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dnn(test): drop non OCV/CPU cases for Int8
- zero code coverage and up to x3-x8 tests slowdown - implementation executes OCV/CPU in all cases - wrong skip conditions
This commit is contained in:
Alexander Alekhin
parent
d58b5ef74b
commit
f55c9ed1ba
@ -8,6 +8,13 @@
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#include <opencv2/dnn/all_layers.hpp>
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namespace opencv_test { namespace {
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testing::internal::ParamGenerator< tuple<Backend, Target> > dnnBackendsAndTargetsInt8()
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{
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std::vector< tuple<Backend, Target> > targets;
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targets.push_back(make_tuple(DNN_BACKEND_OPENCV, DNN_TARGET_CPU));
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return testing::ValuesIn(targets);
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}
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template<typename TString>
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static std::string _tf(TString filename)
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{
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@ -341,7 +348,7 @@ TEST_P(Test_Int8_layers, Eltwise)
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testLayer("split_max", "ONNX", 0.004, 0.012);
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}
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INSTANTIATE_TEST_CASE_P(/**/, Test_Int8_layers, dnnBackendsAndTargets());
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INSTANTIATE_TEST_CASE_P(/**/, Test_Int8_layers, dnnBackendsAndTargetsInt8());
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class Test_Int8_nets : public DNNTestLayer
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{
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@ -657,11 +664,6 @@ TEST_P(Test_Int8_nets, CaffeNet)
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if (target == DNN_TARGET_OPENCL && !ocl::Device::getDefault().isIntel())
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applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2019030000)
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD
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&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
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#endif
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float l1 = 4e-5, lInf = 0.0025;
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testONNXNet("caffenet", l1, lInf);
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}
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@ -679,11 +681,6 @@ TEST_P(Test_Int8_nets, RCNN_ILSVRC13)
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if (target == DNN_TARGET_OPENCL && !ocl::Device::getDefault().isIntel())
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applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2019030000)
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD
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&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
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#endif
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float l1 = 0.02, lInf = 0.042;
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testONNXNet("rcnn_ilsvrc13", l1, lInf);
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}
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@ -715,12 +712,6 @@ TEST_P(Test_Int8_nets, Shufflenet)
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if (target == DNN_TARGET_OPENCL && !ocl::Device::getDefault().isIntel())
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applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
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{
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if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
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if (target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
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if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
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}
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testONNXNet("shufflenet", default_l1, default_lInf);
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}
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@ -767,12 +758,6 @@ TEST_P(Test_Int8_nets, MobileNet_v1_SSD_PPN)
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if (target == DNN_TARGET_OPENCL && !ocl::Device::getDefault().isIntel())
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applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2018050000)
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
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applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16,
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CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
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#endif
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Net net = readNetFromTensorflow(findDataFile("dnn/ssd_mobilenet_v1_ppn_coco.pb", false),
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findDataFile("dnn/ssd_mobilenet_v1_ppn_coco.pbtxt"));
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@ -792,11 +777,6 @@ TEST_P(Test_Int8_nets, Inception_v2_SSD)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
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applyTestTag(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB);
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2019010000)
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD &&
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getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
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#endif
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Net net = readNetFromTensorflow(findDataFile("dnn/ssd_inception_v2_coco_2017_11_17.pb", false),
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findDataFile("dnn/ssd_inception_v2_coco_2017_11_17.pbtxt"));
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@ -875,25 +855,9 @@ TEST_P(Test_Int8_nets, FasterRCNN_resnet50)
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if (target == DNN_TARGET_OPENCL && !ocl::Device::getDefault().isIntel())
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applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
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#ifdef INF_ENGINE_RELEASE
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 &&
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(INF_ENGINE_VER_MAJOR_LT(2019020000) || target != DNN_TARGET_CPU))
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
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if (INF_ENGINE_VER_MAJOR_GT(2019030000) &&
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backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
