mirror of
https://github.com/opencv/opencv.git
synced 2025-06-26 14:20:57 +08:00
347d673a87
dnn: add ONNX TopK #23279 Merge with https://github.com/opencv/opencv_extra/pull/1200 Partially fixes #22890 and #20258 To-do: - [x] TopK forward impl - [x] add tests - [x] support Opset 1 & 10 if possible - [ ] ~Support other backends~ (TopK has two outputs, which is not supported by other backends, such as openvino) Perf: M1 (time in millisecond) | input shape | axis | dnn | ort | | --------------- | ---- | ---- | ---- | | (1000, 100) | 0 | 1.68 | 4.07 | | (1000, 100) K5 | 0 | 1.13 | 0.12 | | (1000, 100) | 1 | 0.96 | 0.77 | | (100, 100, 100) | 0 | 10.00 | 31.13 | | (100, 100, 100) | 1 | 7.33 | 9.17 | | (100, 100, 100) | 2 | 7.52 | 9.48 | M2 (time in milisecond) | input shape | axis | dnn | ort | | --------------- | ---- | ---- | ---- | | (1000, 100) | 0 | 0.76 | 2.44 | | (1000, 100) K5 | 0 | 0.68 | 0.07 | | (1000, 100) | 1 | 0.41 | 0.50 | | (100, 100, 100) | 0 | 4.83 | 17.52| | (100, 100, 100) | 1 | 3.60 | 5.08 | | (100, 100, 100) | 2 | 3.73 | 5.10 | ONNXRuntime performance testing script: https://gist.github.com/fengyuentau/a119f94fd16721ec9974b8c7b0a45d4c ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [x] There is a reference to the original bug report and related work - [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [x] The feature is well documented and sample code can be built with the project CMake
248 lines
13 KiB
C++
248 lines
13 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(MatMul, MatMulLayer);
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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(GatherElements, GatherElementsLayer);
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CV_DNN_REGISTER_LAYER_CLASS(LayerNormalization, LayerNormLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Expand, ExpandLayer);
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CV_DNN_REGISTER_LAYER_CLASS(InstanceNormalization, InstanceNormLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Attention, AttentionLayer);
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CV_DNN_REGISTER_LAYER_CLASS(GroupNormalization, GroupNormLayer);
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CV_DNN_REGISTER_LAYER_CLASS(DepthToSpace, DepthToSpaceLayer)
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CV_DNN_REGISTER_LAYER_CLASS(SpaceToDepth, SpaceToDepthLayer)
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CV_DNN_REGISTER_LAYER_CLASS(DepthToSpaceInt8, DepthToSpaceLayer)
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CV_DNN_REGISTER_LAYER_CLASS(SpaceToDepthInt8, SpaceToDepthLayer)
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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(Einsum, EinsumLayer);
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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(TopK, TopKLayer);
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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(HardSwishInt8, 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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