mirror/opencv
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2024-11-25 19:50:38 +08:00
Code Issues Actions Packages Projects Releases Wiki Activity

dnn(IE): use HAVE_DNN_IE_NN_BUILDER_2019 for NN Builder API code

Browse Source 
- CMake option: OPENCV_DNN_IE_NN_BUILDER_2019
This commit is contained in:
Alexander Alekhin 2020-02-27 16:02:32 +03:00 committed by Alexander Alekhin
parent db5f1c3554
commit 29d214474f
32 changed files with 340 additions and 143 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

6
modules/dnn/CMakeLists.txt
View File

@ -113,9 +113,15 @@ endif()
set(dnn_runtime_libs "")
if(INF_ENGINE_TARGET)
ocv_option(OPENCV_DNN_IE_NN_BUILDER_2019 "Build with Inference Engine NN Builder API support" ON)
if(OPENCV_DNN_IE_NN_BUILDER_2019)
message(STATUS "DNN: Enabling Inference Engine NN Builder API support")
add_definitions(-DHAVE_DNN_IE_NN_BUILDER_2019=1)
endif()
list(APPEND dnn_runtime_libs ${INF_ENGINE_TARGET})
endif()
if(HAVE_NGRAPH)
message(STATUS "DNN: Enabling Inference Engine nGraph API support")
add_definitions(-DHAVE_DNN_NGRAPH)
list(APPEND dnn_runtime_libs ngraph::ngraph)
endif()

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

@ -185,30 +185,40 @@ private:
#ifdef HAVE_INF_ENGINE
if (checkIETarget(DNN_TARGET_CPU)) {
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019, DNN_TARGET_CPU));
#endif
#ifdef HAVE_DNN_NGRAPH
backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, DNN_TARGET_CPU));
#endif
}
if (checkIETarget(DNN_TARGET_MYRIAD)) {
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019, DNN_TARGET_MYRIAD));
#endif
#ifdef HAVE_DNN_NGRAPH
backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, DNN_TARGET_MYRIAD));
#endif
}
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
if (checkIETarget(DNN_TARGET_FPGA))
backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019, DNN_TARGET_FPGA));
#endif
#ifdef HAVE_OPENCL
if (cv::ocl::useOpenCL() && ocl::Device::getDefault().isIntel())
{
if (checkIETarget(DNN_TARGET_OPENCL)) {
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019, DNN_TARGET_OPENCL));
#endif
#ifdef HAVE_DNN_NGRAPH
backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, DNN_TARGET_OPENCL));
#endif
}
if (checkIETarget(DNN_TARGET_OPENCL_FP16)) {
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019, DNN_TARGET_OPENCL_FP16));
#endif
#ifdef HAVE_DNN_NGRAPH
backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, DNN_TARGET_OPENCL_FP16));
#endif
@ -797,7 +807,7 @@ struct DataLayer : public Layer
}
}
#ifdef HAVE_INF_ENGINE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{
CV_CheckEQ(inputsData.size(), (size_t)1, "");
@ -829,7 +839,7 @@ struct DataLayer : public Layer
addConstantData("biases", biases, ieLayer);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
}
#endif // HAVE_INF_ENGINE
#endif // HAVE_DNN_IE_NN_BUILDER_2019
std::vector<String> outNames;
std::vector<MatShape> shapes;
@ -1087,10 +1097,10 @@ static Ptr<BackendWrapper> wrapMat(int backendId, int targetId, cv::Mat& m)
}
else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
#ifdef HAVE_INF_ENGINE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
return Ptr<BackendWrapper>(new InfEngineBackendWrapper(targetId, m));
#else
CV_Error(Error::StsNotImplemented, "This OpenCV version is built without Inference Engine API support");
CV_Error(Error::StsNotImplemented, "This OpenCV version is built without Inference Engine NN Builder API support");
#endif
}
else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
@ -1587,10 +1597,10 @@ struct Net::Impl
initHalideBackend();
else if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
#ifdef HAVE_INF_ENGINE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
initInfEngineBackend(blobsToKeep_);
#else
CV_Assert(false && "This OpenCV version is built without Inference Engine API support");
CV_Assert(false && "This OpenCV version is built without Inference Engine NN Builder API support");
#endif
}
else if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
@ -1664,7 +1674,7 @@ struct Net::Impl
}
}
#ifdef HAVE_INF_ENGINE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
// Before launching Inference Engine graph we need to specify output blobs.
// This function requests output blobs based on inputs references of
// layers from default backend or layers from different graphs.
@ -1969,7 +1979,7 @@ struct Net::Impl
}
}
}
#endif // HAVE_INF_ENGINE
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
@ -3377,8 +3387,12 @@ struct Net::Impl
CV_Assert(preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) {
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
Ptr<InfEngineBackendWrapper> wrapper = ld.outputBlobsWrappers[pin.oid].dynamicCast<InfEngineBackendWrapper>();
return std::move(wrapper->futureMat);
#else
CV_Error(Error::StsNotImplemented, "This OpenCV version is built without Inference Engine NN Builder API support");
#endif
}
else if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
@ -3470,9 +3484,13 @@ Net Net::Impl::createNetworkFromModelOptimizer(InferenceEngine::CNNNetwork& ieNe
else
#endif
{
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
Ptr<InfEngineBackendNode> backendNodeNN(new InfEngineBackendNode(InferenceEngine::Builder::Layer("")));
backendNodeNN->net = Ptr<InfEngineBackendNet>(new InfEngineBackendNet(ieNet));
backendNode = backendNodeNN;
#else
CV_Error(Error::StsNotImplemented, "This OpenCV version is built without Inference Engine NN Builder API support");
#endif
}
for (auto& it : ieNet.getOutputsInfo())
@ -3499,6 +3517,7 @@ Net Net::Impl::createNetworkFromModelOptimizer(InferenceEngine::CNNNetwork& ieNe
else
#endif
{
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
Ptr<Layer> cvLayer(new InfEngineBackendLayer(ieNet));
InferenceEngine::CNNLayerPtr ieLayer = ieNet.getLayerByName(it.first.c_str());
@ -3509,6 +3528,9 @@ Net Net::Impl::createNetworkFromModelOptimizer(InferenceEngine::CNNNetwork& ieNe
ld.layerInstance = cvLayer;
ld.backendNodes[DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019] = backendNode;
#else
CV_Error(Error::StsNotImplemented, "This OpenCV version is built without Inference Engine NN Builder API support");
#endif
}
for (int i = 0; i < inputsNames.size(); ++i)

