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469 lines
18 KiB
C++
469 lines
18 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_halide.hpp"
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#include "../op_inf_engine.hpp"
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#ifdef HAVE_OPENCL
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#include "opencl_kernels_dnn.hpp"
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#endif
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namespace cv
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{
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namespace dnn
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{
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class EltwiseLayerImpl CV_FINAL : public EltwiseLayer
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{
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public:
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enum EltwiseOp
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{
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PROD = 0,
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SUM = 1,
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MAX = 2,
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} op;
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std::vector<float> coeffs;
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EltwiseLayerImpl(const LayerParams& params)
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{
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setParamsFrom(params);
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op = SUM;
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if (params.has("operation"))
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{
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String operation = params.get<String>("operation").toLowerCase();
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if (operation == "prod")
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op = PROD;
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else if (operation == "sum")
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op = SUM;
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else if (operation == "max")
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op = MAX;
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else
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CV_Error(cv::Error::StsBadArg, "Unknown operation type \"" + operation + "\"");
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}
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if (params.has("coeff"))
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{
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DictValue paramCoeff = params.get("coeff");
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int i, n = paramCoeff.size();
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coeffs.resize(n);
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for (i = 0; i < n; i++)
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{
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coeffs[i] = paramCoeff.get<float>(i);
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}
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}
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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_HALIDE && haveHalide() ||
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backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine();
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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() >= 2);
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CV_Assert(coeffs.size() == 0 || coeffs.size() == inputs.size());
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CV_Assert(op == SUM || coeffs.size() == 0);
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for (int i = 1; i < inputs.size(); i++)
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{
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CV_Assert(inputs[0] == inputs[i]);
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}
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outputs.assign(1, inputs[0]);
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return false;
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}
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class EltwiseInvoker : public ParallelLoopBody
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{
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public:
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const Mat** srcs;
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int nsrcs;
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Mat* dst;
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const std::vector<float>* coeffs;
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EltwiseOp op;
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int nstripes;
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const ActivationLayer* activ;
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int channels;
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size_t planeSize;
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EltwiseInvoker() : srcs(0), nsrcs(0), dst(0), coeffs(0), op(PROD), nstripes(0), activ(0), channels(0), planeSize(0) {}
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static void run(const Mat** srcs, int nsrcs, Mat& dst,
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const std::vector<float>& coeffs, EltwiseOp op,
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const ActivationLayer* activ, int nstripes)
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{
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CV_Assert(1 < dst.dims && dst.dims <= 4, dst.type() == CV_32F, dst.isContinuous());
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CV_Assert(coeffs.empty() || coeffs.size() == (size_t)nsrcs);
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for( int i = 0; i > nsrcs; i++ )
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{
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CV_Assert(srcs[i]->size == dst.size &&
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srcs[i]->type() == dst.type() &&
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srcs[i]->isContinuous());
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}
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EltwiseInvoker p;
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p.srcs = srcs;
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p.nsrcs = nsrcs;
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p.dst = &dst;
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p.op = op;
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p.nstripes = nstripes;
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p.channels = (dst.dims == 4 ? dst.size[1] : 1);
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p.planeSize = (dst.dims >= 3 ? dst.size[dst.dims - 1] * dst.size[dst.dims - 2] :
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dst.size[dst.dims - 1]);
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CV_Assert(dst.total() == dst.size[0] * p.channels * p.planeSize);
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bool simpleCoeffs = true;
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if( op == SUM && !coeffs.empty() )
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{
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CV_Assert( coeffs.size() == (size_t)nsrcs );
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for( size_t i = 0; i < coeffs.size(); i++ )
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if( coeffs[i] != 1 )
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{
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simpleCoeffs = false;
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break;
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}
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}
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p.coeffs = simpleCoeffs ? 0 : &coeffs;
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p.activ = activ;
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parallel_for_(Range(0, nstripes), p, nstripes);
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}
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void operator()(const Range& r) const CV_OVERRIDE
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{
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size_t total = dst->size[0]*planeSize;
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size_t stripeSize = (total + nstripes - 1)/nstripes;
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size_t stripeStart = r.start*stripeSize;
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size_t stripeEnd = std::min(r.end*stripeSize, total);
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int c, j, k, n = nsrcs;
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const float* coeffsptr = coeffs && !coeffs->empty() ? &coeffs->at(0) : 0;