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#endif
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
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if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
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if (backend == DNN_BACKEND_CUDA && target == DNN_TARGET_CUDA_FP16)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA_FP16);
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Net net = readNetFromTensorflow(findDataFile("dnn/faster_rcnn_resnet50_coco_2018_01_28.pb", false),
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findDataFile("dnn/faster_rcnn_resnet50_coco_2018_01_28.pbtxt"));
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@ -918,25 +882,9 @@ TEST_P(Test_Int8_nets, FasterRCNN_inceptionv2)
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if (target == DNN_TARGET_OPENCL && !ocl::Device::getDefault().isIntel())
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applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
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#ifdef INF_ENGINE_RELEASE
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 &&
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(INF_ENGINE_VER_MAJOR_LT(2019020000) || target != DNN_TARGET_CPU))
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
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if (INF_ENGINE_VER_MAJOR_GT(2019030000) &&
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backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
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#endif
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
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if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
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if (backend == DNN_BACKEND_CUDA && target == DNN_TARGET_CUDA_FP16)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA_FP16);
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Net net = readNetFromTensorflow(findDataFile("dnn/faster_rcnn_inception_v2_coco_2018_01_28.pb", false),
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findDataFile("dnn/faster_rcnn_inception_v2_coco_2018_01_28.pbtxt"));
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@ -965,17 +913,6 @@ TEST_P(Test_Int8_nets, FasterRCNN_vgg16)
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if (target == DNN_TARGET_OPENCL && !ocl::Device::getDefault().isIntel())
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applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
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#if defined(INF_ENGINE_RELEASE)
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if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
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applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
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#endif
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Net net = readNetFromCaffe(findDataFile("dnn/faster_rcnn_vgg16.prototxt"),
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findDataFile("dnn/VGG16_faster_rcnn_final.caffemodel", false));
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@ -1003,17 +940,6 @@ TEST_P(Test_Int8_nets, FasterRCNN_zf)
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if (target == DNN_TARGET_OPENCL && !ocl::Device::getDefault().isIntel())
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applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
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if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ||
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backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && target == DNN_TARGET_OPENCL_FP16)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
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if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ||
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backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && target == DNN_TARGET_MYRIAD)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
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if (target == DNN_TARGET_CUDA_FP16)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA_FP16);
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Net net = readNetFromCaffe(findDataFile("dnn/faster_rcnn_zf.prototxt"),
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findDataFile("dnn/ZF_faster_rcnn_final.caffemodel", false));
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@ -1038,14 +964,6 @@ TEST_P(Test_Int8_nets, RFCN)
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if (target == DNN_TARGET_OPENCL && !ocl::Device::getDefault().isIntel())
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applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
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if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ||
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backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && target == DNN_TARGET_OPENCL_FP16)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
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if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ||
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backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && target == DNN_TARGET_MYRIAD)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
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Net net = readNetFromCaffe(findDataFile("dnn/rfcn_pascal_voc_resnet50.prototxt"),
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findDataFile("dnn/resnet50_rfcn_final.caffemodel", false));
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@ -1072,22 +990,6 @@ TEST_P(Test_Int8_nets, YoloVoc)
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if (target == DNN_TARGET_OPENCL && !ocl::Device::getDefault().isIntel())
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applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2020040000)
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
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#endif
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_OPENCL_FP16)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
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#endif
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#if defined(INF_ENGINE_RELEASE)
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if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) &&
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target == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
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#endif
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Mat ref = (Mat_<float>(6, 7) << 0, 6, 0.750469f, 0.577374f, 0.127391f, 0.902949f, 0.300809f,
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0, 1, 0.780879f, 0.270762f, 0.264102f, 0.732475f, 0.745412f,
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0, 11, 0.901615f, 0.1386f, 0.338509f, 0.421337f, 0.938789f,
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@ -1119,18 +1021,6 @@ TEST_P(Test_Int8_nets, TinyYoloVoc)
