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
235
|
# Copyright 2015 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 DynamicPartition op."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from six.moves import xrange # pylint: disable=redefined-builtin
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import data_flow_ops
from tensorflow.python.ops import gradients_impl
import tensorflow.python.ops.data_flow_grad # pylint: disable=unused-import
from tensorflow.python.platform import test
class DynamicPartitionTest(test.TestCase):
def testSimpleOneDimensional(self):
with self.test_session(use_gpu=True) as sess:
data = constant_op.constant([0, 13, 2, 39, 4, 17], dtype=dtypes.float32)
indices = constant_op.constant([0, 0, 2, 3, 2, 1])
partitions = data_flow_ops.dynamic_partition(
data, indices, num_partitions=4)
partition_vals = sess.run(partitions)
self.assertAllEqual([0, 13], partition_vals[0])
self.assertAllEqual([17], partition_vals[1])
self.assertAllEqual([2, 4], partition_vals[2])
self.assertAllEqual([39], partition_vals[3])
# Vector data input to DynamicPartition results in
# `num_partitions` vectors of unknown length.
self.assertEqual([None], partitions[0].get_shape().as_list())
self.assertEqual([None], partitions[1].get_shape().as_list())
self.assertEqual([None], partitions[2].get_shape().as_list())
self.assertEqual([None], partitions[3].get_shape().as_list())
def testSimpleTwoDimensional(self):
with self.test_session(use_gpu=True) as sess:
data = constant_op.constant([[0, 1, 2], [3, 4, 5], [6, 7, 8], [9, 10, 11],
[12, 13, 14], [15, 16, 17]],
dtype=dtypes.float32)
indices = constant_op.constant([0, 0, 2, 3, 2, 1])
partitions = data_flow_ops.dynamic_partition(
data, indices, num_partitions=4)
partition_vals = sess.run(partitions)
self.assertAllEqual([[0, 1, 2], [3, 4, 5]], partition_vals[0])
self.assertAllEqual([[15, 16, 17]], partition_vals[1])
self.assertAllEqual([[6, 7, 8], [12, 13, 14]], partition_vals[2])
self.assertAllEqual([[9, 10, 11]], partition_vals[3])
# Vector data input to DynamicPartition results in
# `num_partitions` matrices with an unknown number of rows, and 3 columns.
self.assertEqual([None, 3], partitions[0].get_shape().as_list())
self.assertEqual([None, 3], partitions[1].get_shape().as_list())
self.assertEqual([None, 3], partitions[2].get_shape().as_list())
self.assertEqual([None, 3], partitions[3].get_shape().as_list())
def testLargeOneDimensional(self):
num = 100000
data_list = [x for x in range(num)]
indices_list = [x % 2 for x in range(num)]
part1 = [x for x in range(num) if x % 2 == 0]
part2 = [x for x in range(num) if x % 2 == 1]
with self.test_session(use_gpu=True) as sess:
data = constant_op.constant(data_list, dtype=dtypes.float32)
indices = constant_op.constant(indices_list, dtype=dtypes.int32)
partitions = data_flow_ops.dynamic_partition(
data, indices, num_partitions=2)
partition_vals = sess.run(partitions)
self.assertAllEqual(part1, partition_vals[0])
self.assertAllEqual(part2, partition_vals[1])
def testLargeTwoDimensional(self):
rows = 100000
cols = 100
data_list = [None] * rows
for i in range(rows):
data_list[i] = [i for _ in range(cols)]
num_partitions = 97
indices_list = [(i ** 2) % num_partitions for i in range(rows)]
parts = [[] for _ in range(num_partitions)]
for i in range(rows):
parts[(i ** 2) % num_partitions].append(data_list[i])
with self.test_session(use_gpu=True) as sess:
data = constant_op.constant(data_list, dtype=dtypes.float32)
indices = constant_op.constant(indices_list, dtype=dtypes.int32)
partitions = data_flow_ops.dynamic_partition(
data, indices, num_partitions=num_partitions)
partition_vals = sess.run(partitions)
for i in range(num_partitions):
# reshape because of empty parts
parts_np = np.array(parts[i], dtype=np.float).reshape(-1, cols)
self.assertAllEqual(parts_np, partition_vals[i])
def testSimpleComplex(self):
data_list = [1 + 2j, 3 + 4j, 5 + 6j, 7 + 8j]
indices_list = [1, 0, 1, 0]
with self.test_session(use_gpu=True) as sess:
data = constant_op.constant(data_list, dtype=dtypes.complex64)
indices = constant_op.constant(indices_list, dtype=dtypes.int32)
