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Merge pull request #25199 from olpipi:fix_openvino_2024_0

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pull/25242/head
Alexander Alekhin 8 months ago
parent 625eebad54 6da2ddcf0e
commit 0c6fc763f4
48 changed files with 647 additions and 1678 deletions
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  1. 64
      cmake/OpenCVDetectInferenceEngine.cmake
  2. 697
      modules/dnn/src/ie_ngraph.cpp
  3. 42
      modules/dnn/src/ie_ngraph.hpp
  4. 12
      modules/dnn/src/int8layers/batch_norm_layer.cpp
  5. 62
      modules/dnn/src/int8layers/convolution_layer.cpp
  6. 20
      modules/dnn/src/int8layers/elementwise_layers.cpp
  7. 8
      modules/dnn/src/int8layers/eltwise_layer.cpp
  8. 32
      modules/dnn/src/int8layers/fully_connected_layer.cpp
  9. 23
      modules/dnn/src/int8layers/pooling_layer.cpp
  10. 14
      modules/dnn/src/int8layers/scale_layer.cpp
  11. 6
      modules/dnn/src/int8layers/softmax_layer.cpp
  12. 14
      modules/dnn/src/layers/batch_norm_layer.cpp
  13. 4
      modules/dnn/src/layers/blank_layer.cpp
  14. 13
      modules/dnn/src/layers/concat_layer.cpp
  15. 16
      modules/dnn/src/layers/const_layer.cpp
  16. 74
      modules/dnn/src/layers/convolution_layer.cpp
  17. 22
      modules/dnn/src/layers/crop_and_resize_layer.cpp
  18. 30
      modules/dnn/src/layers/detection_output_layer.cpp
  19. 92
      modules/dnn/src/layers/elementwise_layers.cpp
  20. 20
      modules/dnn/src/layers/eltwise_layer.cpp
  21. 7
      modules/dnn/src/layers/flatten_layer.cpp
  22. 18
      modules/dnn/src/layers/fully_connected_layer.cpp
  23. 26
      modules/dnn/src/layers/gemm_layer.cpp
  24. 23
      modules/dnn/src/layers/instance_norm_layer.cpp
  25. 25
      modules/dnn/src/layers/layer_norm.cpp
  26. 4
      modules/dnn/src/layers/lrn_layer.cpp
  27. 8
      modules/dnn/src/layers/matmul_layer.cpp
  28. 22
      modules/dnn/src/layers/max_unpooling_layer.cpp
  29. 8
      modules/dnn/src/layers/mvn_layer.cpp
  30. 26
      modules/dnn/src/layers/nary_eltwise_layers.cpp
  31. 14
      modules/dnn/src/layers/normalize_bbox_layer.cpp
  32. 12
      modules/dnn/src/layers/padding_layer.cpp
  33. 6
      modules/dnn/src/layers/permute_layer.cpp
  34. 45
      modules/dnn/src/layers/pooling_layer.cpp
  35. 39
      modules/dnn/src/layers/prior_box_layer.cpp
  36. 16
      modules/dnn/src/layers/proposal_layer.cpp
  37. 155
      modules/dnn/src/layers/region_layer.cpp
  38. 8
      modules/dnn/src/layers/reorg_layer.cpp
  39. 6
      modules/dnn/src/layers/reshape_layer.cpp
  40. 74
      modules/dnn/src/layers/resize_layer.cpp
  41. 27
      modules/dnn/src/layers/scale_layer.cpp
  42. 16
      modules/dnn/src/layers/slice_layer.cpp
  43. 4
      modules/dnn/src/layers/softmax_layer.cpp
  44. 112
      modules/dnn/src/net_openvino.cpp
  45. 139
      modules/dnn/src/op_inf_engine.cpp
  46. 73
      modules/dnn/src/op_inf_engine.hpp
  47. 2
      modules/dnn/src/op_webnn.hpp
  48. 145
      modules/dnn/test/test_ie_models.cpp

64
cmake/OpenCVDetectInferenceEngine.cmake
Unescape View File

@ -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}")

697
modules/dnn/src/ie_ngraph.cpp
Unescape View File

@ -14,7 +14,7 @@
#include <opencv2/dnn/shape_utils.hpp>
#ifdef HAVE_DNN_NGRAPH
#include <ie_extension.h>
#include <openvino/core/extension.hpp>
#endif // HAVE_DNN_NGRAPH
#include <opencv2/core/utils/configuration.private.hpp>
@ -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<Mat>& 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<std::vector<size_t> >& 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<Ptr<NgraphBackendWrapper> >
ngraphWrappers(const std::vector<Ptr<BackendWrapper> >& ptrs)
{
@ -78,13 +48,11 @@ ngraphWrappers(const std::vector<Ptr<BackendWrapper> >& 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<Layer>& cvLayer, const std::vector<Mat>& outputs, const std::vector<Mat>& internals):
NgraphCustomOp(const ov::OutputVector& inputs, Ptr<Layer>& cvLayer, const std::vector<Mat>& outputs, const std::vector<Mat>& internals):
Op(inputs), cvLayer(cvLayer), outputs(outputs), internals(internals)
{
constructor_validate_and_infer_types();
@ -103,7 +71,7 @@ public:
}
}
std::shared_ptr<ngraph::Node> clone_with_new_inputs(const ngraph::OutputVector& new_args) const override
std::shared_ptr<ov::Node> clone_with_new_inputs(const ov::OutputVector& new_args) const override
{
return std::make_shared<NgraphCustomOp>(new_args, cvLayer, outputs, internals);
}
@ -131,265 +99,13 @@ public:
std::vector<Mat> 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<uint64_t>(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<std::string, InferenceEngine::Parameter>& params = {}):
Op(inputs), params(params)
{
constructor_validate_and_infer_types();
}
~NgraphCustomOp()
{
// nothing
}
void validate_and_infer_types() override
{
std::vector<std::vector<size_t> > 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<ngraph::Node> clone_with_new_inputs(const ngraph::OutputVector& new_args) const override
{
return std::make_shared<NgraphCustomOp>(new_args, params);
}
#else
std::shared_ptr<ngraph::Node> 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<NgraphCustomOp>(ngraph::as_output_vector(new_args), params);
#else
return std::make_shared<NgraphCustomOp>(new_args, params);
#endif
}
#endif
bool visit_attributes(ngraph::AttributeVisitor& visitor) override
{
for (auto& attr : params)
{
if (attr.second.is<std::string>())
visitor.on_attribute(attr.first, attr.second.as<std::string>());
}
return true;
}
std::map<std::string, InferenceEngine::Parameter> params;
};
class InfEngineNgraphCustomLayer : public InferenceEngine::ILayerExecImpl
{
public:
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2020_2)
explicit InfEngineNgraphCustomLayer(const std::shared_ptr<ngraph::Node>& _node)
{
node = std::dynamic_pointer_cast<NgraphCustomOp>(_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<std::vector<size_t> > 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<int>(shapes[i].begin(), shapes[i].end()), CV_32F);
}
~InfEngineNgraphCustomLayer()
{
// nothing
}
virtual InferenceEngine::StatusCode execute(std::vector<InferenceEngine::Blob::Ptr>& inputs,
std::vector<InferenceEngine::Blob::Ptr>& outputs,
InferenceEngine::ResponseDesc *resp) noexcept
{
std::vector<Mat> 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<InferenceEngine::LayerConfig>& conf,
InferenceEngine::ResponseDesc* resp) noexcept
{
std::vector<InferenceEngine::DataConfig> inDataConfig;
std::vector<InferenceEngine::DataConfig> 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<NgraphCustomOp> node;
#else
InferenceEngine::CNNLayer cnnLayer;
#endif
dnn::Layer* cvLayer;
std::vector<Mat> 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<InferenceEngine::ILayerImpl::Ptr>& impls,
InferenceEngine::ResponseDesc* resp) noexcept override
{
impls.push_back(std::make_shared<InfEngineNgraphCustomLayer>(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<std::string> getImplTypes(const std::shared_ptr<ngraph::Node>& node) override {
return {"CPU"};
}
InferenceEngine::ILayerImpl::Ptr getImplementation(const std::shared_ptr<ngraph::Node>& node, const std::string& implType) override {
if (std::dynamic_pointer_cast<NgraphCustomOp>(node) && implType == "CPU") {
return std::make_shared<InfEngineNgraphCustomLayer>(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<ngraph::Node>&& _node)
InfEngineNgraphNode::InfEngineNgraphNode(ov::Output<ov::Node>&& _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<ngraph::Node>& _node)
InfEngineNgraphNode::InfEngineNgraphNode(const ov::Output<ov::Node>& _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<Ptr<BackendNode> >& n
std::vector<Mat>& outputs, std::vector<Mat>& 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<InfEngineNgraphNode>()->node);
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2022_1)
node = std::make_shared<NgraphCustomOp>(inp_nodes, cvLayer, outputs, internals);
#else
std::ostringstream oss;
oss << (size_t)cvLayer.get();
std::map<std::string, InferenceEngine::Parameter> params = {
{"impl", oss.str()},
{"outputs", shapesToStr(outputs)},
{"internals", shapesToStr(internals)}
};
node = std::make_shared<NgraphCustomOp>(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<ov::Model>& 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<ngraph::op::Result>(output_node_it->second->node);
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2022_1)
auto out = std::make_shared<ov::op::v0::Result>(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<ngraph::Function>(outs, inputs_vec);
ngraph_function = std::make_shared<ov::Model>(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<ngraph::op::Parameter>(node);
if (parameter && parameter->get_element_type() == ngraph::element::f32)
{
parameter->set_element_type(ngraph::element::f16);
}
auto constant = std::dynamic_pointer_cast<ngraph::op::Constant>(node);
if (constant && constant->get_element_type() == ngraph::element::f32)
{
const float* floatsData = constant->get_data_ptr<float>();
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::op::Constant>(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<cv::Mat>& inputs,
ov::ParameterVector InfEngineNgraphNet::setInputs(const std::vector<cv::Mat>& inputs,
const std::vector<std::string>& names) {
CV_Assert_N(inputs.size() == names.size());
ngraph::ParameterVector current_inp;
ov::ParameterVector current_inp;
for (size_t i = 0; i < inputs.size(); i++)
{
std::vector<size_t> shape = getShape<size_t>(inputs[i]);
auto inp = std::make_shared<ngraph::op::Parameter>(ngraph::element::f32, ngraph::Shape(shape));
auto inp = std::make_shared<ov::op::v0::Parameter>(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<ngraph::op::Parameter>& a) {
[&inp](const std::shared_ptr<ov::op::v0::Parameter>& 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<cv::Mat>
}
void InfEngineNgraphNet::initPlugin(InferenceEngine::CNNNetwork& net)
void InfEngineNgraphNet::initPlugin(std::shared_ptr<ov::Model>& net)
{
CV_Assert(!isInitialized());
try
{
AutoLock lock(getInitializationMutex());
InferenceEngine::Core& ie = getCore(device_name);
ov::Core& ie = getCore(device_name);
{
isInit = true;
std::vector<std::string> 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<InferenceEngine::Extension>(libName);
#else
InferenceEngine::make_so_pointer<InferenceEngine::IExtension>(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<InfEngineNgraphExtension>(), "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<std::string, std::string> 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<MatShape> &inputs,
std::vector<MatShape> &outputs,
std::vector<MatShape> &internals) const
{
auto ngraphFunction = t_net.getFunction();
bool equal_flag = true;
std::map<std::string, std::vector<size_t> > inShapes;
std::map<std::string, ov::PartialShape> inShapes;
int i = 0;
for (const auto& inp : ngraphFunction->get_parameters())
for (const auto& inp : t_net->get_parameters())
{
std::vector<size_t> oldShape = inp->get_shape();
std::vector<size_t> 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<MatShape> &inputs,
if (!equal_flag)
{
InferenceEngine::CNNNetwork curr_t_net(t_net);
curr_t_net.reshape(inShapes);
std::shared_ptr<ov::Model> curr_t_net(t_net);
curr_t_net->reshape(inShapes);
}
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2022_1)
std::vector<size_t> 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<size_t> 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<size_t> shape = getShape<size_t>(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<size_t>& shape,
InferenceEngine::Layout layout)
{
if (m.type() == CV_32F)
return InferenceEngine::make_shared_blob<float>(
{InferenceEngine::Precision::FP32, shape, layout}, (float*)m.data);
else if (m.type() == CV_8U)
return InferenceEngine::make_shared_blob<uint8_t>(
{InferenceEngine::Precision::U8, shape, layout}, (uint8_t*)m.data);
else if (m.type() == CV_32SC1)
return InferenceEngine::make_shared_blob<int32_t>(
{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<size_t> shape = getShape<size_t>(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<float>(description);
}
else if (precision == InferenceEngine::Precision::U8)
{
copy = InferenceEngine::make_shared_blob<uint8_t>(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<Ptr<BackendWrapper> >& outBlo
reqWrapper = Ptr<NgraphReqWrapper>(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<Ptr<BackendWrapper> >& 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<Ptr<BackendWrapper> >& 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<Ptr<BackendWrapper> >& 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<std::function<void(InferenceEngine::InferRequest, InferenceEngine::StatusCode)>>(
[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<Ptr<BackendWrapper> >& 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<ngraph::Node> ngraphQuantize(ngraph::Output<ngraph::Node> input, float output_sc, float output_zp) {
ov::Output<ov::Node> ngraphQuantize(ov::Output<ov::Node> 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<ngraph::op::FakeQuantize>(input,
std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{1}, &inpLow),
std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{1}, &inpHigh),
std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{1}, &outLow),
std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{1}, &outHigh),
return std::make_shared<ov::op::v0::FakeQuantize>(input,
std::make_shared<ov::op::v0::Constant>(ov::element::f32, ov::Shape{1}, &inpLow),
std::make_shared<ov::op::v0::Constant>(ov::element::f32, ov::Shape{1}, &inpHigh),
std::make_shared<ov::op::v0::Constant>(ov::element::f32, ov::Shape{1}, &outLow),
std::make_shared<ov::op::v0::Constant>(ov::element::f32, ov::Shape{1}, &outHigh),
256 // levels
);
}
ngraph::Output<ngraph::Node> ngraphDequantize(ngraph::Output<ngraph::Node> input, float input_sc, float input_zp) {
ov::Output<ov::Node> ngraphDequantize(ov::Output<ov::Node> 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<ngraph::op::FakeQuantize>(input,
std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{1}, &inpLow),
std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{1}, &inpHigh),
std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{1}, &outLow),
std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{1}, &outHigh),
return std::make_shared<ov::op::v0::FakeQuantize>(input,
std::make_shared<ov::op::v0::Constant>(ov::element::f32, ov::Shape{1}, &inpLow),
std::make_shared<ov::op::v0::Constant>(ov::element::f32, ov::Shape{1}, &inpHigh),
std::make_shared<ov::op::v0::Constant>(ov::element::f32, ov::Shape{1}, &outLow),
std::make_shared<ov::op::v0::Constant>(ov::element::f32, ov::Shape{1}, &outHigh),
256 // levels
);
}

