@ -177,48 +177,29 @@ private:
# ifdef HAVE_INF_ENGINE
if ( checkIETarget ( DNN_TARGET_CPU ) ) {
# ifdef HAVE_DNN_IE_NN_BUILDER_2019
backends . push_back ( std : : make_pair ( DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 , DNN_TARGET_CPU ) ) ;
# endif
# ifdef HAVE_DNN_NGRAPH
backends . push_back ( std : : make_pair ( DNN_BACKEND_INFERENCE_ENGINE_NGRAPH , DNN_TARGET_CPU ) ) ;
# endif
}
if ( checkIETarget ( DNN_TARGET_MYRIAD ) ) {
# ifdef HAVE_DNN_IE_NN_BUILDER_2019
backends . push_back ( std : : make_pair ( DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 , DNN_TARGET_MYRIAD ) ) ;
# endif
# ifdef HAVE_DNN_NGRAPH
backends . push_back ( std : : make_pair ( DNN_BACKEND_INFERENCE_ENGINE_NGRAPH , DNN_TARGET_MYRIAD ) ) ;
# endif
}
if ( checkIETarget ( DNN_TARGET_HDDL ) ) {
# ifdef HAVE_DNN_IE_NN_BUILDER_2019
backends . push_back ( std : : make_pair ( DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 , DNN_TARGET_HDDL ) ) ;
# endif
# ifdef HAVE_DNN_NGRAPH
backends . push_back ( std : : make_pair ( DNN_BACKEND_INFERENCE_ENGINE_NGRAPH , DNN_TARGET_HDDL ) ) ;
# endif
}
# ifdef HAVE_DNN_IE_NN_BUILDER_2019
if ( checkIETarget ( DNN_TARGET_FPGA ) )
backends . push_back ( std : : make_pair ( DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 , DNN_TARGET_FPGA ) ) ;
# endif
# ifdef HAVE_OPENCL
if ( cv : : ocl : : useOpenCL ( ) & & ocl : : Device : : getDefault ( ) . isIntel ( ) )
{
if ( checkIETarget ( DNN_TARGET_OPENCL ) ) {
# ifdef HAVE_DNN_IE_NN_BUILDER_2019
backends . push_back ( std : : make_pair ( DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 , DNN_TARGET_OPENCL ) ) ;
# endif
# ifdef HAVE_DNN_NGRAPH
backends . push_back ( std : : make_pair ( DNN_BACKEND_INFERENCE_ENGINE_NGRAPH , DNN_TARGET_OPENCL ) ) ;
# endif
}
if ( checkIETarget ( DNN_TARGET_OPENCL_FP16 ) ) {
# ifdef HAVE_DNN_IE_NN_BUILDER_2019
backends . push_back ( std : : make_pair ( DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 , DNN_TARGET_OPENCL_FP16 ) ) ;
# endif
# ifdef HAVE_DNN_NGRAPH
backends . push_back ( std : : make_pair ( DNN_BACKEND_INFERENCE_ENGINE_NGRAPH , DNN_TARGET_OPENCL_FP16 ) ) ;
# endif
@ -273,7 +254,7 @@ std::vector<Target> getAvailableTargets(Backend be)
be = ( Backend ) PARAM_DNN_BACKEND_DEFAULT ;
# ifdef HAVE_INF_ENGINE
if ( be = = DNN_BACKEND_INFERENCE_ENGINE )
be = getInferenceEngineBackendTypeParam ( ) ;
be = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH ;
# endif
std : : vector < Target > result ;
@ -637,8 +618,7 @@ struct DataLayer : public Layer
virtual bool supportBackend ( int backendId ) CV_OVERRIDE
{
return backendId = = DNN_BACKEND_OPENCV | |
( backendId = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 & & inputsData . size ( ) = = 1 ) ;
return backendId = = DNN_BACKEND_OPENCV ;
}
void forward ( InputArrayOfArrays inputs_arr , OutputArrayOfArrays outputs_arr , OutputArrayOfArrays internals_arr ) CV_OVERRIDE
@ -829,39 +809,6 @@ struct DataLayer : public Layer
}
}
# ifdef HAVE_DNN_IE_NN_BUILDER_2019
virtual Ptr < BackendNode > initInfEngine ( const std : : vector < Ptr < BackendWrapper > > & ) CV_OVERRIDE
{
CV_CheckEQ ( inputsData . size ( ) , ( size_t ) 1 , " " ) ;
CV_CheckEQ ( inputsData [ 0 ] . dims , 4 , " " ) ;
const size_t numChannels = inputsData [ 0 ] . size [ 1 ] ;
CV_Assert ( numChannels < = 4 ) ;
// Scale
InferenceEngine : : TensorDesc td ( InferenceEngine : : Precision : : FP32 , { numChannels } ,
InferenceEngine : : Layout : : C ) ;
auto weights = InferenceEngine : : make_shared_blob < float > ( td ) ;
weights - > allocate ( ) ;
float * weight_buf = weights - > buffer ( ) . as < float * > ( ) ;
std : : fill ( weight_buf , weight_buf + numChannels , scaleFactors [ 0 ] ) ;
// Mean subtraction
auto biases = InferenceEngine : : make_shared_blob < float > ( td ) ;
biases - > allocate ( ) ;
float * bias_buf = biases - > buffer ( ) . as < float * > ( ) ;
for ( int i = 0 ; i < numChannels ; + + i )
{
