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efc9837df1
DNN: avoid CV_16S usage for FP16 #24892 **Merge after**: #24918 TODO: - [x] measure performance changes - [x] optimize convertTo for OpenCL: #24918 12700K iGPU: |Name of Test|0|1|1 vs 0 (x-factor)| |---|:-:|:-:|:-:| |AlexNet::DNNTestNetwork::OCV/OCL_FP16|7.441|7.480|0.99| |CRNN::DNNTestNetwork::OCV/OCL_FP16|10.776|10.736|1.00| |DenseNet_121::DNNTestNetwork::OCV/OCL_FP16|52.762|52.833|1.00| |EAST_text_detection::DNNTestNetwork::OCV/OCL_FP16|60.694|60.721|1.00| |EfficientNet::DNNTestNetwork::OCV/OCL_FP16|33.373|33.173|1.01| |FastNeuralStyle_eccv16::DNNTestNetwork::OCV/OCL_FP16|81.840|81.724|1.00| |GoogLeNet::DNNTestNetwork::OCV/OCL_FP16|20.965|20.927|1.00| |Inception_5h::DNNTestNetwork::OCV/OCL_FP16|22.204|22.173|1.00| |Inception_v2_SSD_TensorFlow::DNNTestNetwork::OCV/OCL_FP16|47.115|47.460|0.99| |MPHand::DNNTestNetwork::OCV/OCL_FP16|6.760|6.670|1.01| |MPPalm::DNNTestNetwork::OCV/OCL_FP16|10.188|10.171|1.00| |MPPose::DNNTestNetwork::OCV/OCL_FP16|12.510|12.561|1.00| |MobileNet_SSD_Caffe::DNNTestNetwork::OCV/OCL_FP16|17.290|17.072|1.01| |MobileNet_SSD_v1_TensorFlow::DNNTestNetwork::OCV/OCL_FP16|19.473|19.306|1.01| |MobileNet_SSD_v2_TensorFlow::DNNTestNetwork::OCV/OCL_FP16|22.874|23.404|0.98| |OpenFace::DNNTestNetwork::OCV/OCL_FP16|9.568|9.517|1.01| |OpenPose_pose_mpi_faster_4_stages::DNNTestNetwork::OCV/OCL_FP16|539.899|539.845|1.00| |PPHumanSeg::DNNTestNetwork::OCV/OCL_FP16|18.015|18.769|0.96| |PPOCRv3::DNNTestNetwork::OCV/OCL_FP16|63.122|63.540|0.99| |ResNet_50::DNNTestNetwork::OCV/OCL_FP16|34.947|34.925|1.00| |SFace::DNNTestNetwork::OCV/OCL_FP16|10.249|10.206|1.00| |SSD::DNNTestNetwork::OCV/OCL_FP16|213.068|213.108|1.00| |SqueezeNet_v1_1::DNNTestNetwork::OCV/OCL_FP16|4.867|4.878|1.00| |VIT_B_32::DNNTestNetwork::OCV/OCL_FP16|200.563|190.788|1.05| |VitTrack::DNNTestNetwork::OCV/OCL_FP16|7.528|7.173|1.05| |YOLOX::DNNTestNetwork::OCV/OCL_FP16|132.858|132.701|1.00| |YOLOv3::DNNTestNetwork::OCV/OCL_FP16|209.559|208.809|1.00| |YOLOv4::DNNTestNetwork::OCV/OCL_FP16|221.357|220.924|1.00| |YOLOv4_tiny::DNNTestNetwork::OCV/OCL_FP16|24.446|24.382|1.00| |YOLOv5::DNNTestNetwork::OCV/OCL_FP16|43.922|44.080|1.00| |YOLOv8::DNNTestNetwork::OCV/OCL_FP16|64.159|63.842|1.00| |YuNet::DNNTestNetwork::OCV/OCL_FP16|10.177|10.231|0.99| |opencv_face_detector::DNNTestNetwork::OCV/OCL_FP16|15.121|15.445|0.98| Co-authored-by: Alexander Alekhin <alexander.a.alekhin@gmail.com>
996 lines
39 KiB
C++
996 lines
39 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_cann.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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#endif
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#ifdef HAVE_CUDA
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#include "../cuda4dnn/primitives/eltwise.hpp"
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#include "../cuda4dnn/primitives/shortcut.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 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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DIV = 3,
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MIN = 4,
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} op;
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std::vector<float> coeffs;
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enum OutputChannelsMode
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{
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ELTWISE_CHANNNELS_SAME = 0, //!< number of channels from inputs must be the same and equal to output's number of channels
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ELTWISE_CHANNNELS_INPUT_0, //!< number of channels from inputs may be different,
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//!< output's number of channels is equal to number of channels of first input
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//!< number of channels of other inputs should not be greater than number of channels of first input
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ELTWISE_CHANNNELS_INPUT_0_TRUNCATE, //!< number of channels from inputs may be different,
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//!< output's number of channels is equal to number of channels of first input
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//!< there is restriction on number of channels of other inputs
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//!< extra channels of other inputs is ignored
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ELTWISE_CHANNNELS_USE_MAX, //!< number of channels from inputs may be different,
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//!< output's number of channels is equal to maximal number of input channels
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//!< @note supported operation: `SUM`
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} channelsModeInput;
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mutable OutputChannelsMode channelsMode; //!< "optimized" channels mode (switch to ELTWISE_CHANNNELS_SAME if number of input channels are equal)
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mutable /*size_t*/int outputChannels;