42
modules/dnn/src/ie_ngraph.hpp
Unescape View File

@ -17,7 +17,8 @@
#pragma warning(disable : 4245)
#pragma warning(disable : 4268)
#endif
#include <ngraph/ngraph.hpp>
#include <openvino/openvino.hpp>
#include <openvino/op/ops.hpp>
#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<ov::Model>& net);
void addOutput(const Ptr<InfEngineNgraphNode>& node);
@ -44,8 +44,8 @@ public:
void forward(const std::vector<Ptr<BackendWrapper> >& outBlobsWrappers, bool isAsync);
void initPlugin(InferenceEngine::CNNNetwork& net);
ngraph::ParameterVector setInputs(const std::vector<cv::Mat>& inputs, const std::vector<std::string>& names);
void initPlugin(std::shared_ptr<ov::Model>& net);
ov::ParameterVector setInputs(const std::vector<cv::Mat>& inputs, const std::vector<std::string>& names);
void addBlobs(const std::vector<cv::Ptr<BackendWrapper> >& ptrs);
@ -56,15 +56,11 @@ public:
//private:
detail::NetImplBase& netImpl_;
ngraph::ParameterVector inputs_vec;
std::shared_ptr<ngraph::Function> ngraph_function;
ov::ParameterVector inputs_vec;
std::shared_ptr<ov::Model> ngraph_function;
InferenceEngine::ExecutableNetwork netExec;
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2022_1)
ov::CompiledModel netExec;
std::map<std::string, ov::Tensor> allBlobs;
#else
InferenceEngine::BlobMap allBlobs;
#endif
std::string device_name;
bool isInit = false;
@ -74,14 +70,14 @@ public:
void makePromises(const std::vector<Ptr<BackendWrapper> >& outs);
InferenceEngine::InferRequest req;
ov::InferRequest req;
std::vector<cv::AsyncPromise> outProms;
std::vector<std::string> outsNames;
bool isReady;
};
std::vector<Ptr<NgraphReqWrapper> > infRequests;
InferenceEngine::CNNNetwork cnn;
std::shared_ptr<ov::Model> cnn;
bool hasNetOwner;
std::unordered_map<std::string, InfEngineNgraphNode*> requestedOutputs;
};
@ -93,13 +89,13 @@ public:
std::vector<Mat*>& inputs, std::vector<Mat>& outputs,
std::vector<Mat>& internals);
InfEngineNgraphNode(ngraph::Output<ngraph::Node>&& _node);
InfEngineNgraphNode(const ngraph::Output<ngraph::Node>& _node);
InfEngineNgraphNode(ov::Output<ov::Node>&& _node);
InfEngineNgraphNode(const ov::Output<ov::Node>& _node);
void setName(const std::string& name);
// Inference Engine network object that allows to obtain the outputs of this layer.
ngraph::Output<ngraph::Node> node;
ov::Output<ov::Node> node;
Ptr<InfEngineNgraphNet> net;
Ptr<dnn::Layer> 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<ov::Model> &t_net_) : t_net(t_net_) {};
virtual bool getMemoryShapes(const std::vector<MatShape> &inputs,
const int requiredOutputs,
@ -145,11 +137,11 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE;
private:
InferenceEngine::CNNNetwork t_net;
std::shared_ptr<ov::Model> t_net;
};
ngraph::Output<ngraph::Node> ngraphQuantize(ngraph::Output<ngraph::Node> input, float output_sc, float output_zp);
ngraph::Output<ngraph::Node> ngraphDequantize(ngraph::Output<ngraph::Node> input, float input_sc, float input_zp);
ov::Output<ov::Node> ngraphQuantize(ov::Output<ov::Node> input, float output_sc, float output_zp);
ov::Output<ov::Node> ngraphDequantize(ov::Output<ov::Node> input, float input_sc, float input_zp);
#endif // HAVE_DNN_NGRAPH

12
modules/dnn/src/int8layers/batch_norm_layer.cpp
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@ -7,8 +7,6 @@
#include "../op_timvx.hpp"
#include "../ie_ngraph.hpp"
#include <opencv2/dnn/shape_utils.hpp>
namespace cv
{
namespace dnn
@ -250,11 +248,11 @@ public:
std::vector<size_t> shape(input.get_shape().size(), 1);
shape[1] = origin_weights.total();
ngraph::Output<ngraph::Node> res;
auto ieWeights = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, shape, origin_weights.data);
auto ieBias = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, shape, origin_bias.data);
res = std::make_shared<ngraph::op::v1::Multiply>(input, ieWeights);
res = std::make_shared<ngraph::op::v1::Add>(res, ieBias);
ov::Output<ov::Node> res;
auto ieWeights = std::make_shared<ov::op::v0::Constant>(ov::element::f32, shape, origin_weights.data);
auto ieBias = std::make_shared<ov::op::v0::Constant>(ov::element::f32, shape, origin_bias.data);
res = std::make_shared<ov::op::v1::Multiply>(input, ieWeights);
res = std::make_shared<ov::op::v1::Add>(res, ieBias);
res = ngraphQuantize(res, output_sc, output_zp);
return new InfEngineNgraphNode(res);

62
modules/dnn/src/int8layers/convolution_layer.cpp
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@ -573,8 +573,8 @@ public:
auto ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
std::vector<size_t> 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<ngraph::Node> ieWeights;
CV_Assert(ieInpNode.get_element_type() == ov::element::f32);
ov::Output<ov::Node> ieWeights;
if (nodes.size() > 1)
ieWeights = nodes[1].dynamicCast<InfEngineNgraphNode>()->node;
const int inpCn = dims[1];
@ -592,18 +592,18 @@ public:
if (nodes.size() == 1)
{
ieWeights = std::make_shared<ngraph::op::Constant>(ngraph::element::i8, kernel_shape, blobs[0].data);
ieWeights = std::make_shared<ov::op::v0::Constant>(ov::element::i8, kernel_shape, blobs[0].data);
}
else
{
auto shape = std::make_shared<ngraph::op::Constant>(ngraph::element::i64,
ngraph::Shape{kernel_shape.size()}, std::vector<int64_t>(kernel_shape.begin(), kernel_shape.end()));
ieWeights = std::make_shared<ngraph::op::v1::Reshape>(ieWeights, shape, true);
auto shape = std::make_shared<ov::op::v0::Constant>(ov::element::i64,
ov::Shape{kernel_shape.size()}, std::vector<int64_t>(kernel_shape.begin(), kernel_shape.end()));
ieWeights = std::make_shared<ov::op::v1::Reshape>(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<ngraph::op::Convert>(ieWeights, ngraph::element::f32);
ieWeights = std::make_shared<ngraph::op::FakeQuantize>(ieWeights,
std::make_shared<ngraph::op::Constant>(ngraph::element::f32, quantShape, inpLows.data()),
std::make_shared<ngraph::op::Constant>(ngraph::element::f32, quantShape, inpHighs.data()),
std::make_shared<ngraph::op::Constant>(ngraph::element::f32, quantShape, outLows.data()),
std::make_shared<ngraph::op::Constant>(ngraph::element::f32, quantShape, outHighs.data()),
ieWeights = std::make_shared<ov::op::v0::Convert>(ieWeights, ov::element::f32);
ieWeights = std::make_shared<ov::op::v0::FakeQuantize>(ieWeights,
std::make_shared<ov::op::v0::Constant>(ov::element::f32, quantShape, inpLows.data()),
std::make_shared<ov::op::v0::Constant>(ov::element::f32, quantShape, inpHighs.data()),
std::make_shared<ov::op::v0::Constant>(ov::element::f32, quantShape, outLows.data()),
std::make_shared<ov::op::v0::Constant>(ov::element::f32, quantShape, outHighs.data()),
256 // levels
);
ngraph::Output<ngraph::Node> conv_node;
ov::Output<ov::Node> conv_node;
if (group != 1) {
conv_node = std::make_shared<ngraph::op::v1::GroupConvolution>(
conv_node = std::make_shared<ov::op::v1::GroupConvolution>(
ieInpNode, ieWeights,
ngraph::Strides(strides),
ngraph::CoordinateDiff(std::vector<std::ptrdiff_t>(pads_begin.begin(), pads_begin.end())),
ngraph::CoordinateDiff(std::vector<std::ptrdiff_t>(pads_end.begin(), pads_end.end())),
ngraph::Strides(dilations),
ov::Strides(strides),
ov::CoordinateDiff(std::vector<std::ptrdiff_t>(pads_begin.begin(), pads_begin.end())),
ov::CoordinateDiff(std::vector<std::ptrdiff_t>(pads_end.begin(), pads_end.end())),
ov::Strides(dilations),
pad_type);
} else {
conv_node = std::make_shared<ngraph::op::v1::Convolution>(
conv_node = std::make_shared<ov::op::v1::Convolution>(
ieInpNode, ieWeights,
ngraph::Strides(strides),
ngraph::CoordinateDiff(std::vector<std::ptrdiff_t>(pads_begin.begin(), pads_begin.end())),
ngraph::CoordinateDiff(std::vector<std::ptrdiff_t>(pads_end.begin(), pads_end.end())),
ngraph::Strides(dilations),
ov::Strides(strides),
ov::CoordinateDiff(std::vector<std::ptrdiff_t>(pads_begin.begin(), pads_begin.end())),
ov::CoordinateDiff(std::vector<std::ptrdiff_t>(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<ngraph::Node> bias;
std::shared_ptr<ov::Node> bias;
if (nodes.size() == 3)
{
auto bias_shape = std::make_shared<ngraph::op::Constant>(ngraph::element::i64,
ngraph::Shape{shape.size()}, std::vector<int64_t>(shape.begin(), shape.end()));
bias = std::make_shared<ngraph::op::v1::Reshape>(nodes[2].dynamicCast<InfEngineNgraphNode>()->node, bias_shape, true);
auto bias_shape = std::make_shared<ov::op::v0::Constant>(ov::element::i64,
ov::Shape{shape.size()}, std::vector<int64_t>(shape.begin(), shape.end()));
bias = std::make_shared<ov::op::v1::Reshape>(nodes[2].dynamicCast<InfEngineNgraphNode>()->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::op::Constant>(ngraph::element::f32, ngraph::Shape(shape), ovBias.data());
bias = std::make_shared<ov::op::v0::Constant>(ov::element::f32, ov::Shape(shape), ovBias.data());
}
conv_node = std::make_shared<ngraph::op::v1::Add>(conv_node, bias, ngraph::op::AutoBroadcastType::NUMPY);
conv_node = std::make_shared<ov::op::v1::Add>(conv_node, bias, ov::op::AutoBroadcastType::NUMPY);
}
conv_node = ngraphQuantize(conv_node, output_sc, output_zp);

20
modules/dnn/src/int8layers/elementwise_layers.cpp
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@ -253,26 +253,26 @@ public:
input = ngraphDequantize(input, input_sc, input_zp);
ngraph::Output<ngraph::Node> res;
ov::Output<ov::Node> res;
if (type == "ReLU6Int8") {
res = std::make_shared<ngraph::op::Clamp>(input, 0.0f, 6.0f);
res = std::make_shared<ov::op::v0::Clamp>(input, 0.0f, 6.0f);
} else if (type == "ReLUInt8") {
if (slope) {
auto param = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{1}, &slope);
res = std::make_shared<ngraph::op::PRelu>(input, param);
auto param = std::make_shared<ov::op::v0::Constant>(ov::element::f32, ov::Shape{1}, &slope);
res = std::make_shared<ov::op::v0::PRelu>(input, param);
} else {
res = std::make_shared<ngraph::op::Relu>(input);
res = std::make_shared<ov::op::v0::Relu>(input);
}
} else if (type == "ELUInt8") {
res = std::make_shared<ngraph::op::Elu>(input, 1.0f);
res = std::make_shared<ov::op::v0::Elu>(input, 1.0f);
} else if (type == "MishInt8") {
res = std::make_shared<ngraph::op::v4::Mish>(input);
res = std::make_shared<ov::op::v4::Mish>(input);
} else if (type == "HardSwishInt8") {
res = std::make_shared<ngraph::op::v4::HSwish>(input);
res = std::make_shared<ov::op::v4::HSwish>(input);
} else if (type == "AbsValInt8") {
res = std::make_shared<ngraph::op::Abs>(input);
res = std::make_shared<ov::op::v0::Abs>(input);
} else if (type == "SigmoidInt8") {
res = std::make_shared<ngraph::op::Sigmoid>(input);
res = std::make_shared<ov::op::v0::Sigmoid>(input);
} else {
CV_Error(Error::StsNotImplemented, type + " activation with OpenVINO");
}

8
modules/dnn/src/int8layers/eltwise_layer.cpp
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@ -375,7 +375,7 @@ public:
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
CV_Assert(nodes.size() >= 2);
std::vector<ngraph::Output<ngraph::Node>> ieInpNodes(nodes.size());
std::vector<ov::Output<ov::Node>> ieInpNodes(nodes.size());
for (size_t i = 0; i < nodes.size(); i++)
{
ieInpNodes[i] = nodes[i].dynamicCast<InfEngineNgraphNode>()->node;
@ -389,9 +389,9 @@ public:
for (size_t i = 1; i < ieInpNodes.size(); i++)
{
switch (op) {
case SUM: res = std::make_shared<ngraph::op::v1::Add>(res, ieInpNodes[i]); break;
case PROD: res = std::make_shared<ngraph::op::v1::Multiply>(res, ieInpNodes[i]); break;
case MAX: res = std::make_shared<ngraph::op::v1::Maximum>(res, ieInpNodes[i]); break;
case SUM: res = std::make_shared<ov::op::v1::Add>(res, ieInpNodes[i]); break;
case PROD: res = std::make_shared<ov::op::v1::Multiply>(res, ieInpNodes[i]); break;
case MAX: res = std::make_shared<ov::op::v1::Maximum>(res, ieInpNodes[i]); break;
default: CV_Error(Error::StsNotImplemented, "Unsupported eltwise operation");
}
}

32
modules/dnn/src/int8layers/fully_connected_layer.cpp
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@ -410,8 +410,8 @@ public:
CV_CheckTypeEQ(blobs[1].type(), CV_32S, ""); // bias
CV_CheckTypeEQ(outputMultiplier.type(), CV_32F, "");
ngraph::Output<ngraph::Node> input = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
ngraph::Output<ngraph::Node> ieWeights, ieBias, matmul;
ov::Output<ov::Node> input = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
ov::Output<ov::Node> 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<InfEngineNgraphNode>()->node;
// matmul = std::make_shared<ngraph::op::MatMul>(ieInpNode, inp2, transA, transB);
// matmul = std::make_shared<ov::op::v0::MatMul>(ieInpNode, inp2, transA, transB);
}
else
{
std::vector<int> shape(1 + normalize_axis(axis, input.get_shape().size()), 0);
shape[shape.size() - 1] = -1;
input = std::make_shared<ngraph::op::v1::Reshape>(
input = std::make_shared<ov::op::v1::Reshape>(
input,
std::make_shared<ngraph::op::Constant>(ngraph::element::i32, ngraph::Shape{shape.size()}, shape.data()),
std::make_shared<ov::op::v0::Constant>(ov::element::i32, ov::Shape{shape.size()}, shape.data()),
true
);
@ -444,16 +444,16 @@ public:
}
std::vector<size_t> weight_shape{(size_t)blobs[0].size[0], (size_t)blobs[0].size[1]};
ieWeights = std::make_shared<ngraph::op::Constant>(ngraph::element::i8, weight_shape, blobs[0].data);
ieWeights = std::make_shared<ngraph::op::Convert>(ieWeights, ngraph::element::f32);
ieWeights = std::make_shared<ngraph::op::FakeQuantize>(ieWeights,
std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{numOutput, 1}, inpLows.data()),
std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{numOutput, 1}, inpHighs.data()),
std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{numOutput, 1}, outLows.data()),
std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{numOutput, 1}, outHighs.data()),
ieWeights = std::make_shared<ov::op::v0::Constant>(ov::element::i8, weight_shape, blobs[0].data);
ieWeights = std::make_shared<ov::op::v0::Convert>(ieWeights, ov::element::f32);
ieWeights = std::make_shared<ov::op::v0::FakeQuantize>(ieWeights,
std::make_shared<ov::op::v0::Constant>(ov::element::f32, ov::Shape{numOutput, 1}, inpLows.data()),
std::make_shared<ov::op::v0::Constant>(ov::element::f32, ov::Shape{numOutput, 1}, inpHighs.data()),
std::make_shared<ov::op::v0::Constant>(ov::element::f32, ov::Shape{numOutput, 1}, outLows.data()),
std::make_shared<ov::op::v0::Constant>(ov::element::f32, ov::Shape{numOutput, 1}, outHighs.data()),
256 // levels
);
matmul = std::make_shared<ngraph::op::MatMul>(input, ieWeights, transA, transB);
matmul = std::make_shared<ov::op::v0::MatMul>(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<float>()[i] * output_sc;
}
auto bias_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32,
ngraph::Shape{blobs[1].total()}, ovBias.data());
matmul = std::make_shared<ngraph::op::v1::Add>(matmul, bias_node);
auto bias_node = std::make_shared<ov::op::v0::Constant>(ov::element::f32,
ov::Shape{blobs[1].total()}, ovBias.data());
matmul = std::make_shared<ov::op::v1::Add>(matmul, bias_node);
}
matmul = ngraphQuantize(matmul, output_sc, output_zp);

