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Merge remote-tracking branch 'upstream/3.4' into merge-3.4

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This commit is contained in:
Alexander Alekhin 2019-04-01 18:11:10 +03:00
commit 33dde339fe
8 changed files with 329 additions and 356 deletions
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12
modules/core/src/lda.cpp
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@ -903,19 +903,19 @@ public:
// given in src. This function is a port of the EigenvalueSolver in JAMA,
// which has been released to public domain by The MathWorks and the
// National Institute of Standards and Technology (NIST).
EigenvalueDecomposition(InputArray src, bool fallbackSymmetric = true) :
EigenvalueDecomposition() :
n(0),
d(NULL), e(NULL), ort(NULL),
V(NULL), H(NULL)
{
compute(src, fallbackSymmetric);
// nothing
}
// This function computes the Eigenvalue Decomposition for a general matrix
// given in src. This function is a port of the EigenvalueSolver in JAMA,
// which has been released to public domain by The MathWorks and the
// National Institute of Standards and Technology (NIST).
void compute(InputArray src, bool fallbackSymmetric)
void compute(InputArray src, bool fallbackSymmetric = true)
{
CV_INSTRUMENT_REGION();
@ -970,7 +970,8 @@ void eigenNonSymmetric(InputArray _src, OutputArray _evals, OutputArray _evects)
else
src64f = src;
EigenvalueDecomposition eigensystem(src64f, false);
EigenvalueDecomposition eigensystem;
eigensystem.compute(src64f, false);
// EigenvalueDecomposition returns transposed and non-sorted eigenvalues
std::vector<double> eigenvalues64f;
@ -1146,7 +1147,8 @@ void LDA::lda(InputArrayOfArrays _src, InputArray _lbls) {
// M = inv(Sw)*Sb
Mat M;
gemm(Swi, Sb, 1.0, Mat(), 0.0, M);
EigenvalueDecomposition es(M);
EigenvalueDecomposition es;
es.compute(M);
_eigenvalues = es.eigenvalues();
_eigenvectors = es.eigenvectors();
// reshape eigenvalues, so they are stored by column

2
modules/core/src/system.cpp
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@ -94,7 +94,7 @@ void* allocSingletonBuffer(size_t size) { return fastMalloc(size); }
#include <cstdlib> // std::abort
#endif
#if defined __ANDROID__ || defined __linux__ || defined __FreeBSD__ || defined __HAIKU__ || defined __Fuchsia__
#if defined __ANDROID__ || defined __linux__ || defined __FreeBSD__ || defined __OpenBSD__ || defined __HAIKU__ || defined __Fuchsia__
# include <unistd.h>
# include <fcntl.h>
# include <elf.h>

2
modules/dnn/CMakeLists.txt
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@ -94,7 +94,7 @@ set(perf_path "${CMAKE_CURRENT_LIST_DIR}/perf")
file(GLOB_RECURSE perf_srcs "${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}
)

6
modules/dnn/perf/perf_common.cpp Normal file
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@ -0,0 +1,6 @@
// 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

291
modules/dnn/test/test_common.cpp
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@ -2,292 +2,5 @@
// 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_INLINE_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;
case DNN_BACKEND_VKCOM: *os << "VKCOM"; 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_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 tuple<cv::dnn::Backend, cv::dnn::Target> v, std::ostream* os)
{
PrintTo(get<0>(v), os);
*os << "/";
PrintTo(get<1>(v), os);
}
CV__DNN_INLINE_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*/,
bool withVkCom /*= 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
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);
}
#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

294
modules/dnn/test/test_common.impl.hpp Normal file
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@ -0,0 +1,294 @@
// 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_INLINE_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_VKCOM: *os << "VKCOM"; 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_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 tuple<cv::dnn::Backend, cv::dnn::Target> v, std::ostream* os)
{
PrintTo(get<0>(v), os);
*os << "/";
PrintTo(get<1>(v), os);
}
CV__DNN_INLINE_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*/,
bool withVkCom /*= 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
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);
}
#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

