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Move Inference Engine to new API

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pull/13539/head
Dmitry Kurtaev 6 years ago
parent 4ced27e149
commit f0ddf302b2
34 changed files with 852 additions and 80 deletions
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  1. 13
      modules/dnn/perf/perf_net.cpp
  2. 74
      modules/dnn/src/dnn.cpp
  3. 9
      modules/dnn/src/layers/batch_norm_layer.cpp
  4. 6
      modules/dnn/src/layers/blank_layer.cpp
  5. 9
      modules/dnn/src/layers/concat_layer.cpp
  6. 102
      modules/dnn/src/layers/convolution_layer.cpp
  7. 15
      modules/dnn/src/layers/crop_layer.cpp
  8. 20
      modules/dnn/src/layers/detection_output_layer.cpp
  9. 74
      modules/dnn/src/layers/elementwise_layers.cpp
  10. 25
      modules/dnn/src/layers/eltwise_layer.cpp
  11. 13
      modules/dnn/src/layers/flatten_layer.cpp
  12. 13
      modules/dnn/src/layers/fully_connected_layer.cpp
  13. 12
      modules/dnn/src/layers/lrn_layer.cpp
  14. 8
      modules/dnn/src/layers/mvn_layer.cpp
  15. 44
      modules/dnn/src/layers/normalize_bbox_layer.cpp
  16. 6
      modules/dnn/src/layers/permute_layer.cpp
  17. 43
      modules/dnn/src/layers/pooling_layer.cpp
  18. 53
      modules/dnn/src/layers/prior_box_layer.cpp
  19. 23
      modules/dnn/src/layers/proposal_layer.cpp
  20. 6
      modules/dnn/src/layers/reorg_layer.cpp
  21. 24
      modules/dnn/src/layers/reshape_layer.cpp
  22. 41
      modules/dnn/src/layers/resize_layer.cpp
  23. 24
      modules/dnn/src/layers/scale_layer.cpp
  24. 19
      modules/dnn/src/layers/slice_layer.cpp
  25. 8
      modules/dnn/src/layers/softmax_layer.cpp
  26. 137
      modules/dnn/src/op_inf_engine.cpp
  27. 66
      modules/dnn/src/op_inf_engine.hpp
  28. 4
      modules/dnn/test/test_backends.cpp
  29. 2
      modules/dnn/test/test_darknet_importer.cpp
  30. 3
      modules/dnn/test/test_halide_layers.cpp
  31. 4
      modules/dnn/test/test_layers.cpp
  32. 10
      modules/dnn/test/test_onnx_importer.cpp
  33. 13
      modules/dnn/test/test_tf_importer.cpp
  34. 9
      modules/dnn/test/test_torch_importer.cpp

13
modules/dnn/perf/perf_net.cpp
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@ -157,8 +157,7 @@ PERF_TEST_P_(DNNTestNetwork, MobileNet_SSD_v2_TensorFlow)
PERF_TEST_P_(DNNTestNetwork, DenseNet_121) PERF_TEST_P_(DNNTestNetwork, DenseNet_121)
{ {
if (backend == DNN_BACKEND_HALIDE || if (backend == DNN_BACKEND_HALIDE)
(backend == DNN_BACKEND_INFERENCE_ENGINE && (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD)))
throw SkipTestException(""); throw SkipTestException("");
processNet("dnn/DenseNet_121.caffemodel", "dnn/DenseNet_121.prototxt", "", processNet("dnn/DenseNet_121.caffemodel", "dnn/DenseNet_121.prototxt", "",
Mat(cv::Size(224, 224), CV_32FC3)); Mat(cv::Size(224, 224), CV_32FC3));
@ -211,8 +210,7 @@ PERF_TEST_P_(DNNTestNetwork, Inception_v2_SSD_TensorFlow)
PERF_TEST_P_(DNNTestNetwork, YOLOv3) PERF_TEST_P_(DNNTestNetwork, YOLOv3)
{ {
if (backend == DNN_BACKEND_HALIDE || if (backend == DNN_BACKEND_HALIDE)
(backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD))
throw SkipTestException(""); throw SkipTestException("");
Mat sample = imread(findDataFile("dnn/dog416.png", false)); Mat sample = imread(findDataFile("dnn/dog416.png", false));
Mat inp; Mat inp;
@ -222,8 +220,11 @@ PERF_TEST_P_(DNNTestNetwork, YOLOv3)
PERF_TEST_P_(DNNTestNetwork, EAST_text_detection) PERF_TEST_P_(DNNTestNetwork, EAST_text_detection)
{ {
if (backend == DNN_BACKEND_HALIDE || if (backend == DNN_BACKEND_HALIDE
(backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)) #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE < 2018030000
|| (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
#endif
)
throw SkipTestException(""); throw SkipTestException("");
processNet("dnn/frozen_east_text_detection.pb", "", "", Mat(cv::Size(320, 320), CV_32FC3)); processNet("dnn/frozen_east_text_detection.pb", "", "", Mat(cv::Size(320, 320), CV_32FC3));
} }

74
modules/dnn/src/dnn.cpp
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@ -701,12 +701,6 @@ struct DataLayer : public Layer
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{ {
#ifdef HAVE_INF_ENGINE #ifdef HAVE_INF_ENGINE
InferenceEngine::LayerParams lp;
lp.name = name;
lp.type = "ScaleShift";
lp.precision = InferenceEngine::Precision::FP32;
std::shared_ptr<InferenceEngine::ScaleShiftLayer> ieLayer(new InferenceEngine::ScaleShiftLayer(lp));
CV_CheckEQ(inputsData.size(), (size_t)1, ""); CV_CheckEQ(inputsData.size(), (size_t)1, "");
CV_CheckEQ(inputsData[0].dims, 4, ""); CV_CheckEQ(inputsData[0].dims, 4, "");
const size_t numChannels = inputsData[0].size[1]; const size_t numChannels = inputsData[0].size[1];
@ -717,7 +711,6 @@ struct DataLayer : public Layer
{numChannels}); {numChannels});
weights->allocate(); weights->allocate();
weights->set(std::vector<float>(numChannels, scaleFactors[0])); weights->set(std::vector<float>(numChannels, scaleFactors[0]));
ieLayer->_weights = weights;
// Mean subtraction // Mean subtraction
auto biases = InferenceEngine::make_shared_blob<float>(InferenceEngine::Precision::FP32, auto biases = InferenceEngine::make_shared_blob<float>(InferenceEngine::Precision::FP32,
@ -729,8 +722,21 @@ struct DataLayer : public Layer
biasesVec[i] = -means[0][i] * scaleFactors[0]; biasesVec[i] = -means[0][i] * scaleFactors[0];
} }
biases->set(biasesVec); biases->set(biasesVec);
ieLayer->_biases = biases;
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
InferenceEngine::Builder::ScaleShiftLayer ieLayer(name);
ieLayer.setWeights(weights);
ieLayer.setBiases(biases);
#else
InferenceEngine::LayerParams lp;
lp.name = name;
lp.type = "ScaleShift";
lp.precision = InferenceEngine::Precision::FP32;
std::shared_ptr<InferenceEngine::ScaleShiftLayer> ieLayer(new InferenceEngine::ScaleShiftLayer(lp));
ieLayer->_weights = weights;
ieLayer->_biases = biases;
#endif
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer)); return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#endif // HAVE_INF_ENGINE #endif // HAVE_INF_ENGINE
return Ptr<BackendNode>(); return Ptr<BackendNode>();
@ -1451,7 +1457,11 @@ struct Net::Impl
if (layerNet != ieInpNode->net) if (layerNet != ieInpNode->net)
{ {
// layerNet is empty or nodes are from different graphs. // layerNet is empty or nodes are from different graphs.
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
ieInpNode->net->addOutput(ieInpNode->layer.getName());
#else
ieInpNode->net->addOutput(ieInpNode->layer->name); ieInpNode->net->addOutput(ieInpNode->layer->name);
#endif
} }
} }
} }
@ -1527,7 +1537,7 @@ struct Net::Impl
// Build Inference Engine networks from sets of layers that support this // Build Inference Engine networks from sets of layers that support this
// backend. Split a whole model on several Inference Engine networks if // backend. Split a whole model on several Inference Engine networks if
// some of layers is not implemented. // some of layers are not implemented.
// Set of all input and output blobs wrappers for current network. // Set of all input and output blobs wrappers for current network.
std::map<LayerPin, Ptr<BackendWrapper> > netBlobsWrappers; std::map<LayerPin, Ptr<BackendWrapper> > netBlobsWrappers;
@ -1543,7 +1553,7 @@ struct Net::Impl
{ {
addInfEngineNetOutputs(ld); addInfEngineNetOutputs(ld);
net = Ptr<InfEngineBackendNet>(); net = Ptr<InfEngineBackendNet>();
netBlobsWrappers.clear(); netBlobsWrappers.clear(); // Is not used for R5 release but we don't wrap it to #ifdef.
layer->preferableTarget = DNN_TARGET_CPU; layer->preferableTarget = DNN_TARGET_CPU;
continue; continue;
} }
@ -1561,12 +1571,13 @@ struct Net::Impl
if (ieInpNode->net != net) if (ieInpNode->net != net)
{ {
net = Ptr<InfEngineBackendNet>(); net = Ptr<InfEngineBackendNet>();
netBlobsWrappers.clear(); netBlobsWrappers.clear(); // Is not used for R5 release but we don't wrap it to #ifdef.
break; break;
} }
} }
} }
#if INF_ENGINE_VER_MAJOR_LT(INF_ENGINE_RELEASE_2018R5)
// The same blobs wrappers cannot be shared between two Inference Engine // The same blobs wrappers cannot be shared between two Inference Engine
// networks because of explicit references between layers and blobs. // networks because of explicit references between layers and blobs.
// So we need to rewrap all the external blobs. // So we need to rewrap all the external blobs.
@ -1583,6 +1594,7 @@ struct Net::Impl
ld.inputBlobsWrappers[i] = it->second; ld.inputBlobsWrappers[i] = it->second;
} }
netBlobsWrappers[LayerPin(ld.id, 0)] = ld.outputBlobsWrappers[0]; netBlobsWrappers[LayerPin(ld.id, 0)] = ld.outputBlobsWrappers[0];
#endif // IE < R5
Ptr<BackendNode> node; Ptr<BackendNode> node;
if (!net.empty()) if (!net.empty())
@ -1613,6 +1625,40 @@ struct Net::Impl
CV_Assert(!ieNode.empty()); CV_Assert(!ieNode.empty());
ieNode->net = net; ieNode->net = net;
// Convert weights in FP16 for specific targets.
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
if ((preferableTarget == DNN_TARGET_OPENCL_FP16 ||
preferableTarget == DNN_TARGET_MYRIAD ||
preferableTarget == DNN_TARGET_FPGA) && !fused)
{
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));
}
}
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);
#else // IE >= R5
auto weightableLayer = std::dynamic_pointer_cast<InferenceEngine::WeightableLayer>(ieNode->layer); auto weightableLayer = std::dynamic_pointer_cast<InferenceEngine::WeightableLayer>(ieNode->layer);
if ((preferableTarget == DNN_TARGET_OPENCL_FP16 || if ((preferableTarget == DNN_TARGET_OPENCL_FP16 ||
preferableTarget == DNN_TARGET_MYRIAD || preferableTarget == DNN_TARGET_MYRIAD ||
@ -1650,10 +1696,10 @@ struct Net::Impl
if (!fused) if (!fused)
net->addLayer(ieNode->layer); net->addLayer(ieNode->layer);
addInfEngineNetOutputs(ld); addInfEngineNetOutputs(ld);
#endif // IE >= R5
} }
// Initialize all networks. // Initialize all networks.
std::set<InfEngineBackendNet> initializedNets;
for (MapIdToLayerData::reverse_iterator it = layers.rbegin(); it != layers.rend(); ++it) for (MapIdToLayerData::reverse_iterator it = layers.rbegin(); it != layers.rend(); ++it)
{ {
LayerData &ld = it->second; LayerData &ld = it->second;
@ -2546,7 +2592,11 @@ Net Net::readFromModelOptimizer(const String& xml, const String& bin)
Net cvNet; Net cvNet;
cvNet.setInputsNames(inputsNames); cvNet.setInputsNames(inputsNames);
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
Ptr<InfEngineBackendNode> backendNode(new InfEngineBackendNode(InferenceEngine::Builder::Layer("")));
#else
Ptr<InfEngineBackendNode> backendNode(new InfEngineBackendNode(0)); Ptr<InfEngineBackendNode> backendNode(new InfEngineBackendNode(0));
#endif
backendNode->net = Ptr<InfEngineBackendNet>(new InfEngineBackendNet(ieNet)); backendNode->net = Ptr<InfEngineBackendNet>(new InfEngineBackendNet(ieNet));
for (auto& it : ieNet.getOutputsInfo()) for (auto& it : ieNet.getOutputsInfo())
{ {