23
modules/dnn/src/int8layers/pooling_layer.cpp
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@ -11,6 +11,7 @@
#include <float.h>
#include <algorithm>
#include <numeric>
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<ngraph::Node> pool;
auto rounding_type = ceilMode ? ov::op::RoundingType::CEIL : ov::op::RoundingType::FLOOR;
ov::Output<ov::Node> pool;
if (type == MAX) {
pool = std::make_shared<ngraph::op::v1::MaxPool>(input, ngraph::Strides(strides),
ngraph::Shape(pads_begin), ngraph::Shape(pads_end), ngraph::Shape(kernel_size),
pool = std::make_shared<ov::op::v1::MaxPool>(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<ngraph::op::v1::AvgPool>(input, ngraph::Strides(strides),
ngraph::Shape(pads_begin), ngraph::Shape(pads_end), ngraph::Shape(kernel_size),
pool = std::make_shared<ov::op::v1::AvgPool>(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<int64_t> 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::op::Constant>(ngraph::element::i64, ngraph::Shape{axes.size()}, axes);
pool = std::make_shared<ngraph::op::v1::ReduceSum>(input, reduction_axes, true);
auto reduction_axes = std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{axes.size()}, axes);
pool = std::make_shared<ov::op::v1::ReduceSum>(input, reduction_axes, true);
} else {
CV_Error(Error::StsNotImplemented, format("INT8 Pooling type: %d", type));
}

14
modules/dnn/src/int8layers/scale_layer.cpp
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@ -191,7 +191,7 @@ public:
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs, const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
std::vector<ngraph::Output<ngraph::Node>> ieInpNodes(nodes.size());
std::vector<ov::Output<ov::Node>> ieInpNodes(nodes.size());
for (int i = 0; i < nodes.size(); ++i) {
ieInpNodes[i] = nodes[i].dynamicCast<InfEngineNgraphNode>()->node;
}
@ -200,7 +200,7 @@ public:
CV_Assert(!blobs.empty() || ieInpNodes.size() == 1 + (int)hasWeights + (int)hasBias);
ngraph::Output<ngraph::Node> weights, bias;
ov::Output<ov::Node> weights, bias;
if (blobs.empty()) {
if (hasWeights)
weights = ieInpNodes[1];
@ -222,17 +222,17 @@ public:
}
if (hasWeights)
weights = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, shape, blobs[0].data);
weights = std::make_shared<ov::op::v0::Constant>(ov::element::f32, shape, blobs[0].data);
if (hasBias)
bias = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, shape, blobs[(int)hasWeights].data);
bias = std::make_shared<ov::op::v0::Constant>(ov::element::f32, shape, blobs[(int)hasWeights].data);
}
ngraph::Output<ngraph::Node> res = ieInpNodes[0];
ov::Output<ov::Node> res = ieInpNodes[0];
if (hasWeights) {
res = std::make_shared<ngraph::op::v1::Multiply>(res, weights);
res = std::make_shared<ov::op::v1::Multiply>(res, weights);
}
if (hasBias) {
res = std::make_shared<ngraph::op::v1::Add>(res, bias);
res = std::make_shared<ov::op::v1::Add>(res, bias);
}
res = ngraphQuantize(res, output_sc, output_zp);

6
modules/dnn/src/int8layers/softmax_layer.cpp
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@ -204,11 +204,11 @@ public:
input = ngraphDequantize(input, input_sc, input_zp);
ngraph::Output<ngraph::Node> res;
ov::Output<ov::Node> res;
if (logSoftMax) {
res = std::make_shared<ngraph::op::v5::LogSoftmax>(input, axis);
res = std::make_shared<ov::op::v5::LogSoftmax>(input, axis);
} else {
res = std::make_shared<ngraph::op::v1::Softmax>(input, axis);
res = std::make_shared<ov::op::v1::Softmax>(input, axis);
}
res = ngraphQuantize(res, output_sc, output_zp);

14
modules/dnn/src/layers/batch_norm_layer.cpp
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@ -18,8 +18,6 @@ Implementation of Batch Normalization layer.
#include "../op_webnn.hpp"
#include "../op_cann.hpp"
#include <opencv2/dnn/shape_utils.hpp>
#ifdef HAVE_OPENCL
#include "opencl_kernels_dnn.hpp"
#endif
@ -459,14 +457,10 @@ public:
auto ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
std::vector<size_t> shape(ieInpNode.get_shape().size(), 1);
shape[1] = weights_.total();
auto weight = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape(shape), weights_.data);
auto bias = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape(shape), bias_.data);
#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2021_2)
auto scale_node = std::make_shared<ngraph::op::v1::Multiply>(ieInpNode, weight, ngraph::op::AutoBroadcastType::NUMPY);
#else
auto scale_node = std::make_shared<ngraph::op::v0::Multiply>(ieInpNode, weight, ngraph::op::AutoBroadcastType::NUMPY);
#endif
auto scale_shift = std::make_shared<ngraph::op::v1::Add>(scale_node, bias, ngraph::op::AutoBroadcastType::NUMPY);
auto weight = std::make_shared<ov::op::v0::Constant>(ov::element::f32, ov::Shape(shape), weights_.data);
auto bias = std::make_shared<ov::op::v0::Constant>(ov::element::f32, ov::Shape(shape), bias_.data);
auto scale_node = std::make_shared<ov::op::v1::Multiply>(ieInpNode, weight, ov::op::AutoBroadcastType::NUMPY);
auto scale_shift = std::make_shared<ov::op::v1::Add>(scale_node, bias, ov::op::AutoBroadcastType::NUMPY);
return Ptr<BackendNode>(new InfEngineNgraphNode(scale_shift));
}
#endif // HAVE_DNN_NGRAPH

4
modules/dnn/src/layers/blank_layer.cpp
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@ -149,8 +149,8 @@ public:
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
auto ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
ngraph::OutputVector inp{ieInpNode};
auto blank = std::make_shared<ngraph::op::Concat>(inp, 0);
ov::OutputVector inp{ieInpNode};
auto blank = std::make_shared<ov::op::v0::Concat>(inp, 0);
return Ptr<BackendNode>(new InfEngineNgraphNode(blank));
}
#endif // HAVE_DNN_NGRAPH

13
modules/dnn/src/layers/concat_layer.cpp
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@ -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<size_t> 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<InfEngineNgraphNode>()->node;
@ -423,14 +422,14 @@ public:
}
if (needPadding)
{
inp_nodes[i] = std::make_shared<ngraph::op::v1::Pad>(
inp_nodes[i] = std::make_shared<ov::op::v1::Pad>(
inp_nodes[i],
std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{begins.size()}, begins.data()),
std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{ends.size()}, ends.data()),
ngraph::op::PadMode::CONSTANT);
std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{begins.size()}, begins.data()),
std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{ends.size()}, ends.data()),
ov::op::PadMode::CONSTANT);
}
}
auto concat = std::make_shared<ngraph::op::Concat>(inp_nodes, cAxis);
auto concat = std::make_shared<ov::op::v0::Concat>(inp_nodes, cAxis);
return Ptr<BackendNode>(new InfEngineNgraphNode(concat));
}
#endif // HAVE_DNN_NGRAPH

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

@ -128,22 +128,22 @@ public:
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
const std::vector<Ptr<BackendNode> >& 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<ngraph::Node> node =
std::make_shared<ngraph::op::Constant>(dType,
std::shared_ptr<ov::Node> node =
std::make_shared<ov::op::v0::Constant>(dType,
getShape<size_t>(blobs[0]),
blobs[0].data);
if (node->get_element_type() != ngraph::element::f32) {
node = std::make_shared<ngraph::op::Convert>(node, ngraph::element::f32);
if (node->get_element_type() != ov::element::f32) {
node = std::make_shared<ov::op::v0::Convert>(node, ov::element::f32);
}
return Ptr<BackendNode>(new InfEngineNgraphNode(node));
}

74
modules/dnn/src/layers/convolution_layer.cpp
Unescape View File

@ -822,7 +822,7 @@ public:
auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
std::vector<size_t> dims = ieInpNode.get_shape();
CV_Check(dims.size(), dims.size() >= 3 && dims.size() <= 5, "");
ngraph::Output<ngraph::Node> ieWeights;
ov::Output<ov::Node> ieWeights;
if (nodes.size() > 1)
ieWeights = nodes[1].dynamicCast<InfEngineNgraphNode>()->node;
const int inpCn = dims[1];
@ -840,49 +840,49 @@ public:
if (nodes.size() == 1)
{
ieWeights = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, kernel_shape, blobs[0].data);
ieWeights = std::make_shared<ov::op::v0::Constant>(ov::element::f32, kernel_shape, blobs[0].data);
if (fusedWeights)
{
if (weightsMat.isContinuous())
{
ieWeights = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, kernel_shape, weightsMat.data);
ieWeights = std::make_shared<ov::op::v0::Constant>(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::op::Constant>(ngraph::element::f32, kernel_shape, newWeights.data);
ieWeights = std::make_shared<ov::op::v0::Constant>(ov::element::f32, kernel_shape, newWeights.data);
}
}
}
else
{
auto shape = std::make_shared<ngraph::op::Constant>(ngraph::element::i64,
ngraph::Shape{kernel_shape.size()}, std::vector<int64_t>(kernel_shape.begin(), kernel_shape.end()));
ieWeights = std::make_shared<ngraph::op::v1::Reshape>(ieWeights, shape, true);
auto shape = std::make_shared<ov::op::v0::Constant>(ov::element::i64,
ov::Shape{kernel_shape.size()}, std::vector<int64_t>(kernel_shape.begin(), kernel_shape.end()));
ieWeights = std::make_shared<ov::op::v1::Reshape>(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<ngraph::Node> conv_node;
std::shared_ptr<ov::Node> conv_node;
if (group != 1) {
conv_node = std::make_shared<ngraph::op::v1::GroupConvolution>(
conv_node = std::make_shared<ov::op::v1::GroupConvolution>(
ieInpNode, ieWeights,
ngraph::Strides(strides),
ngraph::CoordinateDiff(std::vector<std::ptrdiff_t>(pads_begin.begin(), pads_begin.end())),
ngraph::CoordinateDiff(std::vector<std::ptrdiff_t>(pads_end.begin(), pads_end.end())),
ngraph::Strides(dilations),
ov::Strides(strides),
ov::CoordinateDiff(std::vector<std::ptrdiff_t>(pads_begin.begin(), pads_begin.end())),
ov::CoordinateDiff(std::vector<std::ptrdiff_t>(pads_end.begin(), pads_end.end())),
ov::Strides(dilations),
pad_type);
} else {
conv_node = std::make_shared<ngraph::op::v1::Convolution>(
conv_node = std::make_shared<ov::op::v1::Convolution>(
ieInpNode, ieWeights,
ngraph::Strides(strides),
ngraph::CoordinateDiff(std::vector<std::ptrdiff_t>(pads_begin.begin(), pads_begin.end())),
ngraph::CoordinateDiff(std::vector<std::ptrdiff_t>(pads_end.begin(), pads_end.end())),
ngraph::Strides(dilations),
ov::Strides(strides),
ov::CoordinateDiff(std::vector<std::ptrdiff_t>(pads_begin.begin(), pads_begin.end())),
ov::CoordinateDiff(std::vector<std::ptrdiff_t>(pads_end.begin(), pads_end.end())),
ov::Strides(dilations),
pad_type);
}
@ -890,18 +890,18 @@ public:
{
std::vector<size_t> shape(conv_node->get_shape().size(), 1);
shape[1] = conv_node->get_shape()[1];
std::shared_ptr<ngraph::Node> bias;
std::shared_ptr<ov::Node> bias;
if (nodes.size() == 3)
{
auto bias_shape = std::make_shared<ngraph::op::Constant>(ngraph::element::i64,
ngraph::Shape{shape.size()}, std::vector<int64_t>(shape.begin(), shape.end()));
bias = std::make_shared<ngraph::op::v1::Reshape>(nodes[2].dynamicCast<InfEngineNgraphNode>()->node, bias_shape, true);
auto bias_shape = std::make_shared<ov::op::v0::Constant>(ov::element::i64,
ov::Shape{shape.size()}, std::vector<int64_t>(shape.begin(), shape.end()));
bias = std::make_shared<ov::op::v1::Reshape>(nodes[2].dynamicCast<InfEngineNgraphNode>()->node, bias_shape, true);
}
else
{
bias = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape(shape), biasvec.data());
bias = std::make_shared<ov::op::v0::Constant>(ov::element::f32, ov::Shape(shape), biasvec.data());
}
auto conv_bias = std::make_shared<ngraph::op::v1::Add>(conv_node, bias, ngraph::op::AutoBroadcastType::NUMPY);
auto conv_bias = std::make_shared<ov::op::v1::Add>(conv_node, bias, ov::op::AutoBroadcastType::NUMPY);
return Ptr<BackendNode>(new InfEngineNgraphNode(conv_bias));
}
return Ptr<BackendNode>(new InfEngineNgraphNode(conv_node));
@ -2252,13 +2252,13 @@ public:
auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
std::vector<size_t> kernel_shape = getShape<size_t>(blobs[0]);
auto ieWeights = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, kernel_shape, blobs[0].data);
auto ieWeights = std::make_shared<ov::op::v0::Constant>(ov::element::f32, kernel_shape, blobs[0].data);
if (fusedWeights)
{
Mat newWeights;
transpose(weightsMat, newWeights);
ieWeights = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, kernel_shape, newWeights.data);
ieWeights = std::make_shared<ov::op::v0::Constant>(ov::element::f32, kernel_shape, newWeights.data);
}
std::vector<size_t> 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<ngraph::op::v1::ConvolutionBackpropData>(
auto deconv = std::make_shared<ov::op::v1::ConvolutionBackpropData>(
ieInpNode,
ieWeights,
ngraph::Strides(strides),
ngraph::CoordinateDiff(std::vector<std::ptrdiff_t>(pads_begin.begin(), pads_begin.end())),
ngraph::CoordinateDiff(std::vector<std::ptrdiff_t>(paddings_end.begin(), paddings_end.end())),
ngraph::Strides(dilations),
ov::Strides(strides),
ov::CoordinateDiff(std::vector<std::ptrdiff_t>(pads_begin.begin(), pads_begin.end())),
ov::CoordinateDiff(std::vector<std::ptrdiff_t>(paddings_end.begin(), paddings_end.end())),
ov::Strides(dilations),
pad_type,
ngraph::CoordinateDiff(std::vector<std::ptrdiff_t>(adjust_pads.begin(), adjust_pads.end())));
ov::CoordinateDiff(std::vector<std::ptrdiff_t>(adjust_pads.begin(), adjust_pads.end())));
if (hasBias() || fusedBias)
{
std::vector<size_t> shape(deconv->get_shape().size(), 1);
shape[1] = numOutput;
auto bias = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape(shape), blobs[1].data);
auto deconv_bias = std::make_shared<ngraph::op::v1::Add>(deconv, bias, ngraph::op::AutoBroadcastType::NUMPY);
auto bias = std::make_shared<ov::op::v0::Constant>(ov::element::f32, ov::Shape(shape), blobs[1].data);
auto deconv_bias = std::make_shared<ov::op::v1::Add>(deconv, bias, ov::op::AutoBroadcastType::NUMPY);
return Ptr<BackendNode>(new InfEngineNgraphNode(deconv_bias));
}

