ONNX Runtime is an open-source inference engine designed to accelerate machine learning models across a variety of hardware platforms. It supports models in the ONNX (Open Neural Network Exchange) format, which allows for interoperability between different machine learning frameworks. The primary goal of ONNX Runtime is to provide high-performance and efficient execution of machine learning models.
When using ONNX Runtime, you might encounter the following error message: ONNXRuntimeError: [ONNXRuntimeError] : 42 : FAIL : Model shape inference failed. This error indicates that there is a problem with the model's shape inference process, which is crucial for ensuring that the model's input and output dimensions are correctly configured.
Shape inference is a process used by ONNX Runtime to determine the dimensions of tensors as they flow through the model. This is essential for validating that the model's operations are compatible with the input and output shapes.
Shape inference can fail due to several reasons, including:
Begin by checking the model's input and output shapes. Ensure that they are correctly defined and match the expected dimensions. You can use tools like ONNX's official tools to inspect the model's graph and verify shape information.
ONNX provides a shape inference tool that can be used to automatically infer shapes within a model. Run the following command to apply shape inference:
python -m onnxruntime.tools.symbolic_shape_infer --input model.onnx --output model_inferred.onnx
This command will generate a new ONNX model file with inferred shapes.
Ensure that all operations in your model are supported by ONNX Runtime. Some operations may not support dynamic shapes or may require specific configurations. Refer to the ONNX Runtime operator documentation for more details.
Ensure you are using the latest version of ONNX Runtime, as updates often include bug fixes and improvements. You can update ONNX Runtime using pip:
pip install --upgrade onnxruntime
By following these steps, you should be able to resolve the shape inference failure in ONNX Runtime. Properly defined shapes and compatible operations are crucial for successful model execution. For further assistance, consider visiting the ONNX Runtime GitHub issues page to seek help from the community.
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