add samples

wangrong

2025-03-19 291afb9c81aae1d15d9e3edcea618d0a2e1fc5a6

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#pragma once 
 
#include <fstream> 
#include <iostream> 
#include <cmath> 
#include <numeric> 
#include <opencv2/opencv.hpp> 
#include "include/cuda_utils.h" 
#include "include/logging.h" 
#include "include/model.h" 
#include "include/postprocess.h" 
#include "include/preprocess.h" 
#include "include/utils.h" 
 
namespace trt_yolov8 { 
    using namespace nvinfer1; 
    class trt_yolov8_classifier 
    { 
    private: 
        void batch_preprocess(std::vector<cv::Mat>& imgs, float* output, int dst_width = 224, int dst_height = 224);  
        std::vector<float> softmax(float *prob, int n); 
        std::vector<int> topk(const std::vector<float>& vec, int k); 
        void prepare_buffers(ICudaEngine* engine, float** gpu_input_buffer, float** gpu_output_buffer, float** cpu_input_buffer, float** output_buffer_host); 
        void infer(IExecutionContext& context, cudaStream_t& stream, void **buffers, float* input, float* output, int batchSize); 
        void serialize_engine(unsigned int max_batchsize, float& gd, float& gw, std::string& wts_name, std::string& engine_name); 
        void deserialize_engine(std::string& engine_name, IRuntime** runtime, ICudaEngine** engine, IExecutionContext** context); 
 
        Logger gLogger; 
        const static int kOutputSize = kClsNumClass; 
        IRuntime* runtime = nullptr; 
        ICudaEngine* engine = nullptr; 
        IExecutionContext* context = nullptr; 
        cudaStream_t stream; 
    public: 
        trt_yolov8_classifier(std::string model_path = ""); 
        ~trt_yolov8_classifier(); 
 
        // classify 
        void classify(std::vector<cv::Mat> images, std::vector<std::vector<Classification>>& classifications, int top_k = 3); 
 
        // serialize wts to plan file for image classify 
        // sub_type: [ n/s/m/l/x/n2/s2/m2/l2/x2/n6/s6/m6/l6/x6 ] 
        bool wts_2_engine(std::string wts_name, std::string engine_name, std::string sub_type); 
    }; 
}