-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
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-//
-// By downloading, copying, installing or using the software you agree to this license.
-// If you do not agree to this license, do not download, install,
-// copy or use the software.
-//
-//
-// License Agreement
-// For Open Source Computer Vision Library
-//
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-// * Redistribution's of source code must retain the above copyright notice,
-// this list of conditions and the following disclaimer.
-//
-// * Redistribution's in binary form must reproduce the above copyright notice,
-// this list of conditions and the following disclaimer in the documentation
-// and/or other materials provided with the distribution.
-//
-// * The name of the copyright holders may not be used to endorse or promote products
-// derived from this software without specific prior written permission.
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-// any express or implied warranties, including, but not limited to, the implied
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-// In no event shall the Intel Corporation or contributors be liable for any direct,
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+// 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
+#define CV_TEST_TAG_DNN_SKIP_HALIDE "dnn_skip_halide"
+#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 "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_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_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"
+
namespace cv { namespace dnn {
CV__DNN_INLINE_NS_BEGIN
-static inline 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;
- case DNN_BACKEND_VKCOM: *os << "VKCOM"; return;
- } // don't use "default:" to emit compiler warnings
- *os << "DNN_BACKEND_UNKNOWN(" << (int)v << ")";
-}
-
-static inline 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_VULKAN: *os << "VULKAN"; 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 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;
-static inline 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);
-}
+void PrintTo(const tuple<cv::dnn::Backend, cv::dnn::Target> v, std::ostream* os);
CV__DNN_INLINE_NS_END
-}} // namespace
+}} // namespace cv::dnn
-static inline const std::string &getOpenCVExtraDir()
-{
- return cvtest::TS::ptr()->get_data_path();
-}
-static inline 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;
+namespace opencv_test {
- double normInf = cvtest::norm(ref, test, cv::NORM_INF);
- EXPECT_LE(normInf, lInf) << comment;
-}
+void initDNNTests();
-static 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);
+using namespace cv::dnn;
- 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;
+static inline const std::string &getOpenCVExtraDir()
+{
+ return cvtest::TS::ptr()->get_data_path();
}
-static inline 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;
- }
+void normAssert(
+ cv::InputArray ref, cv::InputArray test, const char *comment = "",
+ double l1 = 0.00001, double lInf = 0.0001);
- // 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;
- }
- }
-}
+std::vector<cv::Rect2d> matToBoxes(const cv::Mat& m);
+
+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);
// 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].
-static inline 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);
-}
-
-static inline 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;
+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);
- content.clear();
+void readFileContent(const std::string& filename, CV_OUT std::vector<char>& content);
- 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;
-}
-
-namespace opencv_test {
-
-using namespace cv::dnn;
+#ifdef HAVE_INF_ENGINE
+bool validateVPUType();
+#endif
-static inline
testing::internal::ParamGenerator< tuple<Backend, Target> > dnnBackendsAndTargets(
bool withInferenceEngine = true,
bool withHalide = false,
bool withCpuOCV = true,
- bool withVkCom = true
-)
-{
- 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));
- }
- if (withInferenceEngine)
- {
- available = getAvailableTargets(DNN_BACKEND_INFERENCE_ENGINE);
- for (std::vector< Target >::const_iterator i = available.begin(); i != available.end(); ++i)
- targets.push_back(make_tuple(DNN_BACKEND_INFERENCE_ENGINE, *i));
- }
- if (withVkCom)
- {
- available = getAvailableTargets(DNN_BACKEND_VKCOM);
- for (std::vector< Target >::const_iterator i = available.begin(); i != available.end(); ++i)
- targets.push_back(make_tuple(DNN_BACKEND_VKCOM, *i));
- }
- {
- 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);
-}
-
-} // namespace
+ bool withVkCom = true,
+ bool withCUDA = true
+);
-namespace opencv_test {
-using namespace cv::dnn;
-
class DNNTestLayer : public TestWithParam<tuple<Backend, Target> >
{
public:
getDefaultThresholds(backend, target, &default_l1, &default_lInf);
}
- static void getDefaultThresholds(int backend, int target, double* l1, double* lInf)
- {
- if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD)
- {
- *l1 = 4e-3;
- *lInf = 2e-2;
- }
- else
- {
- *l1 = 1e-5;
- *lInf = 1e-4;
- }
- }
+ static void getDefaultThresholds(int backend, int target, double* l1, double* lInf)
+ {
+ if (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)
{
- if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
- {
- if (inp && ref && inp->dims == 4 && ref->dims == 4 &&
- inp->size[0] != 1 && inp->size[0] != ref->size[0])
- throw SkipTestException("Inconsistent batch size of input and output blobs for Myriad plugin");
- }
- }
+ if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
+ {
+ if (inp && ref && inp->dims == 4 && ref->dims == 4 &&
+ inp->size[0] != 1 && inp->size[0] != ref->size[0])
+ {
+ applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
+ throw SkipTestException("Inconsistent batch size of input and output blobs for Myriad plugin");
+ }
+ }
+ }
+
+ void expectNoFallbacks(Net& net)
+ {
+ // 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<double> timings;
+ net.getPerfProfile(timings);
+ std::vector<String> names = net.getLayerNames();
+ CV_Assert(names.size() == timings.size());
+
+ for (int i = 0; i < names.size(); ++i)
+ {
+ Ptr<dnn::Layer> l = net.getLayer(net.getLayerId(names[i]));
+ bool fused = !timings[i];
+ if ((!l->supportBackend(backend) || l->preferableTarget != target) && !fused)
+ CV_Error(Error::StsNotImplemented, "Layer [" + l->name + "] of type [" +
+ l->type + "] is expected to has backend implementation");
+ }
+ }
+
+ void expectNoFallbacksFromIE(Net& net)
+ {
+ if (backend == DNN_BACKEND_INFERENCE_ENGINE)
+ expectNoFallbacks(net);
+ }
+
+ void expectNoFallbacksFromCUDA(Net& net)
+ {
+ if (backend == DNN_BACKEND_CUDA)
+ expectNoFallbacks(net);
+ }
protected:
void checkBackend(Mat* inp = 0, Mat* ref = 0)
} // namespace
+
+// 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))
+
#endif
projects / platform / upstream / opencv.git / blobdiff