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276 lines
10 KiB
C++
276 lines
10 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
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// Copyright (C) 2017, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "../precomp.hpp"
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#include "layers_common.hpp"
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#include "../op_inf_engine.hpp"
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namespace cv { namespace dnn {
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class NormalizeBBoxLayerImpl CV_FINAL : public NormalizeBBoxLayer
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{
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public:
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NormalizeBBoxLayerImpl(const LayerParams& params)
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{
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setParamsFrom(params);
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pnorm = params.get<float>("p", 2);
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epsilon = params.get<float>("eps", 1e-10f);
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acrossSpatial = params.get<bool>("across_spatial", true);
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startAxis = params.get<int>("start_axis", 1);
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CV_Assert(!params.has("across_spatial") || !params.has("end_axis"));
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endAxis = params.get<int>("end_axis", acrossSpatial ? -1 : startAxis);
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CV_Assert(pnorm > 0);
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}
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virtual bool supportBackend(int backendId) CV_OVERRIDE
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{
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return backendId == DNN_BACKEND_OPENCV ||
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backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine() &&
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pnorm == 2 && !blobs.empty();
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}
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bool getMemoryShapes(const std::vector<MatShape> &inputs,
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const int requiredOutputs,
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std::vector<MatShape> &outputs,
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std::vector<MatShape> &internals) const CV_OVERRIDE
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{
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CV_Assert(inputs.size() == 1);
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Layer::getMemoryShapes(inputs, requiredOutputs, outputs, internals);
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internals.resize(1, inputs[0]);
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internals[0][0] = 1; // Batch size.
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return true;
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}
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#ifdef HAVE_OPENCL
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bool forward_ocl(InputArrayOfArrays inputs_, OutputArrayOfArrays outputs_, OutputArrayOfArrays internals_)
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{
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std::vector<UMat> inputs;
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std::vector<UMat> outputs;
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std::vector<UMat> internals;
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if (inputs_.depth() == CV_16S)
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return false;
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inputs_.getUMatVector(inputs);
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outputs_.getUMatVector(outputs);
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internals_.getUMatVector(internals);
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CV_Assert(inputs.size() == 1 && outputs.size() == 1);
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CV_Assert(inputs[0].total() == outputs[0].total());
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const UMat& inp0 = inputs[0];
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UMat& buffer = internals[0];
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startAxis = clamp(startAxis, inp0.dims);
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endAxis = clamp(endAxis, inp0.dims);
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size_t num = total(shape(inp0.size), 0, startAxis);
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size_t numPlanes = total(shape(inp0.size), startAxis, endAxis + 1);
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size_t planeSize = inp0.total() / (num * numPlanes);
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MatShape s = shape(1, inputs[0].total());
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UMat inp = inputs[0].reshape(1, s.size(), &s[0]).reshape(1, num);
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UMat out = outputs[0].reshape(1, s.size(), &s[0]).reshape(1, num);
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for (size_t i = 0; i < num; ++i)
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{
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s = shape(numPlanes, planeSize);
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UMat src = inp.row(i).reshape(1, s.size(), &s[0]);
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UMat dst = out.row(i).reshape(1, s.size(), &s[0]);
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UMat abs_mat;
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absdiff(src, cv::Scalar::all(0), abs_mat);
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pow(abs_mat, pnorm, buffer);
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if (planeSize == 1)
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{
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// add eps to avoid overflow
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float absSum = sum(buffer)[0] + epsilon;
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float norm = pow(absSum, 1.0f / pnorm);
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multiply(src, 1.0f / norm, dst);
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}
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else
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{
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Mat norm;
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reduce(buffer, norm, 0, REDUCE_SUM);
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norm += epsilon;
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// compute inverted norm to call multiply instead divide
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cv::pow(norm, -1.0f / pnorm, norm);
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repeat(norm, numPlanes, 1, buffer);
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multiply(src, buffer, dst);
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}
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if (!blobs.empty())
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{
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// scale the output
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Mat scale = blobs[0];
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if (scale.total() == 1)
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{
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// _scale: 1 x 1
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multiply(dst, scale.at<float>(0, 0), dst);
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}
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else
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{
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// _scale: _channels x 1
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CV_Assert(scale.total() == numPlanes);
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repeat(scale, 1, dst.cols, buffer);