bias_buf [ i ] = - means [ 0 ] [ i ] * scaleFactors [ 0 ] ;
}
InferenceEngine : : Builder : : Layer ieLayer = InferenceEngine : : Builder : : ScaleShiftLayer ( name ) ;
addConstantData ( " weights " , weights , ieLayer ) ;
addConstantData ( " biases " , biases , ieLayer ) ;
return Ptr < BackendNode > ( new InfEngineBackendNode ( ieLayer ) ) ;
}
# endif // HAVE_DNN_IE_NN_BUILDER_2019
std : : vector < String > outNames ;
std : : vector < MatShape > shapes ;
@ -1119,18 +1066,14 @@ static Ptr<BackendWrapper> wrapMat(int backendId, int targetId, cv::Mat& m)
}
else if ( backendId = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
{
# ifdef HAVE_DNN_IE_NN_BUILDER_2019
return Ptr < BackendWrapper > ( new InfEngineBackendWrapper ( targetId , m ) ) ;
# else
CV_Error ( Error : : StsNotImplemented , " This OpenCV version is built without Inference Engine NN Builder API support " ) ;
# endif
CV_ERROR_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ;
}
else if ( backendId = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
{
# ifdef HAVE_DNN_NGRAPH
return Ptr < BackendWrapper > ( new NgraphBackendWrapper ( targetId , m ) ) ;
# else
CV_Error ( Error : : StsNotImplemented , " This OpenCV version is built without support of Inference Engine + nGraph " ) ;
CV_Error ( Error : : StsNotImplemented , " This OpenCV version is built without support of OpenVINO / Inference Engine + nGraph " ) ;
# endif
}
else if ( backendId = = DNN_BACKEND_WEBNN )
@ -1278,7 +1221,7 @@ struct Net::Impl : public detail::NetImplBase
}
else if ( preferableBackend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
{
return wrapMat ( preferableBackend , preferableTarget , host ) ;
CV_ERROR_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ;
}
else if ( preferableBackend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
{
@ -1407,7 +1350,7 @@ struct Net::Impl : public detail::NetImplBase
preferableBackend = ( Backend ) PARAM_DNN_BACKEND_DEFAULT ;
# ifdef HAVE_INF_ENGINE
if ( preferableBackend = = DNN_BACKEND_INFERENCE_ENGINE )
preferableBackend = getInferenceEngineBackendTypeParam ( ) ;
preferableBackend = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH ; // = getInferenceEngineBackendTypeParam();
# endif
CV_Assert ( preferableBackend ! = DNN_BACKEND_OPENCV | |
@ -1418,8 +1361,7 @@ struct Net::Impl : public detail::NetImplBase
preferableTarget = = DNN_TARGET_CPU | |
preferableTarget = = DNN_TARGET_OPENCL ) ;
# ifdef HAVE_INF_ENGINE
if ( preferableBackend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 | |
preferableBackend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
if ( preferableBackend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
{
CV_Assert (
( preferableTarget = = DNN_TARGET_CPU & & ( ! isArmComputePlugin ( ) | | preferableBackend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH ) ) | |
@ -1721,14 +1663,6 @@ struct Net::Impl : public detail::NetImplBase
}
else if ( preferableBackend = = DNN_BACKEND_HALIDE )
initHalideBackend ( ) ;
else if ( preferableBackend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
{
# ifdef HAVE_DNN_IE_NN_BUILDER_2019
initInfEngineBackend ( blobsToKeep_ ) ;
# else
CV_Assert ( false & & " This OpenCV version is built without Inference Engine NN Builder API support " ) ;
# endif
}
else if ( preferableBackend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
{
# ifdef HAVE_DNN_NGRAPH
@ -1750,7 +1684,7 @@ struct Net::Impl : public detail::NetImplBase
else if ( preferableBackend = = DNN_BACKEND_CUDA )
initCUDABackend ( blobsToKeep_ ) ;
else
CV_Error ( Error : : StsNotImplemented , " Unknown backend identifier " ) ;
CV_Error ( Error : : StsNotImplemented , cv : : format ( " Unknown backend identifier: %d " , preferableBackend ) ) ;
}
void initHalideBackend ( )
@ -1808,316 +1742,6 @@ struct Net::Impl : public detail::NetImplBase
}
}
# ifdef HAVE_DNN_IE_NN_BUILDER_2019
// Before launching Inference Engine graph we need to specify output blobs.
