RT-ODLab/deployment/ONNXRuntime

YOLO ONNXRuntime

Convert Your Model to ONNX

First, you should move to by:

cd <RT-ODLab>
cd tools/

Then, you can:

1. Convert a standard YOLO model by:

   python3 export_onnx.py -m yolov1 --weight ../weight/coco/yolov1/yolov1_coco.pth -nc 80 --img_size 640
   

Notes:

• -n: specify a model name. The model name must be one of the [yolox-s,m,l,x and yolox-nano, yolox-tiny, yolov3]
• -c: the model you have trained
• -o: opset version, default 11. However, if you will further convert your onnx model to OpenVINO, please specify the opset version to 10.
• --no-onnxsim: disable onnxsim

• To customize an input shape for onnx model, modify the following code in tools/export_onnx.py:

  dummy_input = torch.randn(args.batch_size, 3, args.img_size, args.img_size)
  

ONNXRuntime Demo

Step1.

cd <YOLOX_HOME>/deployment/ONNXRuntime

Step2.

python3 onnx_inference.py --model ../../weights/onnx/11/yolov1.onnx -i ../test_image.jpg -s 0.3 --img_size 640

Notes:

• --model: your converted onnx model
• -i: input_image
• -s: score threshold for visualization.
• --img_size: should be consistent with the shape you used for onnx convertion.