// 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. #ifndef __OPENCV_TEST_COMMON_HPP__ #define __OPENCV_TEST_COMMON_HPP__ #include "opencv2/dnn/utils/inference_engine.hpp" #ifdef HAVE_OPENCL #include "opencv2/core/ocl.hpp" #endif // src/op_inf_engine.hpp #define INF_ENGINE_VER_MAJOR_GT(ver) (((INF_ENGINE_RELEASE) / 10000) > ((ver) / 10000)) #define INF_ENGINE_VER_MAJOR_GE(ver) (((INF_ENGINE_RELEASE) / 10000) >= ((ver) / 10000)) #define INF_ENGINE_VER_MAJOR_LT(ver) (((INF_ENGINE_RELEASE) / 10000) < ((ver) / 10000)) #define INF_ENGINE_VER_MAJOR_LE(ver) (((INF_ENGINE_RELEASE) / 10000) <= ((ver) / 10000)) #define INF_ENGINE_VER_MAJOR_EQ(ver) (((INF_ENGINE_RELEASE) / 10000) == ((ver) / 10000)) #define CV_TEST_TAG_DNN_SKIP_OPENCV_BACKEND "dnn_skip_opencv_backend" #define CV_TEST_TAG_DNN_SKIP_HALIDE "dnn_skip_halide" #define CV_TEST_TAG_DNN_SKIP_CPU "dnn_skip_cpu" #define CV_TEST_TAG_DNN_SKIP_CPU_FP16 "dnn_skip_cpu_fp16" #define CV_TEST_TAG_DNN_SKIP_OPENCL "dnn_skip_ocl" #define CV_TEST_TAG_DNN_SKIP_OPENCL_FP16 "dnn_skip_ocl_fp16" #define CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER "dnn_skip_ie_nn_builder" #define CV_TEST_TAG_DNN_SKIP_IE_NGRAPH "dnn_skip_ie_ngraph" #define CV_TEST_TAG_DNN_SKIP_IE "dnn_skip_ie" #define CV_TEST_TAG_DNN_SKIP_IE_2018R5 "dnn_skip_ie_2018r5" #define CV_TEST_TAG_DNN_SKIP_IE_2019R1 "dnn_skip_ie_2019r1" #define CV_TEST_TAG_DNN_SKIP_IE_2019R1_1 "dnn_skip_ie_2019r1_1" #define CV_TEST_TAG_DNN_SKIP_IE_2019R2 "dnn_skip_ie_2019r2" #define CV_TEST_TAG_DNN_SKIP_IE_2019R3 "dnn_skip_ie_2019r3" #define CV_TEST_TAG_DNN_SKIP_IE_CPU "dnn_skip_ie_cpu" #define CV_TEST_TAG_DNN_SKIP_IE_OPENCL "dnn_skip_ie_ocl" #define CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16 "dnn_skip_ie_ocl_fp16" #define CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_2 "dnn_skip_ie_myriad2" #define CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X "dnn_skip_ie_myriadx" #define CV_TEST_TAG_DNN_SKIP_IE_MYRIAD CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_2, CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X #define CV_TEST_TAG_DNN_SKIP_IE_ARM_CPU "dnn_skip_ie_arm_cpu" #define CV_TEST_TAG_DNN_SKIP_VULKAN "dnn_skip_vulkan" #define CV_TEST_TAG_DNN_SKIP_CUDA "dnn_skip_cuda" #define CV_TEST_TAG_DNN_SKIP_CUDA_FP16 "dnn_skip_cuda_fp16" #define CV_TEST_TAG_DNN_SKIP_CUDA_FP32 "dnn_skip_cuda_fp32" #define CV_TEST_TAG_DNN_SKIP_ONNX_CONFORMANCE "dnn_skip_onnx_conformance" #define CV_TEST_TAG_DNN_SKIP_PARSER "dnn_skip_parser" #define CV_TEST_TAG_DNN_SKIP_GLOBAL "dnn_skip_global" #define CV_TEST_TAG_DNN_SKIP_TIMVX "dnn_skip_timvx" #define CV_TEST_TAG_DNN_SKIP_CANN "dnn_skip_cann" #ifdef HAVE_INF_ENGINE #if INF_ENGINE_VER_MAJOR_EQ(2018050000) # define CV_TEST_TAG_DNN_SKIP_IE_VERSION CV_TEST_TAG_DNN_SKIP_IE, CV_TEST_TAG_DNN_SKIP_IE_2018R5 #elif INF_ENGINE_VER_MAJOR_EQ(2019010000) # if INF_ENGINE_RELEASE < 2019010100 # define CV_TEST_TAG_DNN_SKIP_IE_VERSION CV_TEST_TAG_DNN_SKIP_IE, CV_TEST_TAG_DNN_SKIP_IE_2019R1 # else # define CV_TEST_TAG_DNN_SKIP_IE_VERSION CV_TEST_TAG_DNN_SKIP_IE, CV_TEST_TAG_DNN_SKIP_IE_2019R1_1 # endif #elif INF_ENGINE_VER_MAJOR_EQ(2019020000) # define CV_TEST_TAG_DNN_SKIP_IE_VERSION CV_TEST_TAG_DNN_SKIP_IE, CV_TEST_TAG_DNN_SKIP_IE_2019R2 #elif INF_ENGINE_VER_MAJOR_EQ(2019030000) # define CV_TEST_TAG_DNN_SKIP_IE_VERSION CV_TEST_TAG_DNN_SKIP_IE, CV_TEST_TAG_DNN_SKIP_IE_2019R3 #endif #endif // HAVE_INF_ENGINE #ifndef CV_TEST_TAG_DNN_SKIP_IE_VERSION # define CV_TEST_TAG_DNN_SKIP_IE_VERSION CV_TEST_TAG_DNN_SKIP_IE #endif namespace cv { namespace dnn { CV__DNN_INLINE_NS_BEGIN void PrintTo(const cv::dnn::Backend& v, std::ostream* os); void PrintTo(const cv::dnn::Target& v, std::ostream* os); using opencv_test::tuple; using opencv_test::get; void PrintTo(const tuple v, std::ostream* os); CV__DNN_INLINE_NS_END }} // namespace cv::dnn namespace opencv_test { void initDNNTests(); using namespace cv::dnn; static inline const std::string &getOpenCVExtraDir() { return cvtest::TS::ptr()->get_data_path(); } void normAssert( cv::InputArray ref, cv::InputArray