YOLONAS_Pose.md

YOLO-NAS-Pose usage

NOTE: The yaml file is not required.

• Convert model
• Edit the config_infer_primary_yolonas_pose file

Convert model

1. Download the YOLO-NAS repo and install the requirements

git clone https://github.com/Deci-AI/super-gradients.git
cd super-gradients
pip3 install -r requirements.txt
python3 setup.py install
pip3 install onnx onnxsim onnxruntime

NOTE: It is recommended to use Python virtualenv.

2. Copy conversor

Copy the export_yolonas_pose.py file from DeepStream-Yolo-Pose/utils directory to the super-gradients folder.

3. Download the model

Download the pth file from YOLO-NAS-Pose releases (example for YOLO-NAS-Pose S)

wget https://sghub.deci.ai/models/yolo_nas_pose_s_coco_pose.pth

NOTE: You can use your custom model.

4. Convert model

Generate the ONNX model file (example for YOLO-NAS-Pose S)

python3 export_yolonas_pose.py -m yolo_nas_pose_s -w yolo_nas_pose_s_coco_pose.pth --dynamic

NOTE: Model names

-m yolo_nas_pose_s

or

-m yolo_nas_pose_m

or

-m yolo_nas_pose_l

NOTE: To change the inference size (defaut: 640)

-s SIZE
--size SIZE
-s HEIGHT WIDTH
--size HEIGHT WIDTH

Example for 1280

-s 1280

or

-s 1280 1280

NOTE: To simplify the ONNX model (DeepStream >= 6.0)

--simplify

NOTE: To use dynamic batch-size (DeepStream >= 6.1)

--dynamic

NOTE: To use static batch-size (example for batch-size = 4)

--batch 4

5. Copy generated files

Copy the generated ONNX model file to the DeepStream-Yolo-Pose folder.

Edit the config_infer_primary_yolonas_pose file

Edit the config_infer_primary_yolonas_pose.txt file according to your model (example for YOLO-NAS-Pose S)

[property]
...
onnx-file=yolo_nas_pose_s_coco_pose.onnx
...
parse-bbox-func-name=NvDsInferParseYoloPoseE
...