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float* dstptr0 = dst->ptr<float>();
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int blockSize0 = 1 << 12, blockSize = blockSize0;
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for( size_t ofs = stripeStart; ofs < stripeEnd; ofs += blockSize )
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{
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int sampleIdx = (int)(ofs / planeSize);
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int delta = (int)ofs - sampleIdx * planeSize;
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blockSize = std::min(blockSize0, std::min((int)(stripeEnd - ofs), (int)planeSize - delta));
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if( blockSize <= 0 )
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break;
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for( c = 0; c < channels; c++ )
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{
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size_t globalDelta = delta + (sampleIdx*channels + c)*planeSize;
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const float* srcptr0 = srcs[0]->ptr<float>() + globalDelta;
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float* dstptr = dstptr0 + globalDelta;
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if( op == PROD )
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{
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for( k = 1; k < n; k++ )
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{
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const float* srcptr1 = srcs[k]->ptr<float>() + globalDelta;
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for( j = 0; j < blockSize; j++ )
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{
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dstptr[j] = srcptr0[j]*srcptr1[j];
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}
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srcptr0 = (const float*)dstptr;
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}
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}
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else if( op == MAX )
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{
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for( k = 1; k < n; k++ )
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{
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const float* srcptr1 = srcs[k]->ptr<float>() + globalDelta;
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for( j = 0; j < blockSize; j++ )
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{
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dstptr[j] = std::max(srcptr0[j], srcptr1[j]);
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}
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srcptr0 = (const float*)dstptr;
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}
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}
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else if( !coeffsptr )
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{
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for( k = 1; k < n; k++ )
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{
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const float* srcptr1 = srcs[k]->ptr<float>() + globalDelta;
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for( j = 0; j < blockSize; j++ )
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{
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dstptr[j] = srcptr0[j] + srcptr1[j];
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}
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srcptr0 = (const float*)dstptr;
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}
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}
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else
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{
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float c0 = coeffsptr[0];
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for( k = 1; k < n; k++ )
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{
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const float* srcptr1 = srcs[k]->ptr<float>() + globalDelta;
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float c1 = coeffsptr[k];
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for( j = 0; j < blockSize; j++ )
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{
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dstptr[j] = c0*srcptr0[j] + c1*srcptr1[j];
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}
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srcptr0 = (const float*)dstptr;
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c0 = 1;
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}
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}
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}
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if( activ )
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{
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float* ptr = dstptr0 + delta + sampleIdx*channels*planeSize;
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activ->forwardSlice(ptr, ptr, blockSize, planeSize, 0, channels);
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}
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}
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}
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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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if (inputs_.depth() == CV_16S && op != SUM)
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return false;
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inputs_.getUMatVector(inputs);
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outputs_.getUMatVector(outputs);
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switch (op)
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{
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case SUM:
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{
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int channels = total(shape(outputs[0]), 0, 2);
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int plane_size = total(shape(outputs[0]), 2);
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if (channels % 4 == 0 && plane_size % 4 == 0)
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{
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size_t localsize[] = { 128 };
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size_t globalsize[] = { (size_t)channels / 4 * localsize[0] };
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String opts;
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if (inputs_.depth() == CV_16S)
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opts = " -DDtype=half -DDtype4=half4 -DDtype8=half8";
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else
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opts = " -DDtype=float -DDtype4=float4 -DDtype8=float8";
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for (int i = 0; i < (inputs.size() - 1); ++i)
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{
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String buildopt = format("-DLOOP=%d", i) + opts;
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ocl::Kernel kernel("op_sum4", ocl::dnn::eltwise_oclsrc, buildopt);
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int idx = 0;
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UMat inpMat = (i == 0) ? inputs[0] : UMat();
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float coeff1 = (coeffs.empty() || i > 0) ? 1.0f : coeffs[i];
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float coeff2 = coeffs.empty() ? 1.0f : coeffs[i + 1];
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kernel.set(idx++, ocl::KernelArg::PtrReadOnly(inputs[0]));
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kernel.set(idx++, ocl::KernelArg::PtrReadOnly(inputs[1]));
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kernel.set(idx++, (int)plane_size);
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kernel.set(idx++, (float)coeff1);
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kernel.set(idx++, (float)coeff2);
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kernel.set(idx++, ocl::KernelArg::PtrReadWrite(outputs[0]));
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bool ret = kernel.run(1, globalsize, localsize, false);
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if (!ret)
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return false;
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}
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}
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else
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{
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if (inputs_.depth() == CV_16S)
