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https://github.com/opencv/opencv.git
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8f99083726
Add layer forward interface with InputArrayOfArrays and OutputArrayOfArrays parameters, it allows UMat buffer to be processed and transferred in the layers. Signed-off-by: Li Peng <peng.li@intel.com>
414 lines
15 KiB
C++
414 lines
15 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 "opencl_kernels_dnn.hpp"
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#include <opencv2/dnn/shape_utils.hpp>
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#ifdef HAVE_OPENCL
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using namespace cv::dnn::ocl4dnn;
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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 FullyConnectedLayerImpl : public InnerProductLayer
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{
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public:
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enum { VEC_ALIGN = 8 };
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#ifdef HAVE_OPENCL
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Ptr<OCL4DNNInnerProduct<float> > innerProductOp;
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std::vector<UMat> umat_blobs;
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#endif
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FullyConnectedLayerImpl(const LayerParams& params)
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{
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setParamsFrom(params);
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CV_Assert(1 <= blobs.size() && blobs.size() <= 2);
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int numOutput = params.get<int>("num_output");
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int innerSize = (int)blobs[0].total() / numOutput;
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bias = params.get<bool>("bias_term", true);
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axis = params.get<int>("axis", 1);
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CV_Assert(blobs[0].dims >= 2 && (size_t)(innerSize * numOutput) == blobs[0].total());
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CV_Assert(!bias || (blobs.size() == 2 && (size_t)numOutput == blobs[1].total()));
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weightsMat = blobs[0] = blobs[0].reshape(1, numOutput);
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int vecsize = weightsMat.cols;
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if( vecsize % VEC_ALIGN != 0 )
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{
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int vecsize_aligned = (int)alignSize(vecsize, VEC_ALIGN);
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Mat weightsBuf(weightsMat.rows, vecsize_aligned, weightsMat.type());
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Mat wpadding = weightsBuf.colRange(vecsize, vecsize_aligned);
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wpadding.setTo(Scalar::all(0.));
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weightsMat = weightsBuf.colRange(0, vecsize);
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blobs[0].copyTo(weightsMat);
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}
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if (bias)
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biasMat = blobs[1] = blobs[1].reshape(1, 1);
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else
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biasMat = Mat::zeros(1, numOutput, weightsMat.type());
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#ifdef HAVE_OPENCL
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size_t n = blobs.size();
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umat_blobs.resize(n);
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for (int i = 0; i < n; i++) umat_blobs[i] = blobs[i].getUMat(ACCESS_READ);
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#endif
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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> &) const
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{
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CV_Assert(inputs.size() == 1);
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CV_Assert(1 <= blobs.size() && blobs.size() <= 2);
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CV_Assert(blobs[0].dims == 2);
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int cAxis = clamp(axis, inputs[0]);
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int numOutput = blobs[0].size[0];
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MatShape outShape(cAxis + 1);
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for (int i = 0; i < cAxis; ++i)
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outShape[i] = inputs[0][i];
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outShape.back() = numOutput;
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outputs.resize(inputs.size(), outShape);
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CV_Assert(!bias || (size_t)numOutput == blobs[1].total());
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return false;
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}
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virtual bool supportBackend(int backendId)
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{
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return backendId == DNN_BACKEND_DEFAULT ||
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backendId == DNN_BACKEND_HALIDE && haveHalide() && axis == 1;
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}
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virtual bool setActivation(const Ptr<ActivationLayer>& layer)
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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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class FullyConnected : public ParallelLoopBody
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{
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public:
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FullyConnected() : srcMat(0), weights(0), biasMat(0), activ(0), dstMat(0), nstripes(0), useAVX(false), useAVX2(false) {}
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static void run(const Mat& srcMat, const Mat& weights, const Mat& biasMat,
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Mat& dstMat, const ActivationLayer* activ, int nstripes)
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{
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CV_Assert( srcMat.dims == 2 && srcMat.cols == weights.cols &&
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dstMat.rows == srcMat.rows && dstMat.cols == weights.rows &&
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srcMat.type() == weights.type() && weights.type() == dstMat.type() &&
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srcMat.type() == CV_32F &&
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(biasMat.empty() || (biasMat.type() == srcMat.type() &&
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biasMat.isContinuous() && (int)biasMat.total() == dstMat.cols)) );
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FullyConnected p;
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p.srcMat = &srcMat;
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p.weights = &weights;
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p.biasMat = &biasMat;
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p.dstMat = &dstMat;
