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Merge pull request #15537 from l-bat:ngraph

Browse Source 
* Support nGraph

* Fix resize
This commit is contained in:
Lubov Batanina 2019-12-02 16:16:06 +03:00 committed by Alexander Alekhin
parent 20ac7f40f6
commit 7523c777c5
49 changed files with 3189 additions and 447 deletions
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32
CMakeLists.txt
View File

@ -276,6 +276,9 @@ OCV_OPTION(WITH_HALIDE "Include Halide support" OFF
OCV_OPTION(WITH_INF_ENGINE "Include Intel Inference Engine support" OFF
VISIBLE_IF TRUE
VERIFY INF_ENGINE_TARGET)
OCV_OPTION(WITH_NGRAPH "Include nGraph support" WITH_INF_ENGINE
VISIBLE_IF TRUE
VERIFY TARGET ngraph::ngraph)
OCV_OPTION(WITH_JASPER "Include JPEG2K support" ON
VISIBLE_IF NOT IOS
VERIFY HAVE_JASPER)
@ -1489,12 +1492,37 @@ if(WITH_INF_ENGINE OR INF_ENGINE_TARGET)
)
get_target_property(_inc ${ie_target} INTERFACE_INCLUDE_DIRECTORIES)
status(" Inference Engine:" "${__msg}")
status(" libs:" "${_lib}")
status(" includes:" "${_inc}")
status(" * libs:" "${_lib}")
status(" * includes:" "${_inc}")
else()
status(" Inference Engine:" "NO")
endif()
endif()
if(WITH_NGRAPH OR HAVE_NGRAPH)
if(HAVE_NGRAPH)
set(__target ngraph::ngraph)
set(__msg "YES (${ngraph_VERSION})")
get_target_property(_lib ${__target} IMPORTED_LOCATION)
get_target_property(_lib_imp_rel ${__target} IMPORTED_IMPLIB_RELEASE)
get_target_property(_lib_imp_dbg ${__target} IMPORTED_IMPLIB_DEBUG)
get_target_property(_lib_rel ${__target} IMPORTED_LOCATION_RELEASE)
get_target_property(_lib_dbg ${__target} IMPORTED_LOCATION_DEBUG)
ocv_build_features_string(_lib
IF _lib THEN "${_lib}"
IF _lib_imp_rel AND _lib_imp_dbg THEN "${_lib_imp_rel} / ${_lib_imp_dbg}"
IF _lib_rel AND _lib_dbg THEN "${_lib_rel} / ${_lib_dbg}"
IF _lib_rel THEN "${_lib_rel}"
IF _lib_dbg THEN "${_lib_dbg}"
ELSE "unknown"
)
get_target_property(_inc ${__target} INTERFACE_INCLUDE_DIRECTORIES)
status(" nGraph:" "${__msg}")
status(" * libs:" "${_lib}")
status(" * includes:" "${_inc}")
else()
status(" nGraph:" "NO")
endif()
endif()
if(WITH_EIGEN OR HAVE_EIGEN)
status(" Eigen:" HAVE_EIGEN THEN "YES (ver ${EIGEN_WORLD_VERSION}.${EIGEN_MAJOR_VERSION}.${EIGEN_MINOR_VERSION})" ELSE NO)

30
cmake/OpenCVDetectInferenceEngine.cmake
View File

@ -35,6 +35,15 @@ function(add_custom_ie_build _inc _lib _lib_rel _lib_dbg _msg)
IMPORTED_IMPLIB_DEBUG "${_lib_dbg}"
INTERFACE_INCLUDE_DIRECTORIES "${_inc}"
)
find_library(ie_builder_custom_lib "inference_engine_nn_builder" PATHS "${INF_ENGINE_LIB_DIRS}" NO_DEFAULT_PATH)
if(EXISTS "${ie_builder_custom_lib}")
add_library(inference_engine_nn_builder UNKNOWN IMPORTED)
set_target_properties(inference_engine_nn_builder PROPERTIES
IMPORTED_LOCATION "${ie_builder_custom_lib}"
)
endif()
if(NOT INF_ENGINE_RELEASE VERSION_GREATER "2018050000")
find_library(INF_ENGINE_OMP_LIBRARY iomp5 PATHS "${INF_ENGINE_OMP_DIR}" NO_DEFAULT_PATH)
if(NOT INF_ENGINE_OMP_LIBRARY)
@ -44,7 +53,12 @@ function(add_custom_ie_build _inc _lib _lib_rel _lib_dbg _msg)
endif()
endif()
set(INF_ENGINE_VERSION "Unknown" CACHE STRING "")
set(INF_ENGINE_TARGET inference_engine PARENT_SCOPE)
set(INF_ENGINE_TARGET inference_engine)
if(TARGET inference_engine_nn_builder)
list(APPEND INF_ENGINE_TARGET inference_engine_nn_builder)
set(_msg "${_msg}, with IE NN Builder API")
endif()
set(INF_ENGINE_TARGET "${INF_ENGINE_TARGET}" PARENT_SCOPE)
message(STATUS "Detected InferenceEngine: ${_msg}")
endfunction()
@ -54,7 +68,7 @@ find_package(InferenceEngine QUIET)
if(InferenceEngine_FOUND)
set(INF_ENGINE_TARGET ${InferenceEngine_LIBRARIES})
set(INF_ENGINE_VERSION "${InferenceEngine_VERSION}" CACHE STRING "")
message(STATUS "Detected InferenceEngine: cmake package")
message(STATUS "Detected InferenceEngine: cmake package (${InferenceEngine_VERSION})")
endif()
if(NOT INF_ENGINE_TARGET AND INF_ENGINE_LIB_DIRS AND INF_ENGINE_INCLUDE_DIRS)
@ -94,3 +108,15 @@ if(INF_ENGINE_TARGET)
INTERFACE_COMPILE_DEFINITIONS "HAVE_INF_ENGINE=1;INF_ENGINE_RELEASE=${INF_ENGINE_RELEASE}"
)
endif()
if(WITH_NGRAPH)
find_package(ngraph QUIET)
if(ngraph_FOUND)
ocv_assert(TARGET ngraph::ngraph)
if(INF_ENGINE_RELEASE VERSION_LESS "2019039999")
message(WARNING "nGraph is not tested with current InferenceEngine version: INF_ENGINE_RELEASE=${INF_ENGINE_RELEASE}")
endif()
message(STATUS "Detected ngraph: cmake package (${ngraph_VERSION})")
set(HAVE_NGRAPH ON)
endif()
endif()

15
modules/dnn/CMakeLists.txt
View File

@ -74,6 +74,7 @@ endif()
set(include_dirs ${fw_inc})
set(sources_options "")
set(libs libprotobuf ${LAPACK_LIBRARIES})
if(OPENCV_DNN_OPENCL AND HAVE_OPENCL)
list(APPEND include_dirs ${OPENCL_INCLUDE_DIRS})
@ -87,10 +88,20 @@ if(CMAKE_CXX_COMPILER_ID STREQUAL "GNU")
elseif(CMAKE_CXX_COMPILER_ID STREQUAL "Clang")
ocv_append_source_files_cxx_compiler_options(fw_srcs "-Wno-inconsistent-missing-override") # Clang
endif()
set(dnn_runtime_libs "")
if(INF_ENGINE_TARGET)
list(APPEND dnn_runtime_libs ${INF_ENGINE_TARGET})
endif()
if(HAVE_NGRAPH)
add_definitions(-DHAVE_DNN_NGRAPH)
list(APPEND dnn_runtime_libs ngraph::ngraph)
endif()
ocv_glob_module_sources(${sources_options} SOURCES ${fw_srcs})
ocv_create_module(${libs} ${INF_ENGINE_TARGET})
ocv_create_module(${libs} ${dnn_runtime_libs})
ocv_add_samples()
ocv_add_accuracy_tests(${INF_ENGINE_TARGET})
ocv_add_accuracy_tests(${dnn_runtime_libs})
set(perf_path "${CMAKE_CURRENT_LIST_DIR}/perf")
file(GLOB_RECURSE perf_srcs "${perf_path}/*.cpp")

20
modules/dnn/include/opencv2/dnn/dnn.hpp
View File

@ -47,9 +47,9 @@
#include "opencv2/core/async.hpp"
#if !defined CV_DOXYGEN && !defined CV_STATIC_ANALYSIS && !defined CV_DNN_DONT_ADD_EXPERIMENTAL_NS
#define CV__DNN_EXPERIMENTAL_NS_BEGIN namespace experimental_dnn_34_v14 {
#define CV__DNN_EXPERIMENTAL_NS_BEGIN namespace experimental_dnn_34_v15 {
#define CV__DNN_EXPERIMENTAL_NS_END }
namespace cv { namespace dnn { namespace experimental_dnn_34_v14 { } using namespace experimental_dnn_34_v14; }}
namespace cv { namespace dnn { namespace experimental_dnn_34_v15 { } using namespace experimental_dnn_34_v15; }}
#else
#define CV__DNN_EXPERIMENTAL_NS_BEGIN
#define CV__DNN_EXPERIMENTAL_NS_END
@ -74,10 +74,18 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
//! DNN_BACKEND_DEFAULT equals to DNN_BACKEND_INFERENCE_ENGINE if
//! OpenCV is built with Intel's Inference Engine library or
//! DNN_BACKEND_OPENCV otherwise.
DNN_BACKEND_DEFAULT,
DNN_BACKEND_DEFAULT = 0,
DNN_BACKEND_HALIDE,
DNN_BACKEND_INFERENCE_ENGINE, //!< Intel's Inference Engine computational backend.
DNN_BACKEND_OPENCV
DNN_BACKEND_INFERENCE_ENGINE, //!< Intel's Inference Engine computational backend
//!< @sa setInferenceEngineBackendType
DNN_BACKEND_OPENCV,
// OpenCV 4.x: DNN_BACKEND_VKCOM,
// OpenCV 4.x: DNN_BACKEND_CUDA,
#ifdef __OPENCV_BUILD
DNN_BACKEND_INFERENCE_ENGINE_NGRAPH = 1000000, // internal - use DNN_BACKEND_INFERENCE_ENGINE + setInferenceEngineBackendType()
DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019, // internal - use DNN_BACKEND_INFERENCE_ENGINE + setInferenceEngineBackendType()
#endif
};
/**
@ -278,6 +286,8 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> > &inputs);
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> > &inputs, const std::vector<Ptr<BackendNode> >& nodes);
/**
* @brief Automatic Halide scheduling based on layer hyper-parameters.
* @param[in] node Backend node with Halide functions.

21
modules/dnn/include/opencv2/dnn/utils/inference_engine.hpp
View File

@ -14,6 +14,27 @@ namespace cv { namespace dnn {
CV__DNN_EXPERIMENTAL_NS_BEGIN
/* Values for 'OPENCV_DNN_BACKEND_INFERENCE_ENGINE_TYPE' parameter */
#define CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API "NN_BUILDER"
#define CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH "NGRAPH"
/** @brief Returns Inference Engine internal backend API.
*
* See values of `CV_DNN_BACKEND_INFERENCE_ENGINE_*` macros.
*
* Default value is controlled through `OPENCV_DNN_BACKEND_INFERENCE_ENGINE_TYPE` runtime parameter (environment variable).
*/
CV_EXPORTS_W cv::String getInferenceEngineBackendType();
/** @brief Specify Inference Engine internal backend API.
*
* See values of `CV_DNN_BACKEND_INFERENCE_ENGINE_*` macros.
*
* @returns previous value of internal backend API
*/
CV_EXPORTS_W cv::String setInferenceEngineBackendType(const cv::String& newBackendType);
/** @brief Release a Myriad device (binded by OpenCV).
*
* Single Myriad device cannot be shared across multiple processes which uses

14
modules/dnn/perf/perf_net.cpp
View File

@ -100,7 +100,7 @@ PERF_TEST_P_(DNNTestNetwork, SqueezeNet_v1_1)
PERF_TEST_P_(DNNTestNetwork, Inception_5h)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE) throw SkipTestException("");
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) throw SkipTestException("");
processNet("dnn/tensorflow_inception_graph.pb", "",
"inception_5h.yml",
Mat(cv::Size(224, 224), CV_32FC3), "softmax2");
@ -108,7 +108,7 @@ PERF_TEST_P_(DNNTestNetwork, Inception_5h)
PERF_TEST_P_(DNNTestNetwork, ENet)
{
if ((backend == DNN_BACKEND_INFERENCE_ENGINE) ||
if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) ||
(backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16))
throw SkipTestException("");
processNet("dnn/Enet-model-best.net", "", "enet.yml",
@ -126,7 +126,7 @@ PERF_TEST_P_(DNNTestNetwork, OpenFace)
if (backend == DNN_BACKEND_HALIDE)
throw SkipTestException("");
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2018050000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
throw SkipTestException("");
#endif
processNet("dnn/openface_nn4.small2.v1.t7", "", "",
@ -168,7 +168,7 @@ PERF_TEST_P_(DNNTestNetwork, DenseNet_121)
PERF_TEST_P_(DNNTestNetwork, OpenPose_pose_mpi_faster_4_stages)
{
if (backend == DNN_BACKEND_HALIDE ||
(backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD))
(backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD))
throw SkipTestException("");
// The same .caffemodel but modified .prototxt
// See https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/src/openpose/pose/poseParameters.cpp
@ -219,15 +219,15 @@ PERF_TEST_P_(DNNTestNetwork, FastNeuralStyle_eccv16)
PERF_TEST_P_(DNNTestNetwork, Inception_v2_Faster_RCNN)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2019010000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
throw SkipTestException("Test is disabled in OpenVINO 2019R1");
#endif
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2019020000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
throw SkipTestException("Test is disabled in OpenVINO 2019R2");
#endif
if (backend == DNN_BACKEND_HALIDE ||
(backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_CPU) ||
(backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU) ||
(backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16))
throw SkipTestException("");
processNet("dnn/faster_rcnn_inception_v2_coco_2018_01_28.pb",

