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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
from absl.testing import parameterized
import numpy as np
from tensorflow.contrib.data.python.ops import get_single_element
from tensorflow.contrib.data.python.ops import grouping
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 sparse_tensor
from tensorflow.python.ops import array_ops
from tensorflow.python.platform import test
class GetSingleElementTest(test.TestCase, parameterized.TestCase):
@parameterized.named_parameters(
("Zero", 0, 1),
("Five", 5, 1),
("Ten", 10, 1),
("Empty", 100, 1, errors.InvalidArgumentError, "Dataset was empty."),
("MoreThanOne", 0, 2, errors.InvalidArgumentError,
"Dataset had more than one element."),
)
def testGetSingleElement(self, skip, take, error=None, error_msg=None):
skip_t = array_ops.placeholder(dtypes.int64, shape=[])
take_t = array_ops.placeholder(dtypes.int64, shape=[])
def make_sparse(x):
x_1d = array_ops.reshape(x, [1])
x_2d = array_ops.reshape(x, [1, 1])
return sparse_tensor.SparseTensor(x_2d, x_1d, x_1d)
dataset = dataset_ops.Dataset.range(100).skip(skip_t).map(
lambda x: (x * x, make_sparse(x))).take(take_t)
element = get_single_element.get_single_element(dataset)
with self.test_session() as sess:
if error is None:
dense_val, sparse_val = sess.run(
element, feed_dict={
skip_t: skip,
take_t: take
})
self.assertEqual(skip * skip, dense_val)
self.assertAllEqual([[skip]], sparse_val.indices)
self.assertAllEqual([skip], sparse_val.values)
self.assertAllEqual([skip], sparse_val.dense_shape)
else:
with self.assertRaisesRegexp(error, error_msg):
sess.run(element, feed_dict={skip_t: skip, take_t: take})
@parameterized.named_parameters(
("SumZero", 0),
("SumOne", 1),
("SumFive", 5),
("SumTen", 10),
)
def testReduceDataset(self, stop):
def init_fn(_):
return np.int64(0)
def reduce_fn(state, value):
return state + value
def finalize_fn(state):
return state
sum_reducer = grouping.Reducer(init_fn, reduce_fn, finalize_fn)
stop_t = array_ops.placeholder(dtypes.int64, shape=[])
dataset = dataset_ops.Dataset.range(stop_t)
element = get_single_element.reduce_dataset(dataset, sum_reducer)
with self.test_session() as sess:
value = sess.run(element, feed_dict={stop_t: stop})
self.assertEqual(stop * (stop - 1) / 2, value)
if __name__ == "__main__":
test.main()
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