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Merge pull request #11929 from alalek:dnn_test_drop_enums

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This commit is contained in:
Alexander Alekhin 2018-07-10 15:31:51 +00:00
commit e9bb26267e
7 changed files with 85 additions and 60 deletions
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18
modules/dnn/test/test_backends.cpp
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@ -278,19 +278,19 @@ TEST_P(DNNTestNetwork, FastNeuralStyle_eccv16)
processNet("dnn/fast_neural_style_eccv16_starry_night.t7", "", inp, "", "", l1, lInf); processNet("dnn/fast_neural_style_eccv16_starry_night.t7", "", inp, "", "", l1, lInf);
} }
const tuple<DNNBackend, DNNTarget> testCases[] = { const tuple<Backend, Target> testCases[] = {
#ifdef HAVE_HALIDE #ifdef HAVE_HALIDE
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_HALIDE, DNN_TARGET_CPU), tuple<Backend, Target>(DNN_BACKEND_HALIDE, DNN_TARGET_CPU),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_HALIDE, DNN_TARGET_OPENCL), tuple<Backend, Target>(DNN_BACKEND_HALIDE, DNN_TARGET_OPENCL),
#endif #endif
#ifdef HAVE_INF_ENGINE #ifdef HAVE_INF_ENGINE
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_CPU), tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_CPU),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL), tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL_FP16), tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL_FP16),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_MYRIAD), tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_MYRIAD),
#endif #endif
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL), tuple<Backend, Target>(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL_FP16) tuple<Backend, Target>(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL_FP16)
}; };
INSTANTIATE_TEST_CASE_P(/*nothing*/, DNNTestNetwork, testing::ValuesIn(testCases)); INSTANTIATE_TEST_CASE_P(/*nothing*/, DNNTestNetwork, testing::ValuesIn(testCases));

10
modules/dnn/test/test_caffe_importer.cpp
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@ -82,7 +82,7 @@ TEST(Test_Caffe, read_googlenet)
ASSERT_FALSE(net.empty()); ASSERT_FALSE(net.empty());
} }
typedef testing::TestWithParam<tuple<bool, DNNTarget> > Reproducibility_AlexNet; typedef testing::TestWithParam<tuple<bool, Target> > Reproducibility_AlexNet;
TEST_P(Reproducibility_AlexNet, Accuracy) TEST_P(Reproducibility_AlexNet, Accuracy)
{ {
bool readFromMemory = get<0>(GetParam()); bool readFromMemory = get<0>(GetParam());
@ -179,7 +179,7 @@ TEST(Reproducibility_SSD, Accuracy)
normAssertDetections(ref, out); normAssertDetections(ref, out);
} }
typedef testing::TestWithParam<DNNTarget> Reproducibility_MobileNet_SSD; typedef testing::TestWithParam<Target> Reproducibility_MobileNet_SSD;
TEST_P(Reproducibility_MobileNet_SSD, Accuracy) TEST_P(Reproducibility_MobileNet_SSD, Accuracy)
{ {
const string proto = findDataFile("dnn/MobileNetSSD_deploy.prototxt", false); const string proto = findDataFile("dnn/MobileNetSSD_deploy.prototxt", false);
@ -234,7 +234,7 @@ TEST_P(Reproducibility_MobileNet_SSD, Accuracy)
INSTANTIATE_TEST_CASE_P(/**/, Reproducibility_MobileNet_SSD, INSTANTIATE_TEST_CASE_P(/**/, Reproducibility_MobileNet_SSD,
Values(DNN_TARGET_CPU, DNN_TARGET_OPENCL, DNN_TARGET_OPENCL_FP16)); Values(DNN_TARGET_CPU, DNN_TARGET_OPENCL, DNN_TARGET_OPENCL_FP16));
typedef testing::TestWithParam<DNNTarget> Reproducibility_ResNet50; typedef testing::TestWithParam<Target> Reproducibility_ResNet50;
TEST_P(Reproducibility_ResNet50, Accuracy) TEST_P(Reproducibility_ResNet50, Accuracy)
{ {
Net net = readNetFromCaffe(findDataFile("dnn/ResNet-50-deploy.prototxt", false), Net net = readNetFromCaffe(findDataFile("dnn/ResNet-50-deploy.prototxt", false),
@ -270,7 +270,7 @@ TEST_P(Reproducibility_ResNet50, Accuracy)
INSTANTIATE_TEST_CASE_P(/**/, Reproducibility_ResNet50, INSTANTIATE_TEST_CASE_P(/**/, Reproducibility_ResNet50,
Values(DNN_TARGET_CPU, DNN_TARGET_OPENCL, DNN_TARGET_OPENCL_FP16)); Values(DNN_TARGET_CPU, DNN_TARGET_OPENCL, DNN_TARGET_OPENCL_FP16));
typedef testing::TestWithParam<DNNTarget> Reproducibility_SqueezeNet_v1_1; typedef testing::TestWithParam<Target> Reproducibility_SqueezeNet_v1_1;
TEST_P(Reproducibility_SqueezeNet_v1_1, Accuracy) TEST_P(Reproducibility_SqueezeNet_v1_1, Accuracy)
{ {
Net net = readNetFromCaffe(findDataFile("dnn/squeezenet_v1.1.prototxt", false), Net net = readNetFromCaffe(findDataFile("dnn/squeezenet_v1.1.prototxt", false),
@ -413,7 +413,7 @@ TEST(Test_Caffe, multiple_inputs)
normAssert(out, first_image + second_image); normAssert(out, first_image + second_image);
} }
typedef testing::TestWithParam<tuple<std::string, DNNTarget> > opencv_face_detector; typedef testing::TestWithParam<tuple<std::string, Target> > opencv_face_detector;
TEST_P(opencv_face_detector, Accuracy) TEST_P(opencv_face_detector, Accuracy)
{ {
std::string proto = findDataFile("dnn/opencv_face_detector.prototxt", false); std::string proto = findDataFile("dnn/opencv_face_detector.prototxt", false);