527
modules/dnn/src/dnn.cpp
View File

@ -42,6 +42,8 @@
#include "precomp.hpp"
#include "op_halide.hpp"
#include "op_inf_engine.hpp"
#include "ie_ngraph.hpp"
#include "halide_scheduler.hpp"
#include <set>
#include <algorithm>
@ -98,11 +100,9 @@ public:
return impl;
}
static inline bool checkIETarget(int target)
#ifdef HAVE_INF_ENGINE
static inline bool checkIETarget(Target target)
{
#ifndef HAVE_INF_ENGINE
return false;
#else
cv::dnn::Net net;
cv::dnn::LayerParams lp;
lp.set("kernel_size", 1);
@ -120,13 +120,14 @@ public:
{
net.forward();
}
catch(...)
catch(const std::exception& e)
{
CV_LOG_INFO(NULL, "checkIETarget(" << (int)target << ") has failed with message: " << e.what());
return false;
}
return true;
#endif
}
#endif
private:
BackendRegistry()
@ -140,21 +141,37 @@ private:
#endif // HAVE_HALIDE
#ifdef HAVE_INF_ENGINE
if (checkIETarget(DNN_TARGET_CPU))
backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_CPU));
if (checkIETarget(DNN_TARGET_MYRIAD))
backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_MYRIAD));
if (checkIETarget(DNN_TARGET_CPU)) {
backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019, DNN_TARGET_CPU));
#ifdef HAVE_DNN_NGRAPH
backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, DNN_TARGET_CPU));
#endif
}
if (checkIETarget(DNN_TARGET_MYRIAD)) {
backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019, DNN_TARGET_MYRIAD));
#ifdef HAVE_DNN_NGRAPH
backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, DNN_TARGET_MYRIAD));
#endif
}
if (checkIETarget(DNN_TARGET_FPGA))
backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_FPGA));
# ifdef HAVE_OPENCL
backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019, DNN_TARGET_FPGA));
#ifdef HAVE_OPENCL
if (cv::ocl::useOpenCL() && ocl::Device::getDefault().isIntel())
{
if (checkIETarget(DNN_TARGET_OPENCL))
backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL));
if (checkIETarget(DNN_TARGET_OPENCL_FP16))
backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL_FP16));
if (checkIETarget(DNN_TARGET_OPENCL)) {
backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019, DNN_TARGET_OPENCL));
#ifdef HAVE_DNN_NGRAPH
backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, DNN_TARGET_OPENCL));
#endif
}
if (checkIETarget(DNN_TARGET_OPENCL_FP16)) {
backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019, DNN_TARGET_OPENCL_FP16));
#ifdef HAVE_DNN_NGRAPH
backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, DNN_TARGET_OPENCL_FP16));
#endif
}
}
# endif
#endif
#endif // HAVE_INF_ENGINE
#ifdef HAVE_OPENCL
@ -181,6 +198,10 @@ std::vector<Target> getAvailableTargets(Backend be)
{
if (be == DNN_BACKEND_DEFAULT)
be = (Backend)PARAM_DNN_BACKEND_DEFAULT;
#ifdef HAVE_INF_ENGINE
if (be == DNN_BACKEND_INFERENCE_ENGINE)
be = getInferenceEngineBackendTypeParam();
#endif
std::vector<Target> result;
const BackendRegistry::BackendsList all_backends = getAvailableBackends();
@ -351,6 +372,7 @@ void imagesFromBlob(const cv::Mat& blob_, OutputArrayOfArrays images_)
}
}
#ifdef HAVE_OPENCL
class OpenCLBackendWrapper : public BackendWrapper
{
public:
@ -440,6 +462,7 @@ private:
Mat* host;
bool hostDirty;
};
#endif
struct LayerPin
{
@ -536,7 +559,7 @@ struct DataLayer : public Layer
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV ||
(backendId == DNN_BACKEND_INFERENCE_ENGINE && inputsData.size() == 1);
(backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && inputsData.size() == 1);
}
void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE
@ -706,9 +729,9 @@ struct DataLayer : public Layer
}
}
#ifdef HAVE_INF_ENGINE
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{
#ifdef HAVE_INF_ENGINE
CV_CheckEQ(inputsData.size(), (size_t)1, "");
CV_CheckEQ(inputsData[0].dims, 4, "");
const size_t numChannels = inputsData[0].size[1];
@ -737,9 +760,8 @@ struct DataLayer : public Layer
addConstantData("weights", weights, ieLayer);
addConstantData("biases", biases, ieLayer);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#endif // HAVE_INF_ENGINE
return Ptr<BackendNode>();
}
#endif // HAVE_INF_ENGINE
std::vector<String> outNames;
// Preprocessing parameters for each network's input.
@ -980,10 +1002,12 @@ static Ptr<BackendWrapper> wrapMat(int backendId, int targetId, cv::Mat& m)
{
if (targetId == DNN_TARGET_CPU)
return Ptr<BackendWrapper>();
#ifdef HAVE_OPENCL
else if (IS_DNN_OPENCL_TARGET(targetId))
return OpenCLBackendWrapper::create(m);
#endif
else
CV_Error(Error::StsNotImplemented, "Unknown target identifier");
CV_Error(Error::StsNotImplemented, "Unknown/unsupported target identifier");
}
else if (backendId == DNN_BACKEND_HALIDE)
{
@ -992,16 +1016,25 @@ static Ptr<BackendWrapper> wrapMat(int backendId, int targetId, cv::Mat& m)
return Ptr<BackendWrapper>(new HalideBackendWrapper(targetId, m));
#endif // HAVE_HALIDE
}
else if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
CV_Assert(haveInfEngine());
#ifdef HAVE_INF_ENGINE
return Ptr<BackendWrapper>(new InfEngineBackendWrapper(targetId, m));
#endif // HAVE_INF_ENGINE
#else
CV_Error(Error::StsNotImplemented, "This OpenCV version is built without Inference Engine API support");
#endif
}
else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
#ifdef HAVE_DNN_NGRAPH
return Ptr<BackendWrapper>(new NgraphBackendWrapper(targetId, m));
#else
CV_Error(Error::StsNotImplemented, "This OpenCV version is built without support of Inference Engine + nGraph");
#endif
}
else
CV_Error(Error::StsNotImplemented, "Unknown backend identifier");
return Ptr<BackendWrapper>();
return Ptr<BackendWrapper>(); // TODO Error?
}
struct Net::Impl
@ -1064,17 +1097,25 @@ struct Net::Impl
Ptr<BackendWrapper> baseBuffer = backendWrappers[data];
if (preferableBackend == DNN_BACKEND_OPENCV)
{
#ifdef HAVE_OPENCL
CV_Assert(IS_DNN_OPENCL_TARGET(preferableTarget));
return OpenCLBackendWrapper::create(baseBuffer, host);
#else
CV_Error(Error::StsInternal, "");
#endif
}
else if (preferableBackend == DNN_BACKEND_HALIDE)
{
CV_Assert(haveHalide());
#ifdef HAVE_HALIDE
#ifdef HAVE_HALIDE
return Ptr<BackendWrapper>(new HalideBackendWrapper(baseBuffer, shape));
#endif // HAVE_HALIDE
#endif
}
else if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE)
else if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
return wrapMat(preferableBackend, preferableTarget, host);
}
else if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
return wrapMat(preferableBackend, preferableTarget, host);
}
@ -1167,6 +1208,10 @@ struct Net::Impl
if (preferableBackend == DNN_BACKEND_DEFAULT)
preferableBackend = (Backend)PARAM_DNN_BACKEND_DEFAULT;
#ifdef HAVE_INF_ENGINE
if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE)
preferableBackend = getInferenceEngineBackendTypeParam();
#endif
CV_Assert(preferableBackend != DNN_BACKEND_OPENCV ||
preferableTarget == DNN_TARGET_CPU ||
@ -1175,12 +1220,17 @@ struct Net::Impl
CV_Assert(preferableBackend != DNN_BACKEND_HALIDE ||
preferableTarget == DNN_TARGET_CPU ||
preferableTarget == DNN_TARGET_OPENCL);
CV_Assert(preferableBackend != DNN_BACKEND_INFERENCE_ENGINE ||
if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ||
preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
CV_Assert(
preferableTarget == DNN_TARGET_CPU ||
preferableTarget == DNN_TARGET_OPENCL ||
preferableTarget == DNN_TARGET_OPENCL_FP16 ||
preferableTarget == DNN_TARGET_MYRIAD ||
preferableTarget == DNN_TARGET_FPGA);
preferableTarget == DNN_TARGET_FPGA
);
}
if (!netWasAllocated || this->blobsToKeep != blobsToKeep_)
{
if (preferableBackend == DNN_BACKEND_OPENCV && IS_DNN_OPENCL_TARGET(preferableTarget))
@ -1360,8 +1410,22 @@ struct Net::Impl
CV_Assert(preferableTarget == DNN_TARGET_CPU || IS_DNN_OPENCL_TARGET(preferableTarget));
else if (preferableBackend == DNN_BACKEND_HALIDE)
initHalideBackend();
else if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE)
else if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
#ifdef HAVE_INF_ENGINE
initInfEngineBackend();
#else
CV_Assert(false && "This OpenCV version is built without Inference Engine API support");
#endif
}
else if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
#ifdef HAVE_DNN_NGRAPH
initNgraphBackend();
#else
CV_Error(Error::StsNotImplemented, "This OpenCV version is built without support of Inference Engine + nGraph");
#endif
}
else
CV_Error(Error::StsNotImplemented, "Unknown backend identifier");
}
@ -1427,6 +1491,7 @@ struct Net::Impl
// layers from default backend or layers from different graphs.
void addInfEngineNetOutputs(LayerData &ld)
{
CV_TRACE_FUNCTION();
Ptr<InfEngineBackendNet> layerNet;
if (ld.backendNodes.find(preferableBackend) != ld.backendNodes.end())
{
@ -1457,13 +1522,11 @@ struct Net::Impl
}
}
}
#endif // HAVE_INF_ENGINE
void initInfEngineBackend()
{
CV_TRACE_FUNCTION();
CV_Assert_N(preferableBackend == DNN_BACKEND_INFERENCE_ENGINE, haveInfEngine());
#ifdef HAVE_INF_ENGINE
CV_Assert_N(preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019, haveInfEngine());
MapIdToLayerData::iterator it;
Ptr<InfEngineBackendNet> net;
@ -1538,7 +1601,7 @@ struct Net::Impl
ld.skip = true;
}
layers[lastLayerId].skip = false;
ieNode->net->init(preferableTarget);
ieNode->net->init((Target)preferableTarget);
return;
}
@ -1713,12 +1776,272 @@ struct Net::Impl
if (!ieNode->net->isInitialized())
{
ieNode->net->init(preferableTarget);
ieNode->net->init((Target)preferableTarget);
ld.skip = false;
}
}
#endif // HAVE_INF_ENGINE
}
#endif // HAVE_INF_ENGINE
#ifdef HAVE_DNN_NGRAPH
void addNgraphOutputs(LayerData &ld)
{
CV_TRACE_FUNCTION();
Ptr<InfEngineNgraphNet> layerNet;
auto it = ld.backendNodes.find(preferableBackend);
if (it != ld.backendNodes.end())
{
Ptr<BackendNode> node = it->second;
if (!node.empty())
{
Ptr<InfEngineNgraphNode> ieNode = node.dynamicCast<InfEngineNgraphNode>();
CV_Assert(!ieNode.empty()); CV_Assert(!ieNode->net.empty());
layerNet = ieNode->net;
}
}
for (int i = 0; i < ld.inputBlobsId.size(); ++i)
{
LayerData &inpLd = layers[ld.inputBlobsId[i].lid];
Ptr<BackendNode> inpNode = inpLd.backendNodes[preferableBackend];
if (!inpNode.empty())
{
Ptr<InfEngineNgraphNode> ieInpNode = inpNode.dynamicCast<InfEngineNgraphNode>();
CV_Assert(!ieInpNode.empty()); CV_Assert(!ieInpNode->net.empty());
if (layerNet != ieInpNode->net)
{
ieInpNode->net->addOutput(ieInpNode->node->get_friendly_name());
ieInpNode->net->setUnconnectedNodes(ieInpNode);
}
}
}
}
void initNgraphBackend()
{
CV_TRACE_FUNCTION();
CV_Assert_N(preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, haveInfEngine());
MapIdToLayerData::iterator it;
Ptr<InfEngineNgraphNet> net;
for (it = layers.begin(); it != layers.end(); ++it)
{
LayerData &ld = it->second;
if (ld.id == 0)
{
CV_Assert((netInputLayer->outNames.empty() && ld.outputBlobsWrappers.size() == 1) ||
(netInputLayer->outNames.size() == ld.outputBlobsWrappers.size()));
for (int i = 0; i < ld.outputBlobsWrappers.size(); ++i)
{
InferenceEngine::DataPtr dataPtr = ngraphDataNode(ld.outputBlobsWrappers[i]);
dataPtr->setName(netInputLayer->outNames.empty() ? ld.name : netInputLayer->outNames[i]);
}
}
else
{
for (int i = 0; i < ld.outputBlobsWrappers.size(); ++i)
{
InferenceEngine::DataPtr dataPtr = ngraphDataNode(ld.outputBlobsWrappers[i]);
dataPtr->setName(ld.name);
}
}
}
if (skipInfEngineInit)
{
Ptr<BackendNode> node = layers[lastLayerId].backendNodes[preferableBackend];
CV_Assert(!node.empty());
Ptr<InfEngineNgraphNode> ieNode = node.dynamicCast<InfEngineNgraphNode>();
CV_Assert(!ieNode.empty());
for (it = layers.begin(); it != layers.end(); ++it)
{
LayerData &ld = it->second;
if (ld.id == 0)
{
for (int i = 0; i < ld.inputBlobsWrappers.size(); ++i)
{
InferenceEngine::DataPtr dataPtr = ngraphDataNode(ld.inputBlobsWrappers[i]);
dataPtr->setName(netInputLayer->outNames[i]);
}
}
else
{
for (int i = 0; i < ld.outputBlobsWrappers.size(); ++i)
{
InferenceEngine::DataPtr dataPtr = ngraphDataNode(ld.outputBlobsWrappers[i]);
dataPtr->setName(ld.name);
}
}
ieNode->net->addBlobs(ld.inputBlobsWrappers);
ieNode->net->addBlobs(ld.outputBlobsWrappers);
ld.skip = true;
}
layers[lastLayerId].skip = false;
ieNode->net->init((Target)preferableTarget);
return;
}
// Build Inference Engine networks from sets of layers that support this
// backend. Split a whole model on several Inference Engine networks if
// some of layers are not implemented.
for (it = layers.begin(); it != layers.end(); ++it)
{
LayerData &ld = it->second;
if (ld.id == 0 && ld.skip)
continue;
bool fused = ld.skip;
Ptr<Layer> layer = ld.layerInstance;
if (!fused && !layer->supportBackend(preferableBackend))
{
addNgraphOutputs(ld);
net = Ptr<InfEngineNgraphNet>();
layer->preferableTarget = DNN_TARGET_CPU;
for (int i = 0; i < ld.inputBlobsId.size(); ++i)
{
LayerData &inpLd = layers[ld.inputBlobsId[i].lid];
Ptr<BackendNode> inpNode = inpLd.backendNodes[preferableBackend];
if (!inpNode.empty()) {
Ptr<InfEngineNgraphNode> ieNode = inpNode.dynamicCast<InfEngineNgraphNode>();
ieNode->net->setUnconnectedNodes(ieNode);
}
}
continue;
}
ld.skip = true; // Initially skip all Inference Engine supported layers.
// Create a new network if one of inputs from different Inference Engine graph.
std::vector<Ptr<BackendNode>> inputNodes;
for (int i = 0; i < ld.inputBlobsId.size(); ++i)
{
// Layer_Test_ROIPooling.Accuracy has 2 inputs inpLD = 0, 0 -> has 4 inputNodes (input, rois, input, rois)
if (inputNodes.size() == ld.inputBlobsId.size()) {
break;
}
LayerData &inpLd = layers[ld.inputBlobsId[i].lid];
Ptr<BackendNode> inpNode = inpLd.backendNodes[preferableBackend];
if (!inpNode.empty())
{
Ptr<InfEngineNgraphNode> ieInpNode = inpNode.dynamicCast<InfEngineNgraphNode>();
CV_Assert(!ieInpNode.empty()); CV_Assert(!ieInpNode->net.empty());
if (ieInpNode->net == net && !fused) {
inputNodes.push_back(inpNode);
continue;
}
}
if (net.empty()) {
net = Ptr<InfEngineNgraphNet>(new InfEngineNgraphNet());
}
if (!fused) {
std::vector<std::string> inputNames;
std::vector<cv::Mat> inputs;
auto curr_pos = inpLd.consumers.begin();
auto compare = [&ld] (const LayerPin& lp) { return lp.lid == ld.id; };
auto cons = curr_pos;
while ((cons = std::find_if(curr_pos, inpLd.consumers.end(), compare)) !=
inpLd.consumers.end()) {
int cons_inp = cons->oid;
Ptr<NgraphBackendWrapper> inpWrapper = inpLd.outputBlobsWrappers[cons_inp].
dynamicCast<NgraphBackendWrapper>();
auto iter = std::find(inputNames.begin(), inputNames.end(),
inpWrapper->dataPtr->getName());
if (iter == inputNames.end()) {
inputNames.push_back(inpWrapper->dataPtr->getName());
inputs.push_back(inpLd.outputBlobs[cons_inp]);
}
curr_pos = cons + 1;
}
auto inps = net->setInputs(inputs, inputNames);
for (auto& inp : inps) {
inputNodes.emplace_back(Ptr<BackendNode>(new InfEngineNgraphNode(inp)));
}
}
}
Ptr<BackendNode> node;
if (!net.empty())
{
if (fused)
{
bool inPlace = ld.inputBlobsId.size() == 1 && ld.outputBlobs.size() == 1 &&
ld.inputBlobs[0]->data == ld.outputBlobs[0].data;
CV_Assert(inPlace);
node = layers[ld.inputBlobsId[0].lid].backendNodes[preferableBackend];
ld.inputBlobsWrappers = layers[ld.inputBlobsId[0].lid].inputBlobsWrappers;
}
}
else {
net = Ptr<InfEngineNgraphNet>(new InfEngineNgraphNet());
}
if (!fused)
{
CV_Assert(!inputNodes.empty());
node = layer->initNgraph(ld.inputBlobsWrappers, inputNodes);
for (int i = 0; i < ld.outputBlobsWrappers.size(); ++i)
{
InferenceEngine::DataPtr dataPtr = ngraphDataNode(ld.outputBlobsWrappers[i]);
node.dynamicCast<InfEngineNgraphNode>()->setName(dataPtr->getName());
}
}
else if (node.empty())
continue;
ld.backendNodes[preferableBackend] = node;
Ptr<InfEngineNgraphNode> ieNode = node.dynamicCast<InfEngineNgraphNode>();
CV_Assert(!ieNode.empty());
ieNode->net = net;
if (ld.consumers.empty()) {
// TF EAST_text_detection
ieNode->net->setUnconnectedNodes(ieNode);
}
ieNode->net->setNodePtr(&ieNode->node);
net->addBlobs(ld.inputBlobsWrappers);
net->addBlobs(ld.outputBlobsWrappers);
addNgraphOutputs(ld);
}
// Initialize all networks.
for (MapIdToLayerData::reverse_iterator it = layers.rbegin(); it != layers.rend(); ++it)
{
LayerData &ld = it->second;
auto iter = ld.backendNodes.find(preferableBackend);
if (iter == ld.backendNodes.end())
continue;
Ptr<BackendNode>& node = iter->second;
if (node.empty())
continue;
Ptr<InfEngineNgraphNode> ieNode = node.dynamicCast<InfEngineNgraphNode>();
if (ieNode.empty())
continue;
CV_Assert(!ieNode->net.empty());
if (!ieNode->net->isInitialized())
{
ieNode->net->setUnconnectedNodes(ieNode);
ieNode->net->createNet((Target)preferableTarget);
ld.skip = false;
}
}
}
#endif // HAVE_DNN_NGRAPH
void allocateLayer(int lid, const LayersShapesMap& layersShapes)
{
@ -1835,12 +2158,13 @@ struct Net::Impl
void fuseLayers(const std::vector<LayerPin>& blobsToKeep_)
{
if( !fusion || (preferableBackend != DNN_BACKEND_OPENCV &&
preferableBackend != DNN_BACKEND_INFERENCE_ENGINE))
return;
CV_TRACE_FUNCTION();
if(!fusion || (preferableBackend != DNN_BACKEND_OPENCV &&
preferableBackend != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 &&
preferableBackend != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH))
return;
// scan through all the layers. If there is convolution layer followed by the activation layer,
// we try to embed this activation into the convolution and disable separate execution of the activation
std::set<LayerPin> pinsToKeep(blobsToKeep_.begin(),
@ -2058,6 +2382,7 @@ struct Net::Impl
{
Mat& output = ld.outputBlobs[0];
UMat umat_output;
#ifdef HAVE_OPENCL
if (!ld.outputBlobsWrappers.empty() &&
(preferableBackend == DNN_BACKEND_OPENCV && IS_DNN_OPENCL_TARGET(preferableTarget)))
{
@ -2082,6 +2407,7 @@ struct Net::Impl
umat_outputBlobs = OpenCLBackendWrapper::getUMatVector(ld.outputBlobsWrappers);
umat_output = umat_outputBlobs[0];
}
#endif
// TODO: in general, this optimization can always be done, but
// many layers currently check that the input/output blobs are
@ -2118,6 +2444,7 @@ struct Net::Impl
// Allocate new memory to prevent collisions during memory
// reusing (see https://github.com/opencv/opencv/pull/10456).
output = output.clone();
#ifdef HAVE_OPENCL
if (preferableBackend == DNN_BACKEND_OPENCV &&
IS_DNN_OPENCL_TARGET(preferableTarget))
{
@ -2126,6 +2453,7 @@ struct Net::Impl
umats[0] = umat_output;
OpenCLBackendWrapper::update(ld.outputBlobsWrappers, umats);
}
#endif
Range chrange[] = { Range::all(), Range::all(), Range::all(), Range::all() };
int ofs = 0;
for( i = 0; i < ninputs; i++ )
@ -2142,12 +2470,14 @@ struct Net::Impl
CV_Assert(output_slice.isContinuous() && output_slice.size == curr_output.size);
Mat* oldPtr = &curr_output;
curr_output = output_slice;
#ifdef HAVE_OPENCL
if (preferableBackend == DNN_BACKEND_OPENCV && IS_DNN_OPENCL_TARGET(preferableTarget))
{
std::vector<UMat> umats(inp_i_data->outputBlobsWrappers.size());
umats[pin.oid] = umat_output(chrange);
OpenCLBackendWrapper::update(inp_i_data->outputBlobsWrappers, umats);
}
#endif
// Layers that refer old input Mat will refer to the
// new data but the same Mat object.
CV_Assert_N(curr_output.data == output_slice.data, oldPtr == &curr_output);
@ -2231,6 +2561,7 @@ struct Net::Impl
CV_Error(Error::StsNotImplemented, format("Layer \"%s\" of type \"%s\" unsupported on OpenCV backend",
ld.name.c_str(), ld.type.c_str()));
#ifdef HAVE_OPENCL
if (preferableBackend == DNN_BACKEND_OPENCV && IS_DNN_OPENCL_TARGET(preferableTarget))
{
std::vector<UMat> umat_inputBlobs = OpenCLBackendWrapper::getUMatVector(ld.inputBlobsWrappers);
@ -2305,6 +2636,7 @@ struct Net::Impl
OpenCLBackendWrapper::update(ld.outputBlobsWrappers, umat_outputBlobs);
}
else
#endif
{
for (int i = 0, n = ld.inputBlobsWrappers.size(); i < n; ++i)
{
@ -2384,10 +2716,14 @@ struct Net::Impl
{
forwardHalide(ld.outputBlobsWrappers, node);
}
else if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE)
else if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
forwardInfEngine(ld.outputBlobsWrappers, node, isAsync);
}
else if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
forwardNgraph(ld.outputBlobsWrappers, node, isAsync);
}
else
{
CV_Error(Error::StsNotImplemented, "Unknown backend identifier");
@ -2550,13 +2886,23 @@ struct Net::Impl
// Transfer data to CPU if it's require.
ld.outputBlobsWrappers[pin.oid]->copyToHost();
}
CV_Assert(preferableBackend == DNN_BACKEND_INFERENCE_ENGINE);
CV_Assert(preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
Ptr<InfEngineBackendWrapper> wrapper = ld.outputBlobsWrappers[pin.oid].dynamicCast<InfEngineBackendWrapper>();
return std::move(wrapper->futureMat);
if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) {
Ptr<InfEngineBackendWrapper> wrapper = ld.outputBlobsWrappers[pin.oid].dynamicCast<InfEngineBackendWrapper>();
return std::move(wrapper->futureMat);
}
else if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
#ifdef HAVE_DNN_NGRAPH
Ptr<NgraphBackendWrapper> wrapper = ld.outputBlobsWrappers[pin.oid].dynamicCast<NgraphBackendWrapper>();
return std::move(wrapper->futureMat);
#else
CV_Error(Error::StsNotImplemented, "DNN_BACKEND_INFERENCE_ENGINE backend is required");
CV_Error(Error::StsNotImplemented, "This OpenCV version is built without support of Inference Engine + nGraph");
#endif
}
#endif // HAVE_INF_ENGINE
CV_Error(Error::StsNotImplemented, "DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 backend is required");
}
AsyncArray getBlobAsync(String outputName)
@ -2575,11 +2921,17 @@ Net Net::readFromModelOptimizer(const String& xml, const String& bin)
#ifndef HAVE_INF_ENGINE
CV_Error(Error::StsError, "Build OpenCV with Inference Engine to enable loading models from Model Optimizer.");
#else
#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R3)
InferenceEngine::CNNNetReader reader;
reader.ReadNetwork(xml);
reader.ReadWeights(bin);
InferenceEngine::CNNNetwork ieNet = reader.getNetwork();
#else
InferenceEngine::Core& ie = getCore();
InferenceEngine::CNNNetwork ieNet = ie.ReadNetwork(xml, bin);
#endif
std::vector<String> inputsNames;
for (auto& it : ieNet.getInputsInfo())
@ -2590,27 +2942,62 @@ Net Net::readFromModelOptimizer(const String& xml, const String& bin)
Net cvNet;
cvNet.setInputsNames(inputsNames);
Ptr<InfEngineBackendNode> backendNode(new InfEngineBackendNode(InferenceEngine::Builder::Layer("")));
backendNode->net = Ptr<InfEngineBackendNet>(new InfEngineBackendNet(ieNet));
Ptr<BackendNode> backendNode;
#ifdef HAVE_DNN_NGRAPH
if (DNN_BACKEND_INFERENCE_ENGINE_NGRAPH == getInferenceEngineBackendTypeParam())
{
auto fake_node = std::make_shared<ngraph::op::Parameter>(ngraph::element::f32, ngraph::Shape{});
Ptr<InfEngineNgraphNode> backendNodeNGraph(new InfEngineNgraphNode(fake_node));
backendNodeNGraph->net = Ptr<InfEngineNgraphNet>(new InfEngineNgraphNet(ieNet));
backendNode = backendNodeNGraph;
}
else
#endif
{
Ptr<InfEngineBackendNode> backendNodeNN(new InfEngineBackendNode(InferenceEngine::Builder::Layer("")));
backendNodeNN->net = Ptr<InfEngineBackendNet>(new InfEngineBackendNet(ieNet));
backendNode = backendNodeNN;
}
for (auto& it : ieNet.getOutputsInfo())
{
Ptr<Layer> cvLayer(new InfEngineBackendLayer(ieNet));
InferenceEngine::CNNLayerPtr ieLayer = ieNet.getLayerByName(it.first.c_str());
CV_Assert(ieLayer);
LayerParams lp;
int lid = cvNet.addLayer(it.first, "", lp);
LayerData& ld = cvNet.impl->layers[lid];
cvLayer->name = it.first;
cvLayer->type = ieLayer->type;
ld.layerInstance = cvLayer;
ld.backendNodes[DNN_BACKEND_INFERENCE_ENGINE] = backendNode;
#ifdef HAVE_DNN_NGRAPH
if (DNN_BACKEND_INFERENCE_ENGINE_NGRAPH == getInferenceEngineBackendTypeParam())
{
Ptr<Layer> cvLayer(new NgraphBackendLayer(ieNet));
InferenceEngine::CNNLayerPtr ieLayer = ieNet.getLayerByName(it.first.c_str());
CV_Assert(ieLayer);
cvLayer->name = it.first;
cvLayer->type = ieLayer->type;
ld.layerInstance = cvLayer;
ld.backendNodes[DNN_BACKEND_INFERENCE_ENGINE_NGRAPH] = backendNode;
}
else
#endif
{
Ptr<Layer> cvLayer(new InfEngineBackendLayer(ieNet));
InferenceEngine::CNNLayerPtr ieLayer = ieNet.getLayerByName(it.first.c_str());
CV_Assert(ieLayer);
cvLayer->name = it.first;
cvLayer->type = ieLayer->type;
ld.layerInstance = cvLayer;
ld.backendNodes[DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019] = backendNode;
}
for (int i = 0; i < inputsNames.size(); ++i)
cvNet.connect(0, i, lid, i);
}
cvNet.setPreferableBackend(DNN_BACKEND_INFERENCE_ENGINE);
cvNet.setPreferableBackend(getInferenceEngineBackendTypeParam());
cvNet.impl->skipInfEngineInit = true;
return cvNet;
@ -2695,8 +3082,8 @@ AsyncArray Net::forwardAsync(const String& outputName)
std::vector<LayerPin> pins(1, impl->getPinByAlias(layerName));
impl->setUpNet(pins);
if (impl->preferableBackend != DNN_BACKEND_INFERENCE_ENGINE)
CV_Error(Error::StsNotImplemented, "Asynchronous forward for backend which is different from DNN_BACKEND_INFERENCE_ENGINE");
if (!(impl->preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || impl->preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH))
CV_Error(Error::StsNotImplemented, "DNN: Asynchronous forward is supported for Inference Engine backends only");
impl->isAsync = true;
impl->forwardToLayer(impl->getLayerData(layerName));
@ -2704,7 +3091,7 @@ AsyncArray Net::forwardAsync(const String& outputName)
return impl->getBlobAsync(layerName);
#else
CV_Error(Error::StsNotImplemented, "Asynchronous forward without C++11");
CV_Error(Error::StsNotImplemented, "DNN: Asynchronous forward requires build with enabled C++11");
#endif // CV_CXX11
}
@ -2757,6 +3144,7 @@ void Net::forward(OutputArrayOfArrays outputBlobs, const String& outputName)
{
std::vector<UMat> & outputvec = *(std::vector<UMat> *)outputBlobs.getObj();
#ifdef HAVE_OPENCL
if (impl->preferableBackend == DNN_BACKEND_OPENCV &&
IS_DNN_OPENCL_TARGET(impl->preferableTarget))
{
@ -2771,6 +3159,7 @@ void Net::forward(OutputArrayOfArrays outputBlobs, const String& outputName)
}
}
else
#endif
{
outputvec.resize(ld.outputBlobs.size());
for (int i = 0; i < outputvec.size(); ++i)
@ -2840,6 +3229,11 @@ void Net::setPreferableBackend(int backendId)
CV_TRACE_FUNCTION();
CV_TRACE_ARG(backendId);
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
backendId = getInferenceEngineBackendTypeParam();
#endif
if( impl->preferableBackend != backendId )
{
impl->preferableBackend = backendId;
@ -3030,7 +3424,9 @@ String Net::dump()
switch (prefBackend) {
case DNN_BACKEND_DEFAULT: backend = "DEFAULT/"; break;
case DNN_BACKEND_HALIDE: backend = "HALIDE/"; break;
case DNN_BACKEND_INFERENCE_ENGINE: backend = "DLIE/"; break;
case DNN_BACKEND_INFERENCE_ENGINE: // fallthru
case DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019: backend = "DLIE/"; break;
case DNN_BACKEND_INFERENCE_ENGINE_NGRAPH: backend = "NGRAPH/"; break;
case DNN_BACKEND_OPENCV: backend = "OCV/"; break;
}
out << "digraph G {" << '\n';
@ -3544,6 +3940,13 @@ Ptr<BackendNode> Layer::initInfEngine(const std::vector<Ptr<BackendWrapper> > &)
return Ptr<BackendNode>();
}
Ptr<BackendNode> Layer::initNgraph(const std::vector<Ptr<BackendWrapper> > & inputs, const std::vector<Ptr<BackendNode> >& nodes)
{
CV_Error(Error::StsNotImplemented, "Inference Engine pipeline of " + type +
" layers is not defined.");
return Ptr<BackendNode>();
}
void Layer::applyHalideScheduler(Ptr<BackendNode>& node, const std::vector<Mat*> &inputs,
const std::vector<Mat> &outputs, int targetId) const
{

