#ifndef _MODEL_H_
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#define _MODEL_H_
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#include "plugin_factory.h"
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#include "trt_utils.h"
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#include "calibrator.h"
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#include "NvInfer.h"
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#include "NvInferPlugin.h"
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#include "NvInferRuntimeCommon.h"
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#include "cuda_runtime_api.h"
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#include <algorithm>
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#include <cmath>
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#include <stdint.h>
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#include <string>
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#include <vector>
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#include "../utils/time_util.h"
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#include "../config.h"
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#include "opencv2/opencv.hpp"
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#include <numeric>
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struct NetworkInfo
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{
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std::string networkType;
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std::string labelsFilePath;
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std::string precision;
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std::string deviceType;
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std::string calibrationTablePath;
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std::string enginePath;
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std::string inputBlobName;
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std::string data_path;
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};
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struct InferParams
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{
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bool printPerfInfo;
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bool printPredictionInfo;
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std::string calibImages;
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std::string calibImagesPath;
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float probThresh;
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float nmsThresh;
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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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uint32_t stride{0};
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uint32_t stride_h{0};
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uint32_t stride_w{0};
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uint32_t gridSize{0};
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uint32_t grid_h{ 0 };
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uint32_t grid_w{ 0 };
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uint32_t numClasses{0};
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uint32_t numBBoxes{0};
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uint64_t volume{0};
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std::vector<uint32_t> masks;
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std::vector<float> anchors;
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int bindingIndex{-1};
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float* hostBuffer{nullptr};
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};
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class Detecter
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{
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public:
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float getNMSThresh() const { return m_NMSThresh; }
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std::string getClassName(const int& label) const { return m_ClassNames.at(label); }
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int getClassId(const int& label) const { return m_ClassIds.at(label); }
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uint32_t getInputH() const { return m_InputH; }
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uint32_t getInputW() const { return m_InputW; }
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uint32_t getNumClasses() const { return static_cast<uint32_t>(m_ClassNames.size()); }
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void doInference(const unsigned char* input, const uint32_t batchSize);
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std::vector<BBoxInfo> decodeDetections(const int& imageIdx,
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const int& imageH,
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const int& imageW);
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Detecter(const NetworkInfo& networkInfo, const InferParams& inferParams, int type);
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~Detecter();
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std::string m_EnginePath;
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const std::string m_DeviceType;
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const std::string m_InputBlobName;
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std::vector<TensorInfo> m_OutputTensors;
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std::vector<std::map<std::string, std::string>> m_configBlocks;
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uint32_t m_InputH;
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uint32_t m_InputW;
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uint32_t m_InputC;
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uint64_t m_InputSize;
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uint32_t _n_classes = 0;
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float _f_depth_multiple = 0;
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float _f_width_multiple = 0;
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const float m_ProbThresh;
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const float m_NMSThresh;
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std::vector<std::string> m_ClassNames;
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const std::vector<int> m_ClassIds{
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1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21,
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22, 23, 24, 25, 27, 28, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44,
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46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,
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67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90};
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uint32_t m_BatchSize = 1;
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nvinfer1::INetworkDefinition* m_Network;
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nvinfer1::IBuilder* m_Builder ;
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nvinfer1::IHostMemory* m_ModelStream;
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nvinfer1::ICudaEngine* m_Engine;
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nvinfer1::IExecutionContext* m_Context;
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std::vector<void*> m_DeviceBuffers;
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int m_InputBindingIndex;
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cudaStream_t m_CudaStream;
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PluginFactory* m_PluginFactory;
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std::vector<BBoxInfo> decodeTensor(const int imageIdx, const int imageH,
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const int imageW, const TensorInfo& tensor);
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BBox convert_bbox_res(const float& bx, const float& by, const float& bw, const float& bh,
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const uint32_t& stride_h_, const uint32_t& stride_w_, const uint32_t& netW, const uint32_t& netH)
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{
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BBox b;
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float x = bx * stride_w_;
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float y = by * stride_h_;
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b.x1 = x - bw / 2;
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b.x2 = x + bw / 2;
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b.y1 = y - bh / 2;
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b.y2 = y + bh / 2;
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b.x1 = clamp(b.x1, 0, netW);
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b.x2 = clamp(b.x2, 0, netW);
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b.y1 = clamp(b.y1, 0, netH);
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b.y2 = clamp(b.y2, 0, netH);
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return b;
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}
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inline void add_bbox_proposal(const float bx, const float by, const float bw, const float bh,
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const uint32_t stride_h_, const uint32_t stride_w_, const float scaleH, const float scaleW, const float xoffset_, const float yoffset, const int maxIndex, const float maxProb,
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const uint32_t image_w, const uint32_t image_h,
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std::vector<BBoxInfo>& binfo)
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{
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BBoxInfo bbi;
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bbi.box = convert_bbox_res(bx, by, bw, bh, stride_h_, stride_w_, m_InputW, m_InputH);
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if ((bbi.box.x1 > bbi.box.x2) || (bbi.box.y1 > bbi.box.y2))
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{
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return;
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}
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bbi.box.x1 = ((float)bbi.box.x1 / (float)m_InputW)*(float)image_w;
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bbi.box.y1 = ((float)bbi.box.y1 / (float)m_InputH)*(float)image_h;
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bbi.box.x2 = ((float)bbi.box.x2 / (float)m_InputW)*(float)image_w;
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bbi.box.y2 = ((float)bbi.box.y2 / (float)m_InputH)*(float)image_h;
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bbi.label = maxIndex;
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bbi.prob = maxProb;
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bbi.classId = getClassId(maxIndex);
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binfo.push_back(bbi);
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};
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inline int64_t volume(const nvinfer1::Dims& d)
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{
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return std::accumulate(d.d,d.d+d.nbDims,1,std::multiplies<int64_t>());
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}
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inline unsigned int getElementSize(nvinfer1::DataType t)
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{
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switch (t)
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{
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case nvinfer1::DataType::kINT32: return 4;
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case nvinfer1::DataType::kFLOAT: return 4;
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case nvinfer1::DataType::kHALF: return 2;
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case nvinfer1::DataType::kBOOL:
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case nvinfer1::DataType::kINT8: return 1;
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}
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throw std::runtime_error("Invalid DataType.");
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return 0;
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}
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void setOutput(int type);
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private:
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Logger m_Logger;
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void createYOLOEngine(const nvinfer1::DataType dataType = nvinfer1::DataType::kFLOAT,
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Int8EntropyCalibrator* calibrator = nullptr);
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void writePlanFileToDisk();
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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 allocateBuffers();
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bool verifyEngine();
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void destroyNetworkUtils(std::vector<nvinfer1::Weights>& trtWeights);
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protected:
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const std::string m_NetworkType;
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std::unique_ptr<YoloTinyMaxpoolPaddingFormula> m_TinyMaxpoolPaddingFormula;
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private:
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Timer _timer;
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int _n_yolo_ind = 0;
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};
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#endif
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