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Merge pull request #9692 from alalek:dnn_perf_net

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This commit is contained in:
Vadim Pisarevsky
2017-09-28 13:44:24 +00:00
parent 68cc2e292d 78788e1efb
commit 22467599c4
10 changed files with 239 additions and 247 deletions
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modules/dnn/perf/perf_convolution.cpp
+8 -19
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@@ -1,27 +1,15 @@
#include "perf_precomp.hpp"
#include <opencv2/dnn/shape_utils.hpp>
namespace cvtest
namespace
{
using std::tr1::tuple;
using std::tr1::get;
using std::tr1::make_tuple;
using std::make_pair;
using namespace perf;
using namespace testing;
using namespace cv;
using namespace cv::dnn;
enum {STRIDE_OFF = 1, STRIDE_ON = 2};
CV_ENUM(StrideSize, STRIDE_OFF, STRIDE_ON);
enum {GROUP_OFF = 1, GROUP_2 = 2};
CV_ENUM(GroupSize, GROUP_OFF, GROUP_2);
//Squared Size
#define SSZ(n) cv::Size(n, n)
typedef std::pair<MatShape, int> InpShapeNumOut;
typedef tuple<Size, InpShapeNumOut, GroupSize, StrideSize> ConvParam; //kernel_size, inp shape, groups, stride
typedef TestBaseWithParam<ConvParam> ConvolutionPerfTest;
@@ -77,11 +65,11 @@ PERF_TEST_P( ConvolutionPerfTest, perf, Combine(
Ptr<Layer> layer = cv::dnn::LayerFactory::createLayerInstance("Convolution", lp);
std::vector<MatShape> inputShapes(1, shape(inpBlob)), outShapes, internals;
layer->getMemoryShapes(inputShapes, 0, outShapes, internals);
for (int i = 0; i < outShapes.size(); i++)
for (size_t i = 0; i < outShapes.size(); i++)
{
outBlobs.push_back(Mat(outShapes[i], CV_32F));
}
for (int i = 0; i < internals.size(); i++)
for (size_t i = 0; i < internals.size(); i++)
{
internalBlobs.push_back(Mat());
if (total(internals[i]))
@@ -95,12 +83,13 @@ PERF_TEST_P( ConvolutionPerfTest, perf, Combine(
Mat outBlob2D = outBlobs[0].reshape(1, outBlobs[0].size[0]);
declare.in(inpBlob2D, wgtBlob2D, WARMUP_RNG).out(outBlob2D).tbb_threads(cv::getNumThreads());
TEST_CYCLE_N(10)
{
layer->forward(inpBlobs, outBlobs, internalBlobs); /// warmup
PERF_SAMPLE_BEGIN()
layer->forward(inpBlobs, outBlobs, internalBlobs);
}
PERF_SAMPLE_END()
SANITY_CHECK_NOTHING();
}
}
} // namespace
modules/dnn/perf/perf_halide_net.cpp
-174
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@@ -1,174 +0,0 @@
// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
//
// Copyright (C) 2017, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
#include "perf_precomp.hpp"
namespace cvtest
{
#ifdef HAVE_HALIDE
using namespace cv;
using namespace dnn;
static void loadNet(std::string weights, std::string proto, std::string scheduler,
int inWidth, int inHeight, const std::string& outputLayer,
const std::string& framework, int targetId, Net* net)
{
Mat input(inHeight, inWidth, CV_32FC3);
randu(input, 0.0f, 1.0f);
weights = findDataFile(weights, false);
if (!proto.empty())
proto = findDataFile(proto, false);
if (!scheduler.empty())
scheduler = findDataFile(scheduler, false);
if (framework == "caffe")
{
*net = cv::dnn::readNetFromCaffe(proto, weights);
}
else if (framework == "torch")
{
*net = cv::dnn::readNetFromTorch(weights);
}
else if (framework == "tensorflow")
{
*net = cv::dnn::readNetFromTensorflow(weights);
