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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 time
import numpy as np
from tensorflow.python.client import session
from tensorflow.python.data.kernel_tests import test_base
from tensorflow.python.data.ops import dataset_ops
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import errors
from tensorflow.python.framework import ops
from tensorflow.python.framework import sparse_tensor
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import functional_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.platform import test
class FilterDatasetTest(test_base.DatasetTestBase):
def testFilterDataset(self):
components = (
np.arange(7, dtype=np.int64),
np.array([[1, 2, 3]], dtype=np.int64) * np.arange(
7, dtype=np.int64)[:, np.newaxis],
np.array(37.0, dtype=np.float64) * np.arange(7)
)
count = array_ops.placeholder(dtypes.int64, shape=[])
modulus = array_ops.placeholder(dtypes.int64)
def _map_fn(x, y, z):
return math_ops.square(x), math_ops.square(y), math_ops.square(z)
iterator = (
dataset_ops.Dataset.from_tensor_slices(components).map(_map_fn)
.repeat(count)
.filter(lambda x, _y, _z: math_ops.equal(math_ops.mod(x, modulus), 0))
.make_initializable_iterator())
init_op = iterator.initializer
get_next = iterator.get_next()
self.assertEqual([c.shape[1:] for c in components],
[t.shape for t in get_next])
with self.cached_session() as sess:
# Test that we can dynamically feed a different modulus value for each
# iterator.
def do_test(count_val, modulus_val):
sess.run(init_op, feed_dict={count: count_val, modulus: modulus_val})
for _ in range(count_val):
for i in [x for x in range(7) if x**2 % modulus_val == 0]:
result = sess.run(get_next)
for component, result_component in zip(components, result):
self.assertAllEqual(component[i]**2, result_component)
with self.assertRaises(errors.OutOfRangeError):
sess.run(get_next)
do_test(14, 2)
do_test(4, 18)
# Test an empty dataset.
do_test(0, 1)
def testFilterRange(self):
dataset = dataset_ops.Dataset.range(100).filter(
lambda x: math_ops.not_equal(math_ops.mod(x, 3), 2))
iterator = dataset.make_one_shot_iterator()
get_next = iterator.get_next()
with self.cached_session() as sess:
self.assertEqual(0, sess.run(get_next))
self.assertEqual(1, sess.run(get_next))
self.assertEqual(3, sess.run(get_next))
def testFilterDict(self):
iterator = (dataset_ops.Dataset.range(10)
.map(lambda x: {"foo": x * 2, "bar": x ** 2})
.filter(lambda d: math_ops.equal(d["bar"] % 2, 0))
.map(lambda d: d["foo"] + d["bar"])
.make_initializable_iterator())
init_op = iterator.initializer
get_next = iterator.get_next()
with self.cached_session() as sess:
sess.run(init_op)
for i in range(10):
if (i ** 2) % 2 == 0:
self.assertEqual(i * 2 + i ** 2, sess.run(get_next))
with self.assertRaises(errors.OutOfRangeError):
sess.run(get_next)
def testUseStepContainerInFilter(self):
input_data = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int64)
# Define a predicate that returns true for the first element of
# the sequence and not the second, and uses `tf.map_fn()`.
def _predicate(xs):
squared_xs = functional_ops.map_fn(lambda x: x * x, xs)
summed = math_ops.reduce_sum(squared_xs)
return math_ops.equal(summed, 1 + 4 + 9)
iterator = (
dataset_ops.Dataset.from_tensor_slices([[1, 2, 3], [4, 5, 6]])
.filter(_predicate)
.make_initializable_iterator())
init_op = iterator.initializer
get_next = iterator.get_next()
with self.cached_session() as sess:
sess.run(init_op)
self.assertAllEqual(input_data[0], sess.run(get_next))
with self.assertRaises(errors.OutOfRangeError):
sess.run(get_next)
def testSparse(self):
def _map_fn(i):
return sparse_tensor.SparseTensorValue(
indices=np.array([[0, 0]]),
values=(i * np.array([1])),
dense_shape=np.array([1, 1])), i
def _filter_fn(_, i):
return math_ops.equal(i % 2, 0)
iterator = (
dataset_ops.Dataset.range(10).map(_map_fn).filter(_filter_fn).map(
lambda x, i: x).make_initializable_iterator())
init_op = iterator.initializer
get_next = iterator.get_next()
with self.cached_session() as sess:
sess.run(init_op)
for i in range(5):
actual = sess.run(get_next)
self.assertTrue(isinstance(actual, sparse_tensor.SparseTensorValue))
self.assertSparseValuesEqual(actual, _map_fn(i * 2)[0])
with self.assertRaises(errors.OutOfRangeError):
sess.run(get_next)
def testShortCircuit(self):
iterator = (
dataset_ops.Dataset.zip(
(dataset_ops.Dataset.range(10),
dataset_ops.Dataset.from_tensors(True).repeat(None)))
.filter(lambda x, y: y).make_initializable_iterator())
init_op = iterator.initializer
get_next = iterator.get_next()
with self.cached_session() as sess:
sess.run(init_op)
for i in range(10):
self.assertEqual((i, True), sess.run(get_next))
with self.assertRaises(errors.OutOfRangeError):
sess.run(get_next)
def testParallelFilters(self):
dataset = dataset_ops.Dataset.range(10).filter(
lambda x: math_ops.equal(x % 2, 0))
iterators = [dataset.make_one_shot_iterator() for _ in range(10)]
next_elements = [iterator.get_next() for iterator in iterators]
with self.cached_session() as sess:
self.assertEqual([0 for _ in range(10)], sess.run(next_elements))
class FilterDatasetBenchmark(test.Benchmark):
def _benchmark(self, predicate, name):
with ops.Graph().as_default():
dataset = (
dataset_ops.Dataset.from_tensors(True).repeat(None).filter(predicate))
iterator = dataset.make_one_shot_iterator()
next_element = iterator.get_next()
with session.Session() as sess:
for _ in range(5):
sess.run(next_element.op)
deltas = []
for _ in range(100):
start = time.time()
for _ in range(100):
sess.run(next_element.op)
end = time.time()
deltas.append(end - start)
median_wall_time = np.median(deltas) / 100
print("Filter dataset using %s. Median wall time: %f" %
(name, median_wall_time))
self.report_benchmark(
iters=100,
wall_time=median_wall_time,
name="benchmark_filter_dataset_%s" % name)
def benchmarkSimpleFunction(self):
self._benchmark(array_ops.identity, "simple_function")
def benchmarkReturnComponentOptimization(self):
self._benchmark(lambda x: x, "return_component")
if __name__ == "__main__":
test.main()
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