# 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()
