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third_party/trt_yolov8/trt_yolov8_classifier.h

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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);
+    };
+}