file(GLOB_RECURSE perf_srcs "${perf_path}/*.cpp")
file(GLOB_RECURSE perf_hdrs "${perf_path}/*.hpp" "${perf_path}/*.h")
ocv_add_perf_tests(${INF_ENGINE_TARGET}
- FILES test_common "${CMAKE_CURRENT_LIST_DIR}/test/test_common.cpp"
+ FILES test_common "${CMAKE_CURRENT_LIST_DIR}/test/test_common.hpp" "${CMAKE_CURRENT_LIST_DIR}/test/test_common.impl.hpp"
FILES Src ${perf_srcs}
FILES Include ${perf_hdrs}
)
-set_property(
- SOURCE "${CMAKE_CURRENT_LIST_DIR}/test/test_common.cpp"
- PROPERTY COMPILE_DEFINITIONS "__OPENCV_TESTS=1"
-)
ocv_option(${the_module}_PERF_CAFFE "Add performance tests of Caffe framework" OFF)
ocv_option(${the_module}_PERF_CLCAFFE "Add performance tests of clCaffe framework" OFF)
--- /dev/null
+// 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.
+
+#include "perf_precomp.hpp"
+#include "../test/test_common.impl.hpp" // shared with accuracy tests
// 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.
-// Used in perf tests too, disabled: #include "test_precomp.hpp"
-#include "opencv2/ts.hpp"
-#include "opencv2/ts/ts_perf.hpp"
-#include "opencv2/core/utility.hpp"
-#include "opencv2/core/ocl.hpp"
-
-#include "opencv2/dnn.hpp"
-#include "test_common.hpp"
-
-#include <opencv2/core/utils/configuration.private.hpp>
-#include <opencv2/core/utils/logger.hpp>
-
-namespace cv { namespace dnn {
-CV__DNN_EXPERIMENTAL_NS_BEGIN
-
-void PrintTo(const cv::dnn::Backend& v, std::ostream* os)
-{
- switch (v) {
- case DNN_BACKEND_DEFAULT: *os << "DEFAULT"; return;
- case DNN_BACKEND_HALIDE: *os << "HALIDE"; return;
- case DNN_BACKEND_INFERENCE_ENGINE: *os << "DLIE"; return;
- case DNN_BACKEND_OPENCV: *os << "OCV"; return;
- } // don't use "default:" to emit compiler warnings
- *os << "DNN_BACKEND_UNKNOWN(" << (int)v << ")";
-}
-
-void PrintTo(const cv::dnn::Target& v, std::ostream* os)
-{
- switch (v) {
- case DNN_TARGET_CPU: *os << "CPU"; return;
- case DNN_TARGET_OPENCL: *os << "OCL"; return;
- case DNN_TARGET_OPENCL_FP16: *os << "OCL_FP16"; return;
- case DNN_TARGET_MYRIAD: *os << "MYRIAD"; return;
- case DNN_TARGET_FPGA: *os << "FPGA"; return;
- } // don't use "default:" to emit compiler warnings
- *os << "DNN_TARGET_UNKNOWN(" << (int)v << ")";
-}
-
-void PrintTo(const tuple<cv::dnn::Backend, cv::dnn::Target> v, std::ostream* os)
-{
- PrintTo(get<0>(v), os);
- *os << "/";
- PrintTo(get<1>(v), os);
-}
-
-CV__DNN_EXPERIMENTAL_NS_END
-}} // namespace
-
-
-
-namespace opencv_test {
-
-void normAssert(
- cv::InputArray ref, cv::InputArray test, const char *comment /*= ""*/,
- double l1 /*= 0.00001*/, double lInf /*= 0.0001*/)
-{
- double normL1 = cvtest::norm(ref, test, cv::NORM_L1) / ref.getMat().total();
- EXPECT_LE(normL1, l1) << comment;
-
- double normInf = cvtest::norm(ref, test, cv::NORM_INF);
- EXPECT_LE(normInf, lInf) << comment;
-}
-
-std::vector<cv::Rect2d> matToBoxes(const cv::Mat& m)
-{
- EXPECT_EQ(m.type(), CV_32FC1);
- EXPECT_EQ(m.dims, 2);
- EXPECT_EQ(m.cols, 4);
-
- std::vector<cv::Rect2d> boxes(m.rows);
- for (int i = 0; i < m.rows; ++i)
- {
- CV_Assert(m.row(i).isContinuous());
- const float* data = m.ptr<float>(i);
- double l = data[0], t = data[1], r = data[2], b = data[3];
- boxes[i] = cv::Rect2d(l, t, r - l, b - t);