}
else
CV_Error(Error::StsNotImplemented, "Unknown framework " + framework);
net->setInput(blobFromImage(input, 1.0, Size(), Scalar(), false));
net->setPreferableBackend(DNN_BACKEND_HALIDE);
net->setPreferableTarget(targetId);
net->setHalideScheduler(scheduler);
net->forward(outputLayer);
}
////////////////////////////////////////////////////////////////////////////////
// CPU target
////////////////////////////////////////////////////////////////////////////////
PERF_TEST(GoogLeNet, HalidePerfTest)
{
Net net;
loadNet("dnn/bvlc_googlenet.caffemodel", "dnn/bvlc_googlenet.prototxt",
"", 224, 224, "prob", "caffe", DNN_TARGET_CPU, &net);
TEST_CYCLE() net.forward();
SANITY_CHECK_NOTHING();
}
PERF_TEST(AlexNet, HalidePerfTest)
{
Net net;
loadNet("dnn/bvlc_alexnet.caffemodel", "dnn/bvlc_alexnet.prototxt",
"dnn/halide_scheduler_alexnet.yml", 227, 227, "prob", "caffe",
DNN_TARGET_CPU, &net);
TEST_CYCLE() net.forward();
SANITY_CHECK_NOTHING();
}
PERF_TEST(ResNet50, HalidePerfTest)
{
Net net;
loadNet("dnn/ResNet-50-model.caffemodel", "dnn/ResNet-50-deploy.prototxt",
"dnn/halide_scheduler_resnet_50.yml", 224, 224, "prob", "caffe",
DNN_TARGET_CPU, &net);
TEST_CYCLE() net.forward();
SANITY_CHECK_NOTHING();
}
PERF_TEST(SqueezeNet_v1_1, HalidePerfTest)
{
Net net;
loadNet("dnn/squeezenet_v1.1.caffemodel", "dnn/squeezenet_v1.1.prototxt",
"dnn/halide_scheduler_squeezenet_v1_1.yml", 227, 227, "prob",
"caffe", DNN_TARGET_CPU, &net);
TEST_CYCLE() net.forward();
SANITY_CHECK_NOTHING();
}
PERF_TEST(Inception_5h, HalidePerfTest)
{
Net net;
loadNet("dnn/tensorflow_inception_graph.pb", "",
"dnn/halide_scheduler_inception_5h.yml",
224, 224, "softmax2", "tensorflow", DNN_TARGET_CPU, &net);
TEST_CYCLE() net.forward("softmax2");
SANITY_CHECK_NOTHING();
}
PERF_TEST(ENet, HalidePerfTest)
{
Net net;
loadNet("dnn/Enet-model-best.net", "", "dnn/halide_scheduler_enet.yml",
512, 256, "l367_Deconvolution", "torch", DNN_TARGET_CPU, &net);
TEST_CYCLE() net.forward();
SANITY_CHECK_NOTHING();
}
////////////////////////////////////////////////////////////////////////////////
// OpenCL target
////////////////////////////////////////////////////////////////////////////////
PERF_TEST(GoogLeNet_opencl, HalidePerfTest)
{
Net net;
loadNet("dnn/bvlc_googlenet.caffemodel", "dnn/bvlc_googlenet.prototxt",
"", 227, 227, "prob", "caffe", DNN_TARGET_OPENCL, &net);
TEST_CYCLE() net.forward();
SANITY_CHECK_NOTHING();
}
PERF_TEST(AlexNet_opencl, HalidePerfTest)
{
Net net;
loadNet("dnn/bvlc_alexnet.caffemodel", "dnn/bvlc_alexnet.prototxt",
"dnn/halide_scheduler_opencl_alexnet.yml", 227, 227, "prob", "caffe",
DNN_TARGET_OPENCL, &net);
TEST_CYCLE() net.forward();
SANITY_CHECK_NOTHING();
}
PERF_TEST(ResNet50_opencl, HalidePerfTest)
{
Net net;
loadNet("dnn/ResNet-50-model.caffemodel", "dnn/ResNet-50-deploy.prototxt",
"dnn/halide_scheduler_opencl_resnet_50.yml", 224, 224, "prob", "caffe",
DNN_TARGET_OPENCL, &net);
TEST_CYCLE() net.forward();
SANITY_CHECK_NOTHING();
}
PERF_TEST(SqueezeNet_v1_1_opencl, HalidePerfTest)
{
Net net;
loadNet("dnn/squeezenet_v1.1.caffemodel", "dnn/squeezenet_v1.1.prototxt",
"dnn/halide_scheduler_opencl_squeezenet_v1_1.yml", 227, 227, "prob",
"caffe", DNN_TARGET_OPENCL, &net);
TEST_CYCLE() net.forward();
