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867804bea1
In addition to ORTModule auto documentation during packaging, this PR also update golden numbers to fix CI
53 lines
1.6 KiB
Python
53 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-simple-usage:
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Load and predict with ONNX Runtime and a very simple model
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==========================================================
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This example demonstrates how to load a model and compute
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the output for an input vector. It also shows how to
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retrieve the definition of its inputs and outputs.
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"""
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import onnxruntime as rt
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import numpy
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from onnxruntime.datasets import get_example
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#########################
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# Let's load a very simple model.
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# The model is available on github `onnx...test_sigmoid <https://github.com/onnx/onnx/tree/master/onnx/backend/test/data/node/test_sigmoid>`_.
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example1 = get_example("sigmoid.onnx")
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sess = rt.InferenceSession(example1)
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#########################
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# Let's see the input name and shape.
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input_name = sess.get_inputs()[0].name
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print("input name", input_name)
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input_shape = sess.get_inputs()[0].shape
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print("input shape", input_shape)
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input_type = sess.get_inputs()[0].type
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print("input type", input_type)
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#########################
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# Let's see the output name and shape.
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output_name = sess.get_outputs()[0].name
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print("output name", output_name)
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output_shape = sess.get_outputs()[0].shape
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print("output shape", output_shape)
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output_type = sess.get_outputs()[0].type
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print("output type", output_type)
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#########################
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# Let's compute its outputs (or predictions if it is a machine learned model).
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import numpy.random
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x = numpy.random.random((3,4,5))
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x = x.astype(numpy.float32)
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res = sess.run([output_name], {input_name: x})
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print(res)
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