mirror of
https://github.com/opencv/opencv.git
synced 2026-09-27 04:09:45 +03:00
Yuantao Feng
8a96e34e33
* first commit * turned C from input to constant; force C constant in impl; better handling 0d/1d cases * integrate with gemm from ficus nn * fix const inputs * adjust threshold for int8 tryQuantize * adjust threshold for int8 quantized 2 * support batched gemm and matmul; tune threshold for rcnn_ilsvrc13; update googlenet * add gemm perf against innerproduct * add perf tests for innerproduct with bias * fix perf * add memset * renamings for next step * add dedicated perf gemm * add innerproduct in perf_gemm * remove gemm and innerproduct perf tests from perf_layer * add perf cases for vit sizes; prepack constants * remove batched gemm; fix wrong trans; optimize KC * remove prepacking for const A; several fixes for const B prepacking * add todos and gemm expression * add optimized branch for avx/avx2 * trigger build * update macros and signature * update signature * fix macro * fix bugs for neon aarch64 & x64 * add backends: cuda, cann, inf_ngraph and vkcom * fix cuda backend * test commit for cuda * test cuda backend * remove debug message from cuda backend * use cpu dispatcher * fix neon macro undef in dispatcher * fix dispatcher * fix inner kernel for neon aarch64 * fix compiling issue on armv7; try fixing accuracy issue on other platforms * broadcast C with beta multiplied; improve func namings * fix bug for avx and avx2 * put all platform-specific kernels in dispatcher * fix typos * attempt to fix compile issues on x64 * run old gemm when neon, avx, avx2 are all not available; add kernel for armv7 neon * fix typo * quick fix: add macros for pack4 * quick fix: use vmlaq_f32 for armv7 * quick fix for missing macro of fast gemm pack f32 4 * disable conformance tests when optimized branches are not supported * disable perf tests when optimized branches are not supported * decouple cv_try_neon and cv_neon_aarch64 * drop googlenet_2023; add fastGemmBatched * fix step in fastGemmBatched * cpu: fix initialization ofb; gpu: support batch * quick followup fix for cuda * add default kernels * quick followup fix to avoid macro redef * optmized kernels for lasx * resolve mis-alignment; remove comments * tune performance for x64 platform * tune performance for neon aarch64 * tune for armv7 * comment time consuming tests * quick follow-up fix
235 lines
12 KiB
C++
235 lines
12 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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// 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 <opencv2/dnn/layer.details.hpp>
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#if defined(HAVE_PROTOBUF) && !defined(BUILD_PLUGIN)
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#include <google/protobuf/stubs/common.h>
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#endif
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namespace cv {
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namespace dnn {
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CV__DNN_INLINE_NS_BEGIN
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static Mutex* __initialization_mutex = NULL;
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Mutex& getInitializationMutex()
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{
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if (__initialization_mutex == NULL)
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__initialization_mutex = new Mutex();
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return *__initialization_mutex;
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}
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// force initialization (single-threaded environment)
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Mutex* __initialization_mutex_initializer = &getInitializationMutex();
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#if defined(HAVE_PROTOBUF) && !defined(BUILD_PLUGIN)
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namespace {
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using namespace google::protobuf;
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class ProtobufShutdown {
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public:
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bool initialized;
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ProtobufShutdown() : initialized(true) {}
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~ProtobufShutdown()
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{
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initialized = false;
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google::protobuf::ShutdownProtobufLibrary();
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}
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};
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} // namespace
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#endif
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void initializeLayerFactory()
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{
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CV_TRACE_FUNCTION();
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#if defined(HAVE_PROTOBUF) && !defined(BUILD_PLUGIN)
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static ProtobufShutdown protobufShutdown; CV_UNUSED(protobufShutdown);
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#endif
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CV_DNN_REGISTER_LAYER_CLASS(Slice, SliceLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Split, SplitLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Concat, ConcatLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Reshape, ReshapeLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Flatten, FlattenLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Resize, ResizeLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Interp, InterpLayer);