9
modules/dnn/src/layers/batch_norm_layer.cpp
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@ -349,6 +349,14 @@ public:
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{ {
#ifdef HAVE_INF_ENGINE #ifdef HAVE_INF_ENGINE
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
InferenceEngine::Builder::ScaleShiftLayer ieLayer(name);
const size_t numChannels = weights_.total();
ieLayer.setWeights(wrapToInfEngineBlob(weights_, {numChannels}, InferenceEngine::Layout::C));
ieLayer.setBiases(wrapToInfEngineBlob(bias_, {numChannels}, InferenceEngine::Layout::C));
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#else
InferenceEngine::LayerParams lp; InferenceEngine::LayerParams lp;
lp.name = name; lp.name = name;
lp.type = "ScaleShift"; lp.type = "ScaleShift";
@ -360,6 +368,7 @@ public:
ieLayer->_biases = wrapToInfEngineBlob(bias_, {numChannels}, InferenceEngine::Layout::C); ieLayer->_biases = wrapToInfEngineBlob(bias_, {numChannels}, InferenceEngine::Layout::C);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer)); return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#endif
#endif // HAVE_INF_ENGINE #endif // HAVE_INF_ENGINE
return Ptr<BackendNode>(); return Ptr<BackendNode>();
} }

6
modules/dnn/src/layers/blank_layer.cpp
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@ -110,6 +110,11 @@ public:
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
{ {
#ifdef HAVE_INF_ENGINE #ifdef HAVE_INF_ENGINE
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
InferenceEngine::Builder::SplitLayer ieLayer(name);
ieLayer.setOutputPorts({InferenceEngine::Port()});
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#else
InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]); InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]);
CV_Assert(!input->dims.empty()); CV_Assert(!input->dims.empty());
@ -123,6 +128,7 @@ public:
ieLayer->params["out_sizes"] = format("%d", (int)input->dims[0]); ieLayer->params["out_sizes"] = format("%d", (int)input->dims[0]);
#endif #endif
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer)); return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#endif
#endif // HAVE_INF_ENGINE #endif // HAVE_INF_ENGINE
return Ptr<BackendNode>(); return Ptr<BackendNode>();
} }

9
modules/dnn/src/layers/concat_layer.cpp
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@ -301,6 +301,14 @@ public:
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
{ {
#ifdef HAVE_INF_ENGINE #ifdef HAVE_INF_ENGINE
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]);
InferenceEngine::Builder::ConcatLayer ieLayer(name);
ieLayer.setAxis(clamp(axis, input->dims.size()));
ieLayer.setInputPorts(std::vector<InferenceEngine::Port>(inputs.size()));
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#else
InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]); InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]);
InferenceEngine::LayerParams lp; InferenceEngine::LayerParams lp;
lp.name = name; lp.name = name;
@ -309,6 +317,7 @@ public:
std::shared_ptr<InferenceEngine::ConcatLayer> ieLayer(new InferenceEngine::ConcatLayer(lp)); std::shared_ptr<InferenceEngine::ConcatLayer> ieLayer(new InferenceEngine::ConcatLayer(lp));
ieLayer->_axis = clamp(axis, input->dims.size()); ieLayer->_axis = clamp(axis, input->dims.size());
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer)); return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#endif
#endif // HAVE_INF_ENGINE #endif // HAVE_INF_ENGINE
return Ptr<BackendNode>(); return Ptr<BackendNode>();
} }

102
modules/dnn/src/layers/convolution_layer.cpp
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@ -451,6 +451,54 @@ public:
const int inpGroupCn = blobs[0].size[1]; const int inpGroupCn = blobs[0].size[1];
const int group = inpCn / inpGroupCn; const int group = inpCn / inpGroupCn;
auto ieWeights = wrapToInfEngineBlob(blobs[0], InferenceEngine::Layout::OIHW);
if (newWeightAndBias)
{
if (weightsMat.isContinuous())
{
Mat fusedWeights = weightsMat.reshape(1, blobs[0].dims, blobs[0].size);
ieWeights = wrapToInfEngineBlob(fusedWeights, InferenceEngine::Layout::OIHW);
}
else
{
ieWeights = InferenceEngine::make_shared_blob<float>(
InferenceEngine::Precision::FP32, InferenceEngine::Layout::OIHW,
ieWeights->dims());
ieWeights->allocate();
Mat newWeights = infEngineBlobToMat(ieWeights).reshape(1, outCn);
Mat fusedWeights = weightsMat.colRange(0, newWeights.cols);
fusedWeights.copyTo(newWeights);
}
}
InferenceEngine::Blob::Ptr ieBiases;
if (hasBias() || fusedBias)
{
Mat biasesMat({outCn}, CV_32F, &biasvec[0]);
ieBiases = wrapToInfEngineBlob(biasesMat, {(size_t)outCn}, InferenceEngine::Layout::C);
}
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
InferenceEngine::Builder::ConvolutionLayer ieLayer(name);
ieLayer.setKernel({kernel.height, kernel.width});
ieLayer.setStrides({stride.height, stride.width});
ieLayer.setDilation({dilation.height, dilation.width});
ieLayer.setPaddingsBegin({pad.height, pad.width});
ieLayer.setPaddingsEnd({pad.height, pad.width});
ieLayer.setGroup(group);
ieLayer.setOutDepth(outCn);
ieLayer.setWeights(ieWeights);
if (ieBiases)
ieLayer.setBiases(ieBiases);
InferenceEngine::Builder::Layer l = ieLayer;
if (!padMode.empty())
l.getParameters()["auto_pad"] = padMode == "VALID" ? std::string("valid") : std::string("same_upper");
return Ptr<BackendNode>(new InfEngineBackendNode(l));
#else
InferenceEngine::LayerParams lp; InferenceEngine::LayerParams lp;
lp.name = name; lp.name = name;
lp.type = "Convolution"; lp.type = "Convolution";
@ -487,32 +535,11 @@ public:
ieLayer->_out_depth = outCn; ieLayer->_out_depth = outCn;
ieLayer->_group = group; ieLayer->_group = group;
ieLayer->_weights = wrapToInfEngineBlob(blobs[0], InferenceEngine::Layout::OIHW); ieLayer->_weights = ieWeights;
if (newWeightAndBias) if (ieBiases)
{ ieLayer->_biases = ieBiases;
if (weightsMat.isContinuous())
{
Mat fusedWeights = weightsMat.reshape(1, blobs[0].dims, blobs[0].size);
ieLayer->_weights = wrapToInfEngineBlob(fusedWeights, InferenceEngine::Layout::OIHW);
}
else
{
ieLayer->_weights = InferenceEngine::make_shared_blob<float>(
InferenceEngine::Precision::FP32, InferenceEngine::Layout::OIHW,
ieLayer->_weights->dims());
ieLayer->_weights->allocate();
Mat newWeights = infEngineBlobToMat(ieLayer->_weights).reshape(1, outCn);
Mat fusedWeights = weightsMat.colRange(0, newWeights.cols);
fusedWeights.copyTo(newWeights);
}
}
if (hasBias() || fusedBias)
{
Mat biasesMat({outCn}, CV_32F, &biasvec[0]);
ieLayer->_biases = wrapToInfEngineBlob(biasesMat, {(size_t)outCn}, InferenceEngine::Layout::C);
}
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer)); return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#endif
#endif // HAVE_INF_ENGINE #endif // HAVE_INF_ENGINE
return Ptr<BackendNode>(); return Ptr<BackendNode>();
} }
@ -1123,6 +1150,9 @@ public:
#ifdef HAVE_INF_ENGINE #ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE) if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
{ {
if (INF_ENGINE_RELEASE == 2018050000 && (adjustPad.height || adjustPad.width))
return false;
const int outGroupCn = blobs[0].size[1]; // Weights are in IOHW layout const int outGroupCn = blobs[0].size[1]; // Weights are in IOHW layout
const int group = numOutput / outGroupCn; const int group = numOutput / outGroupCn;
if (group != 1) if (group != 1)
@ -1677,6 +1707,27 @@ public:
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> > &) CV_OVERRIDE virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> > &) CV_OVERRIDE
{ {
#ifdef HAVE_INF_ENGINE #ifdef HAVE_INF_ENGINE
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
const int outGroupCn = blobs[0].size[1]; // Weights are in IOHW layout
const int group = numOutput / outGroupCn;
InferenceEngine::Builder::DeconvolutionLayer ieLayer(name);
ieLayer.setKernel({kernel.height, kernel.width});
ieLayer.setStrides({stride.height, stride.width});
ieLayer.setDilation({dilation.height, dilation.width});
ieLayer.setPaddingsBegin({pad.height, pad.width});
ieLayer.setPaddingsEnd({pad.height, pad.width});
ieLayer.setGroup(group);
ieLayer.setOutDepth(numOutput);
ieLayer.setWeights(wrapToInfEngineBlob(blobs[0], InferenceEngine::Layout::OIHW));
if (hasBias())
{
ieLayer.setBiases(wrapToInfEngineBlob(blobs[1], {(size_t)numOutput}, InferenceEngine::Layout::C));
}
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#else
const int outGroupCn = blobs[0].size[1]; // Weights are in IOHW layout const int outGroupCn = blobs[0].size[1]; // Weights are in IOHW layout
const int group = numOutput / outGroupCn; const int group = numOutput / outGroupCn;
@ -1716,6 +1767,7 @@ public:
ieLayer->_biases = wrapToInfEngineBlob(blobs[1], {(size_t)numOutput}, InferenceEngine::Layout::C); ieLayer->_biases = wrapToInfEngineBlob(blobs[1], {(size_t)numOutput}, InferenceEngine::Layout::C);
} }
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer)); return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#endif
#endif // HAVE_INF_ENGINE #endif // HAVE_INF_ENGINE
return Ptr<BackendNode>(); return Ptr<BackendNode>();
} }

