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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
// Copyright (C) 2016, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
/*
Implementation of shift layer, which adds up const values to blob.
*/
#include "../precomp.hpp"
#include "op_inf_engine.hpp"
#include <opencv2/dnn/shape_utils.hpp>
namespace cv
{
namespace dnn
{
class ShiftLayerImpl : public ShiftLayer
{
public:
ShiftLayerImpl(const LayerParams ¶ms)
{
setParamsFrom(params);
CV_Assert(blobs.size() == 1);
}
virtual bool supportBackend(int backendId)
{
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,
std::vector<MatShape> &internals) const
{
Layer::getMemoryShapes(inputs, requiredOutputs, outputs, internals);
internals.assign(1, shape(1, total(inputs[0], 2)));
return true;
}
void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr)
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
Layer::forward_fallback(inputs_arr, outputs_arr, internals_arr);
}
virtual void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals)
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
CV_Assert(inputs.size() > 0);
CV_Assert(blobs.size() > 0);
if(inputs[0]->dims == blobs[0].dims)
{
for (size_t ii = 0; ii < outputs.size(); ii++)
{
Mat &inpBlob = *inputs[ii];
Mat &outBlob = outputs[ii];
outBlob = inpBlob + blobs[0];
}
}
else
{
Mat biasOnesMat = internals[0];
biasOnesMat.setTo(1);
for (size_t ii = 0; ii < outputs.size(); ii++)
{
Mat &inpBlob = *inputs[ii];
Mat &outBlob = outputs[ii];
inpBlob.copyTo(outBlob);
for (int n = 0; n < inpBlob.size[0]; n++)
{
Mat dstMat(inpBlob.size[1], inpBlob.size[2] * inpBlob.size[3],
outBlob.type(), outBlob.ptr(n));
gemm(blobs[0], biasOnesMat, 1, dstMat, 1, dstMat); //TODO: gemv
}
}
}
}
virtual Ptr<BackendNode> tryAttach(const Ptr<BackendNode>& node)
{
switch (node->backendId)
{
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)
{
fuseConvWeights(conv, Mat(), blobs[0]);
return base;
}
#endif // HAVE_INF_ENGINE
break;
}
}
return Ptr<BackendNode>();
}
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&)
{
#ifdef HAVE_INF_ENGINE
// Inference Engine has no layer just for biases. Create a linear
// transformation layer with ones weights.
InferenceEngine::LayerParams lp;
lp.name = name;
lp.type = "ScaleShift";
lp.precision = InferenceEngine::Precision::FP32;
std::shared_ptr<InferenceEngine::ScaleShiftLayer> ieLayer(new InferenceEngine::ScaleShiftLayer(lp));
auto weights = InferenceEngine::make_shared_blob<float>(InferenceEngine::Precision::FP32,
{blobs[0].total()});
weights->allocate();
std::vector<float> ones(blobs[0].total(), 1);
weights->set(ones);
ieLayer->_weights = weights;
ieLayer->_biases = wrapToInfEngineBlob(blobs[0]);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#endif // HAVE_INF_ENGINE
return Ptr<BackendNode>();
}
void getScaleShift(Mat& scale, Mat& shift) const
{
scale = Mat();
shift = blobs[0];
}
virtual int64 getFLOPS(const std::vector<MatShape> &inputs,
const std::vector<MatShape> &outputs) const
{
(void)outputs; // suppress unused variable warning
long flops = 0;
for(int i= 0; i < inputs.size(); i++)
{
flops += total(inputs[i]);
}
return flops;
}
};
Ptr<ShiftLayer> ShiftLayer::create(const LayerParams& params)
{
return Ptr<ShiftLayer>(new ShiftLayerImpl(params));
}
}
}