onnxruntime/docs/api/python/downloads/3e23fa9ebb26f4728ee8426ed7da0f63/plot_backend.py
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[Automated]: Update Python API docs (#13926)
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Co-authored-by: natke <natke@users.noreply.github.com>
2022-12-09 15:55:42 -08:00

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Python

# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
"""
.. _l-example-backend-api:
ONNX Runtime Backend for ONNX
=============================
*ONNX Runtime* extends the
`onnx backend API <https://github.com/onnx/onnx/blob/main/docs/ImplementingAnOnnxBackend.md>`_
to run predictions using this runtime.
Let's use the API to compute the prediction
of a simple logistic regression model.
"""
import numpy as np
from onnx import load
import onnxruntime.backend as backend
########################################
# The device depends on how the package was compiled,
# GPU or CPU.
from onnxruntime import datasets, get_device
from onnxruntime.capi.onnxruntime_pybind11_state import InvalidArgument
device = get_device()
name = datasets.get_example("logreg_iris.onnx")
model = load(name)
rep = backend.prepare(model, device)
x = np.array([[-1.0, -2.0]], dtype=np.float32)
try:
label, proba = rep.run(x)
print("label={}".format(label))
print("probabilities={}".format(proba))
except (RuntimeError, InvalidArgument) as e:
print(e)
########################################
# The backend can also directly load the model
# without using *onnx*.
rep = backend.prepare(name, device)
x = np.array([[-1.0, -2.0]], dtype=np.float32)
try:
label, proba = rep.run(x)
print("label={}".format(label))
print("probabilities={}".format(proba))
except (RuntimeError, InvalidArgument) as e:
print(e)
#######################################
# The backend API is implemented by other frameworks
# and makes it easier to switch between multiple runtimes
# with the same API.