diff --git a/cmake/OpenCVDetectInferenceEngine.cmake b/cmake/OpenCVDetectInferenceEngine.cmake index 319fd5bf0a..9a2eb38b03 100644 --- a/cmake/OpenCVDetectInferenceEngine.cmake +++ b/cmake/OpenCVDetectInferenceEngine.cmake @@ -13,67 +13,3 @@ if(WITH_OPENVINO) return() endif() endif() - -# ====================== - -if(WITH_OPENVINO) - find_package(OpenVINO QUIET) - if(OpenVINO_FOUND) - message(STATUS "OpenVINO FOUND: ${OpenVINO_VERSION}") - math(EXPR ver "${OpenVINO_VERSION_MAJOR} * 1000000 + ${OpenVINO_VERSION_MINOR} * 10000 + ${OpenVINO_VERSION_PATCH} * 100") - ocv_add_external_target(openvino "" "openvino::runtime" "INF_ENGINE_RELEASE=${ver};HAVE_NGRAPH;HAVE_DNN_NGRAPH;HAVE_INF_ENGINE") - set(HAVE_OPENVINO 1) - return() - endif() -endif() - -# ====================== - -find_package(InferenceEngine QUIET) -if(InferenceEngine_FOUND) - set(INF_ENGINE_TARGET ${InferenceEngine_LIBRARIES}) - set(INF_ENGINE_VERSION "${InferenceEngine_VERSION}") - message(STATUS "Detected InferenceEngine: cmake package (${InferenceEngine_VERSION})") -endif() - -if(DEFINED InferenceEngine_VERSION) - message(STATUS "InferenceEngine: ${InferenceEngine_VERSION}") - if(NOT INF_ENGINE_RELEASE AND NOT (InferenceEngine_VERSION VERSION_LESS "2021.4")) - math(EXPR INF_ENGINE_RELEASE_INIT "${InferenceEngine_VERSION_MAJOR} * 1000000 + ${InferenceEngine_VERSION_MINOR} * 10000 + ${InferenceEngine_VERSION_PATCH} * 100") - endif() -endif() -if(NOT INF_ENGINE_RELEASE AND NOT INF_ENGINE_RELEASE_INIT) - message(STATUS "WARNING: InferenceEngine version has not been set, 2021.4.2 will be used by default. Set INF_ENGINE_RELEASE variable if you experience build errors.") - set(INF_ENGINE_RELEASE_INIT "2021040200") -elseif(DEFINED INF_ENGINE_RELEASE) - set(INF_ENGINE_RELEASE_INIT "${INF_ENGINE_RELEASE}") -endif() -set(INF_ENGINE_RELEASE "${INF_ENGINE_RELEASE_INIT}" CACHE STRING "Force IE version, should be in form YYYYAABBCC (e.g. 2020.1.0.2 -> 2020010002)") - -set(tgts) -set(defs) - -# Add more features to the target -if(INF_ENGINE_TARGET) - set_target_properties(${INF_ENGINE_TARGET} PROPERTIES - INTERFACE_COMPILE_DEFINITIONS "HAVE_INF_ENGINE=1;INF_ENGINE_RELEASE=${INF_ENGINE_RELEASE}" - ) - list(APPEND tgts ${INF_ENGINE_TARGET}) - list(APPEND defs "INF_ENGINE_RELEASE=${INF_ENGINE_RELEASE}" "HAVE_INF_ENGINE") -endif() - -if(WITH_NGRAPH OR NOT DEFINED WITH_NGRAPH) - find_package(ngraph QUIET) - if(ngraph_FOUND) - ocv_assert(TARGET ngraph::ngraph) - if(INF_ENGINE_RELEASE VERSION_LESS "2019039999") - message(WARNING "nGraph is not tested with current InferenceEngine version: INF_ENGINE_RELEASE=${INF_ENGINE_RELEASE}") - endif() - message(STATUS "Detected ngraph: cmake package (${ngraph_VERSION})") - set(HAVE_NGRAPH ON) - list(APPEND tgts ngraph::ngraph) - list(APPEND defs "HAVE_NGRAPH" "HAVE_DNN_NGRAPH") - endif() -endif() - -ocv_add_external_target(openvino "" "${tgts}" "${defs}") diff --git a/modules/dnn/src/ie_ngraph.cpp b/modules/dnn/src/ie_ngraph.cpp index 18aa8cc3b6..6e7b9f9be5 100644 --- a/modules/dnn/src/ie_ngraph.cpp +++ b/modules/dnn/src/ie_ngraph.cpp @@ -14,7 +14,7 @@ #include #ifdef HAVE_DNN_NGRAPH -#include +#include #endif // HAVE_DNN_NGRAPH #include @@ -35,36 +35,6 @@ static bool DNN_IE_SERIALIZE = utils::getConfigurationParameterBool("OPENCV_DNN_ static std::string kDefaultInpLayerName = "opencv_ngraph_empty_inp_layer_name"; static constexpr const char* kOpenCVLayersType = "opencv_ngraph_layer"; -#if INF_ENGINE_VER_MAJOR_LT(INF_ENGINE_RELEASE_2022_1) -static std::string shapesToStr(const std::vector& mats) -{ - std::ostringstream shapes; - shapes << mats.size() << " "; - for (const Mat& m : mats) - { - shapes << m.dims << " "; - for (int i = 0; i < m.dims; ++i) - shapes << m.size[i] << " "; - } - return shapes.str(); -} - -static void strToShapes(const std::string& str, std::vector >& shapes) -{ - std::istringstream ss(str); - int num, dims; - ss >> num; - shapes.resize(num); - for (int i = 0; i < num; ++i) - { - ss >> dims; - shapes[i].resize(dims); - for (int j = 0; j < dims; ++j) - ss >> shapes[i][j]; - } -} -#endif // OpenVINO < 2022.1 - static std::vector > ngraphWrappers(const std::vector >& ptrs) { @@ -78,13 +48,11 @@ ngraphWrappers(const std::vector >& ptrs) return wrappers; } -#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2022_1) - class NgraphCustomOp: public ov::op::Op { public: OPENVINO_OP(kOpenCVLayersType); - NgraphCustomOp(const ngraph::OutputVector& inputs, Ptr& cvLayer, const std::vector& outputs, const std::vector& internals): + NgraphCustomOp(const ov::OutputVector& inputs, Ptr& cvLayer, const std::vector& outputs, const std::vector& internals): Op(inputs), cvLayer(cvLayer), outputs(outputs), internals(internals) { constructor_validate_and_infer_types(); @@ -103,7 +71,7 @@ public: } } - std::shared_ptr clone_with_new_inputs(const ngraph::OutputVector& new_args) const override + std::shared_ptr clone_with_new_inputs(const ov::OutputVector& new_args) const override { return std::make_shared(new_args, cvLayer, outputs, internals); } @@ -131,265 +99,13 @@ public: std::vector outputs, internals; }; -#else - -class NgraphCustomOp: public ngraph::op::Op { -public: - const ngraph::NodeTypeInfo& get_type_info() const override - { - static constexpr ngraph::NodeTypeInfo type_info{kOpenCVLayersType, static_cast(0)}; - return type_info; - } - -#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2020_3) - NgraphCustomOp(const ngraph::OutputVector& inputs, -#else - NgraphCustomOp(const ngraph::NodeVector& inputs, -#endif - const std::map& params = {}): - Op(inputs), params(params) - { - constructor_validate_and_infer_types(); - } - - ~NgraphCustomOp() - { - // nothing - } - - void validate_and_infer_types() override - { - std::vector > shapes; - strToShapes(params["outputs"], shapes); - set_output_size(shapes.size()); - for (size_t i = 0; i < shapes.size(); ++i) - { - ngraph::Shape output_shape(shapes[i]); - set_output_type(i, get_input_element_type(0), output_shape); - } - } - -#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2020_4) - std::shared_ptr clone_with_new_inputs(const ngraph::OutputVector& new_args) const override - { - return std::make_shared(new_args, params); - } -#else - std::shared_ptr copy_with_new_args(const ngraph::NodeVector& new_args) const override - { -#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2020_3) - return std::make_shared(ngraph::as_output_vector(new_args), params); -#else - return std::make_shared(new_args, params); -#endif - } -#endif - - bool visit_attributes(ngraph::AttributeVisitor& visitor) override - { - for (auto& attr : params) - { - if (attr.second.is()) - visitor.on_attribute(attr.first, attr.second.as()); - } - return true; - } - - std::map params; -}; - - -class InfEngineNgraphCustomLayer : public InferenceEngine::ILayerExecImpl -{ -public: -#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2020_2) - explicit InfEngineNgraphCustomLayer(const std::shared_ptr& _node) - { - node = std::dynamic_pointer_cast(_node); - CV_Assert(node); - std::string implStr = node->params["impl"]; - std::istringstream iss(implStr); -#else - explicit InfEngineNgraphCustomLayer(const InferenceEngine::CNNLayer& layer) : cnnLayer(layer) - { - std::istringstream iss(layer.GetParamAsString("impl")); -#endif - size_t ptr; - iss >> ptr; - cvLayer = (Layer*)ptr; - - std::vector > shapes; -#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2020_2) - strToShapes(node->params["internals"], shapes); -#else - strToShapes(layer.GetParamAsString("internals"), shapes); -#endif - internals.resize(shapes.size()); - for (int i = 0; i < shapes.size(); ++i) - internals[i].create(std::vector(shapes[i].begin(), shapes[i].end()), CV_32F); - } - - ~InfEngineNgraphCustomLayer() - { - // nothing - } - - virtual InferenceEngine::StatusCode execute(std::vector& inputs, - std::vector& outputs, - InferenceEngine::ResponseDesc *resp) noexcept - { - std::vector inpMats, outMats; - infEngineBlobsToMats(inputs, inpMats); - infEngineBlobsToMats(outputs, outMats); - - try - { - cvLayer->forward(inpMats, outMats, internals); - return InferenceEngine::StatusCode::OK; - } - catch (...) - { - return InferenceEngine::StatusCode::GENERAL_ERROR; - } - } - - virtual InferenceEngine::StatusCode - getSupportedConfigurations(std::vector& conf, - InferenceEngine::ResponseDesc* resp) noexcept - { - std::vector inDataConfig; - std::vector outDataConfig; -#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2020_2) - InferenceEngine::SizeVector order; - for (int i = 0; i < node->get_input_size(); ++i) - { - InferenceEngine::DataConfig conf; - auto shape = node->input_value(i).get_shape(); - order.resize(shape.size()); - std::iota(order.begin(), order.end(), 0); - conf.desc = InferenceEngine::TensorDesc(InferenceEngine::Precision::FP32, shape, {shape, order}); - inDataConfig.push_back(conf); - } - - for (int i = 0; i < node->get_output_size(); ++i) - { - InferenceEngine::DataConfig conf; - auto shape = node->output(i).get_shape(); - order.resize(shape.size()); - std::iota(order.begin(), order.end(), 0); - conf.desc = InferenceEngine::TensorDesc(InferenceEngine::Precision::FP32, shape, {shape, order}); - outDataConfig.push_back(conf); - } -#else - for (auto& it : cnnLayer.insData) - { - InferenceEngine::DataConfig conf; - conf.desc = it.lock()->getTensorDesc(); - inDataConfig.push_back(conf); - } - - for (auto& it : cnnLayer.outData) - { - InferenceEngine::DataConfig conf; - conf.desc = it->getTensorDesc(); - outDataConfig.push_back(conf); - } -#endif - - InferenceEngine::LayerConfig layerConfig; - layerConfig.inConfs = inDataConfig; - layerConfig.outConfs = outDataConfig; - - conf.push_back(layerConfig); - return InferenceEngine::StatusCode::OK; - } - - InferenceEngine::StatusCode init(InferenceEngine::LayerConfig& config, - InferenceEngine::ResponseDesc *resp) noexcept - { - return InferenceEngine::StatusCode::OK; - } - -private: -#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2020_2) - std::shared_ptr node; -#else - InferenceEngine::CNNLayer cnnLayer; -#endif - dnn::Layer* cvLayer; - std::vector internals; -}; - -#if INF_ENGINE_VER_MAJOR_LT(INF_ENGINE_RELEASE_2020_2) -class InfEngineNgraphCustomLayerFactory : public InferenceEngine::ILayerImplFactory { -public: - explicit InfEngineNgraphCustomLayerFactory(const InferenceEngine::CNNLayer* layer) : cnnLayer(*layer) - { - // nothing - } - - InferenceEngine::StatusCode - getImplementations(std::vector& impls, - InferenceEngine::ResponseDesc* resp) noexcept override - { - impls.push_back(std::make_shared(cnnLayer)); - return InferenceEngine::StatusCode::OK; - } - -private: - InferenceEngine::CNNLayer cnnLayer; -}; -#endif - - -class InfEngineNgraphExtension : public InferenceEngine::IExtension -{ -public: - void Unload() noexcept override {} - void Release() noexcept override { delete this; } - void GetVersion(const InferenceEngine::Version*&) const noexcept override {} - -#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2020_2) - std::vector getImplTypes(const std::shared_ptr& node) override { - return {"CPU"}; - } - - InferenceEngine::ILayerImpl::Ptr getImplementation(const std::shared_ptr& node, const std::string& implType) override { - if (std::dynamic_pointer_cast(node) && implType == "CPU") { - return std::make_shared(node); - } - return nullptr; - } -#else - virtual void SetLogCallback(InferenceEngine::IErrorListener&) noexcept {} - - virtual InferenceEngine::StatusCode getPrimitiveTypes(char**&, unsigned int&, - InferenceEngine::ResponseDesc*) noexcept - { - return InferenceEngine::StatusCode::OK; - } - - InferenceEngine::StatusCode getFactoryFor(InferenceEngine::ILayerImplFactory*& factory, - const InferenceEngine::CNNLayer* cnnLayer, - InferenceEngine::ResponseDesc* resp) noexcept - { - if (cnnLayer->type != kOpenCVLayersType) - return InferenceEngine::StatusCode::NOT_IMPLEMENTED; - factory = new InfEngineNgraphCustomLayerFactory(cnnLayer); - return InferenceEngine::StatusCode::OK; - } -#endif -}; - -#endif // OpenVINO >= 2022.1 - -InfEngineNgraphNode::InfEngineNgraphNode(ngraph::Output&& _node) +InfEngineNgraphNode::InfEngineNgraphNode(ov::Output&& _node) : BackendNode(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH), node(std::move(_node)) { CV_Assert(node.get_node()); CV_Assert(node.get_node_shared_ptr()); } -InfEngineNgraphNode::InfEngineNgraphNode(const ngraph::Output& _node) +InfEngineNgraphNode::InfEngineNgraphNode(const ov::Output& _node) : BackendNode(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH), node(_node) { CV_Assert(node.get_node()); CV_Assert(node.get_node_shared_ptr()); @@ -400,27 +116,11 @@ InfEngineNgraphNode::InfEngineNgraphNode(const std::vector >& n std::vector& outputs, std::vector& internals) : BackendNode(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH), cvLayer(cvLayer_) { -#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2020_3) - ngraph::OutputVector inp_nodes; -#else - ngraph::NodeVector inp_nodes; -#endif + ov::OutputVector inp_nodes; for (const auto& node : nodes) inp_nodes.emplace_back(node.dynamicCast()->node); -#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2022_1) node = std::make_shared(inp_nodes, cvLayer, outputs, internals); -#else - std::ostringstream oss; - oss << (size_t)cvLayer.get(); - std::map params = { - {"impl", oss.str()}, - {"outputs", shapesToStr(outputs)}, - {"internals", shapesToStr(internals)} - }; - node = std::make_shared(inp_nodes, params); -#endif - CV_Assert(!cvLayer->name.empty()); setName(cvLayer->name); } @@ -436,7 +136,7 @@ InfEngineNgraphNet::InfEngineNgraphNet(detail::NetImplBase& netImpl) device_name = "CPU"; } -InfEngineNgraphNet::InfEngineNgraphNet(detail::NetImplBase& netImpl, InferenceEngine::CNNNetwork& net) +InfEngineNgraphNet::InfEngineNgraphNet(detail::NetImplBase& netImpl, std::shared_ptr& net) : netImpl_(netImpl) , cnn(net) { @@ -455,64 +155,31 @@ void InfEngineNgraphNet::createNet(Target targetId) { if (!hasNetOwner) { CV_Assert(!requestedOutputs.empty()); - ngraph::ResultVector outs; + ov::ResultVector outs; for (auto output_node_it = requestedOutputs.begin(); output_node_it != requestedOutputs.end(); ++output_node_it) { CV_LOG_DEBUG(NULL, "DNN/NGRAPH: Add 'Result' output: " << output_node_it->first); CV_Assert(output_node_it->second); - auto out = std::make_shared(output_node_it->second->node); -#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2022_1) + auto out = std::make_shared(output_node_it->second->node); out->set_friendly_name(output_node_it->first + (output_node_it->second->node.get_node()->get_output_size() == 1 ? "" : ".0")); -#endif outs.push_back(out); } CV_Assert_N(!inputs_vec.empty(), !outs.empty()); - ngraph_function = std::make_shared(outs, inputs_vec); + ngraph_function = std::make_shared(outs, inputs_vec); init(targetId); } } -#if INF_ENGINE_VER_MAJOR_LT(INF_ENGINE_RELEASE_2022_1) -static inline -InferenceEngine::Layout estimateLayout(size_t dims); -#endif - void InfEngineNgraphNet::init(Target targetId) { if (!hasNetOwner) { if (targetId == DNN_TARGET_OPENCL_FP16) { -#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2022_1) ov::pass::ConvertFP32ToFP16().run_on_model(ngraph_function); -#else - auto nodes = ngraph_function->get_ordered_ops(); - for (auto& node : nodes) - { - auto parameter = std::dynamic_pointer_cast(node); - if (parameter && parameter->get_element_type() == ngraph::element::f32) - { - parameter->set_element_type(ngraph::element::f16); - } - auto constant = std::dynamic_pointer_cast(node); - if (constant && constant->get_element_type() == ngraph::element::f32) - { - const float* floatsData = constant->get_data_ptr(); - size_t total = ngraph::shape_size(constant->get_shape()); - Mat floats(1, total, CV_32F, (void*)floatsData); - Mat halfs; - floats.convertTo(halfs, CV_16F); - - auto new_const = std::make_shared(ngraph::element::f16, constant->get_shape(), halfs.data); - new_const->set_friendly_name(constant->get_friendly_name()); - ngraph::replace_node(constant, new_const); - } - } - ngraph_function->validate_nodes_and_infer_types(); -#endif // OpenVINO >= 2022.1 } - cnn = InferenceEngine::CNNNetwork(ngraph_function); + cnn = ngraph_function; if (DNN_IE_SERIALIZE) { @@ -520,7 +187,7 @@ void InfEngineNgraphNet::init(Target targetId) std::string dumpFileNameBase = netImpl_.getDumpFileNameBase(); try { - cnn.serialize(dumpFileNameBase + "_ngraph.xml", dumpFileNameBase + "_ngraph.bin"); + ov::pass::Serialize(dumpFileNameBase + "_ngraph.xml", dumpFileNameBase + "_ngraph.bin").run_on_model(cnn); } catch (const std::exception& e) { @@ -558,11 +225,9 @@ void InfEngineNgraphNet::init(Target targetId) CV_Error(Error::StsNotImplemented, "Unknown target"); }; -#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2022_1) - auto model = cnn.getFunction(); - ov::preprocess::PrePostProcessor ppp(model); + ov::preprocess::PrePostProcessor ppp(cnn); int i = 0; - for (const auto& inp : model->inputs()) { // TODO: not sure why but ngraph_function->inputs() here causes segfault. + for (const auto& inp : cnn->inputs()) { // TODO: not sure why but ngraph_function->inputs() here causes segfault. const std::string& name = inp.get_node()->get_friendly_name(); auto blobIt = allBlobs.find(name); CV_Assert(blobIt != allBlobs.end()); @@ -574,7 +239,7 @@ void InfEngineNgraphNet::init(Target targetId) } i = 0; - for (const auto& it : model->outputs()) + for (const auto& it : cnn->outputs()) { const std::string& name = it.get_node()->get_friendly_name(); auto blobIt = allBlobs.find(name); @@ -595,47 +260,21 @@ void InfEngineNgraphNet::init(Target targetId) ppp.build(); -#else - - for (const auto& it : cnn.getInputsInfo()) - { - const std::string& name = it.first; - auto blobIt = allBlobs.find(name); - CV_Assert(blobIt != allBlobs.end()); - it.second->setPrecision(blobIt->second->getTensorDesc().getPrecision()); - } - - for (const auto& it : cnn.getOutputsInfo()) - { - const std::string& name = it.first; - auto blobIt = allBlobs.find(name); - CV_Assert(blobIt != allBlobs.end()); - InferenceEngine::TensorDesc& desc = blobIt->second->getTensorDesc(); - - auto outShape = it.second->getDims(); - if (outShape != desc.getDims()) { - desc.reshape(outShape, estimateLayout(outShape.size())); - } - - it.second->setPrecision(blobIt->second->getTensorDesc().getPrecision()); // Should be always FP32 - } -#endif // OpenVINO >= 2022.1 - initPlugin(cnn); } -ngraph::ParameterVector InfEngineNgraphNet::setInputs(const std::vector& inputs, +ov::ParameterVector InfEngineNgraphNet::setInputs(const std::vector& inputs, const std::vector& names) { CV_Assert_N(inputs.size() == names.size()); - ngraph::ParameterVector current_inp; + ov::ParameterVector current_inp; for (size_t i = 0; i < inputs.size(); i++) { std::vector shape = getShape(inputs[i]); - auto inp = std::make_shared(ngraph::element::f32, ngraph::Shape(shape)); + auto inp = std::make_shared(ov::element::f32, ov::Shape(shape)); inp->set_friendly_name(names[i]); auto it = std::find_if(inputs_vec.begin(), inputs_vec.end(), - [&inp](const std::shared_ptr& a) { + [&inp](const std::shared_ptr& a) { return a->get_friendly_name() == inp->get_friendly_name(); }); if (it == inputs_vec.end()) { @@ -649,14 +288,14 @@ ngraph::ParameterVector InfEngineNgraphNet::setInputs(const std::vector } -void InfEngineNgraphNet::initPlugin(InferenceEngine::CNNNetwork& net) +void InfEngineNgraphNet::initPlugin(std::shared_ptr& net) { CV_Assert(!isInitialized()); try { AutoLock lock(getInitializationMutex()); - InferenceEngine::Core& ie = getCore(device_name); + ov::Core& ie = getCore(device_name); { isInit = true; std::vector candidates; @@ -671,18 +310,7 @@ void InfEngineNgraphNet::initPlugin(InferenceEngine::CNNNetwork& net) const std::string& libName = candidates[i]; try { -#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2022_1) ie.add_extension(libName); -#else - InferenceEngine::IExtensionPtr extension = -#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2021_4) - std::make_shared(libName); -#else - InferenceEngine::make_so_pointer(libName); -#endif - - ie.AddExtension(extension, "CPU"); -#endif CV_LOG_INFO(NULL, "DNN-IE: Loaded extension plugin: " << libName); found = true; break; @@ -693,30 +321,11 @@ void InfEngineNgraphNet::initPlugin(InferenceEngine::CNNNetwork& net) { CV_LOG_WARNING(NULL, "DNN-IE: Can't load extension plugin (extra layers for some networks). Specify path via OPENCV_DNN_IE_EXTRA_PLUGIN_PATH parameter"); } -#if INF_ENGINE_VER_MAJOR_LT(INF_ENGINE_RELEASE_2022_1) - // Some of networks can work without a library of extra layers. - // OpenCV fallbacks as extensions. - try - { - ie.AddExtension(std::make_shared(), "CPU"); - } - catch(const std::exception& e) - { - CV_LOG_INFO(NULL, "DNN-IE: Can't register OpenCV custom layers nGraph extension: " << e.what()); - } -#endif // OpenVINO < 2022.1 #ifndef _WIN32 // Limit the number of CPU threads. if (device_name == "CPU") -#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2022_1) ie.set_property(device_name, ov::inference_num_threads(getNumThreads())); -#else - ie.SetConfig({{ - InferenceEngine::PluginConfigParams::KEY_CPU_THREADS_NUM, format("%d", getNumThreads()), - }}, device_name); -#endif // OpenVINO >= 2022.1 #endif -#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2021_2) if (device_name.find("GPU") == 0) { #if OPENCV_HAVE_FILESYSTEM_SUPPORT @@ -727,24 +336,13 @@ void InfEngineNgraphNet::initPlugin(InferenceEngine::CNNNetwork& net) if (!cache_path.empty() && cache_path != "disabled") { CV_LOG_INFO(NULL, "OpenCV/nGraph: using GPU kernels cache: " << cache_path); -#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2022_1) ie.set_property(device_name, ov::cache_dir(cache_path)); -#else - ie.SetConfig({{ - InferenceEngine::PluginConfigParams::KEY_CACHE_DIR, cache_path, - }}, device_name); -#endif // OpenVINO >= 2022.1 } } -#endif } - std::map config; + ov::AnyMap config; if (device_name == "MYRIAD" || device_name == "HDDL") { -#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2020_4) config.emplace("MYRIAD_DETECT_NETWORK_BATCH", "NO"); -#else - config.emplace("VPU_DETECT_NETWORK_BATCH", "NO"); -#endif } bool isHetero = device_name == "FPGA"; @@ -752,7 +350,7 @@ void InfEngineNgraphNet::initPlugin(InferenceEngine::CNNNetwork& net) // We do not check IR models because they can be with version less than IRv10 if (!isHetero && device_name != "CPU" && !hasNetOwner) { - for (auto& node : net.getFunction()->get_ops()) + for (auto& node : net->get_ops()) { if (node->description() == kOpenCVLayersType) { @@ -764,7 +362,7 @@ void InfEngineNgraphNet::initPlugin(InferenceEngine::CNNNetwork& net) std::string ieDevice = isHetero ? ("HETERO:" + device_name + ",CPU") : device_name; CV_LOG_INFO(NULL, "DNN/IE: Calling LoadNetwork(device=" << ieDevice << ")..."); - netExec = ie.LoadNetwork(net, ieDevice, config); + netExec = ie.compile_model(net, ieDevice, config); } catch (const std::exception& ex) { @@ -782,14 +380,13 @@ bool NgraphBackendLayer::getMemoryShapes(const std::vector &inputs, std::vector &outputs, std::vector &internals) const { - auto ngraphFunction = t_net.getFunction(); bool equal_flag = true; - std::map > inShapes; + std::map inShapes; int i = 0; - for (const auto& inp : ngraphFunction->get_parameters()) + for (const auto& inp : t_net->get_parameters()) { - std::vector oldShape = inp->get_shape(); - std::vector newShape(inputs[i].begin(), inputs[i].end()); + ov::Shape oldShape = inp->get_shape(); + ov::Shape newShape(inputs[i].begin(), inputs[i].end()); inShapes.insert({inp->get_friendly_name(), newShape}); if (oldShape != newShape) { @@ -800,21 +397,17 @@ bool NgraphBackendLayer::getMemoryShapes(const std::vector &inputs, if (!equal_flag) { - InferenceEngine::CNNNetwork curr_t_net(t_net); - curr_t_net.reshape(inShapes); + std::shared_ptr curr_t_net(t_net); + curr_t_net->reshape(inShapes); } -#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2022_1) std::vector dims; - for (const auto& it : ngraphFunction->outputs()) { + for (const auto& it : t_net->outputs()) { if (it.get_node()->get_friendly_name() == name) { dims = it.get_partial_shape().get_max_shape(); } } if (dims.empty()) CV_Error(Error::StsError, format("Unable find result with name %s", name.c_str())); -#else - std::vector dims = t_net.getOutputsInfo()[name]->getDims(); -#endif outputs.push_back(MatShape(dims.begin(), dims.end())); return false; } @@ -832,8 +425,6 @@ void NgraphBackendLayer::forward(InputArrayOfArrays inputs, OutputArrayOfArrays CV_Error(Error::StsInternal, "Choose Inference Engine as a preferable backend."); } -#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2022_1) - ov::Tensor wrapToNgraphBlob(const Mat& m) { std::vector shape = getShape(m); if (m.type() == CV_32F) @@ -848,60 +439,6 @@ ov::Tensor wrapToNgraphBlob(const Mat& m) { CV_Error(Error::StsNotImplemented, format("Unsupported data type %s", typeToString(m.type()).c_str())); } -#else - -static InferenceEngine::Layout estimateLayout(int dims) -{ - if (dims == 4) - return InferenceEngine::Layout::NCHW; - else if (dims == 3) - return InferenceEngine::Layout::CHW; - else if (dims == 2) - return InferenceEngine::Layout::NC; - else if (dims == 1) - return InferenceEngine::Layout::C; - else if (dims == 5) - return InferenceEngine::Layout::NCDHW; - else - return InferenceEngine::Layout::ANY; -} -static inline -InferenceEngine::Layout estimateLayout(size_t dims) -{ - return estimateLayout((int)dims); -} - -static inline -InferenceEngine::Layout estimateLayout(const Mat& m) -{ - return estimateLayout(m.dims); -} - -InferenceEngine::Blob::Ptr wrapToNgraphBlob(const Mat& m, const std::vector& shape, - InferenceEngine::Layout layout) -{ - if (m.type() == CV_32F) - return InferenceEngine::make_shared_blob( - {InferenceEngine::Precision::FP32, shape, layout}, (float*)m.data); - else if (m.type() == CV_8U) - return InferenceEngine::make_shared_blob( - {InferenceEngine::Precision::U8, shape, layout}, (uint8_t*)m.data); - else if (m.type() == CV_32SC1) - return InferenceEngine::make_shared_blob( - {InferenceEngine::Precision::I32, shape, layout}, (int32_t*)m.data); - else - CV_Error(Error::StsNotImplemented, format("Unsupported data type %s", typeToString(m.type()).c_str())); -} - -InferenceEngine::Blob::Ptr wrapToNgraphBlob(const Mat& m, InferenceEngine::Layout layout) -{ - std::vector shape = getShape(m); - return wrapToNgraphBlob(m, shape, layout); -} - -InferenceEngine::Blob::Ptr wrapToNgraphBlob(const Mat& m) { return wrapToNgraphBlob(m, estimateLayout(m)); } - -#endif // OpenVINO >= 2022.1 NgraphBackendWrapper::NgraphBackendWrapper(int targetId, const cv::Mat& m) : BackendWrapper(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, targetId) @@ -941,36 +478,10 @@ void NgraphBackendWrapper::setHostDirty() //CV_Error(Error::StsNotImplemented, ""); } -#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2022_1) ov::Tensor copyBlob(const ov::Tensor& blob) { return ov::Tensor(blob.get_element_type(), blob.get_shape()); } -#else -InferenceEngine::Blob::Ptr copyBlob(const InferenceEngine::Blob::Ptr& blob) -{ - InferenceEngine::Blob::Ptr copy; - auto description = blob->getTensorDesc(); - InferenceEngine::Precision precision = description.getPrecision(); - if (precision == InferenceEngine::Precision::FP32) - { - copy = InferenceEngine::make_shared_blob(description); - } - else if (precision == InferenceEngine::Precision::U8) - { - copy = InferenceEngine::make_shared_blob(description); - } - else - { - std::ostringstream msg; - msg << precision; - CV_Error_(Error::StsNotImplemented, ("Unsupported blob precision: %s", msg.str().c_str())); - } - copy->allocate(); - return copy; -} - -#endif // OpenVINO < 2022.1 void InfEngineNgraphNet::reset() { @@ -1022,7 +533,7 @@ void InfEngineNgraphNet::forward(const std::vector >& outBlo reqWrapper = Ptr(new NgraphReqWrapper()); try { - reqWrapper->req = netExec.CreateInferRequest(); + reqWrapper->req = netExec.create_infer_request(); } catch (const std::exception& ex) { @@ -1030,7 +541,6 @@ void InfEngineNgraphNet::forward(const std::vector >& outBlo } infRequests.push_back(reqWrapper); -#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2022_1) int i = 0; for (const auto& it : netExec.inputs()) { @@ -1048,27 +558,7 @@ void InfEngineNgraphNet::forward(const std::vector >& outBlo CV_Assert(blobIt != allBlobs.end()); reqWrapper->req.set_output_tensor(i++, isAsync ? copyBlob(blobIt->second) : blobIt->second); } -#else - InferenceEngine::BlobMap inpBlobs, outBlobs; - for (const auto& it : cnn.getInputsInfo()) - { - const std::string& name = it.first; - auto blobIt = allBlobs.find(name); - CV_Assert(blobIt != allBlobs.end()); - inpBlobs[name] = isAsync ? copyBlob(blobIt->second) : blobIt->second; - } - for (const auto& it : cnn.getOutputsInfo()) - { - const std::string& name = it.first; - auto blobIt = allBlobs.find(name); - CV_Assert(blobIt != allBlobs.end()); - outBlobs[name] = isAsync ? copyBlob(blobIt->second) : blobIt->second; - } - reqWrapper->req.SetInput(inpBlobs); - reqWrapper->req.SetOutput(outBlobs); -#endif -#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2022_1) if (isAsync) { bool* isReady = &reqWrapper->isReady; auto* promises = &reqWrapper->outProms; @@ -1112,86 +602,13 @@ void InfEngineNgraphNet::forward(const std::vector >& outBlo *isReady = true; }); } -#else // OpenVINO >= 2022.1 - -#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2021_4) - InferenceEngine::InferRequest infRequest = reqWrapper->req; - NgraphReqWrapper* wrapperPtr = reqWrapper.get(); - CV_Assert(wrapperPtr && "Internal error"); -#else - InferenceEngine::IInferRequest::Ptr infRequestPtr = reqWrapper->req; - CV_Assert(infRequestPtr); - InferenceEngine::IInferRequest& infRequest = *infRequestPtr.get(); - infRequest.SetUserData(reqWrapper.get(), 0); -#endif - -#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2021_4) - // do NOT capture 'reqWrapper' (smart ptr) in the lambda callback - infRequest.SetCompletionCallback>( - [wrapperPtr](InferenceEngine::InferRequest /*request*/, InferenceEngine::StatusCode status) -#else - infRequest.SetCompletionCallback( - [](InferenceEngine::IInferRequest::Ptr requestPtr, InferenceEngine::StatusCode status) -#endif - { - CV_LOG_DEBUG(NULL, "DNN(nGraph): completionCallback(" << (int)status << ")"); -#if !INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2021_4) - CV_Assert(requestPtr); - InferenceEngine::IInferRequest& request = *requestPtr.get(); - - NgraphReqWrapper* wrapperPtr; - request.GetUserData((void**)&wrapperPtr, 0); - CV_Assert(wrapperPtr && "Internal error"); -#endif - NgraphReqWrapper& wrapper = *wrapperPtr; - - size_t processedOutputs = 0; - try - { - for (; processedOutputs < wrapper.outProms.size(); ++processedOutputs) - { - const std::string& name = wrapper.outsNames[processedOutputs]; - Mat m = infEngineBlobToMat(wrapper.req.GetBlob(name)); - - try - { - CV_Assert(status == InferenceEngine::StatusCode::OK); - wrapper.outProms[processedOutputs].setValue(m.clone()); - } - catch (...) - { - try { - wrapper.outProms[processedOutputs].setException(std::current_exception()); - } catch(...) { - CV_LOG_ERROR(NULL, "DNN: Exception occurred during async inference exception propagation"); - } - } - } - } - catch (...) - { - std::exception_ptr e = std::current_exception(); - for (; processedOutputs < wrapper.outProms.size(); ++processedOutputs) - { - try { - wrapper.outProms[processedOutputs].setException(e); - } catch(...) { - CV_LOG_ERROR(NULL, "DNN: Exception occurred during async inference exception propagation"); - } - } - } - wrapper.isReady = true; - } - ); -#endif // OpenVINO >= 2022.1 } -#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2022_1) if (isAsync) { // Copy actual data to infer request's input blobs. int i = 0; - for (const auto& it : cnn.getFunction()->get_parameters()) + for (const auto& it : cnn->get_parameters()) { const std::string& name = it->get_friendly_name(); auto blobIt = allBlobs.find(name); @@ -1210,54 +627,30 @@ void InfEngineNgraphNet::forward(const std::vector >& outBlo { reqWrapper->req.infer(); } -#else - if (isAsync) - { - // Copy actual data to infer request's input blobs. - for (const auto& it : cnn.getInputsInfo()) - { - const std::string& name = it.first; - auto blobIt = allBlobs.find(name); - Mat srcMat = infEngineBlobToMat(blobIt->second); - Mat dstMat = infEngineBlobToMat(reqWrapper->req.GetBlob(name)); - srcMat.copyTo(dstMat); - } - - // Set promises to output blobs wrappers. - reqWrapper->makePromises(outBlobsWrappers); - - reqWrapper->isReady = false; - reqWrapper->req.StartAsync(); - } - else - { - reqWrapper->req.Infer(); - } -#endif // OpenVINO >= 2022.1 } -ngraph::Output ngraphQuantize(ngraph::Output input, float output_sc, float output_zp) { +ov::Output ngraphQuantize(ov::Output input, float output_sc, float output_zp) { float outLow = -128, outHigh = 127; float inpLow = output_sc * (outLow - output_zp); float inpHigh = output_sc * (outHigh - output_zp); - return std::make_shared(input, - std::make_shared(ngraph::element::f32, ngraph::Shape{1}, &inpLow), - std::make_shared(ngraph::element::f32, ngraph::Shape{1}, &inpHigh), - std::make_shared(ngraph::element::f32, ngraph::Shape{1}, &outLow), - std::make_shared(ngraph::element::f32, ngraph::Shape{1}, &outHigh), + return std::make_shared(input, + std::make_shared(ov::element::f32, ov::Shape{1}, &inpLow), + std::make_shared(ov::element::f32, ov::Shape{1}, &inpHigh), + std::make_shared(ov::element::f32, ov::Shape{1}, &outLow), + std::make_shared(ov::element::f32, ov::Shape{1}, &outHigh), 256 // levels ); } -ngraph::Output ngraphDequantize(ngraph::Output input, float input_sc, float input_zp) { +ov::Output ngraphDequantize(ov::Output input, float input_sc, float input_zp) { float inpLow = -128, inpHigh = 127; float outLow = input_sc * (inpLow - input_zp); float outHigh = input_sc * (inpHigh - input_zp); - return std::make_shared(input, - std::make_shared(ngraph::element::f32, ngraph::Shape{1}, &inpLow), - std::make_shared(ngraph::element::f32, ngraph::Shape{1}, &inpHigh), - std::make_shared(ngraph::element::f32, ngraph::Shape{1}, &outLow), - std::make_shared(ngraph::element::f32, ngraph::Shape{1}, &outHigh), + return std::make_shared(input, + std::make_shared(ov::element::f32, ov::Shape{1}, &inpLow), + std::make_shared(ov::element::f32, ov::Shape{1}, &inpHigh), + std::make_shared(ov::element::f32, ov::Shape{1}, &outLow), + std::make_shared(ov::element::f32, ov::Shape{1}, &outHigh), 256 // levels ); } diff --git a/modules/dnn/src/ie_ngraph.hpp b/modules/dnn/src/ie_ngraph.hpp index 8672f1a3c2..19e07d62ac 100644 --- a/modules/dnn/src/ie_ngraph.hpp +++ b/modules/dnn/src/ie_ngraph.hpp @@ -17,7 +17,8 @@ #pragma warning(disable : 4245) #pragma warning(disable : 4268) #endif -#include +#include +#include #ifdef _MSC_VER #pragma warning(pop) #endif @@ -30,12 +31,11 @@ namespace cv { namespace dnn { class InfEngineNgraphNode; - class InfEngineNgraphNet { public: InfEngineNgraphNet(detail::NetImplBase& netImpl); - InfEngineNgraphNet(detail::NetImplBase& netImpl, InferenceEngine::CNNNetwork& net); + InfEngineNgraphNet(detail::NetImplBase& netImpl, std::shared_ptr& net); void addOutput(const Ptr& node); @@ -44,8 +44,8 @@ public: void forward(const std::vector >& outBlobsWrappers, bool isAsync); - void initPlugin(InferenceEngine::CNNNetwork& net); - ngraph::ParameterVector setInputs(const std::vector& inputs, const std::vector& names); + void initPlugin(std::shared_ptr& net); + ov::ParameterVector setInputs(const std::vector& inputs, const std::vector& names); void addBlobs(const std::vector >& ptrs); @@ -56,15 +56,11 @@ public: //private: detail::NetImplBase& netImpl_; - ngraph::ParameterVector inputs_vec; - std::shared_ptr ngraph_function; + ov::ParameterVector inputs_vec; + std::shared_ptr ngraph_function; - InferenceEngine::ExecutableNetwork netExec; -#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2022_1) + ov::CompiledModel netExec; std::map allBlobs; -#else - InferenceEngine::BlobMap allBlobs; -#endif std::string device_name; bool isInit = false; @@ -74,14 +70,14 @@ public: void makePromises(const std::vector >& outs); - InferenceEngine::InferRequest req; + ov::InferRequest req; std::vector outProms; std::vector outsNames; bool isReady; }; std::vector > infRequests; - InferenceEngine::CNNNetwork cnn; + std::shared_ptr cnn; bool hasNetOwner; std::unordered_map requestedOutputs; }; @@ -93,13 +89,13 @@ public: std::vector& inputs, std::vector& outputs, std::vector& internals); - InfEngineNgraphNode(ngraph::Output&& _node); - InfEngineNgraphNode(const ngraph::Output& _node); + InfEngineNgraphNode(ov::Output&& _node); + InfEngineNgraphNode(const ov::Output& _node); void setName(const std::string& name); // Inference Engine network object that allows to obtain the outputs of this layer. - ngraph::Output node; + ov::Output node; Ptr net; Ptr cvLayer; }; @@ -118,11 +114,7 @@ public: Mat* host; std::string name; -#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2022_1) ov::Tensor blob; -#else - InferenceEngine::Blob::Ptr blob; -#endif AsyncArray futureMat; }; @@ -132,7 +124,7 @@ public: class NgraphBackendLayer : public Layer { public: - NgraphBackendLayer(const InferenceEngine::CNNNetwork &t_net_) : t_net(t_net_) {}; + NgraphBackendLayer(const std::shared_ptr &t_net_) : t_net(t_net_) {}; virtual bool getMemoryShapes(const std::vector &inputs, const int requiredOutputs, @@ -145,11 +137,11 @@ public: virtual bool supportBackend(int backendId) CV_OVERRIDE; private: - InferenceEngine::CNNNetwork t_net; + std::shared_ptr t_net; }; -ngraph::Output ngraphQuantize(ngraph::Output input, float output_sc, float output_zp); -ngraph::Output ngraphDequantize(ngraph::Output input, float input_sc, float input_zp); +ov::Output ngraphQuantize(ov::Output input, float output_sc, float output_zp); +ov::Output ngraphDequantize(ov::Output input, float input_sc, float input_zp); #endif // HAVE_DNN_NGRAPH diff --git a/modules/dnn/src/int8layers/batch_norm_layer.cpp b/modules/dnn/src/int8layers/batch_norm_layer.cpp index 7ef169deea..3fbf8cd191 100644 --- a/modules/dnn/src/int8layers/batch_norm_layer.cpp +++ b/modules/dnn/src/int8layers/batch_norm_layer.cpp @@ -7,8 +7,6 @@ #include "../op_timvx.hpp" #include "../ie_ngraph.hpp" -#include - namespace cv { namespace dnn @@ -250,11 +248,11 @@ public: std::vector shape(input.get_shape().size(), 1); shape[1] = origin_weights.total(); - ngraph::Output res; - auto ieWeights = std::make_shared(ngraph::element::f32, shape, origin_weights.data); - auto ieBias = std::make_shared(ngraph::element::f32, shape, origin_bias.data); - res = std::make_shared(input, ieWeights); - res = std::make_shared(res, ieBias); + ov::Output res; + auto ieWeights = std::make_shared(ov::element::f32, shape, origin_weights.data); + auto ieBias = std::make_shared(ov::element::f32, shape, origin_bias.data); + res = std::make_shared(input, ieWeights); + res = std::make_shared(res, ieBias); res = ngraphQuantize(res, output_sc, output_zp); return new InfEngineNgraphNode(res); diff --git a/modules/dnn/src/int8layers/convolution_layer.cpp b/modules/dnn/src/int8layers/convolution_layer.cpp