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if (target == DNN_TARGET_OPENCL && !ocl::Device::getDefault().isIntel())
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applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2020040000)
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
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#endif
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#if defined(INF_ENGINE_RELEASE)
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if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) &&
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target == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
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#endif
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Mat ref = (Mat_<float>(4, 7) << 0, 6, 0.761967f, 0.579042f, 0.159161f, 0.894482f, 0.31994f,
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0, 11, 0.780595f, 0.129696f, 0.386467f, 0.445275f, 0.920994f,
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1, 6, 0.651450f, 0.460526f, 0.458019f, 0.522527f, 0.5341f,
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@ -1160,16 +1050,6 @@ TEST_P(Test_Int8_nets, YOLOv3)
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if (target == DNN_TARGET_OPENCL && !ocl::Device::getDefault().isIntel())
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applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2020040000)
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
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#endif
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
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const int N0 = 3;
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const int N1 = 6;
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static const float ref_[/* (N0 + N1) * 7 */] = {
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@ -1195,19 +1075,6 @@ TEST_P(Test_Int8_nets, YOLOv3)
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testDarknetModel(config_file, weights_file, ref.rowRange(0, N0), scoreDiff, iouDiff, confThreshold);
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}
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#if defined(INF_ENGINE_RELEASE)
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
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{
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if (target == DNN_TARGET_OPENCL)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
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else if (target == DNN_TARGET_OPENCL_FP16 && INF_ENGINE_VER_MAJOR_LE(202010000))
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
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else if (target == DNN_TARGET_MYRIAD &&
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getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
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}
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#endif
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{
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SCOPED_TRACE("batch size 2");
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testDarknetModel(config_file, weights_file, ref, scoreDiff, iouDiff, confThreshold);
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@ -1223,17 +1090,6 @@ TEST_P(Test_Int8_nets, YOLOv4)
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if (target == DNN_TARGET_OPENCL && !ocl::Device::getDefault().isIntel())
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applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2020040000)
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
|
||||
#endif
|
||||
#if defined(INF_ENGINE_RELEASE)
|
||||
if (target == DNN_TARGET_MYRIAD)
|
||||
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
|
||||
#endif
|
||||
|
||||
const int N0 = 3;
|
||||
const int N1 = 7;
|
||||
static const float ref_[/* (N0 + N1) * 7 */] = {
|
||||
@ -1262,19 +1118,6 @@ TEST_P(Test_Int8_nets, YOLOv4)
|
||||
{
|
||||
SCOPED_TRACE("batch size 2");
|
||||
|
||||
#if defined(INF_ENGINE_RELEASE)
|
||||
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
|
||||
{
|
||||
if (target == DNN_TARGET_OPENCL)
|
||||
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
|
||||
else if (target == DNN_TARGET_OPENCL_FP16 && INF_ENGINE_VER_MAJOR_LE(202010000))
|
||||
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
|
||||
else if (target == DNN_TARGET_MYRIAD &&
|
||||
getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
|
||||
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
|
||||
}
|
||||
#endif
|
||||
|
||||
testDarknetModel(config_file, weights_file, ref, scoreDiff, iouDiff);
|
||||
}
|
||||
}
|
||||
@ -1290,11 +1133,6 @@ TEST_P(Test_Int8_nets, YOLOv4_tiny)
|
||||
if (target == DNN_TARGET_OPENCL && !ocl::Device::getDefault().isIntel())
|
||||
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
|
||||
|
||||
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2021010000)
|
||||
if (target == DNN_TARGET_MYRIAD)
|
||||
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
|
||||
#endif
|
||||
|
||||
const float confThreshold = 0.6;
|
||||
|
||||
const int N0 = 2;
|
||||
@ -1314,38 +1152,20 @@ TEST_P(Test_Int8_nets, YOLOv4_tiny)
|
||||
double scoreDiff = 0.12;
|
||||
double iouDiff = target == DNN_TARGET_OPENCL_FP16 ? 0.2 : 0.082;
|
||||
|
||||
#if defined(INF_ENGINE_RELEASE)
|
||||
if (target == DNN_TARGET_MYRIAD) // bad accuracy
|
||||
iouDiff = std::numeric_limits<double>::quiet_NaN();
|
||||
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_OPENCL)
|
||||
iouDiff = std::numeric_limits<double>::quiet_NaN();
|
||||
if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ||
|
||||
backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && target == DNN_TARGET_OPENCL_FP16)
|
||||
iouDiff = std::numeric_limits<double>::quiet_NaN();
|
||||
#endif
|
||||
|
||||
{
|
||||
SCOPED_TRACE("batch size 1");
|
||||
testDarknetModel(config_file, weights_file, ref.rowRange(0, N0), scoreDiff, iouDiff, confThreshold);
|
||||
}
|
||||
|
||||
throw SkipTestException("batch2: bad accuracy on second image");
|
||||
/* bad accuracy on second image
|
||||
{
|
||||
SCOPED_TRACE("batch size 2");
|
||||
testDarknetModel(config_file, weights_file, ref, scoreDiff, iouDiff, confThreshold);
|
||||
}
|
||||
*/
|
||||
|
||||
#if defined(INF_ENGINE_RELEASE)
|
||||
if (target == DNN_TARGET_MYRIAD) // bad accuracy
|
||||
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
|
||||
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_OPENCL)
|
||||
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
|
||||
if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ||
|
||||
backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && target == DNN_TARGET_OPENCL_FP16)
|
||||
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
|
||||
#endif
|
||||
}
|
||||
|
||||
INSTANTIATE_TEST_CASE_P(/**/, Test_Int8_nets, dnnBackendsAndTargets());
|
||||
INSTANTIATE_TEST_CASE_P(/**/, Test_Int8_nets, dnnBackendsAndTargetsInt8());
|
||||
|
||||
}} // namespace
|
||||
|
||||
Loading…
Reference in New Issue
Block a user