22
modules/dnn/src/layers/crop_and_resize_layer.cpp
Unescape View File

@ -138,23 +138,23 @@ public:
offsets[3] = 2;
dims[3] = 7;
auto lower_bounds = std::make_shared<ngraph::op::Constant>(ngraph::element::i64,
ngraph::Shape{offsets.size()}, offsets.data());
auto upper_bounds = std::make_shared<ngraph::op::Constant>(ngraph::element::i64,
ngraph::Shape{dims.size()}, dims.data());
auto strides = std::make_shared<ngraph::op::Constant>(ngraph::element::i64,
ngraph::Shape{dims.size()}, std::vector<int64_t>((int64_t)dims.size(), 1));
auto slice = std::make_shared<ngraph::op::v1::StridedSlice>(rois,
auto lower_bounds = std::make_shared<ov::op::v0::Constant>(ov::element::i64,
ov::Shape{offsets.size()}, offsets.data());
auto upper_bounds = std::make_shared<ov::op::v0::Constant>(ov::element::i64,
ov::Shape{dims.size()}, dims.data());
auto strides = std::make_shared<ov::op::v0::Constant>(ov::element::i64,
ov::Shape{dims.size()}, std::vector<int64_t>((int64_t)dims.size(), 1));
auto slice = std::make_shared<ov::op::v1::StridedSlice>(rois,
lower_bounds, upper_bounds, strides, std::vector<int64_t>{}, std::vector<int64_t>{});
// Reshape rois from 4D to 2D
std::vector<int64_t> shapeData = {dims[2], 5};
auto shape = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{2}, shapeData.data());
auto reshape = std::make_shared<ngraph::op::v1::Reshape>(slice, shape, true);
auto shape = std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{2}, shapeData.data());
auto reshape = std::make_shared<ov::op::v1::Reshape>(slice, shape, true);
auto roiPooling =
std::make_shared<ngraph::op::v0::ROIPooling>(input, reshape,
ngraph::Shape{(size_t)outHeight, (size_t)outWidth},
std::make_shared<ov::op::v0::ROIPooling>(input, reshape,
ov::Shape{(size_t)outHeight, (size_t)outWidth},
1.0f, "bilinear");
return Ptr<BackendNode>(new InfEngineNgraphNode(roiPooling));

30
modules/dnn/src/layers/detection_output_layer.cpp
Unescape View File

@ -55,11 +55,7 @@
#ifdef HAVE_DNN_NGRAPH
#include "../ie_ngraph.hpp"
#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2020_4)
#include <ngraph/op/detection_output.hpp>
#else
#include <ngraph/op/experimental/layers/detection_output.hpp>
#endif
#include <openvino/op/detection_output.hpp>
#endif
#ifdef HAVE_CUDA
@ -1012,26 +1008,26 @@ public:
if (_locPredTransposed) {
// Convert box predictions from yxYX to xyXY
box_logits = std::make_shared<ngraph::op::v1::Reshape>(box_logits,
std::make_shared<ngraph::op::Constant>(ngraph::element::i32, ngraph::Shape{3}, std::vector<int32_t>{0, -1, 2}),
box_logits = std::make_shared<ov::op::v1::Reshape>(box_logits,
std::make_shared<ov::op::v0::Constant>(ov::element::i32, ov::Shape{3}, std::vector<int32_t>{0, -1, 2}),
true
);
int axis = 2;
box_logits = std::make_shared<ngraph::op::v1::Reverse>(box_logits,
std::make_shared<ngraph::op::Constant>(ngraph::element::i32, ngraph::Shape{1}, &axis),
ngraph::op::v1::Reverse::Mode::INDEX
box_logits = std::make_shared<ov::op::v1::Reverse>(box_logits,
std::make_shared<ov::op::v0::Constant>(ov::element::i32, ov::Shape{1}, &axis),
ov::op::v1::Reverse::Mode::INDEX
);
}
auto shape = std::make_shared<ngraph::op::Constant>(ngraph::element::i32, ngraph::Shape{2}, std::vector<int32_t>{0, -1});
box_logits = std::make_shared<ngraph::op::v1::Reshape>(box_logits, shape, true);
class_preds = std::make_shared<ngraph::op::v1::Reshape>(class_preds, shape, true);
proposals = std::make_shared<ngraph::op::v1::Reshape>(proposals,
std::make_shared<ngraph::op::Constant>(ngraph::element::i32, ngraph::Shape{3}, std::vector<int32_t>{0, _varianceEncodedInTarget ? 1 : 2, -1}),
auto shape = std::make_shared<ov::op::v0::Constant>(ov::element::i32, ov::Shape{2}, std::vector<int32_t>{0, -1});
box_logits = std::make_shared<ov::op::v1::Reshape>(box_logits, shape, true);
class_preds = std::make_shared<ov::op::v1::Reshape>(class_preds, shape, true);
proposals = std::make_shared<ov::op::v1::Reshape>(proposals,
std::make_shared<ov::op::v0::Constant>(ov::element::i32, ov::Shape{3}, std::vector<int32_t>{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<ngraph::op::DetectionOutput>(box_logits, class_preds,
auto det_out = std::make_shared<ov::op::v0::DetectionOutput>(box_logits, class_preds,
proposals, attrs);
return Ptr<BackendNode>(new InfEngineNgraphNode(det_out));
}

92
modules/dnn/src/layers/elementwise_layers.cpp
Unescape View File

@ -490,13 +490,13 @@ struct ReLUFunctor : public BaseFunctor
#endif
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const ngraph::Output<ngraph::Node>& node)
std::shared_ptr<ov::Node> initNgraphAPI(const ov::Output<ov::Node>& node)
{
if (slope) {
auto param = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{1}, &slope);
return std::make_shared<ngraph::op::PRelu>(node, param);
auto param = std::make_shared<ov::op::v0::Constant>(ov::element::f32, ov::Shape{1}, &slope);
return std::make_shared<ov::op::v0::PRelu>(node, param);
}
return std::make_shared<ngraph::op::Relu>(node);
return std::make_shared<ov::op::v0::Relu>(node);
}
#endif // HAVE_DNN_NGRAPH
@ -674,9 +674,9 @@ struct ReLU6Functor : public BaseFunctor
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const ngraph::Output<ngraph::Node>& node)
std::shared_ptr<ov::Node> initNgraphAPI(const ov::Output<ov::Node>& node)
{
return std::make_shared<ngraph::op::Clamp>(node, minValue, maxValue);
return std::make_shared<ov::op::v0::Clamp>(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<ngraph::Node> initNgraphAPI(const ngraph::Output<ngraph::Node>& node)
std::shared_ptr<ov::Node> initNgraphAPI(const ov::Output<ov::Node>& node)
{
CV_Error(Error::StsNotImplemented, "");
}
@ -929,9 +929,9 @@ struct TanHFunctor : public BaseDefaultFunctor<TanHFunctor>
#endif // HAVE_CANN
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const ngraph::Output<ngraph::Node>& node)
std::shared_ptr<ov::Node> initNgraphAPI(const ov::Output<ov::Node>& node)
{
return std::make_shared<ngraph::op::Tanh>(node);
return std::make_shared<ov::op::v0::Tanh>(node);
}
#endif // HAVE_DNN_NGRAPH
@ -998,10 +998,10 @@ struct SwishFunctor : public BaseDefaultFunctor<SwishFunctor>
#endif // HAVE_CANN
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const ngraph::Output<ngraph::Node>& node)
std::shared_ptr<ov::Node> initNgraphAPI(const ov::Output<ov::Node>& node)
{
auto sigmoid = std::make_shared<ngraph::op::Sigmoid>(node);
return std::make_shared<ngraph::op::v1::Multiply>(node, sigmoid);
auto sigmoid = std::make_shared<ov::op::v0::Sigmoid>(node);
return std::make_shared<ov::op::v1::Multiply>(node, sigmoid);
}
#endif // HAVE_DNN_NGRAPH
@ -1074,9 +1074,9 @@ struct MishFunctor : public BaseDefaultFunctor<MishFunctor>
#endif // HAVE_CANN
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const ngraph::Output<ngraph::Node>& node)
std::shared_ptr<ov::Node> initNgraphAPI(const ov::Output<ov::Node>& node)
{
return std::make_shared<ngraph::op::v4::Mish>(node);
return std::make_shared<ov::op::v4::Mish>(node);
}
#endif // HAVE_DNN_NGRAPH
@ -1151,9 +1151,9 @@ struct SigmoidFunctor : public BaseDefaultFunctor<SigmoidFunctor>
#endif // HAVE_CANN
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const ngraph::Output<ngraph::Node>& node)
std::shared_ptr<ov::Node> initNgraphAPI(const ov::Output<ov::Node>& node)
{
return std::make_shared<ngraph::op::Sigmoid>(node);
return std::make_shared<ov::op::v0::Sigmoid>(node);
}
#endif // HAVE_DNN_NGRAPH
@ -1231,9 +1231,9 @@ struct ELUFunctor : public BaseDefaultFunctor<ELUFunctor>
#endif // HAVE_CANN
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const ngraph::Output<ngraph::Node>& node)
std::shared_ptr<ov::Node> initNgraphAPI(const ov::Output<ov::Node>& node)
{
return std::make_shared<ngraph::op::Elu>(node, alpha);
return std::make_shared<ov::op::v0::Elu>(node, alpha);
}
#endif // HAVE_DNN_NGRAPH
@ -1301,9 +1301,9 @@ struct AbsValFunctor : public BaseDefaultFunctor<AbsValFunctor>
#endif // HAVE_CANN
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const ngraph::Output<ngraph::Node>& node)
std::shared_ptr<ov::Node> initNgraphAPI(const ov::Output<ov::Node>& node)
{
return std::make_shared<ngraph::op::Abs>(node);
return std::make_shared<ov::op::v0::Abs>(node);
}
#endif // HAVE_DNN_NGRAPH
@ -1594,9 +1594,9 @@ struct SqrtFunctor : public BaseDefaultFunctor<SqrtFunctor>
#endif // HAVE_HALIDE
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const ngraph::Output<ngraph::Node>& node)
std::shared_ptr<ov::Node> initNgraphAPI(const ov::Output<ov::Node>& node)
{
return std::make_shared<ngraph::op::v0::Sqrt>(node);
return std::make_shared<ov::op::v0::Sqrt>(node);
}
#endif // HAVE_DNN_NGRAPH
@ -2343,22 +2343,22 @@ struct PowerFunctor : public BaseFunctor
#endif // HAVE_CANN
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const ngraph::Output<ngraph::Node>& node)
std::shared_ptr<ov::Node> initNgraphAPI(const ov::Output<ov::Node>& node)
{
auto scale_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32,
ngraph::Shape{1}, &scale);
auto shift_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32,
ngraph::Shape{1}, &shift);
auto scale_node = std::make_shared<ov::op::v0::Constant>(ov::element::f32,
ov::Shape{1}, &scale);
auto shift_node = std::make_shared<ov::op::v0::Constant>(ov::element::f32,
ov::Shape{1}, &shift);
auto mul = std::make_shared<ngraph::op::v1::Multiply>(scale_node, node, ngraph::op::AutoBroadcastType::NUMPY);
auto scale_shift = std::make_shared<ngraph::op::v1::Add>(mul, shift_node, ngraph::op::AutoBroadcastType::NUMPY);
auto mul = std::make_shared<ov::op::v1::Multiply>(scale_node, node, ov::op::AutoBroadcastType::NUMPY);
auto scale_shift = std::make_shared<ov::op::v1::Add>(mul, shift_node, ov::op::AutoBroadcastType::NUMPY);
if (power == 1)
return scale_shift;
auto power_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32,
ngraph::Shape{1}, &power);
return std::make_shared<ngraph::op::v1::Power>(scale_shift, power_node, ngraph::op::AutoBroadcastType::NUMPY);
auto power_node = std::make_shared<ov::op::v0::Constant>(ov::element::f32,
ov::Shape{1}, &power);
return std::make_shared<ov::op::v1::Power>(scale_shift, power_node, ov::op::AutoBroadcastType::NUMPY);
}
#endif // HAVE_DNN_NGRAPH
@ -2453,15 +2453,15 @@ struct ExpFunctor : public BaseDefaultFunctor<ExpFunctor>
#endif // HAVE_HALIDE
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const ngraph::Output<ngraph::Node>& node)
std::shared_ptr<ov::Node> initNgraphAPI(const ov::Output<ov::Node>& node)
{
auto scale_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32,
ngraph::Shape{1}, &normScale);
auto shift_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32,
ngraph::Shape{1}, &normShift);
auto mul = std::make_shared<ngraph::op::v1::Multiply>(scale_node, node, ngraph::op::AutoBroadcastType::NUMPY);
auto scale_shift = std::make_shared<ngraph::op::v1::Add>(mul, shift_node, ngraph::op::AutoBroadcastType::NUMPY);
return std::make_shared<ngraph::op::v0::Exp>(scale_shift);
auto scale_node = std::make_shared<ov::op::v0::Constant>(ov::element::f32,
ov::Shape{1}, &normScale);
auto shift_node = std::make_shared<ov::op::v0::Constant>(ov::element::f32,
ov::Shape{1}, &normShift);
auto mul = std::make_shared<ov::op::v1::Multiply>(scale_node, node, ov::op::AutoBroadcastType::NUMPY);
auto scale_shift = std::make_shared<ov::op::v1::Add>(mul, shift_node, ov::op::AutoBroadcastType::NUMPY);
return std::make_shared<ov::op::v0::Exp>(scale_shift);
}
#endif // HAVE_DNN_NGRAPH
@ -2612,11 +2612,11 @@ struct ChannelsPReLUFunctor : public BaseFunctor
#endif // HAVE_CANN
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const ngraph::Output<ngraph::Node>& node)
std::shared_ptr<ov::Node> initNgraphAPI(const ov::Output<ov::Node>& node)
{
const size_t numChannels = scale.total();
auto slope = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{numChannels}, scale.data);
return std::make_shared<ngraph::op::PRelu>(node, slope);
auto slope = std::make_shared<ov::op::v0::Constant>(ov::element::f32, ov::Shape{numChannels}, scale.data);
return std::make_shared<ov::op::v0::PRelu>(node, slope);
}
#endif // HAVE_DNN_NGRAPH
@ -2692,11 +2692,11 @@ struct PReLUFunctor : public ChannelsPReLUFunctor
}
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const ngraph::Output<ngraph::Node>& node)
std::shared_ptr<ov::Node> initNgraphAPI(const ov::Output<ov::Node>& node)
{
auto shape = getShape<size_t>(scale);
auto slope = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, shape, scale.ptr<float>());
return std::make_shared<ngraph::op::PRelu>(node, slope);
auto slope = std::make_shared<ov::op::v0::Constant>(ov::element::f32, shape, scale.ptr<float>());
return std::make_shared<ov::op::v0::PRelu>(node, slope);
}
#endif // HAVE_DNN_NGRAPH
};

20
modules/dnn/src/layers/eltwise_layer.cpp
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@ -899,24 +899,24 @@ public:
CV_Assert(nodes.size() >= 2);
auto curr_node = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
if (!coeffs.empty()) {
auto coeff = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{1}, &coeffs[0]);
curr_node = std::make_shared<ngraph::op::v1::Multiply>(curr_node, coeff, ngraph::op::AutoBroadcastType::NUMPY);
auto coeff = std::make_shared<ov::op::v0::Constant>(ov::element::f32, ov::Shape{1}, &coeffs[0]);
curr_node = std::make_shared<ov::op::v1::Multiply>(curr_node, coeff, ov::op::AutoBroadcastType::NUMPY);
}
std::shared_ptr<ngraph::Node> res;
std::shared_ptr<ov::Node> res;
for (size_t i = 1; i < nodes.size(); i++)
{
auto next_node = nodes[i].dynamicCast<InfEngineNgraphNode>()->node;
if (!coeffs.empty()) {
auto coeff = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{1}, &coeffs[i]);
next_node = std::make_shared<ngraph::op::v1::Multiply>(next_node, coeff, ngraph::op::AutoBroadcastType::NUMPY);
auto coeff = std::make_shared<ov::op::v0::Constant>(ov::element::f32, ov::Shape{1}, &coeffs[i]);
next_node = std::make_shared<ov::op::v1::Multiply>(next_node, coeff, ov::op::AutoBroadcastType::NUMPY);
}
switch (op) {
case SUM: res = std::make_shared<ngraph::op::v1::Add>(curr_node, next_node); break;
case PROD: res = std::make_shared<ngraph::op::v1::Multiply>(curr_node, next_node); break;
case DIV: res = std::make_shared<ngraph::op::v1::Divide>(curr_node, next_node); break;
case MAX: res = std::make_shared<ngraph::op::v1::Maximum>(curr_node, next_node); break;
case MIN: res = std::make_shared<ngraph::op::v1::Minimum>(curr_node, next_node); break;
case SUM: res = std::make_shared<ov::op::v1::Add>(curr_node, next_node); break;
case PROD: res = std::make_shared<ov::op::v1::Multiply>(curr_node, next_node); break;
case DIV: res = std::make_shared<ov::op::v1::Divide>(curr_node, next_node); break;
case MAX: res = std::make_shared<ov::op::v1::Maximum>(curr_node, next_node); break;
case MIN: res = std::make_shared<ov::op::v1::Minimum>(curr_node, next_node); break;
default: CV_Error(Error::StsNotImplemented, "Unsupported eltwise operation");
}
curr_node = res;