SANITY_CHECK_NOTHING();
}
PERF_TEST(Inception_5h_opencl, HalidePerfTest)
{
Net net;
loadNet("dnn/tensorflow_inception_graph.pb", "",
"dnn/halide_scheduler_opencl_inception_5h.yml",
224, 224, "softmax2", "tensorflow", DNN_TARGET_OPENCL, &net);
TEST_CYCLE() net.forward("softmax2");
SANITY_CHECK_NOTHING();
}
PERF_TEST(ENet_opencl, HalidePerfTest)
{
Net net;
loadNet("dnn/Enet-model-best.net", "", "dnn/halide_scheduler_opencl_enet.yml",
512, 256, "l367_Deconvolution", "torch", DNN_TARGET_OPENCL, &net);
TEST_CYCLE() net.forward();
SANITY_CHECK_NOTHING();
}
#endif // HAVE_HALIDE
} // namespace cvtest
modules/dnn/perf/perf_net.cpp
+149
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@@ -0,0 +1,149 @@
// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
//
// Copyright (C) 2017, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
#include "perf_precomp.hpp"
#include "opencv2/core/ocl.hpp"
#include "opencv2/dnn/shape_utils.hpp"
namespace
{
#ifdef HAVE_HALIDE
#define TEST_DNN_BACKEND DNN_BACKEND_DEFAULT, DNN_BACKEND_HALIDE
#else
#define TEST_DNN_BACKEND DNN_BACKEND_DEFAULT
#endif
#define TEST_DNN_TARGET DNN_TARGET_CPU, DNN_TARGET_OPENCL
CV_ENUM(DNNBackend, DNN_BACKEND_DEFAULT, DNN_BACKEND_HALIDE)
CV_ENUM(DNNTarget, DNN_TARGET_CPU, DNN_TARGET_OPENCL)
class DNNTestNetwork : public ::perf::TestBaseWithParam< tuple<DNNBackend, DNNTarget> >
{
public:
dnn::Backend backend;
dnn::Target target;
dnn::Net net;
void processNet(std::string weights, std::string proto, std::string halide_scheduler,
int inWidth, int inHeight, const std::string& outputLayer,
const std::string& framework)
{
backend = (dnn::Backend)(int)get<0>(GetParam());
target = (dnn::Target)(int)get<1>(GetParam());
if (backend == DNN_BACKEND_DEFAULT && target == DNN_TARGET_OPENCL)
{
#if 0 //defined(HAVE_OPENCL)
if (!cv::ocl::useOpenCL())
#endif
{
throw ::SkipTestException("OpenCL is not available/disabled in OpenCV");
}
}
Mat input(inHeight, inWidth, CV_32FC3);
randu(input, 0.0f, 1.0f);
weights = findDataFile(weights, false);
if (!proto.empty())
proto = findDataFile(proto, false);
if (!halide_scheduler.empty() && backend == DNN_BACKEND_HALIDE)
halide_scheduler = findDataFile(std::string("dnn/halide_scheduler_") + (target == DNN_TARGET_OPENCL ? "opencl_" : "") + halide_scheduler, true);
if (framework == "caffe")
{
net = cv::dnn::readNetFromCaffe(proto, weights);
}
else if (framework == "torch")
{
net = cv::dnn::readNetFromTorch(weights);
}
else if (framework == "tensorflow")
{
net = cv::dnn::readNetFromTensorflow(weights);
}
else
CV_Error(Error::StsNotImplemented, "Unknown framework " + framework);
net.setInput(blobFromImage(input, 1.0, Size(), Scalar(), false));
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
if (backend == DNN_BACKEND_HALIDE)
{
net.setHalideScheduler(halide_scheduler);
}
MatShape netInputShape = shape(1, 3, inHeight, inWidth);
size_t weightsMemory = 0, blobsMemory = 0;
net.getMemoryConsumption(netInputShape, weightsMemory, blobsMemory);
int64 flops = net.getFLOPS(netInputShape);
net.forward(outputLayer); // warmup
std::cout << "Memory consumption:" << std::endl;
std::cout << " Weights(parameters): " << divUp(weightsMemory, 1u<<20) << " Mb" << std::endl;
std::cout << " Blobs: " << divUp(blobsMemory, 1u<<20) << " Mb" << std::endl;
