ONNX Runtime ONNXRuntimeError: [ONNXRuntimeError] : 10 : INVALID_ARGUMENT : Output shape mismatch

What is ONNX Runtime ONNXRuntimeError: [ONNXRuntimeError] : 10 : INVALID_ARGUMENT : Output shape mismatch

Understanding ONNX Runtime

ONNX Runtime is a high-performance inference engine for deploying machine learning models. It supports models in the Open Neural Network Exchange (ONNX) format, which is an open standard for representing machine learning models. ONNX Runtime is designed to be fast and flexible, enabling developers to run models across different platforms and hardware.

Identifying the Symptom

When working with ONNX Runtime, you might encounter the following error message: ONNXRuntimeError: [ONNXRuntimeError] : 10 : INVALID_ARGUMENT : Output shape mismatch. This error indicates that there is a discrepancy between the expected output shape and the actual output shape produced by a node in the model.

What You Observe

During model inference, the execution is halted, and the above error message is displayed. This typically occurs when the model's output does not conform to the expected dimensions, leading to a mismatch.

Exploring the Issue

The INVALID_ARGUMENT error with the message Output shape mismatch suggests that the model's output shape does not match the shape defined in the model's schema or expected by the consuming application. This can happen due to incorrect model definition, changes in input data dimensions, or errors in the model conversion process.

Common Causes

Incorrect model conversion from another framework to ONNX. Changes in input data dimensions that affect the output shape. Errors in the model's architecture or layer configuration.

Steps to Resolve the Issue

To resolve the output shape mismatch error, follow these steps:

1. Verify Model Output Shape

Check the expected output shape of your model. You can do this by inspecting the model's architecture or using tools like ONNX's model checker to validate the model structure.

import onnx# Load the ONNX modelmodel = onnx.load('your_model.onnx')# Check the modelonnx.checker.check_model(model)

2. Inspect Input Data

Ensure that the input data dimensions are consistent with what the model expects. Mismatched input dimensions can lead to incorrect output shapes.

# Example: Check input shapeinput_data = np.random.randn(1, 3, 224, 224).astype(np.float32)assert input_data.shape == (1, 3, 224, 224), "Input shape mismatch!"

3. Adjust Model Definition

If the model's architecture is incorrect, modify the layers or operations to produce the correct output shape. This might involve changing layer parameters or adding/removing layers.

Additional Resources

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

ONNX Runtime Documentation ONNX Tutorials ONNX GitHub Issues

By following these steps and utilizing the resources provided, you should be able to resolve the output shape mismatch error and ensure your model runs smoothly with ONNX Runtime.