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953 lines
36 KiB
C++
953 lines
36 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_cuda.hpp"
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#include "../op_halide.hpp"
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#include "../op_inf_engine.hpp"
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#include "../ie_ngraph.hpp"
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#include "../op_webnn.hpp"
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#include "../op_cann.hpp"
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#include "../op_vkcom.hpp"
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#include <opencv2/dnn/shape_utils.hpp>
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#ifdef HAVE_OPENCL
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#include "opencl_kernels_dnn.hpp"
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using namespace cv::dnn::ocl4dnn;
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#endif
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#ifdef HAVE_CUDA
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#include "../cuda4dnn/primitives/matmul.hpp"
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#include "../cuda4dnn/primitives/inner_product.hpp"
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using namespace cv::dnn::cuda4dnn;
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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 CV_FINAL : 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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std::vector<UMat> half_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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transA = params.get<bool>("transA", false);
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transB = params.get<bool>("transB", false);
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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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isMatMul = params.get<bool>("is_matmul", false);
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if (!blobs.empty())
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{
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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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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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blobs[0].copyTo(oriMat);
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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 if(isMatMul)
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biasMat = Mat::zeros(1, oriMat.size[oriMat.dims - 2], weightsMat.type());
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else
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biasMat = Mat::zeros(1, numOutput, weightsMat.type());
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transB = !transB;
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}
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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 CV_OVERRIDE
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{
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int numOutput, cAxis;
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std::vector<MatShape> inputsTmp;
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inputsTmp.assign(inputs.begin(), inputs.end());
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if (blobs.empty())
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{
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CV_CheckEQ(inputsTmp.size(), (size_t)2, "");
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if (transA)
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{
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CV_CheckEQ(inputsTmp[0].size(), (size_t)2, "");
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std::swap(inputsTmp[0][0], inputsTmp[0][1]);
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}
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if (transB)
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{
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CV_CheckEQ(inputsTmp[1].size(), (size_t)2, "");
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std::swap(inputsTmp[1][0], inputsTmp[1][1]);
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}
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numOutput = inputsTmp[1].back();
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cAxis = inputsTmp[0].size() - 1;
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int dims = inputsTmp[0].size();
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CV_CheckEQ(inputsTmp[1].size(), (size_t)dims, "");
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CV_CheckGE(dims, 2, "");
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for (int i = 0; i < dims - 2; i++)
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CV_CheckEQ(inputsTmp[0][i], inputsTmp[1][i], "");
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CV_CheckEQ(inputsTmp[0].back(), inputsTmp[1][dims - 2], "");
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}
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else
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{
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CV_CheckEQ(inputsTmp.size(), (size_t)1, "");
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CV_CheckEQ(blobs[0].dims, 2, "");
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if(isMatMul)
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numOutput = oriMat.size[oriMat.dims - 2];