2
modules/dnn/test/test_googlenet.cpp
View File

@ -52,7 +52,7 @@ static std::string _tf(TString filename)
return (getOpenCVExtraDir() + "/dnn/") + filename; return (getOpenCVExtraDir() + "/dnn/") + filename;
} }
typedef testing::TestWithParam<DNNTarget> Reproducibility_GoogLeNet; typedef testing::TestWithParam<Target> Reproducibility_GoogLeNet;
TEST_P(Reproducibility_GoogLeNet, Batching) TEST_P(Reproducibility_GoogLeNet, Batching)
{ {
Net net = readNetFromCaffe(findDataFile("dnn/bvlc_googlenet.prototxt", false), Net net = readNetFromCaffe(findDataFile("dnn/bvlc_googlenet.prototxt", false),

48
modules/dnn/test/test_halide_layers.cpp
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@ -41,21 +41,21 @@ static void test(LayerParams& params, Mat& input, int backendId, int targetId)
test(input, net, backendId, targetId); test(input, net, backendId, targetId);
} }
static testing::internal::ParamGenerator<tuple<DNNBackend, DNNTarget> > dnnBackendsAndTargetsWithHalide() static testing::internal::ParamGenerator<tuple<Backend, Target> > dnnBackendsAndTargetsWithHalide()
{ {
static const tuple<DNNBackend, DNNTarget> testCases[] = { static const tuple<Backend, Target> testCases[] = {
#ifdef HAVE_HALIDE #ifdef HAVE_HALIDE
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_HALIDE, DNN_TARGET_CPU), tuple<Backend, Target>(DNN_BACKEND_HALIDE, DNN_TARGET_CPU),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_HALIDE, DNN_TARGET_OPENCL), tuple<Backend, Target>(DNN_BACKEND_HALIDE, DNN_TARGET_OPENCL),
#endif #endif
#ifdef HAVE_INF_ENGINE #ifdef HAVE_INF_ENGINE
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_CPU), tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_CPU),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL), tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL_FP16), tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL_FP16),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_MYRIAD), tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_MYRIAD),
#endif #endif
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL), tuple<Backend, Target>(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL_FP16) tuple<Backend, Target>(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL_FP16)
}; };
return testing::ValuesIn(testCases); return testing::ValuesIn(testCases);
} }
@ -89,7 +89,7 @@ TEST_P(Test_Halide_layers, Padding)
//////////////////////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////////////////////////
// Convolution // Convolution
//////////////////////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////////////////////////
typedef TestWithParam<tuple<Vec3i, Size, Size, Size, Size, Size, bool, tuple<DNNBackend, DNNTarget> > > Convolution; typedef TestWithParam<tuple<Vec3i, Size, Size, Size, Size, Size, bool, tuple<Backend, Target> > > Convolution;
TEST_P(Convolution, Accuracy) TEST_P(Convolution, Accuracy)
{ {
int inChannels = get<0>(GetParam())[0]; int inChannels = get<0>(GetParam())[0];
@ -154,7 +154,7 @@ INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, Convolution, Combine(
//////////////////////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////////////////////////
// Deconvolution // Deconvolution
//////////////////////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////////////////////////
typedef TestWithParam<tuple<Vec3i, Size, Size, Size, Size, Vec4i, bool, tuple<DNNBackend, DNNTarget> > > Deconvolution; typedef TestWithParam<tuple<Vec3i, Size, Size, Size, Size, Vec4i, bool, tuple<Backend, Target> > > Deconvolution;
