Merge remote-tracking branch 'upstream/3.4' into merge-3.4

modules/core/include/opencv2/core/mat.hpp

@@ -1318,7 +1318,7 @@ public:
     /** @brief Returns a zero array of the specified size and type.
 
     The method returns a Matlab-style zero array initializer. It can be used to quickly form a constant
-    array as a function parameter, part of a matrix expression, or as a matrix initializer. :
+    array as a function parameter, part of a matrix expression, or as a matrix initializer:
     @code
         Mat A;
         A = Mat::zeros(3, 3, CV_32F);
@@ -1354,6 +1354,8 @@ public:
     The above operation does not form a 100x100 matrix of 1's and then multiply it by 3. Instead, it
     just remembers the scale factor (3 in this case) and use it when actually invoking the matrix
     initializer.
+    @note In case of multi-channels type, only the first channel will be initialized with 1's, the
+    others will be set to 0's.
     @param rows Number of rows.
     @param cols Number of columns.
     @param type Created matrix type.
@@ -1381,6 +1383,8 @@ public:
         // make a 4x4 diagonal matrix with 0.1's on the diagonal.
         Mat A = Mat::eye(4, 4, CV_32F)*0.1;
     @endcode
+    @note In case of multi-channels type, identity matrix will be initialized only for the first channel,
+    the others will be set to 0's
     @param rows Number of rows.
     @param cols Number of columns.
     @param type Created matrix type.

modules/dnn/test/test_backends.cpp

@@ -278,19 +278,19 @@ TEST_P(DNNTestNetwork, FastNeuralStyle_eccv16)
     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
-    tuple<DNNBackend, DNNTarget>(DNN_BACKEND_HALIDE, DNN_TARGET_CPU),
-    tuple<DNNBackend, DNNTarget>(DNN_BACKEND_HALIDE, DNN_TARGET_OPENCL),
+    tuple<Backend, Target>(DNN_BACKEND_HALIDE, DNN_TARGET_CPU),
+    tuple<Backend, Target>(DNN_BACKEND_HALIDE, DNN_TARGET_OPENCL),
 #endif
 #ifdef HAVE_INF_ENGINE
-    tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_CPU),
-    tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL),
-    tuple<DNNBackend, DNNTarget>(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_CPU),
+    tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL),
+    tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL_FP16),
+    tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_MYRIAD),
 #endif
-    tuple<DNNBackend, DNNTarget>(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),
+    tuple<Backend, Target>(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL_FP16)
 };
 
 INSTANTIATE_TEST_CASE_P(/*nothing*/, DNNTestNetwork, testing::ValuesIn(testCases));

modules/dnn/test/test_caffe_importer.cpp

@@ -82,7 +82,7 @@ TEST(Test_Caffe, read_googlenet)
     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)
 {
     bool readFromMemory = get<0>(GetParam());
@@ -179,7 +179,7 @@ TEST(Reproducibility_SSD, Accuracy)
     normAssertDetections(ref, out);
 }
 
-typedef testing::TestWithParam<DNNTarget> Reproducibility_MobileNet_SSD;
+typedef testing::TestWithParam<Target> Reproducibility_MobileNet_SSD;
 TEST_P(Reproducibility_MobileNet_SSD, Accuracy)
 {
     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,
                         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)
 {
     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,
                         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)
 {
     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);
 }
 
-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)
 {
     std::string proto = findDataFile("dnn/opencv_face_detector.prototxt", false);

modules/dnn/test/test_googlenet.cpp

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

modules/dnn/test/test_halide_layers.cpp

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

modules/dnn/test/test_precomp.hpp

@@ -49,15 +49,41 @@
 #include "opencv2/dnn.hpp"
 #include "test_common.hpp"
 
-namespace opencv_test { namespace {
-using namespace cv::dnn;
+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_ENUM(DNNBackend, DNN_BACKEND_DEFAULT, DNN_BACKEND_HALIDE, DNN_BACKEND_INFERENCE_ENGINE, DNN_BACKEND_OPENCV)
-CV_ENUM(DNNTarget, DNN_TARGET_CPU, DNN_TARGET_OPENCL, DNN_TARGET_OPENCL_FP16, DNN_TARGET_MYRIAD)
+CV__DNN_EXPERIMENTAL_NS_END
+}} // namespace
 
-static testing::internal::ParamGenerator<DNNTarget> availableDnnTargets()
+namespace opencv_test {
+using namespace cv::dnn;
+
+static testing::internal::ParamGenerator<Target> availableDnnTargets()
 {
-    static std::vector<DNNTarget> targets;
+    static std::vector<Target> targets;
     if (targets.empty())
     {
         targets.push_back(DNN_TARGET_CPU);
@@ -69,23 +95,23 @@ static testing::internal::ParamGenerator<DNNTarget> availableDnnTargets()
     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
-        tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_CPU),
-        tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL),
-        tuple<DNNBackend, DNNTarget>(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_CPU),
+        tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL),
+        tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL_FP16),
+        tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_MYRIAD),
     #endif
-        tuple<DNNBackend, DNNTarget>(DNN_BACKEND_OPENCV, DNN_TARGET_CPU),
-        tuple<DNNBackend, DNNTarget>(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL),
-        tuple<DNNBackend, DNNTarget>(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL_FP16)
+        tuple<Backend, Target>(DNN_BACKEND_OPENCV, DNN_TARGET_CPU),
+        tuple<Backend, Target>(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL),
+        tuple<Backend, Target>(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL_FP16)
     };
     return testing::ValuesIn(testCases);
 }
 
-class DNNTestLayer : public TestWithParam <tuple<DNNBackend, DNNTarget> >
+class DNNTestLayer : public TestWithParam<tuple<Backend, Target> >
 {
 public:
     dnn::Backend backend;
@@ -156,6 +182,5 @@ protected:
     }
 };
 
-}}
-
+} // namespace
 #endif

modules/dnn/test/test_tf_importer.cpp

@@ -243,7 +243,7 @@ TEST_P(Test_TensorFlow_layers, 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)
 {

modules/dnn/test/test_torch_importer.cpp

@@ -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)
 {
@@ -208,7 +208,7 @@ TEST_P(Test_Torch_layers, net_non_spatial)
 
 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)
 {