test, const char *comment = "", double l1 = 0.00001, double lInf = 0.0001); std::vector matToBoxes(const cv::Mat& m); void normAssertDetections( const std::vector& refClassIds, const std::vector& refScores, const std::vector& refBoxes, const std::vector& testClassIds, const std::vector& testScores, const std::vector& testBoxes, const char *comment = "", double confThreshold = 0.0, double scores_diff = 1e-5, double boxes_iou_diff = 1e-4); // For SSD-based object detection networks which produce output of shape 1x1xNx7 // where N is a number of detections and an every detection is represented by // a vector [batchId, classId, confidence, left, top, right, bottom]. void normAssertDetections( cv::Mat ref, cv::Mat out, const char *comment = "", double confThreshold = 0.0, double scores_diff = 1e-5, double boxes_iou_diff = 1e-4); // For text detection networks // Curved text polygon is not supported in the current version. // (concave polygon is invalid input to intersectConvexConvex) void normAssertTextDetections( const std::vector>& gtPolys, const std::vector>& testPolys, const char *comment = "", double boxes_iou_diff = 1e-4); void readFileContent(const std::string& filename, CV_OUT std::vector& content); bool validateVPUType(); testing::internal::ParamGenerator< tuple > dnnBackendsAndTargets( bool withInferenceEngine = true, bool withHalide = false, bool withCpuOCV = true, bool withVkCom = true, bool withCUDA = true, bool withNgraph = true, bool withWebnn = true, bool withCann = true ); testing::internal::ParamGenerator< tuple > dnnBackendsAndTargetsIE(); class DNNTestLayer : public TestWithParam > { public: dnn::Backend backend; dnn::Target target; double default_l1, default_lInf; DNNTestLayer() { backend = (dnn::Backend)(int)get<0>(GetParam()); target = (dnn::Target)(int)get<1>(GetParam()); getDefaultThresholds(backend, target, &default_l1, &default_lInf); } static void getDefaultThresholds(int backend, int target, double* l1, double* lInf) { if (target == DNN_TARGET_CPU_FP16 || target == DNN_TARGET_CUDA_FP16 || target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) { *l1 = 4e-3; *lInf = 2e-2; } else { *l1 = 1e-5; *lInf = 1e-4; } } static void checkBackend(int backend, int target, Mat* inp = 0, Mat* ref = 0) { CV_UNUSED(backend); CV_UNUSED(target); CV_UNUSED(inp); CV_UNUSED(ref); #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021000000) if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && target == DNN_TARGET_MYRIAD) { if (inp && ref && inp->dims == 4 && ref->dims == 4 && inp->size[0] != 1 && inp->size[0] != ref->size[0]) { std::cout << "Inconsistent batch size of input and output blobs for Myriad plugin" << std::endl; applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD); } } #endif } void expectNoFallbacks(Net& net, bool raiseError = true) { // Check if all the layers are supported with current backend and target. // Some layers might be fused so their timings equal to zero. std::vector timings; net.getPerfProfile(timings); std::vector names = net.getLayerNames(); CV_Assert(names.size() == timings.size()); bool hasFallbacks = false; for (int i = 0; i < names.size(); ++i) { Ptr l = net.getLayer(net.getLayerId(names[i])); bool fused = !timings[i]; if ((!l->supportBackend(backend) || l->preferableTarget != target) && !fused) { hasFallbacks = true; std::cout << "FALLBACK: Layer [" << l->type << "]:[" << l->name << "] is expected to have backend implementation" << endl; } } if (hasFallbacks && raiseError) CV_Error(Error::StsNotImplemented, "Implementation fallbacks are not expected in this test"); } void expectNoFallbacksFromIE(Net& net) { if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) expectNoFallbacks(net); if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) expectNoFallbacks(net, false); } void expectNoFallbacksFromCUDA(Net& net) { if (backend == DNN_BACKEND_CUDA) expectNoFallbacks(net); } size_t getTopMemoryUsageMB(); protected: void checkBackend(Mat* inp = 0, Mat* ref = 0) { checkBackend(backend, target, inp, ref); } }; } // namespace #endif