7
modules/dnn/src/layers/flatten_layer.cpp
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@ -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::op::Constant>(ngraph::element::i64,
ngraph::Shape({outputShapeVec.size()}), outputShapeVec.data());
auto reshape = std::make_shared<ngraph::op::v1::Reshape>(ieInpNode, shape, true);
auto shape = std::make_shared<ov::op::v0::Constant>(ov::element::i64,
ov::Shape({outputShapeVec.size()}), outputShapeVec.data());
auto reshape = std::make_shared<ov::op::v1::Reshape>(ieInpNode, shape, true);
return Ptr<BackendNode>(new InfEngineNgraphNode(reshape));
}
#endif // HAVE_DNN_NGRAPH

18
modules/dnn/src/layers/fully_connected_layer.cpp
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@ -787,20 +787,20 @@ public:
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
std::shared_ptr<ngraph::Node> matmul;
std::shared_ptr<ov::Node> matmul;
if (nodes.size() == 2)
{
auto& inp2 = nodes[1].dynamicCast<InfEngineNgraphNode>()->node;
matmul = std::make_shared<ngraph::op::MatMul>(ieInpNode, inp2, transA, transB);
matmul = std::make_shared<ov::op::v0::MatMul>(ieInpNode, inp2, transA, transB);
}
else
{
std::vector<int> shape(1 + normalize_axis(axis, ieInpNode.get_shape().size()), 0);
shape[shape.size() - 1] = -1;
auto inp = std::make_shared<ngraph::op::v1::Reshape>(
auto inp = std::make_shared<ov::op::v1::Reshape>(
ieInpNode,
std::make_shared<ngraph::op::Constant>(ngraph::element::i32, ngraph::Shape{shape.size()}, shape.data()),
std::make_shared<ov::op::v0::Constant>(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::op::Constant>(ngraph::element::f32, weight_shape, blobs[0].data);
matmul = std::make_shared<ngraph::op::MatMul>(inp, ieWeights, transA, transB);
auto ieWeights = std::make_shared<ov::op::v0::Constant>(ov::element::f32, weight_shape, blobs[0].data);
matmul = std::make_shared<ov::op::v0::MatMul>(inp, ieWeights, transA, transB);
}
if (bias) {
auto bias_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32,
ngraph::Shape{(size_t)blobs[1].size[1]}, blobs[1].data);
matmul = std::make_shared<ngraph::op::v1::Add>(matmul, bias_node, ngraph::op::AutoBroadcastType::NUMPY);
auto bias_node = std::make_shared<ov::op::v0::Constant>(ov::element::f32,
ov::Shape{(size_t)blobs[1].size[1]}, blobs[1].data);
matmul = std::make_shared<ov::op::v1::Add>(matmul, bias_node, ov::op::AutoBroadcastType::NUMPY);
}
return Ptr<BackendNode>(new InfEngineNgraphNode(matmul));
}

26
modules/dnn/src/layers/gemm_layer.cpp
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@ -285,45 +285,45 @@ public:
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
auto ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
std::shared_ptr<ngraph::Node> matmul;
std::shared_ptr<ov::Node> matmul;
if (nodes.size() == 2)
{
auto& inp2 = nodes[1].dynamicCast<InfEngineNgraphNode>()->node;
matmul = std::make_shared<ngraph::op::MatMul>(ieInpNode, inp2, trans_a, trans_b);
matmul = std::make_shared<ov::op::v0::MatMul>(ieInpNode, inp2, trans_a, trans_b);
}
else
{
std::shared_ptr<ngraph::Node> ieWeights = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, getShape(blobs[0]), blobs[0].data);
std::shared_ptr<ov::Node> ieWeights = std::make_shared<ov::op::v0::Constant>(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<int> shape(1 + flatten_axis, 0);
shape[shape.size() - 1] = -1;
ieInpNode = std::make_shared<ngraph::op::v1::Reshape>(
ieInpNode = std::make_shared<ov::op::v1::Reshape>(
ieInpNode,
std::make_shared<ngraph::op::Constant>(ngraph::element::i32, ngraph::Shape{shape.size()}, shape.data()),
std::make_shared<ov::op::v0::Constant>(ov::element::i32, ov::Shape{shape.size()}, shape.data()),
true
);
}
matmul = std::make_shared<ngraph::op::MatMul>(ieInpNode, ieWeights, trans_a, trans_b);
matmul = std::make_shared<ov::op::v0::MatMul>(ieInpNode, ieWeights, trans_a, trans_b);
}
if (alpha != 1.0f) {
matmul = std::make_shared<ngraph::op::v1::Multiply>(matmul,
std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{1}, &alpha)
matmul = std::make_shared<ov::op::v1::Multiply>(matmul,
std::make_shared<ov::op::v0::Constant>(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<ngraph::Node> bias_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, shape, bias.data);
auto shape = bias.total() == bias.size[0] ? ov::Shape{bias.total()} : getShape(bias);
std::shared_ptr<ov::Node> bias_node = std::make_shared<ov::op::v0::Constant>(ov::element::f32, shape, bias.data);
if (beta != 1.0f) {
bias_node = std::make_shared<ngraph::op::v1::Multiply>(bias_node,
std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{1}, &beta)
bias_node = std::make_shared<ov::op::v1::Multiply>(bias_node,
std::make_shared<ov::op::v0::Constant>(ov::element::f32, ov::Shape{1}, &beta)
);
}
matmul = std::make_shared<ngraph::op::v1::Add>(matmul, bias_node, ngraph::op::AutoBroadcastType::NUMPY);
matmul = std::make_shared<ov::op::v1::Add>(matmul, bias_node, ov::op::AutoBroadcastType::NUMPY);
}
return Ptr<BackendNode>(new InfEngineNgraphNode(matmul));
}

23
modules/dnn/src/layers/instance_norm_layer.cpp
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@ -225,33 +225,26 @@ public:
auto ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
const auto &input_shape = ieInpNode.get_shape();
std::shared_ptr<ngraph::Node> mvn, result;
std::shared_ptr<ov::Node> 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<ngraph::op::MVN>(ieInpNode, across_channels, normalize_variance, epsilon);
#else
// https://docs.openvino.ai/2023.1/openvino_docs_ops_normalization_MVN_6.html
std::vector<int64_t> 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::op::Constant>(ngraph::element::i64, ngraph::Shape{axes_v.size()}, axes_v.data());
auto axes = std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{axes_v.size()}, axes_v.data());
bool normalize_variance = true;
mvn = std::make_shared<ngraph::op::v6::MVN>(ieInpNode, axes, normalize_variance, epsilon, ngraph::op::MVNEpsMode::INSIDE_SQRT);
#endif
mvn = std::make_shared<ov::op::v6::MVN>(ieInpNode, axes, normalize_variance, epsilon, ov::op::MVNEpsMode::INSIDE_SQRT);
// instance norm = scale * mvn + bias
auto scale = nodes[1].dynamicCast<InfEngineNgraphNode>()->node;
std::vector<int64_t> shared_shape_v(input_shape.size(), 1);
shared_shape_v[1] = -1;
auto shared_shape = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{shared_shape_v.size()}, shared_shape_v.data());
scale = std::make_shared<ngraph::op::v1::Reshape>(scale, shared_shape, true);
result = std::make_shared<ngraph::op::v1::Multiply>(mvn, scale);
auto shared_shape = std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{shared_shape_v.size()}, shared_shape_v.data());
scale = std::make_shared<ov::op::v1::Reshape>(scale, shared_shape, true);
result = std::make_shared<ov::op::v1::Multiply>(mvn, scale);
auto bias = nodes[2].dynamicCast<InfEngineNgraphNode>()->node;
bias = std::make_shared<ngraph::op::v1::Reshape>(bias, shared_shape, true);
result = std::make_shared<ngraph::op::v1::Add>(result, bias);
bias = std::make_shared<ov::op::v1::Reshape>(bias, shared_shape, true);
result = std::make_shared<ov::op::v1::Add>(result, bias);
return Ptr<BackendNode>(new InfEngineNgraphNode(result));
}

25
modules/dnn/src/layers/layer_norm.cpp
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@ -263,42 +263,35 @@ public:
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE {
auto ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
const auto &input_shape = ieInpNode.get_shape();
std::shared_ptr<ngraph::Node> mvn, result;
std::shared_ptr<ov::Node> 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<ngraph::op::MVN>(ieInpNode, across_channels, normalize_variance, epsilon);
#else
// https://docs.openvino.ai/2023.1/openvino_docs_ops_normalization_MVN_6.html
std::vector<int64_t> axes_v(input_shape.size() - axis);
std::iota(axes_v.begin(), axes_v.end(), axis);
auto axes = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{axes_v.size()}, axes_v.data());
auto axes = std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{axes_v.size()}, axes_v.data());
bool normalize_variance = true;
mvn = std::make_shared<ngraph::op::v6::MVN>(ieInpNode, axes, normalize_variance, epsilon, ngraph::op::MVNEpsMode::INSIDE_SQRT);
#endif
mvn = std::make_shared<ov::op::v6::MVN>(ieInpNode, axes, normalize_variance, epsilon, ov::op::MVNEpsMode::INSIDE_SQRT);
// layer norm = scale * mvn + bias
auto scale = nodes[1].dynamicCast<InfEngineNgraphNode>()->node;
ngraph::Output<ngraph::Node> bias;
ov::Output<ov::Node> bias;
if (nodes.size() == 3) {
bias = nodes[2].dynamicCast<InfEngineNgraphNode>()->node;
}
if (axis == -1 || axis == input_shape.size() - 1) { // special case for 1D tensor (2D mat)
std::vector<int64_t> shared_shape_v(input_shape.size(), 1);
shared_shape_v.back() = -1;
auto shared_shape = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{shared_shape_v.size()}, shared_shape_v.data());
scale = std::make_shared<ngraph::op::v1::Reshape>(scale, shared_shape, true);
auto shared_shape = std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{shared_shape_v.size()}, shared_shape_v.data());
scale = std::make_shared<ov::op::v1::Reshape>(scale, shared_shape, true);
if (nodes.size() == 3) {
bias = std::make_shared<ngraph::op::v1::Reshape>(bias, shared_shape, true);
bias = std::make_shared<ov::op::v1::Reshape>(bias, shared_shape, true);
}
}
result = std::make_shared<ngraph::op::v1::Multiply>(mvn, scale);
result = std::make_shared<ov::op::v1::Multiply>(mvn, scale);
if (nodes.size() == 3) {
result = std::make_shared<ngraph::op::v1::Add>(result, bias);
result = std::make_shared<ov::op::v1::Add>(result, bias);
}
return Ptr<BackendNode>(new InfEngineNgraphNode(result));

4
modules/dnn/src/layers/lrn_layer.cpp
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@ -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::op::Constant>(ngraph::element::i64, ngraph::Shape{axes.size()}, axes.data());
auto lrn = std::make_shared<ngraph::op::LRN>(ieInpNode, ngraph_axes, alphaSize, beta, bias, size);
auto ngraph_axes = std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{axes.size()}, axes.data());
auto lrn = std::make_shared<ov::op::v0::LRN>(ieInpNode, ngraph_axes, alphaSize, beta, bias, size);
return Ptr<BackendNode>(new InfEngineNgraphNode(lrn));
}
#endif // HAVE_DNN_NGRAPH

8
modules/dnn/src/layers/matmul_layer.cpp
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@ -216,15 +216,15 @@ class MatMulLayerImpl CV_FINAL : public MatMulLayer {
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE {
auto& input_A_node = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
std::shared_ptr<ngraph::Node> matmul;
std::shared_ptr<ov::Node> matmul;
if (nodes.size() == 2) {
auto &input_B_node = nodes[1].dynamicCast<InfEngineNgraphNode>()->node;
matmul = std::make_shared<ngraph::op::MatMul>(input_A_node, input_B_node, trans_a, trans_b);
matmul = std::make_shared<ov::op::v0::MatMul>(input_A_node, input_B_node, trans_a, trans_b);
} else {
auto input_B_shape = getShape<size_t>(blobs[0]);
auto input_B_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, input_B_shape, blobs[0].data);
matmul = std::make_shared<ngraph::op::MatMul>(input_A_node, input_B_node, trans_a, trans_b);
auto input_B_node = std::make_shared<ov::op::v0::Constant>(ov::element::f32, input_B_shape, blobs[0].data);
matmul = std::make_shared<ov::op::v0::MatMul>(input_A_node, input_B_node, trans_a, trans_b);
}
return Ptr<BackendNode>(new InfEngineNgraphNode(matmul));

22
modules/dnn/src/layers/max_unpooling_layer.cpp
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@ -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::op::Constant>(ngraph::element::f32, ngraph::Shape{zeros.total()}, zeros.data);
auto zeroInp = std::make_shared<ov::op::v0::Constant>(ov::element::f32, ov::Shape{zeros.total()}, zeros.data);
int newShape = -1;
features = std::make_shared<ngraph::op::v1::Reshape>(
features = std::make_shared<ov::op::v1::Reshape>(
features,
std::make_shared<ngraph::op::Constant>(ngraph::element::i32, ngraph::Shape{1}, &newShape),
std::make_shared<ov::op::v0::Constant>(ov::element::i32, ov::Shape{1}, &newShape),
true
);
indices = std::make_shared<ngraph::op::v1::Reshape>(
indices = std::make_shared<ov::op::v1::Reshape>(
indices,
std::make_shared<ngraph::op::Constant>(ngraph::element::i32, ngraph::Shape{1}, &newShape),
std::make_shared<ov::op::v0::Constant>(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<ngraph::op::Convert>(indices, ngraph::element::i64);
if (indices.get_element_type() != ov::element::i32 && indices.get_element_type() != ov::element::i64) {
indices = std::make_shared<ov::op::v0::Convert>(indices, ov::element::i64);
}
int axis = 0;
std::shared_ptr<ngraph::Node> unpool = std::make_shared<ngraph::op::ScatterElementsUpdate>(zeroInp, indices, features,
std::make_shared<ngraph::op::Constant>(ngraph::element::i32, ngraph::Shape{1}, &axis));
std::shared_ptr<ov::Node> unpool = std::make_shared<ov::op::v3::ScatterElementsUpdate>(zeroInp, indices, features,
std::make_shared<ov::op::v0::Constant>(ov::element::i32, ov::Shape{1}, &axis));
auto shape = std::make_shared<ngraph::op::Constant>(ngraph::element::i32, ngraph::Shape{outShapes[0].size()}, outShapes[0].data());
unpool = std::make_shared<ngraph::op::v1::Reshape>(unpool, shape, true);
auto shape = std::make_shared<ov::op::v0::Constant>(ov::element::i32, ov::Shape{outShapes[0].size()}, outShapes[0].data());
unpool = std::make_shared<ov::op::v1::Reshape>(unpool, shape, true);
return Ptr<BackendNode>(new InfEngineNgraphNode(unpool));
}

8
modules/dnn/src/layers/mvn_layer.cpp
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@ -336,15 +336,11 @@ public:
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2021_2)
auto mvn = std::make_shared<ngraph::op::MVN>(ieInpNode, acrossChannels, normVariance, eps);
#else
int64_t start_axis = acrossChannels ? 1 : 2;
std::vector<int64_t> axes_v(ieInpNode.get_shape().size() - start_axis);
std::iota(axes_v.begin(), axes_v.end(), start_axis);
auto axes = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{axes_v.size()}, axes_v.data());
auto mvn = std::make_shared<ngraph::op::v6::MVN>(ieInpNode, axes, normVariance, eps, ngraph::op::MVNEpsMode::INSIDE_SQRT);
#endif
auto axes = std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{axes_v.size()}, axes_v.data());
auto mvn = std::make_shared<ov::op::v6::MVN>(ieInpNode, axes, normVariance, eps, ov::op::MVNEpsMode::INSIDE_SQRT);
return Ptr<BackendNode>(new InfEngineNgraphNode(mvn));
}
#endif // HAVE_DNN_NGRAPH

