def testTFRecordDatasetConstructorErrorsTensorInput(self):
with self.assertRaisesRegex(TypeError,
"filenames.*must be.*Tensor.*string"):
readers.TFRecordDataset([1, 2, 3])
with self.assertRaisesRegex(TypeError,
"filenames.*must be.*Tensor.*string"):
readers.TFRecordDataset(constant_op.constant([1, 2, 3]))
# convert_to_tensor raises different errors in graph and eager
with self.assertRaises(Exception):
readers.TFRecordDataset(object())
def testConstructorErrorsTensorInput(self):
with self.assertRaisesRegex(
TypeError,
"The `filenames` argument must contain `tf.string` elements. Got "
"`tf.int32` elements."):
readers.TFRecordDataset([1, 2, 3])
with self.assertRaisesRegex(
TypeError,
"The `filenames` argument must contain `tf.string` elements. Got "
"`tf.int32` elements."):
readers.TFRecordDataset(constant_op.constant([1, 2, 3]))
# convert_to_tensor raises different errors in graph and eager
with self.assertRaises(Exception):
readers.TFRecordDataset(object())
def make_dataset(self,
num_epochs,
batch_size=1,
compression_type=None,
buffer_size=None):
filenames = self._createFiles()
if compression_type == "ZLIB":
zlib_files = []
for i, fn in enumerate(filenames):
with open(fn, "rb") as f:
cdata = zlib.compress(f.read())
zfn = os.path.join(self.get_temp_dir(),
"tfrecord_%s.z" % i)
with open(zfn, "wb") as f:
f.write(cdata)
zlib_files.append(zfn)
filenames = zlib_files
elif compression_type == "GZIP":
gzip_files = []
for i, fn in enumerate(self._filenames):
with open(fn, "rb") as f:
gzfn = os.path.join(self.get_temp_dir(),
"tfrecord_%s.gz" % i)
with gzip.GzipFile(gzfn, "wb") as gzf:
gzf.write(f.read())
gzip_files.append(gzfn)
filenames = gzip_files
return readers.TFRecordDataset(
filenames, compression_type,
buffer_size=buffer_size).repeat(num_epochs).batch(batch_size)
def testName(self):
files = [self._filenames[0]]
expected_output = [self._record(0, i) for i in range(self._num_records)]
ds = readers.TFRecordDataset(files, name="tf_record_dataset")
self.assertDatasetProduces(
ds, expected_output=expected_output, assert_items_equal=True)
def testPathlib(self):
files = [pathlib.Path(self._filenames[0])]
expected_output = [self._record(0, i) for i in range(self._num_records)]
ds = readers.TFRecordDataset(files)
self.assertDatasetProduces(
ds, expected_output=expected_output, assert_items_equal=True)
def testReadFromDatasetOfFiles(self):
files = dataset_ops.Dataset.from_tensor_slices(self.test_filenames)
expected_output = []
for j in range(self._num_files):
expected_output.extend(
[self._record(j, i) for i in range(self._num_records)])
dataset = readers.TFRecordDataset(files)
self.assertDatasetProduces(dataset, expected_output=expected_output)
def testDirectFilenameTFRecordReaderPipeline(self):
dataset = core_readers.TFRecordDataset(self._filenames)
dataset = distribute._AutoShardDataset(dataset, 5, 0)
expected = [
b"Record %d of file %d" % (r, f) # pylint:disable=g-complex-comprehension
for f in (0, 5) for r in range(0, 10)
]
self.assertDatasetProduces(dataset, expected)
def testZip(self):
dataset1 = readers.TFRecordDataset(self._createTFRecordFiles())
dataset2 = readers.TextLineDataset(self._createTextFiles())
dataset = dataset_ops.Dataset.zip((dataset1, dataset2))
dataset = input_ops.auto_shard_dataset(dataset, self._num_shards,
self._shard_index)
record_fn = lambda r, f: (self._record(r, f), self._text_line(r, f))
self._verifySimpleShardingOutput(dataset, record_fn)
def testReadWithBuffer(self):
one_mebibyte = 2**20
dataset = readers.TFRecordDataset(self.test_filenames,
buffer_size=one_mebibyte)
expected_output = []
for j in range(self._num_files):
expected_output.extend(
[self._record(j, i) for i in range(self._num_records)])
self.assertDatasetProduces(dataset, expected_output=expected_output)
def testReadTenEpochsFromDatasetOfFilesInParallel(self):
files = dataset_ops.Dataset.from_tensor_slices(
self.test_filenames).repeat(10)
expected_output = []
for j in range(self._num_files):
expected_output.extend(
[self._record(j, i) for i in range(self._num_records)])
dataset = readers.TFRecordDataset(files, num_parallel_reads=4)
self.assertDatasetProduces(
dataset, expected_output=expected_output * 10, assert_items_equal=True)
def setUp(self):
super(TFRecordWriterTest, self).setUp()
self._num_records = 7
self.filename = array_ops.placeholder(dtypes.string, shape=[])
self.compression_type = array_ops.placeholder_with_default("", shape=[])
input_dataset = readers.TFRecordDataset([self.filename],
self.compression_type)
self.writer = writers.TFRecordWriter(
self._outputFilename(), self.compression_type).write(input_dataset)
def testReadWithBuffer(self):
one_mebibyte = 2**20
d = readers.TFRecordDataset(self.test_filenames, buffer_size=one_mebibyte)
iterator = d.make_one_shot_iterator()
with self.test_session() as sess:
for j in range(self._num_files):
for i in range(self._num_records):
self.assertAllEqual(self._record(j, i), sess.run(iterator.get_next()))
with self.assertRaises(errors.OutOfRangeError):
sess.run(iterator.get_next())
def testReadFromDatasetOfFiles(self):
files = dataset_ops.Dataset.from_tensor_slices(self.test_filenames)
d = readers.TFRecordDataset(files)
iterator = d.make_one_shot_iterator()
next_element = iterator.get_next()
with self.test_session() as sess:
for j in range(self._num_files):
for i in range(self._num_records):
self.assertAllEqual(self._record(j, i), sess.run(next_element))
with self.assertRaises(errors.OutOfRangeError):
sess.run(next_element)
def _dataset_factory(self,
filenames,
compression_type="",
num_epochs=1,
batch_size=None):
repeat_dataset = readers.TFRecordDataset(
filenames, compression_type).repeat(num_epochs)
if batch_size:
return repeat_dataset.batch(batch_size)
return repeat_dataset
def __init__(self, filenames, compression_type=None, buffer_size=None):
"""Creates a `TFRecordDataset`.
