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
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
|
# 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 `tf.data.experimental.prefetch_to_device()`."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.core.protobuf import config_pb2
from tensorflow.python.data.experimental.ops import prefetching_ops
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.framework import test_util
from tensorflow.python.platform import test
class PrefetchToDeviceTest(test_base.DatasetTestBase):
def testPrefetchToDevice(self):
host_dataset = dataset_ops.Dataset.range(10)
device_dataset = host_dataset.apply(
prefetching_ops.prefetch_to_device("/cpu:1"))
# NOTE(mrry): This device block creates the "host" dataset and iterator on
# /cpu:0, and ensures that the prefetching is across devices. In typical use
# this would not be necessary, because the GPU device would not support any
# of the dataset-related ops.
with ops.device("/cpu:0"):
iterator = device_dataset.make_one_shot_iterator()
self.assertEqual(host_dataset.output_types, device_dataset.output_types)
self.assertEqual(host_dataset.output_types, iterator.output_types)
self.assertEqual(host_dataset.output_shapes, device_dataset.output_shapes)
self.assertEqual(host_dataset.output_shapes, iterator.output_shapes)
self.assertEqual(host_dataset.output_classes, device_dataset.output_classes)
self.assertEqual(host_dataset.output_classes, iterator.output_classes)
next_element = iterator.get_next()
self.assertEqual(dtypes.int64, next_element.dtype)
self.assertEqual([], next_element.shape)
worker_config = config_pb2.ConfigProto(device_count={"CPU": 2})
with self.test_session(config=worker_config) as sess:
for i in range(10):
self.assertEqual(i, sess.run(next_element))
with self.assertRaises(errors.OutOfRangeError):
sess.run(next_element)
def testPrefetchToSameDevice(self):
host_dataset = dataset_ops.Dataset.range(10)
device_dataset = host_dataset.apply(
prefetching_ops.prefetch_to_device(
"/job:localhost/replica:0/task:0/device:CPU:0"))
# NOTE(mrry): This device block creates the "host" dataset and iterator on
# /cpu:0, and ensures that the prefetching is across devices. In typical use
# this would not be necessary, because the GPU device would not support any
# of the dataset-related ops.
with ops.device("/cpu:0"):
iterator = device_dataset.make_one_shot_iterator()
self.assertEqual(host_dataset.output_types, device_dataset.output_types)
self.assertEqual(host_dataset.output_types, iterator.output_types)
self.assertEqual(host_dataset.output_shapes, device_dataset.output_shapes)
self.assertEqual(host_dataset.output_shapes, iterator.output_shapes)
self.assertEqual(host_dataset.output_classes, device_dataset.output_classes)
self.assertEqual(host_dataset.output_classes, iterator.output_classes)
next_element = iterator.get_next()
self.assertEqual(dtypes.int64, next_element.dtype)
self.assertEqual([], next_element.shape)
with self.cached_session() as sess:
for i in range(10):
self.assertEqual(i, sess.run(next_element))
with self.assertRaises(errors.OutOfRangeError):
sess.run(next_element)
def testPrefetchDictToDevice(self):
host_dataset = dataset_ops.Dataset.range(10).map(lambda x: {"a": x})
device_dataset = host_dataset.apply(
prefetching_ops.prefetch_to_device("/cpu:1"))
# NOTE(mrry): This device block creates the "host" dataset and iterator on
# /cpu:0, and ensures that the prefetching is across devices. In typical use
# this would not be necessary, because the GPU device would not support any
# of the dataset-related ops.
with ops.device("/cpu:0"):
iterator = device_dataset.make_one_shot_iterator()
self.assertEqual(host_dataset.output_types, device_dataset.output_types)
self.assertEqual(host_dataset.output_types, iterator.output_types)
self.assertEqual(host_dataset.output_shapes, device_dataset.output_shapes)
self.assertEqual(host_dataset.output_shapes, iterator.output_shapes)
self.assertEqual(host_dataset.output_classes, device_dataset.output_classes)
self.assertEqual(host_dataset.output_classes, iterator.output_classes)
next_element = iterator.get_next()
self.assertEqual(dtypes.int64, next_element["a"].dtype)
self.assertEqual([], next_element["a"].shape)
worker_config = config_pb2.ConfigProto(device_count={"CPU": 2})
with self.test_session(config=worker_config) as sess:
for i in range(10):
self.assertEqual({"a": i}, sess.run(next_element))
with self.assertRaises(errors.OutOfRangeError):
sess.run(next_element)
def testPrefetchSparseTensorsToDevice(self):
def make_tensor(i):
return sparse_tensor.SparseTensorValue(
indices=[[0, 0]], values=(i*[1]), dense_shape=[2, 2])
host_dataset = dataset_ops.Dataset.range(10).map(make_tensor)
device_dataset = host_dataset.apply(
prefetching_ops.prefetch_to_device("/cpu:1"))