// This function requests output blobs based on inputs references of
// layers from default backend or layers from different graphs.
void addInfEngineNetOutputs ( LayerData & ld )
{
CV_TRACE_FUNCTION ( ) ;
Ptr < InfEngineBackendNet > layerNet ;
if ( ld . backendNodes . find ( preferableBackend ) ! = ld . backendNodes . end ( ) )
{
Ptr < BackendNode > node = ld . backendNodes [ preferableBackend ] ;
if ( ! node . empty ( ) )
{
Ptr < InfEngineBackendNode > ieNode = node . dynamicCast < InfEngineBackendNode > ( ) ;
CV_Assert ( ! ieNode . empty ( ) ) ; CV_Assert ( ! ieNode - > net . empty ( ) ) ;
layerNet = ieNode - > net ;
}
}
// For an every input reference we check that it belongs to one of
// the Inference Engine backend graphs. Request an output blob if it is.
// Do nothing if layer's input is from the same graph.
for ( int i = 0 ; i < ld . inputBlobsId . size ( ) ; + + i )
{
LayerData & inpLd = layers [ ld . inputBlobsId [ i ] . lid ] ;
Ptr < BackendNode > inpNode = inpLd . backendNodes [ preferableBackend ] ;
if ( ! inpNode . empty ( ) )
{
Ptr < InfEngineBackendNode > ieInpNode = inpNode . dynamicCast < InfEngineBackendNode > ( ) ;
CV_Assert ( ! ieInpNode . empty ( ) ) ; CV_Assert ( ! ieInpNode - > net . empty ( ) ) ;
if ( layerNet ! = ieInpNode - > net )
{
// layerNet is empty or nodes are from different graphs.
ieInpNode - > net - > addOutput ( ieInpNode - > layer . getName ( ) ) ;
}
}
}
}
void initInfEngineBackend ( const std : : vector < LayerPin > & blobsToKeep_ )
{
CV_TRACE_FUNCTION ( ) ;
CV_Assert_N ( preferableBackend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 , haveInfEngine ( ) ) ;
MapIdToLayerData : : iterator it ;
Ptr < InfEngineBackendNet > net ;
for ( it = layers . begin ( ) ; it ! = layers . end ( ) ; + + it )
{
LayerData & ld = it - > second ;
if ( ld . id = = 0 )
{
CV_Assert ( ( netInputLayer - > outNames . empty ( ) & & ld . outputBlobsWrappers . size ( ) = = 1 ) | |
( netInputLayer - > outNames . size ( ) = = ld . outputBlobsWrappers . size ( ) ) ) ;
for ( int i = 0 ; i < ld . outputBlobsWrappers . size ( ) ; + + i )
{
InferenceEngine : : DataPtr dataPtr = infEngineDataNode ( ld . outputBlobsWrappers [ i ] ) ;
# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2019010000)
dataPtr - > name = netInputLayer - > outNames . empty ( ) ? ld . name : netInputLayer - > outNames [ i ] ;
# else
dataPtr - > setName ( netInputLayer - > outNames . empty ( ) ? ld . name : netInputLayer - > outNames [ i ] ) ;
# endif
}
}
else
{
for ( int i = 0 ; i < ld . outputBlobsWrappers . size ( ) ; + + i )
{
InferenceEngine : : DataPtr dataPtr = infEngineDataNode ( ld . outputBlobsWrappers [ i ] ) ;
# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2019010000)
dataPtr - > name = ld . name ;
# else
dataPtr - > setName ( ld . name ) ;
# endif
}
}
}
if ( skipInfEngineInit )
{
Ptr < BackendNode > node = layers [ lastLayerId ] . backendNodes [ preferableBackend ] ;
CV_Assert ( ! node . empty ( ) ) ;
Ptr < InfEngineBackendNode > ieNode = node . dynamicCast < InfEngineBackendNode > ( ) ;
CV_Assert ( ! ieNode . empty ( ) ) ;
ieNode - > net - > reset ( ) ;
for ( it = layers . begin ( ) ; it ! = layers . end ( ) ; + + it )
{
LayerData & ld = it - > second ;
if ( ld . id = = 0 )
{
for ( int i = 0 ; i < ld . inputBlobsWrappers . size ( ) ; + + i )
{
InferenceEngine : : DataPtr dataPtr = infEngineDataNode ( ld . inputBlobsWrappers [ i ] ) ;
# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2019010000)
dataPtr - > name = netInputLayer - > outNames [ i ] ;
# else
dataPtr - > setName ( netInputLayer - > outNames [ i ] ) ;
# endif
}
}
else
{
for ( int i = 0 ; i < ld . outputBlobsWrappers . size ( ) ; + + i )
{
InferenceEngine : : DataPtr dataPtr = infEngineDataNode ( ld . outputBlobsWrappers [ i ] ) ;
# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2019010000)
dataPtr - > name = ld . name ;
# else
dataPtr - > setName ( ld . name ) ;
# endif
}
}
ieNode - > net - > addBlobs ( ld . inputBlobsWrappers ) ;
ieNode - > net - > addBlobs ( ld . outputBlobsWrappers ) ;
ld . skip = true ;
}
layers [ lastLayerId ] . skip = false ;
ieNode - > net - > init ( ( Target ) preferableTarget ) ;
return ;
}
// Build Inference Engine networks from sets of layers that support this
// backend. Split a whole model on several Inference Engine networks if
// some of layers are not implemented.