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EltwiseLayerImpl(const LayerParams& params)
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: outputChannels(0)
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{
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setParamsFrom(params);
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hasVecInput = false;
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op = SUM;
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if (params.has("operation"))
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{
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String operation = toLowerCase(params.get<String>("operation"));
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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 if (operation == "min")
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op = MIN;
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else if (operation == "div")
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op = DIV;
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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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channelsModeInput = ELTWISE_CHANNNELS_SAME;
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if (params.has("output_channels_mode"))
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{
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String v = toLowerCase(params.get<String>("output_channels_mode"));
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if (v == "same")
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{
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channelsModeInput = ELTWISE_CHANNNELS_SAME;
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}
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else if (v == "input_0")
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{
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channelsModeInput = ELTWISE_CHANNNELS_INPUT_0;
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}
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else if (v == "input_0_truncate")
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{
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channelsModeInput = ELTWISE_CHANNNELS_INPUT_0_TRUNCATE;
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}
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else if (v == "max_input_channels")
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{
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channelsModeInput = ELTWISE_CHANNNELS_USE_MAX;
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if (op != SUM)
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CV_Error(cv::Error::StsBadArg, "[" + type + "]:(" + name + ") 'max' channels mode is limited to SUM operation only");
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}
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else
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CV_Error(cv::Error::StsBadArg, "[" + type + "]:(" + name + ") unknown channels mode: \"" + v + "\"");
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}
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channelsMode = channelsModeInput;
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// TODO Must have checks for other unknown options
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}
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virtual bool supportBackend(int backendId) CV_OVERRIDE
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{
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if (hasVecInput && ELTWISE_CHANNNELS_SAME)
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return backendId == DNN_BACKEND_OPENCV;
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#ifdef HAVE_INF_ENGINE
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if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
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return channelsMode == ELTWISE_CHANNNELS_SAME;
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#endif
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#ifdef HAVE_CANN
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if (backendId == DNN_BACKEND_CANN)
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return channelsMode == ELTWISE_CHANNNELS_SAME && coeffs.empty();
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#endif
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if (backendId == DNN_BACKEND_CUDA)
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{
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if(channelsModeInput == ELTWISE_CHANNNELS_INPUT_0 || channelsModeInput == ELTWISE_CHANNNELS_INPUT_0_TRUNCATE)
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return op == SUM && coeffs.empty();
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return channelsModeInput == ELTWISE_CHANNNELS_SAME;
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}
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return backendId == DNN_BACKEND_OPENCV ||
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(backendId == DNN_BACKEND_HALIDE && op != DIV) // TODO: not implemented, see PR #15811