144
modules/dnn/src/ie_ngraph.cpp
View File

@ -25,8 +25,8 @@ namespace cv { namespace dnn {
// For networks with input layer which has an empty name, IE generates a name id[some_number].
// OpenCV lets users use an empty input name and to prevent unexpected naming,
// we can use some predefined name.
static std::string kDefaultInpLayerName = "empty_inp_layer_name";
static constexpr const char* kOpenCVLayersType = "OpenCVLayer";
static std::string kDefaultInpLayerName = "opencv_ngraph_empty_inp_layer_name";
static constexpr const char* kOpenCVLayersType = "opencv_ngraph_layer";
static std::string shapesToStr(const std::vector<Mat>& mats)
{
@ -77,7 +77,6 @@ public:
return type_info;
}
NgraphCustomOp() {};
NgraphCustomOp(const ngraph::NodeVector& inputs,
const std::map<std::string, InferenceEngine::Parameter>& params = {}):
Op(inputs), params(params)
@ -85,6 +84,11 @@ public:
constructor_validate_and_infer_types();
}
~NgraphCustomOp()
{
// nothing
}
void validate_and_infer_types() override
{
std::vector<std::vector<size_t> > shapes;
@ -116,6 +120,136 @@ private:
std::map<std::string, InferenceEngine::Parameter> params;
};
class InfEngineNgraphCustomLayer : public InferenceEngine::ILayerExecImpl
{
public:
explicit InfEngineNgraphCustomLayer(const InferenceEngine::CNNLayer& layer) : cnnLayer(layer)
{
std::istringstream iss(layer.GetParamAsString("impl"));
size_t ptr;
iss >> ptr;
cvLayer = (Layer*)ptr;
std::vector<std::vector<size_t> > shapes;
strToShapes(layer.GetParamAsString("internals"), shapes);
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;
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);
}
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:
InferenceEngine::CNNLayer cnnLayer;
dnn::Layer* cvLayer;
std::vector<Mat> internals;
};
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;
};
class InfEngineNgraphExtension : public InferenceEngine::IExtension
{
public:
virtual void SetLogCallback(InferenceEngine::IErrorListener&) noexcept {}
virtual void Unload() noexcept {}
virtual void Release() noexcept {}
virtual void GetVersion(const InferenceEngine::Version*&) const noexcept {}
virtual InferenceEngine::StatusCode getPrimitiveTypes(char**&, unsigned int&,
InferenceEngine::ResponseDesc*) noexcept
{
return InferenceEngine::StatusCode::OK;
}
InferenceEngine::StatusCode getFactoryFor(InferenceEngine::ILayerImplFactory*& factory,
const InferenceEngine::CNNLayer* cnnLayer,
InferenceEngine::ResponseDesc* resp) noexcept
{
if (cnnLayer->type != kOpenCVLayersType)
return InferenceEngine::StatusCode::NOT_IMPLEMENTED;
factory = new InfEngineNgraphCustomLayerFactory(cnnLayer);
return InferenceEngine::StatusCode::OK;
}
};
InfEngineNgraphNode::InfEngineNgraphNode(std::shared_ptr<ngraph::Node>&& _node)
: BackendNode(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH), node(std::move(_node)) {}
@ -423,11 +557,11 @@ void InfEngineNgraphNet::initPlugin(InferenceEngine::CNNNetwork& net)
// OpenCV fallbacks as extensions.
try
{
ie.AddExtension(std::make_shared<InfEngineExtension>(), "CPU");
ie.AddExtension(std::make_shared<InfEngineNgraphExtension>(), "CPU");
}
catch(const std::exception& e)
{
CV_LOG_INFO(NULL, "DNN-IE: Can't register OpenCV custom layers extension: " << e.what());
CV_LOG_INFO(NULL, "DNN-IE: Can't register OpenCV custom layers nGraph extension: " << e.what());
}
#ifndef _WIN32
// Limit the number of CPU threads.