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else
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numOutput = blobs[0].size[0];
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CV_Assert(!bias || (size_t)numOutput == blobs[1].total());
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cAxis = normalize_axis(axis, inputsTmp[0]);
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}
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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] = inputsTmp[0][i];
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outShape.back() = numOutput;
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outputs.resize(1, outShape);
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return false;
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}
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virtual bool supportBackend(int backendId) CV_OVERRIDE
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{
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bool tranAorB = transA || transB;
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return backendId == DNN_BACKEND_OPENCV ||
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backendId == DNN_BACKEND_CUDA ||
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(backendId == DNN_BACKEND_HALIDE && haveHalide() && axis == 1 && !tranAorB) ||
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(backendId == DNN_BACKEND_WEBNN && axis == 1 && !tranAorB) ||
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backendId == DNN_BACKEND_CANN ||
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backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH ||
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(backendId == DNN_BACKEND_VKCOM && haveVulkan() && !tranAorB);
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}
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virtual bool setActivation(const Ptr<ActivationLayer>& layer) CV_OVERRIDE
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{
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if (activ.empty() || layer.empty())
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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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else
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return false;
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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), useAVX512(false), useRVV(false), useLASX(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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p.useAVX512 = CV_CPU_HAS_SUPPORT_AVX512_SKX;
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p.useRVV = checkHardwareSupport(CPU_RVV);
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p.useLASX = checkHardwareSupport(CPU_LASX);
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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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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(srcbuf.data(), (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_AVX512_SKX
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if( useAVX512 )
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opt_AVX512_SKX::fastGEMM1T( sptr, wptr, wstep, biasptr, dptr, nw, vecsize_aligned);
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else
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#endif
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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_aligned);
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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_aligned);
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else
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#endif
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#if CV_TRY_RVV
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if( useRVV )
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opt_RVV::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_LASX
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if( useLASX )
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opt_LASX::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);
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v_float32x4 vs1 = v_setall_f32(0.f);
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v_float32x4 vs2 = v_setall_f32(0.f);
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v_float32x4 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_fma(v, v_load_aligned(wptr + k), vs0);
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vs1 = v_fma(v, v_load_aligned(wptr + wstep + k), vs1);
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vs2 = v_fma(v, v_load_aligned(wptr + wstep*2 + k), vs2);
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vs3 = v_fma(v, v_load_aligned(wptr + wstep*3 + k), vs3);
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}
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v_float32x4 s = v_reduce_sum4(vs0, vs1, vs2, vs3);
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s = v_add(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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bool useAVX512;
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bool useRVV;
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bool useLASX;
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};
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#ifdef HAVE_OPENCL
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virtual void finalize(InputArrayOfArrays, OutputArrayOfArrays) CV_OVERRIDE
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{
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innerProductOp.release();