bool supportsCPUFallback = preferableTarget = = DNN_TARGET_CPU | |
BackendRegistry : : checkIETarget ( DNN_TARGET_CPU ) ;
// Set of all input and output blobs wrappers for current network.
std : : map < LayerPin , Ptr < BackendWrapper > > netBlobsWrappers ;
for ( it = layers . begin ( ) ; it ! = layers . end ( ) ; + + it )
{
LayerData & ld = it - > second ;
if ( ld . id = = 0 & & ld . skip )
continue ;
bool fused = ld . skip ;
Ptr < Layer > layer = ld . layerInstance ;
if ( ! fused & & ! layer - > supportBackend ( preferableBackend ) )
{
bool customizable = ld . id ! = 0 & &
INF_ENGINE_VER_MAJOR_GE ( INF_ENGINE_RELEASE_2019R2 ) & &
supportsCPUFallback ;
// TODO: there is a bug in Myriad plugin with custom layers shape infer.
if ( preferableTarget = = DNN_TARGET_MYRIAD | | preferableTarget = = DNN_TARGET_HDDL )
{
for ( int i = 0 ; customizable & & i < ld . inputBlobs . size ( ) ; + + i )
{
customizable = ld . inputBlobs [ i ] - > size [ 0 ] = = 1 ;
}
}
// TODO: fix these workarounds
if ( preferableTarget = = DNN_TARGET_MYRIAD | |
preferableTarget = = DNN_TARGET_HDDL | |
preferableTarget = = DNN_TARGET_OPENCL | |
preferableTarget = = DNN_TARGET_OPENCL_FP16 )
customizable & = ld . type ! = " Concat " ;
if ( preferableTarget = = DNN_TARGET_OPENCL | |
preferableTarget = = DNN_TARGET_OPENCL_FP16 )
customizable & = ld . type ! = " Power " ;
if ( preferableTarget = = DNN_TARGET_OPENCL )
customizable & = ld . type ! = " Eltwise " ;
if ( ! customizable )
{
addInfEngineNetOutputs ( ld ) ;
net = Ptr < InfEngineBackendNet > ( ) ;
netBlobsWrappers . clear ( ) ; // Is not used for R5 release but we don't wrap it to #ifdef.
layer - > preferableTarget = DNN_TARGET_CPU ;
continue ;
}
}
ld . skip = true ; // Initially skip all Inference Engine supported layers.
// Create a new network if one of inputs from different Inference Engine graph.
for ( int i = 0 ; i < ld . inputBlobsId . size ( ) ; + + i )
{
LayerData & inpLd = layers [ ld . inputBlobsId [ i ] . lid ] ;
Ptr < BackendNode > inpNode = inpLd . backendNodes [ preferableBackend ] ;
if ( ! inpNode . empty ( ) )
{
Ptr < InfEngineBackendNode > ieInpNode = inpNode . dynamicCast < InfEngineBackendNode > ( ) ;
CV_Assert ( ! ieInpNode . empty ( ) ) ; CV_Assert ( ! ieInpNode - > net . empty ( ) ) ;
if ( ieInpNode - > net ! = net )
{
net = Ptr < InfEngineBackendNet > ( ) ;
netBlobsWrappers . clear ( ) ; // Is not used for R5 release but we don't wrap it to #ifdef.