Args:
filenames: A `tf.string` tensor containing one or more filenames.
compression_type: (Optional.) A `tf.string` scalar evaluating to one of
`""` (no compression), `"ZLIB"`, or `"GZIP"`.
buffer_size: (Optional.) A `tf.int64` scalar representing the number of
bytes in the read buffer. 0 means no buffering.
"""
dataset = readers.TFRecordDataset(filenames, compression_type,
buffer_size)
super(TFRecordDataset, self).__init__(dataset)
def testAsFunctionFromReader(self):
with ops.device("CPU"):
file_path = os.path.join(
self.get_temp_dir(),
"{}.tfrecord.gz".format("tf_record_asset"))
with tf_record.TFRecordWriter(file_path, "GZIP") as f:
for v in ["a", "aa", "aaa"]:
f.write(str(v))
original_dataset = readers.TFRecordDataset([file_path],
compression_type="GZIP")
fn = original_dataset._trace_variant_creation()
variant = fn()
revived_dataset = dataset_ops._VariantDataset(
variant, original_dataset.element_spec)
self.assertDatasetProduces(revived_dataset, ["a", "aa", "aaa"])
def testReadWithEquivalentDataset(self):
features = {
"file": parsing_ops.FixedLenFeature([], dtypes.int64),
"record": parsing_ops.FixedLenFeature([], dtypes.int64),
}
dataset = (core_readers.TFRecordDataset(
self._filenames).map(lambda x: parsing_ops.parse_single_example(
x, features)).repeat(10).batch(2))
next_element = self.getNext(dataset)
for file_batch, _, _, _, record_batch, _ in self._next_expected_batch(
range(self._num_files), 2, 10):
actual_batch = self.evaluate(next_element())
self.assertAllEqual(file_batch, actual_batch["file"])
self.assertAllEqual(record_batch, actual_batch["record"])
with self.assertRaises(errors.OutOfRangeError):
self.evaluate(next_element())
def testReadTenEpochsFromDatasetOfFilesInParallel(self):
files = dataset_ops.Dataset.from_tensor_slices(
self.test_filenames).repeat(10)
d = readers.TFRecordDataset(files, num_parallel_reads=4)
iterator = d.make_one_shot_iterator()
next_element = iterator.get_next()
expected = []
actual = []
with self.test_session() as sess:
for _ in range(10):
for j in range(self._num_files):
for i in range(self._num_records):
expected.append(self._record(j, i))
actual.append(sess.run(next_element))
with self.assertRaises(errors.OutOfRangeError):
sess.run(next_element)
self.assertEqual(sorted(expected), sorted(actual))
def testConcat(self):
dataset1 = readers.TFRecordDataset(self._createTFRecordFiles())
dataset2 = readers.TextLineDataset(self._createTextFiles())
dataset = dataset1.concatenate(dataset2)
dataset = input_ops.auto_shard_dataset(
dataset, self._num_shards, self._shard_index)
next_element_fn = self._getNext(dataset)
for f in range(self._shard_index, self._num_files, self._num_shards):
for r in range(self._num_records):
self.assertAllEqual(
self._record(r, f), self.evaluate(next_element_fn()))
for f in range(self._shard_index, self._num_files, self._num_shards):
for r in range(self._num_records):
self.assertAllEqual(
self._text_line(r, f), self.evaluate(next_element_fn()))
with self.assertRaises(errors.OutOfRangeError):
self.evaluate(next_element_fn())
def setUp(self):
super(TFRecordDatasetTestBase, self).setUp()
self._num_files = 2
self._num_records = 7
self.test_filenames = self._createFiles()
self.filenames = array_ops.placeholder(dtypes.string, shape=[None])
self.num_epochs = array_ops.placeholder_with_default(
constant_op.constant(1, dtypes.int64), shape=[])
self.compression_type = array_ops.placeholder_with_default("", shape=[])
self.batch_size = array_ops.placeholder(dtypes.int64, shape=[])
repeat_dataset = core_readers.TFRecordDataset(
self.filenames, self.compression_type).repeat(self.num_epochs)
batch_dataset = repeat_dataset.batch(self.batch_size)
iterator = iterator_ops.Iterator.from_structure(batch_dataset.output_types)
self.init_op = iterator.make_initializer(repeat_dataset)
self.init_batch_op = iterator.make_initializer(batch_dataset)
self.get_next = iterator.get_next()
def testReadWithEquivalentDataset(self):
features = {
"file": parsing_ops.FixedLenFeature([], dtypes.int64),