4
modules/dnn/src/layers/batch_norm_layer.cpp
View File

@ -373,7 +373,7 @@ public:
}
#endif // HAVE_HALIDE
#ifdef HAVE_INF_ENGINE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{
InferenceEngine::Builder::Layer ieLayer = InferenceEngine::Builder::ScaleShiftLayer(name);
@ -382,7 +382,7 @@ public:
addConstantData("biases", wrapToInfEngineBlob(bias_, {numChannels}, InferenceEngine::Layout::C), ieLayer);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
}
#endif // HAVE_INF_ENGINE
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs, const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE

4
modules/dnn/src/layers/blank_layer.cpp
View File

@ -127,7 +127,7 @@ public:
}
#endif
#ifdef HAVE_INF_ENGINE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
{
InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]);
@ -150,7 +150,7 @@ public:
ieLayer.setOutputPorts(std::vector<InferenceEngine::Port>(1));
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
}
#endif // HAVE_INF_ENGINE
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH

4
modules/dnn/src/layers/concat_layer.cpp
View File

@ -335,7 +335,7 @@ public:
return Ptr<BackendNode>();
}
#ifdef HAVE_INF_ENGINE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
{
InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]);
@ -345,7 +345,7 @@ public:
ieLayer.setInputPorts(std::vector<InferenceEngine::Port>(inputs.size()));
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
}
#endif // HAVE_INF_ENGINE
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH

6
modules/dnn/src/layers/const_layer.cpp
View File

@ -75,14 +75,14 @@ public:
blobs[0].copyTo(outputs[0]);
}
#ifdef HAVE_INF_ENGINE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{
InferenceEngine::Builder::ConstLayer ieLayer(name);
ieLayer.setData(wrapToInfEngineBlob(blobs[0]));
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
}
#endif // HAVE_INF_ENGINE
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
@ -93,7 +93,7 @@ public:
blobs[0].data);
return Ptr<BackendNode>(new InfEngineNgraphNode(node));
}
#endif // HAVE_INF_ENGINE
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_CUDA
Ptr<BackendNode> initCUDA(

13
modules/dnn/src/layers/convolution_layer.cpp
View File

@ -622,7 +622,7 @@ public:
return Ptr<BackendNode>();
}
#ifdef HAVE_INF_ENGINE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> > &inputs) CV_OVERRIDE
{
InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]);
@ -683,7 +683,7 @@ public:
return Ptr<BackendNode>(new InfEngineBackendNode(l));
}
#endif // HAVE_INF_ENGINE
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> > &inputs,
@ -1558,6 +1558,7 @@ public:
return group == 1;
}
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (kernel_size.size() == 3 && preferableTarget != DNN_TARGET_CPU) {
@ -1601,10 +1602,12 @@ public:
return std::accumulate(dilations.begin(), dilations.end(), 1, std::multiplies<size_t>()) == 1;
return true;
}
else
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#endif // HAVE_INF_ENGINE
{
return backendId == DNN_BACKEND_CUDA ||
(kernel_size.size() == 2 && (backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_HALIDE));
}
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,
@ -2244,7 +2247,7 @@ public:
return Ptr<BackendNode>();
}
#ifdef HAVE_INF_ENGINE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> > &) CV_OVERRIDE
{
InferenceEngine::Layout layout = blobs[0].dims == 5? InferenceEngine::Layout::NCDHW :
@ -2299,7 +2302,7 @@ public:
addConstantData("biases", wrapToInfEngineBlob(biasesMat, {(size_t)numOutput}, InferenceEngine::Layout::C), l);
return Ptr<BackendNode>(new InfEngineBackendNode(l));
}
#endif // HAVE_INF_ENGINE
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH

4
modules/dnn/src/layers/detection_output_layer.cpp
View File

@ -924,7 +924,7 @@ public:
}
}
#ifdef HAVE_INF_ENGINE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{
InferenceEngine::Builder::DetectionOutputLayer ieLayer(name);
@ -946,7 +946,7 @@ public:
return Ptr<BackendNode>(new InfEngineBackendNode(l));
}
#endif // HAVE_INF_ENGINE
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH

52
modules/dnn/src/layers/elementwise_layers.cpp
View File

@ -163,14 +163,14 @@ public:
return Ptr<BackendNode>();
}
#ifdef HAVE_INF_ENGINE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{
InferenceEngine::Builder::Layer ieLayer = func.initInfEngineBuilderAPI();
ieLayer.setName(this->name);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
}
#endif // HAVE_INF_ENGINE
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs, const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
@ -299,9 +299,11 @@ struct ReLUFunctor : public BaseFunctor
bool supportBackend(int backendId, int)
{
#ifdef HAVE_INF_ENGINE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
return slope >= 0 || !INF_ENGINE_VER_MAJOR_EQ(INF_ENGINE_RELEASE_2019R1);
#endif
#ifdef HAVE_DNN_NGRAPH
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
return true;
#endif
@ -408,12 +410,12 @@ struct ReLUFunctor : public BaseFunctor
}
#endif // HAVE_HALIDE
#ifdef HAVE_INF_ENGINE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
{
return InferenceEngine::Builder::ReLULayer("").setNegativeSlope(slope);
}
#endif // HAVE_INF_ENGINE
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
@ -535,12 +537,12 @@ struct ReLU6Functor : public BaseFunctor
}
#endif // HAVE_HALIDE
#ifdef HAVE_INF_ENGINE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
{
return InferenceEngine::Builder::ClampLayer("").setMinValue(minValue).setMaxValue(maxValue);
}
#endif // HAVE_INF_ENGINE
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
@ -627,12 +629,12 @@ struct TanHFunctor : public BaseFunctor
}
#endif // HAVE_HALIDE
#ifdef HAVE_INF_ENGINE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
{
return InferenceEngine::Builder::TanHLayer("");
}
#endif // HAVE_INF_ENGINE
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
@ -718,12 +720,12 @@ struct SwishFunctor : public BaseFunctor
}
#endif // HAVE_HALIDE
#ifdef HAVE_INF_ENGINE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
{
CV_Error(Error::StsNotImplemented, "");
}
#endif // HAVE_INF_ENGINE
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
@ -810,12 +812,12 @@ struct MishFunctor : public BaseFunctor
}
#endif // HAVE_HALIDE
#ifdef HAVE_INF_ENGINE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
{
CV_Error(Error::StsNotImplemented, "");
}
#endif // HAVE_INF_ENGINE
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
@ -908,12 +910,12 @@ struct SigmoidFunctor : public BaseFunctor
}
#endif // HAVE_HALIDE
#ifdef HAVE_INF_ENGINE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
{
return InferenceEngine::Builder::SigmoidLayer("");
}
#endif // HAVE_INF_ENGINE
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
@ -1000,12 +1002,12 @@ struct ELUFunctor : public BaseFunctor
}
#endif // HAVE_HALIDE
#ifdef HAVE_INF_ENGINE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
{
return InferenceEngine::Builder::ELULayer("");
}
#endif // HAVE_INF_ENGINE
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
@ -1095,12 +1097,12 @@ struct AbsValFunctor : public BaseFunctor
}
#endif // HAVE_HALIDE
#ifdef HAVE_INF_ENGINE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
{
return InferenceEngine::Builder::ReLULayer("").setNegativeSlope(-0.999999f);
}
#endif // HAVE_INF_ENGINE
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
@ -1191,12 +1193,12 @@ struct BNLLFunctor : public BaseFunctor
}
#endif // HAVE_HALIDE
#ifdef HAVE_INF_ENGINE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
{
CV_Error(Error::StsNotImplemented, "");
}
#endif // HAVE_INF_ENGINE
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
@ -1328,14 +1330,14 @@ struct PowerFunctor : public BaseFunctor
}
#endif // HAVE_HALIDE
#ifdef HAVE_INF_ENGINE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
{
return InferenceEngine::Builder::PowerLayer("").setPower(power)
.setScale(scale)
.setShift(shift);
}
#endif // HAVE_INF_ENGINE
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
@ -1497,7 +1499,7 @@ struct ChannelsPReLUFunctor : public BaseFunctor
}
#endif // HAVE_HALIDE
#ifdef HAVE_INF_ENGINE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
{
InferenceEngine::Builder::Layer l = InferenceEngine::Builder::PReLULayer("");
@ -1505,7 +1507,7 @@ struct ChannelsPReLUFunctor : public BaseFunctor
addConstantData("weights", wrapToInfEngineBlob(scale, {numChannels}, InferenceEngine::Layout::C), l);
return l;
}
#endif // HAVE_INF_ENGINE
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)

