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Fix Windows CI builds by updating test scripts to work with numpy 1.20. (#6518)
* Update onnxruntime_test_python.py to work with numpy 1.20. Some aliases are deprecated in favor of the built-in python types. See https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations np.array with bytes for entries and dtype of np.void no longer automatically pads. Change a test to adjust for that. * Fix another test script
This commit is contained in:
Scott McKay
GitHub
parent
e5cbcec17f
commit
5b69cbe80e
2 changed files with 21 additions and 18 deletions
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@ -332,7 +332,7 @@ class TestInferenceSession(unittest.TestCase):
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def testStringListAsInput(self):
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sess = onnxrt.InferenceSession(get_name("identity_string.onnx"))
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x = np.array(['this', 'is', 'identity', 'test'], dtype=np.str).reshape((2, 2))
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x = np.array(['this', 'is', 'identity', 'test'], dtype=str).reshape((2, 2))
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x_name = sess.get_inputs()[0].name
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res = sess.run([], {x_name: x.tolist()})
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np.testing.assert_equal(x, res[0])
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@ -360,8 +360,8 @@ class TestInferenceSession(unittest.TestCase):
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def testBooleanInputs(self):
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sess = onnxrt.InferenceSession(get_name("logicaland.onnx"))
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a = np.array([[True, True], [False, False]], dtype=np.bool)
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b = np.array([[True, False], [True, False]], dtype=np.bool)
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a = np.array([[True, True], [False, False]], dtype=bool)
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b = np.array([[True, False], [True, False]], dtype=bool)
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# input1:0 is first in the protobuf, and input:0 is second
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# and we maintain the original order.
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@ -386,13 +386,13 @@ class TestInferenceSession(unittest.TestCase):
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output_type = sess.get_outputs()[0].type
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self.assertEqual(output_type, 'tensor(bool)')
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output_expected = np.array([[True, False], [False, False]], dtype=np.bool)
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output_expected = np.array([[True, False], [False, False]], dtype=bool)
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res = sess.run([output_name], {a_name: a, b_name: b})
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np.testing.assert_equal(output_expected, res[0])
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def testStringInput1(self):
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sess = onnxrt.InferenceSession(get_name("identity_string.onnx"))
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x = np.array(['this', 'is', 'identity', 'test'], dtype=np.str).reshape((2, 2))
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x = np.array(['this', 'is', 'identity', 'test'], dtype=str).reshape((2, 2))
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x_name = sess.get_inputs()[0].name
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self.assertEqual(x_name, "input:0")
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@ -413,7 +413,7 @@ class TestInferenceSession(unittest.TestCase):
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def testStringInput2(self):
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sess = onnxrt.InferenceSession(get_name("identity_string.onnx"))
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x = np.array(['Olá', '你好', '여보세요', 'hello'], dtype=np.unicode).reshape((2, 2))
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x = np.array(['Olá', '你好', '여보세요', 'hello'], dtype=str).reshape((2, 2))
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x_name = sess.get_inputs()[0].name
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self.assertEqual(x_name, "input:0")
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@ -476,7 +476,9 @@ class TestInferenceSession(unittest.TestCase):
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def testInputVoid(self):
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sess = onnxrt.InferenceSession(get_name("identity_string.onnx"))
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x = np.array([b'this', b'is', b'identity', b'test'], np.void).reshape((2, 2))
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# numpy 1.20+ doesn't automatically pad the bytes based entries in the array when dtype is np.void,
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# so we use inputs where that is the case
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x = np.array([b'must', b'have', b'same', b'size'], dtype=np.void).reshape((2, 2))
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x_name = sess.get_inputs()[0].name
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self.assertEqual(x_name, "input:0")
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@ -494,14 +496,13 @@ class TestInferenceSession(unittest.TestCase):
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res = sess.run([output_name], {x_name: x})
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expr = np.array([['this\x00\x00\x00\x00', 'is\x00\x00\x00\x00\x00\x00'], ['identity', 'test\x00\x00\x00\x00']],
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dtype=object)
