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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 the experimental input pipeline ops."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.contrib.data.python.ops import optimization
from tensorflow.core.framework import graph_pb2
from tensorflow.python.data.ops import dataset_ops
from tensorflow.python.framework import errors
from tensorflow.python.platform import test
class OptimizeDatasetTest(test.TestCase):
def testDefaultOptimizations(self):
dataset = dataset_ops.Dataset.range(10).map(lambda x: x * x).batch(
10).apply(optimization.optimize())
iterator = dataset.make_one_shot_iterator()
get_next = iterator.get_next()
with self.test_session() as sess:
graph = graph_pb2.GraphDef().FromString(
sess.run(dataset._as_serialized_graph()))
self.assertTrue(
all([node.op != "MapAndBatchDatasetV2" for node in graph.node]))
self.assertAllEqual([x * x for x in range(10)], sess.run(get_next))
with self.assertRaises(errors.OutOfRangeError):
sess.run(get_next)
def testEmptyOptimizations(self):
dataset = dataset_ops.Dataset.range(10).map(lambda x: x * x).batch(
10).apply(optimization.optimize([]))
iterator = dataset.make_one_shot_iterator()
get_next = iterator.get_next()
with self.test_session() as sess:
graph = graph_pb2.GraphDef().FromString(
sess.run(dataset._as_serialized_graph()))
self.assertTrue(
all([node.op != "MapAndBatchDatasetV2" for node in graph.node]))
self.assertAllEqual([x * x for x in range(10)], sess.run(get_next))
with self.assertRaises(errors.OutOfRangeError):
sess.run(get_next)
def testOptimization(self):
dataset = dataset_ops.Dataset.range(10).map(lambda x: x * x).batch(
10).apply(optimization.optimize(["map_and_batch_fusion"]))
iterator = dataset.make_one_shot_iterator()
get_next = iterator.get_next()
with self.test_session() as sess:
graph = graph_pb2.GraphDef().FromString(
sess.run(dataset._as_serialized_graph()))
self.assertTrue(
any([node.op == "MapAndBatchDatasetV2" for node in graph.node]))
self.assertAllEqual([x * x for x in range(10)], sess.run(get_next))
with self.assertRaises(errors.OutOfRangeError):
sess.run(get_next)
def testFunctionLibraryDefinitionModification(self):
dataset = dataset_ops.Dataset.from_tensors(0).map(lambda x: x).apply(
optimization.optimize(["_test_only_function_rename"]))
iterator = dataset.make_one_shot_iterator()
get_next = iterator.get_next()
with self.test_session() as sess:
with self.assertRaisesRegexp(errors.NotFoundError,
"Function .* is not defined."):
sess.run(get_next)
if __name__ == "__main__":
test.main()
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