- }
- return boxes;
-}
-
-void normAssertDetections(
- const std::vector<int>& refClassIds,
- const std::vector<float>& refScores,
- const std::vector<cv::Rect2d>& refBoxes,
- const std::vector<int>& testClassIds,
- const std::vector<float>& testScores,
- const std::vector<cv::Rect2d>& testBoxes,
- const char *comment /*= ""*/, double confThreshold /*= 0.0*/,
- double scores_diff /*= 1e-5*/, double boxes_iou_diff /*= 1e-4*/)
-{
- std::vector<bool> matchedRefBoxes(refBoxes.size(), false);
- for (int i = 0; i < testBoxes.size(); ++i)
- {
- double testScore = testScores[i];
- if (testScore < confThreshold)
- continue;
-
- int testClassId = testClassIds[i];
- const cv::Rect2d& testBox = testBoxes[i];
- bool matched = false;
- for (int j = 0; j < refBoxes.size() && !matched; ++j)
- {
- if (!matchedRefBoxes[j] && testClassId == refClassIds[j] &&
- std::abs(testScore - refScores[j]) < scores_diff)
- {
- double interArea = (testBox & refBoxes[j]).area();
- double iou = interArea / (testBox.area() + refBoxes[j].area() - interArea);
- if (std::abs(iou - 1.0) < boxes_iou_diff)
- {
- matched = true;
- matchedRefBoxes[j] = true;
- }
- }
- }
- if (!matched)
- std::cout << cv::format("Unmatched prediction: class %d score %f box ",
- testClassId, testScore) << testBox << std::endl;
- EXPECT_TRUE(matched) << comment;
- }
-
- // Check unmatched reference detections.
- for (int i = 0; i < refBoxes.size(); ++i)
- {
- if (!matchedRefBoxes[i] && refScores[i] > confThreshold)
- {
- std::cout << cv::format("Unmatched reference: class %d score %f box ",
- refClassIds[i], refScores[i]) << refBoxes[i] << std::endl;
- EXPECT_LE(refScores[i], confThreshold) << comment;
- }
- }
-}
-
-// 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*/)
-{
- CV_Assert(ref.total() % 7 == 0);
- CV_Assert(out.total() % 7 == 0);
- ref = ref.reshape(1, ref.total() / 7);
- out = out.reshape(1, out.total() / 7);
-
- cv::Mat refClassIds, testClassIds;
- ref.col(1).convertTo(refClassIds, CV_32SC1);
- out.col(1).convertTo(testClassIds, CV_32SC1);
- std::vector<float> refScores(ref.col(2)), testScores(out.col(2));
- std::vector<cv::Rect2d> refBoxes = matToBoxes(ref.colRange(3, 7));
- std::vector<cv::Rect2d> testBoxes = matToBoxes(out.colRange(3, 7));
- normAssertDetections(refClassIds, refScores, refBoxes, testClassIds, testScores,
- testBoxes, comment, confThreshold, scores_diff, boxes_iou_diff);
-}
-
-bool readFileInMemory(const std::string& filename, std::string& content)
-{
- std::ios::openmode mode = std::ios::in | std::ios::binary;
- std::ifstream ifs(filename.c_str(), mode);
- if (!ifs.is_open())
- return false;
-
- content.clear();
-
- ifs.seekg(0, std::ios::end);
- content.reserve(ifs.tellg());
- ifs.seekg(0, std::ios::beg);
-
- content.assign((std::istreambuf_iterator<char>(ifs)),
- std::istreambuf_iterator<char>());
-
- return true;
-}
-
-
-testing::internal::ParamGenerator< tuple<Backend, Target> > dnnBackendsAndTargets(
- bool withInferenceEngine /*= true*/,
- bool withHalide /*= false*/,
- bool withCpuOCV /*= true*/
-)
-{
-#ifdef HAVE_INF_ENGINE
- bool withVPU = validateVPUType();
-#endif
-
- std::vector< tuple<Backend, Target> > targets;