4
modules/dnn/src/layers/eltwise_layer.cpp
View File

@ -715,7 +715,7 @@ public:
return Ptr<BackendNode>();
}
#ifdef HAVE_INF_ENGINE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
{
InferenceEngine::Builder::EltwiseLayer ieLayer(name);
@ -739,7 +739,7 @@ public:
return Ptr<BackendNode>(new InfEngineBackendNode(l));
}
#endif // HAVE_INF_ENGINE
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH

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

@ -183,7 +183,7 @@ public:
}
#endif
#ifdef HAVE_INF_ENGINE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
{
InferenceEngine::Builder::Layer ieLayer(name);
@ -195,7 +195,7 @@ public:
ieLayer.setOutputPorts(std::vector<InferenceEngine::Port>(1));
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
}
#endif // HAVE_INF_ENGINE
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
@ -223,7 +223,6 @@ virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inp
return Ptr<BackendNode>(new InfEngineNgraphNode(reshape));
}
#endif // HAVE_DNN_NGRAPH
// HAVE_INF_ENGINE
int _startAxis;
int _endAxis;

4
modules/dnn/src/layers/fully_connected_layer.cpp
View File

@ -466,7 +466,7 @@ public:
return Ptr<BackendNode>();
}
#ifdef HAVE_INF_ENGINE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{
InferenceEngine::Builder::FullyConnectedLayer ieLayer(name);
@ -481,7 +481,7 @@ public:
return Ptr<BackendNode>(new InfEngineBackendNode(l));
}
#endif // HAVE_INF_ENGINE
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH

4
modules/dnn/src/layers/lrn_layer.cpp
View File

@ -444,7 +444,7 @@ public:
#endif // HAVE_HALIDE
}
#ifdef HAVE_INF_ENGINE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{
float alphaSize = alpha;
@ -461,7 +461,7 @@ public:
l.getParameters()["k"] = bias;
return Ptr<BackendNode>(new InfEngineBackendNode(l));
}
#endif // HAVE_INF_ENGINE
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs, const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE

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

@ -118,14 +118,17 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
#ifdef HAVE_INF_ENGINE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
return !zeroDev && (preferableTarget != DNN_TARGET_MYRIAD || eps <= 1e-7f);
else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
#endif
#ifdef HAVE_DNN_NGRAPH
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
return true;
else
#endif // HAVE_INF_ENGINE
#endif
{
return backendId == DNN_BACKEND_OPENCV;
}
}
#ifdef HAVE_OPENCL
@ -375,7 +378,7 @@ public:
}
}
#ifdef HAVE_INF_ENGINE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{
InferenceEngine::Builder::MVNLayer ieLayer(name);
@ -384,7 +387,7 @@ public:
ieLayer.setEpsilon(eps);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
}
#endif // HAVE_INF_ENGINE
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,