15
modules/dnn/src/layers/crop_layer.cpp
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@ -67,8 +67,12 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE virtual bool supportBackend(int backendId) CV_OVERRIDE
{ {
return backendId == DNN_BACKEND_OPENCV || #ifdef HAVE_INF_ENGINE
(backendId == DNN_BACKEND_INFERENCE_ENGINE && crop_ranges.size() == 4); if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
return INF_ENGINE_VER_MAJOR_LT(INF_ENGINE_RELEASE_2018R5) && crop_ranges.size() == 4;
else
#endif
return backendId == DNN_BACKEND_OPENCV;
} }
bool getMemoryShapes(const std::vector<MatShape> &inputs, bool getMemoryShapes(const std::vector<MatShape> &inputs,
@ -145,9 +149,10 @@ public:
input(&crop_ranges[0]).copyTo(outputs[0]); input(&crop_ranges[0]).copyTo(outputs[0]);
} }
#ifdef HAVE_INF_ENGINE
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{ {
#ifdef HAVE_INF_ENGINE #if INF_ENGINE_VER_MAJOR_LT(INF_ENGINE_RELEASE_2018R5)
InferenceEngine::LayerParams lp; InferenceEngine::LayerParams lp;
lp.name = name; lp.name = name;
lp.type = "Crop"; lp.type = "Crop";
@ -181,9 +186,11 @@ public:
ieLayer->dim.push_back(crop_ranges[3].end - crop_ranges[3].start); ieLayer->dim.push_back(crop_ranges[3].end - crop_ranges[3].start);
#endif #endif
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer)); return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#endif // HAVE_INF_ENGINE #else
return Ptr<BackendNode>(); return Ptr<BackendNode>();
#endif // IE < R5
} }
#endif
std::vector<Range> crop_ranges; std::vector<Range> crop_ranges;
}; };

20
modules/dnn/src/layers/detection_output_layer.cpp
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@ -939,6 +939,25 @@ public:
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{ {
#ifdef HAVE_INF_ENGINE #ifdef HAVE_INF_ENGINE
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
InferenceEngine::Builder::DetectionOutputLayer ieLayer(name);
ieLayer.setNumClasses(_numClasses);
ieLayer.setShareLocation(_shareLocation);
ieLayer.setBackgroudLabelId(_backgroundLabelId);
ieLayer.setNMSThreshold(_nmsThreshold);
ieLayer.setTopK(_topK);
ieLayer.setKeepTopK(_keepTopK);
ieLayer.setConfidenceThreshold(_confidenceThreshold);
ieLayer.setVariantEncodedInTarget(_varianceEncodedInTarget);
ieLayer.setCodeType("caffe.PriorBoxParameter." + _codeType);
ieLayer.setInputPorts(std::vector<InferenceEngine::Port>(3));
InferenceEngine::Builder::Layer l = ieLayer;
l.getParameters()["eta"] = std::string("1.0");
return Ptr<BackendNode>(new InfEngineBackendNode(l));
#else
InferenceEngine::LayerParams lp; InferenceEngine::LayerParams lp;
lp.name = name; lp.name = name;
lp.type = "DetectionOutput"; lp.type = "DetectionOutput";
@ -956,6 +975,7 @@ public:
ieLayer->params["variance_encoded_in_target"] = _varianceEncodedInTarget ? "1" : "0"; ieLayer->params["variance_encoded_in_target"] = _varianceEncodedInTarget ? "1" : "0";
ieLayer->params["code_type"] = "caffe.PriorBoxParameter." + _codeType; ieLayer->params["code_type"] = "caffe.PriorBoxParameter." + _codeType;
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer)); return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#endif
#endif // HAVE_INF_ENGINE #endif // HAVE_INF_ENGINE
return Ptr<BackendNode>(); return Ptr<BackendNode>();
} }

74
modules/dnn/src/layers/elementwise_layers.cpp
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@ -152,10 +152,16 @@ public:
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{ {
#ifdef HAVE_INF_ENGINE #ifdef HAVE_INF_ENGINE
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
InferenceEngine::Builder::Layer ieLayer = func.initInfEngineBuilderAPI();
ieLayer.setName(this->name);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#else
InferenceEngine::LayerParams lp; InferenceEngine::LayerParams lp;
lp.name = this->name; lp.name = this->name;
lp.precision = InferenceEngine::Precision::FP32; lp.precision = InferenceEngine::Precision::FP32;
return Ptr<BackendNode>(new InfEngineBackendNode(func.initInfEngine(lp))); return Ptr<BackendNode>(new InfEngineBackendNode(func.initInfEngine(lp)));
#endif
#endif // HAVE_INF_ENGINE #endif // HAVE_INF_ENGINE
return Ptr<BackendNode>(); return Ptr<BackendNode>();
} }
@ -345,6 +351,12 @@ struct ReLUFunctor
#endif // HAVE_HALIDE #endif // HAVE_HALIDE
#ifdef HAVE_INF_ENGINE #ifdef HAVE_INF_ENGINE
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
{
return InferenceEngine::Builder::ReLULayer("").setNegativeSlope(slope);
}
#else
InferenceEngine::CNNLayerPtr initInfEngine(InferenceEngine::LayerParams& lp) InferenceEngine::CNNLayerPtr initInfEngine(InferenceEngine::LayerParams& lp)
{ {
lp.type = "ReLU"; lp.type = "ReLU";
@ -353,6 +365,7 @@ struct ReLUFunctor
ieLayer->params["negative_slope"] = format("%f", slope); ieLayer->params["negative_slope"] = format("%f", slope);
return ieLayer; return ieLayer;
} }
#endif
#endif // HAVE_INF_ENGINE #endif // HAVE_INF_ENGINE
bool tryFuse(Ptr<dnn::Layer>&) { return false; } bool tryFuse(Ptr<dnn::Layer>&) { return false; }
@ -452,6 +465,12 @@ struct ReLU6Functor
#endif // HAVE_HALIDE #endif // HAVE_HALIDE
#ifdef HAVE_INF_ENGINE #ifdef HAVE_INF_ENGINE
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
{
return InferenceEngine::Builder::ClampLayer("").setMinValue(minValue).setMaxValue(maxValue);
}
#else
InferenceEngine::CNNLayerPtr initInfEngine(InferenceEngine::LayerParams& lp) InferenceEngine::CNNLayerPtr initInfEngine(InferenceEngine::LayerParams& lp)
{ {
lp.type = "Clamp"; lp.type = "Clamp";
@ -462,6 +481,7 @@ struct ReLU6Functor
ieLayer->params["max"] = format("%f", maxValue); ieLayer->params["max"] = format("%f", maxValue);
return ieLayer; return ieLayer;
} }
#endif
#endif // HAVE_INF_ENGINE #endif // HAVE_INF_ENGINE
bool tryFuse(Ptr<dnn::Layer>&) { return false; } bool tryFuse(Ptr<dnn::Layer>&) { return false; }
@ -530,12 +550,19 @@ struct TanHFunctor
#endif // HAVE_HALIDE #endif // HAVE_HALIDE
#ifdef HAVE_INF_ENGINE #ifdef HAVE_INF_ENGINE
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
{
return InferenceEngine::Builder::TanHLayer("");
}
#else
InferenceEngine::CNNLayerPtr initInfEngine(InferenceEngine::LayerParams& lp) InferenceEngine::CNNLayerPtr initInfEngine(InferenceEngine::LayerParams& lp)
{ {
lp.type = "TanH"; lp.type = "TanH";
std::shared_ptr<InferenceEngine::CNNLayer> ieLayer(new InferenceEngine::CNNLayer(lp)); std::shared_ptr<InferenceEngine::CNNLayer> ieLayer(new InferenceEngine::CNNLayer(lp));
return ieLayer; return ieLayer;
} }
#endif
#endif // HAVE_INF_ENGINE #endif // HAVE_INF_ENGINE
bool tryFuse(Ptr<dnn::Layer>&) { return false; } bool tryFuse(Ptr<dnn::Layer>&) { return false; }
@ -604,12 +631,19 @@ struct SigmoidFunctor
#endif // HAVE_HALIDE #endif // HAVE_HALIDE
#ifdef HAVE_INF_ENGINE #ifdef HAVE_INF_ENGINE
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
{
return InferenceEngine::Builder::SigmoidLayer("");
}
#else
InferenceEngine::CNNLayerPtr initInfEngine(InferenceEngine::LayerParams& lp) InferenceEngine::CNNLayerPtr initInfEngine(InferenceEngine::LayerParams& lp)
{ {
lp.type = "Sigmoid"; lp.type = "Sigmoid";
std::shared_ptr<InferenceEngine::CNNLayer> ieLayer(new InferenceEngine::CNNLayer(lp)); std::shared_ptr<InferenceEngine::CNNLayer> ieLayer(new InferenceEngine::CNNLayer(lp));
return ieLayer; return ieLayer;
} }
#endif
#endif // HAVE_INF_ENGINE #endif // HAVE_INF_ENGINE
bool tryFuse(Ptr<dnn::Layer>&) { return false; } bool tryFuse(Ptr<dnn::Layer>&) { return false; }
@ -680,11 +714,18 @@ struct ELUFunctor
#endif // HAVE_HALIDE #endif // HAVE_HALIDE
#ifdef HAVE_INF_ENGINE #ifdef HAVE_INF_ENGINE
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
{
return InferenceEngine::Builder::ELULayer("");
}
#else
InferenceEngine::CNNLayerPtr initInfEngine(InferenceEngine::LayerParams& lp) InferenceEngine::CNNLayerPtr initInfEngine(InferenceEngine::LayerParams& lp)
{ {
lp.type = "ELU"; lp.type = "ELU";
return InferenceEngine::CNNLayerPtr(new InferenceEngine::CNNLayer(lp)); return InferenceEngine::CNNLayerPtr(new InferenceEngine::CNNLayer(lp));
} }
#endif
#endif // HAVE_INF_ENGINE #endif // HAVE_INF_ENGINE
bool tryFuse(Ptr<dnn::Layer>&) { return false; } bool tryFuse(Ptr<dnn::Layer>&) { return false; }
@ -753,6 +794,12 @@ struct AbsValFunctor
#endif // HAVE_HALIDE #endif // HAVE_HALIDE
#ifdef HAVE_INF_ENGINE #ifdef HAVE_INF_ENGINE
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
{
return InferenceEngine::Builder::ReLULayer("").setNegativeSlope(-1);
}
#else
InferenceEngine::CNNLayerPtr initInfEngine(InferenceEngine::LayerParams& lp) InferenceEngine::CNNLayerPtr initInfEngine(InferenceEngine::LayerParams& lp)
{ {
lp.type = "ReLU"; lp.type = "ReLU";
@ -761,6 +808,7 @@ struct AbsValFunctor
ieLayer->params["negative_slope"] = "-1.0"; ieLayer->params["negative_slope"] = "-1.0";
return ieLayer; return ieLayer;
} }
#endif
#endif // HAVE_INF_ENGINE #endif // HAVE_INF_ENGINE
bool tryFuse(Ptr<dnn::Layer>&) { return false; } bool tryFuse(Ptr<dnn::Layer>&) { return false; }
@ -808,11 +856,18 @@ struct BNLLFunctor
#endif // HAVE_HALIDE #endif // HAVE_HALIDE
#ifdef HAVE_INF_ENGINE #ifdef HAVE_INF_ENGINE
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
{
CV_Error(Error::StsNotImplemented, "");
}
#else
InferenceEngine::CNNLayerPtr initInfEngine(InferenceEngine::LayerParams& lp) InferenceEngine::CNNLayerPtr initInfEngine(InferenceEngine::LayerParams& lp)
{ {
CV_Error(Error::StsNotImplemented, "BNLL"); CV_Error(Error::StsNotImplemented, "BNLL");
return InferenceEngine::CNNLayerPtr(); return InferenceEngine::CNNLayerPtr();
} }
#endif
#endif // HAVE_INF_ENGINE #endif // HAVE_INF_ENGINE
bool tryFuse(Ptr<dnn::Layer>&) { return false; } bool tryFuse(Ptr<dnn::Layer>&) { return false; }
@ -917,6 +972,14 @@ struct PowerFunctor
#endif // HAVE_HALIDE #endif // HAVE_HALIDE
#ifdef HAVE_INF_ENGINE #ifdef HAVE_INF_ENGINE
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
{
return InferenceEngine::Builder::PowerLayer("").setPower(power)
.setScale(scale)
.setShift(shift);
}
#else
InferenceEngine::CNNLayerPtr initInfEngine(InferenceEngine::LayerParams& lp) InferenceEngine::CNNLayerPtr initInfEngine(InferenceEngine::LayerParams& lp)
{ {
if (power == 1.0f && scale == 1.0f && shift == 0.0f) if (power == 1.0f && scale == 1.0f && shift == 0.0f)
@ -936,6 +999,7 @@ struct PowerFunctor
return ieLayer; return ieLayer;
} }
} }
#endif
#endif // HAVE_INF_ENGINE #endif // HAVE_INF_ENGINE
bool tryFuse(Ptr<dnn::Layer>& top) bool tryFuse(Ptr<dnn::Layer>& top)
@ -1067,6 +1131,15 @@ struct ChannelsPReLUFunctor
#endif // HAVE_HALIDE #endif // HAVE_HALIDE
#ifdef HAVE_INF_ENGINE #ifdef HAVE_INF_ENGINE
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
{
InferenceEngine::Builder::PReLULayer ieLayer("");
const size_t numChannels = scale.total();
ieLayer.setWeights(wrapToInfEngineBlob(scale, {numChannels}, InferenceEngine::Layout::C));
return ieLayer;
}
#else
InferenceEngine::CNNLayerPtr initInfEngine(InferenceEngine::LayerParams& lp) InferenceEngine::CNNLayerPtr initInfEngine(InferenceEngine::LayerParams& lp)
{ {
lp.type = "PReLU"; lp.type = "PReLU";
@ -1075,6 +1148,7 @@ struct ChannelsPReLUFunctor
ieLayer->_weights = wrapToInfEngineBlob(scale, {numChannels}, InferenceEngine::Layout::C); ieLayer->_weights = wrapToInfEngineBlob(scale, {numChannels}, InferenceEngine::Layout::C);
return ieLayer; return ieLayer;
} }
#endif
#endif // HAVE_INF_ENGINE #endif // HAVE_INF_ENGINE
bool tryFuse(Ptr<dnn::Layer>&) { return false; } bool tryFuse(Ptr<dnn::Layer>&) { return false; }

