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

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This commit is contained in:
Alexander Alekhin 2022-02-11 17:32:37 +00:00
commit 19926e2979
46 changed files with 323 additions and 2889 deletions
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108
cmake/OpenCVDetectInferenceEngine.cmake
View File

@ -29,84 +29,10 @@ endif()
# ======================
macro(ocv_ie_find_extra_libraries find_prefix find_suffix)
file(GLOB libraries "${INF_ENGINE_LIB_DIRS}/${find_prefix}inference_engine*${find_suffix}")
foreach(full_path IN LISTS libraries)
get_filename_component(library "${full_path}" NAME_WE)
string(REPLACE "${find_prefix}" "" library "${library}")
if(library STREQUAL "inference_engine" OR library STREQUAL "inference_engined")
# skip
else()
add_library(${library} UNKNOWN IMPORTED)
set_target_properties(${library} PROPERTIES
IMPORTED_LOCATION "${full_path}")
list(APPEND custom_libraries ${library})
endif()
endforeach()
endmacro()
function(add_custom_ie_build _inc _lib _lib_rel _lib_dbg _msg)
if(NOT _inc OR NOT (_lib OR _lib_rel OR _lib_dbg))
return()
endif()
if(NOT _lib)
if(_lib_rel)
set(_lib "${_lib_rel}")
else()
set(_lib "${_lib_dbg}")
endif()
endif()
add_library(inference_engine UNKNOWN IMPORTED)
set_target_properties(inference_engine PROPERTIES
IMPORTED_LOCATION "${_lib}"
IMPORTED_IMPLIB_RELEASE "${_lib_rel}"
IMPORTED_IMPLIB_DEBUG "${_lib_dbg}"
INTERFACE_INCLUDE_DIRECTORIES "${_inc}"
)
set(custom_libraries "")
set(__prefixes "${CMAKE_FIND_LIBRARY_PREFIXES}")
if(NOT __prefixes)
set(__prefixes "_empty_")
endif()
foreach(find_prefix ${__prefixes})
if(find_prefix STREQUAL "_empty_") # foreach doesn't iterate over empty elements
set(find_prefix "")
endif()
if(NOT DEFINED INFERENCE_ENGINE_FIND_LIBRARY_SUFFIXES) # allow custom override
set(INFERENCE_ENGINE_FIND_LIBRARY_SUFFIXES ${CMAKE_FIND_LIBRARY_SUFFIXES})
if(APPLE)
ocv_list_filterout(INFERENCE_ENGINE_FIND_LIBRARY_SUFFIXES "^.so$") # skip plugins (can't be linked)
endif()
endif()
foreach(find_suffix ${INFERENCE_ENGINE_FIND_LIBRARY_SUFFIXES})
ocv_ie_find_extra_libraries("${find_prefix}" "${find_suffix}")
endforeach()
if(NOT CMAKE_FIND_LIBRARY_SUFFIXES)
ocv_ie_find_extra_libraries("${find_prefix}" "")
endif()
endforeach()
if(NOT INF_ENGINE_RELEASE VERSION_GREATER "2018050000")
find_library(INF_ENGINE_OMP_LIBRARY iomp5 PATHS "${INF_ENGINE_OMP_DIR}" NO_DEFAULT_PATH)
if(NOT INF_ENGINE_OMP_LIBRARY)
message(WARNING "OpenMP for IE have not been found. Set INF_ENGINE_OMP_DIR variable if you experience build errors.")
endif()
endif()
if(EXISTS "${INF_ENGINE_OMP_LIBRARY}")
set_target_properties(inference_engine PROPERTIES IMPORTED_LINK_INTERFACE_LIBRARIES "${INF_ENGINE_OMP_LIBRARY}")
endif()
set(INF_ENGINE_VERSION "Unknown" CACHE STRING "")
set(INF_ENGINE_TARGET "inference_engine;${custom_libraries}" PARENT_SCOPE)
message(STATUS "Detected InferenceEngine: ${_msg}")
endfunction()
# ======================
find_package(InferenceEngine QUIET)
if(InferenceEngine_FOUND)
set(INF_ENGINE_TARGET ${InferenceEngine_LIBRARIES})
set(INF_ENGINE_VERSION "${InferenceEngine_VERSION}" CACHE STRING "")
set(INF_ENGINE_VERSION "${InferenceEngine_VERSION}")
message(STATUS "Detected InferenceEngine: cmake package (${InferenceEngine_VERSION})")
endif()
@ -124,38 +50,6 @@ elseif(DEFINED INF_ENGINE_RELEASE)
endif()
set(INF_ENGINE_RELEASE "${INF_ENGINE_RELEASE_INIT}" CACHE STRING "Force IE version, should be in form YYYYAABBCC (e.g. 2020.1.0.2 -> 2020010002)")
if(NOT INF_ENGINE_TARGET AND INF_ENGINE_LIB_DIRS AND INF_ENGINE_INCLUDE_DIRS)
find_path(ie_custom_inc "inference_engine.hpp" PATHS "${INF_ENGINE_INCLUDE_DIRS}" NO_DEFAULT_PATH)
if(CMAKE_BUILD_TYPE STREQUAL "Debug")
find_library(ie_custom_lib_dbg "inference_engined" PATHS "${INF_ENGINE_LIB_DIRS}" NO_DEFAULT_PATH) # Win32 and MacOSX
endif()
find_library(ie_custom_lib "inference_engine" PATHS "${INF_ENGINE_LIB_DIRS}" NO_DEFAULT_PATH)
find_library(ie_custom_lib_rel "inference_engine" PATHS "${INF_ENGINE_LIB_DIRS}/Release" NO_DEFAULT_PATH)
find_library(ie_custom_lib_dbg "inference_engine" PATHS "${INF_ENGINE_LIB_DIRS}/Debug" NO_DEFAULT_PATH)
add_custom_ie_build("${ie_custom_inc}" "${ie_custom_lib}" "${ie_custom_lib_rel}" "${ie_custom_lib_dbg}" "INF_ENGINE_{INCLUDE,LIB}_DIRS")
endif()
set(_loc "$ENV{INTEL_OPENVINO_DIR}")
if(NOT _loc AND DEFINED ENV{INTEL_CVSDK_DIR})
set(_loc "$ENV{INTEL_CVSDK_DIR}") # OpenVINO 2018.x
endif()
if(NOT INF_ENGINE_TARGET AND _loc)
if(NOT INF_ENGINE_RELEASE VERSION_GREATER "2018050000")
set(INF_ENGINE_PLATFORM_DEFAULT "ubuntu_16.04")
else()
set(INF_ENGINE_PLATFORM_DEFAULT "")
endif()
set(INF_ENGINE_PLATFORM "${INF_ENGINE_PLATFORM_DEFAULT}" CACHE STRING "InferenceEngine platform (library dir)")
find_path(ie_custom_env_inc "inference_engine.hpp" PATHS "${_loc}/deployment_tools/inference_engine/include" NO_DEFAULT_PATH)
if(CMAKE_BUILD_TYPE STREQUAL "Debug")
find_library(ie_custom_env_lib_dbg "inference_engined" PATHS "${_loc}/deployment_tools/inference_engine/lib/${INF_ENGINE_PLATFORM}/intel64" NO_DEFAULT_PATH)
endif()
find_library(ie_custom_env_lib "inference_engine" PATHS "${_loc}/deployment_tools/inference_engine/lib/${INF_ENGINE_PLATFORM}/intel64" NO_DEFAULT_PATH)
find_library(ie_custom_env_lib_rel "inference_engine" PATHS "${_loc}/deployment_tools/inference_engine/lib/intel64/Release" NO_DEFAULT_PATH)
find_library(ie_custom_env_lib_dbg "inference_engine" PATHS "${_loc}/deployment_tools/inference_engine/lib/intel64/Debug" NO_DEFAULT_PATH)
add_custom_ie_build("${ie_custom_env_inc}" "${ie_custom_env_lib}" "${ie_custom_env_lib_rel}" "${ie_custom_env_lib_dbg}" "OpenVINO (${_loc})")
endif()
set(tgts)
set(defs)

4
modules/core/include/opencv2/core/persistence.hpp
View File

@ -309,8 +309,8 @@ public:
READ = 0, //!< value, open the file for reading
WRITE = 1, //!< value, open the file for writing
APPEND = 2, //!< value, open the file for appending
MEMORY = 4, //!< flag, read data from source or write data to the internal buffer (which is
//!< returned by FileStorage::release)
MEMORY = 4, /**< flag, read data from source or write data to the internal buffer (which is
returned by FileStorage::release) */
FORMAT_MASK = (7<<3), //!< mask for format flags
FORMAT_AUTO = 0, //!< flag, auto format
FORMAT_XML = (1<<3), //!< flag, XML format

25
modules/core/src/hal_internal.cpp
View File

@ -64,6 +64,16 @@
#define HAL_LU_SMALL_MATRIX_THRESH 100
#define HAL_CHOLESKY_SMALL_MATRIX_THRESH 100
#if defined(__clang__) && defined(__has_feature)
#if __has_feature(memory_sanitizer)
#define CV_ANNOTATE_MEMORY_IS_INITIALIZED(address, size) \
__msan_unpoison(adresse, size)
#endif
#endif
#ifndef CV_ANNOTATE_MEMORY_IS_INITIALIZED
#define CV_ANNOTATE_MEMORY_IS_INITIALIZED(address, size) do { } while(0)
#endif
//lapack stores matrices in column-major order so transposing is needed everywhere
template <typename fptype> static inline void
transpose_square_inplace(fptype *src, size_t src_ld, size_t m)
@ -239,20 +249,16 @@ lapack_SVD(fptype* a, size_t a_step, fptype *w, fptype* u, size_t u_step, fptype
else if(typeid(fptype) == typeid(double))
OCV_LAPACK_FUNC(dgesdd)(mode, &m, &n, (double*)a, &lda, (double*)w, (double*)u, &ldu, (double*)vt, &ldv, (double*)buffer, &lwork, iworkBuf, info);
#if defined(__clang__) && defined(__has_feature)
#if __has_feature(memory_sanitizer)
// Make sure MSAN sees the memory as having been written.
// MSAN does not think it has been written because a different language was called.
__msan_unpoison(a, a_step * n);
__msan_unpoison(buffer, sizeof(fptype) * (lwork + 1));
CV_ANNOTATE_MEMORY_IS_INITIALIZED(a, a_step * n);
CV_ANNOTATE_MEMORY_IS_INITIALIZED(buffer, sizeof(fptype) * (lwork + 1));
if (u)
__msan_unpoison(u, u_step * m);
CV_ANNOTATE_MEMORY_IS_INITIALIZED(u, u_step * m);
if (vt)
__msan_unpoison(vt, v_step * n);
CV_ANNOTATE_MEMORY_IS_INITIALIZED(vt, v_step * n);
if (w)
__msan_unpoison(w, sizeof(fptype) * std::min(m, n));
#endif // __has_feature(memory_sanitizer)
#endif // defined(__clang__) && defined(__has_feature)
CV_ANNOTATE_MEMORY_IS_INITIALIZED(w, sizeof(fptype) * std::min(m, n));
if(!(flags & CV_HAL_SVD_NO_UV))
transpose_square_inplace(vt, ldv, n);
@ -357,6 +363,7 @@ lapack_QR(fptype* a, size_t a_step, int m, int n, int k, fptype* b, size_t b_ste
dgeqrf_(&m, &n, (double*)tmpA, &ldtmpA, (double*)dst, (double*)buffer, &lwork, info);
}
CV_ANNOTATE_MEMORY_IS_INITIALIZED(info, sizeof(int));
if (m == n)
transpose_square_inplace(a, lda, m);
else

7
modules/dnn/CMakeLists.txt
View File

@ -165,7 +165,12 @@ elseif(CMAKE_CXX_COMPILER_ID STREQUAL "Clang")
endif()
set(dnn_runtime_libs "")
if(TARGET ocv.3rdparty.openvino)
ocv_option(OPENCV_DNN_OPENVINO "Build with OpenVINO support (2021.4+)" (TARGET ocv.3rdparty.openvino))
if(TARGET ocv.3rdparty.openvino AND OPENCV_DNN_OPENVINO)
if(NOT HAVE_OPENVINO AND NOT HAVE_NGRAPH)
message(FATAL_ERROR "DNN: Inference Engine is not supported without enabled 'nGraph'. Check build configuration.")
endif()
list(APPEND dnn_runtime_libs ocv.3rdparty.openvino)
endif()

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

@ -302,8 +302,6 @@ CV__DNN_INLINE_NS_BEGIN
*/
virtual Ptr<BackendNode> initHalide(const std::vector<Ptr<BackendWrapper> > &inputs);
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);
virtual Ptr<BackendNode> initVkCom(const std::vector<Ptr<BackendWrapper> > &inputs);

