forked from tensorflow/tensorflow
-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathdivision_past_test.py
More file actions
66 lines (55 loc) · 2.17 KB
/
Copy pathdivision_past_test.py
File metadata and controls
66 lines (55 loc) · 2.17 KB
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
# 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 division with division imported from __future__.
This file should be exactly the same as division_past_test.py except
for the __future__ division line.
"""
from __future__ import absolute_import
# from __future__ import division # Intentionally skip this import
from __future__ import print_function
import numpy as np
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import ops
from tensorflow.python.platform import test
class DivisionTestCase(test.TestCase):
def testDivision(self):
"""Test all the different ways to divide."""
values = [1, 2, 7, 11]
functions = (lambda x: x), constant_op.constant
# TODO(irving): Test int8, int16 once we support casts for those.
dtypes = np.int32, np.int64, np.float32, np.float64
def check(x, y):
if isinstance(x, ops.Tensor):
x = x.eval()
if isinstance(y, ops.Tensor):
y = y.eval()
self.assertEqual(x.dtype, y.dtype)
self.assertEqual(x, y)
with self.test_session():
for dtype in dtypes:
for x in map(dtype, values):
for y in map(dtype, values):
for fx in functions:
for fy in functions:
tf_x = fx(x)
tf_y = fy(y)
div = x / y
tf_div = tf_x / tf_y
check(div, tf_div)
floordiv = x // y
tf_floordiv = tf_x // tf_y
check(floordiv, tf_floordiv)
if __name__ == "__main__":
test.main()