S3315_RK3588/AITracker/HI3559A/dasiamrpn/use_mnn.hpp

/*
 * @Author: error: error: git config user.name & please set dead value or install git && error: git config user.email & please set dead value or install git & please set dead value or install git
 * @Date: 2024-03-29 11:44:17
 * @LastEditors: error: error: git config user.name & please set dead value or install git && error: git config user.email & please set dead value or install git & please set dead value or install git
 * @LastEditTime: 2024-03-29 14:35:20
 * @FilePath: \dev_uniform_merge_55kg\mpp\sample\Custom_Sample\main_app_hi3559a\mpp\ai_track\ai\use_mnn.hpp
 * @Description: 这是默认设置,请设置`customMade`, 打开koroFileHeader查看配置 进行设置: https://github.com/OBKoro1/koro1FileHeader/wiki/%E9%85%8D%E7%BD%AE
 */

#ifndef NNIE_LITE_USE_MNN_HPP
#define NNIE_LITE_USE_MNN_HPP
#include <unistd.h>
#include <vector>
#include <chrono>
#include <memory>
#include <iostream>
#include <string>
#include <sys/time.h>
#include <opencv2/core.hpp>
#include <opencv2/imgproc.hpp>
#include "gd_alg_type.h"
#include "MNN/Interpreter.hpp"
#include "MNN/MNNDefine.h"
#include "MNN/Tensor.hpp"
#include "MNN/ImageProcess.hpp"

#define DEBUG_TMP               (0)
#define D_CPU_BIGCORE           (1)

using namespace std::chrono;

namespace GUD {
    namespace ALG{
        class ForwardNet {
        public:
            explicit ForwardNet();

            explicit ForwardNet(const gud_siamrpn_config_t conf);

            ~ForwardNet();

            int net_init(int model_idx, const char* model_path);

            int net_deinit(int model_idx);

            int clsMnnForward(const float *cmap_data, float* coutput_data, bool dataReady);

            int regMnnForward(const float *rmap_data, float* routput_data, bool dataReady);

            int updateTwoKernels(const float *ckernel_data, const float *rkernel_data);

            int updateTwoMaps(const float *cm_data, const float *rm_data);

            void showTensor(MNN::Tensor *TensorIn, int len, int diff);


        public:
            int Thread2done_;
            int InputDataDone_;
            int clsForwardDone_;
            int regForwardDone_;
            int updataCKDone_;
            int updataRKDone_;
            int updataCMDone_;
            int updataRMDone_;

        private:
            int deviceType_;
            bool initialized_;
            int numThread_;
            int prec_type_;
            int clsMpSize_;
            int regMpSize_;
            int clsOutSize_;
            int regOutSize_;

            std::string clsKernelName = "clskernel";
            std::string clsMapName = "x";
            std::string clsOutputName = "55";

            std::string regKernelNane = "regkernel";
            std::string regMapName    = "y";
            std::string regOutputName = "53";

            std::shared_ptr<MNN::Interpreter> net_interpreter_;
            MNN::Session* net_sess_ = nullptr;
            MNN::Tensor* input_tensor = nullptr;
            std::map<std::string, MNN::Tensor*> inputClsTensor;
            std::map<std::string, MNN::Tensor*> inputRegTensor;
            std::map<std::string, MNN::Tensor*> input4Tensors;
            std::vector<int> InputDim;

            ////  Edit By YANGXI on 240426 to solve tracker init slow problem
            std::vector<int> clsMap_dims_;
            std::vector<int> regMap_dims_;
            std::vector<int> ck_dims_;
            std::vector<int> rk_dims_;
            std::vector<int> clsOut_dims_;
            std::vector<int> regOut_dims_;

            std::shared_ptr<MNN::Interpreter> net_cls_;
            MNN::ScheduleConfig MNNconfig_cls_;
            char *MnnModel_cls_;
            MNN::Session *session_cls_;

            std::shared_ptr<MNN::Interpreter> net_reg_;
            MNN::ScheduleConfig MNNconfig_reg_;
            char *MnnModel_reg_;
            MNN::Session *session_reg_;


            MNN::Tensor *clsKernelTensor_;
            MNN::Tensor *regKernelTensor_;
            MNN::Tensor *clsMapTensor_;
            MNN::Tensor *regMapTensor_;
            MNN::Tensor *clsOutTensor_;
            MNN::Tensor *regOutTensor_;

            steady_clock::time_point t1_;
            steady_clock::time_point t2_;
            duration<double> time_span_;
            double time_all_ = 0;
        };
    };
}

#endif //NNIE_LITE_USE_MNN_HPP