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umat_blobs.clear();
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half_blobs.clear();
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}
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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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bool use_half = (inps.depth() == CV_16F);
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inps.getUMatVector(inputs);
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outs.getUMatVector(outputs);
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if (inputs.size() == 2)
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{
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int dims = outputs[0].dims;
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int m = inputs[0].size[dims - 2];
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int n = inputs[0].size[dims - 1];
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int k = inputs[1].size[dims - 1];
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int rows = inputs[0].total() / (m * n);
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MatShape sh_A = shape(rows, m * n);
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MatShape sh_B = shape(rows, n * k);
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MatShape sh_C = shape(rows, m * k);
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UMat inp = inputs[0].reshape(1, sh_A.size(), &sh_A[0]);
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UMat weight = inputs[1].reshape(1, sh_B.size(), &sh_B[0]);
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UMat out = outputs[0].reshape(1, sh_C.size(), &sh_C[0]);
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UMat A, B, C, A_fp32, B_fp32, C_fp32;
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for (int i = 0; i < rows; ++i)
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{
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A = inp.row(i).reshape(1, m);
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B = weight.row(i).reshape(1, n);
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C = out.row(i).reshape(1, m);
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if (use_half)
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{
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A.convertTo(A_fp32, CV_32F);
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B.convertTo(B_fp32, CV_32F);
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C.convertTo(C_fp32, CV_32F);
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}
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else
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{
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A_fp32 = A;
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B_fp32 = B;
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C_fp32 = C;
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}
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cv::gemm(A_fp32, B_fp32, 1, noArray(), 0, C_fp32);
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if (use_half)
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{
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A_fp32.convertTo(A, CV_16F);
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B_fp32.convertTo(B, CV_16F);
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C_fp32.convertTo(C, CV_16F);
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}
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}
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return true;
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}
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int axisCan = normalize_axis(axis, inputs[0].dims);
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int numOutput = blobs[0].size[0];
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int innerSize = 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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size_t n = blobs.size();
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umat_blobs.resize(n);
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for (int i = 0; i < n; i++) blobs[i].copyTo(umat_blobs[i]);
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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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config.use_half = use_half;
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if (use_half)
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{
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half_blobs.resize(umat_blobs.size());
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for (int i = 0; i < umat_blobs.size(); i++)
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{
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if (!umat_blobs[i].empty())
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umat_blobs[i].convertTo(half_blobs[i], CV_16F);
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}
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}
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innerProductOp = Ptr<OCL4DNNInnerProduct<float> >(new OCL4DNNInnerProduct<float>(config));
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}
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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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if (!innerProductOp->Forward(srcMat, (use_half) ? half_blobs[0] : umat_blobs[0],
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(bias) ? (use_half ? half_blobs[1] : umat_blobs[1]) : UMat(),