26
modules/dnn/src/layers/nary_eltwise_layers.cpp
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@ -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<ngraph::op::v0::Convert>(inp0, dtype);
inp0 = std::make_shared<ov::op::v0::Convert>(inp0, dtype);
if (inp1.get_element_type() != dtype)
inp1 = std::make_shared<ngraph::op::v0::Convert>(inp1, dtype);
inp1 = std::make_shared<ov::op::v0::Convert>(inp1, dtype);
}
std::shared_ptr<ngraph::Node> node;
std::shared_ptr<ov::Node> node;
if (op == OPERATION::ADD)
node = std::make_shared<ngraph::op::v1::Add>(inp0, inp1);
node = std::make_shared<ov::op::v1::Add>(inp0, inp1);
else if (op == OPERATION::PROD)
node = std::make_shared<ngraph::op::v1::Multiply>(inp0, inp1);
node = std::make_shared<ov::op::v1::Multiply>(inp0, inp1);
else if (op == OPERATION::GREATER_EQUAL)
node = std::make_shared<ngraph::op::v1::GreaterEqual>(inp0, inp1);
node = std::make_shared<ov::op::v1::GreaterEqual>(inp0, inp1);
else if (op == OPERATION::LESS_EQUAL)
node = std::make_shared<ngraph::op::v1::LessEqual>(inp0, inp1);
node = std::make_shared<ov::op::v1::LessEqual>(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<ngraph::op::Convert>(inp0, ngraph::element::i64);
auto inp1_i64 = std::make_shared<ngraph::op::Convert>(inp1, ngraph::element::i64);
auto mod = std::make_shared<ngraph::op::v1::FloorMod>(inp0_i64, inp1_i64);
node = std::make_shared<ngraph::op::Convert>(mod, ngraph::element::f32);
auto inp0_i64 = std::make_shared<ov::op::v0::Convert>(inp0, ov::element::i64);
auto inp1_i64 = std::make_shared<ov::op::v0::Convert>(inp1, ov::element::i64);
auto mod = std::make_shared<ov::op::v1::FloorMod>(inp0_i64, inp1_i64);
node = std::make_shared<ov::op::v0::Convert>(mod, ov::element::f32);
}
else if (op == OPERATION::FMOD)
node = std::make_shared<ngraph::op::v1::Mod>(inp0, inp1);
node = std::make_shared<ov::op::v1::Mod>(inp0, inp1);
else
CV_Error(Error::StsNotImplemented, "Operation is not implemented for nGraph backend");
return Ptr<BackendNode>(new InfEngineNgraphNode(node));

14
modules/dnn/src/layers/normalize_bbox_layer.cpp
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@ -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::op::Constant>(ngraph::element::i64, ngraph::Shape{axes_data.size()}, axes_data);
auto norm = std::make_shared<ngraph::op::v0::NormalizeL2>(ieInpNode, axes, epsilon, ngraph::op::EpsMode::ADD);
auto axes = std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{axes_data.size()}, axes_data);
auto norm = std::make_shared<ov::op::v0::NormalizeL2>(ieInpNode, axes, epsilon, ov::op::EpsMode::ADD);
CV_Assert(blobs.empty() || numChannels == blobs[0].total());
std::vector<size_t> shape(ieInpNode.get_shape().size(), 1);
@ -292,13 +292,9 @@ public:
shape[1] = numChannels;
if (!blobs.empty())
{
auto weight = std::make_shared<ngraph::op::Constant>(
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<ngraph::op::v1::Multiply>(norm, weight, ngraph::op::AutoBroadcastType::NUMPY);
#else
auto mul = std::make_shared<ngraph::op::v0::Multiply>(norm, weight, ngraph::op::AutoBroadcastType::NUMPY);
#endif
auto weight = std::make_shared<ov::op::v0::Constant>(
ov::element::f32, ov::Shape(shape), blobs[0].data);
auto mul = std::make_shared<ov::op::v1::Multiply>(norm, weight, ov::op::AutoBroadcastType::NUMPY);
return Ptr<BackendNode>(new InfEngineNgraphNode(mul));
}
return Ptr<BackendNode>(new InfEngineNgraphNode(norm));

12
modules/dnn/src/layers/padding_layer.cpp
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@ -268,14 +268,14 @@ public:
begins[i] = static_cast<int64_t>(paddings[i].first);
ends[i] = static_cast<int64_t>(paddings[i].second);
}
auto padding_below = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{begins.size()}, begins.data());
auto padding_above = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{ends.size()}, ends.data());
auto pad_mode = paddingType == "constant" ? ngraph::op::PadMode::CONSTANT : ngraph::op::PadMode::REFLECT; // SYMMETRIC
auto arg_pad_value = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{}, &paddingValue);;
auto padding_below = std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{begins.size()}, begins.data());
auto padding_above = std::make_shared<ov::op::v0::Constant>(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::op::v0::Constant>(ov::element::f32, ov::Shape{}, &paddingValue);;
auto pad = paddingType == "constant" ?
std::make_shared<ngraph::op::v1::Pad>(ieInpNode, padding_below, padding_above, arg_pad_value, pad_mode) :
std::make_shared<ngraph::op::v1::Pad>(ieInpNode, padding_below, padding_above, pad_mode);
std::make_shared<ov::op::v1::Pad>(ieInpNode, padding_below, padding_above, arg_pad_value, pad_mode) :
std::make_shared<ov::op::v1::Pad>(ieInpNode, padding_below, padding_above, pad_mode);
return Ptr<BackendNode>(new InfEngineNgraphNode(pad));
}
#endif

6
modules/dnn/src/layers/permute_layer.cpp
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@ -475,9 +475,9 @@ public:
{
auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
std::vector<int64_t> order(_order.begin(), _order.end());
auto tr_axes = std::make_shared<ngraph::op::Constant>(ngraph::element::i64,
ngraph::Shape({order.size()}), order.data());
auto transpose = std::make_shared<ngraph::op::Transpose>(ieInpNode, tr_axes);
auto tr_axes = std::make_shared<ov::op::v0::Constant>(ov::element::i64,
ov::Shape({order.size()}), order.data());
auto transpose = std::make_shared<ov::op::v1::Transpose>(ieInpNode, tr_axes);
return Ptr<BackendNode>(new InfEngineNgraphNode(transpose));
}
#endif // HAVE_DNN_NGRAPH

45
modules/dnn/src/layers/pooling_layer.cpp
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@ -51,13 +51,8 @@
#ifdef HAVE_DNN_NGRAPH
#include "../ie_ngraph.hpp"
#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2020_4)
#include <ngraph/op/roi_pooling.hpp>
#include <ngraph/op/psroi_pooling.hpp>
#else
#include <ngraph/op/experimental/layers/roi_pooling.hpp>
#include <ngraph/op/experimental/layers/psroi_pooling.hpp>
#endif
#include <openvino/op/roi_pooling.hpp>
#include <openvino/op/psroi_pooling.hpp>
#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<InfEngineNgraphNode>()->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<ngraph::op::v1::AvgPool>(ieInpNode, ngraph::Strides(strides),
ngraph::Shape(pads_begin), ngraph::Shape(pads_end), ngraph::Shape(kernel_size),
auto ave_pool = std::make_shared<ov::op::v1::AvgPool>(ieInpNode, ov::Strides(strides),
ov::Shape(pads_begin), ov::Shape(pads_end), ov::Shape(kernel_size),
exclude_pad, rounding_type, pad_type);
return Ptr<BackendNode>(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<int64_t> 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::op::Constant>(ngraph::element::i64, ngraph::Shape{axes.size()}, axes);
auto reduce_sum = std::make_shared<ngraph::op::v1::ReduceSum>(ieInpNode, reduction_axes, true);
auto reduction_axes = std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{axes.size()}, axes);
auto reduce_sum = std::make_shared<ov::op::v1::ReduceSum>(ieInpNode, reduction_axes, true);
return Ptr<BackendNode>(new InfEngineNgraphNode(reduce_sum));
}
else if (type == MAX) {
std::shared_ptr<ngraph::Node> max_pool;
std::shared_ptr<ov::Node> max_pool;
if (computeMaxIdx) {
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2022_1)
std::vector<size_t> dilations(kernel_size.size(), 1);
max_pool = std::make_shared<ngraph::op::v8::MaxPool>(ieInpNode, ngraph::Strides(strides), ngraph::Strides(dilations),
ngraph::Shape(pads_begin), ngraph::Shape(pads_end), ngraph::Shape(kernel_size),
max_pool = std::make_shared<ov::op::v8::MaxPool>(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<ngraph::op::v1::MaxPool>(ieInpNode, ngraph::Strides(strides),
ngraph::Shape(pads_begin), ngraph::Shape(pads_end), ngraph::Shape(kernel_size),
max_pool = std::make_shared<ov::op::v1::MaxPool>(ieInpNode, ov::Strides(strides),
ov::Shape(pads_begin), ov::Shape(pads_end), ov::Shape(kernel_size),
rounding_type, pad_type);
}
return Ptr<BackendNode>(new InfEngineNgraphNode(max_pool));
}
else if (type == ROI) {
auto& coords = nodes[1].dynamicCast<InfEngineNgraphNode>()->node;
auto roi = std::make_shared<ngraph::op::ROIPooling>(ieInpNode, coords,
ngraph::Shape{(size_t)pooledSize.height, (size_t)pooledSize.width}, spatialScale, "max");
auto roi = std::make_shared<ov::op::v0::ROIPooling>(ieInpNode, coords,
ov::Shape{(size_t)pooledSize.height, (size_t)pooledSize.width}, spatialScale, "max");
return Ptr<BackendNode>(new InfEngineNgraphNode(roi));
}
else if (type == PSROI) {
auto& coords = nodes[1].dynamicCast<InfEngineNgraphNode>()->node;
auto psroi = std::make_shared<ngraph::op::PSROIPooling>(ieInpNode, coords,
auto psroi = std::make_shared<ov::op::v0::PSROIPooling>(ieInpNode, coords,
(size_t)psRoiOutChannels, (size_t)pooledSize.width, spatialScale, 1, 1, "average");
return Ptr<BackendNode>(new InfEngineNgraphNode(psroi));
}

39
modules/dnn/src/layers/prior_box_layer.cpp
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@ -47,13 +47,8 @@
#ifdef HAVE_DNN_NGRAPH
#include "../ie_ngraph.hpp"
#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2020_4)
#include <ngraph/op/prior_box.hpp>
#include <ngraph/op/prior_box_clustered.hpp>
#else
#include <ngraph/op/experimental/layers/prior_box.hpp>
#include <ngraph/op/experimental/layers/prior_box_clustered.hpp>
#endif
#include <openvino/op/prior_box.hpp>
#include <openvino/op/prior_box_clustered.hpp>
#endif
#include "../op_vkcom.hpp"
@ -513,23 +508,23 @@ public:
CV_Assert(nodes.size() == 2);
auto layer = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
auto image = nodes[1].dynamicCast<InfEngineNgraphNode>()->node;
auto layer_shape = std::make_shared<ngraph::op::ShapeOf>(layer);
auto image_shape = std::make_shared<ngraph::op::ShapeOf>(image);
auto layer_shape = std::make_shared<ov::op::v3::ShapeOf>(layer);
auto image_shape = std::make_shared<ov::op::v3::ShapeOf>(image);
auto lower_bounds = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{1}, std::vector<int64_t>{2});
auto upper_bounds = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{1}, std::vector<int64_t>{4});
auto strides = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{1}, std::vector<int64_t>{1});
auto lower_bounds = std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{1}, std::vector<int64_t>{2});
auto upper_bounds = std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{1}, std::vector<int64_t>{4});
auto strides = std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{1}, std::vector<int64_t>{1});
auto slice_layer = std::make_shared<ngraph::op::v1::StridedSlice>(layer_shape,
auto slice_layer = std::make_shared<ov::op::v1::StridedSlice>(layer_shape,
lower_bounds, upper_bounds, strides, std::vector<int64_t>{}, std::vector<int64_t>{});
auto slice_image = std::make_shared<ngraph::op::v1::StridedSlice>(image_shape,
auto slice_image = std::make_shared<ov::op::v1::StridedSlice>(image_shape,
lower_bounds, upper_bounds, strides, std::vector<int64_t>{}, std::vector<int64_t>{});
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<ngraph::op::PriorBoxClustered>(slice_layer, slice_image, attrs);
auto axis = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{1}, std::vector<int64_t>{0});
auto unsqueeze = std::make_shared<ngraph::op::v0::Unsqueeze>(priorBox, axis);
auto priorBox = std::make_shared<ov::op::v0::PriorBoxClustered>(slice_layer, slice_image, attrs);
auto axis = std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{1}, std::vector<int64_t>{0});
auto unsqueeze = std::make_shared<ov::op::v0::Unsqueeze>(priorBox, axis);
return Ptr<BackendNode>(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<ngraph::op::PriorBox>(slice_layer, slice_image, attrs);
auto axis = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{1}, std::vector<int64_t>{0});
auto unsqueeze = std::make_shared<ngraph::op::v0::Unsqueeze>(priorBox, axis);
auto priorBox = std::make_shared<ov::op::v0::PriorBox>(slice_layer, slice_image, attrs);
auto axis = std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{1}, std::vector<int64_t>{0});
auto unsqueeze = std::make_shared<ov::op::v0::Unsqueeze>(priorBox, axis);
return Ptr<BackendNode>(new InfEngineNgraphNode(unsqueeze));
}
}

16
modules/dnn/src/layers/proposal_layer.cpp
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@ -10,11 +10,7 @@
#ifdef HAVE_DNN_NGRAPH
#include "../ie_ngraph.hpp"
#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2020_4)
#include <ngraph/op/proposal.hpp>
#else
#include <ngraph/op/experimental/layers/proposal.hpp>
#endif
#include <openvino/op/proposal.hpp>
#endif
namespace cv { namespace dnn {
@ -344,7 +340,7 @@ public:
const std::vector<Ptr<BackendNode> >& 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<InfEngineNgraphNode>()->node;
CV_Assert_N(image_shape.get_shape().size() == 2, image_shape.get_shape().front() == 1);
auto shape = std::make_shared<ngraph::op::Constant>(ngraph::element::i64,
ngraph::Shape{1},
auto shape = std::make_shared<ov::op::v0::Constant>(ov::element::i64,
ov::Shape{1},
std::vector<int64_t>{(int64_t)image_shape.get_shape().back()});
auto reshape = std::make_shared<ngraph::op::v1::Reshape>(image_shape, shape, true);
auto reshape = std::make_shared<ov::op::v1::Reshape>(image_shape, shape, true);
auto proposal = std::make_shared<ngraph::op::Proposal>(class_probs, class_logits, reshape, attr);
auto proposal = std::make_shared<ov::op::v0::Proposal>(class_probs, class_logits, reshape, attr);
return Ptr<BackendNode>(new InfEngineNgraphNode(proposal));
}
#endif // HAVE_DNN_NGRAPH