TEST_P(Deconvolution, Accuracy) TEST_P(Deconvolution, Accuracy)
{ {
int inChannels = get<0>(GetParam())[0]; int inChannels = get<0>(GetParam())[0];
@ -220,7 +220,7 @@ INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, Deconvolution, Combine(
//////////////////////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////////////////////////
// LRN // LRN
//////////////////////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////////////////////////
typedef TestWithParam<tuple<Vec3i, int, Vec3f, bool, std::string, tuple<DNNBackend, DNNTarget> > > LRN; typedef TestWithParam<tuple<Vec3i, int, Vec3f, bool, std::string, tuple<Backend, Target> > > LRN;
TEST_P(LRN, Accuracy) TEST_P(LRN, Accuracy)
{ {
int inChannels = get<0>(GetParam())[0]; int inChannels = get<0>(GetParam())[0];
@ -265,7 +265,7 @@ INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, LRN, Combine(
//////////////////////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////////////////////////
// Average pooling // Average pooling
//////////////////////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////////////////////////
typedef TestWithParam<tuple<int, Size, Size, Size, tuple<DNNBackend, DNNTarget> > > AvePooling; typedef TestWithParam<tuple<int, Size, Size, Size, tuple<Backend, Target> > > AvePooling;
TEST_P(AvePooling, Accuracy) TEST_P(AvePooling, Accuracy)
{ {
int inChannels = get<0>(GetParam()); int inChannels = get<0>(GetParam());
@ -305,7 +305,7 @@ INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, AvePooling, Combine(
//////////////////////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////////////////////////
// Maximum pooling // Maximum pooling
//////////////////////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////////////////////////
typedef TestWithParam<tuple<int, Size, Size, Size, Size, tuple<DNNBackend, DNNTarget> > > MaxPooling; typedef TestWithParam<tuple<int, Size, Size, Size, Size, tuple<Backend, Target> > > MaxPooling;
TEST_P(MaxPooling, Accuracy) TEST_P(MaxPooling, Accuracy)
{ {
int inChannels = get<0>(GetParam()); int inChannels = get<0>(GetParam());
@ -344,7 +344,7 @@ INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, MaxPooling, Combine(
//////////////////////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////////////////////////
// Fully-connected // Fully-connected
//////////////////////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////////////////////////
typedef TestWithParam<tuple<int, Size, int, bool, tuple<DNNBackend, DNNTarget> > > FullyConnected; typedef TestWithParam<tuple<int, Size, int, bool, tuple<Backend, Target> > > FullyConnected;
TEST_P(FullyConnected, Accuracy) TEST_P(FullyConnected, Accuracy)
{ {
int inChannels = get<0>(GetParam()); int inChannels = get<0>(GetParam());
@ -387,7 +387,7 @@ INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, FullyConnected, Combine(
//////////////////////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////////////////////////
// SoftMax // SoftMax
//////////////////////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////////////////////////
typedef TestWithParam<tuple<int, tuple<DNNBackend, DNNTarget> > > SoftMax; typedef TestWithParam<tuple<int, tuple<Backend, Target> > > SoftMax;
TEST_P(SoftMax, Accuracy) TEST_P(SoftMax, Accuracy)
{ {
