yolov8导onnx

from ultralytics import YOLO
import torch

# model = YOLO(r"D:\ultralytics\yolov8n.pt")  # load a custom trained
model = torch.load(r"D:\ultralytics\yolov8n.pt")
data = torch.randn(1,3,640,640).half().cuda()

output_names = ["output0"]
dynamic = {"images": {0: "batch", 2: "height", 3: "width"}}
dynamic["output0"] = {0: "batch", 2: "anchors"}
model = torch.load('yolov8n.pt')['model'].cuda()
dummy = torch.zeros(1, 3, 640, 640).half().cuda()
torch.onnx.export(
        model,
        dummy,
        "test.onnx",
        verbose=False,
        opset_version=11,
        do_constant_folding=True,  # WARNING: DNN inference with torch>=1.12 may require do_constant_folding=False
        input_names=["images"],
        output_names=output_names,
        dynamic_axes=dynamic or None,
    )