679
modules/dnn/src/ie_ngraph.cpp Normal file
View File

@ -0,0 +1,679 @@
// 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.
//
// Copyright (C) 2018-2019, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
#include "precomp.hpp"
#include "ie_ngraph.hpp"
#include <opencv2/dnn/shape_utils.hpp>
#ifdef HAVE_DNN_NGRAPH
#include <ie_extension.h>
#include <ie_plugin_dispatcher.hpp>
#endif // HAVE_DNN_NGRAPH
#include <opencv2/core/utils/configuration.private.hpp>
#include <opencv2/core/utils/logger.hpp>
namespace cv { namespace dnn {
#ifdef HAVE_DNN_NGRAPH
// For networks with input layer which has an empty name, IE generates a name id[some_number].
// OpenCV lets users use an empty input name and to prevent unexpected naming,
// we can use some predefined name.
static std::string kDefaultInpLayerName = "empty_inp_layer_name";
static std::vector<Ptr<NgraphBackendWrapper> >
ngraphWrappers(const std::vector<Ptr<BackendWrapper> >& ptrs)
{
std::vector<Ptr<NgraphBackendWrapper> > wrappers(ptrs.size());
for (int i = 0; i < ptrs.size(); ++i)
{
CV_Assert(!ptrs[i].empty());
wrappers[i] = ptrs[i].dynamicCast<NgraphBackendWrapper>();
CV_Assert(!wrappers[i].empty());
}
return wrappers;
}
InfEngineNgraphNode::InfEngineNgraphNode(std::shared_ptr<ngraph::Node>&& _node)
: BackendNode(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH), node(std::move(_node)) {}
InfEngineNgraphNode::InfEngineNgraphNode(std::shared_ptr<ngraph::Node>& _node)
: BackendNode(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH), node(_node) {}
void InfEngineNgraphNode::setName(const std::string& name) {
node->set_friendly_name(name);
}
InfEngineNgraphNet::InfEngineNgraphNet()
{
hasNetOwner = false;
device_name = "CPU";
}
InfEngineNgraphNet::InfEngineNgraphNet(InferenceEngine::CNNNetwork& net) : cnn(net)
{
hasNetOwner = true;
device_name = "CPU";
}
void InfEngineNgraphNet::addOutput(const std::string& name)
{
requestedOutputs.push_back(name);
}
void InfEngineNgraphNet::setNodePtr(std::shared_ptr<ngraph::Node>* ptr) {
all_nodes.emplace((*ptr)->get_friendly_name(), ptr);
}
void InfEngineNgraphNet::release() {
for (auto& node : components.back()) {
if (!(node->is_parameter() || node->is_output() || node->is_constant()) ) {
auto it = all_nodes.find(node->get_friendly_name());
if (it != all_nodes.end()) {
unconnectedNodes.erase(*(it->second));
it->second->reset();
all_nodes.erase(it);
}
}
}
}
void InfEngineNgraphNet::dfs(std::shared_ptr<ngraph::Node>& node,
std::vector<std::shared_ptr<ngraph::Node>>& comp,
std::unordered_map<std::string, bool>& used) {
used[node->get_friendly_name()] = true;
comp.push_back(node);
auto inputs = node->get_users();
for (size_t i = 0; i < node->get_input_size(); ++i) {
inputs.push_back(node->input_value(i).get_node()->shared_from_this());
}
for (auto& to : inputs) {
if (!used[to->get_friendly_name()]) {
dfs(to, comp, used);
}
}
}
int InfEngineNgraphNet::getNumComponents() {
if (!components.empty()) {
return components.size();
}
std::unordered_map<std::string, bool> used;
auto inputs = ngraph_function->get_ordered_ops();
for (auto& node : inputs) {
used.emplace(node->get_friendly_name(), false);
}
for (auto& node : inputs) {
if (!used[node->get_friendly_name()]) {
std::vector<std::shared_ptr<ngraph::Node>> current_comp;
dfs(node, current_comp, used);
components.push_back(current_comp);
}
}
return components.size();
}
void InfEngineNgraphNet::createNet(Target targetId) {
if (!hasNetOwner)
{
CV_Assert(!unconnectedNodes.empty());
ngraph::ResultVector outs;
for (auto& node : unconnectedNodes)
{
auto out = std::make_shared<ngraph::op::Result>(node);
outs.push_back(out);
}
CV_Assert_N(!inputs_vec.empty(), !outs.empty());
ngraph_function = std::make_shared<ngraph::Function>(outs, inputs_vec);
int num_comp = getNumComponents();
if (num_comp > 1) {
for (int i = num_comp - 1; i >= 0; --i) {
ngraph::ResultVector outputs;
ngraph::ParameterVector inps;
for (auto& node : components.back()) {
if (node->is_parameter()) {
auto parameter = std::dynamic_pointer_cast<ngraph::op::Parameter>(node);
inps.push_back(parameter);
}
else if (node->is_output()) {
auto result = std::dynamic_pointer_cast<ngraph::op::Result>(node);
outputs.push_back(result);
}
}
isInit = false;
CV_Assert_N(!inps.empty(), !outputs.empty());
ngraph_function = std::make_shared<ngraph::Function>(outputs, inps);
release();
components.pop_back();
init(targetId);
}
} else {
release();
components.clear();
init(targetId);
}
}
}
void InfEngineNgraphNet::init(Target targetId)
{
if (!hasNetOwner)
{
if (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) {
auto nodes = ngraph_function->get_ordered_ops();
for (auto& node : nodes) {
auto parameter = std::dynamic_pointer_cast<ngraph::op::Parameter>(node);
if (parameter && parameter->get_element_type() == ngraph::element::f32) {
parameter->set_element_type(ngraph::element::f16);
}
auto constant = std::dynamic_pointer_cast<ngraph::op::Constant>(node);
if (constant && constant->get_element_type() == ngraph::element::f32) {
auto data = constant->get_vector<float>();
std::vector<ngraph::float16> new_data(data.size());
for (size_t i = 0; i < data.size(); ++i) {
new_data[i] = ngraph::float16(data[i]);
}
auto new_const = std::make_shared<ngraph::op::Constant>(ngraph::element::f16, constant->get_shape(), new_data);
new_const->set_friendly_name(constant->get_friendly_name());
ngraph::replace_node(constant, new_const);
}
}
ngraph_function->validate_nodes_and_infer_types();
}
cnn = InferenceEngine::CNNNetwork(ngraph_function);
#ifdef _DEBUG // TODO
//cnn.serialize("/tmp/cnn.xml", "/tmp/cnn.bin");
#endif
}
switch (targetId)
{
case DNN_TARGET_CPU:
device_name = "CPU";
break;
case DNN_TARGET_OPENCL:
case DNN_TARGET_OPENCL_FP16:
device_name = "GPU";
break;
case DNN_TARGET_MYRIAD:
device_name = "MYRIAD";
break;
case DNN_TARGET_FPGA:
device_name = "FPGA";
break;
default:
CV_Error(Error::StsNotImplemented, "Unknown target");
};
if (!hasNetOwner) {
for (size_t i = 0; i < ngraph_function->get_output_size(); ++i) {
auto node = ngraph_function->output(i).get_node();
for (size_t j = 0; j < node->get_input_size(); ++j) {
std::string name = node->input_value(j).get_node()->get_friendly_name();
auto iter = std::find(requestedOutputs.begin(), requestedOutputs.end(), name);
if (iter != requestedOutputs.end()) {
requestedOutputs.erase(iter);
cnn.addOutput(name);
}
}
}
} else {
for (const auto& name : requestedOutputs)
{
cnn.addOutput(name);
}
}
for (const auto& it : cnn.getInputsInfo())
{
const std::string& name = it.first;
auto blobIt = allBlobs.find(name);
CV_Assert(blobIt != allBlobs.end());
it.second->setPrecision(blobIt->second->getTensorDesc().getPrecision());
}
for (const auto& it : cnn.getOutputsInfo())
{
const std::string& name = it.first;
auto blobIt = allBlobs.find(name);
CV_Assert(blobIt != allBlobs.end());
it.second->setPrecision(blobIt->second->getTensorDesc().getPrecision()); // Should be always FP32
}
initPlugin(cnn);
}
ngraph::ParameterVector InfEngineNgraphNet::setInputs(const std::vector<cv::Mat>& inputs,
const std::vector<std::string>& names) {
CV_Assert_N(inputs.size() == names.size());
ngraph::ParameterVector current_inp;
for (size_t i = 0; i < inputs.size(); i++)
{
std::vector<size_t> shape = getShape<size_t>(inputs[i]);
auto inp = std::make_shared<ngraph::op::Parameter>(ngraph::element::f32, ngraph::Shape(shape));
inp->set_friendly_name(names[i]);
auto it = std::find_if(inputs_vec.begin(), inputs_vec.end(),
[&inp](const std::shared_ptr<ngraph::op::Parameter>& a) {
return a->get_friendly_name() == inp->get_friendly_name();
});
if (it == inputs_vec.end()) {
inputs_vec.push_back(inp);
current_inp.push_back(inp);
} else {
current_inp.push_back(*it);
}
}
return current_inp;
}
void InfEngineNgraphNet::setUnconnectedNodes(Ptr<InfEngineNgraphNode>& node) {
unconnectedNodes.insert(node->node);
}
void InfEngineNgraphNet::initPlugin(InferenceEngine::CNNNetwork& net)
{
CV_Assert(!isInitialized());
try
{
AutoLock lock(getInitializationMutex());
InferenceEngine::Core& ie = getCore();
{
isInit = true;
std::vector<std::string> candidates;
std::string param_pluginPath = utils::getConfigurationParameterString("OPENCV_DNN_IE_EXTRA_PLUGIN_PATH", "");
if (!param_pluginPath.empty())
{
candidates.push_back(param_pluginPath);
}
bool found = false;
for (size_t i = 0; i != candidates.size(); ++i)
{
const std::string& libName = candidates[i];
try
{
InferenceEngine::IExtensionPtr extension =
InferenceEngine::make_so_pointer<InferenceEngine::IExtension>(libName);
ie.AddExtension(extension, "CPU");
CV_LOG_INFO(NULL, "DNN-IE: Loaded extension plugin: " << libName);
found = true;
break;
}
catch(...) {}
}
if (!found && !candidates.empty())
{
CV_LOG_WARNING(NULL, "DNN-IE: Can't load extension plugin (extra layers for some networks). Specify path via OPENCV_DNN_IE_EXTRA_PLUGIN_PATH parameter");
}
// Some of networks can work without a library of extra layers.
// OpenCV fallbacks as extensions.
ie.AddExtension(std::make_shared<InfEngineExtension>(), "CPU");
#ifndef _WIN32
// Limit the number of CPU threads.
if (device_name == "CPU")
ie.SetConfig({{
InferenceEngine::PluginConfigParams::KEY_CPU_THREADS_NUM, format("%d", getNumThreads()),
}}, device_name);
#endif
}
std::map<std::string, std::string> config;
if (device_name == "MYRIAD") {
config.emplace("VPU_DETECT_NETWORK_BATCH", CONFIG_VALUE(NO));
}
netExec = ie.LoadNetwork(net, device_name, config);
}
catch (const std::exception& ex)
{
CV_Error(Error::StsError, format("Failed to initialize Inference Engine backend (device = %s): %s", device_name.c_str(), ex.what()));
}
}
bool InfEngineNgraphNet::isInitialized()
{
return isInit;
}
bool NgraphBackendLayer::getMemoryShapes(const std::vector<MatShape> &inputs,
const int requiredOutputs,
std::vector<MatShape> &outputs,
std::vector<MatShape> &internals) const
{
InferenceEngine::ICNNNetwork::InputShapes inShapes = t_net.getInputShapes();
InferenceEngine::ICNNNetwork::InputShapes::iterator itr;
bool equal_flag = true;
size_t i = 0;
for (itr = inShapes.begin(); itr != inShapes.end(); ++itr)
{
InferenceEngine::SizeVector currentInShape(inputs[i].begin(), inputs[i].end());
if (itr->second != currentInShape)
{
itr->second = currentInShape;
equal_flag = false;
}
i++;
}
if (!equal_flag)
{
InferenceEngine::CNNNetwork curr_t_net(t_net);
curr_t_net.reshape(inShapes);
}
std::vector<size_t> dims = t_net.getOutputsInfo()[name]->getDims();
outputs.push_back(MatShape(dims.begin(), dims.end()));
return false;
}
bool NgraphBackendLayer::supportBackend(int backendId)
{
CV_LOG_DEBUG(NULL, "NgraphBackendLayer::supportBackend(" << backendId << ")");
return backendId == DNN_BACKEND_DEFAULT ||
(backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
}
void NgraphBackendLayer::forward(InputArrayOfArrays inputs, OutputArrayOfArrays outputs,
OutputArrayOfArrays internals)
{
CV_Error(Error::StsInternal, "Choose Inference Engine as a preferable backend.");
}
static InferenceEngine::Layout estimateLayout(const Mat& m)
{
if (m.dims == 4)
return InferenceEngine::Layout::NCHW;
else if (m.dims == 2)
return InferenceEngine::Layout::NC;
else
return InferenceEngine::Layout::ANY;
}
static InferenceEngine::DataPtr wrapToInfEngineDataNode(const Mat& m, const std::string& name = "")
{
std::vector<size_t> shape = getShape<size_t>(m);
if (m.type() == CV_32F)
return InferenceEngine::DataPtr(new InferenceEngine::Data(name,
{InferenceEngine::Precision::FP32, shape, estimateLayout(m)}));
else if (m.type() == CV_8U)
return InferenceEngine::DataPtr(new InferenceEngine::Data(name,
{InferenceEngine::Precision::U8, shape, estimateLayout(m)}));
else
CV_Error(Error::StsNotImplemented, format("Unsupported data type %s", typeToString(m.type()).c_str()));
}
InferenceEngine::Blob::Ptr wrapToNgraphBlob(const Mat& m, const std::vector<size_t>& shape,
InferenceEngine::Layout layout)
{
if (m.type() == CV_32F)
return InferenceEngine::make_shared_blob<float>(
{InferenceEngine::Precision::FP32, shape, layout}, (float*)m.data);
else if (m.type() == CV_8U)
return InferenceEngine::make_shared_blob<uint8_t>(
{InferenceEngine::Precision::U8, shape, layout}, (uint8_t*)m.data);
else
CV_Error(Error::StsNotImplemented, format("Unsupported data type %s", typeToString(m.type()).c_str()));
}
InferenceEngine::Blob::Ptr wrapToNgraphBlob(const Mat& m, InferenceEngine::Layout layout)
{
std::vector<size_t> shape = getShape<size_t>(m);
return wrapToNgraphBlob(m, shape, layout);
}
NgraphBackendWrapper::NgraphBackendWrapper(int targetId, const cv::Mat& m)
: BackendWrapper(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, targetId)
{
dataPtr = wrapToInfEngineDataNode(m);
blob = wrapToNgraphBlob(m, estimateLayout(m));
}
NgraphBackendWrapper::NgraphBackendWrapper(Ptr<BackendWrapper> wrapper)
: BackendWrapper(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, wrapper->targetId)
{
Ptr<NgraphBackendWrapper> ieWrapper = wrapper.dynamicCast<NgraphBackendWrapper>();
CV_Assert(!ieWrapper.empty());
InferenceEngine::DataPtr srcData = ieWrapper->dataPtr;
dataPtr = InferenceEngine::DataPtr(new InferenceEngine::Data(srcData->getName(), srcData->getTensorDesc()));
blob = ieWrapper->blob;
}
Ptr<BackendWrapper> NgraphBackendWrapper::create(Ptr<BackendWrapper> wrapper)
{
return Ptr<BackendWrapper>(new NgraphBackendWrapper(wrapper));
}
NgraphBackendWrapper::~NgraphBackendWrapper()
{
// nothing
}
void NgraphBackendWrapper::copyToHost()
{
CV_LOG_DEBUG(NULL, "NgraphBackendWrapper::copyToHost()");
//CV_Error(Error::StsNotImplemented, "");
}
void NgraphBackendWrapper::setHostDirty()
{
CV_LOG_DEBUG(NULL, "NgraphBackendWrapper::setHostDirty()");
//CV_Error(Error::StsNotImplemented, "");
}
InferenceEngine::Blob::Ptr copyBlob(const InferenceEngine::Blob::Ptr& blob)
{
InferenceEngine::Blob::Ptr copy;
auto description = blob->getTensorDesc();
InferenceEngine::Precision precision = description.getPrecision();
if (precision == InferenceEngine::Precision::FP32)
{
copy = InferenceEngine::make_shared_blob<float>(description);
}
else if (precision == InferenceEngine::Precision::U8)
{
copy = InferenceEngine::make_shared_blob<uint8_t>(description);
}
else
CV_Error(Error::StsNotImplemented, "Unsupported blob precision");
copy->allocate();
return copy;
}
InferenceEngine::DataPtr ngraphDataNode(const Ptr<BackendWrapper>& ptr)
{
CV_Assert(!ptr.empty());
Ptr<NgraphBackendWrapper> p = ptr.dynamicCast<NgraphBackendWrapper>();
CV_Assert(!p.empty());
return p->dataPtr;
}
void forwardNgraph(const std::vector<Ptr<BackendWrapper> >& outBlobsWrappers,
Ptr<BackendNode>& node, bool isAsync)
{
CV_Assert(!node.empty());
Ptr<InfEngineNgraphNode> ieNode = node.dynamicCast<InfEngineNgraphNode>();
CV_Assert(!ieNode.empty());
ieNode->net->forward(outBlobsWrappers, isAsync);
}
void InfEngineNgraphNet::addBlobs(const std::vector<cv::Ptr<BackendWrapper> >& ptrs)
{
auto wrappers = ngraphWrappers(ptrs);
for (const auto& wrapper : wrappers)
{
std::string name = wrapper->dataPtr->getName();
name = name.empty() ? kDefaultInpLayerName : name;
allBlobs.insert({name, wrapper->blob});
}
}
void InfEngineNgraphNet::NgraphReqWrapper::makePromises(const std::vector<Ptr<BackendWrapper> >& outsWrappers)
{
auto outs = ngraphWrappers(outsWrappers);
outProms.clear();
outProms.resize(outs.size());
outsNames.resize(outs.size());
for (int i = 0; i < outs.size(); ++i)
{
outs[i]->futureMat = outProms[i].getArrayResult();
outsNames[i] = outs[i]->dataPtr->getName();
}
}
Mat ngraphBlobToMat(const InferenceEngine::Blob::Ptr& blob)
{
std::vector<size_t> dims = blob->getTensorDesc().getDims();
std::vector<int> size(dims.begin(), dims.end());
auto precision = blob->getTensorDesc().getPrecision();
int type = -1;
switch (precision)
{
case InferenceEngine::Precision::FP32: type = CV_32F; break;
case InferenceEngine::Precision::U8: type = CV_8U; break;
default:
CV_Error(Error::StsNotImplemented, "Unsupported blob precision");
}
return Mat(size, type, (void*)blob->buffer());
}
void InfEngineNgraphNet::forward(const std::vector<Ptr<BackendWrapper> >& outBlobsWrappers, bool isAsync)
{
CV_LOG_DEBUG(NULL, "InfEngineNgraphNet::forward(" << (isAsync ? "async" : "sync") << ")");
// Look for finished requests.
Ptr<NgraphReqWrapper> reqWrapper;
for (auto& wrapper : infRequests)
{
if (wrapper->isReady)
{
reqWrapper = wrapper;
break;
}
}
if (reqWrapper.empty())
{
reqWrapper = Ptr<NgraphReqWrapper>(new NgraphReqWrapper());
try
{
reqWrapper->req = netExec.CreateInferRequest();
}
catch (const std::exception& ex)
{
CV_Error(Error::StsAssert, format("Failed to initialize Inference Engine backend: %s", ex.what()));
}
infRequests.push_back(reqWrapper);
InferenceEngine::BlobMap inpBlobs, outBlobs;
for (const auto& it : cnn.getInputsInfo())
{
const std::string& name = it.first;
auto blobIt = allBlobs.find(name);
CV_Assert(blobIt != allBlobs.end());
inpBlobs[name] = isAsync ? copyBlob(blobIt->second) : blobIt->second;
}
for (const auto& it : cnn.getOutputsInfo())
{
const std::string& name = it.first;
auto blobIt = allBlobs.find(name);
CV_Assert(blobIt != allBlobs.end());
outBlobs[name] = isAsync ? copyBlob(blobIt->second) : blobIt->second;
}
reqWrapper->req.SetInput(inpBlobs);
reqWrapper->req.SetOutput(outBlobs);
InferenceEngine::IInferRequest::Ptr infRequestPtr = reqWrapper->req;
infRequestPtr->SetUserData(reqWrapper.get(), 0);
infRequestPtr->SetCompletionCallback(
[](InferenceEngine::IInferRequest::Ptr request, InferenceEngine::StatusCode status)
{
CV_LOG_DEBUG(NULL, "DNN(nGraph): completionCallback(" << (int)status << ")");
NgraphReqWrapper* wrapper;
request->GetUserData((void**)&wrapper, 0);
CV_Assert(wrapper && "Internal error");
size_t processedOutputs = 0;
try
{
for (; processedOutputs < wrapper->outProms.size(); ++processedOutputs)
{
const std::string& name = wrapper->outsNames[processedOutputs];
Mat m = ngraphBlobToMat(wrapper->req.GetBlob(name));
try
{
CV_Assert(status == InferenceEngine::StatusCode::OK);
wrapper->outProms[processedOutputs].setValue(m.clone());
}
catch (...)
{
try {
wrapper->outProms[processedOutputs].setException(std::current_exception());
} catch(...) {
CV_LOG_ERROR(NULL, "DNN: Exception occured during async inference exception propagation");
}
}
}
}
catch (...)
{
std::exception_ptr e = std::current_exception();
for (; processedOutputs < wrapper->outProms.size(); ++processedOutputs)
{
try {
wrapper->outProms[processedOutputs].setException(e);
} catch(...) {
CV_LOG_ERROR(NULL, "DNN: Exception occured during async inference exception propagation");
}
}
}
wrapper->isReady = true;
}
);
}
if (isAsync)
{
// Copy actual data to infer request's input blobs.
for (const auto& it : cnn.getInputsInfo())
{
const std::string& name = it.first;
auto blobIt = allBlobs.find(name);
Mat srcMat = ngraphBlobToMat(blobIt->second);
Mat dstMat = ngraphBlobToMat(reqWrapper->req.GetBlob(name));
srcMat.copyTo(dstMat);
}
// Set promises to output blobs wrappers.
reqWrapper->makePromises(outBlobsWrappers);
reqWrapper->isReady = false;
reqWrapper->req.StartAsync();
}
else
{
reqWrapper->req.Infer();
}
}
#else
void forwardNgraph(const std::vector<Ptr<BackendWrapper> >& outBlobsWrappers,
Ptr<BackendNode>& node, bool isAsync)
{
CV_Assert(false && "nGraph is not enabled in this OpenCV build");
}
#endif
}}

144
modules/dnn/src/ie_ngraph.hpp Normal file
View File

@ -0,0 +1,144 @@
// 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.
//
// Copyright (C) 2018-2019, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
#ifndef __OPENCV_DNN_IE_NGRAPH_HPP__
#define __OPENCV_DNN_IE_NGRAPH_HPP__
#include "op_inf_engine.hpp"
#ifdef HAVE_DNN_NGRAPH
#include <ngraph/ngraph.hpp>
#endif // HAVE_DNN_NGRAPH
namespace cv { namespace dnn {
#ifdef HAVE_DNN_NGRAPH
class InfEngineNgraphNode;
class InfEngineNgraphNet
{
public:
InfEngineNgraphNet();
InfEngineNgraphNet(InferenceEngine::CNNNetwork& net);
void addOutput(const std::string& name);
bool isInitialized();
void init(Target targetId);
void forward(const std::vector<Ptr<BackendWrapper> >& outBlobsWrappers, bool isAsync);
void initPlugin(InferenceEngine::CNNNetwork& net);
ngraph::ParameterVector setInputs(const std::vector<cv::Mat>& inputs, const std::vector<std::string>& names);
void setUnconnectedNodes(Ptr<InfEngineNgraphNode>& node);
void addBlobs(const std::vector<cv::Ptr<BackendWrapper> >& ptrs);
void createNet(Target targetId);
void setNodePtr(std::shared_ptr<ngraph::Node>* ptr);
private:
void release();
int getNumComponents();
void dfs(std::shared_ptr<ngraph::Node>& node, std::vector<std::shared_ptr<ngraph::Node>>& comp,
std::unordered_map<std::string, bool>& used);
ngraph::ParameterVector inputs_vec;
std::shared_ptr<ngraph::Function> ngraph_function;
std::vector<std::vector<std::shared_ptr<ngraph::Node>>> components;
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>* > all_nodes;
InferenceEngine::ExecutableNetwork netExec;
InferenceEngine::BlobMap allBlobs;
std::string device_name;
bool isInit = false;
struct NgraphReqWrapper
{
NgraphReqWrapper() : isReady(true) {}
void makePromises(const std::vector<Ptr<BackendWrapper> >& outs);
InferenceEngine::InferRequest req;
std::vector<cv::AsyncPromise> outProms;
std::vector<std::string> outsNames;
bool isReady;
};
std::vector<Ptr<NgraphReqWrapper> > infRequests;
InferenceEngine::CNNNetwork cnn;
bool hasNetOwner;
std::vector<std::string> requestedOutputs;
std::unordered_set<std::shared_ptr<ngraph::Node>> unconnectedNodes;
};
class InfEngineNgraphNode : public BackendNode
{
public:
InfEngineNgraphNode(std::shared_ptr<ngraph::Node>&& _node);
InfEngineNgraphNode(std::shared_ptr<ngraph::Node>& _node);
void setName(const std::string& name);
// Inference Engine network object that allows to obtain the outputs of this layer.
std::shared_ptr<ngraph::Node> node;
Ptr<InfEngineNgraphNet> net;
};
class NgraphBackendWrapper : public BackendWrapper
{
public:
NgraphBackendWrapper(int targetId, const Mat& m);
NgraphBackendWrapper(Ptr<BackendWrapper> wrapper);
~NgraphBackendWrapper();
static Ptr<BackendWrapper> create(Ptr<BackendWrapper> wrapper);
virtual void copyToHost() CV_OVERRIDE;
virtual void setHostDirty() CV_OVERRIDE;
InferenceEngine::DataPtr dataPtr;
InferenceEngine::Blob::Ptr blob;
AsyncArray futureMat;
};
InferenceEngine::DataPtr ngraphDataNode(const Ptr<BackendWrapper>& ptr);
// This is a fake class to run networks from Model Optimizer. Objects of that
// class simulate responses of layers are imported by OpenCV and supported by
// Inference Engine. The main difference is that they do not perform forward pass.
class NgraphBackendLayer : public Layer
{
public:
NgraphBackendLayer(const InferenceEngine::CNNNetwork &t_net_) : t_net(t_net_) {};
virtual bool getMemoryShapes(const std::vector<MatShape> &inputs,
const int requiredOutputs,
std::vector<MatShape> &outputs,
std::vector<MatShape> &internals) const CV_OVERRIDE;
virtual void forward(InputArrayOfArrays inputs, OutputArrayOfArrays outputs,
OutputArrayOfArrays internals) CV_OVERRIDE;
virtual bool supportBackend(int backendId) CV_OVERRIDE;
private:
InferenceEngine::CNNNetwork t_net;
};
#endif // HAVE_DNN_NGRAPH
void forwardNgraph(const std::vector<Ptr<BackendWrapper> >& outBlobsWrappers,
Ptr<BackendNode>& node, bool isAsync);
}} // namespace cv::dnn
#endif // __OPENCV_DNN_IE_NGRAPH_HPP__

18
modules/dnn/src/layers/batch_norm_layer.cpp
View File

@ -13,6 +13,8 @@ Implementation of Batch Normalization layer.
#include "layers_common.hpp"
#include "../op_halide.hpp"
#include "../op_inf_engine.hpp"
#include "../ie_ngraph.hpp"
#include <opencv2/dnn/shape_utils.hpp>
#ifdef HAVE_OPENCL
@ -156,7 +158,7 @@ public:
{
return (backendId == DNN_BACKEND_OPENCV) ||
(backendId == DNN_BACKEND_HALIDE && haveHalide()) ||
(backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine() && (preferableTarget == DNN_TARGET_CPU || dims == 4));
((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && haveInfEngine() && (preferableTarget == DNN_TARGET_CPU || dims == 4));
}
#ifdef HAVE_OPENCL
@ -363,6 +365,20 @@ public:
}
#endif // HAVE_INF_ENGINE
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs, const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
auto ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
std::vector<size_t> shape(ieInpNode->get_shape().size(), 1);
shape[1] = weights_.total();
auto weight = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape(shape), weights_.data);
auto bias = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape(shape), bias_.data);
auto scale_node = std::make_shared<ngraph::op::v1::Multiply>(ieInpNode, weight, ngraph::op::AutoBroadcastType::NUMPY);
auto scale_shift = std::make_shared<ngraph::op::v1::Add>(scale_node, bias, ngraph::op::AutoBroadcastType::NUMPY);
return Ptr<BackendNode>(new InfEngineNgraphNode(scale_shift));
}
#endif // HAVE_DNN_NGRAPH
virtual int64 getFLOPS(const std::vector<MatShape> &inputs,
const std::vector<MatShape> &outputs) const CV_OVERRIDE
{

15
modules/dnn/src/layers/blank_layer.cpp
View File

@ -41,6 +41,7 @@
//M*/
#include "../precomp.hpp"
#include "../op_inf_engine.hpp"
#include "../ie_ngraph.hpp"
namespace cv
{
@ -57,7 +58,7 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV ||
(backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine());
((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && haveInfEngine());
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,
@ -131,6 +132,18 @@ public:
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
}
#endif // HAVE_INF_ENGINE
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
ngraph::NodeVector inp{ieInpNode};
auto blank = std::make_shared<ngraph::op::Concat>(inp, 0);
return Ptr<BackendNode>(new InfEngineNgraphNode(blank));
}
#endif // HAVE_DNN_NGRAPH
};
Ptr<Layer> BlankLayer::create(const LayerParams& params)

20
modules/dnn/src/layers/concat_layer.cpp
View File

@ -44,6 +44,7 @@
#include "layers_common.hpp"
#include "../op_halide.hpp"
#include "../op_inf_engine.hpp"
#include "../ie_ngraph.hpp"
#ifdef HAVE_OPENCL
#include "opencl_kernels_dnn.hpp"
@ -105,7 +106,7 @@ public:
{
return backendId == DNN_BACKEND_OPENCV ||
(backendId == DNN_BACKEND_HALIDE && haveHalide() && axis == 1 && !padding) || // By channels
(backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine() && !padding);
((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && haveInfEngine() && !padding);
}
class ChannelConcatInvoker : public ParallelLoopBody
@ -309,6 +310,23 @@ public:
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
}
#endif // HAVE_INF_ENGINE
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
CV_Assert(inputs.size() == nodes.size());
ngraph::NodeVector inp_nodes;
for (auto& node : nodes) {
inp_nodes.push_back(node.dynamicCast<InfEngineNgraphNode>()->node);
}
InferenceEngine::DataPtr data = ngraphDataNode(inputs[0]);
auto concat = std::make_shared<ngraph::op::Concat>(inp_nodes, clamp(axis, data->getDims().size()));
return Ptr<BackendNode>(new InfEngineNgraphNode(concat));
}
#endif // HAVE_DNN_NGRAPH
};
Ptr<ConcatLayer> ConcatLayer::create(const LayerParams& params)

