how to use onnx model in python

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How to Use ONNX Model in Python

Overview

ONNX (Open Neural Network Exchange) is an open-source format for representing deep learning models. It allows models to be trained in one framework and then transferred to another framework for inference. In this tutorial, we will learn how to use an ONNX model in Python.

Steps

  1. Install Required Libraries: To use ONNX models in Python, we need to install the onnx library using the following command:
    shell
    pip install onnx

  2. Load the ONNX Model: We can load an ONNX model using the onnx.load function. Here is an example:
    “`python
    import onnx

model_path = “path_to_onnx_model.onnx”
model = onnx.load(model_path)
“`

  1. Prepare Input Data: Depending on the model, we may need to preprocess the input data. We may need to resize the input images or normalize the values, etc.

  2. Create an ONNX Runtime Session: ONNX Runtime is a high-performance inference engine for ONNX models. We need to create a session using the onnxruntime.InferenceSession class. Here is an example of creating a session:
    “`python
    import onnxruntime

session = onnxruntime.InferenceSession(model_path)
“`

  1. Run the Inference: To run inference on the model, we need to provide the input data in the appropriate format and run the session. Here is an example:
    “`python
    import numpy as np

Prepare input data

input_data = np.array(…) # Input data with appropriate shape and dtype

Run inference

output = session.run(None, {‘input’: input_data})
“`

  1. Process the Output: The output returned by the session.run method will be a list of numpy arrays. You can process these output arrays according to your requirement.

  2. Cleanup: After running the inference, you should release the resources by closing the session.
    python
    session.close()

That’s it! You have successfully used an ONNX model in Python for inference. You can now integrate this code into your application or pipeline for further use.

Note: Make sure you have the appropriate versions of ONNX, ONNX Runtime, and other required libraries installed for smooth execution.

Happy coding!