rtmpose_tensorrt

Description

This repository is use the TensorRT to deploy RTMDet and RTMPose. Your computer should have these components:

• NVIDIA GPU
• CUDA
• cudnn
• TensorRT 8.x
• OPENCV
• VS2019

The effect of the code is as follows:

Multi person pose estimation using EasyPose

EasyPose is a simple library of human pose estimation algorithms that can perform multi person pose estimation with fewer dependency packages. It can be easily applied to various applications, click here to jump to it.

Get Started

I. Convert Model

1. RTMDet

When you start to convert a RTMDet model, you can use convert_rtmdet.py to convert pth file to onnx.

python convert_rtmdet.py --config <model cfg> --checkpoint <checkpoint> --output <output path>

Note that RTMDet should be the mmdetection version, and the conversion of mmyolo is not supported.

2. RTMPose

You can use mmdeploy to convert RTMPose. The mmdeploy config file should use configs/mmpose/pose-detection_simcc_onnxruntime_dynamic.py. The convert command as follow:

python tools/deploy.py <deploy cfg> <model cfg> <checkpoint> <image path>

3. Convert to TensorRT engine file

You can use trtexec to convert an ONNX file to engine file. The command as follow:

trtexec --onnx=<ONNX file> --saveEngine=<output file>

Note that the engine files included in the project are only for storing examples. As the engine files generated by TensorRT are related to hardware, it is necessary to regenerate the engine files on the computer where the code needs to be run.

If you want to get the trained ONNX files, please obtain them from EasyPose (RTMDet, RTMPose).

II. Run

At first, you should fill in the model locations for RTMDet and RTMPose as follows:

// set engine file path
string detEngineFile = "./model/rtmdet.engine";
string poseEngineFile = "./model/rtmpose_m.engine";

Then, you can set the cap to video file or camera.

// open cap
cv::VideoCapture cap(0);

If you want to change iou threshold or confidence threshold, you can change them when you initialize RTMDet model.

RTMDet det_model(detEngineFile, logger, 0.5, 0.65);

Finally, you can run the main.cpp file to get result.