c0d71900fd
* added unit test * added python test * using pword approach * Added passing path to onnx reader * support for wstring * Added more tests * Apply suggestions from code review Co-authored-by: Michał Karzyński <4430709+postrational@users.noreply.github.com> * fix build for Windows * styles applied * Fixed Windows tests * styles applied * fixed styles in tests * review remarks * cmake order * Used target_compile_definitions instead of add_definitions * Move ONNX_TEST_MODELS to other scope Co-authored-by: Michał Karzyński <4430709+postrational@users.noreply.github.com>
42 lines
1.5 KiB
Python
42 lines
1.5 KiB
Python
# ******************************************************************************
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# Copyright 2017-2020 Intel Corporation
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ******************************************************************************
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import os
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import numpy as np
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import ngraph as ng
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from openvino.inference_engine import IECore
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from tests.runtime import get_runtime
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def test_import_onnx_with_external_data():
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model_path = os.path.join(os.path.dirname(__file__), "models/external_data.prototxt")
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ie = IECore()
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ie_network = ie.read_network(model=model_path)
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ng_function = ng.function_from_cnn(ie_network)
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dtype = np.float32
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value_a = np.array([1.0, 3.0, 5.0], dtype=dtype)
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value_b = np.array([3.0, 5.0, 1.0], dtype=dtype)
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# third input [5.0, 1.0, 3.0] read from external file
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runtime = get_runtime()
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computation = runtime.computation(ng_function)
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result = computation(value_a, value_b)
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assert np.allclose(result, np.array([3.0, 3.0, 3.0], dtype=dtype))
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