- std::vector< Target > available;
- if (withHalide)
- {
- available = getAvailableTargets(DNN_BACKEND_HALIDE);
- for (std::vector< Target >::const_iterator i = available.begin(); i != available.end(); ++i)
- targets.push_back(make_tuple(DNN_BACKEND_HALIDE, *i));
- }
-#ifdef HAVE_INF_ENGINE
- if (withInferenceEngine)
- {
- available = getAvailableTargets(DNN_BACKEND_INFERENCE_ENGINE);
- for (std::vector< Target >::const_iterator i = available.begin(); i != available.end(); ++i)
- {
- if (*i == DNN_TARGET_MYRIAD && !withVPU)
- continue;
- targets.push_back(make_tuple(DNN_BACKEND_INFERENCE_ENGINE, *i));
- }
- }
-#else
- CV_UNUSED(withInferenceEngine);
-#endif
- {
- available = getAvailableTargets(DNN_BACKEND_OPENCV);
- for (std::vector< Target >::const_iterator i = available.begin(); i != available.end(); ++i)
- {
- if (!withCpuOCV && *i == DNN_TARGET_CPU)
- continue;
- targets.push_back(make_tuple(DNN_BACKEND_OPENCV, *i));
- }
- }
- if (targets.empty()) // validate at least CPU mode
- targets.push_back(make_tuple(DNN_BACKEND_OPENCV, DNN_TARGET_CPU));
- return testing::ValuesIn(targets);
-}
-
-
-#ifdef HAVE_INF_ENGINE
-static std::string getTestInferenceEngineVPUType()
-{
- static std::string param_vpu_type = utils::getConfigurationParameterString("OPENCV_TEST_DNN_IE_VPU_TYPE", "");
- return param_vpu_type;
-}
-
-static bool validateVPUType_()
-{
- std::string test_vpu_type = getTestInferenceEngineVPUType();
- if (test_vpu_type == "DISABLED" || test_vpu_type == "disabled")
- {
- return false;
- }
-
- std::vector<Target> available = getAvailableTargets(DNN_BACKEND_INFERENCE_ENGINE);
- bool have_vpu_target = false;
- for (std::vector<Target>::const_iterator i = available.begin(); i != available.end(); ++i)
- {
- if (*i == DNN_TARGET_MYRIAD)
- {
- have_vpu_target = true;
- break;
- }
- }
-
- if (test_vpu_type.empty())
- {
- if (have_vpu_target)
- {
- CV_LOG_INFO(NULL, "OpenCV-DNN-Test: VPU type for testing is not specified via 'OPENCV_TEST_DNN_IE_VPU_TYPE' parameter.")
- }
- }
- else
- {
- if (!have_vpu_target)
- {
- 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.");
- exit(1);
- }
- std::string dnn_vpu_type = getInferenceEngineVPUType();
- if (dnn_vpu_type != test_vpu_type)
- {
- CV_LOG_FATAL(NULL, "OpenCV-DNN-Test: 'testing' and 'detected' VPU types mismatch: '" << test_vpu_type << "' vs '" << dnn_vpu_type << "'. STOP.");
- exit(1);
- }
- }
- return true;
-}
-
-bool validateVPUType()
-{
- static bool result = validateVPUType_();
- return result;
-}
-#endif // HAVE_INF_ENGINE
-
-} // namespace
+#include "test_precomp.hpp"
+#include "test_common.impl.hpp" // shared with perf tests
--- /dev/null
+// 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.
+
+// Used in accuracy and perf tests as a content of .cpp file
+// Note: don't use "precomp.hpp" here
+#include "opencv2/ts.hpp"
+#include "opencv2/ts/ts_perf.hpp"
+#include "opencv2/core/utility.hpp"
+#include "opencv2/core/ocl.hpp"
+
+#include "opencv2/dnn.hpp"
+#include "test_common.hpp"
+
+#include <opencv2/core/utils/configuration.private.hpp>
+#include <opencv2/core/utils/logger.hpp>
+
+namespace cv { namespace dnn {
+CV__DNN_EXPERIMENTAL_NS_BEGIN
+
