-2 opencv module_error ai_generated partial

cv2.error: OpenCV(4.8.0) /tmp/opencv-4.8.0/modules/dnn/src/onnx/onnx_importer.cpp:203: error: (-2:Unspecified error) Failed to parse ONNX model: No Op registered for 'NonMaxSuppression' with domain_version of 12 in function 'readNetFromONNX'

ID: opencv/dnn-readnet-from-onnx-parse-error

Also available as: JSON · Markdown · 中文
80%Fix Rate
88%Confidence
1Evidence
2023-06-20First Seen

Version Compatibility

VersionStatusIntroducedDeprecatedNotes
4.7.0 active — — —
4.8.0 active — — —
4.9.0 active — — —
4.10.0 active — — —

Root Cause

ONNX model uses an operator (e.g., NonMaxSuppression) that is not supported by the OpenCV DNN module for the given opset version.

generic

中文

ONNX 模型使用了 OpenCV DNN 模块在给定操作集版本中不支持的操作符（例如 NonMaxSuppression）。

Official Documentation

https://docs.opencv.org/4.x/d6/d0f/group__dnn.html#ga29f34df9376379a603acd8df581ac8d7

Workarounds

  1. 75% success Export the ONNX model with a lower opset version (e.g., 11) that includes all operators supported by OpenCV: `torch.onnx.export(model, dummy_input, 'model.onnx', opset_version=11)` 
    Export the ONNX model with a lower opset version (e.g., 11) that includes all operators supported by OpenCV: `torch.onnx.export(model, dummy_input, 'model.onnx', opset_version=11)`
  2. 70% success Use OpenCV's DNN with a different backend like Intel OpenVINO: `net.setPreferableBackend(cv2.dnn.DNN_BACKEND_INFERENCE_ENGINE)` 
    Use OpenCV's DNN with a different backend like Intel OpenVINO: `net.setPreferableBackend(cv2.dnn.DNN_BACKEND_INFERENCE_ENGINE)`
  3. 65% success Convert the ONNX model to a different format (e.g., TensorFlow SavedModel) and load with OpenCV's TensorFlow importer. 
    Convert the ONNX model to a different format (e.g., TensorFlow SavedModel) and load with OpenCV's TensorFlow importer.

中文步骤

  1. Export the ONNX model with a lower opset version (e.g., 11) that includes all operators supported by OpenCV: `torch.onnx.export(model, dummy_input, 'model.onnx', opset_version=11)`
  2. Use OpenCV's DNN with a different backend like Intel OpenVINO: `net.setPreferableBackend(cv2.dnn.DNN_BACKEND_INFERENCE_ENGINE)`
  3. Convert the ONNX model to a different format (e.g., TensorFlow SavedModel) and load with OpenCV's TensorFlow importer.

Dead Ends

Common approaches that don't work:

  1. 60% fail

    Downgrading OpenCV may remove support for newer ONNX opsets but won't add missing operators.

  2. 80% fail

    ONNX Runtime is a separate inference engine; installing it doesn't modify OpenCV's DNN module.

  3. 90% fail

    Changing the model file name or path doesn't affect operator parsing.