8
modules/dnn/include/opencv2/dnn/utils/inference_engine.hpp
View File

@ -15,14 +15,18 @@ CV__DNN_INLINE_NS_BEGIN
/* Values for 'OPENCV_DNN_BACKEND_INFERENCE_ENGINE_TYPE' parameter */
/// @deprecated
#define CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API "NN_BUILDER"
/// @deprecated
#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).
* `OPENCV_DNN_BACKEND_INFERENCE_ENGINE_TYPE` runtime parameter (environment variable) is ignored since 4.6.0.
*
* @deprecated
*/
CV_EXPORTS_W cv::String getInferenceEngineBackendType();
@ -31,6 +35,8 @@ CV_EXPORTS_W cv::String getInferenceEngineBackendType();
* See values of `CV_DNN_BACKEND_INFERENCE_ENGINE_*` macros.
*
* @returns previous value of internal backend API
*
* @deprecated
*/
CV_EXPORTS_W cv::String setInferenceEngineBackendType(const cv::String& newBackendType);

552
modules/dnn/src/dnn.cpp
View File

@ -177,48 +177,29 @@ private:
#ifdef HAVE_INF_ENGINE
if (checkIETarget(DNN_TARGET_CPU)) {
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019, DNN_TARGET_CPU));
#endif
#ifdef HAVE_DNN_NGRAPH
backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, DNN_TARGET_CPU));
#endif
}
if (checkIETarget(DNN_TARGET_MYRIAD)) {
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019, DNN_TARGET_MYRIAD));
#endif
#ifdef HAVE_DNN_NGRAPH
backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, DNN_TARGET_MYRIAD));
#endif
}
if (checkIETarget(DNN_TARGET_HDDL)) {
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019, DNN_TARGET_HDDL));
#endif
#ifdef HAVE_DNN_NGRAPH
backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, DNN_TARGET_HDDL));
#endif
}
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
if (checkIETarget(DNN_TARGET_FPGA))
backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019, DNN_TARGET_FPGA));
#endif
#ifdef HAVE_OPENCL
if (cv::ocl::useOpenCL() && ocl::Device::getDefault().isIntel())
{
if (checkIETarget(DNN_TARGET_OPENCL)) {
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019, DNN_TARGET_OPENCL));
#endif
#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)) {
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019, DNN_TARGET_OPENCL_FP16));
#endif
#ifdef HAVE_DNN_NGRAPH
backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, DNN_TARGET_OPENCL_FP16));
#endif
@ -273,7 +254,7 @@ std::vector<Target> getAvailableTargets(Backend be)
be = (Backend)PARAM_DNN_BACKEND_DEFAULT;
#ifdef HAVE_INF_ENGINE
if (be == DNN_BACKEND_INFERENCE_ENGINE)
be = getInferenceEngineBackendTypeParam();
be = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH;
#endif
std::vector<Target> result;
@ -637,8 +618,7 @@ struct DataLayer : public Layer
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV ||
(backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && inputsData.size() == 1);
return backendId == DNN_BACKEND_OPENCV;
}
void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE
@ -829,39 +809,6 @@ struct DataLayer : public Layer
}
}
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{
CV_CheckEQ(inputsData.size(), (size_t)1, "");
CV_CheckEQ(inputsData[0].dims, 4, "");
const size_t numChannels = inputsData[0].size[1];
CV_Assert(numChannels <= 4);
// Scale
InferenceEngine::TensorDesc td(InferenceEngine::Precision::FP32, {numChannels},
InferenceEngine::Layout::C);
auto weights = InferenceEngine::make_shared_blob<float>(td);
weights->allocate();
float* weight_buf = weights->buffer().as<float*>();
std::fill(weight_buf, weight_buf + numChannels, scaleFactors[0]);
// Mean subtraction
auto biases = InferenceEngine::make_shared_blob<float>(td);
biases->allocate();
float* bias_buf = biases->buffer().as<float*>();
for (int i = 0; i < numChannels; ++i)
{
bias_buf[i] = -means[0][i] * scaleFactors[0];
}
InferenceEngine::Builder::Layer ieLayer = InferenceEngine::Builder::ScaleShiftLayer(name);
addConstantData("weights", weights, ieLayer);
addConstantData("biases", biases, ieLayer);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
std::vector<String> outNames;
std::vector<MatShape> shapes;
@ -1119,18 +1066,14 @@ static Ptr<BackendWrapper> wrapMat(int backendId, int targetId, cv::Mat& m)
}
else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
return Ptr<BackendWrapper>(new InfEngineBackendWrapper(targetId, m));
#else
CV_Error(Error::StsNotImplemented, "This OpenCV version is built without Inference Engine NN Builder API support");
#endif
CV_ERROR_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019;
}
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");
CV_Error(Error::StsNotImplemented, "This OpenCV version is built without support of OpenVINO / Inference Engine + nGraph");
#endif
}
else if (backendId == DNN_BACKEND_WEBNN)
@ -1278,7 +1221,7 @@ struct Net::Impl : public detail::NetImplBase
}
else if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
return wrapMat(preferableBackend, preferableTarget, host);
CV_ERROR_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019;
}
else if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
@ -1407,7 +1350,7 @@ struct Net::Impl : public detail::NetImplBase
preferableBackend = (Backend)PARAM_DNN_BACKEND_DEFAULT;
#ifdef HAVE_INF_ENGINE
if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE)
preferableBackend = getInferenceEngineBackendTypeParam();
preferableBackend = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH; // = getInferenceEngineBackendTypeParam();
#endif
CV_Assert(preferableBackend != DNN_BACKEND_OPENCV ||
@ -1418,8 +1361,7 @@ struct Net::Impl : public detail::NetImplBase
preferableTarget == DNN_TARGET_CPU ||
preferableTarget == DNN_TARGET_OPENCL);
#ifdef HAVE_INF_ENGINE
if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ||
preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
CV_Assert(
(preferableTarget == DNN_TARGET_CPU && (!isArmComputePlugin() || preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)) ||
@ -1721,14 +1663,6 @@ struct Net::Impl : public detail::NetImplBase
}
else if (preferableBackend == DNN_BACKEND_HALIDE)
initHalideBackend();
else if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
initInfEngineBackend(blobsToKeep_);
#else
CV_Assert(false && "This OpenCV version is built without Inference Engine NN Builder API support");
#endif
}
else if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
#ifdef HAVE_DNN_NGRAPH
@ -1750,7 +1684,7 @@ struct Net::Impl : public detail::NetImplBase
else if (preferableBackend == DNN_BACKEND_CUDA)
initCUDABackend(blobsToKeep_);
else
CV_Error(Error::StsNotImplemented, "Unknown backend identifier");
CV_Error(Error::StsNotImplemented, cv::format("Unknown backend identifier: %d", preferableBackend));
}
void initHalideBackend()
@ -1808,316 +1742,6 @@ struct Net::Impl : public detail::NetImplBase
}
}
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
// Before launching Inference Engine graph we need to specify output blobs.
// This function requests output blobs based on inputs references of
// 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())
{
Ptr<BackendNode> node = ld.backendNodes[preferableBackend];
if (!node.empty())
{
Ptr<InfEngineBackendNode> ieNode = node.dynamicCast<InfEngineBackendNode>();
CV_Assert(!ieNode.empty()); CV_Assert(!ieNode->net.empty());
layerNet = ieNode->net;
}
}
// For an every input reference we check that it belongs to one of
// the Inference Engine backend graphs. Request an output blob if it is.
// Do nothing if layer's input is from the same graph.
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<InfEngineBackendNode> ieInpNode = inpNode.dynamicCast<InfEngineBackendNode>();
CV_Assert(!ieInpNode.empty()); CV_Assert(!ieInpNode->net.empty());
if (layerNet != ieInpNode->net)
{
// layerNet is empty or nodes are from different graphs.
ieInpNode->net->addOutput(ieInpNode->layer.getName());
}
}
}
}
void initInfEngineBackend(const std::vector<LayerPin>& blobsToKeep_)
{
CV_TRACE_FUNCTION();
CV_Assert_N(preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019, haveInfEngine());
MapIdToLayerData::iterator it;
Ptr<InfEngineBackendNet> 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 = infEngineDataNode(ld.outputBlobsWrappers[i]);
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2019010000)
dataPtr->name = netInputLayer->outNames.empty() ? ld.name : netInputLayer->outNames[i];
#else
dataPtr->setName(netInputLayer->outNames.empty() ? ld.name : netInputLayer->outNames[i]);
#endif
}
}
else
{
for (int i = 0; i < ld.outputBlobsWrappers.size(); ++i)
{
InferenceEngine::DataPtr dataPtr = infEngineDataNode(ld.outputBlobsWrappers[i]);
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2019010000)
dataPtr->name = ld.name;
#else
dataPtr->setName(ld.name);
#endif
}
}
}
if (skipInfEngineInit)
{
Ptr<BackendNode> node = layers[lastLayerId].backendNodes[preferableBackend];
CV_Assert(!node.empty());
Ptr<InfEngineBackendNode> ieNode = node.dynamicCast<InfEngineBackendNode>();
CV_Assert(!ieNode.empty());
ieNode->net->reset();
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 = infEngineDataNode(ld.inputBlobsWrappers[i]);
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2019010000)
dataPtr->name = netInputLayer->outNames[i];
#else
dataPtr->setName(netInputLayer->outNames[i]);
#endif
}
}
else
{
for (int i = 0; i < ld.outputBlobsWrappers.size(); ++i)
{
InferenceEngine::DataPtr dataPtr = infEngineDataNode(ld.outputBlobsWrappers[i]);
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2019010000)
dataPtr->name = ld.name;
#else
dataPtr->setName(ld.name);
#endif
}
}
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.
bool supportsCPUFallback = preferableTarget == DNN_TARGET_CPU ||
BackendRegistry::checkIETarget(DNN_TARGET_CPU);
// Set of all input and output blobs wrappers for current network.
std::map<LayerPin, Ptr<BackendWrapper> > netBlobsWrappers;
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))
{
bool customizable = ld.id != 0 &&
INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R2) &&
supportsCPUFallback;
// TODO: there is a bug in Myriad plugin with custom layers shape infer.
if (preferableTarget == DNN_TARGET_MYRIAD || preferableTarget == DNN_TARGET_HDDL)
{
for (int i = 0; customizable && i < ld.inputBlobs.size(); ++i)
{
customizable = ld.inputBlobs[i]->size[0] == 1;
}
}
// TODO: fix these workarounds
if (preferableTarget == DNN_TARGET_MYRIAD ||
preferableTarget == DNN_TARGET_HDDL ||
preferableTarget == DNN_TARGET_OPENCL ||
preferableTarget == DNN_TARGET_OPENCL_FP16)
customizable &= ld.type != "Concat";
if (preferableTarget == DNN_TARGET_OPENCL ||
preferableTarget == DNN_TARGET_OPENCL_FP16)
customizable &= ld.type != "Power";
if (preferableTarget == DNN_TARGET_OPENCL)
customizable &= ld.type != "Eltwise";
if (!customizable)
{
addInfEngineNetOutputs(ld);
net = Ptr<InfEngineBackendNet>();
netBlobsWrappers.clear(); // Is not used for R5 release but we don't wrap it to #ifdef.
layer->preferableTarget = DNN_TARGET_CPU;
continue;
}
}
ld.skip = true; // Initially skip all Inference Engine supported layers.
// Create a new network if one of inputs from different Inference Engine graph.
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<InfEngineBackendNode> ieInpNode = inpNode.dynamicCast<InfEngineBackendNode>();
CV_Assert(!ieInpNode.empty()); CV_Assert(!ieInpNode->net.empty());
if (ieInpNode->net != net)
{
net = Ptr<InfEngineBackendNet>();
netBlobsWrappers.clear(); // Is not used for R5 release but we don't wrap it to #ifdef.
break;
}
}
}
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<InfEngineBackendNet>(new InfEngineBackendNet());
if (!fused)
{
if (layer->supportBackend(preferableBackend))
node = layer->initInfEngine(ld.inputBlobsWrappers);
else
{
node = Ptr<BackendNode>(new InfEngineBackendNode(