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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> &internals) const CV_OVERRIDE
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{
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CV_Assert(inputs.size() >= 2);
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CV_Assert(inputs[0].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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int dims = inputs[0].size();
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// Number of channels in output shape is determined by the first input tensor.
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bool variableChannels = false;
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int numChannels = inputs[0][1];
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for (size_t i = 1; i < inputs.size(); i++)
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{
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CV_Assert(inputs[0][0] == inputs[i][0]); // batch sizes are equal
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int input_channels = inputs[i][1];
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if (numChannels != input_channels)
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variableChannels = true;
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if (channelsModeInput == ELTWISE_CHANNNELS_SAME)
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{
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CV_Assert(numChannels == input_channels);
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}
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else if (channelsModeInput == ELTWISE_CHANNNELS_INPUT_0)
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{
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CV_Assert(numChannels >= input_channels);
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}
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else if (channelsModeInput == ELTWISE_CHANNNELS_INPUT_0_TRUNCATE)
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{
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// nothing to check
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}
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else if (channelsModeInput == ELTWISE_CHANNNELS_USE_MAX)
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{
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numChannels = std::max(numChannels, input_channels);
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}
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else
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{
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CV_Assert(0 && "Internal error");
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}
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}
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channelsMode = variableChannels ? channelsModeInput : ELTWISE_CHANNNELS_SAME;
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outputChannels = numChannels;
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outputs.assign(1, inputs[0]);
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outputs[0][1] = numChannels;
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if (dims > 2)
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{
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size_t vecIdx = 0;
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bool isVecFound = false;
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for (size_t i = 0; i < inputs.size(); i++)
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{
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bool allOnes = isAllOnes(inputs[i], 2, dims);
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if (!allOnes && !isVecFound)
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{
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vecIdx = i;
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isVecFound = true;
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}
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if (!allOnes && i != vecIdx)
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{
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for (size_t j = 2; j < dims; j++)
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{
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CV_Assert(inputs[vecIdx][j] == inputs[i][j]);
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}
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}
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}
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if (channelsModeInput == ELTWISE_CHANNNELS_SAME && isVecFound)
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{
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for (size_t j = 2; j < dims; j++)
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{
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outputs[0][j] = inputs[vecIdx][j];
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}
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}
