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329 lines
11 KiB
C++
329 lines
11 KiB
C++
// 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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// Used in accuracy and perf tests as a content of .cpp file
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// Note: don't use "precomp.hpp" here
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#include "opencv2/ts.hpp"
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#include "opencv2/ts/ts_perf.hpp"
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#include "opencv2/core/utility.hpp"
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#include "opencv2/core/ocl.hpp"
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#include "opencv2/dnn.hpp"
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#include "test_common.hpp"
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#include <opencv2/core/utils/configuration.private.hpp>
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#include <opencv2/core/utils/logger.hpp>
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namespace cv { namespace dnn {
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CV__DNN_EXPERIMENTAL_NS_BEGIN
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void PrintTo(const cv::dnn::Backend& v, std::ostream* os)
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{
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switch (v) {
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case DNN_BACKEND_DEFAULT: *os << "DEFAULT"; return;
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case DNN_BACKEND_HALIDE: *os << "HALIDE"; return;
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case DNN_BACKEND_INFERENCE_ENGINE: *os << "DLIE"; return;
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case DNN_BACKEND_OPENCV: *os << "OCV"; return;
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} // don't use "default:" to emit compiler warnings
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*os << "DNN_BACKEND_UNKNOWN(" << (int)v << ")";
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}
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void PrintTo(const cv::dnn::Target& v, std::ostream* os)
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{
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switch (v) {
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case DNN_TARGET_CPU: *os << "CPU"; return;
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case DNN_TARGET_OPENCL: *os << "OCL"; return;
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case DNN_TARGET_OPENCL_FP16: *os << "OCL_FP16"; return;
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case DNN_TARGET_MYRIAD: *os << "MYRIAD"; return;
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case DNN_TARGET_FPGA: *os << "FPGA"; return;
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} // don't use "default:" to emit compiler warnings
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*os << "DNN_TARGET_UNKNOWN(" << (int)v << ")";
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}
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void PrintTo(const tuple<cv::dnn::Backend, cv::dnn::Target> v, std::ostream* os)
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{
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PrintTo(get<0>(v), os);
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*os << "/";
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PrintTo(get<1>(v), os);
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}
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CV__DNN_EXPERIMENTAL_NS_END
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}} // namespace
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namespace opencv_test {
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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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{
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double normL1 = cvtest::norm(ref, test, cv::NORM_L1) / ref.getMat().total();
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EXPECT_LE(normL1, l1) << comment;
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double normInf = cvtest::norm(ref, test, cv::NORM_INF);
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EXPECT_LE(normInf, lInf) << comment;
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}
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std::vector<cv::Rect2d> matToBoxes(const cv::Mat& m)
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{
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EXPECT_EQ(m.type(), CV_32FC1);
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EXPECT_EQ(m.dims, 2);
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EXPECT_EQ(m.cols, 4);
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std::vector<cv::Rect2d> boxes(m.rows);
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for (int i = 0; i < m.rows; ++i)
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{
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CV_Assert(m.row(i).isContinuous());
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const float* data = m.ptr<float>(i);
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double l = data[0], t = data[1], r = data[2], b = data[3];
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boxes[i] = cv::Rect2d(l, t, r - l, b - t);
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}
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return boxes;
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}
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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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{
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std::vector<bool> matchedRefBoxes(refBoxes.size(), false);
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for (int i = 0; i < testBoxes.size(); ++i)
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{
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double testScore = testScores[i];
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if (testScore < confThreshold)
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continue;
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int testClassId = testClassIds[i];
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const cv::Rect2d& testBox = testBoxes[i];
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bool matched = false;
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for (int j = 0; j < refBoxes.size() && !matched; ++j)
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{
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if (!matchedRefBoxes[j] && testClassId == refClassIds[j] &&
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std::abs(testScore - refScores[j]) < scores_diff)
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{
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double interArea = (testBox & refBoxes[j]).area();
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double iou = interArea / (testBox.area() + refBoxes[j].area() - interArea);
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if (std::abs(iou - 1.0) < boxes_iou_diff)
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{
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matched = true;
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matchedRefBoxes[j] = true;
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}
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}
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}
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if (!matched)
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std::cout << cv::format("Unmatched prediction: class %d score %f box ",
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testClassId, testScore) << testBox << std::endl;
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EXPECT_TRUE(matched) << comment;
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}
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// Check unmatched reference detections.
