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Blank and L2-normalization layers from Intel's Inference Engine

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This commit is contained in:
Dmitry Kurtaev 2018-04-12 15:21:08 +03:00
parent 0b9d075958
commit b92c3182ab
3 changed files with 73 additions and 5 deletions
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20
modules/dnn/src/layers/blank_layer.cpp
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@ -40,6 +40,7 @@
//
//M*/
#include "../precomp.hpp"
#include "../op_inf_engine.hpp"
namespace cv
{
@ -53,6 +54,12 @@ public:
setParamsFrom(params);
}
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_DEFAULT ||
backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine();
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,
const int requiredOutputs,
std::vector<MatShape> &outputs,
@ -104,6 +111,19 @@ public:
if (outputs[i].data != inputs[i]->data)
inputs[i]->copyTo(outputs[i]);
}
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{
#ifdef HAVE_INF_ENGINE
InferenceEngine::LayerParams lp;
lp.name = name;
lp.type = "Split";
lp.precision = InferenceEngine::Precision::FP32;
std::shared_ptr<InferenceEngine::SplitLayer> ieLayer(new InferenceEngine::SplitLayer(lp));
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#endif // HAVE_INF_ENGINE
return Ptr<BackendNode>();
}
};
Ptr<Layer> BlankLayer::create(const LayerParams& params)

30
modules/dnn/src/layers/normalize_bbox_layer.cpp
View File

@ -42,6 +42,7 @@
#include "../precomp.hpp"
#include "layers_common.hpp"
#include "../op_inf_engine.hpp"
namespace cv { namespace dnn {
@ -60,6 +61,13 @@ public:
CV_Assert(pnorm > 0);
}
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_DEFAULT ||
backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine() &&
pnorm == 2 && !blobs.empty();
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,
const int requiredOutputs,
std::vector<MatShape> &outputs,
@ -228,6 +236,28 @@ public:
}
}
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{
#ifdef HAVE_INF_ENGINE
InferenceEngine::LayerParams lp;
lp.name = name;
lp.type = "Normalize";
lp.precision = InferenceEngine::Precision::FP32;
std::shared_ptr<InferenceEngine::CNNLayer> ieLayer(new InferenceEngine::CNNLayer(lp));
CV_Assert(!blobs.empty());
ieLayer->params["eps"] = format("%f", epsilon);
ieLayer->params["across_spatial"] = acrossSpatial ? "1" : "0";
ieLayer->params["channel_shared"] = blobs[0].total() == 1 ? "1" : "0";
const int numChannels = blobs[0].total();
ieLayer->blobs["weights"] = wrapToInfEngineBlob(blobs[0], {numChannels}, InferenceEngine::Layout::C);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#endif // HAVE_INF_ENGINE
return Ptr<BackendNode>();
}
private:
int startAxis, endAxis;
};

28
modules/dnn/src/op_inf_engine.cpp
View File

@ -18,6 +18,11 @@ namespace cv { namespace dnn {
#ifdef HAVE_INF_ENGINE
static int infEngineVersion()
{
return std::atoi(InferenceEngine::GetInferenceEngineVersion()->buildNumber);
}
InfEngineBackendNode::InfEngineBackendNode(const InferenceEngine::CNNLayerPtr& _layer)
: BackendNode(DNN_BACKEND_INFERENCE_ENGINE), layer(_layer) {}
@ -58,9 +63,23 @@ static InferenceEngine::DataPtr wrapToInfEngineDataNode(const Mat& m, const std:
{
std::vector<size_t> reversedShape(&m.size[0], &m.size[0] + m.dims);
std::reverse(reversedShape.begin(), reversedShape.end());
return InferenceEngine::DataPtr(
new InferenceEngine::Data(name, reversedShape, InferenceEngine::Precision::FP32)
);
if (infEngineVersion() > 5855)
{
InferenceEngine::Layout l = InferenceEngine::Layout::ANY;
if (m.dims == 4)
l = InferenceEngine::Layout::NCHW;
else if (m.dims == 2)
l = InferenceEngine::Layout::NC;
return InferenceEngine::DataPtr(
new InferenceEngine::Data(name, reversedShape, InferenceEngine::Precision::FP32, l)
);
}
else
{
return InferenceEngine::DataPtr(
new InferenceEngine::Data(name, reversedShape, InferenceEngine::Precision::FP32)
);
}
}
InferenceEngine::TBlob<float>::Ptr wrapToInfEngineBlob(const Mat& m, const std::vector<size_t>& shape,
@ -336,10 +355,9 @@ void InfEngineBackendNet::initPlugin(InferenceEngine::ICNNNetwork& net)
InferenceEngine::StatusCode status;
InferenceEngine::ResponseDesc resp;
const InferenceEngine::Version* v = InferenceEngine::GetInferenceEngineVersion();
plugin = InferenceEngine::PluginDispatcher({""}).getSuitablePlugin(targetDevice);
if (std::atoi(v->buildNumber) > 5855)
if (infEngineVersion() > 5855 && targetDevice == InferenceEngine::TargetDevice::eCPU)
{
#ifdef _WIN32
InferenceEngine::IExtensionPtr extension =