"record": parsing_ops.FixedLenFeature([], dtypes.int64),
}
dataset = (core_readers.TFRecordDataset(self.test_filenames).map(
lambda x: parsing_ops.parse_single_example(x, features)).repeat(
10).batch(2))
iterator = dataset.make_initializable_iterator()
init_op = iterator.initializer
next_element = iterator.get_next()
with self.test_session() as sess:
sess.run(init_op)
for file_batch, _, _, _, record_batch in self._next_expected_batch(
range(self._num_files), 2, 10):
actual_batch = sess.run(next_element)
self.assertAllEqual(file_batch, actual_batch["file"])
self.assertAllEqual(record_batch, actual_batch["record"])
with self.assertRaises(errors.OutOfRangeError):
sess.run(next_element)
def testConcat(self):
dataset1 = readers.TFRecordDataset(self._createTFRecordFiles())
dataset2 = readers.TextLineDataset(self._createTextFiles())
dataset = dataset1.concatenate(dataset2)
dataset = input_ops.auto_shard_dataset(dataset, self._num_shards,
self._shard_index)
iterator = dataset.make_one_shot_iterator()
next_element = iterator.get_next()
with self.cached_session() as sess:
for f in range(self._shard_index, self._num_files,
self._num_shards):
for r in range(self._num_records):
self.assertAllEqual(self._record(r, f),
sess.run(next_element))
for f in range(self._shard_index, self._num_files,
self._num_shards):
for r in range(self._num_records):
self.assertAllEqual(self._text_line(r, f),
sess.run(next_element))
with self.assertRaises(errors.OutOfRangeError):
sess.run(next_element)
def testShard(self):
filename = self._createFile()
dataset = readers.TFRecordDataset([filename])
def reduce_func(key, dataset):
shard_filename = string_ops.string_join(
[filename, string_ops.as_string(key)])
writer = writers.TFRecordWriter(shard_filename)
writer.write(dataset.map(lambda _, x: x))
return dataset_ops.Dataset.from_tensors(shard_filename)
dataset = dataset.enumerate()
dataset = dataset.apply(
grouping.group_by_window(lambda i, _: i % 2, reduce_func,
dtypes.int64.max))
get_next = self.getNext(dataset)
for i in range(2):
shard_filename = (filename + str(i)).encode()
self.assertEqual(self.evaluate(get_next()), shard_filename)
for j, r in enumerate(
tf_record.tf_record_iterator(shard_filename)):
self.assertAllEqual(self._record(i + 2 * j), r)
def testTFRecordDatasetIgnoreError(self):
filenames = []
for i in range(5):
fn = os.path.join(self.get_temp_dir(), "tf_record.%d.txt" % i)
filenames.append(fn)
writer = python_io.TFRecordWriter(fn)
for _ in range(10):
writer.write(b"record")
writer.close()
# Append corrupted data
with open(fn, "a") as f:
f.write("corrupted data")
dataset = readers.TFRecordDataset(filenames).apply(
error_ops.ignore_errors())
get_next = self.getNext(dataset)
# All of the files are present.
for _ in filenames:
for _ in range(10):
self.assertEqual(b"record", self.evaluate(get_next()))
with self.assertRaises(errors.OutOfRangeError):
self.evaluate(get_next())
def _TFRecordDataset(filename: Text) -> dataset_ops.Dataset:
buffer_size = 8 * 1024 * 1024 # 8 MiB per file
dataset = readers.TFRecordDataset(filename, buffer_size=buffer_size)
return dataset
class DatasetTest(test_base.DatasetTestBase, parameterized.TestCase):
def testAsSerializedGraph(self):
dataset = dataset_ops.Dataset.range(10)
graph = graph_pb2.GraphDef().FromString(
self.evaluate(dataset._as_serialized_graph()))
self.assertTrue(any([node.op != "RangeDataset" for node in graph.node]))
@staticmethod
def make_apply_fn(dataset):
def apply_fn(dataset):
def _apply_fn(dataset):
return dataset.cache()
return dataset.apply(_apply_fn)
return apply_fn
@staticmethod
def make_gen():
def gen():
yield 42
return gen
@staticmethod
def make_interleave_fn(dataset, num_parallel_calls=None):
def interleave_fn(dataset):
return dataset.interleave(
lambda x: dataset_ops.Dataset.range(0),
cycle_length=2,
num_parallel_calls=num_parallel_calls)
return interleave_fn
@parameterized.named_parameters(
("FixedLengthRecord",
lambda: readers.FixedLengthRecordDataset("", 42)),