std::cout << "Calculation complexity: " << flops * 1e-9 << " GFlops" << std::endl;
PERF_SAMPLE_BEGIN()
net.forward();
PERF_SAMPLE_END()
SANITY_CHECK_NOTHING();
}
};
PERF_TEST_P_(DNNTestNetwork, AlexNet)
{
processNet("dnn/bvlc_alexnet.caffemodel", "dnn/bvlc_alexnet.prototxt",
"alexnet.yml", 227, 227, "prob", "caffe");
}
PERF_TEST_P_(DNNTestNetwork, GoogLeNet)
{
processNet("dnn/bvlc_googlenet.caffemodel", "dnn/bvlc_googlenet.prototxt",
"", 224, 224, "prob", "caffe");
}
PERF_TEST_P_(DNNTestNetwork, ResNet50)
{
processNet("dnn/ResNet-50-model.caffemodel", "dnn/ResNet-50-deploy.prototxt",
"resnet_50.yml", 224, 224, "prob", "caffe");
}
PERF_TEST_P_(DNNTestNetwork, SqueezeNet_v1_1)
{
processNet("dnn/squeezenet_v1.1.caffemodel", "dnn/squeezenet_v1.1.prototxt",
"squeezenet_v1_1.yml", 227, 227, "prob", "caffe");
}
PERF_TEST_P_(DNNTestNetwork, Inception_5h)
{
processNet("dnn/tensorflow_inception_graph.pb", "",
"inception_5h.yml",
224, 224, "softmax2", "tensorflow");
}
PERF_TEST_P_(DNNTestNetwork, ENet)
{
processNet("dnn/Enet-model-best.net", "", "enet.yml",
512, 256, "l367_Deconvolution", "torch");
}
INSTANTIATE_TEST_CASE_P(/*nothing*/, DNNTestNetwork,
testing::Combine(
::testing::Values(TEST_DNN_BACKEND),
DNNTarget::all()
)
);
} // namespace
modules/dnn/perf/perf_precomp.hpp
+5 -8
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@@ -1,11 +1,3 @@
#ifdef __GNUC__
# pragma GCC diagnostic ignored "-Wmissing-declarations"
# if defined __clang__ || defined __APPLE__
# pragma GCC diagnostic ignored "-Wmissing-prototypes"
# pragma GCC diagnostic ignored "-Wextra"
# endif
#endif
#ifndef __OPENCV_PERF_PRECOMP_HPP__
#define __OPENCV_PERF_PRECOMP_HPP__
@@ -14,4 +6,9 @@
#include <opencv2/highgui.hpp>
#include <opencv2/dnn.hpp>
using namespace cvtest;
using namespace perf;
using namespace cv;
using namespace dnn;
#endif
modules/ts/include/opencv2/ts.hpp
+9 -2
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@@ -58,8 +58,8 @@
# define GTEST_USES_POSIX_RE 0
#endif
#define PARAM_TEST_CASE(name, ...) struct name : testing::TestWithParam< std::tr1::tuple< __VA_ARGS__ > >
#define GET_PARAM(k) std::tr1::get< k >(GetParam())
#define PARAM_TEST_CASE(name, ...) struct name : testing::TestWithParam< testing::tuple< __VA_ARGS__ > >
#define GET_PARAM(k) testing::get< k >(GetParam())
namespace cvtest
{
@@ -70,6 +70,13 @@ using namespace cv;
using testing::Values;
using testing::Combine;
// Tuple stuff from Google Tests
using testing::get;
using testing::make_tuple;
using testing::tuple;
using testing::tuple_size;
using testing::tuple_element;
class SkipTestException: public cv::Exception
{
modules/ts/include/opencv2/ts/cuda_perf.hpp
+2 -2
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@@ -62,9 +62,9 @@ namespace perf
#define CUDA_CHANNELS_1_3_4 testing::Values(MatCn(Gray), MatCn(BGR), MatCn(BGRA))
#define CUDA_CHANNELS_1_3 testing::Values(MatCn(Gray), MatCn(BGR))
#define GET_PARAM(k) std::tr1::get< k >(GetParam())
#define GET_PARAM(k) testing::get< k >(GetParam())
#define DEF_PARAM_TEST(name, ...) typedef ::perf::TestBaseWithParam< std::tr1::tuple< __VA_ARGS__ > > name
#define DEF_PARAM_TEST(name, ...) typedef ::perf::TestBaseWithParam< testing::tuple< __VA_ARGS__ > > name
