ViTPose-Pytorch no mmcv needed

ViTPose (https://github.com/ViTAE-Transformer/ViTPose) + ByteTrack (https://github.com/ifzhang/ByteTrack) + yolov5 (https://github.com/ultralytics/yolov5)

Preparation

Download the official weights from ViTPose. Create a dictionary called models. Tested with both the vitpose-b models.

mkdir models

mv <weight_path> ~/ViTPose-Pytorch/models/

Requirements

If nvidia-tensorrt is not found install nvidia-pyindex first.

pip3 insltall -r requirements.txt

How to run

Export ViTPose TRT model

python3 export.py --include engine --device 0

Export yolov5 engine, either by cloning the official yolov5 repo and using the export function or via cd /.cache/torch/hub/yolov5_master and exporting from there. Move the engine file into the root folder of ViTPose-Pytorch.

With a webcam or another device

ls /dev/vid*

3 demo examples are provided . To use this lib as a package run

python3 -m build

Install the whl in the dist folder via pip.

Examples

With TRT