mirror of
https://github.com/saymrwulf/onnxruntime.git
synced 2026-07-08 17:17:15 +00:00
77 lines
3.2 KiB
Python
77 lines
3.2 KiB
Python
# Copyright (c) Microsoft Corporation. All rights reserved.
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# Licensed under the MIT License.
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# -*- coding: UTF-8 -*-
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import unittest
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import os
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import sys
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import numpy as np
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import onnxruntime as onnxrt
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from onnxruntime import datasets
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import onnxruntime.backend as backend
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from onnxruntime.backend.backend import OnnxRuntimeBackend as ort_backend
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from onnx import load
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class TestBackend(unittest.TestCase):
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def get_name(self, name):
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if os.path.exists(name):
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return name
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rel = os.path.join("testdata", name)
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if os.path.exists(rel):
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return rel
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this = os.path.dirname(__file__)
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data = os.path.join(this, "..", "testdata")
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res = os.path.join(data, name)
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if os.path.exists(res):
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return res
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raise FileNotFoundError("Unable to find '{0}' or '{1}' or '{2}'".format(name, rel, res))
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def testRunModel(self):
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name = self.get_name("mul_1.onnx")
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rep = backend.prepare(name)
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x = np.array([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], dtype=np.float32)
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res = rep.run(x)
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output_expected = np.array([[1.0, 4.0], [9.0, 16.0], [25.0, 36.0]], dtype=np.float32)
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np.testing.assert_allclose(output_expected, res[0], rtol=1e-05, atol=1e-08)
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def testRunModelNonTensor(self):
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name = self.get_name("pipeline_vectorize.onnx")
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rep = backend.prepare(name)
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x = {0: 25.0, 1: 5.13, 2: 0.0, 3: 0.453, 4: 5.966}
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res = rep.run(x)
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output_expected = np.array([[49.752754]], dtype=np.float32)
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np.testing.assert_allclose(output_expected, res[0], rtol=1e-05, atol=1e-08)
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def testRunModelProto(self):
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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)
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x = np.array([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], dtype=np.float32)
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res = rep.run(x)
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output_expected = np.array([0, 0, 0], dtype=np.float32)
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np.testing.assert_allclose(output_expected, res[0], rtol=1e-05, atol=1e-08)
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output_expected = [{0: 0.950599730014801, 1: 0.027834169566631317, 2: 0.02156602405011654},
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{0: 0.9974970817565918, 1: 5.6299926654901356e-05, 2: 0.0024466661270707846},
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{0: 0.9997311234474182, 1: 1.1918064757310276e-07, 2: 0.00026869276189245284}]
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self.assertEqual(output_expected, res[1])
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def testRunModelProtoApi(self):
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name = datasets.get_example("logreg_iris.onnx")
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model = load(name)
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inputs = np.array([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], dtype=np.float32)
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outputs = ort_backend.run_model(model, inputs)
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output_expected = np.array([0, 0, 0], dtype=np.float32)
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np.testing.assert_allclose(output_expected, outputs[0], rtol=1e-05, atol=1e-08)
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output_expected = [{0: 0.950599730014801, 1: 0.027834169566631317, 2: 0.02156602405011654},
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{0: 0.9974970817565918, 1: 5.6299926654901356e-05, 2: 0.0024466661270707846},
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{0: 0.9997311234474182, 1: 1.1918064757310276e-07, 2: 0.00026869276189245284}]
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self.assertEqual(output_expected, outputs[1])
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if __name__ == '__main__':
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unittest.main(module=__name__, buffer=True)
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