ONNX Runtime ONNXRuntimeError: [ONNXRuntimeError] : 37 : FAIL : Invalid model input

What is ONNX Runtime ONNXRuntimeError: [ONNXRuntimeError] : 37 : FAIL : Invalid model input

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 and providing optimized performance, ONNX Runtime enables developers to efficiently run their models in production environments.

Identifying the Symptom

When using ONNX Runtime, you may encounter the error message: ONNXRuntimeError: [ONNXRuntimeError] : 37 : FAIL : Invalid model input. This error indicates that there is an issue with the input data being fed into the model during inference.

What You Observe

Upon attempting to run inference using ONNX Runtime, the process fails, and the error message is displayed. This prevents the model from processing the input data and producing the desired output.

Explaining the Issue

The error code 37 signifies a failure due to invalid model input. This typically occurs when the input data does not match the expected format or dimensions required by the model. It is crucial to ensure that the input data adheres to the model's specifications, including data type, shape, and any preprocessing steps.

Common Causes

Incorrect data type: The input data type does not match the model's expected input type. Shape mismatch: The dimensions of the input data do not align with the model's input layer requirements. Missing preprocessing: Necessary preprocessing steps, such as normalization or resizing, have not been applied to the input data.

Steps to Fix the Issue

To resolve the Invalid model input error, follow these steps:

1. Verify Input Data Type

Ensure that the input data type matches the model's expected input type. For example, if the model expects a float32 tensor, make sure the input data is converted to this type. You can use libraries like NumPy to convert data types:

import numpy as np# Convert input data to float32input_data = np.array(your_input_data, dtype=np.float32)

2. Check Input Shape

Confirm that the input data shape matches the model's input layer requirements. You can inspect the model's input shape using ONNX tools or by examining the model's documentation. Adjust the input data shape accordingly:

# Reshape input data to match model's input shapeinput_data = input_data.reshape(expected_shape)

3. Apply Necessary Preprocessing

Ensure that any required preprocessing steps, such as normalization or resizing, are applied to the input data. This may involve scaling pixel values for image data or tokenizing text data for NLP models.

4. Validate with ONNX Model

Use tools like ONNX to validate the model and input data. This can help identify discrepancies between the model's expectations and the provided input.

Additional Resources

For further assistance, consider exploring the following resources:

ONNX Runtime Official Documentation ONNX Runtime GitHub Repository ONNX Official Website