index a5a98c9764..603100ae11 100644 --- a/modules/dnn/src/int8layers/convolution_layer.cpp +++ b/modules/dnn/src/int8layers/convolution_layer.cpp @@ -573,8 +573,8 @@ public: auto ieInpNode = nodes[0].dynamicCast()->node; std::vector dims = ieInpNode.get_shape(); CV_Check(dims.size(), dims.size() >= 3 && dims.size() <= 5, ""); - CV_Assert(ieInpNode.get_element_type() == ngraph::element::f32); - ngraph::Output ieWeights; + CV_Assert(ieInpNode.get_element_type() == ov::element::f32); + ov::Output ieWeights; if (nodes.size() > 1) ieWeights = nodes[1].dynamicCast()->node; const int inpCn = dims[1]; @@ -592,18 +592,18 @@ public: if (nodes.size() == 1) { - ieWeights = std::make_shared(ngraph::element::i8, kernel_shape, blobs[0].data); + ieWeights = std::make_shared(ov::element::i8, kernel_shape, blobs[0].data); } else { - auto shape = std::make_shared(ngraph::element::i64, - ngraph::Shape{kernel_shape.size()}, std::vector(kernel_shape.begin(), kernel_shape.end())); - ieWeights = std::make_shared(ieWeights, shape, true); + auto shape = std::make_shared(ov::element::i64, + ov::Shape{kernel_shape.size()}, std::vector(kernel_shape.begin(), kernel_shape.end())); + ieWeights = std::make_shared(ieWeights, shape, true); } - ngraph::op::PadType pad_type = ngraph::op::PadType::EXPLICIT; + ov::op::PadType pad_type = ov::op::PadType::EXPLICIT; if (!padMode.empty()) - pad_type = padMode == "VALID" ? ngraph::op::PadType::VALID : ngraph::op::PadType::SAME_UPPER; + pad_type = padMode == "VALID" ? ov::op::PadType::VALID : ov::op::PadType::SAME_UPPER; ieInpNode = ngraphDequantize(ieInpNode, input_sc, input_zp); @@ -627,31 +627,31 @@ public: outLows[i] = low * outputMultiplier[i] * output_sc / input_sc; outHighs[i] = high * outputMultiplier[i] * output_sc / input_sc; } - ieWeights = std::make_shared(ieWeights, ngraph::element::f32); - ieWeights = std::make_shared(ieWeights, - std::make_shared(ngraph::element::f32, quantShape, inpLows.data()), - std::make_shared(ngraph::element::f32, quantShape, inpHighs.data()), - std::make_shared(ngraph::element::f32, quantShape, outLows.data()), - std::make_shared(ngraph::element::f32, quantShape, outHighs.data()), + ieWeights = std::make_shared(ieWeights, ov::element::f32); + ieWeights = std::make_shared(ieWeights, + std::make_shared(ov::element::f32, quantShape, inpLows.data()), + std::make_shared(ov::element::f32, quantShape, inpHighs.data()), + std::make_shared(ov::element::f32, quantShape, outLows.data()), + std::make_shared(ov::element::f32, quantShape, outHighs.data()), 256 // levels ); - ngraph::Output conv_node; + ov::Output conv_node; if (group != 1) { - conv_node = std::make_shared( + conv_node = std::make_shared( ieInpNode, ieWeights, - ngraph::Strides(strides), - ngraph::CoordinateDiff(std::vector(pads_begin.begin(), pads_begin.end())), - ngraph::CoordinateDiff(std::vector(pads_end.begin(), pads_end.end())), - ngraph::Strides(dilations), + ov::Strides(strides), + ov::CoordinateDiff(std::vector(pads_begin.begin(), pads_begin.end())), + ov::CoordinateDiff(std::vector(pads_end.begin(), pads_end.end())), + ov::Strides(dilations), pad_type); } else { - conv_node = std::make_shared( + conv_node = std::make_shared( ieInpNode, ieWeights, - ngraph::Strides(strides), - ngraph::CoordinateDiff(std::vector(pads_begin.begin(), pads_begin.end())), - ngraph::CoordinateDiff(std::vector(pads_end.begin(), pads_end.end())), - ngraph::Strides(dilations), + ov::Strides(strides), + ov::CoordinateDiff(std::vector(pads_begin.begin(), pads_begin.end())), + ov::CoordinateDiff(std::vector(pads_end.begin(), pads_end.end())), + ov::Strides(dilations), pad_type); } @@ -659,12 +659,12 @@ public: shape[1] = conv_node.get_shape()[1]; if (biasvec.size() || nodes.size() == 3) { - std::shared_ptr bias; + std::shared_ptr bias; if (nodes.size() == 3) { - auto bias_shape = std::make_shared(ngraph::element::i64, - ngraph::Shape{shape.size()}, std::vector(shape.begin(), shape.end())); - bias = std::make_shared(nodes[2].dynamicCast()->node, bias_shape, true); + auto bias_shape = std::make_shared(ov::element::i64, + ov::Shape{shape.size()}, std::vector(shape.begin(), shape.end())); + bias = std::make_shared(nodes[2].dynamicCast()->node, bias_shape, true); } else { @@ -672,9 +672,9 @@ public: for (int i = 0; i < numOutput; ++i) { ovBias[i] = (biasvec[i] + input_zp * cv::sum(blobs[0].row(i))[0]) * outputMultiplier[i] * output_sc; } - bias = std::make_shared(ngraph::element::f32, ngraph::Shape(shape), ovBias.data()); + bias = std::make_shared(ov::element::f32, ov::Shape(shape), ovBias.data()); } - conv_node = std::make_shared(conv_node, bias, ngraph::op::AutoBroadcastType::NUMPY); + conv_node = std::make_shared(conv_node, bias, ov::op::AutoBroadcastType::NUMPY); } conv_node = ngraphQuantize(conv_node, output_sc, output_zp); diff --git a/modules/dnn/src/int8layers/elementwise_layers.cpp b/modules/dnn/src/int8layers/elementwise_layers.cpp index 5c533840f3..f522efa0c1 100644 --- a/modules/dnn/src/int8layers/elementwise_layers.cpp +++ b/modules/dnn/src/int8layers/elementwise_layers.cpp @@ -253,26 +253,26 @@ public: input = ngraphDequantize(input, input_sc, input_zp); - ngraph::Output res; + ov::Output res; if (type == "ReLU6Int8") { - res = std::make_shared(input, 0.0f, 6.0f); + res = std::make_shared(input, 0.0f, 6.0f); } else if (type == "ReLUInt8") { if (slope) { - auto param = std::make_shared(ngraph::element::f32, ngraph::Shape{1}, &slope); - res = std::make_shared(input, param); + auto param = std::make_shared(ov::element::f32, ov::Shape{1}, &slope); + res = std::make_shared(input, param); } else { - res = std::make_shared(input); + res = std::make_shared(input); } } else if (type == "ELUInt8") { - res = std::make_shared(input, 1.0f); + res = std::make_shared(input, 1.0f); } else if (type == "MishInt8") { - res = std::make_shared(input); + res = std::make_shared(input); } else if (type == "HardSwishInt8") { - res = std::make_shared(input); + res = std::make_shared(input); } else if (type == "AbsValInt8") { - res = std::make_shared(input); + res = std::make_shared(input); } else if (type == "SigmoidInt8") { - res = std::make_shared(input); + res = std::make_shared(input); } else { CV_Error(Error::StsNotImplemented, type + " activation with OpenVINO"); } diff --git a/modules/dnn/src/int8layers/eltwise_layer.cpp b/modules/dnn/src/int8layers/eltwise_layer.cpp index a3bb6ec2d6..214d11525a 100644 --- a/modules/dnn/src/int8layers/eltwise_layer.cpp +++ b/modules/dnn/src/int8layers/eltwise_layer.cpp @@ -375,7 +375,7 @@ public: const std::vector >& nodes) CV_OVERRIDE { CV_Assert(nodes.size() >= 2); - std::vector> ieInpNodes(nodes.size()); + std::vector> ieInpNodes(nodes.size()); for (size_t i = 0; i < nodes.size(); i++) { ieInpNodes[i] = nodes[i].dynamicCast()->node; @@ -389,9 +389,9 @@ public: for (size_t i = 1; i < ieInpNodes.size(); i++) { switch (op) { - case SUM: res = std::make_shared(res, ieInpNodes[i]); break; - case PROD: res = std::make_shared(res, ieInpNodes[i]); break; - case MAX: res = std::make_shared(res, ieInpNodes[i]); break; + case SUM: res = std::make_shared(res, ieInpNodes[i]); break; + case PROD: res = std::make_shared(res, ieInpNodes[i]); break; + case MAX: res = std::make_shared(res, ieInpNodes[i]); break; default: CV_Error(Error::StsNotImplemented, "Unsupported eltwise operation"); } } diff --git a/modules/dnn/src/int8layers/fully_connected_layer.cpp b/modules/dnn/src/int8layers/fully_connected_layer.cpp index 3a560ddda6..6691128990 100644 --- a/modules/dnn/src/int8layers/fully_connected_layer.cpp +++ b/modules/dnn/src/int8layers/fully_connected_layer.cpp @@ -410,8 +410,8 @@ public: CV_CheckTypeEQ(blobs[1].type(), CV_32S, ""); // bias CV_CheckTypeEQ(outputMultiplier.type(), CV_32F, ""); - ngraph::Output input = nodes[0].dynamicCast()->node; - ngraph::Output ieWeights, ieBias, matmul; + ov::Output input = nodes[0].dynamicCast()->node; + ov::Output ieWeights, ieBias, matmul; bool transA = false, transB = true; size_t numOutput = blobs[0].size[0]; @@ -419,15 +419,15 @@ public: { CV_Error(Error::StsNotImplemented, ""); // auto inp2 = nodes[1].dynamicCast()->node; - // matmul = std::make_shared(ieInpNode, inp2, transA, transB); + // matmul = std::make_shared(ieInpNode, inp2, transA, transB); } else { std::vector shape(1 + normalize_axis(axis, input.get_shape().size()), 0); shape[shape.size() - 1] = -1; - input = std::make_shared( + input = std::make_shared( input, - std::make_shared(ngraph::element::i32, ngraph::Shape{shape.size()}, shape.data()), + std::make_shared(ov::element::i32, ov::Shape{shape.size()}, shape.data()), true ); @@ -444,16 +444,16 @@ public: } std::vector weight_shape{(size_t)blobs[0].size[0], (size_t)blobs[0].size[1]}; - ieWeights = std::make_shared(ngraph::element::i8, weight_shape, blobs[0].data); - ieWeights = std::make_shared(ieWeights, ngraph::element::f32); - ieWeights = std::make_shared(ieWeights, - std::make_shared(ngraph::element::f32, ngraph::Shape{numOutput, 1}, inpLows.data()), - std::make_shared(ngraph::element::f32, ngraph::Shape{numOutput, 1}, inpHighs.data()), - std::make_shared(ngraph::element::f32, ngraph::Shape{numOutput, 1}, outLows.data()), - std::make_shared(ngraph::element::f32, ngraph::Shape{numOutput, 1}, outHighs.data()), + ieWeights = std::make_shared(ov::element::i8, weight_shape, blobs[0].data); + ieWeights = std::make_shared(ieWeights, ov::element::f32); + ieWeights = std::make_shared(ieWeights, + std::make_shared(ov::element::f32, ov::Shape{numOutput, 1}, inpLows.data()), + std::make_shared(ov::element::f32, ov::Shape{numOutput, 1}, inpHighs.data()), + std::make_shared(ov::element::f32, ov::Shape{numOutput, 1}, outLows.data()), + std::make_shared(ov::element::f32, ov::Shape{numOutput, 1}, outHighs.data()), 256 // levels ); - matmul = std::make_shared(input, ieWeights, transA, transB); + matmul = std::make_shared(input, ieWeights, transA, transB); } if (blobs.size() > 1) { @@ -462,9 +462,9 @@ public: for (int i = 0; i < ovBias.size(); ++i) { ovBias[i] = (bias[i] + input_zp * cv::sum(blobs[0].row(i))[0]) * outputMultiplier.ptr()[i] * output_sc; } - auto bias_node = std::make_shared(ngraph::element::f32, - ngraph::Shape{blobs[1].total()}, ovBias.data()); - matmul = std::make_shared(matmul, bias_node); + auto bias_node = std::make_shared(ov::element::f32, + ov::Shape{blobs[1].total()}, ovBias.data()); + matmul = std::make_shared(matmul, bias_node); } matmul = ngraphQuantize(matmul, output_sc, output_zp); diff --git a/modules/dnn/src/int8layers/pooling_layer.cpp b/modules/dnn/src/int8layers/pooling_layer.cpp index b321d730f7..cfd04bd2f4 100644 --- a/modules/dnn/src/int8layers/pooling_layer.cpp +++ b/modules/dnn/src/int8layers/pooling_layer.cpp @@ -11,6 +11,7 @@ #include #include #include + using std::max; using std::min; @@ -284,22 +285,22 @@ public: input = ngraphDequantize(input, input_sc, input_zp); - ngraph::op::PadType pad_type = ngraph::op::PadType::EXPLICIT; + ov::op::PadType pad_type = ov::op::PadType::EXPLICIT; if (!padMode.empty()) - pad_type = padMode == "VALID" ? ngraph::op::PadType::VALID : ngraph::op::PadType::SAME_UPPER; + pad_type = padMode == "VALID" ? ov::op::PadType::VALID : ov::op::PadType::SAME_UPPER; - auto rounding_type = ceilMode ? ngraph::op::RoundingType::CEIL : ngraph::op::RoundingType::FLOOR; - ngraph::Output pool; + auto rounding_type = ceilMode ? ov::op::RoundingType::CEIL : ov::op::RoundingType::FLOOR; + ov::Output pool; if (type == MAX) { - pool = std::make_shared(input, ngraph::Strides(strides), - ngraph::Shape(pads_begin), ngraph::Shape(pads_end), ngraph::Shape(kernel_size), + pool = std::make_shared(input, ov::Strides(strides), + ov::Shape(pads_begin), ov::Shape(pads_end), ov::Shape(kernel_size), rounding_type, pad_type); } else if (type == AVE) { - pool = std::make_shared(input, ngraph::Strides(strides), - ngraph::Shape(pads_begin), ngraph::Shape(pads_end), ngraph::Shape(kernel_size), + pool = std::make_shared(input, ov::Strides(strides), + ov::Shape(pads_begin), ov::Shape(pads_end), ov::Shape(kernel_size), !avePoolPaddedArea, rounding_type, pad_type); } else if (type == SUM) { - ngraph::Shape inpShape = input.get_shape(); + ov::Shape inpShape = input.get_shape(); CV_Assert(inpShape.size() == 2 + kernel_size.size()); std::vector axes; for (size_t i = 0; i < kernel_size.size(); i++) @@ -307,8 +308,8 @@ public: if (inpShape[2 + i] == kernel_size[i]) axes.push_back(2 + i); } - auto reduction_axes = std::make_shared(ngraph::element::i64, ngraph::Shape{axes.size()}, axes); - pool = std::make_shared(input, reduction_axes, true); + auto reduction_axes = std::make_shared(ov::element::i64, ov::Shape{axes.size()}, axes); + pool = std::make_shared(input, reduction_axes, true); } else { CV_Error(Error::StsNotImplemented, format("INT8 Pooling type: %d", type)); } diff --git a/modules/dnn/src/int8layers/scale_layer.cpp b/modules/dnn/src/int8layers/scale_layer.cpp index e50c4cea0e..25d48e3d17 100644 --- a/modules/dnn/src/int8layers/scale_layer.cpp +++ b/modules/dnn/src/int8layers/scale_layer.cpp @@ -191,7 +191,7 @@ public: #ifdef HAVE_DNN_NGRAPH virtual Ptr initNgraph(const std::vector >& inputs, const std::vector >& nodes) CV_OVERRIDE { - std::vector> ieInpNodes(nodes.size()); + std::vector> ieInpNodes(nodes.size()); for (int i = 0; i < nodes.size(); ++i) { ieInpNodes[i] = nodes[i].dynamicCast()->node; } @@ -200,7 +200,7 @@ public: CV_Assert(!blobs.empty() || ieInpNodes.size() == 1 + (int)hasWeights + (int)hasBias); - ngraph::Output weights, bias; + ov::Output weights, bias; if (blobs.empty()) { if (hasWeights) weights = ieInpNodes[1]; @@ -222,17 +222,17 @@ public: } if (hasWeights) - weights = std::make_shared(ngraph::element::f32, shape, blobs[0].data); + weights = std::make_shared(ov::element::f32, shape, blobs[0].data); if (hasBias) - bias = std::make_shared(ngraph::element::f32, shape, blobs[(int)hasWeights].data); + bias = std::make_shared(ov::element::f32, shape, blobs[(int)hasWeights].data); } - ngraph::Output res = ieInpNodes[0]; + ov::Output res = ieInpNodes[0]; if (hasWeights) { - res = std::make_shared(res, weights); + res = std::make_shared(res, weights); } if (hasBias) { - res = std::make_shared(res, bias); + res = std::make_shared(res, bias); } res = ngraphQuantize(res, output_sc, output_zp); diff --git a/modules/dnn/src/int8layers/softmax_layer.cpp b/modules/dnn/src/int8layers/softmax_layer.cpp index 28c6837cca..e81b82b99f 100644 --- a/modules/dnn/src/int8layers/softmax_layer.cpp +++ b/modules/dnn/src/int8layers/softmax_layer.cpp @@ -204,11 +204,11 @@ public: input = ngraphDequantize(input, input_sc, input_zp); - ngraph::Output res; + ov::Output res; if (logSoftMax) { - res = std::make_shared(input, axis); + res = std::make_shared(input, axis); } else { - res = std::make_shared(input, axis); + res = std::make_shared(input, axis); } res = ngraphQuantize(res, output_sc, output_zp); diff --git a/modules/dnn/src/layers/batch_norm_layer.cpp b/modules/dnn/src/layers/batch_norm_layer.cpp index ccc8354b42..3cdbdc222b 100644 --- a/modules/dnn/src/layers/batch_norm_layer.cpp +++ b/modules/dnn/src/layers/batch_norm_layer.cpp @@ -18,8 +18,6 @@ Implementation of Batch Normalization layer. #include "../op_webnn.hpp" #include "../op_cann.hpp" -#include - #ifdef HAVE_OPENCL #include "opencl_kernels_dnn.hpp" #endif @@ -459,14 +457,10 @@ public: auto ieInpNode = nodes[0].dynamicCast()->node; std::vector shape(ieInpNode.get_shape().size(), 1); shape[1] = weights_.total(); - auto weight = std::make_shared(ngraph::element::f32, ngraph::Shape(shape), weights_.data); - auto bias = std::make_shared(ngraph::element::f32, ngraph::Shape(shape), bias_.data); -#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2021_2) - auto scale_node = std::make_shared(ieInpNode, weight, ngraph::op::AutoBroadcastType::NUMPY); -#else - auto scale_node = std::make_shared(ieInpNode, weight, ngraph::op::AutoBroadcastType::NUMPY); -#endif - auto scale_shift = std::make_shared(scale_node, bias, ngraph::op::AutoBroadcastType::NUMPY); + auto weight = std::make_shared(ov::element::f32, ov::Shape(shape), weights_.data); + auto bias = std::make_shared(ov::element::f32, ov::Shape(shape), bias_.data); + auto scale_node = std::make_shared(ieInpNode, weight, ov::op::AutoBroadcastType::NUMPY); + auto scale_shift = std::make_shared(scale_node, bias, ov::op::AutoBroadcastType::NUMPY); return Ptr(new InfEngineNgraphNode(scale_shift)); } #endif // HAVE_DNN_NGRAPH diff --git a/modules/dnn/src/layers/blank_layer.cpp b/modules/dnn/src/layers/blank_layer.cpp index 16de23b15e..9723975723 100644 --- a/modules/dnn/src/layers/blank_layer.cpp +++ b/modules/dnn/src/layers/blank_layer.cpp @@ -149,8 +149,8 @@ public: const std::vector >& nodes) CV_OVERRIDE { auto ieInpNode = nodes[0].dynamicCast()->node; - ngraph::OutputVector inp{ieInpNode}; - auto blank = std::make_shared(inp, 0); + ov::OutputVector inp{ieInpNode}; + auto blank = std::make_shared(inp, 0); return Ptr(new InfEngineNgraphNode(blank)); } #endif // HAVE_DNN_NGRAPH diff --git a/modules/dnn/src/layers/concat_layer.cpp b/modules/dnn/src/layers/concat_layer.cpp index 3b4b622b2a..3a6466bd80 100644 --- a/modules/dnn/src/layers/concat_layer.cpp +++ b/modules/dnn/src/layers/concat_layer.cpp @@ -59,7 +59,6 @@ #include "../cuda4dnn/primitives/concat.hpp" using namespace cv::dnn::cuda4dnn; #endif - namespace cv { namespace dnn @@ -397,7 +396,7 @@ public: std::vector maxDims(numDims, 0); CV_Assert(inputs.size() == nodes.size()); - ngraph::OutputVector inp_nodes; + ov::OutputVector inp_nodes; for (int i = 0; i < nodes.size(); ++i) { auto inp = nodes[i].dynamicCast()->node; @@ -423,14 +422,14 @@ public: } if (needPadding) { - inp_nodes[i] = std::make_shared( + inp_nodes[i] = std::make_shared( inp_nodes[i], - std::make_shared(ngraph::element::i64, ngraph::Shape{begins.size()}, begins.data()), - std::make_shared(ngraph::element::i64, ngraph::Shape{ends.size()}, ends.data()), - ngraph::op::PadMode::CONSTANT); + std::make_shared(ov::element::i64, ov::Shape{begins.size()}, begins.data()), + std::make_shared(ov::element::i64, ov::Shape{ends.size()}, ends.data()), + ov::op::PadMode::CONSTANT); } } - auto concat = std::make_shared(inp_nodes, cAxis); + auto concat = std::make_shared(inp_nodes, cAxis); return Ptr(new InfEngineNgraphNode(concat)); } #endif // HAVE_DNN_NGRAPH diff --git a/modules/dnn/src/layers/const_layer.cpp b/modules/dnn/src/layers/const_layer.cpp index a3f510c496..2246c16320 100644 --- a/modules/dnn/src/layers/const_layer.cpp +++ b/modules/dnn/src/layers/const_layer.cpp @@ -128,22 +128,22 @@ public: virtual Ptr initNgraph(const std::vector >& inputs, const std::vector >& nodes) CV_OVERRIDE { - ngraph::element::Type dType; + ov::element::Type dType; if (blobs[0].depth() == CV_32F) { - dType = ngraph::element::f32; + dType = ov::element::f32; } else if (blobs[0].depth() == CV_32S) { - dType = ngraph::element::i32; + dType = ov::element::i32; } else if (blobs[0].depth() == CV_8S) { - dType = ngraph::element::i8; + dType = ov::element::i8; } else { CV_Error(Error::StsNotImplemented, format("Unexpected Const data depth: %d", blobs[0].depth())); } - std::shared_ptr node = - std::make_shared(dType, + std::shared_ptr node = + std::make_shared(dType, getShape(blobs[0]), blobs[0].data); - if (node->get_element_type() != ngraph::element::f32) { - node = std::make_shared(node, ngraph::element::f32); + if (node->get_element_type() != ov::element::f32) { + node = std::make_shared(node, ov::element::f32); } return Ptr(new InfEngineNgraphNode(node)); } diff --git a/modules/dnn/src/layers/convolution_layer.cpp