNG seed: 12345
- drop non-informative legacy test code (ts->printf, etc)
This commit is contained in:
Alexander Alekhin 2017-12-05 02:28:05 +03:00 committed by Alexander Alekhin
parent 611cf8d86f
commit d48d2d7fe2
1 changed files with 175 additions and 246 deletions
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183
modules/core/test/test_mat.cpp
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@ -286,13 +286,8 @@ void Core_ReduceTest::run( int )
#define CHECK_C #define CHECK_C
class Core_PCATest : public cvtest::BaseTest TEST(Core_PCA, accuracy)
{ {
public:
Core_PCATest() {}
protected:
void run(int)
{
const Size sz(200, 500); const Size sz(200, 500);
double diffPrjEps, diffBackPrjEps, double diffPrjEps, diffBackPrjEps,
@ -301,7 +296,7 @@ protected:
int maxComponents = 100; int maxComponents = 100;
double retainedVariance = 0.95; double retainedVariance = 0.95;
Mat rPoints(sz, CV_32FC1), rTestPoints(sz, CV_32FC1); Mat rPoints(sz, CV_32FC1), rTestPoints(sz, CV_32FC1);
RNG& rng = ts->get_rng(); RNG rng(12345);
rng.fill( rPoints, RNG::UNIFORM, Scalar::all(0.0), Scalar::all(1.0) ); rng.fill( rPoints, RNG::UNIFORM, Scalar::all(0.0), Scalar::all(1.0) );
rng.fill( rTestPoints, RNG::UNIFORM, Scalar::all(0.0), Scalar::all(1.0) ); rng.fill( rTestPoints, RNG::UNIFORM, Scalar::all(0.0), Scalar::all(1.0) );
@ -326,13 +321,13 @@ protected:
Mat subEval( maxComponents, 1, eval.type(), eval.ptr() ), Mat subEval( maxComponents, 1, eval.type(), eval.ptr() ),
subEvec( maxComponents, evec.cols, evec.type(), evec.ptr() ); subEvec( maxComponents, evec.cols, evec.type(), evec.ptr() );
#ifdef CHECK_C #ifdef CHECK_C
Mat prjTestPoints, backPrjTestPoints, cPoints = rPoints.t(), cTestPoints = rTestPoints.t(); Mat prjTestPoints, backPrjTestPoints, cPoints = rPoints.t(), cTestPoints = rTestPoints.t();
CvMat _points, _testPoints, _avg, _eval, _evec, _prjTestPoints, _backPrjTestPoints; CvMat _points, _testPoints, _avg, _eval, _evec, _prjTestPoints, _backPrjTestPoints;
#endif #endif
// check eigen() // check eigen()
double eigenEps = 1e-6; double eigenEps = 1e-4;
double err; double err;
for(int i = 0; i < Q.rows; i++ ) for(int i = 0; i < Q.rows; i++ )
{ {
@ -340,47 +335,37 @@ protected:
Mat Qv = Q * v; Mat Qv = Q * v;
Mat lv = eval.at<float>(i,0) * v; Mat lv = eval.at<float>(i,0) * v;
err = cvtest::norm( Qv, lv, NORM_L2 ); err = cvtest::norm(Qv, lv, NORM_L2 | NORM_RELATIVE);
if( err > eigenEps ) EXPECT_LE(err, eigenEps) << "bad accuracy of eigen(); i = " << i;
{
ts->printf( cvtest::TS::LOG, "bad accuracy of eigen(); err = %f\n", err );
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
return;
}
} }
// check pca eigenvalues // check pca eigenvalues
evalEps = 1e-6, evecEps = 1e-3; evalEps = 1e-5, evecEps = 5e-3;
err = cvtest::norm( rPCA.eigenvalues, subEval, NORM_L2 ); err = cvtest::norm(rPCA.eigenvalues, subEval, NORM_L2 | NORM_RELATIVE);
if( err > evalEps ) EXPECT_LE(err , evalEps) << "pca.eigenvalues is incorrect (CV_PCA_DATA_AS_ROW)";
{
ts->printf( cvtest::TS::LOG, "pca.eigenvalues is incorrect (CV_PCA_DATA_AS_ROW); err = %f\n", err );
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
return;
}
// check pca eigenvectors // check pca eigenvectors
for(int i = 0; i < subEvec.rows; i++) for(int i = 0; i < subEvec.rows; i++)
{ {
Mat r0 = rPCA.eigenvectors.row(i); Mat r0 = rPCA.eigenvectors.row(i);
Mat r1 = subEvec.row(i); Mat r1 = subEvec.row(i);
err = cvtest::norm( r0, r1, CV_L2 ); // eigenvectors have normalized length, but both directions v and -v are valid
