def test_tfds_source_splits(self):
default_splits_src = dataset_providers.TfdsDataSource("fake:0.0.0")
self.assertSameElements(["train", "validation"], default_splits_src.splits)
validation_split_src = dataset_providers.TfdsDataSource(
"fake:0.0.0", splits=["validation"])
self.assertSameElements(["validation"], validation_split_src.splits)
sliced_split_src = dataset_providers.TfdsDataSource(
"fake:0.0.0", splits={"validation": "train[0:1%]"})
self.assertSameElements(["validation"], sliced_split_src.splits)
def test_tfds_splits(self):
self.assertSameElements(
["train", "validation"],
dataset_providers.TfdsDataSource(tfds_name="fake:0.0.0").splits)
self.assertSameElements(["validation"],
dataset_providers.TfdsDataSource(
tfds_name="fake:0.0.0",
splits=["validation"]).splits)
self.assertSameElements(["validation"],
dataset_providers.TfdsDataSource(
tfds_name="fake:0.0.0",
splits={
"validation": "train"
}).splits)
def _prepare_sources_and_tasks(self):
clear_tasks()
clear_mixtures()
# Prepare TfdsSource
# Note we don't use mock.Mock since they fail to pickle.
fake_tfds_paths = {
"train": [
{ # pylint:disable=g-complex-comprehension
"filename": "train.tfrecord-%05d-of-00002" % i,
"skip": 0,
"take": -1
} for i in range(2)
],
"validation": [{
"filename": "validation.tfrecord-00000-of-00001",
"skip": 0,
"take": -1
}],
}
def _load_shard(shard_instruction, shuffle_files, seed):
del shuffle_files
del seed
fname = shard_instruction["filename"]
if "train" in fname:
ds = get_fake_dataset("train")
if fname.endswith("00000-of-00002"):
return ds.take(2)
else:
return ds.skip(2)
else:
return get_fake_dataset("validation")
fake_tfds = FakeLazyTfds(name="fake:0.0.0",
load=get_fake_dataset,
load_shard=_load_shard,
info=FakeTfdsInfo(splits={
"train": None,
"validation": None
}),
files=fake_tfds_paths.get,
size=lambda x: 30 if x == "train" else 10)
self._tfds_patcher = mock.patch("t5.seqio.utils.LazyTfdsLoader",
new=mock.Mock(return_value=fake_tfds))
self._tfds_patcher.start()
# Set up data directory.
self.test_tmpdir = self.get_tempdir()
self.test_data_dir = os.path.join(self.test_tmpdir, "test_data")
shutil.copytree(TEST_DATA_DIR, self.test_data_dir)
for root, dirs, _ in os.walk(self.test_data_dir):
for d in dirs + [""]:
os.chmod(os.path.join(root, d), 0o777)
# Prepare uncached TextLineTask.
self.tfds_source = dataset_providers.TfdsDataSource(
tfds_name="fake:0.0.0", splits=("train", "validation"))
self.add_task("tfds_task", source=self.tfds_source)
# Prepare TextLineSource.
_dump_fake_dataset(os.path.join(self.test_data_dir,
"train.tsv"), _FAKE_DATASET["train"],
[2, 1], _dump_examples_to_tsv)
self.text_line_source = dataset_providers.TextLineDataSource(
split_to_filepattern={
"train": os.path.join(self.test_data_dir, "train.tsv*"),
},
skip_header_lines=1,
)
self.add_task("text_line_task",
source=self.text_line_source,
preprocessors=(split_tsv_preprocessor, ) +
self.DEFAULT_PREPROCESSORS)
# Prepare TFExampleSource.
_dump_fake_dataset(os.path.join(self.test_data_dir, "train.tfrecord"),
_FAKE_DATASET["train"], [2, 1],
_dump_examples_to_tfrecord)
self.tf_example_source = dataset_providers.TFExampleDataSource(
split_to_filepattern={
"train": os.path.join(self.test_data_dir, "train.tfrecord*"),
},
feature_description={
"prefix": tf.io.FixedLenFeature([], tf.string),
"suffix": tf.io.FixedLenFeature([], tf.string),
})
self.add_task("tf_example_task", source=self.tf_example_source)
# Prepare FunctionDataSource
self.function_source = dataset_providers.FunctionDataSource(
dataset_fn=get_fake_dataset, splits=["train", "validation"])
self.add_task("function_task", source=self.function_source)
# Prepare Task that is tokenized and preprocessed before caching.
self.add_task("fully_processed_precache",
source=self.function_source,
preprocessors=(
test_text_preprocessor,
preprocessors.tokenize,
token_preprocessor_no_sequence_length,
dataset_providers.CacheDatasetPlaceholder(),
))
# Prepare Task that is tokenized after caching.
self.add_task("tokenized_postcache",
source=self.function_source,
preprocessors=(
test_text_preprocessor,
dataset_providers.CacheDatasetPlaceholder(),
preprocessors.tokenize,
token_preprocessor_no_sequence_length,
))
# Prepare Task with randomization.
self.random_task = self.add_task(
"random_task",
source=self.function_source,
preprocessors=(
test_text_preprocessor,
dataset_providers.CacheDatasetPlaceholder(),
preprocessors.tokenize,
random_token_preprocessor,
))
self.uncached_task = self.add_task("uncached_task",
source=self.tfds_source)
# Prepare cached task.
dataset_utils.set_global_cache_dirs([self.test_data_dir])
self.cached_task_dir = os.path.join(self.test_data_dir, "cached_task")
_dump_fake_dataset(
os.path.join(self.cached_task_dir,
"train.tfrecord"), _FAKE_TOKENIZED_DATASET["train"],
[2, 1], _dump_examples_to_tfrecord)
_dump_fake_dataset(
os.path.join(self.cached_task_dir, "validation.tfrecord"),
_FAKE_TOKENIZED_DATASET["validation"], [2],
_dump_examples_to_tfrecord)
self.cached_task = self.add_task("cached_task",
source=self.tfds_source)
# Prepare cached plaintext task.
_dump_fake_dataset(
os.path.join(self.test_data_dir, "cached_plaintext_task",
"train.tfrecord"),
_FAKE_PLAINTEXT_TOKENIZED_DATASET["train"], [2, 1],
_dump_examples_to_tfrecord)
self.cached_plaintext_task = self.add_task(
"cached_plaintext_task",
source=self.tfds_source,
preprocessors=self.DEFAULT_PREPROCESSORS +
(test_token_preprocessor, ))
def test_no_tfds_version(self):
with self.assertRaisesWithLiteralMatch(
ValueError,
"TFDS name must contain a version number, got: fake"):
dataset_providers.TfdsDataSource(tfds_name="fake")