1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
|
# 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 random
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 errors
from tensorflow.python.framework import sparse_tensor
from tensorflow.python.ops import sparse_ops
from tensorflow.python.platform import test
from tensorflow.python.training import server_lib
class FlatMapDatasetTest(test_base.DatasetTestBase):
# pylint: disable=g-long-lambda
def testFlatMapDataset(self):
repeats = [1, 2, 3, 4, 5, 0, 1]
components = np.array(repeats, dtype=np.int64)
iterator = (
dataset_ops.Dataset.from_tensor_slices(components)
.flat_map(lambda x: dataset_ops.Dataset.from_tensors([x]).repeat(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 repeats:
for _ in range(i):
self.assertEqual(i, sess.run(get_next))
with self.assertRaises(errors.OutOfRangeError):
sess.run(get_next)
def testNestedFlatMapDataset(self):
repeats = [[1, 2], [3, 4], [5, 0], [1, 7]]
components = np.array(repeats, dtype=np.int64)
iterator = (
dataset_ops.Dataset.from_tensor_slices(components)
.flat_map(lambda x: dataset_ops.Dataset.from_tensor_slices(x)
.flat_map(lambda y: dataset_ops.Dataset.from_tensors(y)
.repeat(y))).make_initializable_iterator())
init_op = iterator.initializer
get_next = iterator.get_next()
with self.cached_session() as sess:
sess.run(init_op)
for row in repeats:
for i in row:
for _ in range(i):
self.assertEqual(i, sess.run(get_next))
with self.assertRaises(errors.OutOfRangeError):
sess.run(get_next)
def testSharedResourceNestedFlatMapDataset(self):
repeats = [[1, 2], [3, 4], [5, 0], [1, 7]]
components = np.array(repeats, dtype=np.int64)
iterator = (
dataset_ops.Dataset.from_tensor_slices(components)
.flat_map(lambda x: dataset_ops.Dataset.from_tensor_slices(x)
.flat_map(lambda y: dataset_ops.Dataset.from_tensors(y)
.repeat(y))).make_initializable_iterator(
shared_name="shared_flat_map_iterator"))
init_op = iterator.initializer
get_next = iterator.get_next()
# Create two concurrent sessions that share the same iterator
# resource on the same server, and verify that a random
# interleaving of `Session.run(get_next)` calls on the two
# sessions yields the expected result.
server = server_lib.Server.create_local_server()
with session.Session(server.target) as sess1:
with session.Session(server.target) as sess2:
for _ in range(3):
sess = random.choice([sess1, sess2])
sess.run(init_op)
for row in repeats:
for i in row:
for _ in range(i):
sess = random.choice([sess1, sess2])
self.assertEqual(i, sess.run(get_next))
with self.assertRaises(errors.OutOfRangeError):
sess = random.choice([sess1, sess2])
sess.run(get_next)
def testMapDict(self):
iterator = (dataset_ops.Dataset.range(10)
.map(lambda x: {"foo": x * 2, "bar": x ** 2})
.flat_map(lambda d: dataset_ops.Dataset.from_tensors(d["foo"])
.repeat(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):
for _ in range(i ** 2):
self.assertEqual(i * 2, sess.run(get_next))
with self.assertRaises(errors.OutOfRangeError):
sess.run(get_next)
# pylint: enable=g-long-lambda
def testSparse(self):
def _map_fn(i):
return sparse_tensor.SparseTensorValue(
indices=[[0, 0], [1, 1]], values=(i * [1, -1]), dense_shape=[2, 2])
def _flat_map_fn(x):
return dataset_ops.Dataset.from_tensor_slices(
sparse_ops.sparse_to_dense(x.indices, x.dense_shape, x.values))
iterator = (
dataset_ops.Dataset.range(10).map(_map_fn).flat_map(_flat_map_fn)
.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):
for j in range(2):
expected = [i, 0] if j % 2 == 0 else [0, -i]
self.assertAllEqual(expected, sess.run(get_next))
with self.assertRaises(errors.OutOfRangeError):
sess.run(get_next)
if __name__ == "__main__":
test.main()
|
