ONNX Runtime ONNXRuntimeError: [ONNXRuntimeError] : 46 : FAIL : Model node input error

What is ONNX Runtime ONNXRuntimeError: [ONNXRuntimeError] : 46 : FAIL : Model node input error

Understanding ONNX Runtime

ONNX Runtime is a high-performance inference engine for machine learning models in the Open Neural Network Exchange (ONNX) format. It is designed to accelerate the deployment of machine learning models across various platforms and devices. By supporting a wide range of hardware, ONNX Runtime allows developers to optimize their models for performance and efficiency.

Identifying the Symptom

When working with ONNX Runtime, you might encounter the following error message: ONNXRuntimeError: [ONNXRuntimeError] : 46 : FAIL : Model node input error. This error indicates that there is an issue with the input provided to a node within your ONNX model.

What You Observe

During model inference or testing, the process fails, and the above error message is displayed. This typically halts the execution and prevents further processing of the model.

Explaining the Issue

The error code 46 in ONNX Runtime refers to a failure related to the inputs of a model node. Each node in an ONNX model represents a computational operation, and it requires specific inputs to function correctly. If these inputs are missing, incorrectly formatted, or incompatible, the runtime will throw this error.

Common Causes

Mismatch in input dimensions or types. Incorrect input names or missing inputs. Incompatible data types or shapes.

Steps to Fix the Issue

To resolve the Model node input error, follow these steps:

Step 1: Verify Input Specifications

Check the model's input specifications to ensure that the inputs you are providing match the expected dimensions and data types. You can inspect the model's input requirements using tools like ONNX's model checker or by examining the model's metadata.

Step 2: Inspect the Model Graph

Use visualization tools such as Netron to view the model graph. This will help you identify the nodes and their expected inputs, making it easier to spot discrepancies.

Step 3: Debugging with ONNX Runtime

Enable verbose logging in ONNX Runtime to get more detailed error messages. This can be done by setting the logging level to VERBOSE in your runtime configuration. This additional information can provide insights into which node is causing the issue.

Step 4: Correct the Inputs

Once you've identified the problematic node and its input requirements, adjust your input data accordingly. Ensure that the data types, shapes, and names match the model's expectations.

Conclusion

By carefully inspecting the model's input requirements and using the right tools to visualize and debug the model, you can effectively resolve the Model node input error in ONNX Runtime. For further reading, consider exploring the ONNX Runtime documentation for more detailed guidance on model deployment and troubleshooting.