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.
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.
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.
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.
To resolve the Invalid model input error, follow these steps:
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 float32
input_data = np.array(your_input_data, dtype=np.float32)
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 shape
input_data = input_data.reshape(expected_shape)
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.
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.
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