dog-qiuqiu / Simple-TensorRT

Secondary encapsulation of NVIDIA TensorRT interface to simplify the calling process

Simple-TensorRT

• Secondary encapsulation of NVIDIA TensorRT interface to simplify the calling process

Compilation dependencies

• Linux
• CUDA
• TensorRT 8.5.1(TensorRT 8.5 GA)
• Opencv 4.7.0

Docker environment

Install NVIDIA Container Toolkit

https://zhuanlan.zhihu.com/p/689473287

Download TensorRT .deb installation package

https://developer.nvidia.cn/tensorrt/download

• Remember to modify the TensorRT .deb installation package path in line 16 of the dockerfile

Build Docker image

cd docker
docker build -t simgpletrt:0.1 .
docker run --gpus all -it -v /home/qiuqiu/Desktop/simple-tensorrt/:/root simgpletrt:0.1

How to compile

Linux platform

sh build.sh

• The compiled libraries and header files and sample programs are saved in the "sdk_out" folder

Interface description

• Open "doc/index.html" in the browser

Examples

Taking resnet50 as an example

1.Export resnet50 onnx model

cd sdk_out/examples/resnet50 && python3 export_onnx.py

2.Convert .onnx model >> tensorrt .engine，base on “trtexec”

static batch mode:

trtexec --onnx=resnet50.onnx --saveEngine=resnet50.engine --fp16

dynamic batch mode:

trtexec --onnx=resnet50_dynamic.onnx --minShapes=input:1x3x224x224 --optShapes=input:4x3x224x224 --maxShapes=input:8x3x224x224 --saveEngine=resnet50_dynamic.engine --fp16

3.run example program

cd sdk_out/examples 

# sync forward
./build/resnet50 resnet50/resnet50.engine resnet50/cat.jpeg
./build/resnet50 resnet50/resnet50_dynamic.engine resnet50/cat.jpeg 

# async forward
./build/resnet50_async resnet50/resnet50.engine resnet50/cat.jpeg resnet50/airplane.jpeg
./build/resnet50_async resnet50/resnet50_dynamic.engine resnet50/cat.jpeg resnet50/airplane.jpeg

Taking yolov8n detection as an example

1.Export yolov8n onnx model

https://docs.ultralytics.com/modes/export/#key-features-of-export-mode

2.Convert .onnx model >> tensorrt .engine，base on “trtexec”

trtexec --onnx=yolov8n.onnx --saveEngine=yolov8n.engine --fp16

3.run example program

cd sdk_out/examples
./build/yolov8_det yolov8_det/yolov8n.engine yolov8_det/test.jpg

Example lists

resnet50: Example of image classification based on ResNet50
yolov8_det: Ultralytics yolov8 detection