if( err > evecEps ) double err1 = cvtest::norm(r0, r1, NORM_L2 | NORM_RELATIVE);
{ double err2 = cvtest::norm(r0, -r1, NORM_L2 | NORM_RELATIVE);
r1 *= -1; err = std::min(err1, err2);
double err2 = cvtest::norm(r0, r1, CV_L2); if (err > evecEps)
if( err2 > evecEps )
{ {
Mat tmp; Mat tmp;
absdiff(rPCA.eigenvectors, subEvec, tmp); absdiff(rPCA.eigenvectors, subEvec, tmp);
double mval = 0; Point mloc; double mval = 0; Point mloc;
minMaxLoc(tmp, 0, &mval, 0, &mloc); minMaxLoc(tmp, 0, &mval, 0, &mloc);
ts->printf( cvtest::TS::LOG, "pca.eigenvectors is incorrect (CV_PCA_DATA_AS_ROW); err = %f\n", err ); EXPECT_LE(err, evecEps) << "pca.eigenvectors is incorrect (CV_PCA_DATA_AS_ROW) at " << i << " "
ts->printf( cvtest::TS::LOG, "max diff is %g at (i=%d, j=%d) (%g vs %g)\n", << cv::format("max diff is %g at (i=%d, j=%d) (%g vs %g)\n",
mval, mloc.y, mloc.x, rPCA.eigenvectors.at<float>(mloc.y, mloc.x), mval, mloc.y, mloc.x, rPCA.eigenvectors.at<float>(mloc.y, mloc.x),
subEvec.at<float>(mloc.y, mloc.x)); subEvec.at<float>(mloc.y, mloc.x))
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); << "r0=" << r0 << std::endl
return; << "r1=" << r1 << std::endl
} << "err1=" << err1 << " err2=" << err2
;
} }
} }
@ -390,21 +375,19 @@ protected:
// check pca project // check pca project
Mat subEvec_t = subEvec.t(); Mat subEvec_t = subEvec.t();
Mat prj = rTestPoints.row(i) - avg; prj *= subEvec_t; Mat prj = rTestPoints.row(i) - avg; prj *= subEvec_t;
err = cvtest::norm(rPrjTestPoints.row(i), prj, CV_RELATIVE_L2); err = cvtest::norm(rPrjTestPoints.row(i), prj, NORM_L2 | NORM_RELATIVE);
if( err > prjEps ) if (err < prjEps)
{ {
ts->printf( cvtest::TS::LOG, "bad accuracy of project() (CV_PCA_DATA_AS_ROW); err = %f\n", err ); EXPECT_LE(err, prjEps) << "bad accuracy of project() (CV_PCA_DATA_AS_ROW)";
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); continue;
return;
} }
// check pca backProject // check pca backProject
Mat backPrj = rPrjTestPoints.row(i) * subEvec + avg; Mat backPrj = rPrjTestPoints.row(i) * subEvec + avg;
err = cvtest::norm( rBackPrjTestPoints.row(i), backPrj, CV_RELATIVE_L2 ); err = cvtest::norm(rBackPrjTestPoints.row(i), backPrj, NORM_L2 | NORM_RELATIVE);
if( err > backPrjEps ) if (err > backPrjEps)
{ {
ts->printf( cvtest::TS::LOG, "bad accuracy of backProject() (CV_PCA_DATA_AS_ROW); err = %f\n", err ); EXPECT_LE(err, backPrjEps) << "bad accuracy of backProject() (CV_PCA_DATA_AS_ROW)";
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); continue;
return;
} }
} }
@ -412,20 +395,10 @@ protected:
cPCA( rPoints.t(), Mat(), CV_PCA_DATA_AS_COL, maxComponents ); cPCA( rPoints.t(), Mat(), CV_PCA_DATA_AS_COL, maxComponents );
diffPrjEps = 1, diffBackPrjEps = 1; diffPrjEps = 1, diffBackPrjEps = 1;
Mat ocvPrjTestPoints = cPCA.project(rTestPoints.t()); Mat ocvPrjTestPoints = cPCA.project(rTestPoints.t());
err = cvtest::norm(cv::abs(ocvPrjTestPoints), cv::abs(rPrjTestPoints.t()), CV_RELATIVE_L2 ); err = cvtest::norm(cv::abs(ocvPrjTestPoints), cv::abs(rPrjTestPoints.t()), NORM_L2 | NORM_RELATIVE);
if( err > diffPrjEps ) ASSERT_LE(err, diffPrjEps) << "bad accuracy of project() (CV_PCA_DATA_AS_COL)";
{ err = cvtest::norm(cPCA.backProject(ocvPrjTestPoints), rBackPrjTestPoints.t(), NORM_L2 | NORM_RELATIVE);
ts->printf( cvtest::TS::LOG, "bad accuracy of project() (CV_PCA_DATA_AS_COL); err = %f\n", err ); ASSERT_LE(err, diffBackPrjEps) << "bad accuracy of backProject() (CV_PCA_DATA_AS_COL)";