155
modules/dnn/src/layers/region_layer.cpp
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@ -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::op::Constant>(ngraph::element::i64, ngraph::Shape{2}, std::vector<int64_t>{cols, rows});
auto tr_axes = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{2}, std::vector<int64_t>{1, 0});
auto shape_node = std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{2}, std::vector<int64_t>{cols, rows});
auto tr_axes = std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{2}, std::vector<int64_t>{1, 0});
std::shared_ptr<ngraph::Node> input2d;
std::shared_ptr<ov::Node> input2d;
{
input2d = std::make_shared<ngraph::op::v1::Reshape>(input, shape_node, true);
input2d = std::make_shared<ngraph::op::Transpose>(input2d, tr_axes);
input2d = std::make_shared<ov::op::v1::Reshape>(input, shape_node, true);
input2d = std::make_shared<ov::op::v1::Transpose>(input2d, tr_axes);
}
std::shared_ptr<ngraph::Node> region;
std::shared_ptr<ov::Node> region;
{
auto new_axes = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{4}, std::vector<int64_t>{0, 3, 1, 2});
auto tr_input = std::make_shared<ngraph::op::Transpose>(input, new_axes);
auto new_axes = std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{4}, std::vector<int64_t>{0, 3, 1, 2});
auto tr_input = std::make_shared<ov::op::v1::Transpose>(input, new_axes);
std::vector<float> anchors_vec(blobs[0].ptr<float>(), blobs[0].ptr<float>() + blobs[0].total());
std::vector<int64_t> mask(anchors, 1);
region = std::make_shared<ngraph::op::RegionYolo>(tr_input, coords, classes, anchors, useSoftmax, mask, 1, 3, anchors_vec);
region = std::make_shared<ov::op::v0::RegionYolo>(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::op::Constant>(ngraph::element::i64,
ngraph::Shape{tr_shape.size()},
auto shape_as_inp = std::make_shared<ov::op::v0::Constant>(ov::element::i64,
ov::Shape{tr_shape.size()},
std::vector<int64_t>(tr_shape.begin(), tr_shape.end()));
region = std::make_shared<ngraph::op::v1::Reshape>(region, shape_as_inp, true);
new_axes = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{4}, std::vector<int64_t>{0, 2, 3, 1});
region = std::make_shared<ngraph::op::Transpose>(region, new_axes);
region = std::make_shared<ov::op::v1::Reshape>(region, shape_as_inp, true);
new_axes = std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{4}, std::vector<int64_t>{0, 2, 3, 1});
region = std::make_shared<ov::op::v1::Transpose>(region, new_axes);
region = std::make_shared<ngraph::op::v1::Reshape>(region, shape_node, true);
region = std::make_shared<ngraph::op::Transpose>(region, tr_axes);
region = std::make_shared<ov::op::v1::Reshape>(region, shape_node, true);
region = std::make_shared<ov::op::v1::Transpose>(region, tr_axes);
}
auto strides = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{2}, std::vector<int64_t>{1, 1});
auto strides = std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{2}, std::vector<int64_t>{1, 1});
std::vector<int64_t> boxes_shape{b, anchors, h, w};
auto shape_3d = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{boxes_shape.size()}, boxes_shape.data());
auto shape_3d = std::make_shared<ov::op::v0::Constant>(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::op::Constant>(ngraph::element::f32, ngraph::Shape{1}, &scale_x_y);
auto shift_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{1}, std::vector<float>{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::op::v0::Constant>(ov::element::f32, ov::Shape{1}, &scale_x_y);
auto shift_node = std::make_shared<ov::op::v0::Constant>(ov::element::f32, ov::Shape{1}, std::vector<float>{0.5});
auto axis = ngraph::op::Constant::create<int64_t>(ngraph::element::i64, ngraph::Shape{}, {0});
auto splits = ngraph::op::Constant::create<int64_t>(ngraph::element::i64, ngraph::Shape{5}, {1, 1, 1, 1, rows - 4});
auto split = std::make_shared<ngraph::op::v1::VariadicSplit>(input2d, axis, splits);
std::shared_ptr<ngraph::Node> box_x;
auto axis = ov::op::v0::Constant::create<int64_t>(ov::element::i64, ov::Shape{}, {0});
auto splits = ov::op::v0::Constant::create<int64_t>(ov::element::i64, ov::Shape{5}, {1, 1, 1, 1, rows - 4});
auto split = std::make_shared<ov::op::v1::VariadicSplit>(input2d, axis, splits);
std::shared_ptr<ov::Node> box_x;
{
box_x = std::make_shared<ngraph::op::Sigmoid>(split->output(0));
box_x = std::make_shared<ngraph::op::v1::Subtract>(box_x, shift_node, ngraph::op::AutoBroadcastType::NUMPY);
box_x = std::make_shared<ngraph::op::v1::Multiply>(box_x, scale_x_y_node, ngraph::op::AutoBroadcastType::NUMPY);
box_x = std::make_shared<ngraph::op::v1::Add>(box_x, shift_node, ngraph::op::AutoBroadcastType::NUMPY);
box_x = std::make_shared<ngraph::op::v1::Reshape>(box_x, shape_3d, true);
box_x = std::make_shared<ov::op::v0::Sigmoid>(split->output(0));
box_x = std::make_shared<ov::op::v1::Subtract>(box_x, shift_node, ov::op::AutoBroadcastType::NUMPY);
box_x = std::make_shared<ov::op::v1::Multiply>(box_x, scale_x_y_node, ov::op::AutoBroadcastType::NUMPY);
box_x = std::make_shared<ov::op::v1::Add>(box_x, shift_node, ov::op::AutoBroadcastType::NUMPY);
box_x = std::make_shared<ov::op::v1::Reshape>(box_x, shape_3d, true);
std::vector<float> 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::op::Constant>(ngraph::element::f32, box_broad_shape, x_indices.data());
box_x = std::make_shared<ngraph::op::v1::Add>(box_x, horiz, ngraph::op::AutoBroadcastType::NUMPY);
auto horiz = std::make_shared<ov::op::v0::Constant>(ov::element::f32, box_broad_shape, x_indices.data());
box_x = std::make_shared<ov::op::v1::Add>(box_x, horiz, ov::op::AutoBroadcastType::NUMPY);
auto cols_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{1}, std::vector<float>{float(w)});
box_x = std::make_shared<ngraph::op::v1::Divide>(box_x, cols_node, ngraph::op::AutoBroadcastType::NUMPY);
auto cols_node = std::make_shared<ov::op::v0::Constant>(ov::element::f32, ov::Shape{1}, std::vector<float>{float(w)});
box_x = std::make_shared<ov::op::v1::Divide>(box_x, cols_node, ov::op::AutoBroadcastType::NUMPY);
}
std::shared_ptr<ngraph::Node> box_y;
std::shared_ptr<ov::Node> box_y;
{
box_y = std::make_shared<ngraph::op::Sigmoid>(split->output(1));
box_y = std::make_shared<ngraph::op::v1::Subtract>(box_y, shift_node, ngraph::op::AutoBroadcastType::NUMPY);
box_y = std::make_shared<ngraph::op::v1::Multiply>(box_y, scale_x_y_node, ngraph::op::AutoBroadcastType::NUMPY);
box_y = std::make_shared<ngraph::op::v1::Add>(box_y, shift_node, ngraph::op::AutoBroadcastType::NUMPY);
box_y = std::make_shared<ngraph::op::v1::Reshape>(box_y, shape_3d, true);
box_y = std::make_shared<ov::op::v0::Sigmoid>(split->output(1));
box_y = std::make_shared<ov::op::v1::Subtract>(box_y, shift_node, ov::op::AutoBroadcastType::NUMPY);
box_y = std::make_shared<ov::op::v1::Multiply>(box_y, scale_x_y_node, ov::op::AutoBroadcastType::NUMPY);
box_y = std::make_shared<ov::op::v1::Add>(box_y, shift_node, ov::op::AutoBroadcastType::NUMPY);
box_y = std::make_shared<ov::op::v1::Reshape>(box_y, shape_3d, true);
std::vector<float> 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::op::Constant>(ngraph::element::f32, ngraph::Shape{1, (size_t)anchors, (size_t)h, 1}, y_indices.data());
box_y = std::make_shared<ngraph::op::v1::Add>(box_y, vert, ngraph::op::AutoBroadcastType::NUMPY);
auto rows_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{1}, std::vector<float>{float(h)});
box_y = std::make_shared<ngraph::op::v1::Divide>(box_y, rows_node, ngraph::op::AutoBroadcastType::NUMPY);
auto vert = std::make_shared<ov::op::v0::Constant>(ov::element::f32, ov::Shape{1, (size_t)anchors, (size_t)h, 1}, y_indices.data());
box_y = std::make_shared<ov::op::v1::Add>(box_y, vert, ov::op::AutoBroadcastType::NUMPY);
auto rows_node = std::make_shared<ov::op::v0::Constant>(ov::element::f32, ov::Shape{1}, std::vector<float>{float(h)});
box_y = std::make_shared<ov::op::v1::Divide>(box_y, rows_node, ov::op::AutoBroadcastType::NUMPY);
}
std::shared_ptr<ngraph::Node> box_w, box_h;
std::shared_ptr<ov::Node> 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<ngraph::op::v0::Exp>(split->output(2));
box_w = std::make_shared<ngraph::op::v1::Reshape>(box_w, shape_3d, true);
auto anchor_w_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, box_broad_shape, bias_w.data());
box_w = std::make_shared<ngraph::op::v1::Multiply>(box_w, anchor_w_node, ngraph::op::AutoBroadcastType::NUMPY);
box_w = std::make_shared<ov::op::v0::Exp>(split->output(2));
box_w = std::make_shared<ov::op::v1::Reshape>(box_w, shape_3d, true);
auto anchor_w_node = std::make_shared<ov::op::v0::Constant>(ov::element::f32, box_broad_shape, bias_w.data());
box_w = std::make_shared<ov::op::v1::Multiply>(box_w, anchor_w_node, ov::op::AutoBroadcastType::NUMPY);
box_h = std::make_shared<ngraph::op::v0::Exp>(split->output(3));
box_h = std::make_shared<ngraph::op::v1::Reshape>(box_h, shape_3d, true);
auto anchor_h_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, box_broad_shape, bias_h.data());
box_h = std::make_shared<ngraph::op::v1::Multiply>(box_h, anchor_h_node, ngraph::op::AutoBroadcastType::NUMPY);
box_h = std::make_shared<ov::op::v0::Exp>(split->output(3));
box_h = std::make_shared<ov::op::v1::Reshape>(box_h, shape_3d, true);
auto anchor_h_node = std::make_shared<ov::op::v0::Constant>(ov::element::f32, box_broad_shape, bias_h.data());
box_h = std::make_shared<ov::op::v1::Multiply>(box_h, anchor_h_node, ov::op::AutoBroadcastType::NUMPY);
}
auto region_splits = ngraph::op::Constant::create<int64_t>(ngraph::element::i64, ngraph::Shape{3}, {4, 1, rows - 5});
auto region_split = std::make_shared<ngraph::op::v1::VariadicSplit>(region, axis, region_splits);
auto region_splits = ov::op::v0::Constant::create<int64_t>(ov::element::i64, ov::Shape{3}, {4, 1, rows - 5});
auto region_split = std::make_shared<ov::op::v1::VariadicSplit>(region, axis, region_splits);
std::shared_ptr<ngraph::Node> scale;
std::shared_ptr<ov::Node> scale;
{
float thr = classfix == -1 ? 0.5 : 0;
auto thresh_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{1}, std::vector<float>{thr});
auto mask = std::make_shared<ngraph::op::v1::Less>(region_split->output(1), thresh_node);
auto zero_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, mask->get_shape(), std::vector<float>(cols, 0));
scale = std::make_shared<ngraph::op::v1::Select>(mask, zero_node, region_split->output(1));
auto thresh_node = std::make_shared<ov::op::v0::Constant>(ov::element::f32, ov::Shape{1}, std::vector<float>{thr});
auto mask = std::make_shared<ov::op::v1::Less>(region_split->output(1), thresh_node);
auto zero_node = std::make_shared<ov::op::v0::Constant>(ov::element::f32, mask->get_shape(), std::vector<float>(cols, 0));
scale = std::make_shared<ov::op::v1::Select>(mask, zero_node, region_split->output(1));
}
std::shared_ptr<ngraph::Node> probs;
std::shared_ptr<ov::Node> probs;
{
probs = std::make_shared<ngraph::op::v1::Multiply>(region_split->output(2), scale, ngraph::op::AutoBroadcastType::NUMPY);
auto thresh_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{1}, &thresh);
auto mask = std::make_shared<ngraph::op::v1::Greater>(probs, thresh_node);
auto zero_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, mask->get_shape(), std::vector<float>((rows - 5) * cols, 0));
probs = std::make_shared<ngraph::op::v1::Select>(mask, probs, zero_node);
probs = std::make_shared<ov::op::v1::Multiply>(region_split->output(2), scale, ov::op::AutoBroadcastType::NUMPY);
auto thresh_node = std::make_shared<ov::op::v0::Constant>(ov::element::f32, ov::Shape{1}, &thresh);
auto mask = std::make_shared<ov::op::v1::Greater>(probs, thresh_node);
auto zero_node = std::make_shared<ov::op::v0::Constant>(ov::element::f32, mask->get_shape(), std::vector<float>((rows - 5) * cols, 0));
probs = std::make_shared<ov::op::v1::Select>(mask, probs, zero_node);
}
auto concat_shape = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{2}, std::vector<int64_t>{1, cols});
box_x = std::make_shared<ngraph::op::v1::Reshape>(box_x, concat_shape, true);
box_y = std::make_shared<ngraph::op::v1::Reshape>(box_y, concat_shape, true);
box_w = std::make_shared<ngraph::op::v1::Reshape>(box_w, concat_shape, true);
box_h = std::make_shared<ngraph::op::v1::Reshape>(box_h, concat_shape, true);
auto concat_shape = std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{2}, std::vector<int64_t>{1, cols});
box_x = std::make_shared<ov::op::v1::Reshape>(box_x, concat_shape, true);
box_y = std::make_shared<ov::op::v1::Reshape>(box_y, concat_shape, true);
box_w = std::make_shared<ov::op::v1::Reshape>(box_w, concat_shape, true);
box_h = std::make_shared<ov::op::v1::Reshape>(box_h, concat_shape, true);
ngraph::NodeVector inp_nodes{box_x, box_y, box_w, box_h, scale, probs};
std::shared_ptr<ngraph::Node> result = std::make_shared<ngraph::op::Concat>(inp_nodes, 0);
result = std::make_shared<ngraph::op::Transpose>(result, tr_axes);
ov::NodeVector inp_nodes{box_x, box_y, box_w, box_h, scale, probs};
std::shared_ptr<ov::Node> result = std::make_shared<ov::op::v0::Concat>(inp_nodes, 0);
result = std::make_shared<ov::op::v1::Transpose>(result, tr_axes);
if (b > 1)
{
std::vector<int64_t> sizes{b, static_cast<int64_t>(result->get_shape()[0]) / b, static_cast<int64_t>(result->get_shape()[1])};
auto shape_node = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{sizes.size()}, sizes.data());
result = std::make_shared<ngraph::op::v1::Reshape>(result, shape_node, true);
auto shape_node = std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{sizes.size()}, sizes.data());
result = std::make_shared<ov::op::v1::Reshape>(result, shape_node, true);
}
return Ptr<BackendNode>(new InfEngineNgraphNode(result));

8
modules/dnn/src/layers/reorg_layer.cpp
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@ -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 <ngraph/op/reorg_yolo.hpp>
#else
#include <ngraph/op/experimental/layers/reorg_yolo.hpp>
#endif
#include <openvino/op/reorg_yolo.hpp>
#endif
#include "../op_cuda.hpp"
@ -205,7 +201,7 @@ public:
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
auto reorg = std::make_shared<ngraph::op::ReorgYolo>(ieInpNode, ngraph::Strides{(size_t)reorgStride});
auto reorg = std::make_shared<ov::op::v0::ReorgYolo>(ieInpNode, ov::Strides{(size_t)reorgStride});
return Ptr<BackendNode>(new InfEngineNgraphNode(reorg));
}
#endif // HAVE_DNN_NGRAPH

6
modules/dnn/src/layers/reshape_layer.cpp
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@ -369,9 +369,9 @@ public:
auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
std::vector<int64_t> out(outShapes[0].begin(), outShapes[0].end());
auto shape = std::make_shared<ngraph::op::Constant>(ngraph::element::i64,
ngraph::Shape{out.size()}, out.data());
auto reshape = std::make_shared<ngraph::op::v1::Reshape>(ieInpNode, shape, true);
auto shape = std::make_shared<ov::op::v0::Constant>(ov::element::i64,
ov::Shape{out.size()}, out.data());
auto reshape = std::make_shared<ov::op::v1::Reshape>(ieInpNode, shape, true);
return Ptr<BackendNode>(new InfEngineNgraphNode(reshape));
}
#endif // HAVE_DNN_NGRAPH

