metadata

license: mit
pipeline_tag: object-detection
tags:
  - Pose Estimation

RTMO / YOLO-NAS-Pose Inference with CUDAExecutionProvider / TensorrtExecutionProvider DEMO

demo.sh: DEMO main program, which will first install rtmlib, and then use rtmo-s to analyze the .mp4 files in the video folder.
demo_batch.sh: Multi-batch version of demo.sh
rtmo_gpu.py: Defines an RTMO_GPU (& RTMO_GPU_BATCH) class, making fine adjustments to CUDA & TensorRT settings.
rtmo_demo.py: Python main program, which has three arguments:
- path: The folder location that contains the .mp4 files to be analyzed.
- model_path: The local path to the ONNX model or a URL pointing to the RTMO model published on mmpose.
- --yolo_nas_pose: If you run inference with YOLO NAS Pose Model instead of RTMO model.
rtmo_demo_batch.py: Multi-batch version of demo_batch.sh
video: Contains one test video.

Note

Original ONNX models come from MMPOSE/RTMO Project Page trained on body7. We did only
DEMO Inferecne Code is modified from rtmlib
TensorrtExecutionProvider only supports Models with fixed batch size (*_batchN.onnx) while CUDAExecutionProvider can run with dynamic batch size.

We did the following to make them work with TensorRTExecutionProvdier

PS. FP16 ONNX model is also provided.