int inChannels = get<0>(GetParam()); int inChannels = get<0>(GetParam());
@ -476,7 +476,7 @@ void testInPlaceActivation(LayerParams& lp, int backendId, int targetId)
test(input, net, backendId, targetId); test(input, net, backendId, targetId);
} }
typedef TestWithParam<tuple<bool, bool, float, tuple<DNNBackend, DNNTarget> > > BatchNorm; typedef TestWithParam<tuple<bool, bool, float, tuple<Backend, Target> > > BatchNorm;
TEST_P(BatchNorm, Accuracy) TEST_P(BatchNorm, Accuracy)
{ {
bool hasWeights = get<0>(GetParam()); bool hasWeights = get<0>(GetParam());
@ -511,7 +511,7 @@ INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, BatchNorm, Combine(
dnnBackendsAndTargetsWithHalide() dnnBackendsAndTargetsWithHalide()
)); ));
typedef TestWithParam<tuple<float, tuple<DNNBackend, DNNTarget> > > ReLU; typedef TestWithParam<tuple<float, tuple<Backend, Target> > > ReLU;
TEST_P(ReLU, Accuracy) TEST_P(ReLU, Accuracy)
{ {
float negativeSlope = get<0>(GetParam()); float negativeSlope = get<0>(GetParam());
@ -530,7 +530,7 @@ INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, ReLU, Combine(
dnnBackendsAndTargetsWithHalide() dnnBackendsAndTargetsWithHalide()
)); ));
typedef TestWithParam<tuple<std::string, tuple<DNNBackend, DNNTarget> > > NoParamActivation; typedef TestWithParam<tuple<std::string, tuple<Backend, Target> > > NoParamActivation;
TEST_P(NoParamActivation, Accuracy) TEST_P(NoParamActivation, Accuracy)
{ {
int backendId = get<0>(get<1>(GetParam())); int backendId = get<0>(get<1>(GetParam()));
@ -546,7 +546,7 @@ INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, NoParamActivation, Combine(
dnnBackendsAndTargetsWithHalide() dnnBackendsAndTargetsWithHalide()
)); ));
typedef TestWithParam<tuple<Vec3f, tuple<DNNBackend, DNNTarget> > > Power; typedef TestWithParam<tuple<Vec3f, tuple<Backend, Target> > > Power;
TEST_P(Power, Accuracy) TEST_P(Power, Accuracy)
{ {
float power = get<0>(GetParam())[0]; float power = get<0>(GetParam())[0];
@ -582,7 +582,7 @@ TEST_P(Test_Halide_layers, ChannelsPReLU)
testInPlaceActivation(lp, backend, target); testInPlaceActivation(lp, backend, target);
} }
typedef TestWithParam<tuple<bool, tuple<DNNBackend, DNNTarget> > > Scale; typedef TestWithParam<tuple<bool, tuple<Backend, Target> > > Scale;
TEST_P(Scale, Accuracy) TEST_P(Scale, Accuracy)
{ {
bool hasBias = get<0>(GetParam()); bool hasBias = get<0>(GetParam());
@ -616,7 +616,7 @@ INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, Scale, Combine(
// `--- conv ----^ ^ ^ // `--- conv ----^ ^ ^
// `---- ... ------' ' // `---- ... ------' '
// `-----------------' // `-----------------'
typedef TestWithParam<tuple<Vec3i, Vec3i, tuple<DNNBackend, DNNTarget> > > Concat; typedef TestWithParam<tuple<Vec3i, Vec3i, tuple<Backend, Target> > > Concat;
TEST_P(Concat, Accuracy) TEST_P(Concat, Accuracy)
{ {
Vec3i inSize = get<0>(GetParam()); Vec3i inSize = get<0>(GetParam());
@ -682,7 +682,7 @@ INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, Concat, Combine(
// `--- conv ----^ ^ ^ // `--- conv ----^ ^ ^
// `---- ... ------' ' // `---- ... ------' '
// `-----------------' // `-----------------'
typedef TestWithParam<tuple<Vec3i, std::string, int, bool, tuple<DNNBackend, DNNTarget> > > Eltwise; typedef TestWithParam<tuple<Vec3i, std::string, int, bool, tuple<Backend, Target> > > Eltwise;
TEST_P(Eltwise, Accuracy) TEST_P(Eltwise, Accuracy)
{ {
Vec3i inSize = get<0>(GetParam()); Vec3i inSize = get<0>(GetParam());

