In this repository, I introduce how to convert from trained weight to onnx model and use it for my own custom app without depending on any module of Yolo.
To start, training in YOLOv7 is the first step. Follow: https://.com/WongKinYiu/yolov7
ICDAR 2019: https://.com/cndplab-founder/ICDAR2019_cTDaR
- Python 3
- Opencv-python
- Pillow
- Flask
- ONNX
- ONNX Runtime
As mentioned above, the first step is training in YOLOv7.
From YOLOv7 directory, run export.py as below:
python export.py --weights best.pt --grid --end2end --simplify --topk-all 100 --iou-thres 0.65 --conf-thres 0.35 --img-size 640 640 --max-wh 640The converted weight (eg. 'best.onnx') then is saved in the directory of Flask app: 'app/controller/weights'.
# Load and Check
cuda = torch.cuda.is_available()
providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] if cuda else ['CPUExecutionProvider']
onnx_model = onnx.load(weights)
onnx.checker.check_model(onnx_model)weights = "best.onnx"
# Start ONNX Runtime session
ort_session = ort.InferenceSession(weights, providers=providers)
outname = [i.name for i in ort_session.get_outputs()]
inname = [i.name for i in ort_session.get_inputs()]
inp = {inname[0]: infer_image}
# ONNX inference
outputs = ort_session.run(outname, inp)[0]The inference phase and showing results are shown in code: detect.py
- A simple guild can be found here: YOLOv7ONNXandTRT.ipynb