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}
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return false;
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}
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void finalize(InputArrayOfArrays inputs_arr, OutputArrayOfArrays) CV_OVERRIDE
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{
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std::vector<Mat> inputs;
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inputs_arr.getMatVector(inputs);
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for (size_t i = 0; i < inputs.size(); i++)
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{
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MatShape inpShape = shape(inputs[i].size);
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if (isAllOnes(inpShape, 2, inputs[i].dims))
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{
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hasVecInput = true;
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return;
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}
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}
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}
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class EltwiseInvoker : public ParallelLoopBody
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{
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EltwiseLayerImpl& self;
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std::vector<const Mat*> srcs;
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std::vector<int> srcNumChannels;
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int nsrcs;
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Mat* dst;
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std::vector<float> coeffs;
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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(EltwiseLayerImpl& self_)
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: self(self_)
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, nsrcs(0), dst(0), nstripes(0), activ(0), channels(0)
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, planeSize(0)
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{}
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public:
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static void run(EltwiseLayerImpl& self,
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const Mat* srcs, int nsrcs, Mat& dst,
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int nstripes)
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{
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const EltwiseOp op = self.op;
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CV_Check(dst.dims, 1 < dst.dims && dst.dims <= 5, ""); CV_CheckTypeEQ(dst.type(), CV_32FC1, ""); CV_Assert(dst.isContinuous());
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CV_Assert(self.coeffs.empty() || self.coeffs.size() == (size_t)nsrcs);
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CV_CheckGE(nsrcs, 2, "");
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CV_Assert(self.outputChannels == dst.size[1]);
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EltwiseInvoker p(self);
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p.srcs.resize(nsrcs);
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p.srcNumChannels.resize(nsrcs);
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p.coeffs = self.coeffs; // can be sorted
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bool sortInputs = false;
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for( int i = 0; i < nsrcs; i++ )
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{
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p.srcs[i] = &srcs[i];
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CV_CheckEQ(srcs[i].dims, dst.dims, "");
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CV_Assert(srcs[i].isContinuous());
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CV_Assert(srcs[i].type() == dst.type());
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p.srcNumChannels[i] = (srcs[i].dims >= 4) ? srcs[i].size[1] : 1;
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if (self.channelsMode == ELTWISE_CHANNNELS_SAME)
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{
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CV_Assert(srcs[i].size == dst.size);
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}
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else if (self.channelsMode == ELTWISE_CHANNNELS_INPUT_0)
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{
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if (i == 0)
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CV_Assert(srcs[0].size == dst.size);
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CV_Assert(self.outputChannels >= p.srcNumChannels[i]);
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sortInputs = true;
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}
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else if (self.channelsMode == ELTWISE_CHANNNELS_INPUT_0_TRUNCATE)
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{