74
modules/dnn/src/layers/resize_layer.cpp
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@ -13,11 +13,7 @@
#ifdef HAVE_DNN_NGRAPH
#include "../ie_ngraph.hpp"
#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2020_4)
#include <ngraph/op/interpolate.hpp>
#else
#include <ngraph/op/experimental/layers/interpolate.hpp>
#endif
#include <openvino/op/interpolate.hpp>
#endif
#ifdef HAVE_CUDA
@ -376,81 +372,39 @@ public:
{
auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->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;
if (interpolation == "nearest") {
attrs.mode = "nearest";
attrs.antialias = false;
} else if (interpolation == "bilinear") {
attrs.mode = "linear";
} else {
CV_Error(Error::StsNotImplemented, "Unsupported interpolation: " + interpolation);
}
std::vector<int64_t> shape = {outHeight, outWidth};
auto out_shape = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{2}, shape.data());
auto interp = std::make_shared<ngraph::op::Interpolate>(ieInpNode, out_shape, attrs);
#else
ngraph::op::v4::Interpolate::InterpolateAttrs attrs;
ov::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;
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 = 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<int64_t> shape = {outHeight, outWidth};
auto out_shape = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{2}, shape.data());
auto out_shape = std::make_shared<ov::op::v0::Constant>(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<float> scales = {static_cast<float>(outHeight) / input_shape[2], static_cast<float>(outWidth) / input_shape[3]};
auto scales_shape = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{2}, scales.data());
auto scales_shape = std::make_shared<ov::op::v0::Constant>(ov::element::f32, ov::Shape{2}, scales.data());
auto axes = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{2}, std::vector<int64_t>{2, 3});
auto interp = std::make_shared<ngraph::op::v4::Interpolate>(ieInpNode, out_shape, scales_shape, axes, attrs);
#endif
auto axes = std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{2}, std::vector<int64_t>{2, 3});
auto interp = std::make_shared<ov::op::v4::Interpolate>(ieInpNode, out_shape, scales_shape, axes, attrs);
return Ptr<BackendNode>(new InfEngineNgraphNode(interp));
}
#endif // HAVE_DNN_NGRAPH

27
modules/dnn/src/layers/scale_layer.cpp
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@ -331,7 +331,7 @@ public:
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs, const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
auto ieInpNode0 = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
ngraph::Output<ngraph::Node> ieInpNode1;
ov::Output<ov::Node> ieInpNode1;
if (nodes.size() > 1)
ieInpNode1 = nodes[1].dynamicCast<InfEngineNgraphNode>()->node;
@ -346,31 +346,26 @@ public:
int cAxis = normalize_axis(axis, shape.size());
shape[cAxis] = numChannels;
std::shared_ptr<ngraph::Node> node;
std::shared_ptr<ov::Node> node;
if (hasWeights)
{
ngraph::Output<ngraph::Node> weight = blobs.empty() ? ieInpNode1 :
std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape(shape), blobs[0].data);
#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2021_2)
node = std::make_shared<ngraph::op::v1::Multiply>(ieInpNode0, weight, ngraph::op::AutoBroadcastType::NUMPY);
#else
node = std::make_shared<ngraph::op::v0::Multiply>(ieInpNode0, weight, ngraph::op::AutoBroadcastType::NUMPY);
#endif
ov::Output<ov::Node> weight = blobs.empty() ? ieInpNode1 :
std::make_shared<ov::op::v0::Constant>(ov::element::f32, ov::Shape(shape), blobs[0].data);
node = std::make_shared<ov::op::v1::Multiply>(ieInpNode0, weight, ov::op::AutoBroadcastType::NUMPY);
}
if (hasBias || !hasWeights)
{
ngraph::Output<ngraph::Node> bias;
ov::Output<ov::Node> bias;
if (hasBias)
{
bias = blobs.empty() ? ieInpNode1 :
std::make_shared<ngraph::op::Constant>(ngraph::element::f32,
ngraph::Shape(shape), blobs.back().data);
std::make_shared<ov::op::v0::Constant>(ov::element::f32,
ov::Shape(shape), blobs.back().data);
}
else
bias = std::make_shared<ngraph::op::Constant>(ngraph::element::f32,
ngraph::Shape(shape), std::vector<float>(numChannels, 0).data());
node = std::make_shared<ngraph::op::v1::Add>(node, bias, ngraph::op::AutoBroadcastType::NUMPY);
bias = std::make_shared<ov::op::v0::Constant>(ov::element::f32,
ov::Shape(shape), std::vector<float>(numChannels, 0).data());
node = std::make_shared<ov::op::v1::Add>(node, bias, ov::op::AutoBroadcastType::NUMPY);
}
return Ptr<BackendNode>(new InfEngineNgraphNode(node));
}

16
modules/dnn/src/layers/slice_layer.cpp
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@ -768,14 +768,14 @@ public:
dims.push_back(finalSliceRanges[0][i].end);
}
auto lower_bounds = std::make_shared<ngraph::op::Constant>(ngraph::element::i64,
ngraph::Shape{offsets.size()}, offsets.data());
auto upper_bounds = std::make_shared<ngraph::op::Constant>(ngraph::element::i64,
ngraph::Shape{dims.size()}, dims.data());
auto strides = std::make_shared<ngraph::op::Constant>(ngraph::element::i64,
ngraph::Shape{dims.size()}, std::vector<int64_t>((int64_t)dims.size(), 1));
auto slice = std::make_shared<ngraph::op::v1::StridedSlice>(ieInpNode,
auto lower_bounds = std::make_shared<ov::op::v0::Constant>(ov::element::i64,
ov::Shape{offsets.size()}, offsets.data());
auto upper_bounds = std::make_shared<ov::op::v0::Constant>(ov::element::i64,
ov::Shape{dims.size()}, dims.data());
auto strides = std::make_shared<ov::op::v0::Constant>(ov::element::i64,
ov::Shape{dims.size()}, std::vector<int64_t>((int64_t)dims.size(), 1));
auto slice = std::make_shared<ov::op::v1::StridedSlice>(ieInpNode,
lower_bounds, upper_bounds, strides, std::vector<int64_t>{}, std::vector<int64_t>{});
return Ptr<BackendNode>(new InfEngineNgraphNode(slice));

4
modules/dnn/src/layers/softmax_layer.cpp
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@ -314,9 +314,9 @@ public:
auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
int axis = normalize_axis(axisRaw, ieInpNode.get_shape().size());
if (logSoftMax) {
return new InfEngineNgraphNode(std::make_shared<ngraph::op::v5::LogSoftmax>(ieInpNode, axis));
return new InfEngineNgraphNode(std::make_shared<ov::op::v5::LogSoftmax>(ieInpNode, axis));
} else {
return new InfEngineNgraphNode(std::make_shared<ngraph::op::v1::Softmax>(ieInpNode, axis));
return new InfEngineNgraphNode(std::make_shared<ov::op::v1::Softmax>(ieInpNode, axis));
}
}
#endif // HAVE_DNN_NGRAPH

112
modules/dnn/src/net_openvino.cpp
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@ -9,6 +9,12 @@
#include <opencv2/core/utils/configuration.private.hpp>
#include <opencv2/core/utils/logger.hpp>
#include "op_inf_engine.hpp"
#ifdef HAVE_INF_ENGINE
#include <openvino/op/util/op_types.hpp>
#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<ov::Model>& ieNet);
}; // NetImplOpenVINO
@ -320,11 +326,6 @@ void NetImplOpenVINO::initBackend(const std::vector<LayerPin>& 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<LayerPin>& blobsToKeep_)
bool fused = ld.skip;
Ptr<Layer> 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<InfEngineNgraphNet>();
layer->preferableTarget = DNN_TARGET_CPU;
for (int i = 0; i < ld.inputBlobsId.size(); ++i)
{
LayerData& inpLd = layers[ld.inputBlobsId[i].lid];
Ptr<BackendNode> inpNode = inpLd.backendNodes[preferableBackend];
if (!inpNode.empty())
{
Ptr<InfEngineNgraphNode> ieNode = inpNode.dynamicCast<InfEngineNgraphNode>();
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<LayerPin>& 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<BackendNode>(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<BackendNode>(new InfEngineNgraphNode(ieInpNode->node->get_output_as_single_output_node(oid)));
#else
inputNodes[i] = Ptr<BackendNode>(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<LayerPin>& 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<ov::Model>& ieNet)
{
CV_TRACE_FUNCTION();
CV_TRACE_REGION("register_inputs");
auto ngraphFunction = ieNet.getFunction();
CV_Assert(ngraphFunction);
std::vector<String> inputsNames;
std::vector<MatShape> inp_shapes;
for (auto& it : ngraphFunction->get_parameters())
for (auto& it : ieNet->get_parameters())
{
inputsNames.push_back(it->get_friendly_name());
std::vector<size_t> 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> backendNode;
{
auto fake_node = std::make_shared<ngraph::op::Parameter>(ngraph::element::f32, ngraph::Shape {});
auto fake_node = std::make_shared<ov::op::v0::Parameter>(ov::element::f32, ov::Shape {});
Ptr<InfEngineNgraphNode> backendNodeNGraph(new InfEngineNgraphNode(fake_node));
backendNodeNGraph->net = Ptr<InfEngineNgraphNet>(new InfEngineNgraphNet(openvino_impl, ieNet));
backendNode = backendNodeNGraph;
@ -767,9 +704,9 @@ Net NetImplOpenVINO::createNetworkFromModelOptimizer(InferenceEngine::CNNNetwork
CV_TRACE_REGION_NEXT("register_outputs");
std::vector<std::shared_ptr<ngraph::Node>> ngraphOperations = ngraphFunction->get_ops();
std::vector<std::shared_ptr<ov::Node>> 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<ov::Model> 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<ov::Model> 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<uint8_t>(tensorDesc, (uint8_t*)bufferWeightsPtr, bufferWeightsSize);
ieNet = ie.ReadNetwork(model, weights_blob);
#endif
}
catch (const std::exception& e)
{

139
modules/dnn/src/op_inf_engine.cpp
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@ -10,7 +10,7 @@
#include <opencv2/dnn/shape_utils.hpp>
#ifdef HAVE_INF_ENGINE
#include <ie_extension.h>
#include <openvino/core/extension.hpp>
#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<ov::Model> model) : model(model) {}
std::shared_ptr<ov::Model> 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<std::string, std::vector<size_t> >& shapes) {
std::map<std::string, ov::PartialShape> 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<std::string> 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<std::string, std::string>& 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<size_t> 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<size_t> dims = blob->getTensorDesc().getDims();
std::vector<int> size(dims.begin(), dims.end());
auto precision = blob->getTensorDesc().getPrecision();
int type = -1;
switch (precision)
{
case InferenceEngine::Precision::FP32: type = CV_32F; break;
case InferenceEngine::Precision::U8: type = CV_8U; break;
default:
CV_Error(Error::StsNotImplemented, "Unsupported blob precision");
}
return Mat(size, type, (void*)blob->buffer());
}
void infEngineBlobsToMats(const std::vector<InferenceEngine::Blob::Ptr>& blobs,
std::vector<Mat>& 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<std::string, std::shared_ptr<InferenceEngine::Core> >& cores)
static ov::Core& retrieveIECore(const std::string& id, std::map<std::string, std::shared_ptr<ov::Core> >& cores)
{
AutoLock lock(getInitializationMutex());
std::map<std::string, std::shared_ptr<InferenceEngine::Core> >::iterator i = cores.find(id);
std::map<std::string, std::shared_ptr<ov::Core> >::iterator i = cores.find(id);
if (i == cores.end())
{
std::shared_ptr<InferenceEngine::Core> core = std::make_shared<InferenceEngine::Core>();
std::shared_ptr<ov::Core> core = std::make_shared<ov::Core>();
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<std::string, std::shared_ptr<InferenceEngine::Core> > cores;
static std::map<std::string, std::shared_ptr<ov::Core> > 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<std::string, std::shared_ptr<InferenceEngine::Core> >* cores =
new std::map<std::string, std::shared_ptr<InferenceEngine::Core> >();
static std::map<std::string, std::shared_ptr<ov::Core> >* cores =
new std::map<std::string, std::shared_ptr<ov::Core> >();
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<std::string> devices = ie.GetAvailableDevices();
ov::Core& ie = getCore("CPU");
const std::vector<std::string> devices = ie.get_available_devices();
for (std::vector<std::string>::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<std::string>();
#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<std::string> devices = ie.GetAvailableDevices();
ov::Core& ie = getCore(device);
const std::vector<std::string> devices = ie.get_available_devices();
for (std::vector<std::string>::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<std::string>();
#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<std::string> devices = getCore("").GetAvailableDevices();
const std::vector<std::string> devices = getCore("").get_available_devices();
for (std::vector<std::string>::const_iterator i = devices.begin(); i != devices.end(); ++i)
{
if (std::string::npos != i->find("MYRIAD") && target == DNN_TARGET_MYRIAD)

73
modules/dnn/src/op_inf_engine.hpp
Unescape View File

@ -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 <openvino/openvino.hpp>
#include <openvino/pass/serialize.hpp>
#include <openvino/pass/convert_fp32_to_fp16.hpp>
#else
#include <inference_engine.hpp>
#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<Mat>& mats);
#else
Mat infEngineBlobToMat(const InferenceEngine::Blob::Ptr& blob);
void infEngineBlobsToMats(const std::vector<InferenceEngine::Blob::Ptr>& blobs,
std::vector<Mat>& 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<ov::Model> model);
std::shared_ptr<ov::Model> getFunction() const;
void serialize(const std::string& xmlPath, const std::string& binPath);
void reshape(const std::map<std::string, std::vector<size_t> >& shapes);
private:
std::shared_ptr<ov::Model> 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<std::string> 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<std::string, std::string>& config);
};
}
#endif // OpenVINO >= 2022.1
InferenceEngine::Core& getCore(const std::string& id);
ov::Core& getCore(const std::string& id);
template<typename T = size_t>
static inline std::vector<T> getShape(const Mat& mat)

2
modules/dnn/src/op_webnn.hpp
Unescape View File

@ -111,7 +111,7 @@ public:
void addBlobs(const std::vector<cv::Ptr<BackendWrapper> >& ptrs);
void createNet(Target targetId);
// void setNodePtr(std::shared_ptr<ngraph::Node>* ptr);
// void setNodePtr(std::shared_ptr<ov::Node>* ptr);
void reset();

145
modules/dnn/test/test_ie_models.cpp
Unescape View File

@ -9,7 +9,6 @@
#ifdef HAVE_INF_ENGINE
#include <opencv2/core/utils/filesystem.hpp>
//
// Synchronize headers include statements with src/op_inf_engine.hpp
//
@ -26,14 +25,11 @@
#pragma GCC visibility push(default)
#endif
#include <inference_engine.hpp>
#include <ie_icnn_network.hpp>
#include <ie_extension.h>
#if defined(__GNUC__)
#pragma GCC visibility pop
#endif
#include <openvino/runtime/core.hpp>
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<size_t>& dims = desc.getDims();
if (desc.getPrecision() == InferenceEngine::Precision::FP32)
{
m.create(std::vector<int>(dims.begin(), dims.end()), CV_32F);
randu(m, -1, 1);
dataPtr = make_shared_blob<float>(desc, (float*)m.data);
}
else if (desc.getPrecision() == InferenceEngine::Precision::I32)
{
m.create(std::vector<int>(dims.begin(), dims.end()), CV_32S);
randu(m, -100, 100);
dataPtr = make_shared_blob<int>(desc, (int*)m.data);
}
else
{
FAIL() << "Unsupported precision: " << desc.getPrecision();
}
}
void runIE(Target target, const std::string& xmlPath, const std::string& binPath,
std::map<std::string, cv::Mat>& inputsMap, std::map<std::string, cv::Mat>& 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
#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);
ov::Core core;
CNNNetwork net = reader.getNetwork();
#endif
auto model = core.read_model(xmlPath, binPath);
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<IExtension>(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<int>(shape.begin(), shape.end()), CV_32F);
randu(m, -1, 1);
}
else if (type == ov::element::i32)
{
m.create(std::vector<int>(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<size_t>& 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<int>(shape.begin(), shape.end()), CV_32F);
}
else if (type == ov::element::i32)
{
m.create(std::vector<int>(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<size_t>& 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,

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