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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/convolution_layer.cpp
+39 -1
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@@ -43,6 +43,7 @@
#include "../precomp.hpp"
#include "layers_common.hpp"
#include "op_halide.hpp"
#include "op_inf_engine.hpp"
#include "opencv2/core/hal/hal.hpp"
#include "opencv2/core/hal/intrin.hpp"
#include <iostream>
@@ -65,7 +66,8 @@ 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 finalize(const std::vector<Mat*> &inputs, std::vector<Mat> &outputs)
@@ -335,6 +337,42 @@ public:
return Ptr<BackendNode>();
}
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> > &inputs)
{
#ifdef HAVE_INF_ENGINE
InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]);
CV_Assert(input->dims.size() == 4);
const int inpCn = input->dims[2]; // NOTE: input->dims are reversed (whcn)
const int outCn = blobs[0].size[0];
const int inpGroupCn = blobs[0].size[1];
const int group = inpCn / inpGroupCn;
InferenceEngine::LayerParams lp;
lp.name = name;
lp.type = "Convolution";
lp.precision = InferenceEngine::Precision::FP32;
std::shared_ptr<InferenceEngine::ConvolutionLayer> ieLayer(new InferenceEngine::ConvolutionLayer(lp));
ieLayer->_kernel_x = kernel.width;
ieLayer->_kernel_y = kernel.height;
ieLayer->_stride_x = stride.width;
ieLayer->_stride_y = stride.height;
ieLayer->_out_depth = outCn;
ieLayer->_padding_x = pad.width;
ieLayer->_padding_y = pad.height;
ieLayer->_dilation_x = dilation.width;
ieLayer->_dilation_y = dilation.height;
ieLayer->_group = group;
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>();
}
class ParallelConv : public cv::ParallelLoopBody
{
public: