feat: 切换后端至PaddleOCR-NCNN，切换工程为CMake
1.项目后端整体迁移至PaddleOCR-NCNN算法，已通过基本的兼容性测试 2.工程改为使用CMake组织，后续为了更好地兼容第三方库，不再提供QMake工程 3.重整权利声明文件，重整代码工程，确保最小化侵权风险 Log: 切换后端至PaddleOCR-NCNN，切换工程为CMake Change-Id: I4d5d2c5d37505a4a24b389b1a4c5d12f17bfa38c
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wangzhengyang
234
3rdparty/opencv-4.5.4/modules/dnn/test/test_common.hpp
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3rdparty/opencv-4.5.4/modules/dnn/test/test_common.hpp
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// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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#ifndef __OPENCV_TEST_COMMON_HPP__
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#define __OPENCV_TEST_COMMON_HPP__
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#include "opencv2/dnn/utils/inference_engine.hpp"
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#ifdef HAVE_OPENCL
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#include "opencv2/core/ocl.hpp"
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#endif
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// src/op_inf_engine.hpp
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#define INF_ENGINE_VER_MAJOR_GT(ver) (((INF_ENGINE_RELEASE) / 10000) > ((ver) / 10000))
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#define INF_ENGINE_VER_MAJOR_GE(ver) (((INF_ENGINE_RELEASE) / 10000) >= ((ver) / 10000))
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#define INF_ENGINE_VER_MAJOR_LT(ver) (((INF_ENGINE_RELEASE) / 10000) < ((ver) / 10000))
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#define INF_ENGINE_VER_MAJOR_LE(ver) (((INF_ENGINE_RELEASE) / 10000) <= ((ver) / 10000))
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#define INF_ENGINE_VER_MAJOR_EQ(ver) (((INF_ENGINE_RELEASE) / 10000) == ((ver) / 10000))
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#define CV_TEST_TAG_DNN_SKIP_HALIDE "dnn_skip_halide"
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#define CV_TEST_TAG_DNN_SKIP_OPENCL "dnn_skip_ocl"
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#define CV_TEST_TAG_DNN_SKIP_OPENCL_FP16 "dnn_skip_ocl_fp16"
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#define CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER "dnn_skip_ie_nn_builder"
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#define CV_TEST_TAG_DNN_SKIP_IE_NGRAPH "dnn_skip_ie_ngraph"
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#define CV_TEST_TAG_DNN_SKIP_IE "dnn_skip_ie"
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#define CV_TEST_TAG_DNN_SKIP_IE_2018R5 "dnn_skip_ie_2018r5"
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#define CV_TEST_TAG_DNN_SKIP_IE_2019R1 "dnn_skip_ie_2019r1"
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#define CV_TEST_TAG_DNN_SKIP_IE_2019R1_1 "dnn_skip_ie_2019r1_1"
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#define CV_TEST_TAG_DNN_SKIP_IE_2019R2 "dnn_skip_ie_2019r2"
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#define CV_TEST_TAG_DNN_SKIP_IE_2019R3 "dnn_skip_ie_2019r3"
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#define CV_TEST_TAG_DNN_SKIP_IE_CPU "dnn_skip_ie_cpu"
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#define CV_TEST_TAG_DNN_SKIP_IE_OPENCL "dnn_skip_ie_ocl"
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#define CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16 "dnn_skip_ie_ocl_fp16"
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#define CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_2 "dnn_skip_ie_myriad2"
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#define CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X "dnn_skip_ie_myriadx"
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#define CV_TEST_TAG_DNN_SKIP_IE_MYRIAD CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_2, CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X
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#define CV_TEST_TAG_DNN_SKIP_IE_ARM_CPU "dnn_skip_ie_arm_cpu"
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#define CV_TEST_TAG_DNN_SKIP_VULKAN "dnn_skip_vulkan"
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#define CV_TEST_TAG_DNN_SKIP_CUDA "dnn_skip_cuda"
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#define CV_TEST_TAG_DNN_SKIP_CUDA_FP16 "dnn_skip_cuda_fp16"
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#define CV_TEST_TAG_DNN_SKIP_CUDA_FP32 "dnn_skip_cuda_fp32"
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#ifdef HAVE_INF_ENGINE
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#if INF_ENGINE_VER_MAJOR_EQ(2018050000)
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# define CV_TEST_TAG_DNN_SKIP_IE_VERSION CV_TEST_TAG_DNN_SKIP_IE, CV_TEST_TAG_DNN_SKIP_IE_2018R5
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#elif INF_ENGINE_VER_MAJOR_EQ(2019010000)
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# if INF_ENGINE_RELEASE < 2019010100
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# define CV_TEST_TAG_DNN_SKIP_IE_VERSION CV_TEST_TAG_DNN_SKIP_IE, CV_TEST_TAG_DNN_SKIP_IE_2019R1
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# else
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# define CV_TEST_TAG_DNN_SKIP_IE_VERSION CV_TEST_TAG_DNN_SKIP_IE, CV_TEST_TAG_DNN_SKIP_IE_2019R1_1
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# endif
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#elif INF_ENGINE_VER_MAJOR_EQ(2019020000)
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# define CV_TEST_TAG_DNN_SKIP_IE_VERSION CV_TEST_TAG_DNN_SKIP_IE, CV_TEST_TAG_DNN_SKIP_IE_2019R2
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#elif INF_ENGINE_VER_MAJOR_EQ(2019030000)
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# define CV_TEST_TAG_DNN_SKIP_IE_VERSION CV_TEST_TAG_DNN_SKIP_IE, CV_TEST_TAG_DNN_SKIP_IE_2019R3
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#endif
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#endif // HAVE_INF_ENGINE
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#ifndef CV_TEST_TAG_DNN_SKIP_IE_VERSION
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# define CV_TEST_TAG_DNN_SKIP_IE_VERSION CV_TEST_TAG_DNN_SKIP_IE
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#endif
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namespace cv { namespace dnn {
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CV__DNN_INLINE_NS_BEGIN
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void PrintTo(const cv::dnn::Backend& v, std::ostream* os);
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void PrintTo(const cv::dnn::Target& v, std::ostream* os);
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using opencv_test::tuple;
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using opencv_test::get;
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void PrintTo(const tuple<cv::dnn::Backend, cv::dnn::Target> v, std::ostream* os);
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CV__DNN_INLINE_NS_END
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}} // namespace cv::dnn
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namespace opencv_test {
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void initDNNTests();
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using namespace cv::dnn;
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static inline const std::string &getOpenCVExtraDir()
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{
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return cvtest::TS::ptr()->get_data_path();
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}
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void normAssert(
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cv::InputArray ref, cv::InputArray test, const char *comment = "",
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double l1 = 0.00001, double lInf = 0.0001);
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std::vector<cv::Rect2d> matToBoxes(const cv::Mat& m);
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void normAssertDetections(
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const std::vector<int>& refClassIds,
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const std::vector<float>& refScores,
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const std::vector<cv::Rect2d>& refBoxes,
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const std::vector<int>& testClassIds,
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const std::vector<float>& testScores,
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const std::vector<cv::Rect2d>& testBoxes,
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const char *comment = "", double confThreshold = 0.0,
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double scores_diff = 1e-5, double boxes_iou_diff = 1e-4);