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dstMat))
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{
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ret = false;
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break;
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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);
|
|
outshape = shape(outerSize, numOutput);
|
|
|
|
UMat srcMat, dstMat, srcMat_fp32, dstMat_fp32;
|
|
srcMat = inputs[i].reshape(1, inshape.size(), &inshape[0]);
|
|
dstMat = outputs[i].reshape(1, outshape.size(), &outshape[0]);
|
|
|
|
if (use_half)
|
|
{
|
|
srcMat.convertTo(srcMat_fp32, CV_32F);
|
|
dstMat.convertTo(dstMat_fp32, CV_32F);
|
|
}
|
|
else
|
|
{
|
|
srcMat_fp32 = srcMat;
|
|
dstMat_fp32 = dstMat;
|
|
}
|
|
|
|
cv::gemm(srcMat_fp32, weights, 1, noArray(), 0, dstMat_fp32, GEMM_2_T);
|
|
|
|
if (bias)
|
|
{
|
|
UMat biasOnesMat = UMat::ones(outerSize, 1, umat_blobs[0].type());
|
|
UMat& biases = umat_blobs[1];
|
|
cv::gemm(biasOnesMat, biases, 1, dstMat_fp32, 1, dstMat_fp32, 0);
|
|
}
|
|
if (use_half)
|
|
{
|
|
srcMat_fp32.convertTo(srcMat, CV_16F);
|
|
dstMat_fp32.convertTo(dstMat, CV_16F);
|
|
}
|
|
}
|
|
|
|
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) && !isMatMul,
|
|
forward_ocl(inputs_arr, outputs_arr, internals_arr))
|
|
|
|
if (inputs_arr.depth() == CV_16F)
|
|
{
|
|
forward_fallback(inputs_arr, outputs_arr, internals_arr);
|
|
return;
|
|
}
|
|
|
|
std::vector<Mat> input, output;
|
|
inputs_arr.getMatVector(input);
|
|
outputs_arr.getMatVector(output);
|
|
|
|
if (!blobs.empty())
|
|
{
|
|
int inp1Dim = input[0].dims;
|
|
if (isMatMul)
|
|
{
|
|
int matNum = input[0].total(0, inp1Dim - 2);
|
|
int rowMatMul = oriMat.size[oriMat.dims - 2];
|
|
Mat srcMatTmp = input[0].reshape(1, matNum);
|
|
Mat dstMatTmp = output[0].reshape(1, matNum);
|
|
|
|
int outerSize = input[0].size[inp1Dim - 2];
|
|
int rowStart = -rowMatMul;
|
|
for (int n = 0; n < matNum; ++n)
|
|
{
|
|
Mat srcMat = srcMatTmp.row(n).reshape(1, outerSize);
|
|
Mat dstMat = dstMatTmp.row(n).reshape(1, outerSize);
|
|
rowStart = (rowStart + rowMatMul) % weightsMat.rows;
|
|
Mat weiMat = weightsMat.rowRange(rowStart, rowStart + rowMatMul);
|
|
|
|
const int nstripes = getNumThreads();
|
|
FullyConnected::run(srcMat, weiMat, biasMat, dstMat, activ.get(), nstripes);
|
|
}
|
|
}
|
|
else
|
|
{
|
|
int axisCan = normalize_axis(axis, inp1Dim);
|
|
int outerSize = input[0].total(0, axisCan);
|
|
|
|
for (size_t i = 0; i < input.size(); i++)
|
|
{
|
|
Mat srcMat = input[i].reshape(1, outerSize);
|
|
Mat dstMat = output[i].reshape(1, outerSize);
|
|
|
|
const int nstripes = getNumThreads();
|
|
FullyConnected::run(srcMat, weightsMat, biasMat, dstMat, activ.get(), nstripes);
|
|
}
|
|
}
|
|
}
|
|
else
|
|
{
|
|
Mat input0 = input[0];
|
|
Mat input1 = input[1];
|
|
|
|
if (transA)
|
|
{
|
|
CV_Assert(input0.dims == 2);
|
|
input0 = input0.t();
|
|
}
|
|
|
|
if (transB)
|
|
{
|
|
CV_Assert(input1.dims == 2);
|
|
input1 = input1.t();
|
|
}
|
|
|
|
float* inpData = input0.ptr<float>();
|
|
float* weightData = input1.ptr<float>();
|
|
float* outData = output[0].ptr<float>();
|
|
|
|
int dims = output[0].dims;
|
|
int numSlice = output[0].total() / output[0].total(dims - 2);
|
|
int m = input0.size[dims - 2];
|
|
int n = input0.size[dims - 1];
|
|
int k = input1.size[dims - 1];
|
|
for (int i = 0; i < numSlice; i++)
|
|
{
|
|
Mat inpSlice(m, n, CV_32F, inpData);
|
|
Mat weightSlice(n, k, CV_32F, weightData);
|
|
Mat outSlice(m, k, CV_32F, outData);
|
|
|
|
outSlice = inpSlice * weightSlice;
|
|
inpData += inpSlice.total();
|
|
weightData += weightSlice.total();
|
|
outData += outSlice.total();
|
|
}
|
|
}
|
|
}
|
|
|
|
#ifdef HAVE_CUDA
|
|
Ptr<BackendNode> initCUDA(
|
|
void *context_,
|
|
const std::vector<Ptr<BackendWrapper>>& inputs,
|
|
const std::vector<Ptr<BackendWrapper>>& outputs
|
|
) override
|
|
{
|
|
auto biasMat_ = bias ? biasMat : Mat();
|
|
auto context = reinterpret_cast<csl::CSLContext*>(context_);
|
|
auto input_wrapper = inputs[0].dynamicCast<CUDABackendWrapper>();
|
|
|
|
if (weightsMat.empty() || isMatMul)
|
|
{
|
|
int inp2Dim;
|
|
// broadcast is not supported with CUDA
|
|
if(weightsMat.empty())
|
|
{
|
|
auto input_wrapper2 = inputs[1].dynamicCast<CUDABackendWrapper>();
|
|
inp2Dim = input_wrapper2->getRank();
|
|
}else
|
|
inp2Dim = oriMat.dims;
|
|
|
|
if(input_wrapper->getRank() == inp2Dim)
|
|
return make_cuda_node<cuda4dnn::MatMulOp>(preferableTarget, std::move(context->stream), std::move(context->cublas_handle), oriMat, biasMat_, transA, transB);
|
|
else {
|
|
CV_LOG_INFO(NULL, "DNN/CUDA: no implementation for MatMul with rank " << input_wrapper->getRank());
|
|
return Ptr<BackendNode>();
|
|
}
|
|
}
|
|
|
|
auto flatten_start_axis = normalize_axis(axis, input_wrapper->getRank());
|
|
return make_cuda_node<cuda4dnn::InnerProductOp>(preferableTarget, std::move(context->stream), std::move(context->cublas_handle), flatten_start_axis, weightsMat, biasMat_);
|
|
}
|
|
#endif
|
|
|
|
#ifdef HAVE_VULKAN
|
|
virtual Ptr<BackendNode> initVkCom(const std::vector<Ptr<BackendWrapper> > &inputs,
|
|
std::vector<Ptr<BackendWrapper> > &outputs) CV_OVERRIDE
|
|
{
|
|
auto biasMat_ = bias ? biasMat : Mat();
|
|
auto input_wrapper = inputs[0].dynamicCast<VkComBackendWrapper>();
|
|
|
|
CV_Assert((inputs.size() == 2 || inputs.size() == 1) && outputs.size() == 1);
|
|
std::vector<Mat> vkBlobs;
|
|
Ptr<vkcom::OpBase> op;
|
|
|
|
if (!biasMat_.empty() || !activ.empty())
|
|
{
|
|
return Ptr<BackendNode>();
|
|
}
|
|
|
|
Ptr<VkComBackendWrapper> outputWrap = outputs[0].dynamicCast<VkComBackendWrapper>();
|
|
CV_Assert(outputWrap);
|
|
// TODO: Currently, we only support the 2D MatMul. Need support the FC layer and bias case in the future.