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p.nstripes = nstripes;
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p.activ = activ;
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p.useAVX = checkHardwareSupport(CPU_AVX);
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p.useAVX2 = checkHardwareSupport(CPU_AVX2);
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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
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{
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int valign = FullyConnectedLayerImpl::VEC_ALIGN;
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int nsamples = srcMat->rows;
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int nw0 = weights->rows;
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int k, vecsize = srcMat->cols;
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int vecsize_aligned = (int)alignSize(vecsize, VEC_ALIGN);
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size_t total = (size_t)nsamples*nw0;
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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 = r.end == nstripes ? total : std::min(r.end*stripeSize, total);
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size_t wstep = weights->step1();
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AutoBuffer<float> srcbuf(vecsize_aligned + valign);
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float* sptr = alignPtr((float*)srcbuf, (int)(valign*sizeof(float)));
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for( k = vecsize; k < vecsize_aligned; k++ )
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sptr[k] = 0.f;
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for( size_t ofs = stripeStart; ofs < stripeEnd; )
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{
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int sampleIdx = (int)(ofs / nw0);
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int delta = (int)(ofs - (size_t)sampleIdx*nw0);
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const float* sptr_ = srcMat->ptr<float>(sampleIdx);
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const float* wptr = weights->ptr<float>(delta);
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float* dptr = dstMat->ptr<float>(sampleIdx) + delta;
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const float* biasptr = biasMat->ptr<float>() + delta;
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int nw = std::min(nw0 - delta, (int)(stripeEnd - ofs));
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memcpy(sptr, sptr_, vecsize*sizeof(sptr[0]));
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#if CV_TRY_AVX2
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if( useAVX2 )
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opt_AVX2::fastGEMM1T( sptr, wptr, wstep, biasptr, dptr, nw, vecsize);
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else
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#endif
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#if CV_TRY_AVX
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if( useAVX )
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opt_AVX::fastGEMM1T( sptr, wptr, wstep, biasptr, dptr, nw, vecsize);
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else
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#endif
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{
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int i = 0;
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#if CV_SIMD128
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for( ; i <= nw - 4; i += 4, wptr += 4*wstep )
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{
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v_float32x4 vs0 = v_setall_f32(0.f), vs1 = v_setall_f32(0.f);
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v_float32x4 vs2 = v_setall_f32(0.f), vs3 = v_setall_f32(0.f);
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for( k = 0; k < vecsize; k += 4 )
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{
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v_float32x4 v = v_load_aligned(sptr + k);
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vs0 += v*v_load_aligned(wptr + k);
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vs1 += v*v_load_aligned(wptr + wstep + k);
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vs2 += v*v_load_aligned(wptr + wstep*2 + k);
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vs3 += v*v_load_aligned(wptr + wstep*3 + k);
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}
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v_float32x4 s = v_reduce_sum4(vs0, vs1, vs2, vs3);
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s += v_load(biasptr + i);
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v_store(dptr + i, s);
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}
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#endif
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for( ; i < nw; i++, wptr += wstep )
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{
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float s0=biasptr[i];
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for( k = 0; k < vecsize; k++ )
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{
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float v = sptr[k];
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s0 += v*wptr[k];
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}
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dptr[i] = s0;
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}
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}
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if(activ)
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activ->forwardSlice(dptr, dptr, 1, 1, delta, delta + nw);
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ofs += nw;
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}
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}
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const Mat *srcMat, *weights, *biasMat;
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const ActivationLayer* activ;
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Mat* dstMat;
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int nstripes;
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bool useAVX;
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bool useAVX2;
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};
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#ifdef HAVE_OPENCL
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bool forward_ocl(InputArrayOfArrays inps, OutputArrayOfArrays outs, InputArrayOfArrays 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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inps.getUMatVector(inputs);
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outs.getUMatVector(outputs);
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int axisCan = clamp(axis, inputs[0].dims);
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int numOutput = umat_blobs[0].size[0];
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int innerSize = umat_blobs[0].size[1];
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int outerSize = total(shape(inputs[0]), 0, axisCan);
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bool ret = true;
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if (innerProductOp.empty())
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{
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OCL4DNNInnerProductConfig config;
