ONNX Runtime ONNXRuntimeError: [ONNXRuntimeError] : 5 : INVALID_PROTOBUF : Protobuf parsing failed

What is ONNX Runtime ONNXRuntimeError: [ONNXRuntimeError] : 5 : INVALID_PROTOBUF : Protobuf parsing failed

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, providing a flexible and efficient solution for model inference.

Identifying the Symptom

While using ONNX Runtime, you might encounter the following error message: ONNXRuntimeError: [ONNXRuntimeError] : 5 : INVALID_PROTOBUF : Protobuf parsing failed. This error indicates that there is an issue with parsing the ONNX model file.

Explaining the Issue

What is Protobuf?

Protocol Buffers (Protobuf) is a method developed by Google for serializing structured data. It is used extensively in ONNX models to define the model structure and parameters.

Why Does This Error Occur?

The error occurs when the ONNX model file is not a valid Protobuf file. This can happen if the model file is corrupted, improperly exported, or not in the correct format.

Steps to Fix the Issue

Step 1: Verify the Model File

Ensure that the ONNX model file is not corrupted. You can do this by checking the file size and comparing it with the expected size. If the file is significantly smaller than expected, it might be incomplete.

Step 2: Re-export the Model

If the file is corrupted or improperly exported, re-export the model from the original framework (e.g., PyTorch, TensorFlow) using the appropriate export function. For example, in PyTorch, use the torch.onnx.export() function to export the model:

import torch# Assuming 'model' is your PyTorch model# and 'dummy_input' is a sample input tensortorch.onnx.export(model, dummy_input, "model.onnx")

Ensure that the export process completes without errors.

Step 3: Validate the ONNX Model

Use the ONNX library to validate the model file. You can use the following command to check the model:

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

If the model is valid, the checker will not raise any errors.

Additional Resources

For more information on exporting models to ONNX, refer to the PyTorch ONNX documentation or the TensorFlow SavedModel guide. These resources provide detailed instructions on exporting models in the correct format.