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CV_DNN_REGISTER_LAYER_CLASS(CropAndResize, CropAndResizeLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Convolution, ConvolutionLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Deconvolution, DeconvolutionLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Pooling, PoolingLayer);
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CV_DNN_REGISTER_LAYER_CLASS(ROIPooling, PoolingLayer);
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CV_DNN_REGISTER_LAYER_CLASS(PSROIPooling, PoolingLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Reduce, ReduceLayer);
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CV_DNN_REGISTER_LAYER_CLASS(LRN, LRNLayer);
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CV_DNN_REGISTER_LAYER_CLASS(InnerProduct, InnerProductLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Gemm, GemmLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Softmax, SoftmaxLayer);
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CV_DNN_REGISTER_LAYER_CLASS(SoftMax, SoftmaxLayer); // For compatibility. See https://github.com/opencv/opencv/issues/16877
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CV_DNN_REGISTER_LAYER_CLASS(MVN, MVNLayer);
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CV_DNN_REGISTER_LAYER_CLASS(ReLU, ReLULayer);
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CV_DNN_REGISTER_LAYER_CLASS(ReLU6, ReLU6Layer);
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CV_DNN_REGISTER_LAYER_CLASS(ChannelsPReLU, ChannelsPReLULayer);
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CV_DNN_REGISTER_LAYER_CLASS(PReLU, ChannelsPReLULayer);
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CV_DNN_REGISTER_LAYER_CLASS(Sigmoid, SigmoidLayer);
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CV_DNN_REGISTER_LAYER_CLASS(TanH, TanHLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Swish, SwishLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Mish, MishLayer);
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CV_DNN_REGISTER_LAYER_CLASS(ELU, ELULayer);
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CV_DNN_REGISTER_LAYER_CLASS(BNLL, BNLLLayer);
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CV_DNN_REGISTER_LAYER_CLASS(AbsVal, AbsLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Power, PowerLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Exp, ExpLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Ceil, CeilLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Floor, FloorLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Log, LogLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Round, RoundLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Sqrt, SqrtLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Not, NotLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Acos, AcosLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Acosh, AcoshLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Asin, AsinLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Asinh, AsinhLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Atan, AtanLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Atanh, AtanhLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Cos, CosLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Cosh, CoshLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Erf, ErfLayer);
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CV_DNN_REGISTER_LAYER_CLASS(HardSwish, HardSwishLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Sin, SinLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Sinh, SinhLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Sign, SignLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Shrink, ShrinkLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Softplus, SoftplusLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Softsign, SoftsignLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Tan, TanLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Celu, CeluLayer);
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CV_DNN_REGISTER_LAYER_CLASS(HardSigmoid, HardSigmoidLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Selu, SeluLayer);
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CV_DNN_REGISTER_LAYER_CLASS(ThresholdedRelu,ThresholdedReluLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Gelu, GeluLayer);
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CV_DNN_REGISTER_LAYER_CLASS(GeluApproximation, GeluApproximationLayer);
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CV_DNN_REGISTER_LAYER_CLASS(BatchNorm, BatchNormLayer);
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CV_DNN_REGISTER_LAYER_CLASS(MaxUnpool, MaxUnpoolLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Dropout, BlankLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Identity, BlankLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Silence, BlankLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Const, ConstLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Arg, ArgLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Reciprocal, ReciprocalLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Gather, GatherLayer);
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CV_DNN_REGISTER_LAYER_CLASS(LayerNormalization, LayerNormLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Crop, CropLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Eltwise, EltwiseLayer);
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CV_DNN_REGISTER_LAYER_CLASS(NaryEltwise, NaryEltwiseLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Permute, PermuteLayer);
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CV_DNN_REGISTER_LAYER_CLASS(ShuffleChannel, ShuffleChannelLayer);
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CV_DNN_REGISTER_LAYER_CLASS(PriorBox, PriorBoxLayer);
