81 lines
2.5 KiB
Python
81 lines
2.5 KiB
Python
"""
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Copyright (C) 2018-2020 Intel Corporation
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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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http://www.apache.org/licenses/LICENSE-2.0
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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 unittest
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import numpy as np
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import onnx
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from extensions.front.onnx.crop_ext import CropFrontExtractor
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from mo.graph.graph import Node
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from mo.utils.unittest.graph import build_graph
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class CropONNXExtractorTest(unittest.TestCase):
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@staticmethod
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def _create_node(attrs: dict):
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pb = onnx.helper.make_node("Crop", ["X"], ["Y"], **attrs)
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graph = build_graph({'node_0': {'pb': pb}}, [])
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return Node(graph, 'node_0')
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@staticmethod
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def _base_attrs():
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# Commonly used attributes in the tests
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# Each test takes these ones and then adds/modifies/deletes particular fields
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return (
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# test input ONNX attributes
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dict(
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border=[5, 10, 15, 20],
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),
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# reference output Node attributes
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dict(
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op='Crop',
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crop_begin=np.array([10, 5]),
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crop_end=np.array([20, 15]),
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axis=np.array([2, 3])
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)
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)
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@staticmethod
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def _extract(inp):
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node = __class__._create_node(inp)
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CropFrontExtractor.extract(node)
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return node.graph.node[node.id]
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def _match(self, out, ref):
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for key in ref.keys():
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status = out[key] == ref[key]
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if type(status) in [list, np.ndarray]:
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status = np.all(status)
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self.assertTrue(status, 'Mismatch for field {}, observed: {}, expected: {}'.format(key, out[key], ref[key]))
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def test_default(self):
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inp, ref = self._base_attrs()
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out = self._extract(inp)
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self._match(out, ref)
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def test_with_scale(self):
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inp, ref = self._base_attrs()
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inp['scale'] = np.array([34, 50])
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del ref['crop_begin']
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del ref['crop_end']
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ref['dim'] = np.array([34, 50])
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ref['offset'] = np.array([10, 5])
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out = self._extract(inp)
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self._match(out, ref)
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