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multiply(dst, buffer, dst);
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}
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}
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}
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return true;
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}
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#endif
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void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE
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{
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CV_TRACE_FUNCTION();
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CV_TRACE_ARG_VALUE(name, "name", name.c_str());
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CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget) &&
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OCL_PERFORMANCE_CHECK(ocl::Device::getDefault().isIntel()),
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forward_ocl(inputs_arr, outputs_arr, internals_arr))
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Layer::forward_fallback(inputs_arr, outputs_arr, internals_arr);
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}
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void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals) CV_OVERRIDE
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{
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CV_TRACE_FUNCTION();
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CV_TRACE_ARG_VALUE(name, "name", name.c_str());
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CV_Assert(inputs.size() == 1 && outputs.size() == 1);
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CV_Assert(inputs[0]->total() == outputs[0].total());
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const Mat& inp0 = *inputs[0];
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Mat& buffer = internals[0];
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startAxis = clamp(startAxis, inp0.dims);
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endAxis = clamp(endAxis, inp0.dims);
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const float* inpData = inp0.ptr<float>();
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float* outData = outputs[0].ptr<float>();
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size_t num = total(shape(inp0.size), 0, startAxis);
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size_t numPlanes = total(shape(inp0.size), startAxis, endAxis + 1);
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size_t planeSize = inp0.total() / (num * numPlanes);
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for (size_t n = 0; n < num; ++n)
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{
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Mat src = Mat(numPlanes, planeSize, CV_32F, (void*)inpData);
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Mat dst = Mat(numPlanes, planeSize, CV_32F, (void*)outData);
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cv::pow(abs(src), pnorm, buffer);
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if (planeSize == 1)
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{
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// add eps to avoid overflow
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float absSum = sum(buffer)[0] + epsilon;
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float norm = pow(absSum, 1.0f / pnorm);
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multiply(src, 1.0f / norm, dst);
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}
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else
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{
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Mat norm;
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reduce(buffer, norm, 0, REDUCE_SUM);
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norm += epsilon;
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// compute inverted norm to call multiply instead divide
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cv::pow(norm, -1.0f / pnorm, norm);
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repeat(norm, numPlanes, 1, buffer);
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multiply(src, buffer, dst);
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}
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if (!blobs.empty())
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{
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// scale the output
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Mat scale = blobs[0];
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if (scale.total() == 1)
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{
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// _scale: 1 x 1
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dst *= scale.at<float>(0, 0);
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}
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else
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{
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// _scale: _channels x 1
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CV_Assert(scale.total() == numPlanes);
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repeat(scale, 1, dst.cols, buffer);
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multiply(dst, buffer, dst);
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}
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}
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inpData += numPlanes * planeSize;
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outData += numPlanes * planeSize;
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}
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}
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virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
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{
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#ifdef HAVE_INF_ENGINE
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InferenceEngine::LayerParams lp;
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lp.name = name;
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lp.type = "Normalize";
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lp.precision = InferenceEngine::Precision::FP32;
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std::shared_ptr<InferenceEngine::CNNLayer> ieLayer(new InferenceEngine::CNNLayer(lp));
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CV_Assert(!blobs.empty());
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ieLayer->params["eps"] = format("%f", epsilon);
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ieLayer->params["across_spatial"] = acrossSpatial ? "1" : "0";
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ieLayer->params["channel_shared"] = blobs[0].total() == 1 ? "1" : "0";
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const size_t numChannels = blobs[0].total();
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ieLayer->blobs["weights"] = wrapToInfEngineBlob(blobs[0], {numChannels}, InferenceEngine::Layout::C);
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return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
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#endif // HAVE_INF_ENGINE
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return Ptr<BackendNode>();
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}
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private:
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int startAxis, endAxis;
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};
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Ptr<NormalizeBBoxLayer> NormalizeBBoxLayer::create(const LayerParams ¶ms)
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{
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return Ptr<NormalizeBBoxLayer>(new NormalizeBBoxLayerImpl(params));
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}
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}
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}
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