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
return;
}
err = cvtest::norm(cPCA.backProject(ocvPrjTestPoints), rBackPrjTestPoints.t(), CV_RELATIVE_L2 );
if( err > diffBackPrjEps )
{
ts->printf( cvtest::TS::LOG, "bad accuracy of backProject() (CV_PCA_DATA_AS_COL); err = %f\n", err );
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
return;
}
// 3. check C++ PCA w/retainedVariance // 3. check C++ PCA w/retainedVariance
cPCA( rPoints.t(), Mat(), CV_PCA_DATA_AS_COL, retainedVariance ); cPCA( rPoints.t(), Mat(), CV_PCA_DATA_AS_COL, retainedVariance );
@ -433,25 +406,15 @@ protected:
Mat rvPrjTestPoints = cPCA.project(rTestPoints.t()); Mat rvPrjTestPoints = cPCA.project(rTestPoints.t());
if( cPCA.eigenvectors.rows > maxComponents) if( cPCA.eigenvectors.rows > maxComponents)
err = cvtest::norm(cv::abs(rvPrjTestPoints.rowRange(0,maxComponents)), cv::abs(rPrjTestPoints.t()), CV_RELATIVE_L2 ); err = cvtest::norm(cv::abs(rvPrjTestPoints.rowRange(0,maxComponents)), cv::abs(rPrjTestPoints.t()), NORM_L2 | NORM_RELATIVE);
else else
err = cvtest::norm(cv::abs(rvPrjTestPoints), cv::abs(rPrjTestPoints.colRange(0,cPCA.eigenvectors.rows).t()), CV_RELATIVE_L2 ); err = cvtest::norm(cv::abs(rvPrjTestPoints), cv::abs(rPrjTestPoints.colRange(0,cPCA.eigenvectors.rows).t()), NORM_L2 | NORM_RELATIVE);
if( err > diffPrjEps ) ASSERT_LE(err, diffPrjEps) << "bad accuracy of project() (CV_PCA_DATA_AS_COL); retainedVariance=" << retainedVariance;
{ err = cvtest::norm(cPCA.backProject(rvPrjTestPoints), rBackPrjTestPoints.t(), NORM_L2 | NORM_RELATIVE);
ts->printf( cvtest::TS::LOG, "bad accuracy of project() (CV_PCA_DATA_AS_COL); retainedVariance=0.95; err = %f\n", err ); ASSERT_LE(err, diffBackPrjEps) << "bad accuracy of backProject() (CV_PCA_DATA_AS_COL); retainedVariance=" << retainedVariance;
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
return;
}
err = cvtest::norm(cPCA.backProject(rvPrjTestPoints), rBackPrjTestPoints.t(), CV_RELATIVE_L2 );
if( err > diffBackPrjEps )
{
ts->printf( cvtest::TS::LOG, "bad accuracy of backProject() (CV_PCA_DATA_AS_COL); retainedVariance=0.95; err = %f\n", err );
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
return;
}
#ifdef CHECK_C #ifdef CHECK_C
// 4. check C PCA & ROW // 4. check C PCA & ROW
_points = rPoints; _points = rPoints;
_testPoints = rTestPoints; _testPoints = rTestPoints;
@ -467,20 +430,10 @@ protected:
cvProjectPCA( &_testPoints, &_avg, &_evec, &_prjTestPoints ); cvProjectPCA( &_testPoints, &_avg, &_evec, &_prjTestPoints );
cvBackProjectPCA( &_prjTestPoints, &_avg, &_evec, &_backPrjTestPoints ); cvBackProjectPCA( &_prjTestPoints, &_avg, &_evec, &_backPrjTestPoints );
err = cvtest::norm(prjTestPoints, rPrjTestPoints, CV_RELATIVE_L2); err = cvtest::norm(prjTestPoints, rPrjTestPoints, NORM_L2 | NORM_RELATIVE);
if( err > diffPrjEps ) ASSERT_LE(err, diffPrjEps) << "bad accuracy of cvProjectPCA() (CV_PCA_DATA_AS_ROW)";
{ err = cvtest::norm(backPrjTestPoints, rBackPrjTestPoints, NORM_L2 | NORM_RELATIVE);
ts->printf( cvtest::TS::LOG, "bad accuracy of cvProjectPCA() (CV_PCA_DATA_AS_ROW); err = %f\n", err ); ASSERT_LE(err, diffBackPrjEps) << "bad accuracy of cvBackProjectPCA() (CV_PCA_DATA_AS_ROW)";
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
return;
}
err = cvtest::norm(backPrjTestPoints, rBackPrjTestPoints, CV_RELATIVE_L2);
if( err > diffBackPrjEps )
{
ts->printf( cvtest::TS::LOG, "bad accuracy of cvBackProjectPCA() (CV_PCA_DATA_AS_ROW); err = %f\n", err );