b/modules/dnn/src/layers/convolution_layer.cpp index 3f4c5e4069..a950ea18c2 100644 --- a/modules/dnn/src/layers/convolution_layer.cpp +++ b/modules/dnn/src/layers/convolution_layer.cpp @@ -822,7 +822,7 @@ public: auto& ieInpNode = nodes[0].dynamicCast()->node; std::vector dims = ieInpNode.get_shape(); CV_Check(dims.size(), dims.size() >= 3 && dims.size() <= 5, ""); - ngraph::Output ieWeights; + ov::Output ieWeights; if (nodes.size() > 1) ieWeights = nodes[1].dynamicCast()->node; const int inpCn = dims[1]; @@ -840,49 +840,49 @@ public: if (nodes.size() == 1) { - ieWeights = std::make_shared(ngraph::element::f32, kernel_shape, blobs[0].data); + ieWeights = std::make_shared(ov::element::f32, kernel_shape, blobs[0].data); if (fusedWeights) { if (weightsMat.isContinuous()) { - ieWeights = std::make_shared(ngraph::element::f32, kernel_shape, weightsMat.data); + ieWeights = std::make_shared(ov::element::f32, kernel_shape, weightsMat.data); } else { Mat newWeights; Mat cvWeights = weightsMat.colRange(0, blobs[0].total() / numOutput); cvWeights.copyTo(newWeights); - ieWeights = std::make_shared(ngraph::element::f32, kernel_shape, newWeights.data); + ieWeights = std::make_shared(ov::element::f32, kernel_shape, newWeights.data); } } } else { - auto shape = std::make_shared(ngraph::element::i64, - ngraph::Shape{kernel_shape.size()}, std::vector(kernel_shape.begin(), kernel_shape.end())); - ieWeights = std::make_shared(ieWeights, shape, true); + auto shape = std::make_shared(ov::element::i64, + ov::Shape{kernel_shape.size()}, std::vector(kernel_shape.begin(), kernel_shape.end())); + ieWeights = std::make_shared(ieWeights, shape, true); } - ngraph::op::PadType pad_type = ngraph::op::PadType::EXPLICIT; + ov::op::PadType pad_type = ov::op::PadType::EXPLICIT; if (!padMode.empty()) - pad_type = padMode == "VALID" ? ngraph::op::PadType::VALID : ngraph::op::PadType::SAME_UPPER; + pad_type = padMode == "VALID" ? ov::op::PadType::VALID : ov::op::PadType::SAME_UPPER; - std::shared_ptr conv_node; + std::shared_ptr conv_node; if (group != 1) { - conv_node = std::make_shared( + conv_node = std::make_shared( ieInpNode, ieWeights, - ngraph::Strides(strides), - ngraph::CoordinateDiff(std::vector(pads_begin.begin(), pads_begin.end())), - ngraph::CoordinateDiff(std::vector(pads_end.begin(), pads_end.end())), - ngraph::Strides(dilations), + ov::Strides(strides), + ov::CoordinateDiff(std::vector(pads_begin.begin(), pads_begin.end())), + ov::CoordinateDiff(std::vector(pads_end.begin(), pads_end.end())), + ov::Strides(dilations), pad_type); } else { - conv_node = std::make_shared( + conv_node = std::make_shared( ieInpNode, ieWeights, - ngraph::Strides(strides), - ngraph::CoordinateDiff(std::vector(pads_begin.begin(), pads_begin.end())), - ngraph::CoordinateDiff(std::vector(pads_end.begin(), pads_end.end())), - ngraph::Strides(dilations), + ov::Strides(strides), + ov::CoordinateDiff(std::vector(pads_begin.begin(), pads_begin.end())), + ov::CoordinateDiff(std::vector(pads_end.begin(), pads_end.end())), + ov::Strides(dilations), pad_type); } @@ -890,18 +890,18 @@ public: { std::vector shape(conv_node->get_shape().size(), 1); shape[1] = conv_node->get_shape()[1]; - std::shared_ptr bias; + std::shared_ptr bias; if (nodes.size() == 3) { - auto bias_shape = std::make_shared(ngraph::element::i64, - ngraph::Shape{shape.size()}, std::vector(shape.begin(), shape.end())); - bias = std::make_shared(nodes[2].dynamicCast()->node, bias_shape, true); + auto bias_shape = std::make_shared(ov::element::i64, + ov::Shape{shape.size()}, std::vector(shape.begin(), shape.end())); + bias = std::make_shared(nodes[2].dynamicCast()->node, bias_shape, true); } else { - bias = std::make_shared(ngraph::element::f32, ngraph::Shape(shape), biasvec.data()); + bias = std::make_shared(ov::element::f32, ov::Shape(shape), biasvec.data()); } - auto conv_bias = std::make_shared(conv_node, bias, ngraph::op::AutoBroadcastType::NUMPY); + auto conv_bias = std::make_shared(conv_node, bias, ov::op::AutoBroadcastType::NUMPY); return Ptr(new InfEngineNgraphNode(conv_bias)); } return Ptr(new InfEngineNgraphNode(conv_node)); @@ -2252,13 +2252,13 @@ public: auto& ieInpNode = nodes[0].dynamicCast()->node; std::vector kernel_shape = getShape(blobs[0]); - auto ieWeights = std::make_shared(ngraph::element::f32, kernel_shape, blobs[0].data); + auto ieWeights = std::make_shared(ov::element::f32, kernel_shape, blobs[0].data); if (fusedWeights) { Mat newWeights; transpose(weightsMat, newWeights); - ieWeights = std::make_shared(ngraph::element::f32, kernel_shape, newWeights.data); + ieWeights = std::make_shared(ov::element::f32, kernel_shape, newWeights.data); } std::vector paddings_end; if (padMode == "SAME") @@ -2270,24 +2270,24 @@ public: } else { paddings_end = pads_end; } - ngraph::op::PadType pad_type = padMode == "VALID" ? ngraph::op::PadType::VALID : ngraph::op::PadType::EXPLICIT; + ov::op::PadType pad_type = padMode == "VALID" ? ov::op::PadType::VALID : ov::op::PadType::EXPLICIT; - auto deconv = std::make_shared( + auto deconv = std::make_shared( ieInpNode, ieWeights, - ngraph::Strides(strides), - ngraph::CoordinateDiff(std::vector(pads_begin.begin(), pads_begin.end())), - ngraph::CoordinateDiff(std::vector(paddings_end.begin(), paddings_end.end())), - ngraph::Strides(dilations), + ov::Strides(strides), + ov::CoordinateDiff(std::vector(pads_begin.begin(), pads_begin.end())), + ov::CoordinateDiff(std::vector(paddings_end.begin(), paddings_end.end())), + ov::Strides(dilations), pad_type, - ngraph::CoordinateDiff(std::vector(adjust_pads.begin(), adjust_pads.end()))); + ov::CoordinateDiff(std::vector(adjust_pads.begin(), adjust_pads.end()))); if (hasBias() || fusedBias) { std::vector shape(deconv->get_shape().size(), 1); shape[1] = numOutput; - auto bias = std::make_shared(ngraph::element::f32, ngraph::Shape(shape), blobs[1].data); - auto deconv_bias = std::make_shared(deconv, bias, ngraph::op::AutoBroadcastType::NUMPY); + auto bias = std::make_shared(ov::element::f32, ov::Shape(shape), blobs[1].data); + auto deconv_bias = std::make_shared(deconv, bias, ov::op::AutoBroadcastType::NUMPY); return Ptr(new InfEngineNgraphNode(deconv_bias)); } diff --git a/modules/dnn/src/layers/crop_and_resize_layer.cpp b/modules/dnn/src/layers/crop_and_resize_layer.cpp index 43373ca4de..6c29d6b8f4 100644 --- a/modules/dnn/src/layers/crop_and_resize_layer.cpp +++ b/modules/dnn/src/layers/crop_and_resize_layer.cpp @@ -138,23 +138,23 @@ public: offsets[3] = 2; dims[3] = 7; - auto lower_bounds = std::make_shared(ngraph::element::i64, - ngraph::Shape{offsets.size()}, offsets.data()); - auto upper_bounds = std::make_shared(ngraph::element::i64, - ngraph::Shape{dims.size()}, dims.data()); - auto strides = std::make_shared(ngraph::element::i64, - ngraph::Shape{dims.size()}, std::vector((int64_t)dims.size(), 1)); - auto slice = std::make_shared(rois, + auto lower_bounds = std::make_shared(ov::element::i64, + ov::Shape{offsets.size()}, offsets.data()); + auto upper_bounds = std::make_shared(ov::element::i64, + ov::Shape{dims.size()}, dims.data()); + auto strides = std::make_shared(ov::element::i64, + ov::Shape{dims.size()}, std::vector((int64_t)dims.size(), 1)); + auto slice = std::make_shared(rois, lower_bounds, upper_bounds, strides, std::vector{}, std::vector{}); // Reshape rois from 4D to 2D std::vector shapeData = {dims[2], 5}; - auto shape = std::make_shared(ngraph::element::i64, ngraph::Shape{2}, shapeData.data()); - auto reshape = std::make_shared(slice, shape, true); + auto shape = std::make_shared(ov::element::i64, ov::Shape{2}, shapeData.data()); + auto reshape = std::make_shared(slice, shape, true); auto roiPooling = - std::make_shared(input, reshape, - ngraph::Shape{(size_t)outHeight, (size_t)outWidth}, + std::make_shared(input, reshape, + ov::Shape{(size_t)outHeight, (size_t)outWidth}, 1.0f, "bilinear"); return Ptr(new InfEngineNgraphNode(roiPooling)); diff --git a/modules/dnn/src/layers/detection_output_layer.cpp b/modules/dnn/src/layers/detection_output_layer.cpp index 9a7a56fe7a..c7b2272550 100644 --- a/modules/dnn/src/layers/detection_output_layer.cpp +++ b/modules/dnn/src/layers/detection_output_layer.cpp @@ -55,11 +55,7 @@ #ifdef HAVE_DNN_NGRAPH #include "../ie_ngraph.hpp" -#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2020_4) -#include -#else -#include -#endif +#include #endif #ifdef HAVE_CUDA @@ -1012,26 +1008,26 @@ public: if (_locPredTransposed) { // Convert box predictions from yxYX to xyXY - box_logits = std::make_shared(box_logits, - std::make_shared(ngraph::element::i32, ngraph::Shape{3}, std::vector{0, -1, 2}), + box_logits = std::make_shared(box_logits, + std::make_shared(ov::element::i32, ov::Shape{3}, std::vector{0, -1, 2}), true ); int axis = 2; - box_logits = std::make_shared(box_logits, - std::make_shared(ngraph::element::i32, ngraph::Shape{1}, &axis), - ngraph::op::v1::Reverse::Mode::INDEX + box_logits = std::make_shared(box_logits, + std::make_shared(ov::element::i32, ov::Shape{1}, &axis), + ov::op::v1::Reverse::Mode::INDEX ); } - auto shape = std::make_shared(ngraph::element::i32, ngraph::Shape{2}, std::vector{0, -1}); - box_logits = std::make_shared(box_logits, shape, true); - class_preds = std::make_shared(class_preds, shape, true); - proposals = std::make_shared(proposals, - std::make_shared(ngraph::element::i32, ngraph::Shape{3}, std::vector{0, _varianceEncodedInTarget ? 1 : 2, -1}), + auto shape = std::make_shared(ov::element::i32, ov::Shape{2}, std::vector{0, -1}); + box_logits = std::make_shared(box_logits, shape, true); + class_preds = std::make_shared(class_preds, shape, true); + proposals = std::make_shared(proposals, + std::make_shared(ov::element::i32, ov::Shape{3}, std::vector{0, _varianceEncodedInTarget ? 1 : 2, -1}), true ); - ngraph::op::DetectionOutputAttrs attrs; + ov::op::v0::DetectionOutput::Attributes attrs; attrs.num_classes = _numClasses; attrs.background_label_id = _backgroundLabelId; attrs.top_k = _topK > 0 ? _topK : _keepTopK; @@ -1044,7 +1040,7 @@ public: attrs.code_type = std::string{"caffe.PriorBoxParameter." + _codeType}; attrs.normalized = true; - auto det_out = std::make_shared(box_logits, class_preds, + auto det_out = std::make_shared(box_logits, class_preds, proposals, attrs); return Ptr(new InfEngineNgraphNode(det_out)); } diff --git a/modules/dnn/src/layers/elementwise_layers.cpp b/modules/dnn/src/layers/elementwise_layers.cpp index 7de854c179..6b7909b1b7 100644 --- a/modules/dnn/src/layers/elementwise_layers.cpp +++ b/modules/dnn/src/layers/elementwise_layers.cpp @@ -490,13 +490,13 @@ struct ReLUFunctor : public BaseFunctor #endif #ifdef HAVE_DNN_NGRAPH - std::shared_ptr initNgraphAPI(const ngraph::Output& node) + std::shared_ptr initNgraphAPI(const ov::Output& node) { if (slope) { - auto param = std::make_shared(ngraph::element::f32, ngraph::Shape{1}, &slope); - return std::make_shared(node, param); + auto param = std::make_shared(ov::element::f32, ov::Shape{1}, &slope); + return std::make_shared(node, param); } - return std::make_shared(node); + return std::make_shared(node); } #endif // HAVE_DNN_NGRAPH @@ -674,9 +674,9 @@ struct ReLU6Functor : public BaseFunctor #ifdef HAVE_DNN_NGRAPH - std::shared_ptr initNgraphAPI(const ngraph::Output& node) + std::shared_ptr initNgraphAPI(const ov::Output& node) { - return std::make_shared(node, minValue, maxValue); + return std::make_shared(node, minValue, maxValue); } #endif // HAVE_DNN_NGRAPH @@ -796,7 +796,7 @@ struct BaseDefaultFunctor : public BaseFunctor #endif // HAVE_CANN #ifdef HAVE_DNN_NGRAPH - std::shared_ptr initNgraphAPI(const ngraph::Output& node) + std::shared_ptr initNgraphAPI(const ov::Output& node) { CV_Error(Error::StsNotImplemented, ""); } @@ -929,9 +929,9 @@ struct TanHFunctor : public BaseDefaultFunctor #endif // HAVE_CANN #ifdef HAVE_DNN_NGRAPH - std::shared_ptr initNgraphAPI(const ngraph::Output& node) + std::shared_ptr initNgraphAPI(const ov::Output& node) { - return std::make_shared(node); + return std::make_shared(node); } #endif // HAVE_DNN_NGRAPH @@ -998,10 +998,10 @@ struct SwishFunctor : public BaseDefaultFunctor #endif // HAVE_CANN #ifdef HAVE_DNN_NGRAPH - std::shared_ptr initNgraphAPI(const ngraph::Output& node) + std::shared_ptr initNgraphAPI(const ov::Output& node) { - auto sigmoid = std::make_shared(node); - return std::make_shared(node, sigmoid); + auto sigmoid = std::make_shared(node); + return std::make_shared(node, sigmoid); } #endif // HAVE_DNN_NGRAPH @@ -1074,9 +1074,9 @@ struct MishFunctor : public BaseDefaultFunctor #endif // HAVE_CANN #ifdef HAVE_DNN_NGRAPH - std::shared_ptr initNgraphAPI(const ngraph::Output& node) + std::shared_ptr initNgraphAPI(const ov::Output& node) { - return std::make_shared(node); + return std::make_shared(node); } #endif // HAVE_DNN_NGRAPH @@ -1151,9 +1151,9 @@ struct SigmoidFunctor : public BaseDefaultFunctor #endif // HAVE_CANN #ifdef HAVE_DNN_NGRAPH - std::shared_ptr initNgraphAPI(const ngraph::Output& node) + std::shared_ptr initNgraphAPI(const ov::Output& node) { - return std::make_shared(node); + return std::make_shared(node); } #endif // HAVE_DNN_NGRAPH @@ -1231,9 +1231,9 @@ struct ELUFunctor : public BaseDefaultFunctor #endif // HAVE_CANN #ifdef HAVE_DNN_NGRAPH - std::shared_ptr initNgraphAPI(const ngraph::Output& node) + std::shared_ptr initNgraphAPI(const ov::Output& node) { - return std::make_shared(node, alpha); + return std::make_shared(node, alpha); } #endif // HAVE_DNN_NGRAPH @@ -1301,9 +1301,9 @@ struct AbsValFunctor : public BaseDefaultFunctor #endif // HAVE_CANN #ifdef HAVE_DNN_NGRAPH - std::shared_ptr initNgraphAPI(const ngraph::Output& node) + std::shared_ptr initNgraphAPI(const ov::Output& node) { - return std::make_shared(node); + return std::make_shared(node); } #endif // HAVE_DNN_NGRAPH @@ -1594,9 +1594,9 @@ struct SqrtFunctor : public BaseDefaultFunctor #endif // HAVE_HALIDE #ifdef HAVE_DNN_NGRAPH - std::shared_ptr initNgraphAPI(const ngraph::Output& node) + std::shared_ptr initNgraphAPI(const ov::Output& node) { - return std::make_shared(node); + return std::make_shared(node); } #endif // HAVE_DNN_NGRAPH @@ -2343,22 +2343,22 @@ struct PowerFunctor : public BaseFunctor #endif // HAVE_CANN #ifdef HAVE_DNN_NGRAPH - std::shared_ptr initNgraphAPI(const ngraph::Output& node) + std::shared_ptr initNgraphAPI(const ov::Output& node) { - auto scale_node = std::make_shared(ngraph::element::f32, - ngraph::Shape{1}, &scale); - auto shift_node = std::make_shared(ngraph::element::f32, - ngraph::Shape{1}, &shift); + auto scale_node = std::make_shared(ov::element::f32, + ov::Shape{1}, &scale); + auto shift_node = std::make_shared(ov::element::f32, + ov::Shape{1}, &shift); - auto mul = std::make_shared(scale_node, node, ngraph::op::AutoBroadcastType::NUMPY); - auto scale_shift = std::make_shared(mul, shift_node, ngraph::op::AutoBroadcastType::NUMPY); + auto mul = std::make_shared(scale_node, node, ov::op::AutoBroadcastType::NUMPY); + auto scale_shift = std::make_shared(mul, shift_node, ov::op::AutoBroadcastType::NUMPY); if (power == 1) return scale_shift; - auto power_node = std::make_shared(ngraph::element::f32, - ngraph::Shape{1}, &power); - return std::make_shared(scale_shift, power_node, ngraph::op::AutoBroadcastType::NUMPY); + auto power_node = std::make_shared(ov::element::f32, + ov::Shape{1}, &power); + return std::make_shared(scale_shift, power_node, ov::op::AutoBroadcastType::NUMPY); } #endif // HAVE_DNN_NGRAPH @@ -2453,15 +2453,15 @@ struct ExpFunctor : public BaseDefaultFunctor #endif // HAVE_HALIDE #ifdef HAVE_DNN_NGRAPH - std::shared_ptr initNgraphAPI(const ngraph::Output& node) + std::shared_ptr initNgraphAPI(const ov::Output& node) { - auto scale_node = std::make_shared(ngraph::element::f32, - ngraph::Shape{1}, &normScale); - auto shift_node = std::make_shared(ngraph::element::f32, - ngraph::Shape{1}, &normShift); - auto mul = std::make_shared(scale_node, node, ngraph::op::AutoBroadcastType::NUMPY); - auto scale_shift = std::make_shared(mul, shift_node, ngraph::op::AutoBroadcastType::NUMPY); - return std::make_shared(scale_shift); + auto scale_node = std::make_shared(ov::element::f32, + ov::Shape{1}, &normScale); + auto shift_node = std::make_shared(ov::element::f32, + ov::Shape{1}, &normShift); + auto mul = std::make_shared(scale_node, node, ov::op::AutoBroadcastType::NUMPY); + auto scale_shift = std::make_shared(mul, shift_node, ov::op::AutoBroadcastType::NUMPY); + return std::make_shared(scale_shift); } #endif // HAVE_DNN_NGRAPH @@ -2612,11 +2612,11 @@ struct ChannelsPReLUFunctor : public BaseFunctor #endif // HAVE_CANN #ifdef HAVE_DNN_NGRAPH - std::shared_ptr initNgraphAPI(const ngraph::Output& node) + std::shared_ptr initNgraphAPI(const ov::Output& node) { const size_t numChannels = scale.total(); - auto slope = std::make_shared(ngraph::element::f32, ngraph::Shape{numChannels}, scale.data); - return std::make_shared(node, slope); + auto slope = std::make_shared(ov::element::f32, ov::Shape{numChannels}, scale.data); + return std::make_shared(node, slope); } #endif // HAVE_DNN_NGRAPH @@ -2692,11 +2692,11 @@ struct PReLUFunctor : public ChannelsPReLUFunctor } #ifdef HAVE_DNN_NGRAPH - std::shared_ptr initNgraphAPI(const ngraph::Output& node) + std::shared_ptr initNgraphAPI(const ov::Output& node) { auto shape = getShape(scale); - auto slope = std::make_shared(ngraph::element::f32, shape, scale.ptr()); - return std::make_shared(node, slope); + auto slope = std::make_shared(ov::element::f32, shape, scale.ptr()); + return std::make_shared(node, slope); } #endif // HAVE_DNN_NGRAPH }; diff --git a/modules/dnn/src/layers/eltwise_layer.cpp b/modules/dnn/src/layers/eltwise_layer.cpp index 32caf29530..e9363bcbea 100644 --- a/modules/dnn/src/layers/eltwise_layer.cpp +++ b/modules/dnn/src/layers/eltwise_layer.cpp @@ -899,24 +899,24 @@ public: CV_Assert(nodes.size() >= 2); auto curr_node = nodes[0].dynamicCast()->node; if (!coeffs.empty()) { - auto coeff = std::make_shared(ngraph::element::f32, ngraph::Shape{1}, &coeffs[0]); - curr_node = std::make_shared(curr_node, coeff, ngraph::op::AutoBroadcastType::NUMPY); + auto coeff = std::make_shared(ov::element::f32, ov::Shape{1}, &coeffs[0]); + curr_node = std::make_shared(curr_node, coeff, ov::op::AutoBroadcastType::NUMPY); } - std::shared_ptr res; + std::shared_ptr res; for (size_t i = 1; i < nodes.size(); i++) { auto next_node = nodes[i].dynamicCast()->node; if (!coeffs.empty()) { - auto coeff = std::make_shared(ngraph::element::f32, ngraph::Shape{1}, &coeffs[i]); - next_node = std::make_shared(next_node, coeff, ngraph::op::AutoBroadcastType::NUMPY); + auto coeff = std::make_shared(ov::element::f32, ov::Shape{1}, &coeffs[i]); + next_node = std::make_shared(next_node, coeff, ov::op::AutoBroadcastType::NUMPY); } switch (op) { - case SUM: res = std::make_shared(curr_node, next_node); break; - case PROD: res = std::make_shared(curr_node, next_node); break; - case DIV: res = std::make_shared(curr_node, next_node); break; - case MAX: res = std::make_shared(curr_node, next_node); break; - case MIN: res = std::make_shared(curr_node, next_node); break; + case SUM: res = std::make_shared(curr_node, next_node); break; + case PROD: res = std::make_shared(curr_node, next_node); break; + case DIV: res = std::make_shared(curr_node, next_node); break; + case MAX: res = std::make_shared(curr_node, next_node); break; + case MIN: res = std::make_shared(curr_node, next_node); break; default: CV_Error(Error::StsNotImplemented, "Unsupported eltwise operation"); } curr_node = res; diff --git a/modules/dnn/src/layers/flatten_layer.cpp b/modules/dnn/src/layers/flatten_layer.cpp