def testBasic(self, compression):
dataset = dataset_ops.Dataset.range(42)
io.save(dataset, self._test_dir, compression=compression)
dataset2 = io.load(self._test_dir,
dataset.element_spec,
compression=compression)
self.assertDatasetProduces(dataset2, range(42))
def testCustomShardFunction(self):
dataset = dataset_ops.Dataset.range(42)
io.save(dataset, self._test_dir, shard_func=lambda x: x // 21)
dataset2 = io.load(self._test_dir, dataset.element_spec)
expected = []
for i in range(21):
expected.extend([i, i + 21])
self.assertDatasetProduces(dataset2, expected)
def testSaveCheckpointingAPIIncorrectArgs(self):
dataset = dataset_ops.Dataset.range(42)
checkpoint_args = {
"directory": self._checkpoint_prefix,
"incorrect_arg": "incorrect_arg"
}
with self.assertRaises(TypeError):
io.save(dataset, self._save_dir, checkpoint_args=checkpoint_args)
def testSaveCheckpointingAPI(self):
dataset = dataset_ops.Dataset.range(40)
checkpoint_args = {"directory": self._checkpoint_prefix, "max_to_keep": 50}
io.save(dataset, self._save_dir, checkpoint_args=checkpoint_args)
num_checkpoint_files = len(list(os.listdir(self._checkpoint_prefix)))
# By default, we checkpoint every increment. Each checkpoint writes a
# file containing the data and a file containing the index. There is
# also an overall checkpoint file. Thus, we expect (2 * 40) + 1 files.
self.assertEqual(81, num_checkpoint_files)
def testCustomReaderFunction(self):
dataset = dataset_ops.Dataset.range(42)
io.save(dataset, self._test_dir, shard_func=lambda x: x % 7)
dataset2 = io.load(self._test_dir,
dataset.element_spec,
reader_func=lambda x: x.flat_map(lambda y: y))
expected = []
for i in range(7):
expected.extend(range(i, 42, 7))
self.assertDatasetProduces(dataset2, expected)
def testElementSpecOptional(self):
range_dataset = dataset_ops.Dataset.range(42)
dict_dataset = dataset_ops.Dataset.from_tensor_slices({"a": [1, 2],
"b": [3, 4]})
tuple_dataset = dataset_ops.Dataset.from_tensor_slices(([1, 2], [3, 4]))
dataset = dataset_ops.Dataset.zip((range_dataset, dict_dataset,
tuple_dataset))
io.save(dataset, self._test_dir)
dataset_loaded = io.load(self._test_dir)
self.assertDatasetsEqual(dataset, dataset_loaded)
def testRepeatAndPrefetch(self):
"""This test reproduces github.com/tensorflow/tensorflow/issues/49165."""
dataset1 = dataset_ops.Dataset.from_tensor_slices(np.random.rand(16, 32))
io.save(dataset1, self._test_dir)
dataset = io.load(self._test_dir)
dataset = dataset.shuffle(buffer_size=16)
dataset = dataset.batch(16)
dataset = dataset.repeat()
dataset = dataset.prefetch(1)
next_element = self.getNext(dataset)
for _ in range(30):
self.evaluate(next_element())
def testSaveCheckpointingAPICustomCheckpointInterval(self):
dataset = dataset_ops.Dataset.range(40)
step_counter = variables.Variable(0, trainable=False)
checkpoint_args = {
"checkpoint_interval": 5,
"step_counter": step_counter,
"directory": self._checkpoint_prefix,
"max_to_keep": 10,
}
io.save(dataset, self._save_dir, checkpoint_args=checkpoint_args)
num_checkpoint_files = len(list(os.listdir(self._checkpoint_prefix)))
# We expect (2 * 8) + 1 files.
self.assertEqual(17, num_checkpoint_files)
def testLoadCheckpointIteratorMultipleBreaks(self):
dataset = dataset_ops.Dataset.range(3)
io.save(dataset, self._save_dir)
loaded_dataset = io.load(self._save_dir)
iterator = iter(loaded_dataset)
get_next = iterator.get_next
checkpoint = trackable_utils.Checkpoint(iterator=iterator)
for i in range(len(dataset)):
save_path = checkpoint.save(self._checkpoint_prefix)
self.assertAllEqual(i, get_next())
checkpoint.restore(save_path).run_restore_ops()
self.assertAllEqual(i, get_next())
with self.assertRaises(errors.OutOfRangeError):
get_next()
def testLoadCheckpointFullyUsedIterator(self):
dataset = dataset_ops.Dataset.range(3)
io.save(dataset, self._save_dir)
loaded_dataset = io.load(self._save_dir)
iterator = iter(loaded_dataset)
get_next = iterator.get_next
checkpoint = trackable_utils.Checkpoint(iterator=iterator)
self.assertAllEqual(0, get_next())
self.assertAllEqual(1, get_next())
self.assertAllEqual(2, get_next())
save_path = checkpoint.save(self._checkpoint_prefix)
checkpoint.restore(save_path).run_restore_ops()
with self.assertRaises(errors.OutOfRangeError):
get_next()
def testCardinality(self): dataset = dataset_ops.Dataset.range(42) io.save(dataset, self._test_dir) dataset2 = io.load(self._test_dir, dataset.element_spec) self.assertEqual(self.evaluate(dataset2.cardinality()), 42)
def save_fn():
io.save(dataset, self._test_dir, compression=compression)
def test(self, verify_fn): dataset = dataset_ops.Dataset.range(42) io.save(dataset, self._save_dir) verify_fn(self, self._build_ds, num_outputs=42)
def testElementSpecRequired(self):
dataset = dataset_ops.Dataset.range(42)
io.save(dataset, self._test_dir)
with self.assertRaises(ValueError):
_ = io.load(self._test_dir)
def test(self, verify_fn):
self.skipTest(
"TODO(jsimsa): Re-enable once snapshot reader supports serialization.")
dataset = dataset_ops.Dataset.range(42)
io.save(dataset, self._save_dir)
verify_fn(self, self._build_ds, num_outputs=42)
