cv2.dnn.readNetFromONNX读取yolov8的onnx报错解决过程

Loading /home/inference/Amplitudemode_AI/all_model_and_pred/xxx/segment/train3/weights/last.onnx for ONNX OpenCV DNN inference...
[ERROR:0@3.062] global onnx_importer.cpp:1051 handleNode DNN/ONNX: ERROR during processing node with 2 inputs and 2 outputs: [Split]:(onnx_node!/model.22/Split) from domain='ai.onnx'
Traceback (most recent call last):
  File "/home/inference/Amplitudemode_AI/all_model_and_pred/AI_Ribfrac_ths/onnx_test_seg/infer-seg.py", line 167, in <module>
    model = AutoBackend(weights="/home/inference/Amplitudemode_AI/all_model_and_pred/xxx/segment/train3/weights/last.onnx", dnn=True)
  File "/home/inference/miniconda3/envs/yolov8/lib/python3.10/site-packages/ultralytics/nn/autobackend.py", line 124, in __init__
    net = cv2.dnn.readNetFromONNX(w)
cv2.error: OpenCV(4.7.0) /io/opencv/modules/dnn/src/onnx/onnx_importer.cpp:1073: error: (-2:Unspecified error) in function 'handleNode'
> Node [Split@ai.onnx]:(onnx_node!/model.22/Split) parse error: OpenCV(4.7.0) /io/opencv/modules/dnn/src/layers/slice_layer.cpp:274: error: (-215:Assertion failed) splits > 0 && inpShape[axis_rw] % splits == 0 in function 'getMemoryShapes'
> 

上述是尝试用opencv读取模型时的报错信息。

接着去github上的yolov8官方项目的问题区搜索，经过尝试最终搜索关键字如下：

ONNX DNN  splits > 0 && inpShape[axis_rw] % splits == 0 in function 'getMemoryShapes 

找到对应问题如下：Exported ONNX cannot be opened in OpenCV · Issue #226 · ultralytics/ultralytics · GitHubSearch before asking I have searched the YOLOv8 issues and found no similar bug report. YOLOv8 Component Export Bug I created a custom object detector and export to ONNX. Attempting to load it in OpenCV results in the following error: `[...https://github.com/ultralytics/ultralytics/issues/226

 找到解决方法如下转换时要设置（关键是添加opset=11）

yolo mode=export model=runs/detect/train/weights/best.pt imgsz=[640,640] format=onnx opset=11

实际转化代码如下：

from ultralytics import YOLO
 
model = YOLO(
    "/home/inference/Amplitudemode_AI/all_model_and_pred/xxx/segment/train3/weights/last.pt")
success = model.export(format="onnx", opset=11, simplify=True)  # export the model to onnx format
assert success

用转换好的onnx调用官方api推理如下：

from ultralytics import YOLO
model = YOLO("/home/inference/Amplitudemode_AI/all_model_and_pred/xxx/segment/train3/weights/last.onnx")  # 模型加载
results = model.predict(
    source='/home/inference/tt',imgsz=640, save=True, boxes=False)  # save plotted images 保存绘制图片

正常推理成功。