def testFixedLengthReader(self):
dataset = readers.FixedLengthRecordDataset(
self._createFixedLengthRecordFiles(), self._record_bytes)
dataset = input_ops.auto_shard_dataset(
dataset, self._num_shards, self._shard_index)
self._verifySimpleShardingOutput(dataset, self._fixed_length_record)
def testTextLineReaderWithFlatMap(self):
dataset = dataset_ops.Dataset.from_tensor_slices(self._createTextFiles())
dataset = dataset.flat_map(readers.TextLineDataset)
dataset = input_ops.auto_shard_dataset(
dataset, self._num_shards, self._shard_index)
self._verifySimpleShardingOutput(dataset, self._text_line)
def testFixedLengthReader(self):
dataset = readers.FixedLengthRecordDataset(
self._createFixedLengthRecordFiles(), self._record_bytes)
dataset = input_ops.auto_shard_dataset(dataset, self._num_shards,
self._shard_index)
self._verifySimpleShardingOutput(dataset, self._fixed_length_record)
def __init__(self,
dataset_fn,
worker_device_map,
prefetch_on_device=None,
auto_shard=False):
"""Initialize the MultiWorkerDataset object.
Args:
dataset_fn: a function that returns a `tf.data.Dataset`.
worker_device_map: a dict mapping from each worker to a list of devices
that belong to this worker.
prefetch_on_device: whether to prefetch to devices.
auto_shard: whether to auto-shard the dataset.
"""
self._worker_device_map = worker_device_map
self._datasets = {}
# TODO(yuefengz, priyag): support different set of jobs for input
# processing.
for i, (worker,
worker_devices) in enumerate(six.iteritems(worker_device_map)):
with ops.device(worker):
worker_input = dataset_fn()
if auto_shard:
worker_input = input_ops.auto_shard_dataset(
worker_input, len(worker_device_map), i)
self._datasets[worker] = PerDeviceDataset(
worker_input,
worker_devices,
prefetch_on_device=prefetch_on_device)
def testFlatMap(self):
dataset = dataset_ops.Dataset.from_tensor_slices(
self._createTFRecordFiles())
dataset = dataset.flat_map(readers.TFRecordDataset)
dataset = input_ops.auto_shard_dataset(dataset, self._num_shards,
self._shard_index)
self._verifySimpleShardingOutput(dataset, self._record)
def testTextLineReaderWithFlatMap(self):
dataset = dataset_ops.Dataset.from_tensor_slices(
self._createTextFiles())
dataset = dataset.flat_map(readers.TextLineDataset)
dataset = input_ops.auto_shard_dataset(dataset, self._num_shards,
self._shard_index)
self._verifySimpleShardingOutput(dataset, self._text_line)
def testFlatMap(self):
dataset = dataset_ops.Dataset.from_tensor_slices(
self._createTFRecordFiles())
dataset = dataset.flat_map(readers.TFRecordDataset)
dataset = input_ops.auto_shard_dataset(
dataset, self._num_shards, self._shard_index)
self._verifySimpleShardingOutput(dataset, self._record)
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 testFixedLengthReaderWithFlatMap(self):
dataset = dataset_ops.Dataset.from_tensor_slices(
self._createFixedLengthRecordFiles())
dataset = dataset.flat_map(
lambda f: readers.FixedLengthRecordDataset(f, self._record_bytes))
dataset = input_ops.auto_shard_dataset(
dataset, self._num_shards, self._shard_index)
self._verifySimpleShardingOutput(dataset, self._fixed_length_record)
def testFixedLengthReaderWithFlatMap(self):
dataset = dataset_ops.Dataset.from_tensor_slices(
self._createFixedLengthRecordFiles())
dataset = dataset.flat_map(
lambda f: readers.FixedLengthRecordDataset(f, self._record_bytes))
dataset = input_ops.auto_shard_dataset(dataset, self._num_shards,
self._shard_index)
self._verifySimpleShardingOutput(dataset, self._fixed_length_record)
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 testInterleave(self):
dataset = dataset_ops.Dataset.from_tensor_slices(
self._createTFRecordFiles())
dataset = dataset.interleave(
readers.TFRecordDataset, cycle_length=4, block_length=self._num_records)
dataset = input_ops.auto_shard_dataset(
dataset, self._num_shards, self._shard_index)
# Since block_length == num records in each file, the output will still
# contain records in order of files.
self._verifySimpleShardingOutput(dataset, self._record)
def testInterleave(self):
dataset = dataset_ops.Dataset.from_tensor_slices(
self._createTFRecordFiles())
dataset = dataset.interleave(readers.TFRecordDataset,
cycle_length=4,
block_length=self._num_records)
dataset = input_ops.auto_shard_dataset(dataset, self._num_shards,
self._shard_index)