index 9ff3bec38b..48950601f2 100644 --- a/modules/dnn/src/layers/flatten_layer.cpp +++ b/modules/dnn/src/layers/flatten_layer.cpp @@ -56,6 +56,7 @@ using namespace cv::dnn::cuda4dnn; #endif + namespace cv { namespace dnn @@ -224,9 +225,9 @@ public: outputShapeVec.push_back(flattenedDimensionSize); outputShapeVec.insert(outputShapeVec.end(), dims.begin() + endAxis + 1, dims.end()); - auto shape = std::make_shared(ngraph::element::i64, - ngraph::Shape({outputShapeVec.size()}), outputShapeVec.data()); - auto reshape = std::make_shared(ieInpNode, shape, true); + auto shape = std::make_shared(ov::element::i64, + ov::Shape({outputShapeVec.size()}), outputShapeVec.data()); + auto reshape = std::make_shared(ieInpNode, shape, true); return Ptr(new InfEngineNgraphNode(reshape)); } #endif // HAVE_DNN_NGRAPH diff --git a/modules/dnn/src/layers/fully_connected_layer.cpp b/modules/dnn/src/layers/fully_connected_layer.cpp index 29090652e1..4ff6fc74a4 100644 --- a/modules/dnn/src/layers/fully_connected_layer.cpp +++ b/modules/dnn/src/layers/fully_connected_layer.cpp @@ -787,20 +787,20 @@ public: const std::vector >& nodes) CV_OVERRIDE { auto& ieInpNode = nodes[0].dynamicCast()->node; - std::shared_ptr matmul; + std::shared_ptr matmul; if (nodes.size() == 2) { auto& inp2 = nodes[1].dynamicCast()->node; - matmul = std::make_shared(ieInpNode, inp2, transA, transB); + matmul = std::make_shared(ieInpNode, inp2, transA, transB); } else { std::vector shape(1 + normalize_axis(axis, ieInpNode.get_shape().size()), 0); shape[shape.size() - 1] = -1; - auto inp = std::make_shared( + auto inp = std::make_shared( ieInpNode, - std::make_shared(ngraph::element::i32, ngraph::Shape{shape.size()}, shape.data()), + std::make_shared(ov::element::i32, ov::Shape{shape.size()}, shape.data()), true ); @@ -810,14 +810,14 @@ public: } else { weight_shape = {(size_t)blobs[0].size[0], (size_t)blobs[0].size[1]}; } - auto ieWeights = std::make_shared(ngraph::element::f32, weight_shape, blobs[0].data); - matmul = std::make_shared(inp, ieWeights, transA, transB); + auto ieWeights = std::make_shared(ov::element::f32, weight_shape, blobs[0].data); + matmul = std::make_shared(inp, ieWeights, transA, transB); } if (bias) { - auto bias_node = std::make_shared(ngraph::element::f32, - ngraph::Shape{(size_t)blobs[1].size[1]}, blobs[1].data); - matmul = std::make_shared(matmul, bias_node, ngraph::op::AutoBroadcastType::NUMPY); + auto bias_node = std::make_shared(ov::element::f32, + ov::Shape{(size_t)blobs[1].size[1]}, blobs[1].data); + matmul = std::make_shared(matmul, bias_node, ov::op::AutoBroadcastType::NUMPY); } return Ptr(new InfEngineNgraphNode(matmul)); } diff --git a/modules/dnn/src/layers/gemm_layer.cpp b/modules/dnn/src/layers/gemm_layer.cpp index 496d3871a2..fd10e9acdf 100644 --- a/modules/dnn/src/layers/gemm_layer.cpp +++ b/modules/dnn/src/layers/gemm_layer.cpp @@ -285,45 +285,45 @@ public: const std::vector >& nodes) CV_OVERRIDE { auto ieInpNode = nodes[0].dynamicCast()->node; - std::shared_ptr matmul; + std::shared_ptr matmul; if (nodes.size() == 2) { auto& inp2 = nodes[1].dynamicCast()->node; - matmul = std::make_shared(ieInpNode, inp2, trans_a, trans_b); + matmul = std::make_shared(ieInpNode, inp2, trans_a, trans_b); } else { - std::shared_ptr ieWeights = std::make_shared(ngraph::element::f32, getShape(blobs[0]), blobs[0].data); + std::shared_ptr ieWeights = std::make_shared(ov::element::f32, getShape(blobs[0]), blobs[0].data); int flatten_axis = ieInpNode.get_shape().size() - ieWeights->get_shape().size(); if (flatten_axis > 0) { std::vector shape(1 + flatten_axis, 0); shape[shape.size() - 1] = -1; - ieInpNode = std::make_shared( + ieInpNode = std::make_shared( ieInpNode, - std::make_shared(ngraph::element::i32, ngraph::Shape{shape.size()}, shape.data()), + std::make_shared(ov::element::i32, ov::Shape{shape.size()}, shape.data()), true ); } - matmul = std::make_shared(ieInpNode, ieWeights, trans_a, trans_b); + matmul = std::make_shared(ieInpNode, ieWeights, trans_a, trans_b); } if (alpha != 1.0f) { - matmul = std::make_shared(matmul, - std::make_shared(ngraph::element::f32, ngraph::Shape{1}, &alpha) + matmul = std::make_shared(matmul, + std::make_shared(ov::element::f32, ov::Shape{1}, &alpha) ); } if (have_bias && const_C) { Mat bias = blobs.back(); - auto shape = bias.total() == bias.size[0] ? ngraph::Shape{bias.total()} : getShape(bias); - std::shared_ptr bias_node = std::make_shared(ngraph::element::f32, shape, bias.data); + auto shape = bias.total() == bias.size[0] ? ov::Shape{bias.total()} : getShape(bias); + std::shared_ptr bias_node = std::make_shared(ov::element::f32, shape, bias.data); if (beta != 1.0f) { - bias_node = std::make_shared(bias_node, - std::make_shared(ngraph::element::f32, ngraph::Shape{1}, &beta) + bias_node = std::make_shared(bias_node, + std::make_shared(ov::element::f32, ov::Shape{1}, &beta) ); } - matmul = std::make_shared(matmul, bias_node, ngraph::op::AutoBroadcastType::NUMPY); + matmul = std::make_shared(matmul, bias_node, ov::op::AutoBroadcastType::NUMPY); } return Ptr(new InfEngineNgraphNode(matmul)); } diff --git a/modules/dnn/src/layers/instance_norm_layer.cpp b/modules/dnn/src/layers/instance_norm_layer.cpp index b6427238f2..ae61f15656 100644 --- a/modules/dnn/src/layers/instance_norm_layer.cpp +++ b/modules/dnn/src/layers/instance_norm_layer.cpp @@ -225,33 +225,26 @@ public: auto ieInpNode = nodes[0].dynamicCast()->node; const auto &input_shape = ieInpNode.get_shape(); - std::shared_ptr mvn, result; + std::shared_ptr mvn, result; // mvn -#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2021_2) - // https://docs.openvino.ai/2021.4/api/ngraph_python_api/_autosummary/ngraph.opset3.mvn.html?highlight=mvn#ngraph.opset3.mvn - bool across_channels = false; - bool normalize_variance = true; - mvn = std::make_shared(ieInpNode, across_channels, normalize_variance, epsilon); -#else // https://docs.openvino.ai/2023.1/openvino_docs_ops_normalization_MVN_6.html std::vector axes_v(input_shape.size() - 2); std::iota(axes_v.begin(), axes_v.end(), 2); // {2, 3, ...} for nd input tensor, n>=3 - auto axes = std::make_shared(ngraph::element::i64, ngraph::Shape{axes_v.size()}, axes_v.data()); + auto axes = std::make_shared(ov::element::i64, ov::Shape{axes_v.size()}, axes_v.data()); bool normalize_variance = true; - mvn = std::make_shared(ieInpNode, axes, normalize_variance, epsilon, ngraph::op::MVNEpsMode::INSIDE_SQRT); -#endif + mvn = std::make_shared(ieInpNode, axes, normalize_variance, epsilon, ov::op::MVNEpsMode::INSIDE_SQRT); // instance norm = scale * mvn + bias auto scale = nodes[1].dynamicCast()->node; std::vector shared_shape_v(input_shape.size(), 1); shared_shape_v[1] = -1; - auto shared_shape = std::make_shared(ngraph::element::i64, ngraph::Shape{shared_shape_v.size()}, shared_shape_v.data()); - scale = std::make_shared(scale, shared_shape, true); - result = std::make_shared(mvn, scale); + auto shared_shape = std::make_shared(ov::element::i64, ov::Shape{shared_shape_v.size()}, shared_shape_v.data()); + scale = std::make_shared(scale, shared_shape, true); + result = std::make_shared(mvn, scale); auto bias = nodes[2].dynamicCast()->node; - bias = std::make_shared(bias, shared_shape, true); - result = std::make_shared(result, bias); + bias = std::make_shared(bias, shared_shape, true); + result = std::make_shared(result, bias); return Ptr(new InfEngineNgraphNode(result)); } diff --git a/modules/dnn/src/layers/layer_norm.cpp b/modules/dnn/src/layers/layer_norm.cpp index 6ea1bee42a..5ebebbd32d 100644 --- a/modules/dnn/src/layers/layer_norm.cpp +++ b/modules/dnn/src/layers/layer_norm.cpp @@ -263,42 +263,35 @@ public: const std::vector >& nodes) CV_OVERRIDE { auto ieInpNode = nodes[0].dynamicCast()->node; const auto &input_shape = ieInpNode.get_shape(); - std::shared_ptr mvn, result; + std::shared_ptr mvn, result; // mvn -#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2021_2) - // https://docs.openvino.ai/2021.4/api/ngraph_python_api/_autosummary/ngraph.opset3.mvn.html?highlight=mvn#ngraph.opset3.mvn - bool across_channels = false; - bool normalize_variance = true; - mvn = std::make_shared(ieInpNode, across_channels, normalize_variance, epsilon); -#else // https://docs.openvino.ai/2023.1/openvino_docs_ops_normalization_MVN_6.html std::vector axes_v(input_shape.size() - axis); std::iota(axes_v.begin(), axes_v.end(), axis); - auto axes = std::make_shared(ngraph::element::i64, ngraph::Shape{axes_v.size()}, axes_v.data()); + auto axes = std::make_shared(ov::element::i64, ov::Shape{axes_v.size()}, axes_v.data()); bool normalize_variance = true; - mvn = std::make_shared(ieInpNode, axes, normalize_variance, epsilon, ngraph::op::MVNEpsMode::INSIDE_SQRT); -#endif + mvn = std::make_shared(ieInpNode, axes, normalize_variance, epsilon, ov::op::MVNEpsMode::INSIDE_SQRT); // layer norm = scale * mvn + bias auto scale = nodes[1].dynamicCast()->node; - ngraph::Output bias; + ov::Output bias; if (nodes.size() == 3) { bias = nodes[2].dynamicCast()->node; } if (axis == -1 || axis == input_shape.size() - 1) { // special case for 1D tensor (2D mat) std::vector shared_shape_v(input_shape.size(), 1); shared_shape_v.back() = -1; - auto shared_shape = std::make_shared(ngraph::element::i64, ngraph::Shape{shared_shape_v.size()}, shared_shape_v.data()); - scale = std::make_shared(scale, shared_shape, true); + auto shared_shape = std::make_shared(ov::element::i64, ov::Shape{shared_shape_v.size()}, shared_shape_v.data()); + scale = std::make_shared(scale, shared_shape, true); if (nodes.size() == 3) { - bias = std::make_shared(bias, shared_shape, true); + bias = std::make_shared(bias, shared_shape, true); } } - result = std::make_shared(mvn, scale); + result = std::make_shared(mvn, scale); if (nodes.size() == 3) { - result = std::make_shared(result, bias); + result = std::make_shared(result, bias); } return Ptr(new InfEngineNgraphNode(result)); diff --git a/modules/dnn/src/layers/lrn_layer.cpp b/modules/dnn/src/layers/lrn_layer.cpp index 7d212dc888..4863be2e3d 100644 --- a/modules/dnn/src/layers/lrn_layer.cpp +++ b/modules/dnn/src/layers/lrn_layer.cpp @@ -483,8 +483,8 @@ public: axes.resize(ieInpNode.get_shape().size() - 2); std::iota(axes.begin(), axes.end(), 2); } - auto ngraph_axes = std::make_shared(ngraph::element::i64, ngraph::Shape{axes.size()}, axes.data()); - auto lrn = std::make_shared(ieInpNode, ngraph_axes, alphaSize, beta, bias, size); + auto ngraph_axes = std::make_shared(ov::element::i64, ov::Shape{axes.size()}, axes.data()); + auto lrn = std::make_shared(ieInpNode, ngraph_axes, alphaSize, beta, bias, size); return Ptr(new InfEngineNgraphNode(lrn)); } #endif // HAVE_DNN_NGRAPH diff --git a/modules/dnn/src/layers/matmul_layer.cpp b/modules/dnn/src/layers/matmul_layer.cpp index 1e9a8dfebe..a571592dfb 100644 --- a/modules/dnn/src/layers/matmul_layer.cpp +++ b/modules/dnn/src/layers/matmul_layer.cpp @@ -216,15 +216,15 @@ class MatMulLayerImpl CV_FINAL : public MatMulLayer { virtual Ptr initNgraph(const std::vector >& inputs, const std::vector >& nodes) CV_OVERRIDE { auto& input_A_node = nodes[0].dynamicCast()->node; - std::shared_ptr matmul; + std::shared_ptr matmul; if (nodes.size() == 2) { auto &input_B_node = nodes[1].dynamicCast()->node; - matmul = std::make_shared(input_A_node, input_B_node, trans_a, trans_b); + matmul = std::make_shared(input_A_node, input_B_node, trans_a, trans_b); } else { auto input_B_shape = getShape(blobs[0]); - auto input_B_node = std::make_shared(ngraph::element::f32, input_B_shape, blobs[0].data); - matmul = std::make_shared(input_A_node, input_B_node, trans_a, trans_b); + auto input_B_node = std::make_shared(ov::element::f32, input_B_shape, blobs[0].data); + matmul = std::make_shared(input_A_node, input_B_node, trans_a, trans_b); } return Ptr(new InfEngineNgraphNode(matmul)); diff --git a/modules/dnn/src/layers/max_unpooling_layer.cpp b/modules/dnn/src/layers/max_unpooling_layer.cpp index d00887db3c..aa645c17ae 100644 --- a/modules/dnn/src/layers/max_unpooling_layer.cpp +++ b/modules/dnn/src/layers/max_unpooling_layer.cpp @@ -200,29 +200,29 @@ public: getMemoryShapes(inpShapes, 1, outShapes, internals); Mat zeros = Mat::zeros(1, total(outShapes[0]), CV_32F); - auto zeroInp = std::make_shared(ngraph::element::f32, ngraph::Shape{zeros.total()}, zeros.data); + auto zeroInp = std::make_shared(ov::element::f32, ov::Shape{zeros.total()}, zeros.data); int newShape = -1; - features = std::make_shared( + features = std::make_shared( features, - std::make_shared(ngraph::element::i32, ngraph::Shape{1}, &newShape), + std::make_shared(ov::element::i32, ov::Shape{1}, &newShape), true ); - indices = std::make_shared( + indices = std::make_shared( indices, - std::make_shared(ngraph::element::i32, ngraph::Shape{1}, &newShape), + std::make_shared(ov::element::i32, ov::Shape{1}, &newShape), true ); - if (indices.get_element_type() != ngraph::element::i32 && indices.get_element_type() != ngraph::element::i64) { - indices = std::make_shared(indices, ngraph::element::i64); + if (indices.get_element_type() != ov::element::i32 && indices.get_element_type() != ov::element::i64) { + indices = std::make_shared(indices, ov::element::i64); } int axis = 0; - std::shared_ptr unpool = std::make_shared(zeroInp, indices, features, - std::make_shared(ngraph::element::i32, ngraph::Shape{1}, &axis)); + std::shared_ptr unpool = std::make_shared(zeroInp, indices, features, + std::make_shared(ov::element::i32, ov::Shape{1}, &axis)); - auto shape = std::make_shared(ngraph::element::i32, ngraph::Shape{outShapes[0].size()}, outShapes[0].data()); - unpool = std::make_shared(unpool, shape, true); + auto shape = std::make_shared(ov::element::i32, ov::Shape{outShapes[0].size()}, outShapes[0].data()); + unpool = std::make_shared(unpool, shape, true); return Ptr(new InfEngineNgraphNode(unpool)); } diff --git a/modules/dnn/src/layers/mvn_layer.cpp b/modules/dnn/src/layers/mvn_layer.cpp index 10ef1cfb34..d59e339ac4 100644 --- a/modules/dnn/src/layers/mvn_layer.cpp +++ b/modules/dnn/src/layers/mvn_layer.cpp @@ -336,15 +336,11 @@ public: const std::vector >& nodes) CV_OVERRIDE { auto& ieInpNode = nodes[0].dynamicCast()->node; -#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2021_2) - auto mvn = std::make_shared(ieInpNode, acrossChannels, normVariance, eps); -#else int64_t start_axis = acrossChannels ? 1 : 2; std::vector axes_v(ieInpNode.get_shape().size() - start_axis); std::iota(axes_v.begin(), axes_v.end(), start_axis); - auto axes = std::make_shared(ngraph::element::i64, ngraph::Shape{axes_v.size()}, axes_v.data()); - auto mvn = std::make_shared(ieInpNode, axes, normVariance, eps, ngraph::op::MVNEpsMode::INSIDE_SQRT); -#endif + auto axes = std::make_shared(ov::element::i64, ov::Shape{axes_v.size()}, axes_v.data()); + auto mvn = std::make_shared(ieInpNode, axes, normVariance, eps, ov::op::MVNEpsMode::INSIDE_SQRT); return Ptr(new InfEngineNgraphNode(mvn)); } #endif // HAVE_DNN_NGRAPH diff --git a/modules/dnn/src/layers/nary_eltwise_layers.cpp b/modules/dnn/src/layers/nary_eltwise_layers.cpp index c369a710a6..e3a8b2a583 100644 --- a/modules/dnn/src/layers/nary_eltwise_layers.cpp +++ b/modules/dnn/src/layers/nary_eltwise_layers.cpp @@ -887,32 +887,32 @@ public: if (inp0.get_element_type() != inp1.get_element_type()) { auto dtype = preferableTarget == DNN_TARGET_OPENCL_FP16 || preferableTarget == DNN_TARGET_MYRIAD ? - ngraph::element::f16 : ngraph::element::f32; + ov::element::f16 : ov::element::f32; if (inp0.get_element_type() != dtype) - inp0 = std::make_shared(inp0, dtype); + inp0 = std::make_shared(inp0, dtype); if (inp1.get_element_type() != dtype) - inp1 = std::make_shared(inp1, dtype); + inp1 = std::make_shared(inp1, dtype); } - std::shared_ptr node; + std::shared_ptr node; if (op == OPERATION::ADD) - node = std::make_shared(inp0, inp1); + node = std::make_shared(inp0, inp1); else if (op == OPERATION::PROD) - node = std::make_shared(inp0, inp1); + node = std::make_shared(inp0, inp1); else if (op == OPERATION::GREATER_EQUAL) - node = std::make_shared(inp0, inp1); + node = std::make_shared(inp0, inp1); else if (op == OPERATION::LESS_EQUAL) - node = std::make_shared(inp0, inp1); + node = std::make_shared(inp0, inp1); // Ideally we should do this but int32 internal blobs are converted to float32 data type in inference. // TODO: Remove data type convertion when we have type inference. else if (op == OPERATION::MOD) { - auto inp0_i64 = std::make_shared(inp0, ngraph::element::i64); - auto inp1_i64 = std::make_shared(inp1, ngraph::element::i64); - auto mod = std::make_shared(inp0_i64, inp1_i64); - node = std::make_shared(mod, ngraph::element::f32); + auto inp0_i64 = std::make_shared(inp0, ov::element::i64); + auto inp1_i64 = std::make_shared(inp1, ov::element::i64); + auto mod = std::make_shared(inp0_i64, inp1_i64); + node = std::make_shared(mod, ov::element::f32); } else if (op == OPERATION::FMOD) - node = std::make_shared(inp0, inp1); + node = std::make_shared(inp0, inp1); else CV_Error(Error::StsNotImplemented, "Operation is not implemented for nGraph backend"); return Ptr(new InfEngineNgraphNode(node)); diff --git a/modules/dnn/src/layers/normalize_bbox_layer.cpp b/modules/dnn/src/layers/normalize_bbox_layer.cpp index 723d3c7f7f..d2324b2a94 100644 --- a/modules/dnn/src/layers/normalize_bbox_layer.cpp +++ b/modules/dnn/src/layers/normalize_bbox_layer.cpp @@ -283,8 +283,8 @@ public: axes_data.resize(ieInpNode.get_shape().size() - 1); std::iota(axes_data.begin(), axes_data.end(), 1); } - auto axes = std::make_shared(ngraph::element::i64, ngraph::Shape{axes_data.size()}, axes_data); - auto norm = std::make_shared(ieInpNode, axes, epsilon, ngraph::op::EpsMode::ADD); + auto axes = std::make_shared(ov::element::i64, ov::Shape{axes_data.size()}, axes_data); + auto norm = std::make_shared(ieInpNode, axes, epsilon, ov::op::EpsMode::ADD); CV_Assert(blobs.empty() || numChannels == blobs[0].total()); std::vector shape(ieInpNode.get_shape().size(), 1); @@ -292,13 +292,9 @@ public: shape[1] = numChannels; if (!blobs.empty()) { - auto weight = std::make_shared( - ngraph::element::f32, ngraph::Shape(shape), blobs[0].data); -#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2021_2) - auto mul = std::make_shared(norm, weight, ngraph::op::AutoBroadcastType::NUMPY); -#else - auto mul = std::make_shared(norm, weight, ngraph::op::AutoBroadcastType::NUMPY); -#endif + auto weight = std::make_shared( + ov::element::f32, ov::Shape(shape), blobs[0].data); + auto mul = std::make_shared(norm, weight, ov::op::AutoBroadcastType::NUMPY); return Ptr(new InfEngineNgraphNode(mul)); } return Ptr(new InfEngineNgraphNode(norm)); diff --git a/modules/dnn/src/layers/padding_layer.cpp b/modules/dnn/src/layers/padding_layer.cpp index edb7f92413..947bca8618 100644 --- a/modules/dnn/src/layers/padding_layer.cpp +++ b/modules/dnn/src/layers/padding_layer.cpp @@ -268,14 +268,14 @@ public: begins[i] = static_cast(paddings[i].first); ends[i] = static_cast(paddings[i].second); } - auto padding_below = std::make_shared(ngraph::element::i64, ngraph::Shape{begins.size()}, begins.data()); - auto padding_above = std::make_shared(ngraph::element::i64, ngraph::Shape{ends.size()}, ends.data()); - auto pad_mode = paddingType == "constant" ? ngraph::op::PadMode::CONSTANT : ngraph::op::PadMode::REFLECT; // SYMMETRIC - auto arg_pad_value = std::make_shared(ngraph::element::f32, ngraph::Shape{}, &paddingValue);; + auto padding_below = std::make_shared(ov::element::i64, ov::Shape{begins.size()}, begins.data()); + auto padding_above = std::make_shared(ov::element::i64, ov::Shape{ends.size()}, ends.data()); + auto pad_mode = paddingType == "constant" ? ov::op::PadMode::CONSTANT : ov::op::PadMode::REFLECT; // SYMMETRIC + auto arg_pad_value = std::make_shared(ov::element::f32, ov::Shape{}, &paddingValue);; auto pad = paddingType == "constant" ? - std::make_shared(ieInpNode, padding_below, padding_above, arg_pad_value, pad_mode) : - std::make_shared(ieInpNode, padding_below, padding_above, pad_mode); + std::make_shared(ieInpNode, padding_below, padding_above, arg_pad_value, pad_mode) : + std::make_shared(ieInpNode, padding_below, padding_above, pad_mode); return Ptr(new InfEngineNgraphNode(pad)); } #endif diff --git a/modules/dnn/src/layers/permute_layer.cpp b/modules/dnn/src/layers/permute_layer.cpp index b7c8bcfdaf..7304302314 100644 --- a/modules/dnn/src/layers/permute_layer.cpp +++ b/modules/dnn/src/layers/permute_layer.cpp @@ -475,9 +475,9 @@ public: { auto& ieInpNode = nodes[0].dynamicCast()->node; std::vector order(_order.begin(), _order.end()); - auto tr_axes = std::make_shared(ngraph::element::i64, - ngraph::Shape({order.size()}), order.data()); - auto transpose = std::make_shared(ieInpNode, tr_axes); + auto tr_axes = std::make_shared(ov::element::i64, + ov::Shape({order.size()}), order.data()); + auto transpose = std::make_shared(ieInpNode, tr_axes); return Ptr(new InfEngineNgraphNode(transpose)); } #endif // HAVE_DNN_NGRAPH diff --git a/modules/dnn/src/layers/pooling_layer.cpp b/modules/dnn/src/layers/pooling_layer.cpp index ba077bdcc4..fbf075f7d3 100644 --- a/modules/dnn/src/layers/pooling_layer.cpp +++ b/modules/dnn/src/layers/pooling_layer.cpp @@ -51,13 +51,8 @@ #ifdef HAVE_DNN_NGRAPH #include "../ie_ngraph.hpp" -#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2020_4) -#include -#include -#else -#include -#include -#endif +#include +#include #endif #include "../op_vkcom.hpp" @@ -588,20 +583,20 @@ public: CV_Assert_N((inputs.size() == 1 && (type == MAX || type == AVE || type == SUM)) || inputs.size() == 2, nodes.size() == inputs.size()); auto& ieInpNode = nodes[0].dynamicCast()->node; - ngraph::op::PadType pad_type = ngraph::op::PadType::EXPLICIT; + ov::op::PadType pad_type = ov::op::PadType::EXPLICIT; if (!padMode.empty()) - pad_type = padMode == "VALID" ? ngraph::op::PadType::VALID : ngraph::op::PadType::SAME_UPPER; + pad_type = padMode == "VALID" ? ov::op::PadType::VALID : ov::op::PadType::SAME_UPPER; - auto rounding_type = ceilMode ? ngraph::op::RoundingType::CEIL : ngraph::op::RoundingType::FLOOR; + auto rounding_type = ceilMode ? ov::op::RoundingType::CEIL : ov::op::RoundingType::FLOOR; if (type == AVE) { auto exclude_pad = !avePoolPaddedArea; - auto ave_pool = std::make_shared(ieInpNode, ngraph::Strides(strides), - ngraph::Shape(pads_begin), ngraph::Shape(pads_end), ngraph::Shape(kernel_size), + auto ave_pool = std::make_shared(ieInpNode, ov::Strides(strides), + ov::Shape(pads_begin), ov::Shape(pads_end), ov::Shape(kernel_size), exclude_pad, rounding_type, pad_type); return Ptr(new InfEngineNgraphNode(ave_pool)); } else if (type == SUM) { - ngraph::Shape inpShape = ieInpNode.get_shape(); + ov::Shape inpShape = ieInpNode.get_shape(); CV_Assert(inpShape.size() == 2 + kernel_size.size()); std::vector axes; for (size_t i = 0; i < kernel_size.size(); i++) @@ -609,37 +604,33 @@ public: if (inpShape[2 + i] == kernel_size[i]) axes.push_back(2 + i); } - auto reduction_axes = std::make_shared(ngraph::element::i64, ngraph::Shape{axes.size()}, axes); - auto reduce_sum = std::make_shared(ieInpNode, reduction_axes, true); + auto reduction_axes = std::make_shared(ov::element::i64, ov::Shape{axes.size()}, axes); + auto reduce_sum = std::make_shared(ieInpNode, reduction_axes, true); return Ptr(new InfEngineNgraphNode(reduce_sum)); } else if (type == MAX) { - std::shared_ptr max_pool; + std::shared_ptr max_pool; if (computeMaxIdx) { -#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2022_1) std::vector dilations(kernel_size.size(), 1); - max_pool = std::make_shared(ieInpNode, ngraph::Strides(strides), ngraph::Strides(dilations), - ngraph::Shape(pads_begin), ngraph::Shape(pads_end), ngraph::Shape(kernel_size), + max_pool = std::make_shared(ieInpNode, ov::Strides(strides), ov::Strides(dilations), + ov::Shape(pads_begin), ov::Shape(pads_end), ov::Shape(kernel_size), rounding_type, pad_type); -#else - CV_Error(Error::StsNotImplemented, "OpenVINO MaxPool with indices"); -#endif } else { - max_pool = std::make_shared(ieInpNode, ngraph::Strides(strides), - ngraph::Shape(pads_begin), ngraph::Shape(pads_end), ngraph::Shape(kernel_size), + max_pool = std::make_shared(ieInpNode, ov::Strides(strides), + ov::Shape(pads_begin), ov::Shape(pads_end), ov::Shape(kernel_size), rounding_type, pad_type); } return Ptr(new InfEngineNgraphNode(max_pool)); } else if (type == ROI) { auto& coords = nodes[1].dynamicCast()->node; - auto roi = std::make_shared(ieInpNode, coords, - ngraph::Shape{(size_t)pooledSize.height, (size_t)pooledSize.width}, spatialScale, "max"); + auto roi = std::make_shared(ieInpNode, coords, + ov::Shape{(size_t)pooledSize.height, (size_t)pooledSize.width}, spatialScale, "max"); return Ptr(new InfEngineNgraphNode(roi)); } else if (type == PSROI) { auto& coords = nodes[1].dynamicCast()->node; - auto psroi = std::make_shared(ieInpNode, coords, + auto psroi = std::make_shared(ieInpNode, coords, (size_t)psRoiOutChannels, (size_t)pooledSize.width, spatialScale, 1, 1, "average"); return Ptr(new InfEngineNgraphNode(psroi)); } diff --git a/modules/dnn/src/layers/prior_box_layer.cpp b/modules/dnn/src/layers/prior_box_layer.cpp index 1dc40b48ce..bb3aa99cd3 100644 --- a/modules/dnn/src/layers/prior_box_layer.cpp +++ b/modules/dnn/src/layers/prior_box_layer.cpp @@ -47,13 +47,8 @@ #ifdef HAVE_DNN_NGRAPH #include "../ie_ngraph.hpp" -#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2020_4) -#include -#include -#else -#include -#include -#endif +#include +#include #endif #include "../op_vkcom.hpp" @@ -513,23 +508,23 @@ public: CV_Assert(nodes.size() == 2); auto layer = nodes[0].dynamicCast()->node; auto image = nodes[1].dynamicCast()->node; - auto layer_shape = std::make_shared(layer); - auto image_shape = std::make_shared(image); + auto layer_shape = std::make_shared(layer); + auto image_shape = std::make_shared(image); - auto lower_bounds = std::make_shared(ngraph::element::i64, ngraph::Shape{1}, std::vector{2}); - auto upper_bounds = std::make_shared(ngraph::element::i64, ngraph::Shape{1}, std::vector{4}); - auto strides = std::make_shared(ngraph::element::i64, ngraph::Shape{1}, std::vector{1}); + auto lower_bounds = std::make_shared(ov::element::i64, ov::Shape{1}, std::vector{2}); + auto upper_bounds = std::make_shared(ov::element::i64, ov::Shape{1}, std::vector{4}); + auto strides = std::make_shared(ov::element::i64, ov::Shape{1}, std::vector{1}); - auto slice_layer = std::make_shared(layer_shape, + auto slice_layer = std::make_shared(layer_shape, lower_bounds, upper_bounds, strides, std::vector{}, std::vector{}); - auto slice_image = std::make_shared(image_shape, + auto slice_image = std::make_shared(image_shape, lower_bounds, upper_bounds, strides, std::vector{}, std::vector{}); if (_explicitSizes) { CV_Assert_N(!_boxWidths.empty(), !_boxHeights.empty(), !_variance.empty()); CV_Assert(_boxWidths.size() == _boxHeights.size()); - ngraph::op::PriorBoxClusteredAttrs attrs; + ov::op::v0::PriorBoxClustered::Attributes attrs; attrs.widths = _boxWidths; attrs.heights = _boxHeights; attrs.clip = _clip; @@ -539,14 +534,14 @@ public: attrs.step_widths = _stepX; attrs.variances = _variance; - auto priorBox = std::make_shared(slice_layer, slice_image, attrs); - auto axis = std::make_shared(ngraph::element::i64, ngraph::Shape{1}, std::vector{0}); - auto unsqueeze = std::make_shared(priorBox, axis); + auto priorBox = std::make_shared(slice_layer, slice_image, attrs); + auto axis = std::make_shared(ov::element::i64, ov::Shape{1}, std::vector{0}); + auto unsqueeze = std::make_shared(priorBox, axis); return Ptr(new InfEngineNgraphNode(unsqueeze)); } else { - ngraph::op::PriorBoxAttrs attrs; + ov::op::v0::PriorBox::Attributes attrs; attrs.min_size = _minSize; attrs.max_size = _maxSize; // doesn't work with empty aspectRatio @@ -560,9 +555,9 @@ public: attrs.step = _stepX; attrs.scale_all_sizes = !_aspectRatios.empty(); - auto priorBox = std::make_shared(slice_layer, slice_image, attrs); - auto axis = std::make_shared(ngraph::element::i64, ngraph::Shape{1}, std::vector{0}); - auto unsqueeze = std::make_shared(priorBox, axis); + auto priorBox = std::make_shared(slice_layer, slice_image, attrs); + auto axis = std::make_shared(ov::element::i64, ov::Shape{1}, std::vector{0}); + auto unsqueeze = std::make_shared(priorBox, axis); return Ptr(new InfEngineNgraphNode(unsqueeze)); } } diff --git a/modules/dnn/src/layers/proposal_layer.cpp b/modules/dnn/src/layers/proposal_layer.cpp index d9df09c642..66559ab9ff 100644 --- a/modules/dnn/src/layers/proposal_layer.cpp +++ b/modules/dnn/src/layers/proposal_layer.cpp @@ -10,11 +10,7 @@ #ifdef HAVE_DNN_NGRAPH #include "../ie_ngraph.hpp" -#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2020_4) -#include -#else -#include -#endif +#include #endif namespace cv { namespace dnn { @@ -344,7 +340,7 @@ public: const std::vector >& nodes) CV_OVERRIDE { CV_Assert(nodes.size() == 3); - ngraph::op::ProposalAttrs attr; + ov::op::v0::Proposal::Attributes attr; attr.base_size = baseSize; attr.nms_thresh = nmsThreshold; attr.feat_stride = featStride; @@ -367,12 +363,12 @@ public: auto& image_shape = nodes[2].dynamicCast()->node; CV_Assert_N(image_shape.get_shape().size() == 2, image_shape.get_shape().front() == 1); - auto shape = std::make_shared(ngraph::element::i64, - ngraph::Shape{1}, + auto shape = std::make_shared(ov::element::i64, + ov::Shape{1}, std::vector{(int64_t)image_shape.get_shape().back()}); - auto reshape = std::make_shared(image_shape, shape, true); + auto reshape = std::make_shared(image_shape, shape, true); - auto proposal = std::make_shared(class_probs, class_logits, reshape, attr); + auto proposal = std::make_shared(class_probs, class_logits, reshape, attr); return Ptr(new InfEngineNgraphNode(proposal)); } #endif // HAVE_DNN_NGRAPH diff --git a/modules/dnn/src/layers/region_layer.cpp b/modules/dnn/src/layers/region_layer.cpp index 159fd5f7e1..409cdfa38b 100644 --- a/modules/dnn/src/layers/region_layer.cpp +++ b/modules/dnn/src/layers/region_layer.cpp @@ -60,7 +60,6 @@ using namespace cv::dnn::cuda4dnn; #endif - namespace cv { namespace dnn @@ -122,7 +121,7 @@ public: { #ifdef HAVE_DNN_NGRAPH if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) - return INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2020_2) && preferableTarget != DNN_TARGET_MYRIAD && new_coords == 0; + return preferableTarget != DNN_TARGET_MYRIAD && new_coords == 0; #endif #ifdef HAVE_CUDA if (backendId == DNN_BACKEND_CUDA) @@ -473,55 +472,55 @@ public: int64_t cols = b * h * w * anchors; int64_t rows = c / anchors; - auto shape_node = std::make_shared(ngraph::element::i64, ngraph::Shape{2}, std::vector{cols, rows}); - auto tr_axes = std::make_shared(ngraph::element::i64, ngraph::Shape{2}, std::vector{1, 0}); + auto shape_node = std::make_shared(ov::element::i64, ov::Shape{2}, std::vector{cols, rows}); + auto tr_axes = std::make_shared(ov::element::i64, ov::Shape{2}, std::vector{1, 0}); - std::shared_ptr input2d; + std::shared_ptr input2d; { - input2d = std::make_shared(input, shape_node, true); - input2d = std::make_shared(input2d, tr_axes); + input2d = std::make_shared(input, shape_node, true); + input2d = std::make_shared(input2d, tr_axes); } - std::shared_ptr region; + std::shared_ptr region; { - auto new_axes = std::make_shared(ngraph::element::i64, ngraph::Shape{4}, std::vector{0, 3, 1, 2}); - auto tr_input = std::make_shared(input, new_axes); + auto new_axes = std::make_shared(ov::element::i64, ov::Shape{4}, std::vector{0, 3, 1, 2}); + auto tr_input = std::make_shared(input, new_axes); std::vector anchors_vec(blobs[0].ptr(), blobs[0].ptr() + blobs[0].total()); std::vector mask(anchors, 1); - region = std::make_shared(tr_input, coords, classes, anchors, useSoftmax, mask, 1, 3, anchors_vec); + region = std::make_shared(tr_input, coords, classes, anchors, useSoftmax, mask, 1, 3, anchors_vec); auto tr_shape = tr_input->get_shape(); - auto shape_as_inp = std::make_shared(ngraph::element::i64, - ngraph::Shape{tr_shape.size()}, + auto shape_as_inp = std::make_shared(ov::element::i64, + ov::Shape{tr_shape.size()}, std::vector(tr_shape.begin(), tr_shape.end())); - region = std::make_shared(region, shape_as_inp, true); - new_axes = std::make_shared(ngraph::element::i64, ngraph::Shape{4}, std::vector{0, 2, 3, 1}); - region = std::make_shared(region, new_axes); + region = std::make_shared(region, shape_as_inp, true); + new_axes = std::make_shared(ov::element::i64, ov::Shape{4}, std::vector{0, 2, 3, 1}); + region = std::make_shared(region, new_axes); - region = std::make_shared(region, shape_node, true); - region = std::make_shared(region, tr_axes); + region = std::make_shared(region, shape_node, true); + region = std::make_shared(region, tr_axes); } - auto strides = std::make_shared(ngraph::element::i64, ngraph::Shape{2}, std::vector{1, 1}); + auto strides = std::make_shared(ov::element::i64, ov::Shape{2}, std::vector{1, 1}); std::vector boxes_shape{b, anchors, h, w}; - auto shape_3d = std::make_shared(ngraph::element::i64, ngraph::Shape{boxes_shape.size()}, boxes_shape.data()); + auto shape_3d = std::make_shared(ov::element::i64, ov::Shape{boxes_shape.size()}, boxes_shape.data()); - ngraph::Shape box_broad_shape{1, (size_t)anchors, (size_t)h, (size_t)w}; - auto scale_x_y_node = std::make_shared(ngraph::element::f32, ngraph::Shape{1}, &scale_x_y); - auto shift_node = std::make_shared(ngraph::element::f32, ngraph::Shape{1}, std::vector{0.5}); + ov::Shape box_broad_shape{1, (size_t)anchors, (size_t)h, (size_t)w}; + auto scale_x_y_node = std::make_shared(ov::element::f32, ov::Shape{1}, &scale_x_y); + auto shift_node = std::make_shared(ov::element::f32, ov::Shape{1}, std::vector{0.5}); - auto axis = ngraph::op::Constant::create(ngraph::element::i64, ngraph::Shape{}, {0}); - auto splits = ngraph::op::Constant::create(ngraph::element::i64, ngraph::Shape{5}, {1, 1, 1, 1, rows - 4}); - auto split = std::make_shared(input2d, axis, splits); - std::shared_ptr box_x; + auto axis = ov::op::v0::Constant::create(ov::element::i64, ov::Shape{}, {0}); + auto splits = ov::op::v0::Constant::create(ov::element::i64, ov::Shape{5}, {1, 1, 1, 1, rows - 4}); + auto split = std::make_shared(input2d, axis, splits); + std::shared_ptr box_x; { - box_x = std::make_shared(split->output(0)); - box_x = std::make_shared(box_x, shift_node, ngraph::op::AutoBroadcastType::NUMPY); - box_x = std::make_shared(box_x, scale_x_y_node, ngraph::op::AutoBroadcastType::NUMPY); - box_x = std::make_shared(box_x, shift_node, ngraph::op::AutoBroadcastType::NUMPY); - box_x = std::make_shared(box_x, shape_3d, true); + box_x = std::make_shared(split->output(0)); + box_x = std::make_shared(box_x, shift_node, ov::op::AutoBroadcastType::NUMPY); + box_x = std::make_shared(box_x, scale_x_y_node, ov::op::AutoBroadcastType::NUMPY); + box_x = std::make_shared(box_x, shift_node, ov::op::AutoBroadcastType::NUMPY); + box_x = std::make_shared(box_x, shape_3d, true); std::vector x_indices(w * h * anchors); auto begin = x_indices.begin(); @@ -534,20 +533,20 @@ public: { std::copy(begin, begin + w * anchors, begin + j * w * anchors); } - auto horiz = std::make_shared(ngraph::element::f32, box_broad_shape, x_indices.data()); - box_x = std::make_shared(box_x, horiz, ngraph::op::AutoBroadcastType::NUMPY); + auto horiz = std::make_shared(ov::element::f32, box_broad_shape, x_indices.data()); + box_x = std::make_shared(box_x, horiz, ov::op::AutoBroadcastType::NUMPY); - auto cols_node = std::make_shared(ngraph::element::f32, ngraph::Shape{1}, std::vector{float(w)}); - box_x = std::make_shared(box_x, cols_node, ngraph::op::AutoBroadcastType::NUMPY); + auto cols_node = std::make_shared(ov::element::f32, ov::Shape{1}, std::vector{float(w)}); + box_x = std::make_shared(box_x, cols_node, ov::op::AutoBroadcastType::NUMPY); } - std::shared_ptr box_y; + std::shared_ptr box_y; { - box_y = std::make_shared(split->output(1)); - box_y = std::make_shared(box_y, shift_node, ngraph::op::AutoBroadcastType::NUMPY); - box_y = std::make_shared(box_y, scale_x_y_node, ngraph::op::AutoBroadcastType::NUMPY); - box_y = std::make_shared(box_y, shift_node, ngraph::op::AutoBroadcastType::NUMPY); - box_y = std::make_shared(box_y, shape_3d, true); + box_y = std::make_shared(split->output(1)); + box_y = std::make_shared(box_y, shift_node, ov::op::AutoBroadcastType::NUMPY); + box_y = std::make_shared(box_y, scale_x_y_node, ov::op::AutoBroadcastType::NUMPY); + box_y = std::make_shared(box_y, shift_node, ov::op::AutoBroadcastType::NUMPY); + box_y = std::make_shared(box_y, shape_3d, true); std::vector y_indices(h * anchors); for (int i = 0; i < h; i++) @@ -555,13 +554,13 @@ public: std::fill(y_indices.begin() + i * anchors, y_indices.begin() + (i + 1) * anchors, i); } - auto vert = std::make_shared(ngraph::element::f32, ngraph::Shape{1, (size_t)anchors, (size_t)h, 1}, y_indices.data()); - box_y = std::make_shared(box_y, vert, ngraph::op::AutoBroadcastType::NUMPY); - auto rows_node = std::make_shared(ngraph::element::f32, ngraph::Shape{1}, std::vector{float(h)}); - box_y = std::make_shared(box_y, rows_node, ngraph::op::AutoBroadcastType::NUMPY); + auto vert = std::make_shared(ov::element::f32, ov::Shape{1, (size_t)anchors, (size_t)h, 1}, y_indices.data()); + box_y = std::make_shared(box_y, vert, ov::op::AutoBroadcastType::NUMPY); + auto rows_node = std::make_shared(ov::element::f32, ov::Shape{1}, std::vector{float(h)}); + box_y = std::make_shared(box_y, rows_node, ov::op::AutoBroadcastType::NUMPY); } - std::shared_ptr box_w, box_h; + std::shared_ptr box_w, box_h; { int hNorm, wNorm; if (nodes.size() > 1) @@ -596,53 +595,53 @@ public: std::copy(bias_h.begin(), bias_h.begin() + h * anchors, bias_h.begin() + i * h * anchors); } - box_w = std::make_shared(split->output(2)); - box_w = std::make_shared(box_w, shape_3d, true); - auto anchor_w_node = std::make_shared(ngraph::element::f32, box_broad_shape, bias_w.data()); - box_w = std::make_shared(box_w, anchor_w_node, ngraph::op::AutoBroadcastType::NUMPY); + box_w = std::make_shared(split->output(2)); + box_w = std::make_shared(box_w, shape_3d, true); + auto anchor_w_node = std::make_shared(ov::element::f32, box_broad_shape, bias_w.data()); + box_w = std::make_shared(box_w, anchor_w_node, ov::op::AutoBroadcastType::NUMPY); - box_h = std::make_shared(split->output(3)); - box_h = std::make_shared(box_h, shape_3d, true); - auto anchor_h_node = std::make_shared(ngraph::element::f32, box_broad_shape, bias_h.data()); - box_h = std::make_shared(box_h, anchor_h_node, ngraph::op::AutoBroadcastType::NUMPY); + box_h = std::make_shared(split->output(3)); + box_h = std::make_shared(box_h, shape_3d, true); + auto anchor_h_node = std::make_shared(ov::element::f32, box_broad_shape, bias_h.data()); + box_h = std::make_shared(box_h, anchor_h_node, ov::op::AutoBroadcastType::NUMPY); } - auto region_splits = ngraph::op::Constant::create(ngraph::element::i64, ngraph::Shape{3}, {4, 1, rows - 5}); - auto region_split = std::make_shared(region, axis, region_splits); + auto region_splits = ov::op::v0::Constant::create(ov::element::i64, ov::Shape{3}, {4, 1, rows - 5}); + auto region_split = std::make_shared(region, axis, region_splits); - std::shared_ptr scale; + std::shared_ptr scale; { float thr = classfix == -1 ? 