ld.layerInstance, ld.inputBlobs, ld.outputBlobs, ld.internals));
}
}
else if (node.empty())
continue;
CV_Assert(!node.empty());
ld.backendNodes[preferableBackend] = node;
Ptr<InfEngineBackendNode> ieNode = node.dynamicCast<InfEngineBackendNode>();
CV_Assert(!ieNode.empty());
ieNode->net = net;
for (const auto& pin : blobsToKeep_)
{
if (pin.lid == ld.id)
{
ieNode->net->addOutput(ieNode->layer.getName());
break;
}
}
// Convert weights in FP16 for specific targets.
if ((preferableTarget == DNN_TARGET_OPENCL_FP16 ||
preferableTarget == DNN_TARGET_MYRIAD ||
preferableTarget == DNN_TARGET_HDDL ||
preferableTarget == DNN_TARGET_FPGA) && !fused)
{
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R1)
for (const std::string& name : {"weights", "biases"})
{
auto it = ieNode->layer.getParameters().find(name);
if (it != ieNode->layer.getParameters().end())
{
InferenceEngine::Blob::Ptr bp = it->second.as<InferenceEngine::Blob::Ptr>();
it->second = convertFp16(std::const_pointer_cast<InferenceEngine::Blob>(bp));
}
}
#else
auto& blobs = ieNode->layer.getConstantData();
if (blobs.empty())
{
// In case of non weightable layer we have to specify
// it's precision adding dummy blob.
auto blob = InferenceEngine::make_shared_blob<int16_t>(
InferenceEngine::Precision::FP16,
InferenceEngine::Layout::C, {1});
blob->allocate();
blobs[""] = blob;
}
else
{
for (auto& it : blobs)
it.second = convertFp16(std::const_pointer_cast<InferenceEngine::Blob>(it.second));
}
#endif
}
if (!fused)
net->addLayer(ieNode->layer);
net->connect(ld.inputBlobsWrappers, ld.outputBlobsWrappers, ieNode->layer.getName());
net->addBlobs(ld.inputBlobsWrappers);
net->addBlobs(ld.outputBlobsWrappers);
addInfEngineNetOutputs(ld);
}
// Initialize all networks.
for (MapIdToLayerData::reverse_iterator it = layers.rbegin(); it != layers.rend(); ++it)
{
LayerData &ld = it->second;
if (ld.backendNodes.find(preferableBackend) == ld.backendNodes.end())
continue;
Ptr<BackendNode> node = ld.backendNodes[preferableBackend];
if (node.empty())
continue;
Ptr<InfEngineBackendNode> ieNode = node.dynamicCast<InfEngineBackendNode>();
if (ieNode.empty())
continue;
CV_Assert(!ieNode->net.empty());
if (!ieNode->net->isInitialized())
{
ieNode->net->init((Target)preferableTarget);
ld.skip = false;
}
}
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
/** mark input pins as outputs from other subnetworks
@ -2162,7 +1786,7 @@ struct Net::Impl : public detail::NetImplBase
void initNgraphBackend(const std::vector<LayerPin>& blobsToKeep_)
{
CV_TRACE_FUNCTION();
CV_Assert_N(preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, haveInfEngine());
CV_CheckEQ(preferableBackend, DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, "");
Ptr<InfEngineNgraphNet> net;
@ -2993,7 +2617,6 @@ struct Net::Impl : public detail::NetImplBase
if(!fusion || (preferableBackend != DNN_BACKEND_OPENCV &&
preferableBackend != DNN_BACKEND_CUDA &&
preferableBackend != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 &&
preferableBackend != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH))
return;
@ -3814,14 +3437,12 @@ struct Net::Impl : public detail::NetImplBase
{
forwardHalide(ld.outputBlobsWrappers, node);
}
else if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
forwardInfEngine(ld.outputBlobsWrappers, node, isAsync);
}
#ifdef HAVE_INF_ENGINE
else if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
forwardNgraph(ld.outputBlobsWrappers, node, isAsync);
}
#endif
else if (preferableBackend == DNN_BACKEND_WEBNN)
{
forwardWebnn(ld.outputBlobsWrappers, node, isAsync);
@ -4181,27 +3802,13 @@ struct Net::Impl : public detail::NetImplBase
// Transfer data to CPU if it's require.
ld.outputBlobsWrappers[pin.oid]->copyToHost();
}
CV_Assert(preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
CV_Assert(preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) {
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
Ptr<InfEngineBackendWrapper> wrapper = ld.outputBlobsWrappers[pin.oid].dynamicCast<InfEngineBackendWrapper>();
return std::move(wrapper->futureMat);
#else
CV_Error(Error::StsNotImplemented, "This OpenCV version is built without Inference Engine NN Builder API support");
#endif
}
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, "This OpenCV version is built without support of Inference Engine + nGraph");
#endif
}
CV_Error(Error::StsNotImplemented, "DNN: OpenVINO/nGraph backend is required");
#endif // HAVE_INF_ENGINE
CV_Error(Error::StsNotImplemented, "DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 backend is required");
}
AsyncArray getBlobAsync(String outputName)
@ -4275,40 +3882,18 @@ Net Net::Impl::createNetworkFromModelOptimizer(InferenceEngine::CNNNetwork& ieNe
CV_TRACE_REGION_NEXT("backendNode");
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(*(cvNet.impl), ieNet));
backendNode = backendNodeNGraph;
}
else
#endif
{
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
Ptr<InfEngineBackendNode> backendNodeNN(new InfEngineBackendNode(InferenceEngine::Builder::Layer("")));
backendNodeNN->net = Ptr<InfEngineBackendNet>(new InfEngineBackendNet(ieNet));
backendNode = backendNodeNN;
#else
CV_Error(Error::StsNotImplemented, "This OpenCV version is built without Inference Engine NN Builder API support");
#endif
}
CV_TRACE_REGION_NEXT("register_outputs");
#ifdef HAVE_DNN_NGRAPH
auto ngraphFunction = ieNet.getFunction();
#if INF_ENGINE_VER_MAJOR_LT(INF_ENGINE_RELEASE_2020_2)
std::list< std::shared_ptr<ngraph::Node> > ngraphOperations;
#else
std::vector< std::shared_ptr<ngraph::Node> > ngraphOperations;
#endif
if (ngraphFunction)
{
ngraphOperations = ngraphFunction->get_ops();
}
#endif
CV_Assert(ngraphFunction);
std::vector< std::shared_ptr<ngraph::Node> > ngraphOperations = ngraphFunction->get_ops();
for (auto& it : ieNet.getOutputsInfo())
{
@ -4320,16 +3905,12 @@ Net Net::Impl::createNetworkFromModelOptimizer(InferenceEngine::CNNNetwork& ieNe
LayerData& ld = cvNet.impl->layers[lid];
#ifdef HAVE_DNN_NGRAPH
if (DNN_BACKEND_INFERENCE_ENGINE_NGRAPH == getInferenceEngineBackendTypeParam())
{
Ptr<Layer> cvLayer(new NgraphBackendLayer(ieNet));
cvLayer->name = outputName;
cvLayer->type = "_unknown_";
auto process_layer = [&](const std::string& name) -> bool
{
if (ngraphFunction)
{
CV_TRACE_REGION("ngraph_function");
for (const auto& op : ngraphOperations)
@ -4343,30 +3924,6 @@ Net Net::Impl::createNetworkFromModelOptimizer(InferenceEngine::CNNNetwork& ieNe
}
}
return false;
}
else
{
#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2020_4)
CV_Error(Error::StsNotImplemented, "This OpenCV version is built with Inference Engine which has dropped IR v7 support");
#else
CV_TRACE_REGION("legacy_cnn_layer");
try
{
InferenceEngine::CNNLayerPtr ieLayer = ieNet.getLayerByName(name.c_str());
CV_Assert(ieLayer);
cvLayer->type = ieLayer->type;
return true;
}
catch (const std::exception& e)
{
CV_UNUSED(e);
CV_LOG_DEBUG(NULL, "IE layer extraction failure: '" << name << "' - " << e.what());
return false;
}
#endif
}
};
bool found = process_layer(outputName);
@ -4385,37 +3942,6 @@ Net Net::Impl::createNetworkFromModelOptimizer(InferenceEngine::CNNNetwork& ieNe
ld.layerInstance = cvLayer;
ld.backendNodes[DNN_BACKEND_INFERENCE_ENGINE_NGRAPH] = backendNode;
}
else
#endif
{
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
Ptr<Layer> cvLayer(new InfEngineBackendLayer(ieNet));
InferenceEngine::CNNLayerPtr ieLayer;
try
{
ieLayer = ieNet.getLayerByName(outputName.c_str());
}
catch (...)
{
auto pos = outputName.rfind('.'); // cut port number: ".0"
if (pos != std::string::npos)
{
std::string layerName = outputName.substr(0, pos);
ieLayer = ieNet.getLayerByName(layerName.c_str());
}
}
CV_Assert(ieLayer);
cvLayer->name = outputName;
cvLayer->type = ieLayer->type;
ld.layerInstance = cvLayer;
ld.backendNodes[DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019] = backendNode;
#else
CV_Error(Error::StsNotImplemented, "This OpenCV version is built without Inference Engine NN Builder API support");
#endif
}
for (int i = 0; i < inputsNames.size(); ++i)
cvNet.connect(0, i, lid, i);
@ -4423,7 +3949,7 @@ Net Net::Impl::createNetworkFromModelOptimizer(InferenceEngine::CNNNetwork& ieNe
CV_TRACE_REGION_NEXT("finalize");
cvNet.setPreferableBackend(getInferenceEngineBackendTypeParam());
cvNet.setPreferableBackend(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
cvNet.impl->skipInfEngineInit = true;
return cvNet;
@ -4440,16 +3966,8 @@ Net Net::readFromModelOptimizer(const String& xml, const String& bin)
FPDenormalsIgnoreHintScope fp_denormals_ignore_scope;
#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
return Impl::createNetworkFromModelOptimizer(ieNet);
#endif // HAVE_INF_ENGINE
@ -4478,26 +3996,6 @@ Net Net::readFromModelOptimizer(
FPDenormalsIgnoreHintScope fp_denormals_ignore_scope;
#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R3)
InferenceEngine::CNNNetReader reader;
try
{
reader.ReadNetwork(bufferModelConfigPtr, bufferModelConfigSize);
InferenceEngine::TensorDesc tensorDesc(InferenceEngine::Precision::U8, { bufferWeightsSize }, InferenceEngine::Layout::C);
InferenceEngine::TBlob<uint8_t>::Ptr weightsBlobPtr(new InferenceEngine::TBlob<uint8_t>(tensorDesc));
weightsBlobPtr->allocate();
std::memcpy(weightsBlobPtr->buffer(), (uchar*)bufferWeightsPtr, bufferWeightsSize);
reader.SetWeights(weightsBlobPtr);
}
catch (const std::exception& e)
{
CV_Error(Error::StsError, std::string("DNN: IE failed to load model: ") + e.what());
}
InferenceEngine::CNNNetwork ieNet = reader.getNetwork();
#else
InferenceEngine::Core& ie = getCore("");
std::string model; model.assign((char*)bufferModelConfigPtr, bufferModelConfigSize);
@ -4514,7 +4012,6 @@ Net Net::readFromModelOptimizer(
{
CV_Error(Error::StsError, std::string("DNN: IE failed to load model: ") + e.what());
}
#endif
return Impl::createNetworkFromModelOptimizer(ieNet);
#endif // HAVE_INF_ENGINE
@ -4621,8 +4118,8 @@ AsyncArray Net::forwardAsync(const String& outputName)
std::vector<LayerPin> pins(1, impl->getPinByAlias(layerName));
impl->setUpNet(pins);
if (!(impl->preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || impl->preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH))
CV_Error(Error::StsNotImplemented, "DNN: Asynchronous forward is supported for Inference Engine backends only");
if (impl->preferableBackend != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
CV_Error(Error::StsNotImplemented, "DNN: Asynchronous forward is supported for Inference Engine backend only");
impl->isAsync = true;
impl->forwardToLayer(impl->getLayerData(layerName));
@ -5047,7 +4544,7 @@ void Net::setPreferableBackend(int backendId)
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
backendId = getInferenceEngineBackendTypeParam();
backendId = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH;
#endif
if( impl->preferableBackend != backendId )
@ -5292,8 +4789,8 @@ string Net::Impl::dump() const
case DNN_BACKEND_DEFAULT: backend = "DEFAULT/"; break;
case DNN_BACKEND_HALIDE: backend = "HALIDE/"; 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_INFERENCE_ENGINE_NN_BUILDER_2019: // fallthru
case DNN_BACKEND_INFERENCE_ENGINE_NGRAPH: backend = "OpenVINO/"; break;
case DNN_BACKEND_OPENCV: backend = "OCV/"; break;
case DNN_BACKEND_VKCOM: backend = "VULKAN/"; break;
case DNN_BACKEND_CUDA: backend = "CUDA/"; break;
@ -5894,13 +5391,6 @@ Ptr<BackendNode> Layer::initHalide(const std::vector<Ptr<BackendWrapper> > &)
return Ptr<BackendNode>();
}
Ptr<BackendNode> Layer::initInfEngine(const std::vector<Ptr<BackendWrapper> > &)
{
CV_Error(Error::StsNotImplemented, "Inference Engine pipeline of " + type +
" layers is not defined.");
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 +