4
modules/dnn/src/layers/normalize_bbox_layer.cpp
View File

@ -295,7 +295,7 @@ public:
}
#endif
#ifdef HAVE_INF_ENGINE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
{
InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]);
@ -344,7 +344,7 @@ public:
return Ptr<BackendNode>(new InfEngineBackendNode(l));
}
}
#endif // HAVE_INF_ENGINE
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,

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

@ -212,7 +212,7 @@ public:
return Ptr<BackendNode>();
}
#ifdef HAVE_INF_ENGINE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{
InferenceEngine::Builder::Layer ieLayer(name);

4
modules/dnn/src/layers/permute_layer.cpp
View File

@ -403,14 +403,14 @@ public:
return Ptr<BackendNode>();
}
#ifdef HAVE_INF_ENGINE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{
InferenceEngine::Builder::PermuteLayer ieLayer(name);
ieLayer.setOrder(_order);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
}
#endif // HAVE_INF_ENGINE
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,

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

@ -188,15 +188,15 @@ public:
{
return type == MAX || type == AVE || type == ROI;
}
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (computeMaxIdx)
return false;
#ifdef HAVE_INF_ENGINE
if (kernel_size.size() == 3)
return preferableTarget == DNN_TARGET_CPU;
if (preferableTarget == DNN_TARGET_MYRIAD) {
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R1)
#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R1)
if (type == MAX && (pad_l == 1 && pad_t == 1) && stride == Size(2, 2) ) {
return !isMyriadX();
}
@ -205,14 +205,13 @@ public:
}
else
return type != STOCHASTIC;
#else
return false;
#endif
}
else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) {
#endif
else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
return !computeMaxIdx && type != STOCHASTIC;
}
else
else if (backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_HALIDE || backendId == DNN_BACKEND_VKCOM)
{
if (kernel_size.size() == 3)
return (backendId == DNN_BACKEND_OPENCV && preferableTarget == DNN_TARGET_CPU);
@ -225,6 +224,7 @@ public:
else
return false;
}
return false;
}
#ifdef HAVE_OPENCL
@ -454,7 +454,7 @@ public:
return Ptr<BackendNode>();
}
#ifdef HAVE_INF_ENGINE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{
if (type == MAX || type == AVE)
@ -500,7 +500,7 @@ public:
CV_Error(Error::StsNotImplemented, "Unsupported pooling type");
return Ptr<BackendNode>();
}
#endif // HAVE_INF_ENGINE
#endif // HAVE_DNN_IE_NN_BUILDER_2019

4
modules/dnn/src/layers/prior_box_layer.cpp
View File

@ -555,7 +555,7 @@ public:
return Ptr<BackendNode>();
}
#ifdef HAVE_INF_ENGINE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{
if (_explicitSizes)
@ -615,7 +615,7 @@ public:
return Ptr<BackendNode>(new InfEngineBackendNode(l));
}
}
#endif // HAVE_INF_ENGINE
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs, const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE

4
modules/dnn/src/layers/proposal_layer.cpp
View File

@ -327,7 +327,7 @@ public:
layerOutputs[0].col(2).copyTo(dst);
}
#ifdef HAVE_INF_ENGINE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{
InferenceEngine::Builder::ProposalLayer ieLayer(name);
@ -351,7 +351,7 @@ public:
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
}
#endif // HAVE_INF_ENGINE
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH

4
modules/dnn/src/layers/reorg_layer.cpp
View File

@ -205,14 +205,14 @@ public:
}
#endif
#ifdef HAVE_INF_ENGINE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{
InferenceEngine::Builder::ReorgYoloLayer ieLayer(name);
ieLayer.setStride(reorgStride);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
}
#endif // HAVE_INF_ENGINE
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> > &inputs,

4
modules/dnn/src/layers/reshape_layer.cpp
View File

@ -279,7 +279,7 @@ public:
}
#endif
#ifdef HAVE_INF_ENGINE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
{
InferenceEngine::Builder::ReshapeLayer ieLayer(name);
@ -287,7 +287,7 @@ public:
ieLayer.setDims(outShapes[0]);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
}
#endif // HAVE_INF_ENGINE
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,