25
modules/dnn/src/layers/eltwise_layer.cpp
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@ -99,7 +99,7 @@ public:
return backendId == DNN_BACKEND_OPENCV || return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_HALIDE || backendId == DNN_BACKEND_HALIDE ||
(backendId == DNN_BACKEND_INFERENCE_ENGINE && (backendId == DNN_BACKEND_INFERENCE_ENGINE &&
(preferableTarget != DNN_TARGET_MYRIAD || coeffs.empty())); (preferableTarget != DNN_TARGET_OPENCL || coeffs.empty()));
} }
bool getMemoryShapes(const std::vector<MatShape> &inputs, bool getMemoryShapes(const std::vector<MatShape> &inputs,
@ -420,9 +420,29 @@ public:
return Ptr<BackendNode>(); return Ptr<BackendNode>();
} }
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
{ {
#ifdef HAVE_INF_ENGINE #ifdef HAVE_INF_ENGINE
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
InferenceEngine::Builder::EltwiseLayer ieLayer(name);
ieLayer.setInputPorts(std::vector<InferenceEngine::Port>(inputs.size()));
if (op == SUM)
ieLayer.setEltwiseType(InferenceEngine::Builder::EltwiseLayer::EltwiseType::SUM);
else if (op == PROD)
ieLayer.setEltwiseType(InferenceEngine::Builder::EltwiseLayer::EltwiseType::MUL);
else if (op == MAX)
ieLayer.setEltwiseType(InferenceEngine::Builder::EltwiseLayer::EltwiseType::MAX);
else
CV_Error(Error::StsNotImplemented, "Unsupported eltwise operation");
InferenceEngine::Builder::Layer l = ieLayer;
if (!coeffs.empty())
l.getParameters()["coeff"] = coeffs;
return Ptr<BackendNode>(new InfEngineBackendNode(l));
#else
InferenceEngine::LayerParams lp; InferenceEngine::LayerParams lp;
lp.name = name; lp.name = name;
lp.type = "Eltwise"; lp.type = "Eltwise";
@ -438,6 +458,7 @@ public:
else else
CV_Error(Error::StsNotImplemented, "Unsupported eltwise operation"); CV_Error(Error::StsNotImplemented, "Unsupported eltwise operation");
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer)); return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#endif
#endif // HAVE_INF_ENGINE #endif // HAVE_INF_ENGINE
return Ptr<BackendNode>(); return Ptr<BackendNode>();
} }

13
modules/dnn/src/layers/flatten_layer.cpp
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@ -152,9 +152,19 @@ public:
} }
} }
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
{ {
#ifdef HAVE_INF_ENGINE #ifdef HAVE_INF_ENGINE
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
InferenceEngine::Builder::Layer ieLayer(name);
ieLayer.setName(name);
ieLayer.setType("Flatten");
ieLayer.getParameters()["axis"] = _startAxis;
ieLayer.getParameters()["end_axis"] = _endAxis;
ieLayer.setInputPorts(std::vector<InferenceEngine::Port>(1));
ieLayer.setOutputPorts(std::vector<InferenceEngine::Port>(1));
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#else
InferenceEngine::LayerParams lp; InferenceEngine::LayerParams lp;
lp.name = name; lp.name = name;
lp.type = "Flatten"; lp.type = "Flatten";
@ -163,6 +173,7 @@ public:
ieLayer->params["axis"] = format("%d", _startAxis); ieLayer->params["axis"] = format("%d", _startAxis);
ieLayer->params["end_axis"] = format("%d", _endAxis); ieLayer->params["end_axis"] = format("%d", _endAxis);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer)); return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#endif
#endif // HAVE_INF_ENGINE #endif // HAVE_INF_ENGINE
return Ptr<BackendNode>(); return Ptr<BackendNode>();
} }

13
modules/dnn/src/layers/fully_connected_layer.cpp
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@ -442,6 +442,18 @@ public:
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{ {
#ifdef HAVE_INF_ENGINE #ifdef HAVE_INF_ENGINE
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
InferenceEngine::Builder::FullyConnectedLayer ieLayer(name);
const int outNum = blobs[0].size[0];
ieLayer.setOutputNum(outNum);
ieLayer.setWeights(wrapToInfEngineBlob(blobs[0], {(size_t)blobs[0].size[0], (size_t)blobs[0].size[1], 1, 1}, InferenceEngine::Layout::OIHW));
if (blobs.size() > 1)
ieLayer.setBiases(wrapToInfEngineBlob(blobs[1], {(size_t)outNum}, InferenceEngine::Layout::C));
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#else
InferenceEngine::LayerParams lp; InferenceEngine::LayerParams lp;
lp.name = name; lp.name = name;
lp.type = "FullyConnected"; lp.type = "FullyConnected";
@ -456,6 +468,7 @@ public:
if (blobs.size() > 1) if (blobs.size() > 1)
ieLayer->_biases = wrapToInfEngineBlob(blobs[1], {(size_t)ieLayer->_out_num}, InferenceEngine::Layout::C); ieLayer->_biases = wrapToInfEngineBlob(blobs[1], {(size_t)ieLayer->_out_num}, InferenceEngine::Layout::C);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer)); return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#endif
#endif // HAVE_INF_ENGINE #endif // HAVE_INF_ENGINE
return Ptr<BackendNode>(); return Ptr<BackendNode>();
} }

12
modules/dnn/src/layers/lrn_layer.cpp
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@ -382,6 +382,17 @@ public:
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{ {
#ifdef HAVE_INF_ENGINE #ifdef HAVE_INF_ENGINE
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
InferenceEngine::Builder::NormLayer ieLayer(name);
ieLayer.setSize(size);
ieLayer.setAlpha(alpha);
ieLayer.setBeta(beta);
ieLayer.setAcrossMaps(type == CHANNEL_NRM);
InferenceEngine::Builder::Layer l = ieLayer;
l.getParameters()["k"] = bias;
return Ptr<BackendNode>(new InfEngineBackendNode(l));
#else
InferenceEngine::LayerParams lp; InferenceEngine::LayerParams lp;
lp.name = name; lp.name = name;
lp.type = "Norm"; lp.type = "Norm";
@ -394,6 +405,7 @@ public:
ieLayer->_alpha = alpha; ieLayer->_alpha = alpha;
ieLayer->_isAcrossMaps = (type == CHANNEL_NRM); ieLayer->_isAcrossMaps = (type == CHANNEL_NRM);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer)); return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#endif
#endif // HAVE_INF_ENGINE #endif // HAVE_INF_ENGINE
return Ptr<BackendNode>(); return Ptr<BackendNode>();
} }