break ;
}
}
}
Ptr < BackendNode > node ;
if ( ! net . empty ( ) )
{
if ( fused )
{
bool inPlace = ld . inputBlobsId . size ( ) = = 1 & & ld . outputBlobs . size ( ) = = 1 & &
ld . inputBlobs [ 0 ] - > data = = ld . outputBlobs [ 0 ] . data ;
CV_Assert ( inPlace ) ;
node = layers [ ld . inputBlobsId [ 0 ] . lid ] . backendNodes [ preferableBackend ] ;
ld . inputBlobsWrappers = layers [ ld . inputBlobsId [ 0 ] . lid ] . inputBlobsWrappers ;
}
}
else
net = Ptr < InfEngineBackendNet > ( new InfEngineBackendNet ( ) ) ;
if ( ! fused )
{
if ( layer - > supportBackend ( preferableBackend ) )
node = layer - > initInfEngine ( ld . inputBlobsWrappers ) ;
else
{
node = Ptr < BackendNode > ( new InfEngineBackendNode (
ld . layerInstance , ld . inputBlobs , ld . outputBlobs , ld . internals ) ) ;
}
}
else if ( node . empty ( ) )
continue ;
CV_Assert ( ! node . empty ( ) ) ;
ld . backendNodes [ preferableBackend ] = node ;
Ptr < InfEngineBackendNode > ieNode = node . dynamicCast < InfEngineBackendNode > ( ) ;
CV_Assert ( ! ieNode . empty ( ) ) ;
ieNode - > net = net ;
for ( const auto & pin : blobsToKeep_ )
{
if ( pin . lid = = ld . id )
{
ieNode - > net - > addOutput ( ieNode - > layer . getName ( ) ) ;
break ;
}
}
// Convert weights in FP16 for specific targets.
if ( ( preferableTarget = = DNN_TARGET_OPENCL_FP16 | |
preferableTarget = = DNN_TARGET_MYRIAD | |
preferableTarget = = DNN_TARGET_HDDL | |
preferableTarget = = DNN_TARGET_FPGA ) & & ! fused )
{
# if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R1)
for ( const std : : string & name : { " weights " , " biases " } )
{
auto it = ieNode - > layer . getParameters ( ) . find ( name ) ;
if ( it ! = ieNode - > layer . getParameters ( ) . end ( ) )
{
InferenceEngine : : Blob : : Ptr bp = it - > second . as < InferenceEngine : : Blob : : Ptr > ( ) ;
it - > second = convertFp16 ( std : : const_pointer_cast < InferenceEngine : : Blob > ( bp ) ) ;
}
}
# else
auto & blobs = ieNode - > layer . getConstantData ( ) ;
if ( blobs . empty ( ) )
{
// In case of non weightable layer we have to specify
// it's precision adding dummy blob.
auto blob = InferenceEngine : : make_shared_blob < int16_t > (
InferenceEngine : : Precision : : FP16 ,
InferenceEngine : : Layout : : C , { 1 } ) ;
blob - > allocate ( ) ;
blobs [ " " ] = blob ;
}
else
{
for ( auto & it : blobs )
it . second = convertFp16 ( std : : const_pointer_cast < InferenceEngine : : Blob > ( it . second ) ) ;
}
# endif
}
if ( ! fused )
net - > addLayer ( ieNode - > layer ) ;
net - > connect ( ld . inputBlobsWrappers , ld . outputBlobsWrappers , ieNode - > layer . getName ( ) ) ;
net - > addBlobs ( ld . inputBlobsWrappers ) ;
net - > addBlobs ( ld . outputBlobsWrappers ) ;
addInfEngineNetOutputs ( ld ) ;
}
// Initialize all networks.
for ( MapIdToLayerData : : reverse_iterator it = layers . rbegin ( ) ; it ! = layers . rend ( ) ; + + it )
{
LayerData & ld = it - > second ;
if ( ld . backendNodes . find ( preferableBackend ) = = ld . backendNodes . end ( ) )
continue ;
Ptr < BackendNode > node = ld . backendNodes [ preferableBackend ] ;
if ( node . empty ( ) )
continue ;
Ptr < InfEngineBackendNode > ieNode = node . dynamicCast < InfEngineBackendNode > ( ) ;
if ( ieNode . empty ( ) )
continue ;
CV_Assert ( ! ieNode - > net . empty ( ) ) ;
if ( ! ieNode - > net - > isInitialized ( ) )
{
ieNode - > net - > init ( ( Target ) preferableTarget ) ;
ld . skip = false ;
}
}
}
# endif // HAVE_DNN_IE_NN_BUILDER_2019
# ifdef HAVE_DNN_NGRAPH
/** mark input pins as outputs from other subnetworks
@ -2162,7 +1786,7 @@ struct Net::Impl : public detail::NetImplBase
void initNgraphBackend ( const std : : vector < LayerPin > & blobsToKeep_ )
{
CV_TRACE_FUNCTION ( ) ;
CV_Assert_N ( preferableBackend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH , haveInfEngine ( ) ) ;
CV_CheckEQ ( preferableBackend , DNN_BACKEND_INFERENCE_ENGINE_NGRAPH , " " ) ;
Ptr < InfEngineNgraphNet > net ;
@ -2993,7 +2617,6 @@ struct Net::Impl : public detail::NetImplBase
if ( ! fusion | | ( preferableBackend ! = DNN_BACKEND_OPENCV & &
preferableBackend ! = DNN_BACKEND_CUDA & &
preferableBackend ! = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 & &
preferableBackend ! = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH ) )
return ;
@ -3814,14 +3437,12 @@ struct Net::Impl : public detail::NetImplBase
{
forwardHalide ( ld . outputBlobsWrappers , node ) ;
}
else if ( preferableBackend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
{
forwardInfEngine ( ld . outputBlobsWrappers , node , isAsync ) ;
}
# ifdef HAVE_INF_ENGINE
else if ( preferableBackend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
{
forwardNgraph ( ld . outputBlobsWrappers , node , isAsync ) ;
}
# endif
else if ( preferableBackend = = DNN_BACKEND_WEBNN )
{
forwardWebnn ( ld . outputBlobsWrappers , node , isAsync ) ;
@ -4181,27 +3802,13 @@ struct Net::Impl : public detail::NetImplBase
// Transfer data to CPU if it's require.