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for (int i = 0; i < refBoxes.size(); ++i)
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{
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if (!matchedRefBoxes[i] && refScores[i] > confThreshold)
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{
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std::cout << cv::format("Unmatched reference: class %d score %f box ",
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refClassIds[i], refScores[i]) << refBoxes[i] << std::endl;
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EXPECT_LE(refScores[i], confThreshold) << comment;
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}
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}
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}
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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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{
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CV_Assert(ref.total() % 7 == 0);
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CV_Assert(out.total() % 7 == 0);
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ref = ref.reshape(1, ref.total() / 7);
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out = out.reshape(1, out.total() / 7);
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cv::Mat refClassIds, testClassIds;
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ref.col(1).convertTo(refClassIds, CV_32SC1);
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out.col(1).convertTo(testClassIds, CV_32SC1);
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std::vector<float> refScores(ref.col(2)), testScores(out.col(2));
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std::vector<cv::Rect2d> refBoxes = matToBoxes(ref.colRange(3, 7));
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std::vector<cv::Rect2d> testBoxes = matToBoxes(out.colRange(3, 7));
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normAssertDetections(refClassIds, refScores, refBoxes, testClassIds, testScores,
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testBoxes, comment, confThreshold, scores_diff, boxes_iou_diff);
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}
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void readFileContent(const std::string& filename, CV_OUT std::vector<char>& content)
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{
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const std::ios::openmode mode = std::ios::in | std::ios::binary;
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std::ifstream ifs(filename.c_str(), mode);
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ASSERT_TRUE(ifs.is_open());
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content.clear();
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ifs.seekg(0, std::ios::end);
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const size_t sz = ifs.tellg();
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content.resize(sz);
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ifs.seekg(0, std::ios::beg);
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ifs.read((char*)content.data(), sz);
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ASSERT_FALSE(ifs.fail());
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}
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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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)
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{
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#ifdef HAVE_INF_ENGINE
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bool withVPU = validateVPUType();
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#endif
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std::vector< tuple<Backend, Target> > targets;
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std::vector< Target > available;
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if (withHalide)
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{
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available = getAvailableTargets(DNN_BACKEND_HALIDE);
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for (std::vector< Target >::const_iterator i = available.begin(); i != available.end(); ++i)
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targets.push_back(make_tuple(DNN_BACKEND_HALIDE, *i));
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}
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#ifdef HAVE_INF_ENGINE
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if (withInferenceEngine)
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{
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available = getAvailableTargets(DNN_BACKEND_INFERENCE_ENGINE);
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for (std::vector< Target >::const_iterator i = available.begin(); i != available.end(); ++i)
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{
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if (*i == DNN_TARGET_MYRIAD && !withVPU)
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continue;
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targets.push_back(make_tuple(DNN_BACKEND_INFERENCE_ENGINE, *i));
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}
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}
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#else
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CV_UNUSED(withInferenceEngine);
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#endif
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{
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available = getAvailableTargets(DNN_BACKEND_OPENCV);
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for (std::vector< Target >::const_iterator i = available.begin(); i != available.end(); ++i)
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{
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if (!withCpuOCV && *i == DNN_TARGET_CPU)
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continue;
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targets.push_back(make_tuple(DNN_BACKEND_OPENCV, *i));
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}
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}
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if (targets.empty()) // validate at least CPU mode
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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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#ifdef HAVE_INF_ENGINE
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static std::string getTestInferenceEngineVPUType()
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{
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static std::string param_vpu_type = utils::getConfigurationParameterString("OPENCV_TEST_DNN_IE_VPU_TYPE", "");
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return param_vpu_type;
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}
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static bool validateVPUType_()
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{
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std::string test_vpu_type = getTestInferenceEngineVPUType();
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if (test_vpu_type == "DISABLED" || test_vpu_type == "disabled")
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{
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return false;
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}
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std::vector<Target> available = getAvailableTargets(DNN_BACKEND_INFERENCE_ENGINE);
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bool have_vpu_target = false;
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for (std::vector<Target>::const_iterator i = available.begin(); i != available.end(); ++i)
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{
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if (*i == DNN_TARGET_MYRIAD)
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{
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have_vpu_target = true;
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break;
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}
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}
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if (test_vpu_type.empty())
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{
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if (have_vpu_target)
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{
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CV_LOG_INFO(NULL, "OpenCV-DNN-Test: VPU type for testing is not specified via 'OPENCV_TEST_DNN_IE_VPU_TYPE' parameter.")
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}
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}
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else
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{
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if (!have_vpu_target)
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{
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CV_LOG_FATAL(NULL, "OpenCV-DNN-Test: 'OPENCV_TEST_DNN_IE_VPU_TYPE' parameter requires VPU of type = '" << test_vpu_type << "', but VPU is not detected. STOP.");
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exit(1);
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}
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std::string dnn_vpu_type = getInferenceEngineVPUType();
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if (dnn_vpu_type != test_vpu_type)
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{
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CV_LOG_FATAL(NULL, "OpenCV-DNN-Test: 'testing' and 'detected' VPU types mismatch: '" << test_vpu_type << "' vs '" << dnn_vpu_type << "'. STOP.");
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exit(1);
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}
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}
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if (have_vpu_target)
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{
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std::string dnn_vpu_type = getInferenceEngineVPUType();
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if (dnn_vpu_type == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_2)
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registerGlobalSkipTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_2);
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if (dnn_vpu_type == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
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registerGlobalSkipTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
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}
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return true;
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}
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bool validateVPUType()
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{
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static bool result = validateVPUType_();
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return result;
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}
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#endif // HAVE_INF_ENGINE
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void initDNNTests()
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{
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const char* extraTestDataPath =
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#ifdef WINRT
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NULL;
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#else
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getenv("OPENCV_DNN_TEST_DATA_PATH");
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#endif
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if (extraTestDataPath)
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cvtest::addDataSearchPath(extraTestDataPath);
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registerGlobalSkipTag(
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CV_TEST_TAG_DNN_SKIP_HALIDE,
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CV_TEST_TAG_DNN_SKIP_OPENCL, CV_TEST_TAG_DNN_SKIP_OPENCL_FP16
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);
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#if defined(INF_ENGINE_RELEASE)
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registerGlobalSkipTag(
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#if INF_ENGINE_VER_MAJOR_EQ(2018050000)
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CV_TEST_TAG_DNN_SKIP_IE_2018R5,
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#elif INF_ENGINE_VER_MAJOR_EQ(2019010000)
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CV_TEST_TAG_DNN_SKIP_IE_2019R1,
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# if INF_ENGINE_RELEASE == 2019010100
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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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CV_TEST_TAG_DNN_SKIP_IE_2019R2,
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#endif
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CV_TEST_TAG_DNN_SKIP_IE
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);
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#endif
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registerGlobalSkipTag(
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// see validateVPUType(): CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_2, CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X
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CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16
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);
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}
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} // namespace
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