8
modules/dnn/src/layers/mvn_layer.cpp
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@ -371,6 +371,13 @@ public:
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{ {
#ifdef HAVE_INF_ENGINE #ifdef HAVE_INF_ENGINE
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
InferenceEngine::Builder::MVNLayer ieLayer(name);
ieLayer.setAcrossChannels(acrossChannels);
ieLayer.setNormalize(normVariance);
ieLayer.setEpsilon(eps);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#else
InferenceEngine::LayerParams lp; InferenceEngine::LayerParams lp;
lp.name = name; lp.name = name;
lp.type = "MVN"; lp.type = "MVN";
@ -380,6 +387,7 @@ public:
ieLayer->params["normalize_variance"] = normVariance ? "1" : "0"; ieLayer->params["normalize_variance"] = normVariance ? "1" : "0";
ieLayer->params["eps"] = format("%f", eps); ieLayer->params["eps"] = format("%f", eps);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer)); return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#endif
#endif // HAVE_INF_ENGINE #endif // HAVE_INF_ENGINE
return Ptr<BackendNode>(); return Ptr<BackendNode>();
} }

44
modules/dnn/src/layers/normalize_bbox_layer.cpp
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@ -264,6 +264,49 @@ public:
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
{ {
#ifdef HAVE_INF_ENGINE #ifdef HAVE_INF_ENGINE
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]);
if (input->dims.size() == 4)
{
InferenceEngine::Builder::NormalizeLayer ieLayer(name);
ieLayer.setChannelShared(false);
ieLayer.setAcrossMaps(acrossSpatial);
ieLayer.setEpsilon(epsilon);
InferenceEngine::Builder::Layer l = ieLayer;
const int numChannels = input->dims[2]; // NOTE: input->dims are reversed (whcn)
if (blobs.empty())
{
auto weights = InferenceEngine::make_shared_blob<float>(InferenceEngine::Precision::FP32,
InferenceEngine::Layout::C,
{(size_t)numChannels});
weights->allocate();
std::vector<float> ones(numChannels, 1);
weights->set(ones);
l.addConstantData("weights", weights);
l.getParameters()["channel_shared"] = false;
}
else
{
CV_Assert(numChannels == blobs[0].total());
l.addConstantData("weights", wrapToInfEngineBlob(blobs[0], {(size_t)numChannels}, InferenceEngine::Layout::C));
l.getParameters()["channel_shared"] = blobs[0].total() == 1;
}
l.getParameters()["across_spatial"] = acrossSpatial;
return Ptr<BackendNode>(new InfEngineBackendNode(l));
}
else
{
InferenceEngine::Builder::GRNLayer ieLayer(name);
ieLayer.setBeta(epsilon);
InferenceEngine::Builder::Layer l = ieLayer;
l.getParameters()["bias"] = epsilon;
return Ptr<BackendNode>(new InfEngineBackendNode(l));
}
#else
InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]); InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]);
InferenceEngine::LayerParams lp; InferenceEngine::LayerParams lp;
@ -307,6 +350,7 @@ public:
ieLayer->params["bias"] = format("%f", epsilon); ieLayer->params["bias"] = format("%f", epsilon);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer)); return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
} }
#endif
#endif // HAVE_INF_ENGINE #endif // HAVE_INF_ENGINE
return Ptr<BackendNode>(); return Ptr<BackendNode>();
} }

6
modules/dnn/src/layers/permute_layer.cpp
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@ -373,6 +373,11 @@ public:
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{ {
#ifdef HAVE_INF_ENGINE #ifdef HAVE_INF_ENGINE
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
InferenceEngine::Builder::PermuteLayer ieLayer(name);
ieLayer.setOrder(_order);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#else
InferenceEngine::LayerParams lp; InferenceEngine::LayerParams lp;
lp.name = name; lp.name = name;
lp.type = "Permute"; lp.type = "Permute";
@ -385,6 +390,7 @@ public:
ieLayer->params["order"] += format(",%d", _order[i]); ieLayer->params["order"] += format(",%d", _order[i]);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer)); return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#endif
#endif // HAVE_INF_ENGINE #endif // HAVE_INF_ENGINE
return Ptr<BackendNode>(); return Ptr<BackendNode>();
} }

43
modules/dnn/src/layers/pooling_layer.cpp
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@ -257,6 +257,48 @@ public:
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{ {
#ifdef HAVE_INF_ENGINE #ifdef HAVE_INF_ENGINE
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
if (type == MAX || type == AVE)
{
InferenceEngine::Builder::PoolingLayer ieLayer(name);
ieLayer.setKernel({kernel.height, kernel.width});
ieLayer.setStrides({stride.height, stride.width});
ieLayer.setPaddingsBegin({pad_t, pad_l});
ieLayer.setPaddingsEnd({pad_b, pad_r});
ieLayer.setPoolingType(type == MAX ?
InferenceEngine::Builder::PoolingLayer::PoolingType::MAX :
InferenceEngine::Builder::PoolingLayer::PoolingType::AVG);
ieLayer.setRoundingType(ceilMode ?
InferenceEngine::Builder::PoolingLayer::RoundingType::CEIL :
InferenceEngine::Builder::PoolingLayer::RoundingType::FLOOR);
ieLayer.setExcludePad(type == AVE && padMode == "SAME");
InferenceEngine::Builder::Layer l = ieLayer;
if (!padMode.empty())
l.getParameters()["auto_pad"] = padMode == "VALID" ? std::string("valid") : std::string("same_upper");
return Ptr<BackendNode>(new InfEngineBackendNode(l));
}
else if (type == ROI)
{
InferenceEngine::Builder::ROIPoolingLayer ieLayer(name);
ieLayer.setSpatialScale(spatialScale);
ieLayer.setPooled({pooledSize.height, pooledSize.width});
ieLayer.setInputPorts(std::vector<InferenceEngine::Port>(2));
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
}
else if (type == PSROI)
{
InferenceEngine::Builder::PSROIPoolingLayer ieLayer(name);
ieLayer.setSpatialScale(spatialScale);
ieLayer.setOutputDim(psRoiOutChannels);
ieLayer.setGroupSize(pooledSize.width);
ieLayer.setInputPorts(std::vector<InferenceEngine::Port>(2));
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
}
else
CV_Error(Error::StsNotImplemented, "Unsupported pooling type");
return Ptr<BackendNode>();
#else
InferenceEngine::LayerParams lp; InferenceEngine::LayerParams lp;
lp.name = name; lp.name = name;
lp.precision = InferenceEngine::Precision::FP32; lp.precision = InferenceEngine::Precision::FP32;
@ -315,6 +357,7 @@ public:
CV_Error(Error::StsNotImplemented, "Unsupported pooling type"); CV_Error(Error::StsNotImplemented, "Unsupported pooling type");
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer)); return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#endif
#endif // HAVE_INF_ENGINE #endif // HAVE_INF_ENGINE
return Ptr<BackendNode>(); return Ptr<BackendNode>();
} }

53
modules/dnn/src/layers/prior_box_layer.cpp
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@ -483,6 +483,58 @@ public:
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{ {
#ifdef HAVE_INF_ENGINE #ifdef HAVE_INF_ENGINE
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
if (_explicitSizes)
{
InferenceEngine::Builder::PriorBoxClusteredLayer ieLayer(name);
CV_Assert(_stepX == _stepY);
ieLayer.setStep(_stepX);
CV_CheckEQ(_offsetsX.size(), (size_t)1, ""); CV_CheckEQ(_offsetsY.size(), (size_t)1, ""); CV_CheckEQ(_offsetsX[0], _offsetsY[0], "");
ieLayer.setOffset(_offsetsX[0]);
ieLayer.setClip(_clip);
ieLayer.setFlip(false); // We already flipped aspect ratios.
InferenceEngine::Builder::Layer l = ieLayer;
CV_Assert_N(!_boxWidths.empty(), !_boxHeights.empty(), !_variance.empty());
CV_Assert(_boxWidths.size() == _boxHeights.size());
l.getParameters()["width"] = _boxWidths;
l.getParameters()["height"] = _boxHeights;
l.getParameters()["variance"] = _variance;
return Ptr<BackendNode>(new InfEngineBackendNode(l));
}
else
{
InferenceEngine::Builder::PriorBoxLayer ieLayer(name);
CV_Assert(!_explicitSizes);
ieLayer.setMinSize(_minSize);
if (_maxSize > 0)
ieLayer.setMaxSize(_maxSize);
CV_Assert(_stepX == _stepY);
ieLayer.setStep(_stepX);
CV_CheckEQ(_offsetsX.size(), (size_t)1, ""); CV_CheckEQ(_offsetsY.size(), (size_t)1, ""); CV_CheckEQ(_offsetsX[0], _offsetsY[0], "");
ieLayer.setOffset(_offsetsX[0]);
ieLayer.setClip(_clip);
ieLayer.setFlip(false); // We already flipped aspect ratios.
InferenceEngine::Builder::Layer l = ieLayer;
if (!_aspectRatios.empty())
{
l.getParameters()["aspect_ratio"] = _aspectRatios;
}
CV_Assert(!_variance.empty());
l.getParameters()["variance"] = _variance;
return Ptr<BackendNode>(new InfEngineBackendNode(l));
}
#else
InferenceEngine::LayerParams lp; InferenceEngine::LayerParams lp;
lp.name = name; lp.name = name;
lp.type = _explicitSizes ? "PriorBoxClustered" : "PriorBox"; lp.type = _explicitSizes ? "PriorBoxClustered" : "PriorBox";
@ -538,6 +590,7 @@ public:
ieLayer->params["offset"] = format("%f", _offsetsX[0]); ieLayer->params["offset"] = format("%f", _offsetsX[0]);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer)); return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#endif
#endif // HAVE_INF_ENGINE #endif // HAVE_INF_ENGINE
return Ptr<BackendNode>(); return Ptr<BackendNode>();
} }

23
modules/dnn/src/layers/proposal_layer.cpp
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@ -328,6 +328,28 @@ public:
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{ {
#ifdef HAVE_INF_ENGINE #ifdef HAVE_INF_ENGINE
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
InferenceEngine::Builder::ProposalLayer ieLayer(name);
ieLayer.setBaseSize(baseSize);
ieLayer.setFeatStride(featStride);
ieLayer.setMinSize(16);
ieLayer.setNMSThresh(nmsThreshold);
ieLayer.setPostNMSTopN(keepTopAfterNMS);
ieLayer.setPreNMSTopN(keepTopBeforeNMS);
std::vector<float> scalesVec(scales.size());
for (int i = 0; i < scales.size(); ++i)
scalesVec[i] = scales.get<float>(i);
ieLayer.setScale(scalesVec);
std::vector<float> ratiosVec(ratios.size());
for (int i = 0; i < ratios.size(); ++i)
ratiosVec[i] = ratios.get<float>(i);
ieLayer.setRatio(ratiosVec);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#else
InferenceEngine::LayerParams lp; InferenceEngine::LayerParams lp;
lp.name = name; lp.name = name;
lp.type = "Proposal"; lp.type = "Proposal";
@ -353,6 +375,7 @@ public:
ieLayer->params["scale"] += format(",%f", scales.get<float>(i)); ieLayer->params["scale"] += format(",%f", scales.get<float>(i));
} }
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer)); return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#endif
#endif // HAVE_INF_ENGINE #endif // HAVE_INF_ENGINE
return Ptr<BackendNode>(); return Ptr<BackendNode>();
} }