("FromGenerator",
lambda: dataset_ops.Dataset.from_generator(
DatasetTest.make_gen(), dtypes.int32),
1),
("FromTensors", lambda: dataset_ops.Dataset.from_tensors([42])),
("FromTensorSlices", lambda: dataset_ops.Dataset.from_tensors([42])),
("Range", lambda: dataset_ops.Dataset.range(10)),
("TextLine", lambda: readers.TextLineDataset("")),
("TFRecord", lambda: readers.TFRecordDataset(""), 1),
)
def testDatasetSimpleSourceInputs(self, dataset_fn, num_inputs=0):
self.assertEqual(num_inputs, len(dataset_fn()._inputs()))
def testDatasetComplexSourceInputs(self):
dataset_fn = dataset_ops.Dataset.from_sparse_tensor_slices(
sparse_tensor.SparseTensor(
indices=np.array([[0, 0], [1, 0], [2, 0]]),
values=np.array([0, 0, 0]),
dense_shape=np.array([3, 1])))
self.assertEqual(0, len(dataset_fn._inputs()))
@parameterized.named_parameters(
("Batch",
lambda x: x.batch(10),
lambda: dataset_ops.Dataset.range(0)),
("Cache",
lambda x: x.cache(),
lambda: dataset_ops.Dataset.range(0)),
("Filter",
lambda x: x.filter(lambda x: True),
lambda: dataset_ops.Dataset.range(0)),
("FlatMap",
lambda x: x.flat_map(lambda x: dataset_ops.Dataset.range(0)),
lambda: dataset_ops.Dataset.range(0)),
("Map",
lambda x: x.map(lambda x: x),
lambda: dataset_ops.Dataset.range(0)),
("PaddedBatch",
lambda x: x.padded_batch(10, []),
lambda: dataset_ops.Dataset.range(0)),
("ParallelMap",
lambda x: x.map(lambda x: x, num_parallel_calls=2),
lambda: dataset_ops.Dataset.range(0)),
("Repeat",
lambda x: x.repeat(),
lambda: dataset_ops.Dataset.range(0)),
("Shuffle",
lambda x: x.shuffle(10),
lambda: dataset_ops.Dataset.range(0)),
("Skip",
lambda x: x.skip(1),
lambda: dataset_ops.Dataset.range(0)),
("Take",
lambda x: x.take(1),
lambda: dataset_ops.Dataset.range(0)),
("Window",
lambda x: x.window(10),
lambda: dataset_ops.Dataset.range(0)),
)
def testUnaryTransformationInputs(self, dataset_fn, input_dataset_fn):
input_dataset = input_dataset_fn()
self.assertEqual([input_dataset], dataset_fn(input_dataset)._inputs())
def testUnaryTransformationInputsApply(self):
input_dataset = dataset_ops.Dataset.range(0)
dataset_fn = self.make_apply_fn(dataset_ops.Dataset.range(0))
self.assertEqual([input_dataset], dataset_fn(input_dataset)._inputs())
@parameterized.named_parameters(
("ParallelInterleave",
[lambda: dataset_ops.Dataset.range(0), 2],
lambda: dataset_ops.Dataset.range(0)),
("Interleave",
[lambda: dataset_ops.Dataset.range(0), None],
lambda: dataset_ops.Dataset.range(0)),
)
def testUnaryTransformationInputsWithInterleaveFn(
self, interleave_fn_args, input_dataset_fn):
input_dataset = input_dataset_fn()
dataset_fn = self.make_interleave_fn(*interleave_fn_args)
self.assertEqual([input_dataset], dataset_fn(input_dataset)._inputs())
@parameterized.named_parameters(
("Concatenate", lambda x, y: x.concatenate(y),
lambda: dataset_ops.Dataset.range(0),
lambda: dataset_ops.Dataset.range(1)))
def testBinaryTransformationInputs(self, dataset_fn, input1_fn, input2_fn):
input1 = input1_fn()
input2 = input2_fn()
self.assertEqual([input1, input2], dataset_fn(input1, input2)._inputs())
@parameterized.named_parameters(
("ZipOne",
dataset_ops.Dataset.zip,
lambda: (dataset_ops.Dataset.range(0))),
("ZipNest",
dataset_ops.Dataset.zip,
lambda: (dataset_ops.Dataset.range(0),
(dataset_ops.Dataset.range(1),
dataset_ops.Dataset.range(2)))),
("ZipTuple",
dataset_ops.Dataset.zip,
lambda: (dataset_ops.Dataset.range(0),
dataset_ops.Dataset.range(1))),
)
def testVariadicTransformationInputs(self, dataset_fn, input_datasets_fn):
input_datasets = input_datasets_fn()
self.assertEqual(
nest.flatten(input_datasets),
dataset_fn(input_datasets)._inputs())
def testCollectInputs(self):
ds1 = dataset_ops.Dataset.range(0)
ds2 = ds1.concatenate(ds1)
ds3 = dataset_ops.Dataset.zip((ds2, ds1, ds2))
inputs = []
queue = [ds3]
while queue:
ds = queue[0]
queue = queue[1:]
queue.extend(ds._inputs())
inputs.append(ds)
self.assertEqual(5, inputs.count(ds1))
self.assertEqual(2, inputs.count(ds2))
self.assertEqual(1, inputs.count(ds3))