17
modules/dnn/src/layers/batch_norm_layer.cpp
View File

@ -170,11 +170,14 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
return preferableTarget == DNN_TARGET_CPU || dims == 4;
#endif
return (backendId == DNN_BACKEND_OPENCV) ||
backendId == DNN_BACKEND_CUDA ||
(backendId == DNN_BACKEND_HALIDE && haveHalide()) ||
backendId == DNN_BACKEND_WEBNN ||
((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && haveInfEngine() && (preferableTarget == DNN_TARGET_CPU || dims == 4));
backendId == DNN_BACKEND_WEBNN;
}
#ifdef HAVE_OPENCL
@ -382,16 +385,6 @@ public:
}
#endif // HAVE_HALIDE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{
InferenceEngine::Builder::Layer ieLayer = InferenceEngine::Builder::ScaleShiftLayer(name);
const size_t numChannels = weights_.total();
addConstantData("weights", wrapToInfEngineBlob(weights_, {numChannels}, InferenceEngine::Layout::C), ieLayer);
addConstantData("biases", wrapToInfEngineBlob(bias_, {numChannels}, InferenceEngine::Layout::C), ieLayer);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs, const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE

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

@ -63,9 +63,12 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
return true;
#endif
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_CUDA ||
((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && haveInfEngine());
backendId == DNN_BACKEND_CUDA;
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,
@ -116,32 +119,6 @@ public:
}
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
{
InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]);
std::vector<size_t> dims = input->getDims();
CV_Assert(!dims.empty());
InferenceEngine::Builder::Layer ieLayer(name);
ieLayer.setName(name);
if (preferableTarget == DNN_TARGET_MYRIAD || preferableTarget == DNN_TARGET_HDDL)
{
ieLayer.setType("Copy");
}
else
{
ieLayer.setType("Split");
ieLayer.getParameters()["axis"] = dims.size() - 1;
ieLayer.getParameters()["out_sizes"] = dims[0];
}
ieLayer.setInputPorts({InferenceEngine::Port(dims)});
ieLayer.setOutputPorts(std::vector<InferenceEngine::Port>(1));
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE

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

@ -113,11 +113,13 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
return true;
#endif
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_CUDA ||
(backendId == DNN_BACKEND_HALIDE && haveHalide() && axis == 1 && !padding) || // By channels
(backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && haveInfEngine() && !padding) ||
backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH ||
(backendId == DNN_BACKEND_WEBNN && !padding) ||
(backendId == DNN_BACKEND_VKCOM && haveVulkan() && !padding);
}
@ -343,18 +345,6 @@ public:
return Ptr<BackendNode>();
}
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
{
InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]);
InferenceEngine::Builder::ConcatLayer ieLayer(name);
ieLayer.setAxis(normalize_axis(axis, input->getDims().size()));
ieLayer.setInputPorts(std::vector<InferenceEngine::Port>(inputs.size()));
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,

16
modules/dnn/src/layers/const_layer.cpp
View File

@ -34,9 +34,11 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
return true;
#endif
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ||
backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH ||
backendId == DNN_BACKEND_WEBNN ||
backendId == DNN_BACKEND_CUDA;
}
@ -78,16 +80,6 @@ public:
}
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{
InferenceEngine::Builder::ConstLayer ieLayer(name);
ieLayer.setData(wrapToInfEngineBlob(blobs[0]));
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE

171
modules/dnn/src/layers/convolution_layer.cpp
View File

@ -330,7 +330,7 @@ public:
}
#endif
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
bool isArmTarget = preferableTarget == DNN_TARGET_CPU && isArmComputePlugin();
if (isArmTarget && blobs.empty())
@ -340,7 +340,7 @@ public:
if (ksize == 3)
return preferableTarget != DNN_TARGET_MYRIAD && !isArmTarget;
bool isMyriad = preferableTarget == DNN_TARGET_MYRIAD || preferableTarget == DNN_TARGET_HDDL;
if ((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || !isMyriad) && blobs.empty())
if (!isMyriad && blobs.empty())
return false;
return (!isMyriad || dilation.width == dilation.height);
}
@ -761,69 +761,6 @@ public:
return Ptr<BackendNode>();
}
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> > &inputs) CV_OVERRIDE
{
CV_Assert(!blobs.empty());
InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]);
std::vector<size_t> dims = input->getDims();
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;
InferenceEngine::Layout layout = (dims.size() == 4) ? InferenceEngine::Layout::OIHW :
InferenceEngine::Layout::NCDHW;
auto ieWeights = wrapToInfEngineBlob(blobs[0], layout);
if (fusedWeights)
{
if (weightsMat.isContinuous())
{
Mat cvWeights = weightsMat.reshape(1, blobs[0].dims, blobs[0].size);
ieWeights = wrapToInfEngineBlob(cvWeights, layout);
}
else
{
ieWeights = InferenceEngine::make_shared_blob<float>({
InferenceEngine::Precision::FP32,
ieWeights->getTensorDesc().getDims(), layout
});
ieWeights->allocate();
Mat newWeights = infEngineBlobToMat(ieWeights).reshape(1, outCn);
Mat cvWeights = weightsMat.colRange(0, newWeights.cols);
cvWeights.copyTo(newWeights);
}
}
InferenceEngine::Blob::Ptr ieBiases;
if (hasBias() || fusedBias)
{
Mat biasesMat({outCn}, CV_32F, &biasvec[0]);
ieBiases = wrapToInfEngineBlob(biasesMat, {(size_t)outCn}, InferenceEngine::Layout::C);
}
InferenceEngine::Builder::ConvolutionLayer ieLayer(name);
ieLayer.setKernel(kernel_size);
ieLayer.setStrides(strides);
ieLayer.setDilation(dilations);
ieLayer.setPaddingsBegin(pads_begin);
ieLayer.setPaddingsEnd(pads_end);
ieLayer.setGroup((size_t)group);
ieLayer.setOutDepth((size_t)outCn);
InferenceEngine::Builder::Layer l = ieLayer;
addConstantData("weights", ieWeights, l);
if (ieBiases)
addConstantData("biases", ieBiases, l);
if (!padMode.empty())
l.getParameters()["auto_pad"] = padMode == "VALID" ? std::string("valid") : std::string("same_upper");
return Ptr<BackendNode>(new InfEngineBackendNode(l));
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> > &inputs,
@ -2319,52 +2256,6 @@ public:
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) {
return group == 1;
}
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (kernel_size.size() == 3 && preferableTarget != DNN_TARGET_CPU) {
return false;
}
if (std::accumulate(adjust_pads.begin(), adjust_pads.end(), 0, std::plus<size_t>()) > 0)
{
if (padMode.empty())
{
if (preferableTarget != DNN_TARGET_CPU && group != 1)
{
for (int i = 0; i < adjust_pads.size(); i++) {
if (adjust_pads[i] && pads_begin[i])
return false;
}
}
for (int i = 0; i < adjust_pads.size(); i++) {
if (pads_end[i] < adjust_pads[i])
return false;
}
return true;
}
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;
}
return true;
}
else if (padMode == "VALID")
return false;
}
if (group != 1)
{
return preferableTarget == DNN_TARGET_CPU;
}
if (preferableTarget == DNN_TARGET_OPENCL || preferableTarget == DNN_TARGET_OPENCL_FP16)
return std::accumulate(dilations.begin(), dilations.end(), 1, std::multiplies<size_t>()) == 1;
return true;
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#endif // HAVE_INF_ENGINE
{
return backendId == DNN_BACKEND_CUDA ||
@ -3022,64 +2913,6 @@ public:
return Ptr<BackendNode>();
}
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> > &) CV_OVERRIDE
{
CV_Assert(!blobs.empty());
InferenceEngine::Layout layout = blobs[0].dims == 5? InferenceEngine::Layout::NCDHW :
InferenceEngine::Layout::OIHW;
auto ieWeights = wrapToInfEngineBlob(blobs[0], layout);
if (fusedWeights)
{
ieWeights = InferenceEngine::make_shared_blob<float>({
InferenceEngine::Precision::FP32,
ieWeights->getTensorDesc().getDims(), layout
});
ieWeights->allocate();
int inpCn = blobs[0].size[0];
Mat newWeights = infEngineBlobToMat(ieWeights).reshape(1, inpCn);
transpose(weightsMat, newWeights);
}
const int outGroupCn = blobs[0].size[1]; // Weights are in IOHW or OIDHW layout
const int group = numOutput / outGroupCn;
InferenceEngine::Builder::DeconvolutionLayer ieLayer(name);
ieLayer.setKernel(kernel_size);
ieLayer.setStrides(strides);
ieLayer.setDilation(dilations);
ieLayer.setPaddingsBegin(pads_begin);
if (padMode.empty())
{
std::vector<size_t> paddings_end;
for (int i = 0; i < pads_end.size(); i++) {
paddings_end.push_back(pads_end[i] - adjust_pads[i]);
}
ieLayer.setPaddingsEnd(paddings_end);
}
else if (padMode == "SAME")
{
std::vector<size_t> paddings_end;
for (int i = 0; i < pads_begin.size(); i++) {
paddings_end.push_back(kernel_size[i] - pads_begin[i] - 1 - adjust_pads[i]);
}
ieLayer.setPaddingsEnd(paddings_end);
}
ieLayer.setGroup((size_t)group);
ieLayer.setOutDepth((size_t)numOutput);
InferenceEngine::Builder::Layer l = ieLayer;
addConstantData("weights", ieWeights, l);
if (hasBias())
addConstantData("biases", wrapToInfEngineBlob(biasesMat, {(size_t)numOutput}, InferenceEngine::Layout::C), l);
return Ptr<BackendNode>(new InfEngineBackendNode(l));
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> > &inputs,

26
modules/dnn/src/layers/detection_output_layer.cpp
View File

@ -221,7 +221,7 @@ public:
{
return backendId == DNN_BACKEND_OPENCV ||
(backendId == DNN_BACKEND_CUDA && !_groupByClasses) ||
((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && !_locPredTransposed && _bboxesNormalized);
(backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && !_locPredTransposed && _bboxesNormalized);
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,
@ -1001,30 +1001,6 @@ public:
}
#endif
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{
InferenceEngine::Builder::DetectionOutputLayer ieLayer(name);
ieLayer.setNumClasses(_numClasses);
ieLayer.setShareLocation(_shareLocation);
ieLayer.setBackgroudLabelId(_backgroundLabelId);
ieLayer.setNMSThreshold(_nmsThreshold);
ieLayer.setTopK(_topK > 0 ? _topK : _keepTopK);
ieLayer.setKeepTopK(_keepTopK);
ieLayer.setConfidenceThreshold(_confidenceThreshold);
ieLayer.setVariantEncodedInTarget(_varianceEncodedInTarget);
ieLayer.setCodeType("caffe.PriorBoxParameter." + _codeType);
ieLayer.setInputPorts(std::vector<InferenceEngine::Port>(3));
InferenceEngine::Builder::Layer l = ieLayer;
l.getParameters()["eta"] = std::string("1.0");
l.getParameters()["clip"] = _clip;
return Ptr<BackendNode>(new InfEngineBackendNode(l));
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs, const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE

120
modules/dnn/src/layers/elementwise_layers.cpp
View File

@ -186,14 +186,6 @@ public:
return Ptr<BackendNode>();
}
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{
InferenceEngine::Builder::Layer ieLayer = func.initInfEngineBuilderAPI();
ieLayer.setName(this->name);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs, const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
@ -341,10 +333,6 @@ struct ReLUFunctor : public BaseFunctor
bool supportBackend(int backendId, int)
{
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
return slope >= 0 || !INF_ENGINE_VER_MAJOR_EQ(INF_ENGINE_RELEASE_2019R1);
#endif
#ifdef HAVE_DNN_NGRAPH
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
return true;
@ -462,13 +450,6 @@ struct ReLUFunctor : public BaseFunctor
}
#endif // HAVE_HALIDE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
{
return InferenceEngine::Builder::ReLULayer("").setNegativeSlope(slope);
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
{
@ -534,11 +515,14 @@ struct ReLU6Functor : public BaseFunctor
bool supportBackend(int backendId, int)
{
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
return true;
#endif
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_CUDA ||
backendId == DNN_BACKEND_HALIDE ||
backendId == DNN_BACKEND_WEBNN ||
backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH;
backendId == DNN_BACKEND_WEBNN;
}
void apply(const float* srcptr, float* dstptr, int len, size_t planeSize, int cn0, int cn1) const
@ -620,12 +604,6 @@ struct ReLU6Functor : public BaseFunctor
}
#endif // HAVE_HALIDE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
{
return InferenceEngine::Builder::ClampLayer("").setMinValue(minValue).setMaxValue(maxValue);
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
@ -743,12 +721,6 @@ struct BaseDefaultFunctor : public BaseFunctor
}
#endif // HAVE_HALIDE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
{
CV_Error(Error::StsNotImplemented, "");
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
@ -782,10 +754,13 @@ struct TanHFunctor : public BaseDefaultFunctor<TanHFunctor>
bool supportBackend(int backendId, int)
{
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
return true;
#endif
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_CUDA ||
backendId == DNN_BACKEND_HALIDE ||
backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH;
backendId == DNN_BACKEND_HALIDE;
}
inline float calculate(float x) const
@ -808,13 +783,6 @@ struct TanHFunctor : public BaseDefaultFunctor<TanHFunctor>
}
#endif // HAVE_HALIDE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
{
return InferenceEngine::Builder::TanHLayer("");
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
{
@ -937,10 +905,13 @@ struct SigmoidFunctor : public BaseDefaultFunctor<SigmoidFunctor>
bool supportBackend(int backendId, int)
{
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
return true;
#endif
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_CUDA ||
backendId == DNN_BACKEND_HALIDE ||
backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH;
backendId == DNN_BACKEND_HALIDE;
}
inline float calculate(float x) const
@ -963,12 +934,6 @@ struct SigmoidFunctor : public BaseDefaultFunctor<SigmoidFunctor>
}
#endif // HAVE_HALIDE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
{
return InferenceEngine::Builder::SigmoidLayer("");
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
@ -992,10 +957,13 @@ struct ELUFunctor : public BaseDefaultFunctor<ELUFunctor>
bool supportBackend(int backendId, int)
{
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
return true;
#endif
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_CUDA ||
backendId == DNN_BACKEND_HALIDE ||
backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH;
backendId == DNN_BACKEND_HALIDE;
}
inline float calculate(float x) const
@ -1023,13 +991,6 @@ struct ELUFunctor : public BaseDefaultFunctor<ELUFunctor>
}
#endif // HAVE_HALIDE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
{
return InferenceEngine::Builder::ELULayer("");
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
{
@ -1050,8 +1011,8 @@ struct AbsValFunctor : public BaseDefaultFunctor<AbsValFunctor>
bool supportBackend(int backendId, int)
{
#ifdef HAVE_INF_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);
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
return true;
#endif
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_CUDA ||
@ -1078,12 +1039,6 @@ struct AbsValFunctor : public BaseDefaultFunctor<AbsValFunctor>
}
#endif // HAVE_HALIDE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
{
return InferenceEngine::Builder::ReLULayer("").setNegativeSlope(-0.999999f);
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
@ -1930,15 +1885,16 @@ struct PowerFunctor : public BaseFunctor
bool supportBackend(int backendId, int targetId)
{
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;
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
return true;
else
#endif
{
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_CUDA ||
backendId == DNN_BACKEND_HALIDE;
}
}
void finalize()
{
@ -2029,14 +1985,6 @@ struct PowerFunctor : public BaseFunctor
}
#endif // HAVE_HALIDE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
{
return InferenceEngine::Builder::PowerLayer("").setPower(power)
.setScale(scale)
.setShift(shift);
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
@ -2189,10 +2137,13 @@ struct ChannelsPReLUFunctor : public BaseFunctor
bool supportBackend(int backendId, int)
{
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
return true;
#endif
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_CUDA ||
backendId == DNN_BACKEND_HALIDE ||
backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH;
backendId == DNN_BACKEND_HALIDE;
}
void apply(const float* srcptr, float* dstptr, int len, size_t planeSize, int cn0, int cn1) const
@ -2282,15 +2233,6 @@ struct ChannelsPReLUFunctor : public BaseFunctor
}
#endif // HAVE_HALIDE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
{
InferenceEngine::Builder::Layer l = InferenceEngine::Builder::PReLULayer("");
const size_t numChannels = scale.total();
addConstantData("weights", wrapToInfEngineBlob(scale, {numChannels}, InferenceEngine::Layout::C), l);
return l;
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)

38
modules/dnn/src/layers/eltwise_layer.cpp
View File

@ -164,6 +164,11 @@ public:
if (hasVecInput && ELTWISE_CHANNNELS_SAME)
return backendId == DNN_BACKEND_OPENCV;
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
return channelsMode == ELTWISE_CHANNNELS_SAME;
#endif
if (backendId == DNN_BACKEND_CUDA)
{
if(channelsModeInput == ELTWISE_CHANNNELS_INPUT_0 || channelsModeInput == ELTWISE_CHANNNELS_INPUT_0_TRUNCATE)
@ -172,9 +177,8 @@ public:
}
return backendId == DNN_BACKEND_OPENCV ||
(backendId == DNN_BACKEND_HALIDE && op != DIV) || // TODO: not implemented, see PR #15811
((((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && (preferableTarget != DNN_TARGET_OPENCL || coeffs.empty()))
|| backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && channelsMode == ELTWISE_CHANNNELS_SAME));
(backendId == DNN_BACKEND_HALIDE && op != DIV) // TODO: not implemented, see PR #15811
;
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,
@ -837,34 +841,6 @@ public:
return Ptr<BackendNode>();
}
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
{
InferenceEngine::Builder::EltwiseLayer ieLayer(name);
ieLayer.setInputPorts(std::vector<InferenceEngine::Port>(inputs.size()));
if (op == SUM)
ieLayer.setEltwiseType(InferenceEngine::Builder::EltwiseLayer::EltwiseType::SUM);
else if (op == PROD)
ieLayer.setEltwiseType(InferenceEngine::Builder::EltwiseLayer::EltwiseType::MUL);
else if (op == DIV)
ieLayer.setEltwiseType(InferenceEngine::Builder::EltwiseLayer::EltwiseType::DIV);
else if (op == MAX)
ieLayer.setEltwiseType(InferenceEngine::Builder::EltwiseLayer::EltwiseType::MAX);
else if (op == MIN)
ieLayer.setEltwiseType(InferenceEngine::Builder::EltwiseLayer::EltwiseType::MIN);
else
CV_Error(Error::StsNotImplemented, "Unsupported eltwise operation");
InferenceEngine::Builder::Layer l = ieLayer;
if (!coeffs.empty())
l.getParameters()["coeff"] = coeffs;
return Ptr<BackendNode>(new InfEngineBackendNode(l));
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,

22
modules/dnn/src/layers/flatten_layer.cpp
View File

@ -72,9 +72,12 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
return true;
#endif
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_CUDA ||
((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && haveInfEngine());
backendId == DNN_BACKEND_CUDA;
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,
@ -171,21 +174,6 @@ public:
}
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
{
InferenceEngine::Builder::Layer ieLayer(name);
ieLayer.setName(name);
ieLayer.setType("Flatten");
ieLayer.getParameters()["axis"] = (size_t)_startAxis;
ieLayer.getParameters()["end_axis"] = _endAxis; // Do not cast to size_t because it might be negative.
ieLayer.setInputPorts(std::vector<InferenceEngine::Port>(1));
ieLayer.setOutputPorts(std::vector<InferenceEngine::Port>(1));
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE

26
modules/dnn/src/layers/fully_connected_layer.cpp
View File

@ -148,12 +148,15 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
return axis == 1;
#endif
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_CUDA ||
(backendId == DNN_BACKEND_HALIDE && haveHalide() && axis == 1) ||
(backendId == DNN_BACKEND_WEBNN && axis == 1) ||
(((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && !blobs.empty()) ||
backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && axis == 1);
(backendId == DNN_BACKEND_WEBNN && axis == 1);
}
virtual bool setActivation(const Ptr<ActivationLayer>& layer) CV_OVERRIDE
@ -570,23 +573,6 @@ public:
return Ptr<BackendNode>();
}
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{
InferenceEngine::Builder::FullyConnectedLayer ieLayer(name);
const int outNum = blobs[0].size[0];
ieLayer.setOutputNum(outNum);
InferenceEngine::Builder::Layer l = ieLayer;
addConstantData("weights", wrapToInfEngineBlob(blobs[0], {(size_t)blobs[0].size[0], (size_t)blobs[0].size[1], 1, 1}, InferenceEngine::Layout::OIHW), l);
if (bias)
addConstantData("biases", wrapToInfEngineBlob(blobs[1], {(size_t)outNum}, InferenceEngine::Layout::C), l);
return Ptr<BackendNode>(new InfEngineBackendNode(l));
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,

26
modules/dnn/src/layers/lrn_layer.cpp
View File

@ -99,12 +99,10 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) {
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
return bias == (int)bias;
}
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) {
return bias == (int)bias;
}
#endif
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_CUDA ||
backendId == DNN_BACKEND_HALIDE ||
@ -444,24 +442,6 @@ public:
#endif // HAVE_HALIDE
}
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{
float alphaSize = alpha;
if (!normBySize)
alphaSize *= (type == SPATIAL_NRM ? size*size : size);
InferenceEngine::Builder::NormLayer ieLayer(name);
ieLayer.setSize(size);
ieLayer.setAlpha(alphaSize);
ieLayer.setBeta(beta);
ieLayer.setAcrossMaps(type == CHANNEL_NRM);
InferenceEngine::Builder::Layer l = ieLayer;
l.getParameters()["k"] = bias;
return Ptr<BackendNode>(new InfEngineBackendNode(l));
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs, const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE

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

@ -124,14 +124,7 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
bool isMyriad = preferableTarget == DNN_TARGET_MYRIAD || preferableTarget == DNN_TARGET_HDDL;
return !zeroDev && (!isMyriad || eps <= 1e-7f);
}
#endif
#ifdef HAVE_DNN_NGRAPH
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
return true;
#endif
@ -387,16 +380,6 @@ public:
}
}
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{
InferenceEngine::Builder::MVNLayer ieLayer(name);
ieLayer.setAcrossChannels(acrossChannels);
ieLayer.setNormalize(normVariance);
ieLayer.setEpsilon(eps);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,

60
modules/dnn/src/layers/normalize_bbox_layer.cpp
View File

@ -70,17 +70,15 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
if (pnorm != 2)
return false;
bool isMyriad = preferableTarget == DNN_TARGET_MYRIAD || preferableTarget == DNN_TARGET_HDDL;
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && isMyriad)
return !acrossSpatial;
return startAxis == 1;
}
#endif
return backendId == DNN_BACKEND_OPENCV ||
(backendId == DNN_BACKEND_CUDA && (pnorm == 1 || pnorm == 2));
}
@ -270,58 +268,6 @@ public:
}
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
{
InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]);
std::vector<size_t> dims = input->getDims();
if (dims.size() == 4)
{
InferenceEngine::Builder::NormalizeLayer ieLayer(name);
ieLayer.setChannelShared(false);
ieLayer.setAcrossMaps(acrossSpatial);
ieLayer.setEpsilon(epsilon);
InferenceEngine::Builder::Layer l = ieLayer;
const int numChannels = dims[1];
InferenceEngine::Blob::Ptr weights;
if (blobs.empty())
{
weights = InferenceEngine::make_shared_blob<float>({
InferenceEngine::Precision::FP32,
{(size_t)numChannels}, InferenceEngine::Layout::C
});
weights->allocate();
Mat weightsMat = infEngineBlobToMat(weights).reshape(1, numChannels);
Mat(numChannels, 1, CV_32F, Scalar(1)).copyTo(weightsMat);
l.getParameters()["channel_shared"] = false;
}
else
{
CV_Assert(numChannels == blobs[0].total());
weights = wrapToInfEngineBlob(blobs[0], {(size_t)numChannels}, InferenceEngine::Layout::C);
l.getParameters()["channel_shared"] = blobs[0].total() == 1;
}
addConstantData("weights", weights, l);
l.getParameters()["across_spatial"] = acrossSpatial;
return Ptr<BackendNode>(new InfEngineBackendNode(l));
}
else
{
InferenceEngine::Builder::GRNLayer ieLayer(name);
ieLayer.setBeta(epsilon);
InferenceEngine::Builder::Layer l = ieLayer;
l.getParameters()["bias"] = epsilon;
return Ptr<BackendNode>(new InfEngineBackendNode(l));
}
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE

5
modules/dnn/src/layers/not_implemented_layer.cpp
View File

@ -87,11 +87,6 @@ public:
CV_Error(Error::StsNotImplemented, msg);
}
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> > &inputs) CV_OVERRIDE
{
CV_Error(Error::StsNotImplemented, msg);
}
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> > &inputs,
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{

28
modules/dnn/src/layers/padding_layer.cpp
View File

@ -102,10 +102,10 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
bool isMyriad = preferableTarget == DNN_TARGET_MYRIAD || preferableTarget == DNN_TARGET_HDDL;
if (INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R1) && isMyriad)
if (isMyriad)
return dstRanges.size() == 4 && paddings[0].first == 0 && paddings[0].second == 0;
return (dstRanges.size() <= 4 || !isArmComputePlugin());
@ -219,30 +219,6 @@ public:
return Ptr<BackendNode>();
}
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{
InferenceEngine::Builder::Layer ieLayer(name);
ieLayer.setName(name);
ieLayer.setType("Pad");
std::vector<int> begins(paddings.size(), 0), ends(paddings.size(), 0);
for (int i = 0; i < paddings.size(); ++i)
{
begins[i] = paddings[i].first;
ends[i] = paddings[i].second;
}
ieLayer.getParameters()["pads_begin"] = begins;
ieLayer.getParameters()["pads_end"] = ends;
ieLayer.getParameters()["pad_mode"] = paddingType;
if (paddingType == "constant")
ieLayer.getParameters()["pad_value"] = paddingValue;
ieLayer.setInputPorts(std::vector<InferenceEngine::Port>(1));
ieLayer.setOutputPorts(std::vector<InferenceEngine::Port>(1));
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
}
#endif
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,