6
modules/dnn/src/layers/reorg_layer.cpp
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@ -181,6 +181,11 @@ public:
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{ {
#ifdef HAVE_INF_ENGINE #ifdef HAVE_INF_ENGINE
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
InferenceEngine::Builder::ReorgYoloLayer ieLayer(name);
ieLayer.setStride(reorgStride);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#else
InferenceEngine::LayerParams lp; InferenceEngine::LayerParams lp;
lp.name = name; lp.name = name;
lp.type = "ReorgYolo"; lp.type = "ReorgYolo";
@ -188,6 +193,7 @@ public:
std::shared_ptr<InferenceEngine::CNNLayer> ieLayer(new InferenceEngine::CNNLayer(lp)); std::shared_ptr<InferenceEngine::CNNLayer> ieLayer(new InferenceEngine::CNNLayer(lp));
ieLayer->params["stride"] = format("%d", reorgStride); ieLayer->params["stride"] = format("%d", reorgStride);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer)); return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#endif
#endif // HAVE_INF_ENGINE #endif // HAVE_INF_ENGINE
return Ptr<BackendNode>(); return Ptr<BackendNode>();
} }

24
modules/dnn/src/layers/reshape_layer.cpp
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@ -203,6 +203,17 @@ public:
return true; return true;
} }
void finalize(InputArrayOfArrays, OutputArrayOfArrays outputs_arr) CV_OVERRIDE
{
std::vector<Mat> outputs;
outputs_arr.getMatVector(outputs);
CV_Assert(!outputs.empty());
outShapes.resize(outputs.size());
for (int i = 0; i < outputs.size(); ++i)
outShapes[i] = shape(outputs[i]);
}
bool forward_ocl(InputArrayOfArrays inps, OutputArrayOfArrays outs, OutputArrayOfArrays internals) bool forward_ocl(InputArrayOfArrays inps, OutputArrayOfArrays outs, OutputArrayOfArrays internals)
{ {
std::vector<UMat> inputs; std::vector<UMat> inputs;
@ -218,8 +229,7 @@ public:
void *dst_handle = outputs[i].handle(ACCESS_WRITE); void *dst_handle = outputs[i].handle(ACCESS_WRITE);
if (src_handle != dst_handle) if (src_handle != dst_handle)
{ {
MatShape outShape = shape(outputs[i]); UMat umat = srcBlob.reshape(1, (int)outShapes[i].size(), &outShapes[i][0]);
UMat umat = srcBlob.reshape(1, (int)outShape.size(), &outShape[0]);
umat.copyTo(outputs[i]); umat.copyTo(outputs[i]);
} }
} }
@ -250,6 +260,12 @@ public:
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
{ {
#ifdef HAVE_INF_ENGINE #ifdef HAVE_INF_ENGINE
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
InferenceEngine::Builder::ReshapeLayer ieLayer(name);
CV_Assert(outShapes.size() == 1);
ieLayer.setDims(outShapes[0]);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#else
InferenceEngine::LayerParams lp; InferenceEngine::LayerParams lp;
lp.name = name; lp.name = name;
lp.type = "Reshape"; lp.type = "Reshape";
@ -265,9 +281,13 @@ public:
ieLayer->shape = std::vector<int>(shapeSrc->dims.rbegin(), shapeSrc->dims.rend()); ieLayer->shape = std::vector<int>(shapeSrc->dims.rbegin(), shapeSrc->dims.rend());
} }
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer)); return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#endif
#endif // HAVE_INF_ENGINE #endif // HAVE_INF_ENGINE
return Ptr<BackendNode>(); return Ptr<BackendNode>();
} }
private:
std::vector<MatShape> outShapes;
}; };
Ptr<ReshapeLayer> ReshapeLayer::create(const LayerParams& params) Ptr<ReshapeLayer> ReshapeLayer::create(const LayerParams& params)

41
modules/dnn/src/layers/resize_layer.cpp
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@ -163,6 +163,33 @@ public:
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{ {
#ifdef HAVE_INF_ENGINE #ifdef HAVE_INF_ENGINE
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
InferenceEngine::Builder::Layer ieLayer(name);
ieLayer.setName(name);
if (interpolation == "nearest")
{
ieLayer.setType("Resample");
ieLayer.getParameters()["type"] = std::string("caffe.ResampleParameter.NEAREST");
ieLayer.getParameters()["antialias"] = false;
if (scaleWidth != scaleHeight)
CV_Error(Error::StsNotImplemented, "resample with sw != sh");
ieLayer.getParameters()["factor"] = 1.0 / scaleWidth;
}
else if (interpolation == "bilinear")
{
ieLayer.setType("Interp");
ieLayer.getParameters()["pad_beg"] = 0;
ieLayer.getParameters()["pad_end"] = 0;
ieLayer.getParameters()["align_corners"] = false;
}
else
CV_Error(Error::StsNotImplemented, "Unsupported interpolation: " + interpolation);
ieLayer.getParameters()["width"] = outWidth;
ieLayer.getParameters()["height"] = outHeight;
ieLayer.setInputPorts(std::vector<InferenceEngine::Port>(1));
ieLayer.setOutputPorts(std::vector<InferenceEngine::Port>(1));
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#else
InferenceEngine::LayerParams lp; InferenceEngine::LayerParams lp;
lp.name = name; lp.name = name;
lp.precision = InferenceEngine::Precision::FP32; lp.precision = InferenceEngine::Precision::FP32;
@ -187,6 +214,7 @@ public:
ieLayer->params["width"] = cv::format("%d", outWidth); ieLayer->params["width"] = cv::format("%d", outWidth);
ieLayer->params["height"] = cv::format("%d", outHeight); ieLayer->params["height"] = cv::format("%d", outHeight);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer)); return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#endif
#endif // HAVE_INF_ENGINE #endif // HAVE_INF_ENGINE
return Ptr<BackendNode>(); return Ptr<BackendNode>();
} }
@ -247,6 +275,18 @@ public:
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{ {
#ifdef HAVE_INF_ENGINE #ifdef HAVE_INF_ENGINE
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
InferenceEngine::Builder::Layer ieLayer(name);
ieLayer.setName(name);
ieLayer.setType("Interp");
ieLayer.getParameters()["pad_beg"] = 0;
ieLayer.getParameters()["pad_end"] = 0;
ieLayer.getParameters()["width"] = outWidth;
ieLayer.getParameters()["height"] = outHeight;
ieLayer.setInputPorts(std::vector<InferenceEngine::Port>(1));
ieLayer.setOutputPorts(std::vector<InferenceEngine::Port>(1));
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#else
InferenceEngine::LayerParams lp; InferenceEngine::LayerParams lp;
lp.name = name; lp.name = name;
lp.type = "Interp"; lp.type = "Interp";
@ -256,6 +296,7 @@ public:
ieLayer->params["pad_beg"] = "0"; ieLayer->params["pad_beg"] = "0";
ieLayer->params["pad_end"] = "0"; ieLayer->params["pad_end"] = "0";
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer)); return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#endif
#endif // HAVE_INF_ENGINE #endif // HAVE_INF_ENGINE
return Ptr<BackendNode>(); return Ptr<BackendNode>();
} }

24
modules/dnn/src/layers/scale_layer.cpp
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@ -197,6 +197,29 @@ public:
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{ {
#ifdef HAVE_INF_ENGINE #ifdef HAVE_INF_ENGINE
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
InferenceEngine::Builder::ScaleShiftLayer ieLayer(name);
CV_Assert(!blobs.empty());
const size_t numChannels = blobs[0].total();
if (hasWeights)
{
ieLayer.setWeights(wrapToInfEngineBlob(blobs[0], {numChannels}, InferenceEngine::Layout::C));
}
else
{
auto weights = InferenceEngine::make_shared_blob<float>(InferenceEngine::Precision::FP32,
{numChannels});
weights->allocate();
std::vector<float> ones(numChannels, 1);
weights->set(ones);
ieLayer.setWeights(weights);
}
if (hasBias)
ieLayer.setBiases(wrapToInfEngineBlob(blobs.back(), {numChannels}, InferenceEngine::Layout::C));
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#else
InferenceEngine::LayerParams lp; InferenceEngine::LayerParams lp;
lp.name = name; lp.name = name;
lp.type = "ScaleShift"; lp.type = "ScaleShift";
@ -223,6 +246,7 @@ public:
ieLayer->_biases = wrapToInfEngineBlob(blobs.back(), {numChannels}, InferenceEngine::Layout::C); ieLayer->_biases = wrapToInfEngineBlob(blobs.back(), {numChannels}, InferenceEngine::Layout::C);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer)); return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#endif
#endif // HAVE_INF_ENGINE #endif // HAVE_INF_ENGINE
return Ptr<BackendNode>(); return Ptr<BackendNode>();
} }

19
modules/dnn/src/layers/slice_layer.cpp
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@ -110,8 +110,15 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE virtual bool supportBackend(int backendId) CV_OVERRIDE
{ {
return backendId == DNN_BACKEND_OPENCV || #ifdef HAVE_INF_ENGINE
(backendId == DNN_BACKEND_INFERENCE_ENGINE && sliceRanges.size() == 1 && sliceRanges[0].size() == 4); if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
{
return INF_ENGINE_VER_MAJOR_LT(INF_ENGINE_RELEASE_2018R5) &&
sliceRanges.size() == 1 && sliceRanges[0].size() == 4;
}
else
#endif
return backendId == DNN_BACKEND_OPENCV;
} }
bool getMemoryShapes(const std::vector<MatShape> &inputs, bool getMemoryShapes(const std::vector<MatShape> &inputs,
@ -254,9 +261,10 @@ public:
} }
} }
#ifdef HAVE_INF_ENGINE
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
{ {
#ifdef HAVE_INF_ENGINE #if INF_ENGINE_VER_MAJOR_LT(INF_ENGINE_RELEASE_2018R5)
InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]); InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]);
InferenceEngine::LayerParams lp; InferenceEngine::LayerParams lp;
lp.name = name; lp.name = name;
@ -286,10 +294,11 @@ public:
ieLayer->dim.push_back(sliceRanges[0][i].end - sliceRanges[0][i].start); ieLayer->dim.push_back(sliceRanges[0][i].end - sliceRanges[0][i].start);
} }
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer)); return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#else
#endif // HAVE_INF_ENGINE
return Ptr<BackendNode>(); return Ptr<BackendNode>();
#endif // IE < R5
} }
#endif
}; };
Ptr<SliceLayer> SliceLayer::create(const LayerParams& params) Ptr<SliceLayer> SliceLayer::create(const LayerParams& params)

8
modules/dnn/src/layers/softmax_layer.cpp
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@ -312,6 +312,13 @@ public:
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
{ {
#ifdef HAVE_INF_ENGINE #ifdef HAVE_INF_ENGINE
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]);
InferenceEngine::Builder::SoftMaxLayer ieLayer(name);
ieLayer.setAxis(clamp(axisRaw, input->dims.size()));
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#else
InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]); InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]);
InferenceEngine::LayerParams lp; InferenceEngine::LayerParams lp;
@ -321,6 +328,7 @@ public:
std::shared_ptr<InferenceEngine::SoftMaxLayer> ieLayer(new InferenceEngine::SoftMaxLayer(lp)); std::shared_ptr<InferenceEngine::SoftMaxLayer> ieLayer(new InferenceEngine::SoftMaxLayer(lp));
ieLayer->axis = clamp(axisRaw, input->dims.size()); ieLayer->axis = clamp(axisRaw, input->dims.size());
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer)); return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#endif
#endif // HAVE_INF_ENGINE #endif // HAVE_INF_ENGINE
return Ptr<BackendNode>(); return Ptr<BackendNode>();
} }