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if (i == 0)
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CV_Assert(srcs[0].size == dst.size);
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sortInputs = true;
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}
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else if (self.channelsMode == ELTWISE_CHANNNELS_USE_MAX)
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{
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CV_Assert(op == SUM);
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CV_Assert(self.outputChannels >= p.srcNumChannels[i]);
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sortInputs = true;
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}
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else
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{
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CV_Assert(0 && "Internal error");
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}
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if (sortInputs)
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{
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// Sort srcs and coefficients in the desc order by number of channels
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for (int j = i; j >= 1; j--)
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{
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if (std::min(self.outputChannels, p.srcs[j - 1]->size[1]) < std::min(self.outputChannels, p.srcs[j]->size[1]))
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{
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std::swap(p.srcs[j - 1], p.srcs[j]);
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std::swap(p.srcNumChannels[j - 1], p.srcNumChannels[j]);
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if (!p.coeffs.empty())
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std::swap(p.coeffs[j - 1], p.coeffs[j]);
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}
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else
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break;
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}
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}
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}
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p.nsrcs = nsrcs;
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p.dst = &dst;
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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.total(dst.dims >= 4 ? 2 : 1);
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CV_CheckEQ(dst.total(), dst.size[0] * p.channels * p.planeSize, "");
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bool simpleCoeffs = true;
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if (op == SUM && !p.coeffs.empty())
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{
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CV_CheckEQ(p.coeffs.size(), (size_t)nsrcs, "");
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for (size_t i = 0; i < p.coeffs.size(); i++)
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{
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if (p.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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}
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if (simpleCoeffs)
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p.coeffs.clear();
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p.activ = self.activ.get();
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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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const EltwiseOp op = self.op;
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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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const float* coeffsptr = !coeffs.empty() ? &coeffs[0] : 0;
|
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float* dstptr0 = dst->ptr<float>();
|
|
int blockSize0 = 1 << 12;
|
|
|
|
for (size_t ofs = stripeStart; ofs < stripeEnd; )
|
|
{
|
|
int sampleIdx = (int)(ofs / planeSize);
|
|
int delta = (int)ofs - sampleIdx * planeSize;
|
|
int blockSize = std::min(blockSize0, std::min((int)(stripeEnd - ofs), (int)planeSize - delta));
|
|
if( blockSize <= 0 )
|
|
break;
|
|
ofs += blockSize;
|
|
|
|
for (int c = 0; c < channels; c++)
|
|
{
|
|
size_t dstIdx = delta + (sampleIdx*channels + c)*planeSize;
|
|
float* dstptr = dstptr0 + dstIdx;
|
|
|
|
// process first two inputs
|
|
{
|
|
const float* srcptr0 = srcs[0]->ptr<float>() + dstIdx;
|
|
|
|
const int inputIdx = 1;
|
|
int src1_channels = srcNumChannels[inputIdx];
|
|
if (c >= src1_channels)
|
|
{
|
|
// no data from second input
|
|
if (!coeffsptr || coeffsptr[0] == 1.0f)
|
|
{
|
|
for (int j = 0; j < blockSize; j++)
|
|
{
|
|
dstptr[j] = srcptr0[j];
|
|
}
|
|
}
|
|
else
|
|
{
|
|
float c0 = coeffsptr[0];
|
|
for (int j = 0; j < blockSize; j++)
|
|
{
|
|
dstptr[j] = c0*srcptr0[j];
|
|
}
|
|
}
|
|
}
|
|
else
|
|
{
|
|
size_t srcIdx = delta + (sampleIdx * src1_channels + c) * planeSize;
|
|