17
modules/dnn/src/layers/permute_layer.cpp
View File

@ -115,13 +115,16 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && preferableTarget == DNN_TARGET_CPU)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
if (preferableTarget == DNN_TARGET_CPU)
return _order.size() <= 4 || !isArmComputePlugin();
return true;
}
#endif
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_CUDA ||
backendId == DNN_BACKEND_WEBNN ||
((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && haveInfEngine()) ||
(backendId == DNN_BACKEND_VKCOM && haveVulkan());
}
@ -418,16 +421,6 @@ public:
}
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{
InferenceEngine::Builder::PermuteLayer ieLayer(name);
ieLayer.setOrder(_order);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE

75
modules/dnn/src/layers/pooling_layer.cpp
View File

@ -199,34 +199,13 @@ public:
{
return type == MAX || type == AVE || type == ROI;
}
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (computeMaxIdx)
return false;
if (kernel_size.size() == 3)
return preferableTarget == DNN_TARGET_CPU;
if (kernel_size.size() == 1)
return false;
if (preferableTarget == DNN_TARGET_MYRIAD || preferableTarget == DNN_TARGET_HDDL) {
#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R1)
if (type == MAX && (pads_begin[1] == 1 && pads_begin[0] == 1) && (strides[0] == 2 && strides[1] == 2)) {
return !isMyriadX();
}
#endif
return type == MAX || type == AVE;
}
else
return type != STOCHASTIC && type != SUM;
}
#endif
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
#ifdef HAVE_DNN_NGRAPH
return !computeMaxIdx && type != STOCHASTIC && kernel_size.size() > 1 && (kernel_size.size() != 3 || !isArmComputePlugin());
#endif
}
else if (backendId == DNN_BACKEND_OPENCV)
#endif
if (backendId == DNN_BACKEND_OPENCV)
{
if (kernel_size.size() == 3)
return preferableTarget == DNN_TARGET_CPU;
@ -550,54 +529,6 @@ public:
return Ptr<BackendNode>();
}
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{
if (type == MAX || type == AVE)
{
InferenceEngine::Builder::PoolingLayer ieLayer(name);
ieLayer.setKernel(kernel_size);
ieLayer.setStrides(strides);
ieLayer.setPaddingsBegin(pads_begin);
ieLayer.setPaddingsEnd(pads_end);
ieLayer.setPoolingType(type == MAX ?
InferenceEngine::Builder::PoolingLayer::PoolingType::MAX :
InferenceEngine::Builder::PoolingLayer::PoolingType::AVG);
ieLayer.setRoundingType(ceilMode ?
InferenceEngine::Builder::PoolingLayer::RoundingType::CEIL :
InferenceEngine::Builder::PoolingLayer::RoundingType::FLOOR);
ieLayer.setExcludePad(!avePoolPaddedArea);
InferenceEngine::Builder::Layer l = ieLayer;
if (!padMode.empty())
l.getParameters()["auto_pad"] = padMode == "VALID" ? std::string("valid") : std::string("same_upper");
return Ptr<BackendNode>(new InfEngineBackendNode(l));
}
else if (type == ROI)
{
InferenceEngine::Builder::ROIPoolingLayer ieLayer(name);
ieLayer.setSpatialScale(spatialScale);
ieLayer.setPooled({pooledSize.height, pooledSize.width});
ieLayer.setInputPorts(std::vector<InferenceEngine::Port>(2));
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
}
else if (type == PSROI)
{
InferenceEngine::Builder::PSROIPoolingLayer ieLayer(name);
ieLayer.setSpatialScale(spatialScale);
ieLayer.setOutputDim(psRoiOutChannels);
ieLayer.setGroupSize(pooledSize.width);
ieLayer.setInputPorts(std::vector<InferenceEngine::Port>(2));
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
}
else
CV_Error(Error::StsNotImplemented, "Unsupported pooling type");
return Ptr<BackendNode>();
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,

67
modules/dnn/src/layers/prior_box_layer.cpp
View File

@ -298,9 +298,7 @@ public:
#endif
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_CUDA ||
(backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && haveInfEngine() &&
( _explicitSizes || (_minSize.size() == 1 && _maxSize.size() <= 1)))
|| (backendId == DNN_BACKEND_VKCOM && haveVulkan());
(backendId == DNN_BACKEND_VKCOM && haveVulkan());
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,
@ -510,69 +508,6 @@ public:
}
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{
if (_explicitSizes)
{
InferenceEngine::Builder::PriorBoxClusteredLayer ieLayer(name);
ieLayer.setSteps({_stepY, _stepX});
CV_CheckEQ(_offsetsX.size(), (size_t)1, ""); CV_CheckEQ(_offsetsY.size(), (size_t)1, ""); CV_CheckEQ(_offsetsX[0], _offsetsY[0], "");
ieLayer.setOffset(_offsetsX[0]);
ieLayer.setClip(_clip);
ieLayer.setFlip(false); // We already flipped aspect ratios.
InferenceEngine::Builder::Layer l = ieLayer;
CV_Assert_N(!_boxWidths.empty(), !_boxHeights.empty(), !_variance.empty());
CV_Assert(_boxWidths.size() == _boxHeights.size());
l.getParameters()["width"] = _boxWidths;
l.getParameters()["height"] = _boxHeights;
l.getParameters()["variance"] = _variance;
return Ptr<BackendNode>(new InfEngineBackendNode(l));
}
else
{
InferenceEngine::Builder::PriorBoxLayer ieLayer(name);
CV_Assert(!_explicitSizes);
ieLayer.setMinSize(_minSize[0]);
if (!_maxSize.empty())
ieLayer.setMaxSize(_maxSize[0]);
CV_CheckEQ(_offsetsX.size(), (size_t)1, ""); CV_CheckEQ(_offsetsY.size(), (size_t)1, ""); CV_CheckEQ(_offsetsX[0], _offsetsY[0], "");
ieLayer.setOffset(_offsetsX[0]);
ieLayer.setClip(_clip);
ieLayer.setFlip(false); // We already flipped aspect ratios.
InferenceEngine::Builder::Layer l = ieLayer;
if (_stepX == _stepY)
{
l.getParameters()["step"] = _stepX;
l.getParameters()["step_h"] = 0.0f;
l.getParameters()["step_w"] = 0.0f;
}
else
{
l.getParameters()["step"] = 0.0f;
l.getParameters()["step_h"] = _stepY;
l.getParameters()["step_w"] = _stepX;
}
if (!_aspectRatios.empty())
{
l.getParameters()["aspect_ratio"] = _aspectRatios;
}
CV_Assert(!_variance.empty());
l.getParameters()["variance"] = _variance;
return Ptr<BackendNode>(new InfEngineBackendNode(l));
}
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs, const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{

28
modules/dnn/src/layers/proposal_layer.cpp
View File

@ -96,7 +96,7 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
bool isMyriad = preferableTarget == DNN_TARGET_MYRIAD || preferableTarget == DNN_TARGET_HDDL;
return !isMyriad;
@ -338,32 +338,6 @@ public:
layerOutputs[0].col(2).copyTo(dst);
}
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{
InferenceEngine::Builder::ProposalLayer ieLayer(name);
ieLayer.setBaseSize(baseSize);
ieLayer.setFeatStride(featStride);
ieLayer.setMinSize(16);
ieLayer.setNMSThresh(nmsThreshold);
ieLayer.setPostNMSTopN(keepTopAfterNMS);
ieLayer.setPreNMSTopN(keepTopBeforeNMS);
std::vector<float> scalesVec(scales.size());
for (int i = 0; i < scales.size(); ++i)
scalesVec[i] = scales.get<float>(i);
ieLayer.setScale(scalesVec);
std::vector<float> ratiosVec(ratios.size());
for (int i = 0; i < ratios.size(); ++i)
ratiosVec[i] = ratios.get<float>(i);
ieLayer.setRatio(ratiosVec);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,

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

@ -151,10 +151,12 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
return true;
#endif
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_CUDA ||
backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ||
backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH;
backendId == DNN_BACKEND_CUDA;
}
#ifdef HAVE_OPENCL
@ -198,16 +200,6 @@ public:
}
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{
InferenceEngine::Builder::ReorgYoloLayer ieLayer(name);
ieLayer.setStride(reorgStride);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> > &inputs,
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE

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

@ -202,10 +202,13 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
return true;
#endif
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_CUDA ||
backendId == DNN_BACKEND_WEBNN ||
((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && haveInfEngine());
backendId == DNN_BACKEND_WEBNN;
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,
@ -306,17 +309,6 @@ public:
}
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
{
InferenceEngine::Builder::ReshapeLayer ieLayer(name);
CV_Assert(outShapes.size() == 1);
ieLayer.setDims(outShapes[0]);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE

34
modules/dnn/src/layers/resize_layer.cpp
View File

@ -78,7 +78,7 @@ public:
return interpolation == "nearest" || interpolation == "bilinear" || interpolation == "opencv_linear";
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
return (interpolation == "nearest" && scaleWidth == scaleHeight) ||
(interpolation == "bilinear");
@ -308,38 +308,6 @@ public:
}
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{
InferenceEngine::Builder::Layer ieLayer(name);
ieLayer.setName(name);
if (interpolation == "nearest")
{
ieLayer.setType("Resample");
ieLayer.getParameters()["type"] = std::string("caffe.ResampleParameter.NEAREST");
ieLayer.getParameters()["antialias"] = false;
if (scaleWidth != scaleHeight)
CV_Error(Error::StsNotImplemented, "resample with sw != sh");
ieLayer.getParameters()["factor"] = 1.0f / scaleWidth;
}
else if (interpolation == "bilinear")
{
ieLayer.setType("Interp");
ieLayer.getParameters()["pad_beg"] = 0;
ieLayer.getParameters()["pad_end"] = 0;
ieLayer.getParameters()["align_corners"] = alignCorners;
}
else
CV_Error(Error::StsNotImplemented, "Unsupported interpolation: " + interpolation);
ieLayer.getParameters()["width"] = outWidth;
ieLayer.getParameters()["height"] = outHeight;
ieLayer.setInputPorts(std::vector<InferenceEngine::Port>(1));
ieLayer.setOutputPorts(std::vector<InferenceEngine::Port>(1));
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE

34
modules/dnn/src/layers/scale_layer.cpp
View File

@ -78,11 +78,13 @@ public:
{
return backendId == DNN_BACKEND_OPENCV;
}
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
return axis > 0;
#endif
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_CUDA ||
backendId == DNN_BACKEND_HALIDE ||
(backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && axis == 1 && !blobs.empty()) ||
(backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && axis > 0) ||
(backendId == DNN_BACKEND_WEBNN && axis >0);
}
@ -314,34 +316,6 @@ public:
}
#endif // HAVE_HALIDE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{
InferenceEngine::Builder::Layer l = InferenceEngine::Builder::ScaleShiftLayer(name);
CV_Assert(!blobs.empty());
const size_t numChannels = blobs[0].total();
if (hasWeights)
{
addConstantData("weights", wrapToInfEngineBlob(blobs[0], {numChannels}, InferenceEngine::Layout::C), l);
}
else
{
auto weights = InferenceEngine::make_shared_blob<float>({
InferenceEngine::Precision::FP32, {(size_t)numChannels},
InferenceEngine::Layout::C
});
weights->allocate();
float* buf = weights->buffer().as<float*>();
std::fill(buf, buf + numChannels, 1);
addConstantData("weights", weights, l);
}
if (hasBias)
addConstantData("biases", wrapToInfEngineBlob(blobs.back(), {numChannels}, InferenceEngine::Layout::C), l);
return Ptr<BackendNode>(new InfEngineBackendNode(l));
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs, const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE

65
modules/dnn/src/layers/slice_layer.cpp
View File

@ -166,12 +166,7 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
return INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R1) &&
sliceRanges.size() == 1 && sliceRanges[0].size() == 4 && !hasSteps;
#endif
#ifdef HAVE_DNN_NGRAPH
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
return sliceRanges.size() == 1 && !hasSteps;
#endif
@ -573,64 +568,6 @@ public:
}
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R1)
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
{
CV_Assert_N(finalSliceRanges.size() == 1, inputs.size() <= 2);
std::vector<size_t> axes, offsets, dims;
int from, to, step;
int numDims = finalSliceRanges[0].size();
if (preferableTarget == DNN_TARGET_MYRIAD || preferableTarget == DNN_TARGET_HDDL)
{
from = axis;
to = numDims;
step = 1;
}
else
{
from = numDims - 1;
to = axis - 1;
step = -1;
}
for (int i = from; i != to; i += step)
{
axes.push_back(i);
offsets.push_back(finalSliceRanges[0][i].start);
dims.push_back(finalSliceRanges[0][i].size());
}
InferenceEngine::Builder::Layer ieLayer(name);
ieLayer.setName(name);
ieLayer.setType("Crop");
ieLayer.getParameters()["axis"] = axes;
ieLayer.getParameters()["dim"] = dims;
ieLayer.getParameters()["offset"] = offsets;
ieLayer.setInputPorts(std::vector<InferenceEngine::Port>(2));
ieLayer.setOutputPorts(std::vector<InferenceEngine::Port>(1));
if (inputs.size() != 2)
{
std::vector<size_t> outShape(numDims);
for (int i = 0; i < numDims; ++i)
outShape[i] = finalSliceRanges[0][i].size();
ieLayer.getInputPorts()[1].setParameter("type", "weights");
auto shapeSource = InferenceEngine::make_shared_blob<float>({
InferenceEngine::Precision::FP32, outShape,
InferenceEngine::Layout::ANY
});
shapeSource->allocate();
addConstantData("weights", shapeSource, ieLayer);
}
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
}
#endif
#endif
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE

17
modules/dnn/src/layers/softmax_layer.cpp
View File

@ -99,6 +99,10 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
return true;
#endif
#ifdef HAVE_WEBNN
if (backendId == DNN_BACKEND_WEBNN) {
// TODO: support logSoftMax
@ -112,8 +116,6 @@ public:
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_CUDA ||
(backendId == DNN_BACKEND_HALIDE && haveHalide() && axisRaw == 1) ||
backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH ||
(backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && haveInfEngine() && !logSoftMax) ||
(backendId == DNN_BACKEND_VKCOM && haveVulkan());
}
@ -360,17 +362,6 @@ public:
return Ptr<BackendNode>();
}
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
{
InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]);
InferenceEngine::Builder::SoftMaxLayer ieLayer(name);
ieLayer.setAxis(normalize_axis(axisRaw, input->getDims().size()));
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,

1025
modules/dnn/src/op_inf_engine.cpp
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File diff suppressed because it is too large Load Diff

192
modules/dnn/src/op_inf_engine.hpp
View File

@ -48,37 +48,16 @@
#pragma GCC diagnostic ignored "-Wsuggest-override"
#endif
#if defined(HAVE_DNN_IE_NN_BUILDER_2019) || INF_ENGINE_VER_MAJOR_EQ(INF_ENGINE_RELEASE_2020_4)
//#define INFERENCE_ENGINE_DEPRECATED // turn off deprecation warnings from IE
//there is no way to suppress warnings from IE only at this moment, so we are forced to suppress warnings globally
#if defined(__GNUC__)
#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
#endif
#ifdef _MSC_VER
#pragma warning(disable: 4996) // was declared deprecated
#endif
#endif
#if defined(__GNUC__) && INF_ENGINE_VER_MAJOR_LT(INF_ENGINE_RELEASE_2020_1)
#pragma GCC visibility push(default)
#endif
#include <inference_engine.hpp>
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
#include <ie_builders.hpp>
#endif
#if defined(__GNUC__) && INF_ENGINE_VER_MAJOR_LT(INF_ENGINE_RELEASE_2020_1)
#pragma GCC visibility pop
#endif
#if defined(__GNUC__) && __GNUC__ >= 5
//#pragma GCC diagnostic pop
#endif
#endif // HAVE_INF_ENGINE
#define CV_ERROR_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 do { CV_Error(Error::StsNotImplemented, "This OpenCV version is built without Inference Engine NN Builder API support (legacy API is not supported anymore)"); } while (0)
namespace cv { namespace dnn {
#ifdef HAVE_INF_ENGINE
@ -90,167 +69,6 @@ Mat infEngineBlobToMat(const InferenceEngine::Blob::Ptr& blob);
void infEngineBlobsToMats(const std::vector<InferenceEngine::Blob::Ptr>& blobs,
std::vector<Mat>& mats);
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
class InfEngineBackendNet
{
public:
InfEngineBackendNet();
InfEngineBackendNet(InferenceEngine::CNNNetwork& net);
void addLayer(InferenceEngine::Builder::Layer& layer);
void addOutput(const std::string& name);
void connect(const std::vector<Ptr<BackendWrapper> >& inputs,
const std::vector<Ptr<BackendWrapper> >& outputs,
const std::string& layerName);
bool isInitialized();
void init(Target targetId);
void forward(const std::vector<Ptr<BackendWrapper> >& outBlobsWrappers,
bool isAsync);
void initPlugin(InferenceEngine::CNNNetwork& net);
void addBlobs(const std::vector<cv::Ptr<BackendWrapper> >& ptrs);
void reset();
private:
InferenceEngine::Builder::Network netBuilder;
InferenceEngine::ExecutableNetwork netExec;
InferenceEngine::BlobMap allBlobs;
std::string device_name;
#if INF_ENGINE_VER_MAJOR_LE(2019010000)
InferenceEngine::InferenceEnginePluginPtr enginePtr;
InferenceEngine::InferencePlugin plugin;
#else
bool isInit = false;
#endif
struct InfEngineReqWrapper
{
InfEngineReqWrapper() : 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<InfEngineReqWrapper> > infRequests;
InferenceEngine::CNNNetwork cnn;
bool hasNetOwner;
std::map<std::string, int> layers;
std::vector<std::string> requestedOutputs;
std::set<std::pair<int, int> > unconnectedPorts;
};
class InfEngineBackendNode : public BackendNode
{
public:
InfEngineBackendNode(const InferenceEngine::Builder::Layer& layer);
InfEngineBackendNode(Ptr<Layer>& layer, std::vector<Mat*>& inputs,
std::vector<Mat>& outputs, std::vector<Mat>& internals);
void connect(std::vector<Ptr<BackendWrapper> >& inputs,
std::vector<Ptr<BackendWrapper> >& outputs);
// Inference Engine network object that allows to obtain the outputs of this layer.
InferenceEngine::Builder::Layer layer;
Ptr<InfEngineBackendNet> net;
// CPU fallback in case of unsupported Inference Engine layer.
Ptr<dnn::Layer> cvLayer;
};
class InfEngineBackendWrapper : public BackendWrapper
{
public:
InfEngineBackendWrapper(int targetId, const Mat& m);
InfEngineBackendWrapper(Ptr<BackendWrapper> wrapper);
~InfEngineBackendWrapper();
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::Blob::Ptr wrapToInfEngineBlob(const Mat& m, InferenceEngine::Layout layout = InferenceEngine::Layout::ANY);
InferenceEngine::Blob::Ptr wrapToInfEngineBlob(const Mat& m, const std::vector<size_t>& shape, InferenceEngine::Layout layout);
InferenceEngine::DataPtr infEngineDataNode(const Ptr<BackendWrapper>& ptr);
// Convert Inference Engine blob with FP32 precision to FP16 precision.
// Allocates memory for a new blob.
InferenceEngine::Blob::Ptr convertFp16(const InferenceEngine::Blob::Ptr& blob);
void addConstantData(const std::string& name, InferenceEngine::Blob::Ptr data, InferenceEngine::Builder::Layer& l);
// 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 InfEngineBackendLayer : public Layer
{
public:
InfEngineBackendLayer(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;
};
class InfEngineExtension : public InferenceEngine::IExtension
{
public:
#if INF_ENGINE_VER_MAJOR_LT(INF_ENGINE_RELEASE_2020_2)
virtual void SetLogCallback(InferenceEngine::IErrorListener&) noexcept {}
#endif
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;
};
#endif // HAVE_DNN_IE_NN_BUILDER_2019
CV__DNN_INLINE_NS_BEGIN
@ -273,14 +91,8 @@ static inline std::vector<T> getShape(const Mat& mat)
return result;
}
#endif // HAVE_INF_ENGINE
bool haveInfEngine();
void forwardInfEngine(const std::vector<Ptr<BackendWrapper> >& outBlobsWrappers,
Ptr<BackendNode>& node, bool isAsync);
}} // namespace dnn, namespace cv
#endif // __OPENCV_DNN_OP_INF_ENGINE_HPP__

10
modules/dnn/test/test_common.impl.hpp
View File

@ -337,16 +337,6 @@ testing::internal::ParamGenerator< tuple<Backend, Target> > dnnBackendsAndTarget
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 || *i == DNN_TARGET_HDDL) && !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)

13
modules/dnn/test/test_ie_models.cpp
View File

@ -371,17 +371,17 @@ TEST_P(DNNTestOpenVINO, models)
|| modelName == "person-vehicle-bike-detection-2004" // 2021.4+: ncDeviceOpen:1013 Failed to find booted device after boot
)
)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
if (targetId == DNN_TARGET_OPENCL && (false
|| modelName == "face-detection-0106" // Operation: 2278 of type ExperimentalDetectronPriorGridGenerator(op::v6) is not supported
)
)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
if (targetId == DNN_TARGET_OPENCL_FP16 && (false
|| modelName == "face-detection-0106" // Operation: 2278 of type ExperimentalDetectronPriorGridGenerator(op::v6) is not supported
)
)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
#if INF_ENGINE_VER_MAJOR_GE(2020020000)
@ -397,12 +397,7 @@ TEST_P(DNNTestOpenVINO, models)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
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";
ASSERT_EQ(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, backendId);
bool isFP16 = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD);

21
modules/dnn/test/test_layers.cpp
View File

@ -1265,12 +1265,7 @@ TEST_P(Layer_Test_Convolution_DLDT, Accuracy)
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";
ASSERT_EQ(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, backendId);
Net netDefault = readNet(_tf("layer_convolution.caffemodel"), _tf("layer_convolution.prototxt"));
Net net = readNet(_tf("layer_convolution.xml"), _tf("layer_convolution.bin"));
@ -1310,12 +1305,7 @@ TEST_P(Layer_Test_Convolution_DLDT, setInput_uint8)
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";
ASSERT_EQ(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, backendId);
int blobSize[] = {2, 6, 75, 113};
Mat inputs[] = {Mat(4, &blobSize[0], CV_8U), Mat()};
@ -1348,12 +1338,7 @@ TEST_P(Layer_Test_Convolution_DLDT, multithreading)
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";
ASSERT_EQ(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, backendId);
std::string xmlPath = _tf("layer_convolution.xml");
std::string binPath = _tf("layer_convolution.bin");

49
modules/dnn/test/test_misc.cpp
View File

@ -117,12 +117,7 @@ void test_readNet_IE_do_not_call_setInput(Backend backendId)
const std::string& model = findDataFile("dnn/layers/layer_convolution.bin");
const std::string& proto = findDataFile("dnn/layers/layer_convolution.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";
ASSERT_EQ(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, backendId);
Net net = readNet(model, proto);
net.setPreferableBackend(backendId);
@ -462,12 +457,7 @@ TEST_P(Async, model_optimizer_pipeline_set_and_forward_single)
const std::string& model = findDataFile("dnn/layers/layer_convolution.bin");
const std::string& proto = findDataFile("dnn/layers/layer_convolution.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";
ASSERT_EQ(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, backendId);
Net netSync = readNet(model, proto);
netSync.setPreferableBackend(backendId);
@ -523,12 +513,7 @@ TEST_P(Async, model_optimizer_pipeline_set_and_forward_all)
const std::string& model = findDataFile("dnn/layers/layer_convolution.bin");
const std::string& proto = findDataFile("dnn/layers/layer_convolution.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";
ASSERT_EQ(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, backendId);
Net netSync = readNet(model, proto);
netSync.setPreferableBackend(backendId);
@ -586,12 +571,7 @@ TEST_P(Async, create_layer_pipeline_set_and_forward_all)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && dtype == CV_8U)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
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";
ASSERT_EQ(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, backendId);
Net netSync;
Net netAsync;
@ -697,12 +677,7 @@ TEST_P(Test_Model_Optimizer, forward_two_nets)
const std::string& model = findDataFile("dnn/layers/layer_convolution.bin");
const std::string& proto = findDataFile("dnn/layers/layer_convolution.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";
ASSERT_EQ(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, backendId);
Net net0 = readNet(model, proto);
net0.setPreferableTarget(targetId);
@ -741,12 +716,7 @@ TEST_P(Test_Model_Optimizer, readFromBuffer)
const std::string& weightsFile = findDataFile("dnn/layers/layer_convolution.bin");
const std::string& modelFile = findDataFile("dnn/layers/layer_convolution.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";
ASSERT_EQ(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, backendId);
Net net1 = readNetFromModelOptimizer(modelFile, weightsFile);
net1.setPreferableBackend(backendId);
@ -793,12 +763,7 @@ TEST_P(Test_Model_Optimizer, flexible_inputs)
const std::string& model = findDataFile("dnn/layers/layer_convolution.bin");
const std::string& proto = findDataFile("dnn/layers/layer_convolution.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";
ASSERT_EQ(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, backendId);
Net net0 = readNet(model, proto);
net0.setPreferableTarget(targetId);

