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| # Lint as: python2, python3 | |
| # Copyright 2018 The TensorFlow Authors All Rights Reserved. | |
| # | |
| # 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. | |
| # ============================================================================== | |
| """Tests for utils.py.""" | |
| import numpy as np | |
| import tensorflow as tf | |
| from deeplab.core import utils | |
| class UtilsTest(tf.test.TestCase): | |
| def testScaleDimensionOutput(self): | |
| self.assertEqual(161, utils.scale_dimension(321, 0.5)) | |
| self.assertEqual(193, utils.scale_dimension(321, 0.6)) | |
| self.assertEqual(241, utils.scale_dimension(321, 0.75)) | |
| def testGetLabelWeightMask_withFloatLabelWeights(self): | |
| labels = tf.constant([0, 4, 1, 3, 2]) | |
| ignore_label = 4 | |
| num_classes = 5 | |
| label_weights = 0.5 | |
| expected_label_weight_mask = np.array([0.5, 0.0, 0.5, 0.5, 0.5], | |
| dtype=np.float32) | |
| with self.test_session() as sess: | |
| label_weight_mask = utils.get_label_weight_mask( | |
| labels, ignore_label, num_classes, label_weights=label_weights) | |
| label_weight_mask = sess.run(label_weight_mask) | |
| self.assertAllEqual(label_weight_mask, expected_label_weight_mask) | |
| def testGetLabelWeightMask_withListLabelWeights(self): | |
| labels = tf.constant([0, 4, 1, 3, 2]) | |
| ignore_label = 4 | |
| num_classes = 5 | |
| label_weights = [0.0, 0.1, 0.2, 0.3, 0.4] | |
| expected_label_weight_mask = np.array([0.0, 0.0, 0.1, 0.3, 0.2], | |
| dtype=np.float32) | |
| with self.test_session() as sess: | |
| label_weight_mask = utils.get_label_weight_mask( | |
| labels, ignore_label, num_classes, label_weights=label_weights) | |
| label_weight_mask = sess.run(label_weight_mask) | |
| self.assertAllEqual(label_weight_mask, expected_label_weight_mask) | |
| def testGetLabelWeightMask_withInvalidLabelWeightsType(self): | |
| labels = tf.constant([0, 4, 1, 3, 2]) | |
| ignore_label = 4 | |
| num_classes = 5 | |
| self.assertRaisesWithRegexpMatch( | |
| ValueError, | |
| '^The type of label_weights is invalid, it must be a float or a list', | |
| utils.get_label_weight_mask, | |
| labels=labels, | |
| ignore_label=ignore_label, | |
| num_classes=num_classes, | |
| label_weights=None) | |
| def testGetLabelWeightMask_withInvalidLabelWeightsLength(self): | |
| labels = tf.constant([0, 4, 1, 3, 2]) | |
| ignore_label = 4 | |
| num_classes = 5 | |
| label_weights = [0.0, 0.1, 0.2] | |
| self.assertRaisesWithRegexpMatch( | |
| ValueError, | |
| '^Length of label_weights must be equal to num_classes if it is a list', | |
| utils.get_label_weight_mask, | |
| labels=labels, | |
| ignore_label=ignore_label, | |
| num_classes=num_classes, | |
| label_weights=label_weights) | |
| if __name__ == '__main__': | |
| tf.test.main() | |