partitions = data_flow_ops.dynamic_partition(
data, indices, num_partitions=2)
partition_vals = sess.run(partitions)
self.assertAllEqual([3 + 4j, 7 + 8j], partition_vals[0])
self.assertAllEqual([1 + 2j, 5 + 6j], partition_vals[1])
def testHigherRank(self):
np.random.seed(7)
with self.test_session(use_gpu=True) as sess:
for n in 2, 3:
for shape in (4,), (4, 5), (4, 5, 2):
partitions = np.random.randint(n, size=np.prod(shape)).reshape(shape)
for extra_shape in (), (6,), (6, 7):
data = np.random.randn(*(shape + extra_shape))
partitions_t = constant_op.constant(partitions, dtype=dtypes.int32)
data_t = constant_op.constant(data)
outputs = data_flow_ops.dynamic_partition(
data_t, partitions_t, num_partitions=n)
self.assertEqual(n, len(outputs))
outputs_val = sess.run(outputs)
for i, output in enumerate(outputs_val):
self.assertAllEqual(output, data[partitions == i])
# Test gradients
outputs_grad = [7 * output for output in outputs_val]
grads = gradients_impl.gradients(outputs, [data_t, partitions_t],
outputs_grad)
self.assertEqual(grads[1], None) # Partitions has no gradients
self.assertAllEqual(7 * data, sess.run(grads[0]))
def testEmptyParts(self):
data_list = [1, 2, 3, 4]
indices_list = [1, 3, 1, 3]
with self.test_session(use_gpu=True) as sess:
data = constant_op.constant(data_list, dtype=dtypes.float32)
indices = constant_op.constant(indices_list, dtype=dtypes.int32)
partitions = data_flow_ops.dynamic_partition(
data, indices, num_partitions=4)
partition_vals = sess.run(partitions)
self.assertAllEqual([], partition_vals[0])
self.assertAllEqual([1, 3], partition_vals[1])
self.assertAllEqual([], partition_vals[2])
self.assertAllEqual([2, 4], partition_vals[3])
def testEmptyDataTwoDimensional(self):
data_list = [[], []]
indices_list = [0, 1]
with self.test_session(use_gpu=True) as sess:
data = constant_op.constant(data_list, dtype=dtypes.float32)
indices = constant_op.constant(indices_list, dtype=dtypes.int32)
partitions = data_flow_ops.dynamic_partition(
data, indices, num_partitions=3)
partition_vals = sess.run(partitions)
self.assertAllEqual([[]], partition_vals[0])
self.assertAllEqual([[]], partition_vals[1])
self.assertAllEqual(np.array([], dtype=np.float).reshape(0, 0),
partition_vals[2])
def testEmptyPartitions(self):
data_list = []
indices_list = []
with self.test_session(use_gpu=True) as sess:
data = constant_op.constant(data_list, dtype=dtypes.float32)
indices = constant_op.constant(indices_list, dtype=dtypes.int32)
partitions = data_flow_ops.dynamic_partition(
data, indices, num_partitions=2)
partition_vals = sess.run(partitions)
self.assertAllEqual([], partition_vals[0])
self.assertAllEqual([], partition_vals[1])
def testErrorIndexOutOfRange(self):
with self.test_session() as sess:
data = constant_op.constant([[0, 1, 2], [3, 4, 5], [6, 7, 8], [9, 10, 11],
[12, 13, 14]])
indices = constant_op.constant([0, 2, 99, 2, 2])
partitions = data_flow_ops.dynamic_partition(
data, indices, num_partitions=4)
with self.assertRaisesOpError(r"partitions\[2\] = 99 is not in \[0, 4\)"):
sess.run(partitions)
def testScalarIndexOutOfRange(self):
with self.test_session() as sess:
bad = 17
data = np.zeros(5)
partitions = data_flow_ops.dynamic_partition(data, bad, num_partitions=7)
with self.assertRaisesOpError(r"partitions = 17 is not in \[0, 7\)"):
sess.run(partitions)
def testHigherRankIndexOutOfRange(self):
with self.test_session() as sess:
shape = (2, 3)
indices = array_ops.placeholder(shape=shape, dtype=np.int32)
data = np.zeros(shape + (5,))
partitions = data_flow_ops.dynamic_partition(
data, indices, num_partitions=7)
for i in xrange(2):
for j in xrange(3):
bad = np.zeros(shape, dtype=np.int32)
bad[i, j] = 17
with self.assertRaisesOpError(
r"partitions\[%d,%d\] = 17 is not in \[0, 7\)" % (i, j)):
sess.run(partitions, feed_dict={indices: bad})
def testErrorWrongDimsIndices(self):
data = constant_op.constant([[0], [1], [2]])
indices = constant_op.constant([[0], [0]])
with self.assertRaises(ValueError):
data_flow_ops.dynamic_partition(data, indices, num_partitions=4)
if __name__ == "__main__":
test.main()
|