# TODO(b/119882922): use-after-free bug in eager mode.
# pylint: disable=g-long-lambda
@parameterized.named_parameters(
("Tensor", lambda: constant_op.constant(37.0),
structure.TensorStructure(dtypes.float32, [])),
("SparseTensor", lambda: sparse_tensor.SparseTensor(
indices=[[0]], values=constant_op.constant([0], dtype=dtypes.int32),
dense_shape=[1]),
structure.SparseTensorStructure(dtypes.int32, [1])),
("Nest", lambda: {
"a": constant_op.constant(37.0),
"b": (constant_op.constant(["Foo"]), constant_op.constant("Bar"))},
structure.NestedStructure({
"a": structure.TensorStructure(dtypes.float32, []),
"b": (structure.TensorStructure(dtypes.string, [1]),
structure.TensorStructure(dtypes.string, []))})),
("Dataset", lambda: dataset_ops.Dataset.from_tensor_slices(
constant_op.constant([1, 2, 3])),
dataset_ops.DatasetStructure(
structure.TensorStructure(dtypes.int32, []))),
("Optional", lambda: optional_ops.Optional.from_value(37.0),
optional_ops.OptionalStructure(
structure.TensorStructure(dtypes.float32, []))),
)
def testSkipEagerDatasetStructure(self, tf_value_fn,
expected_element_structure):
dataset = dataset_ops.Dataset.from_tensors(0).map(lambda _: tf_value_fn())
dataset_structure = structure.Structure.from_value(dataset)
self.assertIsInstance(dataset_structure, dataset_ops.DatasetStructure)
# TODO(b/110122868): Add a public API to `tf.data.Dataset` for accessing
# the element structure.
self.assertTrue(expected_element_structure.is_compatible_with(
dataset_structure._element_structure))
self.assertTrue(dataset_structure._element_structure.is_compatible_with(
expected_element_structure))
self.assertEqual([dtypes.variant], dataset_structure._flat_types)
self.assertEqual([tensor_shape.scalar()], dataset_structure._flat_shapes)
# Assert that the `Dataset` survives a round-trip via _from_tensor_list()
# and _to_tensor_list().
round_trip_dataset = dataset_structure._from_tensor_list(
dataset_structure._to_tensor_list(dataset))
value = tf_value_fn()
if isinstance(value, dataset_ops.Dataset):
self.assertDatasetsEqual(value, dataset.flat_map(lambda x: x))
elif isinstance(value, optional_ops.Optional):
self.assertDatasetProduces(
round_trip_dataset.map(lambda opt: opt.get_value()),
[self.evaluate(value.get_value())],
requires_initialization=True)
else:
self.assertDatasetProduces(
round_trip_dataset, [self.evaluate(tf_value_fn())],
requires_initialization=True)
@test_util.run_deprecated_v1
def testSkipEagerSameGraphErrorOneShot(self):
dataset = dataset_ops.Dataset.range(10)
with ops.Graph().as_default():
dataset = dataset.batch(2)
with test.mock.patch.object(logging, "warning") as mock_log:
_ = dataset.make_one_shot_iterator()
self.assertRegexpMatches(
str(mock_log.call_args), "Please ensure that all datasets in the "
"pipeline are created in the same graph as the iterator.")
@test_util.run_deprecated_v1
def testSkipEagerSameGraphErrorOneShotSimple(self):
dataset = dataset_ops.Dataset.range(10)
with ops.Graph().as_default():
with test.mock.patch.object(logging, "warning") as mock_log:
_ = dataset.make_one_shot_iterator()
self.assertRegexpMatches(
str(mock_log.call_args), "Please ensure that all datasets in the "
"pipeline are created in the same graph as the iterator.")
@test_util.run_deprecated_v1
def testSkipEagerSameGraphErrorInitializable(self):
dataset = dataset_ops.Dataset.range(10)
with ops.Graph().as_default():
dataset = dataset.batch(2)
with self.assertRaisesRegexp(ValueError, "must be from the same graph"):
_ = dataset.make_initializable_iterator()
class DatasetTest(test_base.DatasetTestBase, parameterized.TestCase):
def testAsSerializedGraph(self):
dataset = dataset_ops.Dataset.range(10)
graph = graph_pb2.GraphDef().FromString(
self.evaluate(dataset._as_serialized_graph()))
self.assertTrue(any([node.op != "RangeDataset" for node in graph.node]))
def testAsFunctionWithMap(self):
if not context.executing_eagerly():
self.skipTest("Only works executing eagerly")
with ops.device("CPU"):
original_dataset = dataset_ops.Dataset.range(5).map(lambda x: x * 2)
fn = original_dataset._trace_variant_creation()
variant = fn()
revived_dataset = _RevivedDataset(
variant, original_dataset._element_structure)
self.assertDatasetProduces(revived_dataset, range(0, 10, 2))
def testAsFunctionWithMapInFlatMap(self):
if not context.executing_eagerly():
self.skipTest("Only works executing eagerly")
with ops.device("CPU"):
original_dataset = dataset_ops.Dataset.range(5).flat_map(
lambda x: dataset_ops.Dataset.range(5).map(lambda x: x * 2))
fn = original_dataset._trace_variant_creation()
variant = fn()
revived_dataset = _RevivedDataset(
variant, original_dataset._element_structure)
