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https://github.com/opencv/opencv.git
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d991c22090
Support FlowNet2 model * Support DataAugmentation layer * Fix warnings * Fix comments * Support Correlation layer * TEST * Support Correlation layer * Supported Accum and FlowWarp layers * Supported ChannelNorm layer * Supported Resample with inputs.size() > 1 * Fixed comments * Refactoring * Added tests * Add resample test * Added asserts in resize layer * Updated DataAugmentation layer * Update convolution layer * Refactoring * Fix data augmentation layer * Fix caffe importer * Fix resize * Switch to Mat ptr * Remove useless resize type * Used ResizeLayer in Accum * Split ChannelNormLayer * Delete duplicate assert * Add sample * Fix sample * Added colormap
145 lines
6.5 KiB
C++
145 lines
6.5 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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#include <google/protobuf/stubs/common.h>
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namespace cv {
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namespace dnn {
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CV__DNN_EXPERIMENTAL_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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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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void initializeLayerFactory()
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{
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CV_TRACE_FUNCTION();
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static ProtobufShutdown protobufShutdown; CV_UNUSED(protobufShutdown);
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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(LRN, LRNLayer);
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CV_DNN_REGISTER_LAYER_CLASS(InnerProduct, InnerProductLayer);
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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(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(Crop, CropLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Eltwise, EltwiseLayer);
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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(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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}
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CV__DNN_EXPERIMENTAL_NS_END
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}} // namespace
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