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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Copyright (C) 2017, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "../precomp.hpp"
#include "layers_common.hpp"
#include "../op_inf_engine.hpp"
namespace cv { namespace dnn {
class NormalizeBBoxLayerImpl CV_FINAL : public NormalizeBBoxLayer
{
public:
NormalizeBBoxLayerImpl(const LayerParams& params)
{
setParamsFrom(params);
pnorm = params.get<float>("p", 2);
epsilon = params.get<float>("eps", 1e-10f);
acrossSpatial = params.get<bool>("across_spatial", true);
startAxis = params.get<int>("start_axis", 1);
CV_Assert(!params.has("across_spatial") || !params.has("end_axis"));
endAxis = params.get<int>("end_axis", acrossSpatial ? -1 : startAxis);
CV_Assert(pnorm > 0);
}
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine() &&
pnorm == 2 && !blobs.empty();
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,
const int requiredOutputs,
std::vector<MatShape> &outputs,
std::vector<MatShape> &internals) const CV_OVERRIDE
{
CV_Assert(inputs.size() == 1);
Layer::getMemoryShapes(inputs, requiredOutputs, outputs, internals);
internals.resize(1, inputs[0]);
internals[0][0] = 1; // Batch size.
return true;
}
#ifdef HAVE_OPENCL
bool forward_ocl(InputArrayOfArrays inputs_, OutputArrayOfArrays outputs_, OutputArrayOfArrays internals_)
{
std::vector<UMat> inputs;
std::vector<UMat> outputs;
std::vector<UMat> internals;
if (inputs_.depth() == CV_16S)
return false;
inputs_.getUMatVector(inputs);
outputs_.getUMatVector(outputs);
internals_.getUMatVector(internals);
CV_Assert(inputs.size() == 1 && outputs.size() == 1);
CV_Assert(inputs[0].total() == outputs[0].total());
const UMat& inp0 = inputs[0];
UMat& buffer = internals[0];
startAxis = clamp(startAxis, inp0.dims);
endAxis = clamp(endAxis, inp0.dims);
size_t num = total(shape(inp0.size), 0, startAxis);
size_t numPlanes = total(shape(inp0.size), startAxis, endAxis + 1);
size_t planeSize = inp0.total() / (num * numPlanes);
MatShape s = shape(1, inputs[0].total());
UMat inp = inputs[0].reshape(1, s.size(), &s[0]).reshape(1, num);
UMat out = outputs[0].reshape(1, s.size(), &s[0]).reshape(1, num);
for (size_t i = 0; i < num; ++i)
{
s = shape(numPlanes, planeSize);
UMat src = inp.row(i).reshape(1, s.size(), &s[0]);
UMat dst = out.row(i).reshape(1, s.size(), &s[0]);
UMat abs_mat;
absdiff(src, cv::Scalar::all(0), abs_mat);
pow(abs_mat, pnorm, buffer);
if (planeSize == 1)
{
// add eps to avoid overflow
float absSum = sum(buffer)[0] + epsilon;
float norm = pow(absSum, 1.0f / pnorm);
multiply(src, 1.0f / norm, dst);
}
else
{
Mat norm;
reduce(buffer, norm, 0, REDUCE_SUM);
norm += epsilon;
// compute inverted norm to call multiply instead divide
cv::pow(norm, -1.0f / pnorm, norm);
repeat(norm, numPlanes, 1, buffer);
multiply(src, buffer, dst);
}
if (!blobs.empty())
{
// scale the output
Mat scale = blobs[0];
if (scale.total() == 1)
{
// _scale: 1 x 1
multiply(dst, scale.at<float>(0, 0), dst);
}
else
{
// _scale: _channels x 1
CV_Assert(scale.total() == numPlanes);
repeat(scale, 1, dst.cols, buffer);
multiply(dst, buffer, dst);
}
}
}
return true;
}
#endif
void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget) &&
OCL_PERFORMANCE_CHECK(ocl::Device::getDefault().isIntel()),
forward_ocl(inputs_arr, outputs_arr, internals_arr))
Layer::forward_fallback(inputs_arr, outputs_arr, internals_arr);
}
void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals) CV_OVERRIDE
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
CV_Assert(inputs.size() == 1 && outputs.size() == 1);
CV_Assert(inputs[0]->total() == outputs[0].total());
const Mat& inp0 = *inputs[0];
Mat& buffer = internals[0];
startAxis = clamp(startAxis, inp0.dims);
endAxis = clamp(endAxis, inp0.dims);
const float* inpData = inp0.ptr<float>();
float* outData = outputs[0].ptr<float>();
size_t num = total(shape(inp0.size), 0, startAxis);
size_t numPlanes = total(shape(inp0.size), startAxis, endAxis + 1);
CV_Assert(num * numPlanes != 0);
size_t planeSize = inp0.total() / (num * numPlanes);
for (size_t n = 0; n < num; ++n)
{
Mat src = Mat(numPlanes, planeSize, CV_32F, (void*)inpData);
Mat dst = Mat(numPlanes, planeSize, CV_32F, (void*)outData);
cv::pow(abs(src), pnorm, buffer);
if (planeSize == 1)
{
// add eps to avoid overflow
float absSum = sum(buffer)[0] + epsilon;
float norm = pow(absSum, 1.0f / pnorm);
multiply(src, 1.0f / norm, dst);
}
else
{
Mat norm;
reduce(buffer, norm, 0, REDUCE_SUM);
norm += epsilon;
// compute inverted norm to call multiply instead divide
cv::pow(norm, -1.0f / pnorm, norm);
repeat(norm, numPlanes, 1, buffer);
multiply(src, buffer, dst);
}
if (!blobs.empty())
{
// scale the output
Mat scale = blobs[0];
if (scale.total() == 1)
{
// _scale: 1 x 1
dst *= scale.at<float>(0, 0);
}
else
{
// _scale: _channels x 1
CV_Assert(scale.total() == numPlanes);
repeat(scale, 1, dst.cols, buffer);
multiply(dst, buffer, dst);
}
}
inpData += numPlanes * planeSize;
outData += numPlanes * planeSize;
}
}
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 size_t 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;
};
Ptr<NormalizeBBoxLayer> NormalizeBBoxLayer::create(const LayerParams ¶ms)
{
return Ptr<NormalizeBBoxLayer>(new NormalizeBBoxLayerImpl(params));
}
}
}