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expr = np.array([['must', 'have'], ['same', 'size']], dtype=object)
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np.testing.assert_equal(expr, res[0])
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def testRaiseWrongNumInputs(self):
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with self.assertRaises(ValueError) as context:
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sess = onnxrt.InferenceSession(get_name("logicaland.onnx"))
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a = np.array([[True, True], [False, False]], dtype=np.bool)
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a = np.array([[True, True], [False, False]], dtype=bool)
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res = sess.run([], {'input:0': a})
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self.assertTrue('Model requires 2 inputs' in str(context.exception))
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@ -559,8 +560,8 @@ class TestInferenceSession(unittest.TestCase):
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opt.graph_optimization_level = onnxrt.GraphOptimizationLevel.ORT_ENABLE_EXTENDED
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self.assertEqual(opt.graph_optimization_level, onnxrt.GraphOptimizationLevel.ORT_ENABLE_EXTENDED)
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sess = onnxrt.InferenceSession(get_name("logicaland.onnx"), sess_options=opt)
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a = np.array([[True, True], [False, False]], dtype=np.bool)
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b = np.array([[True, False], [True, False]], dtype=np.bool)
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a = np.array([[True, True], [False, False]], dtype=bool)
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b = np.array([[True, False], [True, False]], dtype=bool)
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res = sess.run([], {'input1:0': a, 'input:0': b})
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@ -125,7 +125,7 @@ class TestInferenceSession(unittest.TestCase):
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output_expected = np.array([3], ndmin=2, dtype=np.int64)
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np.testing.assert_allclose(output_expected, res[0], rtol=1e-05, atol=1e-08)
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x = np.array(['4'], ndmin=2, dtype=np.object)
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x = np.array(['4'], ndmin=2, dtype=object)
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res = sess.run([output_name], {input_name: x})
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output_expected = np.array([3], ndmin=2, dtype=np.int64)
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np.testing.assert_allclose(output_expected, res[0], rtol=1e-05, atol=1e-08)
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@ -134,11 +134,12 @@ class TestInferenceSession(unittest.TestCase):
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available_providers = onnxrt.get_available_providers()
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# The Windows GPU CI pipeline builds the wheel with both CUDA and DML enabled and ORT does not support cases
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# where one node is asigned to CUDA and one node to DML as it doesn't have the data transfer capabilities to deal with
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# potentially different device memory. Hence, use a session with only DML and CPU (excluding CUDA) for this test as it breaks
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# with both CUDA and DML registered.
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# where one node is assigned to CUDA and one node to DML, as it doesn't have the data transfer capabilities to
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# deal with potentially different device memory. Hence, use a session with only DML and CPU (excluding CUDA)
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# for this test as it breaks with both CUDA and DML registered.
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if ('CUDAExecutionProvider' in available_providers and 'DmlExecutionProvider' in available_providers):
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sess = onnxrt.InferenceSession(get_name("mlnet_encoder.onnx"), None, ['DmlExecutionProvider', 'CPUExecutionProvider'])
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sess = onnxrt.InferenceSession(get_name("mlnet_encoder.onnx"), None,
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['DmlExecutionProvider', 'CPUExecutionProvider'])
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else:
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sess = onnxrt.InferenceSession(get_name("mlnet_encoder.onnx"))
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@ -160,11 +161,12 @@ class TestInferenceSession(unittest.TestCase):
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# (to save space). It does not have this behaviour for void
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# but as a result, numpy does not know anymore the size
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# of each element, they all have the same size.
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c1 = np.array([b'A\0A\0\0', b"B\0B\0", b"C\0C\0"], np.void).reshape(1, 3)
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c1 = np.array([b'A\0A\0\0', b"B\0B\0\0", b"C\0C\0\0"], np.void).reshape(1, 3)
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res = sess.run(None, {'C0': c0, 'C1': c1})
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mat = res[1]
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total = mat.sum()
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self.assertEqual(total, 0)
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if __name__ == '__main__':
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unittest.main()
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