|
|
|
|
if (inputs.size() == 2)
|
|
{
|
|
Ptr<VkComBackendWrapper> inputWrap0 = inputs[0].dynamicCast<VkComBackendWrapper>();
|
|
Ptr<VkComBackendWrapper> inputWrap1 = inputs[1].dynamicCast<VkComBackendWrapper>();
|
|
CV_Assert(inputWrap0 && inputWrap1);
|
|
|
|
MatShape inpShape0 = shape(*inputWrap0->getMat());
|
|
MatShape inpShape1 = shape(*inputWrap1->getMat());
|
|
MatShape outShape = shape(*outputWrap->getMat());
|
|
|
|
// TODO Currently, vulkan only support 2D matmul. Try to support 3D and 4D matmul.
|
|
if (inpShape0.size() != 2 || inpShape1.size() != 2)
|
|
return Ptr<BackendNode>();
|
|
|
|
op = (new vkcom::OpMatMul(vkBlobs, inpShape0[0], inpShape0[1], outShape[1]));
|
|
}
|
|
else
|
|
{
|
|
CV_Assert(!weightsMat.empty());
|
|
Mat wm;
|
|
weightsMat.copyTo(wm); // to handle the case of isContinuous() == false
|
|
wm = wm.reshape(1, blobs[0].dims, blobs[0].size);
|
|
vkBlobs.push_back(wm.t());
|
|
|
|
Ptr<VkComBackendWrapper> inputWrap = inputs[0].dynamicCast<VkComBackendWrapper>();
|
|
CV_Assert(inputWrap);
|
|
|
|
MatShape inpShape = shape(*inputWrap->getMat());
|
|
MatShape outShape = shape(*outputWrap->getMat());
|
|
MatShape wShape = shape(weightsMat);
|
|
|
|
// TODO Currently, vulkan only support 2D matmul. Try to support 3D and 4D matmul.
|
|
if (inpShape.size() != 2 || wShape.size() != 2)
|
|
return Ptr<BackendNode>();
|
|
|
|
// TODO: Currently, only focus on 2D MatMul.
|
|
CV_Assert(inpShape.size() == 2 && outShape.size() == 2 && wShape.size() == 2);
|
|
CV_Assert(inpShape[1] == outShape[0]);
|
|
op = (new vkcom::OpMatMul(vkBlobs, inpShape[0], inpShape[1], outShape[1]));
|
|
}
|
|
|
|
return Ptr<BackendNode>(new VkComBackendNode(inputs, op, outputs));
|
|
}
|
|
#endif
|
|
|
|
|
|
virtual Ptr<BackendNode> initHalide(const std::vector<Ptr<BackendWrapper> > &inputs) CV_OVERRIDE
|
|
{
|
|
#ifdef HAVE_HALIDE
|
|
int inW, inH, inC, inN, outC = blobs[0].size[0];
|
|
Halide::Buffer<float> inputBuffer = halideBuffer(inputs[0]);
|
|
getCanonicalSize(inputBuffer, &inW, &inH, &inC, &inN);
|
|
auto weights = wrapToHalideBuffer(blobs[0], {inW, inH, inC, outC});
|
|
|
|
Halide::Var x("x"), y("y"), c("c"), n("n");
|
|
Halide::Func top = (name.empty() ? Halide::Func() : Halide::Func(name));
|
|
Halide::RDom r(0, inW, 0, inH, 0, inC);
|
|
Halide::Expr topExpr = sum(inputBuffer(r.x, r.y, r.z, n) *
|
|
weights(r.x, r.y, r.z, c));
|
|
if (bias)
|
|
{
|
|
Halide::Buffer<float> bias = wrapToHalideBuffer(blobs[1], {outC});
|
|
topExpr += bias(c);
|
|
}
|
|
top(x, y, c, n) = topExpr;
|
|
return Ptr<BackendNode>(new HalideBackendNode(top));
