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# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# 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 input pipeline modifications for distribution strategies."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from tensorflow.contrib.distribute.python import input_ops
from tensorflow.python.data.ops import dataset_ops
from tensorflow.python.data.ops import readers
from tensorflow.python.framework import errors
from tensorflow.python.lib.io import python_io
from tensorflow.python.platform import test
from tensorflow.python.util import compat
class AutoShardDatasetTest(test.TestCase):
def setUp(self):
super(AutoShardDatasetTest, self).setUp()
self._num_files = 10
self._num_records = 4
self._num_shards = 2
self._shard_index = 0
self._record_bytes = 10
def _record(self, r, f):
return compat.as_bytes("Record %d of file %d" % (r, f))
def _text_line(self, r, f):
return compat.as_bytes("Text line %d of file %d" % (r, f))
def _fixed_length_record(self, r, f):
return compat.as_bytes(str((r * f) % 10) * self._record_bytes)
def _createTFRecordFiles(self):
filenames = []
for i in range(self._num_files):
fn = os.path.join(self.get_temp_dir(), "tf_record.%d.txt" % i)
filenames.append(fn)
writer = python_io.TFRecordWriter(fn)
for j in range(self._num_records):
record = self._record(j, i)
writer.write(record)
writer.close()
return filenames
def _createTextFiles(self):
filenames = []
for i in range(self._num_files):
fn = os.path.join(self.get_temp_dir(), "text_line.%d.txt" % i)
filenames.append(fn)
contents = []
for j in range(self._num_records):
contents.append(self._text_line(j, i))
if j + 1 != self._num_records or i == 0:
contents.append(b"\r\n")
contents = b"".join(contents)
with open(fn, "wb") as f:
f.write(contents)
return filenames
def _createFixedLengthRecordFiles(self):
filenames = []
for i in range(self._num_files):
fn = os.path.join(self.get_temp_dir(), "fixed_length_record.%d.txt" % i)
filenames.append(fn)
with open(fn, "wb") as f:
for j in range(self._num_records):
f.write(self._fixed_length_record(j, i))
return filenames
def _verifySimpleShardingOutput(self, dataset, record_fn):
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(record_fn(r, f), sess.run(next_element))
with self.assertRaises(errors.OutOfRangeError):
sess.run(next_element)
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 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 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 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 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 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 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 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 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 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)
if __name__ == "__main__":
test.main()
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