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# Copyright 2017 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 `tf.data.experimental.shuffle_and_repeat()`."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl.testing import parameterized
import numpy as np
from tensorflow.python.data.experimental.ops import shuffle_ops
from tensorflow.python.data.kernel_tests import test_base
from tensorflow.python.data.ops import dataset_ops
from tensorflow.python.framework import errors
from tensorflow.python.framework import ops
from tensorflow.python.platform import test
class ShuffleAndRepeatTest(test_base.DatasetTestBase, parameterized.TestCase):
def _build_ds(self, seed, count=5, num_elements=20):
return dataset_ops.Dataset.range(num_elements).apply(
shuffle_ops.shuffle_and_repeat(buffer_size=5, count=count, seed=seed))
def _gen_outputs(self, ds_fn, num_outputs, verify_exhausted=True):
get_next = ds_fn().make_one_shot_iterator().get_next()
outputs = []
with self.cached_session() as sess:
for _ in range(num_outputs):
outputs.append(sess.run(get_next))
if verify_exhausted:
with self.assertRaises(errors.OutOfRangeError):
sess.run(get_next)
return outputs
def testCorrectOutput(self):
output = self._gen_outputs(lambda: self._build_ds(10), 100)
self.assertSequenceEqual(
sorted(output), sorted(
np.array([range(20) for _ in range(5)]).flatten()))
for i in range(5):
self.assertSequenceEqual(sorted(output[i * 20:(i + 1) * 20]), range(20))
def testReshuffling(self):
# Check that the output orders of different epochs are indeed different.
output = self._gen_outputs(lambda: self._build_ds(10), 100)
for i in range(4):
epoch1 = output[i * 20:(i + 1) * 20]
epoch2 = output[(i + 1) * 20:(i + 2) * 20]
self.assertNotEqual(epoch1, epoch2)
def testSameOrderForSameSeeds(self):
output1 = self._gen_outputs(lambda: self._build_ds(10), 100)
output2 = self._gen_outputs(lambda: self._build_ds(10), 100)
self.assertEqual(output1, output2)
def testDifferentOrderForDifferentSeeds(self):
output1 = self._gen_outputs(lambda: self._build_ds(10), 100)
output2 = self._gen_outputs(lambda: self._build_ds(20), 100)
self.assertNotEqual(output1, output2)
self.assertEqual(sorted(output1), sorted(output2))
def testCountNone(self):
output1 = self._gen_outputs(
lambda: self._build_ds(10, count=None), 100, verify_exhausted=False)
output2 = self._gen_outputs(
lambda: self._build_ds(20, count=None), 100, verify_exhausted=False)
self.assertNotEqual(output1, output2)
self.assertEqual(sorted(output1), sorted(output2))
def testCountMinusOne(self):
output1 = self._gen_outputs(
lambda: self._build_ds(10, count=-1), 100, verify_exhausted=False)
output2 = self._gen_outputs(
lambda: self._build_ds(20, count=-1), 100, verify_exhausted=False)
self.assertNotEqual(output1, output2)
self.assertEqual(sorted(output1), sorted(output2))
def testInfiniteOutputs(self):
# Asserting the iterator is exhausted after producing 100 items should fail.
with self.assertRaises(AssertionError):
self._gen_outputs(lambda: self._build_ds(10, count=None), 100)
with self.assertRaises(AssertionError):
self._gen_outputs(lambda: self._build_ds(10, count=-1), 100)
def testInfiniteEmpty(self):
with self.assertRaises(errors.OutOfRangeError):
self._gen_outputs(lambda: self._build_ds(10, count=None, num_elements=0),
100)
with self.assertRaises(errors.OutOfRangeError):
self._gen_outputs(lambda: self._build_ds(10, count=-1, num_elements=0),
100)
def testLargeBufferSize(self):
with ops.Graph().as_default() as g:
ds = dataset_ops.Dataset.range(20).apply(
shuffle_ops.shuffle_and_repeat(buffer_size=21))
get_next_op = ds.make_one_shot_iterator().get_next()
with self.session(graph=g) as sess:
sess.run(get_next_op)
@parameterized.named_parameters(
("NoSeed", None),
("WithSeed", 42),
)
def testShuffleAndRepeatAndZipDataset(self, seed):
dataset = dataset_ops.Dataset.range(10).apply(
shuffle_ops.shuffle_and_repeat(10, count=3, seed=seed))
dataset = dataset_ops.Dataset.zip((dataset, dataset))
iterator = dataset.make_one_shot_iterator()
next_element = iterator.get_next()
with self.cached_session() as sess:
for _ in range(30):
x, y = sess.run(next_element)
self.assertEqual(x, y)
with self.assertRaises(errors.OutOfRangeError):
sess.run(next_element)
if __name__ == "__main__":
test.main()
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