137
modules/dnn/src/op_inf_engine.cpp
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@ -18,6 +18,10 @@ namespace cv { namespace dnn {
#ifdef HAVE_INF_ENGINE #ifdef HAVE_INF_ENGINE
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
InfEngineBackendNode::InfEngineBackendNode(const InferenceEngine::Builder::Layer& _layer)
: BackendNode(DNN_BACKEND_INFERENCE_ENGINE), layer(_layer) {}
#else
InfEngineBackendNode::InfEngineBackendNode(const InferenceEngine::CNNLayerPtr& _layer) InfEngineBackendNode::InfEngineBackendNode(const InferenceEngine::CNNLayerPtr& _layer)
: BackendNode(DNN_BACKEND_INFERENCE_ENGINE), layer(_layer) {} : BackendNode(DNN_BACKEND_INFERENCE_ENGINE), layer(_layer) {}
@ -40,6 +44,7 @@ void InfEngineBackendNode::connect(std::vector<Ptr<BackendWrapper> >& inputs,
layer->outData[0] = dataPtr; layer->outData[0] = dataPtr;
dataPtr->creatorLayer = InferenceEngine::CNNLayerWeakPtr(layer); dataPtr->creatorLayer = InferenceEngine::CNNLayerWeakPtr(layer);
} }
#endif
static std::vector<Ptr<InfEngineBackendWrapper> > static std::vector<Ptr<InfEngineBackendWrapper> >
infEngineWrappers(const std::vector<Ptr<BackendWrapper> >& ptrs) infEngineWrappers(const std::vector<Ptr<BackendWrapper> >& ptrs)
@ -54,6 +59,129 @@ infEngineWrappers(const std::vector<Ptr<BackendWrapper> >& ptrs)
return wrappers; return wrappers;
} }
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
InfEngineBackendNet::InfEngineBackendNet() : netBuilder("")
{
hasNetOwner = false;
targetDevice = InferenceEngine::TargetDevice::eCPU;
}
InfEngineBackendNet::InfEngineBackendNet(InferenceEngine::CNNNetwork& net) : netBuilder(""), cnn(net)
{
hasNetOwner = true;
targetDevice = InferenceEngine::TargetDevice::eCPU;
}
void InfEngineBackendNet::connect(const std::vector<Ptr<BackendWrapper> >& inputs,
const std::vector<Ptr<BackendWrapper> >& outputs,
const std::string& layerName)
{
std::vector<Ptr<InfEngineBackendWrapper> > inpWrappers = infEngineWrappers(inputs);
std::map<std::string, int>::iterator it = layers.find(layerName);
CV_Assert(it != layers.end());
const int layerId = it->second;
for (int i = 0; i < inpWrappers.size(); ++i)
{
const auto& inp = inpWrappers[i];
const std::string& inpName = inp->dataPtr->name;
int inpId;
it = layers.find(inpName);
if (it == layers.end())
{
InferenceEngine::Builder::InputLayer inpLayer(inpName);
std::vector<size_t> shape(inp->blob->dims());
std::reverse(shape.begin(), shape.end());
inpLayer.setPort(InferenceEngine::Port(shape));
inpId = netBuilder.addLayer(inpLayer);
layers.insert({inpName, inpId});
}
else
inpId = it->second;
netBuilder.connect(inpId, {layerId, i});
unconnectedLayersIds.erase(inpId);
}
CV_Assert(!outputs.empty());
InferenceEngine::DataPtr dataPtr = infEngineDataNode(outputs[0]);
dataPtr->name = layerName;
}
void InfEngineBackendNet::init(int targetId)
{
if (!hasNetOwner)
{
CV_Assert(!unconnectedLayersIds.empty());
for (int id : unconnectedLayersIds)
{
InferenceEngine::Builder::OutputLayer outLayer("myconv1");
netBuilder.addLayer({id}, outLayer);
}
cnn = InferenceEngine::CNNNetwork(InferenceEngine::Builder::convertToICNNNetwork(netBuilder.build()));
}
switch (targetId)
{
case DNN_TARGET_CPU:
targetDevice = InferenceEngine::TargetDevice::eCPU;
break;
case DNN_TARGET_OPENCL: case DNN_TARGET_OPENCL_FP16:
targetDevice = InferenceEngine::TargetDevice::eGPU;
break;
case DNN_TARGET_MYRIAD:
targetDevice = InferenceEngine::TargetDevice::eMYRIAD;
break;
case DNN_TARGET_FPGA:
targetDevice = InferenceEngine::TargetDevice::eFPGA;
break;
default:
CV_Error(Error::StsError, format("Unknown target identifier: %d", targetId));
}
for (const auto& name : requestedOutputs)
{
cnn.addOutput(name);
}
for (const auto& it : cnn.getInputsInfo())
{
const std::string& name = it.first;
auto blobIt = allBlobs.find(name);
CV_Assert(blobIt != allBlobs.end());
inpBlobs[name] = blobIt->second;
it.second->setPrecision(blobIt->second->precision());
}
for (const auto& it : cnn.getOutputsInfo())
{
const std::string& name = it.first;
auto blobIt = allBlobs.find(name);
CV_Assert(blobIt != allBlobs.end());
outBlobs[name] = blobIt->second;
it.second->setPrecision(blobIt->second->precision()); // Should be always FP32
}
initPlugin(cnn);
}
void InfEngineBackendNet::addLayer(const InferenceEngine::Builder::Layer& layer)
{
int id = netBuilder.addLayer(layer);
const std::string& layerName = layer.getName();
CV_Assert(layers.insert({layerName, id}).second);
unconnectedLayersIds.insert(id);
}
void InfEngineBackendNet::addOutput(const std::string& name)
{
requestedOutputs.push_back(name);
}
#endif // IE >= R5
static InferenceEngine::Layout estimateLayout(const Mat& m) static InferenceEngine::Layout estimateLayout(const Mat& m)
{ {
if (m.dims == 4) if (m.dims == 4)
@ -148,6 +276,7 @@ void InfEngineBackendWrapper::setHostDirty()
} }
#if INF_ENGINE_VER_MAJOR_LT(INF_ENGINE_RELEASE_2018R5)
InfEngineBackendNet::InfEngineBackendNet() InfEngineBackendNet::InfEngineBackendNet()
{ {
targetDevice = InferenceEngine::TargetDevice::eCPU; targetDevice = InferenceEngine::TargetDevice::eCPU;
@ -491,6 +620,8 @@ void InfEngineBackendNet::init(int targetId)
initPlugin(*this); initPlugin(*this);
} }
#endif // IE < R5
static std::map<InferenceEngine::TargetDevice, InferenceEngine::InferenceEnginePluginPtr> sharedPlugins; static std::map<InferenceEngine::TargetDevice, InferenceEngine::InferenceEnginePluginPtr> sharedPlugins;
void InfEngineBackendNet::initPlugin(InferenceEngine::ICNNNetwork& net) void InfEngineBackendNet::initPlugin(InferenceEngine::ICNNNetwork& net)
@ -566,7 +697,11 @@ void InfEngineBackendNet::addBlobs(const std::vector<Ptr<BackendWrapper> >& ptrs
auto wrappers = infEngineWrappers(ptrs); auto wrappers = infEngineWrappers(ptrs);
for (const auto& wrapper : wrappers) for (const auto& wrapper : wrappers)
{ {
allBlobs.insert({wrapper->dataPtr->name, wrapper->blob}); std::string name = wrapper->dataPtr->name;
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
name = name.empty() ? "id1" : name; // TODO: drop the magic input name.
#endif
allBlobs.insert({name, wrapper->blob});
} }
} }

66
modules/dnn/src/op_inf_engine.hpp
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@ -35,6 +35,11 @@
#define INF_ENGINE_VER_MAJOR_GT(ver) (((INF_ENGINE_RELEASE) / 10000) > ((ver) / 10000)) #define INF_ENGINE_VER_MAJOR_GT(ver) (((INF_ENGINE_RELEASE) / 10000) > ((ver) / 10000))
#define INF_ENGINE_VER_MAJOR_GE(ver) (((INF_ENGINE_RELEASE) / 10000) >= ((ver) / 10000)) #define INF_ENGINE_VER_MAJOR_GE(ver) (((INF_ENGINE_RELEASE) / 10000) >= ((ver) / 10000))
#define INF_ENGINE_VER_MAJOR_LT(ver) (((INF_ENGINE_RELEASE) / 10000) < ((ver) / 10000))
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
#include <ie_builders.hpp>
#endif
#endif // HAVE_INF_ENGINE #endif // HAVE_INF_ENGINE
@ -42,6 +47,7 @@ namespace cv { namespace dnn {
#ifdef HAVE_INF_ENGINE #ifdef HAVE_INF_ENGINE
#if INF_ENGINE_VER_MAJOR_LT(INF_ENGINE_RELEASE_2018R5)
class InfEngineBackendNet : public InferenceEngine::ICNNNetwork class InfEngineBackendNet : public InferenceEngine::ICNNNetwork
{ {
public: public:
@ -146,17 +152,75 @@ private:
void initPlugin(InferenceEngine::ICNNNetwork& net); void initPlugin(InferenceEngine::ICNNNetwork& net);
}; };
#else // IE < R5
class InfEngineBackendNet
{
public:
InfEngineBackendNet();
InfEngineBackendNet(InferenceEngine::CNNNetwork& net);
void addLayer(const InferenceEngine::Builder::Layer& layer);
void addOutput(const std::string& name);
void connect(const std::vector<Ptr<BackendWrapper> >& inputs,
const std::vector<Ptr<BackendWrapper> >& outputs,
const std::string& layerName);
bool isInitialized();
void init(int targetId);
void forward();
void initPlugin(InferenceEngine::ICNNNetwork& net);
void addBlobs(const std::vector<Ptr<BackendWrapper> >& ptrs);
private:
InferenceEngine::Builder::Network netBuilder;
InferenceEngine::InferenceEnginePluginPtr enginePtr;
InferenceEngine::InferencePlugin plugin;
InferenceEngine::ExecutableNetwork netExec;
InferenceEngine::InferRequest infRequest;
InferenceEngine::BlobMap allBlobs;
InferenceEngine::BlobMap inpBlobs;
InferenceEngine::BlobMap outBlobs;
InferenceEngine::TargetDevice targetDevice;
InferenceEngine::CNNNetwork cnn;
bool hasNetOwner;
std::map<std::string, int> layers;
std::vector<std::string> requestedOutputs;
std::set<int> unconnectedLayersIds;
};
#endif // IE < R5
class InfEngineBackendNode : public BackendNode class InfEngineBackendNode : public BackendNode
{ {
public: public:
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
InfEngineBackendNode(const InferenceEngine::Builder::Layer& layer);
#else
InfEngineBackendNode(const InferenceEngine::CNNLayerPtr& layer); InfEngineBackendNode(const InferenceEngine::CNNLayerPtr& layer);
#endif
void connect(std::vector<Ptr<BackendWrapper> >& inputs, void connect(std::vector<Ptr<BackendWrapper> >& inputs,
std::vector<Ptr<BackendWrapper> >& outputs); std::vector<Ptr<BackendWrapper> >& outputs);
InferenceEngine::CNNLayerPtr layer;
// Inference Engine network object that allows to obtain the outputs of this layer. // Inference Engine network object that allows to obtain the outputs of this layer.
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
InferenceEngine::Builder::Layer layer;
Ptr<InfEngineBackendNet> net; Ptr<InfEngineBackendNet> net;
#else
InferenceEngine::CNNLayerPtr layer;
Ptr<InfEngineBackendNet> net;
#endif
}; };
class InfEngineBackendWrapper : public BackendWrapper class InfEngineBackendWrapper : public BackendWrapper