2
modules/dnn/src/layers/const_layer.cpp
View File

@ -26,7 +26,7 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_INFERENCE_ENGINE;
return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019;
}
virtual bool getMemoryShapes(const std::vector<MatShape> &inputs,

154
modules/dnn/src/layers/convolution_layer.cpp
View File

@ -44,6 +44,8 @@
#include "layers_common.hpp"
#include "../op_halide.hpp"
#include "../op_inf_engine.hpp"
#include "../ie_ngraph.hpp"
#include "opencv2/core/hal/hal.hpp"
#include "opencv2/core/hal/intrin.hpp"
#include <iostream>
@ -253,7 +255,7 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
if (kernel_size.size() == 3)
return preferableTarget == DNN_TARGET_CPU;
@ -528,6 +530,73 @@ public:
}
#endif // HAVE_INF_ENGINE
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> > &inputs,
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
CV_Assert_N(inputs.size() == 1, nodes.size() == 1);
auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
std::vector<size_t> dims = ieInpNode->get_shape();
CV_Assert(dims.size() == 4 || dims.size() == 5);
const int inpCn = dims[1];
const int outCn = blobs[0].size[0];
const int inpGroupCn = blobs[0].size[1];
const int group = inpCn / inpGroupCn;
std::vector<size_t> kernel_shape = getShape<size_t>(blobs[0]);
auto ieWeights = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, kernel_shape, blobs[0].data);
if (fusedWeights)
{
if (weightsMat.isContinuous())
{
ieWeights = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, kernel_shape, weightsMat.data);
}
else
{
Mat newWeights = blobs[0].reshape(1, outCn);
Mat cvWeights = weightsMat.colRange(0, newWeights.cols);
cvWeights.copyTo(newWeights);
ieWeights = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, kernel_shape, blobs[0].data);
}
}
ngraph::op::PadType pad_type = ngraph::op::PadType::EXPLICIT;
if (!padMode.empty())
pad_type = padMode == "VALID" ? ngraph::op::PadType::VALID : ngraph::op::PadType::SAME_UPPER;
std::shared_ptr<ngraph::Node> conv_node;
if (group != 1) {
conv_node = std::make_shared<ngraph::op::GroupConvolution>(
ieInpNode, ieWeights,
ngraph::Strides(strides),
ngraph::Strides(dilations),
ngraph::CoordinateDiff(std::vector<std::ptrdiff_t>(pads_begin.begin(), pads_begin.end())),
ngraph::CoordinateDiff(std::vector<std::ptrdiff_t>(pads_end.begin(), pads_end.end())),
ngraph::Strides{},
group,
pad_type);
} else {
conv_node = std::make_shared<ngraph::op::v1::Convolution>(
ieInpNode, ieWeights,
ngraph::Strides(strides),
ngraph::CoordinateDiff(std::vector<std::ptrdiff_t>(pads_begin.begin(), pads_begin.end())),
ngraph::CoordinateDiff(std::vector<std::ptrdiff_t>(pads_end.begin(), pads_end.end())),
ngraph::Strides(dilations),
pad_type);
}
if (hasBias() || fusedBias)
{
std::vector<size_t> shape(conv_node->get_shape().size(), 1);
shape[1] = outCn;
auto bias = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape(shape), biasvec.data());
auto conv_bias = std::make_shared<ngraph::op::v1::Add>(conv_node, bias, ngraph::op::AutoBroadcastType::NUMPY);
return Ptr<BackendNode>(new InfEngineNgraphNode(conv_bias));
}
return Ptr<BackendNode>(new InfEngineNgraphNode(conv_node));
}
#endif // HAVE_DNN_NGRAPH
class ParallelConv : public cv::ParallelLoopBody
{
public:
@ -1251,7 +1320,24 @@ public:
const int outGroupCn = blobs[0].size[1]; // Weights are in IOHW or IODHW layout
const int group = numOutput / outGroupCn;
if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) {
if (padMode.empty()) {
for (int i = 0; i < adjust_pads.size(); i++) {
if (pads_end[i] < adjust_pads[i])
return false;
}
} else if (padMode == "SAME") {
for (int i = 0; i < adjust_pads.size(); i++) {
if (kernel_size[i] < pads_begin[i] + 1 + adjust_pads[i])
return false;
}
} else if (padMode == "VALID")
return false;
return group == 1;
}
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (kernel_size.size() == 3 && preferableTarget != DNN_TARGET_CPU) {
return false;
@ -1932,6 +2018,70 @@ public:
}
#endif // HAVE_INF_ENGINE
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> > &inputs,
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
const int outGroupCn = blobs[0].size[1];
const int group = numOutput / outGroupCn;
CV_Assert(group == 1);
auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
std::vector<size_t> kernel_shape = getShape<size_t>(blobs[0]);
auto ieWeights = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, kernel_shape, blobs[0].data);
if (fusedWeights)
{
int inpCn = blobs[0].size[0];
Mat newWeights = blobs[0].reshape(1, inpCn);
transpose(weightsMat, newWeights);
}
size_t batch = ieInpNode->get_shape()[0];
std::vector<size_t> out_shape = {batch, (size_t)numOutput};
std::vector<size_t> paddings_end;
std::vector<size_t> inpShape = ieInpNode->get_shape();
if (padMode.empty())
{
for (int i = 0; i < pads_end.size(); i++) {
out_shape.push_back(strides[i] * (inpShape[2 + i] - 1) +
kernel_size[i] - pads_begin[i] - pads_end[i] + adjust_pads[i]);
paddings_end.push_back(pads_end[i] - adjust_pads[i]);
}
}
else if (padMode == "SAME")
{
for (int i = 0; i < pads_begin.size(); i++) {
out_shape.push_back(strides[i] * (inpShape[2 + i] - 1) + 1 + adjust_pads[i]);
paddings_end.push_back(kernel_size[i] - pads_begin[i] - 1 - adjust_pads[i]);
}
} else {
paddings_end = pads_end;
}
auto deconv = std::make_shared<ngraph::op::ConvolutionBackpropData>(
ngraph::Shape{out_shape},
ieWeights,
ieInpNode,
ngraph::Strides(strides),
ngraph::Strides(dilations),
ngraph::CoordinateDiff(std::vector<std::ptrdiff_t>(pads_begin.begin(), pads_begin.end())),
ngraph::CoordinateDiff(std::vector<std::ptrdiff_t>(paddings_end.begin(), paddings_end.end())),
(strides.size() == 2 ? ngraph::Strides{1, 1} : ngraph::Strides{1, 1, 1}));
if (hasBias() || fusedBias)
{
std::vector<size_t> shape(deconv->get_shape().size(), 1);
shape[1] = numOutput;
auto bias = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape(shape), blobs[1].data);
auto deconv_bias = std::make_shared<ngraph::op::v1::Add>(deconv, bias, ngraph::op::AutoBroadcastType::NUMPY);
return Ptr<BackendNode>(new InfEngineNgraphNode(deconv_bias));
}
return Ptr<BackendNode>(new InfEngineNgraphNode(deconv));
}
#endif // HAVE_DNN_NGRAPH
virtual int64 getFLOPS(const std::vector<MatShape> &inputs,
const std::vector<MatShape> &outputs) const CV_OVERRIDE
{

68
modules/dnn/src/layers/detection_output_layer.cpp
View File

@ -43,6 +43,7 @@
#include "../precomp.hpp"
#include "layers_common.hpp"
#include "../op_inf_engine.hpp"
#include <float.h>
#include <string>
#include "../nms.inl.hpp"
@ -51,6 +52,41 @@
#include "opencl_kernels_dnn.hpp"
#endif
#ifdef HAVE_DNN_NGRAPH
#include "../ie_ngraph.hpp"
#include <ngraph/op/experimental/layers/detection_output.hpp>
namespace ngraph {
namespace op {
class Dummy : public Op {
public:
Dummy() : Op("Dummy", {}) {
constructor_validate_and_infer_types();
}
void validate_and_infer_types() override {
set_output_type(0, ngraph::element::Type(), {});
}
std::shared_ptr<Node> copy_with_new_args(const NodeVector& new_args) const override {
if (!new_args.empty())
throw ngraph_error("Incorrect number of new arguments");
return std::make_shared<Dummy>();
}
static constexpr NodeTypeInfo type_info{"Dummy", 1};
const NodeTypeInfo& get_type_info() const override {
return type_info;
}
};
constexpr NodeTypeInfo Dummy::type_info;
} // namespace op
} // namespace ngraph
#endif
namespace cv
{
namespace dnn
@ -198,7 +234,7 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV ||
(backendId == DNN_BACKEND_INFERENCE_ENGINE && !_locPredTransposed && _bboxesNormalized);
((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && !_locPredTransposed && _bboxesNormalized);
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,
@ -941,6 +977,36 @@ public:
return Ptr<BackendNode>(new InfEngineBackendNode(l));
}
#endif // HAVE_INF_ENGINE
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs, const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
CV_Assert(nodes.size() == 3);
auto& box_logits = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
auto& class_preds = nodes[1].dynamicCast<InfEngineNgraphNode>()->node;
auto& proposals = nodes[2].dynamicCast<InfEngineNgraphNode>()->node;
ngraph::op::DetectionOutputAttrs attrs;
attrs.num_classes = _numClasses;
attrs.background_label_id = _backgroundLabelId;
attrs.top_k = _topK > 0 ? _topK : _keepTopK;
attrs.variance_encoded_in_target = _varianceEncodedInTarget;
attrs.keep_top_k = {_keepTopK};
attrs.nms_threshold = _nmsThreshold;
attrs.confidence_threshold = _confidenceThreshold;
attrs.share_location = _shareLocation;
attrs.clip_before_nms = _clip;
attrs.code_type = std::string{"caffe.PriorBoxParameter." + _codeType};
attrs.normalized = true;
auto aux_class_preds = std::make_shared<ngraph::op::Dummy>();
auto aux_box_preds = std::make_shared<ngraph::op::Dummy>();
auto det_out = std::make_shared<ngraph::op::DetectionOutput>(box_logits, class_preds,
proposals, aux_class_preds, aux_box_preds, attrs);
return Ptr<BackendNode>(new InfEngineNgraphNode(det_out));
}
#endif // HAVE_DNN_NGRAPH
};
float util::caffe_box_overlap(const util::NormalizedBBox& a, const util::NormalizedBBox& b)

127
modules/dnn/src/layers/elementwise_layers.cpp
View File

@ -44,6 +44,8 @@
#include "layers_common.hpp"
#include "../op_halide.hpp"
#include "../op_inf_engine.hpp"
#include "../ie_ngraph.hpp"
#include <opencv2/dnn/shape_utils.hpp>
#include <iostream>
@ -158,6 +160,15 @@ public:
}
#endif // HAVE_INF_ENGINE
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs, const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
auto node = func.initNgraphAPI(ieInpNode);
return Ptr<BackendNode>(new InfEngineNgraphNode(node));
}
#endif // HAVE_DNN_NGRAPH
virtual bool tryFuse(Ptr<dnn::Layer>& top) CV_OVERRIDE
{
return func.tryFuse(top);
@ -249,8 +260,10 @@ struct ReLUFunctor
bool supportBackend(int backendId, int)
{
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
return slope >= 0 || !INF_ENGINE_VER_MAJOR_EQ(INF_ENGINE_RELEASE_2019R1);
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
return true;
#endif
return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_HALIDE;
}
@ -352,6 +365,17 @@ struct ReLUFunctor
}
#endif // HAVE_INF_ENGINE
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
{
if (slope) {
auto param = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{1}, &slope);
return std::make_shared<ngraph::op::PRelu>(node, param);
}
return std::make_shared<ngraph::op::Relu>(node);
}
#endif // HAVE_DNN_NGRAPH
bool tryFuse(Ptr<dnn::Layer>&) { return false; }
void getScaleShift(Mat&, Mat&) const {}
@ -373,7 +397,7 @@ struct ReLU6Functor
bool supportBackend(int backendId, int)
{
return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_HALIDE ||
backendId == DNN_BACKEND_INFERENCE_ENGINE;
backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH;
}
void apply(const float* srcptr, float* dstptr, int len, size_t planeSize, int cn0, int cn1) const
@ -455,6 +479,13 @@ struct ReLU6Functor
}
#endif // HAVE_INF_ENGINE
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
{
return std::make_shared<ngraph::op::Clamp>(node, minValue, maxValue);
}
#endif // HAVE_DNN_NGRAPH
bool tryFuse(Ptr<dnn::Layer>&) { return false; }
void getScaleShift(Mat&, Mat&) const {}
@ -469,7 +500,7 @@ struct TanHFunctor
bool supportBackend(int backendId, int)
{
return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_HALIDE ||
backendId == DNN_BACKEND_INFERENCE_ENGINE;
backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH;
}
void apply(const float* srcptr, float* dstptr, int len, size_t planeSize, int cn0, int cn1) const
@ -527,6 +558,13 @@ struct TanHFunctor
}
#endif // HAVE_INF_ENGINE
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
{
return std::make_shared<ngraph::op::Tanh>(node);
}
#endif // HAVE_DNN_NGRAPH
bool tryFuse(Ptr<dnn::Layer>&) { return false; }
void getScaleShift(Mat&, Mat&) const {}
@ -599,6 +637,13 @@ struct SwishFunctor
}
#endif // HAVE_INF_ENGINE
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
{
CV_Error(Error::StsNotImplemented, "");
}
#endif // HAVE_DNN_NGRAPH
bool tryFuse(Ptr<dnn::Layer>&) { return false; }
void getScaleShift(Mat&, Mat&) const {}
@ -672,6 +717,13 @@ struct MishFunctor
}
#endif // HAVE_INF_ENGINE
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
{
CV_Error(Error::StsNotImplemented, "");
}
#endif // HAVE_DNN_NGRAPH
bool tryFuse(Ptr<dnn::Layer>&) { return false; }
void getScaleShift(Mat&, Mat&) const {}
@ -687,7 +739,7 @@ struct SigmoidFunctor
bool supportBackend(int backendId, int)
{
return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_HALIDE ||
backendId == DNN_BACKEND_INFERENCE_ENGINE;
backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH;
}
void apply(const float* srcptr, float* dstptr, int len, size_t planeSize, int cn0, int cn1) const
@ -745,6 +797,13 @@ struct SigmoidFunctor
}
#endif // HAVE_INF_ENGINE
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
{
return std::make_shared<ngraph::op::Sigmoid>(node);
}
#endif // HAVE_DNN_NGRAPH
bool tryFuse(Ptr<dnn::Layer>&) { return false; }
void getScaleShift(Mat&, Mat&) const {}
@ -761,7 +820,7 @@ struct ELUFunctor
bool supportBackend(int backendId, int)
{
return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_HALIDE ||
backendId == DNN_BACKEND_INFERENCE_ENGINE;
backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH;
}
void apply(const float* srcptr, float* dstptr, int len, size_t planeSize, int cn0, int cn1) const
@ -819,6 +878,13 @@ struct ELUFunctor
}
#endif // HAVE_INF_ENGINE
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
{
return std::make_shared<ngraph::op::Elu>(node, 1.0);
}
#endif // HAVE_DNN_NGRAPH
bool tryFuse(Ptr<dnn::Layer>&) { return false; }
void getScaleShift(Mat&, Mat&) const {}
@ -833,7 +899,7 @@ struct AbsValFunctor
bool supportBackend(int backendId, int)
{
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
return !INF_ENGINE_VER_MAJOR_EQ(INF_ENGINE_RELEASE_2019R1);
#endif
return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_HALIDE;
@ -894,6 +960,16 @@ struct AbsValFunctor
}
#endif // HAVE_INF_ENGINE
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
{
float coeff = -0.999999f;
// float coeff = preferableTarget == DNN_TARGET_MYRIAD ? -0.999f : -0.999999f;
auto slope = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{1}, &coeff);
return std::make_shared<ngraph::op::PRelu>(node, slope);
}
#endif // HAVE_DNN_NGRAPH
bool tryFuse(Ptr<dnn::Layer>&) { return false; }
void getScaleShift(Mat&, Mat&) const {}
@ -967,6 +1043,13 @@ struct BNLLFunctor
}
#endif // HAVE_INF_ENGINE
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
{
CV_Error(Error::StsNotImplemented, "");
}
#endif // HAVE_DNN_NGRAPH
bool tryFuse(Ptr<dnn::Layer>&) { return false; }
void getScaleShift(Mat&, Mat&) const {}
@ -987,10 +1070,10 @@ struct PowerFunctor
bool supportBackend(int backendId, int targetId)
{
if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
return (targetId != DNN_TARGET_OPENCL && targetId != DNN_TARGET_OPENCL_FP16) || power == 1.0 || power == 0.5;
else
return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_HALIDE;
return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_HALIDE || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH;
}
void apply(const float* srcptr, float* dstptr, int len, size_t planeSize, int cn0, int cn1) const
@ -1077,6 +1160,22 @@ struct PowerFunctor
}
#endif // HAVE_INF_ENGINE
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
{
auto scale_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32,
ngraph::Shape{1}, &scale);
auto shift_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32,
ngraph::Shape{1}, &shift);
auto power_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32,
ngraph::Shape{1}, &power);
auto mul = std::make_shared<ngraph::op::v1::Multiply>(scale_node, node, ngraph::op::AutoBroadcastType::NUMPY);
auto scale_shift = std::make_shared<ngraph::op::v1::Add>(mul, shift_node, ngraph::op::AutoBroadcastType::NUMPY);
return std::make_shared<ngraph::op::v1::Power>(scale_shift, power_node, ngraph::op::AutoBroadcastType::NUMPY);
}
#endif // HAVE_DNN_NGRAPH
bool tryFuse(Ptr<dnn::Layer>& top)
{
if (power != 1.0f && shift != 0.0f)
@ -1122,7 +1221,7 @@ struct ChannelsPReLUFunctor
bool supportBackend(int backendId, int)
{
return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_HALIDE ||
backendId == DNN_BACKEND_INFERENCE_ENGINE;
backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH;
}
void apply(const float* srcptr, float* dstptr, int len, size_t planeSize, int cn0, int cn1) const
@ -1215,6 +1314,16 @@ struct ChannelsPReLUFunctor
}
#endif // HAVE_INF_ENGINE
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
{
const size_t numChannels = scale.total();
auto slope = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{numChannels}, scale.data);
return std::make_shared<ngraph::op::PRelu>(node, slope);
}
#endif // HAVE_DNN_NGRAPH
bool tryFuse(Ptr<dnn::Layer>&) { return false; }
void getScaleShift(Mat&, Mat&) const {}

35
modules/dnn/src/layers/eltwise_layer.cpp
View File

@ -44,6 +44,7 @@
#include "layers_common.hpp"
#include "../op_halide.hpp"
#include "../op_inf_engine.hpp"
#include "../ie_ngraph.hpp"
#ifdef HAVE_OPENCL
#include "opencl_kernels_dnn.hpp"
@ -102,8 +103,8 @@ public:
{
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_HALIDE ||
(backendId == DNN_BACKEND_INFERENCE_ENGINE && !variableChannels &&
(preferableTarget != DNN_TARGET_OPENCL || coeffs.empty()));
((((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && (preferableTarget != DNN_TARGET_OPENCL || coeffs.empty()))
|| backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && !variableChannels));
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,
@ -521,6 +522,36 @@ public:
}
#endif // HAVE_INF_ENGINE
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
auto curr_node = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
if (!coeffs.empty()) {
auto coeff = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{1}, &coeffs[0]);
curr_node = std::make_shared<ngraph::op::v1::Multiply>(curr_node, coeff, ngraph::op::AutoBroadcastType::NUMPY);
}
for (size_t i = 1; i < nodes.size(); i++)
{
auto next_node = nodes[i].dynamicCast<InfEngineNgraphNode>()->node;
if (!coeffs.empty()) {
auto coeff = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{1}, &coeffs[i]);
next_node = std::make_shared<ngraph::op::v1::Multiply>(next_node, coeff, ngraph::op::AutoBroadcastType::NUMPY);
}
switch (op) {
case SUM: curr_node = std::make_shared<ngraph::op::v1::Add>(curr_node, next_node); break;
case PROD: curr_node = std::make_shared<ngraph::op::v1::Multiply>(curr_node, next_node); break;
case DIV: curr_node = std::make_shared<ngraph::op::v1::Divide>(curr_node, next_node); break;
case MAX: curr_node = std::make_shared<ngraph::op::v1::Maximum>(curr_node, next_node); break;
default: CV_Error(Error::StsNotImplemented, "Unsupported eltwise operation");
}
}
return Ptr<BackendNode>(new InfEngineNgraphNode(curr_node));
}
#endif // HAVE_DNN_NGRAPH
virtual int64 getFLOPS(const std::vector<MatShape> &inputs,
const std::vector<MatShape> &outputs) const CV_OVERRIDE
{

32
modules/dnn/src/layers/flatten_layer.cpp
View File

@ -43,6 +43,8 @@
#include "../precomp.hpp"
#include "layers_common.hpp"
#include "../op_inf_engine.hpp"
#include "../ie_ngraph.hpp"
#include <float.h>
#include <algorithm>
#include <opencv2/dnn/shape_utils.hpp>
@ -65,7 +67,7 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV ||
(backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine());
((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && haveInfEngine());
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,
@ -176,6 +178,34 @@ public:
}
#endif // HAVE_INF_ENGINE
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
std::vector<size_t> dims = ieInpNode->get_shape();
int numAxes = dims.size();
int startAxis = clamp(_startAxis, numAxes);
int endAxis = clamp(_endAxis, numAxes);
CV_Assert(startAxis >= 0);
CV_Assert(endAxis >= startAxis && endAxis < numAxes);
int64_t flattenedDimensionSize = std::accumulate(dims.begin() + startAxis,
dims.begin() + endAxis + 1, 1, std::multiplies<size_t>());
std::vector<int64_t> outputShapeVec(dims.begin(), dims.begin() + startAxis);
outputShapeVec.push_back(flattenedDimensionSize);
outputShapeVec.insert(outputShapeVec.end(), dims.begin() + endAxis + 1, dims.end());
auto shape = std::make_shared<ngraph::op::Constant>(ngraph::element::i64,
ngraph::Shape({outputShapeVec.size()}), outputShapeVec.data());
auto reshape = std::make_shared<ngraph::op::v1::Reshape>(ieInpNode, shape, true);
return Ptr<BackendNode>(new InfEngineNgraphNode(reshape));
}
#endif // HAVE_DNN_NGRAPH
// HAVE_INF_ENGINE
int _startAxis;
int _endAxis;
};

32
modules/dnn/src/layers/fully_connected_layer.cpp
View File

@ -44,6 +44,7 @@
#include "layers_common.hpp"
#include "../op_halide.hpp"
#include "../op_inf_engine.hpp"
#include <opencv2/dnn/shape_utils.hpp>
#ifdef HAVE_OPENCL
@ -51,6 +52,10 @@
using namespace cv::dnn::ocl4dnn;
#endif
#ifdef HAVE_DNN_NGRAPH
#include "../ie_ngraph.hpp"
#endif
namespace cv
{
namespace dnn
@ -124,7 +129,7 @@ public:
{
return backendId == DNN_BACKEND_OPENCV ||
(backendId == DNN_BACKEND_HALIDE && haveHalide() && axis == 1) ||
(backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine() && axis == 1);
((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && haveInfEngine() && axis == 1);
}
virtual bool setActivation(const Ptr<ActivationLayer>& layer) CV_OVERRIDE
@ -456,6 +461,31 @@ public:
}
#endif // HAVE_INF_ENGINE
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
int batch = ieInpNode->get_shape()[0];
std::vector<size_t> data = {(size_t)batch, (size_t)blobs[0].size[1]};
auto new_shape = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{2}, data.data());
auto inp = std::make_shared<ngraph::op::v1::Reshape>(ieInpNode, new_shape, true);
std::vector<size_t> weight_shape{(size_t)blobs[0].size[0], (size_t)blobs[0].size[1]};
auto ieWeights = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, weight_shape, blobs[0].data);
auto matmul = std::make_shared<ngraph::op::MatMul>(inp, ieWeights, false, true);
if (bias) {
auto bias_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32,
ngraph::Shape{(size_t)blobs[1].size[1]}, blobs[1].data);
auto fc = std::make_shared<ngraph::op::v1::Add>(matmul, bias_node, ngraph::op::AutoBroadcastType::NUMPY);
return Ptr<BackendNode>(new InfEngineNgraphNode(fc));
}
return Ptr<BackendNode>(new InfEngineNgraphNode(matmul));
}
#endif // HAVE_DNN_NGRAPH
virtual int64 getFLOPS(const std::vector<MatShape> &inputs,
const std::vector<MatShape> &outputs) const CV_OVERRIDE
{

21
modules/dnn/src/layers/lrn_layer.cpp
View File

@ -44,6 +44,8 @@
#include "layers_common.hpp"
#include "../op_halide.hpp"
#include "../op_inf_engine.hpp"
#include "../ie_ngraph.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/dnn/shape_utils.hpp"
#include "opencv2/core/hal/hal.hpp"
@ -90,8 +92,12 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) {
return bias == (int)bias;
}
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) {
return type == CHANNEL_NRM && bias == (int)bias;
}
return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_HALIDE;
}
@ -398,6 +404,19 @@ public:
}
#endif // HAVE_INF_ENGINE
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs, const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
float alphaSize = alpha;
if (!normBySize)
alphaSize *= (type == SPATIAL_NRM ? size*size : size);
auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
auto lrn = std::make_shared<ngraph::op::LRN>(ieInpNode, (double)alphaSize, (double)beta, (double)bias, (size_t)size);
return Ptr<BackendNode>(new InfEngineNgraphNode(lrn));
}
#endif // HAVE_DNN_NGRAPH
virtual int64 getFLOPS(const std::vector<MatShape> &inputs,
const std::vector<MatShape> &outputs) const CV_OVERRIDE
{

