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 support for a wide range of hardware accelerators. ONNX Runtime is widely used for its efficiency and flexibility, allowing developers to integrate machine learning models into their applications seamlessly.
When working with ONNX Runtime, you might encounter the following error message: ONNXRuntimeError: [ONNXRuntimeError] : 9 : INVALID_ARGUMENT : Node input type mismatch. This error indicates that there is a mismatch between the data type of the input provided to a node and the expected data type defined in the model.
The error code INVALID_ARGUMENT suggests that the input provided to a node in the ONNX model does not match the expected type. This can occur if the input data is not pre-processed correctly or if there is a discrepancy between the model's input specifications and the data being fed into it. Such mismatches can lead to runtime errors, preventing the model from executing as intended.
To resolve the Node input type mismatch error, follow these steps:
Check the ONNX model's input specifications to determine the expected data types. You can use tools like ONNX's official Python API to inspect the model's input signature:
import onnx
# Load the ONNX model
model = onnx.load('model.onnx')
# Print the model's input specifications
for input_tensor in model.graph.input:
print(input_tensor.name, input_tensor.type)
Ensure that the input data is pre-processed to match the expected data types. For example, if the model expects a float32 tensor, make sure your input data is converted accordingly:
import numpy as np
# Example of converting input data to float32
input_data = np.array([1, 2, 3], dtype=np.float32)
Review and update your data pipeline to ensure that the data format remains consistent from pre-processing to inference. This might involve adjusting data loaders or conversion functions to maintain the correct data types.
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