ONNX Runtime ONNXRuntimeError: [ONNXRuntimeError] : 24 : FAIL : Model input/output mismatch

What is ONNX Runtime ONNXRuntimeError: [ONNXRuntimeError] : 24 : FAIL : Model input/output mismatch

Understanding ONNX Runtime

ONNX Runtime is an open-source library designed to accelerate machine learning model inference. It supports models in the ONNX (Open Neural Network Exchange) format, allowing developers to run models across various platforms and devices efficiently. ONNX Runtime is widely used for its performance optimization capabilities and cross-platform support.

Identifying the Symptom

When working with ONNX Runtime, you might encounter the error: ONNXRuntimeError: [ONNXRuntimeError] : 24 : FAIL : Model input/output mismatch. This error indicates a discrepancy between the expected and provided inputs or outputs when loading or running a model.

What You Observe

Upon attempting to run an ONNX model, the process fails, and the error message mentioned above is displayed. This halts the execution and prevents the model from running as expected.

Explaining the Issue

The error occurs when the number of inputs or outputs provided to the ONNX model does not match the model's definition. Each ONNX model has a specific structure that defines the number and type of inputs and outputs it expects. A mismatch can happen due to various reasons, such as incorrect data preparation or model misconfiguration.

Common Causes

Incorrect number of inputs or outputs provided. Mismatch in data types or shapes of inputs/outputs. Changes in the model architecture without updating the input/output handling code.

Steps to Fix the Issue

To resolve the input/output mismatch error, follow these steps:

1. Verify Model Specifications

Check the model's input and output specifications. You can use tools like ONNX's official tools to inspect the model structure. Use the following command to print the model's input and output details:

import onnxmodel = onnx.load('your_model.onnx')print(onnx.helper.printable_graph(model.graph))

2. Adjust Input/Output Data

Ensure that the data you provide matches the model's expected input and output specifications. Adjust the data shapes and types accordingly. For example, if the model expects a tensor of shape (1, 3, 224, 224), ensure your input data matches this shape.

3. Update Code for Model Changes

If the model architecture has been updated, ensure that your code reflects these changes. Update the input/output handling logic to align with the new model structure.

4. Test the Solution

After making the necessary adjustments, test the model again to ensure the error is resolved. If the issue persists, revisit the model specifications and data preparation steps.

Additional Resources

For more information on ONNX Runtime and troubleshooting, consider exploring the following resources:

ONNX Runtime Official Documentation ONNX Runtime GitHub Repository ONNX Official Website