14
modules/dnn/src/layers/mvn_layer.cpp
View File

@ -43,6 +43,8 @@
#include "../precomp.hpp"
#include "layers_common.hpp"
#include "../op_inf_engine.hpp"
#include "../ie_ngraph.hpp"
#include <opencv2/dnn/shape_utils.hpp>
#ifdef HAVE_OPENCL
@ -117,7 +119,7 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
return !zeroDev && (preferableTarget != DNN_TARGET_MYRIAD || eps <= 1e-7f);
else
#endif // HAVE_INF_ENGINE
@ -382,6 +384,16 @@ public:
}
#endif // HAVE_INF_ENGINE
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
auto mvn = std::make_shared<ngraph::op::MVN>(ieInpNode, acrossChannels, normVariance, eps);
return Ptr<BackendNode>(new InfEngineNgraphNode(mvn));
}
#endif // HAVE_DNN_NGRAPH
virtual int64 getFLOPS(const std::vector<MatShape> &inputs,
const std::vector<MatShape> &outputs) const CV_OVERRIDE
{

42
modules/dnn/src/layers/normalize_bbox_layer.cpp
View File

@ -43,6 +43,7 @@
#include "../precomp.hpp"
#include "layers_common.hpp"
#include "../op_inf_engine.hpp"
#include "../ie_ngraph.hpp"
namespace cv { namespace dnn {
@ -63,7 +64,7 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
if (pnorm != 2)
return false;
@ -308,6 +309,45 @@ public:
}
#endif // HAVE_INF_ENGINE
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
const size_t batch = ieInpNode->get_shape()[0];
const size_t numChannels = ieInpNode->get_shape()[1];
std::vector<int64_t> axes_data;
if (!acrossSpatial) {
axes_data.push_back(1);
} else {
axes_data.resize(ieInpNode->get_shape().size());
std::iota(axes_data.begin(), axes_data.end(), 0);
}
auto axes = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{axes_data.size()}, axes_data);
auto norm = std::make_shared<ngraph::op::NormalizeL2>(ieInpNode, axes, epsilon, ngraph::op::EpsMode::ADD);
CV_Assert(blobs.empty() || numChannels == blobs[0].total());
std::vector<size_t> shape(ieInpNode->get_shape().size(), 1);
shape[0] = blobs.empty() ? 1 : batch;
shape[1] = numChannels;
std::shared_ptr<ngraph::op::Constant> weight;
if (blobs.empty())
{
std::vector<float> ones(numChannels, 1);
weight = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape(shape), ones.data());
}
else
{
// weight->get_shape().size() > 1 ~> channel_shared = false
weight = std::make_shared<ngraph::op::Constant>(
ngraph::element::f32, ngraph::Shape(shape), blobs[0].data);
}
auto mul = std::make_shared<ngraph::op::v1::Multiply>(norm, weight, ngraph::op::AutoBroadcastType::NUMPY);
return Ptr<BackendNode>(new InfEngineNgraphNode(mul));
}
#endif // HAVE_DNN_NGRAPH
private:
int startAxis, endAxis;
};

27
modules/dnn/src/layers/padding_layer.cpp
View File

@ -13,6 +13,8 @@ Implementation of padding layer, which adds paddings to input blob.
#include "layers_common.hpp"
#include "../op_halide.hpp"
#include "../op_inf_engine.hpp"
#include "../ie_ngraph.hpp"
#include <vector>
namespace cv
@ -94,7 +96,7 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
return INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R1) &&
(preferableTarget != DNN_TARGET_MYRIAD ||
(dstRanges.size() == 4 && paddings[0].first == 0 && paddings[0].second == 0));
@ -207,6 +209,29 @@ public:
}
#endif
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
std::vector<int64_t> begins(paddings.size(), 0), ends(paddings.size(), 0);
for (int i = 0; i < paddings.size(); ++i)
{
begins[i] = static_cast<int64_t>(paddings[i].first);
ends[i] = static_cast<int64_t>(paddings[i].second);
}
auto padding_below = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{begins.size()}, begins.data());
auto padding_above = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{ends.size()}, ends.data());
auto pad_mode = paddingType == "constant" ? ngraph::op::PadMode::CONSTANT : ngraph::op::PadMode::REFLECT; // SYMMETRIC
auto arg_pad_value = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{}, &paddingValue);;
auto pad = paddingType == "constant" ?
std::make_shared<ngraph::op::v1::Pad>(ieInpNode, padding_below, padding_above, arg_pad_value, pad_mode) :
std::make_shared<ngraph::op::v1::Pad>(ieInpNode, padding_below, padding_above, pad_mode);
return Ptr<BackendNode>(new InfEngineNgraphNode(pad));
}
#endif
private:
std::vector<std::pair<int, int> > paddings; // Pairs pad before, pad after.
std::vector<Range> dstRanges;

15
modules/dnn/src/layers/permute_layer.cpp
View File

@ -43,6 +43,7 @@
#include "../precomp.hpp"
#include "layers_common.hpp"
#include "../op_inf_engine.hpp"
#include "../ie_ngraph.hpp"
#include <float.h>
#include <algorithm>
@ -105,7 +106,7 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV ||
(backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine());
((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && haveInfEngine());
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,
@ -379,6 +380,18 @@ public:
}
#endif // HAVE_INF_ENGINE
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
auto tr_axes = std::make_shared<ngraph::op::Constant>(ngraph::element::i64,
ngraph::Shape({_order.size()}), _order.data());
auto transpose = std::make_shared<ngraph::op::Transpose>(ieInpNode, tr_axes);
return Ptr<BackendNode>(new InfEngineNgraphNode(transpose));
}
#endif // HAVE_DNN_NGRAPH
size_t _count;
std::vector<size_t> _order;

56
modules/dnn/src/layers/pooling_layer.cpp
View File

@ -45,6 +45,13 @@
#include "opencv2/core/hal/intrin.hpp"
#include "../op_halide.hpp"
#include "../op_inf_engine.hpp"
#ifdef HAVE_DNN_NGRAPH
#include "../ie_ngraph.hpp"
#include <ngraph/op/experimental/layers/roi_pooling.hpp>
#include <ngraph/op/experimental/layers/psroi_pooling.hpp>
#endif
#include <float.h>
#include <algorithm>
#include <numeric>
@ -160,7 +167,7 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (computeMaxIdx)
return false;
@ -181,6 +188,9 @@ public:
return false;
#endif
}
else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) {
return type != STOCHASTIC;
}
else
return (kernel_size.size() == 3 && backendId == DNN_BACKEND_OPENCV && preferableTarget == DNN_TARGET_CPU) ||
((kernel_size.empty() || kernel_size.size() == 2) && (backendId == DNN_BACKEND_OPENCV ||
@ -333,6 +343,50 @@ public:
#endif // HAVE_INF_ENGINE
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
CV_Assert_N((inputs.size() == 1 && (type == MAX || type == AVE)) || inputs.size() == 2, nodes.size() == inputs.size());
auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
ngraph::op::PadType pad_type = ngraph::op::PadType::EXPLICIT;
if (!padMode.empty())
pad_type = padMode == "VALID" ? ngraph::op::PadType::VALID : ngraph::op::PadType::SAME_UPPER;
auto rounding_type = ceilMode ? ngraph::op::RoundingType::CEIL : ngraph::op::RoundingType::FLOOR;
if (type == AVE) {
auto exclude_pad = !avePoolPaddedArea;
auto ave_pool = std::make_shared<ngraph::op::v1::AvgPool>(ieInpNode, ngraph::Strides(strides),
ngraph::Shape(pads_begin), ngraph::Shape(pads_end), ngraph::Shape(kernel_size),
exclude_pad, rounding_type, pad_type);
return Ptr<BackendNode>(new InfEngineNgraphNode(ave_pool));
}
else if (type == MAX) {
auto max_pool = std::make_shared<ngraph::op::v1::MaxPool>(ieInpNode, ngraph::Strides(strides),
ngraph::Shape(pads_begin), ngraph::Shape(pads_end), ngraph::Shape(kernel_size),
rounding_type, pad_type);
return Ptr<BackendNode>(new InfEngineNgraphNode(max_pool));
}
else if (type == ROI) {
auto& coords = nodes[1].dynamicCast<InfEngineNgraphNode>()->node;
auto roi = std::make_shared<ngraph::op::ROIPooling>(ieInpNode, coords,
ngraph::Shape{(size_t)pooledSize.height, (size_t)pooledSize.width}, spatialScale, "max");
return Ptr<BackendNode>(new InfEngineNgraphNode(roi));
}
else if (type == PSROI) {
auto& coords = nodes[1].dynamicCast<InfEngineNgraphNode>()->node;
auto psroi = std::make_shared<ngraph::op::PSROIPooling>(ieInpNode, coords,
(size_t)psRoiOutChannels, (size_t)pooledSize.width, spatialScale, 1, 1, "average");
return Ptr<BackendNode>(new InfEngineNgraphNode(psroi));
}
else
CV_Error(Error::StsNotImplemented, "Unsupported pooling type");
}
#endif // HAVE_DNN_NGRAPH
class PoolingInvoker : public ParallelLoopBody
{
public:

73
modules/dnn/src/layers/prior_box_layer.cpp
View File

@ -43,6 +43,13 @@
#include "../precomp.hpp"
#include "layers_common.hpp"
#include "../op_inf_engine.hpp"
#ifdef HAVE_DNN_NGRAPH
#include "../ie_ngraph.hpp"
#include <ngraph/op/experimental/layers/prior_box.hpp>
#include <ngraph/op/experimental/layers/prior_box_clustered.hpp>
#endif
#include <float.h>
#include <algorithm>
#include <cmath>
@ -272,8 +279,12 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
#ifdef HAVE_DNN_NGRAPH
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
return _explicitSizes || _stepX == _stepY;
#endif
return backendId == DNN_BACKEND_OPENCV ||
(backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine() &&
(backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && haveInfEngine() &&
( _explicitSizes || (_minSize.size() == 1 && _maxSize.size() <= 1)));
}
@ -545,6 +556,66 @@ public:
}
#endif // HAVE_INF_ENGINE
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs, const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
CV_Assert(nodes.size() == 2);
auto layer = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
auto image = nodes[1].dynamicCast<InfEngineNgraphNode>()->node;
auto layer_shape = std::make_shared<ngraph::op::ShapeOf>(layer);
auto image_shape = std::make_shared<ngraph::op::ShapeOf>(image);
auto lower_bounds = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{1}, std::vector<int64_t>{2});
auto upper_bounds = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{1}, std::vector<int64_t>{4});
auto strides = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{1}, std::vector<int64_t>{1});
auto slice_layer = std::make_shared<ngraph::op::DynSlice>(layer_shape, lower_bounds, upper_bounds, strides);
auto slice_image = std::make_shared<ngraph::op::DynSlice>(image_shape, lower_bounds, upper_bounds, strides);
if (_explicitSizes)
{
CV_Assert_N(!_boxWidths.empty(), !_boxHeights.empty(), !_variance.empty());
CV_Assert(_boxWidths.size() == _boxHeights.size());
ngraph::op::PriorBoxClusteredAttrs attrs;
attrs.widths = _boxWidths;
attrs.heights = _boxHeights;
attrs.clip = _clip;
CV_CheckEQ(_offsetsX.size(), (size_t)1, ""); CV_CheckEQ(_offsetsY.size(), (size_t)1, ""); CV_CheckEQ(_offsetsX[0], _offsetsY[0], "");
attrs.offset = _offsetsX[0];
attrs.step_heights = _stepY;
attrs.step_widths = _stepX;
attrs.variances = _variance;
auto priorBox = std::make_shared<ngraph::op::PriorBoxClustered>(slice_layer, slice_image, attrs);
auto axis = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{1}, std::vector<int64_t>{0});
auto unsqueeze = std::make_shared<ngraph::op::Unsqueeze>(priorBox, axis);
return Ptr<BackendNode>(new InfEngineNgraphNode(unsqueeze));
}
else
{
ngraph::op::PriorBoxAttrs attrs;
attrs.min_size = _minSize;
attrs.max_size = _maxSize;
// doesn't work with empty aspectRatio
attrs.aspect_ratio = !_aspectRatios.empty()? _aspectRatios : std::vector<float>{1.0f};
attrs.clip = _clip;
attrs.flip = false;
attrs.variance = _variance;
CV_CheckEQ(_offsetsX.size(), (size_t)1, ""); CV_CheckEQ(_offsetsY.size(), (size_t)1, ""); CV_CheckEQ(_offsetsX[0], _offsetsY[0], "");
attrs.offset = _offsetsX[0];
attrs.step = _stepX;
attrs.scale_all_sizes = !_aspectRatios.empty();
auto priorBox = std::make_shared<ngraph::op::PriorBox>(slice_layer, slice_image, attrs);
auto axis = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{1}, std::vector<int64_t>{0});
auto unsqueeze = std::make_shared<ngraph::op::Unsqueeze>(priorBox, axis);
return Ptr<BackendNode>(new InfEngineNgraphNode(unsqueeze));
}
}
#endif // HAVE_DNN_NGRAPH
virtual int64 getFLOPS(const std::vector<MatShape> &inputs,
const std::vector<MatShape> &outputs) const CV_OVERRIDE
{

46
modules/dnn/src/layers/proposal_layer.cpp
View File

@ -8,6 +8,11 @@
#include "layers_common.hpp"
#include "../op_inf_engine.hpp"
#ifdef HAVE_DNN_NGRAPH
#include "../ie_ngraph.hpp"
#include <ngraph/op/experimental/layers/proposal.hpp>
#endif
namespace cv { namespace dnn {
class ProposalLayerImpl CV_FINAL : public ProposalLayer
@ -87,7 +92,7 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV ||
(backendId == DNN_BACKEND_INFERENCE_ENGINE && preferableTarget != DNN_TARGET_MYRIAD);
((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && preferableTarget != DNN_TARGET_MYRIAD);
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,
@ -348,6 +353,45 @@ public:
}
#endif // HAVE_INF_ENGINE
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
CV_Assert(nodes.size() == 3);
ngraph::op::ProposalAttrs attr;
attr.base_size = baseSize;
attr.nms_thresh = nmsThreshold;
attr.feat_stride = featStride;
attr.min_size = 16;
attr.pre_nms_topn = keepTopBeforeNMS;
attr.post_nms_topn = keepTopAfterNMS;
std::vector<float> ratiosVec(ratios.size());
for (int i = 0; i < ratios.size(); ++i)
ratiosVec[i] = ratios.get<float>(i);
attr.ratio = ratiosVec;
std::vector<float> scalesVec(scales.size());
for (int i = 0; i < scales.size(); ++i)
scalesVec[i] = scales.get<float>(i);
attr.scale = scalesVec;
auto& class_probs = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
auto& class_logits = nodes[1].dynamicCast<InfEngineNgraphNode>()->node;
auto& image_shape = nodes[2].dynamicCast<InfEngineNgraphNode>()->node;
CV_Assert_N(image_shape->get_shape().size() == 2, image_shape->get_shape().front() == 1);
auto shape = std::make_shared<ngraph::op::Constant>(ngraph::element::i64,
ngraph::Shape{1},
std::vector<int64_t>{(int64_t)image_shape->get_shape().back()});
auto reshape = std::make_shared<ngraph::op::v1::Reshape>(image_shape, shape, true);
auto proposal = std::make_shared<ngraph::op::Proposal>(class_probs, class_logits, reshape, attr);
return Ptr<BackendNode>(new InfEngineNgraphNode(proposal));
}
#endif // HAVE_DNN_NGRAPH
private:
// A first half of channels are background scores. We need only a second one.
static Mat getObjectScores(const Mat& m)

19
modules/dnn/src/layers/reorg_layer.cpp
View File

@ -42,6 +42,7 @@
#include "../precomp.hpp"
#include "../op_inf_engine.hpp"
#include <opencv2/dnn/shape_utils.hpp>
#include <opencv2/dnn/all_layers.hpp>
@ -49,6 +50,11 @@
#include "opencl_kernels_dnn.hpp"
#endif
#ifdef HAVE_DNN_NGRAPH
#include "../ie_ngraph.hpp"
#include <ngraph/op/experimental/layers/reorg_yolo.hpp>
#endif
namespace cv
{
namespace dnn
@ -135,7 +141,8 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_INFERENCE_ENGINE;
return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ||
backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH;
}
#ifdef HAVE_OPENCL
@ -187,6 +194,16 @@ public:
}
#endif // HAVE_INF_ENGINE
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> > &inputs,
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
auto reorg = std::make_shared<ngraph::op::ReorgYolo>(ieInpNode, ngraph::Strides{(size_t)reorgStride});
return Ptr<BackendNode>(new InfEngineNgraphNode(reorg));
}
#endif // HAVE_DNN_NGRAPH
virtual int64 getFLOPS(const std::vector<MatShape> &inputs,
const std::vector<MatShape> &outputs) const CV_OVERRIDE
{

19
modules/dnn/src/layers/reshape_layer.cpp
View File

@ -43,6 +43,8 @@
#include "../precomp.hpp"
#include "layers_common.hpp"
#include "../op_inf_engine.hpp"
#include "../ie_ngraph.hpp"
#include <opencv2/dnn/shape_utils.hpp>
namespace cv
@ -179,7 +181,7 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV ||
(backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine());
((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && haveInfEngine());
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,
@ -268,6 +270,21 @@ public:
}
#endif // HAVE_INF_ENGINE
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
CV_Assert(outShapes.size() == 1);
auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
std::vector<int64_t> out(outShapes[0].begin(), outShapes[0].end());
auto shape = std::make_shared<ngraph::op::Constant>(ngraph::element::i64,
ngraph::Shape{out.size()}, out.data());
auto reshape = std::make_shared<ngraph::op::v1::Reshape>(ieInpNode, shape, true);
return Ptr<BackendNode>(new InfEngineNgraphNode(reshape));
}
#endif // HAVE_DNN_NGRAPH
private:
std::vector<MatShape> outShapes;
};

61
modules/dnn/src/layers/resize_layer.cpp
View File

@ -9,6 +9,11 @@
#include "../op_inf_engine.hpp"
#include <opencv2/imgproc.hpp>
#ifdef HAVE_DNN_NGRAPH
#include "../ie_ngraph.hpp"
#include <ngraph/op/experimental/layers/interpolate.hpp>
#endif
namespace cv { namespace dnn {
class ResizeLayerImpl : public ResizeLayer
@ -52,7 +57,8 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ||
backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
return (interpolation == "nearest" && scaleWidth == scaleHeight) ||
(interpolation == "bilinear");
@ -191,6 +197,35 @@ public:
return Ptr<BackendNode>();
}
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
ngraph::op::InterpolateAttrs attrs;
attrs.pads_begin.push_back(0);
attrs.pads_end.push_back(0);
attrs.axes = ngraph::AxisSet{2, 3};
attrs.align_corners = false;
if (interpolation == "nearest") {
attrs.mode = "nearest";
attrs.antialias = false;
} else if (interpolation == "bilinear") {
attrs.mode = "linear";
} else {
CV_Error(Error::StsNotImplemented, "Unsupported interpolation: " + interpolation);
}
std::vector<int64_t> shape = {outHeight, outWidth};
auto out_shape = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{2}, shape.data());
auto interp = std::make_shared<ngraph::op::Interpolate>(ieInpNode, out_shape, attrs);
return Ptr<BackendNode>(new InfEngineNgraphNode(interp));
}
#endif // HAVE_DNN_NGRAPH
protected:
int outWidth, outHeight, zoomFactorWidth, zoomFactorHeight;
String interpolation;
@ -224,7 +259,12 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_INFERENCE_ENGINE;
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019
|| backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
return true;
#endif
return backendId == DNN_BACKEND_OPENCV;
}
virtual void finalize(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr) CV_OVERRIDE
@ -260,6 +300,23 @@ public:
}
#endif // HAVE_INF_ENGINE
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
ngraph::op::InterpolateAttrs attrs;
attrs.pads_begin.push_back(0);
attrs.pads_end.push_back(0);
attrs.axes = ngraph::AxisSet{2, 3};
attrs.mode = "linear";
std::vector<int64_t> shape = {outHeight, outWidth};
auto out_shape = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{2}, shape.data());
auto interp = std::make_shared<ngraph::op::Interpolate>(ieInpNode, out_shape, attrs);
return Ptr<BackendNode>(new InfEngineNgraphNode(interp));
}
#endif // HAVE_DNN_NGRAPH
};
Ptr<Layer> InterpLayer::create(const LayerParams& params)

32
modules/dnn/src/layers/scale_layer.cpp
View File

@ -13,6 +13,8 @@ Implementation of Scale layer.
#include "layers_common.hpp"
#include "../op_halide.hpp"
#include "../op_inf_engine.hpp"
#include "../ie_ngraph.hpp"
#include <opencv2/dnn/shape_utils.hpp>
namespace cv
@ -51,7 +53,7 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_HALIDE ||
(backendId == DNN_BACKEND_INFERENCE_ENGINE && axis == 1);
((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && axis == 1);
}
void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE
@ -222,6 +224,34 @@ public:
}
#endif // HAVE_INF_ENGINE
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs, const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
CV_Assert(!blobs.empty());
const size_t numChannels = blobs[0].total();
auto ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
std::vector<size_t> shape(ieInpNode->get_shape().size(), 1);
shape[1] = numChannels;
auto weight = hasWeights ?
std::make_shared<ngraph::op::Constant>(ngraph::element::f32,
ngraph::Shape(shape), blobs[0].data) :
std::make_shared<ngraph::op::Constant>(ngraph::element::f32,
ngraph::Shape(shape), std::vector<float>(numChannels, 1).data());
auto bias = hasBias ?
std::make_shared<ngraph::op::Constant>(ngraph::element::f32,
ngraph::Shape(shape), blobs.back().data) :
std::make_shared<ngraph::op::Constant>(ngraph::element::f32,
ngraph::Shape(shape), std::vector<float>(numChannels, 0).data());
auto scale_node = std::make_shared<ngraph::op::v1::Multiply>(ieInpNode, weight, ngraph::op::AutoBroadcastType::NUMPY);
auto scale_shift = std::make_shared<ngraph::op::v1::Add>(scale_node, bias, ngraph::op::AutoBroadcastType::NUMPY);
return Ptr<BackendNode>(new InfEngineNgraphNode(scale_shift));
}
#endif // HAVE_DNN_NGRAPH
void getScaleShift(Mat& scale, Mat& shift) const CV_OVERRIDE
{
scale = hasWeights ? blobs[0] : Mat();

33
modules/dnn/src/layers/slice_layer.cpp
View File

@ -42,6 +42,8 @@
#include "../precomp.hpp"
#include "../op_inf_engine.hpp"
#include "../ie_ngraph.hpp"
#include "layers_common.hpp"
#include <opencv2/dnn/shape_utils.hpp>
@ -112,7 +114,7 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV ||
(backendId == DNN_BACKEND_INFERENCE_ENGINE &&
((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) &&
#ifdef HAVE_INF_ENGINE
INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R1) &&
#endif
@ -316,6 +318,35 @@ public:
}
#endif
#endif
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
CV_Assert_N(nodes.size() <= 2);
auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
CV_Assert(sliceRanges[0].size() == ieInpNode->get_shape().size());
std::vector<int64_t> offsets, dims;
for (int i = 0; i < sliceRanges[0].size(); ++i)
{
offsets.push_back(sliceRanges[0][i].start);
dims.push_back(sliceRanges[0][i].end);
}
auto lower_bounds = std::make_shared<ngraph::op::Constant>(ngraph::element::i64,
ngraph::Shape{offsets.size()}, offsets.data());
auto upper_bounds = std::make_shared<ngraph::op::Constant>(ngraph::element::i64,
ngraph::Shape{dims.size()}, dims.data());
auto strides = std::make_shared<ngraph::op::Constant>(ngraph::element::i64,
ngraph::Shape{dims.size()}, std::vector<int64_t>((int64_t)dims.size(), 1));
auto slice = std::make_shared<ngraph::op::DynSlice>(ieInpNode, lower_bounds, upper_bounds,
strides, ngraph::AxisSet{}, ngraph::AxisSet{});
return Ptr<BackendNode>(new InfEngineNgraphNode(slice));
}
#endif // HAVE_DNN_NGRAPH
};
class CropLayerImpl CV_FINAL : public SliceLayerImpl

15
modules/dnn/src/layers/softmax_layer.cpp
View File

@ -44,6 +44,8 @@
#include "layers_common.hpp"
#include "../op_halide.hpp"
#include "../op_inf_engine.hpp"
#include "../ie_ngraph.hpp"
#include <algorithm>
#include <stdlib.h>
using std::max;
@ -90,7 +92,7 @@ public:
{
return backendId == DNN_BACKEND_OPENCV ||
(backendId == DNN_BACKEND_HALIDE && haveHalide() && axisRaw == 1) ||
(backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine() && !logSoftMax);
((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && haveInfEngine() && !logSoftMax);
}
#ifdef HAVE_OPENCL
@ -321,6 +323,17 @@ public:
}
#endif // HAVE_INF_ENGINE
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
int axis = clamp(axisRaw, ieInpNode->get_shape().size());
auto softmax = std::make_shared<ngraph::op::v1::Softmax>(ieInpNode, axis);
return Ptr<BackendNode>(new InfEngineNgraphNode(softmax));
}
#endif // HAVE_DNN_NGRAPH
int64 getFLOPS(const std::vector<MatShape> &inputs,
const std::vector<MatShape> &outputs) const CV_OVERRIDE
{