self.assertDatasetProduces(revived_dataset, list(original_dataset))
@staticmethod
def make_apply_fn(dataset):
def apply_fn(dataset):
def _apply_fn(dataset):
return dataset.cache()
return dataset.apply(_apply_fn)
return apply_fn
@staticmethod
def make_gen():
def gen():
yield 42
return gen
@staticmethod
def make_interleave_fn(dataset, num_parallel_calls=None):
def interleave_fn(dataset):
return dataset.interleave(
lambda x: dataset_ops.Dataset.range(0),
cycle_length=2,
num_parallel_calls=num_parallel_calls)
return interleave_fn
@parameterized.named_parameters(
("FixedLengthRecord",
lambda: readers.FixedLengthRecordDataset("", 42)),
("FromGenerator",
lambda: dataset_ops.Dataset.from_generator(
DatasetTest.make_gen(), dtypes.int32),
1),
("FromTensors", lambda: dataset_ops.Dataset.from_tensors([42])),
("FromTensorSlices", lambda: dataset_ops.Dataset.from_tensors([42])),
("Range", lambda: dataset_ops.Dataset.range(10)),
("TextLine", lambda: readers.TextLineDataset("")),
("TFRecord", lambda: readers.TFRecordDataset(""), 1),
)
def testDatasetSimpleSourceInputs(self, dataset_fn, num_inputs=0):
self.assertLen(dataset_fn()._inputs(), num_inputs)
@test_util.run_v1_only("deprecated API, no eager or V2 test coverage")
def testDatasetComplexSourceInputs(self):
dataset_fn = dataset_ops.Dataset.from_sparse_tensor_slices(
sparse_tensor.SparseTensor(
indices=np.array([[0, 0], [1, 0], [2, 0]]),
values=np.array([0, 0, 0]),
dense_shape=np.array([3, 1])))
self.assertEmpty(dataset_fn._inputs())
@parameterized.named_parameters(
("Batch",
lambda x: x.batch(10),
lambda: dataset_ops.Dataset.range(0)),
("Cache",
lambda x: x.cache(),
lambda: dataset_ops.Dataset.range(0)),
("Filter",
lambda x: x.filter(lambda x: True),
lambda: dataset_ops.Dataset.range(0)),
("FlatMap",
lambda x: x.flat_map(lambda x: dataset_ops.Dataset.range(0)),
lambda: dataset_ops.Dataset.range(0)),
("Map",
lambda x: x.map(lambda x: x),
lambda: dataset_ops.Dataset.range(0)),
("PaddedBatch",
lambda x: x.padded_batch(10, []),
lambda: dataset_ops.Dataset.range(0)),
("ParallelMap",
lambda x: x.map(lambda x: x, num_parallel_calls=2),
lambda: dataset_ops.Dataset.range(0)),
("Repeat",
lambda x: x.repeat(),
lambda: dataset_ops.Dataset.range(0)),
("Shuffle",
lambda x: x.shuffle(10),
lambda: dataset_ops.Dataset.range(0)),
("Skip",
lambda x: x.skip(1),
lambda: dataset_ops.Dataset.range(0)),
("Take",
lambda x: x.take(1),
lambda: dataset_ops.Dataset.range(0)),
("Window",
lambda x: x.window(10),
lambda: dataset_ops.Dataset.range(0)),
)
def testUnaryTransformationInputs(self, dataset_fn, input_dataset_fn):
input_dataset = input_dataset_fn()
self.assertEqual([input_dataset], dataset_fn(input_dataset)._inputs())
def testUnaryTransformationInputsApply(self):
input_dataset = dataset_ops.Dataset.range(0)
dataset_fn = self.make_apply_fn(dataset_ops.Dataset.range(0))
self.assertEqual([input_dataset], dataset_fn(input_dataset)._inputs())
@parameterized.named_parameters(
("ParallelInterleave",
[lambda: dataset_ops.Dataset.range(0), 2],
lambda: dataset_ops.Dataset.range(0)),
("Interleave",
[lambda: dataset_ops.Dataset.range(0), None],
lambda: dataset_ops.Dataset.range(0)),
)
def testUnaryTransformationInputsWithInterleaveFn(
self, interleave_fn_args, input_dataset_fn):
input_dataset = input_dataset_fn()
dataset_fn = self.make_interleave_fn(*interleave_fn_args)
self.assertEqual([input_dataset], dataset_fn(input_dataset)._inputs())
def testNoWarnings(self):
with test.mock.patch.object(warnings, "warn") as mock_log:
dataset_fn = self.make_interleave_fn(dataset_ops.Dataset.range(10))
dataset_fn(dataset_ops.Dataset.range(10))
self.assertEmpty(mock_log.call_args_list)
@parameterized.named_parameters(
("Concatenate", lambda x, y: x.concatenate(y),
lambda: dataset_ops.Dataset.range(0),
lambda: dataset_ops.Dataset.range(1)))
def testBinaryTransformationInputs(self, dataset_fn, input1_fn, input2_fn):
input1 = input1_fn()
input2 = input2_fn()
self.assertEqual([input1, input2], dataset_fn(input1, input2)._inputs())
@parameterized.named_parameters(
("ZipOne",
dataset_ops.Dataset.zip,
lambda: (dataset_ops.Dataset.range(0))),
("ZipNest",
dataset_ops.Dataset.zip,
lambda: (dataset_ops.Dataset.range(0),
(dataset_ops.Dataset.range(1),
dataset_ops.Dataset.range(2)))),
("ZipTuple",
dataset_ops.Dataset.zip,
lambda: (dataset_ops.Dataset.range(0),
dataset_ops.Dataset.range(1))),
)
def testVariadicTransformationInputs(self, dataset_fn, input_datasets_fn):
input_datasets = input_datasets_fn()
self.assertEqual(
nest.flatten(input_datasets),
dataset_fn(input_datasets)._inputs())
def testFunctions(self):
dataset = dataset_ops.Dataset.range(5).map(lambda x: x * 2)
self.assertLen(dataset._functions(), 1)