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// For SSD-based object detection networks which produce output of shape 1x1xNx7
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// where N is a number of detections and an every detection is represented by
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// a vector [batchId, classId, confidence, left, top, right, bottom].
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void normAssertDetections(
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cv::Mat ref, cv::Mat out, const char *comment = "",
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double confThreshold = 0.0, double scores_diff = 1e-5,
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double boxes_iou_diff = 1e-4);
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// For text detection networks
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// Curved text polygon is not supported in the current version.
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// (concave polygon is invalid input to intersectConvexConvex)
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void normAssertTextDetections(
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const std::vector<std::vector<Point>>& gtPolys,
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const std::vector<std::vector<Point>>& testPolys,
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const char *comment = "", double boxes_iou_diff = 1e-4);
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void readFileContent(const std::string& filename, CV_OUT std::vector<char>& content);
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#ifdef HAVE_INF_ENGINE
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bool validateVPUType();
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#endif
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testing::internal::ParamGenerator< tuple<Backend, Target> > dnnBackendsAndTargets(
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bool withInferenceEngine = true,
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bool withHalide = false,
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bool withCpuOCV = true,
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bool withVkCom = true,
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bool withCUDA = true,
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bool withNgraph = true
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);
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testing::internal::ParamGenerator< tuple<Backend, Target> > dnnBackendsAndTargetsIE();
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class DNNTestLayer : public TestWithParam<tuple<Backend, Target> >
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{
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public:
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dnn::Backend backend;
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dnn::Target target;
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double default_l1, default_lInf;
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DNNTestLayer()
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{
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backend = (dnn::Backend)(int)get<0>(GetParam());
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target = (dnn::Target)(int)get<1>(GetParam());
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getDefaultThresholds(backend, target, &default_l1, &default_lInf);
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}
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static void getDefaultThresholds(int backend, int target, double* l1, double* lInf)
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{
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if (target == DNN_TARGET_CUDA_FP16 || target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD)
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{
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*l1 = 4e-3;
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*lInf = 2e-2;
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}
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else
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{
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*l1 = 1e-5;
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*lInf = 1e-4;
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}
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}
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static void checkBackend(int backend, int target, Mat* inp = 0, Mat* ref = 0)
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{
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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)
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{
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if (inp && ref && inp->dims == 4 && ref->dims == 4 &&
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inp->size[0] != 1 && inp->size[0] != ref->size[0])
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{
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
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throw SkipTestException("Inconsistent batch size of input and output blobs for Myriad plugin");
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}
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}
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}
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void expectNoFallbacks(Net& net, bool raiseError = true)
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{
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// Check if all the layers are supported with current backend and target.
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// Some layers might be fused so their timings equal to zero.
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std::vector<double> timings;
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net.getPerfProfile(timings);
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std::vector<String> names = net.getLayerNames();
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CV_Assert(names.size() == timings.size());
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bool hasFallbacks = false;
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for (int i = 0; i < names.size(); ++i)
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{
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Ptr<dnn::Layer> l = net.getLayer(net.getLayerId(names[i]));
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bool fused = !timings[i];
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if ((!l->supportBackend(backend) || l->preferableTarget != target) && !fused)
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{
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hasFallbacks = true;
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std::cout << "FALLBACK: Layer [" << l->type << "]:[" << l->name << "] is expected to has backend implementation" << endl;
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}
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}
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if (hasFallbacks && raiseError)
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CV_Error(Error::StsNotImplemented, "Implementation fallbacks are not expected in this test");
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}
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void expectNoFallbacksFromIE(Net& net)
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{
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
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expectNoFallbacks(net);
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
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expectNoFallbacks(net, false);
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}
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void expectNoFallbacksFromCUDA(Net& net)
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{
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if (backend == DNN_BACKEND_CUDA)
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expectNoFallbacks(net);
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}
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protected:
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void checkBackend(Mat* inp = 0, Mat* ref = 0)
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{
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checkBackend(backend, target, inp, ref);
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}
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};
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} // namespace
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#endif
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