8
modules/dnn/src/layers/resize_layer.cpp
View File

@ -65,8 +65,7 @@ public:
return interpolation == "nearest" || interpolation == "bilinear";
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ||
backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
return (interpolation == "nearest" && scaleWidth == scaleHeight) ||
(interpolation == "bilinear");
@ -192,9 +191,9 @@ public:
}
#endif
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{
#ifdef HAVE_INF_ENGINE
InferenceEngine::Builder::Layer ieLayer(name);
ieLayer.setName(name);
if (interpolation == "nearest")
@ -220,9 +219,8 @@ public:
ieLayer.setInputPorts(std::vector<InferenceEngine::Port>(1));
ieLayer.setOutputPorts(std::vector<InferenceEngine::Port>(1));
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#endif // HAVE_INF_ENGINE
return Ptr<BackendNode>();
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH

4
modules/dnn/src/layers/scale_layer.cpp
View File

@ -227,7 +227,7 @@ public:
}
#endif // HAVE_HALIDE
#ifdef HAVE_INF_ENGINE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{
InferenceEngine::Builder::Layer l = InferenceEngine::Builder::ScaleShiftLayer(name);
@ -253,7 +253,7 @@ public:
addConstantData("biases", wrapToInfEngineBlob(blobs.back(), {numChannels}, InferenceEngine::Layout::C), l);
return Ptr<BackendNode>(new InfEngineBackendNode(l));
}
#endif // HAVE_INF_ENGINE
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH

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

@ -119,14 +119,17 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_CUDA ||
(backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && sliceRanges.size() == 1) ||
(backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 &&
#ifdef HAVE_INF_ENGINE
INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R1) &&
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
return INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R1) &&
sliceRanges.size() == 1 && sliceRanges[0].size() == 4;
#endif
sliceRanges.size() == 1 && sliceRanges[0].size() == 4);
#ifdef HAVE_DNN_NGRAPH
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
return sliceRanges.size() == 1;
#endif
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_CUDA;
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,
@ -292,7 +295,7 @@ public:
}
#endif
#ifdef HAVE_INF_ENGINE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R1)
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
{

4
modules/dnn/src/layers/softmax_layer.cpp
View File

@ -348,7 +348,7 @@ public:
return Ptr<BackendNode>();
}
#ifdef HAVE_INF_ENGINE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
{
InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]);
@ -358,7 +358,7 @@ public:
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
}
#endif // HAVE_INF_ENGINE
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,

70
modules/dnn/src/op_inf_engine.cpp
View File

@ -42,8 +42,8 @@ Backend& getInferenceEngineBackendTypeParam()
{
static Backend param = parseInferenceEngineBackendType(
utils::getConfigurationParameterString("OPENCV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019_TYPE",
#ifdef HAVE_NGRAPH
CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API // future: CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH
#ifndef HAVE_DNN_IE_NN_BUILDER_2019
CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH
#else
CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API
#endif
@ -69,6 +69,36 @@ cv::String setInferenceEngineBackendType(const cv::String& newBackendType)
CV__DNN_INLINE_NS_END
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]);
}
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
// For networks with input layer which has an empty name, IE generates a name id[some_number].
// OpenCV lets users use an empty input name and to prevent unexpected naming,
// we can use some predefined name.
@ -556,6 +586,7 @@ void InfEngineBackendWrapper::setHostDirty()
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R1)
static std::map<std::string, InferenceEngine::InferenceEnginePluginPtr>& getSharedPlugins()
@ -687,6 +718,9 @@ static bool detectMyriadX_()
}
#endif // !defined(OPENCV_DNN_IE_VPU_TYPE_DEFAULT)
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
void InfEngineBackendNet::initPlugin(InferenceEngine::CNNNetwork& net)
{
CV_Assert(!isInitialized());
@ -985,32 +1019,6 @@ void InfEngineBackendNet::forward(const std::vector<Ptr<BackendWrapper> >& outBl
}
}
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]);
}
bool InfEngineBackendLayer::getMemoryShapes(const std::vector<MatShape> &inputs,
const int requiredOutputs,
std::vector<MatShape> &outputs,
@ -1077,6 +1085,8 @@ void addConstantData(const std::string& name, InferenceEngine::Blob::Ptr data,
#endif
}
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#endif // HAVE_INF_ENGINE
bool haveInfEngine()
@ -1092,11 +1102,13 @@ void forwardInfEngine(const std::vector<Ptr<BackendWrapper> >& outBlobsWrappers,
Ptr<BackendNode>& node, bool isAsync)
{
CV_Assert(haveInfEngine());
#ifdef HAVE_INF_ENGINE
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
CV_Assert(!node.empty());
Ptr<InfEngineBackendNode> ieNode = node.dynamicCast<InfEngineBackendNode>();
CV_Assert(!ieNode.empty());
ieNode->net->forward(outBlobsWrappers, isAsync);
#else
CV_Error(Error::StsNotImplemented, "This OpenCV version is built without Inference Engine NN Builder API support");
#endif // HAVE_INF_ENGINE
}