const float* srcptrI = srcs[inputIdx]->ptr<float>() + srcIdx;
|
|
|
|
if (op == PROD)
|
|
{
|
|
for (int j = 0; j < blockSize; j++)
|
|
{
|
|
dstptr[j] = srcptr0[j] * srcptrI[j];
|
|
}
|
|
}
|
|
else if (op == DIV)
|
|
{
|
|
for (int j = 0; j < blockSize; j++)
|
|
{
|
|
dstptr[j] = srcptr0[j] / srcptrI[j];
|
|
}
|
|
}
|
|
else if (op == MAX)
|
|
{
|
|
for (int j = 0; j < blockSize; j++)
|
|
{
|
|
dstptr[j] = std::max(srcptr0[j], srcptrI[j]);
|
|
}
|
|
}
|
|
else if (op == MIN)
|
|
{
|
|
for (int j = 0; j < blockSize; j++)
|
|
{
|
|
dstptr[j] = std::min(srcptr0[j], srcptrI[j]);
|
|
}
|
|
}
|
|
else if (op == SUM)
|
|
{
|
|
if (!coeffsptr || (coeffsptr[0] == 1.0f && coeffsptr[1] == 1.0f))
|
|
{
|
|
for (int j = 0; j < blockSize; j++)
|
|
{
|
|
dstptr[j] = srcptr0[j] + srcptrI[j];
|
|
}
|
|
}
|
|
else
|
|
{
|
|
float c0 = coeffsptr[0];
|
|
float c1 = coeffsptr[1];
|
|
for (int j = 0; j < blockSize; j++)
|
|
{
|
|
dstptr[j] = c0*srcptr0[j] + c1*srcptrI[j];
|
|
}
|
|
}
|
|
}
|
|
else
|
|
CV_Error(Error::StsInternal, "");
|
|
}
|
|
}
|
|
|
|
// aggregate other inputs (3+)
|
|
for (size_t inputIdx = 2; inputIdx < nsrcs; inputIdx++)
|
|
{
|
|
int srcI_channels = srcNumChannels[inputIdx];
|
|
if (c >= srcI_channels)
|
|
continue; // no data from second input
|
|
size_t srcIdx = delta + (sampleIdx * srcI_channels + c) * planeSize;
|
|
const float* srcptrI = srcs[inputIdx]->ptr<float>() + srcIdx;
|
|
|
|
if (op == PROD)
|
|
{
|
|
for (int j = 0; j < blockSize; j++)
|
|
{
|
|
dstptr[j] *= srcptrI[j];
|
|
}
|
|
}
|
|
else if (op == DIV)
|
|
{
|
|
for (int j = 0; j < blockSize; j++)
|
|
{
|
|
dstptr[j] /= srcptrI[j];
|
|
}
|
|
}
|
|
else if (op == MAX)
|
|
{
|
|
for (int j = 0; j < blockSize; j++)
|
|
{
|
|
dstptr[j] = std::max(dstptr[j], srcptrI[j]);
|
|
}
|
|
}
|
|
else if (op == MIN)
|
|
{
|
|
for (int j = 0; j < blockSize; j++)
|
|
{
|
|
dstptr[j] = std::min(dstptr[j], srcptrI[j]);
|
|
}
|
|
}
|
|
else if (op == SUM)
|
|
{
|
|
if (!coeffsptr || coeffsptr[inputIdx] == 1.0f)
|
|
{
|
|
for (int j = 0; j < blockSize; j++)
|
|
{
|
|
dstptr[j] += srcptrI[j];
|
|
}
|
|
}
|
|
else
|
|
{
|
|
float cI = coeffsptr[inputIdx];
|
|
for (int j = 0; j < blockSize; j++)
|
|
{
|
|
dstptr[j] += cI * srcptrI[j];
|
|
}
|
|
}
|
|
}
|
|
else
|
|
CV_Error(Error::StsInternal, "");
|
|
}
|
|
}
|
|
|
|
if( activ )
|
|
{
|
|
float* ptr = dstptr0 + delta + sampleIdx*channels*planeSize;
|
|
activ->forwardSlice(ptr, ptr, blockSize, planeSize, 0, channels);
|
|
}
|
|
}
|
|
}
|
|
};
|
|
|
|
#ifdef HAVE_OPENCL
|
|
bool forward_ocl(InputArrayOfArrays inputs_, OutputArrayOfArrays outputs_, OutputArrayOfArrays internals_)
|
|
{
|
|
std::vector<UMat> inputs;
|
|
std::vector<UMat> outputs;
|
|
|
|
if ((inputs_.depth() == CV_16F && op != SUM) || (channelsMode != ELTWISE_CHANNNELS_SAME))
|
|
return false;
|
|
|
|
if (hasVecInput)
|
|
return false; // TODO not implemented yet: https://github.com/opencv/opencv/pull/19477
|
|
|
|
inputs_.getUMatVector(inputs);
|
|
outputs_.getUMatVector(outputs);
|
|
|
|
switch (op)
|
|
{
|
|
case SUM:
|
|
{
|
|
int channels = total(shape(outputs[0]), 0, 2);
|
|
int plane_size = total(shape(outputs[0]), 2);
|
|
if (channels % 4 == 0 && plane_size % 4 == 0)
|
|
{
|
|
size_t localsize[] = { 128 };
|
|
size_t globalsize[] = { (size_t)channels / 4 * localsize[0] };
|
|
String opts;
|
|
if (inputs_.depth() == CV_16F)
|
|
opts = " -DDtype=half -DDtype4=half4 -DDtype8=half8";
|
|
else
|
|
opts = " -DDtype=float -DDtype4=float4 -DDtype8=float8";
|
|
|
|
for (int i = 0; i < (inputs.size() - 1); ++i)
|
|
{
|
|
String buildopt = format("-DLOOP=%d", i) + opts;
|
|
ocl::Kernel kernel("op_sum4", ocl::dnn::eltwise_oclsrc, buildopt);
|
|
int idx = 0;
|
|
UMat inpMat = (i == 0) ? inputs[0] : UMat();
|
|
float coeff1 = (coeffs.empty() || i > 0) ? 1.0f : coeffs[i];
|
|
float coeff2 = coeffs.empty() ? 1.0f : coeffs[i + 1];
|
|
kernel.set(idx++, ocl::KernelArg::PtrReadOnly(inputs[0]));
|
|
kernel.set(idx++, ocl::KernelArg::PtrReadOnly(inputs[1]));
|
|
kernel.set(idx++, (int)plane_size);
|
|
kernel.set(idx++, (float)coeff1);
|
|
kernel.set(idx++, (float)coeff2);
|
|
kernel.set(idx++, ocl::KernelArg::PtrReadWrite(outputs[0]));
|
|
bool ret = kernel.run(1, globalsize, localsize, false);
|
|
if (!ret)
|
|
return false;
|
|
}
|
|
}
|
|
else
|
|
{
|
|
if (inputs_.depth() == CV_16F)
|
|
return false;
|
|
|
|
float coeff1 = coeffs.empty() ? 1.f : coeffs[0];
|
|
float coeff2 = coeffs.empty() ? 1.f : coeffs[1];
|
|
UMat mul0, mul1;
|
|
multiply(coeff1, inputs[0], mul0);
|
|
multiply(coeff2, inputs[1], mul1);
|
|
add(mul0, mul1, outputs[0]);
|
|
for (int i = 2; i < inputs.size(); ++i)
|
|
{
|
|
float coeff = coeffs.empty() ? 1.f : coeffs[i];
|
|
multiply(coeff, inputs[i], mul0);
|
|
add(mul0, outputs[0], outputs[0]);
|
|
}
|
|
}
|
|
}
|
|
break;
|
|
case PROD:
|
|
multiply(inputs[0], inputs[1], outputs[0]);
|
|
for (int i = 2; i < inputs.size(); ++i)
|
|
multiply(inputs[i], outputs[0], outputs[0]);
|
|
break;
|
|
case DIV:
|
|
divide(inputs[0], inputs[1], outputs[0]);
|
|
for (int i = 2; i < inputs.size(); ++i)
|
|
divide(outputs[0], inputs[i], outputs[0]);
|
|
break;
|
|
case MAX:
|
|
max(inputs[0], inputs[1], outputs[0]);
|
|
for (int i = 2; i < inputs.size(); ++i)
|
|