120
modules/dnn/src/op_inf_engine.cpp
View File

@ -21,6 +21,54 @@ namespace cv { namespace dnn {
#ifdef HAVE_INF_ENGINE
static Backend parseInferenceEngineBackendType(const cv::String& backend)
{
CV_Assert(!backend.empty());
if (backend == CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
return DNN_BACKEND_INFERENCE_ENGINE_NGRAPH;
if (backend == CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API)
return DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019;
CV_Error(Error::StsBadArg, cv::format("Unknown IE backend: %s", backend.c_str()));
}
static const char* dumpInferenceEngineBackendType(Backend backend)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
return CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH;
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
return CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API;
CV_Error(Error::StsBadArg, cv::format("Invalid backend ID for IE: %d", backend));
}
Backend& getInferenceEngineBackendTypeParam()
{
static Backend param = parseInferenceEngineBackendType(
utils::getConfigurationParameterString("OPENCV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019_TYPE",
#ifdef HAVE_NGRAPH
CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API // future: CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH
#else
CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API
#endif
)
);
return param;
}
CV__DNN_EXPERIMENTAL_NS_BEGIN
cv::String getInferenceEngineBackendType()
{
return dumpInferenceEngineBackendType(getInferenceEngineBackendTypeParam());
}
cv::String setInferenceEngineBackendType(const cv::String& newBackendType)
{
Backend newBackend = parseInferenceEngineBackendType(newBackendType);
Backend& param = getInferenceEngineBackendTypeParam();
Backend old = param;
param = newBackend;
return dumpInferenceEngineBackendType(old);
}
CV__DNN_EXPERIMENTAL_NS_END
// For networks with input layer which has an empty name, IE generates a name id[some_number].
// OpenCV lets users use an empty input name and to prevent unexpected naming,
// we can use some predefined name.
@ -161,38 +209,25 @@ private:
InferenceEngine::CNNLayer cnnLayer;
};
class InfEngineExtension : public InferenceEngine::IExtension
InferenceEngine::StatusCode InfEngineExtension::getFactoryFor(
InferenceEngine::ILayerImplFactory*& factory,
const InferenceEngine::CNNLayer* cnnLayer,
InferenceEngine::ResponseDesc* resp
) noexcept
{
public:
virtual void SetLogCallback(InferenceEngine::IErrorListener&) noexcept {}
virtual void Unload() noexcept {}
virtual void Release() noexcept {}
virtual void GetVersion(const InferenceEngine::Version*&) const noexcept {}
virtual InferenceEngine::StatusCode getPrimitiveTypes(char**&, unsigned int&,
InferenceEngine::ResponseDesc*) noexcept
{
return InferenceEngine::StatusCode::OK;
}
InferenceEngine::StatusCode getFactoryFor(InferenceEngine::ILayerImplFactory*& factory,
const InferenceEngine::CNNLayer* cnnLayer,
InferenceEngine::ResponseDesc* resp) noexcept
{
if (cnnLayer->type != kOpenCVLayersType)
return InferenceEngine::StatusCode::NOT_IMPLEMENTED;
factory = new InfEngineCustomLayerFactory(cnnLayer);
return InferenceEngine::StatusCode::OK;
}
};
if (cnnLayer->type != kOpenCVLayersType)
return InferenceEngine::StatusCode::NOT_IMPLEMENTED;
factory = new InfEngineCustomLayerFactory(cnnLayer);
return InferenceEngine::StatusCode::OK;
}
InfEngineBackendNode::InfEngineBackendNode(const InferenceEngine::Builder::Layer& _layer)
: BackendNode(DNN_BACKEND_INFERENCE_ENGINE), layer(_layer) {}
: BackendNode(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019), layer(_layer) {}
InfEngineBackendNode::InfEngineBackendNode(Ptr<Layer>& cvLayer_, std::vector<Mat*>& inputs,
std::vector<Mat>& outputs,
std::vector<Mat>& internals)
: BackendNode(DNN_BACKEND_INFERENCE_ENGINE), layer(cvLayer_->name),
: BackendNode(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019), layer(cvLayer_->name),
cvLayer(cvLayer_)
{
CV_Assert(!cvLayer->name.empty());
@ -269,7 +304,7 @@ void InfEngineBackendNet::connect(const std::vector<Ptr<BackendWrapper> >& input
#endif
}
void InfEngineBackendNet::init(int targetId)
void InfEngineBackendNet::init(Target targetId)
{
if (!hasNetOwner)
{
@ -403,7 +438,7 @@ static InferenceEngine::Layout estimateLayout(const Mat& m)
static InferenceEngine::DataPtr wrapToInfEngineDataNode(const Mat& m, const std::string& name = "")
{
std::vector<size_t> shape(&m.size[0], &m.size[0] + m.dims);
std::vector<size_t> shape = getShape<size_t>(m);
if (m.type() == CV_32F)
return InferenceEngine::DataPtr(new InferenceEngine::Data(name,
{InferenceEngine::Precision::FP32, shape, estimateLayout(m)}));
@ -429,7 +464,7 @@ InferenceEngine::Blob::Ptr wrapToInfEngineBlob(const Mat& m, const std::vector<s
InferenceEngine::Blob::Ptr wrapToInfEngineBlob(const Mat& m, InferenceEngine::Layout layout)
{
std::vector<size_t> shape(&m.size[0], &m.size[0] + m.dims);
std::vector<size_t> shape = getShape<size_t>(m);
return wrapToInfEngineBlob(m, shape, layout);
}
@ -461,14 +496,14 @@ InferenceEngine::DataPtr infEngineDataNode(const Ptr<BackendWrapper>& ptr)
}
InfEngineBackendWrapper::InfEngineBackendWrapper(int targetId, const cv::Mat& m)
: BackendWrapper(DNN_BACKEND_INFERENCE_ENGINE, targetId)
: BackendWrapper(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019, targetId)
{
dataPtr = wrapToInfEngineDataNode(m);
blob = wrapToInfEngineBlob(m, estimateLayout(m));
}
InfEngineBackendWrapper::InfEngineBackendWrapper(Ptr<BackendWrapper> wrapper)
: BackendWrapper(DNN_BACKEND_INFERENCE_ENGINE, wrapper->targetId)
: BackendWrapper(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019, wrapper->targetId)
{
Ptr<InfEngineBackendWrapper> ieWrapper = wrapper.dynamicCast<InfEngineBackendWrapper>();
CV_Assert(!ieWrapper.empty());
@ -505,7 +540,7 @@ static std::map<std::string, InferenceEngine::InferenceEnginePluginPtr>& getShar
return sharedPlugins;
}
#else
static InferenceEngine::Core& getCore()
InferenceEngine::Core& getCore()
{
static InferenceEngine::Core core;
return core;
@ -564,7 +599,11 @@ static bool detectMyriadX_()
#else
try
{
#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R3)
auto netExec = getCore().LoadNetwork(cnn, "MYRIAD", {{"VPU_PLATFORM", "VPU_2480"}});
#else
auto netExec = getCore().LoadNetwork(cnn, "MYRIAD", {{"VPU_MYRIAD_PLATFORM", "VPU_MYRIAD_2480"}});
#endif
#endif
auto infRequest = netExec.CreateInferRequest();
} catch(...) {
@ -703,7 +742,7 @@ void InfEngineBackendNet::initPlugin(InferenceEngine::CNNNetwork& net)
}
catch (const std::exception& ex)
{
CV_Error(Error::StsAssert, format("Failed to initialize Inference Engine backend: %s", ex.what()));
CV_Error(Error::StsError, format("Failed to initialize Inference Engine backend (device = %s): %s", device_name.c_str(), ex.what()));
}
}
@ -743,6 +782,7 @@ void InfEngineBackendNet::InfEngineReqWrapper::makePromises(const std::vector<Pt
void InfEngineBackendNet::forward(const std::vector<Ptr<BackendWrapper> >& outBlobsWrappers,
bool isAsync)
{
CV_LOG_DEBUG(NULL, "InfEngineBackendNet::forward(" << (isAsync ? "async" : "sync") << ")");
// Look for finished requests.
Ptr<InfEngineReqWrapper> reqWrapper;
for (auto& wrapper : infRequests)
@ -790,6 +830,8 @@ void InfEngineBackendNet::forward(const std::vector<Ptr<BackendWrapper> >& outBl
infRequestPtr->SetCompletionCallback(
[](InferenceEngine::IInferRequest::Ptr request, InferenceEngine::StatusCode status)
{
CV_LOG_DEBUG(NULL, "DNN(IE): completionCallback(" << (int)status << ")");
InfEngineReqWrapper* wrapper;
request->GetUserData((void**)&wrapper, 0);
CV_Assert(wrapper && "Internal error");
@ -915,8 +957,9 @@ bool InfEngineBackendLayer::getMemoryShapes(const std::vector<MatShape> &inputs,
bool InfEngineBackendLayer::supportBackend(int backendId)
{
CV_LOG_DEBUG(NULL, "InfEngineBackendLayer::supportBackend(" << backendId << ")");
return backendId == DNN_BACKEND_DEFAULT ||
(backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine());
(backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019);
}
void InfEngineBackendLayer::forward(InputArrayOfArrays inputs, OutputArrayOfArrays outputs,
@ -1029,7 +1072,18 @@ cv::String getInferenceEngineVPUType()
static cv::String vpu_type = getInferenceEngineVPUType_();
return vpu_type;
}
#else // HAVE_INF_ENGINE
cv::String getInferenceEngineBackendType()
{
CV_Error(Error::StsNotImplemented, "This OpenCV build doesn't include InferenceEngine support");
}
cv::String setInferenceEngineBackendType(const cv::String& newBackendType)
{
CV_UNUSED(newBackendType);
CV_Error(Error::StsNotImplemented, "This OpenCV build doesn't include InferenceEngine support");
}
cv::String getInferenceEngineVPUType()
{
CV_Error(Error::StsNotImplemented, "This OpenCV build doesn't include InferenceEngine support");

36
modules/dnn/src/op_inf_engine.hpp
View File

@ -71,6 +71,8 @@ namespace cv { namespace dnn {
#ifdef HAVE_INF_ENGINE
Backend& getInferenceEngineBackendTypeParam();
class InfEngineBackendNet
{
public:
@ -88,7 +90,7 @@ public:
bool isInitialized();
void init(int targetId);
void init(Target targetId);
void forward(const std::vector<Ptr<BackendWrapper> >& outBlobsWrappers,
bool isAsync);
@ -210,12 +212,44 @@ private:
InferenceEngine::CNNNetwork t_net;
};
class InfEngineExtension : public InferenceEngine::IExtension
{
public:
virtual void SetLogCallback(InferenceEngine::IErrorListener&) noexcept {}
virtual void Unload() noexcept {}
virtual void Release() noexcept {}
virtual void GetVersion(const InferenceEngine::Version*&) const noexcept {}
virtual InferenceEngine::StatusCode getPrimitiveTypes(char**&, unsigned int&,
InferenceEngine::ResponseDesc*) noexcept
{
return InferenceEngine::StatusCode::OK;
}
InferenceEngine::StatusCode getFactoryFor(InferenceEngine::ILayerImplFactory*& factory,
const InferenceEngine::CNNLayer* cnnLayer,
InferenceEngine::ResponseDesc* resp) noexcept;
};
CV__DNN_EXPERIMENTAL_NS_BEGIN
bool isMyriadX();
CV__DNN_EXPERIMENTAL_NS_END
InferenceEngine::Core& getCore();
template<typename T = size_t>
static inline std::vector<T> getShape(const Mat& mat)
{
std::vector<T> result(mat.dims);
for (int i = 0; i < mat.dims; i++)
result[i] = (T)mat.size[i];
return result;
}
#endif // HAVE_INF_ENGINE
bool haveInfEngine();

2
modules/dnn/src/tensorflow/tf_importer.cpp
View File

@ -1350,6 +1350,8 @@ void TFImporter::populateNet(Net dstNet)
setKSize(layerParams, layer);
setStrides(layerParams, layer);
setPadding(layerParams, layer);
// Test_TensorFlow_nets.EAST_text_detection/1, NGRAPH/CPU
layerParams.set("ceil_mode", false);
int id = dstNet.addLayer(name, "Pooling", layerParams);
layer_id[name] = id;

46
modules/dnn/test/test_backends.cpp
View File

@ -78,7 +78,7 @@ public:
{
if (outputLayer == "detection_out")
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
// Inference Engine produces detections terminated by a row which starts from -1.
out = out.reshape(1, out.total() / 7);
@ -142,7 +142,7 @@ TEST_P(DNNTestNetwork, Inception_5h)
{
applyTestTag(CV_TEST_TAG_MEMORY_512MB);
double l1 = default_l1, lInf = default_lInf;
if (backend == DNN_BACKEND_INFERENCE_ENGINE && (target == DNN_TARGET_CPU || target == DNN_TARGET_OPENCL))
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && (target == DNN_TARGET_CPU || target == DNN_TARGET_OPENCL))
{
l1 = 1.72e-5;
lInf = 8e-4;
@ -157,8 +157,10 @@ TEST_P(DNNTestNetwork, Inception_5h)
TEST_P(DNNTestNetwork, ENet)
{
applyTestTag(target == DNN_TARGET_CPU ? "" : CV_TEST_TAG_MEMORY_512MB);
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
processNet("dnn/Enet-model-best.net", "", Size(512, 512), "l367_Deconvolution",
@ -187,7 +189,7 @@ TEST_P(DNNTestNetwork, MobileNet_SSD_Caffe_Different_Width_Height)
if (backend == DNN_BACKEND_HALIDE)
applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
#endif
@ -221,13 +223,13 @@ TEST_P(DNNTestNetwork, MobileNet_SSD_v1_TensorFlow_Different_Width_Height)
if (backend == DNN_BACKEND_HALIDE)
applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
#endif
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2019020000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE, CV_TEST_TAG_DNN_SKIP_IE_2019R2);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
Mat sample = imread(findDataFile("dnn/street.png"));
@ -276,9 +278,9 @@ TEST_P(DNNTestNetwork, OpenPose_pose_coco)
if (backend == DNN_BACKEND_HALIDE)
applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2018050000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_2018R5);
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
const float l1 = (target == DNN_TARGET_MYRIAD) ? 0.0056 : 0.0;
@ -295,9 +297,9 @@ TEST_P(DNNTestNetwork, OpenPose_pose_mpi)
if (backend == DNN_BACKEND_HALIDE)
applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2018050000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_2018R5);
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
// output range: [-0.001, 0.97]
@ -314,9 +316,9 @@ TEST_P(DNNTestNetwork, OpenPose_pose_mpi_faster_4_stages)
if (backend == DNN_BACKEND_HALIDE)
applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2018050000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_2018R5);
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
// The same .caffemodel but modified .prototxt
@ -330,8 +332,8 @@ TEST_P(DNNTestNetwork, OpenFace)
{
#if defined(INF_ENGINE_RELEASE)
#if INF_ENGINE_VER_MAJOR_EQ(2018050000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_2018R5);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
#endif
if (backend == DNN_BACKEND_HALIDE)
@ -359,13 +361,13 @@ TEST_P(DNNTestNetwork, Inception_v2_SSD_TensorFlow)
CV_TEST_TAG_DEBUG_LONG
);
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
#endif
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2019020000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE, CV_TEST_TAG_DNN_SKIP_IE_2019R2);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
if (backend == DNN_BACKEND_HALIDE)
applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
@ -404,13 +406,13 @@ TEST_P(DNNTestNetwork, FastNeuralStyle_eccv16)
if (backend == DNN_BACKEND_HALIDE)
applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
#if defined(INF_ENGINE_RELEASE)
#if INF_ENGINE_VER_MAJOR_LE(2018050000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_2018R5);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_OPENCL)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
#endif

20
modules/dnn/test/test_caffe_importer.cpp
View File

@ -112,8 +112,10 @@ TEST(Test_Caffe, read_googlenet)
TEST_P(Test_Caffe_nets, Axpy)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
String proto = _tf("axpy.prototxt");
Net net = readNetFromCaffe(proto);
@ -299,7 +301,7 @@ TEST_P(Reproducibility_MobileNet_SSD, Accuracy)
}
// There is something wrong with Reshape layer in Myriad plugin.
if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (targetId == DNN_TARGET_MYRIAD || targetId == DNN_TARGET_OPENCL_FP16)
return;
@ -628,10 +630,10 @@ TEST_P(Test_Caffe_nets, FasterRCNN_vgg16)
);
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
#endif
@ -647,9 +649,9 @@ TEST_P(Test_Caffe_nets, FasterRCNN_zf)
(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB),
CV_TEST_TAG_DEBUG_LONG
);
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
static Mat ref = (Mat_<float>(3, 7) << 0, 2, 0.90121, 120.407, 115.83, 570.586, 528.395,
0, 7, 0.988779, 469.849, 75.1756, 718.64, 186.762,
@ -664,9 +666,9 @@ TEST_P(Test_Caffe_nets, RFCN)
CV_TEST_TAG_LONG,
CV_TEST_TAG_DEBUG_VERYLONG
);
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
double scoreDiff = (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16) ? 4e-3 : default_l1;
double iouDiff = (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16) ? 8e-2 : default_lInf;

60
modules/dnn/test/test_common.hpp
View File

@ -11,9 +11,19 @@
#include "opencv2/core/ocl.hpp"
#endif
// 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))
#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_NN_BUILDER "dnn_skip_ie_nn_builder"
#define CV_TEST_TAG_DNN_SKIP_IE_NGRAPH "dnn_skip_ie_ngraph"
#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"
@ -27,6 +37,26 @@
#define CV_TEST_TAG_DNN_SKIP_IE_MYRIAD CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_2, CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X
#ifdef HAVE_INF_ENGINE
#if INF_ENGINE_VER_MAJOR_EQ(2018050000)
# define CV_TEST_TAG_DNN_SKIP_IE_VERSION CV_TEST_TAG_DNN_SKIP_IE, CV_TEST_TAG_DNN_SKIP_IE_2018R5
#elif INF_ENGINE_VER_MAJOR_EQ(2019010000)
# if INF_ENGINE_RELEASE < 2019010100
# define CV_TEST_TAG_DNN_SKIP_IE_VERSION CV_TEST_TAG_DNN_SKIP_IE, CV_TEST_TAG_DNN_SKIP_IE_2019R1
# else
# define CV_TEST_TAG_DNN_SKIP_IE_VERSION CV_TEST_TAG_DNN_SKIP_IE, CV_TEST_TAG_DNN_SKIP_IE_2019R1_1
# endif
#elif INF_ENGINE_VER_MAJOR_EQ(2019020000)
# define CV_TEST_TAG_DNN_SKIP_IE_VERSION CV_TEST_TAG_DNN_SKIP_IE, CV_TEST_TAG_DNN_SKIP_IE_2019R2
#elif INF_ENGINE_VER_MAJOR_EQ(2019030000)
# define CV_TEST_TAG_DNN_SKIP_IE_VERSION CV_TEST_TAG_DNN_SKIP_IE, CV_TEST_TAG_DNN_SKIP_IE_2019R3
#endif
#endif // HAVE_INF_ENGINE
#ifndef CV_TEST_TAG_DNN_SKIP_IE_VERSION
# define CV_TEST_TAG_DNN_SKIP_IE_VERSION CV_TEST_TAG_DNN_SKIP_IE
#endif
namespace cv { namespace dnn {
CV__DNN_EXPERIMENTAL_NS_BEGIN
@ -86,9 +116,12 @@ bool validateVPUType();
testing::internal::ParamGenerator< tuple<Backend, Target> > dnnBackendsAndTargets(
bool withInferenceEngine = true,
bool withHalide = false,
bool withCpuOCV = true
bool withCpuOCV = true,
bool withNgraph = true
);
testing::internal::ParamGenerator< tuple<Backend, Target> > dnnBackendsAndTargetsIE();
class DNNTestLayer : public TestWithParam<tuple<Backend, Target> >
{
@ -120,7 +153,8 @@ public:
static void checkBackend(int backend, int target, Mat* inp = 0, Mat* ref = 0)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
&& target == DNN_TARGET_MYRIAD)
{
if (inp && ref && inp->dims == 4 && ref->dims == 4 &&
inp->size[0] != 1 && inp->size[0] != ref->size[0])
@ -131,7 +165,7 @@ public:
}
}
void expectNoFallbacks(Net& net)
void expectNoFallbacks(Net& net, bool raiseError = true)
{
// Check if all the layers are supported with current backend and target.
// Some layers might be fused so their timings equal to zero.
@ -140,20 +174,27 @@ public:
std::vector<String> names = net.getLayerNames();
CV_Assert(names.size() == timings.size());
bool hasFallbacks = false;
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");
{
hasFallbacks = true;
std::cout << "FALLBACK: Layer [" << l->type << "]:[" << l->name << "] is expected to has backend implementation" << endl;
}
}
if (hasFallbacks && raiseError)
CV_Error(Error::StsNotImplemented, "Implementation fallbacks are not expected in this test");
}
void expectNoFallbacksFromIE(Net& net)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
expectNoFallbacks(net);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
expectNoFallbacks(net, false);
}
protected:
@ -166,11 +207,4 @@ protected:
} // 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

69
modules/dnn/test/test_common.impl.hpp
View File

@ -21,10 +21,13 @@ 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;
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_INFERENCE_ENGINE_NN_BUILDER_2019: *os << "DLIE"; return;
case DNN_BACKEND_INFERENCE_ENGINE_NGRAPH: *os << "NGRAPH"; return;
default: /* do nothing */;
} // don't use "default:" to emit compiler warnings
*os << "DNN_BACKEND_UNKNOWN(" << (int)v << ")";
}
@ -179,7 +182,8 @@ void readFileContent(const std::string& filename, CV_OUT std::vector<char>& cont
testing::internal::ParamGenerator< tuple<Backend, Target> > dnnBackendsAndTargets(
bool withInferenceEngine /*= true*/,
bool withHalide /*= false*/,
bool withCpuOCV /*= true*/
bool withCpuOCV /*= true*/,
bool withNgraph /*= true*/
)
{
#ifdef HAVE_INF_ENGINE
@ -197,14 +201,25 @@ testing::internal::ParamGenerator< tuple<Backend, Target> > dnnBackendsAndTarget
#ifdef HAVE_INF_ENGINE
if (withInferenceEngine)
{
available = getAvailableTargets(DNN_BACKEND_INFERENCE_ENGINE);
available = getAvailableTargets(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019);
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));
targets.push_back(make_tuple(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019, *i));
}
}
if (withNgraph)
{
available = getAvailableTargets(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
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_NGRAPH, *i));
}
}
#else
CV_UNUSED(withInferenceEngine);
#endif
@ -222,6 +237,40 @@ testing::internal::ParamGenerator< tuple<Backend, Target> > dnnBackendsAndTarget
return testing::ValuesIn(targets);
}
testing::internal::ParamGenerator< tuple<Backend, Target> > dnnBackendsAndTargetsIE()
{
#ifdef HAVE_INF_ENGINE
bool withVPU = validateVPUType();
std::vector< tuple<Backend, Target> > targets;
std::vector< Target > available;
{
available = getAvailableTargets(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019);
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_NN_BUILDER_2019, *i));
}
}
{
available = getAvailableTargets(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
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_NGRAPH, *i));
}
}
return testing::ValuesIn(targets);
#else
return testing::ValuesIn(std::vector< tuple<Backend, Target> >());
#endif
}
#ifdef HAVE_INF_ENGINE
static std::string getTestInferenceEngineVPUType()
@ -306,6 +355,7 @@ void initDNNTests()
);
#if defined(INF_ENGINE_RELEASE)
registerGlobalSkipTag(
CV_TEST_TAG_DNN_SKIP_IE,
#if INF_ENGINE_VER_MAJOR_EQ(2018050000)
CV_TEST_TAG_DNN_SKIP_IE_2018R5,
#elif INF_ENGINE_VER_MAJOR_EQ(2019010000)
@ -318,7 +368,10 @@ void initDNNTests()
#elif INF_ENGINE_VER_MAJOR_EQ(2019030000)
CV_TEST_TAG_DNN_SKIP_IE_2019R3,
#endif
CV_TEST_TAG_DNN_SKIP_IE
#ifdef HAVE_DNN_NGRAPH
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH,
#endif
CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER
);
#endif
registerGlobalSkipTag(