75
modules/dnn/test/test_ie_models.cpp
View File

@ -177,34 +177,17 @@ TEST_P(DNNTestOpenVINO, models)
{
Target target = (dnn::Target)(int)get<0>(GetParam());
std::string modelName = get<1>(GetParam());
std::string precision = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? "FP16" : "FP32";
#ifdef INF_ENGINE_RELEASE
#if INF_ENGINE_RELEASE <= 2018030000
if (target == DNN_TARGET_MYRIAD && (modelName == "landmarks-regression-retail-0001" ||
modelName == "semantic-segmentation-adas-0001" ||
modelName == "face-reidentification-retail-0001"))
throw SkipTestException("");
#elif INF_ENGINE_RELEASE == 2018040000
if (modelName == "single-image-super-resolution-0034" ||
(target == DNN_TARGET_MYRIAD && (modelName == "license-plate-recognition-barrier-0001" ||
modelName == "landmarks-regression-retail-0009" ||
modelName == "semantic-segmentation-adas-0001")))
throw SkipTestException("");
#elif INF_ENGINE_RELEASE == 2018050000
if (modelName == "single-image-super-resolution-0063" ||
modelName == "single-image-super-resolution-1011" ||
modelName == "single-image-super-resolution-1021" ||
(target == DNN_TARGET_OPENCL_FP16 && modelName == "face-reidentification-retail-0095") ||
(target == DNN_TARGET_MYRIAD && (modelName == "license-plate-recognition-barrier-0001" ||
modelName == "semantic-segmentation-adas-0001")))
throw SkipTestException("");
#endif
#endif
std::string precision = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? "FP16" : "FP32";
#if INF_ENGINE_RELEASE <= 2018050000
std::string prefix = utils::fs::join("intel_models",
utils::fs::join(modelName,
utils::fs::join(precision, modelName)));
#endif
#endif
initDLDTDataPath();
std::string xmlPath = findDataFile(prefix + ".xml");
std::string binPath = findDataFile(prefix + ".bin");
@ -221,49 +204,21 @@ TEST_P(DNNTestOpenVINO, models)
{
auto dstIt = cvOutputsMap.find(srcIt.first);
CV_Assert(dstIt != cvOutputsMap.end());
double normInfIE = cvtest::norm(srcIt.second, cv::NORM_INF);
double normInf = cvtest::norm(srcIt.second, dstIt->second, cv::NORM_INF);
double eps = 0;
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD)
{
double fp16_eps = 1.0/1024;
eps = fp16_eps * 1/*ULP*/ * std::max(normInfIE, 1.0);
}
EXPECT_LE(normInf, eps) << "IE: " << normInfIE;
EXPECT_EQ(normInf, 0);
}
}
static testing::internal::ParamGenerator<String> intelModels()
{
initDLDTDataPath();
std::vector<String> modelsNames;
std::string path;
try
{
path = findDataDirectory("intel_models", false);
}
catch (...)
{
std::cerr << "ERROR: Can't find OpenVINO models. Check INTEL_CVSDK_DIR environment variable (run setup.sh)" << std::endl;
return ValuesIn(modelsNames); // empty list
}
cv::utils::fs::glob_relative(path, "", modelsNames, false, true);
modelsNames.erase(
std::remove_if(modelsNames.begin(), modelsNames.end(),
[&](const String& dir){ return !utils::fs::isDirectory(utils::fs::join(path, dir)); }),
modelsNames.end()
);
CV_Assert(!modelsNames.empty());
return ValuesIn(modelsNames);
}
INSTANTIATE_TEST_CASE_P(/**/,
DNNTestOpenVINO,
Combine(testing::ValuesIn(getAvailableTargets(DNN_BACKEND_INFERENCE_ENGINE)), intelModels())
Combine(testing::ValuesIn(getAvailableTargets(DNN_BACKEND_INFERENCE_ENGINE)),
testing::Values(
"age-gender-recognition-retail-0013",
"face-person-detection-retail-0002",
"head-pose-estimation-adas-0001",
"person-detection-retail-0002",
"vehicle-detection-adas-0002"
))
);
}}

3
modules/imgcodecs/src/bitstrm.cpp
View File

@ -175,8 +175,11 @@ void RBaseStream::setPos( int pos )
}
int offset = pos % m_block_size;
int old_block_pos = m_block_pos;
m_block_pos = pos - offset;
m_current = m_start + offset;
if (old_block_pos != m_block_pos)
readBlock();
}