ld . outputBlobsWrappers [ pin . oid ] - > copyToHost ( ) ;
}
CV_Assert ( preferableBackend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 | | preferableBackend = = DNN_BACKEND_INFERENCE_ENGINE_N GRAPH ) ;
CV_Assert ( preferableBackend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH ) ;
if ( preferableBackend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ) {
# ifdef HAVE_DNN_IE_NN_BUILDER_2019
Ptr < InfEngineBackendWrapper > wrapper = ld . outputBlobsWrappers [ pin . oid ] . dynamicCast < InfEngineBackendWrapper > ( ) ;
return std : : move ( wrapper - > futureMat ) ;
# else
CV_Error ( Error : : StsNotImplemented , " This OpenCV version is built without Inference Engine NN Builder API support " ) ;
# endif
}
else if ( preferableBackend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
{
# ifdef HAVE_DNN_NGRAPH
Ptr < NgraphBackendWrapper > wrapper = ld . outputBlobsWrappers [ pin . oid ] . dynamicCast < NgraphBackendWrapper > ( ) ;
return std : : move ( wrapper - > futureMat ) ;
# else
CV_Error ( Error : : StsNotImplemented , " This OpenCV version is built without support of Inference Engine + nGraph " ) ;
# endif
}
CV_Error ( Error : : StsNotImplemented , " DNN: OpenVINO/nGraph backend is required " ) ;
# endif // HAVE_INF_ENGINE
CV_Error ( Error : : StsNotImplemented , " DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 backend is required " ) ;
}
AsyncArray getBlobAsync ( String outputName )
@ -4275,40 +3882,18 @@ Net Net::Impl::createNetworkFromModelOptimizer(InferenceEngine::CNNNetwork& ieNe
CV_TRACE_REGION_NEXT ( " backendNode " ) ;
Ptr < BackendNode > backendNode ;
# ifdef HAVE_DNN_NGRAPH
if ( DNN_BACKEND_INFERENCE_ENGINE_NGRAPH = = getInferenceEngineBackendTypeParam ( ) )
{
auto fake_node = std : : make_shared < ngraph : : op : : Parameter > ( ngraph : : element : : f32 , ngraph : : Shape { } ) ;
Ptr < InfEngineNgraphNode > backendNodeNGraph ( new InfEngineNgraphNode ( fake_node ) ) ;
backendNodeNGraph - > net = Ptr < InfEngineNgraphNet > ( new InfEngineNgraphNet ( * ( cvNet . impl ) , ieNet ) ) ;
backendNode = backendNodeNGraph ;
}
else
# endif
{
# ifdef HAVE_DNN_IE_NN_BUILDER_2019
Ptr < InfEngineBackendNode > backendNodeNN ( new InfEngineBackendNode ( InferenceEngine : : Builder : : Layer ( " " ) ) ) ;
backendNodeNN - > net = Ptr < InfEngineBackendNet > ( new InfEngineBackendNet ( ieNet ) ) ;
backendNode = backendNodeNN ;
# else
CV_Error ( Error : : StsNotImplemented , " This OpenCV version is built without Inference Engine NN Builder API support " ) ;
# endif
}
CV_TRACE_REGION_NEXT ( " register_outputs " ) ;
# ifdef HAVE_DNN_NGRAPH
auto ngraphFunction = ieNet . getFunction ( ) ;
# if INF_ENGINE_VER_MAJOR_LT(INF_ENGINE_RELEASE_2020_2)
std : : list < std : : shared_ptr < ngraph : : Node > > ngraphOperations ;
# else
std : : vector < std : : shared_ptr < ngraph : : Node > > ngraphOperations ;
# endif
if ( ngraphFunction )
{
ngraphOperations = ngraphFunction - > get_ops ( ) ;
}
# endif
CV_Assert ( ngraphFunction ) ;
std : : vector < std : : shared_ptr < ngraph : : Node > > ngraphOperations = ngraphFunction - > get_ops ( ) ;
for ( auto & it : ieNet . getOutputsInfo ( ) )
{
@ -4320,16 +3905,12 @@ Net Net::Impl::createNetworkFromModelOptimizer(InferenceEngine::CNNNetwork& ieNe
LayerData & ld = cvNet . impl - > layers [ lid ] ;
# ifdef HAVE_DNN_NGRAPH
if ( DNN_BACKEND_INFERENCE_ENGINE_NGRAPH = = getInferenceEngineBackendTypeParam ( ) )
{
Ptr < Layer > cvLayer ( new NgraphBackendLayer ( ieNet ) ) ;