|
|
#endif // HAVE_HALIDE
|
|
return Ptr<BackendNode>();
|
|
}
|
|
|
|
#ifdef HAVE_CANN
|
|
virtual Ptr<BackendNode> initCann(const std::vector<Ptr<BackendWrapper> > &inputs,
|
|
const std::vector<Ptr<BackendWrapper> > &outputs,
|
|
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
|
|
{
|
|
auto x1 = inputs[0].dynamicCast<CannBackendWrapper>();
|
|
auto x1_desc = x1->getTensorDesc();
|
|
auto op_x1 = nodes[0].dynamicCast<CannBackendNode>()->getOp();
|
|
auto output_desc = std::make_shared<ge::TensorDesc>(ge::Shape(), ge::FORMAT_NCHW, ge::DT_FLOAT);
|
|
|
|
auto op = std::make_shared<ge::op::MatMulV2>(name);
|
|
|
|
if (!blobs.empty()) // if B is const
|
|
{
|
|
// set attributes
|
|
op->set_attr_transpose_x1(false);
|
|
// weightMat always needs to be transposed, since CPU backend
|
|
// implementation is input * weight.im2row
|
|
op->set_attr_transpose_x2(true);
|
|
|
|
// set inputs
|
|
// set inputs : x2 (weight)
|
|
auto op_const_weight = std::make_shared<CannConstOp>(weightsMat.data, weightsMat.type(), shape(weightsMat), cv::format("%s_w", name.c_str()));
|
|
op->set_input_x2_by_name(*(op_const_weight->getOp()), "y");
|
|
op->update_input_desc_x2(*(op_const_weight->getTensorDesc()));
|
|
}
|
|
else
|
|
{
|
|
// A and B are variable inputs; non-const bias is not considered
|
|
CV_Assert(inputs.size() == 2);
|
|
CV_Assert(nodes.size() == 2);
|
|
|
|
// set attributes
|
|
op->set_attr_transpose_x1(transA);
|
|
op->set_attr_transpose_x2(transB);
|
|
|
|
// set inputs : x2 (weight)
|
|
auto op_x2 = nodes[1].dynamicCast<CannBackendNode>()->getOp();
|
|
auto x2_desc = inputs[1].dynamicCast<CannBackendWrapper>()->getTensorDesc();
|
|
op->set_input_x2_by_name(*op_x2, "y");
|
|
op->update_input_desc_x2(*x2_desc);
|
|
}
|
|
|
|
// set inputs
|
|
// set inputs : x1 (input)
|
|
op->set_input_x1_by_name(*op_x1, x1->name.c_str());
|
|
op->update_input_desc_x1(*x1_desc);
|
|
// set inputs : bias (bias)
|
|
auto bias_mat = bias ? biasMat : Mat::zeros(1, weightsMat.size[0], weightsMat.type());
|
|
std::vector<int> bias_shape{weightsMat.size[0]};
|
|
auto op_const_bias = std::make_shared<CannConstOp>(bias_mat.data, bias_mat.type(), bias_shape, cv::format("%s_b", name.c_str()));
|
|
op->set_input_bias(*(op_const_bias->getOp()));
|
|
op->update_input_desc_bias(*(op_const_bias->getTensorDesc()));
|
|
|
|
// set outputs
|
|
op->update_output_desc_y(*output_desc);
|
|
|
|
return Ptr<BackendNode>(new CannBackendNode(op));
|
|
}
|
|
#endif
|
|
|
|
#ifdef HAVE_DNN_NGRAPH
|
|
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