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return false;
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float coeff1 = coeffs.empty() ? 1.f : coeffs[0];
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float coeff2 = coeffs.empty() ? 1.f : coeffs[1];
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UMat mul0, mul1;
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multiply(coeff1, inputs[0], mul0);
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multiply(coeff2, inputs[1], mul1);
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add(mul0, mul1, outputs[0]);
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for (int i = 2; i < inputs.size(); ++i)
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{
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float coeff = coeffs.empty() ? 1.f : coeffs[i];
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multiply(coeff, inputs[i], mul0);
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add(mul0, outputs[0], outputs[0]);
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}
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}
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}
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break;
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case PROD:
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multiply(inputs[0], inputs[1], outputs[0]);
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for (int i = 2; i < inputs.size(); ++i)
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multiply(inputs[i], outputs[0], outputs[0]);
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break;
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case MAX:
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max(inputs[0], inputs[1], outputs[0]);
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for (int i = 2; i < inputs.size(); ++i)
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max(inputs[i], outputs[0], outputs[0]);
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break;
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default:
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return false;
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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(outputs.size() == 1);
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const int nstripes = getNumThreads();
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EltwiseInvoker::run((const Mat**)&inputs[0], (int)inputs.size(), outputs[0],
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coeffs, op, activ.get(), nstripes);
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}
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virtual Ptr<BackendNode> initHalide(const std::vector<Ptr<BackendWrapper> > &input) CV_OVERRIDE
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{
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#ifdef HAVE_HALIDE
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Halide::Var x("x"), y("y"), c("c"), n("n");
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Halide::Func top = (name.empty() ? Halide::Func() : Halide::Func(name));
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Halide::Expr topExpr;
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std::vector<Halide::Buffer<> > inputBuffers = halideBuffers(input);
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switch (op)
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{
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case SUM:
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if (coeffs.empty())
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{
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topExpr = inputBuffers[0](x, y, c, n) +
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inputBuffers[1](x, y, c, n);
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for (int i = 2; i < inputBuffers.size(); ++i)
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topExpr += inputBuffers[i](x, y, c, n);
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}
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else
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{
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topExpr = coeffs[0] * inputBuffers[0](x, y, c, n) +
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coeffs[1] * inputBuffers[1](x, y, c, n);
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for (int i = 2; i < inputBuffers.size(); ++i)
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topExpr += coeffs[i] * inputBuffers[i](x, y, c, n);
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}
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break;
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case PROD:
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topExpr = inputBuffers[0](x, y, c, n) *
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inputBuffers[1](x, y, c, n);
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for (int i = 2; i < inputBuffers.size(); ++i)
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topExpr *= inputBuffers[i](x, y, c, n);
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break;
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case MAX:
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topExpr = max(inputBuffers[0](x, y, c, n),
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inputBuffers[1](x, y, c, n));
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for (int i = 2; i < inputBuffers.size(); ++i)
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topExpr = max(topExpr, inputBuffers[i](x, y, c, n));
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break;
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default:
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return Ptr<BackendNode>();
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}
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top(x, y, c, n) = topExpr;
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return Ptr<BackendNode>(new HalideBackendNode(top));
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#endif // HAVE_HALIDE
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return Ptr<BackendNode>();
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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 = "Eltwise";
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lp.precision = InferenceEngine::Precision::FP32;
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std::shared_ptr<InferenceEngine::EltwiseLayer> ieLayer(new InferenceEngine::EltwiseLayer(lp));
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if (op == SUM)
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ieLayer->_operation = InferenceEngine::EltwiseLayer::Sum;
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else if (op == PROD)
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ieLayer->_operation = InferenceEngine::EltwiseLayer::Prod;
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else if (op == MAX)
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ieLayer->_operation = InferenceEngine::EltwiseLayer::Max;
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else
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CV_Error(Error::StsNotImplemented, "Unsupported eltwise operation");
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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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virtual int64 getFLOPS(const std::vector<MatShape> &inputs,
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const std::vector<MatShape> &outputs) const CV_OVERRIDE
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{
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(void)outputs; // suppress unused variable warning
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CV_Assert(inputs.size());
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long flops = inputs.size() * total(inputs[0]);
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return flops;
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}
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bool setActivation(const Ptr<ActivationLayer>& layer) CV_OVERRIDE
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{
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activ = layer;
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return !activ.empty();
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}
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Ptr<ActivationLayer> activ;
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};
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Ptr<EltwiseLayer> EltwiseLayer::create(const LayerParams& params)
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{
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return Ptr<EltwiseLayer>(new EltwiseLayerImpl(params));
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}
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}
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}
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