4
modules/dnn/test/test_backends.cpp
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@ -180,7 +180,7 @@ TEST_P(DNNTestNetwork, MobileNet_SSD_v2_TensorFlow)
throw SkipTestException(""); throw SkipTestException("");
Mat sample = imread(findDataFile("dnn/street.png", false)); Mat sample = imread(findDataFile("dnn/street.png", false));
Mat inp = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false); Mat inp = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false);
float l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.013 : 0.0; float l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.013 : 2e-5;
float lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.062 : 0.0; float lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.062 : 0.0;
processNet("dnn/ssd_mobilenet_v2_coco_2018_03_29.pb", "dnn/ssd_mobilenet_v2_coco_2018_03_29.pbtxt", processNet("dnn/ssd_mobilenet_v2_coco_2018_03_29.pb", "dnn/ssd_mobilenet_v2_coco_2018_03_29.pbtxt",
inp, "detection_out", "", l1, lInf, 0.25); inp, "detection_out", "", l1, lInf, 0.25);
@ -288,7 +288,7 @@ TEST_P(DNNTestNetwork, FastNeuralStyle_eccv16)
Mat inp = blobFromImage(img, 1.0, Size(320, 240), Scalar(103.939, 116.779, 123.68), false, false); Mat inp = blobFromImage(img, 1.0, Size(320, 240), Scalar(103.939, 116.779, 123.68), false, false);
// Output image has values in range [-143.526, 148.539]. // Output image has values in range [-143.526, 148.539].
float l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.3 : 4e-5; float l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.3 : 4e-5;
float lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 7.0 : 2e-3; float lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 7.28 : 2e-3;
processNet("dnn/fast_neural_style_eccv16_starry_night.t7", "", inp, "", "", l1, lInf); processNet("dnn/fast_neural_style_eccv16_starry_night.t7", "", inp, "", "", l1, lInf);
} }

2
modules/dnn/test/test_darknet_importer.cpp
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@ -306,7 +306,7 @@ TEST_P(Test_Darknet_nets, TinyYoloVoc)
// batch size 1 // batch size 1
testDarknetModel(config_file, weights_file, ref.rowRange(0, 2), scoreDiff, iouDiff); testDarknetModel(config_file, weights_file, ref.rowRange(0, 2), scoreDiff, iouDiff);
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE >= 2018040000 #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE == 2018040000
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_MYRIAD) if (backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_MYRIAD)
#endif #endif
// batch size 2 // batch size 2

3
modules/dnn/test/test_halide_layers.cpp
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@ -163,7 +163,7 @@ TEST_P(Deconvolution, Accuracy)
bool hasBias = get<6>(GetParam()); bool hasBias = get<6>(GetParam());
Backend backendId = get<0>(get<7>(GetParam())); Backend backendId = get<0>(get<7>(GetParam()));
Target targetId = get<1>(get<7>(GetParam())); Target targetId = get<1>(get<7>(GetParam()));
if (backendId == DNN_BACKEND_INFERENCE_ENGINE && targetId == DNN_TARGET_CPU && if (backendId == DNN_BACKEND_INFERENCE_ENGINE && (targetId == DNN_TARGET_CPU || targetId == DNN_TARGET_MYRIAD) &&
dilation.width == 2 && dilation.height == 2) dilation.width == 2 && dilation.height == 2)
throw SkipTestException(""); throw SkipTestException("");
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE >= 2018040000 #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE >= 2018040000
@ -466,6 +466,7 @@ void testInPlaceActivation(LayerParams& lp, Backend backendId, Target targetId)
pool.set("stride_w", 2); pool.set("stride_w", 2);
pool.set("stride_h", 2); pool.set("stride_h", 2);
pool.type = "Pooling"; pool.type = "Pooling";
pool.name = "ave_pool";
Net net; Net net;
int poolId = net.addLayer(pool.name, pool.type, pool); int poolId = net.addLayer(pool.name, pool.type, pool);

4
modules/dnn/test/test_layers.cpp
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@ -295,10 +295,6 @@ TEST_P(Test_Caffe_layers, Eltwise)
{ {
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD) if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
throw SkipTestException(""); throw SkipTestException("");
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE == 2018050000
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL)
throw SkipTestException("Test is disabled for OpenVINO 2018R5");
#endif
testLayerUsingCaffeModels("layer_eltwise"); testLayerUsingCaffeModels("layer_eltwise");
} }

10
modules/dnn/test/test_onnx_importer.cpp
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@ -351,6 +351,10 @@ TEST_P(Test_ONNX_nets, LResNet100E_IR)
l1 = 0.009; l1 = 0.009;
lInf = 0.035; lInf = 0.035;
} }
else if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_CPU) {
l1 = 4.5e-5;
lInf = 1.9e-4;
}
testONNXModels("LResNet100E_IR", pb, l1, lInf); testONNXModels("LResNet100E_IR", pb, l1, lInf);
} }
@ -366,6 +370,10 @@ TEST_P(Test_ONNX_nets, Emotion_ferplus)
l1 = 0.021; l1 = 0.021;
lInf = 0.034; lInf = 0.034;
} }
else if (backend == DNN_BACKEND_INFERENCE_ENGINE && (target == DNN_TARGET_CPU || target == DNN_TARGET_OPENCL)) {
l1 = 2.4e-4;
lInf = 6e-4;
}
testONNXModels("emotion_ferplus", pb, l1, lInf); testONNXModels("emotion_ferplus", pb, l1, lInf);
} }
@ -389,7 +397,7 @@ TEST_P(Test_ONNX_nets, Inception_v1)
{ {
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE == 2018050000 #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE == 2018050000
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD) if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
throw SkipTestException(""); throw SkipTestException("Test is disabled for OpenVINO 2018R5");
#endif #endif
testONNXModels("inception_v1", pb); testONNXModels("inception_v1", pb);
} }

13
modules/dnn/test/test_tf_importer.cpp
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@ -351,8 +351,8 @@ TEST_P(Test_TensorFlow_nets, MobileNet_v1_SSD)
Mat out = net.forward(); Mat out = net.forward();
Mat ref = blobFromNPY(findDataFile("dnn/tensorflow/ssd_mobilenet_v1_coco_2017_11_17.detection_out.npy")); Mat ref = blobFromNPY(findDataFile("dnn/tensorflow/ssd_mobilenet_v1_coco_2017_11_17.detection_out.npy"));
float scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 7e-3 : 1e-5; float scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 7e-3 : 1.5e-5;
float iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.0098 : 1e-3; float iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.012 : 1e-3;
normAssertDetections(ref, out, "", 0.3, scoreDiff, iouDiff); normAssertDetections(ref, out, "", 0.3, scoreDiff, iouDiff);
} }
@ -366,6 +366,7 @@ TEST_P(Test_TensorFlow_nets, Faster_RCNN)
(backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)) (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16))
throw SkipTestException(""); throw SkipTestException("");
double scoresDiff = backend == DNN_BACKEND_INFERENCE_ENGINE ? 2.9e-5 : 1e-5;
for (int i = 0; i < 2; ++i) for (int i = 0; i < 2; ++i)
{ {
std::string proto = findDataFile("dnn/" + names[i] + ".pbtxt", false); std::string proto = findDataFile("dnn/" + names[i] + ".pbtxt", false);
@ -381,7 +382,7 @@ TEST_P(Test_TensorFlow_nets, Faster_RCNN)
Mat out = net.forward(); Mat out = net.forward();
Mat ref = blobFromNPY(findDataFile("dnn/tensorflow/" + names[i] + ".detection_out.npy")); Mat ref = blobFromNPY(findDataFile("dnn/tensorflow/" + names[i] + ".detection_out.npy"));
normAssertDetections(ref, out, names[i].c_str(), 0.3); normAssertDetections(ref, out, names[i].c_str(), 0.3, scoresDiff);
} }
} }
@ -406,7 +407,7 @@ TEST_P(Test_TensorFlow_nets, MobileNet_v1_SSD_PPN)
net.setInput(blob); net.setInput(blob);
Mat out = net.forward(); Mat out = net.forward();
double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.011 : default_l1; double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.011 : 1.1e-5;
double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.021 : default_lInf; double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.021 : default_lInf;
normAssertDetections(ref, out, "", 0.4, scoreDiff, iouDiff); normAssertDetections(ref, out, "", 0.4, scoreDiff, iouDiff);
} }
@ -568,10 +569,6 @@ TEST_P(Test_TensorFlow_layers, slice)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && if (backend == DNN_BACKEND_INFERENCE_ENGINE &&
(target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
throw SkipTestException(""); throw SkipTestException("");
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE == 2018050000
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
throw SkipTestException("");
#endif
runTensorFlowNet("slice_4d"); runTensorFlowNet("slice_4d");
} }

9
modules/dnn/test/test_torch_importer.cpp
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@ -260,6 +260,11 @@ TEST_P(Test_Torch_layers, run_paralel)
TEST_P(Test_Torch_layers, net_residual) TEST_P(Test_Torch_layers, net_residual)
{ {
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE == 2018050000
if (backend == DNN_BACKEND_INFERENCE_ENGINE && (target == DNN_TARGET_OPENCL ||
target == DNN_TARGET_OPENCL_FP16))
throw SkipTestException("Test is disabled for OpenVINO 2018R5");
#endif
runTorchNet("net_residual", "", false, true); runTorchNet("net_residual", "", false, true);
} }
@ -390,10 +395,6 @@ TEST_P(Test_Torch_nets, ENet_accuracy)
// -model models/instance_norm/feathers.t7 // -model models/instance_norm/feathers.t7
TEST_P(Test_Torch_nets, FastNeuralStyle_accuracy) TEST_P(Test_Torch_nets, FastNeuralStyle_accuracy)
{ {
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE == 2018050000
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
throw SkipTestException("");
#endif
checkBackend(); checkBackend();
std::string models[] = {"dnn/fast_neural_style_eccv16_starry_night.t7", std::string models[] = {"dnn/fast_neural_style_eccv16_starry_night.t7",
"dnn/fast_neural_style_instance_norm_feathers.t7"}; "dnn/fast_neural_style_instance_norm_feathers.t7"};

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