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CV_DNN_REGISTER_LAYER_CLASS(PriorBoxClustered, PriorBoxLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Reorg, ReorgLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Region, RegionLayer);
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CV_DNN_REGISTER_LAYER_CLASS(DetectionOutput, DetectionOutputLayer);
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CV_DNN_REGISTER_LAYER_CLASS(NormalizeBBox, NormalizeBBoxLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Normalize, NormalizeBBoxLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Shift, ShiftLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Padding, PaddingLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Proposal, ProposalLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Scale, ScaleLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Compare, CompareLayer);
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CV_DNN_REGISTER_LAYER_CLASS(DataAugmentation, DataAugmentationLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Correlation, CorrelationLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Accum, AccumLayer);
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CV_DNN_REGISTER_LAYER_CLASS(FlowWarp, FlowWarpLayer);
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CV_DNN_REGISTER_LAYER_CLASS(LSTM, LSTMLayer);
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CV_DNN_REGISTER_LAYER_CLASS(GRU, GRULayer);
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CV_DNN_REGISTER_LAYER_CLASS(CumSum, CumSumLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Scatter, ScatterLayer);
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CV_DNN_REGISTER_LAYER_CLASS(ScatterND, ScatterNDLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Tile, TileLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Quantize, QuantizeLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Dequantize, DequantizeLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Requantize, RequantizeLayer);
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CV_DNN_REGISTER_LAYER_CLASS(ConvolutionInt8, ConvolutionLayerInt8);
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CV_DNN_REGISTER_LAYER_CLASS(InnerProductInt8, InnerProductLayerInt8);
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CV_DNN_REGISTER_LAYER_CLASS(PoolingInt8, PoolingLayerInt8);
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CV_DNN_REGISTER_LAYER_CLASS(EltwiseInt8, EltwiseLayerInt8);
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CV_DNN_REGISTER_LAYER_CLASS(BatchNormInt8, BatchNormLayerInt8);
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CV_DNN_REGISTER_LAYER_CLASS(ScaleInt8, ScaleLayerInt8);
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CV_DNN_REGISTER_LAYER_CLASS(ShiftInt8, ShiftLayerInt8);
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CV_DNN_REGISTER_LAYER_CLASS(ReLUInt8, ActivationLayerInt8);
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CV_DNN_REGISTER_LAYER_CLASS(ReLU6Int8, ActivationLayerInt8);
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CV_DNN_REGISTER_LAYER_CLASS(SigmoidInt8, ActivationLayerInt8);
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CV_DNN_REGISTER_LAYER_CLASS(TanHInt8, ActivationLayerInt8);
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CV_DNN_REGISTER_LAYER_CLASS(SwishInt8, ActivationLayerInt8);
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CV_DNN_REGISTER_LAYER_CLASS(MishInt8, ActivationLayerInt8);
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CV_DNN_REGISTER_LAYER_CLASS(ELUInt8, ActivationLayerInt8);
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CV_DNN_REGISTER_LAYER_CLASS(BNLLInt8, ActivationLayerInt8);
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CV_DNN_REGISTER_LAYER_CLASS(AbsValInt8, ActivationLayerInt8);
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CV_DNN_REGISTER_LAYER_CLASS(SoftmaxInt8, SoftmaxLayerInt8);
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CV_DNN_REGISTER_LAYER_CLASS(SoftMaxInt8, SoftmaxLayerInt8);
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CV_DNN_REGISTER_LAYER_CLASS(ConcatInt8, ConcatLayer);
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CV_DNN_REGISTER_LAYER_CLASS(FlattenInt8, FlattenLayer);
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CV_DNN_REGISTER_LAYER_CLASS(PaddingInt8, PaddingLayer);
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CV_DNN_REGISTER_LAYER_CLASS(BlankInt8, BlankLayer);
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CV_DNN_REGISTER_LAYER_CLASS(DropoutInt8, BlankLayer);
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CV_DNN_REGISTER_LAYER_CLASS(IdentityInt8, BlankLayer);
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CV_DNN_REGISTER_LAYER_CLASS(SilenceInt8, BlankLayer);
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CV_DNN_REGISTER_LAYER_CLASS(ConstInt8, ConstLayer);
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CV_DNN_REGISTER_LAYER_CLASS(ReshapeInt8, ReshapeLayer);
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CV_DNN_REGISTER_LAYER_CLASS(ResizeInt8, ResizeLayer);
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CV_DNN_REGISTER_LAYER_CLASS(SplitInt8, SplitLayer);
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CV_DNN_REGISTER_LAYER_CLASS(SliceInt8, SliceLayer);
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CV_DNN_REGISTER_LAYER_CLASS(CropInt8, CropLayer);
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CV_DNN_REGISTER_LAYER_CLASS(PermuteInt8, PermuteLayer);
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CV_DNN_REGISTER_LAYER_CLASS(ReorgInt8, ReorgLayer);
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CV_DNN_REGISTER_LAYER_CLASS(ShuffleChannelInt8, ShuffleChannelLayer);
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}
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CV__DNN_INLINE_NS_END
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}} // namespace
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