max(inputs[i], outputs[0], outputs[0]);
|
|
break;
|
|
case MIN:
|
|
min(inputs[0], inputs[1], outputs[0]);
|
|
for (int i = 2; i < inputs.size(); ++i)
|
|
min(inputs[i], outputs[0], outputs[0]);
|
|
break;
|
|
default:
|
|
return false;
|
|
}
|
|
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),
|
|
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> inputs, outputs;
|
|
inputs_arr.getMatVector(inputs);
|
|
outputs_arr.getMatVector(outputs);
|
|
|
|
CV_Assert(outputs.size() == 1);
|
|
const int nstripes = getNumThreads();
|
|
|
|
if (channelsModeInput == ELTWISE_CHANNNELS_SAME && inputs[0].dims > 2)
|
|
{
|
|
for (size_t i = 0; i < inputs.size(); i++)
|
|
{
|
|
MatShape inpShape = shape(inputs[i].size);
|
|
bool allOnes = isAllOnes(inpShape, 2, inputs[i].dims);
|
|
|
|
if (allOnes)
|
|
{
|
|
Mat tmpInput = inputs[i];
|
|
MatShape outShape = shape(outputs[0].size);
|
|
size_t xSize = outShape[2];
|
|
for (size_t j = 3; j < outShape.size(); j++)
|
|
xSize *= outShape[j];
|
|
|
|
int dimVec[3] = {outShape[0], outShape[1], (int) xSize};
|
|
std::vector<int> matSizesVec(&dimVec[0], &dimVec[0] + 3);
|
|
inputs[i] = Mat(matSizesVec, tmpInput.type());
|
|
|
|
std::vector<int> idx(outShape.size(), 0);
|
|
std::vector<int> outIdx(inpShape.size(), 0);
|
|
|
|
for (size_t j = 0; j < outShape[0]; j++)
|
|
{
|
|
outIdx[0] = idx[0] = j;
|
|
for(size_t k = 0; k < outShape[1]; k++)
|
|
{
|
|
outIdx[1] = idx[1] = k;
|
|
for (size_t x = 0; x < xSize; x++)
|
|
{
|
|
outIdx[2] = x;
|
|
inputs[i].at<float>(outIdx.data()) = tmpInput.at<float>(idx.data());
|
|
}
|
|
}
|
|
}
|
|
inputs[i] = inputs[i].reshape(0, outShape);
|
|
}
|
|
}
|
|
}
|
|
|
|
EltwiseInvoker::run(*this,
|
|
&inputs[0], (int)inputs.size(), outputs[0],
|
|
nstripes);
|
|
}
|
|
|
|
#ifdef HAVE_CUDA
|
|
Ptr<BackendNode> initCUDA(
|
|
void *context_,
|
|
const std::vector<Ptr<BackendWrapper>>& inputs,
|
|
const std::vector<Ptr<BackendWrapper>>& outputs
|
|
) override
|
|
{
|
|
auto context = reinterpret_cast<csl::CSLContext*>(context_);
|
|
|
|
CV_Assert(channelsModeInput == ELTWISE_CHANNNELS_INPUT_0 ||
|
|
channelsModeInput == ELTWISE_CHANNNELS_INPUT_0_TRUNCATE ||
|
|
channelsModeInput == ELTWISE_CHANNNELS_SAME);
|
|
|
|
if(channelsModeInput == ELTWISE_CHANNNELS_INPUT_0 || channelsModeInput == ELTWISE_CHANNNELS_INPUT_0_TRUNCATE)
|
|
{
|
|
auto input_wrapper = inputs[0].dynamicCast<CUDABackendWrapper>();
|
|
for (int i = 1; i < inputs.size(); i++)
|
|
{
|
|
auto from_wrapper = inputs[i].dynamicCast<CUDABackendWrapper>();
|
|
if (input_wrapper->getShape()[1] != from_wrapper->getShape()[1])
|
|
{
|
|
CV_Assert(op == SUM);
|
|
CV_Assert(coeffs.empty());
|
|
return make_cuda_node<cuda4dnn::ShortcutOp>(preferableTarget, std::move(context->stream));
|
|
}
|
|
}
|
|
}
|
|
|
|
auto op_ = [this] {
|
|
switch (op) {
|
|
case MAX: return cuda4dnn::EltwiseOpType::MAX;
|
|
case MIN: return cuda4dnn::EltwiseOpType::MIN;
|
|
case SUM: return cuda4dnn::EltwiseOpType::SUM;
|
|
case PROD: return cuda4dnn::EltwiseOpType::PRODUCT;
|
|
case DIV: return cuda4dnn::EltwiseOpType::DIV;
|
|
}
|
|
return cuda4dnn::EltwiseOpType::SUM;
|
|
}();
|
|
|
|
return make_cuda_node<cuda4dnn::EltwiseOp>(preferableTarget, std::move(context->stream), op_, coeffs);
|
|
}
|
|
#endif
|
|
|
|
virtual Ptr<BackendNode> initHalide(const std::vector<Ptr<BackendWrapper> > &input) CV_OVERRIDE
|
|
{
|
|
#ifdef HAVE_HALIDE
|
|
Halide::Var x("x"), y("y"), c("c"), n("n");
|
|
Halide::Func top = (name.empty() ? Halide::Func() : Halide::Func(name));
|
|
Halide::Expr topExpr;
|
|
std::vector<Halide::Buffer<> > inputBuffers = halideBuffers(input);
|
|
switch (op)
|
|
{
|
|
case SUM:
|
|
if (coeffs.empty())
|
|
{
|
|
topExpr = inputBuffers[0](x, y, c, n) +
|
|
inputBuffers[1](x, y, c, n);
|
|
for (int i = 2; i < inputBuffers.size(); ++i)
|
|
topExpr += inputBuffers[i](x, y, c, n);
|
|
}
|
|
else
|
|
{
|
|
topExpr = coeffs[0] * inputBuffers[0](x, y, c, n) +
|
|
coeffs[1] * inputBuffers[1](x, y, c, n);
|
|
for (int i = 2; i < inputBuffers.size(); ++i)
|
|
topExpr += coeffs[i] * inputBuffers[i](x, y, c, n);
|
|
}
|
|
break;
|
|
case PROD:
|
|
topExpr = inputBuffers[0](x, y, c, n) *
|
|
inputBuffers[1](x, y, c, n);
|
|
for (int i = 2; i < inputBuffers.size(); ++i)
|
|
topExpr *= inputBuffers[i](x, y, c, n);
|
|
break;
|
|
case DIV:
|
|
topExpr = inputBuffers[0](x, y, c, n) /
|
|
inputBuffers[1](x, y, c, n);
|
|
for (int i = 2; i < inputBuffers.size(); ++i)
|
|
topExpr /= inputBuffers[i](x, y, c, n);
|
|
break;
|
|
case MAX:
|
|
topExpr = max(inputBuffers[0](x, y, c, n),
|
|
inputBuffers[1](x, y, c, n));
|
|
for (int i = 2; i < inputBuffers.size(); ++i)
|
|
topExpr = max(topExpr, inputBuffers[i](x, y, c, n));
|
|
break;
|
|
case MIN:
|
|
topExpr = min(inputBuffers[0](x, y, c, n),
|
|
inputBuffers[1](x, y, c, n));
|
|
for (int i = 2; i < inputBuffers.size(); ++i)
|
|
topExpr = min(topExpr, inputBuffers[i](x, y, c, n));
|
|
break;
|
|
default:
|
|
return Ptr<BackendNode>();
|
|
}
|
|
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
|
|
{
|
|
CV_Assert(inputs.size() == 2);
|
|
CV_Assert(nodes.size() == 2);
|
|
|
|
auto op_x1 = nodes[0].dynamicCast<CannBackendNode>()->getOp();
|
|
auto x1 = inputs[0].dynamicCast<CannBackendWrapper>();
|
|