|
|
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
|
|
{
|
|
auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
|
|
std::shared_ptr<ov::Node> matmul;
|
|
|
|
if (nodes.size() == 2)
|
|
{
|
|
auto& inp2 = nodes[1].dynamicCast<InfEngineNgraphNode>()->node;
|
|
matmul = std::make_shared<ov::op::v0::MatMul>(ieInpNode, inp2, transA, transB);
|
|
}
|
|
else
|
|
{
|
|
std::vector<int> shape(1 + normalize_axis(axis, ieInpNode.get_shape().size()), 0);
|
|
shape[shape.size() - 1] = -1;
|
|
auto inp = std::make_shared<ov::op::v1::Reshape>(
|
|
ieInpNode,
|
|
std::make_shared<ov::op::v0::Constant>(ov::element::i32, ov::Shape{shape.size()}, shape.data()),
|
|
true
|
|
);
|
|
|
|
std::vector<size_t> weight_shape;
|
|
if (isMatMul) {
|
|
weight_shape = getShape<size_t>(oriMat);
|
|
} else {
|
|
weight_shape = {(size_t)blobs[0].size[0], (size_t)blobs[0].size[1]};
|
|
}
|
|
auto ieWeights = std::make_shared<ov::op::v0::Constant>(ov::element::f32, weight_shape, blobs[0].data);
|
|
matmul = std::make_shared<ov::op::v0::MatMul>(inp, ieWeights, transA, transB);
|
|
}
|
|
|
|
if (bias) {
|
|
auto bias_node = std::make_shared<ov::op::v0::Constant>(ov::element::f32,
|
|
ov::Shape{(size_t)blobs[1].size[1]}, blobs[1].data);
|
|
matmul = std::make_shared<ov::op::v1::Add>(matmul, bias_node, ov::op::AutoBroadcastType::NUMPY);
|
|
}
|
|
return Ptr<BackendNode>(new InfEngineNgraphNode(matmul));
|
|
}
|
|
#endif // HAVE_DNN_NGRAPH
|
|
|
|
virtual bool tryQuantize(const std::vector<std::vector<float> > &scales,
|
|
const std::vector<std::vector<int> > &zeropoints, LayerParams& params) CV_OVERRIDE
|
|
{
|
|
if (blobs.empty())
|
|
return false;
|
|
|
|
int numOutput = blobs[0].size[0];
|
|
float inputScale = scales[0][0], outputScale = scales[1][0];
|
|
int inputZp = zeropoints[0][0];
|
|
|
|
Mat weightsQuantized(weightsMat.rows, weightsMat.cols, CV_8S);
|
|
Mat biasQuantized(1, numOutput, CV_32S);
|
|
Mat outputMultiplier(1, numOutput, CV_32F);
|
|
bool perChannel = params.get<bool>("per_channel", true);
|
|
|
|
if (perChannel) // per-Channel quantization.
|
|
{
|
|
for (int i = 0; i < numOutput; i++)
|
|
{
|
|
double weightsScale = getWeightScale(weightsMat.row(i));
|
|
|
|
weightsMat.row(i).convertTo(weightsQuantized.row(i), CV_8S, 1.f/weightsScale);
|
|
float biasScale = inputScale * weightsScale;
|
|
biasQuantized.at<int>(i) = cvRound(biasMat.at<float>(i)/biasScale) - inputZp*(cv::sum(weightsQuantized.row(i))[0]);
|
|
outputMultiplier.at<float>(i) = biasScale / outputScale;
|
|
}
|
|
}
|
|
else // per-Tensor quantization.