#define DEF_PARAM_TEST_1(name, param_type) typedef ::perf::TestBaseWithParam< param_type > name
DEF_PARAM_TEST_1(Sz, cv::Size);
modules/ts/include/opencv2/ts/ocl_perf.hpp
-3
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@@ -52,9 +52,6 @@ namespace ocl {
using namespace perf;
using std::tr1::get;
using std::tr1::tuple;
#define OCL_PERF_STRATEGY PERF_STRATEGY_SIMPLE
#define OCL_PERF_TEST(fixture, name) SIMPLE_PERF_TEST(fixture, name)
modules/ts/include/opencv2/ts/ocl_test.hpp
+1 -1
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@@ -325,7 +325,7 @@ struct CV_EXPORTS TSTestWithParam : public TestUtils, public ::testing::TestWith
};
#undef PARAM_TEST_CASE
#define PARAM_TEST_CASE(name, ...) struct name : public ::cvtest::ocl::TSTestWithParam< std::tr1::tuple< __VA_ARGS__ > >
#define PARAM_TEST_CASE(name, ...) struct name : public ::cvtest::ocl::TSTestWithParam< testing::tuple< __VA_ARGS__ > >
#ifndef IMPLEMENT_PARAM_CLASS
#define IMPLEMENT_PARAM_CLASS(name, type) \
modules/ts/include/opencv2/ts/ts_ext.hpp
+15 -12
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@@ -12,17 +12,17 @@ namespace cvtest {
void checkIppStatus();
}
#define CV_TEST_INIT \
#define CV__TEST_INIT \
cv::ipp::setIppStatus(0); \
cv::theRNG().state = cvtest::param_seed;
#define CV_TEST_CLEANUP ::cvtest::checkIppStatus();
#define CV_TEST_BODY_IMPL(name) \
#define CV__TEST_CLEANUP ::cvtest::checkIppStatus();
#define CV__TEST_BODY_IMPL(name) \
{ \
CV__TRACE_APP_FUNCTION_NAME(name); \
try { \
CV_TEST_INIT \
CV__TEST_INIT \
Body(); \
CV_TEST_CLEANUP \
CV__TEST_CLEANUP \
} \
catch (cvtest::SkipTestException& e) \
{ \
@@ -54,7 +54,7 @@ void checkIppStatus();
::testing::Test::TearDownTestCase, \
new ::testing::internal::TestFactoryImpl<\
GTEST_TEST_CLASS_NAME_(test_case_name, test_name)>);\
void GTEST_TEST_CLASS_NAME_(test_case_name, test_name)::TestBody() CV_TEST_BODY_IMPL( #test_case_name "_" #test_name ) \
void GTEST_TEST_CLASS_NAME_(test_case_name, test_name)::TestBody() CV__TEST_BODY_IMPL( #test_case_name "_" #test_name ) \
void GTEST_TEST_CLASS_NAME_(test_case_name, test_name)::Body()
#undef TEST_F
@@ -80,17 +80,17 @@ void checkIppStatus();
test_fixture::TearDownTestCase, \
new ::testing::internal::TestFactoryImpl<\
GTEST_TEST_CLASS_NAME_(test_fixture, test_name)>);\
void GTEST_TEST_CLASS_NAME_(test_fixture, test_name)::TestBody() CV_TEST_BODY_IMPL( #test_fixture "_" #test_name ) \
void GTEST_TEST_CLASS_NAME_(test_fixture, test_name)::TestBody() CV__TEST_BODY_IMPL( #test_fixture "_" #test_name ) \
void GTEST_TEST_CLASS_NAME_(test_fixture, test_name)::Body()
#undef TEST_P
#define TEST_P(test_case_name, test_name) \
// Don't use directly
#define CV__TEST_P(test_case_name, test_name, bodyMethodName, BODY_IMPL/*(name_str)*/) \
class GTEST_TEST_CLASS_NAME_(test_case_name, test_name) \
: public test_case_name { \
public: \
GTEST_TEST_CLASS_NAME_(test_case_name, test_name)() {} \
private: \
virtual void Body(); \
virtual void bodyMethodName(); \
virtual void TestBody(); \
static int AddToRegistry() { \
::testing::UnitTest::GetInstance()->parameterized_test_registry(). \
@@ -112,7 +112,10 @@ void checkIppStatus();
int GTEST_TEST_CLASS_NAME_(test_case_name, \
test_name)::gtest_registering_dummy_ = \