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config.num_output = numOutput;
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config.bias_term = bias;
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config.M = outerSize;
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config.K = innerSize;
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innerProductOp = Ptr<OCL4DNNInnerProduct<float> >(new OCL4DNNInnerProduct<float>(config));
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}
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UMat biasOnesMat = UMat::ones(outerSize, 1, umat_blobs[0].type());
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for (size_t i = 0; i < inputs.size(); i++)
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{
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UMat& srcMat = inputs[i];
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UMat& dstMat = outputs[i];
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dstMat.setTo(0.0f);
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if (!innerProductOp->Forward(srcMat, umat_blobs[0], (bias) ? umat_blobs[1] : UMat(), dstMat))
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{
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ret = false;
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break;
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}
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if (bias && (outerSize > 1))
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{
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UMat& biases = umat_blobs[1];
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cv::gemm(biasOnesMat, biases, 1, dstMat, 1, dstMat, 0);
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}
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}
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if (ret) return true;
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UMat& weights = umat_blobs[0];
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for (size_t i = 0; i < inputs.size(); i++)
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{
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MatShape inshape, outshape;
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inshape = shape(outerSize, innerSize);
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outshape = shape(outerSize, numOutput);
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UMat srcMat, dstMat;
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srcMat = inputs[i].reshape(1, inshape.size(), &inshape[0]);
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dstMat = outputs[i].reshape(1, outshape.size(), &outshape[0]);
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cv::gemm(srcMat, weights, 1, noArray(), 0, dstMat, GEMM_2_T);
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if (bias)
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{
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UMat& biases = umat_blobs[1];
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cv::gemm(biasOnesMat, biases, 1, dstMat, 1, dstMat, 0);
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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)
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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((preferableTarget == DNN_TARGET_OPENCL) &&
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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*> &input, std::vector<Mat> &output, std::vector<Mat> &)
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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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int axisCan = clamp(axis, input[0]->dims);
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int outerSize = input[0]->total(0, axisCan);
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for (size_t i = 0; i < input.size(); i++)
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{
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Mat srcMat = input[i]->reshape(1, outerSize);
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Mat dstMat = output[i].reshape(1, outerSize);
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const int nstripes = getNumThreads();
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FullyConnected::run(srcMat, weightsMat, biasMat, dstMat, activ.get(), nstripes);
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}
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}
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virtual Ptr<BackendNode> initHalide(const std::vector<Ptr<BackendWrapper> > &inputs)
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{
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#ifdef HAVE_HALIDE
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int inW, inH, inC, inN, outC = blobs[0].size[0];
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Halide::Buffer<float> inputBuffer = halideBuffer(inputs[0]);
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getCanonicalSize(inputBuffer, &inW, &inH, &inC, &inN);
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auto weights = wrapToHalideBuffer(blobs[0], {inW, inH, inC, outC});
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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::RDom r(0, inW, 0, inH, 0, inC);
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Halide::Expr topExpr = sum(inputBuffer(r.x, r.y, r.z, n) *
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weights(r.x, r.y, r.z, c));
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if (bias)
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{
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Halide::Buffer<float> bias = wrapToHalideBuffer(blobs[1], {outC});
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topExpr += bias(c);
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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 int64 getFLOPS(const std::vector<MatShape> &inputs,
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const std::vector<MatShape> &outputs) const
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{
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(void)inputs; // suppress unused variable warning
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long flops = 0;
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int innerSize = blobs[0].size[1];
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for(int i = 0; i < outputs.size(); i++)
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{
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flops += 3*innerSize*total(outputs[i]);
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}
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return flops;
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}
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bool bias;
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Mat weightsMat, biasMat;
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Ptr<ActivationLayer> activ;
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};
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Ptr<InnerProductLayer> InnerProductLayer::create(const LayerParams& params)
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{
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return Ptr<InnerProductLayer>(new FullyConnectedLayerImpl(params));
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}
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}
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}
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