40
modules/dnn/test/test_darknet_importer.cpp
View File

@ -261,11 +261,11 @@ TEST_P(Test_Darknet_nets, YoloVoc)
applyTestTag(CV_TEST_TAG_LONG, CV_TEST_TAG_MEMORY_1GB);
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
#endif
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X); // need to update check function
#endif
@ -301,7 +301,7 @@ TEST_P(Test_Darknet_nets, TinyYoloVoc)
applyTestTag(CV_TEST_TAG_MEMORY_512MB);
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X); // need to update check function
#endif
@ -331,15 +331,23 @@ TEST_P(Test_Darknet_nets, TinyYoloVoc)
#ifdef HAVE_INF_ENGINE
static const std::chrono::milliseconds async_timeout(10000);
typedef testing::TestWithParam<tuple<std::string, Target> > Test_Darknet_nets_async;
typedef testing::TestWithParam<tuple<std::string, tuple<Backend, Target> > > Test_Darknet_nets_async;
TEST_P(Test_Darknet_nets_async, Accuracy)
{
if (INF_ENGINE_VER_MAJOR_LT(2019020000))
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE);
Backend backendId = get<0>(get<1>(GetParam()));
Target targetId = get<1>(get<1>(GetParam()));
if (INF_ENGINE_VER_MAJOR_LT(2019020000) && backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
applyTestTag(CV_TEST_TAG_MEMORY_512MB);
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
std::string prefix = get<0>(GetParam());
int target = get<1>(GetParam());
if (backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
throw SkipTestException("No support for async forward");
const int numInputs = 2;
std::vector<Mat> inputs(numInputs);
@ -352,7 +360,8 @@ TEST_P(Test_Darknet_nets_async, Accuracy)
Net netSync = readNet(findDataFile("dnn/" + prefix + ".cfg"),
findDataFile("dnn/" + prefix + ".weights", false));
netSync.setPreferableTarget(target);
netSync.setPreferableBackend(backendId);
netSync.setPreferableTarget(targetId);
// Run synchronously.
std::vector<Mat> refs(numInputs);
@ -364,7 +373,8 @@ TEST_P(Test_Darknet_nets_async, Accuracy)
Net netAsync = readNet(findDataFile("dnn/" + prefix + ".cfg"),
findDataFile("dnn/" + prefix + ".weights", false));
netAsync.setPreferableTarget(target);
netAsync.setPreferableBackend(backendId);
netAsync.setPreferableTarget(targetId);
// Run asynchronously. To make test more robust, process inputs in the reversed order.
for (int i = numInputs - 1; i >= 0; --i)
@ -381,7 +391,7 @@ TEST_P(Test_Darknet_nets_async, Accuracy)
INSTANTIATE_TEST_CASE_P(/**/, Test_Darknet_nets_async, Combine(
Values("yolo-voc", "tiny-yolo-voc", "yolov3"),
ValuesIn(getAvailableTargets(DNN_BACKEND_INFERENCE_ENGINE))
dnnBackendsAndTargets()
));
#endif
@ -408,7 +418,7 @@ TEST_P(Test_Darknet_nets, YOLOv3)
std::string weights_file = "yolov3.weights";
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD &&
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD &&
getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
{
scoreDiff = 0.04;
@ -422,16 +432,16 @@ TEST_P(Test_Darknet_nets, YOLOv3)
}
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (INF_ENGINE_VER_MAJOR_LE(2018050000) && target == DNN_TARGET_OPENCL)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_2018R5);
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
else if (INF_ENGINE_VER_MAJOR_EQ(2019020000))
{
if (target == DNN_TARGET_OPENCL)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_2019R2);
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
if (target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_2019R2);
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
}
else if (target == DNN_TARGET_MYRIAD &&
getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)

58
modules/dnn/test/test_halide_layers.cpp
View File

@ -165,7 +165,7 @@ TEST_P(Deconvolution, Accuracy)
Target targetId = get<1>(get<7>(GetParam()));
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE && targetId == DNN_TARGET_MYRIAD
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X
&& inChannels == 6 && outChannels == 4 && group == 1
&& kernel == Size(1, 3) && pad == Size(1, 0)
@ -278,7 +278,7 @@ TEST_P(AvePooling, Accuracy)
Target targetId = get<1>(get<4>(GetParam()));
#if defined(INF_ENGINE_RELEASE)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE && targetId == DNN_TARGET_MYRIAD
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X
&& kernel == Size(1, 1) && (stride == Size(1, 1) || stride == Size(2, 2)))
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
@ -324,29 +324,34 @@ TEST_P(MaxPooling, Accuracy)
Target targetId = get<1>(get<5>(GetParam()));
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2018050000)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE && targetId == DNN_TARGET_MYRIAD
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_MYRIAD
&& inSize == Size(7, 6) && kernel == Size(3, 2)
&& (stride == Size(1, 1) || stride == Size(2, 2))
&& (pad == Size(0, 1) || pad == Size(1, 1))
)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_2018R5);
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2018050000)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE && targetId == DNN_TARGET_MYRIAD
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_MYRIAD
&& (kernel == Size(2, 2) || kernel == Size(3, 2))
&& stride == Size(1, 1) && (pad == Size(0, 0) || pad == Size(0, 1))
)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_2018R5);
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE && targetId == DNN_TARGET_MYRIAD
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X
&& (stride == Size(1, 1) || stride == Size(2, 2))
&& (pad == Size(0, 1) || pad == Size(1, 1))
)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_2019R1, CV_TEST_TAG_DNN_SKIP_IE_2019R1_1);
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
#if defined(INF_ENGINE_RELEASE)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && stride != Size(1, 1) && pad != Size(0, 0))
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
#endif
LayerParams lp;
@ -386,7 +391,7 @@ TEST_P(FullyConnected, Accuracy)
bool hasBias = get<3>(GetParam());
Backend backendId = get<0>(get<4>(GetParam()));
Target targetId = get<1>(get<4>(GetParam()));
if (backendId == DNN_BACKEND_INFERENCE_ENGINE && (targetId == DNN_TARGET_OPENCL_FP16 ||
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && (targetId == DNN_TARGET_OPENCL_FP16 ||
(targetId == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X))) {
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
@ -447,8 +452,10 @@ INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, SoftMax, Combine(
//////////////////////////////////////////////////////////////////////////////
TEST_P(Test_Halide_layers, MaxPoolUnpool)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
LayerParams pool;
pool.set("pool", "max");
@ -555,8 +562,8 @@ TEST_P(ReLU, Accuracy)
Target targetId = get<1>(get<1>(GetParam()));
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019020000)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE && targetId == DNN_TARGET_MYRIAD && negativeSlope < 0)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_2019R3, CV_TEST_TAG_DNN_SKIP_IE_2019R2, CV_TEST_TAG_DNN_SKIP_IE);
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_MYRIAD && negativeSlope < 0)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
LayerParams lp;
@ -666,17 +673,17 @@ TEST_P(Concat, Accuracy)
Target targetId = get<1>(get<2>(GetParam()));
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2018050000)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE && targetId == DNN_TARGET_MYRIAD
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_MYRIAD
&& inSize == Vec3i(1, 4, 5) && numChannels == Vec3i(1, 6, 2)
)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_2018R5); // crash
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION); // crash
#endif
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE && targetId == DNN_TARGET_CPU
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_CPU
&& inSize == Vec3i(1, 4, 5) && numChannels == Vec3i(1, 6, 2)
)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE, CV_TEST_TAG_DNN_SKIP_IE_2019R1, CV_TEST_TAG_DNN_SKIP_IE_2019R1_1); // TODO: IE_CPU
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION); // TODO: IE_CPU
#endif
Net net;
@ -748,20 +755,25 @@ TEST_P(Eltwise, Accuracy)
Target targetId = get<1>(get<4>(GetParam()));
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2018050000)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE && targetId == DNN_TARGET_MYRIAD &&
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_MYRIAD &&
inSize == Vec3i(1, 4, 5))
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_2018R5);
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE && numConv > 1)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE, CV_TEST_TAG_DNN_SKIP_IE_2019R1, CV_TEST_TAG_DNN_SKIP_IE_2019R1_1);
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && numConv > 1)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
#if defined(INF_ENGINE_RELEASE)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE && targetId == DNN_TARGET_OPENCL &&
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_OPENCL &&
op == "sum" && numConv == 1 && !weighted)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE);
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
#endif
#if defined(INF_ENGINE_RELEASE)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && numConv > 1)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
Net net;

32
modules/dnn/test/test_ie_models.cpp
View File

@ -268,14 +268,16 @@ std::vector<String> getOutputsNames(const Net& net)
return names;
}
void runCV(Target target, const std::string& xmlPath, const std::string& binPath,
void runCV(Backend backendId, Target targetId, const std::string& xmlPath, const std::string& binPath,
const std::map<std::string, cv::Mat>& inputsMap,
std::map<std::string, cv::Mat>& outputsMap)
{
Net net = readNet(xmlPath, binPath);
for (auto& it : inputsMap)
net.setInput(it.second, it.first);
net.setPreferableTarget(target);
net.setPreferableBackend(backendId);
net.setPreferableTarget(targetId);
std::vector<String> outNames = getOutputsNames(net);
std::vector<Mat> outs;
@ -289,14 +291,26 @@ void runCV(Target target, const std::string& xmlPath, const std::string& binPath
}
}
typedef TestWithParam<tuple<Target, std::string> > DNNTestOpenVINO;
typedef TestWithParam<tuple< tuple<Backend, Target>, std::string> > DNNTestOpenVINO;
TEST_P(DNNTestOpenVINO, models)
{
initDLDTDataPath();
Target target = (dnn::Target)(int)get<0>(GetParam());
const Backend backendId = get<0>(get<0>(GetParam()));
const Target targetId = get<1>(get<0>(GetParam()));
if (backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
throw SkipTestException("No support for async forward");
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API);
else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
else
FAIL() << "Unknown backendId";
std::string modelName = get<1>(GetParam());
bool isFP16 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD);
bool isFP16 = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD);
const std::map<std::string, OpenVINOModelTestCaseInfo>& models = getOpenVINOTestModels();
const auto it = models.find(modelName);
@ -310,10 +324,10 @@ TEST_P(DNNTestOpenVINO, models)
std::map<std::string, cv::Mat> inputsMap;
std::map<std::string, cv::Mat> ieOutputsMap, cvOutputsMap;
// Single Myriad device cannot be shared across multiple processes.
if (target == DNN_TARGET_MYRIAD)
if (targetId == DNN_TARGET_MYRIAD)
resetMyriadDevice();
runIE(target, xmlPath, binPath, inputsMap, ieOutputsMap);
runCV(target, xmlPath, binPath, inputsMap, cvOutputsMap);
runIE(targetId, xmlPath, binPath, inputsMap, ieOutputsMap);
runCV(backendId, targetId, xmlPath, binPath, inputsMap, cvOutputsMap);
EXPECT_EQ(ieOutputsMap.size(), cvOutputsMap.size());
for (auto& srcIt : ieOutputsMap)
@ -328,7 +342,7 @@ TEST_P(DNNTestOpenVINO, models)
INSTANTIATE_TEST_CASE_P(/**/,
DNNTestOpenVINO,
Combine(testing::ValuesIn(getAvailableTargets(DNN_BACKEND_INFERENCE_ENGINE)),
Combine(dnnBackendsAndTargetsIE(),
testing::ValuesIn(getOpenVINOTestModelsList())
)
);

175
modules/dnn/test/test_layers.cpp
View File

@ -146,8 +146,11 @@ TEST_P(Test_Caffe_layers, DeConvolution)
TEST_P(Test_Caffe_layers, InnerProduct)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
testLayerUsingCaffeModels("layer_inner_product", true);
@ -235,12 +238,20 @@ TEST_P(Test_Caffe_layers, Concat)
{
#if defined(INF_ENGINE_RELEASE)
#if INF_ENGINE_VER_MAJOR_GE(2019010000) && INF_ENGINE_VER_MAJOR_LT(2019020000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_2019R1, CV_TEST_TAG_DNN_SKIP_IE_2019R1_1);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#elif INF_ENGINE_VER_MAJOR_EQ(2019020000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_2019R2);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 &&
(target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16,
CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH &&
(target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
testLayerUsingCaffeModels("layer_concat");
testLayerUsingCaffeModels("layer_concat_optim", true, false);
@ -249,8 +260,9 @@ TEST_P(Test_Caffe_layers, Concat)
TEST_P(Test_Caffe_layers, Fused_Concat)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16,
CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
checkBackend();
@ -294,14 +306,15 @@ TEST_P(Test_Caffe_layers, Fused_Concat)
TEST_P(Test_Caffe_layers, Eltwise)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
testLayerUsingCaffeModels("layer_eltwise");
}
TEST_P(Test_Caffe_layers, PReLU)
{
testLayerUsingCaffeModels("layer_prelu", true);
double lInf = (target == DNN_TARGET_MYRIAD || target == DNN_TARGET_OPENCL_FP16) ? 0.021 : 0.0;
testLayerUsingCaffeModels("layer_prelu", true, true, 0.0, lInf);
}
// TODO: fix an unstable test case
@ -317,8 +330,10 @@ TEST_P(Test_Caffe_layers, layer_prelu_fc)
TEST_P(Test_Caffe_layers, Reshape_Split_Slice)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
Net net = readNetFromCaffe(_tf("reshape_and_slice_routines.prototxt"));
ASSERT_FALSE(net.empty());
@ -339,8 +354,8 @@ TEST_P(Test_Caffe_layers, Reshape_Split_Slice)
TEST_P(Test_Caffe_layers, Conv_Elu)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE <= 2018050000
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_2018R5);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
Net net = readNetFromTensorflow(_tf("layer_elu_model.pb"));
@ -553,29 +568,38 @@ TEST_F(Layer_RNN_Test, get_set_test)
EXPECT_EQ(shape(outputs[1]), shape(nT, nS, nH));
}
TEST(Layer_Test_ROIPooling, Accuracy)
TEST_P(Test_Caffe_layers, ROIPooling_Accuracy)
{
Net net = readNetFromCaffe(_tf("net_roi_pooling.prototxt"));
ASSERT_FALSE(net.empty());
Mat inp = blobFromNPY(_tf("net_roi_pooling.input.npy"));
Mat rois = blobFromNPY(_tf("net_roi_pooling.rois.npy"));
Mat ref = blobFromNPY(_tf("net_roi_pooling.npy"));
checkBackend(&inp, &ref);
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
net.setInput(inp, "input");
net.setInput(rois, "rois");
net.setPreferableBackend(DNN_BACKEND_OPENCV);
Mat out = net.forward();
normAssert(out, ref);
double l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 1e-3 : 1e-5;
double lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 1e-3 : 1e-4;
normAssert(out, ref, "", l1, lInf);
}
TEST_P(Test_Caffe_layers, FasterRCNN_Proposal)
{
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
Net net = readNetFromCaffe(_tf("net_faster_rcnn_proposal.prototxt"));
@ -807,16 +831,21 @@ TEST_P(Test_Caffe_layers, PriorBox_repeated)
randu(shape, -1.0f, 1.0f);
net.setInput(inp, "data");
net.setInput(shape, "shape");
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
Mat out = net.forward();
Mat ref = blobFromNPY(_tf("priorbox_output.npy"));
normAssert(out, ref, "");
double l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 1e-3 : 1e-5;
double lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 1e-3 : 1e-4;
normAssert(out, ref, "", l1, lInf);
}
// Test PriorBoxLayer in case of no aspect ratios (just squared proposals).
TEST_P(Test_Caffe_layers, PriorBox_squares)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
LayerParams lp;
lp.name = "testPriorBox";
lp.type = "PriorBox";
@ -970,10 +999,21 @@ INSTANTIATE_TEST_CASE_P(/**/, Layer_Test_DWconv_Prelu, Combine(Values(3, 6), Val
// Using Intel's Model Optimizer generate .xml and .bin files:
// ./ModelOptimizer -w /path/to/caffemodel -d /path/to/prototxt \
// -p FP32 -i -b ${batch_size} -o /path/to/output/folder
typedef testing::TestWithParam<Target> Layer_Test_Convolution_DLDT;
typedef testing::TestWithParam<tuple<Backend, Target> > Layer_Test_Convolution_DLDT;
TEST_P(Layer_Test_Convolution_DLDT, Accuracy)
{
Target targetId = GetParam();
const Backend backendId = get<0>(GetParam());
const Target targetId = get<1>(GetParam());
if (backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
throw SkipTestException("No support for async forward");
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API);
else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
else
FAIL() << "Unknown backendId";
std::string suffix = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? "_fp16" : "";
Net netDefault = readNet(_tf("layer_convolution.caffemodel"), _tf("layer_convolution.prototxt"));
@ -986,6 +1026,7 @@ TEST_P(Layer_Test_Convolution_DLDT, Accuracy)
Mat outDefault = netDefault.forward();
net.setInput(inp);
net.setPreferableBackend(backendId);
net.setPreferableTarget(targetId);
Mat out = net.forward();
@ -1001,10 +1042,22 @@ TEST_P(Layer_Test_Convolution_DLDT, Accuracy)
TEST_P(Layer_Test_Convolution_DLDT, setInput_uint8)
{
Target targetId = GetParam();
Mat inp = blobFromNPY(_tf("blob.npy"));
const Backend backendId = get<0>(GetParam());
const Target targetId = get<1>(GetParam());
if (backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
throw SkipTestException("No support for async forward");
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API);
else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
else
FAIL() << "Unknown backendId";
int blobSize[] = {2, 6, 75, 113};
Mat inputs[] = {Mat(4, &blobSize[0], CV_8U), Mat()};
Mat inputs[] = {Mat(inp.dims, inp.size, CV_8U), Mat()};
randu(inputs[0], 0, 255);
inputs[0].convertTo(inputs[1], CV_32F);
@ -1014,6 +1067,7 @@ TEST_P(Layer_Test_Convolution_DLDT, setInput_uint8)
for (int i = 0; i < 2; ++i)
{
Net net = readNet(_tf("layer_convolution" + suffix + ".xml"), _tf("layer_convolution" + suffix + ".bin"));
net.setPreferableBackend(backendId);
net.setPreferableTarget(targetId);
net.setInput(inputs[i]);
outs[i] = net.forward();
@ -1025,7 +1079,19 @@ TEST_P(Layer_Test_Convolution_DLDT, setInput_uint8)
TEST_P(Layer_Test_Convolution_DLDT, multithreading)
{
Target targetId = GetParam();
const Backend backendId = get<0>(GetParam());
const Target targetId = get<1>(GetParam());
if (backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
throw SkipTestException("No support for async forward");
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API);
else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
else
FAIL() << "Unknown backendId";
std::string suffix = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? "_fp16" : "";
std::string xmlPath = _tf("layer_convolution" + suffix + ".xml");
std::string binPath = _tf("layer_convolution" + suffix + ".bin");
@ -1035,7 +1101,9 @@ TEST_P(Layer_Test_Convolution_DLDT, multithreading)
firstNet.setInput(inp);
secondNet.setInput(inp);
firstNet.setPreferableBackend(backendId);
firstNet.setPreferableTarget(targetId);
secondNet.setPreferableBackend(backendId);
secondNet.setPreferableTarget(targetId);
Mat out1, out2;
@ -1053,7 +1121,8 @@ TEST_P(Layer_Test_Convolution_DLDT, multithreading)
}
INSTANTIATE_TEST_CASE_P(/**/, Layer_Test_Convolution_DLDT,
testing::ValuesIn(getAvailableTargets(DNN_BACKEND_INFERENCE_ENGINE)));
dnnBackendsAndTargetsIE()
);
// 1. Create a .prototxt file with the following network:
// layer {
@ -1112,17 +1181,18 @@ std::vector< std::vector<int> > list_sizes{ {1, 2, 3}, {3, 2, 1}, {5, 5, 5}, {13
INSTANTIATE_TEST_CASE_P(/*nothing*/, Test_DLDT_two_inputs_3dim, Combine(
Values(CV_8U, CV_32F), Values(CV_8U, CV_32F),
testing::ValuesIn(getAvailableTargets(DNN_BACKEND_INFERENCE_ENGINE)),
testing::ValuesIn(getAvailableTargets(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)),
testing::ValuesIn(list_sizes)
));
typedef testing::TestWithParam<tuple<int, int, Target> > Test_DLDT_two_inputs;
typedef testing::TestWithParam<tuple<int, int, tuple<Backend, Target> > > Test_DLDT_two_inputs;
TEST_P(Test_DLDT_two_inputs, as_backend)
{
static const float kScale = 0.5f;
static const float kScaleInv = 1.0f / kScale;
Target targetId = get<2>(GetParam());
Backend backendId = get<0>(get<2>(GetParam()));
Target targetId = get<1>(get<2>(GetParam()));
Net net;
LayerParams lp;
@ -1141,7 +1211,7 @@ TEST_P(Test_DLDT_two_inputs, as_backend)
net.setInputsNames({"data", "second_input"});
net.setInput(firstInp, "data", kScale);
net.setInput(secondInp, "second_input", kScaleInv);
net.setPreferableBackend(DNN_BACKEND_INFERENCE_ENGINE);
net.setPreferableBackend(backendId);
net.setPreferableTarget(targetId);
Mat out = net.forward();
@ -1155,7 +1225,7 @@ TEST_P(Test_DLDT_two_inputs, as_backend)
INSTANTIATE_TEST_CASE_P(/*nothing*/, Test_DLDT_two_inputs, Combine(
Values(CV_8U, CV_32F), Values(CV_8U, CV_32F),
testing::ValuesIn(getAvailableTargets(DNN_BACKEND_INFERENCE_ENGINE))
dnnBackendsAndTargets()
));
class UnsupportedLayer : public Layer
@ -1176,10 +1246,11 @@ public:
virtual void forward(cv::InputArrayOfArrays inputs, cv::OutputArrayOfArrays outputs, cv::OutputArrayOfArrays internals) CV_OVERRIDE {}
};
TEST(Test_DLDT, fused_output)
typedef DNNTestLayer Test_DLDT_layers;
static void test_dldt_fused_output(Backend backend, Target target)
{
static const int kNumChannels = 3;
CV_DNN_REGISTER_LAYER_CLASS(Unsupported, UnsupportedLayer);
Net net;
{
LayerParams lp;
@ -1203,13 +1274,31 @@ TEST(Test_DLDT, fused_output)
LayerParams lp;
net.addLayerToPrev("unsupported_layer", "Unsupported", lp);
}
net.setPreferableBackend(DNN_BACKEND_INFERENCE_ENGINE);
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
net.setInput(Mat({1, 1, 1, 1}, CV_32FC1, Scalar(1)));
ASSERT_NO_THROW(net.forward());
net.forward();
}
TEST_P(Test_DLDT_layers, fused_output)
{
CV_DNN_REGISTER_LAYER_CLASS(Unsupported, UnsupportedLayer);
try
{
test_dldt_fused_output(backend, target);
}
catch (const std::exception& e)
{
ADD_FAILURE() << "Exception: " << e.what();
}
catch(...)
{
ADD_FAILURE() << "Unknown exception";
}
LayerFactory::unregisterLayer("Unsupported");
}
TEST(Test_DLDT, multiple_networks)
TEST_P(Test_DLDT_layers, multiple_networks)
{
Net nets[2];
for (int i = 0; i < 2; ++i)
@ -1224,7 +1313,8 @@ TEST(Test_DLDT, multiple_networks)
lp.name = format("testConv_%d", i);
lp.blobs.push_back(Mat({1, 1, 1, 1}, CV_32F, Scalar(1 + i)));
nets[i].addLayerToPrev(lp.name, lp.type, lp);
nets[i].setPreferableBackend(DNN_BACKEND_INFERENCE_ENGINE);
nets[i].setPreferableBackend(backend);
nets[i].setPreferableTarget(target);
nets[i].setInput(Mat({1, 1, 1, 1}, CV_32FC1, Scalar(1)));
}
Mat out_1 = nets[0].forward();
@ -1233,6 +1323,9 @@ TEST(Test_DLDT, multiple_networks)
out_1 = nets[0].forward();
normAssert(2 * out_1, out_2);
}
INSTANTIATE_TEST_CASE_P(/*nothing*/, Test_DLDT_layers, dnnBackendsAndTargets());
#endif // HAVE_INF_ENGINE
// Test a custom layer.
@ -1348,7 +1441,7 @@ TEST_P(Test_Caffe_layers, Interp)
TEST_P(Test_Caffe_layers, DISABLED_Interp) // requires patched protobuf (available in OpenCV source tree only)
#endif
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
// Test a custom layer.