16
modules/dnn/src/op_inf_engine.hpp
View File

@ -41,6 +41,7 @@
#pragma GCC diagnostic ignored "-Wsuggest-override"
#endif
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
//#define INFERENCE_ENGINE_DEPRECATED // turn off deprecation warnings from IE
//there is no way to suppress warnings from IE only at this moment, so we are forced to suppress warnings globally
#if defined(__GNUC__)
@ -49,6 +50,7 @@
#ifdef _MSC_VER
#pragma warning(disable: 4996) // was declared deprecated
#endif
#endif // HAVE_DNN_IE_NN_BUILDER_2019
#if defined(__GNUC__) && INF_ENGINE_VER_MAJOR_LT(INF_ENGINE_RELEASE_2020_1)
#pragma GCC visibility push(default)
@ -74,6 +76,13 @@ namespace cv { namespace dnn {
Backend& getInferenceEngineBackendTypeParam();
Mat infEngineBlobToMat(const InferenceEngine::Blob::Ptr& blob);
void infEngineBlobsToMats(const std::vector<InferenceEngine::Blob::Ptr>& blobs,
std::vector<Mat>& mats);
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
class InfEngineBackendNet
{
public:
@ -180,11 +189,6 @@ InferenceEngine::Blob::Ptr wrapToInfEngineBlob(const Mat& m, const std::vector<s
InferenceEngine::DataPtr infEngineDataNode(const Ptr<BackendWrapper>& ptr);
Mat infEngineBlobToMat(const InferenceEngine::Blob::Ptr& blob);
void infEngineBlobsToMats(const std::vector<InferenceEngine::Blob::Ptr>& blobs,
std::vector<Mat>& mats);
// Convert Inference Engine blob with FP32 precision to FP16 precision.
// Allocates memory for a new blob.
InferenceEngine::Blob::Ptr convertFp16(const InferenceEngine::Blob::Ptr& blob);
@ -233,6 +237,8 @@ public:
InferenceEngine::ResponseDesc* resp) noexcept;
};
#endif // HAVE_DNN_IE_NN_BUILDER_2019
CV__DNN_INLINE_NS_BEGIN

5
modules/dnn/test/test_common.impl.hpp
View File

@ -392,7 +392,10 @@ void initDNNTests()
#ifdef HAVE_DNN_NGRAPH
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH,
#endif
CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER,
#endif
""
);
#endif
registerGlobalSkipTag(

4
modules/dnn/test/test_misc.cpp
View File

@ -130,14 +130,18 @@ void test_readNet_IE_do_not_call_setInput(Backend backendId)
EXPECT_TRUE(res.empty()) << res.size;
}
#ifdef HAVE_DNN_IE_NN_BUILDER_2019
TEST(readNet, do_not_call_setInput_IE_NN_BUILDER_2019)
{
test_readNet_IE_do_not_call_setInput(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019);
}
#endif
#ifdef HAVE_DNN_NGRAPH
TEST(readNet, do_not_call_setInput_IE_NGRAPH)
{
test_readNet_IE_do_not_call_setInput(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
}
#endif
#endif // HAVE_INF_ENGINE
typedef testing::TestWithParam<tuple<Backend, Target> > dump;

2
modules/ts/src/ts_tags.cpp
View File

@ -62,6 +62,8 @@ static std::vector<std::string>& getTestTagsSkipList()
void registerGlobalSkipTag(const std::string& skipTag)
{
if (skipTag.empty())
return; // do nothing
std::vector<std::string>& skipTags = getTestTagsSkipList();
for (size_t i = 0; i < skipTags.size(); ++i)
{