ONNX Runtime is an open-source project designed to accelerate machine learning model inference. It supports models in the ONNX (Open Neural Network Exchange) format, which allows developers to use models across different frameworks. ONNX Runtime is optimized for performance and is widely used in production environments to run models efficiently on various hardware platforms.
When working with ONNX Runtime, you might encounter the following error message:
ONNXRuntimeError: [ONNXRuntimeError] : 50 : FAIL : Model node attribute type error
This error indicates that there is a type mismatch in one of the node attributes within your ONNX model.
The error occurs when a node in the ONNX model has an attribute with a type that does not match the expected type defined in the model's schema. This can happen due to incorrect model conversion or manual editing of the model file.
In ONNX models, nodes represent operations, and each node can have attributes that define specific parameters for the operation. These attributes must adhere to the expected types, such as integers, floats, or strings.
First, inspect your ONNX model to identify the node causing the issue. You can use tools like Netron to visualize the model and examine the attributes of each node.
Once you've identified the problematic node, check the attribute types against the expected types defined in the ONNX Operator documentation. Ensure that each attribute matches the required type.
If you find a mismatch, modify the model to correct the attribute type. This might involve editing the model file directly or re-exporting the model from the original framework with the correct settings.
After making the necessary corrections, test the model again using ONNX Runtime to ensure the error is resolved. You can use the following Python code to load and run the model:
import onnxruntime as ort
# Load the model
session = ort.InferenceSession('path/to/your/model.onnx')
# Run inference
outputs = session.run(None, {'input_name': input_data})
By following these steps, you should be able to resolve the "Model node attribute type error" in ONNX Runtime. Ensuring that your model's node attributes have the correct types is crucial for successful inference. For more detailed guidance, refer to the ONNX Runtime documentation.
(Perfect for DevOps & SREs)
(Perfect for DevOps & SREs)