+void PrintTo(const cv::dnn::Backend& v, std::ostream* os)
+{
+ switch (v) {
+ case DNN_BACKEND_DEFAULT: *os << "DEFAULT"; return;
+ case DNN_BACKEND_HALIDE: *os << "HALIDE"; return;
+ case DNN_BACKEND_INFERENCE_ENGINE: *os << "DLIE"; return;
+ case DNN_BACKEND_OPENCV: *os << "OCV"; return;
+ } // don't use "default:" to emit compiler warnings
+ *os << "DNN_BACKEND_UNKNOWN(" << (int)v << ")";
+}
+
+void PrintTo(const cv::dnn::Target& v, std::ostream* os)
+{
+ switch (v) {
+ case DNN_TARGET_CPU: *os << "CPU"; return;
+ case DNN_TARGET_OPENCL: *os << "OCL"; return;
+ case DNN_TARGET_OPENCL_FP16: *os << "OCL_FP16"; return;
+ case DNN_TARGET_MYRIAD: *os << "MYRIAD"; return;
+ case DNN_TARGET_FPGA: *os << "FPGA"; return;
+ } // don't use "default:" to emit compiler warnings
+ *os << "DNN_TARGET_UNKNOWN(" << (int)v << ")";
+}
+
+void PrintTo(const tuple<cv::dnn::Backend, cv::dnn::Target> v, std::ostream* os)
+{
+ PrintTo(get<0>(v), os);
+ *os << "/";
+ PrintTo(get<1>(v), os);
+}
+
+CV__DNN_EXPERIMENTAL_NS_END
+}} // namespace
+
+
+
+namespace opencv_test {
+
+void normAssert(
+ cv::InputArray ref, cv::InputArray test, const char *comment /*= ""*/,
+ double l1 /*= 0.00001*/, double lInf /*= 0.0001*/)
+{
+ double normL1 = cvtest::norm(ref, test, cv::NORM_L1) / ref.getMat().total();
+ EXPECT_LE(normL1, l1) << comment;
+
+ double normInf = cvtest::norm(ref, test, cv::NORM_INF);
+ EXPECT_LE(normInf, lInf) << comment;
+}
+
+std::vector<cv::Rect2d> matToBoxes(const cv::Mat& m)
+{
+ EXPECT_EQ(m.type(), CV_32FC1);
+ EXPECT_EQ(m.dims, 2);
+ EXPECT_EQ(m.cols, 4);
+
+ std::vector<cv::Rect2d> boxes(m.rows);
+ for (int i = 0; i < m.rows; ++i)
+ {
+ CV_Assert(m.row(i).isContinuous());
+ const float* data = m.ptr<float>(i);
+ double l = data[0], t = data[1], r = data[2], b = data[3];
+ boxes[i] = cv::Rect2d(l, t, r - l, b - t);
+ }
+ return boxes;
+}
+
+void normAssertDetections(
+ const std::vector<int>& refClassIds,
+ const std::vector<float>& refScores,
+ const std::vector<cv::Rect2d>& refBoxes,
+ const std::vector<int>& testClassIds,
+ const std::vector<float>& testScores,
+ const std::vector<cv::Rect2d>& testBoxes,
+ const char *comment /*= ""*/, double confThreshold /*= 0.0*/,
+ double scores_diff /*= 1e-5*/, double boxes_iou_diff /*= 1e-4*/)
+{
+ std::vector<bool> matchedRefBoxes(refBoxes.size(), false);
+ for (int i = 0; i < testBoxes.size(); ++i)
+ {
+ double testScore = testScores[i];
+ if (testScore < confThreshold)
+ continue;
+
+ int testClassId = testClassIds[i];
+ const cv::Rect2d& testBox = testBoxes[i];
+ bool matched = false;
+ for (int j = 0; j < refBoxes.size() && !matched; ++j)
+ {
+ if (!matchedRefBoxes[j] && testClassId == refClassIds[j] &&
+ std::abs(testScore - refScores[j]) < scores_diff)
+ {
+ double interArea = (testBox & refBoxes[j]).area();
+ double iou = interArea / (testBox.area() + refBoxes[j].area() - interArea);
+ if (std::abs(iou - 1.0) < boxes_iou_diff)
+ {
+ matched = true;
+ matchedRefBoxes[j] = true;
+ }
+ }
+ }
+ if (!matched)
+ std::cout << cv::format("Unmatched prediction: class %d score %f box ",
+ testClassId, testScore) << testBox << std::endl;
+ EXPECT_TRUE(matched) << comment;
+ }
+
+ // Check unmatched reference detections.
