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