# Since block_length == num records in each file, the output will still
# contain records in order of files.
self._verifySimpleShardingOutput(dataset, self._record)
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 testListfiles(self):
filenames = self._createTFRecordFiles()
file_pattern = filenames[0].rsplit("/", 1)[0] + "/tf_record.*.txt"
dataset = dataset_ops.Dataset.list_files(file_pattern, shuffle=False)
dataset = dataset.flat_map(readers.TFRecordDataset)
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:
actual, expected = [], []
for f in range(self._shard_index, self._num_files, self._num_shards):
for r in range(self._num_records):
actual.append(sess.run(next_element))
expected.append(self._record(r, f))
with self.assertRaises(errors.OutOfRangeError):
sess.run(next_element)
self.assertAllEqual(expected, actual)
def testListfiles(self):
filenames = self._createTFRecordFiles()
file_pattern = filenames[0].rsplit("/", 1)[0] + "/tf_record.*.txt"
dataset = dataset_ops.Dataset.list_files(file_pattern, shuffle=False)
dataset = dataset.flat_map(readers.TFRecordDataset)
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:
actual, expected = [], []
for f in range(self._shard_index, self._num_files,
self._num_shards):
for r in range(self._num_records):
actual.append(sess.run(next_element))
expected.append(self._record(r, f))
with self.assertRaises(errors.OutOfRangeError):
sess.run(next_element)
self.assertAllEqual(expected, actual)
def __init__(self, dataset_fn, worker_device_map, prefetch_on_device=None):
"""Initialize the MultiWorkerDataset object.
Args:
dataset_fn: a function that returns a `tf.data.Dataset`.
worker_device_map: a dict mapping from each worker to a list of devices
that belong to this worker.
prefetch_on_device: whether to prefetch to devices.
"""
self._worker_device_map = worker_device_map
self._datasets = {}
# TODO(yuefengz, priyag): support different set of jobs for input
# processing.
for i, (worker, worker_devices) in enumerate(
six.iteritems(worker_device_map)):
with ops.device(worker):
worker_input = dataset_fn()
worker_input = input_ops.auto_shard_dataset(
worker_input, len(worker_device_map), i)
self._datasets[worker] = PerDeviceDataset(
worker_input, worker_devices, prefetch_on_device=prefetch_on_device)
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 testComplexPipeline(self):
# Setup a complex input pipeline.
batch_size = 2
num_epochs = 5
dataset = dataset_ops.Dataset.from_tensor_slices(
self._createTFRecordFiles())
dataset = dataset.shuffle(buffer_size=self._num_files)
dataset = dataset.flat_map(readers.TFRecordDataset)
dataset = dataset.prefetch(buffer_size=batch_size)
dataset = dataset.shuffle(2 * self._num_files * self._num_records)
dataset = dataset.repeat(num_epochs)
dataset = dataset.map(lambda x: x)
dataset = dataset.batch(batch_size)
dataset = dataset.prefetch(buffer_size=None)
# Auto shard.
dataset = input_ops.auto_shard_dataset(
dataset, self._num_shards, self._shard_index)
# Verify output.
iterator = dataset.make_one_shot_iterator()
next_element = iterator.get_next()
with self.cached_session() as sess:
actual = []
num_iterations = (self._num_files * self._num_records * num_epochs) // (
self._num_shards * batch_size)
for _ in range(num_iterations):
actual.extend(sess.run(next_element))
with self.assertRaises(errors.OutOfRangeError):
sess.run(next_element)
expected = []
for f in range(0, self._num_files, self._num_shards):
for r in range(self._num_records):
expected.append(self._record(r, f))
expected *= num_epochs
self.assertAllEqual(sorted(expected), sorted(actual))
def testComplexPipeline(self):
# Setup a complex input pipeline.
batch_size = 2
num_epochs = 5
dataset = dataset_ops.Dataset.from_tensor_slices(
self._createTFRecordFiles())
dataset = dataset.shuffle(buffer_size=self._num_files)
dataset = dataset.flat_map(readers.TFRecordDataset)
dataset = dataset.prefetch(buffer_size=batch_size)
dataset = dataset.shuffle(2 * self._num_files * self._num_records)
dataset = dataset.repeat(num_epochs)
dataset = dataset.map(lambda x: x)
dataset = dataset.batch(batch_size)
dataset = dataset.prefetch(buffer_size=None)
# Auto shard.
dataset = input_ops.auto_shard_dataset(dataset, self._num_shards,
self._shard_index)
# Verify output.
iterator = dataset.make_one_shot_iterator()
next_element = iterator.get_next()
with self.cached_session() as sess:
actual = []
num_iterations = (self._num_files * self._num_records *
num_epochs) // (self._num_shards * batch_size)
for _ in range(num_iterations):
actual.extend(sess.run(next_element))
with self.assertRaises(errors.OutOfRangeError):
sess.run(next_element)
expected = []
for f in range(0, self._num_files, self._num_shards):
for r in range(self._num_records):
expected.append(self._record(r, f))
expected *= num_epochs
self.assertAllEqual(sorted(expected), sorted(actual))
def testTFRecordDataset(self):
dataset = readers.TFRecordDataset(self._createTFRecordFiles())
dataset = input_ops.auto_shard_dataset(
dataset, self._num_shards, self._shard_index)
self._verifySimpleShardingOutput(dataset, self._record)
def testTextLineReader(self):
dataset = readers.TextLineDataset(self._createTextFiles())
dataset = input_ops.auto_shard_dataset(dataset, self._num_shards,
self._shard_index)
self._verifySimpleShardingOutput(dataset, self._text_line)
def testTFRecordDataset(self):
dataset = readers.TFRecordDataset(self._createTFRecordFiles())
dataset = input_ops.auto_shard_dataset(dataset, self._num_shards,
self._shard_index)
self._verifySimpleShardingOutput(dataset, self._record)
def testTextLineReader(self):
dataset = readers.TextLineDataset(self._createTextFiles())
dataset = input_ops.auto_shard_dataset(
dataset, self._num_shards, self._shard_index)
self._verifySimpleShardingOutput(dataset, self._text_line)