51
modules/imgproc/include/opencv2/imgproc.hpp
View File

@ -3559,10 +3559,11 @@ a mask and then extract the contour, or copy the region to another image, and so
function unless the #FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See
the details below.
@param mask Operation mask that should be a single-channel 8-bit image, 2 pixels wider and 2 pixels
taller than image. Since this is both an input and output parameter, you must take responsibility
of initializing it. Flood-filling cannot go across non-zero pixels in the input mask. For example,
taller than image. If an empty Mat is passed it will be created automatically. Since this is both an
input and output parameter, you must take responsibility of initializing it.
Flood-filling cannot go across non-zero pixels in the input mask. For example,
an edge detector output can be used as a mask to stop filling at edges. On output, pixels in the
mask corresponding to filled pixels in the image are set to 1 or to the a value specified in flags
mask corresponding to filled pixels in the image are set to 1 or to the specified value in flags
as described below. Additionally, the function fills the border of the mask with ones to simplify
internal processing. It is therefore possible to use the same mask in multiple calls to the function
to make sure the filled areas do not overlap.
@ -4832,13 +4833,11 @@ CV_EXPORTS_W double getFontScaleFromHeight(const int fontFace,
const int pixelHeight,
const int thickness = 1);
/** @brief Line iterator
/** @brief Class for iterating over all pixels on a raster line segment.
The class is used to iterate over all the pixels on the raster line
segment connecting two specified points.
The class LineIterator is used to get each pixel of a raster line. It
can be treated as versatile implementation of the Bresenham algorithm
The class LineIterator is used to get each pixel of a raster line connecting
two specified points.
It can be treated as a versatile implementation of the Bresenham algorithm
where you can stop at each pixel and do some extra processing, for
example, grab pixel values along the line or draw a line with an effect
(for example, with XOR operation).
@ -4867,14 +4866,19 @@ for(int i = 0; i < it2.count; i++, ++it2)
class CV_EXPORTS LineIterator
{
public:
/** @brief initializes the iterator
/** @brief Initializes iterator object for the given line and image.
creates iterators for the line connecting pt1 and pt2
the line will be clipped on the image boundaries
the line is 8-connected or 4-connected
If leftToRight=true, then the iteration is always done
from the left-most point to the right most,
not to depend on the ordering of pt1 and pt2 parameters;
The returned iterator can be used to traverse all pixels on a line that
connects the given two points.
The line will be clipped on the image boundaries.
@param img Underlying image.
@param pt1 First endpoint of the line.
@param pt2 The other endpoint of the line.
@param connectivity Pixel connectivity of the iterator. Valid values are 4 (iterator can move
up, down, left and right) and 8 (iterator can also move diagonally).
@param leftToRight If true, the line is traversed from the leftmost endpoint to the rightmost
endpoint. Otherwise, the line is traversed from \p pt1 to \p pt2.
*/
LineIterator( const Mat& img, Point pt1, Point pt2,
int connectivity = 8, bool leftToRight = false )
@ -4907,16 +4911,23 @@ public:
}
void init(const Mat* img, Rect boundingAreaRect, Point pt1, Point pt2, int connectivity, bool leftToRight);
/** @brief returns pointer to the current pixel
/** @brief Returns pointer to the current pixel.
*/
uchar* operator *();
/** @brief prefix increment operator (++it). shifts iterator to the next pixel
/** @brief Moves iterator to the next pixel on the line.
This is the prefix version (++it).
*/
LineIterator& operator ++();
/** @brief postfix increment operator (it++). shifts iterator to the next pixel
/** @brief Moves iterator to the next pixel on the line.
This is the postfix version (it++).
*/
LineIterator operator ++(int);
/** @brief returns coordinates of the current pixel
/** @brief Returns coordinates of the current pixel.
*/
Point pos() const;

44
modules/imgproc/src/floodfill.cpp
View File

@ -477,11 +477,10 @@ int cv::floodFill( InputOutputArray _image, InputOutputArray _mask,
nv_buf._[0] = nv_buf._[1] = nv_buf._[2] = nv_buf._[3] = 0;
struct { Vec3b b; Vec3i i; Vec3f f; } ld_buf, ud_buf;
Mat img = _image.getMat(), mask;
if( !_mask.empty() )
mask = _mask.getMat();
Size size = img.size();
Mat img = _image.getMat(), mask;
Size size = img.size();
int type = img.type();
int depth = img.depth();
int cn = img.channels();
@ -495,6 +494,20 @@ int cv::floodFill( InputOutputArray _image, InputOutputArray _mask,
if( connectivity != 0 && connectivity != 4 && connectivity != 8 )
CV_Error( CV_StsBadFlag, "Connectivity must be 4, 0(=4) or 8" );
if( _mask.empty() )
{
_mask.create( size.height + 2, size.width + 2, CV_8UC1 );
_mask.setTo(0);
}
mask = _mask.getMat();
CV_CheckTypeEQ( mask.type(), CV_8U, "" );
CV_CheckEQ( mask.rows, size.height + 2, "" );
CV_CheckEQ( mask.cols, size.width + 2, "" );
Mat mask_inner = mask( Rect(1, 1, mask.cols - 2, mask.rows - 2) );
copyMakeBorder( mask_inner, mask, 1, 1, 1, 1, BORDER_ISOLATED | BORDER_CONSTANT, Scalar(1) );
bool is_simple = mask.empty() && (flags & FLOODFILL_MASK_ONLY) == 0;
for( i = 0; i < cn; i++ )
@ -544,26 +557,6 @@ int cv::floodFill( InputOutputArray _image, InputOutputArray _mask,
}
}
if( mask.empty() )
{
Mat tempMask( size.height + 2, size.width + 2, CV_8UC1 );
tempMask.setTo(Scalar::all(0));
mask = tempMask;
}
else
{
CV_Assert( mask.rows == size.height+2 && mask.cols == size.width+2 );
CV_Assert( mask.type() == CV_8U );
}
memset( mask.ptr(), 1, mask.cols );
memset( mask.ptr(mask.rows-1), 1, mask.cols );
for( i = 1; i <= size.height; i++ )
{
mask.at<uchar>(i, 0) = mask.at<uchar>(i, mask.cols-1) = (uchar)1;
}
if( depth == CV_8U )
for( i = 0; i < cn; i++ )
{
@ -632,7 +625,8 @@ int cv::floodFill( InputOutputArray _image, Point seedPoint,
{
CV_INSTRUMENT_REGION();
return floodFill(_image, Mat(), seedPoint, newVal, rect, loDiff, upDiff, flags);
Mat mask;
return floodFill(_image, mask, seedPoint, newVal, rect, loDiff, upDiff, flags);
}

4
modules/imgproc/test/test_floodfill.cpp
View File

@ -531,11 +531,11 @@ TEST(Imgproc_FloodFill, maskValue)
{
const int n = 50;
Mat img = Mat::zeros(n, n, CV_8U);
Mat mask = Mat::zeros(n + 2, n + 2, CV_8U);
Mat mask;
circle(img, Point(n/2, n/2), 20, Scalar(100), 4);
int flags = 4 + CV_FLOODFILL_MASK_ONLY;
int flags = 4 + FLOODFILL_MASK_ONLY;
floodFill(img, mask, Point(n/2 + 13, n/2), Scalar(100), NULL, Scalar(), Scalar(), flags);
ASSERT_EQ(1, cvtest::norm(mask.rowRange(1, n-1).colRange(1, n-1), NORM_INF));

29
platforms/winpack_dldt/2021.4.2/cmake/InferenceEngineConfig-version.cmake Normal file
View File

@ -0,0 +1,29 @@
# Copyright (C) 2018-2021 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
#
set(PACKAGE_VERSION_MAJOR 2021)
set(PACKAGE_VERSION_MINOR 4)
set(PACKAGE_VERSION_PATCH 2)
set(PACKAGE_VERSION "${PACKAGE_VERSION_MAJOR}.${PACKAGE_VERSION_MINOR}.${PACKAGE_VERSION_PATCH}")
set(PACKAGE_VERSION_EXACT False)
set(PACKAGE_VERSION_COMPATIBLE False)
# Compatibility with old versioning for 2.x
if(PACKAGE_FIND_VERSION_MAJOR VERSION_EQUAL 2)
set(PACKAGE_VERSION_COMPATIBLE True)
if(${CMAKE_FIND_PACKAGE_NAME}_FIND_REQUIRED)
message(WARNING "Inference Engine versioning has changed. Use ${PACKAGE_VERSION} instead of ${PACKAGE_FIND_VERSION}")
endif()
endif()
if(PACKAGE_FIND_VERSION VERSION_EQUAL PACKAGE_VERSION)
set(PACKAGE_VERSION_EXACT True)
set(PACKAGE_VERSION_COMPATIBLE True)
endif()
if(PACKAGE_FIND_VERSION_MAJOR EQUAL PACKAGE_VERSION_MAJOR AND
PACKAGE_FIND_VERSION VERSION_LESS PACKAGE_VERSION)
set(PACKAGE_VERSION_COMPATIBLE True)
endif()

31
platforms/winpack_dldt/2021.4.2/cmake/InferenceEngineConfig.cmake Normal file
View File

@ -0,0 +1,31 @@
# Inference Engine CMake config for OpenCV windows package
get_filename_component(_IMPORT_PREFIX "${CMAKE_CURRENT_LIST_FILE}" PATH)
get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
set(InferenceEngine_LIBRARIES IE::inference_engine)
add_library(IE::inference_engine SHARED IMPORTED)
set_target_properties(IE::inference_engine PROPERTIES
INTERFACE_INCLUDE_DIRECTORIES "${_IMPORT_PREFIX}/deployment_tools/inference_engine/include"
)
# Import target "IE::inference_engine" for configuration "Debug"
set_property(TARGET IE::inference_engine APPEND PROPERTY IMPORTED_CONFIGURATIONS DEBUG)
set_target_properties(IE::inference_engine PROPERTIES
IMPORTED_IMPLIB_DEBUG "${_IMPORT_PREFIX}/deployment_tools/inference_engine/lib/intel64/inference_engined.lib"
IMPORTED_LINK_DEPENDENT_LIBRARIES_DEBUG ""
IMPORTED_LOCATION_DEBUG "${_IMPORT_PREFIX}/bin/inference_engined.dll"
)
# Import target "IE::inference_engine" for configuration "Release"
set_property(TARGET IE::inference_engine APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
set_target_properties(IE::inference_engine PROPERTIES
IMPORTED_IMPLIB_RELEASE "${_IMPORT_PREFIX}/deployment_tools/inference_engine/lib/intel64/inference_engine.lib"
IMPORTED_LINK_DEPENDENT_LIBRARIES_RELEASE ""
IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/bin/inference_engine.dll"
)
set(InferenceEngine_FOUND ON)

2
platforms/winpack_dldt/2021.4.2/sysroot.config.py
View File

@ -1,3 +1,5 @@
copytree(self.cpath / 'cmake', self.sysrootdir / 'deployment_tools' / 'inference_engine' / 'cmake')
sysroot_bin_dir = prepare_dir(self.sysrootdir / 'bin')
copytree(self.build_dir / 'install', self.sysrootdir / 'ngraph')
#rm_one(self.sysrootdir / 'ngraph' / 'lib' / 'ngraph.dll')

7
platforms/winpack_dldt/build_package.py
View File

@ -388,10 +388,9 @@ class Builder:
if self.config.dldt_release:
cmake_vars['INF_ENGINE_RELEASE'] = str(self.config.dldt_release)
cmake_vars['INF_ENGINE_LIB_DIRS:PATH'] = str(builderDLDT.sysrootdir / 'deployment_tools/inference_engine/lib/intel64')
assert os.path.exists(cmake_vars['INF_ENGINE_LIB_DIRS:PATH']), cmake_vars['INF_ENGINE_LIB_DIRS:PATH']
cmake_vars['INF_ENGINE_INCLUDE_DIRS:PATH'] = str(builderDLDT.sysrootdir / 'deployment_tools/inference_engine/include')
assert os.path.exists(cmake_vars['INF_ENGINE_INCLUDE_DIRS:PATH']), cmake_vars['INF_ENGINE_INCLUDE_DIRS:PATH']
InferenceEngine_DIR = str(builderDLDT.sysrootdir / 'deployment_tools' / 'inference_engine' / 'cmake')
assert os.path.exists(InferenceEngine_DIR), InferenceEngine_DIR
cmake_vars['InferenceEngine_DIR:PATH'] = InferenceEngine_DIR
ngraph_DIR = str(builderDLDT.sysrootdir / 'ngraph/cmake')
if not os.path.exists(ngraph_DIR):

2
samples/dnn/text_detection.py
View File

@ -195,7 +195,7 @@ def main():
indices = cv.dnn.NMSBoxesRotated(boxes, confidences, confThreshold, nmsThreshold)
for i in indices:
# get 4 corners of the rotated rect
vertices = cv.boxPoints(boxes[i[0]])
vertices = cv.boxPoints(boxes[i])
# scale the bounding box coordinates based on the respective ratios
for j in range(4):
vertices[j][0] *= rW