132 lines
4.0 KiB
C++
132 lines
4.0 KiB
C++
/*
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* Copyright (c) 2019-2020, NVIDIA CORPORATION. All rights reserved.
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*
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* Permission is hereby granted, free of charge, to any person obtaining a
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* copy of this software and associated documentation files (the "Software"),
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* to deal in the Software without restriction, including without limitation
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* the rights to use, copy, modify, merge, publish, distribute, sublicense,
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* and/or sell copies of the Software, and to permit persons to whom the
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* Software is furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in
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* all copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
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* THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
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* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
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* DEALINGS IN THE SOFTWARE.
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*
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* Edited by Marcos Luciano
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* https://www.github.com/marcoslucianops
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*/
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#ifndef _YOLO_H_
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#define _YOLO_H_
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#include "NvInferPlugin.h"
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#include "nvdsinfer_custom_impl.h"
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#include "layers/convolutional_layer.h"
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#include "layers/deconvolutional_layer.h"
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#include "layers/batchnorm_layer.h"
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#include "layers/implicit_layer.h"
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#include "layers/channels_layer.h"
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#include "layers/shortcut_layer.h"
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#include "layers/sam_layer.h"
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#include "layers/route_layer.h"
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#include "layers/upsample_layer.h"
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#include "layers/pooling_layer.h"
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#include "layers/reorg_layer.h"
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struct NetworkInfo
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{
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std::string inputBlobName;
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std::string networkType;
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std::string modelName;
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std::string onnxWtsFilePath;
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std::string darknetWtsFilePath;
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std::string darknetCfgFilePath;
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uint batchSize;
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int implicitBatch;
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std::string int8CalibPath;
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std::string deviceType;
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uint numDetectedClasses;
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int clusterMode;
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std::string networkMode;
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float scaleFactor;
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const float* offsets;
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};
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struct TensorInfo
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{
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std::string blobName;
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uint gridSizeX {0};
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uint gridSizeY {0};
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uint numBBoxes {0};
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float scaleXY;
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std::vector<float> anchors;
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std::vector<int> mask;
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};
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class Yolo : public IModelParser {
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public:
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Yolo(const NetworkInfo& networkInfo);
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~Yolo() override;
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bool hasFullDimsSupported() const override { return false; }
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const char* getModelName() const override {
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return m_NetworkType == "onnx" ? m_OnnxWtsFilePath.substr(0, m_OnnxWtsFilePath.find(".onnx")).c_str() :
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m_DarknetCfgFilePath.substr(0, m_DarknetCfgFilePath.find(".cfg")).c_str();
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}
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NvDsInferStatus parseModel(nvinfer1::INetworkDefinition& network) override;
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nvinfer1::ICudaEngine* createEngine(nvinfer1::IBuilder* builder, nvinfer1::IBuilderConfig* config);
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protected:
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const std::string m_InputBlobName;
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const std::string m_NetworkType;
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const std::string m_ModelName;
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const std::string m_OnnxWtsFilePath;
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const std::string m_DarknetWtsFilePath;
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const std::string m_DarknetCfgFilePath;
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const uint m_BatchSize;
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const int m_ImplicitBatch;
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const std::string m_Int8CalibPath;
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const std::string m_DeviceType;
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const uint m_NumDetectedClasses;
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const int m_ClusterMode;
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const std::string m_NetworkMode;
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const float m_ScaleFactor;
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const float* m_Offsets;
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uint m_InputC;
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uint m_InputH;
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uint m_InputW;
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uint64_t m_InputSize;
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uint m_NumClasses;
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uint m_LetterBox;
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uint m_NewCoords;
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uint m_YoloCount;
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std::vector<TensorInfo> m_YoloTensors;
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std::vector<std::map<std::string, std::string>> m_ConfigBlocks;
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std::vector<nvinfer1::Weights> m_TrtWeights;
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private:
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NvDsInferStatus buildYoloNetwork(std::vector<float>& weights, nvinfer1::INetworkDefinition& network);
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std::vector<std::map<std::string, std::string>> parseConfigFile(const std::string cfgFilePath);
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void parseConfigBlocks();
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void destroyNetworkUtils();
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};
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#endif // _YOLO_H_
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