auto x1_desc = x1->getTensorDesc();
|
|
auto op_x2 = nodes[1].dynamicCast<CannBackendNode>()->getOp();
|
|
auto x2 = inputs[1].dynamicCast<CannBackendWrapper>();
|
|
auto x2_desc = x2->getTensorDesc();
|
|
auto output_desc = std::make_shared<ge::TensorDesc>(ge::Shape(), ge::FORMAT_NCHW, ge::DT_FLOAT);
|
|
|
|
std::shared_ptr<ge::Operator> eltwise_operator = nullptr;
|
|
// add, mul, div, max, min
|
|
switch (op)
|
|
{
|
|
#define BUILD_CANN_ELTWISE_OP(op_type, class_name, op_name) \
|
|
case op_type: { \
|
|
auto eltwise_op = \
|
|
std::make_shared<ge::op::class_name>(op_name); \
|
|
eltwise_op->set_input_x1_by_name(*op_x1, x1->name.c_str()); \
|
|
eltwise_op->set_input_x2_by_name(*op_x2, x2->name.c_str()); \
|
|
eltwise_op->update_input_desc_x1(*x1_desc); \
|
|
eltwise_op->update_input_desc_x2(*x2_desc); \
|
|
eltwise_op->update_output_desc_y(*output_desc); \
|
|
eltwise_operator = eltwise_op; \
|
|
} break;
|
|
BUILD_CANN_ELTWISE_OP(SUM, Add, name);
|
|
BUILD_CANN_ELTWISE_OP(PROD, Mul, name);
|
|
BUILD_CANN_ELTWISE_OP(DIV, Xdivy, name);
|
|
BUILD_CANN_ELTWISE_OP(MAX, Maximum, name);
|
|
BUILD_CANN_ELTWISE_OP(MIN, Minimum, name);
|
|
#undef BUILD_CANN_ELTWISE_OP
|
|
default: CV_Error(Error::StsNotImplemented, "Unsupported eltwise operation");
|
|
}
|
|
|
|
return Ptr<BackendNode>(new CannBackendNode(eltwise_operator));
|
|
}
|
|
#endif // HAVE_CANN
|
|
|
|
#ifdef HAVE_DNN_NGRAPH
|
|
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
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|
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
|
|
{
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|
CV_Assert(nodes.size() >= 2);
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|
auto curr_node = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
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|
if (!coeffs.empty()) {
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|
auto coeff = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{1}, &coeffs[0]);
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|
curr_node = std::make_shared<ngraph::op::v1::Multiply>(curr_node, coeff, ngraph::op::AutoBroadcastType::NUMPY);
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|
}
|
|
|
|
std::shared_ptr<ngraph::Node> res;
|
|
for (size_t i = 1; i < nodes.size(); i++)
|
|
{
|
|
auto next_node = nodes[i].dynamicCast<InfEngineNgraphNode>()->node;
|
|
if (!coeffs.empty()) {
|
|
auto coeff = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{1}, &coeffs[i]);
|
|
next_node = std::make_shared<ngraph::op::v1::Multiply>(next_node, coeff, ngraph::op::AutoBroadcastType::NUMPY);
|
|
}
|
|
switch (op) {
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|
case SUM: res = std::make_shared<ngraph::op::v1::Add>(curr_node, next_node); break;
|
|
case PROD: res = std::make_shared<ngraph::op::v1::Multiply>(curr_node, next_node); break;
|
|
case DIV: res = std::make_shared<ngraph::op::v1::Divide>(curr_node, next_node); break;
|
|
case MAX: res = std::make_shared<ngraph::op::v1::Maximum>(curr_node, next_node); break;
|
|
case MIN: res = std::make_shared<ngraph::op::v1::Minimum>(curr_node, next_node); break;
|
|
default: CV_Error(Error::StsNotImplemented, "Unsupported eltwise operation");
|
|
}
|
|
curr_node = res;
|
|
}
|
|
return Ptr<BackendNode>(new InfEngineNgraphNode(res));
|
|
}
|
|
#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
|
|
{
|
|
params.set("input_scales", DictValue::arrayReal(scales[0].data(), scales[0].size()));
|
|
params.set("input_zeropoints", DictValue::arrayInt(zeropoints[0].data(), zeropoints[0].size()));
|
|
if (op == SUM)
|
|
{
|
|
std::vector<float> newCoeffs;
|
|
float offset = zeropoints[1][0];
|
|
float out_sc = scales[1][0];
|
|
for (int i = 0; i < scales[0].size(); i++)
|
|
{
|
|
float coeff = coeffs.empty() ? 1.f : coeffs[i];
|
|
float newcoeff = (scales[0][i] * coeff) / out_sc;
|
|
newCoeffs.push_back(newcoeff);
|
|
offset -= (newcoeff * zeropoints[0][i]);
|
|
}
|
|
params.set("coeff", DictValue::arrayReal(newCoeffs.data(), newCoeffs.size()));
|
|
params.set("offset", offset);
|
|
return true;
|
|
}
|
|
else if (op == PROD)
|
|
{
|
|
std::vector<float> newCoeffs = scales[0];
|
|
newCoeffs[0] /= scales[1][0];
|
|
params.set("coeff", DictValue::arrayReal(newCoeffs.data(), newCoeffs.size()));
|
|
params.set("offset", zeropoints[1][0]);
|
|
return true;
|
|
}
|
|
return op == MAX;
|
|
}
|
|
|
|
virtual int64 getFLOPS(const std::vector<MatShape> &inputs,
|
|
const std::vector<MatShape> &outputs) const CV_OVERRIDE
|
|
{
|
|
CV_UNUSED(outputs); // suppress unused variable warning
|
|
CV_Assert(inputs.size());
|
|
|
|
// FIXIT: handle inputs with different number of channels
|
|
long flops = inputs.size() * total(inputs[0]);
|
|
|
|
return flops;
|
|
}
|
|
|
|
bool setActivation(const Ptr<ActivationLayer>& layer) CV_OVERRIDE
|
|
{
|
|
if (activ.empty() || layer.empty())
|
|
{
|
|
activ = layer;
|
|
return !activ.empty();
|
|
}
|
|
else
|
|
return false;
|
|
}
|
|
|
|
Ptr<ActivationLayer> activ;
|
|
|
|
private:
|
|
bool hasVecInput;
|
|
};
|
|
|
|
Ptr<EltwiseLayer> EltwiseLayer::create(const LayerParams& params)
|
|
{
|
|
return Ptr<EltwiseLayer>(new EltwiseLayerImpl(params));
|
|
}
|
|
|
|
}
|
|
}
|