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867804bea1
In addition to ORTModule auto documentation during packaging, this PR also update golden numbers to fix CI
57 lines
1.6 KiB
Python
57 lines
1.6 KiB
Python
# Copyright (c) Microsoft Corporation. All rights reserved.
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# Licensed under the MIT License.
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"""
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.. _l-example-backend-api:
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ONNX Runtime Backend for ONNX
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=============================
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*ONNX Runtime* extends the
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`onnx backend API <https://github.com/onnx/onnx/blob/master/docs/ImplementingAnOnnxBackend.md>`_
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to run predictions using this runtime.
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Let's use the API to compute the prediction
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of a simple logistic regression model.
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"""
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import numpy as np
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from onnxruntime import datasets
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from onnxruntime.capi.onnxruntime_pybind11_state import InvalidArgument
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import onnxruntime.backend as backend
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from onnx import load
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########################################
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# The device depends on how the package was compiled,
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# GPU or CPU.
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from onnxruntime import get_device
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device = get_device()
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name = datasets.get_example("logreg_iris.onnx")
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model = load(name)
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rep = backend.prepare(model, device)
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x = np.array([[-1.0, -2.0]], dtype=np.float32)
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try:
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label, proba = rep.run(x)
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print("label={}".format(label))
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print("probabilities={}".format(proba))
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except (RuntimeError, InvalidArgument) as e:
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print(e)
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########################################
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# The backend can also directly load the model
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# without using *onnx*.
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rep = backend.prepare(name, device)
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x = np.array([[-1.0, -2.0]], dtype=np.float32)
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try:
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label, proba = rep.run(x)
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print("label={}".format(label))
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print("probabilities={}".format(proba))
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except (RuntimeError, InvalidArgument) as e:
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print(e)
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#######################################
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# The backend API is implemented by other frameworks
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# and makes it easier to switch between multiple runtimes
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# with the same API.
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