ONNX Runtime ONNXRuntimeError: [ONNXRuntimeError] : 25 : FAIL : Invalid tensor data

What is ONNX Runtime ONNXRuntimeError: [ONNXRuntimeError] : 25 : FAIL : Invalid tensor data

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 providing a consistent API and runtime environment, ONNX Runtime enables developers to optimize and run models efficiently, ensuring compatibility and performance across different hardware configurations.

Identifying the Symptom

When working with ONNX Runtime, you might encounter the following error message: ONNXRuntimeError: [ONNXRuntimeError] : 25 : FAIL : Invalid tensor data. This error indicates that there is an issue with the tensor data being processed by the runtime, which prevents the model from executing as expected.

Exploring the Issue

What Does the Error Mean?

The error code 25 signifies a failure related to the tensor data. This typically occurs when the data provided to the model is either incorrectly formatted or corrupted. Tensors are multi-dimensional arrays that represent the data inputs and outputs for machine learning models, and any discrepancies in their structure or content can lead to runtime errors.

Common Causes

Incorrect data types or shapes in the tensor. Corrupted data files or incomplete data loading. Mismatch between the model's expected input and the provided data.

Steps to Fix the Issue

Verify Tensor Data Integrity

Start by ensuring that the tensor data is correctly formatted and not corrupted. You can use data validation tools or scripts to check the integrity of your data files. For example, if you are using Python, you can utilize libraries like NumPy to inspect the shape and type of your tensors:

import numpy as npdata = np.load('your_tensor_data.npy')print(data.shape)print(data.dtype)

Ensure that the shape and data type match the model's requirements.

Check Model Input Specifications

Review the model's input specifications to confirm that the tensor data aligns with what the model expects. You can inspect the model's input nodes using ONNX tools:

import onnxmodel = onnx.load('your_model.onnx')for input in model.graph.input: print(input.name, input.type)

Ensure that your tensor data matches these specifications in terms of shape and data type.

Reprocess or Reload Data

If the data appears corrupted, consider reprocessing or reloading it from the original source. Ensure that the data loading pipeline is robust and handles exceptions gracefully. For large datasets, consider using data streaming techniques to avoid memory issues.

Additional Resources

For more information on handling tensor data and troubleshooting ONNX Runtime errors, consider visiting the following resources:

ONNX Runtime Documentation ONNX Runtime GitHub Repository NumPy Documentation

By following these steps and utilizing the resources provided, you can effectively diagnose and resolve the 'Invalid tensor data' error in ONNX Runtime, ensuring smooth and efficient model execution.