multimodal/YOLOX/demo/OpenVINO/python/README.md

YOLOX-OpenVINO in Python

This tutorial includes a Python demo for OpenVINO, as well as some converted models.

Download OpenVINO models.

Model	Parameters	GFLOPs	Test Size	mAP	Weights
YOLOX-Nano	0.91M	1.08	416x416	25.8	github
YOLOX-Tiny	5.06M	6.45	416x416	32.8	github
YOLOX-S	9.0M	26.8	640x640	40.5	github
YOLOX-M	25.3M	73.8	640x640	47.2	github
YOLOX-L	54.2M	155.6	640x640	50.1	github
YOLOX-Darknet53	63.72M	185.3	640x640	48.0	github
YOLOX-X	99.1M	281.9	640x640	51.5	github

Install OpenVINO Toolkit

Please visit Openvino Homepage for more details.

Set up the Environment

For Linux

Option1. Set up the environment tempororally. You need to run this command everytime you start a new shell window.

source /opt/intel/openvino_2021/bin/setupvars.sh

Option2. Set up the environment permenantly.

Step1. For Linux:

vim ~/.bashrc

Step2. Add the following line into your file:

source /opt/intel/openvino_2021/bin/setupvars.sh

Step3. Save and exit the file, then run:

source ~/.bashrc

Convert model

1. Export ONNX model

   Please refer to the ONNX tutorial. Note that you should set --opset to 10, otherwise your next step will fail.

2. Convert ONNX to OpenVINO

   cd <INSTSLL_DIR>/openvino_2021/deployment_tools/model_optimizer
   

   Install requirements for convert tool

   sudo ./install_prerequisites/install_prerequisites_onnx.sh
   

   Then convert model.

   python3 mo.py --input_model <ONNX_MODEL> --input_shape <INPUT_SHAPE> [--data_type FP16]
   

   For example:

   python3 mo.py --input_model yolox.onnx --input_shape [1,3,640,640] --data_type FP16 --output_dir converted_output
   

Demo

python

python openvino_inference.py -m <XML_MODEL_PATH> -i <IMAGE_PATH> 

or

python openvino_inference.py -m <XML_MODEL_PATH> -i <IMAGE_PATH> -o <OUTPUT_DIR> -s <SCORE_THR> -d <DEVICE>