# Copyright 2022 The TensorFlow Datasets Authors. # # 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 tensorflow_datasets.image.celebahq.""" from tensorflow_datasets.image import celebahq import tensorflow_datasets.testing as tfds_test class CelebAHQTest(tfds_test.DatasetBuilderTestCase): DATASET_CLASS = celebahq.CelebAHq BUILDER_CONFIG_NAMES_TO_TEST = ["512"] SPLITS = { "train": 3, } if __name__ == "__main__": tfds_test.test_main()
"""Tests for libritts dataset module.""" from tensorflow_datasets import testing from tensorflow_datasets.audio import libritts class LibriTTSTest(testing.DatasetBuilderTestCase): DATASET_CLASS = libritts.Libritts SPLITS = { "train_clean100": 2, "train_clean360": 2, "train_other500": 2, "test_clean": 2, "test_other": 2, "dev_clean": 2, "dev_other": 2, } DL_DOWNLOAD_RESULT = { "train_clean100": "train-clean-100.tar.gz", "train_clean360": "train-clean-360.tar.gz", "train_other500": "train-other-500.tar.gz", "test_clean": "test-clean.tar.gz", "test_other": "test-other.tar.gz", "dev_clean": "dev-clean.tar.gz", "dev_other": "dev-other.tar.gz", } if __name__ == "__main__": testing.test_main()
bools([0, 0, 0, 1, 1, 0, 0, 0, 1, 0]), ints([0, 3, 1, 2, 0, 3, 1, 1, 0, 1]), ), ) rle_items = [] for dense, encoding in rle_dense_and_encodings: rle_items.append( test_utils.FeatureExpectationItem( value=dense, expected=dense, # expected_serialized=list(encoding), )) self.assertFeature( feature=sds_features.RunLengthEncodedFeature(dtype=tf.bool), dtype=tf.bool, shape=(None, ), tests=rle_items, ) self.assertFeature( feature=sds_features.RunLengthEncodedFeature(size=size, dtype=tf.bool), dtype=tf.bool, shape=shape, tests=rle_items, ) if __name__ == "__main__": test_main()