cvLayer - > name = outputName ;
cvLayer - > type = " _unknown_ " ;
auto process_layer = [ & ] ( const std : : string & name ) - > bool
{
if ( ngraphFunction )
{
CV_TRACE_REGION ( " ngraph_function " ) ;
for ( const auto & op : ngraphOperations )
@ -4343,30 +3924,6 @@ Net Net::Impl::createNetworkFromModelOptimizer(InferenceEngine::CNNNetwork& ieNe
}
}
return false ;
}
else
{
# if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2020_4)
CV_Error ( Error : : StsNotImplemented , " This OpenCV version is built with Inference Engine which has dropped IR v7 support " ) ;
# else
CV_TRACE_REGION ( " legacy_cnn_layer " ) ;
try
{
InferenceEngine : : CNNLayerPtr ieLayer = ieNet . getLayerByName ( name . c_str ( ) ) ;
CV_Assert ( ieLayer ) ;
cvLayer - > type = ieLayer - > type ;
return true ;
}
catch ( const std : : exception & e )
{
CV_UNUSED ( e ) ;
CV_LOG_DEBUG ( NULL , " IE layer extraction failure: ' " < < name < < " ' - " < < e . what ( ) ) ;
return false ;
}
# endif
}
} ;
bool found = process_layer ( outputName ) ;
@ -4385,37 +3942,6 @@ Net Net::Impl::createNetworkFromModelOptimizer(InferenceEngine::CNNNetwork& ieNe
ld . layerInstance = cvLayer ;
ld . backendNodes [ DNN_BACKEND_INFERENCE_ENGINE_NGRAPH ] = backendNode ;
}
else
# endif
{
# ifdef HAVE_DNN_IE_NN_BUILDER_2019
Ptr < Layer > cvLayer ( new InfEngineBackendLayer ( ieNet ) ) ;
InferenceEngine : : CNNLayerPtr ieLayer ;
try
{
ieLayer = ieNet . getLayerByName ( outputName . c_str ( ) ) ;
}
catch ( . . . )
{
auto pos = outputName . rfind ( ' . ' ) ; // cut port number: ".0"
if ( pos ! = std : : string : : npos )
{
std : : string layerName = outputName . substr ( 0 , pos ) ;
ieLayer = ieNet . getLayerByName ( layerName . c_str ( ) ) ;
}
}
CV_Assert ( ieLayer ) ;
cvLayer - > name = outputName ;
cvLayer - > type = ieLayer - > type ;
ld . layerInstance = cvLayer ;
ld . backendNodes [ DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ] = backendNode ;
# else
CV_Error ( Error : : StsNotImplemented , " This OpenCV version is built without Inference Engine NN Builder API support " ) ;
# endif
}
for ( int i = 0 ; i < inputsNames . size ( ) ; + + i )
cvNet . connect ( 0 , i , lid , i ) ;
@ -4423,7 +3949,7 @@ Net Net::Impl::createNetworkFromModelOptimizer(InferenceEngine::CNNNetwork& ieNe
CV_TRACE_REGION_NEXT ( " finalize " ) ;
cvNet . setPreferableBackend ( getInferenceEngineBackendTypeParam ( ) ) ;
cvNet . setPreferableBackend ( DNN_BACKEND_INFERENCE_ENGINE_NGRAPH ) ;
cvNet . impl - > skipInfEngineInit = true ;
return cvNet ;
@ -4440,16 +3966,8 @@ Net Net::readFromModelOptimizer(const String& xml, const String& bin)
FPDenormalsIgnoreHintScope fp_denormals_ignore_scope ;
# if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R3)
InferenceEngine : : CNNNetReader reader ;
reader . ReadNetwork ( xml ) ;
reader . ReadWeights ( bin ) ;
InferenceEngine : : CNNNetwork ieNet = reader . getNetwork ( ) ;
# else
InferenceEngine : : Core & ie = getCore ( " " ) ;
InferenceEngine : : CNNNetwork ieNet = ie . ReadNetwork ( xml , bin ) ;
# endif
return Impl : : createNetworkFromModelOptimizer ( ieNet ) ;
# endif // HAVE_INF_ENGINE
@ -4478,26 +3996,6 @@ Net Net::readFromModelOptimizer(
FPDenormalsIgnoreHintScope fp_denormals_ignore_scope ;
# if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R3)
InferenceEngine : : CNNNetReader reader ;
try
{
reader . ReadNetwork ( bufferModelConfigPtr , bufferModelConfigSize ) ;