# NOTE(mrry): This device block creates the "host" dataset and iterator on
# /cpu:0, and ensures that the prefetching is across devices. In typical use
# this would not be necessary, because the GPU device would not support any
# of the dataset-related ops.
with ops.device("/cpu:0"):
iterator = device_dataset.make_one_shot_iterator()
self.assertEqual(host_dataset.output_types, device_dataset.output_types)
self.assertEqual(host_dataset.output_types, iterator.output_types)
self.assertEqual(host_dataset.output_shapes, device_dataset.output_shapes)
self.assertEqual(host_dataset.output_shapes, iterator.output_shapes)
self.assertEqual(host_dataset.output_classes, device_dataset.output_classes)
self.assertEqual(host_dataset.output_classes, iterator.output_classes)
next_element = iterator.get_next()
self.assertEqual(dtypes.int64, next_element.dtype)
worker_config = config_pb2.ConfigProto(device_count={"CPU": 2})
with self.test_session(config=worker_config) as sess:
for i in range(10):
actual = sess.run(next_element)
self.assertAllEqual([i], actual.values)
self.assertAllEqual([[0, 0]], actual.indices)
self.assertAllEqual([2, 2], actual.dense_shape)
with self.assertRaises(errors.OutOfRangeError):
sess.run(next_element)
def testPrefetchToDeviceGpu(self):
if not test_util.is_gpu_available():
self.skipTest("No GPU available")
host_dataset = dataset_ops.Dataset.range(10)
device_dataset = host_dataset.apply(
prefetching_ops.prefetch_to_device("/gpu:0"))
iterator = device_dataset.make_one_shot_iterator()
next_element = iterator.get_next()
with self.cached_session() as sess:
for i in range(10):
self.assertEqual(i, sess.run(next_element))
with self.assertRaises(errors.OutOfRangeError):
sess.run(next_element)
def testPrefetchToDeviceWithReInit(self):
host_dataset = dataset_ops.Dataset.range(10)
device_dataset = host_dataset.apply(
prefetching_ops.prefetch_to_device("/cpu:1"))
# NOTE(mrry): This device block creates the "host" dataset and iterator on
# /cpu:0, and ensures that the prefetching is across devices. In typical use
# this would not be necessary, because the GPU device would not support any
# of the dataset-related ops.
with ops.device("/cpu:0"):
iterator = device_dataset.make_initializable_iterator()
self.assertEqual(host_dataset.output_types, device_dataset.output_types)
self.assertEqual(host_dataset.output_types, iterator.output_types)
self.assertEqual(host_dataset.output_shapes, device_dataset.output_shapes)
self.assertEqual(host_dataset.output_shapes, iterator.output_shapes)
self.assertEqual(host_dataset.output_classes, device_dataset.output_classes)
self.assertEqual(host_dataset.output_classes, iterator.output_classes)
next_element = iterator.get_next()
self.assertEqual(dtypes.int64, next_element.dtype)
self.assertEqual([], next_element.shape)
worker_config = config_pb2.ConfigProto(device_count={"CPU": 2})
with self.test_session(config=worker_config) as sess:
sess.run(iterator.initializer)
for i in range(5):
self.assertEqual(i, sess.run(next_element))
sess.run(iterator.initializer)
for i in range(10):
self.assertEqual(i, sess.run(next_element))
with self.assertRaises(errors.OutOfRangeError):
sess.run(next_element)
def testPrefetchToDeviceGpuWithReInit(self):
if not test_util.is_gpu_available():
self.skipTest("No GPU available")
host_dataset = dataset_ops.Dataset.range(10)
device_dataset = host_dataset.apply(
prefetching_ops.prefetch_to_device("/gpu:0"))
iterator = device_dataset.make_initializable_iterator()
next_element = iterator.get_next()
with self.cached_session() as sess:
sess.run(iterator.initializer)
for i in range(5):
self.assertEqual(i, sess.run(next_element))
sess.run(iterator.initializer)
for i in range(10):
self.assertEqual(i, sess.run(next_element))
with self.assertRaises(errors.OutOfRangeError):
sess.run(next_element)
if __name__ == "__main__":
test.main()
|