GTEST_TEST_CLASS_NAME_(test_case_name, test_name)::AddToRegistry(); \
void GTEST_TEST_CLASS_NAME_(test_case_name, test_name)::TestBody() CV_TEST_BODY_IMPL( #test_case_name "_" #test_name ) \
void GTEST_TEST_CLASS_NAME_(test_case_name, test_name)::Body()
void GTEST_TEST_CLASS_NAME_(test_case_name, test_name)::TestBody() BODY_IMPL( #test_case_name "_" #test_name ) \
void GTEST_TEST_CLASS_NAME_(test_case_name, test_name)::bodyMethodName()
#undef TEST_P
#define TEST_P(test_case_name, test_name) CV__TEST_P(test_case_name, test_name, Body, CV__TEST_BODY_IMPL)
#endif // OPENCV_TS_EXT_HPP
modules/ts/include/opencv2/ts/ts_perf.hpp
+50 -26
View File
@@ -27,40 +27,49 @@
// declare major namespaces to avoid errors on unknown namespace
namespace cv { namespace cuda {} namespace ocl {} }
namespace cvtest { }
namespace perf
{
// Tuple stuff from Google Tests
using testing::get;
using testing::make_tuple;
using testing::tuple;
using testing::tuple_size;
using testing::tuple_element;
class TestBase;
/*****************************************************************************************\
* Predefined typical frame sizes and typical test parameters *
\*****************************************************************************************/
const cv::Size szQVGA = cv::Size(320, 240);
const cv::Size szVGA = cv::Size(640, 480);
const cv::Size szSVGA = cv::Size(800, 600);
const cv::Size szXGA = cv::Size(1024, 768);
const cv::Size szSXGA = cv::Size(1280, 1024);
const cv::Size szWQHD = cv::Size(2560, 1440);
const static cv::Size szQVGA = cv::Size(320, 240);
const static cv::Size szVGA = cv::Size(640, 480);
const static cv::Size szSVGA = cv::Size(800, 600);
const static cv::Size szXGA = cv::Size(1024, 768);
const static cv::Size szSXGA = cv::Size(1280, 1024);
const static cv::Size szWQHD = cv::Size(2560, 1440);
const cv::Size sznHD = cv::Size(640, 360);
const cv::Size szqHD = cv::Size(960, 540);
const cv::Size sz240p = szQVGA;
const cv::Size sz720p = cv::Size(1280, 720);
const cv::Size sz1080p = cv::Size(1920, 1080);
const cv::Size sz1440p = szWQHD;
const cv::Size sz2160p = cv::Size(3840, 2160);//UHDTV1 4K
const cv::Size sz4320p = cv::Size(7680, 4320);//UHDTV2 8K
const static cv::Size sznHD = cv::Size(640, 360);
const static cv::Size szqHD = cv::Size(960, 540);
const static cv::Size sz240p = szQVGA;
const static cv::Size sz720p = cv::Size(1280, 720);
const static cv::Size sz1080p = cv::Size(1920, 1080);
const static cv::Size sz1440p = szWQHD;
const static cv::Size sz2160p = cv::Size(3840, 2160);//UHDTV1 4K
const static cv::Size sz4320p = cv::Size(7680, 4320);//UHDTV2 8K
const cv::Size sz3MP = cv::Size(2048, 1536);
const cv::Size sz5MP = cv::Size(2592, 1944);
const cv::Size sz2K = cv::Size(2048, 2048);
const static cv::Size sz3MP = cv::Size(2048, 1536);
const static cv::Size sz5MP = cv::Size(2592, 1944);
const static cv::Size sz2K = cv::Size(2048, 2048);
const cv::Size szODD = cv::Size(127, 61);
const static cv::Size szODD = cv::Size(127, 61);
const cv::Size szSmall24 = cv::Size(24, 24);
const cv::Size szSmall32 = cv::Size(32, 32);
const cv::Size szSmall64 = cv::Size(64, 64);
const cv::Size szSmall128 = cv::Size(128, 128);
const static cv::Size szSmall24 = cv::Size(24, 24);
const static cv::Size szSmall32 = cv::Size(32, 32);