InferenceEngine : : TensorDesc tensorDesc ( InferenceEngine : : Precision : : U8 , { bufferWeightsSize } , InferenceEngine : : Layout : : C ) ;
InferenceEngine : : TBlob < uint8_t > : : Ptr weightsBlobPtr ( new InferenceEngine : : TBlob < uint8_t > ( tensorDesc ) ) ;
weightsBlobPtr - > allocate ( ) ;
std : : memcpy ( weightsBlobPtr - > buffer ( ) , ( uchar * ) bufferWeightsPtr , bufferWeightsSize ) ;
reader . SetWeights ( weightsBlobPtr ) ;
}
catch ( const std : : exception & e )
{
CV_Error ( Error : : StsError , std : : string ( " DNN: IE failed to load model: " ) + e . what ( ) ) ;
}
InferenceEngine : : CNNNetwork ieNet = reader . getNetwork ( ) ;
# else
InferenceEngine : : Core & ie = getCore ( " " ) ;
std : : string model ; model . assign ( ( char * ) bufferModelConfigPtr , bufferModelConfigSize ) ;
@ -4514,7 +4012,6 @@ Net Net::readFromModelOptimizer(
{
CV_Error ( Error : : StsError , std : : string ( " DNN: IE failed to load model: " ) + e . what ( ) ) ;
}
# endif
return Impl : : createNetworkFromModelOptimizer ( ieNet ) ;
# endif // HAVE_INF_ENGINE
@ -4621,8 +4118,8 @@ AsyncArray Net::forwardAsync(const String& outputName)
std : : vector < LayerPin > pins ( 1 , impl - > getPinByAlias ( layerName ) ) ;
impl - > setUpNet ( pins ) ;
if ( ! ( impl - > preferableBackend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 | | impl - > preferableBackend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH ) )
CV_Error ( Error : : StsNotImplemented , " DNN: Asynchronous forward is supported for Inference Engine backends only " ) ;
if ( impl - > preferableBackend ! = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
CV_Error ( Error : : StsNotImplemented , " DNN: Asynchronous forward is supported for Inference Engine backend only " ) ;
impl - > isAsync = true ;
impl - > forwardToLayer ( impl - > getLayerData ( layerName ) ) ;
@ -5047,7 +4544,7 @@ void Net::setPreferableBackend(int backendId)
# ifdef HAVE_INF_ENGINE
if ( backendId = = DNN_BACKEND_INFERENCE_ENGINE )
backendId = getInferenceEngineBackendTypeParam ( ) ;
backendId = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH ;
# endif
if ( impl - > preferableBackend ! = backendId )
@ -5292,8 +4789,8 @@ string Net::Impl::dump() const
case DNN_BACKEND_DEFAULT : backend = " DEFAULT/ " ; break ;
case DNN_BACKEND_HALIDE : backend = " HALIDE/ " ; break ;
case DNN_BACKEND_INFERENCE_ENGINE : // fallthru
case DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 : backend = " DLIE/ " ; break ;
case DNN_BACKEND_INFERENCE_ENGINE_NGRAPH : backend = " NGRAPH /" ; break ;
case DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 : // fallthru
case DNN_BACKEND_INFERENCE_ENGINE_NGRAPH : backend = " OpenVINO /" ; break ;
case DNN_BACKEND_OPENCV : backend = " OCV/ " ; break ;
case DNN_BACKEND_VKCOM : backend = " VULKAN/ " ; break ;
case DNN_BACKEND_CUDA : backend = " CUDA/ " ; break ;
@ -5894,13 +5391,6 @@ Ptr<BackendNode> Layer::initHalide(const std::vector<Ptr<BackendWrapper> > &)
return Ptr < BackendNode > ( ) ;
}
Ptr < BackendNode > Layer : : initInfEngine ( const std : : vector < Ptr < BackendWrapper > > & )
{
CV_Error ( Error : : StsNotImplemented , " Inference Engine pipeline of " + type +
" layers is not defined. " ) ;
return Ptr < BackendNode > ( ) ;
}
Ptr < BackendNode > Layer : : initNgraph ( const std : : vector < Ptr < BackendWrapper > > & inputs , const std : : vector < Ptr < BackendNode > > & nodes )
{
CV_Error ( Error : : StsNotImplemented , " Inference Engine pipeline of " + type +
Alexander Alekhin
3 years ago