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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 the experimental input pipeline ops."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.python.data.experimental.ops import interleave_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 random_seed
from tensorflow.python.platform import test
class DirectedInterleaveDatasetTest(test_base.DatasetTestBase):
def testBasic(self):
selector_dataset = dataset_ops.Dataset.range(10).repeat(100)
input_datasets = [
dataset_ops.Dataset.from_tensors(i).repeat(100) for i in range(10)
]
dataset = interleave_ops._DirectedInterleaveDataset(selector_dataset,
input_datasets)
iterator = dataset.make_initializable_iterator()
next_element = iterator.get_next()
with self.cached_session() as sess:
sess.run(iterator.initializer)
for _ in range(100):
for i in range(10):
self.assertEqual(i, sess.run(next_element))
with self.assertRaises(errors.OutOfRangeError):
sess.run(next_element)
def _normalize(self, vec):
return vec / vec.sum()
def _chi2(self, expected, actual):
actual = np.asarray(actual)
expected = np.asarray(expected)
diff = actual - expected
chi2 = np.sum(diff * diff / expected, axis=0)
return chi2
def _testSampleFromDatasetsHelper(self, weights, num_datasets, num_samples):
# Create a dataset that samples each integer in `[0, num_datasets)`
# with probability given by `weights[i]`.
dataset = interleave_ops.sample_from_datasets([
dataset_ops.Dataset.from_tensors(i).repeat(None)
for i in range(num_datasets)
], weights)
dataset = dataset.take(num_samples)
iterator = dataset.make_one_shot_iterator()
next_element = iterator.get_next()
with self.cached_session() as sess:
freqs = np.zeros([num_datasets])
for _ in range(num_samples):
freqs[sess.run(next_element)] += 1
with self.assertRaises(errors.OutOfRangeError):
sess.run(next_element)
return freqs
def testSampleFromDatasets(self):
random_seed.set_random_seed(1619)
num_samples = 5000
rand_probs = self._normalize(np.random.random_sample((15,)))
# Use chi-squared test to assert that the observed distribution matches the
# expected distribution. Based on the implementation in
# "third_party/tensorflow/python/kernel_tests/multinomial_op_test.py".
for probs in [[.85, .05, .1], rand_probs, [1.]]:
probs = np.asarray(probs)
classes = len(probs)
freqs = self._testSampleFromDatasetsHelper(probs, classes, num_samples)
self.assertLess(self._chi2(probs, freqs / num_samples), 1e-2)
# Also check that `weights` as a dataset samples correctly.
probs_ds = dataset_ops.Dataset.from_tensors(probs).repeat()
freqs = self._testSampleFromDatasetsHelper(probs_ds, classes, num_samples)
self.assertLess(self._chi2(probs, freqs / num_samples), 1e-2)
def testSelectFromDatasets(self):
words = [b"foo", b"bar", b"baz"]
datasets = [dataset_ops.Dataset.from_tensors(w).repeat() for w in words]
choice_array = np.random.randint(3, size=(15,), dtype=np.int64)
choice_dataset = dataset_ops.Dataset.from_tensor_slices(choice_array)
dataset = interleave_ops.choose_from_datasets(datasets, choice_dataset)
iterator = dataset.make_one_shot_iterator()
next_element = iterator.get_next()
with self.cached_session() as sess:
for i in choice_array:
self.assertEqual(words[i], sess.run(next_element))
with self.assertRaises(errors.OutOfRangeError):
sess.run(next_element)
def testErrors(self):
with self.assertRaisesRegexp(ValueError,
r"vector of length `len\(datasets\)`"):
interleave_ops.sample_from_datasets(
[dataset_ops.Dataset.range(10),
dataset_ops.Dataset.range(20)],
weights=[0.25, 0.25, 0.25, 0.25])
with self.assertRaisesRegexp(TypeError, "`tf.float32` or `tf.float64`"):
interleave_ops.sample_from_datasets(
[dataset_ops.Dataset.range(10),
dataset_ops.Dataset.range(20)],
weights=[1, 1])
with self.assertRaisesRegexp(TypeError, "must have the same type"):
interleave_ops.sample_from_datasets([
dataset_ops.Dataset.from_tensors(0),
dataset_ops.Dataset.from_tensors(0.0)
])
with self.assertRaisesRegexp(TypeError, "tf.int64"):
interleave_ops.choose_from_datasets([
dataset_ops.Dataset.from_tensors(0),
dataset_ops.Dataset.from_tensors(1)
], choice_dataset=dataset_ops.Dataset.from_tensors(1.0))
with self.assertRaisesRegexp(TypeError, "scalar"):
interleave_ops.choose_from_datasets([
dataset_ops.Dataset.from_tensors(0),
dataset_ops.Dataset.from_tensors(1)
], choice_dataset=dataset_ops.Dataset.from_tensors([1.0]))
if __name__ == "__main__":
test.main()
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