61
modules/dnn/test/test_precomp.hpp
View File

@ -49,15 +49,41 @@
#include "opencv2/dnn.hpp" #include "opencv2/dnn.hpp"
#include "test_common.hpp" #include "test_common.hpp"
namespace opencv_test { namespace { namespace cv {
namespace dnn {
CV__DNN_EXPERIMENTAL_NS_BEGIN
static inline void PrintTo(const cv::dnn::Backend& v, std::ostream* os)
{
switch (v) {
case DNN_BACKEND_DEFAULT: *os << "DNN_BACKEND_DEFAULT"; return;
case DNN_BACKEND_HALIDE: *os << "DNN_BACKEND_HALIDE"; return;
case DNN_BACKEND_INFERENCE_ENGINE: *os << "DNN_BACKEND_INFERENCE_ENGINE"; return;
case DNN_BACKEND_OPENCV: *os << "DNN_BACKEND_OPENCV"; return;
} // don't use "default:" to emit compiler warnings
*os << "DNN_BACKEND_UNKNOWN(" << v << ")";
}
static inline void PrintTo(const cv::dnn::Target& v, std::ostream* os)
{
switch (v) {
case DNN_TARGET_CPU: *os << "DNN_TARGET_CPU"; return;
case DNN_TARGET_OPENCL: *os << "DNN_TARGET_OPENCL"; return;
case DNN_TARGET_OPENCL_FP16: *os << "DNN_TARGET_OPENCL_FP16"; return;
case DNN_TARGET_MYRIAD: *os << "DNN_TARGET_MYRIAD"; return;
} // don't use "default:" to emit compiler warnings
*os << "DNN_TARGET_UNKNOWN(" << v << ")";
}
CV__DNN_EXPERIMENTAL_NS_END
}} // namespace
namespace opencv_test {
using namespace cv::dnn; using namespace cv::dnn;
CV_ENUM(DNNBackend, DNN_BACKEND_DEFAULT, DNN_BACKEND_HALIDE, DNN_BACKEND_INFERENCE_ENGINE, DNN_BACKEND_OPENCV) static testing::internal::ParamGenerator<Target> availableDnnTargets()
CV_ENUM(DNNTarget, DNN_TARGET_CPU, DNN_TARGET_OPENCL, DNN_TARGET_OPENCL_FP16, DNN_TARGET_MYRIAD)
static testing::internal::ParamGenerator<DNNTarget> availableDnnTargets()
{ {
static std::vector<DNNTarget> targets; static std::vector<Target> targets;
if (targets.empty()) if (targets.empty())
{ {
targets.push_back(DNN_TARGET_CPU); targets.push_back(DNN_TARGET_CPU);
@ -69,23 +95,23 @@ static testing::internal::ParamGenerator<DNNTarget> availableDnnTargets()
return testing::ValuesIn(targets); return testing::ValuesIn(targets);
} }
static testing::internal::ParamGenerator<tuple<DNNBackend, DNNTarget> > dnnBackendsAndTargets() static testing::internal::ParamGenerator<tuple<Backend, Target> > dnnBackendsAndTargets()
{ {
static const tuple<DNNBackend, DNNTarget> testCases[] = { static const tuple<Backend, Target> testCases[] = {
#ifdef HAVE_INF_ENGINE #ifdef HAVE_INF_ENGINE
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_CPU), tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_CPU),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL), tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL_FP16), tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL_FP16),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_MYRIAD), tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_MYRIAD),
#endif #endif
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_OPENCV, DNN_TARGET_CPU), tuple<Backend, Target>(DNN_BACKEND_OPENCV, DNN_TARGET_CPU),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL), tuple<Backend, Target>(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL_FP16) tuple<Backend, Target>(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL_FP16)
}; };
return testing::ValuesIn(testCases); return testing::ValuesIn(testCases);
} }
class DNNTestLayer : public TestWithParam <tuple<DNNBackend, DNNTarget> > class DNNTestLayer : public TestWithParam<tuple<Backend, Target> >
{ {
public: public:
dnn::Backend backend; dnn::Backend backend;
@ -156,6 +182,5 @@ protected:
} }
}; };
}} } // namespace
#endif #endif

2
modules/dnn/test/test_tf_importer.cpp
View File

@ -243,7 +243,7 @@ TEST_P(Test_TensorFlow_layers, l2_normalize_3d)
runTensorFlowNet("l2_normalize_3d"); runTensorFlowNet("l2_normalize_3d");
} }
typedef testing::TestWithParam<DNNTarget> Test_TensorFlow_nets; typedef testing::TestWithParam<Target> Test_TensorFlow_nets;
TEST_P(Test_TensorFlow_nets, MobileNet_SSD) TEST_P(Test_TensorFlow_nets, MobileNet_SSD)
{ {

4
modules/dnn/test/test_torch_importer.cpp
View File

@ -100,7 +100,7 @@ static void runTorchNet(String prefix, int targetId = DNN_TARGET_CPU, String out
} }
} }
typedef testing::TestWithParam<DNNTarget> Test_Torch_layers; typedef testing::TestWithParam<Target> Test_Torch_layers;
TEST_P(Test_Torch_layers, run_convolution) TEST_P(Test_Torch_layers, run_convolution)
{ {
@ -208,7 +208,7 @@ TEST_P(Test_Torch_layers, net_non_spatial)
INSTANTIATE_TEST_CASE_P(/**/, Test_Torch_layers, availableDnnTargets()); INSTANTIATE_TEST_CASE_P(/**/, Test_Torch_layers, availableDnnTargets());
typedef testing::TestWithParam<DNNTarget> Test_Torch_nets; typedef testing::TestWithParam<Target> Test_Torch_nets;
TEST_P(Test_Torch_nets, OpenFace_accuracy) TEST_P(Test_Torch_nets, OpenFace_accuracy)
{ {