/infer

A new tensorrt integrate. 多线程推理

Primary LanguageCudaMIT LicenseMIT

New tensorrt package, easy to integrate many tasks

  • Easily implement producer-consumer models for various tasks and perform high-performance inference
  • No complex packaging, no coupling!

For the Yolo-Demo

  • Currently supports Yolo series 3/4/5/x/7/8
  • YoloV8-Segment is supported
  • 🚀 Pre-processing about 1ms
  • 🚀 Post-processing about 0.5ms

Description

  • cpm.hpp Producer-consumer model
    • For direct inference tasks, cpm.hpp can be turned into an automatic multi-batch producer-consumer model
  • infer.hpp A repackaging of tensorRT. Simple interface
  • yolo.hpp Wrapper for yolo tasks. Based on infer.hpp

Inference flow of trt

step1 Compile the model, e.g.

trtexec --onnx=yolov5s.onnx --saveEngine=yolov5s.engine

step2: Use infer inference

model = trt::load("yolov5s.engine");
... preprocess ...

// Configure the dynamic batch size.
auto dims = model->static_dims();
dims[0] = batch;
model->set_run_dims(dims);
model->forward({input_device, output_device}, stream);

... postprocess ...

step2: Use yolo inference

cv::Mat image = cv::imread("image.jpg");
auto model = yolo::load("yolov5s.engine");
auto objs = model->forward(yolo::Image(image.data, image.cols, image.rows));
// use objs to draw to image.

Use of CPM (wrapping the inference as producer-consumer)

cpm::Instance<yolo::BoxArray, yolo::Image, yolo::Infer> cpmi;
cpmi.start([]{
    return yolo::load("yolov5s.engine", yolo::Type::V5);
}, batch);

auto result_futures = cpmi.commits(images);
for(auto& fut : result_futures){
    auto objs = fut.get();
    ... process ...
}

Reference