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Intel Inference Engine deep learning backend (#10608)

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* Intel Inference Engine deep learning backend.

* OpenFace network using Inference Engine backend
This commit is contained in:
Dmitry Kurtaev
2018-02-06 11:57:35 +03:00
committed by Vadim Pisarevsky
parent 292dfc2d72
commit 10e1de74d2
26 changed files with 1379 additions and 49 deletions
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modules/dnn/src/layers/scale_layer.cpp
+35 -1
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@@ -12,6 +12,7 @@ Implementation of Scale layer.
#include "../precomp.hpp"
#include "layers_common.hpp"
#include "op_halide.hpp"
#include "op_inf_engine.hpp"
#include <opencv2/dnn/shape_utils.hpp>
namespace cv
@@ -42,7 +43,8 @@ public:
virtual bool supportBackend(int backendId)
{
return backendId == DNN_BACKEND_DEFAULT ||
backendId == DNN_BACKEND_HALIDE && haveHalide();
backendId == DNN_BACKEND_HALIDE && haveHalide() ||
backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine();
}
void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr)
@@ -130,6 +132,20 @@ public:
#endif // HAVE_HALIDE
break;
}
case DNN_BACKEND_INFERENCE_ENGINE:
{
#ifdef HAVE_INF_ENGINE
auto base = node.dynamicCast<InfEngineBackendNode>();
auto conv = std::dynamic_pointer_cast<InferenceEngine::ConvolutionLayer>(base->layer);
if (conv)
{
Mat bias = hasBias ? blobs[1] : Mat();
fuseConvWeights(conv, blobs[0], bias);
return base;
}
#endif // HAVE_INF_ENGINE
break;
}
}
return Ptr<BackendNode>();
}
@@ -167,6 +183,24 @@ public:
}
#endif // HAVE_HALIDE
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&)
{
#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));
ieLayer->_weights = wrapToInfEngineBlob(blobs[0]);
if (hasBias)
ieLayer->_biases = wrapToInfEngineBlob(blobs[1]);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#endif // HAVE_INF_ENGINE
return Ptr<BackendNode>();
}
virtual int64 getFLOPS(const std::vector<MatShape> &inputs,
const std::vector<MatShape> &outputs) const
{