+ for (int i = 0; i < refBoxes.size(); ++i)
+ {
+ if (!matchedRefBoxes[i] && refScores[i] > confThreshold)
+ {
+ std::cout << cv::format("Unmatched reference: class %d score %f box ",
+ refClassIds[i], refScores[i]) << refBoxes[i] << std::endl;
+ EXPECT_LE(refScores[i], confThreshold) << comment;
+ }
+ }
+}
+
+// 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*/)
+{
+ CV_Assert(ref.total() % 7 == 0);
+ CV_Assert(out.total() % 7 == 0);
+ ref = ref.reshape(1, ref.total() / 7);
+ out = out.reshape(1, out.total() / 7);
+
+ cv::Mat refClassIds, testClassIds;
+ ref.col(1).convertTo(refClassIds, CV_32SC1);
+ out.col(1).convertTo(testClassIds, CV_32SC1);
+ std::vector<float> refScores(ref.col(2)), testScores(out.col(2));
+ std::vector<cv::Rect2d> refBoxes = matToBoxes(ref.colRange(3, 7));
+ std::vector<cv::Rect2d> testBoxes = matToBoxes(out.colRange(3, 7));
+ normAssertDetections(refClassIds, refScores, refBoxes, testClassIds, testScores,
+ testBoxes, comment, confThreshold, scores_diff, boxes_iou_diff);
+}
+
+bool readFileInMemory(const std::string& filename, std::string& content)
+{
+ std::ios::openmode mode = std::ios::in | std::ios::binary;
+ std::ifstream ifs(filename.c_str(), mode);
+ if (!ifs.is_open())
+ return false;
+
+ content.clear();
+
+ ifs.seekg(0, std::ios::end);
+ content.reserve(ifs.tellg());
+ ifs.seekg(0, std::ios::beg);
+
+ content.assign((std::istreambuf_iterator<char>(ifs)),
+ std::istreambuf_iterator<char>());
+
+ return true;
+}
+
+
+testing::internal::ParamGenerator< tuple<Backend, Target> > dnnBackendsAndTargets(
+ bool withInferenceEngine /*= true*/,
+ bool withHalide /*= false*/,
+ bool withCpuOCV /*= true*/
+)
+{
+#ifdef HAVE_INF_ENGINE
+ bool withVPU = validateVPUType();
+#endif
+
+ std::vector< tuple<Backend, Target> > targets;
+ std::vector< Target > available;
+ if (withHalide)
+ {
+ available = getAvailableTargets(DNN_BACKEND_HALIDE);
+ for (std::vector< Target >::const_iterator i = available.begin(); i != available.end(); ++i)
+ targets.push_back(make_tuple(DNN_BACKEND_HALIDE, *i));
+ }
+#ifdef HAVE_INF_ENGINE
+ if (withInferenceEngine)
+ {
+ available = getAvailableTargets(DNN_BACKEND_INFERENCE_ENGINE);
+ for (std::vector< Target >::const_iterator i = available.begin(); i != available.end(); ++i)
+ {
+ if (*i == DNN_TARGET_MYRIAD && !withVPU)
+ continue;
+ targets.push_back(make_tuple(DNN_BACKEND_INFERENCE_ENGINE, *i));
+ }
+ }
+#else
+ CV_UNUSED(withInferenceEngine);
+#endif
+ {
+ available = getAvailableTargets(DNN_BACKEND_OPENCV);
+ for (std::vector< Target >::const_iterator i = available.begin(); i != available.end(); ++i)
+ {
+ if (!withCpuOCV && *i == DNN_TARGET_CPU)
+ continue;
+ targets.push_back(make_tuple(DNN_BACKEND_OPENCV, *i));
+ }
+ }
+ if (targets.empty()) // validate at least CPU mode
+ targets.push_back(make_tuple(DNN_BACKEND_OPENCV, DNN_TARGET_CPU));
+ return testing::ValuesIn(targets);
+}
+
+
+#ifdef HAVE_INF_ENGINE
+static std::string getTestInferenceEngineVPUType()
+{
+ static std::string param_vpu_type = utils::getConfigurationParameterString("OPENCV_TEST_DNN_IE_VPU_TYPE", "");
+ return param_vpu_type;
+}
+
+static bool validateVPUType_()
+{
+ std::string test_vpu_type = getTestInferenceEngineVPUType();
+ if (test_vpu_type == "DISABLED" || test_vpu_type == "disabled")
+ {
+ return false;
+ }
+
+ std::vector<Target> available = getAvailableTargets(DNN_BACKEND_INFERENCE_ENGINE);
+ bool have_vpu_target = false;
+ for (std::vector<Target>::const_iterator i = available.begin(); i != available.end(); ++i)
+ {
+ if (*i == DNN_TARGET_MYRIAD)
+ {
+ have_vpu_target = true;
+ break;
+ }
+ }
+
+ if (test_vpu_type.empty())
+ {
+ if (have_vpu_target)
+ {
+ CV_LOG_INFO(NULL, "OpenCV-DNN-Test: VPU type for testing is not specified via 'OPENCV_TEST_DNN_IE_VPU_TYPE' parameter.")
+ }
+ }
+ else
+ {
+ if (!have_vpu_target)
+ {
+ 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.");
+ exit(1);
+ }
+ std::string dnn_vpu_type = getInferenceEngineVPUType();
+ if (dnn_vpu_type != test_vpu_type)
+ {
+ CV_LOG_FATAL(NULL, "OpenCV-DNN-Test: 'testing' and 'detected' VPU types mismatch: '" << test_vpu_type << "' vs '" << dnn_vpu_type << "'. STOP.");
+ exit(1);
+ }
+ }
+ return true;
+}
+
+bool validateVPUType()
+{
+ static bool result = validateVPUType_();
+ return result;
+}
+#endif // HAVE_INF_ENGINE
+
+} // namespace
projects / platform / upstream / opencv.git / commitdiff