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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()
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