""" Copyright (c) 2017-2018 Intel Corporation Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ import unittest from unittest.mock import patch import numpy as np from extensions.front.caffe.data_augmentation_ext import DataAugmentationFrontExtractor from extensions.ops.data_augmentation import DataAugmentationOp from mo.utils.unittest.extractors import FakeMultiParam from mo.utils.unittest.graph import FakeNode from mo.ops.op import Op class FakeDAProtoLayer: def __init__(self, val): self.augmentation_param = val class TestDA(unittest.TestCase): @classmethod def setUpClass(cls): Op.registered_ops['DataAugmentation'] = DataAugmentationOp def test_da_no_pb_no_ml(self): self.assertRaises(AttributeError, DataAugmentationFrontExtractor.extract, None) @patch('extensions.front.caffe.data_augmentation_ext.merge_attrs') def test_da_ext_ideal_numbers(self, merge_attrs_mock): params = { 'crop_width': 0, 'crop_height': 0, 'write_augmented': "", 'max_multiplier': 255.0, 'augment_during_test': True, 'recompute_mean': 0, 'write_mean': "", 'mean_per_pixel': False, 'mean': 0, 'mode': "add", 'bottomwidth': 0, 'bottomheight': 0, 'num': 0, 'chromatic_eigvec': [0.0] } merge_attrs_mock.return_value = { **params, 'test': 54, 'test2': 'test3' } fake_pl = FakeDAProtoLayer(FakeMultiParam(params)) fake_node = FakeNode(fake_pl, None) DataAugmentationFrontExtractor.extract(fake_node) exp_res = { 'type': 'DataAugmentation', 'op': 'DataAugmentation', 'crop_width': 0, 'crop_height': 0, 'write_augmented': "", 'max_multiplier': 255.0, 'augment_during_test': 1, 'recompute_mean': 0, 'write_mean': "", 'mean_per_pixel': 0, 'mean': 0, 'mode': "add", 'bottomwidth': 0, 'bottomheight': 0, 'num': 0, 'chromatic_eigvec': [0.0], 'infer': DataAugmentationOp.data_augmentation_infer } for key in exp_res.keys(): if key in ('chromatic_eigvec',): np.testing.assert_equal(exp_res[key], fake_node[key]) else: self.assertEqual(exp_res[key], fake_node[key])