const static cv::Size szSmall64 = cv::Size(64, 64);
const static cv::Size szSmall128 = cv::Size(128, 128);
#define SZ_ALL_VGA ::testing::Values(::perf::szQVGA, ::perf::szVGA, ::perf::szSVGA)
#define SZ_ALL_GA ::testing::Values(::perf::szQVGA, ::perf::szVGA, ::perf::szSVGA, ::perf::szXGA, ::perf::szSXGA)
@@ -492,7 +501,7 @@ public:
template<typename T> class TestBaseWithParam: public TestBase, public ::testing::WithParamInterface<T> {};
typedef std::tr1::tuple<cv::Size, MatType> Size_MatType_t;
typedef tuple<cv::Size, MatType> Size_MatType_t;
typedef TestBaseWithParam<Size_MatType_t> Size_MatType;
/*****************************************************************************************\
@@ -514,6 +523,13 @@ CV_EXPORTS void PrintTo(const Size& sz, ::std::ostream* os);
/*****************************************************************************************\
* Macro definitions for performance tests *
\*****************************************************************************************/
#define CV__PERF_TEST_BODY_IMPL(name) \
{ \
CV__TRACE_APP_FUNCTION_NAME("PERF_TEST: " name); \
RunPerfTestBody(); \
}
#define PERF_PROXY_NAMESPACE_NAME_(test_case_name, test_name) \
test_case_name##_##test_name##_perf_namespace_proxy
@@ -538,7 +554,7 @@ CV_EXPORTS void PrintTo(const Size& sz, ::std::ostream* os);
protected:\
virtual void PerfTestBody();\
};\
TEST_F(test_case_name, test_name){ CV_TRACE_REGION("PERF_TEST: " #test_case_name "_" #test_name); RunPerfTestBody(); }\
TEST_F(test_case_name, test_name){ CV__PERF_TEST_BODY_IMPL(#test_case_name "_" #test_name); }\
}\
void PERF_PROXY_NAMESPACE_NAME_(test_case_name, test_name)::test_case_name::PerfTestBody()
@@ -576,12 +592,20 @@ CV_EXPORTS void PrintTo(const Size& sz, ::std::ostream* os);
protected:\
virtual void PerfTestBody();\
};\
TEST_F(fixture, testname){ CV_TRACE_REGION("PERF_TEST: " #fixture "_" #testname); RunPerfTestBody(); }\
TEST_F(fixture, testname){ CV__PERF_TEST_BODY_IMPL(#fixture "_" #testname); }\
}\
void PERF_PROXY_NAMESPACE_NAME_(fixture, testname)::fixture::PerfTestBody()
// Defines a parametrized performance test.
//
// @Note PERF_TEST_P() below violates behavior of original Google Tests - there is no tests instantiation in original TEST_P()
// This macro is intended for usage with separate INSTANTIATE_TEST_CASE_P macro
#define PERF_TEST_P_(test_case_name, test_name) CV__TEST_P(test_case_name, test_name, PerfTestBody, CV__PERF_TEST_BODY_IMPL)
// Defines a parametrized performance test.
//
// @Note Original TEST_P() macro doesn't instantiate tests with parameters. To keep original usage use PERF_TEST_P_() macro
//
// The first parameter is the name of the test fixture class, which
// also doubles as the test case name. The second parameter is the
// name of the test within the test case.
@@ -609,7 +633,7 @@ CV_EXPORTS void PrintTo(const Size& sz, ::std::ostream* os);
protected:\
virtual void PerfTestBody();\
};\
TEST_P(fixture##_##name, name /*perf*/){ CV_TRACE_REGION("PERF_TEST: " #fixture "_" #name); RunPerfTestBody(); }\
CV__TEST_P(fixture##_##name, name, PerfTestBodyDummy, CV__PERF_TEST_BODY_IMPL){} \
INSTANTIATE_TEST_CASE_P(/*none*/, fixture##_##name, params);\
void fixture##_##name::PerfTestBody()