0.5 : 0; - auto thresh_node = std::make_shared(ngraph::element::f32, ngraph::Shape{1}, std::vector{thr}); - auto mask = std::make_shared(region_split->output(1), thresh_node); - auto zero_node = std::make_shared(ngraph::element::f32, mask->get_shape(), std::vector(cols, 0)); - scale = std::make_shared(mask, zero_node, region_split->output(1)); + auto thresh_node = std::make_shared(ov::element::f32, ov::Shape{1}, std::vector{thr}); + auto mask = std::make_shared(region_split->output(1), thresh_node); + auto zero_node = std::make_shared(ov::element::f32, mask->get_shape(), std::vector(cols, 0)); + scale = std::make_shared(mask, zero_node, region_split->output(1)); } - std::shared_ptr probs; + std::shared_ptr probs; { - probs = std::make_shared(region_split->output(2), scale, ngraph::op::AutoBroadcastType::NUMPY); - auto thresh_node = std::make_shared(ngraph::element::f32, ngraph::Shape{1}, &thresh); - auto mask = std::make_shared(probs, thresh_node); - auto zero_node = std::make_shared(ngraph::element::f32, mask->get_shape(), std::vector((rows - 5) * cols, 0)); - probs = std::make_shared(mask, probs, zero_node); + probs = std::make_shared(region_split->output(2), scale, ov::op::AutoBroadcastType::NUMPY); + auto thresh_node = std::make_shared(ov::element::f32, ov::Shape{1}, &thresh); + auto mask = std::make_shared(probs, thresh_node); + auto zero_node = std::make_shared(ov::element::f32, mask->get_shape(), std::vector((rows - 5) * cols, 0)); + probs = std::make_shared(mask, probs, zero_node); } - auto concat_shape = std::make_shared(ngraph::element::i64, ngraph::Shape{2}, std::vector{1, cols}); - box_x = std::make_shared(box_x, concat_shape, true); - box_y = std::make_shared(box_y, concat_shape, true); - box_w = std::make_shared(box_w, concat_shape, true); - box_h = std::make_shared(box_h, concat_shape, true); + auto concat_shape = std::make_shared(ov::element::i64, ov::Shape{2}, std::vector{1, cols}); + box_x = std::make_shared(box_x, concat_shape, true); + box_y = std::make_shared(box_y, concat_shape, true); + box_w = std::make_shared(box_w, concat_shape, true); + box_h = std::make_shared(box_h, concat_shape, true); - ngraph::NodeVector inp_nodes{box_x, box_y, box_w, box_h, scale, probs}; - std::shared_ptr result = std::make_shared(inp_nodes, 0); - result = std::make_shared(result, tr_axes); + ov::NodeVector inp_nodes{box_x, box_y, box_w, box_h, scale, probs}; + std::shared_ptr result = std::make_shared(inp_nodes, 0); + result = std::make_shared(result, tr_axes); if (b > 1) { std::vector sizes{b, static_cast(result->get_shape()[0]) / b, static_cast(result->get_shape()[1])}; - auto shape_node = std::make_shared(ngraph::element::i64, ngraph::Shape{sizes.size()}, sizes.data()); - result = std::make_shared(result, shape_node, true); + auto shape_node = std::make_shared(ov::element::i64, ov::Shape{sizes.size()}, sizes.data()); + result = std::make_shared(result, shape_node, true); } return Ptr(new InfEngineNgraphNode(result)); diff --git a/modules/dnn/src/layers/reorg_layer.cpp b/modules/dnn/src/layers/reorg_layer.cpp index 7281190cdd..986bb64c82 100644 --- a/modules/dnn/src/layers/reorg_layer.cpp +++ b/modules/dnn/src/layers/reorg_layer.cpp @@ -52,11 +52,7 @@ #include "../op_inf_engine.hpp" #ifdef HAVE_DNN_NGRAPH #include "../ie_ngraph.hpp" -#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2020_4) -#include -#else -#include -#endif +#include #endif #include "../op_cuda.hpp" @@ -205,7 +201,7 @@ public: const std::vector >& nodes) CV_OVERRIDE { auto& ieInpNode = nodes[0].dynamicCast()->node; - auto reorg = std::make_shared(ieInpNode, ngraph::Strides{(size_t)reorgStride}); + auto reorg = std::make_shared(ieInpNode, ov::Strides{(size_t)reorgStride}); return Ptr(new InfEngineNgraphNode(reorg)); } #endif // HAVE_DNN_NGRAPH diff --git a/modules/dnn/src/layers/reshape_layer.cpp b/modules/dnn/src/layers/reshape_layer.cpp index a72236c472..f259629e96 100644 --- a/modules/dnn/src/layers/reshape_layer.cpp +++ b/modules/dnn/src/layers/reshape_layer.cpp @@ -369,9 +369,9 @@ public: auto& ieInpNode = nodes[0].dynamicCast()->node; std::vector out(outShapes[0].begin(), outShapes[0].end()); - auto shape = std::make_shared(ngraph::element::i64, - ngraph::Shape{out.size()}, out.data()); - auto reshape = std::make_shared(ieInpNode, shape, true); + auto shape = std::make_shared(ov::element::i64, + ov::Shape{out.size()}, out.data()); + auto reshape = std::make_shared(ieInpNode, shape, true); return Ptr(new InfEngineNgraphNode(reshape)); } #endif // HAVE_DNN_NGRAPH diff --git a/modules/dnn/src/layers/resize_layer.cpp b/modules/dnn/src/layers/resize_layer.cpp index 5b8b9f812f..bf8f1b674c 100644 --- a/modules/dnn/src/layers/resize_layer.cpp +++ b/modules/dnn/src/layers/resize_layer.cpp @@ -13,11 +13,7 @@ #ifdef HAVE_DNN_NGRAPH #include "../ie_ngraph.hpp" -#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2020_4) -#include -#else -#include -#endif +#include #endif #ifdef HAVE_CUDA @@ -376,81 +372,39 @@ public: { auto& ieInpNode = nodes[0].dynamicCast()->node; -#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2021_2) - ngraph::op::InterpolateAttrs attrs; - attrs.pads_begin.push_back(0); - attrs.pads_end.push_back(0); - attrs.axes = ngraph::AxisSet{2, 3}; - attrs.align_corners = alignCorners; + ov::op::v4::Interpolate::InterpolateAttrs attrs; if (interpolation == "nearest") { - attrs.mode = "nearest"; - attrs.antialias = false; + attrs.mode = ov::op::v4::Interpolate::InterpolateMode::NEAREST; + attrs.coordinate_transformation_mode = ov::op::v4::Interpolate::CoordinateTransformMode::HALF_PIXEL; } else if (interpolation == "bilinear") { - attrs.mode = "linear"; - } else { - CV_Error(Error::StsNotImplemented, "Unsupported interpolation: " + interpolation); - } - - std::vector shape = {outHeight, outWidth}; - auto out_shape = std::make_shared(ngraph::element::i64, ngraph::Shape{2}, shape.data()); - auto interp = std::make_shared(ieInpNode, out_shape, attrs); -#else - ngraph::op::v4::Interpolate::InterpolateAttrs attrs; - -#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2022_1) - if (interpolation == "nearest") { - attrs.mode = ngraph::op::v4::Interpolate::InterpolateMode::NEAREST; - attrs.coordinate_transformation_mode = ngraph::op::v4::Interpolate::CoordinateTransformMode::HALF_PIXEL; - } else if (interpolation == "bilinear") { - attrs.mode = ngraph::op::v4::Interpolate::InterpolateMode::LINEAR_ONNX; - attrs.coordinate_transformation_mode = ngraph::op::v4::Interpolate::CoordinateTransformMode::ASYMMETRIC; + attrs.mode = ov::op::v4::Interpolate::InterpolateMode::LINEAR_ONNX; + attrs.coordinate_transformation_mode = ov::op::v4::Interpolate::CoordinateTransformMode::ASYMMETRIC; } else { CV_Error(Error::StsNotImplemented, format("Unsupported interpolation: %s", interpolation.c_str())); } - attrs.shape_calculation_mode = ngraph::op::v4::Interpolate::ShapeCalcMode::SIZES; + attrs.shape_calculation_mode = ov::op::v4::Interpolate::ShapeCalcMode::SIZES; CV_Assert(!halfPixelCenters || !alignCorners); if (halfPixelCenters) { - attrs.coordinate_transformation_mode = ngraph::op::v4::Interpolate::CoordinateTransformMode::HALF_PIXEL; + attrs.coordinate_transformation_mode = ov::op::v4::Interpolate::CoordinateTransformMode::HALF_PIXEL; } else if (alignCorners) { - attrs.coordinate_transformation_mode = ngraph::op::v4::Interpolate::CoordinateTransformMode::ALIGN_CORNERS; + attrs.coordinate_transformation_mode = ov::op::v4::Interpolate::CoordinateTransformMode::ALIGN_CORNERS; } - attrs.nearest_mode = ngraph::op::v4::Interpolate::NearestMode::ROUND_PREFER_FLOOR; -#else - if (interpolation == "nearest") { - attrs.mode = ngraph::op::v4::Interpolate::InterpolateMode::nearest; - attrs.coordinate_transformation_mode = ngraph::op::v4::Interpolate::CoordinateTransformMode::half_pixel; - } else if (interpolation == "bilinear") { - attrs.mode = ngraph::op::v4::Interpolate::InterpolateMode::linear_onnx; - attrs.coordinate_transformation_mode = ngraph::op::v4::Interpolate::CoordinateTransformMode::asymmetric; - } else { - CV_Error(Error::StsNotImplemented, format("Unsupported interpolation: %s", interpolation.c_str())); - } - attrs.shape_calculation_mode = ngraph::op::v4::Interpolate::ShapeCalcMode::sizes; + attrs.nearest_mode = ov::op::v4::Interpolate::NearestMode::ROUND_PREFER_FLOOR; - CV_Assert(!halfPixelCenters || !alignCorners); - if (halfPixelCenters) { - attrs.coordinate_transformation_mode = ngraph::op::v4::Interpolate::CoordinateTransformMode::half_pixel; - } else if (alignCorners) { - attrs.coordinate_transformation_mode = ngraph::op::v4::Interpolate::CoordinateTransformMode::align_corners; - } - - attrs.nearest_mode = ngraph::op::v4::Interpolate::NearestMode::round_prefer_floor; -#endif // OpenVINO >= 2022.1 std::vector shape = {outHeight, outWidth}; - auto out_shape = std::make_shared(ngraph::element::i64, ngraph::Shape{2}, shape.data()); + auto out_shape = std::make_shared(ov::element::i64, ov::Shape{2}, shape.data()); auto& input_shape = ieInpNode.get_shape(); CV_Assert_N(input_shape[2] != 0, input_shape[3] != 0); std::vector scales = {static_cast(outHeight) / input_shape[2], static_cast(outWidth) / input_shape[3]}; - auto scales_shape = std::make_shared(ngraph::element::f32, ngraph::Shape{2}, scales.data()); + auto scales_shape = std::make_shared(ov::element::f32, ov::Shape{2}, scales.data()); - auto axes = std::make_shared(ngraph::element::i64, ngraph::Shape{2}, std::vector{2, 3}); - auto interp = std::make_shared(ieInpNode, out_shape, scales_shape, axes, attrs); -#endif + auto axes = std::make_shared(ov::element::i64, ov::Shape{2}, std::vector{2, 3}); + auto interp = std::make_shared(ieInpNode, out_shape, scales_shape, axes, attrs); return Ptr(new InfEngineNgraphNode(interp)); } #endif // HAVE_DNN_NGRAPH diff --git a/modules/dnn/src/layers/scale_layer.cpp b/modules/dnn/src/layers/scale_layer.cpp index 00b1281399..4c2d585ce0 100644 --- a/modules/dnn/src/layers/scale_layer.cpp +++ b/modules/dnn/src/layers/scale_layer.cpp @@ -331,7 +331,7 @@ public: virtual Ptr initNgraph(const std::vector >& inputs, const std::vector >& nodes) CV_OVERRIDE { auto ieInpNode0 = nodes[0].dynamicCast()->node; - ngraph::Output ieInpNode1; + ov::Output ieInpNode1; if (nodes.size() > 1) ieInpNode1 = nodes[1].dynamicCast()->node; @@ -346,31 +346,26 @@ public: int cAxis = normalize_axis(axis, shape.size()); shape[cAxis] = numChannels; - std::shared_ptr node; + std::shared_ptr node; if (hasWeights) { - ngraph::Output weight = blobs.empty() ? ieInpNode1 : - std::make_shared(ngraph::element::f32, ngraph::Shape(shape), blobs[0].data); - -#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2021_2) - node = std::make_shared(ieInpNode0, weight, ngraph::op::AutoBroadcastType::NUMPY); -#else - node = std::make_shared(ieInpNode0, weight, ngraph::op::AutoBroadcastType::NUMPY); -#endif + ov::Output weight = blobs.empty() ? ieInpNode1 : + std::make_shared(ov::element::f32, ov::Shape(shape), blobs[0].data); + node = std::make_shared(ieInpNode0, weight, ov::op::AutoBroadcastType::NUMPY); } if (hasBias || !hasWeights) { - ngraph::Output bias; + ov::Output bias; if (hasBias) { bias = blobs.empty() ? ieInpNode1 : - std::make_shared(ngraph::element::f32, - ngraph::Shape(shape), blobs.back().data); + std::make_shared(ov::element::f32, + ov::Shape(shape), blobs.back().data); } else - bias = std::make_shared(ngraph::element::f32, - ngraph::Shape(shape), std::vector(numChannels, 0).data()); - node = std::make_shared(node, bias, ngraph::op::AutoBroadcastType::NUMPY); + bias = std::make_shared(ov::element::f32, + ov::Shape(shape), std::vector(numChannels, 0).data()); + node = std::make_shared(node, bias, ov::op::AutoBroadcastType::NUMPY); } return Ptr(new InfEngineNgraphNode(node)); } diff --git a/modules/dnn/src/layers/slice_layer.cpp b/modules/dnn/src/layers/slice_layer.cpp index 08172f3cdb..de302ec291 100644 --- a/modules/dnn/src/layers/slice_layer.cpp +++ b/modules/dnn/src/layers/slice_layer.cpp @@ -768,14 +768,14 @@ public: dims.push_back(finalSliceRanges[0][i].end); } - auto lower_bounds = std::make_shared(ngraph::element::i64, - ngraph::Shape{offsets.size()}, offsets.data()); - auto upper_bounds = std::make_shared(ngraph::element::i64, - ngraph::Shape{dims.size()}, dims.data()); - auto strides = std::make_shared(ngraph::element::i64, - ngraph::Shape{dims.size()}, std::vector((int64_t)dims.size(), 1)); + auto lower_bounds = std::make_shared(ov::element::i64, + ov::Shape{offsets.size()}, offsets.data()); + auto upper_bounds = std::make_shared(ov::element::i64, + ov::Shape{dims.size()}, dims.data()); + auto strides = std::make_shared(ov::element::i64, + ov::Shape{dims.size()}, std::vector((int64_t)dims.size(), 1)); - auto slice = std::make_shared(ieInpNode, + auto slice = std::make_shared(ieInpNode, lower_bounds, upper_bounds, strides, std::vector{}, std::vector{}); return Ptr(new InfEngineNgraphNode(slice)); diff --git a/modules/dnn/src/layers/softmax_layer.cpp b/modules/dnn/src/layers/softmax_layer.cpp index 18f4d6b61e..239ad1574b 100644 --- a/modules/dnn/src/layers/softmax_layer.cpp +++ b/modules/dnn/src/layers/softmax_layer.cpp @@ -314,9 +314,9 @@ public: auto& ieInpNode = nodes[0].dynamicCast()->node; int axis = normalize_axis(axisRaw, ieInpNode.get_shape().size()); if (logSoftMax) { - return new InfEngineNgraphNode(std::make_shared(ieInpNode, axis)); + return new InfEngineNgraphNode(std::make_shared(ieInpNode, axis)); } else { - return new InfEngineNgraphNode(std::make_shared(ieInpNode, axis)); + return new InfEngineNgraphNode(std::make_shared(ieInpNode, axis)); } } #endif // HAVE_DNN_NGRAPH diff --git a/modules/dnn/src/net_openvino.cpp b/modules/dnn/src/net_openvino.cpp index adcfea60f0..501a596e5d 100644 --- a/modules/dnn/src/net_openvino.cpp +++ b/modules/dnn/src/net_openvino.cpp @@ -9,6 +9,12 @@ #include #include +#include "op_inf_engine.hpp" + +#ifdef HAVE_INF_ENGINE +#include +#endif + #include "net_impl.hpp" #include "backend.hpp" @@ -146,7 +152,7 @@ public: //string dump(bool forceAllocation = false) const override; static - Net createNetworkFromModelOptimizer(InferenceEngine::CNNNetwork& ieNet); + Net createNetworkFromModelOptimizer(std::shared_ptr& ieNet); }; // NetImplOpenVINO @@ -320,11 +326,6 @@ void NetImplOpenVINO::initBackend(const std::vector& blobsToKeep_) return; } -#if INF_ENGINE_VER_MAJOR_LT(INF_ENGINE_RELEASE_2022_1) - bool supportsCPUFallback = !isArmComputePlugin() && (preferableTarget == DNN_TARGET_CPU || - openvino::checkTarget(DNN_TARGET_CPU)); -#endif - // 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. @@ -342,49 +343,8 @@ void NetImplOpenVINO::initBackend(const std::vector& blobsToKeep_) bool fused = ld.skip; Ptr layer = ld.layerInstance; -#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2022_1) if (ld.id == 0) continue; -#else - if (!fused && !layer->supportBackend(preferableBackend)) - { - CV_LOG_DEBUG(NULL, "DNN/IE: NOT supported!"); - bool customizable = ld.id != 0 && 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; - } - } - - if (preferableTarget == DNN_TARGET_OPENCL) - customizable &= ld.type != "Eltwise"; - - if (!customizable) - { - CV_LOG_DEBUG(NULL, "DNN/IE: NOT customizable!"); - addNgraphOutputs(ld); - net = Ptr(); - layer->preferableTarget = DNN_TARGET_CPU; - - for (int i = 0; i < ld.inputBlobsId.size(); ++i) - { - LayerData& inpLd = layers[ld.inputBlobsId[i].lid]; - Ptr inpNode = inpLd.backendNodes[preferableBackend]; - if (!inpNode.empty()) - { - Ptr ieNode = inpNode.dynamicCast(); - CV_Assert(!ieNode.empty()); - ieNode->net->addOutput(ieNode); - } - } - continue; - } - } -#endif ld.skip = true; // Initially skip all Inference Engine supported layers. // Create a new network if one of inputs from different Inference Engine graph. @@ -480,25 +440,14 @@ void NetImplOpenVINO::initBackend(const std::vector& blobsToKeep_) continue; // Handle parameters from other subnets. Output port is not used in this case -#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2020_4) - if ((ngraph::op::is_parameter(ngraph_input_node) || ngraph::op::is_constant(ngraph_input_node)) && -#else - if ((ngraph_input_node->is_parameter() || ngraph_input_node->is_constant()) && -#endif - - ngraph_input_node->get_output_size() == 1) + if ((ov::op::util::is_parameter(ngraph_input_node) || ov::op::util::is_constant(ngraph_input_node)) && + ngraph_input_node->get_output_size() == 1) { inputNodes[i] = Ptr(new InfEngineNgraphNode(ngraph_input_node)); continue; } CV_CheckLT((size_t)oid, ngraph_input_node->get_output_size(), ""); -#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2020_4) inputNodes[i] = new InfEngineNgraphNode(ngraph_input_node->output(oid)); -#elif INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2020_3) - inputNodes[i] = Ptr(new InfEngineNgraphNode(ieInpNode->node->get_output_as_single_output_node(oid))); -#else - inputNodes[i] = Ptr(new InfEngineNgraphNode(ieInpNode->node->get_output_as_single_output_node(oid, false))); -#endif } if (layer->supportBackend(preferableBackend)) @@ -606,9 +555,7 @@ void NetImplOpenVINO::initBackend(const std::vector& blobsToKeep_) } } } -#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2022_1) CV_Assert(uniqueNets.size() == 1); -#endif } @@ -708,18 +655,15 @@ void switchToOpenVINOBackend(Net& net) /*static*/ -Net NetImplOpenVINO::createNetworkFromModelOptimizer(InferenceEngine::CNNNetwork& ieNet) +Net NetImplOpenVINO::createNetworkFromModelOptimizer(std::shared_ptr& ieNet) { CV_TRACE_FUNCTION(); CV_TRACE_REGION("register_inputs"); - auto ngraphFunction = ieNet.getFunction(); - CV_Assert(ngraphFunction); - std::vector inputsNames; std::vector inp_shapes; - for (auto& it : ngraphFunction->get_parameters()) + for (auto& it : ieNet->get_parameters()) { inputsNames.push_back(it->get_friendly_name()); std::vector dims = it->get_shape(); @@ -728,16 +672,9 @@ Net NetImplOpenVINO::createNetworkFromModelOptimizer(InferenceEngine::CNNNetwork // nGraph models produce output "Result" layers which have "/sink_port" suffix in their names. // Their inputs are actual model outputs and we change friendly name to it. // By this workaround, we produce similar outputs names comparing to ieNet.getOutputsInfo() - for (int i = 0; i < ngraphFunction->get_output_size(); ++i) { - auto res = ngraphFunction->output(i); -#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2022_1) + for (int i = 0; i < ieNet->get_output_size(); ++i) { + auto res = ieNet->output(i); const std::string& name = res.get_any_name(); -#else - auto out = res.get_node()->input(0).get_source_output(); - std::string name = out.get_node()->get_friendly_name(); - if (out.get_node()->get_output_size() > 1) - name += "." + std::to_string(out.get_index()); -#endif if (res.get_node()->get_friendly_name() != name) res.get_node()->set_friendly_name(name); } @@ -759,7 +696,7 @@ Net NetImplOpenVINO::createNetworkFromModelOptimizer(InferenceEngine::CNNNetwork Ptr backendNode; { - auto fake_node = std::make_shared(ngraph::element::f32, ngraph::Shape {}); + auto fake_node = std::make_shared(ov::element::f32, ov::Shape {}); Ptr backendNodeNGraph(new InfEngineNgraphNode(fake_node)); backendNodeNGraph->net = Ptr(new InfEngineNgraphNet(openvino_impl, ieNet)); backendNode = backendNodeNGraph; @@ -767,9 +704,9 @@ Net NetImplOpenVINO::createNetworkFromModelOptimizer(InferenceEngine::CNNNetwork CV_TRACE_REGION_NEXT("register_outputs"); - std::vector> ngraphOperations = ngraphFunction->get_ops(); + std::vector> ngraphOperations = ieNet->get_ops(); - for (auto& it : ngraphFunction->get_results()) + for (auto& it : ieNet->get_results()) { CV_TRACE_REGION("output"); const auto& outputName = it->get_friendly_name(); @@ -834,11 +771,11 @@ Net openvino_readNetwork(const String& modelPath, const String& binPath) { FPDenormalsIgnoreHintScope fp_denormals_ignore_scope; - InferenceEngine::Core& ie = getCore(""); - InferenceEngine::CNNNetwork ieNet; + ov::Core& ie = getCore(""); + std::shared_ptr ieNet; try { - ieNet = ie.ReadNetwork(modelPath, binPath); + ieNet = ie.read_model(modelPath, binPath); } catch (const std::exception& e) { @@ -857,22 +794,15 @@ Net openvino_readNetwork( { FPDenormalsIgnoreHintScope fp_denormals_ignore_scope; - InferenceEngine::Core& ie = getCore(""); + ov::Core& ie = getCore(""); std::string model; model.assign((char*)bufferModelConfigPtr, bufferModelConfigSize); - InferenceEngine::CNNNetwork ieNet; + std::shared_ptr ieNet; try { -#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2022_1) ov::Tensor weights_blob(ov::element::u8, {bufferWeightsSize}, (void*)bufferWeightsPtr); ieNet = ie.read_model(model, weights_blob); -#else - InferenceEngine::TensorDesc tensorDesc(InferenceEngine::Precision::U8, { bufferWeightsSize }, InferenceEngine::Layout::C); - InferenceEngine::Blob::CPtr weights_blob = InferenceEngine::make_shared_blob(tensorDesc, (uint8_t*)bufferWeightsPtr, bufferWeightsSize); - - ieNet = ie.ReadNetwork(model, weights_blob); -#endif } catch (const std::exception& e) { diff --git a/modules/dnn/src/op_inf_engine.cpp b/modules/dnn/src/op_inf_engine.cpp index f9e3993d20..04f1da7c71 100644 --- a/modules/dnn/src/op_inf_engine.cpp +++ b/modules/dnn/src/op_inf_engine.cpp @@ -10,7 +10,7 @@ #include #ifdef HAVE_INF_ENGINE -#include +#include #elif defined(ENABLE_PLUGINS) // using plugin API #include "backend.hpp" @@ -39,60 +39,6 @@ cv::String setInferenceEngineBackendType(const cv::String& newBackendType) CV__DNN_INLINE_NS_END -#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2022_1) -namespace InferenceEngine { - -CNNNetwork::CNNNetwork() {} - -CNNNetwork::CNNNetwork(std::shared_ptr model) : model(model) {} - -std::shared_ptr CNNNetwork::getFunction() const { - return model; -} - -void CNNNetwork::serialize(const std::string& xmlPath, const std::string& binPath) { - ov::pass::Serialize(xmlPath, binPath).run_on_model(model); -} - -void CNNNetwork::reshape(const std::map >& shapes) { - std::map partialShapes; - for (const auto& it : shapes) { - ov::PartialShape shape; - shape.insert(shape.begin(), it.second.begin(), it.second.end()); - partialShapes.insert({it.first, shape}); - } - model->reshape(partialShapes); -} - -std::vector Core::GetAvailableDevices() { - return get_available_devices(); -} - -void Core::UnregisterPlugin(const std::string& id) { - unload_plugin(id); -} - -CNNNetwork Core::ReadNetwork(const std::string& xmlPath, const std::string& binPath) { - return read_model(xmlPath, binPath); -} - -ExecutableNetwork Core::LoadNetwork(CNNNetwork net, const std::string& device, - const std::map& config) { - ov::AnyMap props; - for (const auto& it : config) { - props.insert(it); - } - return compile_model(net.getFunction(), device, props); -} - -ExecutableNetwork::ExecutableNetwork() {} - -ExecutableNetwork::ExecutableNetwork(const ov::CompiledModel& copy) : CompiledModel(copy) {} - -ov::InferRequest ExecutableNetwork::CreateInferRequest() { return create_infer_request(); } - -} // namespace InferenceEngine - Mat infEngineBlobToMat(const ov::Tensor& blob) { std::vector dims = blob.get_shape(); @@ -118,67 +64,40 @@ void infEngineBlobsToMats(const ov::TensorVector& blobs, mats[i] = infEngineBlobToMat(blobs[i]); } -#else - -Mat infEngineBlobToMat(const InferenceEngine::Blob::Ptr& blob) -{ - // NOTE: Inference Engine sizes are reversed. - std::vector dims = blob->getTensorDesc().getDims(); - std::vector size(dims.begin(), dims.end()); - auto precision = blob->getTensorDesc().getPrecision(); - - int type = -1; - switch (precision) - { - case InferenceEngine::Precision::FP32: type = CV_32F; break; - case InferenceEngine::Precision::U8: type = CV_8U; break; - default: - CV_Error(Error::StsNotImplemented, "Unsupported blob precision"); - } - return Mat(size, type, (void*)blob->buffer()); -} - -void infEngineBlobsToMats(const std::vector& blobs, - std::vector& mats) -{ - mats.resize(blobs.size()); - for (int i = 0; i < blobs.size(); ++i) - mats[i] = infEngineBlobToMat(blobs[i]); -} -#endif // OpenVINO >= 2022.1 static bool init_IE_plugins() { // load and hold IE plugins - static InferenceEngine::Core* init_core = new InferenceEngine::Core(); // 'delete' is never called - (void)init_core->GetAvailableDevices(); + static ov::Core* init_core = new ov::Core(); // 'delete' is never called + (void)init_core->get_available_devices(); return true; } -static InferenceEngine::Core& retrieveIECore(const std::string& id, std::map >& cores) +static ov::Core& retrieveIECore(const std::string& id, std::map >& cores) { AutoLock lock(getInitializationMutex()); - std::map >::iterator i = cores.find(id); + std::map >::iterator i = cores.find(id); if (i == cores.end()) { - std::shared_ptr core = std::make_shared(); + std::shared_ptr core = std::make_shared(); cores[id] = core; return *core.get(); } return *(i->second).get(); } -static InferenceEngine::Core& create_IE_Core_instance(const std::string& id) +static ov::Core& create_IE_Core_instance(const std::string& id) { - static std::map > cores; + static std::map > cores; return retrieveIECore(id, cores); } -static InferenceEngine::Core& create_IE_Core_pointer(const std::string& id) +static ov::Core& create_IE_Core_pointer(const std::string& id) { // load and hold IE plugins - static std::map >* cores = - new std::map >(); + static std::map >* cores = + new std::map >(); return retrieveIECore(id, *cores); } -InferenceEngine::Core& getCore(const std::string& id) + +ov::Core& getCore(const std::string& id) { // to make happy memory leak tools use: // - OPENCV_DNN_INFERENCE_ENGINE_HOLD_PLUGINS=0 @@ -195,7 +114,7 @@ InferenceEngine::Core& getCore(const std::string& id) #endif ); - InferenceEngine::Core& core = param_DNN_INFERENCE_ENGINE_CORE_LIFETIME_WORKAROUND + ov::Core& core = param_DNN_INFERENCE_ENGINE_CORE_LIFETIME_WORKAROUND ? create_IE_Core_pointer(id) : create_IE_Core_instance(id); return core; @@ -204,17 +123,13 @@ InferenceEngine::Core& getCore(const std::string& id) static bool detectArmPlugin_() { - InferenceEngine::Core& ie = getCore("CPU"); - const std::vector devices = ie.GetAvailableDevices(); + ov::Core& ie = getCore("CPU"); + const std::vector devices = ie.get_available_devices(); for (std::vector::const_iterator i = devices.begin(); i != devices.end(); ++i) { if (i->find("CPU") != std::string::npos) { -#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2022_1) const std::string name = ie.get_property(*i, ov::device::full_name); -#else - const std::string name = ie.GetMetric(*i, METRIC_KEY(FULL_DEVICE_NAME)).as(); -#endif CV_LOG_INFO(NULL, "CPU plugin: " << name); return name.find("arm_compute::NEON") != std::string::npos; } @@ -228,17 +143,13 @@ static bool detectMyriadX_(const std::string& device) AutoLock lock(getInitializationMutex()); // Lightweight detection - InferenceEngine::Core& ie = getCore(device); - const std::vector devices = ie.GetAvailableDevices(); + ov::Core& ie = getCore(device); + const std::vector devices = ie.get_available_devices(); for (std::vector::const_iterator i = devices.begin(); i != devices.end(); ++i) { if (i->find(device) != std::string::npos) { -#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2022_1) const std::string name = ie.get_property(*i, ov::device::full_name); -#else - const std::string name = ie.GetMetric(*i, METRIC_KEY(FULL_DEVICE_NAME)).as(); -#endif CV_LOG_INFO(NULL, "Myriad device: " << name); return name.find("MyriadX") != std::string::npos || name.find("Myriad X") != std::string::npos || name.find("HDDL") != std::string::npos; } @@ -259,11 +170,11 @@ void resetMyriadDevice() AutoLock lock(getInitializationMutex()); - InferenceEngine::Core& ie = getCore("MYRIAD"); + ov::Core& ie = getCore("MYRIAD"); try { - ie.UnregisterPlugin("MYRIAD"); - ie.UnregisterPlugin("HETERO"); + ie.unload_plugin("MYRIAD"); + ie.unload_plugin("HETERO"); } catch (...) {} #endif // HAVE_INF_ENGINE @@ -276,11 +187,11 @@ void releaseHDDLPlugin() AutoLock lock(getInitializationMutex()); - InferenceEngine::Core& ie = getCore("HDDL"); + ov::Core& ie = getCore("HDDL"); try { - ie.UnregisterPlugin("HDDL"); - ie.UnregisterPlugin("HETERO"); + ie.unload_plugin("HDDL"); + ie.unload_plugin("HETERO"); } catch (...) {} #endif // HAVE_INF_ENGINE @@ -351,7 +262,7 @@ namespace openvino { bool checkTarget(Target target) { // Lightweight detection - const std::vector devices = getCore("").GetAvailableDevices(); + const std::vector devices = getCore("").get_available_devices(); for (std::vector::const_iterator i = devices.begin(); i != devices.end(); ++i) { if (std::string::npos != i->find("MYRIAD") && target == DNN_TARGET_MYRIAD) diff --git a/modules/dnn/src/op_inf_engine.hpp b/modules/dnn/src/op_inf_engine.hpp index c5a2e58683..236b21b1a3 100644 --- a/modules/dnn/src/op_inf_engine.hpp +++ b/modules/dnn/src/op_inf_engine.hpp @@ -19,19 +19,13 @@ #ifdef HAVE_INF_ENGINE -#define INF_ENGINE_RELEASE_2020_2 2020020000 -#define INF_ENGINE_RELEASE_2020_3 2020030000 -#define INF_ENGINE_RELEASE_2020_4 2020040000 -#define INF_ENGINE_RELEASE_2021_1 2021010000 -#define INF_ENGINE_RELEASE_2021_2 2021020000 -#define INF_ENGINE_RELEASE_2021_3 2021030000 -#define INF_ENGINE_RELEASE_2021_4 2021040000 #define INF_ENGINE_RELEASE_2022_1 2022010000 #define INF_ENGINE_RELEASE_2023_0 2023000000 +#define INF_ENGINE_RELEASE_2024_0 2024000000 #ifndef INF_ENGINE_RELEASE -#warning("IE version have not been provided via command-line. Using 2021.4 by default") -#define INF_ENGINE_RELEASE INF_ENGINE_RELEASE_2021_4 +#warning("IE version have not been provided via command-line. Using 2022.1 by default") +#define INF_ENGINE_RELEASE INF_ENGINE_RELEASE_2022_1 #endif #define INF_ENGINE_VER_MAJOR_GT(ver) (((INF_ENGINE_RELEASE) / 10000) > ((ver) / 10000)) @@ -45,13 +39,9 @@ #pragma GCC diagnostic ignored "-Wsuggest-override" #endif -#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2022_1) #include #include #include -#else -#include -#endif #if defined(__GNUC__) && __GNUC__ >= 5 //#pragma GCC diagnostic pop @@ -76,18 +66,10 @@ CV__DNN_INLINE_NS_END Backend& getInferenceEngineBackendTypeParam(); -#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2022_1) Mat infEngineBlobToMat(const ov::Tensor& blob); void infEngineBlobsToMats(const ov::TensorVector& blobs, std::vector& mats); -#else -Mat infEngineBlobToMat(const InferenceEngine::Blob::Ptr& blob); - -void infEngineBlobsToMats(const std::vector& blobs, - std::vector& mats); -#endif // OpenVINO >= 2022.1 - CV__DNN_INLINE_NS_BEGIN @@ -99,54 +81,7 @@ bool isArmComputePlugin(); CV__DNN_INLINE_NS_END -// A series of wrappers for classes from OpenVINO API 2.0. -// Need just for less conditional compilation inserts. -#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2022_1) -namespace InferenceEngine { - -class CNNNetwork { -public: - CNNNetwork(); - - CNNNetwork(std::shared_ptr model); - - std::shared_ptr getFunction() const; - - void serialize(const std::string& xmlPath, const std::string& binPath); - - void reshape(const std::map >& shapes); - -private: - std::shared_ptr model = nullptr; -}; - -typedef ov::InferRequest InferRequest; - -class ExecutableNetwork : public ov::CompiledModel { -public: - ExecutableNetwork(); - - ExecutableNetwork(const ov::CompiledModel& copy); - - ov::InferRequest CreateInferRequest(); -}; - -class Core : public ov::Core { -public: - std::vector GetAvailableDevices(); - - void UnregisterPlugin(const std::string& id); - - CNNNetwork ReadNetwork(const std::string& xmlPath, const std::string& binPath); - - ExecutableNetwork LoadNetwork(CNNNetwork net, const std::string& device, - const std::map& config); -}; - -} -#endif // OpenVINO >= 2022.1 - -InferenceEngine::Core& getCore(const std::string& id); +ov::Core& getCore(const std::string& id); template static inline std::vector getShape(const Mat& mat) diff --git a/modules/dnn/src/op_webnn.hpp b/modules/dnn/src/op_webnn.hpp index 5b77b10827..6f96289b80 100644 --- a/modules/dnn/src/op_webnn.hpp +++ b/modules/dnn/src/op_webnn.hpp @@ -111,7 +111,7 @@ public: void addBlobs(const std::vector >& ptrs); void createNet(Target targetId); - // void setNodePtr(std::shared_ptr* ptr); + // void setNodePtr(std::shared_ptr* ptr); void reset(); diff --git a/modules/dnn/test/test_ie_models.cpp b/modules/dnn/test/test_ie_models.cpp index c6667c7ad2..eff389035d 100644 --- a/modules/dnn/test/test_ie_models.cpp +++ b/modules/dnn/test/test_ie_models.cpp @@ -9,7 +9,6 @@ #ifdef HAVE_INF_ENGINE #include - // // Synchronize headers include statements with src/op_inf_engine.hpp // @@ -26,14 +25,11 @@ #pragma GCC visibility push(default) #endif -#include -#include -#include - #if defined(__GNUC__) #pragma GCC visibility pop #endif +#include namespace opencv_test { namespace { @@ -62,7 +58,6 @@ static void initDLDTDataPath() using namespace cv; using namespace cv::dnn; -using namespace InferenceEngine; struct OpenVINOModelTestCaseInfo { @@ -161,27 +156,6 @@ inline static std::string getOpenVINOModel(const std::string &modelName, bool is return std::string(); } -static inline void genData(const InferenceEngine::TensorDesc& desc, Mat& m, Blob::Ptr& dataPtr) -{ - const std::vector& dims = desc.getDims(); - if (desc.getPrecision() == InferenceEngine::Precision::FP32) - { - m.create(std::vector(dims.begin(), dims.end()), CV_32F); - randu(m, -1, 1); - dataPtr = make_shared_blob(desc, (float*)m.data); - } - else if (desc.getPrecision() == InferenceEngine::Precision::I32) - { - m.create(std::vector(dims.begin(), dims.end()), CV_32S); - randu(m, -100, 100); - dataPtr = make_shared_blob(desc, (int*)m.data); - } - else - { - FAIL() << "Unsupported precision: " << desc.getPrecision(); - } -} - void runIE(Target target, const std::string& xmlPath, const std::string& binPath, std::map& inputsMap, std::map& outputsMap) { @@ -189,25 +163,12 @@ void runIE(Target target, const std::string& xmlPath, const std::string& binPath std::string device_name; -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GT(2019010000) - Core ie; -#else - InferenceEnginePluginPtr enginePtr; - InferencePlugin plugin; -#endif + ov::Core core; -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GT(2019030000) - CNNNetwork net = ie.ReadNetwork(xmlPath, binPath); -#else - CNNNetReader reader; - reader.ReadNetwork(xmlPath); - reader.ReadWeights(binPath); + auto model = core.read_model(xmlPath, binPath); - CNNNetwork net = reader.getNetwork(); -#endif - - ExecutableNetwork netExec; - InferRequest infRequest; + ov::CompiledModel compiledModel; + ov::InferRequest infRequest; try { @@ -230,10 +191,6 @@ void runIE(Target target, const std::string& xmlPath, const std::string& binPath CV_Error(Error::StsNotImplemented, "Unknown target"); }; -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2019010000) - auto dispatcher = InferenceEngine::PluginDispatcher({""}); - enginePtr = dispatcher.getPluginByDevice(device_name); -#endif if (target == DNN_TARGET_CPU || target == DNN_TARGET_FPGA) { std::string suffixes[] = {"_avx2", "_sse4", ""}; @@ -255,68 +212,90 @@ void runIE(Target target, const std::string& xmlPath, const std::string& binPath #endif // _WIN32 try { - IExtensionPtr extension = make_so_pointer(libName); -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GT(2019010000) - ie.AddExtension(extension, device_name); -#else - enginePtr->AddExtension(extension, 0); -#endif + core.add_extension(libName); break; } catch(...) {} } // Some of networks can work without a library of extra layers. } -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GT(2019010000) - netExec = ie.LoadNetwork(net, device_name); -#else - plugin = InferencePlugin(enginePtr); - netExec = plugin.LoadNetwork(net, {}); -#endif - infRequest = netExec.CreateInferRequest(); + compiledModel = core.compile_model(model, device_name); + infRequest = compiledModel.create_infer_request(); } catch (const std::exception& ex) { CV_Error(Error::StsAssert, format("Failed to initialize Inference Engine backend: %s", ex.what())); } - // Fill input blobs. + // Fill input tensors. inputsMap.clear(); - BlobMap inputBlobs; - for (auto& it : net.getInputsInfo()) + for (auto&& it : model->inputs()) { - const InferenceEngine::TensorDesc& desc = it.second->getTensorDesc(); - genData(desc, inputsMap[it.first], inputBlobs[it.first]); + auto type = it.get_element_type(); + auto shape = it.get_shape(); + auto& m = inputsMap[it.get_any_name()]; + + auto tensor = ov::Tensor(type, shape); + if (type == ov::element::f32) + { + m.create(std::vector(shape.begin(), shape.end()), CV_32F); + randu(m, -1, 1); + } + else if (type == ov::element::i32) + { + m.create(std::vector(shape.begin(), shape.end()), CV_32S); + randu(m, -100, 100); + } + else + { + FAIL() << "Unsupported precision: " << type; + } + std::memcpy(tensor.data(), m.data, tensor.get_byte_size()); + if (cvtest::debugLevel > 0) { - const std::vector& dims = desc.getDims(); - std::cout << "Input: '" << it.first << "' precision=" << desc.getPrecision() << " dims=" << dims.size() << " ["; - for (auto d : dims) + std::cout << "Input: '" << it.get_any_name() << "' precision=" << type << " dims=" << shape << " ["; + for (auto d : shape) std::cout << " " << d; - std::cout << "] ocv_mat=" << inputsMap[it.first].size << " of " << typeToString(inputsMap[it.first].type()) << std::endl; + std::cout << "] ocv_mat=" << inputsMap[it.get_any_name()].size << " of " << typeToString(inputsMap[it.get_any_name()].type()) << std::endl; } + infRequest.set_tensor(it, tensor); } - infRequest.SetInput(inputBlobs); + infRequest.infer(); - // Fill output blobs. + + // Fill output tensors. outputsMap.clear(); - BlobMap outputBlobs; - for (auto& it : net.getOutputsInfo()) + for (const auto& it : model->outputs()) { - const InferenceEngine::TensorDesc& desc = it.second->getTensorDesc(); - genData(desc, outputsMap[it.first], outputBlobs[it.first]); + auto type = it.get_element_type(); + auto shape = it.get_shape(); + auto& m = outputsMap[it.get_any_name()]; + + auto tensor = infRequest.get_tensor(it); + if (type == ov::element::f32) + { + m.create(std::vector(shape.begin(), shape.end()), CV_32F); + } + else if (type == ov::element::i32) + { + m.create(std::vector(shape.begin(), shape.end()), CV_32S); + } + else + { + FAIL() << "Unsupported precision: " << type; + } + std::memcpy(m.data, tensor.data(), tensor.get_byte_size()); + if (cvtest::debugLevel > 0) { - const std::vector& dims = desc.getDims(); - std::cout << "Output: '" << it.first << "' precision=" << desc.getPrecision() << " dims=" << dims.size() << " ["; - for (auto d : dims) + std::cout << "Output: '" << it.get_any_name() << "' precision=" << type << " dims=" << shape << " ["; + for (auto d : shape) std::cout << " " << d; - std::cout << "] ocv_mat=" << outputsMap[it.first].size << " of " << typeToString(outputsMap[it.first].type()) << std::endl; + std::cout << "] ocv_mat=" << outputsMap[it.get_any_name()].size << " of " << typeToString(outputsMap[it.get_any_name()].type()) << std::endl; } - } - infRequest.SetOutput(outputBlobs); - infRequest.Infer(); + } } void runCV(Backend backendId, Target targetId, const std::string& xmlPath, const std::string& binPath,