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
|
# 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 utilities working with arbitrarily nested structures."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.data.util import random_seed as data_random_seed
from tensorflow.python.eager import context
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import random_seed
from tensorflow.python.framework import test_util
from tensorflow.python.platform import test
class RandomSeedTest(test.TestCase):
@test_util.run_in_graph_and_eager_modes
def testRandomSeed(self):
zero_t = constant_op.constant(0, dtype=dtypes.int64, name='zero')
one_t = constant_op.constant(1, dtype=dtypes.int64, name='one')
intmax_t = constant_op.constant(
2**31 - 1, dtype=dtypes.int64, name='intmax')
test_cases = [
# Each test case is a tuple with input to get_seed:
# (input_graph_seed, input_op_seed)
# and output from get_seed:
# (output_graph_seed, output_op_seed)
((None, 1), (random_seed.DEFAULT_GRAPH_SEED, 1)),
((1, 1), (1, 1)),
((0, 0), (0, 2**31 - 1)), # Avoid nondeterministic (0, 0) output
((2**31 - 1, 0), (0, 2**31 - 1)), # Don't wrap to (0, 0) either
((0, 2**31 - 1), (0, 2**31 - 1)), # Wrapping for the other argument
# Once more, with tensor-valued arguments
((None, one_t), (random_seed.DEFAULT_GRAPH_SEED, 1)),
((1, one_t), (1, 1)),
((0, zero_t), (0, 2**31 - 1)), # Avoid nondeterministic (0, 0) output
((2**31 - 1, zero_t), (0, 2**31 - 1)), # Don't wrap to (0, 0) either
((0, intmax_t), (0, 2**31 - 1)), # Wrapping for the other argument
]
for tc in test_cases:
tinput, toutput = tc[0], tc[1]
random_seed.set_random_seed(tinput[0])
g_seed, op_seed = data_random_seed.get_seed(tinput[1])
g_seed = self.evaluate(g_seed)
op_seed = self.evaluate(op_seed)
msg = 'test_case = {0}, got {1}, want {2}'.format(
tinput, (g_seed, op_seed), toutput)
self.assertEqual((g_seed, op_seed), toutput, msg=msg)
random_seed.set_random_seed(None)
if not context.executing_eagerly():
random_seed.set_random_seed(1)
tinput = (1, None)
toutput = (1, ops.get_default_graph()._last_id) # pylint: disable=protected-access
random_seed.set_random_seed(tinput[0])
g_seed, op_seed = data_random_seed.get_seed(tinput[1])
g_seed = self.evaluate(g_seed)
op_seed = self.evaluate(op_seed)
msg = 'test_case = {0}, got {1}, want {2}'.format(1, (g_seed, op_seed),
toutput)
self.assertEqual((g_seed, op_seed), toutput, msg=msg)
random_seed.set_random_seed(None)
@test_util.run_in_graph_and_eager_modes
def testNondeterministicRandomSeed(self):
random_seed.set_random_seed(None)
op_seeds = []
for _ in range(50):
g_seed, op_seed = data_random_seed.get_seed(None)
g_seed = self.evaluate(g_seed)
op_seed = self.evaluate(op_seed)
self.assertEqual(0, g_seed)
self.assertNotEqual(0, op_seed)
op_seeds.append(op_seed)
self.assertGreater(len(set(op_seeds)), 1)
if __name__ == '__main__':
test.main()
|