|
|
{
|
|
double weightsScale = getWeightScale(weightsMat);
|
|
|
|
weightsMat.convertTo(weightsQuantized, CV_8S, 1.f/weightsScale);
|
|
float biasScale = inputScale * weightsScale;
|
|
|
|
for (int i = 0; i < numOutput; i++)
|
|
{
|
|
biasQuantized.at<int>(i) = cvRound(biasMat.at<float>(i)/biasScale) - inputZp*(cv::sum(weightsQuantized.row(i))[0]);
|
|
outputMultiplier.at<float>(i) = biasScale / outputScale;
|
|
}
|
|
}
|
|
|
|
params.blobs.clear();
|
|
params.set("per_channel", perChannel);
|
|
params.blobs.push_back(weightsQuantized.reshape(1, shape(blobs[0])));
|
|
params.blobs.push_back(biasQuantized);
|
|
params.blobs.push_back(outputMultiplier);
|
|
params.set("input_scale", inputScale);
|
|
params.set("input_zeropoint", inputZp);
|
|
return true;
|
|
}
|
|
|
|
#ifdef HAVE_WEBNN
|
|
virtual Ptr<BackendNode> initWebnn(const std::vector<Ptr<BackendWrapper> >& inputs, const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
|
|
{
|
|
Ptr<WebnnBackendNode> node = nodes[0].dynamicCast<WebnnBackendNode>();
|
|
auto& webnnInpOperand = node->operand;
|
|
auto& webnnGraphBuilder = node->net->builder;
|
|
ml::GemmOptions gemmOptions = {};
|
|
if (bias)
|
|
{
|
|
std::vector<int32_t> biasDims = {(int32_t)blobs[1].size[1]};
|
|
ml::Operand bias = webnn::BuildConstant(webnnGraphBuilder, biasDims, blobs[1].data, blobs[1].total()*blobs[1].elemSize(), ml::OperandType::Float32);
|
|
gemmOptions.c = bias;
|
|
}
|
|
ml::Operand result = nullptr;
|
|
if (nodes.size() == 2)
|
|
{
|
|
auto& inp2 = nodes[1].dynamicCast<WebnnBackendNode>()->operand;
|
|
result = webnnGraphBuilder.Gemm(webnnInpOperand, inp2, &gemmOptions);
|
|
}
|
|
else
|
|
{
|
|
std::vector<int32_t> input_shape(2, -1);
|
|
input_shape[1] = blobs[0].size[1];
|
|
ml::Operand webnnInpOperand_reshaped = webnnGraphBuilder.Reshape(webnnInpOperand, input_shape.data(), input_shape.size());
|
|
std::vector<int32_t> weight_shape = {(int32_t)blobs[0].size[0], (int32_t)blobs[0].size[1]};
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// std::cout<<"weight size: "<<weight_shape[1]<<" "<<weight_shape[0]<<std::endl;
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ml::Operand inp2 = webnn::BuildConstant(webnnGraphBuilder, weight_shape, blobs[0].data, blobs[0].total()*blobs[0].elemSize(), ml::OperandType::Float32);
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|
gemmOptions.bTranspose = true;
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|
result = webnnGraphBuilder.Gemm(webnnInpOperand_reshaped, inp2, &gemmOptions);
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|
}
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|
return Ptr<BackendNode>(new WebnnBackendNode(result));
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|
}
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|
#endif // HAVE_WEBNN
|
|
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|
virtual int64 getFLOPS(const std::vector<MatShape> &inputs,
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|
const std::vector<MatShape> &outputs) const CV_OVERRIDE
|
|
{
|
|
CV_UNUSED(inputs); // suppress unused variable warning
|
|
long flops = 0;
|
|
int innerSize = 0;
|
|
|
|
if (!blobs.empty())
|
|
{
|
|
innerSize = blobs[0].size[1];
|
|
}
|
|
else
|
|
{
|
|
CV_Assert(inputs.size() == 2);
|
|
if (transB)
|
|
innerSize = inputs[1][1];
|
|
else
|
|
innerSize = inputs[1][0];
|
|
}
|
|
|
|
for(int i = 0; i < outputs.size(); i++)
|
|
{
|
|
flops += CV_BIG_INT(3)*innerSize*total(outputs[i]);
|
|
}
|
|
|
|
return flops;
|
|
}
|
|
|
|
bool bias;
|
|
Mat weightsMat, biasMat, oriMat;
|
|
bool transA, transB;
|
|
bool isMatMul = false;
|
|
Ptr<ActivationLayer> activ;
|
|
};
|
|
|
|
Ptr<InnerProductLayer> InnerProductLayer::create(const LayerParams& params)
|
|
{
|
|
return Ptr<InnerProductLayer>(new FullyConnectedLayerImpl(params));
|
|
}
|
|
|
|
}
|
|
}
|