174
modules/dnn/test/test_misc.cpp
View File

@ -367,21 +367,34 @@ static const std::chrono::milliseconds async_timeout(10000);
// This test runs network in synchronous mode for different inputs and then
// runs the same model asynchronously for the same inputs.
typedef testing::TestWithParam<tuple<int, Target> > Async;
TEST_P(Async, set_and_forward_single)
typedef testing::TestWithParam<tuple<int, tuple<Backend, Target> > > Async;
TEST_P(Async, model_optimizer_pipeline_set_and_forward_single)
{
const int dtype = get<0>(GetParam());
const int target = get<1>(GetParam());
const Backend backendId = get<0>(get<1>(GetParam()));
const Target targetId = get<1>(get<1>(GetParam()));
const std::string suffix = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? "_fp16" : "";
if (backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
throw SkipTestException("No support for async forward");
const std::string suffix = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? "_fp16" : "";
const std::string& model = findDataFile("dnn/layers/layer_convolution" + suffix + ".bin");
const std::string& proto = findDataFile("dnn/layers/layer_convolution" + suffix + ".xml");
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API);
else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
else
FAIL() << "Unknown backendId";
Net netSync = readNet(model, proto);
netSync.setPreferableTarget(target);
netSync.setPreferableBackend(backendId);
netSync.setPreferableTarget(targetId);
Net netAsync = readNet(model, proto);
netAsync.setPreferableTarget(target);
netAsync.setPreferableBackend(backendId);
netAsync.setPreferableTarget(targetId);
// Generate inputs.
const int numInputs = 10;
@ -414,21 +427,33 @@ TEST_P(Async, set_and_forward_single)
}
}
TEST_P(Async, set_and_forward_all)
TEST_P(Async, model_optimizer_pipeline_set_and_forward_all)
{
const int dtype = get<0>(GetParam());
const int target = get<1>(GetParam());
const Backend backendId = get<0>(get<1>(GetParam()));
const Target targetId = get<1>(get<1>(GetParam()));
const std::string suffix = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? "_fp16" : "";
if (backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
throw SkipTestException("No support for async forward");
const std::string suffix = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? "_fp16" : "";
const std::string& model = findDataFile("dnn/layers/layer_convolution" + suffix + ".bin");
const std::string& proto = findDataFile("dnn/layers/layer_convolution" + suffix + ".xml");
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API);
else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
else
FAIL() << "Unknown backendId";
Net netSync = readNet(model, proto);
netSync.setPreferableTarget(target);
netSync.setPreferableBackend(backendId);
netSync.setPreferableTarget(targetId);
Net netAsync = readNet(model, proto);
netAsync.setPreferableTarget(target);
netAsync.setPreferableBackend(backendId);
netAsync.setPreferableTarget(targetId);
// Generate inputs.
const int numInputs = 10;
@ -465,25 +490,136 @@ TEST_P(Async, set_and_forward_all)
}
}
TEST_P(Async, create_layer_pipeline_set_and_forward_all)
{
const int dtype = get<0>(GetParam());
const Backend backendId = get<0>(get<1>(GetParam()));
const Target targetId = get<1>(get<1>(GetParam()));
if (backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
throw SkipTestException("No support for async forward");
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API);
else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
else
FAIL() << "Unknown backendId";
Net netSync;
Net netAsync;
{
int inChannels = 4;
int outChannels = 12;
int group = 3;
Size inSize(113, 75);
Size kernel(4, 5);
Size stride(2, 3);
Size pad(0, 1);
Size dilation(1, 1);
bool hasBias = true;
int sz[] = {outChannels, inChannels / group, kernel.height, kernel.width};
Mat weights(4, &sz[0], CV_32F);
randu(weights, -1.0f, 1.0f);
LayerParams lp;
lp.set("kernel_w", kernel.width);
lp.set("kernel_h", kernel.height);
lp.set("pad_w", pad.width);
lp.set("pad_h", pad.height);
lp.set("stride_w", stride.width);
lp.set("stride_h", stride.height);
lp.set("dilation_w", dilation.width);
lp.set("dilation_h", dilation.height);
lp.set("num_output", outChannels);
lp.set("group", group);
lp.set("bias_term", hasBias);
lp.type = "Convolution";
lp.name = "testLayer";
lp.blobs.push_back(weights);
if (hasBias)
{
Mat bias(1, outChannels, CV_32F);
randu(bias, -1.0f, 1.0f);
lp.blobs.push_back(bias);
}
int inpSz[] = {1, inChannels, inSize.height, inSize.width};
Mat input(4, &inpSz[0], CV_32F);
netSync.addLayerToPrev(lp.name, lp.type, lp);
netAsync.addLayerToPrev(lp.name, lp.type, lp);
}
netSync.setPreferableBackend(backendId);
netSync.setPreferableTarget(targetId);
netAsync.setPreferableBackend(backendId);
netAsync.setPreferableTarget(targetId);
// Generate inputs.
const int numInputs = 10;
std::vector<Mat> inputs(numInputs);
int blobSize[] = {1, 4, 75, 113};
for (int i = 0; i < numInputs; ++i)
{
inputs[i].create(4, &blobSize[0], dtype);
randu(inputs[i], 0, 255);
}
// Run synchronously.
std::vector<Mat> refs(numInputs);
for (int i = 0; i < numInputs; ++i)
{
netSync.setInput(inputs[i]);
refs[i] = netSync.forward().clone();
}
// Run asynchronously. To make test more robust, process inputs in the reversed order.
std::vector<AsyncArray> outs(numInputs);
for (int i = numInputs - 1; i >= 0; --i)
{
netAsync.setInput(inputs[i]);
outs[i] = netAsync.forwardAsync();
}
for (int i = numInputs - 1; i >= 0; --i)
{
ASSERT_TRUE(outs[i].valid());
Mat result;
EXPECT_TRUE(outs[i].get(result, async_timeout));
normAssert(refs[i], result, format("Index: %d", i).c_str(), 0, 0);
}
}
INSTANTIATE_TEST_CASE_P(/**/, Async, Combine(
Values(CV_32F, CV_8U),
testing::ValuesIn(getAvailableTargets(DNN_BACKEND_INFERENCE_ENGINE))
Values(CV_32F, CV_8U),
dnnBackendsAndTargetsIE()
));
typedef testing::TestWithParam<Target> Test_Model_Optimizer;
typedef testing::TestWithParam<tuple<Backend, Target> > Test_Model_Optimizer;
TEST_P(Test_Model_Optimizer, forward_two_nets)
{
const int target = GetParam();
const Backend backendId = get<0>(GetParam());
const Target targetId = get<1>(GetParam());
const std::string suffix = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? "_fp16" : "";
const std::string suffix = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? "_fp16" : "";
const std::string& model = findDataFile("dnn/layers/layer_convolution" + suffix + ".bin");
const std::string& proto = findDataFile("dnn/layers/layer_convolution" + suffix + ".xml");
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API);
else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
else
FAIL() << "Unknown backendId";
Net net0 = readNet(model, proto);
net0.setPreferableTarget(target);
net0.setPreferableTarget(targetId);
Net net1 = readNet(model, proto);
net1.setPreferableTarget(target);
net1.setPreferableTarget(targetId);
// Generate inputs.
int blobSize[] = {2, 6, 75, 113};
@ -502,7 +638,7 @@ TEST_P(Test_Model_Optimizer, forward_two_nets)
normAssert(ref0, ref2, 0, 0);
}
INSTANTIATE_TEST_CASE_P(/**/, Test_Model_Optimizer,
testing::ValuesIn(getAvailableTargets(DNN_BACKEND_INFERENCE_ENGINE))
dnnBackendsAndTargetsIE()
);
#endif // HAVE_INF_ENGINE

151
modules/dnn/test/test_onnx_importer.cpp
View File

@ -98,7 +98,7 @@ TEST_P(Test_ONNX_layers, Convolution)
TEST_P(Test_ONNX_layers, Convolution3D)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000)
throw SkipTestException("Test is enabled starts from 2019R1");
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
if (target != DNN_TARGET_CPU)
throw SkipTestException("Only CPU is supported");
@ -109,10 +109,10 @@ TEST_P(Test_ONNX_layers, Convolution3D)
TEST_P(Test_ONNX_layers, Two_convolution)
{
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X
)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
#endif
// Reference output values are in range [-0.855, 0.611]
testONNXModels("two_convolution");
@ -129,10 +129,10 @@ TEST_P(Test_ONNX_layers, Deconvolution)
TEST_P(Test_ONNX_layers, Deconvolution3D)
{
#if defined(INF_ENGINE_RELEASE)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_2018R5);
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2018050000)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
if (backend != DNN_BACKEND_INFERENCE_ENGINE || target != DNN_TARGET_CPU)
if (backend == DNN_BACKEND_OPENCV || target != DNN_TARGET_CPU)
throw SkipTestException("Only DLIE backend on CPU is supported");
testONNXModels("deconv3d");
testONNXModels("deconv3d_bias");
@ -169,6 +169,10 @@ TEST_P(Test_ONNX_layers, ReduceMean)
TEST_P(Test_ONNX_layers, ReduceMean3D)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); // Only CPU on DLIE backend is supported
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // Only CPU on DLIE backend is supported
if (target != DNN_TARGET_CPU)
throw SkipTestException("Only CPU is supported");
testONNXModels("reduce_mean3d");
@ -181,11 +185,11 @@ TEST_P(Test_ONNX_layers, MaxPooling_Sigmoid)
TEST_P(Test_ONNX_layers, Concatenation)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
if (target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL);
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
}
testONNXModels("concatenation");
}
@ -193,10 +197,12 @@ TEST_P(Test_ONNX_layers, Concatenation)
TEST_P(Test_ONNX_layers, Eltwise3D)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000)
throw SkipTestException("Test is enabled starts from 2019R1");
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_CPU)
throw SkipTestException("Only CPU on DLIE backend is supported");
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); // Only CPU on DLIE backend is supported
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // Only CPU on DLIE backend is supported
testONNXModels("eltwise3d");
}
@ -208,8 +214,12 @@ TEST_P(Test_ONNX_layers, AveragePooling)
TEST_P(Test_ONNX_layers, MaxPooling3D)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000)
throw SkipTestException("Test is enabled starts from 2019R1");
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); // Only CPU on DLIE backend is supported
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // Only CPU on DLIE backend is supported
if (target != DNN_TARGET_CPU)
throw SkipTestException("Only CPU is supported");
testONNXModels("max_pool3d", npy, 0, 0, false, false);
@ -218,8 +228,12 @@ TEST_P(Test_ONNX_layers, MaxPooling3D)
TEST_P(Test_ONNX_layers, AvePooling3D)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000)
throw SkipTestException("Test is enabled starts from 2019R1");
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); // Only CPU on DLIE backend is supported
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // Only CPU on DLIE backend is supported
if (target != DNN_TARGET_CPU)
throw SkipTestException("Only CPU is supported");
testONNXModels("ave_pool3d");
@ -228,8 +242,12 @@ TEST_P(Test_ONNX_layers, AvePooling3D)
TEST_P(Test_ONNX_layers, PoolConv3D)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000)
throw SkipTestException("Test is enabled starts from 2019R1");
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); // Only CPU on DLIE backend is supported
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // Only CPU on DLIE backend is supported
if (target != DNN_TARGET_CPU)
throw SkipTestException("Only CPU is supported");
testONNXModels("pool_conv_3d");
@ -242,22 +260,22 @@ TEST_P(Test_ONNX_layers, BatchNormalization)
TEST_P(Test_ONNX_layers, BatchNormalization3D)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
if (target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL);
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
}
testONNXModels("batch_norm_3d");
}
TEST_P(Test_ONNX_layers, Transpose)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
if (target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL);
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
}
testONNXModels("transpose");
}
@ -266,17 +284,17 @@ TEST_P(Test_ONNX_layers, Multiplication)
{
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
testONNXModels("mul");
}
TEST_P(Test_ONNX_layers, Constant)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2018050000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_2018R5);
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
testONNXModels("constant");
}
@ -342,10 +360,10 @@ TEST_P(Test_ONNX_layers, Div)
TEST_P(Test_ONNX_layers, DynamicReshape)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
if (target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL);
if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
}
testONNXModels("dynamic_reshape");
}
@ -382,8 +400,10 @@ TEST_P(Test_ONNX_layers, Softmax)
TEST_P(Test_ONNX_layers, Split_EltwiseMax)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
testONNXModels("split_max");
}
@ -425,8 +445,8 @@ TEST_P(Test_ONNX_nets, Squeezenet)
TEST_P(Test_ONNX_nets, Googlenet)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
const String model = _tf("models/googlenet.onnx", false);
@ -455,9 +475,9 @@ TEST_P(Test_ONNX_nets, CaffeNet)
{
applyTestTag(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB);
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2019030000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_2019R3);
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
testONNXModels("caffenet", pb);
}
@ -466,9 +486,9 @@ TEST_P(Test_ONNX_nets, RCNN_ILSVRC13)
{
applyTestTag(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB);
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2019030000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_2019R3);
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
// Reference output values are in range [-4.992, -1.161]
testONNXModels("rcnn_ilsvrc13", pb, 0.0045);
@ -510,12 +530,12 @@ TEST_P(Test_ONNX_nets, ResNet101_DUC_HDC)
applyTestTag(CV_TEST_TAG_VERYLONG);
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE, CV_TEST_TAG_DNN_SKIP_IE_2019R1, CV_TEST_TAG_DNN_SKIP_IE_2019R1_1);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
#endif
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_OPENCL)
{
@ -533,15 +553,15 @@ TEST_P(Test_ONNX_nets, TinyYolov2)
if (cvtest::skipUnstableTests)
throw SkipTestException("Skip unstable test");
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019
&& (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)
)
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X
)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
#endif
// output range: [-11; 8]
@ -568,11 +588,11 @@ TEST_P(Test_ONNX_nets, LResNet100E_IR)
(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB),
CV_TEST_TAG_DEBUG_LONG
);
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
if (target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL);
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
}
double l1 = default_l1;
@ -582,7 +602,7 @@ TEST_P(Test_ONNX_nets, LResNet100E_IR)
l1 = 0.009;
lInf = 0.035;
}
else if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_CPU) {
else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_CPU) {
l1 = 4.6e-5;
lInf = 1.9e-4;
}
@ -592,10 +612,10 @@ TEST_P(Test_ONNX_nets, LResNet100E_IR)
TEST_P(Test_ONNX_nets, Emotion_ferplus)
{
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X
)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
#endif
double l1 = default_l1;
@ -604,12 +624,12 @@ TEST_P(Test_ONNX_nets, Emotion_ferplus)
// Output values are in range [-2.011, 2.111]
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
l1 = 0.007;
else if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16)
else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_OPENCL_FP16)
{
l1 = 0.021;
lInf = 0.034;
}
else if (backend == DNN_BACKEND_INFERENCE_ENGINE && (target == DNN_TARGET_CPU || target == DNN_TARGET_OPENCL)) {
else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && (target == DNN_TARGET_CPU || target == DNN_TARGET_OPENCL)) {
l1 = 2.4e-4;
lInf = 6e-4;
}
@ -632,7 +652,7 @@ TEST_P(Test_ONNX_nets, DenseNet121)
TEST_P(Test_ONNX_nets, Inception_v1)
{
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
#endif
testONNXModels("inception_v1", pb);
@ -640,11 +660,11 @@ TEST_P(Test_ONNX_nets, Inception_v1)
TEST_P(Test_ONNX_nets, Shufflenet)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
if (target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL);
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
}
testONNXModels("shufflenet", pb);
}
@ -652,8 +672,12 @@ TEST_P(Test_ONNX_nets, Shufflenet)
TEST_P(Test_ONNX_nets, Resnet34_kinetics)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000)
throw SkipTestException("Test is enabled starts from 2019R1");
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); // Only CPU on DLIE backend is supported
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // Only CPU on DLIE backend is supported
if (target != DNN_TARGET_CPU)
throw SkipTestException("Only CPU is supported");
@ -675,6 +699,9 @@ TEST_P(Test_ONNX_nets, Resnet34_kinetics)
lp.set("order", DictValue::arrayInt<int*>(&order[0], 4));
permute.addLayerToPrev("perm", "Permute", lp);
permute.setPreferableBackend(backend);
permute.setPreferableTarget(target);
permute.setInput(blob0);
Mat input0 = permute.forward().clone();

153
modules/dnn/test/test_tf_importer.cpp
View File

@ -134,8 +134,12 @@ TEST_P(Test_TensorFlow_layers, conv)
TEST_P(Test_TensorFlow_layers, Convolution3D)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000)
throw SkipTestException("Test is enabled starts from 2019R1");
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); // Only CPU on DLIE backend is supported
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // Only CPU on DLIE backend is supported
if (target != DNN_TARGET_CPU)
throw SkipTestException("Only CPU is supported");
runTensorFlowNet("conv3d");
@ -147,12 +151,12 @@ TEST_P(Test_TensorFlow_layers, padding)
runTensorFlowNet("spatial_padding");
runTensorFlowNet("mirror_pad");
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019020000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_2019R3, CV_TEST_TAG_DNN_SKIP_IE_2019R2);
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
if (target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_2019R3, CV_TEST_TAG_DNN_SKIP_IE_2019R2);
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
}
#endif
runTensorFlowNet("keras_pad_concat");
@ -199,11 +203,11 @@ TEST_P(Test_TensorFlow_layers, batch_norm)
TEST_P(Test_TensorFlow_layers, batch_norm3D)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_CPU)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU)
{
if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
if (target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL);
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
throw SkipTestException("");
}
runTensorFlowNet("batch_norm3d");
@ -211,8 +215,10 @@ TEST_P(Test_TensorFlow_layers, batch_norm3D)
TEST_P(Test_TensorFlow_layers, slim_batch_norm)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
// Output values range: [-40.0597, 207.827]
double l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.041 : default_l1;
double lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.33 : default_lInf;
@ -229,8 +235,10 @@ TEST_P(Test_TensorFlow_layers, pooling)
TEST_P(Test_TensorFlow_layers, max_pool_grad)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
runTensorFlowNet("max_pool_grad");
}
@ -239,10 +247,10 @@ TEST_P(Test_TensorFlow_layers, ave_pool_same)
{
// Reference output values are in range [-0.519531, 0.112976]
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X
)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
runTensorFlowNet("ave_pool_same");
}
@ -250,8 +258,12 @@ TEST_P(Test_TensorFlow_layers, ave_pool_same)
TEST_P(Test_TensorFlow_layers, MaxPooling3D)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000)
throw SkipTestException("Test is enabled starts from 2019R1");
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); // Only CPU on DLIE backend is supported
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // Only CPU on DLIE backend is supported
if (target != DNN_TARGET_CPU)
throw SkipTestException("Only CPU is supported");
runTensorFlowNet("max_pool3d");
@ -260,8 +272,12 @@ TEST_P(Test_TensorFlow_layers, MaxPooling3D)
TEST_P(Test_TensorFlow_layers, AvePooling3D)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000)
throw SkipTestException("Test is enabled starts from 2019R1");
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); // Only CPU on DLIE backend is supported
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // Only CPU on DLIE backend is supported
if (target != DNN_TARGET_CPU)
throw SkipTestException("Only CPU is supported");
runTensorFlowNet("ave_pool3d");
@ -293,8 +309,10 @@ TEST_P(Test_TensorFlow_layers, matmul)
TEST_P(Test_TensorFlow_layers, reshape)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
runTensorFlowNet("shift_reshape_no_reorder");
runTensorFlowNet("reshape_no_reorder");
runTensorFlowNet("reshape_reduce");
@ -304,10 +322,10 @@ TEST_P(Test_TensorFlow_layers, reshape)
TEST_P(Test_TensorFlow_layers, flatten)
{
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_2
)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_2);
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_2, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
#endif
runTensorFlowNet("flatten", true);
@ -322,8 +340,8 @@ TEST_P(Test_TensorFlow_layers, unfused_flatten)
TEST_P(Test_TensorFlow_layers, leaky_relu)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2018050000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_2018R5);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_OPENCL)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
runTensorFlowNet("leaky_relu_order1");
runTensorFlowNet("leaky_relu_order2");
@ -333,10 +351,10 @@ TEST_P(Test_TensorFlow_layers, leaky_relu)
TEST_P(Test_TensorFlow_layers, l2_normalize)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X
)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
runTensorFlowNet("l2_normalize");
@ -346,14 +364,15 @@ TEST_P(Test_TensorFlow_layers, l2_normalize)
TEST_P(Test_TensorFlow_layers, l2_normalize_3d)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2018050000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019
&& (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)
)
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16,
CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
#endif
runTensorFlowNet("l2_normalize_3d");
@ -364,11 +383,11 @@ class Test_TensorFlow_nets : public DNNTestLayer {};
TEST_P(Test_TensorFlow_nets, MobileNet_SSD)
{
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
{
#if INF_ENGINE_VER_MAJOR_GE(2019020000)
if (getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
}
#endif
@ -403,9 +422,9 @@ TEST_P(Test_TensorFlow_nets, Inception_v2_SSD)
{
applyTestTag(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB);
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2019010000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD &&
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD &&
getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
checkBackend();
@ -457,7 +476,7 @@ TEST_P(Test_TensorFlow_nets, MobileNet_v1_SSD)
float detectionConfThresh = (target == DNN_TARGET_MYRIAD) ? 0.35 : 0.3;
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD &&
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD &&
getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
{
scoreDiff = 0.061;
@ -480,16 +499,22 @@ TEST_P(Test_TensorFlow_nets, Faster_RCNN)
static std::string names[] = {"faster_rcnn_inception_v2_coco_2018_01_28",
"faster_rcnn_resnet50_coco_2018_01_28"};
checkBackend();
#ifdef INF_ENGINE_RELEASE
if (backend == DNN_BACKEND_INFERENCE_ENGINE &&
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 &&
(INF_ENGINE_VER_MAJOR_LT(2019020000) || target != DNN_TARGET_CPU))
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE);
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
// segfault: inference-engine/thirdparty/clDNN/src/gpu/detection_output_cpu.cpp:111:
// Assertion `prior_height > 0' failed.
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
double scoresDiff = backend == DNN_BACKEND_INFERENCE_ENGINE ? 2.9e-5 : 1e-5;
checkBackend();
double scoresDiff = backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ? 2.9e-5 : 1e-5;
for (int i = 0; i < 2; ++i)
{
std::string proto = findDataFile("dnn/" + names[i] + ".pbtxt");
@ -512,8 +537,9 @@ TEST_P(Test_TensorFlow_nets, Faster_RCNN)
TEST_P(Test_TensorFlow_nets, MobileNet_v1_SSD_PPN)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2018050000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16,
CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
checkBackend();
std::string proto = findDataFile("dnn/ssd_mobilenet_v1_ppn_coco.pbtxt");
@ -583,12 +609,12 @@ TEST_P(Test_TensorFlow_nets, EAST_text_detection)
);
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16 &&
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_OPENCL_FP16 &&
INF_ENGINE_VER_MAJOR_EQ(2019020000))
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_2019R2);
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
checkBackend();
@ -621,7 +647,7 @@ TEST_P(Test_TensorFlow_nets, EAST_text_detection)
double l1_geometry = default_l1, lInf_geometry = default_lInf;
if (target == DNN_TARGET_OPENCL_FP16)
{
lInf_scores = backend == DNN_BACKEND_INFERENCE_ENGINE ? 0.16 : 0.11;
lInf_scores = backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ? 0.16 : 0.11;
l1_geometry = 0.28; lInf_geometry = 5.94;
}
else if (target == DNN_TARGET_MYRIAD)
@ -679,8 +705,10 @@ TEST_P(Test_TensorFlow_layers, quantized)
TEST_P(Test_TensorFlow_layers, lstm)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
runTensorFlowNet("lstm", true);
@ -689,12 +717,18 @@ TEST_P(Test_TensorFlow_layers, lstm)
TEST_P(Test_TensorFlow_layers, split)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD &&
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD &&
getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_2)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_2);
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_2, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
runTensorFlowNet("split");
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE);
}
TEST_P(Test_TensorFlow_layers, split_equals)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
runTensorFlowNet("split_equals");
}
@ -706,9 +740,10 @@ TEST_P(Test_TensorFlow_layers, resize_nearest_neighbor)
TEST_P(Test_TensorFlow_layers, slice)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE &&
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 &&
(target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16,
CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
runTensorFlowNet("slice_4d");
runTensorFlowNet("strided_slice");
}
@ -722,10 +757,10 @@ TEST_P(Test_TensorFlow_layers, softmax)
TEST_P(Test_TensorFlow_layers, slim_softmax_v2)
{
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD &&
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD &&
getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_2
)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_2);
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_2, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
#endif
runTensorFlowNet("slim_softmax_v2");
}
@ -738,8 +773,10 @@ TEST_P(Test_TensorFlow_layers, relu6)
TEST_P(Test_TensorFlow_layers, subpixel)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
runTensorFlowNet("subpixel");
}
@ -758,10 +795,10 @@ TEST_P(Test_TensorFlow_layers, resize_bilinear)
TEST_P(Test_TensorFlow_layers, squeeze)
{
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_2
)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_2);
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_2, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
#endif
int inpShapes[][4] = {{1, 3, 4, 2}, {1, 3, 1, 2}, {1, 3, 4, 1}, {1, 3, 4, 1}}; // TensorFlow's shape (NHWC)
int outShapes[][3] = {{3, 4, 2}, {1, 3, 2}, {1, 3, 4}, {1, 3, 4}};

40
modules/dnn/test/test_torch_importer.cpp
View File

@ -100,7 +100,7 @@ public:
lInf = lInf ? lInf : default_lInf;
normAssert(outRef, outBlobs[0], "", l1, lInf);
if (check2ndBlob && backend != DNN_BACKEND_INFERENCE_ENGINE)
if (check2ndBlob && backend == DNN_BACKEND_OPENCV)
{
Mat out2 = outBlobs[1];
Mat ref2 = readTorchBlob(_tf(prefix + "_output_2" + suffix), isBinary);
@ -136,8 +136,8 @@ TEST_P(Test_Torch_layers, run_reshape_change_batch_size)
TEST_P(Test_Torch_layers, run_reshape)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
runTorchNet("net_reshape_batch");
runTorchNet("net_reshape_channels", "", false, true);
}
@ -209,8 +209,8 @@ TEST_P(Test_Torch_layers, net_lp_pooling)
TEST_P(Test_Torch_layers, net_conv_gemm_lrn)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
runTorchNet("net_conv_gemm_lrn", "", false, true, true,
target == DNN_TARGET_OPENCL_FP16 ? 0.046 : 0.0,
target == DNN_TARGET_OPENCL_FP16 ? 0.023 : 0.0);
@ -235,9 +235,10 @@ TEST_P(Test_Torch_layers, net_padding)
TEST_P(Test_Torch_layers, net_non_spatial)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE &&
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 &&
(target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16,
CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
runTorchNet("net_non_spatial", "", false, true);
}
@ -251,9 +252,10 @@ TEST_P(Test_Torch_layers, run_paralel)
TEST_P(Test_Torch_layers, net_residual)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE == 2018050000
if (backend == DNN_BACKEND_INFERENCE_ENGINE && (target == DNN_TARGET_OPENCL ||
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && (target == DNN_TARGET_OPENCL ||
target == DNN_TARGET_OPENCL_FP16))
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16,
CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
runTorchNet("net_residual", "", false, true);
}
@ -263,8 +265,8 @@ class Test_Torch_nets : public DNNTestLayer {};
TEST_P(Test_Torch_nets, OpenFace_accuracy)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2018050000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
#endif
checkBackend();
@ -339,11 +341,11 @@ TEST_P(Test_Torch_nets, ENet_accuracy)
checkBackend();
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
throw SkipTestException("");
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_CPU)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU)
{
if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
if (target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL);
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
throw SkipTestException("");
}
@ -395,17 +397,17 @@ TEST_P(Test_Torch_nets, ENet_accuracy)
TEST_P(Test_Torch_nets, FastNeuralStyle_accuracy)
{
#if defined INF_ENGINE_RELEASE
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
#endif
checkBackend();
#if defined(INF_ENGINE_RELEASE)
#if INF_ENGINE_RELEASE <= 2018050000
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_2018R5);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_OPENCL)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
#endif

2
modules/ts/include/opencv2/ts.hpp
View File

@ -219,6 +219,8 @@ static inline void applyTestTag(const std::string& tag1, const std::string& tag2
{ applyTestTag_(tag1); applyTestTag_(tag2); applyTestTag_(tag3); checkTestTags(); }
static inline void applyTestTag(const std::string& tag1, const std::string& tag2, const std::string& tag3, const std::string& tag4)
{ applyTestTag_(tag1); applyTestTag_(tag2); applyTestTag_(tag3); applyTestTag_(tag4); checkTestTags(); }
static inline void applyTestTag(const std::string& tag1, const std::string& tag2, const std::string& tag3, const std::string& tag4, const std::string& tag5)
{ applyTestTag_(tag1); applyTestTag_(tag2); applyTestTag_(tag3); applyTestTag_(tag4); applyTestTag_(tag5); checkTestTags(); }
/** Append global skip test tags