def testCollectInputs(self):
ds1 = dataset_ops.Dataset.range(0)
ds2 = ds1.concatenate(ds1)
ds3 = dataset_ops.Dataset.zip((ds2, ds1, ds2))
inputs = []
queue = [ds3]
while queue:
ds = queue[0]
queue = queue[1:]
queue.extend(ds._inputs())
inputs.append(ds)
self.assertEqual(5, inputs.count(ds1))
self.assertEqual(2, inputs.count(ds2))
self.assertEqual(1, inputs.count(ds3))
# pylint: disable=g-long-lambda
@parameterized.named_parameters(
("Tensor", lambda: constant_op.constant(37.0),
structure.TensorStructure(dtypes.float32, [])),
("SparseTensor", lambda: sparse_tensor.SparseTensor(
indices=[[0]], values=constant_op.constant([0], dtype=dtypes.int32),
dense_shape=[1]),
structure.SparseTensorStructure(dtypes.int32, [1])),
("Nest", lambda: {
"a": constant_op.constant(37.0),
"b": (constant_op.constant(["Foo"]), constant_op.constant("Bar"))},
structure.NestedStructure({
"a": structure.TensorStructure(dtypes.float32, []),
"b": (structure.TensorStructure(dtypes.string, [1]),
structure.TensorStructure(dtypes.string, []))})),
("Dataset", lambda: dataset_ops.Dataset.from_tensor_slices(
constant_op.constant([1, 2, 3])),
dataset_ops.DatasetStructure(
structure.TensorStructure(dtypes.int32, []))),
("Optional", lambda: optional_ops.Optional.from_value(37.0),
optional_ops.OptionalStructure(
structure.TensorStructure(dtypes.float32, []))),
)
def testDatasetStructure(self, tf_value_fn, expected_element_structure):
dataset = dataset_ops.Dataset.from_tensors(0).map(lambda _: tf_value_fn())
dataset_structure = structure.Structure.from_value(dataset)
self.assertIsInstance(dataset_structure, dataset_ops.DatasetStructure)
# TODO(b/110122868): Add a public API to `tf.data.Dataset` for accessing
# the element structure.
self.assertTrue(expected_element_structure.is_compatible_with(
dataset_structure._element_structure))
self.assertTrue(dataset_structure._element_structure.is_compatible_with(
expected_element_structure))
self.assertEqual([dtypes.variant], dataset_structure._flat_types)
self.assertEqual([tensor_shape.scalar()], dataset_structure._flat_shapes)
# Assert that the `Dataset` survives a round-trip via _from_tensor_list()
# and _to_tensor_list().
round_trip_dataset = dataset_structure._from_tensor_list(
dataset_structure._to_tensor_list(dataset))
value = tf_value_fn()
if isinstance(value, dataset_ops.Dataset):
self.assertDatasetsEqual(value, dataset.flat_map(lambda x: x))
elif isinstance(value, optional_ops.Optional):
self.assertDatasetProduces(
round_trip_dataset.map(lambda opt: opt.get_value()),
[self.evaluate(value.get_value())],
requires_initialization=True)
else:
self.assertDatasetProduces(
round_trip_dataset, [self.evaluate(tf_value_fn())],
requires_initialization=True)
@test_util.run_v1_only("graph mode specific, no eager or V2 test coverage")
def testSkipEagerSameGraphErrorOneShot(self):
dataset = dataset_ops.Dataset.range(10)
with ops.Graph().as_default():
with self.assertRaisesRegexp(ValueError, "must be from the same graph"):
dataset = dataset.batch(2)
@test_util.run_v1_only("graph mode specific, no eager or V2 test coverage")
def testSkipEagerSameGraphErrorOneShotSimple(self):
dataset = dataset_ops.Dataset.range(10)
with ops.Graph().as_default():
with test.mock.patch.object(logging, "warning") as mock_log:
_ = dataset_ops.make_one_shot_iterator(dataset)
self.assertRegexpMatches(
str(mock_log.call_args), "Please ensure that all datasets in the "
"pipeline are created in the same graph as the iterator.")
@test_util.run_v1_only("graph mode specific, no eager or V2 test coverage")
def testSkipEagerSameGraphErrorInitializable(self):
dataset = dataset_ops.Dataset.range(10)
with ops.Graph().as_default():
with self.assertRaisesRegexp(ValueError, "must be from the same graph"):
dataset = dataset.batch(2)
@parameterized.named_parameters(
("Async", context.ASYNC),
("Sync", context.SYNC),
)
def testDatasetEagerIteration(self, execution_mode):
with context.eager_mode(), context.execution_mode(execution_mode):
val = 0
dataset = dataset_ops.Dataset.range(10)
for foo in dataset:
self.assertEqual(val, foo.numpy())
val += 1
def testDatasetAsFunctionArgument(self):
@def_function.function
def _uses_dataset(d):
accumulator = array_ops.zeros([], dtype=dtypes.int64)
for value in d:
accumulator += value
return accumulator
with ops.device("CPU"):
first_dataset = dataset_ops.Dataset.range(10)
self.assertEqual(45, self.evaluate(_uses_dataset(first_dataset)))
second_dataset = dataset_ops.Dataset.range(11)
self.assertEqual(55, self.evaluate(_uses_dataset(second_dataset)))
first_concrete = _uses_dataset.get_concrete_function(first_dataset)
# The dataset should not be a captured input
self.assertEmpty(first_concrete.graph.captures)
