4070s 推理占用90%

[fhqmwy]

#include"../utils/yolo.h" #include"yolov8.h" #include <Windows.h>

void setParameters(utils::InitParameter& initParameters) { initParameters.class_names = utils::dataSets::coco80; //initParameters.class_names = utils::dataSets::voc20; initParameters.num_class = 2; // for coco //initParameters.num_class = 20; // for voc2012 initParameters.batch_size = 1; initParameters.dst_h = 640; initParameters.dst_w = 640; initParameters.input_output_names = { "images", "output0" }; initParameters.conf_thresh = 0.25f; initParameters.iou_thresh = 0.45f; initParameters.save_path = ""; }

void task(YOLOV8& yolo, const utils::InitParameter& param, std::vectorcv::Mat& imgsBatch, const int& delayTime, const int& batchi, const bool& isShow, const bool& isSave) { utils::DeviceTimer d_t0; yolo.copy(imgsBatch); float t0 = d_t0.getUsedTime(); utils::DeviceTimer d_t1; yolo.preprocess(imgsBatch); float t1 = d_t1.getUsedTime(); utils::DeviceTimer d_t2; yolo.infer(); float t2 = d_t2.getUsedTime(); utils::DeviceTimer d_t3; yolo.postprocess(imgsBatch); float t3 = d_t3.getUsedTime(); sample::gLogInfo << //"copy time = " << t0 / param.batch_size << "; " "preprocess time = " << t1 / param.batch_size << "; " "infer time = " << t2 / param.batch_size << "; " "postprocess time = " << t3 / param.batch_size << std::endl;

if (isShow)
	utils::show(yolo.getObjectss(), param.class_names, delayTime, imgsBatch);
if (isSave)
	utils::save(yolo.getObjectss(), param.class_names, param.save_path, imgsBatch, param.batch_size, batchi);
yolo.reset();

}

int main(int argc, char** argv) { // parameters utils::InitParameter param; setParameters(param); // path //std::string model_path = "E:/app/TensorRT/TensorRT-8.6.1.6/bin/pubgv8p2.trt"; std::string model_path = "C:/Users/10420/pubgv8.trt"; std::string image_path = "C:/Users/10420/Pictures/0SbOi7H9.jpg"; cv::Mat img = cv::imread(image_path); // camera' id int camera_id = 0;

// get input
utils::InputStream source;
source = utils::InputStream::IMAGE;
//source = utils::InputStream::VIDEO;
//source = utils::InputStream::CAMERA;

// update params from command line parser
bool is_show = true;
bool is_save = false;

param.batch_size = 1;
int total_batches = 1;
int delay_time = 0;

param.src_h = img.rows;
param.src_w = img.cols;

YOLOV8 yolo(param);
// read model
std::vector<unsigned char> trt_file = utils::loadModel(model_path);
if (trt_file.empty())
{
	sample::gLogError << "trt_file is empty!" << std::endl;
	return -1;
}
// init model
if (!yolo.init(trt_file))
{
	sample::gLogError << "initEngine() ocur errors!" << std::endl;
	return -1;
}
yolo.check();
std::vector<cv::Mat> imgs_batch;
imgs_batch.reserve(param.batch_size);
sample::gLogInfo << imgs_batch.capacity() << std::endl;
int batchi = 0;
imgs_batch.emplace_back(img);
task(yolo, param, imgs_batch, delay_time, batchi, false, false);
Sleep(3000);
for (size_t i = 0; ; i++)
{
	task(yolo, param, imgs_batch, delay_time, batchi, false, false);
}

return  -1;

}