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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#ifndef __OPENCV_TEST_COMMON_HPP__
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#define __OPENCV_TEST_COMMON_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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namespace cv { namespace dnn {
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CV__DNN_EXPERIMENTAL_NS_BEGIN
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static inline 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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static inline 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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using opencv_test::tuple;
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using opencv_test::get;
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static inline 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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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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static inline void normAssert(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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static 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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static inline void normAssertDetections(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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static inline void normAssertDetections(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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static inline bool readFileInMemory(const std::string& filename, std::string& content)
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{
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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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if (!ifs.is_open())
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return false;
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content.clear();
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ifs.seekg(0, std::ios::end);
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content.reserve(ifs.tellg());
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ifs.seekg(0, std::ios::beg);
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content.assign((std::istreambuf_iterator<char>(ifs)),
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std::istreambuf_iterator<char>());
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return true;
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}
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namespace opencv_test {
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using namespace cv::dnn;
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static inline
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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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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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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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targets.push_back(make_tuple(DNN_BACKEND_INFERENCE_ENGINE, *i));
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}
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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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targets.push_back(make_tuple(DNN_BACKEND_OPENCV, *i));
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}
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return testing::ValuesIn(targets);
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}
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} // namespace
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namespace opencv_test {
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using namespace cv::dnn;
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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_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 && target == DNN_TARGET_MYRIAD)
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{
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE < 2018030000
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if (inp && ref && inp->size[0] != 1)
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{
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// Myriad plugin supports only batch size 1. Slice a single sample.
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if (inp->size[0] == ref->size[0])
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{
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std::vector<cv::Range> range(inp->dims, Range::all());
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range[0] = Range(0, 1);
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*inp = inp->operator()(range);
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range = std::vector<cv::Range>(ref->dims, Range::all());
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range[0] = Range(0, 1);
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*ref = ref->operator()(range);
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}
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else
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throw SkipTestException("Myriad plugin supports only batch size 1");
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}
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#else
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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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throw SkipTestException("Inconsistent batch size of input and output blobs for Myriad plugin");
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#endif
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}
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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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