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
return;
}
// 5. check C PCA & COL // 5. check C PCA & COL
_points = cPoints; _points = cPoints;
@ -495,21 +448,11 @@ protected:
cvProjectPCA( &_testPoints, &_avg, &_evec, &_prjTestPoints ); cvProjectPCA( &_testPoints, &_avg, &_evec, &_prjTestPoints );
cvBackProjectPCA( &_prjTestPoints, &_avg, &_evec, &_backPrjTestPoints ); cvBackProjectPCA( &_prjTestPoints, &_avg, &_evec, &_backPrjTestPoints );
err = cvtest::norm(cv::abs(prjTestPoints), cv::abs(rPrjTestPoints.t()), CV_RELATIVE_L2 ); err = cvtest::norm(cv::abs(prjTestPoints), cv::abs(rPrjTestPoints.t()), NORM_L2 | NORM_RELATIVE);
if( err > diffPrjEps ) ASSERT_LE(err, diffPrjEps) << "bad accuracy of cvProjectPCA() (CV_PCA_DATA_AS_COL)";
{ err = cvtest::norm(backPrjTestPoints, rBackPrjTestPoints.t(), NORM_L2 | NORM_RELATIVE);
ts->printf( cvtest::TS::LOG, "bad accuracy of cvProjectPCA() (CV_PCA_DATA_AS_COL); err = %f\n", err ); ASSERT_LE(err, diffBackPrjEps) << "bad accuracy of cvBackProjectPCA() (CV_PCA_DATA_AS_COL)";
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); #endif
return;
}
err = cvtest::norm(backPrjTestPoints, rBackPrjTestPoints.t(), CV_RELATIVE_L2);
if( err > diffBackPrjEps )
{
ts->printf( cvtest::TS::LOG, "bad accuracy of cvBackProjectPCA() (CV_PCA_DATA_AS_COL); err = %f\n", err );
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
return;
}
#endif
// Test read and write // Test read and write
FileStorage fs( "PCA_store.yml", FileStorage::WRITE ); FileStorage fs( "PCA_store.yml", FileStorage::WRITE );
rPCA.write( fs ); rPCA.write( fs );
@ -518,26 +461,13 @@ protected:
PCA lPCA; PCA lPCA;
fs.open( "PCA_store.yml", FileStorage::READ ); fs.open( "PCA_store.yml", FileStorage::READ );
lPCA.read( fs.root() ); lPCA.read( fs.root() );
err = cvtest::norm( rPCA.eigenvectors, lPCA.eigenvectors, CV_RELATIVE_L2 ); err = cvtest::norm(rPCA.eigenvectors, lPCA.eigenvectors, NORM_L2 | NORM_RELATIVE);
if( err > 0 ) EXPECT_LE(err, 0) << "bad accuracy of write/load functions (YML)";
{ err = cvtest::norm(rPCA.eigenvalues, lPCA.eigenvalues, NORM_L2 | NORM_RELATIVE);
ts->printf( cvtest::TS::LOG, "bad accuracy of write/load functions (YML); err = %f\n", err ); EXPECT_LE(err, 0) << "bad accuracy of write/load functions (YML)";
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); err = cvtest::norm(rPCA.mean, lPCA.mean, NORM_L2 | NORM_RELATIVE);
} EXPECT_LE(err, 0) << "bad accuracy of write/load functions (YML)";
err = cvtest::norm( rPCA.eigenvalues, lPCA.eigenvalues, CV_RELATIVE_L2 ); }
if( err > 0 )
{
ts->printf( cvtest::TS::LOG, "bad accuracy of write/load functions (YML); err = %f\n", err );
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
}
err = cvtest::norm( rPCA.mean, lPCA.mean, CV_RELATIVE_L2 );
if( err > 0 )
{
ts->printf( cvtest::TS::LOG, "bad accuracy of write/load functions (YML); err = %f\n", err );
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
}
}
};
class Core_ArrayOpTest : public cvtest::BaseTest class Core_ArrayOpTest : public cvtest::BaseTest
{ {
@ -1227,7 +1157,6 @@ protected:
} }
}; };
TEST(Core_PCA, accuracy) { Core_PCATest test; test.safe_run(); }
TEST(Core_Reduce, accuracy) { Core_ReduceTest test; test.safe_run(); } TEST(Core_Reduce, accuracy) { Core_ReduceTest test; test.safe_run(); }
TEST(Core_Array, basic_operations) { Core_ArrayOpTest test; test.safe_run(); } TEST(Core_Array, basic_operations) { Core_ArrayOpTest test; test.safe_run(); }