How to run an ONNX file in Python?

How to run an ONNX file in Python?

To run an ONNX (Open Neural Network Exchange) file in Python, you can use the onnx and onnxruntime libraries. Here's a step-by-step guide on how to do it:

Install Required Libraries

First, make sure you have installed the required libraries. You can install them using pip:

pip install onnx onnxruntime

Load the ONNX Model

Now that you have the libraries installed, let's load the ONNX model:

import onnx
Load the ONNX file
onnx_model = onnx.load("path/to/your/model.onnx")

Replace "path/to/your/model.onnx" with the actual path to your ONNX file.

Create an ONNX Runtime Session

Next, create an ONNX runtime session:

import onnxruntime as ort
Create a new session
session = ort.InferenceSession(onnx_model.SerializeToString())

The SerializeToString() method converts the ONNX model into a string that can be used to create a new session.

Run the Model

Now you're ready to run the model:

# Get the input name and output names from the model
input_name = onnx_model.graph.input[0].name
output_names = [onx.name for onx in onnx_model.graph.output]
Create a new input tensor with the same shape as the model's input
input_data = np.random.rand(1, 28, 28).astype(np.float32)
Run the model
result = session.run(output_names=output_names, input_feed={input_name: input_data})
Get the output tensors from the result
output_tensors = [tensor for tensor in result]

In this example, we're assuming that your ONNX model takes a single float32 tensor as input and produces a single float32 tensor as output. You may need to modify this code depending on the specifics of your model.

Postprocessing

Finally, you can perform any post-processing tasks you like:

# Convert the output tensors to NumPy arrays
output_arrays = [array.from_buffer(obj) for obj in output_tensors]
Do something with the output arrays...
print(output_arrays)

And that's it! You've successfully run an ONNX file in Python using the onnx and onnxruntime libraries.

Inference ONNX model Python

I'm happy to respond in English!

Inference with ONNX models using Python is a popular task, especially for deep learning applications. To get started, you'll need to install the necessary libraries:

Now, let's dive into the process of inferring with an ONNX model using Python:

import onnx
from onnx import helper, numpy_helper
Load your trained model
model_path = 'path/to/model'
model = tf.keras.models.load_model(model_path)
Convert the model to ONNX format
onnx_model = onnx.convert_tf(model)
with open('output.onnx', 'wb') as f:
f.write(onnx_model.SerializeToString())

import onnxruntime as ort
Load the ONNX model
ort_session = ort.InferenceSession('output.onnx')

import numpy as np
Create some sample input data (e.g., images)
input_data = np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]])

# Perform inference on the input data
output = ort_session.run(None, {'input': input_data})
print(output)

This will output the predicted values based on the input data.

Tips and Tricks:

That's it! With these steps, you should be able to perform inference with an ONNX model using Python. Happy inferencing!