# The two datasets have the same structure and so should re-use a trace.
self.assertIs(first_concrete,
_uses_dataset.get_concrete_function(second_dataset))
# With a different structure we should use a different trace.
self.assertIsNot(
first_concrete,
_uses_dataset.get_concrete_function(
dataset_ops.Dataset.zip((first_dataset, second_dataset))))
def testLimitedRetracing(self):
trace_count = [0]
@def_function.function
def f(ds):
trace_count[0] += 1
counter = np.int64(0)
for elem in ds:
counter += elem
return counter
dataset = dataset_ops.Dataset.range(5)
dataset2 = dataset_ops.Dataset.range(10)
for _ in range(10):
self.assertEqual(self.evaluate(f(dataset)), 10)
self.assertEqual(self.evaluate(f(dataset2)), 45)
self.assertEqual(trace_count[0], 1)
def testTFRecordInputs(self):
dataset = readers.TFRecordDataset("")
self.checkNumInputs(dataset, 1)
def _TFRecordDataset(filename): buffer_size = 8 * 1024 * 1024 # 8 MiB per file dataset = readers.TFRecordDataset(filename, buffer_size=buffer_size) return dataset
def make_tf_record_dataset(file_pattern,
batch_size,
parser_fn=None,
num_epochs=None,
shuffle=True,
shuffle_buffer_size=None,
shuffle_seed=None,
prefetch_buffer_size=optimization.AUTOTUNE,
num_parallel_reads=None,
num_parallel_parser_calls=None,
drop_final_batch=False):
"""Reads and optionally parses TFRecord files into a dataset.
Provides common functionality such as batching, optional parsing, shuffling,
and performant defaults.
Args:
file_pattern: List of files or patterns of TFRecord file paths.
See `tf.io.gfile.glob` for pattern rules.
batch_size: An int representing the number of records to combine
in a single batch.
parser_fn: (Optional.) A function accepting string input to parse
and process the record contents. This function must map records
to components of a fixed shape, so they may be batched. By
default, uses the record contents unmodified.
num_epochs: (Optional.) An int specifying the number of times this
dataset is repeated. If None (the default), cycles through the
dataset forever.
shuffle: (Optional.) A bool that indicates whether the input
should be shuffled. Defaults to `True`.
shuffle_buffer_size: (Optional.) Buffer size to use for
shuffling. A large buffer size ensures better shuffling, but
increases memory usage and startup time.
shuffle_seed: (Optional.) Randomization seed to use for shuffling.
prefetch_buffer_size: (Optional.) An int specifying the number of
feature batches to prefetch for performance improvement.
Defaults to auto-tune. Set to 0 to disable prefetching.
num_parallel_reads: (Optional.) Number of threads used to read
records from files. By default or if set to a value >1, the
results will be interleaved.
num_parallel_parser_calls: (Optional.) Number of parallel
records to parse in parallel. Defaults to an automatic selection.
drop_final_batch: (Optional.) Whether the last batch should be
dropped in case its size is smaller than `batch_size`; the
default behavior is not to drop the smaller batch.
Returns:
A dataset, where each element matches the output of `parser_fn`
except it will have an additional leading `batch-size` dimension,
or a `batch_size`-length 1-D tensor of strings if `parser_fn` is
unspecified.
"""
files = dataset_ops.Dataset.list_files(
file_pattern, shuffle=shuffle, seed=shuffle_seed)
if num_parallel_reads is None:
# Note: We considered auto-tuning this value, but there is a concern
# that this affects the mixing of records from different files, which
# could affect training convergence/accuracy, so we are defaulting to
# a constant for now.
num_parallel_reads = 24
dataset = core_readers.TFRecordDataset(
files, num_parallel_reads=num_parallel_reads)
if shuffle_buffer_size is None:
# TODO(josh11b): Auto-tune this value when not specified
shuffle_buffer_size = 10000
dataset = _maybe_shuffle_and_repeat(
dataset, num_epochs, shuffle, shuffle_buffer_size, shuffle_seed)
# NOTE(mrry): We set `drop_final_batch=True` when `num_epochs is None` to
# improve the shape inference, because it makes the batch dimension static.
# It is safe to do this because in that case we are repeating the input
# indefinitely, and all batches will be full-sized.
drop_final_batch = drop_final_batch or num_epochs is None
if parser_fn is None:
dataset = dataset.batch(batch_size, drop_remainder=drop_final_batch)
else:
# TODO(josh11b): if num_parallel_parser_calls is None, use some function
# of num cores instead of map_and_batch's default behavior of one batch.
dataset = dataset.apply(batching.map_and_batch(
parser_fn, batch_size, num_parallel_calls=num_parallel_parser_calls,
drop_remainder=drop_final_batch))
if prefetch_buffer_size == 0:
return dataset
else:
return dataset.prefetch(buffer_size=prefetch_buffer_size)